Depletion-dependent activity-based protein profiling using SWATH/DIA-MS detects serine hydrolase lipid remodeling in lung adenocarcinoma progression

Tatjana Sajic (), Matej Vizovišek, Stephan Arni, Rodolfo Ciuffa, Martin Mehnert, Sébastien Lenglet, Walter Weder, Hector Gallart-Ayala, Julijana Ivanisevic, Marija Buljan, Aurelien Thomas, Sven Hillinger () and Ruedi Aebersold ()
Additional contact information
Tatjana Sajic: ETH
Matej Vizovišek: ETH
Stephan Arni: University Hospital Zurich (UHZ)
Rodolfo Ciuffa: ETH
Martin Mehnert: ETH
Sébastien Lenglet: Lausanne
Walter Weder: University Hospital Zurich (UHZ)
Hector Gallart-Ayala: Quartier UNIL-CHUV
Julijana Ivanisevic: Quartier UNIL-CHUV
Marija Buljan: Swiss Federal Laboratories for Materials Science and Technology
Aurelien Thomas: University of Lausanne
Sven Hillinger: University Hospital Zurich (UHZ)
Ruedi Aebersold: ETH

Nature Communications, 2025, vol. 16, issue 1, 1-24

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-59564-x

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