Diethylene glycol dimethyl ether DEG dimethyl ether
- Known as:
- Diethylene glycol dimethyl ether DEG dimethyl ether
- Catalog number:
- 111-96-6
- Product Quantity:
- 1g
- Category:
- -
- Supplier:
- chemotra
- Gene target:
- Diethylene glycol dimethyl ether DEG
Related genes to: Diethylene glycol dimethyl ether DEG dimethyl ether
- Gene:
- BST2 NIH gene
- Name:
- bone marrow stromal cell antigen 2
- Previous symbol:
- -
- Synonyms:
- CD317, tetherin
- Chromosome:
- 19p13.11
- Locus Type:
- gene with protein product
- Date approved:
- 1994-11-17
- Date modifiied:
- 2016-07-29
Related products to: Diethylene glycol dimethyl ether DEG dimethyl ether
(+ per -)-2,2-Dimethyl-1,3-dioxolane-4-methanol 97% (+ per -)-2,2-Dimethyl-1,3-dioxolane-4-methanol 97% (+ per -)-2,2-Dimethyl-1,3-dioxolane-4-methanol 97% (+)-(2S,6Z,9S,10E)-9-{[tert-Butyl(dimethyl)silyl]oxy}-2,6,10-trimethyl-11-(2-methyl-1,3-thiazol-4-yl)-undeca-6,10-dien-1-ol C24H43NO2SSi CAS: 210690-99-6(+)-(2S,6Z,9S,10E)-9-{[tert-Butyl(dimethyl)silyl]oxy}-2,6,10-trimethyl-11-(2-methyl-1,3-thiazol-4-yl)-undeca-6,10-dien-1-ol CAS: 210690-99-6 Formula:(+)-(3R)-3-{[tert-Butyl(dimethyl)silyl]oxy}dihydrofuran-2(3H)-one C10H20O3Si CAS: 669000-31-1(+)-(3R)-3-{[tert-Butyl(dimethyl)silyl]oxy}dihydrofuran-2(3H)-one CAS: 669000-31-1 Formula: C10H20O3Si(+)-(3R,4R)-3,4-Dimethyl-4-(3-hydroxyphenyl)piperidine C13H19NO CAS: 119193-19-0(+)-(3R,4R)-3,4-Dimethyl-4-(3-hydroxyphenyl)piperidine CAS: 119193-19-0 Formula: C13H19NO(+)-N-Acetyl 3,4,4a,5,6,10b-Hexahydro-2H-naphtho[1,2-b][1,4]oxazine-9-ol Triisopropylsilyl Ether CAS: 1034706-81-4 Formula: C23H37NO3Si(-)-(2Z,5S,6E)-5-{[tert-Butyl(dimethyl)silyl]oxy}-2,6-dimethyl-7-(2-methyl-1,3-thiazol-4-yl)hepta-2,6-dien-1-ol C19H33NO2SSi CAS: 218614-16-5(-)-(2Z,5S,6E)-5-{[tert-Butyl(dimethyl)silyl]oxy}-2,6-dimethyl-7-(2-methyl-1,3-thiazol-4-yl)hepta-2,6-dien-1-ol CAS: 218614-16-5 Formula: C19H33NO2SS(-)-(3S)-3-{[tert-Butyl(dimethyl)silyl]oxy}-5-hydroxypentan-2-one C11H24O3Si CAS: 218615-21-5(-)-(3S)-3-{[tert-Butyl(dimethyl)silyl]oxy}dihydrofuran-2(3H)-one C10H20O3Si CAS: 164264-14-6(-)-(3S)-3-{[tert-Butyl(dimethyl)silyl]oxy}dihydrofuran-2(3H)-one CAS: 164264-14-6 Formula: C10H20O3Si Related articles to: Diethylene glycol dimethyl ether DEG dimethyl ether
- Pancreatic ductal adenocarcinoma (PDAC) features an immunosuppressive tumor microenvironment (TME) that resists immunotherapy. Tumor-associated macrophages, abundant in the TME, modulate T cell responses. Bone marrow stromal antigen 2-positive (BST2) macrophages increase in Kras; Trp53; Pdx1-Cre mouse models during PDAC progression. However, their role in PDAC remains elusive. Our findings reveal a negative correlation between BST2 macrophage levels and PDAC patient prognosis. Moreover, an increased ratio of exhausted CD8 T cells is observed in tumors with up-regulated BST2 macrophages. Mechanistically, BST2 macrophages secrete CXCL7 through the ERK pathway and bind with CXCR2 to activate the AKT/mTOR pathway, promoting CD8 T cell exhaustion. The combined blockade of CXCL7 and programmed death-ligand 1 successfully decelerates tumor growth. Additionally, cGAS-STING pathway activation in macrophages induces interferon (IFN)α synthesis leading to BST2 overexpression in the PDAC TME. This study provides insights into IFNα-induced BST2 macrophages driving an immune-suppressive TME through ERK-CXCL7 signaling to regulate CD8 T cell exhaustion in PDAC. - Source: PubMed
Publication date: 2024/04/10
Zheng ChenleiWang JunliZhou YuDuan YiZheng RujiaXie YutingWei XiaobaoWu JiangchaoShen HangYe MaoKong BoLiu YunhuaXu PinglongZhang QiLiang Tingbo - The human orthopneumovirus, Respiratory Syncytial Virus (RSV), is the causative agent of severe lower respiratory tract infections (LRTI) and exacerbations of chronic lung diseases. In immune competent hosts, RSV productively infects highly differentiated epithelial cells, where it elicits robust anti-viral, cytokine and remodeling programs. By contrast, basal cells are relatively resistant to RSV infection, in part, because of constitutive expression of an intrinsic innate immune response (IIR) consisting of a subgroup of interferon (IFN) responsive genes. The mechanisms controlling the intrinsic IIR are not known. - Source: PubMed
