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1. Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Author

Hong-Wen Tang, Kerstin Spirohn, Yanhui Hu, Tong Hao, István A. Kovács, Yue Gao, Richard Binari, Donghui Yang-Zhou, Kenneth H. Wan, Joel S. Bader, Dawit Balcha, Wenting Bian, Benjamin W. Booth, Atina G. Coté, Steffi de Rouck, Alice Desbuleux, Kah Yong Goh, Dae-Kyum Kim, Jennifer J. Knapp, Wen Xing Lee, Irma Lemmens, Cathleen Li, Mian Li, Roujia Li, Hyobin Julianne Lim, Yifang Liu, Katja Luck, Dylan Markey, Carl Pollis, Sudharshan Rangarajan, Jonathan Rodiger, Sadie Schlabach, Yun Shen, Dayag Sheykhkarimli, Bridget TeeKing, Frederick P. Roth, Jan Tavernier, Michael A. Calderwood, David E. Hill, Susan E. Celniker, Marc Vidal, Norbert Perrimon, and Stephanie E. Mohr

Subjects
Science
Abstract

Abstract Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the ‘FlyBi’ dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Published
2023
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2. Network-based prediction of protein interactions

Author

István A. Kovács, Katja Luck, Kerstin Spirohn, Yang Wang, Carl Pollis, Sadie Schlabach, Wenting Bian, Dae-Kyum Kim, Nishka Kishore, Tong Hao, Michael A. Calderwood, Marc Vidal, and Albert-László Barabási

Subjects
Science
Abstract

Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate.

Published
2019
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3. A reference map of the human binary protein interactome.

Author

Katja Luck, Dae-Kyum Kim, Luke Lambourne, Kerstin Spirohn, Bridget E. Begg, Wenting Bian, Ruth Brignall, Tiziana Cafarelli, Francisco J. Campos-Laborie, Benoît Charloteaux, Dong-Sic Choi, Atina G. Coté, Meaghan Daley, Steven Deimling, Alice Desbuleux, Amélie Dricot, Marinella Gebbia, Madeleine F. Hardy, Nishka Kishore, Jennifer J. Knapp, István A. Kovács, Irma Lemmens, Miles W. Mee, Joseph C. Mellor, Carl Pollis, Carles Pons, Aaron D. Richardson, Sadie Schlabach, Bridget Teeking, Anupama Yadav, Mariana Babor, Dawit Balcha, Omer Basha, Christian Bowman-Colin, Suet-Feung Chin, Soon-gang Choi, Claudia Colabella, Georges Coppin, Cassandra D'Amata, David De Ridder, Steffi De Rouck, Miquel Duran-Frigola, Hanane Ennajdaoui, Florian Goebels, Liana Goehring, Anjali Gopal, Ghazal Haddad, Elodie Hatchi, Mohamed Helmy, Yves Jacob, Yoseph Kassa, Serena Landini, Roujia Li, Natascha van Lieshout, Andrew MacWilliams, Dylan Markey, Joseph N. Paulson, Sudharshan Rangarajan, John Rasla, Ashyad Rayhan, Thomas Rolland, Adriana San-Miguel, Yun Shen, Dayag Sheykhkarimli, Gloria M. Sheynkman, Eyal Simonovsky, Murat Tasan, Alexander Tejeda, Vincent Tropepe, Jean-Claude Twizere, Yang Wang 0081, Robert J. Weatheritt, Jochen Weile, Yu Xia 0002, Xinping Yang, Esti Yeger Lotem, Quan Zhong, Patrick Aloy, Gary D. Bader, Javier De Las Rivas, Suzanne Gaudet, Tong Hao, Janusz Rak, Jan Tavernier, David E. Hill, Marc Vidal, Frederick P. Roth, and Michael A. Calderwood

Published
2020
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4. Next-generation large-scale binary protein interaction network for Drosophila

