Structural Basis of Ferroportin Inhibition by Minihepcidin PR73

Ferroportin (Fpn) is the only known iron exporter in humans and is essential for maintaining iron homeostasis. Fpn activity is suppressed by hepcidin, an endogenous peptide hormone, which inhibits iron export and promotes endocytosis of Fpn. Hepcidin deficiency leads to hemochromatosis and iron-loading anemia. Previous studies have shown that small peptides that mimic the first few residues of hepcidin, i.e., minihepcidins, are more potent than hepcidin. However, the mechanism of enhanced inhibition by minihepcidins remains unclear. Here, we report the structure of human ferroportin in complex with a minihepcidin, PR73 that mimics the first 9 residues of hepcidin, at 2.7 Å overall resolution. The structure reveals novel interactions that were not present between Fpn and hepcidin. We validate PR73-Fpn interactions through binding and transport assays. These results provide insights into how minihepcidins increase inhibition potency and will guide future development of Fpn inhibitors

Optimal design of label-free silicon “lab on a chip” biosensors

This paper reported the optimal design of label-free silicon on insulator (SOI) “lab on a chip” biosensors. These devices are designed on the basis of the evanescent field detection principles and interferometer technologies. The well-established silicon device process technology can be applied to fabricate and test these biosensor devices. In addition, these devices can be monolithically integrated with CMOS electronics and microfluidics. For these biosensor devices, multi-mode interferometer (MMI) was employed to combine many stand-alone biosensors to form chip-level biosensor arrays, which enable real-time and label-free monitoring and parallel detection of various analytes in multiple test samples. This sensing and detection technology features the highest detection sensitivity, which can detect analytes at extremely low concentrations instantaneously. This research can lead to innovative commercial development of the new generation of high sensitivity biosensors for a wide range of applications in many fields, such as environmental monitoring, food security control, medical and biological applications

miR‐155 promotes macrophage pyroptosis induced by Porphyromonas gingivalis through regulating the NLRP3 inflammasome

ObjectiveThe aim of this study is to detect pyroptosis in macrophages stimulated with Porphyromonas gingivalis and elucidate the mechanism by which P. gingivalis induces pyroptosis in macrophages.MethodsThe immortalized human monocyte cell line U937 was stimulated with P. gingivalis W83. Flow cytometry was carried out to detect pyroptosis in macrophages. The expression of miR‐155 was detected by real‐time PCR and inhibited using RNAi. Suppressor of cytokine signaling (SOCS) 1, cleaved GSDMD, caspase (CAS)‐1, caspase‐11, apoptosis‐associated speck‐like protein (ASC), and NOD‐like receptor protein 3 (NLRP3) were detected by Western blotting, and IL‐1β and IL‐18 were detected by ELISA.ResultsThe rate of pyroptosis in macrophages and the expression of miR‐155 increased upon stimulation with P. gingivalis and pyroptosis rate decreased when miR‐155 was silenced. GSDMD‐NT, CAS‐11, CAS‐1, ASC, NLRP3, IL‐1β, and IL‐18 levels increased, but SOCS1 decreased in U937 cells after stimulated with P. gingivalis. These changes were weakened in P. gingivalis‐stimulated U937 macrophages transfected with lentiviruses carrying miR‐155 shRNA compared to those transfected with non‐targeting control sequence. However, there was no significant difference in ASC expression between P. gingivalis‐stimulated shCont and shMiR‐155 cells.ConclusionsPorphyromonas gingivalis promotes pyroptosis in macrophages during early infection. miR‐155 is involved in this process through regulating the NLRP3 inflammasome.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/1/odi13198_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/2/odi13198.pd

A Sialidase‐Deficient Porphyromonas gingivalis Mutant Strain Induces Less Interleukin‐1β and Tumor Necrosis Factor‐α in Epi4 Cells Than W83 Strain Through Regulation of c‐Jun N‐Terminal Kinase Pathway

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142178/1/jpere129.pd

N,N′-Dibenzyl-N,N,N′,N′-tetra­methyl­ethylenediammonium dibromide dihydrate

In the title compound, C20H30N2 2+·2Br−·2H2O, the asymmetric unit consists of half of the N,N′-dibenzyl-N,N,N′,N′-tetra­methyl­ethylenediammonium cation lying across an inversion center, a bromide ion and a water mol­ecule of solvation. There is an eight-membered dibromide dihydrate ring, which is formed via hydrogen bonds of the type O—H⋯Br

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively