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Wiest A, Kielkowski P. Improved deconvolution of natural products' protein targets using diagnostic ions from chemical proteomics linkers. Beilstein J Org Chem 2024; 20:2323-2341. [PMID: 39290210 PMCID: PMC11406061 DOI: 10.3762/bjoc.20.199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 08/27/2024] [Indexed: 09/19/2024] Open
Abstract
Identification of interactions between proteins and natural products or similar active small molecules is crucial for understanding of their mechanism of action on a molecular level. To search elusive, often labile, and low-abundant conjugates between proteins and active compounds, chemical proteomics introduces a feasible strategy that allows to enrich and detect these conjugates. Recent advances in mass spectrometry techniques and search algorithms provide unprecedented depth of proteome coverage and the possibility to detect desired modified peptides with high sensitivity. The chemical 'linker' connecting an active compound-protein conjugate with a detection tag is the critical component of all chemical proteomic workflows. In this review, we discuss the properties and applications of different chemical proteomics linkers with special focus on their fragmentation releasing diagnostic ions and how these may improve the confidence in identified active compound-peptide conjugates. The application of advanced search options improves the identification rates and may help to identify otherwise difficult to find interactions between active compounds and proteins, which may result from unperturbed conditions, and thus are of high physiological relevance.
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Affiliation(s)
- Andreas Wiest
- LMU Munich, Department of Chemistry, Butenandtstr. 5-13, 81377 Munich, Germany
| | - Pavel Kielkowski
- LMU Munich, Department of Chemistry, Butenandtstr. 5-13, 81377 Munich, Germany
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2
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Chen SY, Fiedler MK, Gronauer TF, Omelko O, von Wrisberg MK, Wang T, Schneider S, Sieber SA, Zacharias M. Unraveling the mechanism of small molecule induced activation of Staphylococcus aureus signal peptidase IB. Commun Biol 2024; 7:895. [PMID: 39043865 PMCID: PMC11266668 DOI: 10.1038/s42003-024-06575-x] [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: 02/16/2024] [Accepted: 07/11/2024] [Indexed: 07/25/2024] Open
Abstract
Staphylococcus aureus signal peptidase IB (SpsB) is an essential enzyme for protein secretion. While inhibition of its activity by small molecules is a well-precedented mechanism to kill bacteria, the mode of activation is however less understood. We here investigate the activation mechanism of a recently introduced activator, the antibiotic compound PK150, and demonstrate by combined experimental and Molecular Dynamics (MD) simulation studies a unique principle of enzyme stimulation. Mass spectrometric studies with an affinity-based probe of PK150 unravel the binding site of PK150 in SpsB which is used as a starting point for MD simulations. Our model shows the localization of the molecule in an allosteric pocket next to the active site which shields the catalytic dyad from excess water that destabilizes the catalytic geometry. This mechanism is validated by the placement of mutations aligning the binding pocket of PK150. While the mutants retain turnover of the SpsB substrate, no stimulation of activity is observed upon PK150 addition. Overall, our study elucidates a previously little investigated mechanism of enzyme activation and serves as a starting point for the development of future enzyme activators.
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Affiliation(s)
- Shu-Yu Chen
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, Zurich, 8093, Switzerland
- TUM School of Natural Sciences, Department Biosciences, Theoretical Biophysics (T38), Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany
| | - Michaela K Fiedler
- TUM School of Natural Sciences, Department Biosciences, Chair of Organic Chemistry II, Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany
| | - Thomas F Gronauer
- TUM School of Natural Sciences, Department Biosciences, Chair of Organic Chemistry II, Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany
| | - Olesia Omelko
- TUM School of Natural Sciences, Department Biosciences, Chair of Organic Chemistry II, Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany
| | - Marie-Kristin von Wrisberg
- Department of Chemistry, Ludwig-Maximilians University Munich (LMU), Butenandtstr. 5-13, Munich, 81377, Germany
| | - Tao Wang
- TUM School of Natural Sciences, Department Biosciences, Chair of Organic Chemistry II, Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany
| | - Sabine Schneider
- Department of Chemistry, Ludwig-Maximilians University Munich (LMU), Butenandtstr. 5-13, Munich, 81377, Germany
| | - Stephan A Sieber
- TUM School of Natural Sciences, Department Biosciences, Chair of Organic Chemistry II, Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany.
| | - Martin Zacharias
- TUM School of Natural Sciences, Department Biosciences, Theoretical Biophysics (T38), Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany.