Author

Hong-Wen Tang, Kerstin Spirohn, Yanhui Hu, Tong Hao, István A. Kovács, Yue Gao, Richard Binari, Donghui Yang-Zhou, Kenneth H. Wan, Joel S. Bader, Dawit Balcha, Wenting Bian, Benjamin W. Booth, Atina G. Cote, Steffi De Rouck, Alice Desbuleux, Dae-Kyum Kim, Jennifer J. Knapp, Wen Xing Lee, Irma Lemmens, Cathleen Li, Mian Li, Roujia Li, Hyobin Julianne Lim, Katja Luck, Dylan Markey, Carl Pollis, Sudharshan Rangarajan, Jonathan Rodiger, Sadie Schlabach, Yun Shen, Bridget TeeKing, Frederick P. Roth, Jan Tavernier, Michael A. Calderwood, David E. Hill, Susan E. Celniker, Marc Vidal, Norbert Perrimon, and Stephanie E. Mohr

Abstract

Generating reference maps of the interactome networks underlying most cellular functions can greatly illuminate genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. Here, we applied state-of-the-art experimental and bioinformatics methods to identify high-confidence binary protein-protein interactions (PPIs) for Drosophila melanogaster. We performed four all-by-all yeast two-hybrid (Y2H) screens of >10,000 Drosophila proteins, resulting in the ‘FlyBi’ dataset of 8,723 PPIs among 2,939 proteins. As part of this effort, we tested subsets of our data and data from previous PPI datasets using an orthogonal assay, which allowed us to normalize data quality across datasets. Next, we integrated our FlyBi data with previous PPI data, resulting in an expanded, high-confidence binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6,511 proteins. These data are accessible through the Molecular Interaction Search Tool (MIST) and other databases. To assess the utility of the PPI resource, we used novel interactions from the FlyBi dataset to generate an autophagy interaction network that we validated in vivo using two different autophagy-related assays. We found that deformed wings (dwg) encodes a protein that is both a regulator and a target of autophagy. Altogether, the resources generated in this project provide a strong foundation for building high-confidence new hypotheses regarding protein networks and function.

Published
2022
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5. A reference map of the human binary protein interactome

Author

Eyal Simonovsky, Joseph C. Mellor, Amélie Dricot, Marc Vidal, Liana Goehring, Miquel Duran-Frigola, Florian Goebels, Dayag Sheykhkarimli, Thomas Rolland, Murat Tasan, John Rasla, Steffi De Rouck, Carles Pons, Sadie Schlabach, Yoseph Kassa, Claudia Colabella, Dong-Sic Choi, Yves Jacob, Joseph N. Paulson, Javier De Las Rivas, Madeleine F. Hardy, Francisco J. Campos-Laborie, Xinping Yang, Soon Gang Choi, Frederick P. Roth, Kerstin Spirohn, Nishka Kishore, Luke Lambourne, Cassandra D’Amata, Dawit Balcha, Adriana San-Miguel, Anupama Yadav, Anjali Gopal, Suet-Feung Chin, Suzanne Gaudet, Yang Wang, István Kovács, Elodie Hatchi, Natascha van Lieshout, Michael A. Calderwood, Yu Xia, Gloria M. Sheynkman, Robert J. Weatheritt, Marinella Gebbia, Atina G. Cote, Bridget E. Begg, Mohamed Helmy, Katja Luck, Bridget Teeking, Quan Zhong, Serena Landini, David E. Hill, Sudharshan Rangarajan, Georges Coppin, Ghazal Haddad, Omer Basha, Carl Pollis, Dylan Markey, Alice Desbuleux, Hanane Ennajdaoui, Dae-Kyum Kim, Vincent Tropepe, Roujia Li, Steven Deimling, Jennifer J. Knapp, Jan Tavernier, Mariana Babor, Benoit Charloteaux, Gary D. Bader, Alexander O. Tejeda, Aaron Richardson, Ruth Brignall, Ashyad Rayhan, Irma Lemmens, Tong Hao, Christian Bowman-Colin, Janusz Rak, David De Ridder, Jochen Weile, Wenting Bian, Jean-Claude Twizere, Patrick Aloy, Esti Yeger-Lotem, Meaghan Daley, Tiziana M. Cafarelli, Andrew MacWilliams, Miles W. Mee, Yun Shen, National Institutes of Health (US), National Human Genome Research Institute (US), Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Fonds de la Recherche Scientifique (Fédération Wallonie-Bruxelles), Epidémiologie et Physiopathologie des Virus Oncogènes (EPVO (UMR_3569 / U-Pasteur_3)), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut Pasteur [Paris], Dana-Farber Cancer Institute [Boston], Harvard Medical School [Boston] (HMS), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), University of Toronto, Mount Sinai Health System, Canadian Institute for Advanced Research (CIFAR), and This work was primarily supported by the National Institutes of Health (NIH) National Human Genome Research Institute (NHGRI) grant U41HG001715 (M.V., F.P.R., D.E.H., M.A.C., G.D.B. and J.T.) with additional support from NIH grants P50HG004233 (M.V. and F.P.R.), U01HL098166 (M.V.), U01HG007690 (M.V.), R01GM109199 (M.A.C.), Canadian Institute for Health Research (CIHR) Foundation Grants (F.P.R. and J. Rak), the Canada Excellence Research Chairs Program (F.P.R.) and an American Heart Association grant 15CVGPS23430000 (M.V.). D.-K.K. was supported by a Banting Postdoctoral Fellowship through the Natural Sciences and Engineering Research Council (NSERC) of Canada and by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2017R1A6A3A03004385). C. Pons was supported by a Ramon Cajal fellowship (RYC-2017-22959). G.M.S. was supported by NIH Training Grant T32CA009361. M.V. is a Chercheur Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium).

