The SUMO protease SENP1 and the chromatin remodeler CHD3 interact and jointly affect chromatin accessibility and gene expression

Abstract

The small ubiquitin-like modifier (SUMO) post-translationally modifies lysine residues of transcription factors and co-regulators and thereby contributes to an important layer of control of the activities of these transcriptional regulators. Likewise, deSUMOylation of these factors by the sentrin-specific proteases (SENPs) also plays a role in gene regulation, but whether SENPs functionally interact with other regulatory factors that control gene expression is unclear. In the present work, we focused on SENP1, specifically, on its role in activation of gene expression investigated through analysis of the SENP1 interactome, which revealed that SENP1 physically interacts with the chromatin remodeler chromodomain helicase DNA-binding protein 3 (CHD3). Using several additional methods, including GST pulldown and co-immunoprecipitation assays, we validated and mapped this interaction, and using CRISPR-Cas9-generated CHD3- and SENP1-KO cells (in the haploid HAP1 cell line), we investigated whether these two proteins are functionally linked in regulating chromatin remodeling and gene expression. Genome-wide ATAC-Seq analysis of the CHD3- and SENP1-KO cells revealed a large degree of overlap in differential chromatin openness between these two mutant cell lines. Moreover, motif analysis and comparison with ChIP-Seq profiles in K562 cells pointed to an association of CHD3 and SENP1 with CCCTC-binding factor (CTCF) and SUMOylated chromatin-associated factors. Lastly, genome-wide RNA-Seq also indicated that these two proteins co-regulate the expression of several genes. We propose that the functional link between chromatin remodeling by CHD3 and deSUMOylation by SENP1 uncovered here provides another level of control of gene expression.

SUMO and Transcriptional Regulation: The Lessons of Large-Scale Proteomic, Modifomic and Genomic Studies

“…This was achieved using either large-scale standard chromatin immunoprecipitation (ChIP)- or genome-wide ChIP-seq approaches coupled to transcriptomic- and, sometimes, proteomic experiments. These were conducted in budding yeast [ 149 , 152 , 173 ] and mammalian (mouse and man) cells [ 22 , 175 , 187 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 ]. Most of them included a comparison of SUMO and SUMOylation enzymes distribution with those of histone marks specifying transcriptional activity or -inactivity and Pol II.…”

“…Another important conclusion of these experiments is that the chromatin SUMOylation landscape can be partly or dramatically cell-specific [ 22 , 137 , 177 ] ( Figure 2 A) and is highly dynamic in response to extracellular cues or depletion of components of the SUMO pathway [ 22 , 175 , 177 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 ]. Importantly, such differences are associated with notable changes in transcriptional programs.…”

“…A last important general conclusion of these studies is that the SUMO signals characterized in ChIP and ChIP-seq experiments are likely to be largely accounted for by SUMOylation events occurring within chromatin as (i) they are most often associated with SUMO E2 and E3 enzyme signals in the same ChIP/ChIP-seq experiments and (ii) RNAi-mediated elimination of the latter enzymes entails substantial reduction of SUMO signals in ChIP/ChIP-seq experiments [ 22 , 149 , 173 , 174 , 175 , 176 , 177 , 190 , 193 , 194 , 195 , 196 , 197 ]. Neither how SUMOylation enzymes are attracted to specific genomic loci, nor how this process is regulated are, however, known yet.…”

iFISH is a publically available resource enabling versatile DNA FISH to study genome architecture

“…b Distributions of mean signal-to-noise ratios (SNR) per cell for the same probes shown in a . c Comparison between the mean SNR of each of the 153 probes analyzed in a and the DNA accessibility of the corresponding genomic target previously assessed by ATAC-seq 45 . R, Pearson’s correlation coefficient.…”

“…To test the possible effect of local DNA accessibility on the quality of the 330 probes, we first downloaded previously published HAP1 ATAC-seq data 45 from the GEO repository GSE111047 (SRR6766909, SRR6766910, SRR6766911), and processed them using the Parker Lab’s ATAC-seq processing pipeline (https://github.com/ParkerLab/ATACseq-Snakemake). We used the bedGraph files generated by the pipeline to calculate the mean ATAC-seq signal inside each of our 330 probes (min probe size: 7099 bp; median: 7938 bp; max: 21,603 bp).…”