Targeting cancer metabolism with small molecules directed at specific enzymes, to which cancer cells are addicted, is challenging because immune cells and non-cancerous cells may depend on the same enzymes for function. As such, whether inhibiting an enzyme pharmacologically will tip the scale toward an anti-tumor or pro-tumor microenvironment or cause side effects in non-cancerous organs cannot be predicted a priori with our current knowledge or tools. Ketogenic diet and fasting, however, have been documented to have anti-tumor effects, particularly when combined with immune checkpoint inhibitors. A snapshot of these issues and opportunities is provided here in celebration of remarkable contributions of Dr. Rakesh Jain to cancer research over many decades.
Glioblastoma multiforme (GBM) is an aggressive brain tumor with a 5-year survival rate below 7
PIEZO2 is a mechanosensitive ion channel that converts mechanical stimuli into cellular signals, is highly expressed in peripheral neurons, and plays a key role in touch and proprioception. Despite its physiological importance, the mechanisms governing its spatial organization within cells remain poorly understood. In particular, the contribution of cell morphology and topographic cues to PIEZO2 localization have yet to be elucidated. To investigate its intracellular organization, we exploited a PIEZO2–Green Lantern fusion construct and expressed it in HEK293T cells, a heterologous model widely used for PIEZO2 gating studies. Cells were cultured on micropatterned substrates designed to induce cell directional polarization, enabling us to assess if and how cell polarization influences PIEZO2 spatial distribution. PIEZO2 channel localization was assessed by confocal fluorescence microscopy using both single-line fluorescence profiling and whole-cell quantitative analysis. Both approaches revealed a redistribution of PIEZO2 toward the cell periphery. Furthermore, a correlation analysis between the cell aspect ratio and a polarization index, that quantifies the PIEZO2 fluorescence signal polarization from the nuclear center to the cell edge along the cell major axis, demonstrated that cell elongation is associated with PIEZO2 enrichment at the cell periphery. To confirm the specificity of this distribution, we also tested a GFP-tagged cytoplasmic protein, as a control, which showed a distribution that was not correlated with cell elongation. Finally, we investigated PIEZO2 channel localization in a model of HEK293T cells with defective cell polarization along GR substrates, and found that here PIEZO2 failed to localize preferentially to the cell edges. These results provide new insight into how cell morphology influences the spatial organization of PIEZO2, shedding light on the interplay between cellular shape and mechanotransduction components.
Proteolytic reaction networks are central to coagulation, complement activation, and inflammatory signaling, yet accurate measurement of protease kinetics in these systems remains challenging. Conventional initial-rate approaches rely on selecting an early linear region from reaction trajectories and typically require measurements across multiple substrate concentrations. Here, we present a progress-curve strategy for estimating kinetic parameters from complete enzymatic reaction time courses. We applied a mathematical framework based on the explicit Schnell–Mendoza solution of the integrated Michaelis–Menten equation. This approach uses the Lambert W function to enable direct nonlinear fitting of complete reaction progress curves without numerical root-finding or subjective selection of initial velocity regions. We also evaluated the Goličnik analytical approximation as an elementary-function alternative for implementing the progress-curve model. The workflow was tested using representative coagulation proteases, including human α-thrombin, factor XIa, and plasma kallikrein. Full progress-curve fitting yielded estimates of K_M and V_max for thrombin, factor XIa, and plasma kallikrein, with parameter identifiability depending strongly on the initial substrate concentration and temporal information contained in the trajectory. Estimates obtained with the Goličnik approximation were similar to those obtained with direct implementation of the Schnell–Mendoza formulation under the conditions examined. These findings support progress-curve analysis as a practical strategy for studying coagulation protease kinetics. By explicitly modeling the reaction time course, the approach reduces dependence on investigator-selected initial-rate windows and provides a framework for extracting kinetic information from complete reaction trajectories.
