Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague.
Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague.
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蛋白质的丰度可以区分鼠疫的耶尔森氏菌（耶尔森尼亚耶尔森尼亚（Yersinia Pestis））的自然菌株和实验室菌株。
DOI：
10.1371/journal.pone.0183478
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Kreuzer HW
中科院分区：
文献类型：
--
作者：
Merkley ED;Sego LH;Lin A;Leiser OP;Kaiser BLD;Adkins JN;Keim PS;Wagner DM;Kreuzer HW
细菌进化的快速步伐使生物体能够通过反复传代来适应实验室环境，从而与天然存在的环境（“野生”）菌株不同。区分野生和实验室菌株对于生物防御和生物法医学显然是重要的;然而，DNA序列数据迄今为止还没有提供明确的特征，这可能是由于缺乏对不同基因组变化如何导致趋同表型的理解，难以检测某些类型的突变，或者可能是因为一些适应性修饰是表观遗传的。监测蛋白质丰度，一种表型的分子测量方法，可以克服其中的一些困难。我们已经从我们自己发表和未发表的工作以及蛋白质组学数据档案中收集了鼠疫耶尔森菌蛋白质组学数据集，并证明蛋白质丰度数据可以清楚地区分实验室适应性和野生性。我们开发了一种套索逻辑回归分类器，该分类器使用二元（存在/不存在）或定量蛋白质丰度测量来预测样品是实验室适应的还是野生的，通过重复的10倍交叉验证判断，该分类器被证明准确率约为98%。分类器选择的蛋白质特征雅阁与我们以前在Y.鼠疫输入数据来自各种不相关的实验，并包含显著的混杂变量。我们表明，分类器是强大的，这些变量。该方法能够发现实验室设施和培养基的签名，这些签名在很大程度上独立于实验室适应的签名。超越我们以前的实验室进化研究，这项工作表明，实验室适应和野生Y。鼠疫是普遍的，可能指向一个也适用于其他物种的过程。此外，我们表明，蛋白质组学数据集（甚至是出于不同目的收集的存档数据）包含区分野生和实验室样本所需的信息。这项工作在生物标志物检测和生物防御方面有明显的应用。
The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental (“wild”) strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in Y. pestis. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild Y. pestis are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.
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ProteinLasso：鸟枪法蛋白质组学中蛋白质推理问题的套索回归方法
影响因子：
3.1
作者：
Huang, Ting;Gong, Haipeng;He, Zengyou
通讯作者：
He, Zengyou
影响因子：
2.2
作者：
Lin, Andy;Merkley, Eric D.;Kreuzer, Helen W.
通讯作者：
Kreuzer, Helen W.
影响因子：
5.6
作者：
Ma NL;Rahmat Z;Lam SS
通讯作者：
Lam SS
影响因子：
3.4
作者：
Cooper TF;Lenski RE
通讯作者：
Lenski RE
DOI：
10.1002/prca.201400107
发表时间：
2015-04
期刊：
Proteomics. Clinical applications
影响因子：
--
作者：
Griss J;Perez-Riverol Y;Hermjakob H;Vizcaíno JA
通讯作者：
Vizcaíno JA