Dataset Information

Untargeted NMR Metabolomics Reveals Alternative Biomarkers and Pathways in Alkaptonuria.

ABSTRACT: Alkaptonuria (AKU) is an ultra-rare metabolic disease caused by the accumulation of homogentisic acid (HGA), an intermediate product of phenylalanine and tyrosine degradation. AKU patients carry variants within the gene coding for homogentisate-1,2-dioxygenase (HGD), which are responsible for reducing the enzyme catalytic activity and the consequent accumulation of HGA and formation of a dark pigment called the ochronotic pigment. In individuals with alkaptonuria, ochronotic pigmentation of connective tissues occurs, leading to inflammation, degeneration, and eventually osteoarthritis. The molecular mechanisms underlying the multisystemic development of the disease severity are still not fully understood and are mostly limited to the metabolic pathway segment involving HGA. In this view, untargeted metabolomics of biofluids in metabolic diseases allows the direct investigation of molecular species involved in pathways alterations and their interplay. Here, we present the untargeted metabolomics study of AKU through the nuclear magnetic resonance of urine from a cohort of Italian patients; the study aims to unravel molecular species and mechanisms underlying the AKU metabolic disorder. Dysregulation of metabolic pathways other than the HGD route and new potential biomarkers beyond homogentisate are suggested, contributing to a more comprehensive molecular signature definition for AKU and the development of future adjuvant treatment.

SUBMITTER: Grasso D

PROVIDER: S-EPMC9779518 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Untargeted NMR Metabolomics Reveals Alternative Biomarkers and Pathways in Alkaptonuria.

Grasso Daniela D Geminiani Michela M Galderisi Silvia S Iacomelli Gabriella G Peruzzi Luana L Marzocchi Barbara B Santucci Annalisa A Bernini Andrea A

International journal of molecular sciences 20221213 24

Alkaptonuria (AKU) is an ultra-rare metabolic disease caused by the accumulation of homogentisic acid (HGA), an intermediate product of phenylalanine and tyrosine degradation. AKU patients carry variants within the gene coding for homogentisate-1,2-dioxygenase (HGD), which are responsible for reducing the enzyme catalytic activity and the consequent accumulation of HGA and formation of a dark pigment called the ochronotic pigment. In individuals with alkaptonuria, ochronotic pigmentation of conn ...[more]

PMID: 36555443

Similar Datasets

Project description:Precision medicine may significantly contribute to rapid disease diagnosis and targeted therapy, but relies on the availability of detailed, subject specific, clinical information. Proton nuclear magnetic resonance (¹H⁻NMR) spectroscopy of body fluids can extract individual metabolic fingerprints. Herein, we studied 64 patients admitted to the Florence main hospital emergency room with severe abdominal pain. A blood sample was drawn from each patient at admission, and the corresponding sera underwent ¹H⁻NMR metabolomics fingerprinting. Unsupervised Principal Component Analysis (PCA) analysis showed a significant discrimination between a group of patients with symptoms of upper abdominal pain and a second group consisting of patients with diffuse abdominal/intestinal pain. Prompted by this observation, supervised statistical analysis (Orthogonal Partial Least Squares⁻Discriminant Analysis (OPLS-DA)) showed a very good discrimination (>90%) between the two groups of symptoms. This is a surprising finding, given that neither of the two symptoms points directly to a specific disease among those studied here. Actually herein, upper abdominal pain may result from either symptomatic gallstones, cholecystitis, or pancreatitis, while diffuse abdominal/intestinal pain may result from either intestinal ischemia, strangulated obstruction, or mechanical obstruction. Although limited by the small number of samples from each of these six conditions, discrimination of these diseases was attempted. In the first symptom group, >70% discrimination accuracy was obtained among symptomatic gallstones, pancreatitis, and cholecystitis, while for the second symptom group >85% classification accuracy was obtained for intestinal ischemia, strangulated obstruction, and mechanical obstruction. No single metabolite stands up as a possible biomarker for any of these diseases, while the contribution of the whole ¹H⁻NMR serum fingerprint seems to be a promising candidate, to be confirmed on larger cohorts, as a first-line discriminator for these diseases.

Project description:Background: Esophageal squamous cell carcinoma (ESCC) is one of the most fatal diseases worldwide. Because early diagnosis is difficult, ESCC is mostly diagnosed at an advanced stage, leading to a poor overall prognosis. The purpose of this study was to explore the differences between plasma metabolic profiles in ESCC patients and healthy controls and to establish a diagnostic model of ESCC. Methods: In this study, a cohort of 310 subjects, containing 140 ESCC patients and 170 healthy controls (HC), was recruited. Participants were randomly separated into a training set (80 ESCCs, 80 HCs) and a validation set (60 ESCCs, 90 HCs) and their plasma metabolomics profiles were analyzed by ultra-performance liquid chromatography-tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF/MS) technique. Univariate statistical analysis and multivariate analysis (MVA) methods were used to identify differential metabolites. Finally, the dysregulated pathways associated with ESCC were further explored and the diagnostic performance of the biomarker panel was evaluated. Results: Metabolic analyses identified 34 significant metabolites involved in the metabolism of amino acids, phospholipids, fatty acids, purine, and choline. Farthermore, an effective diagnostic model for ESCC was constructed based on eight metabolites. This panel of biomarkers consisted of hypoxanthine, proline betaine, indoleacrylic acid, inosine, 9-decenoylcarnitine, tetracosahexaenoic acid, LPE (20:4), and LPC (20:5). The model was verified and evaluated in the validation set. The AUC value of the ROC curve was 0.991(95% CI: 0.981-1.000, CI, Confidence interval), with a sensitivity (SE) of 98.8% and a specificity (SP) of 94.9% for the training set and 0.965(95% CI: 0.936-0.993), with a SE of 88.3% and a SP of 88.9% for the validation set. Among them, three biomarkers, indoleacrylic acid, LPC (20:5), and LPE (20:4), exhibited a trend associated with the ESCC progression. Conclusions: Our study identified a novel plasma biomarker panel, which clearly distinguishes ESCC patients and provides insight into the mechanisms of ESCC. This finding may form the basis for the development of a minimally invasive method for ESCC detection.

Dataset Information

Untargeted NMR Metabolomics Reveals Alternative Biomarkers and Pathways in Alkaptonuria.

Publications

Untargeted NMR Metabolomics Reveals Alternative Biomarkers and Pathways in Alkaptonuria.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets