1
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Deutsch EW, Kok LW, Mudge JM, Ruiz-Orera J, Fierro-Monti I, Sun Z, Abelin JG, Alba MM, Aspden JL, Bazzini AA, Bruford EA, Brunet MA, Calviello L, Carr SA, Carvunis AR, Chothani S, Clauwaert J, Dean K, Faridi P, Frankish A, Hubner N, Ingolia NT, Magrane M, Martin MJ, Martinez TF, Menschaert G, Ohler U, Orchard S, Rackham O, Roucou X, Slavoff SA, Valen E, Wacholder A, Weissman JS, Wu W, Xie Z, Choudhary J, Bassani-Sternberg M, Vizcaíno JA, Ternette N, Moritz RL, Prensner JR, van Heesch S. High-quality peptide evidence for annotating non-canonical open reading frames as human proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.612016. [PMID: 39314370 PMCID: PMC11419116 DOI: 10.1101/2024.09.09.612016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, WA, 98109, USA
| | - Leron W Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, 3584 CS, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, Germany
| | - Ivo Fierro-Monti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, WA, 98109, USA
| | | | - M Mar Alba
- Hospital del Mar Research Institute, Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Julie L Aspden
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Ariel A Bazzini
- Stowers Institute for Medical Research, Kansas City, MO, 64110, USA
- Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Elspeth A Bruford
- HUGO Gene Nomenclature Committee (HGNC), Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Marie A Brunet
- Pediatrics Department, University of Sherbrooke, Sherbrooke, Québec, Canada
- Centre de Recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS), Sherbrooke, Québec, Canada
| | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Anne-Ruxandra Carvunis
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Sonia Chothani
- Centre for Computational Biology and Program in Cardiovascular and Metabolic Disorders, Duke-NUS (National University of Singapore) Medical School, Singapore
| | - Jim Clauwaert
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Kellie Dean
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pouya Faridi
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Monash Proteomics & Metabolomics Platform, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Norbert Hubner
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, Germany
- Charité-Universitätsmedizin Berlin, Berlin, 10117, Germany
- Helmholtz-Institute for Translational AngioCardioScience (HI-TAC) of the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) at Heidelberg University, Heidelberg, 69117, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, 13347, Germany
| | - Nicholas T Ingolia
- Department of Molecular and Cell Biology, Center for Computational Biology, University of California, Berkeley, Berkeley, CA, 94720-3202, USA
| | - Michele Magrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Thomas F Martinez
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92617, USA
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92617, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92617, USA
| | - Gerben Menschaert
- Biobix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium
| | - Uwe Ohler
- Department of Biology, Humboldt University Berlin, Berlin, 10117, Germany
- Berlin Institute of Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, 10115, Germany
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | | | - Xavier Roucou
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Sarah A Slavoff
- Department of Chemistry, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Institute for Biomolecular Design and Discovery, Yale University, West Haven, CT, 06516, USA
| | - Eivind Valen
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Aaron Wacholder
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
- Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, 02138, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Wei Wu
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore
- Department of Pharmacy & Pharmaceutical sciences, National University of Singapore (NUS), Singapore
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jyoti Choudhary
- Functional Proteomics Group, Institute of Cancer Research, Chester Betty Labs, London, SW3 6JB, UK
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, 1005, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Lausanne, 1005, Switzerland
- Agora Cancer Research Centre, Lausanne, 1011, Switzerland
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Nicola Ternette
- School of Life Sciences, Division Cell Signalling and Immunology, University of Dundee, Dundee, DD1 5EH, UK
- Centre for Immuno-Oncology, University of Oxford, Oxford, OX37DQ, UK
| | - Robert L Moritz
- Institute for Systems Biology (ISB), Seattle, WA, 98109, USA
| | - John R Prensner
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Sebastiaan van Heesch
- Princess Máxima Center for Pediatric Oncology, Utrecht, 3584 CS, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
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2
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Meng W, Takeuchi Y, Ward JP, Sultan H, Arthur CD, Mardis ER, Artyomov MN, Lichti CF, Schreiber RD. Improvement of Tumor Neoantigen Detection by High-Field Asymmetric Waveform Ion Mobility Mass Spectrometry. Cancer Immunol Res 2024; 12:988-1006. [PMID: 38768391 PMCID: PMC11456315 DOI: 10.1158/2326-6066.cir-23-0900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/05/2024] [Accepted: 05/17/2024] [Indexed: 05/22/2024]
Abstract
Cancer neoantigens have been shown to elicit cancer-specific T-cell responses and have garnered much attention for their roles in both spontaneous and therapeutically induced antitumor responses. Mass spectrometry (MS) profiling of tumor immunopeptidomes has been used, in part, to identify MHC-bound mutant neoantigen ligands. However, under standard conditions, MS-based detection of such rare but clinically relevant neoantigens is relatively insensitive, requiring 300 million cells or more. Here, to quantitatively define the minimum detectable amounts of therapeutically relevant MHC-I and MHC-II neoantigen peptides, we analyzed different dilutions of immunopeptidomes isolated from the well-characterized T3 mouse methylcholanthrene (MCA)-induced cell line by MS. Using either data-dependent acquisition or parallel reaction monitoring (PRM), we established the minimum amount of material required to detect the major T3 neoantigens in the presence or absence of high field asymmetric waveform ion mobility spectrometry (FAIMS). This analysis yielded a 14-fold enhancement of sensitivity in detecting the major T3 MHC-I neoantigen (mLama4) with FAIMS-PRM compared with PRM without FAIMS, allowing ex vivo detection of this neoantigen from an individual 100 mg T3 tumor. These findings were then extended to two other independent MCA-sarcoma lines (1956 and F244). This study demonstrates that FAIMS substantially increases the sensitivity of MS-based characterization of validated neoantigens from tumors.
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Affiliation(s)
- Wei Meng
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Yoshiko Takeuchi
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Jeffrey P. Ward
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Hussein Sultan
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Cora D. Arthur
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Elaine R. Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine at Nationwide Children’s Hospital, Columbus, OH 43215, U.S.A
| | - Maxim N. Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Cheryl F. Lichti
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
| | - Robert D. Schreiber
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO 63110, U.S.A
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3
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Bresser K, Nicolet BP, Jeko A, Wu W, Loayza-Puch F, Agami R, Heck AJR, Wolkers MC, Schumacher TN. Gene and protein sequence features augment HLA class I ligand predictions. Cell Rep 2024; 43:114325. [PMID: 38870014 DOI: 10.1016/j.celrep.2024.114325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 04/22/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.
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Affiliation(s)
- Kaspar Bresser
- Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands
| | - Benoit P Nicolet
- Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Anita Jeko
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands
| | - Wei Wu
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands
| | - Fabricio Loayza-Puch
- Translational Control and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Reuven Agami
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands
| | - Monika C Wolkers
- Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Ton N Schumacher
- Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands.
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4
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Kennedy PH, Alborzian Deh Sheikh A, Balakar M, Jones AC, Olive ME, Hegde M, Matias MI, Pirete N, Burt R, Levy J, Little T, Hogan PG, Liu DR, Doench JG, Newton AC, Gottschalk RA, de Boer CG, Alarcón S, Newby GA, Myers SA. Post-translational modification-centric base editor screens to assess phosphorylation site functionality in high throughput. Nat Methods 2024; 21:1033-1043. [PMID: 38684783 DOI: 10.1038/s41592-024-02256-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/20/2024] [Indexed: 05/02/2024]
Abstract
Signaling pathways that drive gene expression are typically depicted as having a dozen or so landmark phosphorylation and transcriptional events. In reality, thousands of dynamic post-translational modifications (PTMs) orchestrate nearly every cellular function, and we lack technologies to find causal links between these vast biochemical pathways and genetic circuits at scale. Here we describe the high-throughput, functional assessment of phosphorylation sites through the development of PTM-centric base editing coupled to phenotypic screens, directed by temporally resolved phosphoproteomics. Using T cell activation as a model, we observe hundreds of unstudied phosphorylation sites that modulate NFAT transcriptional activity. We identify the phosphorylation-mediated nuclear localization of PHLPP1, which promotes NFAT but inhibits NFκB activity. We also find that specific phosphosite mutants can alter gene expression in subtle yet distinct patterns, demonstrating the potential for fine-tuning transcriptional responses. Overall, base editor screening of PTM sites provides a powerful platform to dissect PTM function within signaling pathways.
