Repositioning drugs by targeting network modules: a Parkinson’s disease case study
© The Author(s). 2017
Published: 28 December 2017
Much 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.
We 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.
In 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.
Parkinson’s Disease (PD) is a disorder characterized by depletion of dopamine in the basal ganglia, including the substantia nigra. While the exact etiology of PD is unknown, major advances have been made in understanding underlying disease mechanisms through technologies in genetics, transcriptomics, epigenetics, proteomics and imaging . These advances have increased recognition of the heterogeneity and etiological complexity of PD as a disease. Nevertheless, there is hope for broad-spectrum therapeutic intervention, as even distinct disease subtypes implicate genes intersecting in common pathways . Recently described “Network Medicine”  approaches offer a platform to study the molecular complexity of a particular disease systematically. These approaches are well-suited to the identification of disease modules and pathways as well as the molecular relationships between apparently distinct phenotypes . Despite progress towards the understanding of genetic factors that contribute to the etiology of PD, current treatments are aimed at clinically apparent PD — after patients are suffering from the onset of neurodegeneration. While, preventative drugs aim at treatment before or during the pre-clinical stage of PD are lacking, as are curative drugs aimed at the underlying molecular mechanisms have had limited success .
The associations discovered in GWAS of PD allow for the identification of disease-specific modules playing a role in triggering the disease. Similarly, gene expression microarray data provides a gross overview of gene expression changes that are associated with diseases like PD. However, future studies of complex diseases will need to move beyond the analysis of single genes and include analysis of interactions between genes or proteins, in order to better understand how functional pathways and networks become dysfunctional . For instance, network-based approaches have already been used to examine various disease molecular mechanisms, e.g., type-2 diabetes , cancer , and neuronal degeneration specifically . Bioinformatics techniques to characterize network topology and functional modules have been developed recently for functional genomics . The identification of disease modules involving specific mutated genes and the molecular pathways to which they belong will provide new targets for drug development. GWAS and whole exome profiling data are combined in systems biology to illustrate upstream perturbations causing dysfunction in pathways and mechanisms leading to the disease phenotype. Therefore, we introduce the approach of discovering disease-specific modules to reveal the etiology of PD.
In this study, we hypothesize that study of PD GWAS  and co-expression data  will enable identification of disease-specific modules caused by a variation in multiple components of a functional pathway or network. Thus, we propose using a network-based approach called Weighted Gene Co-expression Network Analysis (WGCNA)  to detect modules of co-expressed gene networks associated with PD. We then integrate these co-expression clusters with gene regulatory network information and perform enrichment analysis to find PD-specific modules. This method, in combination with functional enrichment and network topology measures, will be used to identify potential targets. This is done by selecting drugs that reverse the altered gene expression signatures found within the PD modules.
PD modules which show significant perturbation is identified by comparing global co-expression networks in PD to regulatory networks identified using GWAS 'hits'. After selecting the PD-specific modules for further analysis, we find significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology terms associated with PD modules. Afterward, we use knowledge of these functional pathways as the basis for “modular drug discovery”—the discovery of drugs that act on many nodes within the disease-specific module. This is accomplished through our innovative Drug Effect Sum Score (DESS) system and then cross-validated through rigorous analysis of published literature.
An overview of the framework
The Drug repositioning section (green) was comprised of four steps. First, we calculated a P-score, which is an intuitive pharmacology score that combines the probability for each interaction and the weight of the drug-target interaction using data from the DMAP database (see details in Methods). Second, we calculated the RP-score, which is a measure of Relevant Protein importance in the PD modules network (see details in Methods). Third, we calculated the Drug Effect Score (DES) of each module. Finally, the DESS was calculated across all modules. Using these steps, we obtained a ranked “modular drug list” consisting of candidate treatments based on PD-specific modules.
Preparation of PD-specific omics, gene-gene interaction, and drug-protein regulation data
Datasets from whole genome expression transcriptional profiling (on the GSE8397-GPL-96 array) were retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8397). In the gene expression profile, 47 samples from PD patients and controls were used in three brain regions: the Frontal Gyrus (FG: 8 tissue samples), Lateral Substantia (LS: 16 tissue samples) and Medial Substantia (MS: 23 tissue samples) . SNP data was obtained from a PD paper , in which a GWAS was carried out. We mapped probe IDs to gene symbols using the NCBI microarray toolkit and assigned gene expression scores by the averaging probe expression values after adjustment and trimming of background noises by using the standard deviation of the mean values from all samples. Since the standard deviation of the mean values were small enough (0.02 in this study), no samples had been trimmed. After performing probe transformation and synonymous gene merging on data from the Affymetrix Human Genome U133A Array [HG-U133A] and Affymetrix Human Genome U133B Array [HG-U133B], 12,995 genes were mapped by 22,283 probes in the merged matrix from the two arrays. In the prior study, 54 genes were reported as having had significant enrichment  in GWAS. The PAGER database  was used to obtain gene-gene regulatory relationships (22,127 pairs curated from 645,385 in total). The HAPPI-2 database  was used to obtain protein-protein interaction (PPI) data. This integrated protein interaction database comprehensively integrates weighted human protein-protein interaction data from a wide variety of protein-protein database sources. After mapping the proteins to genes using UniProt IDs, we obtained 2,658,799 gene-gene interactions. The drug-target regulatory relationships data was from the DMAP database , which consisted of curated 438,004 drug-protein regulatory relationships.
