Background: Monogenetic inborn errors of metabolism (IEM) cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we focus on inherited mitochondrial fatty-acid oxidation (mFAO) deficiencies, particularly on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of mFAO disorder. It is an enigma why some MCADD patients – if untreated – are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients.
Results: We built and experimentally validated a computational model of the human liver mFAO based on detailed kinetic analysis of human enzymes. Metabolite pools were partitioned between the enzymes in the mitochondrial matrix and the membrane-bound enzymes according to their hydrophobicity. Metabolite partitioning improved model predictions. MCADD substantially reduced pathway flux and CoASH concentration, the latter due to the sequestration of CoA in medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed excessive accumulation of medium- and short-chain acyl-carnitines, in agreement with the acyl-CoA pool in the MCADD model. According to the model, upregulation of the enzymes SCAD, MTP and ACOT or downregulation of CPT2 would rescue both flux and CoASH. Proteome analysis of MCADD patient-derived fibroblasts revealed that elevated levels of MTP and SCAD indeed correlated with a clinically asymptomatic state. Personalised models based on these proteomics data confirmed an increased pathway flux and free CoASH concentration in the model of an asymptomatic patient compared to those of symptomatic MCADD patients.
Conclusion: We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides an explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management, and monitoring for IEMs.
Publication type: Journal
Created: 15th Nov 2022 at 12:11