Identification and validation of novel biomarkers and therapeutics for pulpitis using connectivity mapping

International audienceAim To create an irreversible pulpitis gene signature from microarray data of healthy and inflamed dental pulps, followed by a bioinformatics approach using connectivity mapping to identify therapeutic compounds that could potentially treat pulpitis. Methodology The Gene Expression Omnibus (GEO) database, an international public repositor

Identification and validation of novel biomarkers and therapeutics for pulpitis using connectivity mapping

Aim: To create an irreversible pulpitis gene signature from microarray data of healthy and inflamed dental pulps, followed by a bioinformatics approach using connectivity mapping to identify therapeutic compounds that could potentially treat pulpitis. // Methodology: The Gene Expression Omnibus (GEO) database, an international public repository of genomics data sets, was searched for human microarray datasets assessing pulpitis. An irreversible pulpitis gene expression signature was generated by differential expression analysis. The statistically significant connectivity map (ssCMap) method was used to identify compounds with a highly correlating gene expression pattern. qPCR was used to validate novel pulpitis genes. An ex vivo pulpitis model was used to test the effects of the compounds identified, and the level of inflammatory cytokines was measured with qPCR, ELISA and multiplex array. Means were compared using the t-test or ANOVA with the level of significance set at p ≤ .05. // Results: Pulpitis gene signatures were created using differential gene expression analysis at cutoff points p = .0001 and .000018. Top upregulated genes were selected as potential pulpitis biomarkers. Among these, IL8, IL6 and MMP9 were previously identified as pulpitis biomarkers. Novel upregulated genes, chemokine (C-C motif) ligand 21 (CCL21), metallothionein 1H (MT1H) and aquaporin 9 (AQP9) were validated in the pulp tissue of teeth clinically diagnosed with irreversible pulpitis using qPCR. ssCMap analysis identified fluvastatin (Statin) and dequalinium chloride (Quaternary ammonium) as compounds with the strongest correlation to the gene signatures (p = .0001). Fluvastatin reduced IL8, IL6, CCL21, AQP9 (p < .001) and MMP9 (p < .05) in the ex vivo pulpitis model, while dequalinium chloride reduced AQP9 (p < .001) but had no significant effect on the other biomarkers. // Conclusions: AQP9, MT1H and CCL21 were identified and validated as novel biomarkers for pulpitis. Fluvastatin and dequalinium chloride identified by the ssCMap as potential therapeutics for pulpitis reduced selected pulpitis biomarkers in an ex vivo pulpitis model. In vivo testing of these licenced drugs is warranted

A connectivity mapping approach predicted acetylsalicylic acid (aspirin) to induce osteo/odontogenic differentiation of dental pulp cells

International audienc

A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond.

Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug-drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug-drug similarity network. The resulting database, 'DrugSimDB' currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http://vafaeelab.com/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information

Resveratrol inhibits development of colorectal adenoma via suppression of LEF1; comprehensive analysis with connectivity map

Although many chemopreventive studies on colorectal tumors have been reported, no effective and safe preventive agent is currently available. We searched for candidate preventive compounds against colorectal tumor comprehensively from United States Food and Drug Administration (FDA)-approved compounds by using connectivity map (CMAP) analysis coupled with in vitro screening with colorectal adenoma (CRA) patient-derived organoids (PDOs). We generated CRA-specific gene signatures based on the DNA microarray analysis of CRA and normal epithelial specimens, applied them to CMAP analysis with 1309 FDA-approved compounds, and identified 121 candidate compounds that should cancel the gene signatures. We narrowed them down to 15 compounds, and evaluated their inhibitory effects on the growth of CRA-PDOs in vitro. We finally identified resveratrol, one of the polyphenolic phytochemicals, as a compound showing the strongest inhibitory effect on the growth of CRA-PDOs compared with normal epithelial PDOs. When resveratrol was administered to ApcMin/+ mice at 15 or 30 mg/kg, the number of polyps (adenomas) was significantly reduced in both groups compared with control mice. Similarly, the number of polyps (adenomas) was significantly reduced in azoxymethane-injected rats treated with 10 or 100 mg/resveratrol compared with control rats. Microarray analysis of adenomas from resveratrol-treated rats revealed the highest change (downregulation) in expression of LEF1, a key molecule in the Wnt signaling pathway. Treatment with resveratrol significantly downregulated the Wnt-target gene (MYC) in CRA-PDOs. Our data demonstrated that resveratrol can be the most effective compound for chemoprevention of colorectal tumors, the efficacy of which is mediated through suppression of LEF1 expression in the Wnt signaling pathway

Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks.

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches

Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design

Drug discovery and development are long and arduous processes; recent figures point to 10 years and $2 billion USD to take a new chemical agent from discovery through to market. Moreover, though an approved blockbuster drug can be lucrative for the controlling pharmaceutical company, new therapeutic agents suffer from a 90% attrition during development, making the chances of success in the drug development process relatively low. Machine learning (ML) has re-emerged in the last several years as a powerful set of tools for unlocking value from large datasets. ML has shown great promise in improving efficiencies across numerous industries with high quality, vast, datasets. In an age of increasing access to highly curated rich sources of biological data, ML shows promise in reversing some of the negative trends shown in drug discovery and development. In this first part of our analysis of the application of ML to the drug discovery and development process, we discuss recent advances in the use of computational techniques in drug target discovery and lead molecule optimisation. We focus our analysis on oncology, though make reference to the wider field of human health and disease