Comprehensive biotransformation analysis of phenylalanine-tyrosine metabolism reveals alternative routes of metabolite clearance in nitisinone-treated alkaptonuria (Serum metabolomic analysis)
Project description:Metabolomic analyses in alkaptonuria (AKU) have recently revealed alternative pathways in phenylalanine-tyrosine (phe-tyr) metabolism from biotransformation of homogentisic acid (HGA), the active molecule in this disease. The aim of this research was to study the phe-tyr metabolic pathway and whether the metabolites upstream of HGA, increased in nitisinone-treated patients, also undergo phase 1 and 2 biotransformation reactions. Metabolomic analyses were performed on serum and urine from patients partaking in the SONIA 2 phase 3 international randomised-controlled trial of nitisinone in AKU (EudraCT no. 2013-001633-41). Serum and urine samples were taken from the same patients at baseline (pre-nitisinone) then at 24 and 48 months on nitisinone treatment (patients N = 47 serum; 53 urine) or no treatment (patients N = 45 serum; 50 urine). Targeted feature extraction was performed to specifically mine data for the entire complement of theoretically predicted phase 1 and 2 biotransformation products derived from phenylalanine, tyrosine, 4-hydroxyphenylpyruvic acid and 4-hydroxyphenyllactic acid, in addition to phenylalanine-derived metabolites with known increases in phenylketonuria. In total, we observed 13 phase 1 and 2 biotransformation products from phenylalanine through to HGA. Each of these products were observed in urine and two were detected in serum. The derivatives of the metabolites upstream of HGA were markedly increased in urine of nitisinone-treated patients (fold change 1.2-16.2) and increases in 12 of these compounds were directly proportional to the degree of nitisinone-induced hypertyrosinaemia (correlation coefficient with serum tyrosine = 0.2-0.7). Increases in the urinary phenylalanine metabolites were also observed across consecutive visits in the treated group. Nitisinone treatment results in marked increases in a wider network of phe-tyr metabolites than shown before. This network comprises alternative biotransformation products from the major metabolites of this pathway, produced by reactions including hydration (phase 1) and bioconjugation (phase 2) of acetyl, methyl, acetylcysteine, glucuronide, glycine and sulfate groups. We propose that these alternative routes of phe-tyr metabolism, predominantly in urine, minimise tyrosinaemia as well as phenylalanaemia.
Project description:BackgroundIdiopathic membranous nephropathy (IMN) is an organ-specific autoimmune disease with multiple and complex pathogenic mechanisms. Currently, renal biopsy is considered the gold standard for diagnosing membranous nephropathy. However, there were limitations to the renal puncture biopsy, such as the relatively high cost, longer time consuming, and the risk of invasive procedures. We investigated the profile of serum metabolites in IMN patients based on the UHPLC-QE-MS metabolomics technique for exploring the potential disease biomarkers and clinical implementation.MethodsIn our research, we collected serum samples from healthy control (n = 15) and IMN patients (n = 25) to perform metabolomics analysis based on the UHPLC-QE-MS technique.ResultWe identified 215 differentially expressed metabolites (DEMs) between the IMN and healthy control (HC) groups. Furthermore, these DEMs were significantly identified in histidine metabolism, arginine and proline metabolism, pyrimidine metabolism, purine metabolism, and steroid hormone biosynthesis. Several key DEMs were significantly correlated with the level of clinical parameters, such as serum albumin, IgG, UTP, and cholesterol. Among them, dehydroepiandrosterone sulfate (DHEAS) was considered the reliable diagnostic biomarker in the IMN group. There was an increased abundance of actinobacteria, phylum proteobacteria, and class gammaproteobacterial in IMN patients for host-microbiome origin analysis.ConclusionOur study revealed the profiles of DEMs from the IMN and HC groups. The result demonstrated that there were disorders of amino acids, nucleotides, and steroids hormones metabolism in IMN patients. The down-regulation of DHEAS may be associated with the imbalance of the immune environment in IMN patients. In host-microbiome origin analysis, the gut microbiota and metabolite disturbances were present in IMN patients.
Project description:BackgroundUntargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways.ObjectiveIdentify metabolic disturbances associated with Parkinson's disease (PD) in two population-based studies using untargeted metabolomics.MethodsWe performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation.ResultsLC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism.ConclusionsOur results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.
Project description:The balance between the different lipid molecules present in biological fluids accurately reflects the health state of the organism and can be used by medical personnel to finely tune therapy to a single patient, a process known as precision medicine. In this work, we developed a miniaturized workflow for the analysis of different lipid classes at the intact level, as well as their fatty acid constituents, starting from human serum. Fatty acids were identified by using flow-modulated comprehensive gas chromatography coupled to mass spectrometry (FM-GC × GC-MS), and their relative amount as well as the ratio of specific FA classes was determined by using FM-GC × GC with a flame ionization detector. Ultra-high-performance liquid chromatography coupled to tandem mass spectrometry was used for the simultaneous quantification of vitamin D metabolites and assessment of different intact lipid classes. An MRM method was developed for the quantification of five vitamin D metabolites (vitamin D2, vitamin D3, 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, 24R,25-dihydroxyvitamin D3), and validated in terms of LoD, LoQ, accuracy, and precision, also using a certified reference material. At the same time, a combination of SCAN, precursor ion scan, and neutral loss scan, in both positive and negative modes, was used for the identification of 81 intact lipid species, such as phospholipids, cholesteryl esters, and triacylglycerols, in less than 25 min. In order to easily monitor the lipid composition and speed up the identification process, a two-dimensional map of the lipidome was generated, by plotting the molecular weight of the identified molecules versus their retention time. Moreover, a relative quantification was performed within each lipid class identified. The combination of untargeted and targeted data could provide useful information about the pathophysiological condition of the organism and evaluate, in a tailored manner, an efficient action.
