A Mitosis Block Links Active Cell Cycle with Human Epidermal Differentiation and Results in Endoreplication

How human self-renewal tissues co-ordinate proliferation with differentiation is unclear. Human epidermis undergoes continuous cell growth and differentiation and is permanently exposed to mutagenic hazard. Keratinocytes are thought to arrest cell growth and cell cycle prior to terminal differentiation. However, a growing body of evidence does not satisfy this model. For instance, it does not explain how skin maintains tissue structure in hyperproliferative benign lesions. We have developed and applied novel cell cycle techniques to human skin in situ and determined the dynamics of key cell cycle regulators of DNA replication or mitosis, such as cyclins E, A and B, or members of the anaphase promoting complex pathway: cdc14A, Ndc80/Hec1 and Aurora kinase B. The results show that actively cycling keratinocytes initiate terminal differentiation, arrest in mitosis, continue DNA replication in a special G2/M state, and become polyploid by mitotic slippage. They unambiguously demonstrate that cell cycle progression coexists with terminal differentiation, thus explaining how differentiating cells increase in size. Epidermal differentiating cells arrest in mitosis and a genotoxic-induced mitosis block rapidly pushes epidermal basal cells into differentiation and polyploidy. These observations unravel a novel mitosis-differentiation link that provides new insight into skin homeostasis and cancer. It might constitute a self-defence mechanism against oncogenic alterations such as Myc deregulation

Neural Network-Based Calculator for Rat Glomerular Filtration Rate

Glomerular filtration is a pivotal process of renal physiology, and its alterations are a central pathological event in acute kidney injury and chronic kidney disease. Creatinine clearance (ClCr), a standard method for glomerular filtration rate (GFR) measurement, requires a long and tedious procedure of timed (usually 24 h) urine collection. We have developed a neural network (NN)-based calculator of rat ClCr from plasma creatinine (pCr) and body weight. For this purpose, matched pCr, weight, and ClCr trios from our historical records on male Wistar rats were used. When evaluated on the training (1165 trios), validation (389), and test sets (660), the model committed an average prediction error of 0.196, 0.178, and 0.203 mL/min and had a correlation coefficient of 0.863, 0.902, and 0.856, respectively. More importantly, for all datasets, the NN seemed especially effective at comparing ClCr among groups within individual experiments, providing results that were often more congruent than those measured experimentally. ACLARA, a friendly interface for this calculator, has been made publicly available to ease and expedite experimental procedures and to enhance animal welfare in alignment with the 3Rs principles by avoiding unnecessary stressing metabolic caging for individual urine collection