1
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Elezaby A, Lin AJ, Vijayan V, Pokhrel S, Kraemer BR, Bechara LRG, Larus I, Sun J, Baena V, Syed ZA, Murphy E, Glancy B, Ostberg NP, Queliconi BB, Campos JC, Ferreira JCB, Haileselassie B, Mochly-Rosen D. Cardiac troponin I directly binds and inhibits mitochondrial ATP synthase with a noncanonical role in the post-ischemic heart. NATURE CARDIOVASCULAR RESEARCH 2024; 3:987-1002. [PMID: 39196031 DOI: 10.1038/s44161-024-00512-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/21/2024] [Indexed: 08/29/2024]
Abstract
Cardiac troponin I (cTnI) is a key regulator of cardiomyocyte contraction. However, its role in mitochondria is unknown. Here we show that cTnI localized to mitochondria in the heart, inhibited mitochondrial functions when stably expressed in noncardiac cells and increased the opening of the mitochondrial permeability transition pore under oxidative stress. Direct, specific and saturable binding of cTnI to F1FO-ATP synthase was demonstrated in vitro using immune-captured ATP synthase and in cells using proximity ligation assay. cTnI binding doubled ATPase activity, whereas skeletal troponin I and several human pathogenic cTnI variants associated with familial hypertrophic cardiomyopathy did not. A rationally designed peptide, P888, inhibited cTnI binding to ATP synthase, inhibited cTnI-induced increase in ATPase activity in vitro and reduced cardiac injury following transient ischemia in vivo. We suggest that cTnI-bound ATP synthase results in lower ATP levels, and releasing this interaction during cardiac ischemia-reperfusion may increase the reservoir of functional mitochondria to reduce cardiac injury.
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Affiliation(s)
- Aly Elezaby
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Amanda J Lin
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Vijith Vijayan
- Department of Pediatrics, Division of Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Suman Pokhrel
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Benjamin R Kraemer
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Luiz R G Bechara
- Department of Anatomy, Institute of Biomedical Sciences, University of Sao Paulo, São Paulo, Brazil
| | - Isabel Larus
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Junhui Sun
- Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Valentina Baena
- Electron Microscopy Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zulfeqhar A Syed
- Electron Microscopy Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth Murphy
- Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brian Glancy
- Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- National Institute of Arthritis, Musculoskeletal, and Skin Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Nicolai P Ostberg
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bruno B Queliconi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Juliane C Campos
- Department of Anatomy, Institute of Biomedical Sciences, University of Sao Paulo, São Paulo, Brazil
| | - Julio C B Ferreira
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Anatomy, Institute of Biomedical Sciences, University of Sao Paulo, São Paulo, Brazil
| | - Bereketeab Haileselassie
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Daria Mochly-Rosen
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
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2
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McWhite CD, Sae-Lee W, Yuan Y, Mallam AL, Gort-Freitas NA, Ramundo S, Onishi M, Marcotte EM. Alternative proteoforms and proteoform-dependent assemblies in humans and plants. Mol Syst Biol 2024; 20:933-951. [PMID: 38918600 PMCID: PMC11297038 DOI: 10.1038/s44320-024-00048-3] [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: 02/01/2023] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.
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Affiliation(s)
- Claire D McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.
| | - Wisath Sae-Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yaning Yuan
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Anna L Mallam
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | | | - Silvia Ramundo
- Gregor Mendel Institute of Molecular Plant Biology, 1030, Wien, Austria
| | - Masayuki Onishi
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Edward M Marcotte
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
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3
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Doron-Mandel E, Bokor BJ, Ma Y, Street LA, Tang LC, Abdou AA, Shah NH, Rosenberger G, Jovanovic M. A Multiplexed SEC-MS Approach to Systematically Study the Interplay Between Protein Assembly-States and Phosphorylation Events. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.12.523793. [PMID: 36711903 PMCID: PMC9882152 DOI: 10.1101/2023.01.12.523793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Protein molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly-states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly-states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Applying SEC-MX to HEK293 and HCT116 cells, we generated a proof-of-concept dataset mapping thousands of phosphopeptides and their assembly-states. Our analysis revealed intricate relationships between phosphorylation events and assembly-states and generated testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.
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4
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Goel RK, Bithi N, Emili A. Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery. Curr Opin Struct Biol 2024; 88:102880. [PMID: 38996623 DOI: 10.1016/j.sbi.2024.102880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/13/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024]
Abstract
Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.
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Affiliation(s)
- Raghuveera Kumar Goel
- Division of Oncology, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University (OHSU), Portland, OR, USA.
| | - Nazmin Bithi
- Division of Oncology, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University (OHSU), Portland, OR, USA
| | - Andrew Emili
- Division of Oncology, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University (OHSU), Portland, OR, USA.
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5
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Cawood EE, Baker E, Edwards TA, Woolfson DN, Karamanos TK, Wilson AJ. Understanding β-strand mediated protein-protein interactions: tuning binding behaviour of intrinsically disordered sequences by backbone modification. Chem Sci 2024; 15:10237-10245. [PMID: 38966365 PMCID: PMC11220606 DOI: 10.1039/d4sc02240h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/24/2024] [Indexed: 07/06/2024] Open
Abstract
A significant challenge in chemical biology is to understand and modulate protein-protein interactions (PPIs). Given that many PPIs involve a folded protein domain and a peptide sequence that is intrinsically disordered in isolation, peptides represent powerful tools to understand PPIs. Using the interaction between small ubiquitin-like modifier (SUMO) and SUMO-interacting motifs (SIMs), here we show that N-methylation of the peptide backbone can effectively restrict accessible peptide conformations, predisposing them for protein recognition. Backbone N-methylation in appropriate locations results in faster target binding, and thus higher affinity, as shown by relaxation-based NMR experiments and computational analysis. We show that such higher affinities occur as a consequence of an increase in the energy of the unbound state, and a reduction in the entropic contribution to the binding and activation energies. Thus, backbone N-methylation may represent a useful modification within the peptidomimetic toolbox to probe β-strand mediated interactions.
