Cellular senescence can be triggered by various intrinsic and extrinsic stimuli. We previously reported that silencing of 3'-phosphoadenosine 5'-phosphosulfate synthetase 2 (PAPSS2) induces cellular senescence through augmented fibroblast growth factor receptor 1 (FGFR1) signaling. However, the exact molecular mechanism connecting heparan sulfation and cellular senescence remains unclear. Here, we investigated the potential involvement of heparan sulfate proteoglycans (HSPGs) in augmented FGFR1 signaling and cellular senescence. Depletion of several types of HSPGs revealed that cells depleted of syndecan 1 (SDC1) exhibited typical senescence phenotypes, and those depleted of PAPSS2-, SDC1-, or heparan sulfate 2-O sulfotransferase 1 (HS2ST1) showed decreased FGFR1 internalization along with hyperresponsiveness to and prolonged activation of fibroblast growth factor 2 (FGF2)-stimulated FGFR1- v-akt murine thymoma viral oncogene homolog (AKT) signaling. Clathrin- and caveolin-mediated FGFR1 endocytosis contributed to cellular senescence through the FGFR1-AKT-p53-p21 signaling pathway. Dynasore treatment triggered senescence phenotypes, augmented FGFR1-AKT-p53-p21 signaling, and decreased SDC1 expression. Finally, the replicatively and prematurely senescent cells were characterized by decreases of SDC1 expression and FGFR1 internalization, and an increase in FGFR1-AKT-p53-p21 signaling. Together, our results demonstrate that properly sulfated SDC1 plays a critical role in preventing cellular senescence through the regulation of FGFR1 endocytosis.
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http://dx.doi.org/10.1096/fj.201902714R | DOI Listing |
Pharmaceutics
January 2025
Department of Pharmacy, "Federico II" University of Naples, 80131 Naples, Italy.
Arginase (ARG) is a binuclear manganese-containing metalloenzyme that can convert L-arginine to L-ornithine and urea and plays a key role in the urea cycle. It also mediates different cellular functions and processes such as proliferation, senescence, apoptosis, autophagy, and inflammatory responses in various cell types. In mammals, there are two isoenzymes, ARG-1 and ARG-2; they are functionally similar, but their coding genes, tissue distribution, subcellular localization, and molecular regulation are distinct.
View Article and Find Full Text PDFNutrients
January 2025
Department of Nutrition, Food Sciences and Physiology, Center for Nutrition and Research, University of Navarra, 31008 Pamplona, Spain.
Background And Aim: Telomere length (TL) is a key biomarker of cellular aging, with shorter telomeres associated with age-related diseases. Lifestyle interventions mitigating telomere shortening are essential for preventing such conditions. This study aimed to examine the effects of two weight loss dietary strategies, based on a moderately high-protein (MHP) diet and a low-fat (LF) diet on TL in individuals with overweight or obesity.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Department of Biology, Chungnam National University, Daejeon 34134, Republic of Korea.
Objectives: The present study describes the comparative effect of 24-week supplementation of beeswax alcohol (BWA, Raydel, 0.5% and 1.0%, wt/wt) and coenzyme Q (CoQ, 0.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Department of Medicine V, Heidelberg University, 69117 Heidelberg, Germany.
To identify the differences between aged and young human hematopoiesis, we performed a direct comparison of aged and young human hematopoietic stem and progenitor cells (HSPCs). Alterations in transcriptome profiles upon aging between humans and mice were then compared. Human specimens consist of CD34+ cells from bone marrow, and mouse specimens of hematopoietic stem cells (HSCs; Lin- Kit+ Sca1+ CD150+).
View Article and Find Full Text PDFInt J Mol Sci
January 2025
School of Environmental Science and Engineering, Hainan University, Haikou 570228, China.
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs).
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