Pyruvate dehydrogenase (PDH) is the gatekeeper enzyme into the tricarboxylic acid (TCA) cycle. Here we show that PARK7/DJ-1, a key familial Parkinson's disease (PD) gene, is a pacemaker controlling PDH activity in CD4 regulatory T cells (Tregs). DJ-1 bound to PDH-E1 beta (PDHB), inhibiting the phosphorylation of PDH-E1 alpha (PDHA), thus promoting PDH activity and oxidative phosphorylation (OXPHOS). Dj-1 depletion impaired Treg proliferation and cellularity maintenance in older mice, increasing the severity during the remission phase of experimental autoimmune encephalomyelitis (EAE). The compromised proliferation and differentiation of Tregs in Dj-1 knockout mice were caused via regulating PDH activity. These findings provide novel insight into the already complicated regulatory machinery of the PDH complex and demonstrate that the DJ-1-PDHB axis represents a potent target to maintain Treg homeostasis, which is dysregulated in many complex diseases.
The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manually generating ground truth labels for model training hampers the day-to-day application in experimental laboratories. Alternatively, traditional computer vision-based solutions do not need expensive labels for their implementation. Our work combines both approaches by training a deep learning network using weak training labels automatically generated with conventional computer vision methods. Our network surpasses the conventional segmentation quality by generalising beyond noisy labels, providing a 25 % increase of mean intersection over union, and simultaneously reducing the development and inference times. Our solution was embedded into an easy-to-use graphical user interface that allows researchers to assess the predictions and correct potential inaccuracies with minimal human input. To demonstrate the feasibility of training a deep learning solution on a large dataset of noisy labels automatically generated by a conventional pipeline, we compared our solution against the common approach of training a model from a small manually curated dataset by several experts. Our work suggests that humans perform better in context interpretation, such as error assessment, while computers outperform in pixel-by-pixel fine segmentation. Such pipelines are illustrated with a case study on image segmentation for autophagy events. This work aims for better translation of new technologies to real-world settings in microscopy-image analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.