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Affiliation(s)
- Patrick H Kennedy
- Laboratory for Immunochemical Circuits, La Jolla Institute for Immunology, La Jolla, CA, USA
- Center of Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, USA
- Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Amin Alborzian Deh Sheikh
- Laboratory for Immunochemical Circuits, La Jolla Institute for Immunology, La Jolla, CA, USA
- Center of Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, USA
- Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, CA, USA
| | | | - Alexander C Jones
- Department of Pharmacology, University of California San Diego, San Diego, CA, USA
- Biomedical Sciences Graduate Program, University of California San Diego, San Diego, CA, USA
| | | | - Mudra Hegde
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Maria I Matias
- Laboratory for Immunochemical Circuits, La Jolla Institute for Immunology, La Jolla, CA, USA
- Center of Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, USA
- Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Natan Pirete
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rajan Burt
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan Levy
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Tamia Little
- Laboratory for Immunochemical Circuits, La Jolla Institute for Immunology, La Jolla, CA, USA
- Center of Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, USA
- Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Patrick G Hogan
- Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, CA, USA
- Program in Immunology, University of California San Diego, San Diego, CA, USA
- Moores Cancer Center, University of California San Diego Health, La Jolla, CA, USA
| | - David R Liu
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - John G Doench
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexandra C Newton
- Department of Pharmacology, University of California San Diego, San Diego, CA, USA
| | - Rachel A Gottschalk
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Carl G de Boer
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Suzie Alarcón
- La Jolla Institute for Immunology, La Jolla, CA, USA
- AUGenomics, San Diego, CA, USA
| | - Gregory A Newby
- Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Samuel A Myers
- Laboratory for Immunochemical Circuits, La Jolla Institute for Immunology, La Jolla, CA, USA.
- Center of Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, USA.
- Division of Signaling and Gene Expression, La Jolla Institute for Immunology, La Jolla, CA, USA.
- Department of Pharmacology, University of California San Diego, San Diego, CA, USA.
- Program in Immunology, University of California San Diego, San Diego, CA, USA.
- Moores Cancer Center, University of California San Diego Health, La Jolla, CA, USA.
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5
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Minegishi Y, Haga Y, Ueda K. Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy. Cancer Sci 2024; 115:1048-1059. [PMID: 38382459 PMCID: PMC11007014 DOI: 10.1111/cas.16118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.
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Affiliation(s)
- Yuriko Minegishi
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| | - Yoshimi Haga
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| | - Koji Ueda
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
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6
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Omenn GS, Lane L, Overall CM, Lindskog C, Pineau C, Packer NH, Cristea IM, Weintraub ST, Orchard S, Roehrl MHA, Nice E, Guo T, Van Eyk JE, Liu S, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. The 2023 Report on the Proteome from the HUPO Human Proteome Project. J Proteome Res 2024; 23:532-549. [PMID: 38232391 PMCID: PMC11026053 DOI: 10.1021/acs.jproteome.3c00591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | - Christopher M. Overall
- University of British Columbia, Vancouver, BC V6T 1Z4, Canada, Yonsei University Republic of Korea
| | | | - Charles Pineau
- University Rennes, Inserm U1085, Irset, 35042 Rennes, France
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | | | - Michael H. A. Roehrl
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, United States
| | | | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory, Westlake University, Hangzhou 310024, Zhejiang Province, China
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 South San Vicente Boulevard, Pavilion, 9th Floor, Los Angeles, CA, 90048, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, CA, 92093, United States
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
- University of Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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7
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Weingarten-Gabbay S, Chen DY, Sarkizova S, Taylor HB, Gentili M, Hernandez GM, Pearlman LR, Bauer MR, Rice CM, Clauser KR, Hacohen N, Carr SA, Abelin JG, Saeed M, Sabeti PC. The HLA-II immunopeptidome of SARS-CoV-2. Cell Rep 2024; 43:113596. [PMID: 38117652 PMCID: PMC10860710 DOI: 10.1016/j.celrep.2023.113596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/08/2023] [Accepted: 12/01/2023] [Indexed: 12/22/2023] Open
Abstract
Targeted synthetic vaccines have the potential to transform our response to viral outbreaks, yet the design of these vaccines requires a comprehensive knowledge of viral immunogens. Here, we report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides that are naturally processed and loaded onto human leukocyte antigen-II (HLA-II) complexes in infected cells. We identify over 500 unique viral peptides from canonical proteins as well as from overlapping internal open reading frames. Most HLA-II peptides colocalize with known CD4+ T cell epitopes in coronavirus disease 2019 patients, including 2 reported immunodominant regions in the SARS-CoV-2 membrane protein. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and nonstructural and noncanonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize vaccine effectiveness.
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Affiliation(s)
- Shira Weingarten-Gabbay
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA; Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA.
| | - Da-Yuan Chen
- Department of Biochemistry & Cell Biology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Matteo Gentili
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | | | - Leah R Pearlman
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Matthew R Bauer
- Harvard Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard University Medical School, Boston, MA, USA
| | - Charles M Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA; Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | | | - Mohsan Saeed
- Department of Biochemistry & Cell Biology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Pardis C Sabeti
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA; Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA; Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA
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8
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Schrader M. Origins, Technological Advancement, and Applications of Peptidomics. Methods Mol Biol 2024; 2758:3-47. [PMID: 38549006 DOI: 10.1007/978-1-0716-3646-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.
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Affiliation(s)
- Michael Schrader
- Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Sciences, Freising, Germany.
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9
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Gritsenko MA, Tsai CF, Kim H, Liu T. Automated Immunoprecipitation Workflow for Comprehensive Acetylome Analysis. Methods Mol Biol 2024; 2823:173-191. [PMID: 39052221 DOI: 10.1007/978-1-0716-3922-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Immunoprecipitation is one of the most effective methods for enrichment of lysine-acetylated peptides for comprehensive acetylome analysis using mass spectrometry. Manual acetyl peptide enrichment method using non-conjugated antibodies and agarose beads has been developed and applied in various studies. However, it is time-consuming and can introduce contaminants and variability that leads to potential sample loss and decreased sensitivity and robustness of the analysis. Here we describe a fast, automated enrichment protocol that enables reproducible and comprehensive acetylome analysis using a magnetic bead-based immunoprecipitation reagent.
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Affiliation(s)
- Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Hyeyoon Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
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10
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Schrader M, Fricker LD. Current Challenges and Future Directions in Peptidomics. Methods Mol Biol 2024; 2758:485-498. [PMID: 38549031 DOI: 10.1007/978-1-0716-3646-6_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
The field of peptidomics has been under development since its start more than 20 years ago. In this chapter we provide a personal outlook for future directions in this field. The applications of peptidomics technologies are spreading more and more from classical research of peptide hormones and neuropeptides towards commercial applications in plant and food-science. Many clinical applications have been developed to analyze the complexity of biofluids, which are being addressed with new instrumentation, automization, and data processing. Additionally, the newly developed field of immunopeptidomics is showing promise for cancer therapies. In conclusion, peptidomics will continue delivering important information in classical fields like neuropeptides and peptide hormones, benefiting from improvements in state-of-the-art technologies. Moreover, new directions of research such as immunopeptidomics will further complement classical omics technologies and may become routine clinical procedures. Taken together, discoveries of new substances, networks, and applications of peptides can be expected in different disciplines.
