54 results found
de Jonge NF, Louwen JJR, Chekmeneva E, et al., 2023, MS2Query: reliable and scalable MS2 mass spectra-based analogue search., Nat Commun, Vol: 14
Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.
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
Augustin A, Le Guennec A, Umamahesan C, et al., 2023, Faecal metabolite deficit, gut inflammation and diet in Parkinson’s disease: integrative analysis indicates inflammatory response syndrome, Clinical and Translational Medicine, Vol: 13, ISSN: 2001-1326
Background:Gut-brain axis is widely implicated in the pathophysiology of Parkinson's disease (PD). We take an integrated approach to considering the gut as a target for disease-modifying intervention, using continuous measurements of disease facets irrespective of diagnostic divide.Methods:We characterised 77 participants with diagnosed-PD, 113 without, by dietary/exogenous substance intake, faecal metabolome, intestinal inflammation, serum cytokines/chemokines, clinical phenotype including colonic transit time. Complete-linkage hierarchical cluster analysis of metabolites discriminant for PD-status was performed.Results:Longer colonic transit was linked to deficits in faecal short-chain-fatty acids outside PD, to a ‘tryptophan-containing metabolite cluster’ overall. Phenotypic cluster analysis aggregated colonic transit with brady/hypokinesia, tremor, sleep disorder and dysosmia, each individually associated with tryptophan-cluster deficit. Overall, a faster pulse was associated with deficits in a metabolite cluster including benzoic acid and an imidazole-ring compound (anti-fungals) and vitamin B3 (anti-inflammatory) and with higher serum CCL20 (chemotactic for lymphocytes/dendritic cells towards mucosal epithelium). The faster pulse in PD was irrespective of postural hypotension. The benzoic acid-cluster deficit was linked to (well-recognised) lower caffeine and alcohol intakes, tryptophan-cluster deficit to higher maltose intake. Free-sugar intake was increased in PD, maltose intake being 63% higher (p = .001). Faecal calprotectin was 44% (95% CI 5%, 98%) greater in PD [p = .001, adjusted for proton-pump inhibitors (p = .001)], with 16% of PD-probands exceeding a cut-point for clinically significant inflammation compatible with inflammatory bowel disease. Higher maltose intake was associated with exceeding this calprotectin cut-point.Conclusions:Emerging picture is of (i) clinical phenotype being described by deficits in microbial metabolites essenti
Mullish BH, Martinez Gili L, Chekmeneva E, et al., 2022, Assessing the clinical value of faecal bile acid profiling to predict recurrence in primary Clostridioides difficile infection, Alimentary Pharmacology and Therapeutics, Vol: 56, Pages: 1556-1569, ISSN: 0269-2813
Background:Factors influencing recurrence risk in primary Clostridioides difficile infection (CDI) are poorly understood, and tools predicting recurrence are lacking. Perturbations in bile acids (BAs) contribute to CDI pathogenesis and may be relevant to primary disease prognosis.Aims:To define stool BA dynamics in patients with primary CDI and explore signatures predicting recurrenceMethodsWeekly stool samples were collected from patients with primary CDI from the last day of anti-CDI therapy until recurrence or, otherwise, through 8 weeks post-completion. Ultra-high performance liquid chromatography-mass spectrometry was used to profile BAs; stool bile salt hydrolase (BSH) activity was measured to determine primary BA bacterial deconjugation capacity. Multivariate and univariate models were used to define differential BA trajectories in patients with recurrence versus those without, and to assess faecal BAs as predictive markers for recurrence.Results:Twenty (36%) of 56 patients (median age: 57, 64% male) had recurrence; 80% of recurrences occurred within the first 9 days post-antibiotic treatment. Principal component analysis of stool BA profiles demonstrated clustering by recurrence status and post-treatment timepoint. Longitudinal faecal BA trajectories showed recovery of secondary BAs and their derivatives only in patients without recurrence. BSH activity increased over time only among non-relapsing patients (β = 0.056; likelihood ratio test p = 0.018). A joint longitudinal-survival model identified five stool BAs with area under the receiver operating characteristic curve >0.73 for predicting recurrence within 9 days post-CDI treatment.Conclusions:Gut BA metabolism dynamics differ in primary CDI patients between those developing recurrence and those who do not. Individual BAs show promise as potential novel biomarkers to predict CDI recurrence.