PD-specific network module identifications
Whole-genome expression data on 12,995 genes was filtered down to 2895 candidate genes, based on a multi-group empirical Bayesian (eBayes) moderated t-test with p-value ≤ 0.05. Next, we performed WGCNA to cluster these genes based on their co-expression. To do this, we first performed our pipeline steps to identify excessive missing values and outlier microarray samples. The detection of the outlier was performed by trimming the hierarchy tree of average Euclidean distance method using cutoff tree height of 100. Second, we chose an exponent for soft thresholding based on analysis of network topology, to further reduce noise and amplify stronger connections in the scale-free topological model. Third, we performed one-step network construction and module detection using hierarchy tree of unsigned TOM-based dissimilarity distance. Fourth, we visualized the genes in modules in a hierarchy tree based on average linkage clustering . Fifth, we analyzed the cluster (Principal Components) and sample (expression data) correlation using Pearson correlation and asymptotic p-value.
In the final step, we performed ClueGO analysis to elucidate mechanisms involved in the PD-specific modules. We applied Bonferroni correction and selected those with post-correction p-value ≤ 0.05 and Kappa score ≥ 0.5 (moderate network strength or stronger) .
Modular drug repositioning
Construction of PD genetic association networks
PD-specific network modules identified
Enrichment analysis results of two PD-specific network modules
The 5 co-expression module enrichment based on GWAS results
Genes in all
ClueGO analysis of PD-specific modules
Gene Ontology - biological processes (GO-BP) relating to the two PD-specific modules
Br module KEGG pathway
Synaptic vesicle cycle
Br module GO-BP
energy coupled proton transport, against electrochemical gradient
T module GO-BP
Identifying drugs with predicted therapeutic effects on the Br and T modules
Compound IDs for the 12 most highly ranked modular drug candidates
Steroids and steroid derivatives
Lipids and lipid-like molecules
Phenylpropanoids and polyketides
Phenylpropanoids and polyketides
component of Tylosterone
Lipids and lipid-like molecules
Conclusions and discussion
In this work, we present a framework that identified candidate drugs for repositioning based on analysis of GWAS and gene expression microarray data. Starting with genes identified through a standard GWAS, we extended the analysis to one-layer extension by gene-gene regulatory relationship and built an extended regulatory network. Significant results based on an enrichment analysis were then used to generate PD modules. We improved gene co-expression module cohesion by removing isolated or weakly connected genes. PD network modules were then further informed by the integration of data from Protein-Protein interaction databases.
Using this approach, we initially identified over 1201 candidates for drug repurposing. We trimmed this to 12 modular drug candidates based on their DESS. There were three important characteristics of finding within these 12 modular drugs. First, they are noteworthy in that they target PD at the level of the gene co-expression module as opposed to a specific target. Second, most of the genes on the list belong to drug families, which should be expected if data relating to drug target efficacy are accurate and internally consistent. Third, there are general 4 drug families found (steroids and steroid derivatives, lipids and lipid-like molecules, phenylpropanoids and polyketides, and other small molecules), and each family of drugs identified has been previously studied in relation to neurodegenerative disease, suggesting the external validity of our findings as well.
The top candidate drug was estradiol, a steroidal estrogen critical in the regulation of the menstrual cycle. It is currently used pharmaceutically in hormone replacement therapies for menopause and hypogonadism. Several studies support a role for the use of estradiol in PD. It has been shown to protect dopaminergic neurons in an MPP+ Parkinson’s disease model , and a study of postmenopausal women found it to be associated with a reduced risk of PD in women . Further, it is well-established that estrogen deprivation leads to the death of dopaminergic neurons. Of note, many clinical reports also suggest an anti-dopaminergic effect of estrogens on symptoms of PD. It is likely that the timing and dosage of estrogen influence the results in these discrepant findings. Our ninth, tenth and eleventh-ranked drugs (dienestrol, diethylstilbestrol, and methyltestosterone respectively) are isomers relating to diethylstilbestrol (also known as follidiene). Diethylstilbestrol is a synthetic non-steroidal estrogen previously used to treat menopausal and postmenopausal disorders. However, it is now known to have teratogenic and carcinogenic properties . Although these compounds may be contraindicated for use in humans, their high prioritization might prompt us to look for similar compounds without carcinogenic side effects. Methyltestosterone, which had the tenth highest DESS, is a synthetic orally active androgenic-anabolic steroid with relatively high estrogenicity. Methyltestosterone is currently used to treat males with androgen deficiency. Interestingly, testosterone deficiency has previously been reported in patients with PD, and PD itself is seen more commonly in men than women . However, clinical trials have shown no improvement in male PD patients when given exogenous testosterone therapy . Finally, our sixth most highly ranked drug was genistein, an estrogen-like isoflavone compound found exclusively in legumes. Genistein is known to act as an angiogenesis inhibitor and was previously shown to have neuroprotective effects on dopaminergic neurons in mouse models of PD .
