A CDK activity buffer ensures mitotic completion.

The eukaryotic cell cycle is driven by the activity of cyclin-dependent kinases (CDKs). CDK activity rises over 50-fold during the cell cycle, from a low level in G1 to a high level in mitosis. However, it is not known whether the entire range of CDK activity is necessary for cell cycle progression, or whether cells can tolerate a reduction in CDK activity level. Here, in fission yeast, we show that sublethal CDK inhibition lengthens the time cells spend in mitosis but does not cause misordering of mitotic events. Maximum attainable CDK activity exceeds the amount necessary for mitosis, and thus forms a CDK activity buffer between sufficient and maximal possible CDK activities. This CDK activity buffer is needed for mitotic completion when CDK activity is compromised, and CDK inhibition only becomes lethal to cells when this buffer is exhausted. Finally, we explore what factors influence this CDK activity buffer, and find that it is influenced by CDK-counteracting phosphatases. Therefore, maximum attainable CDK activity is not necessary for mitosis but provides robustness to CDK activity reduction to ensure mitotic completion.

Metabolic engineering generates a transgene-free safety switch for cell therapy.

Safeguard mechanisms can ameliorate the potential risks associated with cell therapies but currently rely on the introduction of transgenes. This limits their application owing to immunogenicity or transgene silencing. We aimed to create a control mechanism for human cells that is not mediated by a transgene. Using genome editing methods, we disrupt uridine monophosphate synthetase (UMPS) in the pyrimidine de novo synthesis pathway in cell lines, pluripotent cells and primary human T cells. We show that this makes proliferation dependent on external uridine and enables us to control cell growth by modulating the uridine supply, both in vitro and in vivo after transplantation in xenograft models. Additionally, disrupting this pathway creates resistance to 5-fluoroorotic acid, which enables positive selection of UMPS-knockout cells. We envision that this approach will add an additional level of safety to cell therapies and therefore enable the development of approaches with higher risks, especially those that are intended for limited treatment durations.

Noisy Cell-Size-Correlated Expression of Cyclin B Drives Probabilistic Cell-Size Homeostasis in Fission Yeast.

How cells correct deviations from a mean cell size at mitosis remains uncertain. Classical cell-size homeostasis models are the sizer, timer, and adder [1]. Sizers postulate that cells divide at some threshold size; timers, that cells grow for a set time; and adders, that cells add a constant volume before division. Here, we show that a size-based probabilistic model of cell-size control at the G2/M transition (P(Div)) can generate realistic cell-size homeostasis in silico. In fission yeast cells, Cyclin BCdc13 scales with size, and we propose that this increases the likelihood of mitotic entry, while molecular noise in its expression adds a probabilistic component to the model. Varying Cdc13 expression levels exogenously using a newly developed tetracycline inducible promoter shows that both the level and variability of its expression influence cell size at division. Our results demonstrate that as cells grow larger, their probability of dividing increases, and this is sufficient to generate cell-size homeostasis. Size-correlated Cdc13 expression forms part of the molecular circuitry of this system.

APC/C Dysfunction Limits Excessive Cancer Chromosomal Instability.

Intercellular heterogeneity, exacerbated by chromosomal instability (CIN), fosters tumor heterogeneity and drug resistance. However, extreme CIN correlates with improved cancer outcome, suggesting that karyotypic diversity required to adapt to selection pressures might be balanced in tumors against the risk of excessive instability. Here, we used a functional genomics screen, genome editing, and pharmacologic approaches to identify CIN-survival factors in diploid cells. We find partial anaphase-promoting complex/cyclosome (APC/C) dysfunction lengthens mitosis, suppresses pharmacologically induced chromosome segregation errors, and reduces naturally occurring lagging chromosomes in cancer cell lines or following tetraploidization. APC/C impairment caused adaptation to MPS1 inhibitors, revealing a likely resistance mechanism to therapies targeting the spindle assembly checkpoint. Finally, CRISPR-mediated introduction of cancer somatic mutations in the APC/C subunit cancer driver gene CDC27 reduces chromosome segregation errors, whereas reversal of an APC/C subunit nonsense mutation increases CIN. Subtle variations in mitotic duration, determined by APC/C activity, influence the extent of CIN, allowing cancer cells to dynamically optimize fitness during tumor evolution. SIGNIFICANCE: We report a mechanism whereby cancers balance the evolutionary advantages associated with CIN against the fitness costs caused by excessive genome instability, providing insight into the consequence of CDC27 APC/C subunit driver mutations in cancer. Lengthening of mitosis through APC/C modulation may be a common mechanism of resistance to cancer therapeutics that increase chromosome segregation errors. Cancer Discov; 7(2); 218-33. ©2017 AACR.See related commentary by Burkard and Weaver, p. 134This article is highlighted in the In This Issue feature, p. 115.

Label-free cell cycle analysis for high-throughput imaging flow cytometry.

Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types.

An Imaging Flow Cytometry-based approach to analyse the fission yeast cell cycle in fixed cells.

Fission yeast (Schizosaccharomyces pombe) is an excellent model organism for studying eukaryotic cell division because many of the underlying principles and key regulators of cell cycle biology are conserved from yeast to humans. As such it can be employed as tool for understanding complex human diseases that arise from dis-regulation in cell cycle controls, including cancers. Conventional Flow Cytometry (CFC) is a high-throughput, multi-parameter, fluorescence-based single cell analysis technology. It is widely used for studying the mammalian cell cycle both in the context of the normal and disease states by measuring changes in DNA content during the transition through G1, S and G2/M using fluorescent DNA-binding dyes. Unfortunately analysis of the fission yeast cell cycle by CFC is not straightforward because, unlike mammalian cells, cytokinesis occurs after S-phase meaning that bi-nucleated G1 cells have the same DNA content as mono-nucleated G2 cells and cannot be distinguished using total integrated fluorescence (pulse area). It has been elegantly shown that the width of the DNA pulse can be used to distinguish G2 cells with a single 2C foci versus G1 cells with two 1C foci, however the accuracy of this measurement is dependent on the orientation of the cell as it traverses the laser beam. To this end we sought to improve the accuracy of the fission yeast cell cycle analysis and have developed an Imaging Flow Cytometry (IFC)-based method that is able to preserve the high throughput, objective analysis afforded by CFC in combination with the spatial and morphometric information provide by microscopy. We have been able to derive an analysis framework for subdividing the yeast cell cycle that is based on intensiometric and morphometric measurements and is thus robust against orientation-based miss-classification. In addition we can employ image-based metrics to define populations of septated/bi-nucleated cells and measure cellular dimensions. To our knowledge, this is the first use of IFC to study fission yeast and we are confident that this will provide a springboard for further IFC-based analysis across all aspects of fission yeast biology.