960 results found
Koller KR, Wilson A, Normolle DP, et al., 2021, Dietary fibre to reduce colon cancer risk in Alaska Native people: the Alaska FIRST randomised clinical trial protocol., BMJ Open, Vol: 11, Pages: 1-9, ISSN: 2044-6055
INTRODUCTION: Diet, shown to impact colorectal cancer (CRC) risk, is a modifiable environmental factor. Fibre foods fermented by gut microbiota produce metabolites that not only provide food for the colonic epithelium but also exert regulatory effects on colonic mucosal inflammation and proliferation. We describe methods used in a double-blinded, randomised, controlled trial with Alaska Native (AN) people to determine if dietary fibre supplementation can substantially reduce CRC risk among people with the highest reported CRC incidence worldwide. METHODS AND ANALYSES: Eligible patients undergoing routine screening colonoscopy consent to baseline assessments and specimen/data collection (blood, urine, stool, saliva, breath and colon mucosal biopsies) at the time of colonoscopy. Following an 8-week stabilisation period to re-establish normal gut microbiota post colonoscopy, study personnel randomise participants to either a high fibre supplement (resistant starch, n=30) or placebo (digestible starch, n=30) condition, repeating stool sample collection. During the 28-day supplement trial, each participant consumes their usual diet plus their supplement under direct observation. On day 29, participants undergo a flexible sigmoidoscopy to obtain mucosal biopsy samples to measure the effect of the supplement on inflammatory and proliferative biomarkers of cancer risk, with follow-up assessments and data/specimen collection similar to baseline. Secondary outcome measures include the impact of a high fibre supplement on the oral and colonic microbiome and biofluid metabolome. ETHICS AND DISSEMINATION: Approvals were obtained from the Alaska Area and University of Pittsburgh Institutional Review Boards and Alaska Native Tribal Health Consortium and Southcentral Foundation research review bodies. A data safety monitoring board, material transfer agreements and weekly study team meetings provide regular oversight throughout the study. Study findings will first be shared with AN
Masuda R, Lodge S, Nitschke P, et al., 2021, Integrative Modeling of Plasma Metabolic and Lipoprotein Biomarkers of SARS-CoV-2 Infection in Spanish and Australian COVID-19 Patient Cohorts, JOURNAL OF PROTEOME RESEARCH, Vol: 20, Pages: 4139-4152, ISSN: 1535-3893
Nicholson JK, 2021, Molecular Phenomic Approaches to Deconvolving the Systemic Effects of SARS-CoV-2 Infection and Post-acute COVID-19 Syndrome, Phenomics, ISSN: 2730-583X
<jats:title>Abstract</jats:title><jats:p>SARS COV-2 infection causes acute and frequently severe respiratory disease with associated multi-organ damage and systemic disturbances in many biochemical pathways. Metabolic phenotyping provides deep insights into the complex immunopathological problems that drive the resulting COVID-19 disease and is also a source of novel metrics for assessing patient recovery. A multiplatform metabolic phenotyping approach to studying the pathology and systemic metabolic sequelae of COVID-19 is considered here, together with a framework for assessing post-acute COVID-19 Syndrome (PACS) that is a major long-term health consequence for many patients. The sudden emergence of the disease presents a biological discovery challenge as we try to understand the pathological mechanisms of the disease and develop effective mitigation strategies. This requires technologies to measure objectively the extent and sub-phenotypes of the disease at the molecular level. Spectroscopic methods can reveal metabolic sub-phenotypes and new biomarkers that can be monitored during the acute disease phase and beyond. This approach is scalable and translatable to other pathologies and provides as an exemplar strategy for the investigation of other emergent zoonotic diseases with complex immunological drivers, multi-system involvements and diverse persistent symptoms.</jats:p>
Gray N, Lawler NG, Zeng AX, et al., 2021, Diagnostic potential of the plasma lipidome in infectious disease: application to acute SARS-CoV-2 infection, Metabolites, Vol: 11, Pages: 1-17, ISSN: 2218-1989
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.
