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Mehlferber MM, Jeffery ED, Saquing J, Jordan BT, Sheynkman L, Murali M, Genet G, Acharya BR, Hirschi KK, Sheynkman GM. Characterization of protein isoform diversity in human umbilical vein endothelial cells via long-read proteogenomics. RNA Biol 2022; 19:1228-1243. [PMID: 36457147 PMCID: PMC9721438 DOI: 10.1080/15476286.2022.2141938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system. Their phenotypes can be modulated by alternative splicing of RNA to produce distinct protein isoforms. To characterize the RNA and protein isoform landscape within ECs, we applied a long read proteogenomics approach to analyse human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream mass-spectrometry (MS) analysis to infer protein isoform expression. We detected 53,863 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted "reference isoform" 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene CDH5, suggesting potential changes in its associated signalling pathways. Finally, we identified novel protein isoforms arising from a diversity of RNA splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high-resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function.[Figure: see text].
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Affiliation(s)
- Madison M. Mehlferber
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA,Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Erin D. Jeffery
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Jamie Saquing
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Ben T. Jordan
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Leon Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Mayank Murali
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Gael Genet
- Department of Cell Biology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Bipul R. Acharya
- Department of Cell Biology, University of Virginia School of Medicine, Charlottesville, VA, USA,Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA,Wellcome Centre for Cell-Matrix Research, Faculty of Biology, Medicine and Health, the University of Manchester, UK
| | - Karen K. Hirschi
- Department of Cell Biology, University of Virginia School of Medicine, Charlottesville, VA, USA,Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Gloria M. Sheynkman
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA,Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA,Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA,UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, Virginia, USA,CONTACT Gloria M. Sheynkman The Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
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Shortreed MR, Millikin RJ, Liu L, Rolfs Z, Miller RM, Schaffer LV, Frey BL, Smith LM. Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus. J Proteome Res 2021; 20:1997-2004. [PMID: 33683901 DOI: 10.1021/acs.jproteome.0c00838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m/z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.
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Affiliation(s)
- Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Robert J Millikin
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lei Liu
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Zach Rolfs
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Rachel M Miller
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Leah V Schaffer
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Brian L Frey
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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Dai Y, Buxton KE, Schaffer LV, Miller RM, Millikin RJ, Scalf M, Frey BL, Shortreed MR, Smith LM. Constructing Human Proteoform Families Using Intact-Mass and Top-Down Proteomics with a Multi-Protease Global Post-Translational Modification Discovery Database. J Proteome Res 2019; 18:3671-3680. [PMID: 31479276 DOI: 10.1021/acs.jproteome.9b00339] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Complex human biomolecular processes are made possible by the diversity of human proteoforms. Constructing proteoform families, groups of proteoforms derived from the same gene, is one way to represent this diversity. Comprehensive, high-confidence identification of human proteoforms remains a central challenge in mass spectrometry-based proteomics. We have previously reported a strategy for proteoform identification using intact-mass measurements, and we have since improved that strategy by mass calibration based on search results, the use of a global post-translational modification discovery database, and the integration of top-down proteomics results with intact-mass analysis. In the present study, we combine these strategies for enhanced proteoform identification in total cell lysate from the Jurkat human T lymphocyte cell line. We collected, processed, and integrated three types of proteomics data (NeuCode-labeled intact-mass, label-free top-down, and multi-protease bottom-up) to maximize the number of confident proteoform identifications. The integrated analysis revealed 5950 unique experimentally observed proteoforms, which were assembled into 848 proteoform families. Twenty percent of the observed proteoforms were confidently identified at a 3.9% false discovery rate, representing 1207 unique proteoforms derived from 484 genes.
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Affiliation(s)
- Yunxiang Dai
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States.,Biophysics Graduate Program , University of Wisconsin , 413 Bock Laboratories, 1525 Linden Drive , Madison , Wisconsin 53706 , United States
| | - Katherine E Buxton
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Leah V Schaffer
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Rachel M Miller
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Robert J Millikin
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Mark Scalf
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Brian L Frey
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Michael R Shortreed
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Lloyd M Smith
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
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