The control of glucose metabolism and the cell cycle must be coordinated in order to guarantee sufficient ATP and anabolic substrates at distinct phases of the cell cycle. The family of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatases (PFKFB1-4) are well established regulators of glucose metabolism via their synthesis of fructose-2,6-bisphosphate (F2,6BP), a potent allosteric activator of 6-phosphofructo-1-kinase (Pfk-1). PFKFB3 is overexpressed in human cancers, regulated by HIF-1α, Akt and PTEN, and required for the survival and growth of multiple cancer types. Although most functional studies of the role of PFKFB3 in cancer progression have invoked its well-recognized function in the regulation of glycolysis, recent observations have established that PFKFB3 also traffics to the nucleus and that its product, F2,6BP, activates cyclin-dependent kinases (Cdks). In particular, F2,6BP stimulates the Cdk-mediated phosphorylation of the Cip/Kip protein p27 (threonine 187), which in turn results in p27's ubiquitination and proteasomal degradation. As p27 is a potent suppressor of the G1/S transition and activator of apoptosis, we hypothesized that the known requirement of PFKFB3 for cell cycle progression and prevention of apoptosis may be partly due to the ability of F2,6BP to activate Cdks. In this study, we demonstrate that siRNA silencing of endogenous PFKFB3 inhibits Cdk1 activity, which in turn stabilizes p27 protein levels causing cell cycle arrest at G1/S and increased apoptosis in HeLa cells. Importantly, we demonstrate that the increase in apoptosis and suppression of the G1/S transition caused by siRNA silencing of PFKFB3 expression is reversed by co-siRNA silencing of p27. Taken together with prior publications, these observations support a model whereby PFKFB3 and F2,6BP function not only as regulators of Pfk-1 but also of Cdk1 activity, and therefore serve to couple glucose metabolism with cell proliferation and survival in transformed cells.

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Aberrations in the centrosome number and structure can readily be detected at all stages of tumor progression and are considered hallmarks of cancer. Centrosome anomalies are closely linked to chromosome instability and, therefore, are proposed to be one of the driving events of tumor formation and progression. This concept, first posited by Boveri over 100 years ago, has been an area of interest to cancer researchers. We have now begun to understand the processes by which these numerical and structural anomalies may lead to cancer, and vice-versa: how key events that occur during carcinogenesis could lead to amplification of centrosomes. Despite the proliferative advantages that having extra centrosomes may confer, their presence can also lead to loss of essential genetic material as a result of segregational errors and cancer cells must deal with these deadly consequences. Here, we review recent advances in the current literature describing the mechanisms by which cancer cells amplify their centrosomes and the methods they employ to tolerate the presence of these anomalies, focusing particularly on centrosomal clustering.

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Deep learning techniques bring together key advantages in biomedical image segmentation. They speed up the process, increase the reproducibility, and reduce the workload in segmentation and classifcation. Deep learning techniques can be used for analysing cell concentration, cell viability, as well as the size and form of each cell. In this study, we develop a deep learning model for automated segmentation of fuorescence cell images, and apply it to fuorescence images recorded with a home-built epi-fuorescence microscope. A deep neural network model based on U-Net architecture was built using a publicly available dataset of cell nuclei images [1]. A model accuracy of 97.3% was reached at the end of model training. Fluorescence cell images acquired with our home-built microscope were then segmented using the developed model. 141 of 151 cells in 5 images were successfully segmented, revealing a segmentation success rate of 93.4%. This deep learning model can be extended to the analysis of diferent cell types and cell viability.

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