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Affiliation(s)
- Emma E Cawood
- Astbury Centre for Structural Molecular Biology, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
- School of Chemistry, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
| | - Emily Baker
- School of Biochemistry, University of Bristol Medical Sciences Building, University Walk Bristol BS8 1TD UK
- BrisSynBio, University of Bristol Life Sciences Building, Tyndall Avenue Bristol BS8 1TQ UK
| | - Thomas A Edwards
- Astbury Centre for Structural Molecular Biology, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
- School of Molecular and Cellular Biology, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
- College of Biomedical Sciences, Larkin University 18301 N Miami Ave #1 Miami FL 33169 USA
| | - Derek N Woolfson
- School of Chemistry, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
- School of Biochemistry, University of Bristol Medical Sciences Building, University Walk Bristol BS8 1TD UK
- School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | | | - Andrew J Wilson
- Astbury Centre for Structural Molecular Biology, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
- School of Chemistry, University of Leeds Woodhouse Lane Leeds LS2 9JT UK
- School of Chemistry, University of Birmingham Edgbaston Birmingham B15 2TT UK
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6
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Liu Y, Sundah NR, Ho NRY, Shen WX, Xu Y, Natalia A, Yu Z, Seet JE, Chan CW, Loh TP, Lim BY, Shao H. Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells. Nat Biomed Eng 2024; 8:909-923. [PMID: 38898172 DOI: 10.1038/s41551-024-01225-3] [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: 01/28/2023] [Accepted: 05/05/2024] [Indexed: 06/21/2024]
Abstract
Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.
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Affiliation(s)
- Yu Liu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Noah R Sundah
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Nicholas R Y Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Wan Xiang Shen
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Yun Xu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Auginia Natalia
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Zhonglang Yu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Ju Ee Seet
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Ching Wan Chan
- Department of Surgery, National University Hospital, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tze Ping Loh
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Brian Y Lim
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore.
| | - Huilin Shao
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore.
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore.
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7
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Liu X, Abad L, Chatterjee L, Cristea IM, Varjosalo M. Mapping protein-protein interactions by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38742660 DOI: 10.1002/mas.21887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.
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Affiliation(s)
- Xiaonan Liu
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Lawrence Abad
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Lopamudra Chatterjee
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Markku Varjosalo
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
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8
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Chen Q, Fan X. Potential role of the protein interactome in translating TCM theory and clinical practice into modern biomedical knowledge. Chin J Nat Med 2024; 22:385-386. [PMID: 38796212 DOI: 10.1016/s1875-5364(24)60635-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Indexed: 05/28/2024]
Affiliation(s)
- Qian Chen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China.
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9
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Shrestha HK, Lee D, Wu Z, Wang Z, Fu Y, Wang X, Serrano GE, Beach TG, Peng J. Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry. J Proteome Res 2024; 23:1221-1231. [PMID: 38507900 PMCID: PMC11065482 DOI: 10.1021/acs.jproteome.3c00685] [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] [Indexed: 03/22/2024]
Abstract
Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.
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Affiliation(s)
- Him K. Shrestha
- Departments of Structural Biology and Developmental Neurobiology
| | - DongGeun Lee
- Departments of Structural Biology and Developmental Neurobiology
| | - Zhiping Wu
- Departments of Structural Biology and Developmental Neurobiology
| | - Zhen Wang
- Departments of Structural Biology and Developmental Neurobiology
| | - Yingxue Fu
- Departments of Structural Biology and Developmental Neurobiology
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | | | - Thomas G. Beach
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology
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10
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Reed TJ, Tyl MD, Tadych A, Troyanskaya OG, Cristea IM. Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts. Nat Methods 2024; 21:488-500. [PMID: 38361019 PMCID: PMC11249048 DOI: 10.1038/s41592-024-02179-9] [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: 04/06/2023] [Accepted: 01/12/2024] [Indexed: 02/17/2024]
Abstract
Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.
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Affiliation(s)
- Tavis J Reed
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Matthew D Tyl
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Alicja Tadych
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA.
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
- Flatiron Institute, Simons Foundation, New York City, NY, USA.
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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11
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Antony F, Brough Z, Zhao Z, Duong van Hoa F. Capture of the Mouse Organ Membrane Proteome Specificity in Peptidisc Libraries. J Proteome Res 2024; 23:857-867. [PMID: 38232390 DOI: 10.1021/acs.jproteome.3c00825] [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] [Indexed: 01/19/2024]
Abstract
Membrane proteins, particularly those on the cell surface, play pivotal roles in diverse physiological processes, and their dysfunction is linked to a broad spectrum of diseases. Despite being crucial biomarkers and therapeutic drug targets, their low abundance and hydrophobic nature pose challenges in isolation and quantification, especially when extracted from tissues and organs. To overcome these hurdles, we developed the membrane-mimicking peptidisc, enabling the isolation of the membrane proteome in a water-soluble library conducive to swift identification through liquid chromatography with tandem mass spectrometry. This study applies the method across five mice organs, capturing between 200 and 450 plasma membrane proteins in each case. More than just membrane protein identification, the peptidisc is used to estimate the relative abundance across organs, linking cell-surface protein molecular functions to organ biological roles, thereby contributing to the ongoing discourse on organ specificity. This contribution holds substantial potential for unveiling new avenues in the exploration of biomarkers and downstream applications involving knowledge of the organ cell-surface proteome.
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Affiliation(s)
- Frank Antony
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Zora Brough
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Zhiyu Zhao
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Franck Duong van Hoa
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
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12
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Chen HM, Liu JX, Liu D, Hao GF, Yang GF. Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection. Rev Med Virol 2024; 34:e2517. [PMID: 38282401 DOI: 10.1002/rmv.2517] [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: 05/11/2023] [Revised: 09/12/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.
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Affiliation(s)
- Hui-Min Chen
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Di Liu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
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13
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Skinnider MA, Akinlaja MO, Foster LJ. Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry. Nat Commun 2023; 14:8365. [PMID: 38102123 PMCID: PMC10724252 DOI: 10.1038/s41467-023-44139-5] [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: 06/24/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.
