1
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Wang X, Xu G, Liu X, Liu Y, Zhang S, Zhang Z. Multiomics: unraveling the panoramic landscapes of SARS-CoV-2 infection. Cell Mol Immunol 2021; 18:2313-2324. [PMID: 34471261 PMCID: PMC8408367 DOI: 10.1038/s41423-021-00754-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023] Open
Abstract
In response to emerging infectious diseases, such as the recent pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is critical to quickly identify and understand responsible pathogens, risk factors, host immune responses, and pathogenic mechanisms at both the molecular and cellular levels. The recent development of multiomic technologies, including genomics, proteomics, metabolomics, and single-cell transcriptomics, has enabled a fast and panoramic grasp of the pathogen and the disease. Here, we systematically reviewed the major advances in the virology, immunology, and pathogenic mechanisms of SARS-CoV-2 infection that have been achieved via multiomic technologies. Based on well-established cohorts, omics-based methods can greatly enhance the mechanistic understanding of diseases, contributing to the development of new diagnostics, drugs, and vaccines for emerging infectious diseases, such as COVID-19.
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Affiliation(s)
- Xin Wang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Gang Xu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Xiaoju Liu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Yang Liu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| | - Shuye Zhang
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
| | - Zheng Zhang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong Province, China.
- Shenzhen Research Center for Communicable Disease Diagnosis and Treatment of Chinese Academy of Medical Science, Shenzhen, Guangdong Province, China.
- Guangdong Key Laboratory for Anti-infection Drug Quality Evaluation, Shenzhen, Guangdong Province, China.
- Shenzhen Bay Laboratory, Shenzhen, Guangdong Province, China.
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2
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Abstract
Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.
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Affiliation(s)
- Rivkah Rogawski
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
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3
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Abstract
iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .
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4
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Macossay-Castillo M, Marvelli G, Guharoy M, Jain A, Kihara D, Tompa P, Wodak SJ. The Balancing Act of Intrinsically Disordered Proteins: Enabling Functional Diversity while Minimizing Promiscuity. J Mol Biol 2019; 431:1650-1670. [PMID: 30878482 DOI: 10.1016/j.jmb.2019.03.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 02/25/2019] [Accepted: 03/03/2019] [Indexed: 10/27/2022]
Abstract
Intrinsically disordered proteins (IDPs) or regions (IDRs) perform diverse cellular functions, but are also prone to forming promiscuous and potentially deleterious interactions. We investigate the extent to which the properties of, and content in, IDRs have adapted to enable functional diversity while limiting interference from promiscuous interactions in the crowded cellular environment. Information on protein sequences, their predicted intrinsic disorder, and 3D structure contents is related to data on protein cellular concentrations, gene co-expression, and protein-protein interactions in the well-studied yeast Saccharomyces cerevisiae. Results reveal that both the protein IDR content and the frequency of "sticky" amino acids in IDRs (those more frequently involved in protein interfaces) decrease with increasing protein cellular concentration. This implies that the IDR content and the amino acid composition of IDRs experience negative selection as the protein concentration increases. In the S. cerevisiae protein-protein interaction network, the higher a protein's IDR content, the more frequently it interacts with IDR-containing partners, and the more functionally diverse the partners are. Employing a clustering analysis of Gene Ontology terms, we newly identify ~600 putative multifunctional proteins in S. cerevisiae. Strikingly, these proteins are enriched in IDRs and contribute significantly to all the observed trends. In particular, IDRs of multi-functional proteins feature more sticky amino acids than IDRs of their non-multifunctional counterparts, or the surfaces of structured yeast proteins. This property likely affords sufficient binding affinity for the functional interactions, commonly mediated by short IDR segments, thereby counterbalancing the loss in overall IDR conformational entropy upon binding.
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Affiliation(s)
- Mauricio Macossay-Castillo
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Giulio Marvelli
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Mainak Guharoy
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, Hockmeyer Structural Biology Building, 249 S. Martin Jischke Dr West Lafayette, IN 47907, USA
| | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudosok korutja 2, 1117 Budapest, Hungary
| | - Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium.
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5
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Tian B, Duan Q, Zhao C, Teng B, He Z. Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:365-376. [PMID: 28534782 DOI: 10.1109/tcbb.2017.2705060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets. The experimental results show that Reinforce can get more stable and accurate inference results than existing algorithms. The source codes of Reinforce and data sets used in the experiments are available at: https://sourceforge.net/projects/reinforce/.
