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Kruk ME, Mehta S, Murray K, Higgins L, Do K, Johnson JE, Wagner R, Wendt CH, O’Connor JB, Harris JK, Laguna TA, Jagtap PD, Griffin TJ. An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease. mSystems 2024; 9:e0092923. [PMID: 38934598 PMCID: PMC11264604 DOI: 10.1128/msystems.00929-23] [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: 09/01/2023] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.
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Affiliation(s)
- Monica E. Kruk
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
| | - Kevin Murray
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
- Center for Metabolomics and Proteomics, University of Minnesota, Minneapolis, Minnesota, USA
| | - LeeAnn Higgins
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
- Center for Metabolomics and Proteomics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Katherine Do
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
| | - James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Reid Wagner
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Chris H. Wendt
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Medical School, University of Minnesota, Minneapolis, Minnesota, USA
- Minneapolis VA Health Care System, Minneapolis, Minnesota, USA
| | - John B. O’Connor
- Department of Pediatrics, Division of Pulmonary and Sleep Medicine, Seattle Children’s Hospital, Seattle, Washington, USA
| | - J. Kirk Harris
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Theresa A. Laguna
- Department of Pediatrics, Division of Pulmonary and Sleep Medicine, Seattle Children’s Hospital, Seattle, Washington, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minneapolis, Minnesota, USA
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2
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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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3
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Bai M, Deng J, Dai C, Pfeuffer J, Sachsenberg T, Perez-Riverol Y. LFQ-Based Peptide and Protein Intensity Differential Expression Analysis. J Proteome Res 2023. [PMID: 37220883 DOI: 10.1021/acs.jproteome.2c00812] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.
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Affiliation(s)
- Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
| | - Jingwen Deng
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Chengxin Dai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing 102206, China
| | - Julianus Pfeuffer
- Algorithmic Bioinformatics, Freie Universität Berlin, Berlin 14195, Germany
- Visualization and Data Analysis, Zuse Institute Berlin, Berlin 14195, Germany
| | - Timo Sachsenberg
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72076, Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hixton, Cambridge CB10 1SD, United Kingdom
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4
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Weaver S, DeRosa CM, Schultz SR, Champion MM. PrIntMap-R: An Online Application for Intraprotein Intensity and Peptide Visualization from Bottom-Up Proteomics. J Proteome Res 2023; 22:432-441. [PMID: 36652611 PMCID: PMC9904286 DOI: 10.1021/acs.jproteome.2c00606] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Bottom-up proteomics (BUP) produces rich data, but visualization and analysis are time-consuming and often require programming skills. Many tools analyze these data at the proteome-level, but fewer options exist for individual proteins. Sequence coverage maps are common, but do not proportion peptide intensity. Abundance-based visualization of sequence coverage facilitates detection of protein isoforms, domains, potential truncation sites, peptide "hot-spots", and localization of post-translational modifications (PTMs). Redundant stacked-sequence coverage is an important tool in designing hydrogen-deuterium exchange (HDX) experiments. Visualization tools often lack graphical and tabular-export of processed data which complicates publication of results. Quantitative peptide abundance across amino acid sequences is an essential and missing tool in proteomics toolkits. Here we created PrIntMap-R, an online application that only requires peptide files from a database search and FASTA protein sequences. PrIntMap-R produces a variety of plots for quantitative visualization of coverage; annotation of specific sequences, PTM's, and comparisons of one or many samples overlaid with calculated fold-change or several intensity metrics. We show use-cases including protein phosphorylation, identification of glycosylation, and the optimization of digestion conditions for HDX experiments. PrIntMap-R is freely available, open source, and can run online with no installation, or locally by downloading source code from GitHub.
