Back

Jk DNA GAGA MOTIFS ARE REQUIRED FOR LOCAL NUCLEOSOME REMODELING AND Vk-Jk RECOMBINATION

Veselits, M.; McLean, K. C.; Wright, N. E.; Veselits, J.; Okoreeh, M.; Maienschein-Cline, M.; Mandal, M.; Clark, M. R.

2025-01-23 cell biology
10.1101/2025.01.21.634169 bioRxiv
Show abstract

Immunoreceptor gene recombination requires complementary 12 bp and 23 bp recombination signal sequences (RSSs). In addition, the RSSs that assemble the RAG proteins, recombination centers, must be accessible yet flanked by a 5 nucleosome decorated with H3K4me3. In Drosophila, DNA GAGA motifs play an important role in nucleosome positioning. Herein, we report that 5 to each functional Jk RSS is a DNA GAGA motif conserved across mammalian species. In mice, the GAGA motif 5 to Jk1 regulated local RSS accessibility and 5 nucleosome placement. Furthermore, it was required for Vk-Jk1 recombination. Murine Jk3 is nonfunctional, having mutations in both RSS and GAGA motifs. Restoring both GAGA and RSS motifs rescued Vk-Jk3 recombination. In contrast, restoring the RSS alone did not. Genome-wide, strong cryptic 23 RSSs were preferentially bound to nucleosomes. Furthermore, evolutionary selection against cRSS only occurred in the A Compartment of B lymphocytes, not embryonic stem cells. These data indicate that in developing B cells, nucleosome positioning both enables and restricts recombination to Jk. Furthermore, our data suggest an expanded definition of recombination center-associated RSSs to include a 5 GAGA sequence that dictates the local epigenetic state required for gene recombination. SummaryRecombination center assembly requires a specific epigenetic topology at recombination signal sequences. Herein, we report that conserved GAGA motifs 5 to each Jk segment are required for establishing this epigenetic topology and subsequent local gene recombination.

Matching journals

The top 5 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.