Human Disease-Drug Network Based on Genomic Expression Profiles

By Guanghui Hu, Pankaj Agarwal. PLoS ONE, 2009

296 citations recorded by the source corpus.

Background Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. Methodology/Principal Findings We performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the ∼24.5 million comparisons between ∼7,000 publicly available transcriptomic profiles. The network includes 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn's disease, and a variety of existing drugs for Huntington's disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen's undesired carcinogenic property. From the ∼37K drug-drug relationships, we discover relationships that aid in target and pathway deconvolution, such as 1) KCNMA1 as a potential molecular target of lobeline, and 2) both apoptotic DNA fragmentation and G2/M DNA damage checkpoint regulation as potential pathway targets of daunorubicin. Conclusions/Significance We have automatically generated thousands of disease and drug expression profiles using GEO datasets, and constructed a large scale disease-drug network for effective and efficient drug repositioning as well as drug target/pathway identification.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4e71fece010f1335becab313116b1ea7dd31eef4

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Citations in the corpus

Exploiting drug-disease relationships for computational drug repositioning (2011, methodology): “Hu and Agarwal [35] created a disease-similarity network using publicly available gene expression profiles, and integrated this network with molecular profiles and knowledge of drugs and drug targets to infer drug repositioning opportunities and suggest molecular targets and mechanisms underlying dr”
Discovery and preclinical validation of drug indications using compendia of public gene expression data (2011, background): “By examining the sets of genes that are up-and down-regulated in a disease state compared to a normal state, it is possible to create a gene expression profile, or signature, of a disease ( 13 – 17 ).”
PREDICT: a method for inferring novel drug indications with application to personalized medicine (2011, unknown): “Table is supplemented as an Excel file.”
Network-based in silico drug efficacy screening (2016, background): “Gene expression proﬁle consistency based approaches aim to overcome these limitations by investigating correlations between the expression signatures of drug perturbations and the expression proﬁles in diseases 22,23 .”
Network-based machine learning and graph theory algorithms for precision oncology (2017, background): “The paper 23 performed a large-scale analysis of ~7000 genomic expression pro ﬁ les in the Gene Expression Omnibus with human disease and drug annotations to create a disease – drug network consisting of drug – drug, drug – disease and disease – disease relations.”
Computational Drug Repositioning: From Data to Therapeutics (2013, background): “These contain raw data and processed results from thousands of individual experiments, making it possible to probe the CMap database with collections of disease signatures 21,22 produced by other researchers.”
Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets (2010, background): “As a matter of fact, very recently, Hu and Agarwal [10] presented an approach to determine disease relationships using only gene expression data.”
Discovery of drug mode of action and drug repositioning from transcriptional responses (2010, background): “Gene signatures can be used to discover “connections” among drugs, pathways, and diseases (8, 11, 12, 18) using a large collection of transcriptional responses following compound treatments, such as the “Connectivity Map” (11, 12).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “…expression changes, disease–drug associations, interactomes, or signaling networks), was used to suggest unexpected, novel uses of existing drugs (Hu & Agarwal, 2009; Iorio et al., 2010, MANTRA server, http://mantra.tigem. it; Kotelnikova et al., 2010; Suthram et al., 2010; Gottlieb et al.,…”
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, background): “The majority of these approaches leverage on known drug properties such as chemical similarity [15], molecular activity similarity [20], molecular docking [17], and gene expression profile similarity [13, 24].”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “A variant of that approach has been pursued by Hu and Agarwal, 74 in which gene expression data are not prospectively collected but can be directly obtained from an existing database, Gene Expression Omnibus.”