Metabolomic profiling in small vessel disease identifies multiple associations with disease severity.

Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches.

Mapping of 1 H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products.

One-dimensional (1D) proton-nuclear magnetic resonance (1 H-NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large-scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma-EDTA spectra, were able to successfully predict the 1 H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv1/2 ) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of 1 H NMR chemical shifts based solely on chemically defined constraints.

1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization.

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10-16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.

peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets.

SUMMARY: Untargeted liquid chromatography-mass spectrometry (LC-MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real-time data quality assessment. AVAILABILITY AND IMPLEMENTATION: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diet Patterns Are Associated with Circulating Metabolites and Lipid Profiles of South Asians in the United States.

BACKGROUND: South Asians are at higher risk for cardiometabolic disease than many other racial/ethnic minority groups. Diet patterns in US South Asians have unique components associated with cardiometabolic disease. OBJECTIVES: We aimed to characterize the metabolites associated with 3 representative diet patterns. METHODS: We included 722 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 y without known cardiovascular disease. Fasting serum specimens and diet and demographic questionnaires were collected at baseline and diet patterns previously generated through principal components analysis. LC-MS-based untargeted metabolomic and lipidomic analysis was conducted with targeted integration of known metabolite and lipid signals. Linear regression models of diet pattern factor score and log-transformed metabolites adjusted for age, sex, caloric intake, and BMI and adjusted for multiple comparisons were performed, followed by elastic net linear regression of significant metabolites. RESULTS: There were 443 metabolites of known identity extracted from the profiling data. The "animal protein" diet pattern was associated with 61 metabolites and lipids, including glycerophospholipids phosphatidylethanolamine PE(O-16:1/20:4) and/or PE(P-16:0/20:4) (ß: 0.13; 95% CI: 0.11, 0.14) and N-acyl phosphatidylethanolamines (NAPEs) NAPE(O-18:1/20:4/18:0) and/or NAPE(P-18:0/20:4/18:0) (ß: 0.13; 95% CI: 0.11, 0.14), lysophosphatidylinositol (LPI) (22:6/0:0) (ß: 0.14; 95% CI: 0.12, 0.17), and fatty acid (FA) (22:6) (ß: 0.15; 95% CI: 0.13, 0.17). The "fried snacks, sweets, high-fat dairy" pattern was associated with 12 lipids, including PC(16:0/22:6) (ß: -0.08; 95% CI: -0.09, -0.06) and FA (22:6) (ß: 0.14; 95% CI: -0.17, -0.10). The "fruits, vegetables, nuts, and legumes" pattern was associated with 5 metabolites including proline betaine (ß: 0.17; 95% CI: 0.09, 0.25) (P < 0.0002). CONCLUSIONS: Three predominant dietary patterns in US South Asians are associated with circulating metabolites differentiated by lipids including glycerophospholipids and PUFAs and the amino acid proline betaine.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

Circulating metabolites and lipids are associated with glycaemic measures in South Asians.

BACKGROUND: South Asians are at higher risk for diabetes (DM) than many other racial/ethnic groups. Circulating metabolites are measurable products of metabolic processes that may explain the aetiology of elevated risk. We characterized metabolites associated with prevalent DM and glycaemic measures in South Asians. METHODS: We included 717 participants from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study, aged 40-84 years. We used baseline fasting serum for metabolomics and demographic, behavioural, glycaemic data from baseline and at 5 years. We performed LC-MS untargeted metabolomic and lipidomic analysis with targeted integration of known signals. Individual linear and ordinal logistic regression models were adjusted for age, sex, BMI, diet, exercise, alcohol, smoking and family history of DM followed by elastic net regression to identify metabolites most associated with the outcome. RESULTS: There were 258 metabolites with detectable signal in >98% of samples. Thirty-four metabolites were associated with prevalent DM in an elastic net model. Predominant metabolites associated with DM were sphingomyelins, proline (OR 15.86; 95% CI 4.72, 53.31) and betaine (OR 0.03; 0.004, 0.14). Baseline tri- and di-acylglycerols [DG (18:0/16:0) (18.36; 11.79, 24.92)] were positively associated with fasting glucose and long-chain acylcarnitines [CAR 26:1 (-0.40; -0.54, -0.27)] were inversely associated with prevalent DM and HbA1c at follow-up. DISCUSSION: A metabolomic signature in South Asians may help determine the unique aetiology of diabetes in this high-risk ethnic group. Future work will externally validate our findings and determine the effects of modifiable risk factors for DM.

