bioRxivGenomicsPreprint
MLL3/MLL4 enzymatic activity shapes DNA replication timing
Deniz GökbugetiD, Ryan M. BoileauiD, Kayla LenshoekiD, Robert H. BlellochiD
Abstract
Mammalian genomes are replicated in a precise order during S phase, which is cell-type-specific1-3 and correlates with local transcriptional activity2,4-8, chromatin modifications9 and chromatin architecture1,10,11,12. However, the causal relationships between these features and the key regulators of DNA replication timing (RT) are largely unknown. Here, machine learning was applied to quantify chromatin features, including epigenetic marks, histone variants and chromatin architectural factors, best predicting local RT under steady-state and RT changes during early embryonic stem (ES) cell differentiation. About one-third of genome exhibited RT changes during the differentiation. Combined, chromatin features predicted steady-state RT and RT changes with high accuracy. Of these features, histone H3 lysine 4 monomethylation (H3K4me1) catalyzed by MLL3/4 (also known as KMT2C/D) emerged as a top predictor. Loss of Mll3/4 (but not Mll3 alone) or their enzymatic activity resulted in erasure of genome-wide RT dynamics during ES cell differentiation. Sites that normally gain H3K4me1 in a MLL3/4-dependent fashion during the transition failed to transition towards earlier RT, often with transcriptional activation unaffected. Further analysis revealed a requirement for MLL3/4 in promoting DNA replication initiation zones through MCM2 recruitment, providing a direct link for its role in regulating RT. Our results uncover MLL3/4-dependent H3K4me1 as a functional regulator of RT and highlight a causal relationship between the epigenome and RT that is largely uncoupled from transcription. These findings uncover a previously unknown role for MLL3/4-dependent chromatin functions which is likely relevant to the numerous diseases associated with MLL3/4 mutations.
- organism: mouse
- assay: ChIP-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: spatial domain detection
- research focus: variant calling
- approach: statistical model
- Citations
- 5
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