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Morselli Gysi D, do Valle Í, Zitnik M, Ameli A, Gan X, Varol O, Ghiassian SD, Patten JJ, Davey RA, Loscalzo J, Barabási AL. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc Natl Acad Sci U S A 2021; 118:e2025581118. [PMID: 33906951 PMCID: PMC8126852 DOI: 10.1073/pnas.2025581118] [Citation(s) in RCA: 164] [Impact Index Per Article: 54.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Ítalo do Valle
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, MA 02115
- Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138
| | - Asher Ameli
- Department of Physics, Northeastern University, Boston, MA 02115
- Data Science Department, Scipher Medicine, Waltham, MA 02453
| | - Xiao Gan
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Onur Varol
- Network Science Institute, Northeastern University, Boston, MA 02115
- Department of Physics, Northeastern University, Boston, MA 02115
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | | | - J J Patten
- Department of Microbiology, National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118
| | - Robert A Davey
- Department of Microbiology, National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA 02115;
- Department of Physics, Northeastern University, Boston, MA 02115
- Department of Network and Data Science, Central European University, Budapest 1051, Hungary
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2
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Mellors T, Withers JB, Ameli A, Jones A, Wang M, Zhang L, Sanchez HN, Santolini M, Do Valle I, Sebek M, Cheng F, Pappas DA, Kremer JM, Curtis JR, Johnson KJ, Saleh A, Ghiassian SD, Akmaev VR. Clinical Validation of a Blood-Based Predictive Test for Stratification of Response to Tumor Necrosis Factor Inhibitor Therapies in Rheumatoid Arthritis Patients. Network and Systems Medicine 2020. [DOI: 10.1089/nsm.2020.0007] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
| | | | - Asher Ameli
- Scipher Medicine, Waltham, Massachusetts, USA
| | - Alex Jones
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | - Lixia Zhang
- Scipher Medicine, Waltham, Massachusetts, USA
| | | | - Marc Santolini
- Center for Research and Interdisciplinarity (CRI), University Paris Descartes, Paris, France
| | - Italo Do Valle
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Michael Sebek
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Feixiong Cheng
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA
| | - Dimitrios A. Pappas
- Division of Rheumatology, College of Physicians and Surgeons, Columbia University, New York, New York, USA
- CORRONA, LCC, Waltham, Massachusetts, USA
| | - Joel M. Kremer
- CORRONA, LCC, Waltham, Massachusetts, USA
- Albany Medical College, The Center for Rheumatology, Albany, New York, USA
| | - Jeffery R. Curtis
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Alif Saleh
- Scipher Medicine, Waltham, Massachusetts, USA
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Morselli Gysi D, Do Valle Í, Zitnik M, Ameli A, Gan X, Varol O, Ghiassian SD, Patten JJ, Davey R, Loscalzo J, Barabási AL. Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19. ArXiv 2020:arXiv:2004.07229v2. [PMID: 32550253 PMCID: PMC7280907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 08/09/2020] [Indexed: 06/11/2023]
Abstract
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
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Affiliation(s)
- Deisy Morselli Gysi
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ítalo Do Valle
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, MA 02115, USA
- Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138, USA
| | - Asher Ameli
- Scipher Medicine, 221 Crescent St, Suite 103A, Waltham, MA 02453
- Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Xiao Gan
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Onur Varol
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | | | - J J Patten
- Department of microbiology, NEIDL, Boston University, Boston, MA, USA
| | - Robert Davey
- Department of microbiology, NEIDL, Boston University, Boston, MA, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Department of Network and Data Science, Central European University, Budapest 1051, Hungary
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Kılıç A, Ameli A, Park JA, Kho AT, Tantisira K, Santolini M, Cheng F, Mitchel JA, McGill M, O'Sullivan MJ, De Marzio M, Sharma A, Randell SH, Drazen JM, Fredberg JJ, Weiss ST. Mechanical forces induce an asthma gene signature in healthy airway epithelial cells. Sci Rep 2020; 10:966. [PMID: 31969610 PMCID: PMC6976696 DOI: 10.1038/s41598-020-57755-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 12/23/2019] [Indexed: 12/27/2022] Open
Abstract
Bronchospasm compresses the bronchial epithelium, and this compressive stress has been implicated in asthma pathogenesis. However, the molecular mechanisms by which this compressive stress alters pathways relevant to disease are not well understood. Using air-liquid interface cultures of primary human bronchial epithelial cells derived from non-asthmatic donors and asthmatic donors, we applied a compressive stress and then used a network approach to map resulting changes in the molecular interactome. In cells from non-asthmatic donors, compression by itself was sufficient to induce inflammatory, late repair, and fibrotic pathways. Remarkably, this molecular profile of non-asthmatic cells after compression recapitulated the profile of asthmatic cells before compression. Together, these results show that even in the absence of any inflammatory stimulus, mechanical compression alone is sufficient to induce an asthma-like molecular signature.
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Affiliation(s)
- Ayşe Kılıç
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Asher Ameli
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Jin-Ah Park
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Alvin T Kho
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Kelan Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Marc Santolini
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Centre for Research and Interdisciplinarity (CRI), Paris, F-75014, France
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, 44106, USA
| | - Jennifer A Mitchel
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Maureen McGill
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Michael J O'Sullivan
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Margherita De Marzio
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott H Randell
- Marsico Lung Institute/Cystic Fibrosis Center, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey M Drazen
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Jeffrey J Fredberg
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Program in Molecular Integrative Phyisological Sciences, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA.
