Genetically personalised organ-specific metabolic models in health and disease

Abstract

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Unravelling variant-to-function relationships in sporulation efficiency through integrative analysis of SNP-specific gene expression networks and metabolic models

“…Several studies have reported complex genetic interactions between identified SNPs in metabolic diseases 46,47 . A recent study has used GWAS variant data to develop personalised organ-specific metabolic models for 524,615 individuals of the INTERVAL and UK Biobank cohorts to clarify the impact of genetic variations on the metabolic processes implicated in coronary artery disease 15 . We argue that studying the population-level data by clustering based on variant combinations rather than phenotype along with GS-DFA analysis can provide additional novel targets for therapeutic interventions in complex human metabolic diseases.…”

“…Several studies have integrated the gene expression data into GSMMs to get valuable insights into understanding human diseases and infections [8][9][10][11][12][13] and the knockout effects of genes 14 . Recent studies have utilised GSMM to reconstruct personalised organ-specific metabolic models to address the V2F challenge 15,16 .…”

Competitive, multi‐objective, and compartmented Flux Balance Analysis for addressing tissue‐specific inborn errors of metabolism

“…We chose Recon2.2 here, rather than Recon3D 22 or a tissue-specific model, 36,37 for four reasons: First, we wanted to compare the biomarker prediction results obtained by our novel method with those in our previous, Recon2-based paper (Thiele et al 21 ). Second, Recon 3D has unrealistic reactions, such as the sink reaction for Phe ("phe__L_c < =>" with reaction bounds [À1000, 1000]: the model could use Phe in the brain without uptake from blood).…”

“…And fourthly, because our new method is generic we wanted to demonstrate it in a more generic model. To verify the robustness of our method, we also used both the Recon3D 22 and the tissue-specific model 36,37 with essentially identical results (see supplementary material).…”

Immunometabolic actions of trabectedin and lurbinectedin on human macrophages: Relevance for their antitumor activity

“…Next, the quadratic Metabolic Transformation Algorithm (qMTA) (29) was applied to simulate the study of a transition from the control to each treatment as a result of the adaptation to the drug challenge (Formula 1). The optimization minimizes the difference between the simulated ux values and target ux (i.e.…”