Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.

BACKGROUND: Dysregulation of l-arginine metabolism has been proposed to occur in patients with severe asthma. The effects of l-arginine supplementation on l-arginine metabolite profiles in these patients are unknown. We hypothesized that individuals with severe asthma with low fractional exhaled nitric oxide (FeNO) would have fewer exacerbations with the addition of l-arginine to their standard asthma medications compared with placebo and would demonstrate the greatest changes in metabolite profiles. METHODS: Participants were enrolled in a single-center, crossover, double-blind l-arginine intervention trial at UCD. Subjects received placebo or l-arginine, dosed orally at 0.05 mg/kg (ideal body weight) twice daily. The primary end point was moderate asthma exacerbations. Longitudinal plasma metabolite levels were measured using mass spectrometry. A linear mixed-effect model with subject-specific intercepts was used for testing treatment effects. RESULTS: A cohort of 50 subjects was included in the final analysis. l-Arginine did not significantly decrease asthma exacerbations in the overall cohort. Higher citrulline levels and a lower arginine availability index (AAI) were associated with higher FeNO ( P = 0.005 and P = 2.51 × 10 ^–9 , respectively). Higher AAI was associated with lower exacerbation events. The eicosanoid prostaglandin H ₂ (PGH ₂ ) and N _α -acetyl-l-arginine were found to be good predictors for differentiating clinical responders and nonresponders. CONCLUSIONS: There was no statistically significant decrease in asthma exacerbations in the overall cohort with l-arginine intervention. PGH ₂ , N _α -acetyl-l-arginine, and the AAI could serve as predictive biomarkers in future clinical trials that intervene in the arginine metabolome. TRIAL REGISTRATION: ClinicalTrials.gov NCT01841281. FUNDING: This study was supported by NIH grants R01HL105573, DK097154, UL1 TR001861, and K08HL114882. Metabolomics analysis was supported in part by a grant from the University of California Tobacco-Related Disease Research Program program (TRDRP).

The authors wish to make the following correction to this paper