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      Immunopeptidomics reveals determinants of Mycobacterium tuberculosis antigen presentation on MHC class I

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          Abstract

          CD8+ T cell recognition of Mycobacterium tuberculosis ( Mtb)-specific peptides presented on major histocompatibility complex class I (MHC-I) contributes to immunity to tuberculosis (TB), but the principles that govern presentation of Mtb antigens on MHC-I are incompletely understood. In this study, mass spectrometry (MS) analysis of the MHC-I repertoire of Mtb-infected primary human macrophages reveals that substrates of Mtb’s type VII secretion systems (T7SS) are overrepresented among Mtb-derived peptides presented on MHC-I. Quantitative, targeted MS shows that ESX-1 activity is required for presentation of Mtb peptides derived from both ESX-1 substrates and ESX-5 substrates on MHC-I, consistent with a model in which proteins secreted by multiple T7SSs access a cytosolic antigen processing pathway via ESX-1-mediated phagosome permeabilization. Chemical inhibition of proteasome activity, lysosomal acidification, or cysteine cathepsin activity did not block presentation of Mtb antigens on MHC-I, suggesting involvement of other proteolytic pathways or redundancy among multiple pathways. Our study identifies Mtb antigens presented on MHC-I that could serve as targets for TB vaccines, and reveals how the activity of multiple T7SSs interacts to contribute to presentation of Mtb antigens on MHC-I.

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          Most cited references85

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          The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

          The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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            NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

            Abstract Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.
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              SignalP 6.0 predicts all five types of signal peptides using protein language models

              Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data. A new version of SignalP predicts all types of signal peptides.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                19 April 2023
                2023
                : 12
                : e84070
                Affiliations
                [1 ] Department of Biological Engineering, Massachusetts Institute of Technology ( https://ror.org/042nb2s44) Cambridge United States
                [2 ] Ragon Institute of Massachusetts General Hospital, Harvard, and MIT ( https://ror.org/002pd6e78) Cambridge United States
                [3 ] Koch Institute for Integrative Cancer Research Cambridge United States
                [4 ] Center for Precision Cancer Medicine Cambridge United States
                University of the Witwatersrand ( https://ror.org/03rp50x72) South Africa
                University of the Witwatersrand ( https://ror.org/03rp50x72) South Africa
                University of the Witwatersrand ( https://ror.org/03rp50x72) South Africa
                University of the Witwatersrand ( https://ror.org/03rp50x72) South Africa
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-3437-1956
                https://orcid.org/0000-0002-1545-1651
                https://orcid.org/0000-0003-1716-6712
                Article
                84070
                10.7554/eLife.84070
                10159623
                37073954
                af844ce5-1908-42df-a1f4-24e0c6ec9f7f
                © 2023, Leddy et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 10 October 2022
                : 17 April 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R35GM142900-01
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01A1022553
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: P42 ES027707
                Award Recipient :
                Funded by: Center for Precision Cancer Medicine;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P30 AI06035
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Immunology and Inflammation
                Microbiology and Infectious Disease
                Custom metadata
                Human macrophages infected with Mycobacterium tuberculosis present peptides derived from substrates of type VII secretion systems on MHC class I via a pathway dependent on the ESX-1 secretion system and independent of antigen processing by the proteasome and cathepsins.

                Life sciences
                m. tuberculosis,antigen presentation,mhc-i,secretion systems,phagosome,macrophages,other
                Life sciences
                m. tuberculosis, antigen presentation, mhc-i, secretion systems, phagosome, macrophages, other

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