Gene Co-expression Network Modules Research Articles

Myeloid spatial and transcriptional molecular signature of ischemia-reperfusion injury in human liver transplantation.

Ischemia-reperfusion injury (IRI) is a significant clinical concern in liver transplantation, with a key influence on short-term and long-term allograft and patient survival. Myeloid cells trigger and sustain tissue inflammation and damage associated with IRI, but the mechanisms regulating these activities are unknown. To address this, we investigated the molecular characteristics of intragraft myeloid cells present in biopsy-proven IRI- and IRI+ liver transplants. RNA-sequencing was performed on 80 pre-reperfusion and post-reperfusion biopsies from 40 human recipients of liver transplantation (23 IRI+, 17 IRI-). We used transcriptional profiling and computational approaches to identify specific gene coexpression network modules correlated with functional subsets of MPO+, lysozyme+, and CD68+ myeloid cells quantified by immunohistochemistry on sequential sections from the same patient biopsies. A global molecular map showed gene signatures related to myeloid activation in all patients regardless of IRI status; however, myeloid cell subsets differed dramatically in their spatial morphology and associated gene signatures. IRI- recipients were found to have a natural corticosteroid production and response profile from pre-reperfusion to post-reperfusion, particularly among monocytes/macrophages. The pre-reperfusion signature of IRI+ recipients included acute inflammatory responses in neutrophils and increased translation of adaptive immune-related genes in monocytes/macrophages coupled with decreased glucocorticoid responses. Subsequent lymphocyte activation at post-reperfusion identified transcriptional programs associated with the transition to adaptive immunity found only among IRI+ recipients. Myeloid subset-specific genes and related signaling pathways provide targets for the development of therapeutic strategies aimed at limiting IRI in the clinical setting of liver transplantation.

Role of SiPHR1 in the Response to Low Phosphate in Foxtail Millet via Comparative Transcriptomic and Co-Expression Network Analyses.

Enhancing the absorption and utilization of phosphorus by crops is an important aim for ensuring food security worldwide. However, the gene regulatory network underlying phosphorus use in foxtail millet remains unclear. In this study, the molecular mechanism underlying low-phosphorus (LP) responsiveness in foxtail millet was evaluated using a comparative transcriptome analysis. LP reduced the chlorophyll content in shoots, increased the anthocyanin content in roots, and up-regulated purple acid phosphatase and phytase activities as well as antioxidant systems (CAT, POD, and SOD). Finally, 13 differentially expressed genes related to LP response were identified and verified using transcriptomic data and qRT-PCR. Two gene co-expression network modules related to phosphorus responsiveness were positively correlated with POD, CAT, and PAPs. Of these, SiPHR1, functionally annotated as PHOSPHATE STARVATION RESPONSE 1, was identified as an MYB transcription factor related to phosphate responsiveness. SiPHR1 overexpression in Arabidopsis significantly modified the root architecture. LP stress caused cellular, physiological, and phenotypic changes in seedlings. SiPHR1 functioned as a positive regulator by activating downstream genes related to LP tolerance. These results improve our understanding of the molecular mechanism underlying responsiveness to LP stress, thereby laying a theoretical foundation for the genetic modification and breeding of new LP-tolerant foxtail millet varieties.

Monosomy X in isogenic human iPSC-derived trophoblast model impacts expression modules preserved in human placenta

Mammalian sex chromosomes encode homologous X/Y gene pairs that were retained on the Y chromosome in males and escape X chromosome inactivation (XCI) in females. Inferred to reflect X/Y pair dosage sensitivity, monosomy X is a leading cause of miscarriage in humans with near full penetrance. This phenotype is shared with many other mammals but not the mouse, which offers sophisticated genetic tools to generate sex chromosomal aneuploidy but also tolerates its developmental impact. To address this critical gap, we generated X-monosomic human induced pluripotent stem cells (hiPSCs) alongside otherwise isogenic euploid controls from male and female mosaic samples. Phased genomic variants in these hiPSC panels enable systematic investigation of X/Y dosage-sensitive features using invitro models of human development. Here, we demonstrate the utility of these validated hiPSC lines to test how X/Y-linked gene dosage impacts a widely used model for human syncytiotrophoblast development. While these isogenic panels trigger a GATA2/3- and TFAP2A/C-driven trophoblast gene circuit irrespective of karyotype, differential expression implicates monosomy X in altered levels of placental genes and in secretion of placental growth factor (PlGF) and human chorionic gonadotropin (hCG). Remarkably, weighted gene coexpression network modules that significantly reflect these changes are also preserved in first-trimester chorionic villi and term placenta. Our results suggest monosomy X may skew trophoblast cell type composition and function, and that the combined haploinsufficiency of the pseudoautosomal region likely plays a key role in these changes.

