TOR signaling regulates GPCR levels on the plasma membrane and suppresses the Saccharomyces cerevisiae mating pathway.

Saccharomyces cerevisiae respond to mating pheromone through the GPCRs Ste2 and Ste3, which promote growth of a mating projection in response to ligand binding. This commitment to mating is nutritionally and energetically taxing, and so we hypothesized that the cell may suppress mating signaling during starvation. We set out to investigate negative regulators of the mating pathway in nutritionally depleted environments. Here, we report that nutrient deprivation led to loss of Ste2 from the plasma membrane. Recapitulating this effect with nitrogen starvation led us to hypothesize that it was due to TORC1 signaling. Rapamycin inhibition of TORC1 impacted membrane levels of all yeast GPCRs. Inhibition of TORC1 also dampened mating pathway output. Deletion analysis revealed that TORC1 repression leads to α-arrestin-directed CME through TORC2-Ypk1 signaling. We then set out to determine whether major downstream effectors of the TOR complexes also downregulate pathway output during mating. We found that autophagy contributes to pathway downregulation through analysis of strains lacking ATG8 . We also show that Ypk1 significantly reduced pathway output. Thus, both autophagy machinery and TORC2-Ypk1 signaling serve as attenuators of pheromone signaling during mating. Altogether, we demonstrate that the stress-responsive TOR complexes coordinate GPCR endocytosis and reduce the magnitude of pheromone signaling, in ligand-independent and ligand-dependent contexts. One Sentence Summary: TOR signaling regulates the localization of all Saccharomyces cerevisiae GPCRs during starvation and suppress the mating pathway in the presence and absence of ligand.

A mechanistic hydro-epidemiological model of liver fluke risk.

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather-water-environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time-space patterns of infection, which will be valuable for decision support.