Transcriptome­based drug repositioning identifies TPCA­1 as a potential selective inhibitor of esophagus squamous carcinoma cell viability.

Esophageal squamous cell carcinoma (ESCC) is a cancer type with limited treatment options. The present study aimed to screen for small molecules that may inhibit ESCC cell viability. The small­molecule­perturbed signatures were extrapolated from the library of integrated network­based cellular signatures (LINCS) database. Since LINCS does not include small­molecule­perturbed signatures of ESCC cells, it was hypothesized that non­ESCC cell lines that display transcriptome profiles similar to those of ESCC may have similar small­molecule­perturbated responses to ESCC cells and that identifying small molecules that inhibit the viability of these non­ESCC cells may also inhibit the viability of ESCC cells. The transcriptomes of >1,000 cancer cell lines from the Cancer Cell Line Encyclopedia database were analyzed and 70 non­ESCC cell lines exhibiting similar transcriptome profiles to those of ESCC cells were identified. Among them, six cell lines with transcriptome signatures upon drug perturbation were available in the LINCS, which were used as reference signatures. A total of 20 ESCC datasets were analyzed and 522 downregulated and 461 upregulated differentially expressed genes (DEGs) that were consistently altered across >50% of the datasets were identified. These DEGs together with the reference signatures were then used as inputs of the ZhangScore method to score small molecules that may reverse transcriptome alterations of ESCC. Among the top­ranked 50 molecules identified by the ZhangScore, four candidates that may inhibit ESCC cell viability were experimentally verified. Furthermore, 2­[(aminocarbonyl)amino]­5­(4­fluorophenyl)­3­-thiophenecarboxamide (TPCA­1), an inhibitor of the NF­κB pathway, was able to preferentially inhibit the viability of ESCC cells compared with non­tumorigenic epithelial Het­1A cells. Mechanistically, TPCA­1 induced ESCC KYSE­450 cell apoptosis by inhibiting the phosphorylation of inhibitor of NF­κB kinase subunit ß, leading to IκBα stabilization and NF­κB signaling pathway inhibition. Collectively, these results demonstrated that LINCS­based drug repositioning may facilitate drug discovery and that TPCA­1 may be a promising candidate molecule in the treatment of ESCC.

Drug connectivity mapping and functional analysis reveal therapeutic small molecules that differentially modulate myelination.

Disruption or loss of oligodendrocytes (OLs) and myelin has devastating effects on CNS function and integrity, which occur in diverse neurological disorders, including Multiple Sclerosis (MS), Alzheimer's disease and neuropsychiatric disorders. Hence, there is a need to develop new therapies that promote oligodendrocyte regeneration and myelin repair. A promising approach is drug repurposing, but most agents have potentially contrasting biological actions depending on the cellular context and their dose-dependent effects on intracellular pathways. Here, we have used a combined systems biology and neurobiological approach to identify compounds that exert positive and negative effects on oligodendroglia, depending on concentration. Notably, next generation pharmacogenomic analysis identified the PI3K/Akt modulator LY294002 as the most highly ranked small molecule with both pro- and anti-oligodendroglial concentration-dependent effects. We validated these in silico findings using multidisciplinary approaches to reveal a profoundly bipartite effect of LY294002 on the generation of OPCs and their differentiation into myelinating oligodendrocytes in both postnatal and adult contexts. Finally, we employed transcriptional profiling and signalling pathway activity assays to determine cell-specific mechanisms of action of LY294002 on oligodendrocytes and resolve optimal in vivo conditions required to promote myelin repair. These results demonstrate the power of multidisciplinary strategies in determining the therapeutic potential of small molecules in neurodegenerative disorders.

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

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.

Repurposable drugs for SARS-CoV-2 and influenza sepsis with scRNA-seq data targeting post-transcription modifications.

Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. The lack of time for new drug discovery and the urgent need for rapid disease control to reduce mortality have led to a search for quick and effective alternatives to novel therapeutics, for example drug repurposing. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from patients with mild and severe COVID-19 (GEO: GSE145926, public data available and accessed on 22 April 2020). We identified 281 FDA-approved drugs that have the potential to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested and demonstrated the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a, two chemical inhibitors of glycosylation (a post-translational modification) on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus as well as on the transcription and translation of host cell cytokines and their regulators (IFNs and ISGs). In conclusion, we have identified and experimentally validated repurposable anti-SARS-CoV-2 and IAV drugs using a systems biology approach, which may have the potential for treating these viral infections and their complications (sepsis).

Integrating LINCS Data to Evaluate Cancer Transcriptome Modifying Potential of Small-molecule Compounds for Drug Repositioning.

BACKGROUND: Conventional high-throughput chemical screens in conjunction with genome-wide gene expression profiling proves to be successful in novel anti-cancer agent discovery and provides comprehensive insights into the mechanisms of action and off-target effects of single small-molecule compound. However, systematic evaluation on heterogeneous transcriptional responses of different cancer cell types to thousands of independent perturbations in a bioinformatics way is still limited. METHOD: Here, we introduce cancer transcriptome modifying potential (CTMP) which uses "Connectivity Score" to quantify and compare the effects of approved antineoplastic drugs on transcriptionally restoring dysregulated (both up- and down-) gene expressions at cancer state towards normal state. As a proof-of-concept, we applied this CTMP computational evaluation on > 10,000 small-molecule compounds using >200,000 Library of Integrated Network-based Cellular Signatures (LINCS) expression profiles generated upon 4 different cancer cell lines. We screened and proposed a candidate list of cancer transcriptome modifying therapeutics (CTMTs), among which the approved on-market drugs are further validated using GDSC drug sensitivity data, highlighting their potential to facilitate direct antineoplastic repositioning. RESULTS: In total, we calculated CTMPs of 85 on-market antineoplastic drugs and ~15,000 smallmolecule compounds using 253,813 transcriptomes across four cancer cell lines of lung, melanoma, prostate, and colon. Our results reveal that regardless of the chemical structure and targeted proteins majority of approved antineoplastic drugs present significant bilateral CTMPs across all 4 cancer cell lines. Bilateral CTMP-based systematic screen further indicates that candidate CTMTs are limited and most notably cancer-type specific. In particular, for each cancer cell type we proposed 3~5 CTMTs that are approved drugs with potent sensitivity data to support development in antineoplastic indications. CONCLUSION: Our work establishes CTMP to evaluate the antineoplastic property of small-molecule compounds and suggests CTMP-based systematic screen of cancer type-specific CTMTs as a feasible strategy in drug repositioning for precise anti-cancer purposes.

Identification of Repurposable Drugs and Adverse Drug Reactions for Various Courses of COVID-19 Based on Single-Cell RNA Sequencing Data.

Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA-approved drugs that have the potential to be effective against SARS-CoV-2 infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus. In conclusion, we have identified a list of repurposable anti-SARS-CoV-2 drugs using a systems biology approach.

A Review of Drug Repositioning Based Chemical-induced Cell Line Expression Data.

Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

Using the Pleiotropic Characteristics of Curcumin to Validate the Potential Application of a Novel Gene Expression Screening Platform.

Curcumin is a polyphenol derived from curcumin longa that exhibits anticancer and anti-inflammatory properties. The consumption of foods at supernutritional levels to obtain health benefits may paradoxically result in negative health outcomes. In the present study, multiple targeting characteristics of curcumin were analyzed using our gene expression screening system, which utilized the gene expression signatures of curcumin from human hepatocellular carcinoma and colorectal cancer cells to query gene expression databases and effectively identify the molecular actions of curcumin. In agreement with prediction, curcumin inhibited NF-κB and Aurora-A, and induced G2/M arrest and apoptosis. Curcumin-suppressed NF-κB was identified through inhibition of PLCG1, PIK3R1, and MALT1 in the CD4-T-cell-receptor-signaling NF-κB cascade pathway. The results suggest that our novel gene expression screening platform is an effective method of rapidly identifying unknown biological functions and side effects of compounds with potential nutraceutical benefits.

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.

Systematic Identification and Assessment of Therapeutic Targets for Breast Cancer Based on Genome-Wide RNA Interference Transcriptomes.

With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.