Wei GZ, Martin KA, Xing PY, et al., 2021, Tryptophan-metabolizing gut microbes regulate adult neurogenesis via the aryl hydrocarbon receptor, Proceedings of the National Academy of Sciences, Vol: 118, Pages: 1-10, ISSN: 0027-8424
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.
Posma JM, Garcia-Perez I, Frost G, et al., 2021, Nutriome-metabolome relationships provide insights into dietary intake and metabolism (vol 1, pg 426, 2020), NATURE FOOD, Vol: 2, Pages: 541-542
Li J, 2021, Roux-en-Y Gastric bypass-induced bacterial perturbation contributes to altered host-bacterial co-metabolic phenotype, Microbiome, Vol: 9, ISSN: 2049-2618
BACKGROUND: Bariatric surgery, used to achieve effective weight loss in individuals with severe obesity, modifies the gut microbiota and systemic metabolism in both humans and animal models. The aim of the current study was to understand better the metabolic functions of the altered gut microbiome by conducting deep phenotyping of bariatric surgery patients and bacterial culturing to investigate causality of the metabolic observations. METHODS: Three bariatric cohorts (n = 84, n = 14 and n = 9) with patients who had undergone Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG) or laparoscopic gastric banding (LGB), respectively, were enrolled. Metabolic and 16S rRNA bacterial profiles were compared between pre- and post-surgery. Faeces from RYGB patients and bacterial isolates were cultured to experimentally associate the observed metabolic changes in biofluids with the altered gut microbiome. RESULTS: Compared to SG and LGB, RYGB induced the greatest weight loss and most profound metabolic and bacterial changes. RYGB patients showed increased aromatic amino acids-based host-bacterial co-metabolism, resulting in increased urinary excretion of 4-hydroxyphenylacetate, phenylacetylglutamine, 4-cresyl sulphate and indoxyl sulphate, and increased faecal excretion of tyramine and phenylacetate. Bacterial degradation of choline was increased as evidenced by altered urinary trimethylamine-N-oxide and dimethylamine excretion and faecal concentrations of dimethylamine. RYGB patients' bacteria had a greater capacity to produce tyramine from tyrosine, phenylalanine to phenylacetate and tryptophan to indole and tryptamine, compared to the microbiota from non-surgery, normal weight individuals. 3-Hydroxydicarboxylic acid metabolism and urinary excretion of primary bile acids, serum BCAAs and dimethyl sulfone were also perturbed following bariatric surgery. CONCLUSION: Altered bacterial composition and metabolism contribute to metabolic observations in biofluid
Bergamaschi L, Mescia F, Turner L, et al., 2021, Longitudinal analysis reveals that delayed bystander CD8(+) T cell activation and early immune pathology distinguish severe COVID-19 from mild disease, IMMUNITY, Vol: 54, Pages: 1257-+, ISSN: 1074-7613
Holmes E, Wist J, Masuda R, et al., 2021, Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome, JOURNAL OF PROTEOME RESEARCH, Vol: 20, Pages: 3315-3329, ISSN: 1535-3893
Kimhofer T, Lodge S, Whiley L, et al., 2021, Correction to "Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection"., J Proteome Res, Vol: 20
Brial F, Chilloux J, Nielsen T, et al., 2021, Human and preclinical studies of the host-gut microbiome co-metabolite hippurate as a marker and mediator of metabolic health., Gut, ISSN: 0017-5749
OBJECTIVE: Gut microbial products are involved in regulation of host metabolism. In human and experimental studies, we explored the potential role of hippurate, a hepatic phase 2 conjugation product of microbial benzoate, as a marker and mediator of metabolic health. DESIGN: In 271 middle-aged non-diabetic Danish individuals, who were stratified on habitual dietary intake, we applied 1H-nuclear magnetic resonance (NMR) spectroscopy of urine samples and shotgun-sequencing-based metagenomics of the gut microbiome to explore links between the urine level of hippurate, measures of the gut microbiome, dietary fat and markers of metabolic health. In mechanistic experiments with chronic subcutaneous infusion of hippurate to high-fat-diet-fed obese mice, we tested for causality between hippurate and metabolic phenotypes. RESULTS: In the human study, we showed that urine hippurate positively associates with microbial gene richness and functional modules for microbial benzoate biosynthetic pathways, one of which is less prevalent in the Bacteroides 2 enterotype compared with Ruminococcaceae or Prevotella enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate concentration, independently of gene richness, accounts for links with metabolic health. In the high-fat-fed mice experiments, we demonstrate causality through chronic infusion of hippurate (20 nmol/day) resulting in improved glucose tolerance and enhanced insulin secretion. CONCLUSION: Our human and experimental studies show that a high urine hippurate concentration is a general marker of metabolic health, and in the context of obesity induced by high-fat diets, hippurate contributes to metabolic improvements, highlighting its potential as a mediator of metabolic health.