SFARI genes and where to find them; modelling Autism Spectrum Disorder specific gene expression dysregulation with RNA-seq data

Autism Spectrum Disorders (ASD) have a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease one approach that is gaining popularity is the combination of gene expression and clinical genetic data, often using the SFARI-gene database, which comprises lists of curated genes considered to have causative roles in ASD when mutated in patients. We build a gene co-expression network to study the relationship between ASD-specific transcriptomic data and SFARI genes and then analyse it at different levels of granularity. No significant evidence is found of association between SFARI genes and differential gene expression patterns when comparing ASD samples to a control group, nor statistical enrichment of SFARI genes in gene co-expression network modules that have a strong correlation with ASD diagnosis. However, classification models that incorporate topological information from the whole ASD-specific gene co-expression network can predict novel SFARI candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. A statistically significant association is also found between the absolute level of gene expression and SFARI’s genes and Scores, which can confound the analysis if uncorrected. We propose a novel approach to correct for this that is general enough to be applied to other problems affected by continuous sources of bias. It was found that only co-expression network analyses that integrate information from the whole network are able to reveal signatures linked to ASD diagnosis and novel candidate genes for the study of ASD, which individual gene or module analyses fail to do. It was also found that the influence of SFARI genes permeates not only other ASD scoring systems, but also lists of genes believed to be involved in other neurodevelopmental disorders.

Identification of hub genes in airway epithelial cells of asthma patients by WGCNA and PPI network

Bronchial asthma is a common chronic disease of airway inflammation, high mucus secretion and airway hyper responsiveness. The pathogenetic mechanisms of asthma remain unclear. In this study, we aimed at identifying genes playing an import role in disease-related pathways in airway epithelial cells of asthma patients. Microarray data GSE41861 of asthma airway epithelial cells was used to screen differentially expressed genes (DEGs) through GEO2R analysis. The weighted gene co-expression network analysis (WGCNA) was performed to identify gene co-expression network modules in bronchial asthma. The DAVID database was then used to perform functional and pathway enrichment analysis of these DEGs. In addition, we have conducted protein-protein interaction (PPI) network of DEGs by STRING, and eventually found key genes and significant modules. A total of 315 DEGs (111 up-regulated and 204 down-regulated) were identified between severe asthma and healthy individual, which were mainly involved in pathways of cilium assembly, cilium morphogenesis, axon guidance, positive regulation of fat cell differentiation, and positive regulation of cell substrate adhesion. A total of 60 genes in the black module and green module were considered to be correlated with the severity of asthma. Combining PPI network, several key genes were identified, such as BP2RY14, PTGS1, SLC18A2, SIGLEC6, RGS13, CPA3, and HPGDS. Our findings revealed several genes that may be involved in the process of development of bronchial asthma and potentially be candidate targets for diagnosis or therapy of bronchial asthma.

The neurobiological basis of divergent thinking: Insight from gene co-expression network-based analysis

Although many efforts have been made to explore the genetic basis of divergent thinking (DT), there is still a gap in the understanding of how these findings relate to the neurobiology of DT. In a combined sample of 1,682 Chinese participants, by integrating GWAS with previously identified brain-specific gene co-expression network modules, this study explored for the first time the functional brain-specific gene co-expression networks underlying DT. The results showed that gene co-expression network modules in anterior cingulate cortex, caudate, amygdala and substantia nigra were enriched with DT association signals. Further functional enrichment analysis showed that these DT-related gene co-expression network modules were enriched for key biological process and cellular component related to myelination, suggesting that cortical and sub-cortical grey matter myelination may serve as important neurobiological basis of DT. Although the underlying mechanisms need to be further refined, this exploratory study may provide new insight into the neurobiology of DT.

Global Survey of the Full-Length Cabbage Transcriptome (Brassica oleracea Var. capitata L.) Reveals Key Alternative Splicing Events Involved in Growth and Disease Response.

Cabbage (Brassica oleracea L. var. capitata L.) is an important vegetable crop cultivated around the world. Previous studies of cabbage gene transcripts were primarily based on next-generation sequencing (NGS) technology which cannot provide accurate information concerning transcript assembly and structure analysis. To overcome these issues and analyze the whole cabbage transcriptome at the isoform level, PacBio RS II Single-Molecule Real-Time (SMRT) sequencing technology was used for a global survey of the full-length transcriptomes of five cabbage tissue types (root, stem, leaf, flower, and silique). A total of 77,048 isoforms, capturing 18,183 annotated genes, were discovered from the sequencing data generated through SMRT. The patterns of both alternative splicing (AS) and alternative polyadenylation (APA) were comprehensively analyzed. In total, we detected 13,468 genes which had isoforms containing APA sites and 8978 genes which underwent AS events. Moreover, 5272 long non-coding RNAs (lncRNAs) were discovered, and most exhibited tissue-specific expression. In total, 3147 transcription factors (TFs) were detected and 10 significant gene co-expression network modules were identified. In addition, we found that Fusarium wilt, black rot and clubroot infection significantly influenced AS in resistant cabbage. In summary, this study provides abundant cabbage isoform transcriptome data, which promotes reannotation of the cabbage genome, deepens our understanding of their post-transcriptional regulation mechanisms, and can be used for future functional genomic research.

