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更新于：2025-05-07

ZNF423

更新于：2025-05-07

基本信息

别名

Ebfaz、hOAZ、JBTS19

+ [8]

简介

Transcription factor that can both act as an activator or a repressor depending on the context. Plays a central role in BMP signaling and olfactory neurogenesis. Associates with SMADs in response to BMP2 leading to activate transcription of BMP target genes. Acts as a transcriptional repressor via its interaction with EBF1, a transcription factor involved in terminal olfactory receptor neurons differentiation; this interaction preventing EBF1 to bind DNA and activate olfactory-specific genes. Involved in olfactory neurogenesis by participating in a developmental switch that regulates the transition from differentiation to maturation in olfactory receptor neurons. Controls proliferation and differentiation of neural precursors in cerebellar vermis formation.

关联

项与 ZNF423 相关的药物

CCT-517

靶点

ZNF423

作用机制

ZNF423 inhibitors

在研机构

Canary Cure Therapeutics Inc

原研机构

Canary Cure Therapeutics Inc

在研适应症

肥胖

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期1800-01-20

CCT-217

靶点

CB1R x ZNF423

作用机制

CB1拮抗剂 [+1]

在研机构

Canary Cure Therapeutics Inc

原研机构

Canary Cure Therapeutics Inc

在研适应症

肥胖

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期1800-01-20

100 项与 ZNF423 相关的临床结果

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100 项与 ZNF423 相关的转化医学

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0 项与 ZNF423 相关的专利（医药）

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110

项与 ZNF423 相关的文献（医药）

2024-12-01·Cell Reports

Thyroid hormones are required for thermogenesis of beige adipocytes induced by Zfp423 inactivation

Article

作者: Strehlau, Christian ; Krause, Kerstin ; Blüher, Matthias ; Sheikh, Bilal ; Tönjes, Anke ; Hagemann, Tobias ; Weiner, Juliane ; Donndorf, Lorenz ; Ghosh, Adhideb ; Wolfrum, Christian ; Noé, Falko ; Roth, Lisa ; Krohn, Knut ; Wagner, Leonie ; Mittag, Jens ; Heiker, John T. ; Stumvoll, Michael ; Hoffmann, Anne ; Heiker, John T ; Klöting, Nora

The significance of thyroid hormones (THs) in beige adipocyte thermogenesis remains incompletely understood. We previously reported that THs directly regulate the expression of zinc-finger protein 423 (ZFP423), an anti-thermogenic factor, in adipose tissue. This study investigates the interaction between THs and adrenergic signaling in regulating thermogenic capacity and activation of beige adipocytes formed in response to Zfp423 deletion. We demonstrate that THs are indispensable for uncoupling protein 1 (UCP1)-dependent thermogenesis, leading to increased energy expenditure in mice with adipocyte-specific Zfp423 knockout. Targeted activation of the thyroid receptor isoform TRβ, which plays a central role in the inguinal depot, is sufficient to enhance energy expenditure in hypothyroid Zfp423iAKO mice. Mechanistically, THs and ZFP423 pathways cooperate to regulate early B cell factor 2 (EBF2)-mediated activation of the Ucp1 gene. RNA sequencing (RNA-seq) analysis of human adipose tissue samples supports the relevance of this regulatory network for human adipose tissue plasticity.

2024-08-01·Heliyon

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning

Article

作者: Wang, Xinyu ; Sheng, Yueyang ; Wang, Ying ; Li, Shan ; Li, Manyu ; Qiu, Cheng ; Wu, Chengai ; Cheng, Yuxi ; Zhang, Yanzhuo ; Liu, Tianyi

BackgroundOsteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples.MethodsWe exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers.ResultsIn total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set.ConclusionWe identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

2024-07-01·Translational Pediatrics

Identification and analysis of inflammation-related biomarkers in tetralogy of Fallot

Article

作者: Zheng, Fengnan ; Wu, Wenzhi ; Meng, Baoying ; Wang, Yuanxiang ; Du, Junzhe ; Wang, Pengcheng ; Liu, Huaipu

BackgroundStudies have revealed that inflammatory response is relevant to the tetralogy of Fallot (TOF). However, there are no studies to systematically explore the role of the inflammation-related genes (IRGs) in TOF. Therefore, based on bioinformatics, we explored the biomarkers related to inflammation in TOF, laying a theoretical foundation for its in-depth study.MethodsTOF-related datasets (GSE36761 and GSE35776) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between TOF and control groups were identified in GSE36761. And DEGs between TOF and control groups were intersected with IRGs to obtain differentially expressed IRGs (DE-IRGs). Afterwards, the least absolute shrinkage and selection operator (LASSO) and random forest (RF) were utilized to identify the biomarkers. Next, immune analysis was carried out. The transcription factor (TF)-mRNA, lncRNA-miRNA-mRNA, and miRNA-single nucleotide polymorphism (SNP)-mRNA networks were created. Finally, the potential drugs targeting the biomarkers were predicted.ResultsThere were 971 DEGs between TOF and control groups, and 29 DE-IRGs were gained through the intersection between DEGs and IRGs. Next, a total of five biomarkers (MARCO, CXCL6, F3, SLC7A2, and SLC7A1) were acquired via two machine learning algorithms. Infiltrating abundance of 18 immune cells was significantly different between TOF and control groups, such as activated B cells, neutrophil, CD56dim natural killer cells, etc. The TF-mRNA network contained 4 mRNAs, 31 TFs, and 33 edges, for instance, ELF1-CXCL6, CBX8-SLC7A2, ZNF423-SLC7A1, ZNF71-F3. The lncRNA-miRNA-mRNA network was created, containing 4 mRNAs, 4 miRNAs, and 228 lncRNAs. Afterwards, nine SNPs locations were identified in the miRNA-SNP-mRNA network. A total of 21 drugs were predicted, such as ornithine, lysine, arginine, etc.ConclusionsOur findings detected five inflammation-related biomarkers (MARCO, CXCL6, F3, SLC7A2, and SLC7A1) for TOF, providing a scientific reference for further studies of TOF.