Porterpearce3759

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptor-type transcription factors with three subtypes (α, δ, and γ) that regulate cell differentiation and metabolism. Co-crystals of human PPARα-ligand-binding domain (LBD)-PPARα ligand for X-ray crystallography have been difficult to obtain. Recombinant human PPARα-LBD proteins contain intrinsic fatty acids (iFAs of Escherichia coli origin) and may be unstable without ligands during crystallization. To circumvent these limitations, we have successfully applied various crystallization techniques, including co-crystallization, cross-seeding, soaking, delipidation, and coactivator peptide supplementation. For complete details on the use and execution of this protocol, please refer to Kamata et al. (2020).Lipid peroxidation of polyunsaturated fatty acid (PUFA) phospholipids induces necrotic cell death through compromised cell membrane integrity during ferroptosis. We established assays to investigate oxidoreductase-mediated oxidative rupture, specifically via NADPH-cytochrome P450 reductase (POR) and NADH-cytochrome b5 reductase (CYB5R1), of PUFA phospholipids in artificially generated protein-free liposomes. Liposome breakage was detected via Tb3+ liposome release and electron microscopy liposome morphology imaging. This protocol was also applied to other oxidoreductases with analogous functions and investigation of ferroptotic membrane damage in cell-free systems. For complete details on the use and execution of this protocol, please refer to Yan et al. (2020).Genetic markers used to define discrete cell populations are seldom expressed exclusively in the population of interest and are, thus, unsuitable when evaluated individually, especially in the absence of spatial and morphological information. Here, we present fluorescence in situ hybridization for flow cytometry to allow simultaneous analysis of multiple marker genes at the single whole-cell level, exemplified by application to the embryonic epicardium. The protocol facilitates multiplexed quantification of gene and protein expression and temporal changes across specific cell populations. For complete details on the use and execution of this protocol, please refer to Lupu et al. (2020).Integrative analysis of next-generation sequencing data can help understand disease mechanisms. Specifically, ChIP-seq can illuminate where transcription regulators bind to regulate transcription. A major obstacle to performing this assay on primary cells is the low numbers obtained from tissues. The extensively validated ChIP-seq protocol presented here uses small volumes and single-pot on-bead library preparation to generate diverse high-quality ChIP-seq data. This protocol allows for medium-to-high-throughput ChIP-seq of low-abundance cells and can also be applied to other mammalian cells. For complete details on the use and execution of this protocol, please refer to Brigidi et al. (2019), Carlin et al. (2018), Heinz et al. (2018), Nott et al. (2019), Sakai et al. (2019), and Seidman et al. (2020).Pseudomonas putida is widely recognized as a spoiler of fresh foods under cold storage, and recently associated also with infections in clinical settings. The presence of antibiotic resistance genes (ARGs) could be acquired and transmitted by horizontal genetic transfer and further increase the risk associated with its persistence in food and the need to be deeper investigated. Thus, in this work we presented a genomic and phenotypic analysis of the psychrotrophic P. Pracinostat in vitro putida ITEM 17297 to provide new insight into AR mechanisms by this species until now widely studied only for its spoilage traits. ITEM 17297 displayed resistance to several classes of antibiotics and it also formed huge amounts of biofilm; this latter registered increases at 15 °C in comparison to the optimum growth condition (30 °C). After ITEM 17297 biofilms exposure to antibiotic concentrations higher than 10-fold their MIC values no eradication occurred; interestingly, biomasses of biofilm cultivated at 15 °C increased their amount in a dose-dependent manner. Genomic analyses revealed determinants (RND-systems, ABC-transporters, and MFS-efflux pumps) for multi-drugs resistance (β-lactams, macrolides, nalidixic acid, tetracycline, fusidic acid and bacitracin) and a novel ampC allele. Biofilm and motility related pathways were depicted underlying their contribution to AR. Based on these results, underestimated psychrotrophic pseudomonas, such as the herein studied ITEM 17297 strain, might assume relevance in relation to the risk associated with the transfer of antimicrobial resistance genes to humans through cold stored contaminated foods. P. putida biofilm and AR related molecular targets herein identified will provide a basis to clarify the interaction between AR and biofilm formation and to develop novel strategies to counteract the persistence of multidrug resistant P. putida in the food chain.The novel Coronavirus, COVID-19, pandemic is being considered the most crucial health calamity of the century. Many organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, The University of Waterloo, along with Darwin AI-a start-up spin-off of this department, has designed the Deep Learning model 'COVID-Net' and created a dataset called 'COVIDx' consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX's Deep Learning Software, VisionPro Deep Learning™, is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.