Rao Prahlad K, Li Qingbo
Center for Pharmaceutical Biotechnology, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois 60607, USA.
J Proteomics Bioinform. 2009 Jan 15;2(1):19-31. doi: 10.4172/jpb.1000058.
The goal of this study is to use principal component analysis (PCA) for multivariate analysis of proteome dynamics based on both protein abundance and turnover information generated by high-resolution mass spectrometry. We previously reported assessing protein dynamics in iron-starved Mycobacterium tuberculosis, revealing interesting interconnection among the cellular processes involving protein synthesis, degradation, and secretion (Anal. Chem. 80, 6860-9). In this study, we use target-decoy database search approach to select peptides for quantitation at a false discovery rate of 4.2%. We further use PCA to reduce the data dimensions for simpler interpretation. The PCA results indicate that the protein turnover and relative abundance properties are approximately orthogonal in the data space defined by the first three principal components. We show the potential of the Hotelling's T2 (T2) value as a quantifiable index for comparing changes between protein functional categories. The T2 value represents the gross change of a protein in both abundance and turnover. Close examination of the antigen 85 complex demonstrates that T2 correctly predicts the coordinated changes of the antigen 85 complex proteins. The multi-dimensional protein dynamics data further reveal the secretion of the antigen 85 complex. Overall, this study demonstrates PCA as an effective means to facilitate interpretation of the multivariate proteome dynamics dataset which otherwise would remain a significant challenge using traditional methods.
本研究的目标是基于高分辨率质谱产生的蛋白质丰度和周转率信息，使用主成分分析（PCA）对蛋白质组动力学进行多变量分析。我们之前报道了评估铁饥饿结核分枝杆菌中的蛋白质动力学，揭示了涉及蛋白质合成、降解和分泌的细胞过程之间有趣的相互联系（《分析化学》80, 6860 - 6869）。在本研究中，我们使用目标 - 诱饵数据库搜索方法以4.2%的错误发现率选择用于定量的肽段。我们进一步使用PCA来降低数据维度以便于更简单的解释。PCA结果表明，在前三个主成分定义的数据空间中，蛋白质周转率和相对丰度属性大致正交。我们展示了霍特林T2（T2）值作为比较蛋白质功能类别之间变化的可量化指标的潜力。T2值代表蛋白质在丰度和周转率方面的总体变化。对抗原85复合物的仔细检查表明，T2正确地预测了抗原85复合物蛋白的协同变化。多维蛋白质动力学数据进一步揭示了抗原85复合物的分泌。总体而言，本研究证明PCA是促进对多变量蛋白质组动力学数据集进行解释的有效手段，否则使用传统方法将仍然是一项重大挑战。
