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Surgical wound excision is a necessary procedure for burn patients that require the removal of eschar. The extent of excision is currently guided by clinical judgement, with excessinto healthy tissue potentially leading to excessive scar, or inadequate debridement increasing risk of infection. Thus, an objective real-time measure to facilitate accurate excision could support clinical judgement and improve this surgical procedure. This study was designed to investigate the potential use of Rapid evaporative ionisation mass spectrometry (REIMS) as a tool to support data-driven objective tissue excision.Data were acquired using a multi-platform approach that consisted of both Rapid Evaporative Ionisation Mass Spectrometry (REIMS) performed on intact skin, and comprehensive liquid chromatography-mass spectrometry (LC-MS/MS) lipidomics performed on homogenised skin tissue extracts. Data were analysed using principal components analysis (PCA) and multivariate orthogonal projections to latent squares discriminant analysis (OPLS-DA) and logistic regression to determine the predictability of the models.PCA and OPLS-DA models of the REIMS and LC-MS/MS lipidomics data reported separation of excised and healthy tissue. Molecular fingerprints generated from REIMS analysis of healthy skin tissue revealed a high degree of heterogeneity, however, intra-individual variance was smaller than inter-individual variance. Both platforms indicated high levels of skin classification accuracy. In addition, OPLS-DA of the LC-MS/MS lipidomic data revealed significant differences in specific lipid classes between healthy control and excised skin samples; including lower free fatty acids (FFA), monoacylglycerols (MAG), lysophosphatidylglycerol (LPG) and lysophosphatidylethanolamines (LPE) in excised tissue and higher lactosylceramides (LCER) and cholesterol esters (CE) compared to healthy control tissue.Having established the heterogeneity in the biochemical composition of healthy skin using REIMS and LC-MS/MS, our data show that REIMS has the potential to distinguish between excied and healthy skin tissue samples. This pilot study suggests that REIMS may be an effective tool to support accurate tissue excision during burn surgery.

We present compelling evidence for the existence of an evolutionary adaptive response to viral agents such as SARS-CoV-2, that results in the humanin vivobiosynthesis of a family of compounds with potential antiviral activity. Using nuclear magnetic resonance (NMR) spectroscopy, we detected a characteristic spin-system motif indicative of the presence of an extended panel of urinary and serum metabolites during the acute viral phase. The structure of eight of nucleoside analogues was elucidated (six of which have not previously been reported in human urine), and subsequently confirmed by total-synthesis and matrix spiking. The molecular structures of the nucleoside analogues and their correlation with an array of serum cytokines, including IFN-α2, IFN-γ and IL-10, suggest an association with the viperin enzyme contributing to an endogenous innate immune defense mechanism against viral infection.

To understand the metabolic consequences following non-severe burns, longitudinal plasma was collected from adults with non-severe burn injuries (n=35) within 48 hours of admission to hospital and at 6 weeks post-surgery, as well as non-burn controls (n=14). Samples were analysed by1H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry to quantify 112 lipoprotein, supramolecular phospholipid composite (SPC), GlycB arising from the glycoprotein acetyl residues and 852 lipid species across 20 chemical subclasses. Alterations in lipoprotein and lipid metabolism were evident in samples taken within 48 hours of admission and found to be sustained at the 6-week timepoint, with increases in GlycB (p-value < 0.0001) and the multivariate importance of the apolipoprotein B100/apolipoprotein A1 (ABA1) ratio, as well as decreases in anti-inflammatory phosphatidylcholines, phosphatidylglycerols, phosphatidylinositols and phosphatidylserines. Low circulating high density lipoproteins, and increases in pro-atherogenic low density lipoproteins and monoacylglyceride (20:4) (p-value < 0.0001) are potentially indicative of an increased risk of cardiovascular disease after non-severe burns which requires further investigation.

Liquid chromatography mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple “dilute-and-shoot” approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences which lack consideration in the relevant metabolomics literature. In this work we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-L-alanine-L-proline betaine (L,L-TMAP) and N,N-dimethyl-L-proline-L-proline betaine (L,L-DMPP), the latter reported here for the first time. Offline LC-MS/MS, MRMS, and NMR spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the co-existence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of the column temperature and mobile phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on the metabolic profile.

To investigate the underlying molecular mechanisms on how the gut microbe metabolite, indoles, regulate host organ growth and function, germ-free male mice were mono-colonized with indole-producing wildtypeEscherichia colior tryptophanase-encodingtnaAknockout mutant indole-non-producingE. coli. The indole mutantE. colirecipient mice exhibited significant multiorgan decline and growth retardation combined with catabolism and energy deficiency despite increased food intake compared to control mice. In addition, indole mutant mice displayed malfunctional intestine, enlarged caecum, reduced numbers of colonic enterochromaffin cells and reduced circulating serotonin levels, resulting in reduced gut motility, diminished digestion, and lower energy harvest. Furthermore, indole mutant mice also displayed decreased expression ofKcnj12gene, suggesting reduced excitability of enteric neurons thus adding to intestinal dysfunctional phenotype. In conclusion, indoles are necessary to maintain adult metabolic homeostasis across multiple organs in vivo. Impairment of indole levels results in multiorgan functional decline suggesting a mechanism whereby gut microbe metabolites may regulate biological ageing and thus increase the risk for disease.Graphical summary

Genome-wide association studies (GWASs) have identified genetic loci associated with the risk of Alzheimer's disease (AD), but the molecular mechanisms by which they confer risk are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWASs using untargeted metabolic profiling (metabolomics) by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single-nucleotide polymorphisms (SNPs) in

One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal agerelated cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life., Australian Research Council FL200100220

We present a multivariate metabotyping approach to assess the functional recovery of nonhospitalized COVID-19 patients and the possible biochemical sequelae of "Post-Acute COVID-19 Syndrome", colloquially known as long-COVID. Blood samples were taken from patients ca. 3 months after acute COVID-19 infection with further assessment of symptoms at 6 months. Some 57% of the patients had one or more persistent symptoms including respiratory-related symptoms like cough, dyspnea, and rhinorrhea or other nonrespiratory symptoms including chronic fatigue, anosmia, myalgia, or joint pain. Plasma samples were quantitatively analyzed for lipoproteins, glycoproteins, amino acids, biogenic amines, and tryptophan pathway intermediates using Nuclear Magnetic Resonance (NMR) spectroscopy and mass spectrometry. Metabolic data for the follow-up patients (n = 27) were compared with controls (n = 41) and hospitalized severe acute respiratory syndrome SARS-CoV-2 positive patients (n = 18, with multiple time-points). Univariate and multivariate statistics revealed variable patterns of functional recovery with many patients exhibiting residual COVID-19 biomarker signatures. Several parameters were persistently perturbed, e.g., elevated taurine (p = 3.6 × 10-3 versus controls) and reduced glutamine/glutamate ratio (p = 6.95 × 10-8 versus controls), indicative of possible liver and muscle damage and a high energy demand linked to more generalized tissue repair or immune function. Some parameters showed near-complete normalization, e.g., the plasma apolipoprotein B100/A1 ratio was similar to that of healthy controls but significantly lower (p = 4.2 × 10-3) than post-acute COVID-19 patients, reflecting partial reversion of the metabolic phenotype (phenoreversion) toward the healthy metabolic state. Plasma neopterin was normalized in all follow-up patients, indicative of a reduction in the adaptive immune activity that has been previously detected in active SARS-CoV-2 infection. Other systemic inflammatory biomarkers such as GlycA and the kynurenine/tryptophan ratio remained elevated in some, but not all, patients. Correlation analysis, principal component analysis (PCA), and orthogonal-partial least-squares discriminant analysis (O-PLS-DA) showed that the follow-up patients were, as a group, metabolically distinct from controls and partially comapped with the acute-phase patients. Significant systematic metabolic differences between asymptomatic and symptomatic follow-up patients were also observed for multiple metabolites. The overall metabolic variance of the symptomatic patients was significantly greater than that of nonsymptomatic patients for multiple parameters (χ2 p = 0.014). Thus, asymptomatic follow-up patients including those with post-acute COVID-19 Syndrome displayed a spectrum of multiple persistent biochemical pathophysiology, suggesting that the metabolic phenotyping approach may be deployed for multisystem functional assessment of individual post-acute COVID-19 patients.

