Selective autophagy underlies many of the important physiological roles that autophagy plays in multicellular organisms, but the mechanisms involved in cargo selection are poorly understood. Here we describe a molecular mechanism that can target conventional endosomes for autophagic degradation. We show that the human transmembrane protein TMEM59 contains a minimal 19-amino-acid peptide in its intracellular domain that promotes LC3 labelling and lysosomal targeting of its own endosomal compartment. Interestingly, this peptide defines a novel protein motif that mediates interaction with the WD-repeat domain of ATG16L1, thus providing a mechanistic basis for the activity. The motif is represented with the same ATG16L1-binding ability in other molecules, suggesting a more general relevance. We propose that this motif may play an important role in targeting specific membranous compartments for autophagic degradation, and therefore it may facilitate the search for adaptor proteins that promote selective autophagy by engaging ATG16L1. Endogenous TMEM59 interacts with ATG16L1 and mediates autophagy in response to Staphylococcus aureus infection.
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http://dx.doi.org/10.1038/emboj.2013.8 | DOI Listing |
Clin Trials
January 2025
Rare Diseases Team, Office of New Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Background/aims: Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
Department of Chemical and Metallurgical Engineering, School of Chemical Engineering, Aalto University, 02150 Espoo, Finland.
Alginate hydrogels have gathered significant attention in biomedical engineering due to their remarkable biocompatibility, biodegradability, and ability to encapsulate cells and bioactive molecules, but much less has been reported on the kinetics of gelation. Scarce experimental data are available on cross-linked alginates (AL) with bioactive components. The present study addressed a novel method for defining the crosslinking mechanism using rheological measurements for aqueous mixtures of AL and calcium chloride (CaCl) with the presence of hydroxyapatite (HAp) as filler particles.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China.
Rapid heating cycle molding technology has recently emerged as a novel injection molding technique, with the uniformity of temperature distribution on the mold cavity surface being a critical factor influencing product quality. A numerical simulation method is employed to investigate the rapid heating process of molds and optimize heating power, with the positions of heating rods as variables. The temperature uniformity coefficient is an indicator used to assess the uniformity of temperature distribution within a system or process, while the thermal response rate plays a crucial role in evaluating the heating efficiency of a heating system.
View Article and Find Full Text PDFPathogens
December 2024
Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK.
Mycobacterial infections are an important emerging zoonosis in companion animals for which diagnostic options remain imperfect, and the canine immunological response to these infections has been poorly investigated. We sought to further define the cellular response of peripheral blood mononuclear cells (PBMCs) from dogs infected with , as determined using a commercial interferon-gamma response assay (IGRA). To this end, PBMCs from healthy or infected dogs were collected.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, 22083 Berlin, Germany.
Predictive machine learning models have made use of a variety of scoring systems to identify clinical deterioration in ICU patients. However, most of these scores include variables that are dependent on medical staff examining the patient. We present the development of a real-time prediction model using clinical variables that are digital and automatically generated for the early detection of patients at risk of deterioration.
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