A clinically validated method to separate and quantify underivatized acylcarnitines and carnitine metabolic intermediates using mixed-mode chromatography with tandem mass spectrometry

Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow-Up in the General Population

BACKGROUND

Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality.

METHODS AND RESULTS

We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R2=0.27 and R2=0.39, respectively). Metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low-density lipoprotein. After 23 years of follow-up, metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality.

CONCLUSIONS

Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.

Development of a second-tier method for C4, C5 and C2 acylcarnitine analysis in plasma

INTRODUCTION

Acylcarnitines are typically analyzed using either a flow injection analysis (FIA) method or liquid chromatography-mass spectrometry (LC-MS/MS) methods. The FIA method is a fast, efficient method, however it does not have the capability to separate compounds with the same molecular weight. These isobaric interferences can be removed by chromatographic separation with LC-MS/MS. In this study, we aimed to develop and optimize a qualitative LC-MS/MS method to separate the isobaric interferences for two-, four- and five-carbon acylcarnitines.

METHODS

The samples were first prepared by acylcarnitine derivatization with butanolic HCl. The developed LC-MS/MS method is a combination of isocratic and gradient elution used to separate acylcarnitines. Multiple reaction monitoring was used for determination of precursor and product ions for each acylcarnitine species as well as known interferences used in our study. We used this method to analyze quality assurance and patient samples with elevated two-, four- and five-carbon acylcarnitines.

RESULTS

Butyryl- and isobutyrylcarnitines as well as valeryl- and isovalerylcarnitines were successfully separated using the developed method. This method was able also to separate and distinguish acetylcarnitine from glutamate interference that has been causing overestimation of acetylcarnitine. In patients, the dominant five-carbon acylcarnitine was found to be isovalerylcarnitine. We confirmed that the majority of analyzed patient samples had additional carnitine adducts present but not valerylcarnitine. Butyryl- and isobutyrylcarnitines, in variable ratios, were present in every patient sample.

CONCLUSION

We developed a qualitative LC-MS/MS method for butyl-ester derivatized acylcarnitines, which can be used as a second-tier method for diagnosis and monitoring of various inborn errors of metabolism in our hospital network.

Dicarboxylic acylcarnitine biomarkers in peroxisome biogenesis disorders

The peroxisome is an essential eukaryotic organelle with diverse metabolic functions. Inherited peroxisomal disorders are associated with a wide spectrum of clinical outcomes and are broadly divided into two classes, those impacting peroxisome biogenesis (PBD) and those impacting specific peroxisomal factors. Prior studies have indicated a role for acylcarnitine testing in the diagnosis of some peroxisomal diseases through the detection of long chain dicarboxylic acylcarnitine abnormalities (C16-DC and C18-DC). However, there remains limited independent corroboration of these initial findings and acylcarnitine testing for peroxisomal diseases has not been widely adopted in clinical laboratories. To explore the utility of acylcarnitine testing in the diagnosis of peroxisomal disorders we applied a LC-MS/MS acylcarnitine method to study a heterogenous clinical sample set (n = 598) that included residual plasma specimens from nineteen patients with PBD caused by PEX1 or PEX6 deficiency, ranging in severity from lethal neonatal onset to mild late onset forms. Multiple dicarboxylic acylcarnitines were significantly elevated in PBD patients including medium to long chain (C8-DC to C18-DC) species as well as previously undescribed elevations of malonylcarnitine (C3-DC) and very long chain dicarboxylic acylcarnitines (C20-DC and C22-DC). The best performing plasma acylcarnitine biomarkers, C20-DC and C22-DC, were detected at elevated levels in 100% and 68% of PBD patients but were rarely elevated in patients that did not have a PBD. We extended our analysis to residual newborn screening blood spot cards and were able to detect dicarboxylic acylcarnitine abnormalities in a newborn with a PBD caused by PEX6 deficiency. Similar to prior studies, we failed to detect substantial dicarboxylic acylcarnitine abnormalities in blood spot cards from patients with x-linked adrenoleukodystrophy (x-ald) indicating that these biomarkers may have utility in quickly narrowing the differential diagnosis in patients with a positive newborn screen for x-ald. Overall, our study identifies widespread dicarboxylic acylcarnitine abnormalities in patients with PBD and highlights key acylcarnitine biomarkers for the detection of this class of inherited metabolic disease.

Loss of succinyl-CoA synthetase in mouse forebrain results in hypersuccinylation with perturbed neuronal transcription and metabolism

Lysine succinylation is a subtype of protein acylation associated with metabolic regulation of succinyl-CoA in the tricarboxylic acid cycle. Deficiency of succinyl-CoA synthetase (SCS), the tricarboxylic acid cycle enzyme catalyzing the interconversion of succinyl-CoA to succinate, results in mitochondrial encephalomyopathy in humans. This report presents a conditional forebrain-specific knockout (KO) mouse model of Sucla2, the gene encoding the ATP-specific beta isoform of SCS, resulting in postnatal deficiency of the entire SCS complex. Results demonstrate that accumulation of succinyl-CoA in the absence of SCS leads to hypersuccinylation within the murine cerebral cortex. Specifically, increased succinylation is associated with functionally significant reduced activity of respiratory chain complex I and widescale alterations in chromatin landscape and gene expression. Integrative analysis of the transcriptomic data also reveals perturbations in regulatory networks of neuronal transcription in the KO forebrain. Together, these findings provide evidence that protein succinylation plays a significant role in the pathogenesis of SCS deficiency.

Validation of a targeted metabolomics panel for improved second-tier newborn screening

Improved second-tier assays are needed to reduce the number of false positives in newborn screening (NBS) for inherited metabolic disorders including those on the Recommended Uniform Screening Panel (RUSP). We developed an expanded metabolite panel for second-tier testing of dried blood spot (DBS) samples from screen-positive cases reported by the California NBS program, consisting of true- and false-positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second-tier analysis of single DBS punches using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a 3-min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true- and false positive cases. Targeted metabolomic analysis of 121 analytes from DBS extracts in combination with RF classification at a sensitivity of 100% reduced false positives for GA1 by 83%, MMA by 84%, OTCD by 100%, and VLCADD by 51%. This performance was driven by a combination of known disease markers (3-hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long-chain acylcarnitine and hydroxy-acylcarnitine species. These findings establish the effectiveness of this second-tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false-positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.