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Biological samples of lipids and metabolites degrade after extensive years in −80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of −80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS–DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of −80 °C storage. With receiver operator characteristic curves, sparse PLS–DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at −80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Amino acids are important in several biochemical pathways as precursors to neurotransmitters which impact biological processes previously linked to functional gastrointestinal disorders (FGIDs). Dietary protein consumption, metabolic host processes, and the gut microbiome can influence the plasma concentration of amino acids and neurotransmitters, and their uptake by tissues. The aim of this analysis was to quantify 19 proteogenic and 4 non-proteogenic amino acids and 19 neurotransmitters (including precursors and catabolites, herein referred to as neurotransmitters) to ascertain if their circulating concentrations differed between healthy participants and those with FGIDs. Plasma proteogenic and non-proteogenic amino acids and neurotransmitters were measured using ultra-performance liquid chromatography and liquid chromatography–mass spectrometry, respectively, from 165 participants (Rome IV: irritable bowel syndrome (IBS-constipation, IBS-diarrhea), functional constipation, functional diarrhea, and healthy controls). There were significant differences (p < 0.05) in pairwise comparisons between healthy controls and specific FGID groups for branched-chain amino acids (BCAAs), ornithine, and alpha-aminobutyric acid. No other significant differences were observed for the neurotransmitters or any other amino acids analyzed. Multivariate and bivariate correlation analyses between proteogenic and non-proteogenic amino acids and neurotransmitters for constipation (constipation (IBS-C and functional constipation) and phenotypes diarrhea (IBS-D and functional diarrhea)) and healthy controls suggested that associations between BCAAs, 5-hydroxytryptophan, and kynurenine in combination with tyrosine, 3,4-dihydroxyphenylalanine, and 3,4-dihydroxyphenylacetic acid and associations with gamma-aminobutyric acid, glutamate, asparagine, and serine are likely disrupted in FGID phenotypes. In conclusion, although correlations were evident between some proteogenic and non-proteogenic amino acids and neurotransmitters, the results showed minor concentration differences in plasma proteogenic and non-proteogenic amino acids, amino acid-derived metabolites, and neurotransmitters between FGID phenotypes and healthy controls.

The milk fat globule membrane (MFGM) appears to play an important role in infant neurocognitive development; however, its mechanism(s) of action remains unclear. This study aimed to investigate the role of a dietary MFGM supplement on the lipid profiles of different neonatal brain regions. Ten-day-old male piglets (4–5 kg) were fed unsupplemented infant formula (control, n = 7) or an infant formula supplemented with low (4%) or high (8%) levels of MFGM (n = 8 each) daily for 21 days. Piglets were then euthanized, and brain tissues were sectioned. Untargeted liquid chromatography-mass spectrometry lipidomics was performed on the cerebellum, hippocampus, prefrontal cortex, and the rest of the brain. The analyses identified 271 and 171 lipids using positive and negative ionization modes, respectively, spanning 16 different lipid classes. MFGM consumption did not significantly alter the lipidome in most brain regions, regardless of dose, compared to the control infant formula. However, 16 triacylglyceride species were increased in the hippocampus (t-test, p-value < 0.05) of the high-supplemented piglets. Most lipids (262 (96.7%) and 160 (93.6%), respectively) differed significantly between different brain regions (ANOVA, false discovery rate corrected p-value < 0.05) independent of diet. Thus, this study highlighted that dietary MFGM altered lipid abundance in the hippocampus and detected large differences in lipid profiles between neonatal piglet brain regions.

This study aimed to examine the changes in lipid and metabolite profiles of ovariectomized (OVX) rats with diet-induced metabolic syndrome-associated osteoarthritis (MetOA) after supplementation with greenshell mussel (GSM) using an untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach. Ninety-six rats were fed with one of four diets: control, control supplemented with GSM + GSM, high fat/high sugar (HFHS), or high fat/high sugar enriched with GSM (HFHS + GSM). After 8 weeks on experimental diets, half of the rats in each group underwent OVX and the other half were sham operated. After being fed for an additional 28 weeks, blood samples were collected for the metabolomics analysis. Lipid and polar metabolites were extracted from plasma and analysed by LC-MS. We identified 29 lipid species from four lipid subclasses (phosphatidylcholine, lysophosphatidylcholine, diacylglycerol, and triacylglycerol) and a set of eight metabolites involved in amino acid metabolism (serine, threonine, lysine, valine, histidine, pipecolic acid, 3-methylcytidine, and cholic acid) as potential biomarkers for the effect of HFHS diet and GSM supplementation. GSM incorporation more specifically in the control diet generated significant alterations in the levels of several lipids and metabolites. Further studies are required to validate these findings that identify potential biomarkers to follow OA progression and to monitor the impact of GSM supplementation.

Bile acids are metabolites involved in nutrient absorption and signaling with levels influenced by dietary intake, metabolic processes, and the gut microbiome. We aimed to quantify 23 bile acids in fecal samples to ascertain if concentrations differed between healthy participants and those with functional gut disorders. Fecal bile acids were measured using liquid chromatography-mass spectrometry (LC-MS) in the COMFORT (The Christchurch IBS cohort to investigate mechanisms for gut relief and improved transit) cohort of 250 participants with Rome IV IBS (IBS-constipation (C), IBS-diarrhea (D), IBS-mixed (M)), functional gut disorders (functional constipation (FC), functional diarrhea (FD)) and healthy controls (FC n = 35, FD n = 13, IBS-C n = 24, IBS-D n = 52, IBS-M n = 29, and control n = 97). Dietary information was recorded to ascertain three-day dietary intake before fecal samples were collected. Fecal bile acid concentrations, predominantly primary bile acids, were significantly different between all functional gut disorder participants and healthy controls (CDCA p = 0.011, CA p = 0.003) and between constipation (FC + IBS-C) and diarrhea (FD + IBS-D) groups (CDCA p = 0.001, CA p = 0.0002). Comparison of bile acids between all functional groups showed four metabolites were significantly different, although analysis of combined groups (FC + IBS-C vs. FD + IBS-D) showed that 10 metabolites were significantly different. The bile acid profiles of FD individuals were similar to those with IBS-D, and likewise, those with FC were similar to IBS-C. Individuals with a diarrhea phenotype (FD + IBS-D) had higher concentrations of bile acids compared to those with constipation (FC + IBS-C). Bile acid metabolites distinguish between individuals with functional gut disorders and healthy controls but are similar in constipation (or diarrhea) whether classified as IBS or not.

Holistic benefits of human milk to infants, particularly brain development and cognitive behavior, have stipulated that infant formula be tailored in composition like human milk. However, the composition of human milk, especially lipids, and their effects on brain development is complex and not fully elucidated. We evaluated brain lipidome profiles in weanling rats fed human milk or infant formula using non-targeted UHPLC-MS techniques. We also compared the lipid composition of human milk and infant formula using conventional GC-FID and HPLC-ELSD techniques. The sphingomyelin class of lipids was significantly higher in brains of rats fed human milk. Lipid species mainly comprising saturated or mono-unsaturated C18 fatty acids contributed significantly higher percentages to their respective classes in human milk compared to infant formula fed samples. In contrast, PUFAs contributed significantly higher percentages in brains of formula fed samples. Differences between human milk and formula lipids included minor fatty acids such as C8:0 and C12:0, which were higher in formula, and C16:1 and C18:1 n11, which were higher in human milk. Formula also contained higher levels of low- to medium-carbon triacylglycerols, whereas human milk had higher levels of high-carbon triacylglycerols. All phospholipid classes, and ceramides, were higher in formula. We show that brain lipid composition differs in weanling rats fed human milk or infant formula, but dietary lipid compositions do not necessarily manifest in the brain lipidome.

When developing a sample preparation protocol for LC−MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC−MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC−MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (

Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package “iontree” that can capture, store and analyze MS2 and MS3 mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS2 spectral comparison, and tools for building platform-independent ion tree (MS2/MS3) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS2 databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.