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5
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Qiao D, Ameli A, Prokopenko D, Chen H, Kho AT, Parker MM, Morrow J, Hobbs BD, Liu Y, Beaty TH, Crapo JD, Barnes KC, Nickerson DA, Bamshad M, Hersh CP, Lomas DA, Agusti A, Make BJ, Calverley PMA, Donner CF, Wouters EF, Vestbo J, Paré PD, Levy RD, Rennard SI, Tal-Singer R, Spitz MR, Sharma A, Ruczinski I, Lange C, Silverman EK, Cho MH. Whole exome sequencing analysis in severe chronic obstructive pulmonary disease. Hum Mol Genet 2018; 27:3801-3812. [PMID: 30060175 PMCID: PMC6196654 DOI: 10.1093/hmg/ddy269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 07/09/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD), one of the leading causes of death worldwide, is substantially influenced by genetic factors. Alpha-1 antitrypsin deficiency demonstrates that rare coding variants of large effect can influence COPD susceptibility. To identify additional rare coding variants in patients with severe COPD, we conducted whole exome sequencing analysis in 2543 subjects from two family-based studies (Boston Early-Onset COPD Study and International COPD Genetics Network) and one case-control study (COPDGene). Applying a gene-based segregation test in the family-based data, we identified significant segregation of rare loss of function variants in TBC1D10A and RFPL1 (P-value < 2x10-6), but were unable to find similar variants in the case-control study. In single-variant, gene-based and pathway association analyses, we were unable to find significant findings that replicated or were significant in meta-analysis. However, we found that the top results in the two datasets were in proximity to each other in the protein-protein interaction network (P-value = 0.014), suggesting enrichment of these results for similar biological processes. A network of these association results and their neighbors was significantly enriched in the transforming growth factor beta-receptor binding and cilia-related pathways. Finally, in a more detailed examination of candidate genes, we identified individuals with putative high-risk variants, including patients harboring homozygous mutations in genes associated with cutis laxa and Niemann-Pick Disease Type C. Our results likely reflect heterogeneity of genetic risk for COPD along with limitations of statistical power and functional annotation, and highlight the potential of network analysis to gain insight into genetic association studies.
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Affiliation(s)
- Dandi Qiao
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Asher Ameli
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Dmitry Prokopenko
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Alvin T Kho
- Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Margaret M Parker
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jarrett Morrow
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yanhong Liu
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - James D Crapo
- National Jewish Health, Denver, Colorado, United States of America
| | - Kathleen C Barnes
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Michael Bamshad
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, Washington , United States of America
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Alvar Agusti
- Respiratory Institute, Hospital Clinic, IDIBAPS, University of Barcelona, CIBERES, Barcelona, Spain
| | - Barry J Make
- National Jewish Health, Denver, Colorado, United States of America
| | | | - Claudio F Donner
- Mondo Medico di I.F.I.M. srl, Multidisciplinary and Rehabilitation Outpatient Clinic, Borgomanero, Novara, Italy
| | - Emiel F Wouters
- Department of Respiratory Medicine, Maastricht University Medical Center, AZ Maastricht, The Netherlands
| | - Jørgen Vestbo
- University of Manchester, Manchester, United Kingdom
| | - Peter D Paré
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, British Columbia V6T, Canada
| | - Robert D Levy
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, British Columbia V6T, Canada
| | - Stephen I Rennard
- University of Nebraska Medical Center, Omaha, Nebraska, United States of America
- AstraZeneca, Cambridge CB2 0RE, United Kingdom
| | - Ruth Tal-Singer
- GSK Research and Development, KingOf Prussia, Pennsylvania, United States of America
| | - Margaret R Spitz
- Dan L. Duncan Comprehensive Cancer Center, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Christoph Lange
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Channing Division of Network Medicine, Longwood Avenue, Boston, MA, USA
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Sharma A, Kitsak M, Cho MH, Ameli A, Zhou X, Jiang Z, Crapo JD, Beaty TH, Menche J, Bakke PS, Santolini M, Silverman EK. Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module. Sci Rep 2018; 8:14439. [PMID: 30262855 PMCID: PMC6160419 DOI: 10.1038/s41598-018-32173-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 08/20/2018] [Indexed: 12/21/2022] Open
Abstract
The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.
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Affiliation(s)
- Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. .,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. .,Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA. .,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
| | - Maksim Kitsak
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Asher Ameli
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Department of Physics, Northeastern University, Boston, MA, 02115, United States
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Zhiqiang Jiang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - James D Crapo
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jörg Menche
- Department of Bioinformatics, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090, Vienna, Austria
| | - Per S Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marc Santolini
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. .,Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA. .,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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7
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Hossieny N, Shaayegan V, Ameli A, Saniei M, Park C. Characterization of hard-segment crystalline phase of thermoplastic polyurethane in the presence of butane and glycerol monosterate and its impact on mechanical property and microcellular morphology. POLYMER 2017. [DOI: 10.1016/j.polymer.2017.02.015] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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