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6
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Ershov PV, Mezentsev YV, Yablokov EO, Kaluzhskiy LA, Florinskaya AV, Gnedenko OV, Zgoda VG, Vakhrushev IV, Raeva OS, Yarygin KN, Gilep AA, Usanov SA, Medvedev AE, Ivanov AS. Direct Molecular Fishing of Protein Partners for Proteins Encoded by Genes of Human Chromosome 18 in HepG2 Cell Lysate. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2019. [DOI: 10.1134/s1068162019010059] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Tramontano A. The computational prediction of protein assemblies. Curr Opin Struct Biol 2017; 46:170-175. [PMID: 29102305 DOI: 10.1016/j.sbi.2017.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 10/18/2022]
Abstract
The function of proteins in the cell is almost always mediated by their interaction with different partners, including other proteins, nucleic acids or small organic molecules. The ability of identifying all of them is an essential step in our quest for understanding life at the molecular level. The inference of the protein complex composition and of its molecular details can also provide relevant clues for the development and the design of drugs. In this short review, I will discuss the computational aspects of the analysis and prediction of protein-protein assemblies and discuss some of the most recent developments as seen in the last Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment.
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Affiliation(s)
- Anna Tramontano
- Physics Department, Sapienza University of Rome, Piazzale Aldo Moro, 5 I-00185 Roma, Italy; Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, Piazzale Aldo Moro, 5 I-00185 Roma, Italy
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8
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Meysman P, Titeca K, Eyckerman S, Tavernier J, Goethals B, Martens L, Valkenborg D, Laukens K. Protein complex analysis: From raw protein lists to protein interaction networks. MASS SPECTROMETRY REVIEWS 2017; 36:600-614. [PMID: 26709718 DOI: 10.1002/mas.21485] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Accepted: 11/17/2015] [Indexed: 06/05/2023]
Abstract
The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.
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Affiliation(s)
- Pieter Meysman
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - Kevin Titeca
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Sven Eyckerman
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Jan Tavernier
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Bart Goethals
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - Lennart Martens
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- IBioStat, Hasselt University, Hasselt, Belgium
- CFP-CeProMa, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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9
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Sala S, Van Troys M, Medves S, Catillon M, Timmerman E, Staes A, Schaffner-Reckinger E, Gevaert K, Ampe C. Expanding the Interactome of TES by Exploiting TES Modules with Different Subcellular Localizations. J Proteome Res 2017; 16:2054-2071. [DOI: 10.1021/acs.jproteome.7b00034] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Stefano Sala
- Department of Biochemistry, Ghent University, 9000 Gent, Belgium
| | | | - Sandrine Medves
- Cytoskeleton
and Cell Plasticity Lab, Life Sciences Research Unit − FSTC, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Laboratory of Experimental Cancer Research, LIH, 1445 Strassen, Luxembourg
| | - Marie Catillon
- Cytoskeleton
and Cell Plasticity Lab, Life Sciences Research Unit − FSTC, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Evy Timmerman
- Department of Biochemistry, Ghent University, 9000 Gent, Belgium
- VIB Medical Biotechnology Center, 9000 Gent, Belgium
| | - An Staes
- Department of Biochemistry, Ghent University, 9000 Gent, Belgium
- VIB Medical Biotechnology Center, 9000 Gent, Belgium
| | - Elisabeth Schaffner-Reckinger
- Cytoskeleton
and Cell Plasticity Lab, Life Sciences Research Unit − FSTC, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Kris Gevaert
- Department of Biochemistry, Ghent University, 9000 Gent, Belgium
- VIB Medical Biotechnology Center, 9000 Gent, Belgium
| | - Christophe Ampe
- Department of Biochemistry, Ghent University, 9000 Gent, Belgium
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10
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Havis S, Moree WJ, Mali S, Bark SJ. Solid support resins and affinity purification mass spectrometry. MOLECULAR BIOSYSTEMS 2017; 13:456-462. [DOI: 10.1039/c6mb00735j] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Solid-support resins are critical components of AP-MS experiments, but their interactions with experimental conditions are underappreciated.
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Affiliation(s)
- Spencer Havis
- Department of Biology and Biochemistry
- The University of Houston
- Houston
- USA
| | - Wilna J. Moree
- Department of Biology and Biochemistry
- The University of Houston
- Houston
- USA
| | - Sujina Mali
- Department of Biology and Biochemistry
- The University of Houston
- Houston
- USA
| | - Steven J. Bark
- Department of Biology and Biochemistry
- The University of Houston
- Houston
- USA
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11
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Eyckerman S, Impens F, Van Quickelberghe E, Samyn N, Vandemoortele G, De Sutter D, Tavernier J, Gevaert K. Intelligent Mixing of Proteomes for Elimination of False Positives in Affinity Purification-Mass Spectrometry. J Proteome Res 2016; 15:3929-3937. [PMID: 27640904 DOI: 10.1021/acs.jproteome.6b00517] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Protein complexes are essential in all organizational and functional aspects of the cell. Different strategies currently exist for analyzing such protein complexes by mass spectrometry, including affinity purification (AP-MS) and proximal labeling-based strategies. However, the high sensitivity of current mass spectrometers typically results in extensive protein lists mainly consisting of nonspecifically copurified proteins. Finding the true positive interactors in these lists remains highly challenging. Here, we report a powerful design based on differential labeling with stable isotopes combined with nonequal mixing of control and experimental samples to discover bona fide interaction partners in AP-MS experiments. We apply this intelligent mixing of proteomes (iMixPro) concept to overexpression experiments for RAF1, RNF41, and TANK and also to engineered cell lines expressing epitope-tagged endogenous PTPN14, JIP3, and IQGAP1. For all baits, we confirmed known interactions and found a number of novel interactions. The results for RNF41 and TANK were compared to a classical affinity purification experiment, which demonstrated the efficiency and specificity of the iMixPro approach.
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Affiliation(s)
- Sven Eyckerman
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Francis Impens
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,VIB Proteomics Expertise Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Emmy Van Quickelberghe
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Noortje Samyn
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Giel Vandemoortele
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Delphine De Sutter
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Jan Tavernier
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Kris Gevaert
- VIB Medical Biotechnology Center , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
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12
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Rosenbluh J, Mercer J, Shrestha Y, Oliver R, Tamayo P, Doench JG, Tirosh I, Piccioni F, Hartenian E, Horn H, Fagbami L, Root DE, Jaffe J, Lage K, Boehm JS, Hahn WC. Genetic and Proteomic Interrogation of Lower Confidence Candidate Genes Reveals Signaling Networks in β-Catenin-Active Cancers. Cell Syst 2016; 3:302-316.e4. [PMID: 27684187 PMCID: PMC5455996 DOI: 10.1016/j.cels.2016.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 05/11/2016] [Accepted: 09/02/2016] [Indexed: 12/20/2022]
Abstract
Genome-scale expression studies and comprehensive loss-of-function genetic screens have focused almost exclusively on the highest confidence candidate genes. Here, we describe a strategy for characterizing the lower confidence candidates identified by such approaches. We interrogated 177 genes that we classified as essential for the proliferation of cancer cells exhibiting constitutive β-catenin activity and integrated data for each of the candidates, derived from orthogonal short hairpin RNA (shRNA) knockdown and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-mediated gene editing knockout screens, to yield 69 validated genes. We then characterized the relationships between sets of these genes using complementary assays: medium-throughput stable isotope labeling by amino acids in cell culture (SILAC)-based mass spectrometry, yielding 3,639 protein-protein interactions, and a CRISPR-mediated pairwise double knockout screen, yielding 375 combinations exhibiting greater- or lesser-than-additive phenotypic effects indicating genetic interactions. These studies identify previously unreported regulators of β-catenin, define functional networks required for the survival of β-catenin-active cancers, and provide an experimental strategy that may be applied to define other signaling networks.
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Affiliation(s)
- Joseph Rosenbluh
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Johnathan Mercer
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Yashaswi Shrestha
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Rachel Oliver
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Pablo Tamayo
- Moores Cancer Center and School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - John G Doench
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Itay Tirosh
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Federica Piccioni
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Ella Hartenian
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Heiko Horn
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Lola Fagbami
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - David E Root
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Jacob Jaffe
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - Kasper Lage
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jesse S Boehm
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
| | - William C Hahn
- Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA 02215, USA.
