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Sci Rep. 2018 Sep 27;8(1):14439. doi: 10.1038/s41598-018-32173-z.

Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module.

Sharma A1,2,3,4, Kitsak M5, Cho MH6,7,8, Ameli A6,9, Zhou X6,8, Jiang Z6, Crapo JD10, Beaty TH11, Menche J12, Bakke PS13, Santolini M6,5,14, Silverman EK15,16,17.

Author information

1
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. amitabh.sharma@channing.harvard.edu.
2
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. amitabh.sharma@channing.harvard.edu.
3
Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA. amitabh.sharma@channing.harvard.edu.
4
Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. amitabh.sharma@channing.harvard.edu.
5
Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA.
6
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.
7
Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
8
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
9
Department of Physics, Northeastern University, Boston, MA, 02115, United States.
10
Department of Medicine, National Jewish Health, Denver, Colorado, USA.
11
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
12
Department of Bioinformatics, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090, Vienna, Austria.
13
Department of Clinical Science, University of Bergen, Bergen, Norway.
14
Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
15
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. ed.silverman@channing.harvard.edu.
16
Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA. ed.silverman@channing.harvard.edu.
17
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. ed.silverman@channing.harvard.edu.

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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