Analysis of common genetic variants associated with neuro-synapse development among 60 family trios affected with sporadic autism spectrum disorders / 中华医学遗传学杂志

Objective@#To explore susceptibility genes for autism spectrum disorders (ASD).@*Methods@#Whole-exome sequencing was carried out for 60 family trios affected with sporadic ASD. Genetic variants discovered in over 10% of the patients were selected for genotype-phenotype correlation and pathway enrichment analysis using Phenolyzer software and metascape database. Combining gene-phenotypic scores, pathway-related genes associated with neural and neurite triggering were screened for the candidates.@*Results@#A total of 170 common variants were found to be associated with the ASD phenotype. Among these, there was only one high-confidence gene [SHANK2 (0.8146)] and four medium-confidence genes [ERBB2 (0.1322), LAMC3 (0.1117), PPFIA4 (0.1059), DISC1 (0.1002)]. Twenty-pathways and four biological processes were found to be statistically significant by pathway enrichment analysis, which included neuron projection morphogenesis (GO: 0048812), regulation of neuroblast proliferation (GO: 1902692), modulation of excitatory postsynaptic potential (GO: 0098815), and dendrite morphogenesis (GO: 0048813). Twenty-one genes were found to be closely associated with neurological and neurite triggering, among which only SHANK2, ERBB2, and DISC1 had above-medium confidence correlation scores with the ASD phenotypes.@*Conclusion@#Abnormal neuron projection morphogenesis (GO: 0048812) may be closely related to the occurrence of ASD. SHANK2, ERBB2, and DISC1 are susceptibility genes for ASD.

Differentially expressed genes in Sjgren's syndrome microarray / 医学研究生学报

Objective Sjgren's Syndrome (SS) is considered to be a common rheumatoid immune disease only second to rheumatoid arthritis in prevalence. This study aimed to screen SS-related genes with gene expression profiling data, explore the pathogenesis of SS and search for the potential drug targets for the treatment of the disease.Methods Using Affymetrix Human Genome U133 Plus 2.0 Array, we obtained the cell expression profiles of human parotid tissue in SS patients from the GEO database, including 24 SS samples and 25 non-SS samples up to the diagnostic criteria. We screened differentially expressed genes with the GEO2R online tools and enriched the functions and pathways of the genes with the DAVID tools. Then we constructed a network of interaction among differentially expressed gene protein products using the STRING database, imported the data into the Cytoscape software, calculated the topological properties, and screened the core genes.Results Totally, 24 up-regulated and 147 down-regulated differentially expressed genes were screened out from the SS samples, involved in cell adhesion molecules, intestinal immune networkIgA secretion, viral myocarditis, rheumatoid arthritis and the leukocyte transendothelial migration pathway, among which PTPRC, CD86, STAT1, FYN and LCP2 were the key genes.Conclusion SS-related biological pathways and key genes can be screened by bioinformatics, which can provide some experimental reference for further revealing the pathogenesis of SS.

Analyzing gene expression profiles in dilated cardiomyopathy via bioinformatics methods

Dilated cardiomyopathy (DCM) is characterized by ventricular dilatation, and it is a common cause of heart failure and cardiac transplantation. This study aimed to explore potential DCM-related genes and their underlying regulatory mechanism using methods of bioinformatics. The gene expression profiles of GSE3586 were downloaded from Gene Expression Omnibus database, including 15 normal samples and 13 DCM samples. The differentially expressed genes (DEGs) were identified between normal and DCM samples using Limma package in R language. Pathway enrichment analysis of DEGs was then performed. Meanwhile, the potential transcription factors (TFs) and microRNAs (miRNAs) of these DEGs were predicted based on their binding sequences. In addition, DEGs were mapped to the cMap database to find the potential small molecule drugs. A total of 4777 genes were identified as DEGs by comparing gene expression profiles between DCM and control samples. DEGs were significantly enriched in 26 pathways, such as lymphocyte TarBase pathway and androgen receptor signaling pathway. Furthermore, potential TFs (SP1, LEF1, and NFAT) were identified, as well as potential miRNAs (miR-9, miR-200 family, and miR-30 family). Additionally, small molecules like isoflupredone and trihexyphenidyl were found to be potential therapeutic drugs for DCM. The identified DEGs (PRSS12 and FOXG1), potential TFs, as well as potential miRNAs, might be involved in DCM.

Gene expression profiles analysis identifies key genes of PBMCs in patients with benign and malignant breast tumor / 中国免疫学杂志

Objective:To observe the changes of gene expression in peripheral blood mononuclear cells( PBMCs) of benign and malignant breast tumor based on gene expression profiling. Methods: Datasets of gene expression profiling were downloaded from the GEO database,including PBMCs profilings of benign breast tumor,breast cancer and healthy controls. GEO2R tool was used to analyze the data to identify the differentially expressed genes (DEGs). Function of DEGs were annotated by DAVID. Protein interaction analysis and hub gene select were then performed using STRING database. Results:563 and 237 DEGs respectively were identified. DEGs in breast cancer involved in biological process of leukocyte activation,angiogenesis and leukocyte transendothelial migration. The hub genes are IL8,RHOB,ITGB1. Conclusion:The data suggests that gene expression patterns of these two profilings are different at a certain degree. PBMCs maybe a better noninvasive material for biomarker detection of benign and malignant breast tumor.

Mechanisms of childhood hemophagocytic lymphohistiocytosis:A bioinformatic analysis / 医学研究生学报

Objective Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening condition characterized by excessive inflammation, with a high incidence in children and a death rate of 40%.This study was to analyze the gene expression profile in child-hood HLH and explore the important pathways of childhood HLH using bioinformatic methods . Methods The childhood HLH gene ex-pression profile data GSE26050 were obtained from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnolo-gy Information.Differentially expressed genes were identified with the GEO 2R online analysis tools released recently .The key pathways of the differentially expressed genes were investigated using the Kyoto Encyclopedia of Genes and Genomes ( KEGG) pathway enrichment a-nalysis. Results A total of 184 differentially expressed genes were identified , 126 upregulated and the other 58 downregulated .They were enriched in 3 pathways, including cytokine-cytokine receptor interaction , hematopoietic cell lineage and NOD-like receptor signaling pathways. Conclusion Bioinformatic tools allow the identification of the key genes and pathways associated with the development and progression of childhood HLH and point out the potential directions for researches on the mechanisms of childhood HLH .