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The original version of this Article contained errors where Table S5 and Table S6 were incorrectly cited. As the result, in the Methods section, under the subheading ‘Germline transformation, crossing setups and insertion junction sequencing’, “Progeny were scored for transformation marker presence during either the larval, pupal and adult stage by using a fluorescence stereo microscope (SteREO Discovery.V8, Zeiss) with appropriate filter sets (Table S4).” now reads: “Progeny were scored for transformation marker presence during either the larval, pupal and adult stage by using a fluorescence stereo microscope (SteREO Discovery.V8, Zeiss) with appropriate filter sets (Table S5).” And, under the subheading ‘Light sheet-based fluorescence microscopy’, “Metadata for the three datasets are provided in Table S5.” now reads: “Metadata for the three datasets are provided in Table S6.” In Data availability section, “Microscopy data can be accessed as described in Table S5.” now reads: “Microscopy data can be accessed as described in Table S6.” Additionally, in the Supplementary Information 8 file, the “Data Access” row was omitted in Table S6. The “Data Access” row now reads: Dataset (DS) DS0001 DS0002 DS0003 Dataset Access DOI: 10.5281/zenodo.4892363 DOI: 10.5281/zenodo.4892373 DOI: 10.5281/zenodo.4892381 The original Supplementary Information 8 file is provided below. Finally, the Supplementary Information 1 and 5 files published with this Article contained tracked changes, these have now been removed. The original Article and accompanying Supplementary Information files have been corrected.
Autophagy is a core molecular pathway for the preservation of cellular and organismal homeostasis. Pharmacological and genetic interventions impairing autophagy responses promote or aggravate disease in a plethora of experimental models. Consistently, mutations in autophagy-related processes cause severe human pathologies. Here, we review and discuss preclinical data linking autophagy dysfunction to the pathogenesis of major human disorders including cancer as well as cardiovascular, neurodegenerative, metabolic, pulmonary, renal, infectious, musculoskeletal, and ocular disorders.
Macrophages exert the primary cellular immune response. Pathogen components like bacterial lipopolysaccharides (LPS) stimulate macrophage migration, phagocytotic activity and cytokine expression. Previously, we identified the poly(A)+ RNA interactome of RAW 264.7 macrophages. Of the 402 RNA-binding proteins (RBPs), 32 were classified as unique in macrophages, including nineteen not reported to interact with nucleic acids before. Remarkably, P23 a HSP90 co-chaperone, also known as cytosolic prostaglandin E2 synthase (PTGES3), exhibited differential poly(A)+ RNA binding in untreated and LPS-induced macrophages. To identify mRNAs bound by P23 and to elucidate potential regulatory RBP functions in macrophages, we immunoprecipitated P23 from cytoplasmic extracts of cross-linked untreated and LPS-induced cells. RNAseq revealed that enrichment of 44 mRNAs was reduced in response to LPS. Kif15 mRNA, which encodes kinesin family member 15 (KIF15), a motor protein implicated in cytoskeletal reorganization and cell mobility was selected for further analysis. Noteworthy, phagocytic activity of LPS-induced macrophages was enhanced by P23 depletion. Specifically, in untreated RAW 264.7 macrophages, decreased P23 results in Kif15 mRNA destabilization, diminished KIF15 expression and accelerated macrophage migration. We show that the unexpected RBP function of P23 contributes to the regulation of macrophage phagocytotic activity and migration.
Cryo-electron tomography provides a snapshot of the cellular proteome. With template matching, the spatial positions of various macromolecular complexes within their native cellular context can be detected. However, the growing awareness of the reference bias introduced by the cross-correlation based approaches, and more importantly the lack of a reliable confidence measurement in the selection of these macromolecular complexes, has restricted the use of these applications. Here we propose a heuristic, in which the reference bias is measured in real space in an analogous way to the R-free value in X-ray crystallography. We measure the reference bias within the mask used to outline the area of the template, and do not modify the template itself. The heuristic works by splitting the mask into a working and a testing area in a volume ratio of 9:1. While the working area is used during the calculation of the cross-correlation function, the information from both areas is explored to calculate the M-free score. We show using artificial data, that the M-free score gives a reliable measure for the reference bias. The heuristic can be applied in template matching and in sub-tomogram averaging. We further test the applicability of the heuristic in tomograms of purified macromolecules, and tomograms of whole Mycoplasma cells.