50 results found
Alexander J, Powell N, Marchesi J, et al., 2023, Considerations for peripheral blood transport and storage during large-scale multicentre metabolome research, Gut, Pages: 1-4, ISSN: 0017-5749
Harvey N, Takis PG, Lindon JC, et al., 2023, Optimization of diffusion-ordered NMR spectroscopy experiments for high-throughput automation in human metabolic phenotyping, Analytical Chemistry, Vol: 95, Pages: 3147-3152, ISSN: 0003-2700
The diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) experiment allows the calculation of diffusion coefficient values of metabolites in complex mixtures. However, this experiment has not yet been broadly used for metabolic profiling due to lack of a standardized protocol. Here we propose a pipeline for the DOSY experimental setup and data processing in metabolic phenotyping studies. Due to the complexity of biological samples, three experiments (a standard DOSY, a relaxation-edited DOSY, and a diffusion-edited DOSY) have been optimized to provide DOSY metabolic profiles with peak-picked diffusion coefficients for over 90% of signals visible in the one-dimensional 1H general biofluid profile in as little as 3 min 36 s. The developed parameter sets and tools are straightforward to implement and can facilitate the use of DOSY for metabolic profiling of human blood plasma and urine samples.
Garcia-Segura ME, Durainayagam BR, Liggi S, et al., 2023, Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer´s Disease mouse model and risk loci carriers, Journal of Neurochemistry, Vol: 164, Pages: 57-76, ISSN: 0022-3042
Alzheimer´s Disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these datasets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic dataset derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetics metabolic pathways were significantly over-represented across the AD multi-omics datasets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modelled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms (SNP)-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabol
Hussain H, Vutipongsatorn K, Jimenez B, et al., 2022, Patient stratification in sepsis: Using metabolomics to detect clinical phenotypes, sub-phenotypes and therapeutic response, Metabolites, Vol: 12, Pages: 1-42, ISSN: 2218-1989
Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.
Sliz E, Shin J, Ahmad S, et al., 2022, Circulating metabolome and white matter hyperintensities in women and men, Circulation, Vol: 145, Pages: 1040-1052, ISSN: 0009-7322
Background:White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.Methods:We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment–stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance–weighted method. Associations with false discovery rate (FDR)–adjusted P values (PFDR)<0.05 were considered significant.Results:In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10−7) and in both the pooled sample (PFDR.full.adj=1.66×10−4) and statin-untreated (PFDR.full.adj=1.65×10−6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingo
Albreht A, Hussain H, Jimenez B, et al., 2022, Structure elucidation and mitigation of endogenous interferences in LC-MS-based metabolic profiling of urine, Analytical Chemistry, Vol: 94, ISSN: 0003-2700
Liquid chromatography mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple “dilute-and-shoot” approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences which lack consideration in the relevant metabolomics literature. In this work we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-L-alanine-L-proline betaine (L,L-TMAP) and N,N-dimethyl-L-proline-L-proline betaine (L,L-DMPP), the latter reported here for the first time. Offline LC-MS/MS, MRMS, and NMR spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the co-existence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of the column temperature and mobile phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on
Takis PG, Jiménez B, Al-Saffar NMS, et al., 2021, A computationally lightweight algorithm for deriving reliable metabolite panel measurements from 1D 1H NMR., Analytical Chemistry, Vol: 93, Pages: 4995-5000, ISSN: 0003-2700
Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.
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.
Whiley L, Chappell KE, D'Hondt E, et al., 2021, Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer's disease, Alzheimers Research & Therapy, Vol: 13, Pages: 1-18, ISSN: 1758-9193
BackgroundBoth serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls.MethodsMetabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations.ResultsResults revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced o
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.
