Nucleobase adducts bind MR1 and stimulate MR1-restricted T cells.

MR1T cells are a recently found class of T cells that recognize antigens presented by the major histocompatibility complex-I-related molecule MR1 in the absence of microbial infection. The nature of the self-antigens that stimulate MR1T cells remains unclear, hampering our understanding of their physiological role and therapeutic potential. By combining genetic, pharmacological, and biochemical approaches, we found that carbonyl stress and changes in nucleobase metabolism in target cells promote MR1T cell activation. Stimulatory compounds formed by carbonyl adducts of nucleobases were detected within MR1 molecules produced by tumor cells, and their abundance and antigenicity were enhanced by drugs that induce carbonyl accumulation. Our data reveal carbonyl-nucleobase adducts as MR1T cell antigens. Recognizing cells under carbonyl stress allows MR1T cells to monitor cellular metabolic changes with physiological and therapeutic implications.

Surface protein and functional analyses identify CD4+CD39+ TCR αß+ and activated TCR Vδ1+ cells with distinct pro-inflammatory functions in Crohn's disease lesions.

Crohn's disease (CD) is a chronic immune-mediated disorder of the gastrointestinal tract. Extensive screening studies have revealed the accumulation of immune cell subsets with unique plasticity and immunoregulatory properties in patients with CD. We performed phenotypic and functional studies on inflamed and non-inflamed bioptic tissue to investigate the presence of distinct T cells in the intestinal mucosa of CD patients. We analysed hundreds of surface molecules expressed on cells isolated from the intestinal tissue of CD patients using anti-CD45 mAbs-based barcoding. A gene ontology enrichment analysis showed that proteins that regulate the activation of T cells were the most enriched group. We, therefore, designed T-cell focused multicolour flow-cytometry panels and performed clustering analysis which revealed an accumulation of activated TEM CD4+CD39+ T cells producing IL-17 and IL-21 and increased frequency of terminally differentiated TCR Vδ1+ cells producing TNF-α and IFN-γ in inflamed tissue of CD patients. The different functional capacities of CD4+ and TCR Vδ1+ cells in CD lesions indicate their non-overlapping contribution to inflammation. The abnormally high number of terminally differentiated TCR Vδ1+ cells suggests that they are continuously activated in inflamed tissue, making them a potential target for novel therapies.

Promiscuous recognition of MR1 drives self-reactive mucosal-associated invariant T cell responses.

Mucosal-associated invariant T (MAIT) cells use canonical semi-invariant T cell receptors (TCR) to recognize microbial riboflavin precursors displayed by the antigen-presenting molecule MR1. The extent of MAIT TCR crossreactivity toward physiological, microbially unrelated antigens remains underexplored. We describe MAIT TCRs endowed with MR1-dependent reactivity to tumor and healthy cells in the absence of microbial metabolites. MAIT cells bearing TCRs crossreactive toward self are rare but commonly found within healthy donors and display T-helper-like functions in vitro. Experiments with MR1-tetramers loaded with distinct ligands revealed significant crossreactivity among MAIT TCRs both ex vivo and upon in vitro expansion. A canonical MAIT TCR was selected on the basis of extremely promiscuous MR1 recognition. Structural and molecular dynamic analyses associated promiscuity to unique TCRß-chain features that were enriched within self-reactive MAIT cells of healthy individuals. Thus, self-reactive recognition of MR1 represents a functionally relevant indication of MAIT TCR crossreactivity, suggesting a potentially broader role of MAIT cells in immune homeostasis and diseases, beyond microbial immunosurveillance.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service.

BACKGROUND: Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS: "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS: During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning.

BACKGROUND: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. METHODS: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. RESULTS: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. CONCLUSIONS: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.