While recent advent empowered the new reality of holistic metabolic phenotyping, we are currently still unable to accurately
determine the source(s) of the underlying metabolites. This is undoubtedly the main stumbling block in inferring causality to new
biomarkers for disease prevention, prediction, and prognosis, particularly given the rapidly increasing burden of metabolic diseases.
At the same time, conventional metabolomics methods do not meet the requirements for adoption in clinical practice. MeMoSA will
address these issues by unraveling the source hierarchy of the gastrointestinal metabolome, ultimately enabling effective
personalized treatments through longitudinal source modulation and follow-up. First, two workflows for 1. high-throughput
comprehensive 2D metabolomics and lipidomics and, 2. rapid clinically applicable ambient ionization metabotyping, will be
developed. Second, molecular fingerprints of our unique deeply phenotyped pediatric cohorts (1.5k children) will be generated and
advanced machine learning algorithms will be used to predict metabolite abundances based on their sources, i.e., diet, lifestyle,
anthropometrics, microbiome, drug intake, psychological factors, clinical markers, etc. Third, a combination of in vitro digestions, in
vivo humanized mice, and in silico experiments with selected source variables will be designed to contribute to our understanding of
source-metabolite causality. These mechanistic insights will be used to build dedicated intervention trials in children with specific
source-dominated metabotypes. MeMoSA will lay the foundation for integrating metabolomics into personalized and preventive
medicine in children through (i) better prediction of individual metabotypes in relation to health; (ii) in-depth insight into metabolite
sources, which will foster a framework for biomarker qualification and unraveling disease etiology; (iii) greater treatment efficacy
through dedicated metabolome-driven source modulation.