Publication date: 2024/03/27
Xu XiaofangQiao DianhuaBrasier Allan R - SARS-CoV-2 typically causes mild symptoms in children, but evidence suggests that persistent immunopathological changes may lead to long COVID (LC). To explore the interplay between LC and innate immunity, we assessed the type I interferon (IFN-I) response in children and adolescents with LC symptoms (LC; n = 28). This was compared with age-matched SARS-CoV-2 recovered participants without LC symptoms (MC; n = 28) and healthy controls (HC; n = 18). We measured the mRNA expression of IFN-I (IFN-α/β/ε/ω), IFN-I receptor (IFNAR1/2), and ISGs (ISG15, ISG56, MxA, IFI27, BST2, LY6E, OAS1, OAS2, OAS3, and MDA5) in PBMCs collected 3-6 months after COVID-19. LC adolescents (12-17 years) had higher transcript levels of IFN-β, IFN-ε, and IFN-ω than HC, whereas LC children (6-11 years) had lower levels than HC. In adolescents, increased levels of IFN-α, IFN-β, and IFN-ω mRNAs were found in the LC group compared with MC, while lower levels were observed in LC children than MC. Adolescents with neurological symptoms had higher IFN-α/β mRNA levels than MC. LC and MC participants showed decreased expression of ISGs and IFNAR1, but increased expression of IFNAR2, than HC. Our results show age-related changes in the expression of transcripts involved in the IFN-I signaling pathway in children and adolescents with LC. - Source: PubMed
Publication date: 2024/03/24
Fracella MatteoMancino EnricaNenna RaffaellaVirgillito ChiaraFrasca FedericaD'Auria AlessandraSorrentino LeonardoPetrarca LauraLa Regina DomenicoMatera LuigiDi Mattia GretaCaputo BeniaminoAntonelli GuidoPierangeli AlessandraViscidi Raphael PMidulla FabioScagnolari Carolina - Plasmacytoid dendritic cells (pDCs) are the major producers of type I interferons (IFNs), which are essential to mount antiviral and antitumoral immune responses. To avoid exaggerated levels of type I IFNs, which pave the way to immune dysregulation and autoimmunity, pDC activation is strictly regulated by a variety of inhibitory receptors (IRs). In tumors, pDCs display an exhausted phenotype and correlate with an unfavorable prognosis, which largely depends on the accumulation of immunosuppressive cytokines and oncometabolites. This review explores the hypothesis that tumor microenvironment may reduce the release of type I IFNs also by a more pDC-specific mechanism, namely the engagement of IRs. Literature shows that many cancer types express , or overexpress, IR ligands (such as BST2, PCNA, CAECAM-1 and modified surface carbohydrates) which often represent a strong predictor of poor outcome and metastasis. In line with this, tumor cells expressing ligands engaging IRs such as BDCA-2, ILT7, TIM3 and CD44 block pDC activation, while this blocking is prevented when IR engagement or signaling is inhibited. Based on this evidence, we propose that the regulation of IFN secretion by IRs may be regarded as an "innate checkpoint", reminiscent of the function of "classical" adaptive immune checkpoints, like PD1 expressed in CD8+ T cells, which restrain autoimmunity and immunopathology but favor chronic infections and tumors. However, we also point out that further work is needed to fully unravel the biology of tumor-associated pDCs, the neat contribution of pDC exhaustion in tumor growth following the engagement of IRs, especially those expressed also by other leukocytes, and their therapeutic potential as targets of combined immune checkpoint blockade in cancer immunotherapy. - Source: PubMed
Publication date: 2024/03/05
Tiberio LauraLaffranchi MattiaZucchi GiovanniSalvi ValentinaSchioppa TizianaSozzani SilvanoDel Prete AnnalisaBosisio Daniela - Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP. - Source: PubMed
Bian JiaminYan JiayuChen ChuYin LiLiu PanpanZhou QiYu JianfengLiang QinHe Qingmei