Bayesian network analysis of targeting interactions in chromatin

Abstract

In eukaryotes, many chromatin proteins together regulate gene expression. Chromatin proteins often direct the genomic binding pattern of other chromatin proteins, for example, by recruitment or competition mechanisms. The network of such targeting interactions in chromatin is complex and still poorly understood. Based on genome-wide binding maps, we constructed a Bayesian network model of the targeting interactions among a broad set of 43 chromatin components in Drosophila cells. This model predicts many novel functional relationships. For example, we found that the homologous proteins HP1 and HP1C each target the heterochromatin protein HP3 to distinct sets of genes in a competitive manner. We also discovered a central role for the remodeling factor Brahma in the targeting of several DNA-binding factors, including GAGA factor, JRA, and SU(VAR)3-7. Our network model provides a global view of the targeting interplay among dozens of chromatin components.

Figure 1.

Chromosomal maps of 43 chromatin components. Example showing the binding levels (log₂ ratios transformed to Z-scores) of 43 Drosophila chromatin components along 300 genes in the proximal part of chromosome 2R. Rows were arranged by genome-wide hierarchical clustering, as shown by the tree to the left of the binding data.

Figure 2.

Bayesian Network model BN₈₀ of the targeting interactions between 43 chromatin components. Nodes represent chromatin components; edges represent predicted targeting interactions with a bootstrap score (combined for both directions) of at least 80%. The size of each arrowhead is proportional to the bootstrap score of the targeting interaction in the corresponding direction.

Figure 3.

Experimental validation of predicted HP3 and HP6 targeting interactions. Density plots (“smoothed histograms”) showing the changes in binding levels of HP3 (A) and HP6 (B) after RNAi knockdown of HP1, compared to a control knockdown. Changes in binding are shown for subsets of genes as indicated. The y-axes show relative frequency (plots in each panel are normalized to have the same surface area). Binding data after knockdown are from (Greil et al. 2007). Nontargets are bound by neither HP1 nor HP1C. P-values are according to two-sided Wilcoxon tests. (C) Cartoon depicting the competitive targeting of HP3 by HP1 and HP1C, as well as the targeting of HP6 by HP1.

Figure 4.

BRM mediates targeting of GAF, JRA, and SU(VAR)3-7. (A) Western blot of BRM protein after RNAi knockdown of brm. RNAi of white (w), a gene that is not expressed in Kc cells, served as a control. Asterisk marks a nonspecific band. Lamin was probed as a loading control. (B–D) Changes in binding levels of GAF, JRA, and SU(VAR)3-7 at BRM target genes (black) and nontarget genes (gray) upon knockdown of brm, relative to control knockdown. (E) No significant changes in HP1 relocation after brm knockdown. (F) brm knockdown does not affect SU(VAR)3-7 binding at heterochromatic genes, defined as HP1 target genes. P-values are according to Wilcoxon tests.

Figure 5.

BRM promotes the interaction of JRA and GAF with their binding motifs through enhancement of chromatin accessibility. (A) Depletion of BRM causes a reduction in the correlation between binding and recognition motif occurrence for GAF and JRA, indicating that BRM enhances binding of these DBFs to their recognition motifs. cntrl, control knockdown; brm kd, brm knockdown. (B) Depletion of BRM causes a slight decrease in chromatin accessibility at BRM target genes. Depicted are changes in accessibility per DpnI-fragment that overlaps with indicated target or nontarget reporters of microarrays used for DamID profiles. (C) Same as B, but for GAF and JRA target genes that are either BRM targets or nontargets. (D) Cartoon model depicting the targeting of GAF through the combined action of BRM and GAGA motifs. A similar model may apply to JRA and SU(VAR)3-7. P-values are according to a test for difference between two dependent correlations (Chen and Popovich 2002) (A) or two-sided Wilcoxon tests (B,C).

Figure 6.

Examples of compartmentalization of regulatory function in the chromatin network. (A) BN₈₀ with the same layout as in Figure 2, with nodes colored according to the mean expression level (in Kc cells) of the target genes of each chromatin component. (B,C) Same as in A, but node colors depicting enrichment (yellow) or depletion (blue) of genes that are expressed in embryonic trunk mesoderm (B) or yolk cells (C). Node sizes in A–C depict the statistical significance of the observed expression level (A, two-sided Wilcoxon test) or the observed enrichment or depletion (B,C, two-sided binomial test), ranging from P > 10⁻³ (smallest nodes) to P ≤ 10⁻⁸ (largest nodes). Tissue expression data in B and C are from (Tomancak et al. 2007).