PharmOmics: A Species- and Tissue-specific Drug Signature Database and Online Tool for Drug Repurposing

Chen, Yen‐Wei; Arneson, Douglas; Diamante, Graciel; García, Jennifer; Zaghari, Nima; Patel, Paul; Allard, P.; Yang, Xia

doi:10.1101/837773

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…69 We also conducted an overlap-based drug repositioning analysis "PharmOmics" based on the identified key driver genes (FDR <0.05) to predict potential drugs or small molecules targeting WMH. 70 PharmOmics comprises a curated drug signature database covering 941 drugs, constructed from transcriptomic data across >20 tissues from rat, human, and mouse. For our analysis, we selected drug signatures from relevant tissues (in vivo human transcriptome data in cardiovascular and nervous system, and in vitro transcriptome data from murine oligodendroglial precursor cells), and examined the overlap between these drug signature genes and key-driver genes from our identified WMH-associated gene sets.…”

Section: Identification Of Biological Pathways Using Multi-dimensiona...mentioning

confidence: 99%

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Yang

Knol

Wang

et al. 2022

Brain

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Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at approximately 450,000 CpG sites in 9,732 middle-aged to older adults from 14 community-based studies. Single-CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single-CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10−8), was associated with F2 expression in blood (P = 6.4 × 10−5), and colocalized with FOLH1 expression in brain (posterior probability =0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single-CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis, and multi-omics colocalization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug repositioning analysis indicated antihyperlipidemic agents, more specifically peroxisome proliferator-activated receptor alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood brain barrier disruption.

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Section: Identification Of Biological Pathways Using Multi-dimensiona...mentioning

confidence: 99%

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Yang

Knol

Wang

et al. 2022

Brain

View full text Add to dashboard Cite

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“…We have recently developed a novel species- and tissue-specific network-based drug repositioning tool, PharmOmics, which is based on in vivo molecular studies of drugs ( 41 ). PharmOmics is a complementary drug repositioning tool to other existing tools, such as CMap ( 42 ) and LINCS L1000 ( 43 ), which are mostly based on in vitro cell line data.…”

Section: Overview and Updates On The Core Functions Of Mergeomicsmentioning

confidence: 99%

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

Ding

Blencowe

Nghiem

et al. 2021

Nucleic Acids Research

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The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

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“…To date, the CMap LINCS dataset encompasses more than 1.5 million gene expression signatures related to up to 5000 small molecules and more than 10,000 genes across a total of 77 cancer cell lines [9]. Such a vast amount of gene expression information enables computational approaches, such as the deep neural network, to learn data patterns, to predict gene regulation effects [11], and drug side effects [24]. In this work, we collected L1000 high-throughput gene expression data from LINCS phase I dataset; the dataset contains perturbation data points on gene expression level under small molecular treatments at different conditions, such as dosages, cell line cultures, and time points.…”

Section: Datasetsmentioning

confidence: 99%

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes

2021

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Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach—DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of “hybrid” chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug–gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.

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PharmOmics: A Species- and Tissue-specific Drug Signature Database and Online Tool for Drug Repurposing

Cited by 4 publications

References 62 publications

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes

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