Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome.

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial "dark matter".

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.

Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes.

Delayed diagnosis of patients with sepsis or septic shock is associated with increased mortality and morbidity. UPLC-MS and NMR spectroscopy were used to measure panels of lipoproteins, lipids, biogenic amines, amino acids, and tryptophan pathway metabolites in blood plasma samples collected from 152 patients within 48 h of admission into the Intensive Care Unit (ICU) where 62 patients had no sepsis, 71 patients had sepsis, and 19 patients had septic shock. Patients with sepsis or septic shock had higher concentrations of neopterin and lower levels of HDL cholesterol and phospholipid particles in comparison to nonsepsis patients. Septic shock could be differentiated from sepsis patients based on different concentrations of 10 lipids, including significantly lower concentrations of five phosphatidylcholine species, three cholesterol esters, one dihydroceramide, and one phosphatidylethanolamine. The Supramolecular Phospholipid Composite (SPC) was reduced in all ICU patients, while the composite markers of acute phase glycoproteins were increased in the sepsis and septic shock patients within 48 h admission into ICU. We show that the plasma metabolic phenotype obtained within 48 h of ICU admission is diagnostic for the presence of sepsis and that septic shock can be differentiated from sepsis based on the lipid profile.

Rapid and Self-Administrable Capillary Blood Microsampling Demonstrates Statistical Equivalence with Standard Venous Collections in NMR-Based Lipoprotein Analysis.

We investigated plasma and serum blood derivatives from capillary blood microsamples (500 µL, MiniCollect tubes) and corresponding venous blood (10 mL vacutainers). Samples from 20 healthy participants were analyzed by 1H NMR, and 112 lipoprotein subfraction parameters; 3 supramolecular phospholipid composite (SPC) parameters from SPC1, SPC2, and SPC3 subfractions; 2 N-acetyl signals from α-1-acid glycoprotein (Glyc), GlycA, and GlycB; and 3 calculated parameters, SPC (total), SPC3/SPC2, and Glyc (total) were assessed. Using linear regression between capillary and venous collection sites, we explained that agreement (Adj. R2 ≥ 0.8, p < 0.001) was witnessed for 86% of plasma parameters (103/120) and 88% of serum parameters (106/120), indicating that capillary lipoprotein, SPC, and Glyc concentrations follow changes in venous concentrations. These results indicate that capillary blood microsamples are suitable for sampling in remote areas and for high-frequency longitudinal sampling of the majority of lipoproteins, SPCs, and Glycs.

Integrative Molecular Structure Elucidation and Construction of an Extended Metabolic Pathway Associated with an Ancient Innate Immune Response in COVID-19 Patients.

We present compelling evidence for the existence of an extended innate viperin-dependent pathway, which provides crucial evidence for an adaptive response to viral agents, such as SARS-CoV-2. We show the in vivo biosynthesis of a family of novel endogenous cytosine metabolites with potential antiviral activities. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy revealed a characteristic spin-system motif, indicating the presence of an extended panel of urinary metabolites during the acute viral replication phase. Mass spectrometry additionally enabled the characterization and quantification of the most abundant serum metabolites, showing the potential diagnostic value of the compounds for viral infections. In total, we unveiled ten nucleoside (cytosine- and uracil-based) analogue structures, eight of which were previously unknown in humans allowing us to propose a new extended viperin pathway for the innate production of antiviral compounds. 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 ancient endogenous innate immune defense mechanism against viral infection.

NMRium: Teaching nuclear magnetic resonance spectra interpretation in an online platform.

NMRium is the first web-based software that allows displaying, processing, interpretation, and teaching of 1D and 2D NMR data in a user-friendly interface. It can import the most common data formats (e.g., JCAMP-DX, Bruker, Varian, and Jeol). While the scope for the use of NMRium encompasses a variety of applications such as being a component in data repositories or electronic lab notebooks (ELN), performing structure elucidation or preparing raw spectral data for publication, it also excels in enhancing teaching of NMR interpretation. In this paper, we present some current possibilities of this new tool. Several series of exercises are already provided on https://www.nmrium.org/teaching.

Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population.

The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants (n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m2) with (n = 57) and without diabetes (n = 58) or overweight/obese (BMI > 24.99 kg/m2) with (n = 102) and without diabetes (n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD+ pathway and SIRT1 activation.

OBJECTIVES: The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata. METHODS: The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data. RESULTS: Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway. CONCLUSIONS: Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.

Gut microbiota and metabolomics profiles in patients with chronic stable angina and acute coronary syndrome.

Cardiovascular disease (CVD) is the leading cause of death worldwide. The gut microbiota and its associated metabolites may be involved in the development and progression of CVD, although the mechanisms and impact on clinical outcomes are not fully understood. This study investigated the gut microbiome profile and associated metabolites in patients with chronic stable angina (CSA) and acute coronary syndrome (ACS) compared with healthy controls. Bacterial alpha diversity in stool from patients with ACS or CSA was comparable to healthy controls at both baseline and follow-up visits. Differential abundance analysis identified operational taxonomic units (OTUs) assigned to commensal taxa differentiating patients with ACS from healthy controls at both baseline and follow-up. Patients with CSA and ACS had significantly higher levels of trimethylamine N-oxide compared with healthy controls (CSA: 0.032 ± 0.023 mmol/L, P < 0.01 vs. healthy, and ACS: 0.032 ± 0.023 mmol/L, P = 0.02 vs. healthy, respectively). Patients with ACS had reduced levels of propionate and butyrate (119 ± 4 vs. 139 ± 5.1 µM, P = 0.001, and 14 ± 4.3 vs. 23.5 ± 8.1 µM, P < 0.001, respectively), as well as elevated serum sCD14 (2245 ± 75.1 vs. 1834 ± 45.8 ng/mL, P < 0.0001) and sCD163 levels (457.3 ± 31.8 vs. 326.8 ± 20.7 ng/mL, P = 0.001), compared with healthy controls at baseline. Furthermore, a modified small molecule metabolomic and lipidomic signature was observed in patients with CSA and ACS compared with healthy controls. These findings provide evidence of a link between gut microbiome composition and gut bacterial metabolites with CVD. Future time course studies in patients to observe temporal changes and subsequent associations with gut microbiome composition are required to provide insight into how these are affected by transient changes following an acute coronary event.NEW & NOTEWORTHY The study found discriminative microorganisms differentiating patients with acute coronary syndrome (ACS) from healthy controls. In addition, reduced levels of certain bacterial metabolites and elevated sCD14 and sCD163 were observed in patients with ACS compared with healthy controls. Furthermore, modified small molecule metabolomic and lipidomic signatures were found in both patient groups. Although it is not known whether these differences in profiles are associated with disease development and/or progression, the findings provide exciting options for potential new disease-related mechanism(s) and associated therapeutic target(s).

Nonsevere Burn Induces a Prolonged Systemic Metabolic Phenotype Indicative of a Persistent Inflammatory Response Postinjury.