Subjects
0301 basic medicine, Multidisciplinary, Proteome, [SDV]Life Sciences [q-bio], Computational biology, Biology, Genome, Phenotype, Interactome, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Article, Protein–protein interaction, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Organ Specificity, Protein Interaction Mapping, Reference map, Humans, Cellular organization, Extracellular Space, 030217 neurology & neurosurgery, Function (biology)
Abstract

et al., Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships1,2. Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes., This work was primarily supported by the National Institutes of Health (NIH) National Human Genome Research Institute (NHGRI) grant U41HG001715 (M.V., F.P.R., D.E.H., M.A.C., G.D.B. and J.T.) with additional support from NIH grants P50HG004233 (M.V. and F.P.R.), U01HL098166 (M.V.), U01HG007690 (M.V.), R01GM109199 (M.A.C.), Canadian Institute for Health Research (CIHR) Foundation Grants (F.P.R. and J. Rak), the Canada Excellence Research Chairs Program (F.P.R.) and an American Heart Association grant 15CVGPS23430000 (M.V.). D.-K.K. was supported by a Banting Postdoctoral Fellowship through the Natural Sciences and Engineering Research Council (NSERC) of Canada and by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2017R1A6A3A03004385). C. Pons was supported by a Ramon Cajal fellowship (RYC-2017-22959). G.M.S. was supported by NIH Training Grant T32CA009361. M.V. is a Chercheurv Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium).

Published
2020
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6. A reference map of the human protein interactome

