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.