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Identification of genes and signaling pathways associated with arthrogryposis‑renal dysfunction‑cholestasis syndrome using weighted correlation network analysis.

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The present study aimed to identify the molecular basis of the arthrogryposis‑renal dysfunction‑cholestasis (ARC) syndrome, which is caused by mutations in the vacuolar protein sorting 33 homolog B (VPS33B) gene.… Click to show full abstract

The present study aimed to identify the molecular basis of the arthrogryposis‑renal dysfunction‑cholestasis (ARC) syndrome, which is caused by mutations in the vacuolar protein sorting 33 homolog B (VPS33B) gene. The microarray dataset GSE83192, which contained six liver tissue samples from VPS33B knockout mice and four liver tissue samples from control mice, was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were screened by the Limma package in R software. The DEGs most relevant to ARC were selected via weighted gene co‑expression network analysis to construct a protein‑protein interaction (PPI) network. In addition, module analysis was performed for the PPI network using the Molecular Complex Detection function. Functional and pathway enrichment analyses were also performed for DEGs in the PPI network. Potential drugs for ARC treatment were predicted using the Connectivity Map database. In total, 768 upregulated and 379 downregulated DEGs were detected in the VPS33B knockout mice, while three modules were identified from the PPI network constructed. The DEGs in module 1 (CD83, IL1B and TLR2) were mainly involved in the positive regulation of cytokine production and the Toll‑like receptor (TLR) signaling pathway. The DEGs in module 2 (COL1A1 and COL1A2) were significantly enriched with respect to cellular component organization, extracellular matrix‑receptor interactions and focal adhesion. The DEGs in module 3 (ABCG8 and ABCG3) were clearly associated with sterol absorption and transport. Furthermore, mercaptopurine was identified to be a potential drug (connectivity score=‑0.939) for ARC treatment. In conclusion, the results of the current study may help to further understand the pathology of ARC, and the DEGs identified in these modules may serve as therapeutic targets.

Keywords: renal dysfunction; network; dysfunction cholestasis; analysis; arthrogryposis renal; network analysis

Journal Title: International journal of molecular medicine
Year Published: 2018

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