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Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA
| | - Mopelola O Akinlaja
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
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14
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Na D, Lim DH, Hong JS, Lee HM, Cho D, Yu MS, Shaker B, Ren J, Lee B, Song JG, Oh Y, Lee K, Oh KS, Lee MY, Choi MS, Choi HS, Kim YH, Bui JM, Lee K, Kim HW, Lee YS, Gsponer J. A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration. Mol Syst Biol 2023; 19:e11801. [PMID: 37984409 DOI: 10.15252/msb.202311801] [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: 06/05/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/22/2023] Open
Abstract
The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3β), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.
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Affiliation(s)
- Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Do-Hwan Lim
- College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Jae-Sang Hong
- College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Daeahn Cho
- Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Myeong-Sang Yu
- Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Bilal Shaker
- Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Jun Ren
- Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Bomi Lee
- College of Life Sciences, Sejong University, Seoul, Republic of Korea
| | - Jae Gwang Song
- College of Life Sciences, Sejong University, Seoul, Republic of Korea
| | - Yuna Oh
- Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Kyungeun Lee
- Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Kwang-Seok Oh
- Information-based Drug Research Center, Korea Research Institute of Chemical Technology, Deajeon, Republic of Korea
| | - Mi Young Lee
- Information-based Drug Research Center, Korea Research Institute of Chemical Technology, Deajeon, Republic of Korea
| | - Min-Seok Choi
- College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
| | - Han Saem Choi
- College of Life Sciences, Sejong University, Seoul, Republic of Korea
| | - Yang-Hee Kim
- College of Life Sciences, Sejong University, Seoul, Republic of Korea
| | - Jennifer M Bui
- Department of Biochemistry and Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Kangseok Lee
- Department of Life Science, Chung-Ang University, Seoul, Republic of Korea
| | - Hyung Wook Kim
- College of Life Sciences, Sejong University, Seoul, Republic of Korea
| | - Young Sik Lee
- College of Life Sciences and Biotechnology, Korea University, Seoul, Republic of Korea
| | - Jörg Gsponer
- Department of Biochemistry and Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
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15
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Zilocchi M, Rahmatbakhsh M, Moutaoufik MT, Broderick K, Gagarinova A, Jessulat M, Phanse S, Aoki H, Aly KA, Babu M. Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain. Nat Protoc 2023; 18:3918-3973. [PMID: 37985878 DOI: 10.1038/s41596-023-00901-z] [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: 05/04/2023] [Accepted: 08/09/2023] [Indexed: 11/22/2023]
Abstract
Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.
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Affiliation(s)
- Mara Zilocchi
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | | | | | - Kirsten Broderick
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Alla Gagarinova
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
- Department of Biology, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Matthew Jessulat
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Hiroyuki Aoki
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Khaled A Aly
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada.
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16
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Kurgan L, Hu G, Wang K, Ghadermarzi S, Zhao B, Malhis N, Erdős G, Gsponer J, Uversky VN, Dosztányi Z. Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins. Nat Protoc 2023; 18:3157-3172. [PMID: 37740110 DOI: 10.1038/s41596-023-00876-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/21/2023] [Indexed: 09/24/2023]
Abstract
Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.
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Affiliation(s)
- Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Kui Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gábor Erdős
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Vladimir N Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
- Byrd Alzheimer's Center and Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
| | - Zsuzsanna Dosztányi
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary.
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17
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Reitz CJ, Kuzmanov U, Gramolini AO. Multi-omic analyses and network biology in cardiovascular disease. Proteomics 2023; 23:e2200289. [PMID: 37691071 DOI: 10.1002/pmic.202200289] [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: 03/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.
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Affiliation(s)
- Cristine J Reitz
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Uros Kuzmanov
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
| | - Anthony O Gramolini
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
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18
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Youssef A, Bian F, Paniikov NS, Crovella M, Emili A. Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations. Proteomics 2023; 23:e2200404. [PMID: 37248827 DOI: 10.1002/pmic.202200404] [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: 04/02/2023] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 05/31/2023]
Abstract
Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.
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Affiliation(s)
- Ahmed Youssef
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
- Center for Network Systems Biology, Boston University, Boston, Massachusetts, USA
| | - Fei Bian
- Center for Network Systems Biology, Boston University, Boston, Massachusetts, USA
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Nicolai S Paniikov
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA
| | - Mark Crovella
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
- Computer Science Department, Boston University, Boston, Massachusetts, USA
- Faculty of Computing and Data Sciences, Boston University, Boston, Massachusetts, USA
| | - Andrew Emili
- Graduate Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
- Center for Network Systems Biology, Boston University, Boston, Massachusetts, USA
- Faculty of Computing and Data Sciences, Boston University, Boston, Massachusetts, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, USA
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19
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Wright SN, Leger BS, Rosenthal SB, Liu SN, Jia T, Chitre AS, Polesskaya O, Holl K, Gao J, Cheng R, Garcia Martinez A, George A, Gileta AF, Han W, Netzley AH, King CP, Lamparelli A, Martin C, St Pierre CL, Wang T, Bimschleger H, Richards J, Ishiwari K, Chen H, Flagel SB, Meyer P, Robinson TE, Solberg Woods LC, Kreisberg JF, Ideker T, Palmer AA. Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks. Cell Rep 2023; 42:112873. [PMID: 37527041 PMCID: PMC10546330 DOI: 10.1016/j.celrep.2023.112873] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.
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Affiliation(s)
- Sarah N Wright
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Program in Biomedical Sciences, University of California San Diego, La Jolla, CA 93093, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology & Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sophie N Liu
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Tongqiu Jia
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Katie Holl
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Riyan Cheng
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Angel Garcia Martinez
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Anthony George
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Alexander F Gileta
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Wenyan Han
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Alesa H Netzley
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christopher P King
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Connor Martin
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Tengfei Wang
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Hannah Bimschleger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Jerry Richards
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Keita Ishiwari
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY 14203, USA
| | - Hao Chen
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Shelly B Flagel
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paul Meyer
- Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | - Terry E Robinson
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Jason F Kreisberg
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
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20
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Hasman M, Mayr M, Theofilatos K. Uncovering Protein Networks in Cardiovascular Proteomics. Mol Cell Proteomics 2023; 22:100607. [PMID: 37356494 PMCID: PMC10460687 DOI: 10.1016/j.mcpro.2023.100607] [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: 11/14/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 06/27/2023] Open
Abstract
Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.
