In silico secretome analysis approach for next generation sequencing transcriptomic data.
In silico secretome analysis approach for next generation sequencing transcriptomic data.
复制标题
DOI：
10.1186/1471-2164-12-s3-s14
复制
发表时间：
2011-11-30
期刊：
影响因子：
4.4
通讯作者：
Ranganathan S
中科院分区：
文献类型：
--
作者：
Garg G;Ranganathan S
排泄/分泌蛋白（ESP）在寄生虫感染中发挥着重要作用，因为它们存在于宿主-寄生虫界面并调节宿主免疫系统。对于寄生蠕虫，转录组学已被广泛用于了解寄生的分子基础并开发针对寄生虫感染的新治疗策略。然而，没有一项转录组学研究广泛涵盖 ES 蛋白预测以识别新的治疗靶点，特别是当寄生虫采用非经典分泌途径时。我们开发了一种半自动计算方法，利用下一代测序平台的转录组数据来预测和注释 ES 蛋白。为了预测非经典分泌蛋白，我们使用了改进的计算策略，以及与实验确定的寄生蠕虫 ES 蛋白数据集的同源匹配。我们应用该方案分析了寄生线虫（Strongyloidesratti）的 454 个短读段。从 296231 个读数中，我们得到了 28901 个重叠群，它们被翻译成 20877 个蛋白质。基于我们改进的ES蛋白预测流程，我们鉴定了2572个ES蛋白，其中407个（1.9％）蛋白具有经典的N端信号肽，923个（4.4％）通过计算鉴定为非经典分泌，而1516个（7.26％）通过与实验鉴定的寄生虫ES蛋白同源性鉴定。在 2572 个 ES 蛋白中，2310 个 (89.8%) ES 蛋白在自由生活的线虫秀丽隐杆线虫中具有同源性，2220 个 (86.3%) 在寄生线虫中具有同源性。我们可以用蛋白质家族和结构域对 1591 个 (61.8%) ES 蛋白质进行功能注释，并为 691 个 (26.8%) 蛋白质建立通路关联。此外，我们还鉴定了 19 种代表性 ES 蛋白作为潜在的治疗靶点，它们在宿主生物体中没有同源物，但与线虫中的致死性 RNAi 表型同源。我们报告了一种使用免费计算工具对 NGS 数据进行分泌组分析的综合方法。该方法已应用于 S.ratti 454 转录组数据，用于计算机排泄/分泌蛋白预测和分析，为开发寄生虫感染的新治疗解决方案奠定了基础。
Excretory/secretory proteins (ESPs) play a major role in parasitic infection as they are present at the host-parasite interface and regulate host immune system. In case of parasitic helminths, transcriptomics has been used extensively to understand the molecular basis of parasitism and for developing novel therapeutic strategies against parasitic infections. However, none of transcriptomic studies have extensively covered ES protein prediction for identifying novel therapeutic targets, especially as parasites adopt non-classical secretion pathways. We developed a semi-automated computational approach for prediction and annotation of ES proteins using transcriptomic data from next generation sequencing platforms. For the prediction of non-classically secreted proteins, we have used an improved computational strategy, together with homology matching to a dataset of experimentally determined parasitic helminth ES proteins. We applied this protocol to analyse 454 short reads of parasitic nematode, Strongyloides ratti. From 296231 reads, we derived 28901 contigs, which were translated into 20877 proteins. Based on our improved ES protein prediction pipeline, we identified 2572 ES proteins, of which 407 (1.9%) proteins have classical N-terminal signal peptides, 923 (4.4%) were computationally identified as non-classically secreted while 1516 (7.26%) were identified by homology to experimentally identified parasitic helminth ES proteins. Out of 2572 ES proteins, 2310 (89.8%) ES proteins had homologues in the free-living nematode Caenorhabditis elegans and 2220 (86.3%) in parasitic nematodes. We could functionally annotate 1591 (61.8%) ES proteins with protein families and domains and establish pathway associations for 691 (26.8%) proteins. In addition, we have identified 19 representative ES proteins, which have no homologues in the host organism but homologous to lethal RNAi phenotypes in C. elegans, as potential therapeutic targets. We report a comprehensive approach using freely available computational tools for the secretome analysis of NGS data. This approach has been applied to S. ratti 454 transcriptomic data for in silico excretory/secretory proteins prediction and analysis, providing a foundation for developing new therapeutic solutions for parasitic infections.