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Lookup NU author(s): Dr Louise MichaelisORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Royal Society of Chemistry 2025.Cow’s milk protein allergy (CMA) is one of the most common food allergies in children worldwide. However, it is still not well understood why certain children outgrow their CMA and others do not. While there is increasing evidence for a link of CMA with the gut microbiome, it is still unclear how the gut microbiome and metabolome interact with the immune system. Integrating data from different omics platforms and clinical data can help to unravel these interactions. In this study, we integrate clinical, microbial, (meta)proteomics, immune and metabolomics data into machine learning (ML) classification, using multi-view learning by late integration. The aim is to group infants into those that outgrew their CMA and those that did not. The results show that integration of microbiome data with clinical, immune, (meta)proteomics and metabolomics data could considerably improve classification of infants on outgrowth of CMA, compared to only considering one type of data. Moreover, pathways previously linked to development of CMA could also be related to outgrowth of this allergy.
Author(s): Hendrickx DM, Savova MV, Zhu P, An R, Boeren S, Klomp K, Mutte SK, Wopereis H, van der Molen RG, Harms AC, Belzer C, Chatchatee P, Nowak-Wegrzyn A, Lange L, Benjaponpitak S, Chong KW, Sangsupawanich P, van Ampting MTJ, Nijhuis MMO, Harthoorn LF, Langford JE, Knol J, Knipping K, Garssen J, Trendelenburg V, Pesek R, Davis CM, Muraro A, Erlewyn-Lajeunesse M, Fox AT, Michaelis LJ, Beyer K, Noimark L, Stiefel G, Schauer U, Hamelmann E, Peroni D, Boner A
Publication type: Article
Publication status: Published
Journal: Molecular Omics
Year: 2025
Issue: ePub ahead of Print
Online publication date: 09/05/2025
Acceptance date: 01/05/2025
Date deposited: 16/06/2025
ISSN (electronic): 2515-4184
Publisher: Royal Society of Chemistry
URL: https://doi.org/10.1039/d4mo00245h
DOI: 10.1039/d4mo00245h
Data Access Statement: Raw sequencing data are publicly available in the European Nucleotide Archive (ENA) (https://www.ebi.ac.uk/ena) under accession number PRJEB56782. Raw proteomics data and Max- Quant search results are publicly available from ProteomeX- change via the PRIDE33 partner repository (https://www.ebi.ac. uk/pride/) under accession number PXD037190. Metabolomics data are publicly available from MetaboLights (https://www.ebi. ac.uk/metabolights/) under accession number MTBLS8954. Clinical data are available from Danone Nutricia Research upon reasonable request (contact: Harm Wopereis, Harm.Woper- eis@danone.com). Olink immune data are available as ESI,† (Gitlab folder) from another manuscript.14 All R code used in this study has been deposited in Gitlab: https://git.wur.nl/afsg- microbiology/publication-supplementary-materials/2024-hendrickx- et-al-earlyfit-presto-machine-learning
PubMed id: 40407702
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