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Affiliation(s)
- Michael Schrader
- Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Sciences, Freising, Germany.
| | - Lloyd D Fricker
- Departments of Molecular Pharmacology and Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
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11
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Bi B, Qiu M, Liu P, Wang Q, Wen Y, Li Y, Li B, Li Y, He Y, Zhao J. Protein post-translational modifications: A key factor in colorectal cancer resistance mechanisms. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2023; 1866:194977. [PMID: 37625568 DOI: 10.1016/j.bbagrm.2023.194977] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/16/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023]
Abstract
Colorectal cancer (CRC) is one of the leading causes of cancer-related death. Despite advances in treatment, drug resistance remains a critical impediment. Post-translational modifications (PTMs) regulate protein stability, localization, and activity, impacting vital cellular processes. Recent research has highlighted the essential role of PTMs in the development of CRC resistance. This review summarizes recent advancements in understanding PTMs' roles in CRC resistance, focusing on the latest discoveries. We discuss the functional impact of PTMs on signaling pathways and molecules involved in CRC resistance, progress in drug development, and potential therapeutic targets. We also summarize the primary enrichment methods for PTMs. Finally, we discuss current challenges and future directions, including the need for more comprehensive PTM analysis methods and PTM-targeted therapies. This review identifies potential therapeutic interventions for addressing medication resistance in CRC, proposes prospective therapeutic options, and gives an overview of the function of PTMs in CRC resistance.
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Affiliation(s)
- Bo Bi
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Miaojuan Qiu
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Peng Liu
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Qiang Wang
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Yingfei Wen
- Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - You Li
- Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Binbin Li
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Yongshu Li
- Hubei Normal University, College of Life Sciences Huangshi, Hubei, China.
| | - Yulong He
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China.
| | - Jing Zhao
- Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China; Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, Guangdong, China.
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12
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Kennedy PH, Deh Sheikh AA, Balakar M, Jones AC, Olive ME, Hegde M, Matias MI, Pirete N, Burt R, Levy J, Little T, Hogan PG, Liu DR, Doench JG, Newton AC, Gottschalk RA, de Boer C, Alarcón S, Newby G, Myers SA. Proteome-wide base editor screens to assess phosphorylation site functionality in high-throughput. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.11.566649. [PMID: 38014346 PMCID: PMC10680671 DOI: 10.1101/2023.11.11.566649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Signaling pathways that drive gene expression are typically depicted as having a dozen or so landmark phosphorylation and transcriptional events. In reality, thousands of dynamic post-translational modifications (PTMs) orchestrate nearly every cellular function, and we lack technologies to find causal links between these vast biochemical pathways and genetic circuits at scale. Here, we describe "signaling-to-transcription network" mapping through the development of PTM-centric base editing coupled to phenotypic screens, directed by temporally-resolved phosphoproteomics. Using T cell activation as a model, we observe hundreds of unstudied phosphorylation sites that modulate NFAT transcriptional activity. We identify the phosphorylation-mediated nuclear localization of the phosphatase PHLPP1 which promotes NFAT but inhibits NFκB activity. We also find that specific phosphosite mutants can alter gene expression in subtle yet distinct patterns, demonstrating the potential for fine-tuning transcriptional responses. Overall, base editor screening of PTM sites provides a powerful platform to dissect PTM function within signaling pathways.
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13
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Meng W, Schreiber RD, Lichti CF. Recent advances in immunopeptidomic-based tumor neoantigen discovery. Adv Immunol 2023; 160:1-36. [PMID: 38042584 DOI: 10.1016/bs.ai.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2023]
Abstract
The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.
