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1. An inter‐species protein–protein interaction network across vast evolutionary distance

Author

Quan Zhong, Samuel J Pevzner, Tong Hao, Yang Wang, Roberto Mosca, Jörg Menche, Mikko Taipale, Murat Taşan, Changyu Fan, Xinping Yang, Patrick Haley, Ryan R Murray, Flora Mer, Fana Gebreab, Stanley Tam, Andrew MacWilliams, Amélie Dricot, Patrick Reichert, Balaji Santhanam, Lila Ghamsari, Michael A Calderwood, Thomas Rolland, Benoit Charloteaux, Susan Lindquist, Albert‐László Barabási, David E Hill, Patrick Aloy, Michael E Cusick, Yu Xia, Frederick P Roth, and Marc Vidal

Subjects
Cross‐species complementation, Network evolution, Selection, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical‐versus‐functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an “inter‐interactome” approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter‐interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra‐species networks. Although substantially reduced relative to intra‐species networks, the levels of functional overlap in the yeast–human inter‐interactome network uncover significant remnants of co‐functionality widely preserved in the two proteomes beyond human–yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co‐functionality. Such non‐functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Published
2016
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3. 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

4. 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

5. Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing

Author

Dawit Balcha, Lila Ghamsari, Andrew MacWilliams, Aaron Richardson, Alyce A. Chen, Song Yi, Benoit Charloteaux, Tong Hao, Jasmin Coulombe-Huntington, Kerstin Spirohn, Shelly A. Trigg, Bridget E. Begg, Quan Zhong, Marc Vidal, Brenda J. Andrews, Frederick P. Roth, Yu Xia, Michael Costanzo, Guihong Tan, David E. Hill, Gloria M. Sheynkman, Yun A. Shen, Samuel J. Pevzner, Nidhi Sahni, Shuli Kang, Michael A. Calderwood, Kourosh Salehi-Ashtiani, Maria D. Rodriguez, Song Sun, Lilia M. Iakoucheva, Patrick Aloy, Ryan R. Murray, Fan Yang, Xinping Yang, Xianghong Jasmine Zhou, Charles Boone, Stanley Tam, and Miquel Duran-Frigola

Subjects
Models, Molecular, 0301 basic medicine, Protein isoform, Gene isoform, Proteome, Genomics, Computational biology, Biology, Interactome, General Biochemistry, Genetics and Molecular Biology, Evolution, Molecular, Open Reading Frames, 03 medical and health sciences, Animals, Humans, Protein Isoforms, Protein Interaction Domains and Motifs, Protein Interaction Maps, Cloning, Molecular, Gene, Genetics, Biochemistry, Genetics and Molecular Biology(all), Alternative splicing, Alternative Splicing, 030104 developmental biology, Human genome
Abstract

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Published
2016
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6. CrY2H-seq interactome screening

Author

Shelly Wanamaker, Shelly Trigg, Renee Garza, Andrew MacWilliams, Joseph R. Nery, Anna Bartlett, Rosa Castanon, Adeline Goubil, Joseph Feeney, Ronan O'Malley, Shao-shan Carol Huang, Zhuzhu Zhang, Mary Galli, and Joseph R. Ecker

Subjects
0301 basic medicine, 03 medical and health sciences, 030104 developmental biology, General Earth and Planetary Sciences, Computational biology, Biology, Interactome, General Environmental Science
Published
2017

7. CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping

Author

Rosa Castanon, Joseph Feeney, Adeline Goubil, Joseph R. Ecker, Shelly A. Trigg, Shao-shan Carol Huang, Renee M. Garza, Zhuzhu Zhang, Anna Bartlett, Joseph R. Nery, Ronan C. O'Malley, Andrew MacWilliams, Mary Galli, University of California [San Diego] (UC San Diego), University of California, Salk Institute for Biological Studies, Plant Molecular and Cellular Biology Laboratory, Institut National de la Recherche Agronomique (INRA), Amazon, United States Department of Energy, Plant Biology Laboratory, The Salk Institute for Biological Studies, Rutgers University [Camden], Rutgers University System (Rutgers), US Department of Energy [DOE-DE SC0007078], National Science Foundation [IOS-1650227, IOS1456950, IOS1546873], Graduate Research Fellowship Program [DGE-1650112], and Mary K. Chapman Foundation