The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data, covering a year post disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct “systemic recovery” profiles with specific progression and resolution of the inflammatory, immune, metabolic and clinical responses, over weeks to several months after infection. In particular, we found a strong intra-patient temporal covariation of innate immune cell numbers, kynurenine- and host lipid-metabolites, which suggested candidate immunometabolic pathways putatively influencing restoration of homeostasis, the risk of death and of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery on the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online toolhttp://shiny.mrc-bsu.cam.ac.uk/apps/covid-systemic-recovery-prediction-app, designed to test our findings prospectively.Graphical abstract

The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multi-platform metabolic phenotyping with Nuclear Magnetic Resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative measurements of lipoprotein sub-fractions, alpha-1-acid glycoprotein, glucose and biogenic amines were made on samples from symptomatic coronavirus disease 19 (COVID-19) patients who had tested positive for the SARS-CoV-2 virus (n = 17) and from age and gender-matched controls (n = 25). Data were analyzed using an orthogonal-projections to latent structures (O-PLS) method and used to construct an exceptionally strong (AUROC=1) hybrid NMR-MS model that enabled detailed metabolic discrimination between the groups and their biochemical relationships. Key discriminant metabolites included markers of inflammation including elevated alpha-1 acid glycoprotein and an increased kynurenine/tryptophan ratio. There was also an abnormal lipoprotein, glucose and amino acid signature consistent with diabetes and coronary artery disease (low total and HDL Apolipoprotein A1, low HDL triglycerides, high LDL and VLDL triglycerides). Plus, multiple highly significant amino acid markers of liver dysfunction (including the elevated glutamine/glutamate and Fischer’s ratios) that present themselves as part of a distinct SARS-CoV-2 infection pattern. A multivariate training-test set model was validated using independent samples from additional SARS-CoV-2 positive patients and controls. The predictive model showed a sensitivity of 100% for SARS-CoV-2 positivity. The breadth of the disturbed pathways indicates a systemic signature of SARS-CoV-2 positivity that includes elements of liver dysfunction, dyslipidaemia, diabetes, and coronary heart disease risk that are consistent with recent reports that COVID-19 is a systemic disease affecting multiple organs and systems. Metabolights study reference: MTBLS2014.

SARS-CoV-2 infection causes a significant reduction in lipoprotein-bound serum phospholipids give rise to supramolecular phospholipid composite (SPC) signals observed in diffusion and relaxation edited

Mild traumatic brain injury (mTBI) causes structural, cellular and biochemical alterations which are difficult to detect in the brain and may persist chronically following single or repeated injury. Lipids are abundant in the brain and readily cross the blood-brain barrier, suggesting that lipidomic analysis of blood samples may provide valuable insight into the neuropathological state. This study used liquid chromatography-mass spectrometry (LC-MS) to examine plasma lipid concentrations at 11 days following sham (no injury), one (1×) or two (2×) mTBI in rats. Eighteen lipid species were identified that distinguished between sham, 1× and 2× mTBI. Three distinct patterns were found: (1) lipids that were altered significantly in concentration after either 1× or 2× F mTBI: cholesterol ester CE (14:0) (increased), phosphoserine PS (14:0/18:2) and hexosylceramide HCER (d18:0/26:0) (decreased), phosphoinositol PI(16:0/18:2) (increased with 1×, decreased with 2× mTBI); (2) lipids that were altered in response to 1× mTBI only: free fatty acid FFA (18:3 and 20:3) (increased); (3) lipids that were altered in response to 2× mTBI only: HCER (22:0), phosphoethanolamine PE (P-18:1/20:4 and P-18:0/20:1) (increased), lysophosphatidylethanolamine LPE (20:1), phosphocholine PC (20:0/22:4), PI (18:1/18:2 and 20:0/18:2) (decreased). These findings suggest that increasing numbers of mTBI induce a range of changes dependent upon the lipid species, which likely reflect a balance of damage and reparative responses.

A growing body of evidence supports the concept of a systemic response to non-severe thermal trauma. This provokes an immunosuppressed state that predisposes paediatric patients to poor recovery and increased risk of secondary morbidity. In this study, to understand the long-term systemic effects of non-severe burns in children, targeted mass spectrometry assays for biogenic amines and tryptophan metabolites were performed on plasma collected from child burn patients at least three years post injury and compared to age and sex matched non-burn (healthy) controls. A panel of 12 metabolites, including urea cycle intermediates, aromatic amino acids and quinolinic acid were present in significantly higher concentrations in children with previous burn injury. Correlation analysis of metabolite levels to previously measured cytokine levels indicated the presence of multiple cytokine-metabolite associations in the burn injury participants that were absent from the healthy controls. These data suggest that there is a sustained immunometabolic imprint of non-severe burn trauma, potentially linked to long-term immune changes that may contribute to the poor long-term health outcomes observed in children after burn injury.

Significance While the effects of gut microbes on brain development and function have been described, the mechanisms remain largely unknown. Here, we report that tryptophan-metabolizing gut microbes secrete indoles that regulate neurogenesis in the adult hippocampus. This stimulatory effect on adult neurogenesis is mediated by the metabolic- and immune-linked aryl hydrocarbon receptor (AhR). Another AhR ligand, the tryptophan metabolite kynurenine, failed to induce neurogenesis, suggesting ligand specificity of AhR-mediated regulation of adult neurogenesis. The indole-AhR signaling pathway elevates transcription factors and signaling proteins that promote adult neurogenesis, as well as key markers of synaptic maturation. Our data demonstrate a symbiotic gut–brain coregulatory axis that connects the metabolic status of gut microbes to the control of neurogenesis in the adult hippocampus., While modulatory effects of gut microbes on neurological phenotypes have been reported, the mechanisms remain largely unknown. Here, we demonstrate that indole, a tryptophan metabolite produced by tryptophanase-expressing gut microbes, elicits neurogenic effects in the adult mouse hippocampus. Neurogenesis is reduced in germ-free (GF) mice and in GF mice monocolonized with a single-gene tnaA knockout (KO) mutant Escherichia coli unable to produce indole. External administration of systemic indole increases adult neurogenesis in the dentate gyrus in these mouse models and in specific pathogen-free (SPF) control mice. Indole-treated mice display elevated synaptic markers postsynaptic density protein 95 and synaptophysin, suggesting synaptic maturation effects in vivo. By contrast, neurogenesis is not induced by indole in aryl hydrocarbon receptor KO (AhR−/−) mice or in ex vivo neurospheres derived from them. Neural progenitor cells exposed to indole exit the cell cycle, terminally differentiate, and mature into neurons that display longer and more branched neurites. These effects are not observed with kynurenine, another AhR ligand. The indole-AhR–mediated signaling pathway elevated the expression of β-catenin, Neurog2, and VEGF-α genes, thus identifying a molecular pathway connecting gut microbiota composition and their metabolic function to neurogenesis in the adult hippocampus. Our data have implications for the understanding of mechanisms of brain aging and for potential next-generation therapeutic opportunities.

Annotation and identification of metabolite biomarkers is critical for their biological interpretation in metabolic phenotyping studies, presenting a significant bottleneck in the successful implementation of untargeted metabolomics. Here, a systematic multi-step protocol was developed for the purification and de novo structural elucidation of urinary metabolites. The protocol is most suited for instances where structure elucidation and metabolite annotation are critical for the downstream biological interpretation of metabolic phenotyping studies. First, a bulk urine pool was desalted using ion-exchange resins enabling large-scale fractionation using precise iterations of analytical scale chromatography. Primary urine fractions were collected and assembled into a “fraction bank” suitable for long-term laboratory storage. Secondary and tertiary fractionations exploited differences in selectivity across a range of reversed-phase chemistries, achieving the purification of metabolites of interest yielding an amount of material suitable for chemical characterisation. To exemplify the application of the systematic workflow in a diverse set of cases, four metabolites with a range of physico-chemical properties were selected and purified from urine and subjected to chemical formula and structure elucidation by respective magnetic resonance mass spectrometry (MRMS) and NMR analyses. Their structures were fully assigned as teterahydropentoxyline, indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent was collected, dried and returned to the fraction bank, demonstrating the viability of the system for repeat use in metabolite annotation with a high degree of efficiency.