Globally, burns are a significant cause of injury that can cause substantial acute trauma as well as lead to increased incidence of chronic comorbidity and disease. To date, research has primarily focused on the systemic response to severe injury, with little in the literature reported on the impact of nonsevere injuries (<15% total burn surface area; TBSA). To elucidate the metabolic consequences of a nonsevere burn injury, longitudinal plasma was collected from adults (n = 35) who presented at hospital with a nonsevere burn injury at admission, and at 6 week follow up. A cross-sectional baseline sample was also collected from nonburn control participants (n = 14). Samples underwent multiplatform metabolic phenotyping using 1H nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry to quantify 112 lipoprotein and glycoprotein signatures and 852 lipid species from across 20 subclasses. Multivariate data modeling (orthogonal projections to latent structures-discriminate analysis; OPLS-DA) revealed alterations in lipoprotein and lipid metabolism when comparing the baseline control to hospital admission samples, with the phenotypic signature found to be sustained at follow up. Univariate (Mann-Whitney U) testing and OPLS-DA indicated specific increases in GlycB (p-value < 1.0e-4), low density lipoprotein-2 subfractions (variable importance in projection score; VIP > 6.83e-1) and monoacyglyceride (20:4) (p-value < 1.0e-4) and decreases in circulating anti-inflammatory high-density lipoprotein-4 subfractions (VIP > 7.75e-1), phosphatidylcholines, phosphatidylglycerols, phosphatidylinositols, and phosphatidylserines. The results indicate a persistent systemic metabolic phenotype that occurs even in cases of a nonsevere burn injury. The phenotype is indicative of an acute inflammatory profile that continues to be sustained postinjury, suggesting an impact on systems health beyond the site of injury. The phenotypes contained metabolic signatures consistent with chronic inflammatory states reported to have an elevated incidence postburn injury. Such phenotypic signatures may provide patient stratification opportunities, to identify individual responses to injury, personalize intervention strategies, and improve acute care, reducing the risk of chronic comorbidity.

Altered dietary behaviour during pregnancy impacts systemic metabolic phenotypes.

Rationale: Evidence suggests consumption of a Mediterranean diet (MD) can positively impact both maternal and offspring health, potentially mediated by a beneficial effect on inflammatory pathways. We aimed to apply metabolic profiling of serum and urine samples to assess differences between women who were stratified into high and low alignment to a MD throughout pregnancy and investigate the relationship of the diet to inflammatory markers. Methods: From the ORIGINS cohort, 51 pregnant women were stratified for persistent high and low alignment to a MD, based on validated MD questionnaires. 1H Nuclear Magnetic Resonance (NMR) spectroscopy was used to investigate the urine and serum metabolite profiles of these women at 36 weeks of pregnancy. The relationship between diet, metabolite profile and inflammatory status was investigated. Results: There were clear differences in both the food choice and metabolic profiles of women who self-reported concordance to a high (HMDA) and low (LMDA) Mediterranean diet, indicating that alignment with the MD was associated with a specific metabolic phenotype during pregnancy. Reduced meat intake and higher vegetable intake in the HMDA group was supported by increased levels of urinary hippurate (p = 0.044) and lower creatine (p = 0.047) levels. Serum concentrations of the NMR spectroscopic inflammatory biomarkers GlycA (p = 0.020) and GlycB (p = 0.016) were significantly lower in the HDMA group and were negatively associated with serum acetate, histidine and isoleucine (p < 0.05) suggesting a greater level of plant-based nutrients in the diet. Serum branched chain and aromatic amino acids were positively associated with the HMDA group while both urinary and serum creatine, urine creatinine and dimethylamine were positively associated with the LMDA group. Conclusion: Metabolic phenotypes of pregnant women who had a high alignment with the MD were significantly different from pregnant women who had a poor alignment with the MD. The metabolite profiles aligned with reported food intake. Differences were most significant biomarkers of systemic inflammation and selected gut-microbial metabolites. This research expands our understanding of the mechanisms driving health outcomes during the perinatal period and provides additional biomarkers for investigation in pregnant women to assess potential health risks.

Insights into the associations between the gut microbiome, its metabolites, and heart failure.

