Back

Yeast DNA Polymerase δ Proofreading Nuclease Suppresses Translesion Synthesis and Facilitates Recombination in DNA Lesion Bypass.

Jun, C.; Wang, Q.; He, J.; Zhang, Z.; Chung, J. M.; Gallagher, D. N.; Hong, X.; Rao, H.; Shim, E. Y.; Haber, J. E.; Niu, H.; Lee, S. E.

2025-01-16 genetics
10.1101/2025.01.14.632785 bioRxiv
Show abstract

Translesion synthesis (TLS) employs specialized polymerases to incorporate nucleotides opposite DNA lesions, allowing replication to continue temporarily without repairing the lesions, while sacrificing replication fidelity (1). TLS depends on a ubiquitin molecule attached to the clamp PCNA at residue Lys164 (2, 3); the precise biochemical and molecular basis how PCNA modification stimulates TLS is still elusive (4-10). In this study, we discovered that DNA polymerase {delta} (Pol {delta}) 3 to 5 "proofreading" exonuclease degrades nascent TLS synthesis opposite pyrimidine dimers and inhibits TLS on both leading and lagging strands. Rad18 E3 ligase and PCNA ubiquitylation become dispensable for TLS when Pol {delta} exonuclease is inactive. Biochemical reconstitution showed that the major UV lesion bypass polymerase Rad30 is intrinsically inefficient at bypassing thymidine dimers and requires multiple turnovers. Pol {delta} exonuclease impedes Rad30 activity both catalytically and non-catalytically during and post-lesion bypass. Importantly, PCNA and PCNA ubiquitylation enhance processivity of Rad30 after thymidine dimer bypass, a critical step for further DNA synthesis extension and overcoming Pol {delta} inhibition. Our study reveals a new paradigm how PCNA ubiquitylation facilitates TLS and the new role of Pol {delta} nuclease in lesion bypass pathway choice and replication fidelity at fork-blocking DNA lesions.

Matching journals

The top 3 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.