APH1A Antibody
- Known as:
- APH1A Antibody
- Catalog number:
- 45-262
- Product Quantity:
- 0.1 mg
- Category:
- -
- Supplier:
- Prosci
- Gene target:
- APH1A Antibody
Related genes to: APH1A Antibody
- Gene:
- APH1A NIH gene
- Name:
- aph-1 homolog A, gamma-secretase subunit
- Previous symbol:
- -
- Synonyms:
- APH-1A, CGI-78
- Chromosome:
- 1q21.2
- Locus Type:
- gene with protein product
- Date approved:
- 2005-02-08
- Date modifiied:
- 2016-05-16
Related products to: APH1A Antibody
Related articles to: APH1A Antibody
- The precise regulation of neural stem cell (NSC) fate is fundamental to neocortical development. MicroRNAs (miRNAs) are critical post-transcriptional regulators in this process, yet the functions of many remain unknown. Here, we found miR-151-5p is expressed in NSCs of the developing mouse cerebral cortex. Conditional knockout of miR-151-5p increased SOX2 expression in NSCs and enhanced their proliferative capacity. Mechanistically, we identified APH1A, a core subunit of the γ-secretase complex, as a direct target of miR-151-5p. Notably, overexpression of APH1A phenocopied the effects of miR-151-5p knockout, promoting NSC proliferation by elevating NICD levels. These findings demonstrate that miR-151-5p biases NSC fate specification by targeting APH1A to modulate the Notch signaling pathway, thereby fine-tuning the balance between NSC maintenance and differentiation. In summary, our study unveils a novel miR-151-5p/APH1A/Notch signaling axis that governs NSC fate, adding a critical layer of post-transcriptional regulation to our understanding of mammalian neocortical development. - Source: PubMed
Publication date: 2026/05/21
Wang XinrunLi LiChen ZhuoZeng YiShu PengchengHou LinYin BinLiu WeiPeng Xiaozhong - The treatment landscape for Alzheimer's disease (AD) faces challenges such as prolonged drug development, high costs, and limited FDA-approved therapies. Given the pathological similarities between Early-Onset AD (EOAD) and Late-Onset AD (LOAD), repurposing existing drugs offers a promising strategy to expedite therapeutic development. In this study, weighted gene coexpression network analysis (WGCNA) was applied to identify AD-associated gene modules, followed by network pharmacology to screen candidate compounds. Parthenolide was selected based on blood-brain barrier permeability and disease relevance. Its effects were evaluated using LPS-stimulated BV2 microglia, N2a-sw and HT22 neuronal models, and transgenic AD mouse models. Transcriptomic integration, transcription factor enrichment, pharmacological inhibition, and behavioral and pathological analyses were employed to elucidate underlying mechanisms. Our findings reveal that parthenolide markedly suppressed microglial activation and reduced pro-inflammatory mediators via modulation of the HIF1α/NF-κB signaling axis. Bioinformatics analysis identified HIF1α as a key hub gene, which was experimentally validated using the selective inhibitor YC-1. Parthenolide attenuated inflammation-induced amyloidogenesis by downregulating amyloid β precursor protein (APP) expression and the γ-secretase component Aph-1A γ-Secretase Subunit (APH1α). , parthenolide administration significantly improved cognitive performance, reduced microglial activation, decreased β-amyloid plaque burden, and suppressed HIF1α/NF-κB-dependent inflammatory signaling in 5 × FAD mouse models. In conclusion, this study demonstrates that parthenolide exerts multitarget therapeutic effects in AD by concurrently suppressing neuroinflammation and amyloidogenic processing. Targeting the HIF1α/NF-κB axis may represent a promising strategy for modulating inflammatory-metabolic-amyloid networks in AD. - Source: PubMed
Publication date: 2026/04/23
