Back

Evolutionarily conserved effects of Notch signaling drive intestinal graft-versus-host disease in mice and non-human primates

Tkachev, V.; Vanderbeck, A.; Perkey, E.; Furlan, S. N.; McGuckin, C.; Gomez Atria, D.; Gerdemann, U.; Rui, X.; Lane, J.; Hunt, D. J.; Zheng, H.; Colonna, L.; Hoffman, M.; Yu, A.; Kelly, S.; Allman, A.; Burbach, B.; Shimizu, Y.; Panoskaltsis-Mortari, A.; Chen, G.; Carpenter, S.; Harari, O.; Kuhnert, F.; Thurston, G.; Blazar, B. R.; Kean, L. S.; Maillard, I.

2022-04-27 immunology
10.1101/2022.04.23.488844 bioRxiv
Show abstract

Notch signaling promotes T-cell pathogenicity and graft-versus-host disease (GVHD) after allogeneic hematopoietic cell transplantation (allo-HCT) in mice, with a dominant role for the Delta-like ligand DLL4. To assess if Notchs effects are evolutionarily conserved and identify key mechanisms, we studied antibody-mediated DLL4 blockade in a non-human primate model similar to human allo-HCT. Short-term DLL4 blockade improved post-transplant survival with striking, durable protection from gastrointestinal GVHD, out of proportion to other disease sites. Unlike prior immunosuppressive strategies, anti-DLL4 interfered with a T-cell transcriptional program associated with intestinal infiltration. In cross-species investigations, Notch inhibition decreased surface abundance of the gut-homing integrin a4b7 in conventional T-cells via b1 competition for a4 binding, while preserving a4b7 in regulatory T-cells. Thereby, DLL4/Notch blockade decreased effector T-cell infiltration into the gut, with increased regulatory to conventional T-cell ratios early after allo-HCT. Our results identify a conserved, biologically unique and targetable role of DLL4/Notch signaling in GVHD. One Sentence SummaryNotch signaling promotes pathogenic effector T cell infiltration of the intestine during acute graft-versus-host disease.

Matching journals

The top 6 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.