Purpose of Experiment: The goal of this study was to identify metabolic differences in mouse heart resulting from knockout of acyl-coenzyme A synthetase. A second component of this study was to examine these metabolic differences in the context of inhibition of mTOR via rapamycin treatment. Experimental design / Summary of Procedure: Global biochemical profiles were determined in 27 samples of mouse heart from WT or acyl coenzyme A synthetase KO animals left untreated or treated with rapamycin (an mTOR inhibitor).
UNC Nutrition Obesity Research Center
American Heart Association
Metabolite data of mouse hearts in the early stages of the cardiac fuel switch
|Type of Resource||software, multimedia|
|Last Date Modified||2018-09|
|Methods||Metabolite raw counts were generated by Metabolon, Inc., Durham, NC, USA.|
Liquid chromatography/Mass Spectrometry (LC/MS, LC/MS2): The LC/MS portion of the platform was based on a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ mass spectrometer, which consisted of an electrospray ionization (ESI) source and linear ion-trap (LIT) mass analyzer. The sample extract was split into two aliquots, dried, then reconstituted in acidic or basic LC-compatible solvents, each of which contained 11 or more injection standards at fixed concentrations. One aliquot was analyzed using acidic positive ion optimized conditions and the other using basic negative ion optimized conditions in two independent injections using separate dedicated columns. Extracts reconstituted in acidic conditions were gradient eluted using water and methanol both containing 0.1% Formic acid, while the basic extracts, which also used water/methanol, contained 6.5mM Ammonium Bicarbonate. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion.
Gas chromatography/Mass Spectrometry (GC/MS): The samples destined for GC/MS analysis were re-dried under vacuum desiccation for a minimum of 24 hours prior to being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide (BSTFA). The GC column was 5% phenyl and the temperature ramp is from 40° to 300° C in a 16 minute period. Samples were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using electron impact ionization. The instrument was tuned and calibrated for mass resolution and mass accuracy on a daily basis.
Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, nominal mass match to the library +/- 0.4amu, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between these molecules based on one of these factors, the use of all three data points can be utilized to distinguish and differentiate biochemicals. More than 3500 commercially available purified standard compounds have been acquired and registered for distribution to both the LC-MS and GC-MS platforms for determination of their analytical characteristics. The annotation of the identified metabolites (321) are found in worksheet “metabolite_annoation”.
The raw counts were analyzed using Metaboanalyst (v3.0) [1-3] run in the statistical package R (v3.3.2). Of the 27 samples and 321 identified metabolites (8667 expected data points), there were 256 (or 3%) missing values. The original values are found in the worksheet “data_original”. Genotypes are indicated (WT/KO) as well as the presence of rapamycin (0/1, no/yes, respectively). Any feature with more than 50% missing data were removed and the remaining missing values were imputed using k-nearest neighbors. Next, non-informative variables were filtered out of downstream analyses using the interquantile range . This filtering removes variables that are near-constant throughout the experimental conditions. The filtered and imputed dataset is found in the worksheet, “data_processed”. Concentrations were log transformed and unit scaled and are found in the worksheet, “data_normalized”.
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4. Hackstadt A. J. and Hess A. M. (2009) Filtering for increased power for microarray data analysis. BMC Bioinformatics 10:11
|Keywords||metabolomics; cardiac metabolism; cardiac hypertrophy; fatty acid oxidation; mTOR; rapamycin|