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13
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Varusai TM, Kolch W, Kholodenko BN, Nguyen LK. Protein-protein interactions generate hidden feedback and feed-forward loops to trigger bistable switches, oscillations and biphasic dose-responses. MOLECULAR BIOSYSTEMS 2016; 11:2750-62. [PMID: 26266875 DOI: 10.1039/c5mb00385g] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein-protein interactions (PPIs) defined as reversible association of two proteins to form a complex, are undoubtedly among the most common interaction motifs featured in cells. Recent large-scale proteomic studies have revealed an enormously complex interactome of the cell, consisting of tens of thousands of PPIs with numerous signalling hubs. PPIs have functional roles in regulating a wide range of cellular processes including signal transduction and post-translational modifications, and de-regulation of PPIs is implicated in many diseases including cancers and neuro-degenerative disorders. Despite the ubiquitous appearance and physiological significance of PPIs, our understanding of the dynamic and functional consequences of these simple motifs remains incomplete, particularly when PPIs occur within large biochemical networks. We employ quantitative, dynamic modelling to computationally analyse salient dynamic features of the PPI motifs and PPI-containing signalling networks varying in topological architecture. Our analyses surprisingly reveal that simple reversible PPI motifs, when being embedded into signalling cascades, could give rise to extremely rich and complex regulatory dynamics in the absence of explicit positive and negative feedback loops. Our work represents a systematic investigation of the dynamic properties of PPIs in signalling networks, and the results shed light on how this simple event may potentiate diverse and intricate behaviours in vivo.
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Affiliation(s)
- Thawfeek M Varusai
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
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14
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Yi Z, Manil-Ségalen M, Sago L, Glatigny A, Redeker V, Legouis R, Mucchielli-Giorgi MH. SAFER, an Analysis Method of Quantitative Proteomic Data, Reveals New Interactors of the C. elegans Autophagic Protein LGG-1. J Proteome Res 2016; 15:1515-23. [PMID: 26999449 DOI: 10.1021/acs.jproteome.5b01158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Affinity purifications followed by mass spectrometric analysis are used to identify protein-protein interactions. Because quantitative proteomic data are noisy, it is necessary to develop statistical methods to eliminate false-positives and identify true partners. We present here a novel approach for filtering false interactors, named "SAFER" for mass Spectrometry data Analysis by Filtering of Experimental Replicates, which is based on the reproducibility of the replicates and the fold-change of the protein intensities between bait and control. To identify regulators or targets of autophagy, we characterized the interactors of LGG1, a ubiquitin-like protein involved in autophagosome formation in C. elegans. LGG-1 partners were purified by affinity, analyzed by nanoLC-MS/MS mass spectrometry, and quantified by a label-free proteomic approach based on the mass spectrometric signal intensity of peptide precursor ions. Because the selection of confident interactions depends on the method used for statistical analysis, we compared SAFER with several statistical tests and different scoring algorithms on this set of data. We show that SAFER recovers high-confidence interactors that have been ignored by the other methods and identified new candidates involved in the autophagy process. We further validated our method on a public data set and conclude that SAFER notably improves the identification of protein interactors.
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Affiliation(s)
- Zhou Yi
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
| | - Marion Manil-Ségalen
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
| | - Laila Sago
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France.,Service d'Identification et de Caractérisation des Protéines par Spectrométrie de masse (SICaPS), CNRS, 91198 Gif-sur-Yvette, France
| | - Annie Glatigny
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
| | - Virginie Redeker
- Service d'Identification et de Caractérisation des Protéines par Spectrométrie de masse (SICaPS), CNRS, 91198 Gif-sur-Yvette, France.,Paris-Saclay Institute of Neuroscience (Neuro-PSI), CNRS, 91198 Gif-sur-Yvette cedex, France
| | - Renaud Legouis
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
| | - Marie-Hélène Mucchielli-Giorgi
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France.,Sorbonne Universités , UPMC Univ Paris 06, UFR927, F-75005, Paris, France
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15
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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16