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Affiliation(s)
- Maria Hasman
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
| | - Manuel Mayr
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
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21
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Hu M, Zhang Y, Yuan Y, Ma W, Zheng Y, Gu Q, Xie XS. Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics. J Phys Chem B 2023. [PMID: 37368753 DOI: 10.1021/acs.jpcb.3c00014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date has focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlation between the translational levels of a pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1000 proteins in a population of homogeneous K562 cells under a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations into bulk samples. However, some correlations of gene or protein expression under the steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.
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Affiliation(s)
- Mo Hu
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Changping Laboratory, Beijing 102206, China
| | - Yutong Zhang
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yuan Yuan
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Wenping Ma
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yinghui Zheng
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | | | - X Sunney Xie
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Changping Laboratory, Beijing 102206, China
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22
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Street L, Rothamel K, Brannan K, Jin W, Bokor B, Dong K, Rhine K, Madrigal A, Al-Azzam N, Kim JK, Ma Y, Abdou A, Wolin E, Doron-Mandel E, Ahdout J, Mujumdar M, Jovanovic M, Yeo GW. Large-scale map of RNA binding protein interactomes across the mRNA life-cycle. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.08.544225. [PMID: 37333282 PMCID: PMC10274859 DOI: 10.1101/2023.06.08.544225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Messenger RNAs (mRNAs) interact with RNA-binding proteins (RBPs) in diverse ribonucleoprotein complexes (RNPs) during distinct life-cycle stages for their processing and maturation. While substantial attention has focused on understanding RNA regulation by assigning proteins, particularly RBPs, to specific RNA substrates, there has been considerably less exploration leveraging protein-protein interaction (PPI) methodologies to identify and study the role of proteins in mRNA life-cycle stages. To address this gap, we generated an RNA-aware RBP-centric PPI map across the mRNA life-cycle by immunopurification (IP-MS) of ~100 endogenous RBPs across the life-cycle in the presence or absence of RNase, augmented by size exclusion chromatography (SEC-MS). Aside from confirming 8,700 known and discovering 20,359 novel interactions between 1125 proteins, we determined that 73% of our IP interactions are regulated by the presence of RNA. Our PPI data enables us to link proteins to life-cycle stage functions, highlighting that nearly half of the proteins participate in at least two distinct stages. We show that one of the most highly interconnected proteins, ERH, engages in multiple RNA processes, including via interactions with nuclear speckles and the mRNA export machinery. We also demonstrate that the spliceosomal protein SNRNP200 participates in distinct stress granule-associated RNPs and occupies different RNA target regions in the cytoplasm during stress. Our comprehensive RBP-focused PPI network is a novel resource for identifying multi-stage RBPs and exploring RBP complexes in RNA maturation.
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Affiliation(s)
- Lena Street
- These authors contributed equally
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Katherine Rothamel
- These authors contributed equally
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kristopher Brannan
- These authors contributed equally
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX, USA
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, USA
| | - Wenhao Jin
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Benjamin Bokor
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Kevin Dong
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kevin Rhine
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Assael Madrigal
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Norah Al-Azzam
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jenny Kim Kim
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Yanzhe Ma
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Ahmed Abdou
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Erica Wolin
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Ella Doron-Mandel
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Joshua Ahdout
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Mayuresh Mujumdar
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Marko Jovanovic
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
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23
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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24
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Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors. Biomolecules 2023; 13:biom13030527. [PMID: 36979462 PMCID: PMC10046894 DOI: 10.3390/biom13030527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023] Open
Abstract
Research in the field of biochemistry and cellular biology has entered a new phase due to the discovery of phase separation driving the formation of biomolecular condensates, or membraneless organelles, in cells. The implications of this novel principle of cellular organization are vast and can be applied at multiple scales, spawning exciting research questions in numerous directions. Of fundamental importance are the molecular mechanisms that underly biomolecular condensate formation within cells and whether insights gained into these mechanisms provide a gateway for accurate predictions of protein phase behavior. Within the last six years, a significant number of predictors for protein phase separation and condensate localization have emerged. Herein, we compare a collection of state-of-the-art predictors on different tasks related to protein phase behavior. We show that the tested methods achieve high AUCs in the identification of biomolecular condensate drivers and scaffolds, as well as in the identification of proteins able to phase separate in vitro. However, our benchmark tests reveal that their performance is poorer when used to predict protein segments that are involved in phase separation or to classify amino acid substitutions as phase-separation-promoting or -inhibiting mutations. Our results suggest that the phenomenological approach used by most predictors is insufficient to fully grasp the complexity of the phenomenon within biological contexts and make reliable predictions related to protein phase behavior at the residue level.
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25
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Schlossarek D, Zhang Y, Sokolowska EM, Fernie AR, Luzarowski M, Skirycz A. Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:904-914. [PMID: 36575913 DOI: 10.1111/tpj.16084] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.
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Affiliation(s)
- Dennis Schlossarek
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Youjun Zhang
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Ewelina M Sokolowska
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Alisdair R Fernie
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Marcin Luzarowski
- Center for Molecular Biology Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Aleksandra Skirycz
- Depeartment One, Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
- Boyce Thompson Institute, Ithaca, NY, 14850, USA
- School of Integrative Plant Science, Cornell University, Ithaca, NY, 14850, USA
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26
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Yang L, Fan W, Xu Y. Chameleon-like microbes promote microecological differentiation of Daqu. Food Microbiol 2023; 109:104144. [DOI: 10.1016/j.fm.2022.104144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 11/28/2022]
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27
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Zambo B, Morlet B, Negroni L, Trave G, Gogl G. Native holdup (nHU) to measure binding affinities from cell extracts. SCIENCE ADVANCES 2022; 8:eade3828. [PMID: 36542723 PMCID: PMC9770967 DOI: 10.1126/sciadv.ade3828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.
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Affiliation(s)
- Boglarka Zambo
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Bastien Morlet
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Luc Negroni
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Gilles Trave
- Équipe Labellisée Ligue 2015, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
- Corresponding author. (G.T.); (G.G.)
| | - Gergo Gogl
- Équipe Labellisée Ligue 2015, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
- Corresponding author. (G.T.); (G.G.)
