Anti_Human, mab CD299 Source Mouse
- Known as:
- Anti_Human, mab CD299 Source Mouse
- Catalog number:
- 101-M302
- Product Quantity:
- 100 µg
- Category:
- -
- Supplier:
- Reliatech
- Gene target:
- Anti_Human mab CD299 Source Mouse
Related genes to: Anti_Human, mab CD299 Source Mouse
- Gene:
- CLEC4M NIH gene
- Name:
- C-type lectin domain family 4 member M
- Previous symbol:
- CD209L, CD299
- Synonyms:
- HP10347, DC-SIGNR, LSIGN, DCSIGNR, DC-SIGN2
- Chromosome:
- 19p13.2
- Locus Type:
- gene with protein product
- Date approved:
- 2000-09-19
- Date modifiied:
- 2016-10-05
Related products to: Anti_Human, mab CD299 Source Mouse
Related articles to: Anti_Human, mab CD299 Source Mouse
- Protein-carbohydrate interactions play a key role in numerous biological processes, including immune response, and glycan-based ligands that can target specific protein receptors on a cell surface represent promising candidates for therapeutics applications. For example, in retinoblastoma, the DC-SIGN mannose receptor is overexpressed on the surface of pathogenic cells and represents an interesting target for mannose-based ligands. At the same time, these ligands should not bind to the MRC1 receptor, which is expressed by adjacent, healthy, retinal pigment epithelial cells and presents a carbohydrate recognition domain (CRD) similar to the one of DC-SIGN. Therefore, the challenge remains to obtain a detailed picture of the recognition process between carbohydrates and proteins, in order to design effective and selective therapeutic compounds. In this work we used classical, all-atom molecular dynamics (MD) simulations to investigate the interaction between several mannose based ligands and the CRDs from DC-SIGN and MRC1. The analysis of the protein-carbohydrate contacts from the resulting trajectories highlights the variability of the mannose binding modes on both CRDs, and shows how the increased affinity of mannose for the MRC1 CRD can be related to a specific mannose binding state that is not accessible in the DC-SIGN CRD. - Source: PubMed
Publication date: 2026/04/30
Geissler SinaSacquin-Mora Sophie - : Advanced liver fibrosis (LF) is a major determinant of prognosis across chronic liver diseases. Current biomarkers are often etiology-specific and lack cross-cohort robustness. Shared molecular drivers across etiologies remain incompletely defined, and effective anti-fibrotic therapies are limited. : We developed a multi-algorithm consensus machine-learning framework to derive a robust LF progression signature. In the training non-alcoholic fatty liver disease (NAFLD) cohort GSE213621 ( = 368), samples were formulated as a binary classification task (mild fibrosis, F0-F2; advanced fibrosis, F3-F4). Candidate genes were screened in parallel using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). Genes selected by at least two algorithms were defined as a high-consensus pool, and genes consistently selected by all four algorithms were prioritized to construct a core signature. Model performance was evaluated by stratified cross-validation in the training cohort and externally validated in four independent cohorts of different etiologies (GSE49541, GSE84044, GSE130970, and GSE276114). Cellular sources of signature genes were characterized using single-cell RNA sequencing (scRNA-seq) datasets GSE136103 (human) and GSE172492 (mouse). For therapeutic discovery, the high-consensus expression profile was queried against the Connectivity Map (CMap) to prioritize compounds predicted to reverse the fibrotic transcriptional program. Withaferin A (WFA) was selected for experimental validation in a carbon tetrachloride (CCl)-induced mouse LF model and in the transforming growth factor-β1 (TGF-β1)-stimulated human hepatic stellate cell line LX-2. Bulk liver RNA-seq profiling was performed to interrogate WFA-associated molecular changes in vivo. : We identified a six-gene signature (CLEC4M, COL25A1, ITGBL1, NALCN, PAPPA, and PEG3) that discriminated advanced from mild fibrosis, achieving a mean AUC of 0.890 in internal cross-validation and an average AUC of 0.864 across external validation cohorts. scRNA-seq analysis revealed cell-type-specific expression with prominent enrichment in fibroblast populations. In vivo, WFA markedly attenuated CCl-induced fibrosis ( < 0.05) and reversed 1314 fibrosis-associated differentially expressed genes (adjusted < 0.05), which were enriched in fatty acid metabolism and PPAR signaling, as well as extracellular matrix (ECM)-receptor interaction and focal adhesion (adjusted < 0.05). In vitro, WFA suppressed TGF-β1-induced LX-2 activation, reducing α-SMA and Fibronectin expression ( < 0.05). : We report a six-gene signature that robustly predicts advanced LF across etiologies, define its cellular context using single-cell atlases, and validate the anti-fibrotic activity of WFA in both in vivo and in vitro models. Bulk liver RNA-seq and cellular evidence further suggest that WFA-associated effects are linked to lipid metabolic programs, ECM remodeling, and attenuation of hepatic stellate cell activation. - Source: PubMed
Publication date: 2026/03/17
Qin YingyingMa ShuoshuoHong HaoyuanZhong DeyuanLiang YuxinSu YuhaoChen YahuiChen XingZhu YizhunHuang Xiaolun - Physical activity (PA) and sedentary behavior (SB) are associated with many diseases, including Alzheimer disease and all-cause dementia. However, the specific biological mechanisms through which PA protects against disease are not entirely understood. This study aims to address this gap, with a specific focus on all-cause dementia. - Source: PubMed
Publication date: 2026/01/23
Arani GayatriArora AmitYang ShuaiWu JingyueKraszewski Jennifer NMartins AmyMiller AlexandraZeba ZebunnesaJafri AyanHu ChengchengFarland Leslie VBea Jennifer WColetta Dawn KAslan Daniel HSayre M KatherineBharadwaj Pradyumna KAlly MadelineMaltagliati SilvioLai Mark H CWilcox RandDE Geus EcoAlexander Gene ERaichlen David AKlimentidis Yann C - Understanding genetic associations of proteins is important for studying the molecular effect of genetic variation. A key component of this is to understand the role of complex genetic effects such as dominance and epistasis that are associated with plasma proteins. Therefore, we develop EIR-auto-GP, a deep learning-based approach, to identify complex effects that are associated with protein quantitative trait loci (pQTLs). Applying this method to the UK Biobank proteomics cohort of 48,594 individuals, we identify 123 proteins that are correlated with non-linear covariates and 15 with genetic dominance and epistasis. We uncover a novel interaction between the ABO and FUT3 loci and demonstrate dominance effects of the ABO locus on plasma levels of pathogen recognition receptors CD209 and CLEC4M. Furthermore, we replicate these findings and the methodology across Olink and mass spectrometry-based cohorts. Our approach presents a systematic, large-scale attempt to identify complex effects of plasma protein levels. - Source: PubMed
Publication date: 2025/12/14
Sigurdsson Arnor IGräf Justus FYang ZhiyuRavn KirstineMeisner JonasThielemann RomanWebel HenrySmit Roelof A JNiu LiliMann Matthias Vilhjalmsson BjarniNeale Benjamin MHolm Jens-ChristianGanna AndreaHansen TorbenLoos Ruth J FRasmussen Simon - Ovarian cancer (OC) demonstrates the poorest prognosis among gynecological malignancies, with five-year survival rates below 45%, primarily due to late-stage diagnosis. To address this challenge, we systematically identified OC-specific differentially expressed genes (DEGs) to develop a robust diagnostic model based on eleven machine learning algorithms. Furthermore, we explored the potential mechanism of key DEG in OC. - Source: PubMed
Publication date: 2025/11/04
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