Gadgil MD, Kanaya AM, Sands C, et al., 2022, Diet Patterns Are Associated with Circulating Metabolites and Lipid Profiles of South Asians in the United States., J Nutr, Vol: 152, Pages: 2358-2366
BACKGROUND: South Asians are at higher risk for cardiometabolic disease than many other racial/ethnic minority groups. Diet patterns in US South Asians have unique components associated with cardiometabolic disease. OBJECTIVES: We aimed to characterize the metabolites associated with 3 representative diet patterns. METHODS: We included 722 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort study aged 40-84 y without known cardiovascular disease. Fasting serum specimens and diet and demographic questionnaires were collected at baseline and diet patterns previously generated through principal components analysis. LC-MS-based untargeted metabolomic and lipidomic analysis was conducted with targeted integration of known metabolite and lipid signals. Linear regression models of diet pattern factor score and log-transformed metabolites adjusted for age, sex, caloric intake, and BMI and adjusted for multiple comparisons were performed, followed by elastic net linear regression of significant metabolites. RESULTS: There were 443 metabolites of known identity extracted from the profiling data. The "animal protein" diet pattern was associated with 61 metabolites and lipids, including glycerophospholipids phosphatidylethanolamine PE(O-16:1/20:4) and/or PE(P-16:0/20:4) (β: 0.13; 95% CI: 0.11, 0.14) and N-acyl phosphatidylethanolamines (NAPEs) NAPE(O-18:1/20:4/18:0) and/or NAPE(P-18:0/20:4/18:0) (β: 0.13; 95% CI: 0.11, 0.14), lysophosphatidylinositol (LPI) (22:6/0:0) (β: 0.14; 95% CI: 0.12, 0.17), and fatty acid (FA) (22:6) (β: 0.15; 95% CI: 0.13, 0.17). The "fried snacks, sweets, high-fat dairy" pattern was associated with 12 lipids, including PC(16:0/22:6) (β: -0.08; 95% CI: -0.09, -0.06) and FA (22:6) (β: 0.14; 95% CI: -0.17, -0.10). The "fruits, vegetables, nuts, and legumes" pattern was associated with 5 metabolites including proline betaine (β: 0.17; 95% CI: 0.0
Dehghan A, Pinto RC, Karaman I, et al., 2022, Metabolome-wide association study on ABCA7 indicates a role of ceramide metabolism in Alzheimer's disease., Proceedings of the National Academy of Sciences of USA, Vol: 119, Pages: 1-12, ISSN: 0027-8424
Genome-wide association studies (GWASs) have identified genetic loci associated with the risk of Alzheimer's disease (AD), but the molecular mechanisms by which they confer risk are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWASs using untargeted metabolic profiling (metabolomics) by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single-nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0 × 10-5 to 1.3 × 10-44). We showed that plasma LacCer concentrations are associated with cognitive performance and genetically modified levels of LacCer are associated with AD risk. We then showed that concentrations of sphingomyelins, ceramides, and hexosylceramides were altered in brain tissue from Abca7 knockout mice, compared with wild type (WT) (P = 0.049-1.4 × 10-5), but not in a mouse model of amyloidosis. Furthermore, activation of microglia increases intracellular concentrations of hexosylceramides in part through induction in the expression of sphingosine kinase, an enzyme with a high control coefficient for sphingolipid and ceramide synthesis. Our work suggests that the risk for AD arising from functional variations in ABCA7 is mediated at least in part through ceramides. Modulation of their metabolism or downstream signaling may offer new therapeutic opportunities for AD.
