Cellular senescence represents an irreversible state of cell-cycle arrest during which cells secrete senescence-associated secretory phenotypes, including inflammatory factors and chemokines. Additionally, these cells exhibit an apoptotic resistance phenotype. Cellular senescence serves a pivotal role not only in embryonic development, tissue regeneration, and tumor suppression but also in the pathogenesis of age-related degenerative diseases, malignancies, metabolic diseases, and kidney diseases. The senescence of renal tubular epithelial cells (RTEC) constitutes a critical cellular event in the progression of acute kidney injury (AKI). RTEC senescence inhibits renal regeneration and repair processes and, concurrently, promotes the transition of AKI to chronic kidney disease via the senescence-associated secretory phenotype. The mechanisms underlying cellular senescence are multifaceted and include telomere shortening or damage, DNA damage, mitochondrial autophagy deficiency, cellular metabolic disorders, endoplasmic reticulum stress, and epigenetic regulation. Strategies aimed at inhibiting RTEC senescence, targeting the clearance of senescent RTEC, or promoting the apoptosis of senescent RTEC hold promise for enhancing the renal prognosis of AKI. This review primarily focuses on the characteristics and mechanisms of RTEC senescence, and the impact of intervening RTEC senescence on the prognosis of AKI, aiming to provide a foundation for understanding the pathogenesis and providing potentially effective approaches for AKI treatment.
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http://dx.doi.org/10.1038/s41420-024-01831-9 | 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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