Ask about this productRelated genes to: GDPD5 antibody
- Gene:
- GDPD5 NIH gene
- Name:
- glycerophosphodiester phosphodiesterase domain containing 5
- Previous symbol:
- -
- Synonyms:
- PP1665, GDE2
- Chromosome:
- 11q13.4-q13.5
- Locus Type:
- gene with protein product
- Date approved:
- 2005-03-15
- Date modifiied:
- 2018-02-13
Related products to: GDPD5 antibody
Related articles to: GDPD5 antibody
- Resistance to neoadjuvant chemoradiotherapy in rectal cancer diminishes survival benefits, potentially due to dysregulated lipid metabolism, though the mechanisms are unclear. Using the MSigDB database and GSE68204 cohort, we identified lipid metabolism genes linked to radiotherapy resistance. We developed resistant cell lines and xenograft models, and through multi-algorithm analysis (SVM-RFE, RF, LASSO), pinpointed key genes. Molecular mechanisms were explored via Western blotting, co-immunoprecipitation, molecular docking, and functional assays, validated in patient-derived organoids. Our study found that radiotherapy-resistant rectal cancer shows a lipid accumulation phenotype, with an inverse relationship between lipid droplet deposition and radiosensitivity in resistant cell models. The multi-algorithm screening identified GDPD5 as a key regulator. Silencing GDPD5 reduced lipid accumulation and increased radiosensitivity. Mechanistically, GDPD5 competes with CD55, disrupting its interaction with EGFR and promoting EGFR nuclear translocation, which suppresses p53 and leads to lipid buildup and radiotherapy resistance in tumors. Clinical samples showed high GDPD5 and low CD55 levels correlate with EGFR nuclear localization. Patient-derived organoids with high GDPD5 also showed increased radiotherapy resistance. Our findings indicate that GDPD5 facilitates EGFR nuclear translocation by binding to CD55, suppressing p53, and causing lipid accumulation and radiotherapy resistance in tumors. Targeting the GDPD5-CD55-EGFR interaction may enhance radiosensitivity. - Source: PubMed
Publication date: 2026/04/07
Zhu RuiqiuLi MingyueShen YiZou LiJin LiminShen YuntianZhu YaqunPeng Qiliang - To develop and validate a genetic diagnostic model for colorectal cancer (CRC). First, differential expression genes (DEGs) between colorectal cancer and normal groups were screened using the TCGA database. Subsequently, a two-sample Mendelian randomization analysis was performed using the eQTL genomic data from the IEU OpenGWAS database and colorectal cancer outcomes from the R12 Finnish database to identify associated genes. The intersecting genes from both methods were selected for the development and validation of the CRC genetic diagnostic model using nine machine learning algorithms: Lasso Regression, XGBoost, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT). A total of 3716 DEGs were identified from the TCGA database, while 121 genes were associated with CRC based on the eQTL Mendelian randomization analysis. The intersection of these two methods yielded 27 genes. Among the nine machine learning methods, XGBoost achieved the highest AUC value of 0.990. The top five genes predicted by the XGBoost method-RIF1, GDPD5, DBNDD1, RCCD1, and CLDN5-along with the five most significantly differentially expressed genes (, , , , and ) in the GSE87211 dataset, were selected for the construction of the final colorectal cancer (CRC) genetic diagnostic model. The ROC curve analysis revealed an AUC (95% CI) of 0.9875 (0.9737-0.9875) for the training set, and 0.9601 (0.9145-0.9601) for the validation set, indicating strong predictive performance of the model. SHAP model interpretation further identified and as the most influential genes in the XGBoost model, with both making positive contributions to the model's predictions. The gene expression profile in colorectal cancer is characterized by enhanced cell proliferation, elevated metabolic activity, and immune evasion. A genetic diagnostic model constructed based on ten genes (, , , , , , , , , and ) demonstrates strong predictive performance. This model holds significant potential for the early diagnosis and intervention of colorectal cancer, contributing to the implementation of third-tier prevention strategies. - Source: PubMed