Gadgil MD, Kanaya AM, Sands C, et al., 2022, Diet Patterns Are Associated with Circulating Metabolites and Lipid Profiles of South Asians in the United States, JOURNAL OF NUTRITION, Vol: 152, Pages: 2358-2366, ISSN: 0022-3166
de Jonge NF, Louwen JR, Chekmeneva E, et al., 2022, MS2Query: Reliable and Scalable MS<sup>2</sup> Mass Spectral-based Analogue Search
<jats:title>Abstract</jats:title><jats:p>Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches, but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrates an improved reliability and scalability. Thereby, MS2Query offers exciting opportunities for further increasing the annotation rate of complex metabolite mixtures and for discovering new biology.</jats:p>
Climaco Pinto R, Karaman I, Lewis MR, et al., 2022, Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets., Analytical Chemistry, Vol: 94, Pages: 5493-5503, ISSN: 0003-2700
Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
Mehta R, Chekmeneva E, Jackson H, et al., 2022, Antiviral metabolite 3’-Deoxy-3’,4’-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19, Med, Vol: 3, Pages: 204-215.e6, ISSN: 2666-6340
Background:There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.Methods:Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections including COVID-19, bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.Findings:3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral to 101 non-viral cases in the discovery cohort, ddhC was the most differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral to 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were amongst the five genes most highly correlated to ddhC abundance.Conclusions:The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.
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
Ferreira MR, Sands CJ, Li J, et al., 2021, Impact of pelvic radiation therapy for prostate cancer on global metabolic profiles and microbiota-driven gastrointestinal late side effects: a longitudinal observational study, International Journal of Radiation: Oncology - Biology - Physics, Vol: 111, Pages: 1204-1213, ISSN: 0360-3016
PurposeRadiation therapy to the prostate and pelvic lymph nodes (PLNRT) is part of the curative treatment of high-risk prostate cancer. Yet, the broader influence of radiation therapy on patient physiology is poorly understood. We conducted comprehensive global metabolomic profiling of urine, plasma, and stools sampled from patients undergoing PLNRT for high-risk prostate cancer.Methods and MaterialsSamples were taken from 32 patients at 6 timepoints: baseline, 2 to 3 and 4 to 5 weeks of PLNRT; and 3, 6, and 12 months after PLNRT. We characterized the global metabolome of urine and plasma using 1H nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography-mass spectrometry, and of stools with nuclear magnetic resonance. Linear mixed-effects modeling was used to investigate metabolic changes between timepoints for each biofluid and assay and determine metabolites of interest.ResultsMetabolites in urine, plasma and stools changed significantly after PLNRT initiation. Metabolic profiles did not return to baseline up to 1 year post-PLNRT in any biofluid. Molecules associated with cardiovascular risk were increased in plasma. Pre-PLNRT fecal butyrate levels directly associated with increasing gastrointestinal side effects, as did a sharper fall in those levels during and up to 1 year postradiation therapy, mirroring our previous results with metataxonomics.ConclusionsWe showed for the first time that an overall metabolic effect is observed in patients undergoing PLNRT up to 1 year posttreatment. These metabolic changes may effect on long-term morbidity after treatment, which warrants further investigation.
Wolfer AM, Correia GDS, Sands CJ, et al., 2021, peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets, BIOINFORMATICS, Vol: 37, Pages: 4886-4888, ISSN: 1367-4803
- Author Web Link
- Citations: 11
Martinez-Gili L, Mullish BH, Correia G, et al., 2021, A DISTINCTIVE SIGNATURE OF FECAL BILE ACIDS AND OTHER NOVEL METABOLITES ACCOMPANYING RECURRENCE AFTER PRIMARY CLOSTRIDIOIDES DIFFICILE INFECTION, Society-for-Surgery-of-the-Alimentary-Tract Annual Meeting at Digestive Disease Week (DDW), Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S368-S368, ISSN: 0016-5085
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.
Sands CJ, Gómez-Romero M, Correia G, et al., 2021, Representing the metabolome with high fidelity: range and response as quality control factors in LC-MS-based global profiling., Analytical Chemistry, Vol: 93, Pages: 1924-1933, ISSN: 0003-2700
Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.
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.
Lau CH, Taylor-Bateman V, Vorkas PA, et al., 2020, Metabolic signatures of gestational weight gain and postpartum weight loss in a lifestyle intervention study of overweight and obese women, Metabolites, Vol: 10, ISSN: 2218-1989
BACKGROUND: Overweight and obesity amongst women of reproductive age are increasingly common in developed economies and are shown to adversely affect birth outcomes and both childhood and adulthood health risks in the offspring. Metabolic profiling in conditions of overweight and obesity in pregnancy could potentially be applied to elucidate the molecular basis of the adverse effects of gestational weight gain (GWG) and postpartum weight loss (WL) on future risks for cardiovascular disease (CVD) and other chronic diseases. METHODS: Biofluid samples were collected from 114 ethnically diverse pregnant women with body mass index (BMI) between 25 and 40 kg/m2 from Chicago (US), as part of a randomized lifestyle intervention trial (Maternal Offspring Metabolics: Family Intervention Trial; NCT01631747). At 15 weeks, 35 weeks of gestation, and at 1 year postpartum, the blood plasma lipidome and metabolic profile of urine samples were analyzed by liquid chromatography mass spectrometry (LC-MS) and 1H nuclear magnetic resonance spectroscopy (1H NMR) respectively. RESULTS: Urinary 4-deoxyerythronic acid and 4-deoxythreonic acid were found to be positively correlated to BMI. Seventeen plasma lipids were found to be associated with GWG and 16 lipids were found to be associated with WL, which included phosphatidylinositols (PI), phosphatidylcholines (PC), lysophospholipids (lyso-), sphingomyelins (SM) and ether phosphatidylcholine (PC-O). Three phospholipids found to be positively associated with GWG all contained palmitate side-chains, and amongst the 14 lipids that were negatively associated with GWG, seven were PC-O. Six of eight lipids found to be negatively associated with WL contained an 18:2 fatty acid side-chain. CONCLUSIONS: Maternal obesity was associated with characteristic urine and plasma metabolic phenotypes, and phospholipid profile was found to be associated with both GWG and postpartum WL in metabolically healthy pregnant women with overweight/obesity. Postpartu
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.
Gibson R, Lau C, Loo RL, et al., 2019, The association of fish consumption and its urinary metabolites with cardiovascular risk factors: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP), American Journal of Clinical Nutrition, Vol: 111, Pages: 280-290, ISSN: 0002-9165
BackgroundResults from observational studies regarding associations between fish (including shellfish) intake and cardiovascular disease risk factors, including blood pressure (BP) and BMI, are inconsistent.ObjectiveTo investigate associations of fish consumption and associated urinary metabolites with BP and BMI in free-living populations.MethodsWe used cross-sectional data from the International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP), including 4680 men and women (40–59 y) from Japan, China, the United Kingdom, and United States. Dietary intakes were assessed by four 24-h dietary recalls and BP from 8 measurements. Urinary metabolites (2 timed 24-h urinary samples) associated with fish intake acquired from NMR spectroscopy were identified. Linear models were used to estimate BP and BMI differences across categories of intake and per 2 SD higher intake of fish and its biomarkers.ResultsNo significant associations were observed between fish intake and BP. There was a direct association with fish intake and BMI in the Japanese population sample (P trend = 0.03; fully adjusted model). In Japan, trimethylamine-N-oxide (TMAO) and taurine, respectively, demonstrated area under the receiver operating characteristic curve (AUC) values of 0.81 and 0.78 in discriminating high against low fish intake, whereas homarine (a metabolite found in shellfish muscle) demonstrated an AUC of 0.80 for high/nonshellfish intake. Direct associations were observed between urinary TMAO and BMI for all regions except Japan (P < 0.0001) and in Western populations between TMAO and BP (diastolic blood pressure: mean difference 1.28; 95% CI: 0.55, 2.02 mmHg; P = 0.0006, systolic blood pressure: mean difference 1.67; 95% CI: 0.60, 2.73 mmHg; P = 0.002).ConclusionsUrinary TMAO showed a stronger association with fish intake in the Japanese compared with the Western population sample. Urinary TMAO was directly associated with BP in the Western but not the Japanese popula
Tzoulaki I, Castagné R, Boulangé CL, et al., 2019, Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease, European Heart Journal, Vol: 40, Pages: 2883-2896, ISSN: 1522-9645