Takis P, Jimenez B, Sands C, et al., 2020, SMolESY: An efficient and quantitative alternative to on-instrument macromolecular ¹H-NMR signal suppression, Chemical Science, Vol: 11, Pages: 6000-6011, ISSN: 2041-6520
One-dimensional (1D) proton-nuclear magnetic resonance (1H-NMR) spectroscopy is an established technique for measuring small molecules in a wide variety of complex biological sample types. It is demonstrably reproducible, easily automatable and consequently ideal for routine and large-scale application. However, samples containing proteins, lipids, polysaccharides and other macromolecules produce broad signals which overlap and convolute those from small molecules. NMR experiment types designed to suppress macromolecular signals during acquisition may be additionally performed, however these approaches add to the overall sample analysis time and cost, especially for large cohort studies, and fail to produce reliably quantitative data. Here, we propose an alternative way of computationally eliminating macromolecular signals, employing the mathematical differentiation of standard 1H-NMR spectra, producing small molecule-enhanced spectra with preserved quantitative capability and increased resolution. Our approach, presented in its simplest form, was implemented in a cheminformatic toolbox and successfully applied to more than 3000 samples of various biological matrices rich or potentially rich with macromolecules, offering an efficient alternative to on-instrument experimentation, facilitating NMR use in routine and large-scale applications.
Andreas NJ, Roy RB, Gomez-Romero M, et al., 2020, Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis, Scientific Reports, Vol: 10, Pages: 1-11, ISSN: 2045-2322
We applied a metabonomic strategy to identify host biomarkers in serum to diagnose paediatric tuberculosis (TB) disease. 112 symptomatic children with presumptive TB were recruited in The Gambia and classified as bacteriologically-confirmed TB, clinically diagnosed TB, or other diseases. Sera were analysed using 1H nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Multivariate data analysis was used to distinguish patients with TB from other diseases. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. Model performance was tested in a validation cohort of 36 children from the UK. Data acquired using 1H NMR demonstrated a sensitivity, specificity and Area Under the Curve (AUC) of 69% (95% confidence interval [CI], 56–73%), 83% (95% CI, 73–93%), and 0.78 respectively, and correctly classified 20% of the validation cohort from the UK. The most discriminatory MS data showed a sensitivity of 67% (95% CI, 60–71%), specificity of 86% (95% CI, 75–93%) and an AUC of 0.78, correctly classifying 83% of the validation cohort. Amongst children with presumptive TB, metabolic profiling of sera distinguished bacteriologically-confirmed and clinical TB from other diseases. This novel approach yielded a diagnostic performance for paediatric TB comparable to that of Xpert MTB/RIF and interferon gamma release assays.
Sands C, Wolfer A, DS Correia G, et al., 2019, The nPYc-Toolbox, a Python module for the pre-processing, quality-control, and analysis of metabolic profiling datasets, Bioinformatics, Vol: 35, Pages: 5359-5360, ISSN: 1367-4803
Summary: As large-scale metabolic phenotyping studies become increasingly common, the need forsystemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysishas 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 visualisation of metabolicphenotyping datasets, either interactively, or in automated pipelines.Availability and Implementation: The nPYc-Toolbox is implemented in Python, and is freelyavailable from the Python package index https://pypi.org/project/nPYc/, source isavailable at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation canbe found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials athttps://github.com/phenomecentre/nPYc-toolbox-tutorials
Whiley L, Chekmeneva E, Berry DJ, et al., 2019, Systematic isolation and structure elucidation of urinary metabolites optimized for the analytical-scale molecular profiling laboratory, Analytical Chemistry, Vol: 91, Pages: 8873-8882, ISSN: 0003-2700
Annotation and identification of metabolite biomarkers is critical for their biological interpretation in metabolic phenotyping studies, presenting a significant bottleneck in the successful implementation of untargeted metabolomics. Here, a systematic multi-step protocol was developed for the purification and de novo structural elucidation of urinary metabolites. The protocol is most suited for instances where structure elucidation and metabolite annotation are critical for the downstream biological interpretation of metabolic phenotyping studies. First, a bulk urine pool was desalted using ion-exchange resins enabling large-scale fractionation using precise iterations of analytical scale chromatography. Primary urine fractions were collected and assembled into a “fraction bank” suitable for long-term laboratory storage. Secondary and tertiary fractionations exploited differences in selectivity across a range of reversed-phase chemistries, achieving the purification of metabolites of interest yielding an amount of material suitable for chemical characterisation. To exemplify the application of the systematic workflow in a diverse set of cases, four metabolites with a range of physico-chemical properties were selected and purified from urine and subjected to chemical formula and structure elucidation by respective magnetic resonance mass spectrometry (MRMS) and NMR analyses. Their structures were fully assigned as teterahydropentoxyline, indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent was collected, dried and returned to the fraction bank, demonstrating the viability of the system for repeat use in metabolite annotation with a high degree of efficiency.
