Back

Inhibition of Retinoic Acid Signaling in Proximal Tubular Epithelial cells Protects against Acute Kidney Injury by Enhancing Kim-1-dependent Efferocytosis

Yang, M.; Lopez, L. N.; Brewer, M.; Delgado, R. D.; Menshikh, A. A.; Clouthier, K. A.; Zhu, Y.; Vanichapol, T.; Yang, H.; Harris, R.; Gewin, L.; Brooks, C. R.; Davidson, A.; de Caestecker, M.

2023-06-15 pathology
10.1101/2023.06.15.545113 bioRxiv
Show abstract

Retinoic acid receptor (RAR) signaling is essential for mammalian kidney development, but in the adult kidney is restricted to occasional collecting duct epithelial cells. We now show there is widespread reactivation of RAR signaling in proximal tubular epithelial cells (PTECs) in human sepsis-associated acute kidney injury (AKI), and in mouse models of AKI. Genetic inhibition of RAR signaling in PTECs protects against experimental AKI but is associated with increased expression of the PTEC injury marker, Kim-1. However, Kim-1 is also expressed by de-differentiated, proliferating PTECs, and protects against injury by increasing apoptotic cell clearance, or efferocytosis. We show that the protective effect of inhibiting PTEC RAR signaling is mediated by increased Kim-1 dependent efferocytosis, and that this is associated with de-differentiation, proliferation, and metabolic reprogramming of PTECs. These data demonstrate a novel functional role that reactivation of RAR signaling plays in regulating PTEC differentiation and function in human and experimental AKI. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/545113v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@140ba6borg.highwire.dtl.DTLVardef@8f02f0org.highwire.dtl.DTLVardef@10a123eorg.highwire.dtl.DTLVardef@33b564_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.