Back

The role of LEDGF in transcription is exploited by HIV-1 to position integration

Pathak, R.; Esnault, C.; Radhakrishnan, R.; Singh, P. K.; Hongen, H.; Dale, R. K.; Anand, A.; Bedwell, G. J.; Engelman, A.; Rabi, A.; Hormoz, S.; Singh, P.; Levin, H. L.

2024-07-02 genomics
10.1101/2024.06.29.601340 bioRxiv
Show abstract

HIV-1 integration occurs across actively transcribed genes due to the interaction of integrase with host chromatin factor LEDGF. Although LEDGF was originally isolated as a co-activator that stimulates promoter activity in purified systems, this role is inconsistent with LEDGF-mediated integration across gene bodies and with data indicating LEDGF is a histone chaperone that promotes transcriptional elongation. We found LEDGF is enriched in pronounced peaks that match the enrichments of H3K4me3 and RNA Pol II at transcription start sites (TSSs) of active promoters. Our genome-wide chromatin mapping revealed that MLL1 had a dominant role in recruiting LEDGF to promoters and the presence of LEDGF recruits RNA Pol II. Enrichment of LEDGF at TSSs correlates strongly with levels of integration across the transcribed sequences, indicating that LEDGF at TSSs contributed to integration across gene bodies. Although the N-terminal Pro-Trp-Trp-Pro (PWWP) domain of LEDGF interacts with nucleosomes containing H3K36me3, a modification thought to recruit LEDGF to chromatin, we found H3K36me3 does not contribute to gene specificity of integration. These data support a dual role model of LEDGF where it is tethered to promoters by MLL1 and recruits RNA Pol II. Subsequently, LEDGF travels across genes to effect HIV-1 integration. Our data also provides a mechanistic context for the contribution made by LEDGF to MLL1-based infant acute leukemia and acute myeloid leukemia in adults.

Matching journals

The top 4 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.