应用 PPIN分析肝移植临床耐受患者PBMC基因表达特点

应用生物信息学方法分析肝移植临床耐受患者PBMC基因表达特征,筛选临床耐受关键基因。从GEO数据库获取19个肝移植临床耐受病例及22个非临床耐受病例基因表达谱数据。应用DAVID网络软件进行差异基因功能注释与聚类分析;通过Cytoscape软件的MiMI插件构建蛋白质相互作用网络(PPIN)筛选肝移植临床耐受关键基因。差异基因涉及蛋白质及RNA代谢、免疫应答、膜结构调节等复杂生物过程。PPIN网络分析获得10个临床耐受核心基因。我们的研究表明:肝移植临床耐受涉及外周血免疫细胞复杂的基因表达调控机制及蛋白质间相互作用;RNA的转录后加工及蛋白质降解在免疫耐受的形成中发挥了重要作用;RBM8A、DHX9、CBL、IKBKB、CSNK2A1、HSPA8等核心基因发挥重要的免疫调节功能。     

Deciphering key genes in cardio-renal syndrome using network analysis

Cardio-renal syndrome (CRS) is a rapidly recognized clinical entity which refers to the inextricably connection between heart and renal impairment, whereby abnormality to one organ directly promotes deterioration of the other one. Biological markers help to gain insight into the pathological processes for early diagnosis with higher accuracy of CRS using known clinical findings. Therefore, it is of interest to identify target genes in associated pathways implicated linked to CRS. Hence, 119 CRS genes were extracted from the literature to construct the PPIN network. We used the MCODE tool to generate modules from network so as to select the top 10 modules from 23 available modules. The modules were further analyzed to identify 12 essential genes in the network. These biomarkers are potential emerging tools for understanding the pathophysiologic mechanisms for the early diagnosis of CRS. Ontological analysis shows that they are rich in MF protease binding and endo-peptidase inhibitor activity. Thus, this data help increase our knowledge on CRS to improve clinical management of the disease.     

Functional combination strategy for prioritization of human miRNA target

MicroRNAs (miRNAs) are a class of non-coding RNAs known to play important regulatory roles through targets, which can affect human cell proliferation, differentiation, and metabolism. Overlaps between different miRNA target prediction algorithms (MTPAs) are small, which limit the understanding of miRNA's biological functions. However, the overlaps increase on functional levels, such as Gene Ontology (GO), Protein–Protein Interaction Network (PPIN) and pathways. Here, we performed prioritization on existing predicted target sets for each miRNA by considering all the possible combinations of 7 functional levels. After analyzing the results of both single and multiple functional levels, we found that functional combination strategies including pathways and GO performed better in the prioritization of human miRNA target. The combination which performed best was “Pathway + GO BP + GO MF + GO CC + Target + PPIN”. For the prioritized result of this combination, the valid target had top ranking, and our method performed better than the MTPAs after comparison adopting the validated ranking levels. Top genes in ranking lists generated by this strategy were either validated by experiments or share same functions with the corresponding miRNA/its validated genes in disease related biological processes.