Disease Gene Network Research Articles

Modular organization of the human disease genes: a text-based network inference.

The analysis of disease phenotype data with genetic information indicated that genes associated with clinically similar diseases tend to be functionally related and work together to perform a specific biological function. Therefore, it is of interest to relate disease phenotype data to mirror modular property implied in the association map of disease genes. Hence, we constructed a textbased human disease gene network (HDGN) by using the phenotypic similarity of their associated disease phenotype records in the OMIM database. Analysis shows that the network is highly modular and it is highly correlated with the physiological classification of genetic diseases. Using a graph clustering algorithm, we found 139 gene modules in the network of 1,865 genes and their gene products (proteins) in these gene modules tend to interact with each other via the computation of PPI intensity. Genes in such gene modules are functionally related and may represent the shared genetic basis of their corresponding diseases. These genes, alone or in combination, could be considered as potential therapeutic targets in future clinical therapy.

Co-controllability of drug-disease-gene network

Controllability of a single network often focuses on the determination of the network’s minimum dominating set, which aims to elaborate how to control the whole network with minimum driver nodes. This paper proposes a new framework, co-controllability of multiple networks, which stresses the control of one network by another network as well as the mutual control characteristics of multiple networks based on minimum dominating sets. We take a drug–disease–gene network that consists of a drug–drug network, a disease–disease network and a gene–gene network as an example to study co-controllability of multiple networks. The results show that driver nodes tend to be conserved, e.g. diseases highly associated with driver nodes of the drug–drug network tend to be driver nodes in the disease–disease network compared with random networks. In addition, co-controllability of multiple networks is probably associated with the networks’ node degree, which is more stringent than controllability of a single network that is mainly determined by the network’s degree distribution. We also find that diseases and drugs tend to be mapped as two different subnetworks of human protein–protein interaction (PPI) network, drugs are inclined to dominate diseases by controlling the PPI network, and the coded proteins of disease-related genes exhibit a low tendency to be drug targets for the control of diseases. The results in this paper not only play an important role in understanding co-controllability of multiple networks, but also are helpful for understanding the mechanisms of drug–disease–gene, disease treatments and drug design in a network-based framework.

Abstract 1085: A bioinformatics approach to evaluate the involvement of CDH1/E-cadherin in solid tumors and to identify breast cancer biomarkers

Abstract Cancer is a complex disease characterized by abnormal proliferation of different cell types among which solid tumors (ST) stand out due to their high incidence and mortality rates. Of many ST, breast cancer (BC) is the 2nd most common cancer worldwide and the most frequent among women (∼1.67 million new cases in 2012). Ductal breast cancer (DBC) and lobular breast cancer (LBC) are the most frequent BC types. The human gene CDH1 encodes Epithelial Cadherin (ECad), a calcium-dependent cell-cell adhesion molecule defined as tumor suppressor since its absence prevents adherens junctions and associates to tumor progression and poor patient outcome. In cancer research, a great deal of data is available in the literature, providing an abundant source of knowledge for biomedical research, although the information is disaggregated. The use of bioinformatics tools (BT) dedicated to text mining (TM) and network biology has become very useful to study cancer pathology from a global perspective since integrate the vast amount of data available. In this study, we have implemented BT (DisGeNET, Cytoscape, COSMIC, Hippie, PANTHER, String) to systematically assess the relationship between CDH1/ECad and ST and to identify potential biomarkers for DBC and LBC. To identify CDH1 related diseases, a gene-disease network analysis was performed using DisGeNET/Cytoscape. In a query restricted to curated databases, CDH1 was associated (biomarker/genetic variation/ therapeutic target) to 29 diseases among which 20 were ST. When all databases (curated, predicted and literature) were included, the terms related to CDH1 expanded to 173 diseases (98 ST). A manual term-curation related to CDH1 (80 ST) was performed, with a total of 286 publications reviewed. In 199/286 (70%) reports, the association between CDH1 and ST was confirmed (67/286 (23%) inconclusive, 20/286 (7%) unconfirmed). As a result, 75/80 ST terms were validated as related to CDH1. An analysis done with the COSMIC BT led to the identification of 376 somatic mutations on the CDH1 gene in 11/20 ST terms from the DisGeNET output (curated databases). For BC, 3045 genes were found using TM tools as related to this term (CDH1: biomarker/genetic variation). For DBC, 42 genes were related with proven evidence (high score: BAG1, CDH1, ATF4, CLDN4, PTGER1, SERPINB5). To analyze protein-protein interaction (PPI), the HIPPIE BT was used. A selected DBC biomarkers were identified from a PPI network of 448 nodes, among which HDAC1, SKP2, CFTR, HSPA8, SMAD7, TGM2, ATF2, MDM2 were listed as putative biomarkers based on the ´Linkage method´. For LBC, 28 genes were found as associated (3 with proven evidence) and generate a PPI network of 216 nodes; ERBB2IP, SRC, EGFR, EP300 y HDAC1 were listed as potential biomarkers. Altogether, BT allowed a systematic assessment of current knowledge of CDH1/ECad and led to the identification of novel putative biomarkers for DBC and LBC. Citation Format: Maria F. Abascal, Maria J. Besso, Evangelina Aparicio, Marina Rosso, Victoria Mencucci, Laura I. Furlong, Monica H. Vazquez-Levin. A bioinformatics approach to evaluate the involvement of CDH1/E-cadherin in solid tumors and to identify breast cancer biomarkers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1085. doi:10.1158/1538-7445.AM2015-1085

