Identification of a thymus microRNA­mRNA regulatory network in Down syndrome.

The present bioinformatics analysis was performed using a multi­step approach to identify a microRNA (miR)­mRNA regulatory network in Down syndrome. miR (GSE69210) and mRNA (GSE70573) data was downloaded and collected from the thymic tissues of both Down syndrome and karyotypically normal subjects and placed in a public repository. Then, weighted gene co­expression network analysis (WGCNA) was performed to screen for miRs and mRNAs associated with Down syndrome. Subsequently, differentially expressed miRs (DEmiRs) and mRNAs/differentially expressed genes (DEGs) were identified following screening and mapping to RNA data. Bidirectional hierarchical clustering analysis was then performed to distinguish DEmiRs and DEGs between Down syndrome samples and normal control samples. DEmiR targets were retrieved using the miRanda database and mapped to the mRNA module screen by WGCNA. A gene co­expression network was constructed and subjected to functional enrichment analysis. During WGCNA, a total of 6 miR modules and 20 mRNA modules associated with Down syndrome were identified. Following mapping of these miRs and mRNAs to the miR and mRNA modules screened using WGNCA, a total of 12 DEmiRs and 237 DEGs were collected. Following comparison with DEmiR targets retrieved from the miRanda database, a total of 255 DEmiR­DEG pairs, including 6 DEmiRs and 106 DEGs were obtained. At expression correlation coefficient >0.9, a total of 231 gene pairs were selected. These gene pairs were enriched in response to stress and response to stimuli following functional annotation and module division. An integrated analysis of miR and mRNA expression in the thymus in Down syndrome is reported in the present study. miR­30c, miR­145, miR­183 and their targets may serve important roles in the pathogenesis and development of complications in Down syndrome. However, further experimental studies are required to verify these results.

Identification of genes and signaling pathways associated with arthrogryposis­renal dysfunction­cholestasis syndrome using weighted correlation network analysis.

The present study aimed to identify the molecular basis of the arthrogryposis­renal dysfunction­cholestasis (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 co­expression network analysis to construct a protein­protein 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 Toll­like receptor (TLR) signaling pathway. The DEGs in module 2 (COL1A1 and COL1A2) were significantly enriched with respect to cellular component organization, extracellular matrix­receptor 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.