Heart failure (HF) is the end stage of most cardiovascular diseases and remains a significant health problem globally. We aimed to assess whether patients with left ventricular ejection fraction ≤45% had alterations in both the gut microbiome profile and production of associated metabolites when compared with a healthy cohort. We also examined the associated inflammatory, metabolomic, and lipidomic profiles of patients with HF. This single center, observational study, recruited 73 patients with HF and 59 healthy volunteers. Blood and stool samples were collected at baseline and 6-mo follow-up, along with anthropometric and clinical data. When compared with healthy controls, patients with HF had reduced gut bacterial alpha diversity at follow-up (P = 0.004) but not at baseline. The stool microbiota of patients with HF was characterized by a depletion of operational taxonomic units representing commensal Clostridia at both baseline and follow-up. Patients with HF also had significantly elevated baseline plasma acetate (P = 0.007), plasma trimethylamine-N-oxide (TMAO) (P = 0.003), serum soluble CD14 (sCD14; P = 0.005), and soluble CD163 (sCD163; P = 0.004) levels compared with healthy controls. Furthermore, patients with HF had a distinct metabolomic and lipidomic profile at baseline when compared with healthy controls. Differences in the composition of the gut microbiome and the levels of associated metabolites were observed in patients with HF when compared with a healthy cohort. This was also associated with an altered metabolomic and lipidomic profile. Our study identifies microorganisms and metabolites that could represent new therapeutic targets and diagnostic tools in the pathogenesis of HF.NEW & NOTEWORTHY We found a reduction in gut bacterial alpha diversity in patients with heart failure (HF) and that the stool microbiota of patients with HF was characterized by depletion of operational taxonomic units representing commensal Clostridia at both baseline and follow-up. Patients with HF also had altered bacterial metabolites and increased inflammatory profiles compared with healthy controls. A distinct metabolomic and lipidomic profile was present in patients with HF at baseline when compared with healthy controls.

Plasma lipoprotein subclass variation in middle-aged and older adults: Sex-stratified distributions and associations with health status and cardiometabolic risk factors.

BACKGROUND: Circulating lipids and lipoproteins mediate cardiovascular risk, however routine plasma lipid biochemistry provides limited information on pro-atherogenic remnant particles. OBJECTIVE: We analysed plasma lipoprotein subclasses including very low-density and intermediate-density lipoprotein (VLDL and IDL); and assessed their associations with health and cardiometabolic risk. METHODS: From 1,976 community-dwelling adults aged 45-67 years, 114/1071 women (10.6%) and 153/905 men (16.9%) were categorised as very healthy. Fasting plasma lipoprotein profiles comprising 112 parameters were measured using 1H nuclear magnetic resonance (NMR) spectroscopy, and associations with health status and cardiometabolic risk factors examined. RESULTS: HDL cholesterol was higher, and IDL and VLDL cholesterol and triglycerides lower, in very healthy women compared to other women, and women compared to men. IDL and VLDL cholesterol and triglyceride were lower in very healthy men compared to other men. HDL cholesterol and apolipoprotein (apo) A-I were inversely, and IDL and VLDL cholesterol, apoB-100, and apoB-100/apoA-I ratio directly associated with body mass index (BMI) in women and men. In women, LDL, IDL and VLDL cholesterol increased with age. Women with diabetes and cardiovascular disease had higher cholesterol, triglycerides, phospholipids and free cholesterol across IDL and VLDL fractions, with similar trends for men with diabetes. CONCLUSION: Lipoprotein subclasses and density fractions, and their lipid and apolipoprotein constituents, are differentially distributed by sex, health status and BMI. Very healthy women and men are distinguished by favorable lipoprotein profiles, particularly lower concentrations of VLDL and IDL, providing reference intervals for comparison with general populations and adults with cardiometabolic risk factors.

Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction.

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.

Effect of Operational Parameters on the Cultivation of the Gut Microbiome in Continuous Bioreactors Inoculated with Feces: A Systematic Review.

Since the early 1980s, multiple researchers have contributed to the development of in vitro models of the human gastrointestinal system for the mechanistic interrogation of the gut microbiome ecology. Using a bioreactor for simulating all the features and conditions of the gastrointestinal system is a massive challenge. Some conditions, such as temperature and pH, are readily controlled, but a more challenging feature to simulate is that both may vary in different regions of the gastrointestinal tract. Promising solutions have been developed for simulating other functionalities, such as dialysis capabilities, peristaltic movements, and biofilm growth. This research field is under constant development, and further efforts are needed to drive these models closer to in vivo conditions, thereby increasing their usefulness for studying the gut microbiome impact on human health. Therefore, understanding the influence of key operational parameters is fundamental for the refinement of the current bioreactors and for guiding the development of more complex models. In this review, we performed a systematic search for operational parameters in 229 papers that used continuous bioreactors seeded with human feces. Despite the reporting of operational parameters for the various bioreactor models being variable, as a result of a lack of standardization, the impact of specific operational parameters on gut microbial ecology is discussed, highlighting the advantages and limitations of the current bioreactor systems.

Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry.

Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.

Targeted lipidomics coupled with machine learning for authenticating the provenance of chicken eggs.

Free-range eggs are ethically desirable but as with all high-value commercial products, the establishment of provenance can be problematic. Here, we compared a simple one-step isopropanol method to a two-step methyl-tert-butyl ether method for extracting lipid species in chicken egg yolks before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The isopropanol method extracted 937 lipid species from 20 major lipid subclasses with high reproducibility (CV < 30 %). Machine learning techniques could differentiate conventional cage, barn, and free-range eggs using an external test dataset with an accuracy of 0.94, 0.82, and 0.82, respectively. Lipid species that differentiated cage eggs were predominantly phosphocholines and phosphoethanolamines whilst the free-range egg lipidomes were dominated by acylglycerides with up to three fatty acids. The lipid profiles were found to be characteristic of the cage, barns, and free-range eggs. The lipidomic analysis together with the statistical modeling approach thus provides an efficient tool for verifying the provenance of conventional chicken eggs.

A patient-centric modeling framework captures recovery from SARS-CoV-2 infection.

The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct 'systemic recovery' profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app , designed to test our findings prospectively.

An NMR-Based Model to Investigate the Metabolic Phenoreversion of COVID-19 Patients throughout a Longitudinal Study.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts.

Characterization of diet-dependent temporal changes in circulating short-chain fatty acid concentrations: A randomized crossover dietary trial.

BACKGROUND: Production of SCFAs from food is a complex and dynamic saccharolytic fermentation process mediated by both human and gut microbial factors. Knowledge of SCFA production and of the relation between SCFA profiles and dietary patterns is lacking. OBJECTIVES: Temporal changes in SCFA concentrations in response to 2 contrasting diets were investigated using a novel GC-MS method. METHODS: Samples were obtained from a randomized, controlled, crossover trial designed to characterize the metabolic response to 4 diets. Participants (n = 19) undertook these diets during an inpatient stay (of 72 h). Serum samples were collected 2 h after breakfast (AB), after lunch (AL), and after dinner (AD) on day 3, and a fasting sample (FA) was obtained on day 4. The 24-h urine samples were collected on day 3. In this substudy, samples from the 2 extreme diets representing a diet with high adherence to WHO healthy eating recommendations and a typical Western diet were analyzed using a bespoke GC-MS method developed to detect and quantify 10 SCFAs and precursors in serum and urine samples. RESULTS: Considerable interindividual variation in serum SCFA concentrations was observed across all time points, and temporal fluctuations were observed for both diets. Although the sample collection timing exerted a greater magnitude of effect on circulating SCFA concentrations, the unhealthy diet was associated with a lower concentration of acetic acid (FA: coefficient: -17.0; SE: 5.8; P-trend = 0.00615), 2-methylbutyric acid (AL: coefficient: -0.1; SE: 0.028; P-trend = 4.13 × 10-4 and AD: coefficient: -0.1; SE: 0.028; P-trend = 2.28 × 10-3), and 2-hydroxybutyric acid (FA: coefficient: -15.8; SE: 5.11; P-trend: 4.09 × 10-3). In contrast, lactic acid was significantly higher in the unhealthy diet (AL: coefficient: 750.2; SE: 315.2; P-trend = 0.024 and AD: coefficient: 1219.3; SE: 322.6; P-trend: 8.28 × 10-4). CONCLUSIONS: The GC-MS method allowed robust mapping of diurnal patterns in SCFA concentrations, which were affected by diet, and highlighted the importance of standardizing the timing of SCFA measurements in dietary studies. This trial was registered on the NIHR UK clinical trial gateway and with ISRCTN as ISRCTN43087333.