Sajic Tatjana, Vizovišek Matej, Arni Stephan, Ciuffa Rodolfo, Mehnert Martin, Lenglet Sébastien, Weder Walter, Gallart-Ayala Hector, Ivanisevic Julijana, Buljan Marija, Thomas Aurelien, Hillinger Sven, Aebersold Ruedi
Department of Biology, Institute of Molecular Systems Biology, ETH, Zurich, Switzerland.
Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
Nat Commun. 2025 May 27;16(1):4889. doi: 10.1038/s41467-025-59564-x.
Systematic inference of enzyme activity in human tumors is key to understanding cancer progression and resistance to therapy. However, standard protein or transcript abundances are blind to the activity status of the measured enzymes, regulated, for example, by active-site amino acid mutations or post-translational protein modifications. Current methods for activity-based proteome profiling (ABPP), which combine mass spectrometry (MS) with chemical probes, quantify the fraction of enzymes that are catalytically active. Here, we describe depletion-dependent ABPP (dd-ABPP) combined with automated SWATH/DIA-MS, which simultaneously determines three molecular layers of studied enzymes: i) catalytically active enzyme fractions, ii) enzyme and background protein abundances, and iii) context-dependent enzyme-protein interactions. We demonstrate the utility of the method in advanced lung adenocarcinoma (LUAD) by monitoring nearly 4000 protein groups and 200 serine hydrolases (SHs) in tumor and adjacent tissue sections routinely collected for patient histopathology. The activity profiles of 23 SHs and the abundance of 59 proteins associated with these enzymes retrospectively classified aggressive LUAD. The molecular signature revealed accelerated lipoprotein depalmitoylation via palmitoyl(protein)hydrolase activities, further confirmed by excess palmitate and its metabolites. The approach is universal and applicable to other enzyme families with available chemical probes, providing clinicians with a biochemical rationale for tumor sample classification.
系统推断人类肿瘤中的酶活性是理解癌症进展和治疗抗性的关键。然而，标准的蛋白质或转录本丰度对所测酶的活性状态视而不见，而酶的活性状态受例如活性位点氨基酸突变或蛋白质翻译后修饰的调控。当前基于活性的蛋白质组分析（ABPP）方法将质谱（MS）与化学探针相结合，可量化具有催化活性的酶的比例。在此，我们描述了与自动化SWATH/DIA-MS相结合的消耗依赖性ABPP（dd-ABPP），它能同时确定所研究酶的三个分子层面：i）具有催化活性的酶组分，ii）酶和背景蛋白丰度，以及iii）依赖于环境的酶-蛋白相互作用。我们通过监测为患者组织病理学常规采集的肿瘤和相邻组织切片中的近4000个蛋白质组和200种丝氨酸水解酶（SHs），证明了该方法在晚期肺腺癌（LUAD）中的实用性。23种SHs的活性谱以及与这些酶相关的59种蛋白质的丰度对侵袭性LUAD进行了回顾性分类。分子特征揭示了通过棕榈酰（蛋白）水解酶活性加速脂蛋白去棕榈酰化，过量的棕榈酸及其代谢物进一步证实了这一点。该方法具有通用性，适用于有可用化学探针的其他酶家族，为临床医生提供了肿瘤样本分类的生化依据。
