MS4A3 Pre-design Chimera RNAi
- Known as:
- MS4A3 Pre-design Chimera RNAi
- Catalog number:
- H00000932-R05
- Product Quantity:
- 10 nmol
- Category:
- -
- Supplier:
- Abno
- Gene target:
- MS4A3 Pre-design Chimera RNAi
Related genes to: MS4A3 Pre-design Chimera RNAi
- Gene:
- MS4A3 NIH gene
- Name:
- membrane spanning 4-domains A3
- Previous symbol:
- CD20L
- Synonyms:
- HTM4
- Chromosome:
- 11q12.1
- Locus Type:
- gene with protein product
- Date approved:
- 1994-12-14
- Date modifiied:
- 2016-01-20
Related products to: MS4A3 Pre-design Chimera RNAi
Related articles to: MS4A3 Pre-design Chimera RNAi
- Spinal Muscular Atrophy (SMA) is a neurodegenerative disease caused by reduced survival motor neuron (SMN) protein levels due to gene mutations. The natural history of SMA has dramatically changed since innovative therapies were approved; among them, Risdiplam (an oral molecule) increases the peripheral levels of SMN by modifying the pre-mRNA slicing of the paralogous that also codes for the protein. - Source: PubMed
Publication date: 2026/06/17
Liguori MariaConsiglio AriannaD'Errico EustachioAntonacci YleniaCoffa MartinaIntrona AlessandroSimone Isabella Laura - Age-related macular degeneration (AMD) is characterized by disruption of the choriocapillaris (CC) and retinal pigment epithelium (RPE) dysfunction, leading to drusen accumulation. The CC and RPE form a tightly interdependent unit that maintains homeostasis where the CC supplies oxygen and nutrients to the RPE, while the RPE produces vascular endothelial growth factor (VEGF) to maintain the CC. Genetic studies link alternative complement pathway variants to AMD, and complement deposition on the CC increases during both aging and AMD. Macrophages express complement protein, receptors, and inhibitors, suggesting that they may be a missing link in understanding the role of complement in AMD. In support, previous groups have shown that macrophage depletion disrupts RPE-CC homeostasis, leading to AMD-like pathology, but the mechanism remains unclear. - Source: PubMed
Publication date: 2026/05/18
Gong JoyceGhotbaldini SanazViniak RitvikRajesh AmritaLavine Jeremy A - Colorectal cancer (CRC) is one of the most common malignant tumors worldwide. Patients with different immunophenotypes of CRC could achieve different effect of immunotherapy and yield different prognosis. With the advancement of bioinformatics, multi-omics analysis of the variations at both genomics and epigenomics levels helps a lot to provide a molecular basis for immunophenotype. Gene expression and clinical data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We calculated Spearman correlation of CD274 (programmed cell death ligand-1, PD-L1) and IFNG (interferon gamma, IFN-γ) expressions with immune cell fraction, and screened different immune cell types with CD274 and IFNG by Lasso regression analysis. Multi-omics analysis was exploited to screen out candidate genes with differential in genetic and epigenetic landscapes between two CRC subtypes with the greatest difference in immune infiltration. Finally, a risk scoring model was established and the role of candidate genes in prognosis and oncoimmunology was evaluated at the pan-cancer level. Two CRC types (cluster A and cluster B) including five subtypes (subclusters A1, A2, B1, B2A, and B2B) were identified by unsupervised clustering analysis. Somatic mutations, CNVs, and DNA methylation differed between subcluster A2 and B2, and analysis of DEGs correlated with CRC immune phenotypes identified FUT9 and MS4A3 as key genes related to CRC immune-phenotypes and prognosis. Furthermore, FUT9 was validated to act as a key gene related to CRC immune escape in vitro. The present study established a risk model for CRC immunophenotyping and prognosis, and highlighted the significance of FUT9 and MS4A3 in oncoimmunology of CRC. - Source: PubMed
Publication date: 2026/03/23
Zhu MinjingDong HaibeiHu YanyanXu XiFang Zejun - Transcriptomic analysis of blood cells can reveal key elements of the dysregulated host response in sepsis and spur biomarker and mechanism identification. We hypothesized that sepsis nonsurvivors exhibit a distinct transcriptional signature in whole blood that reflects insights into sepsis mortality. We conducted a prospective observational cohort study of 161 critically ill sepsis patients. Whole blood RNA was collected within 24 hours of intensive care unit admission. Gene expression levels were measured using microarrays, and changes in gene levels were compared between 30-day nonsurvivors and survivors, adjusting for age, sex, and neutrophil count. Pathway overrepresentation analysis and weighted gene co-expression analysis were performed to identify biological pathways and gene co-expression groups, respectively, associated with sepsis mortality. Gene- and pathway-based results were compared to findings in an independent cohort of 479 sepsis patients with 28-day mortality data. Thirty-day mortality in the enrolled sepsis cohort was 37% (60 of 161 patients). We identified 1106 differentially expressed genes in nonsurvivors (Benjamini-Hochberg-adjusted P-value <.05), including several neutrophil-related genes (CEACAM8, ELANE, PRTN3, MPO, CEACAM6, DEFA4, MS4A3) with expression levels over 1.8 times higher in nonsurvivors despite adjusting for neutrophil counts. The neutrophil degranulation pathway was prominent based on its overrepresentation in (1) differentially expressed genes in both cohorts, (2) overrepresentation by gene set enrichment analysis, and (3) 4 of the 6 gene co-expression groups correlated with sepsis mortality. Our findings highlight the involvement of neutrophil degranulation genes in sepsis mortality, prompting further study to better understand whether they constitute a modifiable target. - Source: PubMed
Giannini Heather MKan MengyuanCosgriff Christopher VMorley Michael PMiano Todd ANarayanan NishaIttner Caroline A GTurner Alexandra PEsperanza Mika PErlich Matthew COniyide OluwatosinAnderson Brian JJones Tiffanie KFeng RuiReilly John PHimes Blanca EShashaty Michael G SMeyer Nuala J - Genome-wide association studies (GWASs) have identified thousands of putative disease-causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show that current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a pipeline for sQTL discovery. MAJIQTL includes two statistical methods: a weighted multiple-testing approach for sGene discovery and a model for sQTL effect-size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the variant rs528823 in Alzheimer disease. Using antisense oligonucleotides, we test this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in MS4A3. - Source: PubMed
Publication date: 2025/11/12
Wang DavidGazzara Matthew RJewell SanWales-McGrath Benjamin DYang KevinBrown Christopher DChoi Peter SBarash Yoseph