AI Insight
VariantFlux is a computational workflow that integrates genetic variation data with genome-scale metabolic modeling to predict how individual genetic variants affect organ metabolism. The researchers analyzed 2,547 individuals from the 1000 Genomes Project, building personalized kidney metabolism models that incorporated gene-dosage effects of damaging variants. Despite approximately 50% of metabolic genes containing variants predicted to be damaging, over 97% of models maintained basic metabolic function, demonstrating significant metabolic robustness, though each individual showed unique metabolic flux rewiring patterns.
Why it matters
This approach enables personalized predictions of how genetic variants influence metabolism at the organ level, with potential applications in precision medicine, drug response prediction, and understanding individual disease risk. The framework could help identify patients who may respond differently to medications or be at higher risk for metabolic disorders based on their unique genetic profiles.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Human genetic variation is a major determinant of organ metabolism, yet how naturally occurring variants shape quantitative metabolic phenotypes remains unclear. We present VariantFlux, a workflow that integrates ancestry-aware variant interpretation into genome-scale metabolic modelling to generate personalised, variant-constrained kidney reconstructions. Using the Human1 v1.19 model, we built a kidney-specific baseline model constrained by 482 metabolites and analysed 2,547 individuals from the 1000 Genomes Project, in whom ~50% of metabolic genes were predicted damaging by at least three computational tools. These variants, whose burden diRered subtly across ancestries, were translated into gene-dosage-anchored flux constraints for homozygous knockouts and graded heterozygous knockdowns. Despite widespread perturbation, >97% of models preserved baseline growth, indicating strong metabolic robustness. Yet individual genomes exhibited distinct flux-rewiring patterns, with frequent individual-specific gain-of-flux events and fewer shared loss-of-flux reactions. Limited ancestry clustering suggests metabolic responses are driven mainly by unique variant combinations. VariantFlux links human genomes to organ-level flux phenotypes, enabling precision medicine, pharmacogenomics, and disease risk prediction.