Anthony C. Dona

Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping

Proton nuclear magnetic resonance (NMR)-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible, and high-throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimize technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions, including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control, and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare.

Assessment of the extent of starch dissolution in dimethyl sulfoxide by 1H NMR spectroscopy

Complete dissolution is needed for the separation, characterization, or homogeneous labeling of whole starch molecules. A method is presented to quantify the extent of starch dissolution in DMSO for the first time; it is validated on a commercial rice starch. It is used directly on starch dispersions containing possible undissolved or co-dissolved species. High-amylose maize starches, known to be digested slowly in vivo, only quantitatively dissolve in the presence of high concentrations of an H-bond disrupter, LiBr, although they form clear dispersions at low LiBr concentrations. Starch quantitatively dissolves from waxy rice flours; non-starch components partially co-dissolve but do not interfere with the dissolution quantification.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments.

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC–MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

Discovery and validation of urinary metabotypes for the diagnosis of hepatocellular carcinoma in West Africans

There is no clinically applicable biomarker for surveillance of hepatocellular carcinoma (HCC), because the sensitivity of serum alpha‐fetoprotein (AFP) is too low for this purpose. Here, we determined the diagnostic performance of a panel of urinary metabolites of HCC patients from West Africa. Urine samples were collected from Nigerian and Gambian patients recruited on the case‐control platform of the Prevention of Liver Fibrosis and Cancer in Africa (PROLIFICA) program. Urinary proton nuclear magnetic resonance (1H‐NMR) spectroscopy was used to metabolically phenotype 290 subjects: 63 with HCC; 32 with cirrhosis (Cir); 107 with noncirrhotic liver disease (DC); and 88 normal control (NC) healthy volunteers. Urine samples from a further cohort of 463 subjects (141 HCC, 56 Cir, 178 DC, and 88 NC) were analyzed, the results of which validated the initial cohort. The urinary metabotype of patients with HCC was distinct from those with Cir, DC, and NC with areas under the receiver operating characteristic (AUROC) curves of 0.86 (0.78‐0.94), 0.93 (0.89‐0.97), and 0.89 (0.80‐0.98) in the training set and 0.81 (0.73‐0.89), 0.96 (0.94‐0.99), and 0.90 (0.85‐0.96), respectively, in the validation cohort. A urinary metabolite panel, comprising inosine, indole‐3‐acetate, galactose, and an N‐acetylated amino acid (NAA), showed a high sensitivity (86.9% [75.8‐94.2]) and specificity (90.3% [74.2‐98.0]) in the discrimination of HCC from cirrhosis, a finding that was corroborated in a validation cohort (AUROC: urinary panel = 0.72; AFP = 0.58). Metabolites that were significantly increased in urine of HCC patients, and which correlated with clinical stage of HCC, were NAA, dimethylglycine, 1‐methylnicotinamide, methionine, acetylcarnitine, 2‐oxoglutarate, choline, and creatine. Conclusion: The urinary metabotyping of this West African cohort identified and validated a metabolite panel that diagnostically outperforms serum AFP. (Hepatology 2014;60:1291–1301)

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

Remote ischemic preconditioning attenuates EGR-1 expression following myocardial ischemia reperfusion injury through activation of the JAK-STAT pathway

BACKGROUND/OBJECTIVES Remote ischemic preconditioning (RIPC) protects the myocardium from ischemia/reperfusion (I/R) injury however the molecular pathways involved in cardioprotection are yet to be fully delineated. Transcription factor Early growth response-1 (Egr-1) is a key upstream activator in a variety of cardiovascular diseases. In this study, we elucidated the role of RIPC in modulating the regulation of Egr-1. METHODS This study subjected rats to transient blockade of the left anterior descending (LAD) coronary artery with or without prior RIPC of the hind-limb muscle and thereafter excised the heart 24h following surgical intervention. In vitro, rat cardiac myoblast H9c2 cells were exposed to ischemic preconditioning by subjecting them to 3cycles of alternating nitrogen-flushed hypoxia and normoxia. These preconditioned media were added to recipient H9c2 cells which were then subjected to 30min of hypoxia followed by 30min of normoxia to simulate myocardial I/R injury. Thereafter, the effects of RIPC on cell viability, apoptosis and inflammatory markers were assessed. RESULTS We showed reduced infarct size and suppressed Egr-1 in the heart of rats when RIPC was administered to the hind leg. In vitro, we showed that RIPC improved cell viability, reduced apoptosis and attenuated Egr-1 in recipient cells. CONCLUSIONS Selective inhibition of intracellular signaling pathways confirmed that RIPC increased production of intracellular nitric oxide (NO) and reactive oxygen species (ROS) via activation of the JAK-STAT pathway which then inactivated I/R-induced ERK 1/2 signaling pathways, ultimately leading to the suppression of Egr-1.

