Project Details
Projekt
Unveiling the role of particulate matter (PM) and micro-/nanoplastic (MNP) in glomerular kidney disease with focus on membranous glomerulonephritis (Acronym: UNPLOK)
Subject Area
Nephrology
Glass, Ceramics and Derived Composites
Glass, Ceramics and Derived Composites
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 523847126
Membranous glomerulonephritis (MGN) is caused by autoantibodies (AABs) binding to podocyte antigens. The most frequent AAB is LA2R antibody. However, it remains unknown what triggers AAB production and how ABBs reach the subepithelial space in MGN, because the glomerular filtration barrier (GFB) is normally almost impermeable for AABs. In the past, we could show that podocyte and glomerular endothelial cell derived microRNAs (miRs) that target nephronectin - an extracellular matrix protein of the GBM produced by podocytes - might play a role in MGN. We hypothesize that particulate matter (PM) or Micro-/ nanoplastic (MNP) induce miRs that target GBM molecules enabling AABs to reach the subpodocyte space and inhibit autophagy in MGN. This hypothesis should be tested in three dimensional glomerular cell culture models, transgenic zebrafish models and mice including proteinuria and autophagy reporter lines, inducible podocyte specific miR overexpressing lines and transgenic Pla2r mice. Cell culture models as well as zebrafish and mice will be exposed to PM and MNP via cell culture medium, water and versatile aerosol concentration enrichment system. The therapeutic potential of miR antagomirs should be investigated in our zebrafish model after PM/ MNP exposure. Samples will be analyzed using miR/ mRNA arrays, Western Blot, proteinuria and autophagy assays together with a unique combination of multi-modal, scale-bridging analytics such as Raman spectroscopy, nanoGPS technology, scanning electron- and ion microscopies (FIB/SEM) and scanning ion conductance microscopy (SCICM). Kidney biopsies of patients with MGN will also be included. The comprehensive data will be correlated, quantitatively evaluated with statistical and machine learning methods and might predict future clinical diagnostic and therapeutic options.
DFG Programme
Research Grants
