The adaptive molecular landscape of reprogrammed telomeric sequences
D'Angiolo, M.; Barre, B. P.; Khaiwal, S.; Muenzner, J.; Hallin, J.; De Chiara, M.; Tellini, N.; Warringer, J.; Ralser, M.; Gilson, E.; Liti, G.
Show abstract
Telomeric sequences vary across the tree of life and intimately co-evolve with telomere-binding protein complexes. However, the molecular mechanisms allowing organisms to adapt to new telomeric sequences are difficult to gauge from extant species. Here, we reprogrammed multiple yeast lines to human-like telomeric repeats to unveil their molecular and fitness response to novel telomeres. Initially, the exchange of telomere sequences resulted in genome instability, proteome remodelling and severe fitness decline. However, adaptive evolution experiments selected for repeated mutations that drove adaptation to the humanized telomeres. These consisted of the recurrent amplification of the telomere-binding protein TBF1, by complex aneuploidies, or in repeated mutations that attenuate the DNA damage response. Overall, our results outline a response that defines the adaptive molecular landscape to novel telomeric sequences.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Mitotic chromosome condensation resets chromatin to maintain transcriptional homeostasis 95%
- Organization principles of dynamic three-dimensional genome architecture associated with centromere clustering states 94%
- Mechanism of in vivo activation of the MutLγ-Exo1 complex for meiotic crossover formation 94%
Similar papers in this journal
- The proteomic landscape of centromeric chromatin reveals an essential role for the Ctf19CCAN complex in meiotic kinetochore assembly 94%
- Mitochondrial function regulates cell growth kinetics to actively maintain mitochondrial homeostasis 93%
- A novel family of secreted proteins linked to plant gall development 93%
"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.