1
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Parres-Gold J, Levine M, Emert B, Stuart A, Elowitz MB. Principles of Computation by Competitive Protein Dimerization Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564854. [PMID: 37961250 PMCID: PMC10634983 DOI: 10.1101/2023.10.30.564854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.
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Affiliation(s)
- Jacob Parres-Gold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Matthew Levine
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Benjamin Emert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Andrew Stuart
- Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Michael B. Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
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2
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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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3
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Li F, Tarkington J, Sherlock G. Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays. J Mol Evol 2023; 91:334-344. [PMID: 36877292 PMCID: PMC10276102 DOI: 10.1007/s00239-023-10098-0] [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: 10/26/2022] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.
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Affiliation(s)
- Fangfei Li
- Department of Genetics, Stanford University, Stanford, USA
| | | | - Gavin Sherlock
- Department of Genetics, Stanford University, Stanford, USA.
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4
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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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5
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Omranian S, Nikoloski Z, Grimm DG. Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward. Comput Struct Biotechnol J 2022; 20:2699-2712. [PMID: 35685359 PMCID: PMC9166428 DOI: 10.1016/j.csbj.2022.05.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 01/05/2023] Open
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6
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Evans-Yamamoto D, Rouleau FD, Nanda P, Makanae K, Liu Y, Després P, Matsuo H, Seki M, Dubé AK, Ascencio D, Yachie N, Landry C. OUP accepted manuscript. Nucleic Acids Res 2022; 50:e54. [PMID: 35137167 PMCID: PMC9122585 DOI: 10.1093/nar/gkac045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/22/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.
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Affiliation(s)
- Daniel Evans-Yamamoto
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
| | - François D Rouleau
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Piyush Nanda
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Koji Makanae
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Yin Liu
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Philippe C Després
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Hitoshi Matsuo
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Motoaki Seki
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biologie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Diana Ascencio
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biologie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Nozomu Yachie
- Correspondence may also be addressed to Nozomu Yachie. Tel: +1 604 822 9512;
| | - Christian R Landry
- To whom correspondence should be addressed. Tel: +1 418 656 3954; Fax: +1 418 656 7176;
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7
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Skinnider MA, Scott NE, Prudova A, Kerr CH, Stoynov N, Stacey RG, Chan QWT, Rattray D, Gsponer J, Foster LJ. An atlas of protein-protein interactions across mouse tissues. Cell 2021; 184:4073-4089.e17. [PMID: 34214469 DOI: 10.1016/j.cell.2021.06.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/05/2021] [Accepted: 06/01/2021] [Indexed: 12/20/2022]
Abstract
Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.
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Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Nichollas E Scott
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Peter Doherty Institute, Department of Microbiology and Immunology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Anna Prudova
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David Rattray
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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8
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Blaszczak E, Lazarewicz N, Sudevan A, Wysocki R, Rabut G. Protein-fragment complementation assays for large-scale analysis of protein-protein interactions. Biochem Soc Trans 2021; 49:1337-1348. [PMID: 34156434 PMCID: PMC8286835 DOI: 10.1042/bst20201058] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/10/2021] [Accepted: 05/14/2021] [Indexed: 12/25/2022]
Abstract
Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.
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Affiliation(s)
- Ewa Blaszczak
- Department of Genetics and Cell Physiology, Faculty of Biological Sciences, University of Wroclaw, Kanonia 6/8, 50-328 Wroclaw, Poland
| | - Natalia Lazarewicz
- Department of Genetics and Cell Physiology, Faculty of Biological Sciences, University of Wroclaw, Kanonia 6/8, 50-328 Wroclaw, Poland
- Univ Rennes, CNRS, IGDR (Institute of Genetics and Development of Rennes) – UMR 6290, F-35000 Rennes, France
| | - Aswani Sudevan
- Univ Rennes, CNRS, IGDR (Institute of Genetics and Development of Rennes) – UMR 6290, F-35000 Rennes, France
| | - Robert Wysocki
- Department of Genetics and Cell Physiology, Faculty of Biological Sciences, University of Wroclaw, Kanonia 6/8, 50-328 Wroclaw, Poland
| | - Gwenaël Rabut
- Univ Rennes, CNRS, IGDR (Institute of Genetics and Development of Rennes) – UMR 6290, F-35000 Rennes, France
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9
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Younis H, Anwar MW, Khan MUG, Sikandar A, Bajwa UI. A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein-Protein Interaction Networks (PPINs). Interdiscip Sci 2021; 13:371-388. [PMID: 33959851 DOI: 10.1007/s12539-021-00433-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.
