Dihydropyrimidinase protects from DNA replication stress caused by cytotoxic metabolites

Basbous, Jihane; Aze, Antoine; Chaloin, Laurent; Lebdy, Rana; Hodroj, Dana; Ribeyre, Cyril; Larroque, Marion; Shepard, Caitlin; Kim, Baek; Pruvost, Alain; Moreaux, Jérôme; Maiorano, Domenico; Méchali, Marcel; Constantinou, Angelos

doi:10.1093/nar/gkz1162

Cited by 20 publications

(16 citation statements)

References 86 publications

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“…Moreover, since deficiency in enzymes downstream of DPD, such as dihydropyrimidase (DHP) and/or β-ureidopropionase (UDP), could alter the 5,6-Dihydrouracil catabolic pathway [57], our finding supports the possibility that DPD function could indicate an error of 5,6-Dihydrouracil metabolism detected in metabolomic analysis in our female ischemic stroke patients [54]. This possibility is supported by other studies [58,59] that DPD function could indicate an error of 5,6-Dihydrouracil metabolism.…”

Section: Discussionsupporting

confidence: 89%

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

et al. 2021

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Background: Stroke is known to affect both men and women; however, incidence and outcomes differ between them. Therefore, the discovery of novel, sex-specific, blood-based biomarkers for acute ischemic stroke (AIS) patients has the potential to enhance the understanding of the etiology of this deadly disease in the content of sex. The objective of this study was to identify serum metabolites associated with male and female AIS patients. Methods: Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification from blood specimens collected from AIS patients. Samples were collected from 36 patients comprising each of 18 men and women with matched controls. Metabolic pathway analysis and principal component analysis (PCA) was used to differentiate metabolite profiles for male and female AIS patients from the control, while logistic regression was used to determine differences in metabolites between male and female AIS patients. Results: In female AIS patients, 14 distinct altered metabolic pathways and 49 corresponding metabolites were identified, while 39 metabolites and 5 metabolic pathways were identified in male patients. Metabolites that are predictive of ischemic stroke in female patients were 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) (AUC = 0.914, 0.765–1.000), 1-(1-enyl-palmitoyl)-2-palmitoyl-GPC (P-16:0/16:0) (AUC = 0.840, 0.656–1.000), and 5,6-dihydrouracil (P-16:0/20:2) (AUC = 0.815, 0.601–1.000). Significant metabolites that were predictive of stroke in male patients were 5alpha-androstan-3alpha,17beta-diol disulfate (AUC = 0.951, 0.857–1.000), alpha-hydroxyisocaproate (AUC = 0.938, 0.832–1.000), threonate (AUC = 0.877, 0.716–1.000), and bilirubin (AUC = 0.817, 0.746–1.000). Conclusions: In the current study, the untargeted serum metabolomics platform identified multiple pathways and metabolites associated with male and female AIS patients. Further research is necessary to characterize how these metabolites are associated with the pathophysiology in male and female AIS patients.

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Section: Discussionsupporting

confidence: 89%

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

et al. 2021

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“…The increasing evidence also suggests that carcinogenesis was associated with metabolic reprogramming of the pyrimidine degradation pathway. Dihydrothymine, a decomposition product of thymine, was also considered as a potential cytotoxic metabolite and was up-regulated in both early lung adenocarcinoma and breast cancer (Basbous et al, 2020;Wikoff et al, 2015;Shaul et al, 2014), but in this study, it was found to be downregulated in the chemotherapyresistant group, which may be associated with enhanced activity of the degradation enzyme dihydropyrimidase (DHP).…”

Section: Discussionmentioning

confidence: 66%

Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS

Liu

Feng

et al. 2021

Front. Pharmacol.

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Tumor cells not only show a vigorous metabolic state, but also reflect the disease progression and prognosis from their metabolites. To judge the progress and prognosis of ovarian cancer is generally based on the formation of ascites, or whether there is ascites recurrence during chemotherapy after ovarian cancer surgery. To explore the relationship between the production of ascites and ovarian cancer tissue, metabolomics was used to screen differential metabolites in this study. The significant markers leading to ascites formation and chemoresistance were screened by analyzing their correlation with the formation of ascites in ovarian cancer and the clinical indicators of patients, and then provided a theoretical basis. The results revealed that nine differential metabolites were screened out from 37 ovarian cancer tissues and their ascites, among which seven differential metabolites were screened from 22 self-paired samples. Sebacic acid and 20-COOH-leukotriene E4 were negatively correlated with the high expression of serum CA125. Carnosine was positively correlated with the high expression of serum uric acid. Hexadecanoic acid was negatively correlated with the high expression of serum γ-GGT and HBDH. 20a,22b-Dihydroxycholesterol was positively correlated with serum alkaline phosphatase and γ-GGT. In the chemotherapy-sensitive and chemotherapy-resistant ovarian cancer tissues, the differential metabolite dihydrothymine was significantly reduced in the chemotherapy-resistant group. In the ascites supernatant of the drug-resistant group, the differential metabolites, 1,25-dihydroxyvitamins D3-26, 23-lactonel and hexadecanoic acid were also significantly reduced. The results indicated that the nine differential metabolites could reflect the prognosis and the extent of liver and kidney damage in patients with ovarian cancer. Three differential metabolites with low expression in the drug-resistant group were proposed as new markers of chemotherapy efficacy in ovarian cancer patients with ascites.

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“…Among the 9 risk genes, the relationship between DPYS and the development of HCC has not been reported. DPYS can affect the levels of dihydrouracil and dihydrothymine by regulating the metabolism of dihydropyrimidine, which is related to cell transformation and cancer progression [36]. Furthermore, we plotted the receiver operator characteristic curves (ROC) and calculated the AUC value to evaluate the sensitivity and speci city of the prognostic model (Fig.…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

Liu

et al. 2020

Preprint

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BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

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Dihydropyrimidinase protects from DNA replication stress caused by cytotoxic metabolites

Cited by 20 publications

References 86 publications

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS

Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

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