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.     

苯扎贝特治疗儿童ApoE Tokyo(Arg25Cys)突变型脂蛋白肾病1例

脂蛋白肾病(Lipoprotein glomerulopathy,LPG),1989年首次由日本学者Saito报道,LPG主要累及肾脏,且以肾小球病变为主[1]。几乎所有患者均有不同程度的蛋白尿,多数表现为肾病综合征,少数表现为轻微蛋白尿和镜下血尿,部分患者伴有不同程度的贫血及高血压,血脂异常易被忽略为肾病综合征的低蛋白血症所致。载脂蛋白E(apolipoprotein E,ApoE)增高是LPG血脂改变的主要特点[2-3]。LPG为一种与脂质代谢紊乱密切相关的肾脏疾病,目前世界范围内有报道的病例不足200例,儿童报道仅10余例[2]。本病进展缓慢,临床常误诊为原发性肾病综合征[4]。因此,为增强对LPG的认识,提高诊治水平,现分析1例确诊的儿童LPG临床资料,总结LPG的临床特点、诊断、治疗及预后。     

血流感染肺炎克雷伯菌中CRISPR-Cas系统的分布及其与毒力基因和耐药的关系

的 了解血流感染肺炎克雷伯菌中CRISPR-Cas系统的分布特征并分析其与毒力基因和耐药的关系。方法 收集非重复血流感染肺炎克雷伯菌248株，使用Vitek2-Compact全自动微生物分析系统进行菌株鉴定及药物敏感性分析，PCR检测CRISPR-Cas系统3个相关基因(CRISPR1、CRISPR2和cas1)、筛查6种常见高毒力荚膜血清型(K1、K2、K5、 K20、K54和K57)、12种毒力基因及检测13种耐药基因，用χ2检验比较携带有CRISPR-Cas系统菌株与不携带CRISPR-Cas系统菌株毒力及耐药差异。结果 CRISPR-Cas系统的检出率为29.8%(74/248)；K1型是携带CRISPR-Cas系统肺炎克雷伯菌的主要荚膜血清型，占 28.4%(21/74)；除kpn基因外，携带CRISPR-Cas系统菌株的毒力基因检出率均大于不携带CRISPR-Cas系统菌株，其中7种差异有统计学意义；除对氨苄西林耐药率达100%外，携带有CRISPR-Cas系统菌株的其他抗菌药物耐药率均小于不携带有CRISPR-Cas系统菌株，其中13种差异有统计学意义；携带CRISPR-Cas系统菌株的耐药基因阳性率小于不携带CRISPR-Cas系统的菌株，且blaKPC、blaSHV、qnrS基因差异有统计学意义。结论 高毒力荚膜血清型肺炎克雷伯菌中主要为K1型携带CRISPR-Cas系统，携带CRISPR-Cas系统的肺炎克雷伯菌相对于不携带CRISPR-Cas系统菌株的毒力基因阳性率高，耐药率低，耐药基因的阳性率低。CRISPR-Cas系统可能能降低耐药基因在肺炎克雷伯菌中的水平传播，尤其是在K1型肺炎克雷伯菌。     

Is low or high body mass index in patients operated for oral squamous cell carcinoma associated with the perioperative complication rate?

The aim of this study was to analyse the effect of body mass index (BMI), both low and high values, on the perioperative complication rate in patients with oral squamous cell carcinoma (OSCC). The medical records of 259 patients operated between 2014 and 2017 for OSCC were reviewed. Univariate and multivariate analyses were performed. Sixty of the 259 patients developed 87 complications. Low or high BMI was not associated with the perioperative complication rate. A longer operating time and increased blood loss were associated with a higher perioperative complication rate and higher Clavien–Dindo grade. Low BMI, American Society of Anesthesiologists score 2 and 3, a longer operating time, and increased blood loss were associated with a longer hospital stay. Low BMI was associated with a longer hospital stay. Neither low nor high BMI was associated with the perioperative complication rate. A longer operating time and increased blood loss were associated with a higher perioperative complication rate and higher Clavien–Dindo grade.     

Identification of key genes and pathways in discoid lupus skin via bioinformatics analysis

Discoid lupus erythematosus (DLE) is the most common skin manifestation of lupus; however, the molecular mechanisms underlying DLE remain unknown. Therefore, we aimed to identify key differentially expressed genes (DEGs) in discoid lupus skin and investigate their potential pathways.To identify candidate genes involved in the occurrence and development of the disease, we downloaded the microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE52471","term_id":"52471"}}GSE52471 and {"type":"entrez-geo","attrs":{"text":"GSE72535","term_id":"72535"}}GSE72535 from the Gene Expression Database (GEO). DEGs between discoid lupus skin and normal controls were selected using the GEO2R tool and Venn diagram software (http://bioinformatics.psb.ugent.be/webtools/Venn/). The Database for Annotation, Visualization, and Integrated Discovery (DAVID), Enrichr, and Cytoscape ClueGo were used to analyze the Kyoto Encyclopedia of Gene and Genome pathways and gene ontology. Protein-protein interactions (PPIs) of these DEGs were further assessed using the Search Tool for the Retrieval Interacting Genes version 10.0.Seventy three DEGs were co-expressed in both datasets. DEGs were predominantly upregulated in receptor signaling pathways of the immune response. In the PPI network, 69 upregulated genes were selected. Furthermore, 4 genes (CXCL10, ISG15, IFIH1, and IRF7) were found to be significantly upregulated in the RIG-I-like receptor signaling pathway, from analysis of Enrichr and Cytoscape ClueGo.The results of this study may provide new insights into the potential molecular mechanisms of DLE. However, further experimentation is required to confirm these findings.     

Efficient polymer nanoparticle-mediated delivery of gene editing reagents into human hematopoietic stem and progenitor cells

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