Barker GF, Pechlivanis A, Bello AT, et al., 2021, Aa022 a high-fiber low-fat diet increases fecal levels of lithocholic acid derivative 3-ketocholanic acid, Digestive Disease Week, Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S393-S394, ISSN: 0016-5085
Hoyles L, Mayneris-Perxachs J, Cardellini M, et al., 2021, Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome, Microbiome, Vol: 9, Pages: 1-18, ISSN: 2049-2618
Background: The gut microbiome and iron status are known to play a role in the pathophysiology of non-alcoholic fatty liver disease (NAFLD), although their complex interaction remains unclear.Results: Here, we applied an integrative systems medicine approach (faecal metagenomics, plasma and urine metabolomics, hepatic transcriptomics) in 2 well-characterised human cohorts of subjects with obesity (discovery n = 49 and validation n = 628) and an independent cohort formed by both individuals with and without obesity (n = 130), combined with in vitro and animal models. Serum ferritin levels, as a markers of liver iron stores, were positively associated with liver fat accumulation in parallel with lower gut microbial gene richness, composition and functionality. Specifically, ferritin had strong negative associations with the Pasteurellaceae, Leuconostocaceae and Micrococcaea families. It also had consistent negative associations with several Veillonella, Bifidobacterium and Lactobacillus species, but positive associations with Bacteroides and Prevotella spp. Notably, the ferritin-associated bacterial families had a strong correlation with iron-related liver genes. In addition, several bacterial functions related to iron metabolism (transport, chelation, heme and siderophore biosynthesis) and NAFLD (fatty acid and glutathione biosynthesis) were also associated with the host serum ferritin levels. This iron-related microbiome signature was linked to a transcriptomic and metabolomic signature associated to the degree of liver fat accumulation through hepatic glucose metabolism. In particular, we found a consistent association among serum ferritin, Pasteurellaceae and Micrococcacea families, bacterial functions involved in histidine transport, the host circulating histidine levels and the liver expression of GYS2 and SEC24B. Serum ferritin was also related to bacterial glycine transporters, the host glycine serum levels and the liver expression of glycine transporters. The
Lawler NG, Gray N, Kimhofer T, et al., 2021, Systemic perturbations in amine and kynurenine metabolism associated with acute SARS-CoV-2 infection and inflammatory cytokine responses, Journal of Proteome Research, Vol: 20, Pages: 2796-2811, ISSN: 1535-3893
We performed quantitative metabolic phenotyping of blood plasma in parallel with cytokine/chemokine analysis from participants who were either SARS-CoV-2 (+) (n = 10) or SARS-CoV-2 (-) (n = 49). SARS-CoV-2 positivity was associated with a unique metabolic phenotype and demonstrated a complex systemic response to infection, including severe perturbations in amino acid and kynurenine metabolic pathways. Nine metabolites were elevated in plasma and strongly associated with infection (quinolinic acid, glutamic acid, nicotinic acid, aspartic acid, neopterin, kynurenine, phenylalanine, 3-hydroxykynurenine, and taurine; p < 0.05), while four metabolites were lower in infection (tryptophan, histidine, indole-3-acetic acid, and citrulline; p < 0.05). This signature supports a systemic metabolic phenoconversion following infection, indicating possible neurotoxicity and neurological disruption (elevations of 3-hydroxykynurenine and quinolinic acid) and liver dysfunction (reduction in Fischer’s ratio and elevation of taurine). Finally, we report correlations between the key metabolite changes observed in the disease with concentrations of proinflammatory cytokines and chemokines showing strong immunometabolic disorder in response to SARS-CoV-2 infection.