Gene expression correlates of advanced epigenetic age and psychopathology in postmortem cortical tissue

Psychiatric stress has been associated with accelerated epigenetic aging (i.e., when estimates of cellular age based on DNA methylation exceed chronological age) in both blood and brain tissue. Little is known about the downstream biological effects of accelerated epigenetic age on gene expression. In this study we examined associations between DNA methylation-derived estimates of cellular age that range from decelerated to accelerated relative to chronological age (“DNAm age residuals”) and transcriptome-wide gene expression. This was examined using tissue from three post-mortem cortical regions (ventromedial and dorsolateral prefrontal cortex and motor cortex, n = 97) from the VA National PTSD Brain Bank. In addition, we examined how posttraumatic stress disorder (PTSD) and alcohol-use disorders (AUD) moderated the association between DNAm age residuals and gene expression. Transcriptome-wide results across brain regions, psychiatric diagnoses, and cohorts (full sample and male and female subsets) revealed experiment-wide differential expression of 11 genes in association with PTSD or AUD in interaction with DNAm age residuals. This included the inflammation-related genes IL1B, RCOR2, and GCNT1. Candidate gene class analyses and gene network enrichment analyses further supported differential expression of inflammation/immune gene networks as well as glucocorticoid, circadian, and oxidative stress-related genes. Gene co-expression network modules suggested enrichment of myelination related processes and oligodendrocyte enrichment in association with DNAm age residuals in the presence of psychopathology. Collectively, results suggest that psychiatric stress accentuates the association between advanced epigenetic age and expression of inflammation genes in the brain. This highlights the role of inflammatory processes in the pathophysiology of accelerated cellular aging and suggests that inflammatory pathways may link accelerated cellular aging to premature disease onset and neurodegeneration, particularly in stressed populations. This suggests that anti-inflammatory interventions may be an important direction to pursue in evaluating ways to prevent or delay cellular aging and increase resilience to diseases of aging.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors’ sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging.

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Inhibitory Effects of PRG4 on Migration and Proliferation of Human Venous Cells

BackgroundProteoglycan 4 (PRG4; lubricin) is a member of two gene co-expression network modules associated with human vein graft failure. However, little is known about PRG4 and the vascular system. Therefore, we have investigated the effects of recombinant human PRG4 (rhPRG4) on cell migration and proliferation in human veins. MethodsEffects of rhPRG4 on cell migration, proliferation, and neointima formation were determined in human venous tissue and cultured venous smooth muscle cells (SMCs), adventitial cells, and endothelial cells. Expression of PRG4 by cultured human saphenous veins, failed vein grafts, and varicose veins was determined by immunostaining or Western blotting. ResultsLimited expression of PRG4 in fresh saphenous veins was dramatically increased around medial SMCs after culture ex vivo. rhPRG4 inhibited the migration of cultured SMCs, adventitial cells, and endothelial cells, as well as the proliferation of endothelial cells. rhPRG4 also inhibited the migration of SMCs and adventitial cells from tissue explants, but there was no effect on cell proliferation or neointima formation in ex vivo whole veins. Finally, PRG4 was largely absent in two examples of venous pathology, that is, failed human vein grafts and varicose veins. ConclusionsAlthough rhPRG4 can inhibit the migration of venous SMCs, endothelial cells, and adventitial cells, and the proliferation of endothelial cells, PRG4 was only increased around medial SMCs in veins after ex vivo culture. PRG4 was not observed around medial SMCs in failed human vein grafts and varicose veins, suggesting the possibility that a failure of PRG4 upregulation may promote these pathologies.

Deletion of the KH1 Domain of Fmr1 Leads to Transcriptional Alterations and Attentional Deficits in Rats.

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by mutations in the FMR1 gene. It is a leading monogenic cause of autism spectrum disorder and inherited intellectual disability and is often comorbid with attention deficits. Most FXS cases are due to an expansion of CGG repeats leading to suppressed expression of fragile X mental retardation protein (FMRP), an RNA-binding protein involved in mRNA metabolism. We found that the previously published Fmr1 knockout rat model of FXS expresses an Fmr1 transcript with an in-frame deletion of exon 8, which encodes for the K-homology (KH) RNA-binding domain, KH1. Notably, 3 pathogenic missense mutations associated with FXS lie in the KH domains. We observed that the deletion of exon 8 in rats leads to attention deficits and to alterations in transcriptional profiles within the medial prefrontal cortex (mPFC), which map to 2 weighted gene coexpression network modules. These modules are conserved in human frontal cortex and enriched for known FMRP targets. Hub genes in these modules represent potential therapeutic targets for FXS. Taken together, these findings indicate that attentional testing might be a reliable cross-species tool for investigating FXS and identify dysregulated conserved gene networks in a relevant brain region.

