Ask about this productRelated genes to: PSMB9 antibody
- Gene:
- PSMB9 NIH gene
- Name:
- proteasome subunit beta 9
- Previous symbol:
- LMP2
- Synonyms:
- RING12, beta1i, PSMB6i
- Chromosome:
- 6p21.32
- Locus Type:
- gene with protein product
- Date approved:
- 1991-12-18
- Date modifiied:
- 2016-10-05
Related products to: PSMB9 antibody
Related articles to: PSMB9 antibody
- Preterm birth (PTB, < 37 weeks of gestation) is a major public health concern in the United States, with Black women experiencing a higher incidence compared to White women. Although some studies have identified social, medical, and obstetric risk factors for PTB, the biological mechanisms underlying spontaneous PTB (sPTB) risk remain unclear. We conducted a secondary analysis using data from Black participants enrolled in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b), (n = 1073) from 2010 to 2013. Peripheral whole blood samples were collected from all participants between 6 + 0/7 and 13 + 6/7 weeks of gestation. Demographic, behavioral and clinical data were gathered through surveys, and pregnancy outcomes were obtained through chart abstraction. We used the Infinium Methylation EPIC v2.0 BeadChip for epigenome-wide association (EWAS) of DNA methylation and sPTB. - Source: PubMed
Publication date: 2026/05/24
Zhao TingtingZhao YihongReho PaoloSamari GoleenWapner RonaldHazi ArielleWu HaotianBarcelona Veronica - Despite significant advances, the molecular basis and thus patient-tailored therapeutic options for inborn errors of immunity remain unknown in a significant number of patients. - Source: PubMed
Publication date: 2025/11/18
Fournier BenjaminPoirier LéaAbramowski VincentMerlin EtienneDeutsch HélèneBastard PaulCallebaut IsabellePicard CapucineRosain JérémieKüry SébastienBézieau StéphaneFabre MoniqueCastelle MartinNeven BénédicteMoshous Despinade Villartay Jean-PierreEbstein Frédéric - Monoallelic variants in catalytic immunoproteasome subunits have recently been linked to proteasome-associated autoinflammatory syndromes with immunodeficiency (PRAAS-ID), yet their molecular mechanisms and clinical spectra are not fully defined. In this study, seven individuals from five unrelated families carrying five distinct monoallelic PSMB8 variants were identified. Individuals presented with neonatal-onset immunodeficiency characterized by recurrent infections, B cell lymphopenia, and hypogammaglobulinemia requiring immunoglobulin replacement. Inflammatory manifestations of variable severity included enteropathy, hepatitis, myositis, and inflammatory lung disease. Additional findings included leukocyte vacuolization in blood and bone marrow. Pathogenic variants in immunoproteasome subunits were analyzed to identify structural features associated with dominant-negative behavior. Immunoproteasome assembly and activity were investigated using complexome profiling, immunoblotting, and in-gel activity assays in proband-derived fibroblasts and transfected HEK293T cells, with downstream effects assessed by proteomic and RT-qPCR analyses. Mutant PSMB8 subunits were inefficiently incorporated into immunoproteasome complexes, leading to impaired assembly, including reduced fully assembled complexes and accumulation of assembly intermediates. This defect was accompanied by activation of the integrated stress response alongside impaired immune signaling. Monoallelic pathogenic variants in PSMB8, PSMB9, and PSMB10 associated with PRAAS-ID affected residues that are highly conserved and biophysically similar between the three immunoproteasome catalytic subunits. These shared structural features may help identify additional variants with similar disruptive effects on immunoproteasome assembly. Together, our data show that monoallelic PSMB8 variants disrupt immunoproteasome assembly, resulting in clinically variable disease with immunodeficiency and systemic inflammation. Our findings support immunoproteasome assembly disruption as a unifying dominant-negative mechanism underlying PRAAS-ID. - Source: PubMed
Publication date: 2026/05/21
Wijngaard Robinvan der Made Caspar IKalkan Uçar SemaRamakrishnan GayatriWang ManBrand JohannesRosenfeld Jill AVogel Tiphanie PNicholas Sarah KWeisz-Hubshman Monika van Karnebeek Clara D MAllenspach Eric JGardiner Taylor EPerera Kimmantudawage SumuduStark ZornitzaArmstrong Ruth KCampbell JanineVolpi StefanoDrago EnricoGattorno MarcoGrossi AliceCeccherini IsabellaCabrera-Orefice AlfredoSiebels BenteMair ThomasSchlüter HartmutSmeets Ruben Lvan Beek RonaldGoebel IngridKüchler KatrinGersting Søren WHoischen AlexanderVissers Lisenka E L MWevers Ron AMeyer-Schwesinger CatherineWortmann Saskia BOud Machteld MGuerrero-Castillo Sergio - Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by progressive joint destruction and functional impairment. Increasing data indicate that glutamate metabolism is critically involved in RA pathogenesis. This analysis aimed to identify glutamate metabolism-related biomarkers and potential RA therapeutics. - Source: PubMed
Publication date: 2026/04/29
Zhu BingruiLi BaoliangZhang ShuxuQi WenzhuoMu ZhouKong PengHan YingguangShi Zhigang - Gene regulatory networks differ substantially across individual cell lines, and population-level network inferences often fail to capture the underlying biological heterogeneity. To better capture this heterogeneity, cell line-specific gene network analysis is required. However, interpreting such high-dimensional cell line-specific networks remains a major challenge in the field of network biology. One interpretative approach is to identify differentially regulated gene networks (DGNs) between phenotypes because these networks can highlight phenotype-specific regulatory mechanisms. Although several methods have been proposed for DGN analysis, they are not suitable for cell line-specific gene network analysis, which is characterized by pronounced heterogeneity across individual networks. To address this problem, we proposed a novel statistical method for identifying DGNs in a cell line-specific scenario. The proposed framework integrates cell line-specific network estimation, a Kullback-Leibler divergence-based comparison of multivariate distributions, and a DKL-ratio statistic to quantify between-phenotype heterogeneity relative to within-phenotype homogeneity. Our method evaluates both between-phenotype heterogeneity and within-phenotype homogeneity, ensuring the robust detection of phenotype-specific network structures. Through Monte Carlo simulation studies, we systematically evaluated the performance of the proposed method and demonstrated that our strategy consistently outperformed existing methods in terms of accuracy, precision, true positive rate (TPR), true negative rate (TNR), and F-measure across diverse network structures and mean shift scenarios. Statistical significance was assessed using a permutation-based framework, confirming that the identified networks are unlikely to arise from random variation. We further applied our strategy to Quizartinib sensitivity-specific gene network analysis and identified immune-related subnetworks enriched in antigen processing and presentation pathways. These subnetworks included hub genes such as IFIT1, PSMB9, and HLA-B, which are known to be associated with immune evasion and drug resistance in acute myeloid leukemia. Our findings demonstrate that the proposed method enables statistically reliable and biologically interpretable identification of phenotype-specific gene regulatory mechanisms, providing insights into potential therapeutic targets. - Source: PubMed
Publication date: 2026/04/27
Oh JooeePark Heewon