Characterising the urinary acylcarnitine and amino acid profiles of HIV/TB co-infection, using LC-MS metabolomics
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ORGANISM(S): Homo sapiens (human)
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PROVIDER: S-BSST1402 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description: Not available
| S-BSST1397 | biostudies-other
Project description:IntroductionAmid the global health crisis, HIV/TB co-infection presents significant challenges, amplifying the burden on patients and healthcare systems alike. Metabolomics offers an innovative window into the metabolic disruptions caused by co-infection, potentially improving diagnosis and treatment monitoring.AimThis study uses untargeted metabolomics to investigate the urinary metabolic signature of HIV/TB co-infection, enhancing understanding of the metabolic interplay between these infections.MethodsUrine samples from South African adults, categorised into four groups - healthy controls, TB-positive, HIV-positive, and HIV/TB co-infected - were analysed using GCxGC-TOFMS. Metabolites showing significant differences among groups were identified through Kruskal-Wallis and Wilcoxon rank sum tests.ResultsVarious metabolites (n = 23) were modulated across the spectrum of health and disease states represented in the cohorts. The metabolomic profiles reflect a pronounced disruption in biochemical pathways involved in energy production, amino acid metabolism, gut microbiome, and the immune response, suggesting a bidirectional exacerbation between HIV and TB. While both diseases independently perturb the host's metabolism, their co-infection leads to a unique metabolic phenotype, indicative of an intricate interplay rather than a simple additive effect.ConclusionMetabolic profiling revealed a unique metabolic landscape shaped by HIV/TB co-infection. The findings highlight the potential of urinary differential metabolites for co-infection, offering a non-invasive tool for enhancing diagnostic precision and tailoring therapeutic interventions. Future research should focus on expanding sample sizes and integrating longitudinal analyses to build upon these foundational insights, paving the way for metabolomic applications in combating these concurrent pandemics.
| S-EPMC11252185 | biostudies-literature
Project description:BackgroundAlzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.MethodsBlood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.ResultsWhile no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.ConclusionUsing a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.
| S-EPMC8479251 | biostudies-literature
Project description:Tandem MS "profiling" of acylcarnitines and amino acids was conceived as a first-tier screening method, and its application to expanded newborn screening has been enormously successful. However, unlike amino acid screening (which uses amino acid analysis as its second-tier validation of screening results), acylcarnitine "profiling" also assumed the role of second-tier validation, due to the lack of a generally accepted second-tier acylcarnitine determination method. In this report, we present results from the application of our validated UHPLC-MS/MS second-tier method for the quantification of total carnitine, free carnitine, butyrobetaine, and acylcarnitines to patient samples with known diagnoses: malonic acidemia, short-chain acyl-CoA dehydrogenase deficiency (SCADD) or isobutyryl-CoA dehydrogenase deficiency (IBD), 3-methyl-crotonyl carboxylase deficiency (3-MCC) or ß-ketothiolase deficiency (BKT), and methylmalonic acidemia (MMA). We demonstrate the assay's ability to separate constitutional isomers and diastereomeric acylcarnitines and generate values with a high level of accuracy and precision. These capabilities are unavailable when using tandem MS "profiles". We also show examples of research interest, where separation of acylcarnitine species and accurate and precise acylcarnitine quantification is necessary.
| S-EPMC5009370 | biostudies-literature
Project description:Acylcarnitines are fatty acyl esters of L-carnitine and facilitate the entry of long-chain fatty acids into mitochondria via the carnitine shuttle, where they are metabolized via ß-oxidation. Alterations of acylcarnitine species can be diagnostic for fatty acid oxidation disorders and organic aciduria and are thus frequently used to screen newborns. Only a subfraction of all known acylcarnitines is thereby monitored and quantified. Therefore, a method for the simultaneous fast and robust detection of all known acylcarnitines was developed using a single concise liquid chromatography mass spectrometry (LC-MS) approach. Derivatization by 3-nitrophenylhydrazine increased the signal intensity of the acylcarnitines and a linear elution from a reversed phase column was observed that was dependent on the length of the carbon chain. This allowed a precise prediction of the exact elution time for each acylcarnitine class, which depended solely on the chemical nature of the carbon chain. This method can be further used to screen for yet unknown acylcarnitine species and adds a layer of confidence for their correct identification. Altogether 123 acylcarnitines species were used to establish a targeted low-resolution LC-MS method. The method was applied to acylcarnitine profiling in several mouse tissues and fluids, in order to identify large differences in the quantity and composition of acylcarnitines.