Bidirectional communication between the Aryl hydrocarbon Receptor (AhR) and the microbiome tunes host metabolism

The ligand-induced transcription factor, aryl hydrocarbon receptor (AhR) is known for its capacity to tune adaptive immunity and xenobiotic metabolism—biological properties subject to regulation by the indigenous microbiome. The objective of this study was to probe the postulated microbiome-AhR crosstalk and whether such an axis could influence metabolic homeostasis of the host. Utilising a systems-biology approach combining in-depth 1H-NMR-based metabonomics (plasma, liver and skeletal muscle) with microbiome profiling (small intestine, colon and faeces) of AhR knockout (AhR−/−) and wild-type (AhR+/+) mice, we assessed AhR function in host metabolism. Microbiome metabolites such as short-chain fatty acids were found to regulate AhR and its target genes in liver and intestine. The AhR signalling pathway, in turn, was able to influence microbiome composition in the small intestine as evident from microbiota profiling of the AhR+/+ and AhR−/− mice fed with diet enriched with a specific AhR ligand or diet depleted of any known AhR ligands. The AhR−/− mice also displayed increased levels of corticosterol and alanine in serum. In addition, activation of gluconeogenic genes in the AhR−/− mice was indicative of on-going metabolic stress. Reduced levels of ketone bodies and reduced expression of genes involved in fatty acid metabolism in the liver further underscored this observation. Interestingly, exposing AhR−/− mice to a high-fat diet showed resilience to glucose intolerance. Our data suggest the existence of a bidirectional AhR-microbiome axis, which influences host metabolic pathways.

Translational and emerging clinical applications of metabolomics in cardiovascular disease diagnosis and treatment

Numerous molecular screening strategies have recently been developed to measure the chemical diversity of a population’s biofluids with the ultimate aim to provide clinicians, medical scientists and epidemiologists with a clearer picture of the presence and severity of cardiovascular disease; prognosis; and response to treatment. Current cardiology practice integrates clinical history and examination with state-of-the-art imaging, invasive measures, and electrical interrogation. Biomarkers in common clinical use are relatively limited to troponin and brain natriuretic peptide, dependent on damage to heart muscle, or myocyte ‘stretch’ respectively. Although they have been recently applied to risk stratification in asymptomatic individuals at higher risk, the development of markers capable of detecting earlier phases of disease development would facilitate targeted strategies to prevent pathological complications in the general community. Metabolomics is the systematic study of small molecules in biological fluids. Profiling strategies aim to comprehensively measure and quantify such biomarkers in a fast, cost-effective and clinically informative manner. Techniques tend to be applied in an unbiased fashion, with advanced statistical methods allowing for identification of signature profiles in particular cohorts. In this manner, metabolomics has the potential to identify new pathophysiological pathways, and thus therapeutic targets, as well as assist in improved risk-stratification and personalized cardiovascular medicine. The latter has great potential in the primary and secondary cardiovascular disease prevention settings, integrating known and as yet unidentified host and environmental factors. The current review discusses applications of metabolomic techniques relevant to both the research and the clinical cardiologist.

1)H NMR Spectroscopy of Fecal Extracts Enables Detection of Advanced Colorectal Neoplasia.

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its etiopathogenesis and earlier diagnosis. Here, we investigated the fecal metabolic phenotype of patients with advanced colorectal neoplasia and controls using (1)H-nuclear magnetic resonance (NMR) spectroscopy and multivariate modeling. The fecal microbiota composition was assessed by quantitative real-time PCR as well as Wif-1 methylation levels in stools, serum, and urine and correlated to the metabolic profile of each patient. The predictivity of the model was 0.507 (Q(2)Y), and the explained variance was 0.755 (R(2)Y). Patients with advanced colorectal neoplasia demonstrated increased fecal concentrations of four short-chain fatty acids (valerate, acetate, propionate, and butyrate) and decreased signals relating to β-glucose, glutamine, and glutamate. The predictive accuracy of the multivariate (1)H NMR model was higher than that of the guaiac-fecal occult blood test and the Wif-1 methylation test for predicting advanced colorectal neoplasia. Correlation analysis between fecal metabolites and bacterial profiles revealed strong associations between Faecalibacterium prausnitzii and Clostridium leptum species with short-chain fatty acids concentration and inverse correlation between Faecalibacterium prausnitzii and glucose. These preliminary results suggest that fecal metabonomics may potentially have a future role in a noninvasive colorectal screening program and may contribute to our understanding of the role of these dysregulated molecules in the cross-talk between the host and its bacterial microbiota.

Kinetics of In Vitro Digestion of Starches Monitored by Time-Resolved 1H Nuclear Magnetic Resonance

A (1)H NMR method is presented that monitors the initial and later stages of in vitro enzymatic digestion of starch suspensions. It allows, for the first time to our knowledge, the accurate analysis of the initial 5% of the extent of hydrolysis. This is significant because rapidly digested starch produces glucose that determines the blood glucose concentration immediately after ingestion of food. The two key hydrolytic enzymes, alpha-amylase and amyloglucosidase, showed clear systematic deviation from Michaelis-Menten kinetics as the starch or wheat flour substrate that was used changed its character during the reaction. Estimates of Michaelis-Menten parameters for amyloglucosidase and alpha-amylase were successfully found by analyzing two stages of digestion separately. The Michaelis-Menten constants for purified starch were (6.4 +/- 0.8) and (1.1 +/- 0.3) g dL(-1) (% w/v), respectively; and the maximum velocities of glucose release by amyloglucosidase, and short oligoglucosides and glucose by alpha-amylase were (1.9 +/- 0.4) x 10(-2) and (1.6 +/- 0.2) x 10(-2) mmol L(-1) s(-1) for the first stage of digestion, and (9.0 +/- 1.0) x 10(-3) and (4.7 +/- 1.4) x 10(-3) mmol L(-1) s(-1) for the second stage, giving a ratio of the two V(max) values of 2.1 and 3.4, respectively.