Kynurenine-3-monooxygenase expression is activated in the pancreatic endocrine cells by diabetes and its blockade improves glucose-stimulated insulin secretion

Liu, Junjun; Bailbé, Danielle; Raynal, Sophie; Carbonne, Christel; Zhen, Delong; Dairou, Julien; Gausserès, Blandine; Armanet, Mathieu; Domet, Thomas; Pitasi, Caterina Luana; Movassat, Jamileh; Lim, Chai K.; Guillemin, Gilles J.; Autier, Valérie; Kergoat, Micheline; Portha, Bernard

doi:10.1016/j.bbadis.2022.166509

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…IGF2 [494], IRF7 [495], E2F1 [496], TEAD4 [497], KCNH2 [498], E2F2 [499], SNHG7 [500], FLI1 [501], CYP3A5 [502], HMGCS2 [503], GOT1 [504], PPARGC1A [505], GC (GC vitamin D binding protein) [506], VNN1 [507], NOX4 [508], SLC2A1 [509], BPGM (bisphosphoglyceratemutase) [164], NR4A3 [354], PFKFB2 [510], CDH2 [511], F11 [512], AQP2 [513], CLDN2 [514], EGF (epidermal growth factor) [515], ANGPT1 [516], KNG1 [517], SERPINA5 [518], HRG (histidine rich glycoprotein) [519], KL (klotho) [520], DEFB1 [521], ACE2 [522], AQP3 [523], CADM1 [188], DPP4 [524], STC1 [525], REN (renin) [526], TRPM6 [527], MSR1 [528], CCR1 [529], TNFRSF11B [530], FZD5 [531], ERBB4 [216], F8 [532], VCAM1 [533], PTGER3 [534] and ALB (albumin) [535] have been shown to influence the genetic risk of sepsis. IGF2 [536], IRF7 [100], PRKCB (protein kinase C beta) [101], CCL5 [537], EEF1A2 [538], ACTN3 [264], FCN1 [539], BRSK2 [540], MNX1 [541], AMH (anti-Mullerian hormone) [542], E2F1 [543], HAP1 [544], PF4 [545], AGER (advanced glycosylation end-product specific receptor) [546], E2F2 [547], TYMP (thymidine phosphorylase) [548], PPP1CC [549], NR2E1 [550], GREM1 [436], GRIN1 [551], WNT3A [552], COMP (cartilage oligomeric matrix protein) [553], BHMT (betaine--homocysteine S-methyltransferase) [554], ANGPTL3 [555], PCK1 [556], KMO (kynurenine 3-monooxygenase) [557], HSD11B2 […”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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“…It is currently still unclear whether elevated plasma levels reflect a causal pathway or represent only epiphenomena of disease development. If the first were to hold true, targeting the KYN pathway might harbour potential to expand the range of therapies to prevent and treat metabolic diseases [ 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Associations of Parameters of the Tryptophan–Kynurenine Pathway with Cardiovascular Risk Factors in Hypertensive Patients

et al. 2023

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Accumulating evidence suggests an association of the tryptophan–kynurenine (TRP-KYN) pathway with atherosclerosis and cardiovascular risk factors. In this cross-sectional analysis we investigated whether TRP-KYN pathway parameters are associated with 24 h blood pressure (BP) and other risk factors in patients with arterial hypertension from a tertiary care centre. In 490 participants, we found no significant and independent association of 24 h systolic and diastolic BP with parameters of the TRP-KYN pathway. However, linear regression analyses of HDL as dependent and TRP, KYN and quinolinic acid (QUIN) as explanatory variables adjusted for BMI and sex showed significant associations. These were found for KYN, BMI and sex (unstandardised beta coefficient −0.182, standard error 0.052, p < 0.001; −0.313 (0.078), p < 0.001; −0.180 (0.024), p < 0.001, respectively) as well as for QUIN, BMI and sex (−0.157 (0.038), p < 0.001; −0.321 (0.079), p < 0.001; −0.193 (0.024), p < 0.001, respectively). Smokers had significantly lower levels of KYN (2.36 µmol/L, IQR 2.01–2.98, versus 2.71 µmol/L, IQR 2.31–3.27, p < 0.001), QUIN (384 nmol/L, IQR 303–448, versus 451 nmol/L, IQR 369–575, p < 0.001) and KYN/TRP ratio (38.2, IQR 33.7–43.2, versus 43.1, IQR 37.5–50.9, p < 0.001) compared to non-smokers. We demonstrated that TRP/KYN pathway metabolites are associated with some cardiovascular risk factors, warranting further studies to elucidate the diagnostic and therapeutic potential of the TRP-KYN pathway for cardiovascular diseases.

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Kynurenine pathway in type 2 diabetes: Role of metformin

Al‐Qahtani,

Al‐kuraishy,

Ali

et al. 2024

Drug Development Research

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The Kynurenine pathway (KP) which is involved in the synthesis of nicotinamide adenine dinucleotide (NAD) from tryptophan (Trp) is intricate in the development of insulin resistance (IR) and type 2 diabetes (T2D). Inflammatory reactions in response to cardiometabolic disorders can induce the development of IR through the augmentation of KP. However, kynurenine (KYN), a precursor of kynurenic acid (KA) is increased following physical exercise and involved in the reduction of IR. Consequently, KP metabolites KA and KYN have anti‐diabetogenic effects while other metabolites have diabetogenic effects. KP modulators, either inhibitors or activators, affect glucose homeostasis and insulin sensitivity in T2D in a bidirectional way, either protective or detrimental, that is not related to the KP effect. However, metformin through inhibition of inflammatory signaling pathways can reduce the activation of KP in T2D. These findings indicated a strong controversy regarding the role of KP in T2D. Therefore, the objectives of this mini review were to clarify how KP induces the development of IR and T2D. In addition, this review aimed to find the mechanistic role of antidiabetic drug metformin on the KP, and how KP modulators affect the pathogenesis of T2D.

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Kynurenine-3-monooxygenase expression is activated in the pancreatic endocrine cells by diabetes and its blockade improves glucose-stimulated insulin secretion

Cited by 6 publications

References 40 publications

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Associations of Parameters of the Tryptophan–Kynurenine Pathway with Cardiovascular Risk Factors in Hypertensive Patients

Kynurenine pathway in type 2 diabetes: Role of metformin

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