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3
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Tkalec KI, Hayes AJ, Lim KS, Lewis JM, Davies MR, Scott NE. Glycan-Tailored Glycoproteomic Analysis Reveals Serine is the Sole Residue Subjected to O-Linked Glycosylation in Acinetobacter baumannii. J Proteome Res 2024; 23:2474-2494. [PMID: 38850255 DOI: 10.1021/acs.jproteome.4c00148] [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: 06/10/2024]
Abstract
Protein glycosylation is a ubiquitous process observed across all domains of life. Within the human pathogen Acinetobacter baumannii, O-linked glycosylation is required for virulence; however, the targets and conservation of glycosylation events remain poorly defined. In this work, we expand our understanding of the breadth and site specificity of glycosylation within A. baumannii by demonstrating the value of strain specific glycan electron-transfer/higher-energy collision dissociation (EThcD) triggering for bacterial glycoproteomics. By coupling tailored EThcD-triggering regimes to complementary glycopeptide enrichment approaches, we assessed the observable glycoproteome of three A. baumannii strains (ATCC19606, BAL062, and D1279779). Combining glycopeptide enrichment techniques including ion mobility (FAIMS), metal oxide affinity chromatography (titanium dioxide), and hydrophilic interaction liquid chromatography (ZIC-HILIC), as well as the use of multiple proteases (trypsin, GluC, pepsin, and thermolysis), we expand the known A. baumannii glycoproteome to 33 unique glycoproteins containing 42 glycosylation sites. We demonstrate that serine is the sole residue subjected to glycosylation with the substitution of serine for threonine abolishing glycosylation in model glycoproteins. An A. baumannii pan-genome built from 576 reference genomes identified that serine glycosylation sites are highly conserved. Combined this work expands our knowledge of the conservation and site specificity of A. baumannii O-linked glycosylation.
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Affiliation(s)
- Kristian I Tkalec
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne 3000, Australia
| | - Andrew J Hayes
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne 3000, Australia
| | - Kataleen S Lim
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne 3000, Australia
| | - Jessica M Lewis
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne 3000, Australia
| | - Mark R Davies
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne 3000, Australia
| | - Nichollas E Scott
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne 3000, Australia
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4
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Landerer C, Scheremetjew M, Moon H, Hersemann L, Toth-Petroczy A. deTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets. Bioinformatics 2024; 40:btae424. [PMID: 38941503 PMCID: PMC11236091 DOI: 10.1093/bioinformatics/btae424] [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: 03/19/2024] [Revised: 05/28/2024] [Accepted: 06/27/2024] [Indexed: 06/30/2024] Open
Abstract
MOTIVATION Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry. RESULTS Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions. AVAILABILITY AND IMPLEMENTATION deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.
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Affiliation(s)
- Cedric Landerer
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Maxim Scheremetjew
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - HongKee Moon
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Lena Hersemann
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Agnes Toth-Petroczy
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
- Cluster of Excellence Physics of Life, TU Dresden, 01062 Dresden, Germany
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Faktor J, Kote S, Bienkowski M, Hupp TR, Marek-Trzonkowska N. Novel FFPE proteomics method suggests prolactin induced protein as hormone induced cytoskeleton remodeling spatial biomarker. Commun Biol 2024; 7:708. [PMID: 38851810 PMCID: PMC11162451 DOI: 10.1038/s42003-024-06354-8] [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: 01/20/2023] [Accepted: 05/20/2024] [Indexed: 06/10/2024] Open
Abstract
Robotically assisted proteomics provides insights into the regulation of multiple proteins achieving excellent spatial resolution. However, developing an effective method for spatially resolved quantitative proteomics of formalin fixed paraffin embedded tissue (FFPE) in an accessible and economical manner remains challenging. We introduce non-robotic In-insert FFPE proteomics approach, combining glass insert FFPE tissue processing with spatial quantitative data-independent mass spectrometry (DIA). In-insert approach identifies 450 proteins from a 5 µm thick breast FFPE tissue voxel with 50 µm lateral dimensions covering several tens of cells. Furthermore, In-insert approach associated a keratin series and moesin (MOES) with prolactin-induced protein (PIP) indicating their prolactin and/or estrogen regulation. Our data suggest that PIP is a spatial biomarker for hormonally triggered cytoskeletal remodeling, potentially useful for screening hormonally affected hotspots in breast tissue. In-insert proteomics represents an alternative FFPE processing method, requiring minimal laboratory equipment and skills to generate spatial proteotype repositories from FFPE tissue.