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28
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Mezentsev Y, Ershov P, Yablokov E, Kaluzhskiy L, Kupriyanov K, Gnedenko O, Ivanov A. Protein Interactome Profiling of Stable Molecular Complexes in Biomaterial Lysate. Int J Mol Sci 2022; 23:ijms232415697. [PMID: 36555337 PMCID: PMC9779103 DOI: 10.3390/ijms232415697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Most proteins function as part of various complexes, forming via stable and dynamic protein-protein interactions (PPIs). The profiling of PPIs expands the fundamental knowledge about the structures, functions, and regulation patterns of protein complexes and intracellular molecular machineries. Protein interactomics aims at solving three main tasks: (1) identification of protein partners and parts of complex intracellular structures; (2) analysis of PPIs parameters (affinity, molecular-recognition specificity, kinetic rate constants, and thermodynamic-parameters determination); (3) the study of the functional role of novel PPIs. The purpose of this work is to update the current state and prospects of multi-omics approaches to profiling of proteins involved in the formation of stable complexes. Methodological paradigm includes a development of protein-extraction and -separation techniques from tissues or cellular lysates and subsequent identification of proteins using mass-spectrometry analysis. In addition, some aspects of authors' experimental platforms, based on high-performance size-exclusion chromatography, procedures of molecular fishing, and protein identification, as well as the possibilities of interactomic taxonomy of each protein, are discussed.
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29
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Barrio-Hernandez I, Beltrao P. Network analysis of genome-wide association studies for drug target prioritisation. Curr Opin Chem Biol 2022; 71:102206. [PMID: 36087372 DOI: 10.1016/j.cbpa.2022.102206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 01/27/2023]
Abstract
Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.
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Affiliation(s)
- Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, 8093, Switzerland.
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30
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Holguin-Cruz JA, Foster LJ, Gsponer J. Where protein structure and cell diversity meet. Trends Cell Biol 2022; 32:996-1007. [PMID: 35537902 DOI: 10.1016/j.tcb.2022.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/21/2023]
Abstract
Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.
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Affiliation(s)
- Jorge A Holguin-Cruz
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada.
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31
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Chihara K, Gerovac M, Hör J, Vogel J. Global profiling of the RNA and protein complexes of Escherichia coli by size exclusion chromatography followed by RNA sequencing and mass spectrometry (SEC-seq). RNA (NEW YORK, N.Y.) 2022; 29:rna.079439.122. [PMID: 36328526 PMCID: PMC9808575 DOI: 10.1261/rna.079439.122] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
New methods for the global identification of RNA-protein interactions have led to greater recognition of the abundance and importance of RNA-binding proteins (RBPs) in bacteria. Here, we expand this tool kit by developing SEC-seq, a method based on a similar concept as the established Grad-seq approach. In Grad-seq, cellular RNA and protein complexes of a bacterium of interest are separated in a glycerol gradient, followed by high-throughput RNA-sequencing and mass spectrometry analyses of individual gradient fractions. New RNA-protein complexes are predicted based on the similarity of their elution profiles. In SEC-seq, we have replaced the glycerol gradient with separation by size exclusion chromatography, which shortens operation times and offers greater potential for automation. Applying SEC-seq to Escherichia coli, we find that the method provides a higher resolution than Grad-seq in the lower molecular weight range up to ~500 kDa. This is illustrated by the ability of SEC-seq to resolve two distinct, but similarly sized complexes of the global translational repressor CsrA with either of its antagonistic small RNAs, CsrB and CsrC. We also characterized changes in the SEC-seq profiles of the small RNA MicA upon deletion of its RNA chaperones Hfq and ProQ and investigated the redistribution of these two proteins upon RNase treatment. Overall, we demonstrate that SEC-seq is a tractable and reproducible method for the global profiling of bacterial RNA-protein complexes that offers the potential to discover yet-unrecognized associations between bacterial RNAs and proteins.
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Affiliation(s)
- Kotaro Chihara
- Helmholtz Institute for RNA-based Infection Research, Würzburg, Germany
| | | | - Jens Hör
- Weizmann Institute, Rehovot, Israel
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32
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Tang K, Tang J, Zeng J, Shen W, Zou M, Zhang C, Sun Q, Ye X, Li C, Sun C, Liu S, Jiang G, Du X. A network view of human immune system and virus-human interaction. Front Immunol 2022; 13:997851. [PMID: 36389817 PMCID: PMC9643829 DOI: 10.3389/fimmu.2022.997851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/11/2022] [Indexed: 11/30/2022] Open
Abstract
The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.
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Affiliation(s)
- Kang Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jing Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Wei Shen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Min Zou
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Chi Zhang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Qianru Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Ye
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chunwei Li
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Caijun Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Xiangjun Du,
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33
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Yang M, Harrison BR, Promislow DEL. In search of a Drosophila core cellular network with single-cell transcriptome data. G3 GENES|GENOMES|GENETICS 2022; 12:6670625. [PMID: 35976114 PMCID: PMC9526075 DOI: 10.1093/g3journal/jkac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/03/2022] [Indexed: 11/29/2022]
Abstract
Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.