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Affiliation(s)
- Simon
D. Weaver
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States,Integrated
Biomedical Sciences Graduate Program, University
of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Christine M. DeRosa
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Sadie R. Schultz
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Matthew M. Champion
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States,Integrated
Biomedical Sciences Graduate Program, University
of Notre Dame, Notre
Dame, Indiana 46556, United States,
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5
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Wang Z, Liu C, Wang S, Hou X, Gong P, Li X, Liang Z, Liu J, Zhang L, Zhang Y. Segmented MS/MS acquisition of a1 ion-based strategy for in-depth proteome quantitation. Anal Chim Acta 2022; 1232:340491. [DOI: 10.1016/j.aca.2022.340491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/17/2022] [Accepted: 10/05/2022] [Indexed: 11/01/2022]
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6
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Sinclair P, Kabbani N. Nicotinic receptor components of amyloid beta 42 proteome regulation in human neural cells. PLoS One 2022; 17:e0270479. [PMID: 35960729 PMCID: PMC9374227 DOI: 10.1371/journal.pone.0270479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/12/2022] [Indexed: 11/30/2022] Open
Abstract
Alzheimer’s disease (AD) is associated with chronic neurodegeneration often accompanied by elevated levels of the neurotoxic peptide amyloid-beta 1–42 (Aβ42) in the brain. Studies show that extracellular Aβ42 binds to various cell surface receptors including the human α7 nicotinic acetylcholine receptor (nAChR) and activates pathways of neurotoxicity leading to cell death. The α7 nAChR is thus considered a promising drug target for therapy against neurodegenerative disease such as AD. In this study, we use mass spectrometry-based label-free precursor ion quantification to identify proteins and pathways that are changed by a 72-hour treatment with Aβ42 or Aβ42 in the presence of the α7 nAChR blocker, α-bungarotoxin (Bgtx) in the human neuroblastoma SH-SY5Y cell line. Bioinformatic gene ontology enrichment analysis was used to identify and characterize proteins and pathways altered by Aβ42 presentation. The results support evidence on the involvement of mitochondrial proteins in Aβ42 responses and define potential mechanisms of α7 nAChR mediated amyloid toxicity. These findings can inform pharmacological strategies for drug design and treatment against amyloid disease.
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Affiliation(s)
- Patricia Sinclair
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, United States of America
| | - Nadine Kabbani
- Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, United States of America
- School of System Biology, George Mason University, Fairfax, VA, United States of America
- * E-mail:
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7
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Pinter N, Glätzer D, Fahrner M, Fröhlich K, Johnson J, Grüning BA, Warscheid B, Drepper F, Schilling O, Föll MC. MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone. J Proteome Res 2022; 21:1558-1565. [PMID: 35503992 DOI: 10.1021/acs.jproteome.2c00051] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Quantitative mass spectrometry-based proteomics has become a high-throughput technology for the identification and quantification of thousands of proteins in complex biological samples. Two frequently used tools, MaxQuant and MSstats, allow for the analysis of raw data and finding proteins with differential abundance between conditions of interest. To enable accessible and reproducible quantitative proteomics analyses in a cloud environment, we have integrated MaxQuant (including TMTpro 16/18plex), Proteomics Quality Control (PTXQC), MSstats, and MSstatsTMT into the open-source Galaxy framework. This enables the web-based analysis of label-free and isobaric labeling proteomics experiments via Galaxy's graphical user interface on public clouds. MaxQuant and MSstats in Galaxy can be applied in conjunction with thousands of existing Galaxy tools and integrated into standardized, sharable workflows. Galaxy tracks all metadata and intermediate results in analysis histories, which can be shared privately for collaborations or publicly, allowing full reproducibility and transparency of published analysis. To further increase accessibility, we provide detailed hands-on training materials. The integration of MaxQuant and MSstats into the Galaxy framework enables their usage in a reproducible way on accessible large computational infrastructures, hence realizing the foundation for high-throughput proteomics data science for everyone.
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Affiliation(s)
- Niko Pinter
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Damian Glätzer
- Biochemistry and Functional Proteomics, Institute of Biology II, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Klemens Fröhlich
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | | | - Bettina Warscheid
- Biochemistry and Functional Proteomics, Institute of Biology II, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.,Faculty of Chemistry and Pharmacy, Department of Biochemistry, Julius Maximilian University of Würzburg, 97074 Würzburg, Germany
| | - Friedel Drepper
- Biochemistry and Functional Proteomics, Institute of Biology II, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), 79106 Freiburg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany.,Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.,Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts 02115, United States
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8
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Rajczewski AT, Han Q, Mehta S, Kumar P, Jagtap PD, Knutson CG, Fox JG, Tretyakova NY, Griffin TJ. Quantitative Proteogenomic Characterization of Inflamed Murine Colon Tissue Using an Integrated Discovery, Verification, and Validation Proteogenomic Workflow. Proteomes 2022; 10:proteomes10020011. [PMID: 35466239 PMCID: PMC9036229 DOI: 10.3390/proteomes10020011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/27/2022] [Accepted: 04/07/2022] [Indexed: 11/24/2022] Open
Abstract
Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2−/−Il10−/− mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.
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Affiliation(s)
- Andrew T. Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Qiyuan Han
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
| | - Charles G. Knutson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (C.G.K.); (J.G.F.)
| | - James G. Fox
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (C.G.K.); (J.G.F.)
| | - Natalia Y. Tretyakova
- Department of Medicinal Chemistry, the Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; (A.T.R.); (Q.H.); (S.M.); (P.K.); (P.D.J.)
- Correspondence:
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9
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Reska D, Czajkowski M, Jurczuk K, Boldak C, Kwedlo W, Bauer W, Koszelew J, Kretowski M. Integration of solutions and services for multi-omics data analysis towards personalized medicine. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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