Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing

A stochastic vs deterministic perspective on the timing of cellular events

Cells are the fundamental units of life, and like all life forms, they change over time. Changes in cell state are driven by molecular processes; of these many are initiated when molecule numbers reach and exceed specific thresholds, a characteristic that can be described as "digital cellular logic". Here we show how molecular and cellular noise profoundly influence the time to cross a critical threshold-the first-passage time-and map out scenarios in which stochastic dynamics result in shorter or longer average first-passage times compared to noise-less dynamics. We illustrate the dependence of the mean first-passage time on noise for a set of exemplar models of gene expression, auto-regulatory feedback control, and enzyme-mediated catalysis. Our theory provides intuitive insight into the origin of these effects and underscores two important insights: (i) deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells; (ii) molecular noise can significantly shift mean first-passage times, particularly within auto-regulatory genetic feedback circuits.

Toward a predictive understanding of epithelial cell death

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

Fatty acid synthase (FASN) signalome: A molecular guide for precision oncology

The initial excitement generated more than two decades ago by the discovery of drugs targeting fatty acid synthase (FASN)-catalyzed de novo lipogenesis for cancer therapy was short-lived. However, the advent of the first clinical-grade FASN inhibitor (TVB-2640; denifanstat), which is currently being studied in various phase II trials, and the exciting advances in understanding the FASN signalome are fueling a renewed interest in FASN-targeted strategies for the treatment and prevention of cancer. Here, we provide a detailed overview of how FASN can drive phenotypic plasticity and cell fate decisions, mitochondrial regulation of cell death, immune escape and organ-specific metastatic potential. We then present a variety of FASN-targeted therapeutic approaches that address the major challenges facing FASN therapy. These include limitations of current FASN inhibitors and the lack of precision tools to maximize the therapeutic potential of FASN inhibitors in the clinic. Rethinking the role of FASN as a signal transducer in cancer pathogenesis may provide molecularly driven strategies to optimize FASN as a long-awaited target for cancer therapeutics.

WITHDRAWN: Mental health risk assessment and intervention of vocational college students with the assistance of mental health education

Ahead of Print article withdrawn by publisher.

Correlation between external regulators governs the mean-noise relationship in stochastic gene expression

Gene transcription in single cells is inherently a probabilistic process. The relationship between variance ($ \sigma^{2} $) and mean expression ($ \mu $) is of paramount importance for investigations into the evolutionary origins and consequences of noise in gene expression. It is often formulated as $ \log \left({{{\sigma}^{2}}}/{{{\mu}^{2}}}\; \right) = \beta\log\mu+\log\alpha $, where $ \beta $ is a key parameter since its sign determines the qualitative dependence of noise on mean. We reveal that the sign of $ \beta $ is controlled completely by external regulation, but independent of promoter structure. Specifically, it is negative if regulators as stochastic variables are independent but positive if they are correlated. The essential mechanism revealed here can well interpret diverse experimental phenomena underlying expression noise. Our results imply that external regulation rather than promoter sequence governs the mean-noise relationship.