Macromolecular interactions dictate Polycomb-mediated epigenetic repression [RNA-seq]
Dynamic regulation of epigenetic states relies on complex macromolecular interactions such as at the protein-protein and protein-DNA level. It remains an unsolved question how H3K27me3, the hallmark histone modification for facultative heterochromatin, and its writer Polycomb repressive complex 2 (PRC2) are spatiotemporally regulated during development. Here we engineered separation-of-function mutants to surgically dissect the roles of individual macromolecular interactions required for PRC2 function. We show that Polycomb-mediated silencing is precisely regulated by the dynamic interactions among the PRC2 core complex, its accessory proteins, DNA sequences, and other histone modifications. Combining CRISPR-mediated engineering of separation-of-function mutants, human stem cell differentiation models, next-generation sequencing approaches, and reconstituted biochemical assays, we identified distinct regulatory functions of these macromolecular interactions on epigenetic repression in human pluripotent stem cells and cardiac differentiation. Disruption of key interactions led to distinct and opposing effects on cardiomyocyte differentiation, suggesting their highly specified roles in cell fate determination. Together, these results reveal the importance of individual macromolecular interactions at the center of PRC2 controlling epigenetic repression. Overall design: Two independent CRSIPR clones of each of the two edited cell lines (WT or mutant) were analyzed. RNA-seq libraries were prepared from 500 ng of total RNA using the KAPA RNA HyperPrep Kit with RiboErase according to the manufacturer's instructions. Libraries were pooled and sequenced on a NovaSeq 6000 or NovaSeq X Plus using a 150-cycle kit (paired end, 2 x 150 bp). RNA-seq analysis was performed similar to what was described previously.14 Adapter sequences were trimmed from the read pairs using Cutadapt (-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT). Trimmed reads were aligned to the human reference genome (hg38) using STAR with GENCODE basic gene annotation (v42). Read counts were calculated as transcripts per million (TPM) using uniquely mapped reads counted using featureCounts in a strand-specific way. Differential expression analysis between the mutant and WT lines was performed by modeling read counts in a generalized linear model accounting for cell lines. Fold change and p-values were calculated using DESeq2, in which two-sided Wald tests for regression coefficients of interest were performed. Differential expression heatmaps were generated using the R package pheatmap. Scatter plots were generated using the Tableau software. Gene ontology analysis was performed using ShinyGO.