Back

Disruptors of sestrin-MAPK interactions rejuvenate T cells and expand TCR specificity

Lanna, A.; D'Ambra, C.; Rinaldi, F.; Chocarro De Erauso, L.; Delpero, M.; Capitani, M.; Karin, M.

2024-05-21 immunology
10.1101/2024.05.17.594698 bioRxiv
Show abstract

Whereas treatments that reactivate exhausted T cells are available, strategies to rejuvenate terminally differentiated senescent lymphocytes are yet to be developed. Senescent T cells, with short telomeres and inactive telomerase, are different from exhausted cells, and may form due to defective telomere transfer reactions upon contact with antigen presenting cells (APCs). Senescent T cells are characterized by presence of sestrin-MAPK kinase activation complexes (sMACs), large immune-inhibitory protein assemblies of sestrins bound to a stress/energy sensing kinase (AMPK) and three functional effector kinases (ERK, JNK and p38 MAPKs). Here we described first in class Disruptors of the Sestrin-MAPK immune-inhibitory Complex (DOS), which target sMAC to ubiquitin-dependent proteasomal degradation, resulting in long-term sestrin transcriptional inhibition, increased T cell fitness, and generation of long-lived stem like memory features. Strikingly, the DOS generated stem T cells present de novo antigen-specific T-cell receptor DNA rearrangements that precede their future expansion. Although largely senescent at the point of treatment, the DOS regenerated T cells, with stem features and new TCRs, initiated immune-protective rejuvenation-dependent responses to new challenges, with or without vaccination. Therefore, it is possible to generate new T cell clones from formerly senescent cells and expand immune specificity. HighlightsO_LIDOS are the first sestrin-MAPK binding disruptors C_LIO_LIDOS rejuvenate T cells (DOS-juvenation) C_LIO_LIDOS-juvenated T cells protect old mice from lethal infections, with or without vaccination C_LIO_LIDOS-juvenated T cells exist as stem-like cells that undergo antigen-specific TCR revisions C_LI

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.