Back

TRF2 Non-Telomeric Function is Indispensable for Neural stemness

Vinayagamurthy, S.; Bhatt, A. K.; Bagri, S.; Ghosh, S.; Parichha, A.; Maitra, A.; Chowdhury, S.

2025-03-03 molecular biology
10.1101/2025.02.28.640739 bioRxiv
Show abstract

Depletion of TRF2 from chromosome ends results in telomeric fusions and genome instability in mammals. Here we show that although TRF2 is indispensable for the proliferation and survival of mouse neural stem cells (mNSCs), surprisingly, this is due to non-telomeric transcriptional function of TRF2, and not telomere protection. Complementing recent work showing TRF2 is dispensable for telomere protection in pluripotent stem cells. Deletion of TRF2 in adult mNSCs (TRF2fl/fl, Nestin-Cre) resulted in markedly reduced proliferation and impaired differentiation into neurons. However, telomere dysregulation-induced DNA damage was not observed, as indicated by the unaltered DNA damage response. Similarly, in SH-SY5Y cells, TRF2 depletion induced differentiation without causing telomere dysfunction. Mechanistically, non-telomeric TRF2 directly binds to the promoters of key genes that regulate differentiation. TRF2-dependent recruitment of the polycomb repressor complex (PRC2) and subsequent H3K27 trimethylation repress differentiation-associated genes, thereby maintaining NSC identity. Interestingly, G-quadruplex (G4) motifs are necessary for TRF2 binding. Disrupting the TRF2-G4 interaction-- either through G4-binding ligands or the G4-specific helicase DHX36--induces differentiation genes, thereby promoting neurogenesis. These findings reveal a pivotal non-telomeric role of TRF2 in NSC survival, providing key mechanistic insights into neurogenesis with implications for aging-related neurodegeneration.

Matching journals

The top 12 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.