Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics

Kevin J. Murray, Erik S. Carlson, Alessia Stornetta, Emily P. Balskus, Peter W Villalta, Silvia Balbo

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Development of high-resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2′-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic fragmentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation patterns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small-molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest.

Original languageEnglish (US)
Pages (from-to)5754-5762
Number of pages9
JournalAnalytical Chemistry
Volume93
Issue number14
DOIs
StatePublished - Apr 13 2021

Bibliographical note

Funding Information:
The authors would like to thank Dr. Romel Dator and Dr. Morwena Solivio of Masonic Cancer Center of the University of Minnesota for their comments and recommendations during workflow testing and Dr. Ansgar Korf for advice developing an MZmine module. This work was funded in part by NIH grants R01 CA220376, R01 CA208834, and F32 CA254165, the Packard Fellowship for Science and Engineering, and the Damon Runyon-Rachleff Innovation Award. Salary support for P.W.V. was provided by the National Cancer Institute (R50-CA211256) and mass spectrometry analysis was supported by the Masonic Cancer Center’s National Cancer Institute support grant CA077598.

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