Crystallin, alphaB ImmunoSet
- Known as:
- Crystallin, alphaB ImmunoSet
- Catalog number:
- ASA960-074
- Product Quantity:
- 5 x 96 Well
- Category:
- -
- Supplier:
- Other suppliers
- Gene target:
- Crystallin alphaB ImmunoSet
Related genes to: Crystallin, alphaB ImmunoSet
- Gene:
- IFNA8 NIH gene
- Name:
- interferon alpha 8
- Previous symbol:
- -
- Synonyms:
- IFN-alphaB
- Chromosome:
- 9p21.3
- Locus Type:
- gene with protein product
- Date approved:
- 1993-01-14
- Date modifiied:
- 2016-10-05
Related products to: Crystallin, alphaB ImmunoSet
4P11,CRYZL1,Homo sapiens,Human,Protein 4P11,QOH-1,Quinone oxidoreductase homolog 1,Quinone oxidoreductase-like protein 1,Zeta-crystallin homologA crystallin A antibody (clone c9F2)A crystallin A antibody (clone c9F2)aB - Crystallin (73 - 92)Albumin, bovine BSA ; bovine, crystallinAlpha A CrystallinAlpha A CrystallinAlpha A CrystallinAlpha A Crystallin antibodyAlpha A Crystallin antibodyAlpha A Crystallin antibodyAlpha A Crystallin antibodyAlpha A Crystallin antibodyAlpha A Crystallin AntibodyAlpha A Crystallin Antibody Related articles to: Crystallin, alphaB ImmunoSet
- Alveolar macrophages (AMs) and recruited monocyte-derived macrophages (MDMs) mediate early lung immune responses to Mycobacterium tuberculosis. Differences in the response of these distinct cell types are poorly understood and may provide insight into mechanisms of tuberculosis pathogenesis. The objective of this study was to determine whether M. tuberculosis induces unique and essential antimicrobial pathways in human AMs compared with MDMs. Using paired human AMs and 5-d MCSF-derived MDMs from six healthy volunteers, we infected cells with M. tuberculosis H37Rv for 6 h, isolated RNA, and analyzed transcriptomic profiles with RNA sequencing. We found 681 genes that were M. tuberculosis dependent in AMs compared with MDMs and 4538 that were M. tuberculosis dependent in MDMs, but not AMs (false discovery rate [FDR] < 0.05). Using hypergeometric enrichment of DEGs in Broad Hallmark gene sets, we found that type I and II IFN Response were the only gene sets selectively induced in M. tuberculosis-infected AM (FDR < 0.05). In contrast, MYC targets, unfolded protein response and MTORC1 signaling, were selectively enriched in MDMs (FDR < 0.05). IFNA1, IFNA8, IFNE, and IFNL1 were specifically and highly upregulated in AMs compared with MDMs at baseline and/or after M. tuberculosis infection. IFNA8 modulated M. tuberculosis-induced proinflammatory cytokines and, compared with other IFNs, stimulated unique transcriptomes. Several DNA sensors and IFN regulatory factors had higher expression at baseline and/or after M. tuberculosis infection in AMs compared with MDMs. These findings demonstrate that M. tuberculosis infection induced unique transcriptional responses in human AMs compared with MDMs, including upregulation of the IFN response pathway and specific DNA sensors. - Source: PubMed
Campo MonicaDill-McFarland Kimberly APeterson Glenna JBenson BasilinSkerrett Shawn JHawn Thomas R - The intensified search for low-threshold herbal anti-viral drugs would be of great advantage in prevention of early stages of infection. Since the SARS-CoV-2 Omicron variant has prevailed in western countries, the course has only been mild, but there are still no widely available drugs that can alleviate or shorten disease progression and counteract the development of Long-COVID. This study aimed to investigate the antiviral effects of a CO-extract from Petasites hybridus (Ze 339). - Source: PubMed
Publication date: 2023/12/07
Jakwerth Constanze AGrass VincentErb AnnaPichlmair AndreasBoonen GeorgButterweck VeronikaSchmidt-Weber Carsten B - In systemic lupus erythematosus (SLE), the relevance of non-hematopoietic sources of type I interferon in human autoimmunity has recently been recognized. Particularly, type I interferon production precedes autoimmunity in early skin lesions related to SLE. However, the relevance of intrarenal type I interferon expression has not been shown in lupus nephritis. From transcriptome array datasets, median-centered log mRNA expression levels of IFNα (, , , , , , , , , , , , and ), IFNω (), and IFNβ () in lupus nephritis were extracted specifically from microdissected tubulointerstitial ( = 32) and glomerular compartments ( = 32). We found an association between proteinuria and tubulointerstitial expression of type I interferon ( = 0.0142), while all others were not significantly associated. By contrast, no such correlation was observed between proteinuria and any type I interferon expression in the glomerular compartment in lupus nephritis. Interestingly, there was no difference between female and male patients ( = 0.8237) and no association between type I interferon expression and kidney function or lupus nephritis progression. Finally, we identified distinct molecular signatures involved in transcriptional regulation (GLI protein-regulated transcription, IRF7 activation, and HSF1-dependent transactivation) and receptor signaling (BMP signaling and GPCR ligand binding) in association with tubulointerstitial expression of type I interferon in the kidney. In summary, this transcriptome array-based approach links proteinuria to the tubulointerstitial expression of type I interferon in lupus nephritis. Because type I interferon receptor subunit I antagonism has recently been investigated in active SLE, the current study further emphasizes the role of type I interferons in lupus nephritis and might also be of relevance for mechanistic studies. - Source: PubMed
Publication date: 2023/06/25
Korsten PeterTampe Björn - Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)-signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism. The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A. A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan-Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression ( = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor ( < 0.001; HR = 3.342, 95%, CI = 1.834-6.088). The 5-year area under time-dependent ROC curve (AUC) reached 0.655. The training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modifications, and the direct interaction between m6A and the four genes. This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC. - Source: PubMed
Publication date: 2022/07/19
Jiang FeiChen XiaoweiShen YanShen Xiaobing - Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune-oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis, , , , and correlated with favorable survival (hazard risks: 0.3-0.5), and , , and , with poor survival (hazard risks = 1.0-2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (), and expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with and and/or rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1). , , and were associated with the germinal center B-cell (GCB) subtype, and , , , and were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted. - Source: PubMed
Publication date: 2021/12/20
Carreras JoaquimHiraiwa ShinichiroKikuti Yara YukieMiyaoka MasashiTomita SakuraIkoma HarukaIto AtsushiKondo YusukeRoncador GiovannaGarcia Juan FAndo KiyoshiHamoudi RifatNakamura Naoya