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Affiliation(s)
- Haseeb Younis
- School of Professional Advancement, University of Management and Technology, Lahore, Pakistan.,Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
| | | | - Muhammad Usman Ghani Khan
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Aisha Sikandar
- Govt. Girls Post Graduate College No.1 Abbottabad, Abbottabad, Pakistan
| | - Usama Ijaz Bajwa
- Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
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10
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Federico A, Monti S. Contextualized Protein-Protein Interactions. PATTERNS 2021; 2:100153. [PMID: 33511361 PMCID: PMC7815950 DOI: 10.1016/j.patter.2020.100153] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/20/2020] [Accepted: 10/28/2020] [Indexed: 12/29/2022]
Abstract
Protein-protein interaction (PPI) databases are an important bioinformatics resource, yet existing literature-curated databases usually represent cell-type-agnostic interactions, which is at variance with our understanding that protein dynamics are context specific and highly dependent on their environment. Here, we provide a resource derived through data mining to infer disease- and tissue-relevant interactions by annotating existing PPI databases with cell-contextual information extracted from reporting studies. This resource is applicable to the reconstruction and analysis of disease-centric molecular interaction networks. We have made the data and method publicly available and plan to release scheduled updates in the future. We expect these resources to be of interest to a wide audience of researchers in the life sciences. We present PPI Context: contextualization of existing literature-curated PPIs A resource for filtering PPIs by cell-line information mined from reporting studies A fast and flexible pipeline implementing the presented data mining method
Existing literature-curated protein-protein interaction (PPI) databases usually aggregate cell-type-agnostic interactions, yet PPIs are dependent on environmental conditions. Thus, new methods and resources for inferring the context in which a PPI is reported will extend their application and use in disease-centric modeling. We expect the resource presented in this article to be of high interest to those querying known interactions of proteins of interest, reconstruction and analyses of molecular interaction networks, and multi-omics data integration approaches.
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Affiliation(s)
- Anthony Federico
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA.,Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Stefano Monti
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA.,Bioinformatics Program, Boston University, Boston, MA 02215, USA
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11
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Liu Z, Miller D, Li F, Liu X, Levy SF. A large accessory protein interactome is rewired across environments. eLife 2020; 9:e62365. [PMID: 32924934 PMCID: PMC7577743 DOI: 10.7554/elife.62365] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 09/04/2020] [Indexed: 12/30/2022] Open
Abstract
To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.
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Affiliation(s)
- Zhimin Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Darach Miller
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
| | - Xianan Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
- SLAC National Accelerator LaboratoryMenlo ParkUnited States
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12
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Genome-Wide Dynamic Evaluation of the UV-Induced DNA Damage Response. G3-GENES GENOMES GENETICS 2020; 10:2981-2988. [PMID: 32732306 PMCID: PMC7466999 DOI: 10.1534/g3.120.401417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Genetic screens in Saccharomyces cerevisiae have allowed for the identification of many genes as sensors or effectors of DNA damage, typically by comparing the fitness of genetic mutants in the presence or absence of DNA-damaging treatments. However, these static screens overlook the dynamic nature of DNA damage response pathways, missing time-dependent or transient effects. Here, we examine gene dependencies in the dynamic response to ultraviolet radiation-induced DNA damage by integrating ultra-high-density arrays of 6144 diploid gene deletion mutants with high-frequency time-lapse imaging. We identify 494 ultraviolet radiation response genes which, in addition to recovering molecular pathways and protein complexes previously annotated to DNA damage repair, include components of the CCR4-NOT complex, tRNA wobble modification, autophagy, and, most unexpectedly, 153 nuclear-encoded mitochondrial genes. Notably, mitochondria-deficient strains present time-dependent insensitivity to ultraviolet radiation, posing impaired mitochondrial function as a protective factor in the ultraviolet radiation response.