Rodriguez-Martinez A, Ayala R, Posma JM, et al., 2019, pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra, Bioinformatics, Vol: 35, Pages: 1916-1922, ISSN: 1367-4803
Motivation: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. Results: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. Availability: The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information: Supplementary data are available at Bioinformatics online.
Harbaum L, Ghataorhe P, Wharton J, et al., 2019, Reduced plasma levels of small HDL particles transporting fibrinolytic proteins in pulmonary arterial hypertension, Thorax, Vol: 74, Pages: 380-389, ISSN: 1468-3296
Background Aberrant lipoprotein metabolism has been implicated in experimental pulmonary hypertension, but the relevance to patients with pulmonary arterial hypertension (PAH) is inconclusive.Objective To investigate the relationship between circulating lipoprotein subclasses and survival in patients with PAH.Methods Using nuclear magnetic resonance spectroscopy, 105 discrete lipoproteins were measured in plasma samples from two cohorts of patients with idiopathic or heritable PAH. Data from 1124 plasma proteins were used to identify proteins linked to lipoprotein subclasses. The physical presence of proteins was confirmed in plasma lipoprotein subfractions separated by ultracentrifugation.Results Plasma levels of three lipoproteins from the small high-density lipoprotein (HDL) subclass, termed HDL-4, were inversely related to survival in both the discovery (n=127) and validation (n=77) cohorts, independent of exercise capacity, comorbidities, treatment, N-terminal probrain natriuretic peptide, C reactive protein and the principal lipoprotein classes. The small HDL subclass rich in apolipoprotein A-2 content (HDL-4-Apo A-2) exhibited the most significant association with survival. None of the other lipoprotein classes, including principal lipoprotein classes HDL and low-density lipoprotein cholesterol, were prognostic. Three out of nine proteins identified to associate with HDL-4-Apo A-2 are involved in the regulation of fibrinolysis, namely, the plasmin regulator, alpha-2-antiplasmin, and two major components of the kallikrein–kinin pathway (coagulation factor XI and prekallikrein), and their physical presence in the HDL-4 subfraction was confirmed.Conclusion Reduced plasma levels of small HDL particles transporting fibrinolytic proteins are associated with poor outcomes in patients with idiopathic and heritable PAH.
Gafson AR, Thorne T, McKechnie CIJ, et al., 2018, Lipoprotein markers associated with disability from multiple sclerosis, Scientific Reports, Vol: 8, ISSN: 2045-2322
Altered lipid metabolism is a feature of chronic infammatory disorders. Increased plasma lipids andlipoproteins have been associated with multiple sclerosis (MS) disease activity. Our objective was tocharacterise the specifc lipids and associated plasma lipoproteins increased in MS and to test for anassociation with disability. Plasma samples were collected from 27 RRMS patients (median EDSS,1.5, range 1–7) and 31 healthy controls. Concentrations of lipids within lipoprotein sub-classes weredetermined from NMR spectra. Plasma cytokines were measured using the MesoScale DiscoveryV-PLEX kit. Associations were tested using multivariate linear regression. Diferences between thepatient and volunteer groups were found for lipids within VLDL and HDL lipoprotein sub-fractions(p<0.05). Multivariate regression demonstrated a high correlation between lipids within VLDLsub-classes and the Expanded Disability Status Scale (EDSS) (p<0.05). An optimal model for EDSSincluded free cholesterol carried by VLDL-2, gender and age (R2=0.38, p<0.05). Free cholesterolcarried by VLDL-2 was highly correlated with plasma cytokines CCL-17 and IL-7 (R2=0.78, p<0.0001).These results highlight relationships between disability, infammatory responses and systemic lipidmetabolism in RRMS. Altered lipid metabolism with systemic infammation may contribute to immuneactivation.
Jimenez B, Holmes E, Heude C, et al., 2018, Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial, Analytical Chemistry, Vol: 90, Pages: 11962-11971, ISSN: 0003-2700
We report an extensive 600 MHz NMR trial of a quantitative lipoprotein and small molecule measurements in human blood serum and plasma. Five centers with eleven 600 MHz NMR spectrometers were used to analyze 98 samples including: 20 QCs, 37 commercially sourced, paired serum and plasma samples and 2 National Institute of Science and Technology, NIST, reference material 1951c replicates. Samples were analyzed using rigorous protocols for sample preparation and experimental acquisition. A commercial lipoprotein subclass analysis was used to quantify 105 lipoprotein subclasses and 24 low molecular weight metabolites from the nuclear magnetic resonance, NMR, spectra. For all spectrometers, the instrument specific variance in measuring internal quality controls, QCs, was lower than the percentage described by the National Cholesterol Education Program, NCEP, criteria for lipid testing (triglycerides<2.7%, cholesterol<2.8%; LDL-cholesterol<2.8%; HDL-cholesterol<2.3%), showing exceptional reproducibility for direct quantitation of lipoproteins in both matrices. The average RSD for the 105 lipoprotein parameters in the 11 instruments was 4.6% and 3.9% for the two NIST samples while it was 38% and 40% for the 37 commercially sourced plasmas and sera, respectively, showing negligible analytical compared to biological variation. The coefficient of variance, CV, obtained for the quantification of the small molecules across the 11 spectrometers was below 15% for 20 out of the 24 metabolites analyzed. This study provides further evidence of the suitability of NMR for high-throughput lipoprotein subcomponent analysis and small molecule quantitation with the exceptional reproducibility required for clinical and other regulatory settings.
Rodriguez-Martinez A, Posma JM, Ayala R, et al., 2017, J-Resolved (1)H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis., Analytical Chemistry, Vol: 89, Pages: 11405-11412, ISSN: 0003-2700
(1)H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D (1)H NMR spectra, we have investigated the use of 2D J-resolved (JRES) (1)H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.
Bray R, Cacciatore S, Jimenez B, et al., 2017, Urinary metabolic phenotyping of women with lower urinary tract symptoms, Journal of Proteome Research, Vol: 16, Pages: 4208-4216, ISSN: 1535-3893
Lower urinary tract symptoms (LUTS), including urinary incontinence, urgency and nocturia, affect approximately half of women worldwide. Current diagnostic methods for LUTS are invasive and costly, while available treatments are limited by side effects leading to poor patient compliance. In this study, we aimed to identify urine metabolic signatures associated with LUTS using proton nuclear magnetic resonance (1H NMR) spectroscopy. A total of 214 urine samples were collected from women attending tertiary urogynecology clinics (cases; n = 176) and healthy control women attending general gynecology clinics (n = 36). Despite high variation in the urine metabolome across the cohort, associations between urine metabolic profiles and BMI, parity, overactive bladder syndrome, frequency, straining, and bladder storage were identified using KODAMA (knowledge discovery by accuracy maximization). Four distinct urinary metabotypes were identified, one of which was associated with increased urinary frequency and low BMI. Urine from these patients was characterized by increased levels of isoleucine and decreased levels of hippurate. Our study suggests that metabolic profiling of urine samples from LUTS patients offers the potential to identify differences in underlying etiology, which may permit stratification of patient populations and the design of more personalized treatment strategies.
