HSP90B1 Antibody.
- Known as:
- HSP90B1 Antibody.
- Catalog number:
- 45-071
- Product Quantity:
- 0.1 mg
- Category:
- -
- Supplier:
- Prosci
- Gene target:
- HSP90B1 Antibody.
Related genes to: HSP90B1 Antibody.
- Gene:
- HSP90B1 NIH gene
- Name:
- heat shock protein 90 beta family member 1
- Previous symbol:
- TRA1
- Synonyms:
- GP96, GRP94
- Chromosome:
- 12q23.3
- Locus Type:
- gene with protein product
- Date approved:
- 1991-07-03
- Date modifiied:
- 2016-10-11
Related products to: HSP90B1 Antibody.
Related articles to: HSP90B1 Antibody.
- Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants linked to diabetes, yet their role in diabetic complications is poorly understood. This study demonstrates that exposure to multiple PFAS subtypes significantly delays wound healing in diabetic rats. Through integrated transcriptomic and network toxicology approaches, we identified heat shock protein 90 beta family member 1 (HSP90B1/GRP94) as a key mediator of this toxicity. A natural product screen suggested ligustilide as a potential protective agent. Computational docking revealed that both ligustilide and the representative PFAS compound OBS target the Asn107 site on GRP94. Subsequent site-directed mutagenesis and knockdown-rescue experiments confirmed that OBS binding at Asn107 induces N-glycosylation of GRP94. Ligustilide competitively occupies this site, thereby blocking OBS-induced glycosylation, restoring GRP94's chaperone function, and alleviating endoplasmic reticulum stress and apoptosis. In PFAS-exposed diabetic rats, ligustilide treatment effectively rescued impaired wound healing, as evidenced by accelerated wound closure, improved tissue perfusion and vascular maturation, and enhanced collagen deposition. Our findings elucidate a novel mechanism through which PFAS disrupt tissue repair via precise glycosylation of GRP94 and highlight ligustilide as a promising therapeutic candidate against PFAS-aggravated diabetic complications. - Source: PubMed
Publication date: 2026/05/20
Yu TanxinXie JingfeiZhang HanwenYu YanLin ShihanLiu BohengWu AiminWang JiangningZhang Xiaolei - Fasting triggers profound systemic metabolic adaptations that are essential for survival during nutrient scarcity, yet the temporal dynamics and cross-tissue coordination of proteomic remodeling remain incompletely characterized. Here, we employed quantitative proteomics to systematically profile the gastrocnemius (GA) muscle and liver of mice subjected to a 72 h fasting challenge at five time points (0, 12, 24, 48, and 72 h). Principal component analysis and hierarchical clustering revealed progressive, time-dependent proteomic reprogramming in both tissues, with distinct temporal trajectories of differentially expressed proteins (DEPs). GA muscle exhibited a biphasic response, with maximal downregulation at 48 h and peak upregulation at 72 h, whereas liver displayed a monotonic increase in DEPs, predominantly characterized by suppression of anabolic programs. Integrative cross-tissue analysis identified 97 conserved fasting-responsive proteins, including molecular chaperones (HSPA5, HSP90B1), complement components (C3), and coagulation factors (FGA, KNG1), which formed highly interconnected protein-protein interaction hubs. Fuzzy c-means clustering resolved five major temporal expression modules in each tissue, revealing coordinated shifts in mitochondrial metabolism, proteostasis, translation, and stress-response pathways. Spearman correlation analysis demonstrated moderate yet stable cross-tissue concordance (r = 0.43-0.48) throughout fasting, suggesting shared systemic regulatory mechanisms. Collectively, our findings provide a comprehensive temporal atlas of fasting-induced proteomic remodeling, revealing tissue-specific adaptive strategies alongside conserved molecular programs that orchestrate multi-organ metabolic homeostasis during prolonged nutrient deprivation. - Source: PubMed
Publication date: 2026/05/21
Cui LeileiZeng LinYang MengqiWang QiquanHuang ChunpingLin LiangLi PingLiu TaoTong WeipengSun JiayiWei HuiLan XinqiangXiang YangSu YuLi Jian - Acute myeloid leukemia (AML) is a hematological malignancy with a high mortality rate and heterogeneous prognosis. Traditional risk stratification is based on the genetic classification in the 2022 guidelines of the European Leukemia Net. However, the risks of some patients remain unclear, and other prognostic assessment methods are required to improve the risk assessment of these patients. Apoptosis-related genes (ARGs) play critical roles in regulating the survival and drug resistance of AML cells. Therefore, we collected gene expression and clinical data from patients with AML from The Cancer Genome Atlas Acute Myeloid Leukemia (TCGA-LAML) datasets to develop a risk assessment model based on 5 ARGs. Using the least absolute shrinkage and selection operator Cox regression (LASSO-Cox) model, we identified 5 key ARGs (, , , , and ) and constructed a 5-ARG prognostic model. Using this model, we successfully stratified patients in both TCGA-LAML training and independent external validation cohorts, with high-risk patients consistently exhibiting significantly poorer clinical outcomes. In addition, high-risk patients exhibited significant enrichment in pathways related to dysfunction, mechanistic target of rapamycin complex 1 (mTORC1) signaling activation, and pro-inflammatory responses, which were closely correlated with co-mutations. Decitabine, sunitinib, and MK-1775 were identified as potential therapeutic agents. In summary, we established a 5-ARG prognostic model that may facilitate risk stratification and inform therapeutic decision-making in AML. - Source: PubMed
Publication date: 2026/05/15
Pei XiangLiu WenbingLi YishuangWei HuiWang MinWang Jianxiang - Alzheimer's Disease (AD) is a devastating neurodegenerative disease, strongly linked to cellular stress originating from the accumulation of the Amyloid-beta (Aβ) peptide and phosphorylated tau protein. Endoplasmic Reticulum (ER) stress and Unfolded Protein Response (UPR) are reported as early events in the AD pathology. Mitochondria-Associated Membrane (MAM) is a proteinaceous tethering between the ER and mitochondria that plays a role in regulating ER stress and related responses. A high level of ER-mitochondria tethering, thereby mitochondrial Calcium (Ca) overload and cell death, has been reported in AD brain cells. Despite the independent recognition of these pathways, a precise mechanism that integrates MAM activity, ER stress response, and AD pathogenesis remains elusive. We used three transcriptomic datasets collected from the NCBI-Gene Expression Omnibus (GEO) database, which deal with AD, ER stress, and MAM, and processed them with a multi-layered bioinformatics approach combining differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), protein interaction network construction, and identification of hub genes using different cytoHubba topological algorithms. Four hub genes, namely Calreticulin (CALR), Calnexin (CANX), Heat Shock Protein 90 Beta Family Member 1 (HSP90B1), and Valosin-Containing Protein (VCP), were identified. CALR, CANX, and HSP90B1 are known chaperones that regulate proteostasis. VCP is an ER ATPase that induces autophagy. These genes are not only associated with MAM regulation and ER stress but also with AD pathology. The results suggest hub genes as a new set of biomarkers and the likely existence of a 'three-component system' among MAM, ER stress, and Neurodegeneration. The study highlights the potential of MAM-related genes as therapeutic targets of AD. - Source: PubMed
Publication date: 2026/05/19
Mohan A AnjanaTalwar Priti - Signal Regulatory Protein Alpha (SIRPA) functions as an inhibitory receptor to suppress phagocytosis of macrophages and promote tumor immune evasion. Recent studies revealed that SIRPA deficiency reprogrammed tumor-associated macrophages toward an antitumor phenotype, and SIRPA functioned independently of CD47. However, the exact role and signaling pathways by which SIRPA macrophages were induced and exerted their functions in glioblastoma have not been fully elucidated. - Source: PubMed
Publication date: 2026/05/11
Yin NanhengZhang ZhichengXia FeiyuZhang XiaopeiLi ZengyangZhong TaoPan JiaxinLiang GengHuang DelongDai XiaoxiaoDong Jun