EPB41L2 _ 4.1G (aa 347 to 357)
- Known as:
- EPB41L2 _ 4.1G (aa 347 357)
- Catalog number:
- Y213821
- Product Quantity:
- 200ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- EPB41L2 _ 4.1G ( 347 357)
Related genes to: EPB41L2 _ 4.1G (aa 347 to 357)
- Gene:
- ANKRD18A NIH gene
- Name:
- ankyrin repeat domain 18A
- Previous symbol:
- -
- Synonyms:
- KIAA2015, FLJ35740
- Chromosome:
- 9p13.1
- Locus Type:
- gene with protein product
- Date approved:
- 2003-11-28
- Date modifiied:
- 2014-11-18
- Gene:
- ARHGAP22-IT1 NIH gene
- Name:
- ARHGAP22 intronic transcript 1
- Previous symbol:
- -
- Synonyms:
- FLJ35752
- Chromosome:
- 10q11.22
- Locus Type:
- RNA, long non-coding
- Date approved:
- 2011-09-07
- Date modifiied:
- 2015-02-25
- Gene:
- ARHGEF40 NIH gene
- Name:
- Rho guanine nucleotide exchange factor 40
- Previous symbol:
- -
- Synonyms:
- solo, FLJ10357
- Chromosome:
- 14q11.2
- Locus Type:
- gene with protein product
- Date approved:
- 2010-09-01
- Date modifiied:
- 2015-11-09
- Gene:
- BCAP31 NIH gene
- Name:
- B cell receptor associated protein 31
- Previous symbol:
- -
- Synonyms:
- DXS1357E, BAP31, 6C6-Ag, CDM
- Chromosome:
- Xq28
- Locus Type:
- gene with protein product
- Date approved:
- 2003-12-22
- Date modifiied:
- 2017-12-06
- Gene:
- C6orf89 NIH gene
- Name:
- chromosome 6 open reading frame 89
- Previous symbol:
- -
- Synonyms:
- FLJ25357, BRAP
- Chromosome:
- 6p21.2
- Locus Type:
- gene with protein product
- Date approved:
- 2003-05-20
- Date modifiied:
- 2018-12-06
Related products to: EPB41L2 _ 4.1G (aa 347 to 357)
OxiSelect In Vitro ROS/RNS Assay Kit (Green Fluorescence), Trial Size
OxiSelect™ In Vitro ROS RNS Assay Kit (Green Fluorescence), Trial Size(5alpha)_4,5_epoxy_3,14_dihydroxy_17_m (5alpha)_4,5_epoxy_3,11,1,2,3,3,3_Hexafluoropropoxybenzene 1,1,2,3,3,3_Hexafluorop1000ul Tecan Pipet Tip 96 Tips/Rack, 2 Racks/Box100ul Filter Gel Tip200ul Yellow Pipet Tips200ul Yellow Pipet Tips Bulk20ul Biomek FX Clear Tips20ul Biomek FX Clear Tips Max. Recovery, Racked2_(Trifluoromethyl)quinoline 2_(Trifluoromethyl)quin2_(Trifluoromethylthio)aniline 2_Aminophenyltrifluoro2_Chloro_N_(2_fluoro_phenyl)_acetamide _347
357
Related articles to: EPB41L2 _ 4.1G (aa 347 to 357)
- Neural functions and circuit formation rely on intricate crosstalk among various cell types during critical periods. Disruptions or delays in this crosstalk between neurons and astrocytes lead to abnormal neural functions and neurodevelopmental disorders. However, the lack of robust mouse models to study the crosstalk between astrocytes and neurons thus renders unclear the implications of impeding such interactions. In this study, we demonstrate that Egfr knockout during the critical period of neuronal maturation results in a transient absence of astrocytes, with recovery observed in adult mice. This model thus provides a unique opportunity to investigate the effects of impaired astrocyte-neuron communication during development. Mechanically, we show that loss of Egfr disrupts the Egfr-pERK-Epb41l2 signaling axis, which in turn prevents glial progenitor cells from migrating outward. More importantly, Egfr deficiency during the critical period compromises astrocyte-neuron communication via the Sema6a-Plxna2/4 ligand-receptor pair. This impaired intercellular crosstalk reduces neuronal dendritic complexity and excitability, ultimately culminating in depressive-like behaviors in adult mice. - Source: PubMed
Publication date: 2026/07/18
Jiang XinQi YanqingYang LinYang FeihongGuo RongliangLi LiangWang KunSun LichenDai DanLiu HanchenGao YanjingSun MenggeSong XiaoleiZhang ZhuangzhiXu ZhejunLuo BinXie YunliYang ZhengangHe MiaoQi DashiZhang XiaodanLiu Guoping - Preclinical translational research has increasingly utilized patient-derived xenograft (PDX) models for mechanistic and experimental therapeutic studies. However, most existing models have been developed from adult cancer types. Here, we describe the establishment of a PDX program to expand the availability of pediatric-specific PDXs for preclinical research and enable studies of pediatric cancer histologies, including ultra-rare diseases. Processes for PDX generation were integrated into established clinical workflows to facilitate universal model