Author

Madeleine F. Hardy, Bridget Teeking, Francisco J. Campos-Laborie, Dayag Sheykhkarimli, Dawit Balcha, Anjali Gopal, Marinella Gebbia, Ashyad Rayhan, Carles Pons, Gloria M. Sheynkman, Yves Jacob, Suzanne Gaudet, Aaron Richardson, Yoseph Kassa, Elodie Hatchi, Kerstin Spirohn, Dong-Sic Choi, Yu Xia, Eyal Simonovsky, David E. Hill, Hanane Ennajdaoui, Steven Deimling, Joseph N. Paulson, Natascha van Lieshout, Vincent Tropepe, Michael A. Calderwood, István Kovács, Gary D. Bader, Luke Lambourne, Sudharshan Rangarajan, Tiziana M. Cafarelli, Carl Pollis, Suet-Feung Chin, Alice Desbuleux, Andrew MacWilliams, Amélie Dricot, Jean-Claude Twizere, Patrick Aloy, Atina G. Cote, Marc Vidal, Jan Tavernier, Javier De Las Rivas, Cassandra D’Amata, Alexander O. Tejeda, Esti Yeger-Lotem, Liana Goehring, Joseph C. Mellor, Meaghan Daley, Irma Lemmens, Soon Gang Choi, Christian Bowman-Colin, Ghazal Haddad, Janusz Rak, Florian Goebels, Robert J. Weatheritt, Mariana Babor, Yang Wang, Thomas Rolland, Steffi De Rouck, Jochen Weile, Serena Landini, John Rasla, Sadie Schlabach, Nishka Kishore, Bridget E. Begg, Ruth Brignall, Quan Zhong, Tong Hao, David De Ridder, Claudia Colabella, Frederick P. Roth, Anupama Yadav, Mohamed Helmy, Katja Luck, Omer Basha, Dae-Kyum Kim, Benoit Charloteaux, Georges Coppin, Dylan Markey, Roujia Li, Miquel Duran-Frigola, Adriana San-Miguel, Wenting Bian, Miles W. Mee, Jennifer J. Knapp, Yun Shen, Murat Tasan, Xinping Yang, Dana-Farber Cancer Institute [Boston], Division of Medical Physics in Radiology [Heidelberg], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Cancer Research UK Cambridge Institute (CRUK), University of Cambridge [UK] (CAM), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut Pasteur [Paris], Harvard Medical School [Boston] (HMS), University of Toronto, Universidad de Salamanca, McGill University Health Center [Montreal] (MUHC), Mount Sinai Hospital [Toronto, Canada] (MSH), Université de Liège, Wigner Research Centre for Physics [Budapest], Hungarian Academy of Sciences (MTA), Northeastern University [Boston], Vlaams Instituut voor Biotechnologie [Ghent, Belgique] (VIB), Universiteit Gent = Ghent University [Belgium] (UGENT), Barcelona Institute of Science and Technology (BIST), Ben-Gurion University of the Negev (BGU), Università degli Studi di Perugia (UNIPG), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Canadian Institute for Advanced Research (CIFAR), Universiteit Gent = Ghent University (UGENT), Università degli Studi di Perugia = University of Perugia (UNIPG), and Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0303 health sciences, Context (language use), Computational biology, Biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Genome, Interactome, Human genetics, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Human interactome, Proteome, 030217 neurology & neurosurgery, Function (biology), 030304 developmental biology
Abstract

Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human “all-by-all” binary reference interactome map, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

Published
2019
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7. Network-based prediction of protein interactions

Author

Katja Luck, Tong Hao, Michael A. Calderwood, Dae-Kyum Kim, Albert-László Barabási, Kerstin Spirohn, István Kovács, Marc Vidal, Wenting Bian, Nishka Kishore, Yang Wang, Carl Pollis, and Sadie Schlabach

Subjects
0301 basic medicine, Computer science, General Physics and Astronomy, Datasets as Topic, 02 engineering and technology, 01 natural sciences, Interactome, 010305 fluids & plasmas, Mice, Human disease, Prediction methods, Protein Interaction Mapping, Drosophila Proteins, Protein Interaction Maps, lcsh:Science, Social network analysis, 0303 health sciences, Sequence, Multidisciplinary, 021001 nanoscience & nanotechnology, Complement (complexity), 0210 nano-technology, Protein Interaction Map, Algorithms, Saccharomyces cerevisiae Proteins, Science, Systems biology, Computational biology, Network topology, Models, Biological, General Biochemistry, Genetics and Molecular Biology, Article, Protein–protein interaction, 03 medical and health sciences, Human interactome, Similarity (psychology), 0103 physical sciences, Animals, Humans, Caenorhabditis elegans Proteins, 030304 developmental biology, Arabidopsis Proteins, Disease mechanisms, Computational Biology, General Chemistry, Triadic closure, 030104 developmental biology, lcsh:Q, Schizosaccharomyces pombe Proteins, Software
Abstract

Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other’s partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome., Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate.

Published
2019

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