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Pesaranghader A, Matwin S, Sokolova M, Beiko RG. simDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes. Bioinformatics 2015; 32:1380-7. [PMID: 26708333 DOI: 10.1093/bioinformatics/btv755] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 12/21/2015] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Measures of protein functional similarity are essential tools for function prediction, evaluation of protein-protein interactions (PPIs) and other applications. Several existing methods perform comparisons between proteins based on the semantic similarity of their GO terms; however, these measures are highly sensitive to modifications in the topological structure of GO, tend to be focused on specific analytical tasks and concentrate on the GO terms themselves rather than considering their textual definitions. RESULTS We introduce simDEF, an efficient method for measuring semantic similarity of GO terms using their GO definitions, which is based on the Gloss Vector measure commonly used in natural language processing. The simDEF approach builds optimized definition vectors for all relevant GO terms, and expresses the similarity of a pair of proteins as the cosine of the angle between their definition vectors. Relative to existing similarity measures, when validated on a yeast reference database, simDEF improves correlation with sequence homology by up to 50%, shows a correlation improvement >4% with gene expression in the biological process hierarchy of GO and increases PPI predictability by > 2.5% in F1 score for molecular function hierarchy. AVAILABILITY AND IMPLEMENTATION Datasets, results and source code are available at http://kiwi.cs.dal.ca/Software/simDEF CONTACT: ahmad.pgh@dal.ca or beiko@cs.dal.ca SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ahmad Pesaranghader
- Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada, Institute for Big Data Analytics, Halifax, NS B3H 4R2, Canada
| | - Stan Matwin
- Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada, Institute for Big Data Analytics, Halifax, NS B3H 4R2, Canada, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland and
| | - Marina Sokolova
- Institute for Big Data Analytics, Halifax, NS B3H 4R2, Canada, Faculty of Medicine and Faculty of Engineering, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Robert G Beiko
- Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
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17
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Titeca K, Meysman P, Gevaert K, Tavernier J, Laukens K, Martens L, Eyckerman S. SFINX: Straightforward Filtering Index for Affinity Purification–Mass Spectrometry Data Analysis. J Proteome Res 2015; 15:332-8. [DOI: 10.1021/acs.jproteome.5b00666] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Kevin Titeca
- VIB Medical Biotechnology Center, A. Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Pieter Meysman
- Advanced
Database Research and Modelling (ADReM), Department of Mathematics
and Computer Science, University of Antwerp, B-2020 Antwerp, Belgium
- Biomedical
Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Kris Gevaert
- VIB Medical Biotechnology Center, A. Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Jan Tavernier
- VIB Medical Biotechnology Center, A. Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Kris Laukens
- Advanced
Database Research and Modelling (ADReM), Department of Mathematics
and Computer Science, University of Antwerp, B-2020 Antwerp, Belgium
- Biomedical
Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Lennart Martens
- VIB Medical Biotechnology Center, A. Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9000 Ghent, Belgium
| | - Sven Eyckerman
- VIB Medical Biotechnology Center, A. Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
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18
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Zhang XF, Ou-Yang L, Hu X, Dai DQ. Identifying binary protein-protein interactions from affinity purification mass spectrometry data. BMC Genomics 2015; 16:745. [PMID: 26438428 PMCID: PMC4595009 DOI: 10.1186/s12864-015-1944-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Accepted: 09/22/2015] [Indexed: 02/04/2023] Open
Abstract
Background The identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells. With the development of affinity purification-mass spectrometry (AP-MS) techniques, several computational scoring methods have been proposed to detect protein interactions from AP-MS data. However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions. Consequently, less is known about the precise physical wiring diagram within cells. Results In this paper, we develop a Binary Interaction Network Model (BINM) to computationally identify direct physical interactions from co-complex interactions which can be inferred from purification data using previous scoring methods. This model provides a mathematical framework for capturing topological relationships between direct physical interactions and observed co-complex interactions. It reassigns a confidence score to each observed interaction to indicate its propensity to be a direct physical interaction. Then observed interactions with high confidence scores are predicted as direct physical interactions. We run our model on two yeast co-complex interaction networks which are constructed by two different scoring methods on a same combined AP-MS data. The direct physical interactions identified by various methods are comprehensively benchmarked against different reference sets that provide both direct and indirect evidence for physical contacts. Experiment results show that our model has a competitive performance over the state-of-the-art methods. Conclusions According to the results obtained in this study, BINM is a powerful scoring method that can solely use network topology to predict direct physical interactions from AP-MS data. This study provides us an alternative approach to explore the information inherent in AP-MS data. The software can be downloaded from https://github.com/Zhangxf-ccnu/BINM. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1944-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Luoyu Road, Wuhan, 430079, China.
| | - Le Ou-Yang
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
| | - Xiaohua Hu
- School of Computer, Central China Normal University, 774 Luoyu Road, Wuhan, 430079, China. .,College of Information Science and Technology, Drexel University, Chestnut Street, Philadelphia, 19104, USA.
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
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