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Affiliation(s)
- Ming Yang
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
| | - Benjamin R Harrison
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
| | - Daniel E L Promislow
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA 98195, USA
- Department of Biology, University of Washington , Seattle, WA 98195, USA
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34
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Gogl G, Zambo B, Kostmann C, Cousido-Siah A, Morlet B, Durbesson F, Negroni L, Eberling P, Jané P, Nominé Y, Zeke A, Østergaard S, Monsellier É, Vincentelli R, Travé G. Quantitative fragmentomics allow affinity mapping of interactomes. Nat Commun 2022; 13:5472. [PMID: 36115835 PMCID: PMC9482650 DOI: 10.1038/s41467-022-33018-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/24/2022] [Indexed: 12/18/2022] Open
Abstract
Human protein networks have been widely explored but most binding affinities remain unknown, hindering quantitative interactome-function studies. Yet interactomes rely on minimal interacting fragments displaying quantifiable affinities. Here, we measure the affinities of 65,000 interactions involving PDZ domains and their target PDZ-binding motifs (PBM) within a human interactome region particularly relevant for viral infection and cancer. We calculate interactomic distances, identify hot spots for viral interference, generate binding profiles and specificity logos, and explain selected cases by crystallographic studies. Mass spectrometry experiments on cell extracts and literature surveys show that quantitative fragmentomics effectively complements protein interactomics by providing affinities and completeness of coverage, putting a full human interactome affinity survey within reach. Finally, we show that interactome hijacking by the viral PBM of human papillomavirus E6 oncoprotein substantially impacts the host cell proteome beyond immediate E6 binders, illustrating the complex system-wide relationship between interactome and function. Protein networks have been widely explored but most binding affinities remain unknown, limiting the quantitative interpretation of interactomes. Here the authors measure affinities of 65,000 interactions involving human PDZ domains and target sequence motifs relevant for viral infection and cancer.
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35
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Dong J, Peng Q, Deng L, Liu J, Huang W, Zhou X, Zhao C, Cai Z. iMS2Net: A multiscale networking methodology to decipher metabolic synergy of organism. iScience 2022; 25:104896. [PMID: 36039290 PMCID: PMC9418851 DOI: 10.1016/j.isci.2022.104896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/04/2022] [Accepted: 08/03/2022] [Indexed: 01/14/2023] Open
Abstract
The metabolic responses of organism to external stimuli are characterized by the multicellular- and multiorgan-based synergistic regulation. Network analysis is a powerful tool to investigate this multiscale interaction. The imaging mass spectrometry (iMS)-based spatial omics provides multidimensional and multiscale information, thus offering the possibility of network analysis to investigate metabolic response of organism to environmental stimuli. We present iMS dataset-sourced multiscale network (iMS2Net) strategy to uncover prenatal environmental pollutant (PM2.5)-induced metabolic responses in the scales of cell and organ from metabolite abundances and metabolite-metabolite interaction using mouse fetal model, including metabotypic similarity, metabolic vulnerability, metabolic co-variability and metabolic diversity within and between organs. Furthermore, network-based analysis results confirm close associations between lipid metabolites and inflammatory cytokine release. This networking methodology elicits particular advantages for modeling the dynamic and adaptive processes of organism under environmental stresses or pathophysiology and provides molecular mechanism to guide the occurrence and development of systemic diseases. IMS2Net, a multiscale networking methodology to decipher iMS-spatial omics data Elaboration of variation and covariation within/between organs to external stimuli Understanding metabolic responses of organisms at cell and organ resolutions A close association between lipid metabolism and inflammatory cytokine release
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Affiliation(s)
- Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Qianwen Peng
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Jianjun Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Wei Huang
- School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, China
| | - Xin Zhou
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China
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36
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Park S, Jang SS, Lee S, Kim M, Sim H, Jeon H, Hong SE, Lee J, Lee J, Jeon EY, Lee J, Lee CR, Kim SY, Kim MJ, Yoon JG, Lim BC, Kim WJ, Kim KJ, Ko JM, Cho A, Lee JS, Choi M, Chae JH. Systematic analysis of inheritance pattern determination in genes that cause rare neurodevelopmental diseases. Front Genet 2022; 13:990015. [PMID: 36212160 PMCID: PMC9533195 DOI: 10.3389/fgene.2022.990015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/23/2022] [Indexed: 01/09/2023] Open
Abstract
Despite recent advancements in our understanding of genetic etiology and its molecular and physiological consequences, it is not yet clear what genetic features determine the inheritance pattern of a disease. To address this issue, we conducted whole exome sequencing analysis to characterize genetic variants in 1,180 Korean patients with neurological symptoms. The diagnostic yield for definitive pathogenic variant findings was 50.8%, after including 33 cases (5.9%) additionally diagnosed by reanalysis. Of diagnosed patients, 33.4% carried inherited variants. At the genetic level, autosomal recessive-inherited genes were characterized by enrichments in metabolic process, muscle organization and metal ion homeostasis pathways. Transcriptome and interactome profiling analyses revealed less brain-centered expression and fewer protein-protein interactions for recessive genes. The majority of autosomal recessive genes were more tolerant of variation, and functional prediction scores of recessively-inherited variants tended to be lower than those of dominantly-inherited variants. Additionally, we were able to predict the rates of carriers for recessive variants. Our results showed that genes responsible for neurodevelopmental disorders harbor different molecular mechanisms and expression patterns according to their inheritance patterns. Also, calculated frequency rates for recessive variants could be utilized to pre-screen rare neurodevelopmental disorder carriers.
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Affiliation(s)
- Soojin Park
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Se Song Jang
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Seungbok Lee
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea,Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Minsoo Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyungtai Sim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyeongseok Jeon
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Sung Eun Hong
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Jean Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Jeongeun Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Eun Young Jeon
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Jeongha Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Cho-Rong Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
| | - Soo Yeon Kim
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Man Jin Kim
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea,Rare Disease Center, Seoul National University Hospital, Seoul, South Korea
| | - Jihoon G. Yoon
- Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Byung Chan Lim
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Woo Joong Kim
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Jung Min Ko
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Jin Sook Lee
- Department of Pediatrics, Seoul National University Hospital Child Cancer and Rare Disease Administration, Seoul National University Children’s Hospital, Seoul, South Korea
| | - Murim Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea,*Correspondence: Murim Choi, ; Jong-Hee Chae,
| | - Jong-Hee Chae
- Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South Korea,Department of Genomic Medicine, Seoul National University Hospital, Seoul, South Korea,*Correspondence: Murim Choi, ; Jong-Hee Chae,
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37
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Tissue-Characteristic Expression of Mouse Proteome. Mol Cell Proteomics 2022; 21:100408. [PMID: 36058520 PMCID: PMC9562433 DOI: 10.1016/j.mcpro.2022.100408] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 07/23/2022] [Accepted: 08/24/2022] [Indexed: 01/18/2023] Open
Abstract
The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition-based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.
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38
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Zhao C, Dong J, Deng L, Tan Y, Jiang W, Cai Z. Molecular network strategy in multi-omics and mass spectrometry imaging. Curr Opin Chem Biol 2022; 70:102199. [PMID: 36027696 DOI: 10.1016/j.cbpa.2022.102199] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/01/2022] [Accepted: 07/10/2022] [Indexed: 11/30/2022]
Abstract
Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.
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Affiliation(s)
- Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Yawen Tan
- Department of Breast and Thyroid Surgery, Shenzhen Second People's Hospital, Shenzhen, China
| | - Wei Jiang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China.
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39
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García-Vaquero ML, Gama-Carvalho M, Pinto FR, De Las Rivas J. Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease. Comput Struct Biotechnol J 2022; 20:3764-3778. [PMID: 35891788 PMCID: PMC9304429 DOI: 10.1016/j.csbj.2022.07.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 07/04/2022] [Accepted: 07/04/2022] [Indexed: 12/29/2022] Open
Abstract
Biological processes are exerted by groups of physically interacting proteins. Proteins display variable biological roles depending on tissue-interactomic context. Tissue-specific protein-protein interaction networks reveal functional diversification. Most disease associated genes/proteins display tissue-specific phenotypes. Protein interaction network analysis is a valuable resource to identify disease genes.
Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.
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Key Words
- BiU, BioInt unit
- Biological function
- CO, CORUM complex
- DEg, Differentially expressed gene
- DG, Disease gene
- Disease gene
- GO-BP, Gene Ontology biological process
- HK, Housekeeping
- Housekeeping gene
- PPI network
- PPI, Protein-protein interaction
- Protein module
- SS, Simpson's similarity
- TE, Tissue enriched
- TS, Tissue-specific
- Tissue-specific gene
- UB, Ubiquitous
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Affiliation(s)
- Marina L García-Vaquero
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal.,Cancer Research Center (CiC-IBMCC, CSIC/USAL and IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca 37007, Spain
| | - Margarida Gama-Carvalho
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal
| | - Francisco R Pinto
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL and IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca 37007, Spain
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40
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Na Z, Dai X, Zheng SJ, Bryant CJ, Loh KH, Su H, Luo Y, Buhagiar AF, Cao X, Baserga SJ, Chen S, Slavoff SA. Mapping subcellular localizations of unannotated microproteins and alternative proteins with MicroID. Mol Cell 2022; 82:2900-2911.e7. [PMID: 35905735 PMCID: PMC9662605 DOI: 10.1016/j.molcel.2022.06.035] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 04/08/2022] [Accepted: 06/29/2022] [Indexed: 11/15/2022]
Abstract
Proteogenomic identification of translated small open reading frames has revealed thousands of previously unannotated, largely uncharacterized microproteins, or polypeptides of less than 100 amino acids, and alternative proteins (alt-proteins) that are co-encoded with canonical proteins and are often larger. The subcellular localizations of microproteins and alt-proteins are generally unknown but can have significant implications for their functions. Proximity biotinylation is an attractive approach to define the protein composition of subcellular compartments in cells and in animals. Here, we developed a high-throughput technology to map unannotated microproteins and alt-proteins to subcellular localizations by proximity biotinylation with TurboID (MicroID). More than 150 microproteins and alt-proteins are associated with subnuclear organelles. One alt-protein, alt-LAMA3, localizes to the nucleolus and functions in pre-rRNA transcription. We applied MicroID in a mouse model, validating expression of a conserved nuclear microprotein, and establishing MicroID for discovery of microproteins and alt-proteins in vivo.
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Affiliation(s)
- Zhenkun Na
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT 06516, USA
| | - Xiaoyun Dai
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA; Systems Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Shu-Jian Zheng
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT 06516, USA
| | - Carson J Bryant
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06529, USA
| | - Ken H Loh
- Laboratory of Molecular Genetics, Howard Hughes Medical Institute, The Rockefeller University, New York, NY 10065, USA
| | - Haomiao Su
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT 06516, USA
| | - Yang Luo
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT 06516, USA
| | - Amber F Buhagiar
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06529, USA
| | - Xiongwen Cao
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT 06516, USA
| | - Susan J Baserga
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06529, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA; Systems Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Sarah A Slavoff
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT 06516, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06529, USA.
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41
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Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery. Nat Commun 2022; 13:4043. [PMID: 35831314 PMCID: PMC9279285 DOI: 10.1038/s41467-022-31809-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 06/29/2022] [Indexed: 12/14/2022] Open
Abstract
Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner. Co-fractionation/mass spectrometry (CF/MS) allows mapping protein interactomes but efficiency and quantitative accuracy are limited. Here, the authors develop a reproducible multiplexed CF/MS method and apply it to characterize interactome rewiring in breast cancer cells.
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42
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43
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Cabrera-Orefice A, Potter A, Evers F, Hevler JF, Guerrero-Castillo S. Complexome Profiling-Exploring Mitochondrial Protein Complexes in Health and Disease. Front Cell Dev Biol 2022; 9:796128. [PMID: 35096826 PMCID: PMC8790184 DOI: 10.3389/fcell.2021.796128] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022] Open
Abstract
Complexome profiling (CP) is a state-of-the-art approach that combines separation of native proteins by electrophoresis, size exclusion chromatography or density gradient centrifugation with tandem mass spectrometry identification and quantification. Resulting data are computationally clustered to visualize the inventory, abundance and arrangement of multiprotein complexes in a biological sample. Since its formal introduction a decade ago, this method has been mostly applied to explore not only the composition and abundance of mitochondrial oxidative phosphorylation (OXPHOS) complexes in several species but also to identify novel protein interactors involved in their assembly, maintenance and functions. Besides, complexome profiling has been utilized to study the dynamics of OXPHOS complexes, as well as the impact of an increasing number of mutations leading to mitochondrial disorders or rearrangements of the whole mitochondrial complexome. Here, we summarize the major findings obtained by this approach; emphasize its advantages and current limitations; discuss multiple examples on how this tool could be applied to further investigate pathophysiological mechanisms and comment on the latest advances and opportunity areas to keep developing this methodology.
