HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions

Chen, Jake Y.; Pandey, Ragini; Nguyen, Thanh

doi:10.1186/s12864-017-3512-1

Cited by 40 publications

(32 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4) We performed enrichment analysis by finding overlapping genes shared between co-expression clusters and GWAS candidate genes, extracting these enriched clusters as PD-specific modules. 5) We constructed PD-specific network module by retrieving the gene-gene interactions for the genes in PD-specific modules from the HAPPI-2 database [ 16 ]. 6) Finally, we annotated PD-specific modules with functional groups using ClueGO [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

“…The PAGER database [ 15 ] was used to obtain gene-gene regulatory relationships (22,127 pairs curated from 645,385 in total). The HAPPI-2 database [ 16 ] 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.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Repositioning drugs by targeting network modules: a Parkinson’s disease case study

et al. 2017

Self Cite

View full text Add to dashboard Cite

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 ...

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Repositioning drugs by targeting network modules: a Parkinson’s disease case study

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…The similar gene expression profile for drug phenotype in patients with different diseases as mapped by connectivity map (cMap), Library Integrated Network based Cellular Signatures (LINCS), expression signatures may have similar therapeutic applications [50][51][52]. Other important public data resources that are used for network-based drug repositioning includes gene set enrichment analysis (GSEA) for drug-drug similarity network [53]; DrugBank [54], online mendelian inheritance in men (OMIM) [55] and GEO [56] for predicting drug-disease network; KEGG [57], STRING [58], BioGrid [59], HAPPI [60] and Reactome [61] for pathway and/or proteinprotein interactions; STITCH drug-gene/protein database [62,63], TTD therapeutic target database [64], SFINX for drug-drug interactions [65]; and SIDER for drug side effects [66]. Also, the recently reported 'Drug Repurposing from Control System theory (DeCoST)' is a comprehensive platform for drug repurposing that encompasses various limitations in the previous databases like variation in number of copies of gene of interest, mutations, lack of reference for normal range of gene expression etc.…”

Section: Strategies For Drug Repositioningmentioning

confidence: 99%

Drug repurposing for breast cancer therapy: Old weapon for new battle

Aggarwal

Verma

Aggarwal

et al. 2021

Seminars in Cancer Biology

View full text Add to dashboard Cite

Despite tremendous resources being invested in prevention and treatment, breast cancer remains a leading cause of cancer deaths in women globally. The available treatment modalities are very costly and produces severe side effects. Drug repurposing that relate to new uses for old drugs has emerged as a novel approach for drug development. Repositioning of old, clinically approved, off patent non-cancer drugs with known targets, into newer indication is like using old weapons for new battle. The advances in genomics, proteomics and information computational biology has facilitated the process of drug repurposing. Repositioning approach not only fastens the process of drug development but also offers more effective, cheaper, safer drugs with lesser/known side effects. During the last decade, drugs such as alkylating agents, anthracyclins, antimetabolite, CDK4/6 inhibitor, aromatase inhibitor, mTOR inhibitor and mitotic inhibitors has been repositioned for breast cancer treatment. The repositioned drugs have been successfully used for the treatment of most aggressive triple negative breast cancer. The literature review suggest that serendipity plays a major role in the drug development. This article describes the comprehensive overview of the current scenario of drug repurposing for the breast cancer treatment. The strategies as well as several examples of repurposed drugs are provided. The challenges associated with drug repurposing are discussed. Current breast cancer therapyUnlike a decade ago, clinicians today have multiple choices for breast cancer treatment depending on the size, stage, grade, metastatic behavior, aggressiveness and intrinsic molecular subtyping of tumor, age, menopausal status, overall health, comorbidities, and preferences of the patient [9-13]. Chemotherapy, hormone therapy, immunotherapy, radiotherapy, and surgery are the common modalities for breast cancer [10,14]. Primary choice of treatment usually includes surgery with the aim of complete resection of the major tumor mass on the first hand. Breast conserving (lumpectomy) and breast reconstruction surgeries, mastectomy or lymph node dissections are performed initially in the breast cancer patients [15]. Surgery may be preceded with the systemic neoadjuvant therapies in order to shrink the tumor for effective surgery and to maximize breast conservation. For example, in HER2+ cases, Trastuzumab (Herceptin) and Pertuzumab (Perjeta)

show abstract

“…In this Figure, the green connectivity shows the types of connectivity for which drug repurposing could utilize to answer the key question: could drug A be re-indicated to treat disease B. The literature and public data sources for these types of connectivity have been thoroughly developed in the recent two decades, such as DrugBank (Law et al, 2013 ) and SFINX (Andersson et al, 2015 ) for drug-drug interaction; DrugBank (Law et al, 2013 ) and STITCH (Kuhn et al, 2012 ) for drug-gene/protein interaction; BioGRID (Chatr-Aryamontri et al, 2013 ), STRING (Szklarczyk et al, 2015 ), HAPPI (Chen et al, 2017 ), KEGG (Kanehisa et al, 2017 ) and Reactome (Croft et al, 2011 ) for protein-protein interaction and human pathway; OMIM (Baxevanis, 2012 ) and GEO (Barrett et al, 2013 ) for disease-specific gene curation and analysis; the human disease network (Goh et al, 2007 ) for disease-disease connectivity; and SIDER for diseases' drug-side-effect (Kuhn et al, 2016 ). The integration of rich data sources enable mathematical system modeling and analysis in system biology to deepen our understanding and predictive capability for biological processes, disease ontology (Hannon and Ruth, 2014 ; Goel and Richter-Dyn, 2016 ; Woodhead et al, 2016 ) and personalized medicine (Weston and Hood, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

DeCoST: A New Approach in Drug Repurposing From Control System Theory

et al. 2018

Self Cite

View full text Add to dashboard Cite

In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from “one: disease-gene-drug” to “multi: gene, dru” and from “lazy guilt-by-association” to “systematic model-based pattern matching,” mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear–quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).

show abstract

HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions

Cited by 40 publications

References 76 publications

Repositioning drugs by targeting network modules: a Parkinson’s disease case study

Repositioning drugs by targeting network modules: a Parkinson’s disease case study

Drug repurposing for breast cancer therapy: Old weapon for new battle

DeCoST: A New Approach in Drug Repurposing From Control System Theory

Contact Info

Product

Resources

About