The loss of epigenetic information accelerates the aging process Aging DNA DNAdamage chromatin epigeneticclock epigenetic senescence CellCellPress
By Suchandrima BhowmikJan 15 2023Reviewed by Emily Henderson, B.Sc. The basis of life depends upon the complex interplay of information stored in the epigenome, genome, and cellular machinery. This is thought to be the software and biological hardware. However, whether a breakdown in the software or hardware causes aging is not yet known. In the 1950s, Szilard and Medawar independently proposed that aging results from the loss of genetic information caused by DNA damage.
Epigenetic changes associated with aging include changes in DNA methylation patterns, H3K27me3, H3K9me3, and H3K9me3. Many epigenetic changes have been observed to follow a specific pattern. However, the reason for changes in the mammalian epigenome is not yet known. A few clues can be obtained from yeast, where DSB is a significant factor whose repair requires epigenetic regulators Esa1, Gcn5, Rpd3, Hst1, and Sir2.
Monochrome multiplex quantitative PCR followed by the assessment of the frailty index was carried out. Lens opacity scoring was performed along with micro CT scanning, quantification of optic nerve axons, quantification of subepidermal thickness, immunohistochemistry for mouse skin, and brain immunohistochemistry, as well as the measurement of mtDNA and ATP.
Study findings The results indicated that HA-I-PpoI was detected in nuclei of ICE cells following the addition of TAM but not in control cells. The number of serine-139-phosphorylated H2AX foci which is a marker of DSB, was observed to reach a 4-fold background in 24 hours in ICE cells. No changes in cell-cycle profile, senescence, apoptosis, mutation frequency, overall translation efficiency, or RNA levels were observed during and after I-PpoI induction. ICE cells were reported to be about 1.
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Transition of amyloid/mutant p53 from tumor suppressor to an oncogene and therapeutic approaches to ameliorate metastasis and cancer stemness - Cancer Cell InternationalThe tumor suppressor p53 when undergoes amyloid formation confers several gain-of-function (GOF) activities that affect molecular pathways crucial for tumorigenesis and progression like some of the p53 mutants. Even after successful cancer treatment, metastasis and recurrence can result in poor survival rates. The major cause of recurrence is mainly the remnant cancer cells with stem cell-like properties, which are resistant to any chemotherapy treatment. Several studies have demonstrated the role of p53 mutants in exacerbating cancer stemness properties and epithelial-mesenchymal transition in these remnant cancer cells. Analyzing the amyloid/mutant p53-mediated signaling pathways that trigger metastasis, relapse or chemoresistance may be helpful for the development of novel or improved individualized treatment plans. In this review, we discuss the changes in the metabolic pathways such as mevalonate pathway and different signaling pathways such as TGF-β, PI3K/AKT/mTOR, NF-κB and Wnt due to p53 amyloid formation, or mutation. In addition to this, we have discussed the role of the regulatory microRNAs and lncRNAs linked with the mutant or amyloid p53 in human malignancies. Such changes promote tumor spread, potential recurrence, and stemness. Importantly, this review discusses the cancer therapies that target either mutant or amyloid p53, restore wild-type functions, and exploit the synthetic lethal interactions with mutant p53.
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Genetic profiling of different phenotypic subsets of breast cancer stem cells (BCSCs) in breast cancer patients - Cancer Cell InternationalBackground Breast cancer stem cells (BCSCs) have a crucial role in breast carcinogenesis, development, and progression. The aim of the current study is to characterize the BCSCs through the genetic profiling of different BCSCs phenotypic subsets to determine their related genetic pathways. Methods Fresh tumor tissue samples were obtained from 31 breast cancer (BC) patients for (1) Mammosphere culture. (2) Magnetic separation of the BCSCs subsets using CD24, CD44, and CD326 Microbeads. (3) Flow cytometry (FCM) assay using CD44, CD24, and EpCAM. (4) RT-PCR profiler Arrays using stem cell (SC) panel of 84 genes for four group of cells (1) CD44+/CD24−/EpCAM− BCSCs, (2) CD44+/CD24− /EpCAM+ BCSCs, (3) mammospheres, and (4) normal breast tissues. Results The BCSCs (CD44+/CD24−/EpCAM−) showed significant downregulation in 13 genes and upregulation in 15, where the CD44, GJB1 and GDF3 showed the maximal expression (P = 0.001, P = 0.003 and P = 0.007); respectively). The CD44+/CD24−/EpCAM+ BCSCs showed significant upregulation in 28 genes, where the CD44, GDF3, and GJB1 showed maximal expression (P | 0.001, P = 0.001 and P = 0.003; respectively). The mammospheres showed significant downregulation in 9 genes and a significant upregulation in 35 genes. The maximal overexpression was observed in GJB1 and FGF2 (P = 0.001, P = 0.001; respectively). The genes which achieved significant overexpression in all SC subsets were CD44, COL9A1, FGF1, FGF2, GDF3, GJA1, GJB1, GJB2, HSPA9, and KRT15. While significant downregulation in BMP2, BMP3, EP300, and KAT8. The genes which were differentially expressed by the mammospheres compared to the other BCSC subsets were CCND2, FGF3, CD4, WNT1, KAT2A, NUMB, ACAN, COL2A1, TUBB3, ASCL2, FOXA2, ISL1, DTX1, and DVL1. Conclusion BCSCs have specific molecular profiles that differ according to their phenotypes which could affect patients’ prognosis and outcome.
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