Liu XinLiu JieWang Pei-YangCui Jun-HeBu Yu-MengWu Shan-ShanZhang Yan-HuiGuo Chuang - : The Notch signaling pathway regulates cell fate, proliferation, and differentiation, and its dysregulation has been implicated in various cancers, including breast cancer. MicroRNAs (miRNAs) are critical post-transcriptional regulators that can modulate Notch pathway components. The aim of this study was to identify miRNAs that may potentially regulate the expression of Notch pathway-related genes across five molecular subtypes of breast cancer in Polish women. : Tumor and adjacent normal tissue samples were collected from 405 patients with five breast cancer subtypes: luminal A ( = 130), HER2-negative luminal B ( = 100), HER2-positive luminal B ( = 96), non-luminal HER2-positive ( = 36), and triple-negative breast cancer ( = 43). Gene expression was profiled using mRNA microarrays and validated with RT-qPCR and ELISA. Candidate regulatory miRNAs were identified by miRNA microarrays and confirmed using the miRDB database. : , , , , , , , , and were consistently dysregulated across all breast cancer subtypes. Overexpression of and may be driven by decreased levels of miR-145, miR-98, and miR-381. Conversely, downregulation of may be associated with elevated expression of miR-196a and miR-155. No regulatory miRNAs meeting the selection criteria were identified for , , , , , or . : The consistent alterations suggest the presence of a shared Notch-driven oncogenic signature in breast cancer, potentially driving cell proliferation, stemness, and resistance to therapy. These findings enhance our understanding of Notch signaling in breast cancer and propose novel miRNA-Notch interactions as candidate targets for therapeutic intervention. - Source: PubMed
Publication date: 2025/12/12
Mitka-Krysiak ElżbietaKról-Jatręga KatarzynaOssowski PiotrZmarzły NikolaBereza KrzysztofOrdon PawełKulej WojciechSirek TomaszSirek AgataBoroń KacperBoroń MaciejBoroń DariuszGrabarek Beniamin Oskar - Cardiotoxicity, particularly drug-induced arrhythmias, poses a significant challenge in drug development, highlighting the importance of early-stage prediction of human ether-a-go-go-related gene (hERG) toxicity. hERG encodes the pore-forming subunit of the cardiac potassium channel. Traditional methods are both costly and time-intensive, necessitating the development of computational approaches. In this study, we introduce AttenhERG, a novel graph neural network framework designed to predict hERG channel blockers reliably and interpretably. AttenhERG demonstrates improved performance compared to existing methods with an AUROC of 0.835, showcasing its efficacy in accurately predicting hERG activity across diverse datasets. Additionally, uncertainty evaluation analysis reveals the model's reliability, enhancing its utility in drug discovery and safety assessment. Case studies illustrate the practical application of AttenhERG in optimizing compounds for hERG toxicity, highlighting its potential in rational drug design.Scientific contributionAttenhERG is a breakthrough framework that significantly improves the interpretability and accuracy of predicting hERG channel blockers. By integrating uncertainty estimation, AttenhERG demonstrates superior reliability compared to benchmark models. Two case studies, involving APH1A and NMT1 inhibitors, further emphasize AttenhERG's practical application in compound optimization. - Source: PubMed
Publication date: 2024/12/23
Yang TianbiaoDing XiaoyuMcMichael ElizabethPun Frank WAliper AlexRen FengZhavoronkov AlexDing Xiao - The NOTCH-signaling pathway is responsible for intercellular interactions and cell fate commitment. Recently, NOTCH pathway genes were demonstrated to play an important role in aortic valve development, leading to an increased calcified aortic valve disease (CAVD) later in life. Here, we further investigate the association between genetic variants in the NOTCH pathway genes and aortic stenosis in a case-control study of 90 CAVD cases and 4723 controls using target panel sequencing of full-length 20 genes from a NOTCH-related pathway (, , , , , , , , , , , , , , , , , , , ). We identified a common intronic variant in , protecting against CAVD development (rs3812603), as well as several rare and unique new variants in NOTCH-pathway genes (, , , , , , ), with a prominent effect of the protein structure and function. - Source: PubMed
Publication date: 2024/07/17
Irtyuga OlgaSkitchenko RostislavBabakekhyan MaryUsoltsev DmitriiTarnovskaya SvetlanaMalashicheva AnnaFomicheva YulyaRotar OksanaMoiseeva OlgaShadrina UlyanaArtomov MykytaKostareva AnnaShlyakhto Evgeny