The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets.

SUMMARY: As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines. AVAILABILITY AND IMPLEMENTATION: The nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.

The effects of kisspeptin on ß-cell function, serum metabolites and appetite in humans.

AIMS: To investigate the effect of kisspeptin on glucose-stimulated insulin secretion and appetite in humans. MATERIALS AND METHODS: In 15 healthy men (age: 25.2 ± 1.1 years; BMI: 22.3 ± 0.5 kg m-2 ), we compared the effects of 1 nmol kg-1 h-1 kisspeptin versus vehicle administration on glucose-stimulated insulin secretion, metabolites, gut hormones, appetite and food intake. In addition, we assessed the effect of kisspeptin on glucose-stimulated insulin secretion in vitro in human pancreatic islets and a human ß-cell line (EndoC-ßH1 cells). RESULTS: Kisspeptin administration to healthy men enhanced insulin secretion following an intravenous glucose load, and modulated serum metabolites. In keeping with this, kisspeptin increased glucose-stimulated insulin secretion from human islets and a human pancreatic cell line in vitro. In addition, kisspeptin administration did not alter gut hormones, appetite or food intake in healthy men. CONCLUSIONS: Collectively, these data demonstrate for the first time a beneficial role for kisspeptin in insulin secretion in humans in vivo. This has important implications for our understanding of the links between reproduction and metabolism in humans, as well as for the ongoing translational development of kisspeptin-based therapies for reproductive and potentially metabolic conditions.

Metabolic phenotyping for enhanced mechanistic stratification of chronic hepatitis C-induced liver fibrosis.

OBJECTIVES: The invasive nature of biopsy alongside issues with categorical staging and sampling error has driven research into noninvasive biomarkers for the assessment of liver fibrosis in order to stratify and personalize treatment of patients with liver disease. Here, we sought to determine whether a metabonomic approach could be used to identify signatures reflective of the dynamic, pathological metabolic perturbations associated with fibrosis in chronic hepatitis C (CHC) patients. METHODS: Plasma nuclear magnetic resonance (NMR) spectral profiles were generated for two independent cohorts of CHC patients and healthy controls (n=50 original and n=63 validation). Spectral data were analyzed and significant discriminant biomarkers associated with fibrosis (as graded by enhanced liver fibrosis (ELF) and METAVIR scores) identified using orthogonal projection to latent structures (O-PLS). RESULTS: Increased severity of fibrosis was associated with higher tyrosine, phenylalanine, methionine, citrate and, very-low-density lipoprotein (vLDL) and lower creatine, low-density lipoprotein (LDL), phosphatidylcholine, and N-Acetyl-α1-acid-glycoprotein. Although area under the receiver operator characteristic curve analysis revealed a high predictive performance for classification based on METAVIR-derived models, <40% of identified biomarkers were validated in the second cohort. In the ELF-derived models, however, over 80% of the biomarkers were validated. CONCLUSIONS: Our findings suggest that modeling against a continuous ELF-derived score of fibrosis provides a more robust assessment of the metabolic changes associated with fibrosis than modeling against the categorical METAVIR score. Plasma metabolic phenotypes reflective of CHC-induced fibrosis primarily define alterations in amino-acid and lipid metabolism, and hence identify mechanistically relevant pathways for further investigation as therapeutic targets.

Diet shapes the metabolite profile in the intact human ileum, which affects PYY release.

