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A Chemical Structure and Machine Learning Approach to Assess the Potential Bioactivity of Endogenous Metabolites and Their Association with Early Childhood Systemic Inflammation.
Lovric, Mario; Wang, Tingting; Staffe, Mads Rønnow; Sunic, Iva; Casni, Kristina; Lasky-Su, Jessica; Chawes, Bo; Rasmussen, Morten Arendt.
Affiliation
  • Lovric M; COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, 2820 Gentofte, Denmark.
  • Wang T; Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia.
  • Staffe MR; The Lisbon Council, 1040 Brussels, Belgium.
  • Sunic I; COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, 2820 Gentofte, Denmark.
  • Casni K; Department of Food Science, University of Copenhagen, 1958 Frederiksberg, Denmark.
  • Lasky-Su J; Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia.
  • Chawes B; Know-Center, 8010 Graz, Austria.
  • Rasmussen MA; Department of Medicine, Boston, MA 02115, USA.
Metabolites ; 14(5)2024 May 10.
Article in En | MEDLINE | ID: mdl-38786755
ABSTRACT
Metabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children's serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure-activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Metabolites Year: 2024 Document type: Article Affiliation country: Denmark Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Metabolites Year: 2024 Document type: Article Affiliation country: Denmark Country of publication: Switzerland