Microsamples are collections usually less than 50 µL, although all devices that we have captured as part of this review do not fit within this definition (as some can perform collections of up to 600 µL); however, they are considered microsamples that can be self-administered. These microsamples have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venepuncture. However, venepuncture remains the sampling gold standard for the metabolic phenotyping of blood. This presents several challenges in metabolic phenotyping workflows: accessibility for individuals in rural and remote areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample collection difficulty in the young and elderly. Furthermore, venous sample stability may be compromised when the temperate conditions necessary for cold-chain transport are beyond control. Alternatively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for microsampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for the employment of microsamples in metabolic phenotyping research. This review supports the translation of research from the ‘bench to the community’.

Genome-wide association studies (GWAS) have identified genetic loci associated with risk of Alzheimer’s disease (AD), but underlying mechanisms are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWAS using untargeted metabolic profiling (metabolomics) by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0x 10−5 to 1.3 x 10−44). We show that plasma LacCer concentrations are associated with cognitive performance in humans and concentrations of sphingomyelins, ceramides, and hexose-ceramides were altered in brain tissue from ABCA7 knock out mice, compared to wild type (WT) (P =0.049 to 1.4 x10−5). We then used Mendelian randomisation to show that the association of LacCer with AD risk is potentially causal. Our work suggests that risk for AD arising from functional variations in ABCA7 are mediated at least in part through ceramides. Modulation of their metabolism or downstream signalling may offer new therapeutic opportunities for AD.

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.

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.

[Image: see text] A targeted ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization (UHPLC-ESI-MS/MS) method has been developed for the quantification of tryptophan and its downstream metabolites from the kynurenine and serotonin pathways. The assay coverage also includes markers of gut health and inflammation, including citrulline and neopterin. The method was designed in 96-well plate format for application in multiday, multiplate clinical and epidemiology population studies. A chromatographic cycle time of 7 min enables the analysis of two 96-well plates in 24 h. To protect chromatographic column lifespan, samples underwent a two-step extraction, using solvent protein precipitation followed by delipidation via solid-phase extraction (SPE). Analytical validation reported accuracy of each analyte

The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multiplatform metabolic phenotyping with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative measurements of lipoprotein subfractions, α-1-acid glycoprotein, glucose, and biogenic amines were made on samples from symptomatic coronavirus disease 19 (COVID-19) patients who had tested positive for the SARS-CoV-2 virus (

Metabolic phenotyping using mass spectrometry (MS) is being applied to ever increasing sample numbers in clinical and epidemiology studies. High-throughput and robust methods are being developed for the accurate measurement of metabolites associated with disease. Traditionally, quantitative assays have utilized triple quadrupole (QQQ) MS based methods; however, the use of such focused methods removes the ability to perform discovery-based metabolic phenotyping. An integrated workflow for the hybrid simultaneous quantification of 34 biogenic amines in combination with full scan high-resolution accurate mass (HRAM) exploratory metabolic phenotyping is presented. Primary and secondary amines are derivatized with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate prior to revered-phase liquid chromatographic separation and mass spectrometric detection. Using the HRAM-MS data, retrospective phenotypic data mining could be performed, demonstrating the versatility of HRAM-MS instrumentation in a clinical and molecular epidemiological environment. Quantitative performance was assessed using two MS detector platforms: Waters TQ-XS (QQQ; n = 3) and Bruker Impact II QToF (HRAMS-MS; n = 2) and three human biofluids (plasma, serum and urine). Finally, each platform was assessed using a certified external reference sample (NIST SRM 1950 plasma). Intra- and inter-day accuracy and precision were comparable between the QQQ and QToF instruments ( 0.99) over the quantification range of 1–400 μmol L−1. Quantitative values were comparable across all instruments for human plasma, serum and urine samples, and calculated concentrations were verified against certified reference values for NIST SRM 1950 plasma as an external reference. As a real-life biological exemplar, the method was applied to plasma samples obtained from SARS-CoV-2 positive patients versus healthy controls. Both the QQQ and QToF approaches were equivalent in being able to correctly classify SARS-CoV-2 positivity. Critically, the use of HRAM full scan data was also assessed for retrospective exploratory mining of data to extract additional biogenic amines of biomarker interest beyond the 34 quantified targets.

