21 results found
Ferreira MR, Sands CJ, Li JV, 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., Int J Radiat Oncol Biol Phys
PURPOSE: Radiation 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 MATERIALS: Samples 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. RESULTS: Metabolites 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. CONCLUSIONS: We 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.
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.
Gadgil MD, Kanaya AM, Sands C, et al., 2020, Circulating metabolites and lipids are associated with glycaemic measures in South Asians, DIABETIC MEDICINE, Vol: 38, ISSN: 0742-3071
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.
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
Wang X, Nijman R, Camuzeaux S, et al., 2019, Plasma lipid profiles discriminate bacterial from viral infection in febrile children, Scientific Reports, Vol: 9, ISSN: 2045-2322
Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The ‘omics’ approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n=20) and confirmed viral infection (n=20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group..A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics.
Izzi-Engbeaya CN, Comninos AN, Clarke S, et al., 2018, The effects of kisspeptin on β-cell function, serum metabolites and appetite in humans, Diabetes, Obesity and Metabolism, Vol: 20, Pages: 2800-2810, ISSN: 1462-8902
AimsTo investigate the effect of kisspeptin on glucose‐stimulated insulin secretion and appetite in humans.Materials and methodsIn 15 healthy men (age: 25.2 ± 1.1 years; BMI: 22.3 ± 0.5 kg m−2), we compared the effects of 1 nmol kg−1 h−1 kisspeptin versus vehicle administration on glucose‐stimulated insulin secretion, metabolites, gut hormones, appetite and food intake. In addition, we assessed the effect of kisspeptin on glucose‐stimulated insulin secretion in vitro in human pancreatic islets and a human β‐cell line (EndoC‐βH1 cells).ResultsKisspeptin administration to healthy men enhanced insulin secretion following an intravenous glucose load, and modulated serum metabolites. In keeping with this, kisspeptin increased glucose‐stimulated insulin secretion from human islets and a human pancreatic cell line in vitro. In addition, kisspeptin administration did not alter gut hormones, appetite or food intake in healthy men.ConclusionsCollectively, these data demonstrate for the first time a beneficial role for kisspeptin in insulin secretion in humans in vivo. This has important implications for our understanding of the links between reproduction and metabolism in humans, as well as for the ongoing translational development of kisspeptin‐based therapies for reproductive and potentially metabolic conditions.
Sands C, Wolfer AM, Sadawi N, 2018, phenomecentre/nPYc-Toolbox: nPYc
Sands C, Wolfer AM, Correia G, et al., 2018, peakPantheR: Peak Picking and ANnoTation of High resolution Experiments in R
An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files.
Robinson O, Toledano MB, Sands C, et al., 2016, Global metabolic changes induced by plant-derived pyrrolizidine alkaloids following a human poisoning outbreak and in a mouse model, Toxicology Research, Vol: 5, Pages: 1594-1603, ISSN: 2045-4538
Several hundred cases of Hirmi Valley Liver Disease (HVLD), an often fatal liver injury, occurred from 2001 to 2011 in a cluster of rural villages in Tigray, Ethiopia. HVLD is principally caused by contamination of the food supply with plant derived pyrrolizidine alkaloids (PAs), with high exposure to the pesticide DDT among villagers increasing their susceptibility. In an untargeted global approach we aimed to identify metabolic changes induced by PA exposure through 1H NMR spectroscopic based metabolic profiling. We analysed spectra acquired from urine collected from HVLD cases and controls and a murine model of PA exposure and PA/DDT co-exposure, using multivariate partial least squares discriminant analysis. In the human models we identified changes in urinary concentrations of tyrosine, pyruvate, bile acids, N-acetylglycoproteins, N-methylnicotinamide and formate, hippurate, p-cresol sulphate, p-hydroxybenzoate and 3-(3-hydroxyphenyl) propionic acid. Tyrosine and p-cresol sulphate were associated with both exposure and disease. Similar changes to tyrosine, one-carbon intermediates and microbial associated metabolites were observed in the mouse model, with tyrosine correlated with the extent of liver damage. These results provide mechanistic insight and implicate the gut microflora in the human response to challenge with toxins. Pathways identified here may be useful in translational research and as “exposome” signals.
