Comprehensive biotransformation analysis of phenylalanine-tyrosine metabolism reveals alternative routes of metabolite clearance in nitisinone-treated alkaptonuria (Urine 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:Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.
Project description:Vitamin D deficiency is common in pregnant women and may contribute to adverse events in pregnancy such as preeclampsia (PET). To date, studies of vitamin D and PET have focused primarily on serum concentrations vitamin D, 25-hydroxyvitamin D3 (25(OH)D3) later in pregnancy. The aim here was to determine whether a more comprehensive analysis of vitamin D metabolites earlier in pregnancy could provide predictors of PET. Using samples from the SCOPE pregnancy cohort, multiple vitamin D metabolites were quantified by liquid chromatography-tandem mass spectrometry in paired serum and urine prior to the onset of PET symptoms. Samples from 50 women at pregnancy week 15 were analysed, with 25 (50%) developing PET by the end of the pregnancy and 25 continuing with uncomplicated pregnancy. Paired serum and urine from non-pregnant women (n = 9) of reproductive age were also used as a control. Serum concentrations of 25(OH)D3, 25(OH)D2, 1,25(OH)2D3, 24,25(OH)2D3 and 3-epi-25(OH)D3 were measured and showed no significant difference between women with uncomplicated pregnancies and those developing PET. As previously reported, serum 1,25(OH)2D3 was higher in all pregnant women (in the second trimester), but serum 25(OH)D2 was also higher compared to non-pregnant women. In urine, 25(OH)D3 and 24,25(OH)2D3 were quantifiable, with both metabolites demonstrating significantly lower (P < 0.05) concentrations of both of these metabolites in those destined to develop PET. These data indicate that analysis of urinary metabolites provides an additional insight into vitamin D and the kidney, with lower urinary 25(OH)D3 and 24,25(OH)2D3 excretion being an early indicator of a predisposition towards developing PET.
Project description:Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.
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: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:The metabolic landscape of Epstein-Barr-virus-associated gastric cancer (EBVaGC) remains to be elucidated. In this study, we used transcriptomics, metabolomics, and lipidomics to comprehensively investigate aberrant metabolism in EBVaGC. Specifically, we conducted gene expression analyses using microarray-based data from gastric adenocarcinoma epithelial cell lines and tissue samples from patients with clinically advanced gastric carcinoma. We also conducted complementary metabolomics and lipidomics using various mass spectrometry platforms. We found a significant downregulation of genes related to metabolic pathways, especially the metabolism of amino acids, lipids, and carbohydrates. The effect of dysregulated metabolic genes was confirmed in a survival analysis of 3951 gastric cancer patients. We found 57 upregulated metabolites and 31 metabolites that were downregulated in EBVaGC compared with EBV-negative gastric cancer. Sixty-nine lipids, mainly ether-linked phospholipids and triacylglycerols, were downregulated, whereas 45 lipids, mainly phospholipids, were upregulated. In total, 15 metabolisms related to polar metabolites and 15 lipid-associated pathways were involved in alteration of metabolites by EBV in gastric cancer. In this work, we have described the metabolic landscape of EBVaGC at the multi-omics level. These findings could help elucidate the mechanism of EBVaGC oncogenesis.
Project description:Grape hyacinth (Muscari) is an important ornamental bulbous plant with an extraordinary blue colour. Muscari armeniacum, whose flowers can be naturally white, provides an opportunity to unravel the complex metabolic networks underlying certain biochemical traits, especially colour. A blue flower cDNA library of M. armeniacum and a white flower library of M. armeniacum f. album were used for transcriptome sequencing. A total of 89 926 uni-transcripts were isolated, 143 of which could be identified as putative homologues of colour-related genes in other species. Based on a comprehensive analysis relating colour compounds to gene expression profiles, the mechanism of colour biosynthesis was studied in M. armeniacum. Furthermore, a new hypothesis explaining the lack of colour phenotype of the grape hyacinth flower is proposed. Alteration of the substrate competition between flavonol synthase (FLS) and dihydroflavonol 4-reductase (DFR) may lead to elimination of blue pigmentation while the multishunt from the limited flux in the cyanidin (Cy) synthesis pathway seems to be the most likely reason for the colour change in the white flowers of M. armeniacum. Moreover, mass sequence data obtained by the deep sequencing of M. armeniacum and its white variant provided a platform for future function and molecular biological research on M. armeniacum.