Current organophosphorus nerve agent medical countermeasures do not directly address the nicotinic effects of poisoning. A series of antinicotinic bispyridinium compounds has been synthesized in our laboratory and screened in vitro. Their actions can include open-channel block at the nicotinic receptor which may contribute to their efficacy. The current lead compound from these studies, MB327 1,1′-(propane-1,3-diyl)bis(4-tert-butylpyridinium) as either the diiodide (I2) or dimethanesulfonate (DMS) has been examined in vivo for efficacy against nerve agent poisoning. MB327 I2 (0–113 mg kg−1) or the oxime HI-6 DMS (0–100 mg kg− 1), in combination with atropine and avizafone (each at 3 mg kg−1) was administered to guinea-pigs 1 min following soman poisoning. Treatment increased the LD50 of soman in a dose-dependent manner. The increase was statistically significant (p < 0.01) at the 33.9 mg kg−1 (MB327) or 30 mg kg−1 (HI-6) dose with a comparable degree of protection obtained for both compounds. Following administration of 10 mg kg−1 (i.m.), MB327 DMS reached plasma Cmax of 22 μM at 12 min with an elimination t1/2 of 22 min. In an adverse effect study, in the absence of nerve agent poisoning, a dose of 100 mg kg−1 or higher of MB327 DMS was lethal to the guinea-pigs. A lower dose of MB327 DMS (30 mg kg−1) caused flaccid paralysis accompanied by respiratory impairment. Respiration normalised by 30 min, although the animals remained incapacitated to 4 h. MB327 or related compounds may be of utility in treatment of nerve agent poisoning as a component of therapy with atropine, anticonvulsant and oxime, or alternatively as an infusion under medical supervision.

Small molecule quantitative tandem mass spectrometry analysis(1) is now widely used in life sciences and medicine. Quantification is usually accomplished by prior fragmentation of standard materials and the use of commercial software to quantitate the resulting peaks(2,3). Despite its widespread application, there is a paucity of public libraries to expedite assay development. Here, we introduce a new library, METLIN-MRM, comprised of more than 15,500 optimized transitions for multiple reaction monitoring of a wide variety of low molecular weight compounds. METLIN-MRM includes (i) transitions optimized following the established protocol with standard materials and (ii) transitions computationally optimized for selectivity. This computational optimization was achieved by the analysis of a large collection of tandem mass spectrometry spectra, where an algorithm selected the most unique transitions for a given compound in comparison with other compounds with a mass within the error of the mass spectrometer. METLIN-MRM streamlines quantitative analyses with minimal resources and development time and also serves as a public repository, allowing the community to upload, share and cite experimental transitions through accession numbers. Additionally, this library has been integrated with XCMS-MRM, a cloud-based data analysis platform that allows for data analysis and sharing across different platforms and laboratories. This platform is publicly accessible at http://metlin.scripps.edu/ and http://xcmsonline-mrm.scripps.edu

