Dunne, T. | Search bioRxiv

bioRxiv2024

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

Target of Rapamycin (TOR) complexes and G-protein-coupled receptors (GPCRs) are crucial signaling hubs that coordinate adaptive responses to environmental inputs. While GPCR-mediated regulation of TOR has been extensively studied, little is known about TOR-mediated regulation of GPCRs. Here, we establish TOR as a regulator of GPCR signaling via its control of receptor endocytosis in the yeast mating system. By pairing fluorescence microscopy with yeast genetic approaches, we identify the machinery that bridges TOR nutrient sensing to GPCR internalization. Furthermore, we find that autophagic machinery is employed during mating to deliver active receptor to the vacuole (lysosome), suppressing the mating pathway. These results demonstrate that TOR regulates the localization and signaling of the yeast mating GPCR in both ligand-dependent and -independent contexts. These pathways are highly conserved, suggesting that TOR-regulation of GPCRs may be a broadly conserved mechanism for integrating competing signals involving metabolic state and external communications.

cell biology↗

bioRxiv2018

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 cannot be employed to assess 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, explicitly linking the parasite life-cycle to key weather-water-environment conditions. First, using epidemiological data, we show that the model can reproduce observed infection levels in time and space over two case studies in the UK. Second, to overcome data limitations, we propose a calibration approach based on Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. Finally, comparison with information from the literature and a widely-used empirical risk index shows that the simulated disease dynamics agree with what has been traditionally observed, and that the new model gives better insight into the time-space patterns of infection, which will be valuable for decision support.

epidemiology↗