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13
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Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
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Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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14
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Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 2020; 579:409-414. [PMID: 32188942 DOI: 10.1038/s41586-020-2094-2] [Citation(s) in RCA: 253] [Impact Index Per Article: 63.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 01/17/2020] [Indexed: 01/05/2023]
Abstract
Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.
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15
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Salas D, Stacey RG, Akinlaja M, Foster LJ. Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks. Mol Cell Proteomics 2020; 19:1-10. [PMID: 31792070 PMCID: PMC6944233 DOI: 10.1074/mcp.r119.001803] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/26/2019] [Indexed: 12/26/2022] Open
Abstract
Understanding how proteins interact is crucial to understanding cellular processes. Among the available interactome mapping methods, co-elution stands out as a method that is simultaneous in nature and capable of identifying interactions between all the proteins detected in a sample. The general workflow in co-elution methods involves the mild extraction of protein complexes and their separation into several fractions, across which proteins bound together in the same complex will show similar co-elution profiles when analyzed appropriately. In this review we discuss the different separation, quantification and bioinformatic strategies used in co-elution studies, and the important considerations in designing these studies. The benefits of co-elution versus other methods makes it a valuable starting point when asking questions that involve the perturbation of the interactome.
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Affiliation(s)
- Daniela Salas
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada; Department of Chemistry, Simon Fraser University, Burnaby, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola Akinlaja
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
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16
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Gauthier L, Stynen B, Serohijos AWR, Michnick SW. Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics. Bioessays 2019; 42:e1900169. [PMID: 31854021 DOI: 10.1002/bies.201900169] [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: 09/16/2019] [Revised: 11/15/2019] [Indexed: 11/07/2022]
Abstract
How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.
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Affiliation(s)
- Louis Gauthier
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Bram Stynen
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Adrian W R Serohijos
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Stephen W Michnick
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
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17
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Wu Z, Liao Q, Liu B. A comprehensive review and evaluation of computational methods for identifying protein complexes from protein–protein interaction networks. Brief Bioinform 2019; 21:1531-1548. [DOI: 10.1093/bib/bbz085] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 06/17/2019] [Accepted: 06/17/2019] [Indexed: 02/04/2023] Open
Abstract
Abstract
Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.
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Affiliation(s)
- Zhourun Wu
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
| | - Qing Liao
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
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18
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Mei S, Zhang K. Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks. Int J Mol Sci 2019; 20:ijms20205075. [PMID: 31614890 PMCID: PMC6829266 DOI: 10.3390/ijms20205075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022] Open
Abstract
Rapid reconstruction of genome-scale protein-protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as "Neglog" this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L2-regularized logistic regression is used as the base classifier to counteract noise and train on a large dataset. Computational results show that the proposed Neglog method outperforms pure random sampling method with sound biological interpretability. In addition, we find that independent test on negative data is indispensable for bias control, which is usually neglected by existing studies. Lastly, we use the Neglog method to validate the PPIs in STRING, which are supported by gene ontology (GO) enrichment analyses.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang 110034, China.
| | - Kun Zhang
- Bioinformatics facility of Xavier NIH RCMI Cancer Research Center, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA.
