肾结石疾病相关蛋白的大数据分析

Analysis of large proteins data associated kidney stone disease

目的本研究拟利用大数据分析方法发现影响肾结石形成的核心蛋白。方法收集已发现的人类肾结石相关蛋白质信息,组入人类蛋白质相互作用数据库,构建肾结石蛋白质相互作用网络,通过网络拓扑分析方法计算Node Degree和Betweeness Centrality,获得蛋白在相互作用网络中的重要程度排序,执行聚类分析显示这些蛋白参与信号通路情况,根据蛋白排序和参与信号通路的数量确定核心蛋白。Microarray数据分析验证核心蛋白基因在肾结石患者的表达情况。进一步使用SIFT,PolyPhen2,phD-SNP和MutPred四种生物学软件分析核心蛋白基因的致病性nsSNPs。结果构建一个包含812个蛋白质的肾结石相互作用网络,拓扑分析获得其中32(3.9%)个关键蛋白,聚类分析发现这些关键蛋白参与55条信号通路,根据蛋白重要程度及参与信号通路的多少对核心蛋白进行排序,发现MAPK1是一个最核心蛋白,参与其中34条信号通路。Microarray数据分析发现MAPK1在肾结石患者肾乳头组织中下调表达。In silico分析识别MAPK1基因中有3个高可信度的致病性ns SNPs。结论MAPK1是一个肾结石疾病相互作用蛋白网络的核心蛋白,在肾结石患者中下调表达,其高致病性nsSNP可能影响MAPK1与疾病的风险。我们的发现可能为肾结石治疗新药开发提供一个新的靶点,并为今后更深入的肾结石机制研究提供基础。

Objective To find the core protein affecting the kidney stone formation using the method of large data analysis. Methods A protein-protein interaction （PPI） network of kidney stone was constructed using reported proteins. The network topology including Node Degree and Belweeness Centrality obtained importance of protein in the network by sorting using the shortest path calculation method. Functional enrichment analysis was performed to identify the signaling pathway. Microarray data was used to analyze the gene expression in kidney stones patients. The deleterious nsSNPs of the core gene were predicted using SIFT, PolyPhen2, PhD-SNP and MutPred. Results A total of 812 proteins were integrated into a kidney stone network. The enrichment analysis showed that the proteins were enriched in 98 pathways. The proteins were sorted according to the number of involving in pathways, MAPK1 was identified as a core protein which is involving in the most pathways. MAPK1 was downregulated in renal papillary tissue of kidney stone patients. Three disease- associated nsSNPs of MAPK1 were predicted in silico. Conclusions MAPK1 is a core protein of protein interaction network of kidney stone. Our findings might provide a new target for the treatment of kidney stone, and provide a basis for further studies on the mechanism of kidney stone.