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Affiliation(s)
- Wei Meng
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States
| | - Robert D Schreiber
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
| | - Cheryl F Lichti
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
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14
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Huang D, Leng Y, Zhang X, Xing M, Ying W, Gao X. Serial and multi-level proteome analysis for microscale protein samples. J Proteomics 2023; 288:104993. [PMID: 37619946 DOI: 10.1016/j.jprot.2023.104993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/26/2023] [Accepted: 08/04/2023] [Indexed: 08/26/2023]
Abstract
Post-translational modifications (PTMs), such as phosphorylation and ubiquitination, play key roles in signal transduction and protein homeostasis. The crosstalk of PTMs greatly expands the components of proteome and protein functions. Multi-level proteome analysis, which involves proteome investigations of total lysate and PTMs in this context, provides a comprehensive approach to explore the PTM crosstalk of a biological system under diverse disturbances. However, multi-level proteome practice remains technically challenging. Here we intended to build a strategy for multi-level proteome analysis, in which we focus on the serial profiling the total proteome, ubiquitinome and phosphoproteome from the microscale of starting material. We started by evaluating five common lysis buffers and found that the sodium deoxycholate buffer provided the best overall performance. We then developed an approach for serial enrichment and profiling of the multi-level proteome. To expand the depth of identification, we customized the variable windows to perform data-independent acquisition (DIA) sequencing for each proteome. In total, we identified 6465 proteins, ∼20,000 GlyGly sites (class 1), and ∼ 19,000 phosphosites (class 1) sequentially using 1 mg of HeLa digest by three DIA measurements. We applied this strategy to analyze MG132-treated HeLa cells and observed the crosstalk between ubiquitination and phosphorylation. Our method can be referenced for other multi-level proteome studies with microscale samples. SIGNIFICANCE: Lysis buffer containing sodium deoxycholate provided the best overall performance in multi-level proteome analysis. One step of ubiquitination enrichment before phosphorylation enrichment does not reduce the reproducibility of phosphoproteome. Customized isolation windows were established for DIA analysis on each level of proteome. Combined the serial enrichment approach and the customized single-shot DIA method enabled the multi-level proteome of microscale protein samples.
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Affiliation(s)
- Dongying Huang
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yeye Leng
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiangye Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Meining Xing
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Wantao Ying
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
| | - Xiaoxia Gao
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, China.
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15
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Stražar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR, Brown EM, Eraslan B, Hung YM, Ortiz K, Clauser KR, Carr SA, Xavier RJ, Graham DB. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 2023; 56:1681-1698.e13. [PMID: 37301199 PMCID: PMC10519123 DOI: 10.1016/j.immuni.2023.05.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/08/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.
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Affiliation(s)
- Martin Stražar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jihye Park
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas K Pedersen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Basak Eraslan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuan-Mao Hung
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kayla Ortiz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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16
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Zittlau K, Nashier P, Cavarischia-Rega C, Macek B, Spät P, Nalpas N. Recent progress in quantitative phosphoproteomics. Expert Rev Proteomics 2023; 20:469-482. [PMID: 38116637 DOI: 10.1080/14789450.2023.2295872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/12/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Protein phosphorylation is a critical post-translational modification involved in the regulation of numerous cellular processes from signal transduction to modulation of enzyme activities. Knowledge of dynamic changes of phosphorylation levels during biological processes, under various treatments or between healthy and disease models is fundamental for understanding the role of each phosphorylation event. Thereby, LC-MS/MS based technologies in combination with quantitative proteomics strategies evolved as a powerful strategy to investigate the function of individual protein phosphorylation events. AREAS COVERED State-of-the-art labeling techniques including stable isotope and isobaric labeling provide precise and accurate quantification of phosphorylation events. Here, we review the strengths and limitations of recent quantification methods and provide examples based on current studies, how quantitative phosphoproteomics can be further optimized for enhanced analytic depth, dynamic range, site localization, and data integrity. Specifically, reducing the input material demands is key to a broader implementation of quantitative phosphoproteomics, not least for clinical samples. EXPERT OPINION Despite quantitative phosphoproteomics is one of the most thriving fields in the proteomics world, many challenges still have to be overcome to facilitate even deeper and more comprehensive analyses as required in the current research, especially at single cell levels and in clinical diagnostics.
