Back

Interlinked roles for HEB and Id3 in fetal gamma-delta T cell commitment and functional programming

Selvaratnam, J. S.; Barbosa da Rocha, J. D.; Rajan, V.; Wang, H.; Reddy, E. C.; Gams, M. S.; Murre, C.; Guidos, C. J.; Zuniga-Pflucker, J. C.; Anderson, M. K.

2025-06-09 immunology
10.1101/2025.06.08.658490 bioRxiv
Show abstract

T cells expressing the {gamma}{delta} T cell receptor (TCR) develop in a stepwise process initiating at the {beta}/{gamma}{delta} T cell branchpoint followed by maturation and acquisition of effector functions, including the ability to produce interleukin-17 (IL-17) as {gamma}{delta}T17 cells. Previous studies linked TCR signal strength and fate choices to the transcriptional regulator HEB (Tcf12) and its antagonist, Id3, but how these factors regulate different stages of {gamma}{delta} T cell development has not been determined. We found that immature fetal {gamma}{delta}TCR+ cells from conditional Tcf12 knockout (HEB cKO) mice were defective in activating the {gamma}{delta}T17 program at an early stage, whereas Id3 deficient (Id3-KO) mice displayed a partial block in {gamma}{delta}T17 maturation and a defect in IL-17 production. We also found that HEB cKO mice failed to upregulate Id3 during {gamma}{delta}T17 development, whereas HEB overexpression elevated the levels of Id3 in collaboration with TCR signaling. Moreover, Egr2 and HEB were bound to several of the same regulatory sites on the Id3 gene locus in the context of early T cell development. Therefore, our findings reveal an interlinked sequence of events during which HEB and TCR signaling synergize to upregulate Id3, which enables maturation and acquisition of the {gamma}{delta}T17 effector program. One Sentence SummaryThe transcription factor HEB synergizes with TCR signaling to upregulate Id3, which is required for the maturation of fetal IL-17-producing {gamma}{delta} T cells.

Published in eLife (predicted rank #1) · training set

Matching journals

The top 1 journal accounts 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.