Background Biomarkers of Alzheimer’s disease (AD) pathology and progression have now been identified across various modalities. The aims of the two studies presented (ANM-MMB and EMIF-AD biomarker discovery) are to discover and replicate previously identified biomarkers of disease pathology and progression, and moreover to determine whether a multimodal biomarker signature may add value in comparison to a biomarker of a single modality. Methods The ANM-MMB cohort is comprised of 718 AD, MCI converters and non-converters, and control subjects selected from the AddNeuroMed, Alzheimer’s research trust and Dementia case register cohorts. Cognitive measures, serum and urine metabolomics, structural MRI, genomics, whole blood transcriptomics and plasma proteomics data was available. The EMIF-AD biomarker discovery cohort consists of 1221 AD, MCI and control subjects, selected from the EMIF catalogue. All subjects had existing amyloid measures (CSF Aβ or amyloid-PET), structural MRI and clinical data, and furthermore plasma proteomics (targeted and untargeted), CSF proteomics (targeted), metabolomics, genomics and epigenetics data were generated. For both studies univariate and multivariate statistics were utilised to identify candidate biomarkers of AD pathology (neurodegeneration and/or brain amyloid burden), rates of cognitive decline, and MCI progression to dementia. pQTL-eQTL-mQTL analyses, network/pathway analysis, and multimodal classifiers were employed to detect multimodal signatures. Results Initial analyses indicate that in the ANM-MMB study a serum and urine derived 15 metabolite classifier predicts MCI progression to AD with 72% accuracy, and the biological significance of the metabolites included in the biomarker panel was identified. Further analyses will examine whether a multimodal classifier is able to predict with even greater accuracy. We will then seek to replicate this in the EMIF-AD biomarker discovery study. Further analyses will also examine single and multimodal biomarker classifiers of other endophenotypes. Conclusions These two studies could be used to identify novel and replicate previously identified single modality biomarker findings. Furthermore the impact of combining the additional modalities with these findings will be discussed. Computational and technical challenges encountered and the bioinformatics pipeline devised in the multimodal analysis of the ANM-MMB cohort will be used to inform the analysis pipeline of the EMIF-AD biomarker discovery study as a replication.

Roux-En-Y Gastric Bypass (RYGB) and Sleeve Gastrectomy (SG) are now both recognised as the most effective treatment options for morbid obesity and its related comorbidities. However, despite similar long-term outcomes, their precise mechanisms of action are yet to be fully understood.

The aim of metabolomics studies is the comprehensive and quantitative analysis of all metabolites in a cell, tissue or organism. This approach requires sample preparation methods to be fast, reproducible and able to extract a wide range of analytes with different polarities, as well as analytical platforms able to detect the extracted metabolites. Recently, we have developed a one-step extraction method consisting of a lipophilic and hydrophilic layer within a single vial insert, in-vial dual extraction (IVDE). In order to check possible application of this method to real biological case, analysis of plasma samples obtained from three streptozotocin-induced diabetic and three control rats was performed. Analytical validity of the method was proved by the calculation (in quality control samples) of relative standard deviation (RSD) for detected metabolites. The percentage of metabolites with RSD < 30% was 93% for Fatty acyls, 80% for Glycerolipids, 93% for Glycerophospholipids, 68% for Sterol lipids, and 91% for Sphingolipids. IVDE allowed for selection of more than 600 different features discriminating two studied groups. For around 40% of these masses putative identification was possible. Adequate, with several considerations described within this paper, application of IVDE method enables wide metabolite coverage from a single 20 μL plasma aliquot. Within the features putatively identified, glycerolipids and glycerophospholipids arose as the most important groups of compounds discriminating diabetic rats from controls. All discriminating metabolites give an idea of the large metabolic differences that can be present in non-controlled type 1 diabetes.

Metabolic fingerprinting of biological tissues has become an important area of research, particularly in the biomarker discovery field. Methods have inherent analytical variation, and new approaches are necessary to ensure that the vast numbers of intact metabolites present in biofluids are detected. Here, we describe an in-vial dual extraction (IVDE) method and a direct injection method that shows the total number of features recovered to be over 4500 from a single 20 μL plasma aliquot. By applying a one-step extraction consisting of a lipophilic and hydrophilic layer within a single vial insert, we showed that analytical variation was decreased. This was achieved by reducing sample preparation stages including procedures of drying and transfers. The two phases in the vial, upper and lower, underwent HPLC-QTOF analysis on individually customized LC gradients in both positive and negative ionization modes. A 60 min lipid profiling HPLC-QTOF method for the lipophilic phase was specifically developed, enabling the separation and putative identification of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and sterols. The aqueous phase of the extract underwent direct injection onto a 45 min gradient, enabling the detection of both polarities. The IVDE method was compared to two traditional extraction methods. The first method was a two-step ether evaporation and IPA resuspension, and the second method was a methanol precipitation typically used in fingerprinting studies. The IVDE provided a 378% increase in reproducible features when compared to evaporation and a 269% increase when compared to the precipitate and inject method. As a proof of concept, the method was applied to an animal model of diabetes. A 2-fold increase in discriminant metabolites was found when comparing diabetic and control rats with IVDE. These discriminant metabolites accounted for around 600 entities, out of which 388 were identified in available databases.