Subjects
0106 biological sciences, 0301 basic medicine, Technology, Proteome, Sequence analysis, High-throughput screening, Systems biology, [SDV]Life Sciences [q-bio], Arabidopsis, Computational biology, Biology, system, Medical and Health Sciences, 01 natural sciences, Biochemistry, Interactome, DNA sequencing, Article, protein-protein interaction, 03 medical and health sciences, yeast 2-hybrid, generation, Two-Hybrid System Techniques, expression, Protein Interaction Mapping, site, Arabidopsis thaliana, Molecular Biology, Transcription factor, Genetics, interaction networks, High-Throughput Nucleotide Sequencing, DNA, Cell Biology, Sequence Analysis, DNA, Biological Sciences, biology.organism_classification, 030104 developmental biology, Sequence Analysis, Functional genomics, reveals, Developmental Biology, 010606 plant biology & botany, Biotechnology, Transcription Factors
Abstract

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination.

Published
2017

8. Next Generation Protein Interactomes for Plant Systems Biology and Biomass Feedstock Research

Author

Adeline Goubil, Mary Galli, Renee M. Garza, Anna Bartlett, Zhuzhu Zhang, Haili Song, Rosa Castanon, Shelly A. Trigg, Joseph R. Ecker, Joseph Feeney, Joaquin Reina, Andrew MacWilliams, Shao-shan Carol Huang, Joseph R. Nery, and Ronan C. O'Malley

Subjects
business.industry, Biomass feedstock, Biochemical engineering, Biology, Plant system, business, Biotechnology
Published
2016

9. An inter‐species protein–protein interaction network across vast evolutionary distance

Author

Benoit Charloteaux, Lila Ghamsari, Ryan R. Murray, P.J. Haley, Jörg Menche, Frederick P. Roth, Stanley Tam, Thomas Rolland, Roberto Mosca, Murat Tasan, Xinping Yang, Marc Vidal, Amélie Dricot, Tong Hao, Yu Xia, David E. Hill, Albert-László Barabási, Quan Zhong, Changyu Fan, Michael A. Calderwood, Mikko Taipale, Yang Wang, Patrick Aloy, Patrick Reichert, Michael E. Cusick, Balaji Santhanam, Susan Lindquist, Andrew MacWilliams, Fana Gebreab, Samuel J. Pevzner, Flora Mer, Massachusetts Institute of Technology. Department of Biology, Whitehead Institute for Biomedical Research, Taipale, Mikko, and Lindquist, Susan

Subjects
0301 basic medicine, Network evolution, Proteome, Evolution, Biology, General Biochemistry, Genetics and Molecular Biology, Protein protein interaction network, Article, Functional networks, Network Biology, Evolution, Molecular, Fungal Proteins, 03 medical and health sciences, Protein Interaction Mapping, Humans, Databases, Protein, Selection, Genetics, Fungal protein, General Immunology and Microbiology, Applied Mathematics, Computational Biology, Articles, 030104 developmental biology, Computational Theory and Mathematics, Evolutionary biology, Genome-Scale & Integrative Biology, Cross‐species complementation, Evolutionary divergence, Cross-species Complementation, General Agricultural and Biological Sciences, Evolutionary selection, Biological network, Information Systems
Abstract

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical‐versus‐functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an “inter‐interactome” approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter‐interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra‐species networks. Although substantially reduced relative to intra‐species networks, the levels of functional overlap in the yeast–human inter‐interactome network uncover significant remnants of co‐functionality widely preserved in the two proteomes beyond human–yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co‐functionality. Such non‐functional interactions, however, represent a reservoir from which nascent functional interactions may arise., National Human Genome Research Institute (U.S.) (R01‐HG006061), National Science Foundation (U.S.) (CCF‐1219007), Natural Sciences and Engineering Research Council of Canada (RGPIN‐2014‐03892), Canada Foundation for Innovation (JELF‐33732), Canada Research Chairs, Canada Excellence Research Chairs Program, Ontario Research Foundation

Published
2016

10. Yeast one-hybrid assays for gene-centered human gene regulatory network mapping

Author

Rachel Patton McCord, Jun Seop Jeong, Kourosh Salehi-Ashtiani, Seth Blackshaw, John S. Reece-Hoyes, A. Rasim Barutcu, Albertha J.M. Walhout, Andrew MacWilliams, Lizhi Jiang, David E. Hill, Heng Zhu, Xinping Yang, and Job Dekker