Dysplastic spondylolysis is caused by mutations in the diastrophic dysplasia sulfate transporter gene

Spondylolysis is a fracture in part of the vertebra with a reported prevalence of about 3-6% in the general population. Genetic etiology of this disorder remains unknown. The present study was aimed at identifying genomic mutations in patients with dysplastic spondylolysis as well as the potential pathogenesis of the abnormalities. Whole-exome sequencing and functional analysis were performed for patients with spondylolysis. We identified a novel heterozygous mutation (c.2286A > T; p.D673V) in the sulfate transporter gene SLC26A2 in five affected subjects of a Chinese family. Two additional mutations (e.g., c.1922A > G; p.H641R and g.18654T > C in the intron 1) in the gene were identified by screening a cohort of 30 unrelated patients with the disease. In situ hybridization analysis showed that SLC26A2 is abundantly expressed in the lumbosacral spine of the mouse embryo at day 14.5. Sulfate uptake activities in CHO cells transfected with mutant SLC26A2 were dramatically reduced compared with the wild type, confirming the pathogenicity of the two missense mutations. Further analysis of the gene-disease network revealed a convergent pathogenic network for the development of lumbosacral spine. To our knowledge, our findings provide the first identification of autosomal dominant SLC26A2 mutations in patients with dysplastic spondylolysis, suggesting a new clinical entity in the pathogenesis of chondrodysplasia involving lumbosacral spine. The analysis of the gene-disease network may shed new light on the study of patients with dysplastic spondylolysis and spondylolisthesis as well as high-risk individuals who are asymptomatic.

Walking on multiple disease-gene networks to prioritize candidate genes.

Uncovering causal genes for human inherited diseases, as the primary step toward understanding the pathogenesis of these diseases, requires a combined analysis of genetic and genomic data. Although bioinformatics methods have been designed to prioritize candidate genes resulting from genetic linkage analysis or association studies, the coverage of both diseases and genes in existing methods is quite limited, thereby preventing the scan of causal genes for a significant proportion of diseases at the whole-genome level. To overcome this limitation, we propose a method named pgWalk to prioritize candidate genes by integrating multiple phenomic and genomic data. We derive three types of phenotype similarities among 7719 diseases and nine types of functional similarities among 20327 genes. Based on a pair of phenotype and gene similarities, we construct a disease-gene network and then simulate the process that a random walker wanders on such a heterogeneous network to quantify the strength of association between a candidate gene and a query disease. A weighted version of the Fisher's method with dependent correction is adopted to integrate 27 scores obtained in this way, and a final q-value is calibrated for prioritizing candidate genes. A series of validation experiments are conducted to demonstrate the superior performance of this approach. We further show the effectiveness of this method in exome sequencing studies of autism and epileptic encephalopathies. An online service and the standalone software of pgWalk can be found at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgwalk.

The human Drug–Disease–Gene Network

How to understand the correlations of drugs, diseases and genes from a view of network-based framework is of great importance for disease diagnosis, treatments and drug discovery. This paper proposes a multi-level network model that integrates drugs, diseases and genes together, called a drug–disease–gene network (DrDiGeN). The network consists of three subnetworks, a drug–drug network (DrDrN), a disease–disease network (DiDiN) and a gene–gene network (GeGeN). The statistic characteristics show that node degree in most of the subnetworks approximately follows a power-law distribution. The results also indicate that if genes in the GeGeN occupy important topological positions, then their associated drugs and diseases always hold critical roles in the DrDrN and the DiDiN respectively. In addition, most drug target genes and disease-causing genes are always different and nonessential, and the both show a lower likelihood to encode hub proteins in human protein–protein interaction (PPI) network, while a little higher tendency is observed in the GeGeN. Gene modules extracted from the GeGeN are highly enriched in Gene Ontology (GO) terms, but poor coexpressed in human tissues compared with that of the PPI network. Furthermore, diseases (or drugs) associated with similar genes highly interact with each other such that tightly related drugs, diseases and genes can easily form co-modules, in which they share a similar pattern. The conserved structures are helpful for the understanding of the interaction mechanisms of drug–disease–gene as well as drug applications and disease treatments in a network-based level.

Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study.

Proton pump inhibitors (PPIs) are extensively used for the treatment of gastric acid-related disorders. PPIs appear to be well tolerated and almost have no short-term side effects. However, the clinical adverse reactions of long-term PPI usage are increasingly reported in recent years. So far, there is no study that elucidates the side effect mechanisms of PPIs comprehensively and systematically. In this study, a well-defined small molecule perturbed microarray data set of 344 compounds and 1695 samples was analyzed. With this high-throughput data set, a new index (Identity, I) was designed to identify PPI-specific differentially expressed genes. Results indicated that (1) up-regulated genes, such as RETSAT, CYP1A1, CYP1A2, and UGT, enhanced vitamin A's metabolism processes in the cellular retinol metabolism pathway; and that (2) down-regulated genes, such as C1QA, C1QC, C4BPA, C4BPB, CFI, and SERPING1, enriched in the complement and coagulation cascades pathway. In addition, strong association was observed between these PPI-specific differentially expressed genes and the reported side effects of PPIs by the gene-disease association network analysis. One potential toxicity mechanism of PPIs as suggested from this systematic PPI-specific gene expression analysis is that PPIs are enriched in acidic organelles where they are activated and inhibit V-ATPases and acid hydrolases, and consequently block the pathways of antigen presentation, the synthesis and secretion of cytokines, and complement component proteins and coagulation factors. The strategies developed in this work could be extended to studies on other drugs.

Adapting simultaneous analysis phylogenomic techniques to study complex disease gene relationships

The characterization of complex diseases remains a great challenge for biomedical researchers due to the myriad interactions of genetic and environmental factors. Network medicine approaches strive to accommodate these factors holistically. Phylogenomic techniques that can leverage available genomic data may provide an evolutionary perspective that may elucidate knowledge for gene networks of complex diseases and provide another source of information for network medicine approaches. Here, an automated method is presented that leverages publicly available genomic data and phylogenomic techniques, resulting in a gene network. The potential of approach is demonstrated based on a case study of nine genes associated with Alzheimer Disease, a complex neurodegenerative syndrome.The developed technique, which is incorporated into an update to a previously described Perl script called “ASAP,” was implemented through a suite of Ruby scripts entitled “ASAP2,” first compiles a list of sequence-similarity based orthologues using PSI-BLAST and a recursive NCBI BLAST+ search strategy, then constructs maximum parsimony phylogenetic trees for each set of nucleotide and protein sequences, and calculates phylogenetic metrics (Incongruence Length Difference between orthologue sets, partitioned Bremer support values, combined branch scores, and Robinson–Foulds distance) to provide an empirical assessment of evolutionary conservation within a given genetic network. In addition to the individual phylogenetic metrics, ASAP2 provides results in a way that can be used to generate a gene network that represents evolutionary similarity based on topological similarity (the Robinson–Foulds distance).The results of this study demonstrate the potential for using phylogenomic approaches that enable the study of multiple genes simultaneously to provide insights about potential gene relationships that can be studied within a network medicine framework that may not have been apparent using traditional, single-gene methods. Furthermore, the results provide an initial integrated evolutionary history of an Alzheimer Disease gene network and identify potentially important co-evolutionary clustering that may warrant further investigation.

The MicroRNA-130/301 Family Controls Vasoconstriction in Pulmonary Hypertension

Pulmonary hypertension (PH) is a complex disorder, spanning several known vascular cell types. Recently, we identified the microRNA-130/301 (miR-130/301) family as a regulator of multiple pro-proliferative pathways in PH, but the true breadth of influence of the miR-130/301 family across cell types in PH may be even more extensive. Here, we employed targeted network theory to identify additional pathogenic pathways regulated by miR-130/301, including those involving vasomotor tone. Guided by these predictions, we demonstrated, via gain- and loss-of-function experimentation in vitro and in vivo, that miR-130/301-specific control of the peroxisome proliferator-activated receptor γ regulates a panel of vasoactive factors communicating between diseased pulmonary vascular endothelial and smooth muscle cells. Of these, the vasoconstrictive factor endothelin-1 serves as an integral point of communication between the miR-130/301-peroxisome proliferator-activated receptor γ axis in endothelial cells and contractile function in smooth muscle cells. Thus, resulting from an in silico analysis of the architecture of the PH disease gene network coupled with molecular experimentation in vivo, these findings clarify the expanded role of the miR-130/301 family in the global regulation of PH. They further emphasize the importance of molecular cross-talk among the diverse cellular populations involved in PH.