Background A better understanding of the biological mechanisms underlying Alzheimer's Disease (AD) is required. Studies have now demonstrated the promise of using associations with blood metabolites, as functional intermediate phenotypes in biomedical and pharmaceutical research1. Studies have further identified genetic variants in metabolism‐related genes that lead to clearly differentiated metabolic phenotypes, ‘genetically influenced metabotypes’ (GIMs), providing new insights to the role of inherited variation in blood metabolic diversity2. Recently, a number of blood metabolomic studies including ours have highlighted the role of lipid compounds in AD3‐4. The aim of this study was to investigate genetic influences on human plasma metabolites in >400 AD patients and healthy controls to survey regions of the genome associated with metabolic traits and identify AD specific associations. Methods We performed a comprehensive untargeted lipidomic analysis, using Ultra‐Performance Liquid Chromatography/Mass Spectrometry generating >2000 features and a Genome Wide Association (GWA) study followed by imputation. Linear regression analyses were run to identify genetic influences on each metabolic feature. Results We identified significant associations, after Bonferonni correction, between loci involved in lipid metabolism and a number of metabolite molecules. The most significantly associated SNP in our analysis was with a SNP on the FADS1 gene cluster, the top SNP identified by Shin et al2. One of the metabolites associated with the FADS locus has been previously identified by our group to be associated with AD in the same cohort4. Additional associations were identified with loci involved in AD risk. Conclusions These findings need to be replicated in larger well‐phenotyped cohorts, and the causal relationship between metabolites and AD to be explored. References: 1. Suhre K et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 2011. 2. Shin SY et al. An atlas of genetic influences on human blood metabolites. Nat Genet 2014. 3. Whiley L et al. Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease. Neurobiol Aging 2014. 4. Proitsi P et al. Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease. Trans Psy 2015.

With the number of patients suffering from Alzheimer’s disease rapidly increasing, there is a major requirement for an accurate biomarker capable of diagnosing the disease early. Much of the research is focused on protein and genetic approaches; however, small molecules may provide viable marker molecules. Examples that support this approach include known abnormalities in lipid metabolism, glucose utilization and oxidative stress, which have been demonstrated in patients suffering from the disease. Therefore, by-products of this irregular metabolism may provide accurate biomarkers. In this review we present the current approaches previously published in the literature used to investigate potential small-molecule and metabolite markers, and report their findings. A wide range of techniques are discussed, including separation approaches (LC, GC and CE), magnetic resonance technologies (NMR and magnetic resonance spectroscopy), and immunoassays.

Aristolochic acids are known to contribute to various renal disorders; therefore, expanding the availability of analytical methodology to detect these compounds is important in order to assess the quality of Chinese herbal medicines in which they can be found. Twelve medicinal herbal samples were procured from various sources and extracted in duplicate prior to a "fingerprint" analysis using conventional HPLC-DAD. Multivariate analysis was performed on the entire chromatographed fingerprints. The resulting output was a partial least-square discriminant analysis model, which was able to evaluate the potential presence of aristolochic acids I and II as well as providing an individual herbal "fingerprint". The results of this study provide evidence that the presence of aristolochic acids contained within certain herbal extractions could be detected using a simple method, although some limitations apply to this method for quality control, since newly detected samples for aristolochic acid (positives) will need further confirmation with purity checks or MS hyphenation.