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水稻中过敏原蛋白全基因组预测及生物学功能分析 被引量:6

Genome-wide Prediction and Biological Function Analysis of Allergens Protein in Rice
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摘要 世界范围内过敏症状的流行趋势日益严峻,水稻作为单子叶模式植物和人类主要粮食作物,对水稻中致敏性物质的研究具有重要意义。但目前基于水稻全基因组水平预测和分析过敏原的研究尚缺少相关报道。本研究利用生物信息学方法在全基因组层面上,在水稻基因组数据库中和过敏原数据库中,共筛选和获得了657个过敏原基因/蛋白(属于109个蛋白家族),并进一步对这些候选的水稻过敏原基因/蛋白进行了表达模式、家族分类和功能预测分析;以及在进化上分析了水稻过敏原基因/蛋白家族成员的同源蛋白在低等到高等植物中分布情况,结果表明致敏性可能随着基因家族中成员的功能分化而变化;进一步结合水稻花粉和种子转录组芯片数据,细化分析和获得了148个水稻花粉过敏原和116个水稻种子过敏原,并在基因家族和功能分类上进行了对比分析。本研究结果初步明确了水稻过敏原基因/蛋白家族的进化特点、组织表达特性和生物学功能,为进一步研究水稻以及其他单子叶植物中的过敏原奠定了基础。 Allergies have long been a major pandemic health problem worldwide. As Rice is monocot model plant and one of the important food crops, the research of allergens in rice is of high significance. However, systematic studies of allergens in rice on whole-genome level are still lacking. In this study, genome-wide prediction and screening of allergens were performed. Based on allergen databases and genome information of rice, 657 putative and known allergens were obtained by using bioinformatics eventually. Then, analysis on expression pattern, protein family, and putative function of allergens were performed. Meanwhile, taxonomic distribution of allergen homologues from lower plants to higher plants was analyzed, which implying that allergenicity of allergens may change along with functional specification of protein families. Furthermore, 148 pollen allergens and 116 seed allergens were screened by transcriptomic data in rice and then their protein families and functional classification were compared. These researches have initially elucidated the evolutionary traits, expression patterns and biological function of rice allergens will provide new information to promote future study of allergens in rice and other monocots.
作者 陈淼林 许杰 Chen Miaolin Xu Jie(School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 20024)
出处 《基因组学与应用生物学》 CAS CSCD 北大核心 2016年第9期2456-2466,共11页 Genomics and Applied Biology
基金 国家自然科学基金面上项目(31370026 31570312和31500297)资助
关键词 水稻 预测 过敏原 转录组 功能分析 Rice, Allergen, Prediction, Transcriptome, Functional analysis
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