The human ileum contains a high density of enteroendocrine L-cells, which release the appetite-suppressing hormones glucagon-like peptide-1 (GLP-1) and peptide tyrosine tyrosine (PYY) in response to food intake. Recent evidence highlighted the potential role of food structures in PYY release, but the link between food structures, ileal metabolites, and appetite hormone release remains unclear owing to limited access to intact human ileum. In a randomized crossover trial (ISRCTN11327221; isrctn.com), we investigated the role of human ileum in GLP-1 and PYY release by giving healthy volunteers diets differing in fiber and food structure: high-fiber (intact or disrupted food structures) or low-fiber disrupted food structures. We used nasoenteric tubes to sample chyme from the intact distal ileum lumina of humans in the fasted state and every 60 min for 480 min postprandially. We demonstrate the highly dynamic, wide-ranging molecular environment of the ileum over time, with a substantial decrease in ileum bacterial numbers and bacterial metabolites after food intake. We also show that high-fiber diets, independent of food structure, increased PYY release compared with a low-fiber diet during 0 to 240 min postprandially. High-fiber diets also increased ileal stachyose, and a disrupted high-fiber diet increased certain ileal amino acids. Treatment of human ileal organoids with ileal fluids or an amino acid and stachyose mixture stimulated PYY expression in a similar profile to blood PYY concentrations, confirming the role of ileal metabolites in PYY release. Our study demonstrates the diet-induced changes over time in the metabolite environment of intact human ileum, which play a role in PYY release.

In vitro and in vivo efficacy of a cosmetic product formulated with new lipid particles for the treatment of aged skin.

BACKGROUND: The cumulative oxidative damage causes an acceleration in the skin aging. OBJECTIVES: To evaluate the ability of a new patented matrix of lipid particles (SIREN CAPSULE TECHNOLOGY™) to have superior anti-aging properties due to its high sensitivity to reactive oxygen species (ROS), testing its efficacy versus free or encapsulated vitamins. METHODS: An in vitro study was conducted to evaluate the protective effects of lipid particles using menadione as an enhancer of oxidative stress. Subsequently, in vivo studies evaluated skin hydration, skin barrier function, and smoothness and wrinkle depth. For this purpose, gels containing free or encapsulated vitamins were used as controls. RESULTS: In vitro, the SIREN CAPSULE TECHNOLOGY™ gel shows inhibitory activity against ROS production through menadione induction. In fact, at both tested concentrations, ROS production is lower than in the control samples (placebo, free vitamins, encapsulated vitamins). In vivo, the net effect of SIREN CAPSULE TECHNOLOGY™ gel versus the others permitted to conclude that lipid particles exert a higher skin moisturizing effect (20.17%) and a stronger effect in reducing transepidermal water loss (-16.29%) after 4 weeks of treatment. As for surface analysis, a gel based on SIREN CAPSULE TECHNOLOGY™ improves the skin texture in a similar way than gel containing encapsulated vitamins (Ra and Rz variations in 4 weeks). CONCLUSIONS: SIREN CAPSULE TECHNOLOGY™ represents an advance and a successful strategy to develop cosmetic products for the treatment of skin conditions associated with an accumulation of ROS. SIREN CAPSULE TECHNOLOGY™ represents a result-oriented breakthrough in the effective delivery of active ingredients to the skin.

B7-H3 Associates with IMPDH2 and Regulates Cancer Cell Survival.

