The ISW1 and CHD1 ATP-dependent chromatin remodelers compete to set nucleosome spacingin vivo

Abstract

Adenosine triphosphate-dependent chromatin remodeling machines play a central role in gene regulation by manipulating chromatin structure. Most genes have a nucleosome-depleted region at the promoter and an array of regularly spaced nucleosomes phased relative to the transcription start site. In vitro, the three known yeast nucleosome spacing enzymes (CHD1, ISW1 and ISW2) form arrays with different spacing. We used genome-wide nucleosome sequencing to determine whether these enzymes space nucleosomes differently in vivo. We find that CHD1 and ISW1 compete to set the spacing on most genes, such that CHD1 dominates genes with shorter spacing and ISW1 dominates genes with longer spacing. In contrast, ISW2 plays a minor role, limited to transcriptionally inactive genes. Heavily transcribed genes show weak phasing and extreme spacing, either very short or very long, and are depleted of linker histone (H1). Genes with longer spacing are enriched in H1, which directs chromatin folding. We propose that CHD1 directs short spacing, resulting in eviction of H1 and chromatin unfolding, whereas ISW1 directs longer spacing, allowing H1 to bind and condense the chromatin. Thus, competition between the two remodelers to set the spacing on each gene may result in a highly dynamic chromatin structure.

Genomic Nucleosome Organization Reconstituted with Pure Proteins

“…This resonates with in vivo derived notions that remodelers are important for nucleosome positioning (Badis et al, 2008; Ganguli et al, 2014; Gkikopoulos et al, 2011; Hartley and Madhani, 2009; Ocampo et al, 2016; Parnell et al, 2008; van Bakel et al, 2013), via dynamic competition of different remodeling activities (Ganguli et al, 2014; Ocampo et al, 2016; Parnell et al, 2015; Yen et al, 2012), but now establishes their direct, specific and sufficient contributions.…”

“…Inasmuch as different remodelers have distinct remodeling activities (Bartholomew, 2014), the relative amounts added were based on ATPase activity units, which all remodelers have in common. Similar to the respective in vivo phenotype (Gkikopoulos et al, 2011; Ocampo et al, 2016), this mutant extract failed to form well-aligned arrays (Figures 1A and 1B, graphs 3 vs. 4), although NFRs and +1 nucleosomes were largely reconstituted but with somewhat less accuracy (peak position, Figure 1C, Table S1) and precision (positioning strength, Table S2). Thus, formation of the NFR and +1 nucleosome is biochemically separable from array formation/alignment, and the former can be achieved in extracts without ISW1/2/Chd1 remodelers.…”

“…This allows individual assembly stages to be dissected, defined, and the role of individual factors deciphered. Current approaches involving in vivo removal or inactivation of one or a few nucleosome-related activities, for example chromatin remodeling enzymes, via genetic manipulation (Badis et al, 2008; Ganguli et al, 2014; Gkikopoulos et al, 2011; Hartley and Madhani, 2009; Ocampo et al., 2016; Parnell et al, 2008; van Bakel et al, 2013; Whitehouse et al, 2007; Yen et al, 2012) have proven insightful for identifying factors involved, their contribution, and their genomic binding locations. Critically, however, direct versus indirect roles are not readily apparent due to the ever-present complex milieu of cellular proteins, and so cannot identify the minimal set of components and core mechanisms that directly establish the primary structure of chromatin.…”

A unified computational framework for modeling genome-wide nucleosome landscape

“…To facilitate comparison between different datasets, we used a common template ${\hat{\beta }}_{\alpha /j}$ for all three datasets. This common template was constructed by combining average nucleotide frequencies of nucleosomes detected by MNase-seq [39] and chemical cleavage [40]; combining these two types of data help eliminate potential MNase digestion biases towards the ends of the nucleosome as well as chemical cleavage biases around the nucleosome dyad (Fig. S8 of Supplementary Material).…”

“…S12 of Supplementary Material), suggesting that CEM was consistently underestimating the nucleosome number in these datasets (Table S5 of Supplementary Material). Visually comparing predicted and observed nucleosome occupancy profiles showed that, for datasets where nucleosome number was significantly underestimated ( Kaplan-MNase-invivo-WT [3] and Ocampo-MNaseinvivo-WT [39]), CEM-predicted nucleosome occupancy was indeed missing a large fraction of nucleosomes evident from experimental data (Fig. S13 of Supplementary Material).…”