Protein-protein interaction network analysis in chronic obstructive pulmonary disease.
BACKGROUND
The aim of this study was to investigate the gene expression profile of chronic obstructive pulmonary disease (COPD) patients and non-COPD patients.


METHODS
Microarray raw data (GSE29133) was downloaded from Gene Expression Omnibus, including three COPD samples and three normal controls. Gene expression profiling was performed using Affymetrix human genome u133 plus 2.0 GeneChip. Differentially expressed genes were identified by Student's t test and genes with p < 0.05 were considered significantly changed. Up- and downregulated genes were submitted to the molecular signatures database (MSigDB) to search for a possible association with other previously published gene expression signatures. Furthermore, we constructed a COPD protein-protein interaction (PPI) network and used the connectivity map (cMap) to query for potential drugs for COPD.


RESULTS
A total of 680 upregulated genes and 530 downregulated genes in COPD were identified. The MSigDB investigation found that upregulated genes were highly similar to gene signatures that respond to interferon and downregulated genes were similar to erythroid progenitor cells from fetal livers of E13.5 embryos with KLF1 knocked out. A PPI network consisting of 814 gene/proteins and 2,613 interactions was identified by Search Tool for the Retrieval of Interacting Genes. The cMap predicted helveticoside, disulfiram, and lanatoside C as the top three possible drugs that could perhaps treat COPD.


CONCLUSIONS
Comprehensive analysis of the gene expression profile for COPD versus control reveals helveticoside, disulfiram, and lanatoside C as potential molecular targets in COPD. This evidence provides a new breakthrough in the medical treatment of patients with COPD.
Department of Respiratory Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, No. 2800 Gongwei Road, Huinan Town, Shanghai, 201399, China.
Hong
H
Bao
Jiaman
J
Wang
Ding
D
Zhou
Zhaoyong
Z
Han
Ling
L
Su
Yuan
Y
Zhang
Xiong
X
Ye
Chunyan
C
Xu
Yuping
Y
Wang
Qinghua
Q
Li
D016022:Case-Control Studies; D057225:Data Mining; D030541:Databases, Genetic; D004071:Digitalis Glycosides / Q000627:therapeutic use; D004221:Disulfiram / Q000627:therapeutic use; D015195:Drug Design; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D020022:Genetic Predisposition to Disease; D006801:Humans; D007799:Lanatosides / Q000627:therapeutic use; D058990:Molecular Targeted Therapy; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D029424:Pulmonary Disease, Chronic Obstructive / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism*; D013328:Strophanthins / Q000627:therapeutic use
D004071:Digitalis Glycosides; D007799:Lanatosides; D011506:Proteins; D013328:Strophanthins; C007914:helveticoside; C018548:lanatoside C; D004221:Disulfiram