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Affiliation(s)
- Jakub Faktor
- International Centre for Cancer Vaccine Science, University of Gdansk, Kladki 24, 80-822, Gdansk, Poland.
| | - Sachin Kote
- International Centre for Cancer Vaccine Science, University of Gdansk, Kladki 24, 80-822, Gdansk, Poland.
| | - Michal Bienkowski
- Medical University of Gdansk, University of Gdansk, Mariana Smoluchowskiego 17, 80-214, Gdansk, Poland
| | - Ted R Hupp
- International Centre for Cancer Vaccine Science, University of Gdansk, Kladki 24, 80-822, Gdansk, Poland
| | - Natalia Marek-Trzonkowska
- International Centre for Cancer Vaccine Science, University of Gdansk, Kladki 24, 80-822, Gdansk, Poland
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Aguzzoli Heberle B, Brandon JA, Page ML, Nations KA, Dikobe KI, White BJ, Gordon LA, Fox GA, Wadsworth ME, Doyle PH, Williams BA, Fox EJ, Shantaraman A, Ryten M, Goodwin S, Ghiban E, Wappel R, Mavruk-Eskipehlivan S, Miller JB, Seyfried NT, Nelson PT, Fryer JD, Ebbert MTW. Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq. Nat Biotechnol 2024:10.1038/s41587-024-02245-9. [PMID: 38778214 DOI: 10.1038/s41587-024-02245-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 04/15/2024] [Indexed: 05/25/2024]
Abstract
Determining whether the RNA isoforms from medically relevant genes have distinct functions could facilitate direct targeting of RNA isoforms for disease treatment. Here, as a step toward this goal for neurological diseases, we sequenced 12 postmortem, aged human frontal cortices (6 Alzheimer disease cases and 6 controls; 50% female) using one Oxford Nanopore PromethION flow cell per sample. We identified 1,917 medically relevant genes expressing multiple isoforms in the frontal cortex where 1,018 had multiple isoforms with different protein-coding sequences. Of these 1,018 genes, 57 are implicated in brain-related diseases including major depression, schizophrenia, Parkinson's disease and Alzheimer disease. Our study also uncovered 53 new RNA isoforms in medically relevant genes, including several where the new isoform was one of the most highly expressed for that gene. We also reported on five mitochondrially encoded, spliced RNA isoforms. We found 99 differentially expressed RNA isoforms between cases with Alzheimer disease and controls.
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Affiliation(s)
- Bernardo Aguzzoli Heberle
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - J Anthony Brandon
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Madeline L Page
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Kayla A Nations
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Ketsile I Dikobe
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Brendan J White
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Lacey A Gordon
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Grant A Fox
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Mark E Wadsworth
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Patricia H Doyle
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Brittney A Williams
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Edward J Fox
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Mina Ryten
- UK Dementia Research Institute at The University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elena Ghiban
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Robert Wappel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Justin B Miller
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
- Division of Biomedical Informatics, Internal Medicine, College of Medicine, University of Kentucky, Lexington, KY, USA
- Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA
- Microbiology, Immunology and Molecular Genetics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Nicholas T Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Peter T Nelson
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - John D Fryer
- Department of Neuroscience, Mayo Clinic, Scottsdale, AZ, USA
| | - Mark T W Ebbert
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA.
- Division of Biomedical Informatics, Internal Medicine, College of Medicine, University of Kentucky, Lexington, KY, USA.