Lung cancer is one of the most common cancers worldwide, and despite improvements in treatment regimens, patient prognosis remains poor. Lung adenocarcinomas develop from the lung epithelia and understanding how specific genetic and environmental factors lead to oncogenic transformation in these cells is of great importance to define the pathways that contribute to tumorigenesis. The recent rise in the use of immunotherapy to treat different cancers has prompted the exploration of immune modulators in tumour cells that may provide new targets to manipulate this process. Of these, the B7 family of cell surface receptors, which includes PD-1, is of particular interest due to its role in modulating immune cell responses within the tumour microenvironment. B7-H3 (CD276) is one family member that is upregulated in many cancer types and suggested to contribute to tumour-immune interactions. However, the function and ligand(s) for this receptor in normal lung epithelia and the mechanisms through which the overexpression of B7-H3 regulate cancer progression in the absence of immune cell interactions remain unclear. Here, we present evidence that B7-H3 is associated with one of the key rate-limiting metabolic enzymes IMPDH2, and the localisation of this complex is altered in human lung cancer cells that express high levels of B7-H3. Mechanistically, the IMPDH2:B7-H3 complex provides a protective role in cancer cells to escape oxidative stress triggered by chemotherapy, thus leading to cell survival. We further demonstrate that the loss of B7-H3 in cancer cells has no effect on growth or migration in 2D but promotes the expansion of 3D spheroids in an IMPDH2-dependent manner. These findings provide new insights into the B7-H3 function in the metabolic homeostasis of normal and transformed lung cancer cells, and whilst this molecule remains an interesting target for immunotherapy, these findings caution against the use of anti-B7-H3 therapies in certain clinical settings.

Discriminatory Changes in Circulating Metabolites as a Predictor of Hepatocellular Cancer in Patients with Metabolic (Dysfunction) Associated Fatty Liver Disease.

Introduction: The burden of metabolic (dysfunction) associated fatty liver disease (MAFLD) is rising mirrored by an increase in hepatocellular cancer (HCC). MAFLD and its sequelae are characterized by perturbations in lipid handling, inflammation, and mitochondrial damage. The profile of circulating lipid and small molecule metabolites with the development of HCC is poorly characterized in MAFLD and could be used in future studies as a biomarker for HCC. Methods: We assessed the profile of 273 lipid and small molecule metabolites by ultra-performance liquid chromatography coupled to high-resolution mass spectrometry in serum from patients with MAFLD (n = 113) and MAFLD-associated HCC (n = 144) from six different centers. Regression models were used to identify a predictive model of HCC. Results: Twenty lipid species and one metabolite, reflecting changes in mitochondrial function and sphingolipid metabolism, were associated with the presence of cancer on a background of MAFLD with high accuracy (AUC 0.789, 95% CI: 0.721-0.858), which was enhanced with the addition of cirrhosis to the model (AUC 0.855, 95% CI: 0.793-0.917). In particular, the presence of these metabolites was associated with cirrhosis in the MAFLD subgroup (p < 0.001). When considering the HCC cohort alone, the metabolic signature was an independent predictor of overall survival (HR 1.42, 95% CI: 1.09-1.83, p < 0.01). Conclusion: These exploratory findings reveal a metabolic signature in serum which is capable of accurately detecting the presence of HCC on a background of MAFLD. This unique serum signature will be taken forward for further investigation of diagnostic performance as biomarker of early stage HCC in patients with MAFLD in the future.

Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS).

To assess their roles in breast cancer diagnostics, we aimed to compare plasma cell-free DNA (cfDNA) levels with the circulating metabolome in a large breast screening cohort of women recalled for mammography, including healthy women and women with mammographically detected breast diseases, ductal carcinoma in situ and invasive breast cancer: the Breast Screening and Monitoring Study (BSMS). In 999 women, plasma was analyzed by nuclear magnetic resonance (NMR) and Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) and then processed to isolate and quantify total cfDNA. NMR and UPLC-MS results were compared with data for 186 healthy women derived from the AIRWAVE cohort. Results showed no significant differences between groups for all metabolites, whereas invasive cancers had significantly higher plasma cfDNA levels than all other groups. When stratified the supervised OPLS-DA analysis and total cfDNA concentration showed high discrimination accuracy between invasive cancers and the disease/medication-free subjects. Furthermore, comparison of OPLS-DA data for invasive breast cancers with the AIRWAVE cohort showed similar discrimination between breast cancers and healthy controls. This is the first report of agreement between metabolomics and plasma cfDNA levels for discriminating breast cancer from healthy subjects in a true screening population. It also emphasizes the importance of sample standardization. Follow on studies will involve analysis of candidate features in a larger validation series as well as comparing results with serial plasma samples taken at the next routine screening mammography appointment. The findings here help establish the role of plasma analysis in the diagnosis of breast cancer in a large real-world cohort.

