CLEC4M antibody - middle region (ARP42752_P050)
- Known as:
- CLEC4M (anti-) - middle region (ARP42752_P050)
- Catalog number:
- arp42752_p050
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- CLEC4M antibody - middle region (ARP42752_P050)
Related genes to: CLEC4M antibody - middle region (ARP42752_P050)
- 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: CLEC4M antibody - middle region (ARP42752_P050)
Related articles to: CLEC4M antibody - middle region (ARP42752_P050)
- Cholangiocarcinoma (CCA) has a complex tumor microenvironment (TME) and poor immunotherapy response. Understanding how the adaptive immune response (AIR) affects the TME and prognosis is critical. - Source: PubMed
Publication date: 2026/07/06
Jie WangJixiang ZhouYang BaoYanting WangLing Liu - Severe fever with thrombocytopenia syndrome virus (SFTSV), also known as Dabie bandavirus, is the pathogen causing severe fever with thrombocytopenia syndrome (SFTS). Human cell receptors are the first barriers to viral entry, proliferation, and host immune response. This study explored the association between single nucleotide polymorphisms (SNPs) in genes encoding receptors or related molecules that mediate SFTSV cell entry, such as dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN), DC-SIGN-related protein (DC-SIGNR), and non-muscle myosin heavy chain IIA (NMMHC-IIA), and SFTSV infection susceptibility in the Chinese Han population, as well as the underlying potential biological mechanisms. From September 2020 to December 2022, 454 subjects from the First Affiliated Hospital with Nanjing Medical University were enrolled and divided into SFTSV-infected and uninfected groups based on SFTSV RNA and antibody test results. TaqMan SNP genotyping was performed on (rs7248637, rs4804800, and rs11465421), (rs2277998), and (rs2269529 and rs2269530). Logistic regression was used to assess the association between the candidate SNPs and SFTSV infection susceptibility, and the online bioinformatics tools were employed to explore potential biological functions of positive SNPs. Logistic regression analysis showed that individuals carrying the rs2277998 AA genotype were more susceptible to SFTSV infection, and bioinformatics analysis generated the hypothesis that this locus might be involved in influencing gene transcription and expression, which warrants further functional validation. rs2277998 is significantly associated with SFTSV infection susceptibility in the Chinese Han population. The exact regulatory mechanisms require further experimental validation.IMPORTANCESevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by severe fever with thrombocytopenia syndrome virus (SFTSV). Host genetic variation may modulate both disease susceptibility and severity by altering key functional proteins. Our study was the first to explore the association between single nucleotide polymorphisms (SNPs) in host genes encoding receptors or related molecules that mediate SFTSV cell entry, such as dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN), DC-SIGN-related protein (DC-SIGNR), and non-muscle myosin heavy chain IIA (NMMHC-IIA), and SFTSV infection susceptibility in the Chinese Han population. The results showed that the homozygous mutation of rs2277998 AA genotype was associated with a significantly higher risk of SFTSV infection in the Chinese Han population, with predictions pointing to a potential, yet unverified, role in regulating the gene transcription and expression, which may provide a new scientific basis for SFTSV prevention and treatment. - Source: PubMed
Publication date: 2026/07/15
Wang MinRen ChengruiDeng GuoqiangShen YanCheng PengQian LiqinQi YongTan WeilongChai YinchuChen YutingZeng TianYin WenZhu ChuanlongYue Ming - Marburg virus (MARV), a highly pathogenic member of the Filoviridae family, causes severe hemorrhagic fever with a high case fatality rate and currently lacks effective therapeutics. The viral entry process, mediated by the interaction between the MARV glycoprotein (GP) and host receptor C-type lectin domain family 4 member M (CLEC4M) (L-SIGN), represents a critical target for early-stage intervention. The active compounds from BindingDB and the decoy from DUDE were used. The RDKit was used for feature engineering. Machine learning models were trained on an initial dataset consisting of 56 active chemicals and 1232 decoys. Among the tested algorithms, the Random Forest model demonstrated superior performance, achieving the highest discriminative ability (AUC = 0.93, MCC = 0.88) on the test set. Virtual screening of 11,032 phytochemicals resulted in 120 predicted actives, of which 42 compounds satisfied drug-likeness criteria. Subsequent molecular docking identified three lead compounds (PubChem IDs: 42608095, 5281601, and 11243993) with moderate-to-promising binding affinities (-6.3 to -6.5 kcal/mol) toward the CLEC4M binding site. ADMET analysis revealed favorable pharmacokinetic and toxicity profiles for the selected lead compounds. DFT calculations of the three compounds highlighted their electronic stability and reactive nature, indicating that PubChem IDs 42608095 and 5281601 possess particularly stable electronic properties conducive to favorable target interactions. Combining machine learning models with molecular docking and Molecular Dynamics (MD) simulations worked well in finding promising phytochemical inhibitors. The MM/GBSA binding free energy calculations further confirmed binding affinities, with values of -10.83 and -11.08 kcal/mol, respectively, suggesting favorable complex stability. These findings provide a pathway for developing new antiviral agents against MARV, pending further experimental validation and optimization. - Source: PubMed
Publication date: 2026/06/12
Almaghrabi MohammedAlturki Mansour S - Exposure to extreme stress within military contexts such as combat, captivity, survival training, or blast exposure triggers complex neurobiological responses that, in susceptible individuals, culminate in conditions such as Post-Traumatic Stress Disorder (PTSD). This inter-individual variability is rooted in profound genetic and epigenetic foundations. This manuscript reviews the critical relationship between chronic military stress and five key molecules: the glucocorticoid receptor (NR3C1), the FK506-binding protein 5 (FKBP5), brain-derived neurotrophic factor (BDNF), neuropeptide Y (NPY), and interleukin-6 (IL6). We examine how the dysregulation of this allostatic network predisposes individuals to PTSD and generates an altered systemic inflammatory and neuroendocrine microenvironment. Seeking an integrative biological perspective, this pathogenic model is linked to discoveries derived from extreme physical environments. Previous investigations by our group involving exposed to microgravity identified genes that are differentially inhibited under spaceflight-induced stress. Interactomic and evolutionary homology analyses revealed that five of these genes (LDHA, DNAJB5, ELOVL1, CLEC4M, SLC17A5) represent oncogenic vulnerabilities in human glandular epithelial tumors. Notably, network analysis demonstrates that and act as primary convergence nodes that interact directly with the allostatic stress network. We propose that the systemic attrition provoked by military stress acts as the physiological trigger that exploits these evolutionarily conserved epithelial vulnerabilities, thereby facilitating neoplastic progression. Understanding this translational convergence is fundamental for the development of predictive biomarkers and targeted therapies in high-risk populations. - Source: PubMed
Publication date: 2026/06/09
Laván DavidArgüelles NataliaRea RosaMorales JoséMontes SofiaHuaman DanielLluncor AlexisMoyano JuanPeña MiltonHerencia-Reyes VilmaGuerra AlcidesCalderón GabrielaVela-Ruiz José MGallo Aly - : 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