EDF1 antibody - N-terminal region (ARP37729_T100)
- Known as:
- EDF1 (anti-) - N-terminal region (ARP37729_T100)
- Catalog number:
- arp37729_t100
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- EDF1 antibody - N-terminal region (ARP37729_T100)
Related genes to: EDF1 antibody - N-terminal region (ARP37729_T100)
- Gene:
- EDF1 NIH gene
- Name:
- endothelial differentiation related factor 1
- Previous symbol:
- -
- Synonyms:
- EDF-1, CFAP280
- Chromosome:
- 9q34.3
- Locus Type:
- gene with protein product
- Date approved:
- 1999-01-07
- Date modifiied:
- 2016-03-15
Related products to: EDF1 antibody - N-terminal region (ARP37729_T100)
Related articles to: EDF1 antibody - N-terminal region (ARP37729_T100)
- Achieving optimal flowering time is a pivotal factor in precision plant breeding. Here, in maize, we characterized an EMS-induced additive delayed-flowering mutant, edf1. edf1 and its heterozygotes are late-flowering under both long- and short-day photoperiods in the field, and harbor a splice-donor mutation in ALTERED PHLOEM DEVELOPMENT (APL). This mutation leads to the accumulation of the minor APL-β transcript, a splice variant expressed across a large natural maize population. Expression of the major splice variant APL-α is associated with flowering initiation; APL knockdown delays flowering and results in taller plants with increased biomass and plot yield. APL-α binds to and activates the promoter of ZCN8 to trigger flowering, while APL-β has impaired DNA-binding activity and antagonizes APL-α function. Base editing of the APL MYB domain in an elite rice cultivar and Arabidopsis results in delayed flowering, providing a feasible approach to regulate flowering time for the molecular breeding of staple crops. - Source: PubMed
Zhao ZhiweiLiang HuafengWu ChengxiuNi YifanWang FeiFeng FanZhang XinLiu JingzhenYi JuanDu XuanZhang ChunyiLu XiaoduoXiao YingjieYan JianbingLiu Hongtao - Alzheimer's disease (AD) is a common neurodegenerative disorder in the elderly population, and early screening can effectively delay the progression of the disease. Mild cognitive impairment (MCI) occurs prior to the onset of AD; however, the accuracy of existing MCI-to-AD prediction methods remains relatively low. Additionally, small sample sizes and high feature dimensions often lead to model overfitting, highlighting the need for effective early screening approaches. To address the aforementioned issues, this study integrated non-paired multi-modal features-including clinical indicators from the ADNI database, blood biomarkers, brain region volume features extracted from MRI, and genetic biomarkers from the GEO database-and proposed a gender-corrected random matching strategy. The Random Forest algorithm was adopted to evaluate this strategy, analyze feature importance, and compare the performance of 9 machine learning algorithms based on the top 40 ranked features. The predictive performance of multi-modal data was superior to that of single-modal data, and the proposed strategy achieved favorable results in early AD screening. 16 specific genetic features (e.g., IFI27, EDF1, RAP2A, KIF5C, SERPINA3, FBXW7, IFITM1, ISG15, PSMB3, APOE4, KCNB1, PSPH, HMGN2, S100A13, IFIT3, and CALM1) and 6 brain region volume features ranked high in terms of importance. When validated using paired datasets from ADNI across the 9 algorithms, ensemble learning models demonstrated significantly stronger fitting capabilities. The non-paired multi-modal fusion approach not only expands the sample size but also enhances the generalization ability and robustness of the model. This provides a theoretical basis for the application of this strategy in the field of small-sample medical research. - Source: PubMed
Publication date: 2026/03/06
