Organellar proteome analyses of ricin toxin-treated HeLa cells
Organellar proteome analyses of ricin toxin-treated HeLa cells
复制标题
蓖麻毒素处理的 HeLa 细胞的细胞器蛋白质组分析
DOI：
10.1177/0748233714549066
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发表时间：
2016-07-01
影响因子：
1.9
通讯作者：
Liu, Wensen
中科院分区：
文献类型：
--
作者：
Liao, Peng;Lie, Yunhu;Liu, Wensen
蓖麻毒素（RT）引发的细胞凋亡以前已与某些细胞器的隔间，但在组成的细胞器蛋白的多样性仍然不清楚。在这里，我们应用鸟枪蛋白质组学策略，研究蛋白质的差异表达的HeLa细胞的线粒体，细胞核和细胞质的处理和不处理RT。数据相结合的全球生物信息学分析和实验证实。共鉴定出3107个蛋白质。生物信息学预测因子（Proteome Analyst、WoLF PSORT、TargetP、MitoPred、Nucleo、MultiLoc和k-最近邻）和整合这些预测因子的贝叶斯模型用于预测1349种不同细胞器蛋白的位置。我们的数据表明，贝叶斯模型比这些预测因子的单独实现更有效。此外，使用生物分子相互作用网络（BIN）分析来识别149个BIN子网络。我们的实验证实表明，某些凋亡相关蛋白（如细胞色素c，烯醇化酶，核纤层蛋白B，Bax和Drp 1）被发现易位，并具有可变的表达水平。这些结果为系统了解RT诱导的细胞凋亡反应提供了新的见解。
Apoptosis triggered by ricin toxin (RT) has previously been associated with certain cellular organellar compartments, but the diversity in the composition of the organellar proteins remains unclear. Here, we applied a shotgun proteomics strategy to examine the differential expression of proteins in the mitochondria, nuclei, and cytoplasm of HeLa cells treated and not treated with RT. Data were combined with a global bioinformatics analysis and experimental confirmations. A total of 3107 proteins were identified. Bioinformatics predictors (Proteome Analyst, WoLF PSORT, TargetP, MitoPred, Nucleo, MultiLoc, and k-nearest neighbor) and a Bayesian model that integrated these predictors were used to predict the locations of 1349 distinct organellar proteins. Our data indicate that the Bayesian model was more efficient than the individual implementation of these predictors. Additionally, a Biomolecular Interaction Network (BIN) analysis was used to identify 149 BIN subnetworks. Our experimental confirmations indicate that certain apoptosis-related proteins (e.g. cytochrome c, enolase, lamin B, Bax, and Drp1) were found to be translocated and had variable expression levels. These results provide new insights for the systematic understanding of RT-induced apoptosis responses.