Summary: (Macro)autophagy is a fundamental degradation process for macromolecules and organelles of vital importance for cell and tissue homeostasis. Autophagy research has gained a strong momentum in recent years because of its relevance to cancer, neurodegenerative diseases, muscular dystrophy, lipid storage disorders, development, ageing and innate immunity. Autophagy has traditionally been thought of as a bulk degradation process that is mobilized upon nutritional starvation to replenish the cell with building blocks and keep up with the energy demand. This view has recently changed dramatically following an array of papers describing various forms of selective autophagy. A main driving force has been the discovery of specific autophagy receptors that sequester cargo into forming autophagosomes (phagophores). At the heart of this selectivity lies the LC3-interacting region (LIR) motif, which ensures the targeting of autophagy receptors to LC3 (or other ATG8 family proteins) anchored in the phagophore membrane. LIR-containing proteins include cargo receptors, members of the basal autophagy apparatus, proteins associated with vesicles and of their transport, Rab GTPase-activating proteins (GAPs) and specific signaling proteins that are degraded by selective autophagy. Here, we comment on these new insights and focus on the interactions of LIR-containing proteins with members of the ATG8 protein family.

Autophagy is a lysosome-dependent degradation system conserved among eukaryotes. The mammalian Atg1 homologues, Unc-51 like kinase (ULK) 1 and 2, are multifunctional proteins with roles in autophagy, neurite outgrowth, and vesicle transport. The mammalian ULK complex involved in autophagy consists of ULK1, ULK2, ATG13, FIP200, and ATG101. We have used pulldown and peptide array overlay assays to study interactions between the ULK complex and six different ATG8 family proteins. Strikingly, in addition to ULK1 and ULK2, ATG13 and FIP200 interacted with human ATG8 proteins, all with strong preference for the GABARAP subfamily. Similarly, yeast and Drosophila Atg1 interacted with their respective Atg8 proteins, demonstrating the evolutionary conservation of the interaction. Use of peptide arrays allowed precise mapping of the functional LIR motifs, and two-dimensional scans of the ULK1 and ATG13 LIR motifs revealed which substitutions that were tolerated. This information, combined with an analysis of known LIR motifs, provides us with a clearer picture of sequence requirements for LIR motifs. In addition to the known requirements of the aromatic and hydrophobic residues of the core motif, we found the interactions to depend strongly on acidic residues surrounding the central core LIR motifs. A preference for either a hydrophobic residue or an acidic residue following the aromatic residue in the LIR motif is also evident. Importantly, the LIR motif is required for starvation-induced association of ULK1 with autophagosomes. Our data suggest that ATG8 proteins act as scaffolds for assembly of the ULK complex at the phagophore.

Autophagy and endocytosis are membrane-vesicle-based cellular pathways for degradation and recycling of intracellular and extracellular components, respectively. These pathways have a common endpoint at the lysosome, where their cargo is degraded. In addition, the two pathways intersect at different stages during vesicle formation, fusion and trafficking, and share parts of the molecular machinery. Accumulating evidence shows that autophagy is dependent upon endocytosis and vice versa. The emerging joint network of autophagy and endocytosis is of vital importance for cellular metabolism and signaling, and thus also highly relevant in disease settings. In this Review, we will discuss examples of how the autophagy machinery impacts on endocytosis and cell signaling, and highlight how endocytosis regulates the different steps in autophagy in mammalian cells. Finally, we will focus on the interplay of these pathways in the quality control of their common endpoint, the lysosome.

Segmenting subcellular structures in living cells from fluorescence microscope images is a ground truth (GT)-deficient problem. The microscopes’ three-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution and the complex morphological manifestations of the structures all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Therefore, manual segmentation relying on heuristics and experience remains the preferred approach. However, this process is tedious, given the countless structures present inside a single cell, and generating analytics across a large population of cells or performing advanced artificial intelligence tasks such as tracking are greatly limited. Here we bring modelling and deep learning to a nexus for solving this GT-hard problem, improving both the accuracy and speed of subcellular segmentation. We introduce a simulation-supervision approach empowered by physics-based GT, which presents two advantages. First, the physics-based GT resolves the GT-hardness. Second, computational modelling of all the relevant physical aspects assists the deep learning models in learning to compensate, to a great extent, for the limitations of physics and the instrument. We show extensive results on the segmentation of small vesicles and mitochondria in diverse and independent living- and fixed-cell datasets. We demonstrate the adaptability of the approach across diverse microscopes through transfer learning, and illustrate biologically relevant applications of automated analytics and motion analysis.

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