Circr, a Computational Tool to Identify miRNA:circRNA Associations

Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for de novo prediction of miRNA binding sites on target sequences (miRanda, RNAhybrid, and TargetScan) and annotates each identified miRNA:target pairs with experimentally validated miRNA:RNA interactions and binding sites for Argonaute proteins derived from either ChIPseq or CLIPseq data. The combination of multiple tools for the identification of a single miRNA recognition site with experimental data allows to efficiently prioritize candidate miRNA:circRNA interactions for functional studies in different organisms. Circr can use its internal annotation database or custom annotation tables to enhance the identification of novel and not previously annotated miRNA:circRNA sites in virtually any species. Circr is written in Python 3.6 and is released under the GNU GPL3.0 License at https://github.com/bicciatolab/Circr

GDA, a web-based tool for Genomics and Drugs integrated analysis

Several major screenings of genetic profiling and drug testing in cancer cell lines proved that the integration of genomic portraits and compound activities is effective in discovering new genetic markers of drug sensitivity and clinically relevant anticancer compounds. Despite most genetic and drug response data are publicly available, the availability of user-friendly tools for their integrative analysis remains limited, thus hampering an effective exploitation of this information. Here, we present GDA, a web-based tool for Genomics and Drugs integrated Analysis that combines drug response data for >50 800 compounds with mutations and gene expression profiles across 73 cancer cell lines. Genomic and pharmacological data are integrated through a modular architecture that allows users to identify compounds active towards cancer cell lines bearing a specific genomic background and, conversely, the mutational or transcriptional status of cells responding or not-responding to a specific compound. Results are presented through intuitive graphical representations and supplemented with information obtained from public repositories. As both personalized targeted therapies and drug-repurposing are gaining increasing attention, GDA represents a resource to formulate hypotheses on the interplay between genomic traits and drug response in cancer. GDA is freely available at http://gda.unimore.it/

A novel RNA aptamer identifies plasma membrane ATP synthase beta subunit as an early marker and therapeutic target in aggressive cancer

PURPOSE: Primary breast and prostate cancers can be cured, but metastatic disease cannot. Identifying cell factors that predict metastatic potential could guide both prognosis and treatment. METHODS: We used Cell-SELEX to screen an RNA aptamer library for differential binding to prostate cancer cell lines with high vs. low metastatic potential. Mass spectroscopy, immunoblot, and immunohistochemistry were used to identify and validate aptamer targets. Aptamer properties were tested in vitro, in xenograft models, and in clinical biopsies. Gene expression datasets were queried for target associations in cancer. RESULTS: We identified a novel aptamer (Apt63) that binds to the beta subunit of F1Fo ATP synthase (ATP5B), present on the plasma membrane of certain normal and cancer cells. Apt63 bound to plasma membranes of multiple aggressive breast and prostate cell lines, but not to normal breast and prostate epithelial cells, and weakly or not at all to non-metastasizing cancer cells; binding led to rapid cell death. A single intravenous injection of Apt63 induced rapid, tumor cell-selective binding and cytotoxicity in MDA-MB-231 xenograft tumors, associated with endonuclease G nuclear translocation and DNA fragmentation. Apt63 was not toxic to non-transformed epithelial cells in vitro or adjacent normal tissue in vivo. In breast cancer tissue arrays, plasma membrane staining with Apt63 correlated with tumor stage (p < 0.0001, n = 416) and was independent of other cancer markers. Across multiple datasets, ATP5B expression was significantly increased relative to normal tissue, and negatively correlated with metastasis-free (p = 0.0063, 0.00039, respectively) and overall (p = 0.050, 0.0198) survival. CONCLUSION: Ecto-ATP5B binding by Apt63 may disrupt an essential survival mechanism in a subset of tumors with high metastatic potential, and defines a novel category of cancers with potential vulnerability to ATP5B-targeted therapy. Apt63 is a unique tool for elucidating the function of surface ATP synthase, and potentially for predicting and treating metastatic breast and prostate cancer

MDP, a database linking drug response data to genomic information, identifies dasatinib and statins as a combinatorial strategy to inhibit YAP/TAZ in cancer cells

Targeted anticancer therapies represent the most effective pharmacological strategies in terms of clinical responses. In this context, genetic alteration of several oncogenes represents an optimal predictor of response to targeted therapy. Integration of large-scale molecular and pharmacological data from cancer cell lines promises to be effective in the discovery of new genetic markers of drug sensitivity and of clinically relevant anticancer compounds. To define novel pharmacogenomic dependencies in cancer, we created the Mutations and Drugs Portal (MDP, http://mdp.unimore.it), a web accessible database that combines the cell-based NCI60 screening of more than 50,000 compounds with genomic data extracted from the Cancer Cell Line Encyclopedia and the NCI60 DTP projects. MDP can be queried for drugs active in cancer cell lines carrying mutations in specific cancer genes or for genetic markers associated to sensitivity or resistance to a given compound. As proof of performance, we interrogated MDP to identify both known and novel pharmacogenomics associations and unveiled an unpredicted combination of two FDA-approved compounds, namely statins and Dasatinib, as an effective strategy to potently inhibit YAP/TAZ in cancer cells

popsicleR: a R Package for pre-processing and quality control analysis of single cell RNA-seq data

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Preprocessing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of preprocessing parameters. Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available https://github.com/biccialolab/popsicleR . (C) 2022 The Authors. Published by Elsevier Ltd

protwis/gpcrdb_data: Release 2023 September

This repository is the collection point of reference data for the GPCRdb. The GPCRdb contains reference data, interactive visualisation and experiment design tools for G protein-coupled receptors (GPCRs)

protwis/protwis: Release 2023 June

Protwis is the backbone of the GPCRdb. The GPCRdb contains reference data, interactive visualisation and experiment design tools for G protein-coupled receptors (GPCRs)

protwis/protwis: Release 2023 September

Protwis is the backbone of the GPCRdb. The GPCRdb contains reference data, interactive visualisation and experiment design tools for G protein-coupled receptors (GPCRs)

Epigenomic landscape of human colorectal cancer unveils an aberrant core of pan-cancer enhancers orchestrated by YAP/TAZ

Cancer is characterized by pervasive epigenetic alterations with enhancer dysfunction orchestrating the aberrant cancer transcriptional programs and transcriptional dependencies. Here, we epigenetically characterize human colorectal cancer (CRC) using de novo chromatin state discovery on a library of different patient-derived organoids. By exploring this resource, we unveil a tumor-specific deregulated enhancerome that is cancer cell-intrinsic and independent of interpatient heterogeneity. We show that the transcriptional coactivators YAP/TAZ act as key regulators of the conserved CRC gained enhancers. The same YAP/TAZ-bound enhancers display active chromatin profiles across diverse human tumors, highlighting a pan-cancer epigenetic rewiring which at single-cell level distinguishes malignant from normal cell populations. YAP/TAZ inhibition in established tumor organoids causes extensive cell death unveiling their essential role in tumor maintenance. This work indicates a common layer of YAP/TAZ-fueled enhancer reprogramming that is key for the cancer cell state and can be exploited for the development of improved therapeutic avenues