Correlations between milk and plasma levels of amino and carboxylic acids in dairy cows.

The objective of this study was to investigate the relationship between the concentrations of 19 amino acids, glucose, and seven carboxylic acids in the blood and milk of dairy cows and their correlations with established markers of ketosis. To that end, blood plasma and milk specimens were collected throughout lactation in two breeds of dairy cows of different milk yield. Plasma concentrations of glucose, pyruvate, lactate, α-aminobutyrate, β-hydroxybutyrate (BHBA), and most amino acids, except for glutamate and aspartate, were on average 9.9-fold higher than their respective milk levels. In contrast, glutamate, aspartate, and the Krebs cycle intermediates succinate, fumarate, malate, and citrate were on average 9.1-fold higher in milk than in plasma. For most metabolites, with the exception of BHBA and threonine, no significant correlations were observed between their levels in plasma and milk. Additionally, milk levels of acetone showed significant direct relationships with the glycine-to-alanine ratio and the BHBA concentration in plasma. The marked decline in plasma concentrations of glucose, pyruvate, lactate, and alanine in cows with plasma BHBA levels above the diagnostic cutoff point for subclinical ketosis suggests that these animals fail to meet their glucose demand and, as a consequence, rely increasingly on ketone bodies as a source of energy. The concomitant increase in plasma glycine may reflect not only the excessive depletion of protein reserves but also a potential deficiency of vitamin B6

Tools and applications for one- and two-dimensional gas chromatography – time-of-flight mass spectrometry-based metabolomics

The study of cellular metabolite profiles and changes therein due to genetic and environmental influences is termed metabolomics. A major, yet to be realized goal is the detection and quantification of all metabolites in a single analysis. In this work, a comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) method was designed and validated that assembles the entire available analytical information from each sample in one data matrix for subsequent statistical evaluation. For the purpose of data merging we developed and implemented the retention time correction and data alignment tool INCA (Integrative Normalization and Comparative Alignment). The INCA module capitalized on the characteristic fragmentation behavior of silylated metabolites upon EI ionization by using the integral of the m/z 73 ion trace of the trimethylsilyl (TMS) group as quantitative measure for all features. The method was applied to reveal differences in metabolite composition between (i) an Escherichia coli wild type and a double-mutant strain lacking the transhydrogenases UdhA and PntAB and (ii) serum and plasma. Subsequently, we evaluated the performance of the Statistical Compare alignment function introduced later by LECO for GC×GC-TOFMS data and compared it to INCA. GC×GC-TOFMS was comprehensively evaluated against various 1D-GC-MS techniques using a set of 43 metabolite standards from different chemical classes and metabolic pathways. GC×GC-TOFMS proved to be the most powerful method with a linear range of more than three orders of magnitude, LLOQs in the sub-micromolar and LODs in the nanomolar range. GC×GC-TOFMS was also employed for absolute quantification of amino acid enantiomers (AAEs) as their methyl chloroformate derivatives and results were compared to those of a previously established 1D-GC-qMS method with single ion monitoring. The coupling of a gamma-cyclodextrin (Rt-gammaDEXsa) with an amino acid selective (ZB-AAA) column resulted in enhanced peak resolution. Twenty AAEs including the critcal peak pair L-leucine/D-isoleucine, which exhibited equal fragmentation behavior upon EI ionization in 1D-GC-MS, could be baseline separated. Except for methionine enantiomers, distinctly improved LLOQs were obtained. The method was applied to the analysis of AAE serum concentrations in patients suffering from liver cirrhosis and showed significantly increased D-AA concentrations and slightly decreased L-AA levels compared to a control group

Metabolic fingerprinting using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF-MS) is applied to the comparative metabolic fingerprinting of physiological fluids. Stable isotope-labeled internal standards plus norvaline serve as extraction standards and are added to the blanks, controls and patient samples prior to protein precipitation with methanol. The extracts are evaporated to complete dryness and derivatized in two steps using methoximation with methoxylamine hydrochloride (MeOx) and silylation with N-methyl-N-trimethylsily-trifluoroacetamide (MSTFA). Between derivatization steps a second internal standard containing odd-numbered, saturated straight chain fatty acids is added for quality control and to normalize retention time shifts. After GC × GC-TOF-MS analysis raw data are processed, aligned, and combined in one data matrix for subsequent statistical evaluation. Both a custom-made and the NIST 05 library are used to preliminarily identify significant metabolites. For verification purposes, commercial standards are run individually. Absolute quantification of selected metabolites is achieved by using a multi-point calibration curve and isotope-labeled internal standards