generation. Methodologies for tissue procurement, processing, and cryopreservation were optimized to enable intra- and inter-institutional PDX model generation. Over a 6-year span, 388 PDX tumor models representing >40 diagnoses were generated, including ultra-rare tumors and longitudinal models established from pre-therapy, post-therapy, and relapse tumors from the same patient. Genomic characterization of these PDXs demonstrated excellent concordance and recapitulation of molecular alterations of the source tumor. Successful PDX generation was enhanced from relapsed samples, was higher in sarcomas compared to other solid tumor types, and was a negative prognosticator for clinical outcome. The utility of the broad portfolio of molecularly annotated models for validating cross-histology biomarker-driven therapeutic strategies was established by demonstrating anti-tumor activity of a MAT2A inhibitor in MTAP-deficient PDXs. Universal model creation also allowed for experimental validation of therapeutic hypotheses on a patient-specific basis, as highlighted by the characterization of a RAF1 fusion (EPB41L2::RAF1) in an osteosarcoma PDX. Overall, development of a diverse collection of pediatric PDX models enables hypothesis-driven and cross-histology studies that expand our understanding of cancer biology and aid ongoing drug prioritization efforts in rare tumors. - Source: PubMed
Publication date: 2026/07/02
Dela Cruz Filemon SYou DaoqiFeinberg Tamar YBrosius SamanthaWang XinyiMcCarter Joseph GDomenico DylanGutiérrez-Abril JesúsArango Ossa Juan EGundem GunesWalch Henry SChatila Walid KXu JieruiLi HongyanGuillan KristinaSiddiquee ArmaanSilber JoachimBenhamida JamalLinkov IrinaNtiamoah PeterSauter Jennifer LBhanot Umeshkumar KJain MalaKombak Faruk EMedina-Martínez Juan SLevine Max FGlodzik DominikGao TengDiolaiti DanielF Coutinho DiegoRose RavenLi ShanitaStockfisch EmilyBouvier NancyRoberts Ryan DYustein JasonRainusso NinoCrompton Brian DOrtiz Michael VSlotkin Emily KThomas Andrika DSait Sameer FaroukMattar Marissa SMeneses MaximilianoRosales NestorKinnaman Michael DRodríguez-Sánchez M IreneLaQuaglia Michael PGerstle Justin TIacobuzio-Donahue Christine AGlade Bender Julia LRoehrl Michael HSchultz NikolausMauguen Audreyde Stanchina ElisaShukla Neerav NPapaemmanuil ElliKung Andrew L - Ovarian cancer (OC) is the most lethal gynecologic malignancy, presenting insidious onset and lacking specific biomarkers. Tumor-associated macrophages (TAMs) modulate immunity via M1/M2 plasticity, yet their heterogeneity and pro-metastatic mechanisms remain unclear. Using scRNA-seq, we compared immune profiles of OC and normal tissues, extracted TAM modules with hdWGCNA, and intersected them with DEGs to screen candidate genes Univariate/multivariate Cox and LASSO refined a seven-gene prognosis model (EPB41L2, AAK1, PRPF38B, RB1, GPR34, CLEC12A, BRD2) and established a nomogram. We also analyzed the DEGs' functional enrichment, immune signature, immune infiltration profile, and drug sensitivity. Unique immune cell infiltration landscape and gene expression profiles in the tumor microenvironment in OC were revealed. Based on the median risk score, OC patients were assigned to high-risk (HR) and low-risk (LR) groups, and the latter had far longer survival time than the former (P < 0.0001), which was validated in GEO (P = 0.018, P = 0.0063). The HR group had significantly increased Dysfunction Score (P < 0.01), MSI Score (P < 0.01), ESTIMATEScore (P = 0.045), and StromalScore (P = 0.0031) rather than TumorPurity Score (P = 0.045). The seven-gene prognosis model possesses a certain capability to predict survival rates and correlates with drug sensitivity and immunotherapy response. Identified immunoregulatory targets provide a theoretical basis for TAM-targeted treatments. - Source: PubMed
Publication date: 2026/06/21