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7
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Landerer C, Poehls J, Toth-Petroczy A. Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery. Mol Biol Evol 2024; 41:msae048. [PMID: 38421032 PMCID: PMC10939442 DOI: 10.1093/molbev/msae048] [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: 09/04/2023] [Revised: 01/30/2024] [Accepted: 02/23/2024] [Indexed: 03/02/2024] Open
Abstract
Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.
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Affiliation(s)
- Cedric Landerer
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Jonas Poehls
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
| | - Agnes Toth-Petroczy
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
- Center for Systems Biology Dresden, 01307 Dresden, Germany
- Cluster of Excellence Physics of Life, TU Dresden, 01062 Dresden, Germany
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Heberle BA, Brandon JA, Page ML, Nations KA, Dikobe KI, White BJ, Gordon LA, Fox GA, Wadsworth ME, Doyle PH, Williams BA, Fox EJ, Shantaraman A, Ryten M, Goodwin S, Ghiban E, Wappel R, Mavruk-Eskipehlivan S, Miller JB, Seyfried NT, Nelson PT, Fryer JD, Ebbert MTW. Using deep long-read RNAseq in Alzheimer's disease brain to assess medical relevance of RNA isoform diversity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.06.552162. [PMID: 37609156 PMCID: PMC10441303 DOI: 10.1101/2023.08.06.552162] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Due to alternative splicing, human protein-coding genes average over eight RNA isoforms, resulting in nearly four distinct protein coding sequences per gene. Long-read RNAseq (IsoSeq) enables more accurate quantification of isoforms, shedding light on their specific roles. To assess the medical relevance of measuring RNA isoform expression, we sequenced 12 aged human frontal cortices (6 Alzheimer's disease cases and 6 controls; 50% female) using one Oxford Nanopore PromethION flow cell per sample. Our study uncovered 53 new high-confidence RNA isoforms in medically relevant genes, including several where the new isoform was one of the most highly expressed for that gene. Specific examples include WDR4 (61%; microcephaly), MYL3 (44%; hypertrophic cardiomyopathy), and MTHFS (25%; major depression, schizophrenia, bipolar disorder). Other notable genes with new high-confidence isoforms include CPLX2 (10%; schizophrenia, epilepsy) and MAOB (9%; targeted for Parkinson's disease treatment). We identified 1,917 medically relevant genes expressing multiple isoforms in human frontal cortex, where 1,018 had multiple isoforms with different protein coding sequences, demonstrating the need to better understand how individual isoforms from a single gene body are involved in human health and disease, if at all. Exactly 98 of the 1,917 genes are implicated in brain-related diseases, including Alzheimer's disease genes such as APP (Aβ precursor protein; five), MAPT (tau protein; four), and BIN1 (eight). As proof of concept, we also found 99 differentially expressed RNA isoforms between Alzheimer's cases and controls, despite the genes themselves not exhibiting differential expression. Our findings highlight the significant knowledge gaps in RNA isoform diversity and their medical relevance. Deep long-read RNA sequencing will be necessary going forward to fully comprehend the medical relevance of individual isoforms for a "single" gene.
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Affiliation(s)
- Bernardo Aguzzoli Heberle
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY
| | | | - Madeline L. Page
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - Kayla A. Nations
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - Ketsile I. Dikobe
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - Brendan J. White
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - Lacey A. Gordon
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - Grant A. Fox
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY
| | - Mark E. Wadsworth
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - Patricia H. Doyle
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY
| | - Brittney A. Williams
- Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, KY
| | - Edward J. Fox
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
| | - Elena Ghiban
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
| | - Robert Wappel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
| | | | - Justin B. Miller
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
- Division of Biomedical Informatics, Internal Medicine, College of Medicine, University of Kentucky, Lexington, KY
- Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA
- Microbiology, Immunology and Molecular Genetics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Peter T. Nelson
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
| | - John D. Fryer
- Department of Neuroscience, Mayo Clinic, Scottsdale, Arizona
| | - Mark T. W. Ebbert
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY
- Division of Biomedical Informatics, Internal Medicine, College of Medicine, University of Kentucky, Lexington, KY
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9
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Tüshaus J, Sakhteman A, Lechner S, The M, Mucha E, Krisp C, Schlegel J, Delbridge C, Kuster B. A region-resolved proteomic map of the human brain enabled by high-throughput proteomics. EMBO J 2023; 42:e114665. [PMID: 37916885 PMCID: PMC10690467 DOI: 10.15252/embj.2023114665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).
