UC Davis

Autophagy is a dynamic intracellular recycling process where cargo of interest is sequestered into double membrane vesicles and degraded down to its basic building blocks. These building blocks are then reused by the cells as nutrients and/or energy to proliferate and promote cell survival. Dysregulation of autophagy is highly implicated in many diseases such as cancer, neurodegeneration, and metabolic disorders, and for this reason, small molecules that modulate the autophagy pathway are promising therapeutics. In the realm of small molecule characterization aimed at identifying favorable candidates, the conventional techniques employed to assess autophagy protein levels after drug administration exhibit limitations in their capacity to quantitatively measure protein expression with high temporal resolution. Furthermore, these methods do not allow for the distinct measurement of rates pertaining to various stages of the pathway. Consequently, crucial information about the time-dependent impact on specific pathway steps are lost, making it challenging to discern the mechanism of action of the small molecule of interest. Through the utilization of a dynamic live-cell fluorescent imaging protocol as developed by Beesabathuni et al. (2022) [1], we conducted quantitative and temporal analyses of two distinct small molecules, each in a different biological context. Specifically, we first employed this methodology to assess the inhibitory effects of an ULK-1 (unc-51 like autophagy initiating kinase 1) inhibitor, MRT-68921, on the autophagy response, unraveling novel insights. Through our approach of quantitative and temporal measurements, we discovered that MRT-68921 exhibited a drastically reduced degradation rate of cargo—3 times less over a 12-hour period compared to negative control cells. Furthermore, in the presence MRT-68921 coupled with mTORC-1 (mammalian target of rapamycin 1)- inhibition, a stimulus known to induce autophagy, the treatment led to an 8X decrease in cargo degradation compared to the negative control. To facilitate the identification of morphological features beyond vesicle numbers that exhibited significant temporal variance, we used imagebased profiling tools as developed by Beesabathuni et al. (2023)[2] . Through this approach, we discovered intriguing phenotypes such as increased cellular and autophagosome areas postMRT-68921 treatment. Furthermore, by employing dimensionality reduction techniques on extracted features, we successfully clustered treatment conditions that exhibited similar cargo degradation rates. This strategy not only extends to a broader spectrum of small molecules but also serves as a foundation for a compiling a robust training dataset, to drive the prediction of cargo degradation for uncharacterized small molecules relative to those experimentally resolved within the training set. In the second context, by collaborating with the Chang-il Hwang lab at UCD, we quantified the autophagy rates following treatment with the small molecule BET inhibitor, JQ1 to quantitatively investigate the role of the pathway in facilitating synthetic lethality in Brca2 deficient murine PDAC cells [3] . Through the resulting rate measurements, we depicted that BRCA2-deficient PDAC cells exhibited a heightened basal autophagy flux compared to control PDAC cells, which was further augmented by JQ1 treatment. Ultimately, these insightful revelations hold great potential in screening small molecules to precisely modulate the autophagy response. By integrating these findings with experimental tools like biosensor-based target activity tracking and proteomic profiling of cargo contents, we can dive deeper into unraveling the intricate mechanisms that underlie the dynamic autophagy response. Additionally, we can comprehend the consequences of these behaviors in both assisting the disease state and identifying potential treatments for such states.