Ask about this productRelated genes to: PGK1 Blocking Peptide
- Gene:
- PGK1 NIH gene
- Name:
- phosphoglycerate kinase 1
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- Xq21.1
- Locus Type:
- gene with protein product
- Date approved:
- 1989-04-24
- Date modifiied:
- 2016-10-05
Related products to: PGK1 Blocking Peptide
Related articles to: PGK1 Blocking Peptide
- Amyotrophic lateral sclerosis (ALS) is characterized by progressive degeneration of motor neurons (MNs) with few available therapeutic options. Previous ALS studies demonstrated a decrease in phosphoglycerate kinase 1 (Pgk1) secreted from NogoA-overexpressing muscle cells, thus reducing interaction between extracellular Pgk1 (ePgk1) and neural membranous Enolase 2 (Eno2) with consequent inhibition of neurite outgrowth of MNs (NOMN). The negatively charged 419th aspartic acid of receptor Eno2 (Eno2-D419) is a critical residue interacting with the positively charged 353rd lysine of ligand ePgk1-K353. To strengthen the charge attraction, we mutated ePgk1-K353 to arginine (ePgk1-K353R). Compared to wild-type Pgk1, supplementary mutant Pgk1-K353R proved more effective in increasing NOMN derived from NSC34 neural cells cultured in Sol8-vector condition medium. In vivo, Pgk1-K353R-immersed zebrafish embryos exhibited increased caudal primary MNs branching. Intravenous injection of Pgk1-K353R into ALS-mice exhibited more preservative in innervated neuromuscular junctions in gastrocnemius muscle and diaphragm, increased grip strength, higher rearing frequency, 1.6-fold greater locomotive distance and longer survival. For example, median survival days for the control, Pgk1 and Pgk1-K353R groups were 131, 137.5 and 148, respectively. Collectively, we found a single-amino-acid mutant Pgk1-K353R that exhibits higher efficacy to ameliorate neurodegeneration in ALS-mice by delaying disease progression compared to that driven by wild-type Pgk1. We suggest this outcome might be due to more electrostatic attraction between ePgk1-K353R and Eno2-D419 region predicted by in silico analysis. Therefore, mutant Pgk1-K353R protein should be considered a promising neuroprotective drug for ALS treatment. - Source: PubMed
Publication date: 2026/06/19
Lee Bing-ChangTsai Jui-CheChang Wei-ZenWang Chun-ChengHour Ai-LingWang Chia-ChuanTsai Huai-Jen - Glioblastoma (GBM) is a notoriously lethal brain tumor, primarily owing to its inevitable resistance to temozolomide (TMZ), a frontline chemotherapy. Hypoxia-driven metabolic adaptations have been implicated in therapeutic failure; however, the role of circular RNAs remains largely underexplored. By integrating multiomics profiling with functional assays in patient-derived GBM cells, orthotopic xenografts, and clinical specimens, this study aimed to elucidate the role of hypoxia-induced hsa_circ_0000745 (circSPECC1) in mediating TMZ resistance. Mechanistic investigation included RNA pulldown, RIP, glycolysis flux analysis, and DNA damage assessment. The circSPECC1 is overexpressed in GBM and correlates with poor prognosis. Hypoxia triggers HIF-1α-mediated transcriptional upregulation of circSPECC1, which scaffolds insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) to stabilize phosphoglycerate kinase 1 (PGK1) mRNA. Importantly, circSPECC1/PGK1 axis activation enhances glycolytic flux, blunts TMZ-induced DNA damage, and confers chemoresistance. Targeting circSPECC1 disrupts PGK1-driven glycolysis, restores TMZ sensitivity, and synergizes with TMZ to extend survival in orthotopic GBM models. In conclusion, this study identifies a previously uncharacterized HIF-1α/circSPECC1/IGF2BP2/PGK1 axis that drives metabolic adaptation and TMZ resistance in GBM. Targeting this axis overcomes acquired chemoresistance, positioning circSPECC1 as both a prognostic biomarker and a therapeutic vulnerability in hypoxic GBM niches. - Source: PubMed
Publication date: 2026/06/17
Zeng YuZhao LiqianQue TianshiZhou LiYang XiaoshengXu XinCao KaihuaWang XizhaoWang XuhuiZhang WenchuanChen Ming - Breast cancer (BC) is ranked among the most prevalent malignant tumors in the global female population. DLGAP5 is implicated in the progression of various tumors. However, the molecular mechanisms of DLGAP5 involved in BC and TAM interactions remain unclear. - Source: PubMed