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Affiliation(s)
- Johanna Tüshaus
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Amirhossein Sakhteman
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Severin Lechner
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Matthew The
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
| | - Eike Mucha
- Bruker Daltonics GmbH & Co. KGBremenGermany
| | | | - Jürgen Schlegel
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Claire Delbridge
- Department of Neuropathology, Klinikum Rechts der ISAR, School of MedicineTechnical University MunichMunichGermany
| | - Bernhard Kuster
- Proteomics and Bioanalytics, Department of Molecular Life Sciences, School of Life SciencesTechnical University of MunichMunichGermany
- German Cancer Consortium (DKTK), Munich SiteHeidelbergGermany
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10
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Geiszler DJ, Polasky DA, Yu F, Nesvizhskii AI. Detecting diagnostic features in MS/MS spectra of post-translationally modified peptides. Nat Commun 2023; 14:4132. [PMID: 37438360 DOI: 10.1038/s41467-023-39828-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 06/23/2023] [Indexed: 07/14/2023] Open
Abstract
Post-translational modifications are an area of great interest in mass spectrometry-based proteomics, with a surge in methods to detect them in recent years. However, post-translational modifications can introduce complexity into proteomics searches by fragmenting in unexpected ways, ultimately hindering the detection of modified peptides. To address these deficiencies, we present a fully automated method to find diagnostic spectral features for any modification. The features can be incorporated into proteomics search engines to improve modified peptide recovery and localization. We show the utility of this approach by interrogating fragmentation patterns for a cysteine-reactive chemoproteomic probe, RNA-crosslinked peptides, sialic acid-containing glycopeptides, and ADP-ribosylated peptides. We also analyze the interactions between a diagnostic ion's intensity and its statistical properties. This method has been incorporated into the open-search annotation tool PTM-Shepherd and the FragPipe computational platform.
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Affiliation(s)
- Daniel J Geiszler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
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11
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Lichti CF, Wan X. Using mass spectrometry to identify neoantigens in autoimmune diseases: The type 1 diabetes example. Semin Immunol 2023; 66:101730. [PMID: 36827760 PMCID: PMC10324092 DOI: 10.1016/j.smim.2023.101730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/24/2023]
Abstract
In autoimmune diseases, recognition of self-antigens presented by major histocompatibility complex (MHC) molecules elicits unexpected attack of tissue by autoantibodies and/or autoreactive T cells. Post-translational modification (PTM) may alter the MHC-binding motif or TCR contact residues in a peptide antigen, transforming the tolerance to self to autoreactivity. Mass spectrometry-based immunopeptidomics provides a valuable mechanism for identifying MHC ligands that contain PTMs and can thus provide valuable insights into pathogenesis and therapeutics of autoimmune diseases. A plethora of PTMs have been implicated in this process, and this review highlights their formation and identification.
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Affiliation(s)
- Cheryl F Lichti
- Department of Pathology and Immunology, Division of Immunobiology, The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, 660 S. Euclid Ave, Campus Box 8118, St. Louis, MO 63110, USA.
| | - Xiaoxiao Wan
- Department of Pathology and Immunology, Division of Immunobiology, The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, 660 S. Euclid Ave, Campus Box 8118, St. Louis, MO 63110, USA.
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12
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Chang HY, Haynes SE, Yu F, Nesvizhskii AI. Implementing the MSFragger Search Engine as a Node in Proteome Discoverer. J Proteome Res 2023; 22:520-525. [PMID: 36475762 DOI: 10.1021/acs.jproteome.2c00485] [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: 12/13/2022]
Abstract
Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/.
