Li, D.-D. | bioRxiv Science

bioRxiv2021

Antibody-induced glomerulonephritis pathology is amplified by RTEC-intrinsic IL-17 signaling and restrained by the endoribonuclease Regnase-1

Antibody-mediated glomerulonephritis (AGN) is a clinical manifestation of many autoimmune kidney diseases for which few effective treatments exist. Chronic inflammatory circuits in renal glomerular and tubular cells lead to tissue damage in AGN. These cells are targeted by the cytokine IL-17, which has recently been shown to be a central driver of the pathogenesis of AGN. However, surprisingly little is known about the regulation of pathogenic IL-17 signaling in the kidney. Here, using a well characterized mouse model of AGN, we show that IL-17 signaling in renal tubular epithelial cells (RTECs) is necessary for AGN development. We also show that Regnase-1, an RNA binding protein with endoribonuclease activity, is a negative regulator of IL-17 signaling in RTECs. Accordingly, mice with a selective Regnase-1 deficiency in RTECs exhibited exacerbated kidney dysfunction in AGN. Mechanistically, Regnase-1 inhibits IL-17-driven expression of the transcription factor I{kappa}B{xi} and consequently its downstream gene targets including Il6 and Lcn2. Moreover, deletion of Regnase-1 in human RTECs reduced inflammatory gene expression in an I{kappa}B{xi}-dependent manner. Overall, these data identify an IL-17-driven inflammatory circuit in RTECs during AGN that is constrained by Regnase-1.

immunology↗

bioRxiv2018

Network analysis of lymphocyte nucleus staining image

Background: A complex network has been studied and applied in various disciplines. As network analysis and image processing are based on matrices, this research analysed the changes in the chromatin image of lymphocyte nuclei in peripheral blood of humans using a network motif and static features (static parameters), so as to complete image classification with network method.\n\nMethods: Image processing technology was used to establish a chromatin image network of a cell nucleus; Network analysis tool Pajek was used to display the special motif of an isolated structural hole with different symmetric line values; afterwards, the frequency of occurrence of this structural hole in patients with nasopharyngeal carcinoma and AIDS, and healthy people was computed. Then by applying the network static features as variables, the chromatin images of stained lymphocytes from the three groups of people were classified and recognised by using an extreme learning machine (ELM).\n\nResults: The frequency of occurrence of the isolated structural hole with different symmetric line values was adopted to distinguish the structures of the chromatins of peripheral blood lymphocytes in patients with nasopharyngeal carcinoma and AIDS, and healthy people. Similarly, The static features of the chromatin image network of a cell nucleus were applied to classify and recognise the morphological and structural changes in chromatins for peripheral blood lymphocytes in the three groups of people.\n\nConclusion: The surface chemical and physical characteristics, as well as the polymerisation link status of biomacromolecules such as DNA, RNA, and protein in the lymphocyte nucleus change under certain pathological conditions. The change influences the combination of small molecular staining materials and any associated biomacromolecules. Therefore, various macroscopic and microscopic changes were found in the chromatin images of the cell nucleus. The microscopic changes include the variations of the extent of staining of chromatin in the nuclei, coarseness and direction of the texture therein, the size of stained conglomerations, etc. These changes contribute to the differences in chromatin image networks among the same type of cells across the three groups. Based on this, the model can be used to classify and reorganise certain diseases. The results prove that using complex network to analyse the chromatin structure of a cell nucleus is of significance.

bioinformatics↗