Subjects
Response element, Biology, Biochemistry, Article, 03 medical and health sciences, 0302 clinical medicine, Two-Hybrid System Techniques, Humans, Gene Regulatory Networks, Protein–DNA interaction, Enhancer, Molecular Biology, 030304 developmental biology, Cis-regulatory module, Genetics, 0303 health sciences, Binding Sites, General transcription factor, Promoter, DNA, Cell Biology, DNA binding site, Genes, TAF4, Software, 030217 neurology & neurosurgery, Transcription Factors, Biotechnology
Abstract

Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA-to-protein) approach to identify the repertoire of transcription factors that can bind a DNA sequence of interest. We present a set of Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, for the interrogation of interactions using either low or high-throughput settings. These approaches detect both known and novel interactions between human DNA regions and transcription factors.

Published
2011

11. A proteome-scale map of the human interactome network

Author

Mike Jin, Julie M. Sahalie, Andrew MacWilliams, Jean-Claude Twizere, Patrick Aloy, Patrick Reichert, Lilia M. Iakoucheva, Song Yi, Martin P. Broly, Roberto Mosca, John Rasla, Stanley Tam, Yu Xia, Amélie Dricot, Marc Vidal, Bryan J. Gutierrez, Viviana Romero, Annemarie Scholz, David E. Hill, Shuli Kang, Irma Lemmens, Michael A. Calderwood, Roser Corominas, Yun Shen, Murat Tasan, Javier De Las Rivas, Pascal Braun, Kerstin Spirohn, Shelly A. Trigg, Guan Ning Lin, Elizabeth Dann, Xinping Yang, Michael E. Cusick, Amitabh Sharma, Nidhi Sahni, Xavier Rambout, Kerwin Vega, Fana Gebreab, Dan Convery-Zupan, Ryan R. Murray, Akash A. Shah, Bridget E. Begg, Eric A. Franzosa, Alexander O. Tejeda, Jasmin Coulombe-Huntington, Anne-Ruxandra Carvunis, Madeleine F. Hardy, Atanas Kamburov, Dawit Balcha, Ruth Kiros, Matthew M. Poulin, Elien Ruyssinck, Alexandre Palagi, Thomas Rolland, Susan Dina Ghiassian, Celia Fontanillo, Lila Ghamsari, Jan Tavernier, Samuel J. Pevzner, Katja Luck, Jörg Menche, Frederick P. Roth, Benoit Charloteaux, Matija Dreze, Tong Hao, Albert-László Barabási, Marc Brehme, Jennifer M. Walsh, Quan Zhong, Changyu Fan, Ministerio de Ciencia e Innovación (España), Consejo Superior de Investigaciones Científicas (España), Instituto de Salud Carlos III, Ministerio de Economía y Competitividad (España), Junta de Castilla y León, Ontario Research Fund, Avon Foundation for Women, Government of Canada, Krembil Foundation, European Commission, Research Foundation - Flanders, European Research Council, Ghent University, EMBO, Dana Foundation, Fonds de la Recherche Scientifique (Fédération Wallonie-Bruxelles), Celgene, National Cancer Institute (US), National Institutes of Health (US), National Science Foundation (US), and National Human Genome Research Institute (US)

Subjects
Proteome, Computational biology, Biology, Interactome, General Biochemistry, Genetics and Molecular Biology, Article, Mice, 03 medical and health sciences, 0302 clinical medicine, Human interactome, Neoplasms, Animals, Humans, Protein Interaction Maps, Databases, Protein, 030304 developmental biology, Genetics, 0303 health sciences, Biochemistry, Genetics and Molecular Biology(all), Human genetics, Homogeneous, Cancer gene, 030217 neurology & neurosurgery, Protein Interaction Map, Genome-Wide Association Study, Reference genome
Abstract

