Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.     

糖尿病前期中医证型及证素特点分析＊

目的 探讨糖尿病前期的中医证型及证素分布特点。方法 检索中国知网、万方及维普三大数据库中收录的自建库以来有关糖尿病前期证型的临床研究文献，对中医证型进行规范整理，建立数据库，提取证素，运用数据挖掘技术中的关联分析、聚类分析探究证素分布规律。结果 共纳入10篇文献，总有效病例1620例，证型经规范处理后整理为18个，主要证型为脾虚痰湿证。共提取证素13个，主要病位证素为脾，主要病性证素为气虚、湿和痰，关联分析显示脾—湿支持度和置信度最高，聚类分析结果可得到3个聚类组。结论 糖尿病前期病位在脾，气虚、脾、痰、湿是常见证素，临床诊治糖尿病前期应注重从脾论治，需辨证施治。