Comprehensive two-dimensional gas chromatography in metabolomics

One of the major objectives in metabolomics is the identification of subtle changes in metabolite profiles as affected by genetic or environmental factors. Comprehensive two-dimensional gas chromatography (GC × GC) hyphenated to a fast-acquisition mass spectrometer is a well-established analytical technique to study the composition of complex samples due to its enhanced separation capacity, sensitivity, peak resolution, and reproducibility. This review reports applications of GC × GC to metabolomics studies of sample of different types (biofluid, cells, tissue, bacteria, yeast, plants), and discusses its advantages and limitations

Comparison of two algorithmic data processing strategies for metabolic fingerprinting by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

The alignment algorithm Statistical Compare (SC) developed by LECO Corporation for the processing of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) data was validated and compared to the in-house developed retention time correction and data alignment tool INCA (Integrative Normalization and Comparative Analysis) by a spike-in experiment and the comparative metabolic fingerprinting of a wild type versus a double mutant strain of Escherichia coli (E. coli). Starting with the same peak lists generated by LECO's ChromaTOF software, the accuracy of peak alignment and detection of 1.1- to 4-fold changes in metabolite concentration was assessed by spiking 20 standard compounds into an aqueous methanol extract of E. coli. To provide the same quality input signals for both alignment routines, the universal m/z 73 trace of the trimethylsilyl (TMS) group was used as a quantitative measure for all features. The performance of data processing and alignment was evaluated and illustrated by ROC curves. Statistical Compare performed marginally better at the lower fold changes, while INCA did so at the higher fold changes. Using SC, quantitative precision could be improved substantially by exploiting the signal intensities of metabolite-specific unique (U) m/z ion traces rather than the universal m/z 73 trace. A list of 56 features that distinguished the two E. coli strains was obtained by the SC alignment using m/z U with an estimated false discovery rate (FDR) of <0.05. Ultimately, 23 metabolites could be identified, one additional and five less than with INCA due to the failure of SC to extract unitized m/z U's across all fingerprints with suitable spectral intensities for the latter metabolites

Metabolite extraction from adherently growing mammalian cells for metabolomics studies: optimization of harvesting and extraction protocols

Trypsin/ethylenediaminetetraacetic acid (EDTA) treatment and cell scraping in a buffer solution were compared for harvesting adherently growing mammalian SW480 cells for metabolomics studies. In addition, direct scraping with a solvent was tested. Trypsinated and scraped cell pellets were extracted using seven different extraction protocols including pure methanol, methanol/water, pure acetone, acetone/water, methanol/chloroform/water, methanol/isopropanol/water, and acid-base methanol. The extracts were analyzed by GC-MS after methoximation/silylation and derivatization with propyl chloroformate, respectively. The metabolic fingerprints were compared and 25 selected metabolites including amino acids and intermediates of energy metabolism were quantitatively determined. Moreover, the influence of freeze/thaw cycles, ultrasonication and homogenization using ceramic beads on extraction yield was tested. Pure acetone yielded the lowest extraction efficiency while methanol, methanol/water, methanol/isopropanol/water, and acid-base methanol recovered similar metabolite amounts with good reproducibility. Based on overall performance, methanol/water was chosen as a suitable extraction solvent. Repeated freeze/thaw cycles, ultrasonication and homogenization did not improve overall metabolite yield of the methanol/water extraction. Trypsin/EDTA treatment caused substantial metabolite leakage proving it inadequate for metabolomics studies. Gentle scraping of the cells in a buffer solution and subsequent extraction with methanol/water resulted on average in a sevenfold lower recovery of quantified metabolites compared with direct scraping using methanol/water, making the latter one the method of choice to harvest and extract metabolites from adherently growing mammalian SW480 cells

