Periodic Reporting for period 1 - FINALphagy (Final act of the autophagy symphony: Whole-organism orchestration of autophagy termination)
Période du rapport: 2023-01-01 au 2025-06-30
When an organism experiences physiological stresses, such as nutrient starvation, it activates several mechanisms to promote its survival. One of these mechanisms is autophagy, which is a catabolic pathway conserved from yeast to man that functions as a recycling system during normal physiology, and which helps provide nutrients to sustain essential cellular processes during starvation. Although autophagy is an intensively studied process that plays a major role both in normal development and in a wide variety of diseases, several important aspects of autophagy remain to be elucidated. For instance, whereas the vast majority of studies have focused on how cells regulate the activation of autophagy, it remains largely unknown how cells and organisms shut off autophagy in response to returning nutrient availability. The limited knowledge of autophagy termination is partly due to a lack of available tools and appropriate methods. Addressing these challenges will not only provide important insight in this fundamental aspect of the autophagic process, but may potentially also identify new targets for development of drugs to manipulate this phase of autophagy. In FINALphagy I will develop new genetic and computational tools to quantify and manipulate the temporal dynamics of autophagy in an entire organism. I will unravel key mechanism of autophagy termination in individual cells within a tissue and dissect how this connects to systemic signaling and behavioral changes. This will generate a comprehensive toolbox for time- and space-resolved manipulation and quantification of autophagy levels, produce a whole-organism map of autophagy responses at the tissue-level, and conceptualize our understanding of the dynamics of autophagy regulation in an entire organism during changing nutrient levels.
We are determining tissue-specific autophagy kinetics based on existing autophagy reporters of single- and tandem-fluorescently tagged Atg8a in Drosophila larvae under starvation and refeeding.
We have constructed new split-fluorescence autophagy reporters that have been tested in S2 cells. The data obtained from the fluorescence imaging of autophagy reporters is used to train a semi-supervised/self-supervised machine learning model based on neural networks. This has first been established for S2 cells and a pipeline for in vivo samples is under development.
We are following up on candidate regulators of autophagy termination in relation to TORC1 and in relation to metabolism. Here we are delineating the mechanisms that impinge on termination of autophagy.
We have constructed new split-fluorescence autophagy reporters that have been tested in S2 cells. The data obtained from the fluorescence imaging of autophagy reporters is used to train a semi-supervised/self-supervised machine learning model based on neural networks. This has first been established for S2 cells and a pipeline for in vivo samples is under development.
We are following up on candidate regulators of autophagy termination in relation to TORC1 and in relation to metabolism. Here we are delineating the mechanisms that impinge on termination of autophagy.
By addressing the objectives of FINALphagy, we will move the current state of knowledge in the autophagy field in a new direction by developing and advancing new tools and methods, which will be used to uncover the regulation of autophagy termination in an entire organism. Key needs to further uptake and success would be further research and connection to potential clinical applications.