The histone variants H3.3 and H2A.Z have recently emerged as two of the most important features in transcriptional regulation, the molecular mechanism of which still remains poorly understood. In this study, we investigated the regulation of H3.3 and H2A.Z on chromatin dynamics during transcriptional activation. Our in vitro biophysical and biochemical investigation showed that H2A.Z promoted chromatin compaction and repressed transcriptional activity. Surprisingly, with only four to five amino acid differences from the canonical H3, H3.3 greatly impaired higher-ordered chromatin folding and promoted gene activation, although it has no significant effect on the stability of mononucleosomes. We further demonstrated that H3.3 actively marks enhancers and determines the transcriptional potential of retinoid acid (RA)-regulated genes via creating an open chromatin signature that enables the binding of RAR/RXR. Additionally, the H3.3-dependent recruitment of H2A.Z on promoter regions resulted in compaction of chromatin to poise transcription, while RA induction results in the incorporation of H3.3 on promoter regions to activate transcription via counteracting H2A.Z-mediated chromatin compaction. Our results provide key insights into the mechanism of how histone variants H3.3 and H2A.Z function together to regulate gene transcription via the modulation of chromatin dynamics over the enhancer and promoter regions.

The hierarchical organization of eukaryotic chromatin plays a central role in gene regulation, by controlling the extent to which the transcription machinery can access DNA. The histone variants H3.3 and H2A.Z have recently been identified as key regulatory players in this process, but the underlying molecular mechanisms by which they permit or restrict gene expression remain unclear. Here, we investigated the regulatory function of H3.3 and H2A.Z on chromatin dynamics and Polycomb-mediated gene silencing. Our ChIP-seq analysis reveals that in mouse embryonic stem (mES) cells, H3K27me3 enrichment correlates strongly with H2A.Z. We further demonstrate that H2A.Z promotes PRC2 activity on H3K27 methylation through facilitating chromatin compaction both in vitro and in mES cells. In contrast, PRC2 activity is counteracted by H3.3 through impairing chromatin compaction. However, a subset of H3.3 may positively regulate PRC2-dependent H3K27 methylation via coordinating depositions of H2A.Z to developmental and signaling genes in mES cells. Using all-trans retinoic acid (tRA)-induced gene as a model, we show that the dynamic deposition of H2A.Z and H3.3 coordinately regulates the PRC2-dependent H3K27 methylation by modulating local chromatin structure at the promoter region during the process of turning genes off. Our study provides key insights into the mechanism of how histone variants H3.3 and H2A.Z function coordinately to finely tune the PRC2 enzymatic activity during gene silencing, through promoting or impairing chromosome compaction respectively.

We propose differential principal component analysis (dPCA) for analyzing multiple ChIP- sequencing datasets to identify differential protein–DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein–DNA interactions.

The driver genes play critical roles for tumorigenesis, and the number of identified driver genes reached plateau. But how they act during different cancer development stages is lack of knowledge. We investigated 138 driver genes’ mutation changes across clinical stages using 3,477 cases in nine cancer types from the Cancer Genome Atlas (TCGA) and constructed their temporal order relationships. We also examined the codon changes for the widely mutated TP53 and PIK3CA in tumor stages. Combinations of one to three driver genes specifically dominated in each cancer. Across the clinical stages, we categorized three patterns for the behaviors of driver genes’ mutation changes in the nine cancer types: recurrently mutated in all the stages and triggering other mutations; certain mutations lost meanwhile other mutations emerged; mutations dominated across entire stages, while other mutations gradually appeared or disappeared. We observed different codon changes dominated in different stages and revealed mutations recurrently occurring on the hotspot regions of the coding sequence may be the core factor for driver genes’ tumorigenesis. Our results highlighted the dynamic changes of oncogenesis roles in different clinical stages and suggested different diagnostic decision making according to the clinical stages of patients.

Background: ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles. Results: We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately. Conclusions: iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB.