小麦锈菌蛋白激酶基因家族的鉴定与生物信息学分析

为深入分析锈菌结构基因组,阐明锈菌毒性变异的分子机制,本研究通过生物信息分析方法,对3种小麦锈菌蛋白激酶(protein kinases,PKs)超家族预测基因进行了系统分析,利用COG(clusters of orthologous groups of proteins)、KEGG(Kyoto encyclopedia of genes and genomes)、GO(gene ontology)和PHI(pathogen-host interactions)数据库进行注释分析,并对小麦锈菌MAPK基因的蛋白质互作网络进行预测。结果表明,条锈菌Puccinia striiformis、叶锈菌P.triticina和秆锈菌P.graminis的PKs基因数量分别为221、159和159个,不同PKs基因家族类型在3种锈菌中的数量分布上表现出很高的保守性。注释分析表明,PKs家族预测功能涉及病原菌生长发育中的调控作用和病原菌-寄主互作机制,包括信号传导和致病因子等。PKs家族核心基因数目在蛋白激酶基因中占比大于1/3。MAPK基因的催化结构域序列呈现高度相似性。以STRING数据库的酿酒酵母蛋白质互作网络信息为参考,对小麦锈菌MAPK基因的蛋白质互作网络进行预测,共鉴定出25个互作关系,包含29个MAPK基因。研究表明这3种锈菌之间MAPK互作蛋白质分布不均衡,这可能反映了锈菌基因组进化的特殊复杂性。

Identification and bioinformatics analysis for protein kinases of the wheat rust fungi

Wheat rusts caused by Puccinia striiformis, P. triticina and P. graminis, which often lead to serious yield losses of wheat, are the most important wheat diseases worldwide. In order to deeply analyze the structural genome for the wheat rust fungi and elucidate their molecular mechanism of pathogenicity variation, this study was performed by bioinformatics approaches to annotate and systematically dissect the gene superfamily coding for protein kinases of the wheat rust fungi, annotated by COG, KEGG, GO and PHI databases. Furthermore, a predicted protein-protein interaction network of MAPK genes for these rust fungi was established with the bioinformatics data. In total, 221, 159 and 159 genes coding protein kinases for P. striiformis, P. triticina and P. graminis were identified respectively, and the number distribution of different categories showed a high conservatism among them. The annotation results indicated that the protein kinase superfamily might play pivotal regulatory role in processes of growth and development, also the host-pathogen interactions, including signal transduction and pathogenic factors. The core gene set analysis of protein kinases revealed that the core genes accounted for more than a third of all proteins kinases. MAPK analysis found that the catalytic structure of MAPK genes showed high similarity. The protein-protein interaction network of MAPK genes was predicted using Saccharomyces cerevisiae as a reference through STRING database and 25 interaction relationships including 29 MAPK genes were identified out. The overall analysis suggested that MAPK interaction of rusts was unbalanced in protein distribution, which might reflect the complexity of the evolution for rust genomes.