Received on April 12, 2007
Accepted on October 2, 2007

Automation and Analytical Techniques

Metabolomics Identifies Perturbations in Human Disorders of Propionate Metabolism

¹ Department of Molecular Biology and The Center for Mass Spectrometry, The Scripps Research Institute, La Jolla, CA
² Department of Pediatrics, University of California–San Diego, School of Medicine, La Jolla, CA

^* To whom correspondence should be addressed. E-mail: bbarshop{at}ucsd.edu.

Background: We applied untargeted mass spectrometry-based metabolomics to the diseases methylmalonic acidemia (MMA) and propionic acidemia (PA).

Methods: We used a screening platform that used untargeted, mass-based metabolomics of methanol-extracted plasma to find significantly different molecular features in human plasma samples from MMA and PA patients and from healthy individuals. Capillary reversed-phase liquid chromatography (4 µL/min) was interfaced to a TOF mass spectrometer, and data were processed using nonlinear alignment software (XCMS) and an online database (METLIN) to find and identify metabolites differentially regulated in disease.

Results: Of the approximately 3500 features measured, propionyl carnitine was easily identified as the best biomarker of disease (P value 1.3 x 10-18), demonstrating the proof-of-concept use of untargeted metabolomics in clinical chemistry discovery. Five additional acylcarnitine metabolites showed significant differentiation between plasma from patients and healthy individuals, and -butyrobetaine was highly increased in a subset of patients. Two acylcarnitine metabolites and numerous unidentified species differentiate MMA and PA. Many metabolites that do not appear in any public database, and that remain unidentified, varied significantly between normal, MMA, and PA, underscoring the complex downstream metabolic effects resulting from the defect in a single enzyme.

Conclusions: This proof-of-concept study demonstrates that metabolomics can expand the range of metabolites associated with human disease and shows that this method may be useful for disease diagnosis and patient clinical evaluation.