Sands CJ, Guha IN, Kyriakides M, et al., 2015, Metabolic phenotyping for enhanced mechanistic stratification of chronic Hepatitis C-Induced liver fibrosis, American Journal of Gastroenterology, Vol: 110, Pages: 159-169, ISSN: 0002-9270
Objectives: The invasive nature of biopsy alongside issues with categorical staging and sampling error has driven research into noninvasive biomarkers for the assessment of liver fibrosis in order to stratify and personalize treatment of patients with liver disease. Here, we sought to determine whether a metabonomic approach could be used to identify signatures reflective of the dynamic, pathological metabolic perturbations associated with fibrosis in chronic hepatitis C (CHC) patients.Methods: Plasma nuclear magnetic resonance (NMR) spectral profiles were generated for two independent cohorts of CHC patients and healthy controls (n=50 original andn=63 validation). Spectral data were analyzed and significant discriminant biomarkers associated with fibrosis (as graded by enhanced liver fibrosis (ELF) and METAVIR scores) identified using orthogonal projection to latent structures (O-PLS).Results: Increased severity of fibrosis was associated with higher tyrosine, phenylalanine, methionine, citrate and, very-low-density lipoprotein (vLDL) and lower creatine, low-density lipoprotein (LDL), phosphatidylcholine, and N-Acetyl-α1-acid-glycoprotein. Although area under the receiver operator characteristic curve analysis revealed a high predictive performance for classification based on METAVIR-derived models, <40% of identified biomarkers were validated in the second cohort. In the ELF-derived models, however, over 80% of the biomarkers were validated.Conclusions: Our findings suggest that modeling against a continuous ELF-derived score of fibrosis provides a more robust assessment of the metabolic changes associated with fibrosis than modeling against the categorical METAVIR score. Plasma metabolic phenotypes reflective of CHC-induced fibrosis primarily define alterations in amino-acid and lipid metabolism, and hence identify mechanistically relevant pathways for further investigation as therapeutic targets.
Li JV, Ashrafian H, Bueter M, et al., 2012, Metabolic surgery profoundly influences gut microbial-host metabolic cross-talk, Gut, Vol: 60, Pages: 1214-1223
Alves AC, Li JV, Garcia-Perez I, et al., 2012, Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures, Metabolomics, Vol: 8, Pages: 1170-1180
Sands CJ, Coen M, Ebbels TMD, et al., 2011, Data-Driven Approach for Metabolite Relationship Recovery in Biological H-1 NMR Data Sets Using Iterative Statistical Total Correlation Spectroscopy, ANALYTICAL CHEMISTRY, Vol: 83, Pages: 2075-2082, ISSN: 0003-2700
Sands CJ, Coen M, Ebbels TMD, et al., 2011, Data-Driven Approach for Metabolite Relationship Recovery in Biological 1H NMR Data Sets Using Iterative Statistical Total Correlation Spectroscopy, Analytical Chemistry, Vol: 83, Pages: 2075-2082
Sands CJ, Coen M, Maher AD, et al., 2009, Statistical Total Correlation Spectroscopy Editing of H-1 NMR Spectra of Biofluids: Application to Drug Metabolite Profile Identification and Enhanced Information Recovery, ANALYTICAL CHEMISTRY, Vol: 81, Pages: 6458-6466, ISSN: 0003-2700
Sands CJ, Coen M, Maher AD, et al., 2009, Statistical Total Correlation Spectroscopy Editing of 1H NMR Spectra of Biofluids: Application to Drug Metabolite Profile Identification and Enhanced Information Recovery, Analytical Chemistry, Vol: 81, Pages: 6458-6466
MartinezArgudo I, Sands C, Jepson MA, 2007, Translocation of enteropathogenic Escherichia coli across an in vitro M cell model is regulated by its type III secretion system, Cellular Microbiology, Vol: 9, Pages: 1538-1546
Pye VE, Dreveny I, Briggs LC, et al., 2006, Going through the motions: The ATPase cycle of p97, JOURNAL OF STRUCTURAL BIOLOGY, Vol: 156, Pages: 12-28, ISSN: 1047-8477
Pye VE, Dreveny I, Briggs LC, et al., 2006, Going through the motions: The ATPase cycle of p97, Journal of Structural Biology, Vol: 156, Pages: 12-28
Buda A, Sands C, Jepson MA, 2005, Use of fluorescence imaging to investigate the structure and function of intestinal M cells, Advanced Drug Delivery Reviews, Vol: 57, Pages: 123-134
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