| S-EPMC6695155 | biostudies-literature
Project description: Not available
| S-BSST1726 | biostudies-other
Project description:To identify metabolic biomarkers that can be used to differentiate sepsis from systemic inflammatory response syndrome (SIRS), assess severity and predict outcomes.65 patients were involved in this study, including 35 patients with sepsis, 15 patients with SIRS and 15 normal patients. Small metabolites that were present in patient serum samples were measured by liquid chromatography mass spectrometry techniques and analysed using multivariate statistical methods.The metabolic profiling of normal patients and patients with SIRS or sepsis was markedly different. A significant decrease in the levels of lactitol dehydrate and S-phenyl-d-cysteine and an increase in the levels of S-(3-methylbutanoyl)-dihydrolipoamide-E and N-nonanoyl glycine were observed in patients with sepsis in comparison to patients with SIRS (p<0.05). Patients with severe sepsis and septic shock displayed lower levels of glyceryl-phosphoryl-ethanolamine, Ne, Ne dimethyllysine, phenylacetamide and d-cysteine (p<0.05) in their sera. The profiles of patients with sepsis 48?h before death illustrated an obvious state of metabolic disorder, such that S-(3-methylbutanoyl)-dihydrolipoamide-E, phosphatidylglycerol (22:2 (13Z, 16Z)/0:0), glycerophosphocholine and S-succinyl glutathione were significantly decreased (p<0.05). The receiver operating characteristic curve of the differential expression of these metabolites was also performed.The body produces significant evidence of metabolic disorder during SIRS or sepsis. Seven metabolites may potentially be used to diagnose sepsis.ClinicalTrial.gov identifier NCT01649440.
| S-EPMC4265126 | biostudies-literature
Project description:BackgroundUntargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.ResultsUsing metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering .ConclusionsOur proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.
| S-EPMC6570933 | biostudies-literature
Project description:Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.
| S-EPMC5441461 | biostudies-literature
Project description:Acylcarnitines are intermediates of fatty acid and amino acid oxidation found in tissues and body fluids. They are important diagnostic markers for inherited diseases of peroxisomal and mitochondrial oxidation processes and were recently described as biomarkers of complex diseases like the metabolic syndrome. Quantification of acylcarnitine species can become challenging because various species occur as isomers and/or have very low concentrations. Here we describe a new LC-MS/MS method for quantification of 56 acylcarnitine species with acyl-chain lengths from C2 to C18. Our method includes amino acid-derived positional isomers, like methacrylyl-carnitine (2-M-C3:1-CN) and crotonyl-carnitine (C4:1-CN), and odd-numbered carbon species, like pentadecanoyl-carnitine (C15:0-CN) and heptadecanoyl-carnitine (C17:0-CN), occurring at very low concentrations in plasma and tissues. Method validation in plasma and liver samples showed high sensitivity and excellent accuracy and precision. In an application to samples from streptozotocin-treated diabetic mice, we identified significantly increased concentrations of acylcarnitines derived from branched-chain amino acid degradation and of odd-numbered straight-chain species, recently proposed as potential biomarkers for the metabolic syndrome. In conclusion, the LC-MS/MS method presented here allows robust quantification of isomeric acylcarnitine species and extends the palette of acylcarnitines with diagnostic potential derived from fatty acid and amino acid metabolism.
| S-EPMC4583086 | biostudies-other