Antcliffe D, Jimenez B, Veselkov K, et al., 2017, Metabolic profiling in patients with pneumonia on intensive care, EBioMedicine, Vol: 18, Pages: 244-253, ISSN: 2352-3964
Clinical features and investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and when patients develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for metabolic profiling to aid the diagnosis in critical care.In this prospective observational study ventilated patients with brain injuries or pneumonia were recruited in the intensive care unit and serum samples were collected soon after the start of ventilation. Metabolic profiles were produced using 1D 1H NMR spectra. Metabolic data were compared using multivariate statistical techniques including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).We recruited 15 patients with pneumonia and 26 with brain injuries, seven of whom went on to develop VAP. Comparison of metabolic profiles using OPLS-DA differentiated those with pneumonia from those with brain injuries (R2Y = 0.91, Q2Y = 0.28, p = 0.02) and those with VAP from those without (R2Y = 0.94, Q2Y = 0.27, p = 0.05). Metabolites that differentiated patients with pneumonia included lipid species, amino acids and glycoproteins.Metabolic profiling shows promise to aid in the diagnosis of pneumonia in ventilated patients and may allow a more timely diagnosis and better use of antibiotics.
Jimenez B, MacIntyre D, 2017, NMR Metabolic Phenotyping in Clinical Studies, ENCYCLOPEDIA OF SPECTROSCOPY AND SPECTROMETRY, 3RD EDITION, VOL 3: N-R, Editors: Lindon, Tranter, Koppenaal, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 140-145
Jiménez B, MacIntyre D, 2016, NMR metabolic phenotyping in clinical studies, Encyclopedia of Spectroscopy and Spectrometry, Pages: 140-145, ISBN: 9780128032244
Metabolic phenotyping comprises the determination of the metabolic signatures of biofluids and tissues. Differences in such metabolic profiles permit the stratification of individuals according to underlying biochemical status. This information allows objective assessment of health status including disease risk, diagnosis and prognosis, treatment response, drug metabolism, and toxicity. A key analytical platform used for metabolic phenotyping is nuclear magnetic resonance (NMR) spectroscopy due to its robustness, reproducibility, inherent quantitative nature, and capacity to be automated for high throughput. In this article, the main challenges faced in NMR-based metabolic phenotyping studies are explored and the role of phenome centers in the translation of NMR spectroscopy to clinical settings is described.
Correia GDS, Ng KW, Wijeyesekera A, et al., 2015, Metabolic Profiling of Children Undergoing Surgery for Congenital Heart Disease, Critical Care Medicine, Vol: 43, Pages: 1467-1476, ISSN: 1530-0293
Inflammation and metabolism are closely interlinked.Both undergo significant dysregulation following surgery for congenitalheart disease, contributing to organ failure and morbidity.In this study, we combined cytokine and metabolic profilingto examine the effect of postoperative tight glycemic controlcompared with conventional blood glucose management onmetabolic and inflammatory outcomes in children undergoingcongenital heart surgery. The aim was to evaluate changes in keymetabolites following congenital heart surgery and to examinethe potential of metabolic profiling for stratifying patients in termsof expected clinical outcomes.Design: Laboratory and clinical study.Setting: University Hospital and Laboratory.Patients: Of 28 children undergoing surgery for congenital heartdisease, 15 underwent tight glycemic control postoperatively and13 were treated conventionally.Interventions: Metabolic profiling of blood plasma was undertakenusing proton nuclear magnetic resonance spectroscopy. A panel ofmetabolites was measured using a curve-fitting algorithm. Inflammatorycytokines were measured by enzyme-linked immunosorbentassay. The data were assessed with respect to clinical markers ofdisease severity (Risk Adjusted Congenital heart surgery score-1,Pediatric