LEF1 Drives a Central Memory Program and Supports Antitumor Activity of Natural Killer T Cells.

Vα24-invariant natural killer T cells (NKT) possess innate antitumor properties that can be exploited for cancer immunotherapy. We have shown previously that the CD62L+ central memory-like subset of these cells drives the in vivo antitumor activity of NKTs, but molecular mediators of NKT central memory differentiation remain unknown. Here, we demonstrate that relative to CD62L- cells, CD62L+ NKTs express a higher level of the gene encoding the Wnt/ß-catenin transcription factor lymphoid enhancer binding factor 1 (LEF1) and maintain active Wnt/ß-catenin signaling. CRISPR/Cas9-mediated LEF1 knockout reduced CD62L+ frequency after antigenic stimulation, whereas Wnt/ß-catenin activator Wnt3a ligand increased CD62L+ frequency. LEF1 overexpression promoted NKT expansion and limited exhaustion following serial tumor challenge and was sufficient to induce a central memory-like transcriptional program in NKTs. In mice, NKTs expressing a GD2-specific chimeric-antigen receptor (CAR) with LEF1 demonstrated superior control of neuroblastoma xenograft tumors compared with control CAR-NKTs. These results identify LEF1 as a transcriptional activator of the NKT central memory program and advance development of NKT cell-based immunotherapy. See related Spotlight by Van Kaer, p. 144.

The gut microbiota and metabolome are associated with diminished COVID-19 vaccine-induced antibody responses in immunosuppressed inflammatory bowel disease patients.

BACKGROUND: Patients with inflammatory bowel disease (IBD) treated with anti-TNF therapy exhibit attenuated humoral immune responses to vaccination against SARS-CoV-2. The gut microbiota and its functional metabolic output, which are perturbed in IBD, play an important role in shaping host immune responses. We explored whether the gut microbiota and metabolome could explain variation in anti-SARS-CoV-2 vaccination responses in immunosuppressed IBD patients. METHODS: Faecal and serum samples were prospectively collected from infliximab-treated patients with IBD in the CLARITY-IBD study undergoing vaccination against SARS-CoV-2. Antibody responses were measured following two doses of either ChAdOx1 nCoV-19 or BNT162b2 vaccine. Patients were classified as having responses above or below the geometric mean of the wider CLARITY-IBD cohort. 16S rRNA gene amplicon sequencing, nuclear magnetic resonance (NMR) spectroscopy and bile acid profiling with ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) were performed on faecal samples. Univariate, multivariable and correlation analyses were performed to determine gut microbial and metabolomic predictors of response to vaccination. FINDINGS: Forty-three infliximab-treated patients with IBD were recruited (30 Crohn's disease, 12 ulcerative colitis, 1 IBD-unclassified; 26 with concomitant thiopurine therapy). Eight patients had evidence of prior SARS-CoV-2 infection. Seventeen patients (39.5%) had a serological response below the geometric mean. Gut microbiota diversity was lower in below average responders (p = 0.037). Bilophila abundance was associated with better serological response, while Streptococcus was associated with poorer response. The faecal metabolome was distinct between above and below average responders (OPLS-DA R2X 0.25, R2Y 0.26, Q2 0.15; CV-ANOVA p = 0.038). Trimethylamine, isobutyrate and omega-muricholic acid were associated with better response, while succinate, phenylalanine, taurolithocholate and taurodeoxycholate were associated with poorer response. INTERPRETATION: Our data suggest that there is an association between the gut microbiota and variable serological response to vaccination against SARS-CoV-2 in immunocompromised patients. Microbial metabolites including trimethylamine may be important in mitigating anti-TNF-induced attenuation of the immune response. FUNDING: JLA is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-502), funded by Imperial College London and The Joyce and Norman Freed Charitable Trust. BHM is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-002). The Division of Digestive Diseases at Imperial College London receives financial and infrastructure support from the NIHR Imperial Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London. Metabolomics studies were performed at the MRC-NIHR National Phenome Centre at Imperial College London; this work was supported by the Medical Research Council (MRC), the National Institute of Health Research (NIHR) (grant number MC_PC_12025) and infrastructure support was provided by the NIHR Imperial Biomedical Research Centre (BRC). The NIHR Exeter Clinical Research Facility is a partnership between the University of Exeter Medical School College of Medicine and Health, and Royal Devon and Exeter NHS Foundation Trust. This project is supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care.

