ABSTRACT
The condition of having a healthy, functional proteome is known as protein homeostasis, or proteostasis. Establishing and maintaining proteostasis is the province of the proteostasis network, approximately 2,700 components that regulate protein synthesis, folding, localization, and degradation. The proteostasis network is a fundamental entity in biology that is essential for cellular health and has direct relevance to many diseases of protein conformation. However, it is not well defined or annotated, which hinders its functional characterization in health and disease. In this series of manuscripts, we aim to operationally define the human proteostasis network by providing a comprehensive, annotated list of its components. We provided in a previous manuscript a list of chaperones and folding enzymes as well as the components that make up the machineries for protein synthesis, protein trafficking into and out of organelles, and organelle-specific degradation pathways. Here, we provide a curated list of 838 unique high-confidence components of the autophagy-lysosome pathway, one of the two major protein degradation systems in human cells.
ABSTRACT
DNA damage is a common cellular feature seen in cancer and neurodegenerative disease, but fast and accurate methods for quantifying DNA damage are lacking. Comet assays are a biochemical tool to measure DNA damage based on the migration of broken DNA strands towards a positive electrode, which creates a quantifiable 'tail' behind the cell. However, a major limitation of this approach is the time needed for analysis of comets in the images with available open-source algorithms. The requirement for manual curation and the laborious pre- and post-processing steps can take hours to days. To overcome these limitations, we developed AutoComet, a new open-source algorithm for comet analysis that utilizes automated comet segmentation and quantification of tail parameters. AutoComet first segments and filters comets based on size and intensity and then filters out comets without a well-connected head and tail, which significantly increases segmentation accuracy. Because AutoComet is fully automated, it minimizes curator bias and is scalable, decreasing analysis time over ten-fold, to less than 3 s per comet. AutoComet successfully detected statistically significant differences in tail parameters between cells with and without induced DNA damage, and was more comparable to the results of manual curation than other open-source software analysis programs. We conclude that the AutoComet algorithm provides a fast, unbiased and accurate method to quantify DNA damage that avoids the inherent limitations of manual curation and will significantly improve the ability to detect DNA damage.