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Affiliation(s)
- Alfredo Cabrera-Orefice
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Alisa Potter
- Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Felix Evers
- Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Johannes F Hevler
- Biomolecular Mass Spectrometry and Proteomics, University of Utrecht, Utrecht, Netherlands.,Bijvoet Center for Biomolecular Research, University of Utrecht, Utrecht, Netherlands.,Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, Netherlands.,Netherlands Proteomics Center, Utrecht, Netherlands
| | - Sergio Guerrero-Castillo
- University Children's Research@Kinder-UKE, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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44
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:243-269. [DOI: 10.1093/bfgp/elac007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
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45
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Li Y, Dittmann NL, Watson AES, de Almeida MMA, Footz T, Voronova A. Hepatoma Derived Growth Factor Enhances Oligodendrocyte Genesis from Subventricular Zone Precursor Cells. ASN Neuro 2022; 14:17590914221086340. [PMID: 35293825 PMCID: PMC8943302 DOI: 10.1177/17590914221086340] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Oligodendrocytes, the myelinating cells of the central nervous system (CNS), perform vital functions in neural protection and communication, as well as cognition. Enhanced production of oligodendrocytes has been identified as a therapeutic approach for neurodegenerative and neurodevelopmental disorders. In the postnatal brain, oligodendrocytes are generated from the neural stem and precursor cells (NPCs) in the subventricular zone (SVZ) and parenchymal oligodendrocyte precursor cells (OPCs). Here, we demonstrate exogenous Hepatoma Derived Growth Factor (HDGF) enhances oligodendrocyte genesis from murine postnatal SVZ NPCs in vitro without affecting neurogenesis or astrogliogenesis. We further show that this is achieved by increasing proliferation of both NPCs and OPCs, as well as OPC differentiation into oligodendrocytes. In vivo results demonstrate that intracerebroventricular infusion of HDGF leads to increased oligodendrocyte genesis from SVZ NPCs, as well as OPC proliferation. Our results demonstrate a novel role for HDGF in regulating SVZ precursor cell proliferation and oligodendrocyte differentiation.
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Affiliation(s)
- Yutong Li
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
| | - Nicole Leanne Dittmann
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2E1, Canada
| | - Adrianne Eve Scovil Watson
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
| | - Monique Marylin Alves de Almeida
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2E1, Canada
| | - Tim Footz
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
| | - Anastassia Voronova
- Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2E1, Canada
- Women and Children’s Health Research Institute, 5-083 Edmonton Clinic Health Academy, University of Alberta, 11405 87 Avenue NW Edmonton, Alberta, Canada, T6G 1C9
- Department of Cell Biology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
- Multiple Sclerosis Centre, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada
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46
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Niinae T, Ishihama Y, Imami K. Biotinylation-based proximity labeling proteomics: Basics, applications, and technical considerations. J Biochem 2021; 170:569-576. [PMID: 34752609 DOI: 10.1093/jb/mvab123] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/07/2021] [Indexed: 11/13/2022] Open
Abstract
Recent advances in biotinylation-based proximity labeling (PL) have opened up new avenues for mapping the protein composition of cellular compartments and protein complexes in living cells at high spatiotemporal resolution. In particular, PL combined with mass spectrometry-based proteomics has been successfully applied to defining protein-protein interactions, protein-nucleic acid interactions, (membraneless) organelle proteomes, and secretomes in various systems ranging from cultured cells to whole animals. In this review, we first summarize the basics and recent biological applications of PL proteomics, and then highlight recent developments in enrichment techniques for biotinylated proteins and peptides, focusing on the advantages of PL and technical considerations.
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Affiliation(s)
- Tomoya Niinae
- Department of Molecular and Cellular BioAnalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Yasushi Ishihama
- Department of Molecular and Cellular BioAnalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan.,Laboratory of Clinical and Analytical Chemistry, National Institute of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka 567-0085, Japan
| | - Koshi Imami
- Department of Molecular and Cellular BioAnalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan.,PRESTO, Japan Science and Technology Agency (JST), 5-3 Yonban-cho, Chiyoda-ku, Tokyo, 102-0075, Japan
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47
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USP15 antagonizes CRL4 CRBN-mediated ubiquitylation of glutamine synthetase and neosubstrates. Proc Natl Acad Sci U S A 2021; 118:2111391118. [PMID: 34583995 DOI: 10.1073/pnas.2111391118] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2021] [Indexed: 12/13/2022] Open
Abstract
Targeted protein degradation by the ubiquitin-proteasome system represents a new strategy to destroy pathogenic proteins in human diseases, including cancer and neurodegenerative diseases. The immunomodulatory drugs (IMiDs) thalidomide, lenalidomide, and pomalidomide have revolutionized the treatment of patients with multiple myeloma (MM) and other hematologic malignancies, but almost all patients eventually develop resistance to IMiDs. CRBN, a substrate receptor of CUL4-RBX1-DDB1-CRBN (CRL4CRBN) E3 ubiquitin ligase, is a direct target for thalidomide teratogenicity and antitumor activity of IMiDs (now known as Cereblon E3 ligase modulators: CELMoDs). Despite recent advances in developing potent CELMoDs and CRBN-based proteolysis-targeting chimeras (PROTACs), many questions apart from clinical efficacy remain unanswered. CRBN is required for the action of IMiDs, but its protein expression levels do not correlate with intrinsic resistance to IMiDs in MM cells, suggesting other factors involved in regulating resistance to IMiDs. Our recent work revealed that the CRL4CRBN-p97 pathway is required for degradation of natural substrate glutamine synthetase (GS) and neosubstrates. Here, I show that USP15 is a key regulator of the CRL4CRBN-p97 pathway to control stability of GS and neosubstrates IKZF1, IKZF3, CK1-α, RNF166, GSPT1, and BRD4, all of which are crucial drug targets in different types of cancer. USP15 antagonizes ubiquitylation of CRL4CRBN target proteins, thereby preventing their degradation. Notably, USP15 is highly expressed in IMiD-resistant cells, and depletion of USP15 sensitizes these cells to lenalidomide. Inhibition of USP15 represents a valuable therapeutic opportunity to potentiate CELMoD and CRBN-based PROTAC therapies for the treatment of cancer.
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