Metabolic surgery profoundly influences gut microbial-host metabolic cross-talk.

BACKGROUND AND AIMS: Bariatric surgery is increasingly performed worldwide to treat morbid obesity and is also known as metabolic surgery to reflect its beneficial metabolic effects especially with respect to improvement in type 2 diabetes. Understanding surgical weight loss mechanisms and metabolic modulation is required to enhance patient benefits and operative outcomes. METHODS: The authors applied a parallel and statistically integrated bacterial profiling and metabonomic approach to characterise Roux-en-Y gastric bypass (RYGB) effects in a non-obese rat model. RESULTS: Substantial shifts of the main gut phyla towards higher concentrations of Proteobacteria (52-fold), specifically Enterobacter hormaechei, are shown. Low concentrations of Firmicutes (4.5-fold) and Bacteroidetes (twofold) in comparison with sham-operated rats were also found. Faecal extraction studies revealed a decrease in faecal bile acids and a shift from protein degradation to putrefaction through decreased faecal tyrosine with concomitant increases in faecal putrescine and diaminoethane. Decreased urinary amines and cresols were found and indices of modulated energy metabolism were demonstrated after RYGB, including decreased urinary succinate, 2-oxoglutarate, citrate and fumarate. These changes could also indicate renal tubular acidosis, which is associated with increased flux of mitochondrial tricarboxylic acid cycle intermediates. A surgically induced effect on the gut-brain-liver metabolic axis is inferred from modulated faecal γ-aminobutyric acid and glutamate. CONCLUSION: This profound co-dependence of mammalian and microbial metabolism, which is systematically altered after RYGB surgery, suggests that RYGB exerts local and global metabolic effects. The effect of RYGB surgery on the host metabolic-microbial cross-talk augments our understanding of the metabolic phenotype of bariatric procedures and can facilitate enhanced treatments for obesity-related diseases.

Associations of NAFLD with circulating ceramides and impaired glycemia.

AIM: Determine the association of circulating ceramides with NAFLD and glycemic impairment. METHODS: Sample: 669 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort aged 40-84 years without cardiovascular disease, cirrhosis, or significant alcohol intake. CLINICAL MEASURES: Computed tomography scans at baseline for hepatic attenuation. Fasting serum specimens at baseline and after 5 years. Lipidomics: LC-MS-based analysis of 19 known ceramide signals. STATISTICAL ANALYSIS: Linear and logistic regression models of log-transformed ceramides, hepatic attenuation and glucose adjusted for age, sex, calories, study site, BMI, exercise, diet quality, alcohol, saturated fat, lipid-lowering medications and fasting glucose. RESULTS: Average age was 55 years, 44% were women, mean BMI was 25.9 kg/m2, and 8% had NAFLD. In adjusted models, Cer(d16:1/20:0) and Cer(d18:1/18:0) were associated with lower mean hepatic attenuation (increased liver fat) (ß -4.29; 95% CI [-5.98, -2.59]) and (ß -3.40; 95% CI [-5.11, -1.70]), and LacCer(d18:1/16:0) with higher attenuation (ß 4.44; 95% CI [2.15, 6.73]). All three ceramides partially mediated the relationship between hepatic attenuation and fasting glucose by 16%, 11% and 5%, respectively, after 5-years. CONCLUSIONS: Three circulating ceramides were strongly associated with NAFLD and fasting glucose after 5 years, and partially mediated this association.