Project description:BACKGROUND:A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance. RESULTS:To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found. CONCLUSION:BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/ . Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca , which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.
Project description:BackgroundWe aimed to comprehensively investigate the prognostic value of pretreatment laboratory parameters in elderly patients with glioblastoma treated with temozolomide (TMZ)-based chemoradiation.MethodsPatients aged ≥ 65 years from 4 institutions with newly diagnosed IDH-wild-type glioblastoma who received radiotherapy (RT) with concurrent TMZ between 2006 and 2021 were included. Patient factors (age, Karnofsky performance status (KPS), temporalis muscle thickness), molecular factors (MGMT promoter methylation, EGFR amplification, TERT promoter mutation, and TP53 mutation status), treatment factors (extent of resection, and RT dose), and pretreatment laboratory parameters (serum De Ritis ratio, glucose level, neutrophil-to-lymphocyte ratio, platelet count, and systemic immune-inflammation index) were included in the analysis. The primary endpoint was overall survival (OS).ResultsIn total, 490 patients were included in the analysis. The median follow-up period was 12.3 months (range, 1.6-149.9 months). Median OS was significantly prolonged in patients with De Ritis ratio < 1.2 (18.2 vs 15.3 months, P = .022) and in patients with glucose level < 150 mg/dL (18.7 vs 16.5 months, P = .034) per univariate analysis. In multivariate analysis, KPS ≥ 70, MGMT promoter methylation, extent of resection greater than partial resection, De Ritis ratio < 1.2, and glucose level < 150 mg/dL were significant prognostic factors for improved OS.ConclusionsAlong with well-known prognostic factors, pre-RT serum biomarkers, including the De Ritis ratio and glucose level, also had prognostic value in elderly patients with glioblastoma treated with TMZ-based chemoradiation.
Project description:BackgroundTyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.AimsTo dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan-Meier plots and logrank analysis were used to clarify survival differences between subtypes.ResultsBased on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan-Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.ConclusionsOur results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.
Project description:Melanomas depend on autocrine signals for proliferation and survival; however, no systematic screen of known receptor tyrosine kinases (RTKs) has been performed to identify which autocrine signaling pathways are activated in melanoma. Here, we performed a comprehensive analysis of 42 RTKs in six individual human melanoma tumor specimens as well as 17 melanoma cell lines, some of which were derived from the tumor specimens. We identified five RTKs that were active in almost every one of the melanoma tissue specimens and cell lines, including two previously unreported receptors, insulin-like growth factor receptor 1 (IGF-1R) and macrophage-stimulating protein receptor (MSPR), in addition to three receptors (vascular endothelial growth factor receptor, fibroblast growth factor receptor, and hepatocyte growth factor receptor) known to be autocrine activated in melanoma. We show, by quantitative real time PCR, that all melanoma cell lines expressed genes for the RTK ligands such as HGF, IGF-1, and MSP. Addition of antibodies to either IGF-1 or HGF, but not to MSP, to the culture medium blocked melanoma cell proliferation, and even caused net loss of melanoma cells. Antibody addition deactivated IGF-1R and hepatocyte growth factor receptors, as well as mitogen-activated protein kinase signaling. Thus, IGF-1 is a new growth factor for autocrine driven proliferation of human melanoma in vitro. Our results suggest that IGF-1-IGF-1R autocrine pathway in melanoma is a possible target for therapy in human melanomas.
Project description:Diabetic retinopathy (DR) is the main cause of vision loss or blindness in working age adults worldwide. The lack of effective diagnostic biomarkers for DR leads to unsatisfactory curative treatments. To define potential metabolite biomarkers for DR diagnosis, a multiplatform-based metabolomics study is performed. In this study, a total of 905 subjects with diabetes without DR (NDR) and with DR at different clinical stages are recruited. Multiplatform metabolomics methods are used to characterize the serum metabolic profiles and to screen and validate the DR biomarkers. Based on the criteria p < 0.05 and false-discovery rate < 0.05, 348 and 290 metabolites are significantly associated with the pathogenesis of DR and early-stage DR, respectively. The biomarker panel consisting of 12-hydroxyeicosatetraenoic acid (12-HETE) and 2-piperidone exhibited better diagnostic performance than hemoglobin A1c (HbA1c) in differentiating DR from diabetes, with AUCs of 0.946 versus 0.691 and 0.928 versus 0.648 in the discovery and validation sets, respectively. In addition, this panel showed higher sensitivity in early-stage DR detection than HbA1c. In conclusion, this multiplatform-based metabolomics study comprehensively revealed the metabolic dysregulation associated with DR onset and progression. The defined biomarker panel can be used for detection of DR and early-stage DR.