Publication date: 2026/06/15
Lin YanmingWu JiancongZhang ShiruYan ZemingLi JieCheng ZhenWu QibiaoYang Zhixiong - We previously demonstrated that a novel growth-friendly system can alleviate pulmonary hypoplasia in piglets with early-onset scoliosis combined with thoracic insufficiency syndrome (EOS + TIS) by improving the mechanical microenvironment. Transcriptomic analyses indicated that mechanical stress (MS) may exert its effects by regulating immune responses and metabolic pathways. Building upon this foundation, the present study further investigates the implications of MS on macrophage polarization and metabolic reprogramming, as well as the underlying molecular mechanisms. In this study, MS at a 10% amplitude effectively downregulated M1-type markers (CD86, IL-1β, iNOS, TNF-α) in RAW264.7 cells, while upregulating M2-type markers (TGF-β, CD206, IL-10). Transcriptomic analysis further suggested that MS influences lung development by regulating glycolysis, metabolic reprogramming, and immune-related pathways. Further experiments demonstrated that the MS downregulated glycolytic enzymes (PKM2, GLUT1, PGK1, and LDHA), reduced lactate production and extracellular acidification rate, while enhancing oxygen consumption rate and promoting the tube-forming capacity of human umbilical vein endothelial cells, induced by conditioned media from RAW264.7 cells. Mechanistically, MS activated the SOCS3/STAT3 pathway by upregulating Integrin Subunit Alpha D (ITGAD) expression, thereby facilitating M2 polarization and suppressing glycolytic metabolic reprogramming in RAW264.7 cells. ITGAD knockdown reversed these implications. In conclusion, MS activates the SOCS3/STAT3 pathway by upregulating ITGAD, thereby promoting macrophage polarization toward the M2 phenotype, suppressing glycolytic metabolic reprogramming, and enhancing their pro-angiogenic function. This discovery reveals a novel mechanism of MS in the pulmonary microenvironment, providing a new molecular perspective for understanding the pathological processes of EOS + TIS-associated pulmonary dysplasia. - Source: PubMed
Publication date: 2026/06/10
Zhang YingLi QuanXu ShixinLuo YangLiu WenyanLiu Yu - Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is fundamentally challenged by severe data sparsity, where pervasive dropout events obscure true regulatory signals and compromise the reliability of downstream inference. Existing supervised methods, while leveraging prior network structures, remain highly susceptible to this noise due to their end-to-end learning paradigm. To address this bottleneck, we propose SGMHA, a novel two-stage framework that decouples representation learning from link prediction. Specifically, SGMHA first employs a self-supervised graph masked autoencoder (GraphMAE) to learn robust gene representations by reconstructing randomly masked expression values, thereby mitigating sparsity-induced distortions. Subsequently, an MHA (multi-head attention)-based fine-tuning module integrates these pre-trained representations with raw expression data to accurately infer directed regulatory links. Extensive benchmarking across seven scRNA-seq datasets demonstrates that SGMHA consistently outperforms eight state-of-the-art methods in both area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). Applying SGMHA to breast cancer metastasis revealed context-specific GRNs and identified 26 high-confidence candidate drivers. Among these, six (NDUFAF4, ENY2, CCT5, PGK1, DCTPP1, and H2AFZ) were validated as prognostic biomarkers, with their mechanistic roles in metastatic adaptation detailed through multi-omics integration. Collectively, SGMHA provides an accurate, scalable, and biologically interpretable tool for GRN inference, holding strong promise for biomarker discovery in complex diseases. - Source: PubMed
Publication date: 2026/06/09
Zhang XujianLi WenhaoPan YuliangWang XupengGuan JihongCao Zhiwei