Activation and antitumor immunity of CD8+ T cells are supported by the glucose transporter GLUT10 and disrupted by lactic acid.

CD8+ T cell activation leads to the rapid proliferation and differentiation of effector T cells (Teffs), which mediate antitumor immunity. Although aerobic glycolysis is preferentially activated in CD8+ Teffs, the mechanisms that regulate CD8+ T cell glucose uptake in the low-glucose and acidic tumor microenvironment (TME) remain poorly understood. Here, we report that the abundance of the glucose transporter GLUT10 is increased during CD8+ T cell activation and antitumor immunity. Specifically, GLUT10 deficiency inhibited glucose uptake, glycolysis, and antitumor efficiency of tumor-infiltrating CD8+ T cells. Supplementation with glucose alone was insufficient to rescue the antitumor function and glucose uptake of CD8+ T cells in the TME. By analyzing tumor environmental metabolites, we found that high concentrations of lactic acid reduced the glucose uptake, activation, and antitumor effects of CD8+ T cells by directly binding to GLUT10's intracellular motif. Disrupting the interaction of lactic acid and GLUT10 by the mimic peptide PG10.3 facilitated CD8+ T cell glucose utilization, proliferation, and antitumor functions. The combination of PG10.3 and GLUT1 inhibition or anti-programmed cell death 1 antibody treatment showed synergistic antitumor effects. Together, our data indicate that GLUT10 is selectively required for glucose uptake of CD8+ T cells and identify that TME accumulated lactic acid inhibits CD8+ T cell effector function by directly binding to GLUT10 and reducing its glucose transport capacity. Last, our study suggests disrupting lactate-GLUT10 binding as a promising therapeutic strategy to enhance CD8+ T cell-mediated antitumor effects.

Robust Switching Time Optimization for Networked Switched Systems via Model Predictive Control.

This article presents a model predictive control (MPC) strategy to find the optimal switching time sequences of networked switched systems with uncertainties. First, based on predicted trajectories under exact discretization, a large-scale MPC problem is formulated; second, a two-level hierarchical optimization structure coupled with a local compensation mechanism is established to solve the formulated MPC problem, where the proposed hierarchical optimization structure is actually a recurrent neural network consisting of a coordination unit (CU) at the upper level and a series of local optimization units (LOUs) related to each subsystem at the lower level. Finally, a real-time switching time optimization algorithm is designed to calculate the optimal switching time sequences.

Rh(III)-Catalyzed C-H Olefination Cascades to Divergently Construct Diverse Polyheterocycles by Tuning Manipulations of Directing Groups.

Inspired by the diversity created by nature, organic chemists have been using a divergent strategy to improve the synthetic efficiency of diverse molecules. Transition-metal-catalyzed C-H functionalization has become one of the most straightforward, powerful, and atom-economical methods to construct complex scaffolds. However, C-H activation initiated divergent transformation to prepare diverse molecules is still limited. To address this challenge, we herein developed Rh(III)-catalyzed C-H olefination/annulation reaction cascades to divergently construct diverse polyheterocycles by tuning manipulations of directing groups (DGs). Up to 9 distinct scaffolds were creatively synthesized under simple conditions with good functional group tolerance, chemo-, and regioselectivity. Such a versatile strategy and its extension may encourage researchers to discover more promising manipulations of DGs for transition-metal-catalyzed C-H bond activation, making diverse available targets and materials that would have been previously out of range.

Synchronization of chaotic neural networks with time delay via distributed delayed impulsive control.

This letter investigates the impulsive synchronization of chaotic neural networks with time delays. A novel impulsive delayed inequality is proposed, where the control effect of distributed delayed impulses is fully considered. Based on the inequality, a distributed delayed impulsive controller is proposed for exponential synchronization of chaotic neural networks. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theoretical results.