PMCID: PMC4266588.-- et al., Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ∼14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ∼30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a >broader> human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help >connect the dots> of the genomic revolution., This work was supported primarily by NHGRI grant R01/U01HG001715 awarded to M.V., D.E.H., F.P.R., and J.T. and in part by the following grants and agencies: NHGRI P50HG004233 to M.V., F.P.R., and A.-L.B.; NHLBI U01HL098166 subaward to M.V.; NHLBI U01HL108630 subaward to A.-L.B.; NCI U54CA112962 subaward to M.V.; NCI R33CA132073 to M.V.; NIH RC4HG006066 to M.V., D.E.H., and T.H.; NICHD ARRA R01HD065288, R21MH104766, and R01MH105524 to L.M.I.; NIMH R01MH091350 to L.M.I. and T.H.; NSF CCF-1219007 and NSERC RGPIN-2014-03892 to Y.X.; Canada Excellence Research Chair, Krembil Foundation, Ontario Research Fund–Research Excellence Award, Avon Foundation, grant CSI07A09 from Junta de Castilla y Leon (Valladolid, Spain), grant PI12/00624 from Ministerio de Economia y Competitividad (AES 2012, ISCiii, Madrid, Spain), and grant i-Link0398 from Consejo Superior de Investigaciones Científicas (CSIC, Madrid, Spain) to J.D.L.R.; Spanish Ministerio de Ciencia e Innovación (BIO2010-22073) and the European Commission through the FP7 project SyStemAge grant agreement n: 306240 to P.A.; Group-ID Multidisciplinary Research Partnerships of Ghent University, grant FWO-V G.0864.10 from the Fund for Scientific Research-Flanders and ERC Advanced Grant N° 340941 to J.T.; EMBO long-term fellowship to A.K.; Institute Sponsored Research funds from the Dana-Farber Cancer Institute Strategic Initiative to M.V. I.L. is a postdoctoral fellow with the FWO-V. M.V. is a “Chercheur Qualifié Honoraire” from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium). Since performing the work described, C. Fontanillo has become an employee of Celgene Research SL, part of the Celgene Corporation.

Published
2014

12. Evidence for Network Evolution in an Arabidopsis Interactome Map

Author

Viviana Romero, Jeffery L. Dangl, Jyotika Mirchandani, M. Shahid Mukhtar, Eric Olivares, Samuel J. Pevzner, Gopalakrishna Ramaswamy, Jonathan D. Chesnut, Geetha M. Swamilingiah, Thomas Rolland, Doreen Ware, William Spooner, Tijana Milenkovic, Edward A. Rietman, Huaming Chen, Rosa Cheuk Kim, Pascal Braun, Frederick P. Roth, Lantian Gai, Matthew M. Poulin, Gourab Ghoshal, Robert J. Schmitz, Balaji Santhanam, Stacy Wu, Murat Tasan, Paul Shinn, Michael E. Cusick, Danielle Byrdsong, Marc Vidal, Andrew MacWilliams, Selma Waaijers, Chris de los Reyes, Jonathan D. Moore, Uday Matrubutham, Fana Gebreab, Patrick Reichert, Claire Lurin, Dario Monachello, Changyu Fan, Jean Vandenhaute, Padmavathi Balumuri, Matija Dreze, Vanessa Bautista, Yong-Yeol Ahn, Albert-László Barabási, Natasa Przulj, Benoit Charloteaux, Joshua C. Stein, Tong Hao, Mary Galli, Joseph R. Ecker, Junshi Yazaki, Amélie Dricot, Suswapna Patnaik, Melissa Duarte, Sabrina Rabello, Evan M. Weiner, Anne-Ruxandra Carvunis, Christopher Kim, Rosa Quan, Patrick Gilles, Bryan J. Gutierrez, David E. Hill, Stanley Tam, Harvard Medical School [Boston] (HMS), Department of Genetics [Boston], Faculté de Médecine, Université de Liège, Faculté Universitaire Notre Dame de la Paix, Partenaires INRAE, Salk Institute for Biological Studies, Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, department of biological chemistry and molecular pharmacology, Life Technologies, Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Department of Biology, Northern Arizona University [Flagstaff], Northeastern University [Boston], University of Notre Dame [Indiana] (UND), University of Warwick, Boston University [Boston] (BU), Department of Computing, Imperial College London, Cold Spring Harbor Laboratory (CSHL), Eagle Genomics Ltd, Eagle Genomics, and University of Warwick [Coventry]

Subjects
0106 biological sciences, binding, [SDV]Life Sciences [q-bio], plant, Computational biology, Biology, 01 natural sciences, Interactome, 03 medical and health sciences, Arabidopsis, Botany, expression, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, genome, 030304 developmental biology, 2. Zero hunger, 0303 health sciences, Multidisciplinary, evolve, fungi, food and beverages, 15. Life on land, biology.organism_classification, duplicated gene, protein interaction network, fate, plasticity, divergence, 010606 plant biology & botany
Abstract

International audience; Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing about 6200 highly reliable interactions between about 2700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops.

Published
2011

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