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Circulating markers of NADH-reductive stress correlate with mitochondrial disease severity
Rohit Sharma, … , Darryl C. De Vivo, Vamsi K. Mootha
Rohit Sharma, … , Darryl C. De Vivo, Vamsi K. Mootha
Published January 19, 2021
Citation Information: J Clin Invest. 2021;131(2):e136055. https://doi.org/10.1172/JCI136055.
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Research Article Genetics Metabolism

Circulating markers of NADH-reductive stress correlate with mitochondrial disease severity

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Abstract

Mitochondrial disorders represent a large collection of rare syndromes that are difficult to manage both because we do not fully understand biochemical pathogenesis and because we currently lack facile markers of severity. The m.3243A>G variant is the most common heteroplasmic mitochondrial DNA mutation and underlies a spectrum of diseases, notably mitochondrial encephalomyopathy lactic acidosis and stroke-like episodes (MELAS). To identify robust circulating markers of m.3243A>G disease, we first performed discovery proteomics, targeted metabolomics, and untargeted metabolomics on plasma from a deeply phenotyped cohort (102 patients, 32 controls). In a validation phase, we measured concentrations of prioritized metabolites in an independent cohort using distinct methods. We validated 20 analytes (1 protein, 19 metabolites) that distinguish patients with MELAS from controls. The collection includes classic (lactate, alanine) and more recently identified (GDF-15, α-hydroxybutyrate) mitochondrial markers. By mining untargeted mass-spectra we uncovered 3 less well-studied metabolite families: N-lactoyl-amino acids, β-hydroxy acylcarnitines, and β-hydroxy fatty acids. Many of these 20 analytes correlate strongly with established measures of severity, including Karnofsky status, and mechanistically, nearly all markers are attributable to an elevated NADH/NAD+ ratio, or NADH-reductive stress. Our work defines a panel of organelle function tests related to NADH-reductive stress that should enable classification and monitoring of mitochondrial disease.

Authors

Rohit Sharma, Bryn Reinstadler, Kristin Engelstad, Owen S. Skinner, Erin Stackowitz, Ronald G. Haller, Clary B. Clish, Kerry Pierce, Melissa A. Walker, Robert Fryer, Devin Oglesbee, Xiangling Mao, Dikoma C. Shungu, Ashok Khatri, Michio Hirano, Darryl C. De Vivo, Vamsi K. Mootha

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Figure 2

Comparison of 1310 proteins and 376 targeted metabolites in plasma of patients with MELAS and controls.

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Comparison of 1310 proteins and 376 targeted metabolites in plasma of pa...
(A) Four proteins discriminate patients with MELAS (n = 16) from controls (n = 24) at the 2% FDR threshold with the indicated fold effects and 95% CI. Plots (B–E, G–J, and L) show box (median with quartiles) and whisker (1.5 × IQR) plots of controls, m.3243A>G non-MELAS and patients with MELAS for each analyte meeting the 2% FDR threshold with individual data points plotted only for outliers. All show log10(AU) after correction for age, sex, BMI, and batch. (B) Growth differentiation factor 15 (GDF-15). (C) HS6ST1, heparan-sulfate 6-O-sulfotransferase 1. (D) sE-selectin, soluble E-selection. (E) RET proto-oncogene (RET). (F) Twenty-three metabolites from the targeted platform significantly discriminate patients with MELAS (n = 20) from controls (n = 32), with the indicated fold effects and 95% CIs. (G–J and L) Box and whisker plots for 18 of the top metabolites. (K) The significances shown as –log10(P value) of each of the 26 different acylcarnitine species identified by the targeted metabolomics platform are shown according to carbon chain length with the gray line at y = 2.92 marking the 2% FDR threshold. The P values shown here are the results of a regression analysis controlling for age, sex, BMI, and batch (see Methods).

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