Untargeted metabolomics reveals distinct metabolic profiles in MUT-type methylmalonic acidemia.

Abstract

Background: Methylmalonic acidemia (MMA), the most prevalent type of inherited metabolic disorder, is inherited in an autosomal recessive pattern due to MUT gene mutations that impair methylmalonyl-CoA mutase (MCM) enzyme activity. These mutations lead to the toxic accumulation of methylmalonic acid, which causes mitochondrial dysfunction, metabolic disruptions, and multisystem damage. Newborn screening followed by confirmatory biochemical and genetic tests, such as acylcarnitine analysis and urine organic acid profiling, are widely accepted and routinely used in biochemical genetics laboratories. However, these conventional methods have limited ability to detect novel, clinically relevant biomarkers that may offer deeper insights into the pathophysiology of MMA. This study highlights the importance of untargeted metabolomics to identify candidate biomarkers that could potentially predict long-term prognosis or represent novel therapeutic targets.

Methods: LC-HRMS was used to analyze serum samples from patients with genetically confirmed MUT-type methylmalonic acidemia (n = 27) and healthy controls (n = 27). Differential metabolite and pathway analyses were performed to identify metabolic alterations associated with MMA.

Results: A total of 267 dysregulated metabolites were identified in patients with MMA, including 185 upregulated and 82 downregulated metabolites. Downregulated metabolites included glutamine, isoleucine, and deamido-NAD⁺, whereas upregulated metabolites included acylcarnitines, succinyladenosine, and leukotriene B4. These dysregulated metabolites were primarily associated with key altered pathways, including arachidonic acid metabolism, nicotinate and nicotinamide metabolism, sphingolipid metabolism, glutathione metabolism, and purine metabolism. Notably, potential biomarkers such as 11,12-epoxyeicosatrienoic acid (AUC = 0.964) and MG (PGF2alpha/0:0/0:0) (AUC = 0.953) are implicated in pathophysiological mechanisms of MMA and its standard treatment through their association with inflammation, oxidative stress, and altered fatty acid metabolism.

Conclusions: These findings identify candidate disease-associated metabolites and provide insight into the metabolic disturbances associated with MMA. However, these results should be considered exploratory and require validation in larger, independent, age-matched, and longitudinal cohorts before clinical application. Methylmalonic acidemia (MMA), the most prevalent type of inherited metabolic disorder, is inherited in an autosomal recessive pattern due to MUT gene mutations that impair methylmalonyl-CoA mutase (MCM) enzyme activity. These mutations lead to the toxic accumulation of methylmalonic acid, which causes mitochondrial dysfunction, metabolic disruptions, and multisystem damage. Newborn screening followed by confirmatory biochemical and genetic tests, such as acylcarnitine analysis and urine organic acid profiling, are widely accepted and routinely used in biochemical genetics labs. However, these conventional methods have limited ability to detect novel, clinically relevant biomarkers that may offer deeper insights into the pathophysiology of MMA. This study highlights the importance of untargeted metabolomics to identify candidate biomarkers that could potentially predict long-term prognosis or represent novel therapeutic targets. LC-HRMS was used to analyze serum samples from patients with genetically confirmed MUT-type methylmalonic acidemia (n = 27) and healthy controls (n = 27). A total of 267 dysregulated metabolites were identified in patients with MMA, including 185 upregulated and 82 downregulated metabolites. Downregulated metabolites included glutamine, isoleucine, deamido-NAD⁺, and the upregulated metabolites included acylcarnitines, succinyladenosine, and leukotriene B4. These dysregulated metabolites were primarily associated with key altered pathways, including arachidonic acid metabolism, nicotinate and nicotinamide metabolism, sphingolipid metabolism, glutathione metabolism, and purine metabolism. Notably, potential biomarkers, such as 11,12-epoxyeicosatrienoic acid (AUC = 0.964) and MG (PGF2alpha/0:0/0:0; AUC = 0.953) are implicated in pathophysiological mechanisms of MMA and its standard treatment through their association with inflammation, oxidative stress, and altered fatty acid metabolism. These findings identify candidate disease-associated metabolites and provide insight into the metabolic disturbances associated with MMA. However, these results should be considered exploratory and require validation in larger, independent, age-matched, and longitudinal cohorts before clinical application.