Gene Coexpression Networks in Whole Blood Implicate Multiple Interrelated Molecular Pathways in Obesity in People with Asthma.

Asthmatic children who develop obesity through adolescence have poorer disease outcomes compared with those who do not. This study aimed to characterize the biology of childhood asthma complicated by adult obesity. Gene expression networks are powerful statistical tools for characterizing human disease that leverage the putative coregulatory relationships of genes to infer relevant biological pathways. Weighted gene coexpression network analysis of gene expression data was performed in whole blood from 514 adult asthmatic subjects. Then, module preservation and association replication analyses were performed in 418 subjects from two independent asthma cohorts (one pediatric and one adult). A multivariate model was identified in which three gene coexpression network modules were associated with incident obesity in the discovery cohort (each P < 0.05). Two module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the obesity modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in subjects with obesity in both replication cohorts (each P < 0.05). The gene coexpression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of asthma with obesity.

Comparisons of gene coexpression network modules in breast cancer and ovarian cancer

BackgroundBreast cancer and ovarian cancer are hormone driven and are known to have some predisposition genes in common such as the two well known cancer genes BRCA1 and BRCA2. The objective of this study is to compare the coexpression network modules of both cancers, so as to infer the potential cancer-related modules.MethodsWe applied the eigen-decomposition to the matrix that integrates the gene coexpression networks of both breast cancer and ovarian cancer. With hierarchical clustering of the related eigenvectors, we obtained the network modules of both cancers simultaneously. Enrichment analysis on Gene Ontology (GO), KEGG pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) in the identified modules was performed.ResultsWe identified 43 modules that are enriched by at least one of the four types of enrichments. 31, 25, and 18 modules are enriched by GO terms, KEGG pathways, and DO terms, respectively. The structure of 29 modules in both cancers is significantly different with p-values less than 0.05, of which 25 modules have larger densities in ovarian cancer. One module was found to be significantly enriched by the terms related to breast cancer from GO, KEGG and DO enrichment. One module was found to be significantly enriched by ovarian cancer related terms.ConclusionBreast cancer and ovarian cancer share some common properties on the module level. Integration of both cancers helps identifying the potential cancer associated modules.

Weighted Gene Co-Expression Network Analysis Identifies Gender Specific Modules and Hub Genes Related to Metabolism and Inflammation in Response to an Acute Lipid Challenge.

Inflammation is characteristic of diet-related diseases including obesity and type 2 diabetes (T2D). However, biomarkers of inflammation that reflect the early stage metabolic derangements are not optimally sensitive. Lipid challenges elicit postprandial inflammatory and metabolic responses. Gender-specific transcriptomic networks of the peripheral blood mononuclear cell (PBMC) were constructed in response to a lipid challenge. Eighty-six adult males and females of comparable age, anthropometric, and biochemical profiles completed an oral lipid tolerance test (OLTT). PBMC transcriptome was profiled following OLTT. Weighted gene coexpression networks were constructed separately for males and females. Functional ontology analysis of network modules was performed and hub genes identified. Two modules of interest were identified in females-an "inflammatory" module and an "energy metabolism" module. NLRP3, which plays a central role in inflammation and STARD3 that is involved in cholesterol metabolism, were identified as hub genes for the respective modules. The OLTT induced some gender-specific correlations of gene coexpression network modules. In females, biological processes relating to energy metabolism and inflammation pathways were evident. This suggests a gender specific link between inflammation and energy metabolism in response to lipids. In contrast, G-protein coupled receptor protein signaling pathway was common to both genders.

Repositioning drugs by targeting network modules: a Parkinson\u2019s disease case study

BackgroundMuch effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson’s Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD.ResultsWe built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions—the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments for PD and 6 hold potential repositioning applications.ConclusionIn this study, we present a systems pharmacology framework which draws on genetic data from GWAS and gene expression microarray data to reposition drugs for PD. Our innovative approach integrates gene co-expression modules with biomolecular interaction network analysis to identify network modules critical to the PD pathway and disease mechanism. We quantify the positive effects of drugs in a DESS score that is based on known drug-target activity profiles. Our results illustrate that this modular approach is promising for repositioning drugs for use in polygenic diseases such as PD, and is capable of addressing challenges of the hindered gene target in drug repositioning approaches to date.