Aims: To characterise serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). Methods and Results: We used untargeted one-dimensional (1D) serum metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy among 3,867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3,569 participants from the Rotterdam and LOLIPOP Studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 NMR measured metabolites were associated with CAC and/or IMT, P =1.3x10-14 to 6.5x10-6 (discovery), P =4.2x10-14 to 4.4x10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched-chain and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide and lactate as well as apolipoprotein B (P <0.05). Conclusion: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclero
McGill D, Chekmeneva E, Lindon J, et al., 2019, Application of novel solid phase extraction-NMR protocols for metabolic profiling of human urine, Faraday Discussions, Vol: 218, Pages: 395-416, ISSN: 1359-6640
Metabolite identification and annotation procedures are necessary for the discovery of biomarkers indicative of phenotypes or disease states, but these processes can be bottlenecked by the sheer complexity of biofluids containing thousands of different compounds. Here we describe low-cost novel SPE-NMR protocols utilising different cartridges and conditions, on both natural and artifical urine mixtures, which produce unique retention profiles useful to metabolic profiling. We find that different SPE methods applied to biofluids such as urine can be used to selectively retain metabolites based on compound taxonomy or other key functional groups, reducing peak overlap through concentration and fractionation of unknowns and hence promising greater control over the metabolite annotation/identification process.
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.
Graça G, Serrano Contreras JI, Chekmeneva E, 2019, NMR Spectroscopy, Techniques, Pulse Sequences for Structural Elucidation of Small Molecules, Encyclopedia of Analytical Science, 3rd edition, Volume 7, ISBN: 9780081019832
NMR spectroscopy is the most comprehensive analytical tool for chemical structure elucidation and verification. This article aims at introducing and explaining the basics of the most useful NMR pulse sequences for structural elucidation of small organic molecules such as metabolites, drugs and natural products. Step by step we introduce the experiments that are needed to determine the backbone structure and stereochemistry, terminating with a brief description of some of the latest developments in pulse sequences for improving spectral resolution and acquisition. The described experiments are available in most modern NMR spectrometers, from high-resolution systems to benchtop systems.
Gibson R, Lau C-H, Loo RL, et al., 2018, American Heart Association's Epidemiology and Prevention/Lifestyle and Cardiometabolic Health 2019 Scientific Sessions, American Heart Association EpiLifestyle
Chekmeneva E, Dos Santos Correia G, Gomez Romero M, et al., 2018, Ultra performance liquid chromatography-high resolution mass spectrometry and direct infusion-high resolution mass spectrometry for combined exploratory and targeted metabolic profiling of human urine, Journal of Proteome Research, Vol: 17, Pages: 3492-3502, ISSN: 1535-3893
The application of metabolic phenotyping to epidemiological studies involving thousands of biofluid samples presents a challenge for the selection of analytical platforms that meet the requirements of high-throughput precision analysis and cost-effectiveness. Here, direct infusion nanoelectrospray (DI-nESI)- was compared to an ultra-performance (UPLC)-high resolution mass spectrometry (HRMS) method for metabolic profiling of an exemplary set of 132 human urine samples from a large epidemiological cohort. Both methods were developed and optimised to allow simultaneous collection of high resolution urinary metabolic profiles and quantitative data for a selected panel of 35 metabolites. The total run time for measuring the sample set in both polarities by UPLC-HRMS was of 5 days compared to 9 hours by DI-nESI-HRMS. To compare the classification ability of the two MS methods we performed exploratory analysis of the full-scan HRMS profiles to detect sex-related differences in biochemical composition. Although metabolite identification is less specific in DI-nESI-HRMS, the significant features responsible for discrimination between sexes were mostly the same in both MS-based platforms. Using the quantitative data we showed that 10 metabolites have strong correlation (Pearson’s r > 0.9 and Passing-Bablok regression slope 0.8-1.3) and good agreement assessed by Bland-Altman plots between UPLC-HRMS and DI-nESI-HRMS and thus, can be measured using a cheaper and less sample- and time-consuming method. Only five metabolites showed weak correlation (Pearson’s r< 0.4) and poor agreement due to the overestimation of the results by DI-nESI-HRMS, and the rest of metabolites showed acceptable correlation between the two methods.
