Integrating UPLC-QTOF-MS Metabolomics and Multiple Bioactivity Phenotypes Reveals Shared Chemical Markers in Fabaceae

This study presents a KNIME-based biochemometric workflow integrating UPLC-QTOF-MS metabolomics data with three biological responses (DPPH, IL-10, and PGE₂) from 47 Fabaceae extracts. The combination of four complementary statistical approaches enabled the prioritization of molecular features, the identification of three features shared across all three biological endpoints, and four tentative chemical annotations through MS/MS analysis. These findings highlight the potential of this integrated, reproducible, and adaptable strategy to guide chemical dereplication and the investigation of potentially bioactive metabolites in complex natural product extracts.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-31
DOI
https://doi.org/10.5281/zenodo.23840928
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
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article

Integrating UPLC-QTOF-MS Metabolomics and Multiple Bioactivity Phenotypes Reveals Shared Chemical Markers in Fabaceae

Cibele Aparecida Cesila, Michael Murgu, Miller Santos Ferreira, Danielle Ferreira Dias et al.
Zenodo (CERN European Organization for Nuclear Research)
Metabolomics and Mass Spectrometry Studies
article

Integrating UPLC-QTOF-MS Metabolomics and Multiple Bioactivity Phenotypes Reveals Shared Chemical Markers in Fabaceae

Cibele Aparecida Cesila, Michael Murgu, Miller Santos Ferreira, Danielle Ferreira Dias, João Henrique G. Lago, Milbya Lima Costa, Mário F. C. Santos, Daniela Aparecida Chagas‐Paula, Welton Rosa, Albert Katchborian‐Neto, Marisi Gomes Soares, Renato Almeida de Oliveira, Vinícius Fortes Silva
article en

Abstract

This study presents a KNIME-based biochemometric workflow integrating UPLC-QTOF-MS metabolomics data with three biological responses (DPPH, IL-10, and PGE₂) from 47 Fabaceae extracts. The combination of four complementary statistical approaches enabled the prioritization of molecular features, the identification of three features shared across all three biological endpoints, and four tentative chemical annotations through MS/MS analysis. These findings highlight the potential of this integrated, reproducible, and adaptable strategy to guide chemical dereplication and the investigation of potentially bioactive metabolites in complex natural product extracts.

Zenodo (CERN European Organization for Nuclear Research)
Federal University of Technology (NG), Universidade Federal de Alfenas (BR), Instituto Federal Fluminense (BR), Universidade Federal do Espírito Santo (BR), Universidade Federal do ABC (BR)
Openalex Percentile: Top 22%
Metabolomics and Mass Spectrometry Studies
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