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Dam SH, Olsen LR, Vitting-Seerup K. Expression and splicing mediate distinct biological signals. BMC Biol 2023; 21:220. [PMID: 37858135 PMCID: PMC10588054 DOI: 10.1186/s12915-023-01724-w] [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: 12/01/2022] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Through alternative splicing, most human genes produce multiple isoforms in a cell-, tissue-, and disease-specific manner. Numerous studies show that alternative splicing is essential for development, diseases, and their treatments. Despite these important examples, the extent and biological relevance of splicing are currently unknown. RESULTS To solve this problem, we developed pairedGSEA and used it to profile transcriptional changes in 100 representative RNA-seq datasets. Our systematic analysis demonstrates that changes in splicing, on average, contribute to 48.1% of the biological signal in expression analyses. Gene-set enrichment analysis furthermore indicates that expression and splicing both convey shared and distinct biological signals. CONCLUSIONS These findings establish alternative splicing as a major regulator of the human condition and suggest that most contemporary RNA-seq studies likely miss out on critical biological insights. We anticipate our results will contribute to the transition from a gene-centric to an isoform-centric research paradigm.
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Affiliation(s)
- Søren Helweg Dam
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lars Rønn Olsen
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Kristoffer Vitting-Seerup
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
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Chen W, Ding Z, Zang Y, Liu X. Characterization of Proteoform Post-Translational Modifications by Top-Down and Bottom-Up Mass Spectrometry in Conjunction with Annotations. J Proteome Res 2023; 22:3178-3189. [PMID: 37728997 PMCID: PMC10563160 DOI: 10.1021/acs.jproteome.3c00207] [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: 04/07/2023] [Indexed: 09/22/2023]
Abstract
Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledge bases, such as UniProt, provide valuable information for PTM characterization and verification. Here, we present a software pipeline PTM-TBA (PTM characterization by Top-down and Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as PTM annotations. We assessed PTM-TBA using a technical triplicate of bottom-up and top-down MS data of SW480 cells. On average, database search of the top-down MS data identified 2000 mass shifts, 814.5 (40.7%) of which were matched to 11 common PTMs and 423 of which were localized. Of the mass shifts identified by top-down MS, PTM-TBA verified 435 mass shifts using the bottom-up MS data and UniProt annotations.
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Affiliation(s)
- Wenrong Chen
- Department
of BioHealth Informatics, Indiana University-Purdue
University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Zhengming Ding
- Department
of Computer Science, Tulane School of Science and Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Yong Zang
- Department
of Biostatics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Xiaowen Liu
- Tulane
Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Deming Department
of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
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McCool EN, Xu T, Chen W, Beller NC, Nolan SM, Hummon AB, Liu X, Sun L. Deep top-down proteomics revealed significant proteoform-level differences between metastatic and nonmetastatic colorectal cancer cells. SCIENCE ADVANCES 2022; 8:eabq6348. [PMID: 36542699 PMCID: PMC9770947 DOI: 10.1126/sciadv.abq6348] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 11/18/2022] [Indexed: 05/23/2023]
Abstract
Understanding cancer metastasis at the proteoform level is crucial for discovering previously unknown protein biomarkers for cancer diagnosis and drug development. We present the first top-down proteomics (TDP) study of a pair of isogenic human nonmetastatic and metastatic colorectal cancer (CRC) cell lines (SW480 and SW620). We identified 23,622 proteoforms of 2332 proteins from the two cell lines, representing nearly fivefold improvement in the number of proteoform identifications (IDs) compared to previous TDP datasets of human cancer cells. We revealed substantial differences between the SW480 and SW620 cell lines regarding proteoform and single amino acid variant (SAAV) profiles. Quantitative TDP unveiled differentially expressed proteoforms between the two cell lines, and the corresponding genes had diversified functions and were closely related to cancer. Our study represents a pivotal advance in TDP toward the characterization of human proteome in a proteoform-specific manner, which will transform basic and translational biomedical research.
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Affiliation(s)
- Elijah N. McCool
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA
| | - Tian Xu
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA
| | - Wenrong Chen
- Department of BioHealth Informatics, Indiana University–Purdue University Indianapolis, 719 Indiana Avenue, Indianapolis, IN 46202, USA
| | - Nicole C. Beller
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, OH 43210, USA
| | - Scott M. Nolan
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA
| | - Amanda B. Hummon
- Department of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, OH 43210, USA
- The Comprehensive Cancer Center, The Ohio State University, 500 West 12th Avenue, Columbus, OH 43210, USA
| | - Xiaowen Liu
- Deming Department of Medicine, School of Medicine, Tulane University, 1441 Canal Street, New Orleans, LA 70112, USA
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA
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Rajczewski AT, Jagtap PD, Griffin TJ. An overview of technologies for MS-based proteomics-centric multi-omics. Expert Rev Proteomics 2022; 19:165-181. [PMID: 35466851 PMCID: PMC9613604 DOI: 10.1080/14789450.2022.2070476] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems. AREAS COVERED Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics. EXPERT COMMENTARY Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.
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Affiliation(s)
- Andrew T. Rajczewski
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA,Coauthor, Research Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA,Department of Biochemistry, Molecular and Cell Biology Building, University of Minnesota, 420 Washington Ave SE 7-129, Minneapolis, MN, 55455, USA
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Sun RX, Wang RM, Luo L, Liu C, Chi H, Zeng WF, He SM. Accurate Proteoform Identification and Quantitation Using pTop 2.0. Methods Mol Biol 2022; 2500:105-129. [PMID: 35657590 DOI: 10.1007/978-1-0716-2325-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics.
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Affiliation(s)
- Rui-Xiang Sun
- National Institute of Biological Sciences, Beijing, China.
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
| | - Rui-Min Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lan Luo
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Chao Liu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Hao Chi
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Wen-Feng Zeng
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Si-Min He
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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