Hsp104, a yeast protein disaggregase, can be potentiated via numerous missense mutations at disparate locations throughout the coiled-coil middle domain (MD). Potentiated Hsp104 variants can counter the toxicity and misfolding of TDP-43, FUS, and α-synuclein, proteins which are implicated in
Background Endothelial cell injury is a common nidus of renal injury in patients and consistent with the high prevalence of acute kidney injury reported during the COVID-19 pandemic. This cell type expresses integrin α5 (ITGA5), which is essential to the Tie2 signaling pathway. The micro-RNA miR-218-5p is upregulated in endothelial progenitor cells following hypoxia, but miRNA regulation of Tie2 in the endothelial progenitor cell (EPC) lineage is unclear. Methods We isolated EPCs derived from the human kidney (hkEPCs) and surveyed microRNA target transcripts. A preclinical model of ischemic kidney injury was used to evaluate the effect of hkEPCs on capillary repair. We used a genetic knockout model to evaluate the effect of deleting endogenous expression of miR-218 specifically in angioblasts. Results Following ischemic in vitro preconditioning, miR-218-5p was elevated in hkEPCs. We found miR-218-5p bound to ITGA5 mRNA transcript and decreased ITGA5 protein expression. Phosphorylation of 42/44 MAPK decreased by 73.6% in hkEPCs treated with miR-218-5p. Cells supplemented with miR-218-5p downregulated ITGA5 synthesis and decreased 42/44 MAPK phosphorylation. In a CD309-Cre/miR-218-2-loxP mammalian model—a conditional knockout mouse model designed to delete pre-miR-218-2 exclusively in CD309+ cells, homozygotes at e18.5 contained avascular glomeruli, whereas heterozygote adults showed susceptibility to kidney injury. Isolated EPCs from the mouse kidney contained high amounts of ITGA5 and showed decreased migratory capacity in three-dimensional cell culture. Conclusions These results demonstrate the critical regulatory role of miR-218-5p in kidney EPC migration, a finding that may inform efforts to treat microvascular kidney injury via therapeutic cell delivery.
Ubiquitination is a common posttranslational modification in which the protein ubiquitin is appended to substrate proteins to target them to the proteasome for degradation among a myriad of other cellular functions. In addition to recognition of ubiquitin, successful proteasomal degradation relies upon engagement at an unstructured region within the substrate protein. However, many proteasome clients intrinsically lack these requisite unstructured regions. Previous studies have suggested that ubiquitin modification may destabilize the target protein. However, experimental characterization of the energetics of ubiquitin-conjugated substrates with native isopeptide bonds has been limited by both purification constraints and complication due to the signal from ubiquitin itself. We have developed a generalizable protocol for purification of milligram quantities of homogenously monoubiquitinated single-lysine proteins for experimental measurement of ubiquitininduced energetic effects. These effects span from no observable changes to dramatic destabilization and depend on the exact site of modification. Further, there exists a trend between ubiquitin-induced substrate destabilization and faster proteasomal degradation rates. We also find that polyubiquitination at destabilizing sites is sufficient to induce the requisite unstructured region required for successful proteasomal degradation. Taken together, these data establish a connection between ubiquitin-induced changes in substrate energetics and proteasomal processing. We propose that ubiquitin's varied and site-specific modulation of substrate energy landscapes plays a consequential role for substrate engagement and successful degradation at the proteasome.
Monitoring the health and safety of forests has become a rising problem with the advent of global wildfires, rampant logging, and reforestation efforts. This paper proposes a model for the automatic segmentation and classification of aerial forest imagery. The model is based on U-net architecture and relies on dice coefficients, binary cross-entropy, and accuracy as loss functions. While models without autoencoder-based structures can only reach a dice coefficient of 45%, the proposed model can achieve a dice coefficient of 79.85%. In addition, for barren adn dense forestry image classification, the proposed model can achieve 82.51%. This paper demonstrates how complex convolutional neural networks can be applied to aerial forest images to help preserve and save the forest environment.
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