Logistic Organ Dysfunction, inotrope score, duration ofventilation and pediatric ICU-free days).Measurements and Main Results: Changes in metabolic andinflammatory profiles were seen over the time course from surgeryto recovery, compared with the preoperative state. Tight glycemiccontrol did not significantly alter the response profile. We identifiedeight metabolites (3-d-hydroxybutyrate, acetone, acetoacetate,citrate, lactate, creatine, creatinine, and alanine) associated withsurgical and disease severity. The strength of proinflammatoryresponse, particularly interleukin-8 and interleukin-6 concentrations,inversely correlated with PICU-free days at 28 days. The interleukin-6/interleukin-10ratio directly correlated wi
Powles STR, Hicks LC, Jimenez B, et al., 2015, Effect of co-morbidities on urinary metabolic profiling in the characterisation of patients with inflammatory bowel disease, 2nd Digestive Disorders Federation Conference, Publisher: BMJ Publishing Group, Pages: A436-A437, ISSN: 0017-5749
Dona AC, Jimenez B, Schaefer H, et al., 2014, Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping, ANALYTICAL CHEMISTRY, Vol: 86, Pages: 9887-9894, ISSN: 0003-2700
Mirnezami R, Jimenez B, Li JV, et al., 2014, Rapid Diagnosis and Staging of Colorectal Cancer via High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) Spectroscopy of Intact Tissue Biopsies, Annals of Surgery, Vol: 259, Pages: 1138-1149, ISSN: 0003-4932
Objective: To develop novel metabolite-based models for diagnosis and staging in colorectal cancer (CRC) using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy.Background: Previous studies have demonstrated that cancer cells harbor unique metabolic characteristics relative to healthy counterparts. This study sought to characterize metabolic properties in CRC using HR-MAS NMR spectroscopy.Methods: Between November 2010 and January 2012, 44 consecutive patients with confirmed CRC were recruited to a prospective observational study. Fresh tissue samples were obtained from center of tumor and 5 cm from tumor margin from surgical resection specimens. Samples were run in duplicate where tissue volume permitted to compensate for anticipated sample heterogeneity. Samples were subjected to HR-MAS NMR spectroscopic profiling and acquired spectral data were imported into SIMCA and MATLAB statistical software packages for unsupervised and supervised multivariate analysis.Results: A total of 171 spectra were acquired (center of tumor, n = 88; 5 cm from tumor margin, n = 83). Cancer tissue contained significantly increased levels of lactate (P < 0.005), taurine (P < 0.005), and isoglutamine (P < 0.005) and decreased levels of lipids/triglycerides (P < 0.005) relative to healthy mucosa (R2Y = 0.94; Q2Y = 0.72; area under the curve, 0.98). Colon cancer samples (n = 49) contained higher levels of acetate (P < 0.005) and arginine (P < 0.005) and lower levels of lactate (P < 0.005) relative to rectal cancer samples (n = 39). In addition unique metabolic profiles were observed for tumors of differing T-stage.Conclusions: HR-MAS NMR profiling demonstrates cancer-specific metabolic signatures in CRC and reveals metabolic differences between colonic and rectal cancers. In addition, this approach reveals that tumor metabolism undergoes modification during local tumor advancement, offering potential in future staging and therapeu
Kinross JM, Drymousis P, Jimenez B, et al., 2013, Metabonomic profiling: A novel approach in neuroendocrine neoplasias, SURGERY, Vol: 154, Pages: 1185-1192, ISSN: 0039-6060
Jimenez B, Mirnezami R, Kinross J, et al., 2013, H-1 HR-MAS NMR Spectroscopy of Tumor-Induced Local Metabolic "Field-Effects" Enables Colorectal Cancer Staging and Prognostication, JOURNAL OF PROTEOME RESEARCH, Vol: 12, Pages: 959-968, ISSN: 1535-3893
Wong A, Jimenez B, Li X, et al., 2012, Evaluation of High Resolution Magic-Angle Coil Spinning NMR Spectroscopy for Metabolic Profiling of Nanoliter Tissue Biopsies, ANALYTICAL CHEMISTRY, Vol: 84, Pages: 3843-3848, ISSN: 0003-2700
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