Chekmeneva E, Correia GDS, Chan Q, et al., 2017, Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 1646-1658, ISSN: 1535-3893
Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.
Oude Griep LM, Chekmeneva E, Stamler J, et al., 2016, Urinary hippurate and proline betaine relative to fruit intake, blood pressure, and body mass index, Summer meeting 2016: New technology in nutrition research and practice, Publisher: Cambridge University Press (CUP), Pages: E178-E178, ISSN: 0029-6651
Gray N, Adesina-Georgiadis K, Chekmeneva E, et al., 2016, Development of a Rapid Microbore Metabolic Profiling (RAMMP) UPLC-MS Approach for High-Throughput Phenotyping Studies., Analytical Chemistry, Vol: 88, Pages: 5742-5751, ISSN: 0003-2700
A rapid gradient microbore UPLC-MS method has been developed to provide a high-throughput analytical platform for the metabolic phenotyping of urine from large sample cohorts. The rapid microbore metabolic profiling (RAMMP) approach was based on scaling a conventional reversed-phase UPLC-MS method for urinary profiling from 2.1 x 100 mm columns to 1 x 50 mm columns, increasing the linear velocity of the solvent, and decreasing the gradient time to provide an analysis time of 2.5 min/sample. Comparison showed that conventional UPLC-MS and rapid gradient approaches provided peak capacities of 150 and 50 respectively, with the conventional method detecting approximately 19,000 features compared to the ca. 6000 found using the rapid gradient method. Similar levels of repeatability were seen for both methods. Despite the reduced peak capacity and the reduction in ions detected, the RAMMP method was able to achieve similar levels of group discrimination as conventional UPLC-MS when applied to rat urine samples obtained from investigative studies on the effects of acute 2-bromophenol and chronic acetaminophen administration. When compared to a direct infusion MS method of similar analysis time the RAMMP method provided superior selectivity. The RAMMP approach provides a robust and sensitive method that is well suited to high-throughput metabonomic analysis of complex mixtures such as urine combined with a five fold reduction in analysis time compared with the conventional UPLC-MS method.
Chekmeneva E, Correia G, Denes J, et al., 2015, Development of nanoelectrospray high resolution isotope dilution mass spectrometry for targeted quantitative analysis of urinary metabolites: application to population profiling and clinical studies, Analytical Methods, Vol: 7, Pages: 5122-5133, ISSN: 1759-9679
An automated chip-based electrospray platform was used to develop a high-throughput nanoelectrospray high resolution mass spectrometry (nESI-HRMS) method for multiplexed parallel untargeted and targeted quantitative metabolic analysis of urine samples. The method was demonstrated to be suitable for metabolic analysis of large sample numbers and can be applied to large-scale epidemiological and stratified medicine studies. The method requires a small amount of sample (5 μL of injectable volume containing 250 nL of original sample), and the analysis time for each sample is three minutes per sample to acquire data in both negative and positive ion modes. Identification of metabolites was based on the high resolution accurate mass and tandem mass spectrometry using authentic standards. The method was validated for 8 targeted metabolites and was shown to be precise and accurate. The mean accuracy of individual measurements being 106% and the intra- and inter-day precision (expressed as relative standard deviations) were 9% and 14%, respectively. Selected metabolites were quantified by standard addition calibration using the stable isotope labelled internal standards in a pooled urine sample, to account for any matrix effect. The multiple point standard addition calibration curves yielded correlation coefficients greater than 0.99, and the linear dynamic range was more than three orders of magnitude. As a proof-of-concept the developed method was applied for targeted quantitative analysis of a set of 101 urine samples obtained from female participants with different pregnancy outcomes. In addition to the specifically targeted metabolites, several other metabolites were quantified relative to the internal standards. Based on the calculated concentrations, some metabolites showed significant differences according to different pregnancy outcomes. The acquired high resolution full-scan data were used for further untargeted fingerprinting and improved the differentiation of
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