Metabolomics

Metabolomics analysis was performed at the West Coast Metabolomics Center and the Mass Spectrometry and Protein Chemistry Service at The Jackson Laboratory. Three platforms were used for the hemi-brain, muscle, liver and spinal cord, which were examined for primary metabolism by GC-TOF-MS. Brain and muscle were also analyzed for lipidomics using CSH-QTOF-MS/MS (positive and negative electrospray ionization) and for biogenic amines by HILIC-QTOF-MS/MS. Exploratory metabolomics of plasma was performed using a Thermo Q-Exactive Orbitrap mass spectrometer coupled to a dual-channel Vanquish Ultra-Performance Liquid Chromatography system with a hydrophobic C18 column and hydrophilic HILIC column under both positive and negative polarity. Detailed methods provided by the WCMC and by the Mass Spectrometry and Protein Chemistry Service at The Jackson Laboratory are provided in ‘Supplementary Methods’. The ages, genotypes, sex and numbers of the mice analyzed are provided in Tables 2 and ​and3.3. Concentration data were uploaded as comma-separated values (.csv) files to MetaboAnalyst. Data integrity checks, missing value imputations and data filtering were performed before normalization. Statistical analysis was performed with the null hypothesis of no difference between the mutant and control samples at each time point. Multiple exploratory, univariate and multivariate and machine learning methods were used in the data analysis (33,34). Samples with 50% or more values missing in either mutant or control or both samples were discarded from further analysis. Missing values were then imputed using half the detection limit and filtered using the interquartile range method (35,36). The data was then log2 transformed and auto-scaled (37,38). Student t-test, fold change and PCA were performed as exploratory data analysis methods. Machine learning models were developed using a support vector machine algorithm (39), with a linear kernel method and random forest analysis (40) to classify the mutant and control samples and rank the importance of metabolites in discriminating the two types of samples (see ‘Supplementary Methods’ for full details of data processing and analysis).