Resveratrol had the second highest DESS. It is a polyphenolic anti-oxidant stilbenoid compound found in food include the skin of grapes, blueberries, raspberries and mulberries, currently under preclinical investigation as a potential pharmaceutical treatment in treating early onset PD patients. Resveratrol was previously studied in a phase-II clinical trial for individuals with mild to moderate Alzheimer’s disease and was found to reduce plasma levels of some AD biomarkers [28–30].
The third drug alitretinoin, fourth drug tretinoin, and fifth drug isotretinoin are most highly ranked candidates also belonging to a single family of compounds, retinoids. The first is retinoic acid, a retinoid morphogen crucial to the embryonic development of the anterior-posterior axis in vertebrates, as well as differentiation and maintenance of neural cell lineage. Currently, in-vivo animal studies suggest the possibility of therapeutic applications of retinoic acid for PD through nanoparticle delivery . Isotretinoin, trademarked under the name Accutane, is prescribed as a treatment for severe acne vulgaris. Although isotretinoin is a known teratogen , it might be well-suited to treatment of PD given its typical later age of onset.
Our seventh and eighth hits, Sirolimus (Rapamune) and Sirolimus (Perceiva), are again related. Perceiva is an ocular formulation of the macrolide compound sirolimus (commonly known as rapamycin) and was developed to treat neovascular age-related macular degeneration. Sirolimus is used for the treatment of Lymphangioleiomyomatosis, as well as in prevention of organ transplant rejection. Interestingly, sirolimus has been shown to improve cognitive deficits in mouse model of Alzheimer’s Diseases through inhibition of the mTOR signaling pathway, a pathway which is thought to protect against neuronal death in mouse models of PD .
In addition to these twelve candidates, our ClueGO analysis suggests that investigation of two additional biological processes may be profitable. Our analysis of KEGG pathways in relation to the T module implicated mitochondrial respiration as a potential disease mechanism . Interestingly, it has previously been reported that defects in mitochondrial respiration are etiologically related to the pathogenesis of PD. Thus, preservation and restoration of mitochondrial function may hold promise as a potential therapeutic intervention to halt the progression of dopaminergic neurodegeneration in PD. Secondly, in PD, neuronal cells undergo mitotic catastrophe and endoreduplication prior to cell death. It has previously been shown  that overexpression of DNA poly β was involved in the rotenone-mediated pathology of cellular and animal models of PD. In a cell culture model, increased levels of DNA poly β promoted rotenone-mediated endoreduplication. Selective injury to dopaminergic neurons by rotenone resulted in the upregulation of DNA poly β as the neuronal cell cycle was reactivated.
In summary, we perform drug repositioning by integrating weighted drug-protein regulations on all genes, using our novel DESS to quantitate drug effects on entire co-expression networks. As biological systems use functional pathways and networks to maintain homeostasis, by selecting drugs that act at the level of a gene module we were able to target a level of the biological organization more relevant to the disease pathologies of complex disorders such as PD. Although this approach is still in its infancy, our results suggest that it may circumvent issues associated with single-gene targeting in polygenic diseases like PD. Our analysis has identified several families of related drug candidates, all of which have previously been investigated in relation to PD and other neurodegenerative diseases. As such, we believe our framework gives internally and externally valid results and may be extended to provide complementary insights to other disease-module findings and drug-repositioning projects.
The significance of our work should be considered in light of its limitations. First, several of the classes of drugs mentioned have already studied in relation to PD and related phenotypes, as described above. However, members of the families of drugs identified have not resulted in a clinically efficacious treatment for PD to date. As such, a future direction for this line of research is to include a mechanism to account for both additive and potentially non-additive interaction effects between drugs on a disease-specific module. In addition, many of the most highly ranked modular drugs we identified show much promise, but have known adverse effects. Future research will include a method of incorporation of drug side effects into the final priority score.
We acknowledge the partial support of this research by the University of Alabama at Birmingham (UAB) and the UAB Informatics Institute during the implementation of the project. Our database servers and web applications are maintained by the UAB high-performance computing group.
The publication cost of the paper is from Dr.Chen’s lab start-up funding in the University of Alabama at Birmingham in informatics institute and UL1 TR001417 “UAB Center for Clinical and Translational Science” grant award.
Availability of data and materials
PAGER database is available online http://discovery.informatics.uab.edu/PAGER.
DMAP database is available online http://discovery.informatics.uab.edu/cmaps.
The supplemental material are available for download.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 18 Supplement 14, 2017: Proceedings of the 14th Annual MCBIOS conference. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-18-supplement-14.
ZLY performed the construction of the framework, data analysis and led the writing of the manuscript under the guidance of JYC. IA, EZ participated in drug repositioning performance evaluations. VL, SLB and JYC participated in the revision of the manuscript. JYC conceived the project, supervised the entire research team with frequent feedback in the design, implementation, and evaluation of the project. All authors contributed to the completion of the manuscripts and approved final manuscript.
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