Dataset Information

Pathway and network analysis of genes related to osteoporosis.

ABSTRACT: As a common degenerative disease, osteoporosis (OS) is characterized by reduced bone mass and microarchitectural deterioration of bone tissue. Both genetic and environmental factors are involved in OS development. To date, ~300 genes have been confirmed to be involved in the pathogenesis of OS, a large majority of which have been independently investigated. As OS is a polygenetic disease, a comprehensive analysis focusing on the biological functions and interactions of OS?related genes would provide valuable information. In this study, OS related research deposited in PubMed was retrieved and genes related to OS were catalogued. Pathways with an enriched biological function for these genes were extracted, and the crosstalk between the enriched pathways was analyzed. A comprehensive network was constructed, and a minimal network was extracted using the Steiner minimal network algorithm. In this study, a total of 294 genes in were retrieved from PubMed. Biological processes found to be enriched included those related to bone metabolism and the immune system. In total, 58 pathways were enriched. Furthermore, the comprehensive network consisting of 3,943 nodes and 7,976 edges was constructed, among which 631 nodes and 2,581 edges contributed to the OS?specific molecular network. In this network, in excess of 300 potential genes associated with OS and two modules were identified. Thus, this study provides a mechanistic insight into OS and suggests more than 300 potential OS?related genes for future research.

SUBMITTER: Guo L

PROVIDER: S-EPMC6625186 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Pathway and network analysis of genes related to osteoporosis.

Guo Lin L Han Jia J Guo Hao H Lv Dongmei D Wang Yun Y

Molecular medicine reports 20190606 2

As a common degenerative disease, osteoporosis (OS) is characterized by reduced bone mass and microarchitectural deterioration of bone tissue. Both genetic and environmental factors are involved in OS development. To date, ~300 genes have been confirmed to be involved in the pathogenesis of OS, a large majority of which have been independently investigated. As OS is a polygenetic disease, a comprehensive analysis focusing on the biological functions and interactions of OS‑related genes would pro ...[more]

PMID: 31173222

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Project description:BackgroundNetwork Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to principal component analysis, which can be employed to select the high-variance genes, NCA makes use of the connectivity structure from transcriptional regulatory networks to infer dynamics of transcription factor activities. Using the budding yeast Saccharomyces cerevisiae as a model system, we aim to deduce regulatory actions of cytokinesis-related genes, using precise spatial proximity (midbody) and/or temporal synchronicity (cytokinesis) to avoid full-scale computation from genome-wide databases.ResultsNCA was applied to infer regulatory actions of transcription factor activity from microarray data and partial transcription factor-gene connectivity information for cytokinesis-related genes, which were a subset of genome-wide datasets. No literature has so far discussed the inferred results through NCA are independent of the scale of the gene expression dataset. To avoid full-scale computation from genome-wide databases, four cytokinesis-related gene cases were selected for NCA by running computational analysis over the transcription factor database to confirm the approach being scale-free. The inferred dynamics of transcription factor activity through NCA were independent of the scale of the data matrix selected from the four cytokinesis-related gene sets. Moreover, the inferred regulatory actions were nearly identical to published observations for the selected cytokinesis-related genes in the budding yeast; namely, Mcm1, Ndd1, and Fkh2, which form a transcription factor complex to control expression of the CLB2 cluster (i.e. BUD4, CHS2, IQG1, and CDC5).ConclusionIn this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes. The regulated action for four selected cytokinesis-related genes (BUD4, CHS2, IQG1, and CDC5) belongs to the M-phase or M/G1 phase, consistent with the empirical observations that in S. cerevisiae, the Mcm1-Ndd1-Fkh2 transcription factor complex can regulate expression of the cytokinesis-related genes BUD4, CHS2, IQG1, and CDC5. Since Bud4, Iqg1, and Cdc5 are highly conserved between human and yeast, results obtained from NCA for cytokinesis in the budding yeast can lead to a suggestion that human cells should have the transcription regulator(s) as the budding yeast Mcm1-Ndd1-Fkh2 transcription factor complex in controlling occurrence of cytokinesis.

Project description:BackgroundOur understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease.MethodIn this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm.ResultsWe compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified.ConclusionBy means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

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Pathway and network analysis of genes related to osteoporosis.

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Pathway and network analysis of genes related to osteoporosis.

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