The pathogenic bacterium replicates in host cells within a distinct ER-associated compartment termed the-containing vacuole (LCV). How the dynamic ER network contributes to pathogen proliferation within the nascent LCV remains elusive. A proteomic analysis of purified LCVs identified the ER tubule-resident large GTPase atlastin3 (Atl3, yeast Sey1p) and the reticulon protein Rtn4 as conserved LCV host components. Here, we report that Sey1/Atl3 and Rtn4 localize to early LCVs and are critical for pathogen vacuole formation. Sey1 overproduction promotes intracellular growth of , whereas a catalytically inactive, dominant-negative GTPase mutant protein, or Atl3 depletion, restricts pathogen replication and impairs LCV maturation. Sey1 is not required for initial recruitment of ER to PtdIns(4)-positive LCVs but for subsequent pathogen vacuole expansion. GTP (but not GDP) catalyzes the Sey1-dependent aggregation of purified, ER-positive LCVs Thus, Sey1/Atl3-dependent ER remodeling contributes to LCV maturation and intracellular replication of.
Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.
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