Xie G, Wang L, Chen T, et al., 2021, A Metabolite Array Technology for Precision Medicine, ANALYTICAL CHEMISTRY, Vol: 93, Pages: 5709-5717, ISSN: 0003-2700
Lodge S, Nitschke P, Kimhofer T, et al., 2021, Diffusion and relaxation edited proton NMR spectroscopy of plasma reveals a high-fidelity supramolecular biomarker signature of SARS-CoV-2 infection, Analytical Chemistry, Vol: 93, Pages: 3976-3986, ISSN: 0003-2700
We have applied nuclear magnetic resonance spectroscopy based plasma phenotyping to reveal diagnostic molecular signatures of SARS-CoV-2 infection via combined diffusional and relaxation editing (DIRE). We compared plasma from healthy age-matched controls (n = 26) with SARS-CoV-2 negative non-hospitalized respiratory patients and hospitalized respiratory patients (n = 23 and 11 respectively) with SARS-CoV-2 rRT-PCR positive respiratory patients (n = 17, with longitudinal sampling time-points). DIRE data were modelled using principal component analysis and orthogonal projections to latent structures discriminant analysis (O-PLS-DA), with statistical cross-validation indices indicating excellent model generalization for the classification of SARS-CoV-2 positivity for all comparator groups (area under the receiver operator characteristic curve = 1). DIRE spectra show biomarker signal combinations conferred by differential concentrations of metabolites with selected molecular mobility properties. These comprise the following: (a) composite N-acetyl signals from α-1-acid glycoprotein and other glycoproteins (designated GlycA and GlycB) that were elevated in SARS-CoV-2 positive patients [p = 2.52 × 10–10 (GlycA) and 1.25 × 10–9 (GlycB) vs controls], (b) two diagnostic supramolecular phospholipid composite signals that were identified (SPC-A and SPC-B) from the –+N–(CH3)3 choline headgroups of lysophosphatidylcholines carried on plasma glycoproteins and from phospholipids in high-density lipoprotein subfractions (SPC-A) together with a phospholipid component of low-density lipoprotein (SPC–B). The integrals of the summed SPC signals (SPCtotal) were reduced in SARS-CoV-2 positive patients relative to both controls (p = 1.40 × 10–7) and SARS-CoV-2 negative patients (p = 4.52 × 10–8) but were not significantly different between controls and SARS-CoV-2 negative patients. The identity of the SPC signal comp
Lodge S, Nitschke P, Loo RL, et al., 2021, Low volume in vitro diagnostic proton NMR spectroscopy of human blood plasma for lipoprotein and metabolite analysis: application to SARS-CoV-2 biomarkers., Journal of Proteome Research, Vol: 20, Pages: 1415-1423, ISSN: 1535-3893
The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance (1H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 μL plasma) and 5 mm SampleJet NMR tubes (300 μL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.