Zhang ZhihaoZhang RuixiaYang WenzhongLv KeWu MiaoXu Lianghui - Systemic characterization of genes and pathways underlying the genetic architecture of type 2 diabetes (T2D) requires scalable functional genomics approaches. Molecular readouts from CRISPR perturbations can effectively uncover the mechanistic effects of underexplored genes. Here we performed single-cell RNA sequencing on pooled CRISPR screens (Perturb-seq) of 61 T2D-associated genes and 40 ribosome-associated quality control (RQC) genes in human pancreatic β cells (EndoC-βH1) for investigations of insulin production and T2D pathology. We identified 21 functional genes, including the uncharacterized KLHL42 and ZZEF1. Findings from global and β cell-specific knockout male mice, islet organoids and human islets reveal that ZZEF1 is a regulator of insulin synthesis and β cell stress through ribosomal stress-surveillance pathways in working and stress status-defined β cell subtypes. ZZEF1 deficiency impairs β cell function by inhibiting the RQC sensor EDF1, which could be improved by azoramide and ISRIB treatments. These Perturb-seq analyses and identification of functional RQC-related genes can provide potential therapeutic targets for T2D. - Source: PubMed
Publication date: 2026/01/02
Nan JingminjieHe XianglongLiu XiaopingRan JianrongChen JiahuanLi PengxiaoLiu DongxueSun YananShan AijingJiang XiuliXie JingWang WeiqingNing GuangCao Yanan - Comprehensive in situ structures of macromolecules can transform our understanding of biology and advance human health. Here, we map protein synthesis inside human cells in detail by combining automated cryo-focused ion beam (FIB) milling and in situ single-particle cryo electron microscopy (cryo-EM). With this in situ cryo-EM approach, we resolved a 2.2 Å consensus structure of the human 80S ribosome and unveiled 23 functional states, nearly all better than 3 Å resolution. Compared to in vitro studies, we observed variations in ribosome structures, distinct environments of ion and polyamine binding, and associated proteins such as EDF1 and NACβ that are typically not enriched with purified ribosomes. We also detected additional peptide-related density features on the ribosome and visualized ribosome-ribosome interactions in helical polysomes. Finally, high-resolution structures from cells treated with homoharringtonine and cycloheximide revealed a distinct translational landscape and a spermidine that interacts with cycloheximide at the E site, one of the numerous polyamines that also bind native ribosomes. These results underscore the value of high-resolution in situ studies in the native environment. - Source: PubMed
Publication date: 2025/11/28
Zheng WeiZhang YuekangWang JiminWang ShuhuiChai PengxinBailey Elizabeth JZhu ChenghaoGuo WangbiaoDevarkar Swapnil CWu ShenpingLin JianfengZhang KaiLiu JunLomakin Ivan BXiong Yong - Abnormal levels of Inhibin B (INH-B), a major regulator of ovarian activity, are closely linked to the development and prognosis of several ovarian disorders. Understanding the molecular mechanisms governing its regulation in granulosa cells is essential for both diagnosis and therapy. Our earlier work demonstrated the precise localization of lncPrep + 96 kb in granulosa cells and its central influence on the estrogen biosynthetic pathway. In this study, the impact of lncPrep + 96 kb on INH-B expression was investigated further. We created knockout mice lacking the long non-coding RNA lncPrep + 96 kb, which is specifically expressed in granulosa cells of the ovary. RNA sequencing revealed that the inhibin subunit βB (INHBB) was significantly elevated in knockout mice. ELISA was utilized to quantify INH-B levels in serum and granulosa cell supernatants, revealing a significant increase in knockout mice compared to wild-type controls. Overexpression of lncPrep + 96 kb fragments (2.2 kb and 2.8 kb) reduced INH-B expression. Endothelial differentiation-related factor 1 (EDF1), a key intracellular transcription factor, was found to be upregulated by lncPrep + 96 kb, resulting in decreased INH-B expression. In summary, lncPrep + 96 kb regulates INH-B secretion in granulosa cells by modulating of EDF1, providing new insights into the mechanism of INH-B expression and offering new research directions for diagnostic and therapeutic studies of abnormal ovarian functions. - Source: PubMed
Publication date: 2025/11/05
Zhang HongdanLiu JianweiMou CanglangTang YingqiLv Yangfan