RESUMO
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.
RESUMO
Cellular events underlying neurodegenerative disease may be captured by longitudinal live microscopy of neurons. While the advent of robot-assisted microscopy has helped scale such efforts to high-throughput regimes with the statistical power to detect transient events, time-intensive human annotation is required. We addressed this fundamental limitation with biomarker-optimized convolutional neural networks (BO-CNNs): interpretable computer vision models trained directly on biosensor activity. We demonstrate the ability of BO-CNNs to detect cell death, which is typically measured by trained annotators. BO-CNNs detected cell death with superhuman accuracy and speed by learning to identify subcellular morphology associated with cell vitality, despite receiving no explicit supervision to rely on these features. These models also revealed an intranuclear morphology signal that is difficult to spot by eye and had not previously been linked to cell death, but that reliably indicates death. BO-CNNs are broadly useful for analyzing live microscopy and essential for interpreting high-throughput experiments.
RESUMO
Huntington's Disease (HD) is a neurodegenerative disorder caused by an expansion in a CAG-tri-nucleotide repeat that introduces a poly-glutamine stretch into the huntingtin protein (mHTT). Mutant huntingtin (mHTT) has been associated with several phenotypes including mood disorders and depression. Additionally, HD patients are known to be more susceptible to type II diabetes mellitus (T2DM), and HD mice model develops diabetes. However, the mechanism and pathways that link Huntington's disease and diabetes have not been well established. Understanding the underlying mechanisms can reveal potential targets for drug development in HD. In this study, we investigated the transcriptome of mHTT cell populations alongside intracellular glucose measurements using a functionalized nanopipette. Several genes related to glucose uptake and glucose homeostasis are affected. We observed changes in intracellular glucose concentrations and identified altered transcript levels of certain genes including Sorcs1, Hh-II and Vldlr. Our data suggest that these can be used as markers for HD progression. Sorcs1 may not only have a role in glucose metabolism and trafficking but also in glutamatergic pathways affecting trafficking of synaptic components.