Lodge S, Nitschke P, Kimhofer T, et al., 2021, NMR spectroscopic windows on the systemic effects of SARS-CoV-2 infection on plasma lipoproteins and metabolites in relation to circulating cytokines., Journal of Proteome Research, Vol: 20, Pages: 1382-1396, ISSN: 1535-3893
To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune resp
Seyfried F, Phetcharaburanin J, Glymenaki M, et al., 2021, Roux-en-Y gastric bypass surgery in Zucker rats induces bacterial and systemic metabolic changes independent of caloric restriction-induced weight loss, Gut Microbes, Vol: 13, Pages: 1-20, ISSN: 1949-0976
Mechanisms of Roux-en-Y gastric bypass (RYGB) surgery are not fully understood. This study aimed to investigate weight loss-independent bacterial and metabolic changes, as well as the absorption of bacterial metabolites and bile acids through the hepatic portal system following RYGB surgery. Three groups of obese Zucker (fa/fa) rats were included: RYGB (n = 11), sham surgery and body weight matched with RYGB (Sham-BWM, n = 5), and sham surgery fed ad libitum (Sham-obese, n = 5). Urine and feces were collected at multiple time points, with portal vein and peripheral blood obtained at the end of the study. Metabolic phenotyping approaches and 16S rRNA gene sequencing were used to determine the biochemical and bacterial composition of the samples, respectively. RYGB surgery-induced distinct metabolic and bacterial disturbances, which were independent of weight loss through caloric restriction. RYGB resulted in lower absorption of phenylalanine and choline, and higher urinary concentrations of host-bacterial co-metabolites (e.g., phenylacetylglycine, indoxyl sulfate), together with higher fecal trimethylamine, suggesting enhanced bacterial aromatic amino acid and choline metabolism. Short chain fatty acids (SCFAs) were lower in feces and portal vein blood from RYGB group compared to Sham-BWM, accompanied with lower abundances of Lactobacillaceae, and Ruminococcaceae known to contain SCFA producers, indicating reduced bacterial fiber fermentation. Fecal γ-amino butyric acid (GABA) was found in higher concentrations in RYGB than that in Sham groups and could play a role in the metabolic benefits associated with RYGB surgery. While no significant difference in urinary BA excretion, RYGB lowered both portal vein and circulating BA compared to Sham groups. These findings provide a valuable resource for how dynamic, multi-systems changes impact on overall metabolic health, and may provide potential therapeutic targets for developing downstream non-surgical treatment for
Letertre MPM, Myridakis A, Whiley L, et al., 2021, A targeted ultra performance liquid chromatography - Tandem mass spectrometric assay for tyrosine and metabolites in urine and plasma: Application to the effects of antibiotics on mice, JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES, Vol: 1164, ISSN: 1570-0232
Jimenez B, Abellona MRU, Drymousis P, et al., 2021, Neuroendocrine neoplasms: identification of novel metabolic circuits of potential diagnostic utility, Cancers, Vol: 13, ISSN: 2072-6694
The incidence of neuroendocrine neoplasms (NEN) is increasing, but established biomarkers have poor diagnostic and prognostic accuracy. Here, we aim to define the systemic metabolic consequences of NEN and to establish the diagnostic utility of proton nuclear magnetic resonance spectroscopy (1H-NMR) for NEN in a prospective cohort of patients through a single-centre, prospective controlled observational study. Urine samples of 34 treatment-naïve NEN patients (median age: 59.3 years, range: 36–85): 18 had pancreatic (Pan) NEN, of which seven were functioning; 16 had small bowel (SB) NEN; 20 age- and sex-matched healthy control individuals were analysed using a 600 MHz Bruker 1H-NMR spectrometer. Orthogonal partial-least-squares-discriminant analysis models were able to discriminate both PanNEN and SBNEN patients from healthy control (Healthy vs. PanNEN: AUC = 0.90, Healthy vs. SBNEN: AUC = 0.90). Secondary metabolites of tryptophan, such as trigonelline and a niacin-related metabolite were also identified to be universally decreased in NEN patients, while upstream metabolites, such as kynurenine, were elevated in SBNEN. Hippurate, a gut-derived metabolite, was reduced in all patients, whereas other gut microbial co-metabolites, trimethylamine-N-oxide, 4-hydroxyphenylacetate and phenylacetylglutamine, were elevated in those with SBNEN. These findings suggest the existence of a new systems-based neuroendocrine circuit, regulated in part by cancer metabolism, neuroendocrine signalling molecules and gut microbial co-metabolism. Metabonomic profiling of NEN has diagnostic potential and could be used for discovering biomarkers for these tumours. These preliminary data require confirmation in a larger cohort.