Publication date: 2026/01/20
Yin YulaiYang ZhenLi XueqingGong ShuoXu Chen - Hippocampal synaptic activity is tightly regulated to ensure appropriate synaptic function and plasticity, which are important for critical cognitive processes such as learning and memory. Altered hippocampal synaptic function can lead to cognitive and behavioral deficits observed in neurodegenerative diseases such as Alzheimer's disease (AD), necessitating a deeper fundamental understanding of hippocampal synaptic control mechanisms. Glycerophosphodiester phosphodiesterase 2 (GDE2 or GDPD5) is a surface transmembrane enzyme that cleaves the glycosylphosphatidylinositol anchor that tethers some proteins to the membrane. Mice lacking GDE2 (KO) display behavioral deficits in learning and memory that are hippocampal-dependent. However, roles of GDE2 in mouse hippocampal function are not known. Here, we show that GDE2 is expressed in pre- and postsynaptic compartments along apical dendrites in hippocampal CA1 cells. KO CA1 cells showed increased dendritic length and complexity and increased numbers of mushroom spines localized to the stratum radiatum. Furthermore, adult KOs displayed an increased frequency of miniature excitatory postsynaptic currents, impaired paired-pulse facilitation, and disrupted -methyl-d-aspartate receptor (NMDAR)-mediated long-term depression (LTD). The phosphatidylinositol 3-kinase-AKT-glycogen synthase kinase 3 (PI3K-AKT-GSK3) signaling pathway, implicated in the inhibition of NMDAR-mediated LTD, was abnormally activated in the 2KO hippocampus, and inhibition of PI3K restored KO NMDAR-mediated LTD to WT levels. These observations identify GDE2 as an essential physiological regulator of CA1 synaptic morphology and hippocampal pre- and postsynaptic function, including the modulation of NMDAR-mediated LTD via the PI3K-AKT-GSK3 signaling axis. - Source: PubMed
Publication date: 2025/07/29
Daudelin DanielSama-Borbon DamaniZhang NanSockanathan Shanthini - Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder that adversely affects pregnant women and their growing fetuses. Evidence suggests that genetic and epigenetic modifications, such as DNA methylation, may contribute to the disease phenotype. This study aimed to identify GDM-related hub-methylated genes involved in GDM pathogenesis. - Source: PubMed
Hamdan Hamdan Z - Glycerophosphodiester phosphodiesterases (GDPDs) enzymes are known to be involved in phospholipids degradation pathways, where glycerophosphodiesters are hydrolyzed to glycerol-3-phosphate (G3P) and corresponding alcohol. In plants, GDPDs are involved in phosphate deficiency adaptive responses and have been shown to impact root length, but the precise mechanism remains unclear. This study focuses on the rice GDPD5 gene and its role in regulating primary root growth. Our research demonstrates that OsGDPD5 encodes a functional GDPD enzyme and could hydrolyze glycerophosphocholine and glycerophosphorylethanolamine. At transcriptional levels, OsGDPD5 is preferentially expressed in the root tip and regulated by transcription factor OsPHR2. We have used CRISPR/Cas9 to generate OsGDPD5 knock-out lines, allowing us to explore its role in root growth. Our findings show that osgdpd5 mutants had a shorter primary root, which could be restored to a normal level by the exogenous application of sugar or G3P. Further, knocking out OsGDPD5 alters endogenous levels of G3P and sugars, affecting auxin biosynthesis in the root and, ultimately, primary root growth. In this manner, OsGDPD5 has a crucial role in regulating physiological processes, specifically sugar and auxin signaling, which are known to be involved in root growth regulation in rice. Our research thus unraveled a link between rice phosphate deficiency-responsive lipid remodeling and root growth via sugar-hormone signaling. - Source: PubMed
Verma LokeshPandey MandaviBhatia ChitraMehra PoonamSingh BhagatGiri Jitender