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Last update 08 May 2025

GADD45B

Last update 08 May 2025

Basic Info

Synonyms

DKFZP566B133, GADD45B, GADD45BETA

+ [5]

Introduction

Involved in the regulation of growth and apoptosis. Mediates activation of stress-responsive MTK1/MEKK4 MAPKKK.

Drugs associated with GADD45B

KES-0001

Target

GADD45B

Mechanism

GADD45B inhibitors [+1]

Active Org.-

Originator Org.

Imperial College London

Active Indication-

Inactive Indication

Diffuse Large B-Cell Lymphoma [+1]

Drug Highest PhasePending

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with GADD45B

100 Translational Medicine associated with GADD45B

0 Patents (Medical) associated with GADD45B

495

Literatures (Medical) associated with GADD45B

01 Jun 2025·Archives of Biochemistry and Biophysics

PANoptosis-related gene biomarkers in aortic dissection

Article

Author: Zhou, Yang ; Chai, Xiangping ; Guo, Tuo ; Yang, Guifang ; Pu, Yuting

INTRODUCTIONProgrammed cell death of vascular smooth muscle cells (VSMCs) is critical in the pathogenesis of aortic dissection (AD), yet the role of PANoptosis-comprising pyroptosis, apoptosis, and necroptosis-remains unclear.METHODSWe utilized the GSE213740 single-cell sequencing dataset to assess PANoptosis levels in VSMCs. Datasets GSE153434 and GSE147026 were employed to identify differentially expressed genes (DEGs) and perform weighted gene co-expression network analysis. PANoptosis gene sets were sourced from the GSEA website, with GSE52093 serving as the validation cohort. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction analyses were conducted, along with assessments of upstream regulators and immune cell infiltration. Validation was performed on aortic tissues from AD patients and mouse models.RESULTSThe single-cell dataset revealed an increased PANoptosis score in VSMCs in AD. Nineteen PANoptosis-related DEGs (PANDEGs) were identified, contributing to VSMC differentiation, DNA damage response, and apoptosis. KEGG analysis highlighted the P53 and TGF-β pathways, with PANDEGs positively correlating with immune cell infiltration. Key PANDEGs GADD45B, CDKN1A, and SOD2 were validated, showing co-expression with α-SMA.CONCLUSIONThe increased PANoptosis score in VSMCs suggests that GADD45B, CDKN1A, and SOD2 play crucial roles in AD.

01 May 2025·Poultry Science

Exploring the functional variations of key candidate genes affecting egg production by hypothalamic-pituitary-ovarian axis in chickens

Article

Author: Liu, Xiaojun ; Dong, Jiajia ; Kang, Xiangtao ; Wei, Guanghui ; Zhu, Yiqian ; Wang, Dandan ; Zhi, Yihao

Potential genetic variants associated with chicken egg production traits have been extensively identified, most of which are located in the non-coding regions of the genome. However, which functional variants really drive the egg-laying phenotype change remain elusive. In the present study, by integrating the previously screened egg-laying related candidate genetic variants, transcriptome data derived from 16 high- and low-yield Gushi chickens, and epigenomic analyses, 22 potential functional variants (PFVs) were systematically identified. These PFVs potentially drive the differences in egg production phenotypes by affecting the expression of 10 egg-laying related key candidate genes in the hypothalamus (ZNF804B, DPP10, NEO1 and GABRG1), pituitary (DPP10 and GNG7), and ovary (PHIP, OSTN, GADD45B, NFXL1 and ADAMTS17). Subsequently, the regulatory activity and function of one PFV, chr3:79510218A>T, located in the third intron of the pleckstrin homology domain interacting protein gene (PHIP), were investigated in chicken ovarian granulosa cells. Association analysis confirmed the significant association of chr3:79510218A>T with egg number from 21 to 43 week of age in Gushi chicken (P = 0.0022) and Guangxi Yao chicken (P = 0.0388), as well as with ovarian PHIP expression levels (P = 0.0010). Functional analysis indicated that the T allele of chr3:79510218A>T enhanced the transcriptional activity and upregulated PHIP expression in vitro by binding transcription factor forkhead box I1 (FOXI1). Furthermore, the knockdown of PHIP led to the inhibition of ovarian granulosa cell proliferation and a reduction in the synthesis and secretion of progesterone (PROG) hormones. Collectively, this study unveil the egg-laying related functional variants and illustrate a potential genetic regulation mechanism, and will help accelerate the molecular design breeding process of chicken egg production.

01 Apr 2025·Journal of Infection

Clustering Mycobacterium tuberculosis-specific CD154+CD4+ T cells for distinguishing tuberculosis disease from infection based on single-cell RNA-seq analysis

Article

Author: Wang, Xiaochen ; Xing, Wenjin ; Wang, Feng ; Hu, Qiongjie ; Wu, Shiji ; Sun, Ziyong ; Hou, Hongyan ; Ren, Yi ; Jiang, Kaishan ; Xin, Qiudan

BACKGROUNDDistinguishing between active tuberculosis disease (TBD) and latent tuberculosis infection (TBI) is crucial for TB control but remains challenging.METHODSSingle-cell RNA sequencing was conducted on purified Mycobacterium tuberculosis (MTB)-specific CD154⁺CD4⁺ T cells.RESULTSWe observe a superior role of CD154 in detecting MTB infection, whereas its ability in distinguishing TBD from TBI is still limited due to patient heterogeneity. Single-cell RNA sequencing of MTB-specific CD154⁺CD4⁺ T cells identifies 10 distinct clusters, including Treg, T_act, Th1_pex, Th1_eff, Tfh, T_na, Th17_ex, Th2, NKT, and Th1_cyt. Notably, effector and apoptotic Th1 cells are predominant in CD154⁺CD4⁺ T cells of TBD. However, Tfh cells are the primary component in TBI. Most Th1_pex cells are positioned at the end of the developmental trajectory and are regulated by key genes associated with apoptosis and early exhaustion, such as GADD45B, FOS, and EZH2. Oxidative stress-induced metabolic disorder, marked by increased metabolism of nitrogen, cysteine, and glutathione, also contributes to the apoptosis of Th1_pex cells. Using seven features including NA, CM, EM, EMRA, CXCR3⁺ Th1, IFN-γ⁺ Th1, and Tfh of CD154⁺CD4⁺ T cells, both TBD and TBI can be classified into different subtypes, and a further established random forest model can accurately differentiate TBD from TBI. Additionally, the key checkpoints of exhausted MTB-specific Th1 cells are identified and blocking ADORA2A efficiently restores their function.CONCLUSIONSWe depict the cellular compositions, transcriptional characteristics, and developmental trajectories of MTB-specific CD154⁺CD4⁺ T cells from TBI to TBD, putting forward a new direction in the diagnosis and prognosis of disease.

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GADD45B

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