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Affiliation(s)
- Hui-Yin Chang
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48105, United States.,Department of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan 320317
| | - Sarah E Haynes
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48105, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48105, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48105, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48105, United States
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13
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Gagné D, Shajari E, Thibault MP, Noël JF, Boisvert FM, Babakissa C, Levy E, Gagnon H, Brunet MA, Grynspan D, Ferretti E, Bertelle V, Beaulieu JF. Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis. Int J Mol Sci 2022; 23:ijms231911601. [PMID: 36232903 PMCID: PMC9569884 DOI: 10.3390/ijms231911601] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/05/2022] Open
Abstract
Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.
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Affiliation(s)
- David Gagné
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Elmira Shajari
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Marie-Pier Thibault
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Noël
- PhenoSwitch Bioscience Inc., 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - François-Michel Boisvert
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Corentin Babakissa
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Emile Levy
- Research Center, Centre Hospitalier Universitaire Ste-Justine, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Hugo Gagnon
- PhenoSwitch Bioscience Inc., 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - Marie A. Brunet
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - David Grynspan
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Colombia, Vancouver, BC V6T 2B5, Canada
| | - Emanuela Ferretti
- Division of Neonatology, Department of Pediatrics, Children’s Hospital of Eastern Ontario (CHEO) and CHEO Research Institute, Ottawa, ON K1H 8L1, Canada
| | - Valérie Bertelle
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Division of Neonatology, Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Beaulieu
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Correspondence:
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14
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Müller M, Gräbnitz F, Barandun N, Shen Y, Wendt F, Steiner SN, Severin Y, Vetterli SU, Mondal M, Prudent JR, Hofmann R, van Oostrum M, Sarott RC, Nesvizhskii AI, Carreira EM, Bode JW, Snijder B, Robinson JA, Loessner MJ, Oxenius A, Wollscheid B. Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks. Nat Commun 2021; 12:7036. [PMID: 34857745 PMCID: PMC8639842 DOI: 10.1038/s41467-021-27280-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 11/09/2021] [Indexed: 12/18/2022] Open
Abstract
The molecular nanoscale organization of the surfaceome is a fundamental regulator of cellular signaling in health and disease. Technologies for mapping the spatial relationships of cell surface receptors and their extracellular signaling synapses would unlock theranostic opportunities to target protein communities and the possibility to engineer extracellular signaling. Here, we develop an optoproteomic technology termed LUX-MS that enables the targeted elucidation of acute protein interactions on and in between living cells using light-controlled singlet oxygen generators (SOG). By using SOG-coupled antibodies, small molecule drugs, biologics and intact viral particles, we demonstrate the ability of LUX-MS to decode ligand receptor interactions across organisms and to discover surfaceome receptor nanoscale organization with direct implications for drug action. Furthermore, by coupling SOG to antigens we achieved light-controlled molecular mapping of intercellular signaling within functional immune synapses between antigen-presenting cells and CD8+ T cells providing insights into T cell activation with spatiotemporal specificity. LUX-MS based decoding of surfaceome signaling architectures thereby provides a molecular framework for the rational development of theranostic strategies. The spatial organization of cell surface receptors is critical for cell signaling and drug action. Here, the authors develop an optoproteomic method for mapping surface protein interactions, revealing cellular responses to antibodies, drugs and viral particles as well as immunosynapse signaling events.
