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Analysis of protein-protein interaction network and functional modules on primary osteoporosis.

ABSTRACT: BACKGROUND: Primary osteoporosis is an age-related disease, and the main cause of this disease is the failure of bone homeostasis. Previous studies have shown that primary osteoporosis is associated with gene mutations.To explore the functional modules of the PPI (protein-protein interaction) network of differentially expressed genes (DEGs), and the related pathways participating in primary osteoporosis. METHODS: The gene expression profile of primary osteoporosis GSE35956 was downloaded from the GEO (Gene Expression Omnibus) database and included five MSC (mesenchymal stem cell) specimens of normal osseous tissue and five MSC specimens of osteoporosis. The DEGs between the two types of MSC specimens were identified by the samr package in R language. In addition, the functions and pathways of DEGs were enriched. Then the DEGs were mapped to String to acquire PPI pairs and the PPI network was constructed with by these PPI pairs. Topological properties of the network were calculated by Network Analyzer, and modules in the network were screened by Cluster ONE software. Subsequently, the fronting five modules whose P-value was less than 1.0e-05 were identified and function analysis was conducted. RESULTS: A total of 797 genes were filtered as DEGs from these ten specimens of GSE35956 with 660 up-regulated genes and 137 down-regulated genes. Meanwhile, up-regulated DEGs were mainly enriched in functions and pathways related to cell cycle and DNA replication. Furthermore, there were 4,135 PPI pairs and 377 nodes in the PPI network. Four modules were enriched in different pathways, including cell cycle and DNA replication pathway in module 2. CONCLUSIONS: In this paper, we explored the genes and pathways involved in primary osteoporosis based on gene expression profiles, and the present findings have the potential to be used clinically for the future treatment of primary osteoporosis.

SUBMITTER: Li GL

PROVIDER: S-EPMC3994448 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Analysis of protein-protein interaction network and functional modules on primary osteoporosis.

Li Gai-Li GL Xu Xian-Hua XH Wang Bing-Ang BA Yao Yi-Min YM Qin Yang Y Bai Shu-Rong SR Rong Jian J Deng Tao T Hu Yong-He YH

European journal of medical research 20140321

<h4>Background</h4>Primary osteoporosis is an age-related disease, and the main cause of this disease is the failure of bone homeostasis. Previous studies have shown that primary osteoporosis is associated with gene mutations.To explore the functional modules of the PPI (protein-protein interaction) network of differentially expressed genes (DEGs), and the related pathways participating in primary osteoporosis.<h4>Methods</h4>The gene expression profile of primary osteoporosis GSE35956 was downl ...[more]

PMID: 24656062

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Project description:In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks.In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways.Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.

Project description:BackgroundAcute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis.MethodsThis study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed into two sub-networks, specific to each phenotype. Finally, both sub-networks were clustered into modules using weighted gene co-expression network analysis (WGCNA) and further analyzed with functional enrichment analysis.ResultsThrough the integration of DC analysis and PPI network, four protein modules were found active under the GC-resistance phenotype but not under the GC-sensitive. Functional enrichment analysis revealed that these modules are related to proteasome, electron transport chain, tRNA-aminoacyl biosynthesis, and peroxisome signaling pathways. These findings are in accordance with previous findings related to GC-resistance in other hematological malignancies such as pediatric ALL.ConclusionsDifferential co-expression analysis is a promising approach to incorporate the dynamic context of gene expression profiles into the well-documented protein interaction networks. The approach allows the detection of relevant protein modules that are highly enriched with DC gene pairs. Functional enrichment analysis of detected protein modules generates new biological hypotheses and may help in explaining the GC-resistance in MLL-rearranged infant ALL patients.

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Analysis of protein-protein interaction network and functional modules on primary osteoporosis.

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Analysis of protein-protein interaction network and functional modules on primary osteoporosis.

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