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19
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Prall W, Sharma B, Gregory BD. Transcription Is Just the Beginning of Gene Expression Regulation: The Functional Significance of RNA-Binding Proteins to Post-transcriptional Processes in Plants. PLANT & CELL PHYSIOLOGY 2019; 60:1939-1952. [PMID: 31155676 DOI: 10.1093/pcp/pcz067] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 04/03/2019] [Indexed: 06/09/2023]
Abstract
Plants have developed sophisticated mechanisms to compensate and respond to ever-changing environmental conditions. Research focus in this area has recently shifted towards understanding the post-transcriptional mechanisms that contribute to RNA transcript maturation, abundance and function as key regulatory steps in allowing plants to properly react and adapt to these never-ending shifts in their environments. At the center of these regulatory mechanisms are RNA-binding proteins (RBPs), the functional mediators of all post-transcriptional processes. In plants, RBPs are becoming increasingly appreciated as the critical modulators of core cellular processes during development and in response to environmental stimuli. With the majority of research on RBPs and their functions historically in prokaryotic and mammalian systems, it has more recently been unveiled that plants have expanded families of conserved and novel RBPs compared with their eukaryotic counterparts. To better understand the scope of RBPs in plants, we present past and current literature detailing specific roles of RBPs during stress response, development and other fundamental transition periods. In this review, we highlight examples of complex regulation coordinated by RBPs with a focus on the diverse mechanisms of plant RBPs and the unique processes they regulate. Additionally, we discuss the importance for additional research into understanding global interactions of RBPs on a systems and network-scale, with genome mining and annotation providing valuable insight for potential uses in improving crop plants in order to maintain high-level production in this era of global climate change.
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Affiliation(s)
- Wil Prall
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Bishwas Sharma
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian D Gregory
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
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20
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Marchant A, Cisneros AF, Dubé AK, Gagnon-Arsenault I, Ascencio D, Jain H, Aubé S, Eberlein C, Evans-Yamamoto D, Yachie N, Landry CR. The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs. eLife 2019; 8:46754. [PMID: 31454312 PMCID: PMC6711710 DOI: 10.7554/elife.46754] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/11/2019] [Indexed: 01/07/2023] Open
Abstract
Gene duplication is a driver of the evolution of new functions. The duplication of genes encoding homomeric proteins leads to the formation of homomers and heteromers of paralogs, creating new complexes after a single duplication event. The loss of these heteromers may be required for the two paralogs to evolve independent functions. Using yeast as a model, we find that heteromerization is frequent among duplicated homomers and correlates with functional similarity between paralogs. Using in silico evolution, we show that for homomers and heteromers sharing binding interfaces, mutations in one paralog can have structural pleiotropic effects on both interactions, resulting in highly correlated responses of the complexes to selection. Therefore, heteromerization could be preserved indirectly due to selection for the maintenance of homomers, thus slowing down functional divergence between paralogs. We suggest that paralogs can overcome the obstacle of structural pleiotropy by regulatory evolution at the transcriptional and post-translational levels.
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Affiliation(s)
- Axelle Marchant
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Département de biologie, Université Laval, Québec, Canada
| | - Angel F Cisneros
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada
| | - Alexandre K Dubé
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Département de biologie, Université Laval, Québec, Canada
| | - Isabelle Gagnon-Arsenault
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Département de biologie, Université Laval, Québec, Canada
| | - Diana Ascencio
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Département de biologie, Université Laval, Québec, Canada
| | - Honey Jain
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, India
| | - Simon Aubé
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada
| | - Chris Eberlein
- PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Département de biologie, Université Laval, Québec, Canada
| | - Daniel Evans-Yamamoto
- Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Nozomu Yachie
- Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan.,Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
| | - Christian R Landry
- Département de biochimie, de microbiologie et de bio-informatique, Université Laval, Québec, Canada.,PROTEO, le réseau québécois de recherche sur la fonction, la structure et l'ingénierie des protéines, Université Laval, Québec, Canada.,Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, Canada.,Département de biologie, Université Laval, Québec, Canada
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21
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Csősz É, Tóth F, Mahdi M, Tsaprailis G, Emri M, Tőzsér J. Analysis of networks of host proteins in the early time points following HIV transduction. BMC Bioinformatics 2019; 20:398. [PMID: 31315557 PMCID: PMC6637640 DOI: 10.1186/s12859-019-2990-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/10/2019] [Indexed: 12/13/2022] Open
Abstract
Background Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible. Results In this study, we applied label-free proteomics, and generated a network model utilizing both qualitative, and quantitative data, in order to examine the early host response to Human Immunodeficiency Virus type 1 (HIV-1). A weighted network model was generated based on the amount of proteins measured by mass spectrometry, and analysis of weighted networks and functional sub-networks revealed upregulation of proteins involved in translation, transcription, and DNA condensation in the early phase of the viral life-cycle. Conclusion A relatively simple strategy for network analysis was created and applied to examine the effect of HIV-1 on host cellular proteome. We believe that our model may prove beneficial in creating algorithms, allowing for both quantitative and qualitative studies of proteome change in various biological and pathological processes by quantitative mass spectrometry. Electronic supplementary material The online version of this article (10.1186/s12859-019-2990-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Éva Csősz
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary.
