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Mucosal associated invariant T cells (MAIT) are innate T lymphocytes that detect a large variety of bacteria and yeasts. This recognition depends on the detection of microbial compounds presented by the evolutionarily conserved major-histocompatibility-complex (MHC) class I molecule, MR1. Here we show that MAIT cells display cytotoxic activity towards MR1 overexpressing non-hematopoietic cells cocultured with bacteria. The NK receptor, CD161, highly expressed by MAIT cells, modulated the cytokine but not the cytotoxic response triggered by bacteria infected cells. MAIT cells are also activated by and kill epithelial cells expressing endogenous levels of MRI after infection with the invasive bacteria Shigella flexneri. In contrast, MAIT cells were not activated by epithelial cells infected by Salmonella enterica Typhimurium. Finally, MAIT cells are activated in human volunteers receiving an attenuated strain of Shigella dysenteriae-1 tested as a potential vaccine. Thus, in humans, MAIT cells are the most abundant T cell subset able to detect and kill bacteria infected cells.

Mucosal-associated invariant T cells are evolutionarily conserved innate lymphocytes whose physiological function has remained unclear. Lantz and colleagues now demonstrate an important antimicrobial function for these cells. Mucosal-associated invariant T lymphocytes (MAIT lymphocytes) are characterized by two evolutionarily conserved features: an invariant T cell antigen receptor (TCR) α-chain and restriction by the major histocompatibility complex (MHC)-related protein MR1. Here we show that MAIT cells were activated by cells infected with various strains of bacteria and yeast, but not cells infected with virus, in both humans and mice. This activation required cognate interaction between the invariant TCR and MR1, which can present a bacteria-derived ligand. In humans, we observed considerably fewer MAIT cells in blood from patients with bacterial infections such as tuberculosis. In the mouse, MAIT cells protected against infection by Mycobacterium abscessus or Escherichia coli . Thus, MAIT cells are evolutionarily conserved innate-like lymphocytes that sense and help fight off microbial infection.

Mucosal associated invariant T cells (MAIT) are innate T lymphocytes that detect a large variety of bacteria and yeasts. This recognition depends on the detection of microbial compounds presented by the evolutionarily conserved major-histocompatibility-complex (MHC) class I molecule, MR1. Here we show that MAIT cells display cytotoxic activity towards MR1 overexpressing non-hematopoietic cells cocultured with bacteria. The NK receptor, CD161, highly expressed by MAIT cells, modulated the cytokine but not the cytotoxic response triggered by bacteria infected cells. MAIT cells are also activated by and kill epithelial cells expressing endogenous levels of MRI after infection with the invasive bacteria Shigella flexneri. In contrast, MAIT cells were not activated by epithelial cells infected by Salmonella enterica Typhimurium. Finally, MAIT cells are activated in human volunteers receiving an attenuated strain of Shigella dysenteriae-1 tested as a potential vaccine. Thus, in humans, MAIT cells are the most abundant T cell subset able to detect and kill bacteria infected cells.

Nat. Immunol. 11, 701–708 (2010); published online 27 June 2010; corrected after print 13 August 2010 In the version of this article initially published, the author Shouxiong Huang (Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA) was not included. This authorshould be listed as author 15 (and affiliation 8).

Mucosal associated invariant T (MAIT) lymphocytes are characterized by two evolutionarily conserved features: an invariant TCRα chain and restriction by the MHC-related protein, MR1. Here we show that MAIT cells are activated by cells infected with different strains of bacteria and yeasts, but not viruses, both in human and mouse. This activation requires cognate interaction between the invariant T cell receptor (TCR) and MR1, which can present a bacteria-derived ligand. In humans, we observe a striking diminution of MAIT cell blood-numbers in patients with bacterial infections such as tuberculosis. In mouse, MAIT cells protect against infections by Mycobacterium and Escherichia coli. Thus, MAIT cells are evolutionarily conserved innate-like lymphocytes that sense and help fight off microbial infections.

Standardized analysis pipelines contribute to making data bioinformatics research compliant with the paradigm of Findability, Accessibility, Interoperability, and Reusability (FAIR), and facilitate collaboration. Nextflow and Snakemake, two popular command-line solutions, are increasingly adopted by users, complementing GUI-based platforms such as Galaxy. We report recent developments of the nf-core framework with the new Nextflow Domain-Specific Language (DSL2). An extensive library of modules and subworkflows enables research communities to adopt common standards progressively, as resources and needs allow. We present an overview of some of the research communities built around nf-core and showcase its adoption by six EuroFAANG farmed animal research consortia.

Our objectives were to better characterize the colorectal function of patients with Spina Bifida (SB). Patients with SB and healthy volunteers (HVs) completed prospectively a standardized questionnaire, clinical evaluation, rectal barostat, colonoscopy with biopsies and faecal collection. The data from 36 adults with SB (age: 38.8 [34.1-47.2]) were compared with those of 16 HVs (age: 39.0 [31.0-46.5]). Compared to HVs, rectal compliance was lower in patients with SB (p = 0.01), whereas rectal tone was higher (p = 0.0015). Ex vivo paracellular permeability was increased in patients with SB (p = 0.0008) and inversely correlated with rectal compliance (r = − 0.563, p = 0.002). The expression of key tight junction proteins and inflammatory markers was comparable between SB and HVs, except for an increase in Claudin-1 immunoreactivity (p = 0.04) in SB compared to HVs. TGFβ1 and GDNF mRNAs were expressed at higher levels in patients with SB (p = 0.02 and p = 0.008). The levels of acetate, propionate and butyrate in faecal samples were reduced (p = 0.04, p = 0.01, and p = 0.02, respectively). Our findings provide evidence that anorectal and epithelial functions are altered in patients with SB. The alterations in these key functions might represent new therapeutic targets, in particular using microbiota-derived approaches. Clinical Trials: NCT02440984 and NCT03054415.

Background Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. Results The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. Conclusion This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipe and contains as testing data all the data used in this paper.