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Affiliation(s)
- Maik Müller
- Department of Health Sciences and Technology (D-HEST), ETH Zurich, Institute of Translational Medicine (ITM), Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Fabienne Gräbnitz
- Department of Biology, ETH Zurich, Institute of Microbiology, Zurich, Switzerland
| | - Niculò Barandun
- Department of Biology, ETH Zurich, Institute of Microbiology, Zurich, Switzerland
| | - Yang Shen
- Institute of Food Nutrition and Health, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Fabian Wendt
- Department of Health Sciences and Technology (D-HEST), ETH Zurich, Institute of Translational Medicine (ITM), Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sebastian N Steiner
- Department of Health Sciences and Technology (D-HEST), ETH Zurich, Institute of Translational Medicine (ITM), Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Yannik Severin
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | | | - Milon Mondal
- Chemistry Department, University of Zurich, Zurich, Switzerland
| | | | - Raphael Hofmann
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Marc van Oostrum
- Department of Health Sciences and Technology (D-HEST), ETH Zurich, Institute of Translational Medicine (ITM), Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Roman C Sarott
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Erick M Carreira
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Jeffrey W Bode
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Berend Snijder
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - John A Robinson
- Chemistry Department, University of Zurich, Zurich, Switzerland
| | - Martin J Loessner
- Institute of Food Nutrition and Health, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Annette Oxenius
- Department of Biology, ETH Zurich, Institute of Microbiology, Zurich, Switzerland
| | - Bernd Wollscheid
- Department of Health Sciences and Technology (D-HEST), ETH Zurich, Institute of Translational Medicine (ITM), Zurich, Switzerland. .,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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15
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Proteomic and lipidomic profiling of demyelinating lesions identifies fatty acids as modulators in lesion recovery. Cell Rep 2021; 37:109898. [PMID: 34706241 PMCID: PMC8567315 DOI: 10.1016/j.celrep.2021.109898] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 08/24/2021] [Accepted: 10/06/2021] [Indexed: 12/25/2022] Open
Abstract
After demyelinating injury of the central nervous system, resolution of the mounting acute inflammation is crucial for the initiation of a regenerative response. Here, we aim to identify fatty acids and lipid mediators that govern the balance of inflammatory reactions within demyelinating lesions. Using lipidomics, we identify bioactive lipids in the resolution phase of inflammation with markedly elevated levels of n-3 polyunsaturated fatty acids. Using fat-1 transgenic mice, which convert n-6 fatty acids to n-3 fatty acids, we find that reduction of the n-6/n-3 ratio decreases the phagocytic infiltrate. In addition, we observe accelerated decline of microglia/macrophages and enhanced generation of oligodendrocytes in aged mice when n-3 fatty acids are shuttled to the brain. Thus, n-3 fatty acids enhance lesion recovery and may, therefore, provide the basis for pro-regenerative medicines of demyelinating diseases in the central nervous system.
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16
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Salz R, Bouwmeester R, Gabriels R, Degroeve S, Martens L, Volders PJ, 't Hoen PAC. Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection. J Proteome Res 2021; 20:3353-3364. [PMID: 33998808 PMCID: PMC8280751 DOI: 10.1021/acs.jproteome.1c00264] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Indexed: 12/30/2022]
Abstract
Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.
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Affiliation(s)
- Renee Salz
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Pieter-Jan Volders
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Peter A C 't Hoen
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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17
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Schulze S, Igiraneza AB, Kösters M, Leufken J, Leidel SA, Garcia BA, Fufezan C, Pohlschroder M. Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal. J Proteome Res 2021; 20:1986-1996. [PMID: 33514075 DOI: 10.1021/acs.jproteome.0c00799] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.
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Affiliation(s)
- Stefan Schulze
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Aime Bienfait Igiraneza
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Manuel Kösters
- Department of Chemistry and Biochemistry, University of Bern, 3012 Bern, Switzerland
| | - Johannes Leufken
- Department of Chemistry and Biochemistry, University of Bern, 3012 Bern, Switzerland
| | - Sebastian A Leidel
- Department of Chemistry and Biochemistry, University of Bern, 3012 Bern, Switzerland
| | - Benjamin A Garcia
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Christian Fufezan
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany
| | - Mechthild Pohlschroder
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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18
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Geiszler DJ, Kong AT, Avtonomov DM, Yu F, Leprevost FDV, Nesvizhskii AI. PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results. Mol Cell Proteomics 2020; 20:100018. [PMID: 33568339 PMCID: PMC7950090 DOI: 10.1074/mcp.tir120.002216] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 11/13/2020] [Accepted: 12/01/2020] [Indexed: 01/17/2023] Open
Abstract
Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of post-translational modification profiles detected in open searches based on attributes, such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multiexperiment comparisons for studying changes in modification profiles, e.g., in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed and paraffin-embedded samples, detects extreme underalkylation of cysteine in some data sets, discovers an artifactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multicenter proteomics profiling study.
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Affiliation(s)
- Daniel J Geiszler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Andy T Kong
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Dmitry M Avtonomov
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
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