Enrichment-Free Deep Proteomics Enables Proteome-Scale Analysis of Methionine Oxidation.

Authors

Hiromasa Mitsui, Yusei Okuda, Ryo Konno, Daisuke Nakajima, Osamu Ohara, Yusuke Kawashima

Published in

DNA research : an international journal for rapid publication of reports on genes and genomes. Jul 18, 2026. Epub Jul 18, 2026.

Abstract

Advances in mass spectrometry (MS)-based proteomics have enabled the large-scale characterization of posttranslational modifications (PTMs) through affinity-based enrichment. However, this technique introduces a bias towards selectively enrichable modifications, thus, leaving oxidative modifications underexplored. Methionine oxidation (methionine sulfoxide) is an important indicator of cellular redox status, but its systematic analysis remains challenging because no enrichment method is available and artifactual oxidation can occur during sample preparation. Here, we developed an enrichment-free proteomic strategy for large-scale detection of methionine oxidation using a deep LC-MS platform. By optimizing acquisition conditions, we identified more than 260k precursors in a single-shot analysis. Under these conditions, methionine oxidation was efficiently detected, whereas many other PTMs remained poorly detected. To improve data reliability, we established a sample preparation workflow that minimized artifactual oxidation. Accordingly, we identified more than 3,500 methionine-oxidized proteins. Integration of methionine oxidation and expression proteomics across subcellular compartments revealed redox patterns under low-serum conditions, including increased mitochondrial oxidation and decreased endoplasmic reticulum oxidation. These changes are associated with metabolic reprogramming and altered antioxidant capacity. Overall, this study established an enrichment-free framework for the proteome-scale methionine oxidation analysis, and demonstrated that integrating oxidation and expression data enables the spatially resolved interpretation of cellular redox states.

PMID:
42470119
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.