Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection.

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography-mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal-Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

Characterisation of capillary ionic liquid columns for gas chromatography-mass spectrometry analysis of fatty acid methyl esters.

Due to their distinct chemical properties, the application of ionic liquid (IL) compounds as gas chromatography (GC) stationary phases offer unique GC separation especially in the analysis of geometric and positional fatty acid methyl ester (FAME) isomers. Elution behaviour of FAME on several commercialised IL capillary columns including phosphonium based SLB-IL59, SLB-IL60, SLB-IL61 and SLB-IL76 and imidazolium based SLB-IL82, SLB-IL100, and SLB-IL111 as well as a general purpose column SLB-5ms, were evaluated in gas chromatography-mass spectrometry (GC-MS) analysis. The phases were further characterised by using a linear solvation energy relationship (LSER) approach according to the equivalent chain length (ECL) index of FAME. Among all tested IL columns, elution temperatures of saturated FAME increased as their McReynolds' polarity value decreased, except for IL60. ECL values increased markedly as the stationary phase polarity increased, particularly for the polyunsaturated FAME. The LSER study indicated a lowest l/e value at 0.864 for IL111, displaying phase selectivity towards unsaturated FAME, with higher peak capacity within a carbon number isomer group. s and e descriptors calculated from LSER were validated by excellent correlation with dipole moments and lowest unoccupied molecular orbital (LUMO) energies, with R(2) values of 0.99 and 0.92 respectively, calculated using GAUSSIAN.

Profiling of soil fatty acids using comprehensive two-dimensional gas chromatography with mass spectrometry detection.

Profiling of phospholipid fatty acids (PLFA) represents a challenging goal for distinguishing the diversity of microbial communities and biomass in the complex and heterogeneous soil ecosystem. Comprehensive two-dimensional gas chromatography (GC×GC) coupled with simultaneous flame ionisation and mass spectrometry detection was applied as a culture-independent method for PLFA profiling of microbial classification in forest soil. A number of column sets were evaluated for the GC×GC separation of fatty acid methyl esters (FAME). Due to better isomeric separation and compound patterns on the 2D contour plot, an apolar-polar column combination was selected for soil microbial PLFA characterisation. A comprehensive view of PLFA composition with carbon chain length varying from 12 to 20 was observed in forest soil samples, with the commonly reported bacterial FAME of iso-/anteiso-, methyl-branched-, cyclopropyl-, and hydroxyl-substituted FA identified by their mass spectral and retention time according to authentic standards. Notably, some uncommon oxygenated FAME were found in high abundance and were further characterised by GC×GC coupled with high resolution mass spectrometry. This tentatively revealed geometric pairs of methyl 9,10-epoxyoctadecanoate isomers.

Integrated multidimensional and comprehensive 2D GC analysis of fatty acid methyl esters.

Fatty acid methyl ester (FAME) profiling in complex fish oil and milk fat samples was studied using integrated comprehensive 2D GC (GC × GC) and multidimensional GC (MDGC). Using GC × GC, FAME compounds--cis- and trans-isomers, and essential fatty acid isomers--ranging from C18 to C22 in fish oil and C18 in milk fat were clearly displayed in contour plot format according to structural properties and patterns, further identified based on authentic standards. Incompletely resolved regions were subjected to MDGC, with Cn (n = 18, 20) zones transferred to a (2)D column. Elution behavior of C18 FAME on various (2)D column phases (ionic liquids IL111, IL100, IL76, and modified PEG) was evaluated. Individual isolated Cn zones demonstrated about four-fold increased peak capacities. The IL100 provided superior separation, good peak shape, and utilization of elution space. For milk fat-derived FAME, the (2)D chromatogram revealed at least three peaks corresponding to C18:1, more than six peaks for cis/trans-C18:2 isomers, and two peaks for C18:3. More than 17 peaks were obtained for the C20 region of fish oil-derived FAMEs using MDGC, compared with ten peaks using GC × GC. The MDGC strategy is useful for improved FAME isomer separation and confirmation.