Let's talk about flux: the rising potential of autophagy rate measurements in disease

Abstract

Macroautophagy/autophagy is increasingly implicated in a variety of diseases, making it an attractive therapeutic target. However, many aspects of autophagy are not fully understood and its impact on many diseases remains debatable and context-specific. The lack of systematic and dynamic measurements in these cases is a key reason for this ambiguity. In recent years, Loos et al. 2014 and Beesabathuni et al. 2022 developed methods to quantitatively measure autophagy holistically. In this commentary, we pose some of the unresolved biological questions regarding autophagy and consider how quantitative measurements may address them. While the applications are ever-expanding, we provide specific use cases in cancer, virus infection, and mechanistic screening. We address how the rate measurements themselves are central to developing cancer therapies and present ways in which these tools can be leveraged to dissect the complexities of virus-autophagy interactions. Screening methods can be combined with rate measurements to mechanistically decipher the labyrinth of autophagy regulation in cancer and virus infection. Taken together, these approaches have the potential to illuminate the underlying mechanisms of various diseases.Abbreviation MAP1LC3/LC3: microtubule-associated protein 1 light chain 3; R₁: rate of autophagosome formation; R₂: rate of autophagosome-lysosome fusion; R₃: rate of autolysosome turnover.

Keywords: Autophagy flux; autophagy perturbation; autophagy temporal dynamics; autophagy-dependent cell death; cancer; virus infection.

Figure 1.

Precise regulation of autophagy is critical for averting further disease.

Figure 2.

Refining dynamic measurements of autophagy flux. (A) Generalized system of autophagy with relevant rates highlighted. Colors represent the combination of fluorescent markers used to identify and count autophagy vesicles. (B-C) Loos et al. developed a system to effectively measure the rates of autophagy during steady state (beige). Beesabathuni et al. [7] built a pipeline that could then capture changes in autophagy flux at non-steady states (white), which may be especially relevant for certain perturbations.

Figure 3.

Schematic representation of relationship between basal autophagy levels, homeostasis region, and cell survival. (A) Basal and homeostasis levels for healthy cell population. (B) Altered basal autophagy levels of a mutant cell population leading to sensitivity to perturbations of similar magnitudes. (C) Altered basal and homeostasis levels of a mutant cell population leading to higher sensitivity to perturbations in two directions.

Figure 4.

Quantification of autophagy rates and selective cargo degradation.

Figure 5.

Genetic perturbations can be combined with dynamic rate measurements to identify rate-limiting components of autophagy pathway.