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

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

Abstract PR16: RNA aptamers specific for tumor-infiltrating myeloid cells

Abstract Myeloid cells have been extensively studied in the tumor microenvironment for their role in promoting cancer cell survival and metastases. However, their peculiar activated phenotype has not been exploited for the targeted delivery of anticancer agents to the tumor site. Here we report a new therapeutic strategy that allows the delivery of cancer drugs to both tumor and metastases by the preferential targeting of genetically stable tumor-infiltrating myeloid cells (TIMC). We identified 4 monoclonal RNA aptamers that specifically recognize myeloid-derived suppressor cells (MDSC) and macrophages infiltrating the tumor, but not the splenic counterparts. The use of these aptamers conjugated to doxorubicin greatly enhances the accumulation of the chemotherapeutic drug in the primary and metastatic tumor sites in multiple cancer models. Compared to their individual components or current therapeutic approaches, doxorubicin-conjugated TIMC-specific aptamers show enhanced anticancer efficacy and no detectable treatment-related toxicity. This strategy has the intrinsic potential to target multiple tumor types through the same reagents. This abstract is also being presented as Poster A35. Citation Format: Adriana De La Fuente, Jimmy Caroli, Dimitri Van Simaeys, Serena Zilio, Emilia Mazza, Vincenzo Bronte, Silvio Bicciato, Paolo Serafini. RNA aptamers specific for tumor-infiltrating myeloid cells [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr PR16

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)

RNA aptamers specific for transmembrane p24 trafficking protein 6 and Clusterin for the targeted delivery of imaging reagents and RNA therapeutics to human β cells

The ability to detect and target β cells in vivo can substantially refine how diabetes is studied and treated. However, the lack of specific probes still hampers a precise characterization of human β cell mass and the delivery of therapeutics in clinical settings. Here, we report the identification of two RNA aptamers that specifically and selectively recognize mouse and human β cells. The putative targets of the two aptamers are transmembrane p24 trafficking protein 6 (TMED6) and clusterin (CLUS). When given systemically in immune deficient mice, these aptamers recognize the human islet graft producing a fluorescent signal proportional to the number of human islets transplanted. These aptamers cross-react with endogenous mouse β cells and allow monitoring the rejection of mouse islet allografts. Finally, once conjugated to saRNA specific for X-linked inhibitor of apoptosis (XIAP), they can efficiently transfect non-dissociated human islets, prevent early graft loss, and improve the efficacy of human islet transplantation in immunodeficient in mice

Aptamers against mouse and human tumor-infiltrating myeloid cells as reagents for targeted chemotherapy

Local delivery of anticancer agents has the potential to maximize treatment efficacy and minimize the acute and long-term systemic toxicities. Here, we used unsupervised systematic evolution of ligands by exponential enrichment to identify four RNA aptamers that specifically recognized mouse and human myeloid cells infiltrating tumors but not their peripheral or circulating counterparts in multiple mouse models and from patients with head and neck squamous cell carcinoma (HNSCC). The use of these aptamers conjugated to doxorubicin enhanced the accumulation and bystander release of the chemotherapeutic drug in both primary and metastatic tumor sites in breast and fibrosarcoma mouse models. In the 4T1 mammary carcinoma model, these doxorubicin-conjugated aptamers outperformed Doxil, the first clinically approved highly optimized nanoparticle for targeted chemotherapy, promoting tumor regression after just three administrations with no detected changes in weight loss or blood chemistry. These RNA aptamers recognized tumor infiltrating myeloid cells in a variety of mouse tumors in vivo and from human HNSCC ex vivo. This work suggests the use of RNA aptamers for the detection of myeloid-derived suppressor cells in humans and for a targeted delivery of chemotherapy to the tumor microenvironment in multiple malignancies