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Motivation: Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. Results: We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Summary Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the drug set enrichment analysis and the Gene2drug analysis. Here, we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database. Availability and implementation The tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: Elucidation of molecular targets of a compound [mode of action (MoA)] and its off-targets is a crucial step in drug development. We developed an online collaborative resource (MANTRA 2.0) that supports this process by exploiting similarities between drug-induced transcriptional profiles. Drugs are organized in a network of nodes (drugs) and edges (similarities) highlighting ‘communities’ of drugs sharing a similar MoA. A user can upload gene expression profiles before and after drug treatment in one or multiple cell types. An automated processing pipeline transforms the gene expression profiles into a unique drug ‘node’ embedded in the drug-network. Visual inspection of the neighbouring drugs and communities helps in revealing its MoA and to suggest new applications of known drugs (drug repurposing). MANTRA 2.0 allows storing and sharing user-generated network nodes, thus making MANTRA 2.0 a collaborative ever-growing resource.Availability and implementation: The web tool is freely available for academic use at http://mantra.tigem.it.Contact: dibernardo@tigem.it [ABSTRACT FROM PUBLISHER]

Background: Inherited retinal diseases (IRD) are genetically heterogeneous disorders that cause the dysfunction or loss of photoreceptor cells and ultimately lead to blindness. To date, next-generation sequencing procedures fail to detect pathogenic sequence variants in coding regions of known IRD disease genes in about 30–40% of patients. One of the possible explanations for this missing heritability is the presence of yet unidentified transcripts of known IRD genes. Here, we aimed to define the transcript composition of IRD genes in the human retina by a meta-analysis of publicly available RNA-seq datasets using an ad-hoc designed pipeline. Results: We analysed 218 IRD genes and identified 5,054 transcripts, 3,367 of which were not previously reported. We assessed their putative expression levels and focused our attention on 435 transcripts predicted to account for at least 5% of the expression of the corresponding gene. We looked at the possible impact of the newly identified transcripts at the protein level and experimentally validated a subset of them. Conclusions: This study provides an unprecedented, detailed overview of the complexity of the human retinal transcriptome that can be instrumental in contributing to the resolution of some cases of missing heritability in IRD patients. [ABSTRACT FROM AUTHOR]

Motivation: Automated screening approaches are able to rapidly identify a set of small molecules inducing a desired phenotype from large small-molecule libraries. However, the resulting set of candidate molecules is usually very diverse pharmacologically, thus little insight on the shared mechanism of action (MoA) underlying their efficacy can be gained. Results: We introduce a computational method (Drug-Set Enrichment Analysis--DSEA) based on drug-induced gene expression profiles, which is able to identify the molecular pathways that are targeted by most of the drugs in the set. By diluting drug-specific effects unrelated to the phenotype of interest, DSEA is able to highlight phenotype-specific pathways, thus helping to formulate hypotheses on the MoA shared by the drugs in the set. We validated the method by analysing five different drug-sets related to well-known pharmacological classes. We then applied DSEA to identify the MoA shared by drugs known to be partially effective in rescuing mutant cystic fibrosis transmembrane conductance regulator (CFTR) gene function in Cystic Fibrosis. [ABSTRACT FROM AUTHOR]

Background Mistrafficking of CFTR protein caused by F508del, the most frequent mutation in cystic fibrosis (CF), can be corrected by cell incubation at low temperature, an effect that may be mediated by altered expression of proteostasis genes. Methods To identify small molecules mimicking low temperature, we compared gene expression profiles of cells kept at 27 °C with those previously generated from more than 1300 compounds. The resulting candidates were tested with a functional assay on a bronchial epithelial cell line. Results We found that anti-inflammatory glucocorticoids, such as mometasone, budesonide, and fluticasone, increased mutant CFTR function. However, this activity was not confirmed in primary bronchial epithelial cells. Actually, glucocorticoids enhanced Na + absorption, an effect that could further impair mucociliary clearance in CF airways. Conclusions Our results suggest that rescue of F508del-CFTR by low temperature cannot be easily mimicked by small molecules and that compounds with closer transcriptional and functional effects need to be found. [ABSTRACT FROM AUTHOR]

