A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model
A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model
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DOI：
10.1038/s41598-019-43091-z
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发表时间：
2019-05-06
影响因子：
4.6
通讯作者：
Aebersold, Ruedi
中科院分区：
文献类型：
--
作者：
Sajic, Tatjana;Ciuffa, Rodolfo;Aebersold, Ruedi
迄今为止，大多数旨在发现基于组织的癌症生物标志物的蛋白质组学研究都比较了病例组和对照组之间所选蛋白质的数量。然而，蛋白质通常与其他蛋白质联合作用以形成定位于特化细胞类型和组织中的特定亚细胞区室中的模块。事实上，蛋白质的亚细胞错误定位已被检测为多种癌细胞中的关键特征。在这里，我们描述了一种基于线粒体的组织生物标志物检测策略！富集倍数（mtFE）评分，其对蛋白质丰度变化以及线粒体和胞质溶胶之间的亚细胞分布变化敏感。mtFE评分整合了来自总细胞裂解物和富含蛋白质的组分的蛋白质丰度数据，并为癌症样品的分类提供了新的信息，这些信息不一定是单独从常规丰度测量中显而易见的。我们将这种新的策略应用于一组野生型和突变型小鼠，这些小鼠具有肝脏受体同源物1（Lrh-1（hep-/-））的肝脏特异性基因缺失，两种品系均含有对照个体以及由二乙基亚硝胺（DEN）诱导的肝癌个体。Lrh-1基因缺失通过线粒体减弱肝细胞中的癌细胞代谢！谷氨酰胺加工我们表明，基于mtFE分数的蛋白质组变化在区分DEN诱导的肝癌与健康肝组织方面优于蛋白质丰度测量，并且对遗传扰动具有独特的鲁棒性。我们验证了所选蛋白质的能力与信息的mtFE分数，以表明在两个独立的小鼠模型的肝细胞癌（HCC）的肝脏恶性变化，从而证明了这种新的方法的生物标志物研究的鲁棒性。总的来说，该方法为癌症生物标志物发现提供了一种新的、敏感的方法，该方法考虑了测试蛋白质的背景信息。
To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondria! fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1(hep-/-)), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondria! glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.