| | - Ferenc Tóth
- Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary
| | - Mohamed Mahdi
- Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary
| | - George Tsaprailis
- Arizona Research Labs, University of Arizona, PO Box 210066, Administration Building, Room 601, Tucson, AZ, 85721-0066, USA.,The Scripps Research Institute, 132 Scripps Way, Jupiter, FL, 33458, USA
| | - Miklós Emri
- Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - József Tőzsér
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary. .,Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary.
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22
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Armean IM, Lilley KS, Trotter MWB, Pilkington NCV, Holden SB. Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation. Bioinformatics 2019; 34:1884-1892. [PMID: 29390084 PMCID: PMC5972588 DOI: 10.1093/bioinformatics/btx803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 01/29/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation Protein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies. Results PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations. Availability and implementation https://github.com/ima23/maxent-ppi Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Irina M Armean
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK
| | - Kathryn S Lilley
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1GA, UK
| | - Matthew W B Trotter
- Celegene Institute for Translational Research Europe (CITRE), Sevilla 41092, Spain
| | - Nicholas C V Pilkington
- Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK
| | - Sean B Holden
- Department of Computer Science, Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK
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23
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Will T, Helms V. Differential analysis of combinatorial protein complexes with CompleXChange. BMC Bioinformatics 2019; 20:300. [PMID: 31159772 PMCID: PMC6547514 DOI: 10.1186/s12859-019-2852-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 04/26/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Although a considerable number of proteins operate as multiprotein complexes and not on their own, organism-wide studies so far are only able to quantify individual proteins or protein-coding genes in a condition-specific manner for a sizeable number of samples, but not their assemblies. Consequently, there exist large amounts of transcriptomic data and an increasing amount of data on proteome abundance, but quantitative knowledge on complexomes is missing. This deficiency impedes the applicability of the powerful tool of differential analysis in the realm of macromolecular complexes. Here, we present a pipeline for differential analysis of protein complexes based on predicted or manually assigned complexes and inferred complex abundances, which can be easily applied on a whole-genome scale. RESULTS We observed for simulated data that results obtained by our complex abundance estimation algorithm were in better agreement with the ground truth and physicochemically more reasonable compared to previous efforts that used linear programming while running in a fraction of the time. The practical usability of the method was assessed in the context of transcription factor complexes in human monocyte and lymphoblastoid samples. We demonstrated that our new method is robust against false-positive detection and reports deregulated complexomes that can only be partially explained by differential analysis of individual protein-coding genes. Furthermore we showed that deregulated complexes identified by the tool potentially harbor significant yet unused information content. CONCLUSIONS CompleXChange allows to analyze deregulation of the protein complexome on a whole-genome scale by integrating a plethora of input data that is already available. A platform-independent Java binary, a user guide with example data and the source code are freely available at https://sourceforge.net/projects/complexchange/ .
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Affiliation(s)
- Thorsten Will
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123, Germany.,Graduate School of Computer Science, Saarland University, Campus E1.3, Saarbrücken, 66123, Germany
| | - Volkhard Helms
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123, Germany.
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iSeq 2.0: A Modular and Interchangeable Toolkit for Interaction Screening in Yeast. Cell Syst 2019; 8:338-344.e8. [PMID: 30954477 DOI: 10.1016/j.cels.2019.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/10/2019] [Accepted: 03/06/2019] [Indexed: 11/24/2022]
Abstract
We developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae, which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3AI, KanMXAI, HphMXAI, and NatMXAI), and an array of ∼24,000 "double-barcoder" strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.
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25
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Sima S, Schmauder L, Richter K. Genome-wide analysis of yeast expression data based on a priori generated co-regulation cliques. MICROBIAL CELL 2019; 6:160-176. [PMID: 30854393 PMCID: PMC6402361 DOI: 10.15698/mic2019.03.671] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
DNA microarrays are highly sensitive tools to evaluate the gene expression status of organismic samples and standardized array formats exist for many different sample types. Differential expression studies usually utilize the strongest upor downregulated genes to generate networks visualizing the relationships among these genes. To include all yeast genes in one analysis and to get broader information on all cellular responses, we test a priori input of predefined genome-wide expression cliques and subsequent statistical analysis of the expression data. To this end, we generate a set of 72 co-regulation cliques using the information from 3196 microarray experiments. The obtained cliques performed highly significant in gene ontology and transcription factor enrichment analyses. We then tested the clique set on individual microarray experiments reporting on responses to pheromone, glycerol versus glucose based growth and the cellular response to heat. In all cases a highly significant determination of affected expression cliques was possible based on their average expression differences, the positions of their genes within hit rankings (UpRegScore) or the enrichment of the Top200 hits in certain cliques. The 72 cliques were finally used to compare experiments, which reported on the transcriptional response to polyglutamine proteins of different lengths. Using the predefined clique set it is possible to identify with high sensitivity and good significance sample and condition specific changes to gene expression. We thus conclude that an analysis, starting with these 72 preformed expression cliques, can complement traditional microarray analyses by visualizing the entire response on a static genome-wide gene set.
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Affiliation(s)
- Siyuan Sima
- Center for integrated protein research at the Department of Chemie, Technische Universität München, Lichtenbergstr. 4, 85748 Garching, Germany
| | - Lukas Schmauder
- Center for integrated protein research at the Department of Chemie, Technische Universität München, Lichtenbergstr. 4, 85748 Garching, Germany
| | - Klaus Richter
- Center for integrated protein research at the Department of Chemie, Technische Universität München, Lichtenbergstr. 4, 85748 Garching, Germany
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26
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Li F, Salit ML, Levy SF. Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies. Cell Syst 2018; 7:521-525.e4. [PMID: 30391162 DOI: 10.1016/j.cels.2018.09.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/13/2018] [Accepted: 09/28/2018] [Indexed: 02/08/2023]
Abstract
Standard practice for phenotyping complex cell pools is to measure the fold enrichment of genotype-specific amplicons after a period of competitive growth. Here, we show that fold-enrichment measures cannot be compared across genotype pools with different fitness distributions. We develop a method to calculate an unbiased estimate of relative fitness by tracking abundances over several time points and show how to optimize experimental protocols to minimize fitness measurement error.
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Affiliation(s)
- Fangfei Li
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Marc L Salit
- Joint Initiative for Metrology in Biology, Stanford, CA 94305, USA; National Institute of Standards and Technology, Gaithersburg, MD 20899, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Sasha F Levy
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Joint Initiative for Metrology in Biology, Stanford, CA 94305, USA; National Institute of Standards and Technology, Gaithersburg, MD 20899, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
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27
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Stacey RG, Skinnider MA, Chik JHL, Foster LJ. Context-specific interactions in literature-curated protein interaction databases. BMC Genomics 2018; 19:758. [PMID: 30340458 PMCID: PMC6194712 DOI: 10.1186/s12864-018-5139-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 10/03/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Databases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study. RESULTS Using raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate. CONCLUSIONS We recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.