Cystic fibrosis (CF) is caused by mutations in CF transmembrane conductance regulator (CFTR). The most frequent mutation (F508del-CFTR) results in altered proteostasis, that is, in the misfolding and intracellular degradation of the protein. The F508del-CFTR proteostasis machinery and its homeostatic regulation are well studied, while the question whether ‘classical’ signalling pathways and phosphorylation cascades might control proteostasis remains barely explored. Here, we have unravelled signalling cascades acting selectively on the F508del-CFTR folding-trafficking defects by analysing the mechanisms of action of F508del-CFTR proteostasis regulator drugs through an approach based on transcriptional profiling followed by deconvolution of their gene signatures. Targeting multiple components of these signalling pathways resulted in potent and specific correction of F508del-CFTR proteostasis and in synergy with pharmacochaperones. These results provide new insights into the physiology of cellular proteostasis and a rational basis for developing effective pharmacological correctors of the F508del-CFTR defect. [ABSTRACT FROM AUTHOR]

Background: Mendelian disorders are mostly caused by single mutations in the DNA sequence of a gene, leading to a phenotype with pathologic consequences. Whole Exome Sequencing of patients can be a cost-effective alternative to standard genetic screenings to find causative mutations of genetic diseases, especially when the number of cases is limited. Analyzing exome sequencing data requires specific expertise, high computational resources and a reference variant database to identify pathogenic variants. Results: We developed a database of variations collected from patients with Mendelian disorders, which is automatically populated thanks to an associated exome-sequencing pipeline. The pipeline is able to automatically identify, annotate and store insertions, deletions and mutations in the database. The resource is freely available online http://exome.tigem.it. The exome sequencing pipeline automates the analysis workflow (quality control and read trimming, mapping on reference genome, post-alignment processing, variation calling and annotation) using state-of-the-art software tools. The exome-sequencing pipeline has been designed to run on a computing cluster in order to analyse several samples simultaneously. The detected variants are annotated by the pipeline not only with the standard variant annotations (e.g. allele frequency in the general population, the predicted effect on gene product activity, etc.) but, more importantly, with allele frequencies across samples progressively collected in the database itself, stratified by Mendelian disorder. Conclusions: We aim at providing a resource for the genetic disease community to automatically analyse whole exome-sequencing samples with a standard and uniform analysis pipeline, thus collecting variant allele frequencies by disorder. This resource may become a valuable tool to help dissecting the genotype underlying the disease phenotype through an improved selection of putative patient-specific causative or phenotype-associated variations. [ABSTRACT FROM AUTHOR]

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing three-dimensional structural similarities among 5452 compounds can be used to automatically group together drugs with similar scaffolds, physicochemical parameters and mode-of-action. We compared the structural network to a network representing transcriptional similarities among a subset of 1309 drugs for which transcriptional response were available in the Connectivity Map data set. Analysis of structurally similar, but transcriptionally different drugs sharing the same MOA enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability of transcription-based approaches to drug discovery and drug repositioning. Cardiac glycosides exhibited the strongest transcriptional responses with a significant induction of pathways related to epigenetic regulation, which suggests an epigenetic mechanism of action for these drugs. Drug classes with the weakest transcriptional responses tended to induce expression of cytochrome P450 enzymes, hinting at drug-induced drug resistance. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a 'toxic' transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We found that this transcriptional signature is shared by 258 compounds and it is associated to the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Finally, we built a predictive Random Forest model of these 258 compounds based on 128 physicochemical parameters, which should help in the early identification of potentially toxic drug candidates. Systems pharmacology: the chemical-transcriptional landscape of small molecules Transcriptional responses to drug treatment can reveal mechanism of action and off-target effects thus enabling drug repositioning, but only if measured in the appropriate cells at clinically relevant concentrations. A team led by Diego di Bernardo and Diego Medina generated a network representing structural similarities among compounds to automatically group together drugs with similar scaffolds and MOA. By comparing the structural drug network with a transcriptional drug network based on similarities in transcriptional response, the team observed broad differences between the two. This observation led to the identification of a transcriptional signature related lysosomal stress and phospholipidosis, and a physicochemical model to identify such compounds. These results provide general guidelines to prevent erroneous conclusion when using transcriptional responses of small molecules for drug discovery and drug repositioning [ABSTRACT FROM AUTHOR]