Improved enantiomer resolution and quantification of free D-amino acids in serum and urine by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

The potential of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) in the quantitative analysis of amino acid enantiomers (AAEs) as their methyl chloroformate (MCF) derivatives in physiological fluids was investigated. Of the two column sets tested, the combination of an Rt-γDEXsa chiral column with a polar ZB-AAA column provided superior selectivity. Twenty AAEs were baseline resolved including L-Leu and D-Ile, which had failed separation by one-dimensional chiral GC-quadrupole-MS (GC-qMS). Lower limits of quantification (LLOQ) were in the range of 0.03-2 μM. Reproducibility of the analysis of a serum specimen in octaplicate ranged from 1.3 to 16.6%. The GC×GC-TOFMS method was validated by analyzing AAEs in 48 urine and 43 serum specimens, respectively, and by comparing the results with data obtained by a previously validated GC-qMS method. Mean recoveries ranged from 78.4% for D-Leu to 116.4% for D-Pro in urine and 72.2% for L-Thr to 129.4% for L-Ile in serum. The method was applied to the comparison of AAE serum levels in patients suffering from liver cirrhosis to a control group. Significantly increased D-AA concentrations were found for the patient group, whereas L-AA levels were slightly decreased

Performance evaluation of gas chromatography-atmospheric pressure chemical ionization-time-of-flight mass spectrometry for metabolic fingerprinting and profiling

Gas chromatography-atmospheric-pressure chemical ionization-time-of-flight mass spectrometry (GC-APCI-TOFMS) was compared to GC × GC-electron ionization (EI)-TOFMS, GC-EI-TOFMS, GC-chemical ionization (CI)-quadrupole mass spectrometry (qMS), and GC-EI-qMS in terms of reproducibility, dynamic range, limit of detection, and quantification using a mix of 43 metabolites and 12 stable isotope-labeled standards. Lower limits of quantification for GC-APCI-TOFMS ranged between 0.06 and 7.81 μM, and relative standard deviations for calibration replicates were between 0.4% and 8.7%. For all compounds and techniques, except in four cases, R(2) values were above 0.99. Regarding limits of quantification, GC-APCI-TOFMS was inferior to only GC × GC-EI-TOFMS, but outperformed all other techniques tested. GC-APCI-TOFMS was further applied to the metabolic fingerprinting of two Escherichia coli strains. Of 45 features that differed significantly (false discovery rate < 0.05) between the strains, 25 metabolites were identified through highly accurate and reproducible (Δm ± SD below 5 mDa over m/z 190-722) mass measurements. Starting from the quasimolecular ion, six additional metabolites were identified that had not been found in a previous study using GC × GC-EI-TOFMS and an EI mass spectral library for identification purposes. Silylation adducts formed in the APCI source assisted the identification of unknown compounds, as their formation is structure-dependent and is not observed for compounds lacking a carboxylic group

Integrative normalization and comparative analysis for metabolic fingerprinting by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC x GC-TOF-MS) was applied to the comparative metabolic fingerprinting of a wild-type versus a double mutant strain of Escherichia coli lacking the transhydrogenases UdhA and PntAB. Using peak lists generated with the Leco ChromaTOF software as input, we developed retention time correction and data alignment tools (INCA). The accuracy of peak alignment and detection of 1.1- to 4-fold changes in metabolite concentration was validated by a spike-in experiment with 20 standard compounds. A list of 48 significant features that differentiated the two E. coli strains was obtained with an estimated false discovery rate (FDR) of <0.05. A total of 27 metabolites, mainly from the citrate cycle, were identified. That the signal intensity of the m/z 73 trace of the trimethylsilyl (TMS) group reflected true differences in metabolite abundance was confirmed by quantification of pyruvate, fumarate, malate, succinate, alpha-ketoglutarate, citrate, cis-aconitate, myo-inositol, and glucose-6-phosphate using compound specific fragment ions and stable isotope labeled standards. Relative standard deviations for metabolite extraction and GC x GC-TOF-MS analysis of those analytes ranged from 13.2 to 26.3% for the universal m/z 73 trace and 7.4 to 24.5% for the analyte specific fragment ion trace