Anti-FCER1G (aa 60-86) polyclonal antibody

Allergic rhinitis is associated with elevated serum IgE levels. IgE response is mediated by the high-affinity IgE receptor (Fc epsilon RI), which is polymorphic. Studies analyzing the association between allergic rhinitis and Fc epsilon RI variants have been conducted with controversial results. The objective of this study is to analyze, in 1,041 individuals, the putative clinical association of allergic rhinitis with common polymorphisms in Fc epsilon RI subunits genes. These SNPs included FECR1A rs2494262, rs2427837 and rs2251746; FECR1B rs1441586, rs569108 and rs512555; FCER1G rs11587213, rs2070901 and rs11421. Statistically significant differences were observed for the FCER1B rs569108 and rs512555 polymorphisms frequencies when comparing patients with allergic rhinitis without asthma and controls. The OR (95% CI) value for the 237Gly allele (rs569108) is equal to 0.26 (0.08-0.86, P = 0.017) and for the G allele (rs512555) it is equal to 0.27 (0.08-0.88, P = 0.020). These two SNPs are linked (D' = 1.0, LOD = 56.05). Also observed was a statistically significant trend towards lower IgE values among allergic rhinitis patients with variant alleles for both SNPs. In conclusion, in patients with allergic rhinitis without asthma, the FCER1B rs569108 and rs512555 polymorphisms are associated with increased risk of developing allergic rhinitis and with lower IgE levels.

Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, identifying a universal and reliable marker of macrophage cell becomes paramount. Traditional approaches have relied upon tissue specific expression patterns. To identify universal biomarkers of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify conserved cell cycle genes. We performed BECC analysis using the known macrophage marker CD14 as a seed gene. The main idea behind BECC is that it uses massive database of public gene expression dataset to establish robust co-expression patterns identified using a combination of correlation, linear regression and Boolean equivalences. Our analysis identified and validated FCER1G and TYROBP as novel universal biomarkers for macrophages in human and mouse tissues.