Listeriolysin O (LLO), an essential virulence determinant of Listeria monocytogenes, is a pore-forming toxin whose primary function is to facilitate cytosolic bacterial replication by breaching the phagosomal membranes, which is critical for the pathogen to evade host immune recognition. The critical role of LLO in the virulence of L. monocytogenes renders it an ideal target for designing novel antivirulence therapeutics. We found that fisetin, a natural flavonoid without antimicrobial activity, is a potent antagonist of LLO-mediated hemolysis. Fisetin effectively inhibits L. monocytogenes infection in both tissue culture and animal infection models. Molecular modeling and mutational analysis revealed that fisetin directly engages loop 2 and loop 3 of LLO, leading to the blockage of cholesterol binding and the reduction of its oligomerization, thus inhibiting its hemolytic activity. Our results establish fisetin as an effective antitoxin agent for LLO, which can be further developed into novel therapeutics against infections caused by L. monocytogenes.

Abstract:The leaf area density (LAD) within a tree canopy is very important for the understanding and modeling of photosynthetic studies of the tree. Terrestrial light detection and ranging (LiDAR) has been applied to obtain the three-dimensional structural properties of vegetation and estimate the LAD. However, there is concern about the efficiency of available approaches. Thus, the objective of this study was to develop an effective means for the LAD estimation of the canopy of individual magnolia trees using high-resolution terrestrial LiDAR data. The normal difference method based on the differences in the structures of the leaf and non-leaf components of trees was proposed and used to segment leaf point clouds. The vertical LAD profiles were estimated using the voxel-based canopy profiling (VCP) model. The influence of voxel size on the LAD estimation was analyzed. The leaf point cloud's extraction accuracy for two magnolia trees was 86.53% and 84.63%, respectively. Compared with the ground measured leaf area index (LAI), the retrieved accuracy was 99.9% and 90.7%, respectively. The LAD (as well as LAI) was highly sensitive to the voxel size. The spatial resolution of point clouds should be the appropriate estimator for the voxel size in the VCP model.

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Non-small cell lung cancer (NSCLC) is the most common form of cancer, resulting in cancer-related deaths worldwide. Exosomes, a subclass of extracellular vesicles, are produced and secreted from various types of cells, including cancer cells. Cancer-derived exosomes can deliver nucleic acids, proteins, and lipids to provide a favorable microenvironment that supports tumor growth through enhancing cell proliferation and metastasis. Our results showed that miR-224-5p was upregulated in NSCLC patient tissues and cell lines, with a tumor-promoting phenotype. Meanwhile, exosome-derived miR-224-5p induced cell proliferation and metastasis in NSCLC and human lung cells. Moreover, we characterized the androgen receptor (AR) as a direct target of miR-224-5p. Tumor xenograft assay experiments revealed that overexpression of miR-224-5p drove NSCLC tumor growth via the suppression of AR and the mediation of epithelial-mesenchymal transition (EMT). Collectively, our results suggest that miR-224-5p-enriched exosomes promote tumorigenesis by directly targeting AR in NSCLC, which may provide novel potential therapeutic and preventive targets for NSCLC.