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Ayhan S, Dursun A. ELFN1 is a new extracellular matrix (ECM)-associated protein. Life Sci 2024; 352:122900. [PMID: 38986898 DOI: 10.1016/j.lfs.2024.122900] [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: 04/15/2024] [Revised: 06/30/2024] [Accepted: 07/06/2024] [Indexed: 07/12/2024]
Abstract
AIMS The ELFN1, discovered in 2007, is a single-pass transmembrane protein. Studies conducted thus far to elucidate the function of the Elfn1 have been limited only to animal studies. These studies have reported that ELFN1 is a universal binding partner of metabotropic glutamate receptors (mGluRs) in the central nervous system and its functional deficiency has been associated with the pathogenesis of neurological and neuropsychiatric diseases. In 2021, we described the first disease-associated human ELFN1 pathogenic gene mutation. Severe joint laxity, which was the most striking finding of this new disease and was clearly seen in the patients since early infancy, showed that the ELFN1 may have a possible function in the connective tissue besides the nervous system. Here, we present the first experimental evidence of the extracellular matrix (ECM)-related function of the ELFN1. MATERIALS AND METHODS Primary skin fibroblasts were isolated from the skin biopsies of ELFN1 mutated patients and healthy foreskin donors. For the clinical trial in a dish, in vitro ECM and DEM (decellularized ECM) models were created from skin fibroblasts. All the in vitro models were comparatively characterized and analyzed. KEY FINDINGS The mutation in the ELFN1 signal peptide region of patients resulted in a severe lack of ELFN1 expression and dramatically altered the characteristic morphology and behavior (growth, proliferation, and motility) of fibroblasts. SIGNIFICANCE We propose that ELFN1 is involved in the cell-ECM attachment, and its deficiency is critical enough to cause a loss of cell motility and soft ECM stiffness.
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Affiliation(s)
- Selda Ayhan
- Department of Pediatrics Metabolism, Institute of Child Health, Hacettepe University, Sıhhıye, Ankara 06100, Turkey.
| | - Ali Dursun
- Department of Pediatrics Metabolism, Faculty of Medicine, Hacettepe University, Sıhhıye, Ankara 06100, Turkey.
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Do K, Mehta S, Wagner R, Bhuming D, Rajczewski AT, Skubitz APN, Johnson JE, Griffin TJ, Jagtap PD. A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. mSphere 2024; 9:e0079323. [PMID: 38780289 DOI: 10.1128/msphere.00793-23] [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: 12/18/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies. IMPORTANCE Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.
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Affiliation(s)
- Katherine Do
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Reid Wagner
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Dechen Bhuming
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Amy P N Skubitz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
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Do K, Mehta S, Wagner R, Bhuming D, Rajczewski AT, Skubitz APN, Johnson JE, Griffin TJ, Jagtap PD. A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568121. [PMID: 38045370 PMCID: PMC10690215 DOI: 10.1101/2023.11.21.568121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.
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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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Kocsmár É, Schmid M, Cosenza-Contreras M, Kocsmár I, Föll M, Krey L, Barta BA, Rácz G, Kiss A, Werner M, Schilling O, Lotz G, Bronsert P. Proteome alterations in human autopsy tissues in relation to time after death. Cell Mol Life Sci 2023; 80:117. [PMID: 37020120 PMCID: PMC10075177 DOI: 10.1007/s00018-023-04754-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 02/17/2023] [Accepted: 03/07/2023] [Indexed: 04/07/2023]
Abstract
Protein expression is a primary area of interest for routine histological diagnostics and tissue-based research projects, but the limitations of its post-mortem applicability remain largely unclear. On the other hand, tissue specimens obtained during autopsies can provide unique insight into advanced disease states, especially in cancer research. Therefore, we aimed to identify the maximum post-mortem interval (PMI) which is still suitable for characterizing protein expression patterns, to explore organ-specific differences in protein degradation, and to investigate whether certain proteins follow specific degradation kinetics. Therefore, the proteome of human tissue samples obtained during routine autopsies of deceased patients with accurate PMI (6, 12, 18, 24, 48, 72, 96 h) and without specific diseases that significantly affect tissue preservation, from lungs, kidneys and livers, was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). For the kidney and liver, significant protein degradation became apparent at 48 h. For the lung, the proteome composition was rather static for up to 48 h and substantial protein degradation was detected only at 72 h suggesting that degradation kinetics appear to be organ specific. More detailed analyses suggested that proteins with similar post-mortem kinetics are not primarily shared in their biological functions. The overrepresentation of protein families with analogous structural motifs in the kidney indicates that structural features may be a common factor in determining similar postmortem stability. Our study demonstrates that a longer post-mortem period may have a significant impact on proteome composition, but sampling within 24 h may be appropriate, as degradation is within acceptable limits even in organs with faster autolysis.
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Affiliation(s)
- Éva Kocsmár
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Marlene Schmid
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Miguel Cosenza-Contreras
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Ildikó Kocsmár
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
- Department of Urology, Semmelweis University, Budapest, Hungary
| | - Melanie Föll
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, 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, USA
| | - Leah Krey
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bálint András Barta
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Gergely Rácz
- Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary
| | - András Kiss
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Martin Werner
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Gábor Lotz
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Peter Bronsert
- Institute of Surgical Pathology, University Medical Center Freiburg, Breisacher Straße 115A, 79106, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Biobank Comprehensive Cancer Center Freiburg, University Medical Center, Freiburg, Germany.
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Kohler D, Kaza M, Pasi C, Huang T, Staniak M, Mohandas D, Sabido E, Choi M, Vitek O. MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments. J Proteome Res 2023; 22:551-556. [PMID: 36622173 DOI: 10.1021/acs.jproteome.2c00603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).
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Affiliation(s)
- Devon Kohler
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Maanasa Kaza
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | - Cristina Pasi
- Universitat Oberta de Catalunya, Barcelona 08018, Spain
| | - Ting Huang
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
| | | | | | - Eduard Sabido
- Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona 08003, Spain.,Universitat Pompeu Fabra, Barcelona 08002, Spain
| | - Meena Choi
- MPL, Genentech, South San Francisco, California 94080, United States
| | - Olga Vitek
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States
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Perez-Riverol Y. Proteomic repository data submission, dissemination, and reuse: key messages. Expert Rev Proteomics 2022; 19:297-310. [PMID: 36529941 PMCID: PMC7614296 DOI: 10.1080/14789450.2022.2160324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
INTRODUCTION The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public. AREAS COVERED Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed. EXPERT OPINION The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.
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Affiliation(s)
- Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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