'RetinoGenetics': a comprehensive mutation database for genes related to inherited retinal degeneration.

Inherited retinal degeneration (IRD), a leading cause of human blindness worldwide, is exceptionally heterogeneous with clinical heterogeneity and genetic variety. During the past decades, tremendous efforts have been made to explore the complex heterogeneity, and massive mutations have been identified in different genes underlying IRD with the significant advancement of sequencing technology. In this study, we developed a comprehensive database, ‘RetinoGenetics’, which contains informative knowledge about all known IRD-related genes and mutations for IRD. ‘RetinoGenetics’ currently contains 4270 mutations in 186 genes, with detailed information associated with 164 phenotypes from 934 publications and various types of functional annotations. Then extensive annotations were performed to each gene using various resources, including Gene Ontology, KEGG pathways, protein–protein interaction, mutational annotations and gene–disease network. Furthermore, by using the search functions, convenient browsing ways and intuitive graphical displays, ‘RetinoGenetics’ could serve as a valuable resource for unveiling the genetic basis of IRD. Taken together, ‘RetinoGenetics’ is an integrative, informative and updatable resource for IRD-related genetic predispositions.Database URL: http://www.retinogenetics.org/.

Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases

Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets.Here we introduce our Yeast Augmented Network Analysis (YANA) approach and test it with the X-linked spinal muscular atrophy (SMA) disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish) SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.

Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases.

Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets. Here we introduce our Yeast Augmented Network Analysis (YANA) approach and test it with the X-linked spinal muscular atrophy (SMA) disease gene UBA1.First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish) SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.

Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns.

Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug–disease–gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT+) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types.Electronic supplementary materialThe online version of this article (doi:10.1007/s13238-013-0016-x) contains supplementary material, which is available to authorized users.

Community detection in disease-gene network based on principal component analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes.

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT+) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types.

Glycolytic enzymes PGK1 and PKM2 as novel transcriptional targets of PPARγ in breast cancer pathophysiology

Peroxisome proliferator-activated receptor γ (PPARγ) is a nuclear receptor and plays important roles in breast cancer cell proliferation. The complexity of the underlying biochemical and molecular mechanisms of breast cancer and the involvement of PPARγ in breast cancer pathophysiology are unclear. In this study, we carried out prediction of the peroxisome proliferator response element (PPRE) motifs in 2332 genes reported to be involved in breast cancer in literature. A total of 178 genes were found to have PPRE (DR1/DR2) and/or PPAR-associated conserved motif (PACM) motifs. We further constructed protein-protein interaction network, disease gene network and gene ontology (GO) analyses to identify novel key genes for experimental validation. We identified two genes in the glycolytic pathway (phosphoglycerate kinase 1 (PGK1) and pyruvate kinase M2 (PKM2)) at the ATP production steps and experimentally validated their repression by PPARγ in two breast cancer cell lines MDA-MB-231 and MCF-7. Further analysis suggested that this repression leads to decrease in ATP levels and apoptosis. These investigations will help us in understanding the molecular mechanisms by which PPARγ regulates the cellular energy pathway and the use of its ligands in human breast cancer therapeutics.

Predicting Disease-Related Subnetworks for Type 1 Diabetes Using a New Network Activity Score

In this study we investigated the advantage of including network information in prioritizing disease genes of type 1 diabetes (T1D). First, a naïve Bayesian network (NBN) model was developed to integrate information from multiple data sources and to define a T1D-involvement probability score (PS) for each individual gene. The algorithm was validated using known functional candidate genes as a benchmark. Genes with higher PS were found to be more likely to appear in T1D-related publications. Next a new network activity metric was proposed to evaluate the T1D relevance of protein-protein interaction (PPI) subnetworks. The metric considered the contribution both from individual genes and from network topological characteristics. The predictions were confirmed by several independent datasets, including a genome wide association study (GWAS), and two large-scale human gene expression studies. We found that novel candidate genes in the T1D subnetworks showed more significant associations with T1D than genes predicted using PS alone. Interestingly, most novel candidates were not encoded within the human leukocyte antigen (HLA) region, and their expression levels showed correlation with disease only in cohorts with low-risk HLA genotypes. The results suggested the importance of mapping disease gene networks in dissecting the genetics of complex diseases, and offered a general approach to network-based disease gene prioritization from multiple data sources.

A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers

Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.

Automatic Filtering and Substantiation of Drug Safety Signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.