Gu YingruiTan JiajiaZhou LinggangYao DiKuang Yan - Macrophages are pivotal in the progression of metabolic dysfunction-associated steatohepatitis (MASH), yet their specific markers remain elusive. Herein, we employed an integrated bioinformatics strategy, combining single-cell and bulk transcriptomic data from human MASH livers, to identify macrophage-related differentially expressed genes (Mϕ-DEGs). We pinpointed five core Mϕ-DEGs- FRMD4B, PTK2B, CPM, SPTLC2, and EPB41L2-that were predominantly expressed in macrophages and enriched within profibrotic M2 subsets. A diagnostic model constructed from these genes demonstrated high accuracy (area under the curve = 0.9865) and was robustly validated in an independent cohort. In human and murine MASH samples, FRMD4B and PTK2B were consistently downregulated, whereas CPM, SPTLC2, and EPB41L2 were upregulated. Protein-level validation by immunohistochemistry and immunofluorescence confirmed these expression patterns in human and mouse livers and in polarized THP-1-derived macrophages. Mendelian randomization analysis identified CPM as a significant causal protective factor, suggesting that its upregulation may represent a compensatory response. These genes correlated with altered immune cell infiltration (e.g., T follicular helper and regulatory cells) and were enriched in key MASH pathways, including fatty acid metabolism, sphingolipid signaling, and transforming growth factor beta/PI3K-Akt. Our findings were further corroborated through a multilevel validation framework encompassing clinical samples, a murine MASH model, and in vitro macrophage cultures. This study characterized a robust macrophage-specific gene signature for MASH diagnosis and offered genetic evidence regarding the causal protective role of CPM. The pronounced enrichment of these genes in M2 macrophages underscores their critical contribution to immunometabolic dysregulation, offering novel insights into MASH pathogenesis and potential diagnostic and therapeutic targets. - Source: PubMed
Publication date: 2026/04/28
Wang TingtingZhu XiayanZhang YiyingSu LihuangPan Tongtong - Osteoarthritis (OA) involves a complex pathogenesis encompassing inflammation, metabolic dysregulation, and aberrant intercellular communication. Despite their crucial role as mediators of intercellular signaling, exosomes remain largely underexplored in OA. This study aims to investigate exosome-related genes (ERGs) and their roles in OA pathogenesis. Four OA-related gene expression datasets retrieved from GEO were harmonized using the ComBat algorithm to mitigate batch effects. Differentially expressed genes (DEGs) were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). ERGs were screened utilizing the ExoCarta and Vesiclepedia databases. Core ERGs were prioritized using LASSO, random forest, and XGBoost algorithms. Predictive models were constructed and subsequently evaluated using SHAP analysis to ascertain feature importance. Additionally, pathway enrichment, immune infiltration, and molecular subtype identification were performed, followed by validation of core ERG expression via RT-qPCR in clinical samples. Integration of the four GEO datasets yielded 231 DEGs significantly enriched within OA-associated pathways (e.g., inflammation, immune cell migration, extracellular matrix remodeling). From a pool of 79 candidate ERGs, 10 core ERGs (EPB41L2, ISLR, HLA-DRB1, HLA-DRA, PGLYRP1, PTEN, TKT, CTNNB1, THSD4, ATP9A) were identified. Random forest models achieved impressive AUCs of 0.991, 1.0, and 0.935 in the training, validation, and external validation sets, respectively, demonstrating substantial clinical net benefit. SHAP analysis underscored CTNNB1 and PGLYRP1 as pivotal predictors. Core ERGs were intricately linked to immune regulation (e.g., M1 macrophage infiltration) and metabolic perturbations (e.g., fatty acid metabolism). Distinct molecular subtypes of OA were delineated based on ERG profiles, thereby revealing the inherent disease heterogeneity. RT-qPCR further corroborated the differential expression of core ERGs in clinical samples. This study comprehensively integrates exosome-related genomic data with advanced machine learning techniques to identify and validate 10 core ERGs associated with OA, thereby elucidating their pivotal roles in immunometabolic regulation. These seminal findings illuminate the intricate molecular heterogeneity of OA, concurrently offering promising novel biomarkers and therapeutic targets for early diagnosis and precision treatment. - Source: PubMed
Publication date: 2025/12/21
You ChuanfeiDai FurenDai BingzhuWu WeijunFang LeJia WeiminHan XuSu ZhiLi Jian