Brignardello J, Fountana S, Posma JM, et al., 2021, Characterization of diet-dependent temporal changes in circulating short-chain fatty acid concentrations: A randomized crossover dietary trial, The American Journal of Clinical Nutrition, ISSN: 0002-9165
Background: Production of Short-chain fatty acids (SCFAs) from food is a complex and dynamic saccharolytic fermentation process mediated by both human and gut microbial factors. SCFA production and knowledge of the relationship between SCFA profiles and dietary patterns is lacking. Objective: Temporal changes in SCFA levels in response to two contrasting diets were investigated using a novel GC-MS method.Design: Samples were obtained from a randomized, controlled, crossover trial designed to characterize the metabolic response to four diets. Participants (n=19) undertook these diets during an inpatient stay (of 72-h). Serum samples were collected 2-h after breakfast (AB), lunch (AL) and dinner (AD) on day 3 and a fasting sample (FA) was obtained on day 4. 24-h urine samples were collected on day 3. In this sub-study, samples from the two 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 inter-individual 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; standard error (SE)=5.8; p-trend=0.00615), 2-methylbutyric acid (AL: coefficient=-0.1; SE=0.028; p-trend=4.13x10-4 and AD: coefficient =-0.1; SE:=0.028; p-trend=2.28x10-3) and 2-hydroxybutyric acid (FA: coefficient=-15.8; standard error=5.11; p-trend: 4.09x10-3). In contrast lactic acid was significantly higher in the unhealthy diet (AL: coefficient=750.2; standard error=315.2; p-trend=0.024 and AD: coefficient=1219.3; standard error=322.6; p-trend: 8.28x10-4). Conclusion: The GC-MS method allowed robust mapping of
Kurbatova N, Garg M, Whiley L, et al., 2020, Urinary metabolic phenotyping for Alzheimer's disease, Scientific Reports, Vol: 10, ISSN: 2045-2322
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.
Gray N, Lawler NG, Yang R, et al., 2020, A simultaneous exploratory and quantitative amino acid and biogenic amine metabolic profiling platform for rapid disease phenotyping via UPLC-QToF-MS, Talanta, ISSN: 0039-9140
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 (<15%), with excellent linearity (R2 > 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 ab
Loo RL, Lodge S, Kimhofer T, et al., 2020, Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples, JOURNAL OF PROTEOME RESEARCH, Vol: 19, Pages: 4428-4441, ISSN: 1535-3893
Loo RL, Chan Q, Antti H, et al., 2020, Manuscript Strategy for improved characterisation of human metabolic phenotypes using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS), Bioinformatics, Vol: 36, Pages: 5229-5236, ISSN: 1367-4803
MOTIVATION: Large-scale population omics data can provide insight into associations between gene-environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets. RESULTS: Here we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is achieved by implementing statistical correlation spectroscopy (STOCSY) on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset is used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS thus provides an efficient semi-automated approach for screening population datasets. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/cheminfo/COMPASS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Vonhof EV, Piotto M, Holmes E, et al., 2020, Improved spatial resolution of metabolites in tissue biopsies using high-resolution magic-angle-spinning slice localization NMR spectroscopy., Analytical Chemistry, Vol: 92, Pages: 11516-11519, ISSN: 0003-2700
High-resolution magic-angle-spinning 1H NMR spectroscopy (HR-MAS NMR) is a well-established technique for assessing the biochemical composition of intact tissue samples. In this study, we utilized a method based on HR-MAS NMR spectroscopy with slice localization (SLS) to achieve spatial resolution of metabolites. The obtained 7 slice spectra from each of the model samples (i.e., chicken thigh muscle with skin and murine renal biopsy including medulla (M) and cortex (C)) showed distinct metabolite compositions. Furthermore, we analyzed previously acquired 1H HR-MAS NMR spectra of separated cortex and medulla samples using multivariate statistical methods. Concentrations of glycerophosphocholine (GPC) were found to be significantly higher in the renal medulla compared to the cortex. Using GPC as a biomarker, we identified the tissue slices that were predominantly the cortex or medulla. This study demonstrates that HR-MAS SLS combined with multivariate statistics has the potential for identifying tissue heterogeneity and detailed biochemical characterization of complex tissue samples.
Kimhofer T, Lodge S, Whiley L, et al., 2020, Integrative modelling of quantitative plasma lipoprotein, metabolic and amino acid data reveals a multi-organ pathological signature of SARS-CoV-2 infection, Journal of Proteome Research, Vol: 19, Pages: 4442-4454, ISSN: 1535-3893
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 referenc
Garcia Perez I, Posma JM, Serrano Contreras JI, et al., 2020, Identifying unknown metabolites using NMR-based metabolic profiling techniques, Nature Protocols, Vol: 15, Pages: 2538-2567, ISSN: 1750-2799
Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopy based metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments.
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