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Affiliation(s)
- R. Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 Canada
| | - Michael A. Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 Canada
| | - Jenny H. L. Chik
- Current Address: International Collaboration On Repair Discoveries (ICORD), Vancouver Coastal Health Research Institute and Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
| | - Leonard J. Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 Canada
- Department of Biochemistry, University of British Columbia, Vancouver, V6T 1Z3 Canada
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28
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Taymaz-Nikerel H, Karabekmez ME, Eraslan S, Kırdar B. Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells. Sci Rep 2018; 8:13672. [PMID: 30209405 PMCID: PMC6135803 DOI: 10.1038/s41598-018-31939-9] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 08/28/2018] [Indexed: 12/17/2022] Open
Abstract
Doxorubicin is one of the most effective chemotherapy drugs used against solid tumors in the treatment of several cancer types. Two different mechanisms, (i) intercalation of doxorubicin into DNA and inhibition of topoisomerase II leading to changes in chromatin structure, (ii) generation of free radicals and oxidative damage to biomolecules, have been proposed to explain the mode of action of this drug in cancer cells. A genome-wide integrative systems biology approach used in the present study to investigate the long-term effect of doxorubicin in Saccharomyces cerevisiae cells indicated the up-regulation of genes involved in response to oxidative stress as well as in Rad53 checkpoint sensing and signaling pathway. Modular analysis of the active sub-network has also revealed the induction of the genes significantly associated with nucleosome assembly/disassembly and DNA repair in response to doxorubicin. Furthermore, an extensive re-wiring of the metabolism was observed. In addition to glycolysis, and sulfate assimilation, several pathways related to ribosome biogenesis/translation, amino acid biosynthesis, nucleotide biosynthesis, de novo IMP biosynthesis and one-carbon metabolism were significantly repressed. Pentose phosphate pathway, MAPK signaling pathway biological processes associated with meiosis and sporulation were found to be induced in response to long-term exposure to doxorubicin in yeast cells.
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Affiliation(s)
- Hilal Taymaz-Nikerel
- Department of Genetics and Bioengineering, Istanbul Bilgi University, 34060, Eyup, Istanbul, Turkey.
- Department of Chemical Engineering, Bogazici University, 34342, Bebek, Istanbul, Turkey.
| | - Muhammed Erkan Karabekmez
- Department of Chemical Engineering, Bogazici University, 34342, Bebek, Istanbul, Turkey
- Department of Bioengineering, Istanbul Medeniyet University, 34000, Kadikoy, Istanbul, Turkey
| | - Serpil Eraslan
- Department of Chemical Engineering, Bogazici University, 34342, Bebek, Istanbul, Turkey
- Koç University Hospital, Diagnosis Centre for Genetic Disorders, Topkapı, Istanbul, Turkey
| | - Betül Kırdar
- Department of Chemical Engineering, Bogazici University, 34342, Bebek, Istanbul, Turkey
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29
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Ignatius Pang CN, Goel A, Wilkins MR. Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis. J Proteome Res 2018; 17:1014-1030. [DOI: 10.1021/acs.jproteome.7b00649] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Chi Nam Ignatius Pang
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Apurv Goel
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Marc R. Wilkins
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
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30
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Celaj A, Schlecht U, Smith JD, Xu W, Suresh S, Miranda M, Aparicio AM, Proctor M, Davis RW, Roth FP, St Onge RP. Quantitative analysis of protein interaction network dynamics in yeast. Mol Syst Biol 2017; 13:934. [PMID: 28705884 PMCID: PMC5527849 DOI: 10.15252/msb.20177532] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.
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Affiliation(s)
- Albi Celaj
- Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada.,Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Ulrich Schlecht
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Justin D Smith
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Weihong Xu
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
| | - Sundari Suresh
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Molly Miranda
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Ana Maria Aparicio
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Proctor
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald W Davis
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Frederick P Roth
- Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada .,Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Canadian Institute for Advanced Research, Toronto, ON, Canada.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Robert P St Onge
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA .,Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
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