Ageing is a common feature of living organisms, showing shared molecular features called hallmarks of ageing. Usually they are quantified in groups of individuals as a function of their chronological age (time passed since birth) and display continuous and progressive changes. Such approaches are based on the assumption that individuals taken at a given chronological age are biological replicates. However, even in genetically homogeneous and synchronised populations individuals do die at different chronological ages. This highlights the difference between chronological age and biological age, the latter being defined by the actual mortality risk of the organism, reflecting its physiology. The Smurf assay, previously described by Rera and colleagues, allows the identification of individuals at higher risk of death from natural causes amongst a population of a given chronological age. We found that the categorization of individuals as Smurf or non-Smurf, permits to distinguish transcriptional changes associated with either chronological or biological age. We show that transcriptional heterogeneity increases with chronological age, while four out of the six currently defined transcriptional hallmarks of ageing are associated with the biological age of individuals, i.e. their Smurf state. In conclusion, we demonstrate that studying properties of ageing by applying the Smurf classification allows us to differentiate the effect of time from the effect of a physiological response triggering an end-of-life switch (i.e. Smurf phase). More specifically, we show that the ability to isolate a pre-death phase of life in vivo enables us not only to study late life mechanisms preceding death, but also investigate early physiological changes triggering such phase. This allowed the identification of novel pro-longevity genetic interventions. We anticipate that the use of the evolutionary conserved Smurf phenotype in ageing studies will allow significant advances in our comprehension of the underlying mechanisms of ageing.
Kallikrein-related peptidases (KLKs) are implicated in many cancer-related processes. KLK6, one of the 15 KLK family members, is a promising biomarker for diagnosis of many cancers and has been associated with poor prognosis of colorectal cancer (CRC) patients. Herein, we evaluated the expression and cellular functions of KLK6 in colon cancer-derived cell lines and in clinical samples from CRC patients. We showed that, although many KLKs transcripts are upregulated in colon cancer-derived cell lines, KLK6, KLK10, and KLK11 are the most highly secreted proteins. KLK6 induced calcium flux in HT29 cells by activation and internalization of protease-activated receptor 2 (PAR2). Furthermore, KLK6 induced extracellular signal–regulated kinases 1 and 2 (ERK1/2) phosphorylation. KLK6 suppression in HCT-116 colon cancer cells decreased the colony formation, increased cell adhesion to extracellular matrix proteins, and reduced spheroid formation and compaction. Immunohistochemistry (IHC) analysis demonstrated ectopic expression of KLK6 in human colon adenocarcinomas but not in normal epithelia. Importantly, high levels of KLK6 protein were detected in the ascites of CRC patients with peritoneal metastasis, but not in benign ascites. These data indicate that KLK6 overexpression is associated with aggressive CRC, and may be applied to differentiate between benign and malignant ascites.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.