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Affiliation(s)
- Katharina Zittlau
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Payal Nashier
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Claudia Cavarischia-Rega
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Boris Macek
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Philipp Spät
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Nicolas Nalpas
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
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17
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Lim Kam Sian TCC, Goncalves G, Steele JR, Shamekhi T, Bramberger L, Jin D, Shahbazy M, Purcell AW, Ramarathinam S, Stoychev S, Faridi P. SAPrIm, a semi-automated protocol for mid-throughput immunopeptidomics. Front Immunol 2023; 14:1107576. [PMID: 37334365 PMCID: PMC10272402 DOI: 10.3389/fimmu.2023.1107576] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/10/2023] [Indexed: 06/20/2023] Open
Abstract
Human leukocyte antigen (HLA) molecules play a crucial role in directing adaptive immune responses based on the nature of their peptide ligands, collectively coined the immunopeptidome. As such, the study of HLA molecules has been of major interest in the development of cancer immunotherapies such as vaccines and T-cell therapies. Hence, a comprehensive understanding and profiling of the immunopeptidome is required to foster the growth of these personalised solutions. We herein describe SAPrIm, an Immunopeptidomics tool for the Mid-Throughput era. This is a semi-automated workflow involving the KingFisher platform to isolate immunopeptidomes using anti-HLA antibodies coupled to a hyper-porous magnetic protein A microbead, a variable window data independent acquisition (DIA) method and the ability to run up to 12 samples in parallel. Using this workflow, we were able to concordantly identify and quantify ~400 - 13000 unique peptides from 5e5 - 5e7 cells, respectively. Overall, we propose that the application of this workflow will be crucial for the future of immunopeptidome profiling, especially for mid-size cohorts and comparative immunopeptidomics studies.
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Affiliation(s)
- Terry C. C. Lim Kam Sian
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Monash Proteomics and Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Gabriel Goncalves
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Joel R. Steele
- Monash Proteomics and Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Tima Shamekhi
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
| | - Liesl Bramberger
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
| | - Dongbin Jin
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
| | - Mohammad Shahbazy
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Anthony W. Purcell
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Sri Ramarathinam
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | | | - Pouya Faridi
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Monash Proteomics and Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
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Weingarten-Gabbay S, Chen DY, Sarkizova S, Taylor HB, Gentili M, Pearlman LR, Bauer MR, Rice CM, Clauser KR, Hacohen N, Carr SA, Abelin JG, Saeed M, Sabeti PC. The HLA-II immunopeptidome of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542482. [PMID: 37398281 PMCID: PMC10312465 DOI: 10.1101/2023.05.26.542482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Targeted synthetic vaccines have the potential to transform our response to viral outbreaks; yet the design of these vaccines requires a comprehensive knowledge of viral immunogens, including T-cell epitopes. Having previously mapped the SARS-CoV-2 HLA-I landscape, here we report viral peptides that are naturally processed and loaded onto HLA-II complexes in infected cells. We identified over 500 unique viral peptides from canonical proteins, as well as from overlapping internal open reading frames (ORFs), revealing, for the first time, the contribution of internal ORFs to the HLA-II peptide repertoire. Most HLA-II peptides co-localized with the known CD4+ T cell epitopes in COVID-19 patients. We also observed that two reported immunodominant regions in the SARS-CoV-2 membrane protein are formed at the level of HLA-II presentation. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and non-structural and non-canonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize the vaccine effectiveness.
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Phulphagar KM, Ctortecka C, Jacome ASV, Klaeger S, Verzani EK, Hernandez GM, Udeshi ND, Clauser KR, Abelin JG, Carr SA. Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry. Mol Cell Proteomics 2023:100563. [PMID: 37142057 DOI: 10.1016/j.mcpro.2023.100563] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and non-canonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.
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