Detection and characterization of silencers and enhancer-blockers in the greater CFTR locus

Silencers and enhancer-blockers (EBs) are cis-acting, negative regulatory elements (NREs) that control interactions between promoters and enhancers. Although relatively uncharacterized in terms of biological mechanisms, these elements are likely to be abundant in the genome. We developed an experimental strategy to identify silencers and EBs using transient transfection assays. A known insulator and EB from the chicken beta-globin locus, cHS4, served as a control element for these assays. We examined 47 sequences from a 1.8-Mb region of human chromosome 7 for silencer and EB activities. The majority of functional elements displayed directional and promoter-specific activities. A limited number of sequences acted in a dual manner, as both silencers and EBs. We examined genomic data, epigenetic modifications, and sequence motifs within these regions. Strong silencer elements contained a novel CT-rich motif, often in multiple copies. Deletion of the motif from three regions caused a measurable loss of silencing ability in these sequences. Moreover, five duplicate occurrences of this motif were identified in the cHS4 insulator. These motifs provided an explanation for an uncharacterized silencing activity we measured in the insulator element. Overall, we identified 15 novel NREs, which contribute new insights into the prevalence and composition of sequences that negatively regulate gene expression

Evidence for multiple roles for grainyhead-like 2 in the establishment and maintenance of human mucociliary airway epithelium

Most of the airways of the human lung are lined by an epithelium made up of ciliated and secretory luminal cells and undifferentiated basal progenitor cells. The integrity of this epithelium and its ability to act as a selective barrier are critical for normal lung function. In other epithelia, there is evidence that transcription factors of the evolutionarily conserved grainyheadlike (GRHL) family play key roles in coordinating multiple cellular processes required for epithelial morphogenesis, differentiation, remodeling, and repair. However, only a few target genes have been identified, and little is known about GRHL function in the adult lung. Here we focus on the role of GRHL2 in primary human bronchial epithelial cells, both as undifferentiated progenitors and as they differentiate in air–liquid interface culture into an organized mucociliary epithelium with transepithelial resistance. Using a dominant-negative protein or shRNA to inhibit GRHL2, we follow changes in epithelial phenotype and gene transcription using RNA sequencing or microarray analysis. We identify several hundreds of genes that are directly or indirectly regulated by GRHL2 in both undifferentiated cells and air–liquid interface cultures. Using ChIP sequencing to map sites of GRHL2 binding in the basal cells, we identify 7,687 potential primary targets and confirm that GRHL2 binding is strongly enriched near GRHL2-regulated genes. Taken together, the results support the hypothesis that GRHL2 plays a key role in regulating many physiological functions of human airway epithelium, including those involving cell morphogenesis, adhesion, and motility

Genome-wide quantification of the effects of DNA methylation on human gene regulation

Changes in DNA methylation are involved in development, disease, and the response to environmental conditions. However, not all regulatory elements are functionally methylation-dependent (MD). Here, we report a method, mSTARR-seq, that assesses the causal effects of DNA methylation on regulatory activity at hundreds of thousands of fragments (millions of CpG sites) simultaneously. Using mSTARR-seq, we identify thousands of MD regulatory elements in the human genome. MD activity is partially predictable using sequence and chromatin state information, and distinct transcription factors are associated with higher activity in unmethylated versus methylated DNA. Further, pioneer TFs linked to higher activity in the methylated state appear to drive demethylation of experimentally methylated sites. MD regulatory elements also predict methylation-gene expression relationships across individuals, where they are 1.6x enriched among sites with strong negative correlations. mSTARR-seq thus provides a map of MD regulatory activity in the human genome and facilitates interpretation of differential methylation studies

Clustering gene expression time series data using an infinite Gaussian process mixture model

<div><p>Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at <a href="https://github.com/PrincetonUniversity/DP_GP_cluster" target="_blank">https://github.com/PrincetonUniversity/DP_GP_cluster</a>.</p></div

Clustered trajectories of differentially expressed transcripts in A549 cells in response to dex.

<p>For each cluster in (A–M), standardized log₂ fold change in expression from pre-dex exposure levels is shown for each transcript, and the posterior cluster mean and ±2 standard deviations according to the cluster-specific GP.</p

DPGP clusters in <i>H. salinarum</i> H₂O₂-exposed gene expression trajectories.

<p>(A–L) For each cluster, standardized log₂ fold change in expression from pre-exposure levels is shown for each gene as well as the posterior cluster mean ±2 standard deviations. Control strain clusters are on left and Δ<i>rosR</i> clusters on right, organized to relate the Δ<i>rosR</i> clusters that correspond to each control cluster. Note that control cluster 5 had no corresponding Δ<i>rosR</i> cluster, but transcripts in this cluster instead distribute to a variety of Δ<i>rosR</i> clusters, none of which had a majority of cluster 5 transcripts. (M) Heatmap displays the proportion of DPGP samples from the Markov chain in which each gene (on the rows and columns) clusters with every other gene in the control strain. Rows and columns were clustered by Ward’s linkage. The predominant blocks of elevated co-clustering are labeled with the control cluster numbers to which the genes that compose the majority of the block belong. As indicated, cluster 6 is dispersed across multiple blocks, primarily the blocks for clusters 3 and 5. (N) Same as (M), except that values are replaced by the proportions in the Δ<i>rosR</i> strain instead of the control strain. Rows and columns ordered as in (M).</p

Differences in TF binding and histone modification occupancy in A549 cells in control conditions for the four largest DPGP clusters.

<p>(A) Heatmap shows the elastic net logistic regression coefficients for the top twenty predictors (sorted by sum of absolute value across clusters) of cluster membership for the four largest clusters. Predictors were log₁₀ library size-normalized binned counts of ChIP-seq TF binding and histone modification occupancy in control conditions. Distance indicated in row names represents the bin of the predictor (e.g., <1 kb means within 1 kb of the TSS). An additional 23 predictors with smaller but non-zero coefficients are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005896#pcbi.1005896.s006" target="_blank">S6 Fig</a>. (B) Kernel density histogram smoothed with a Gaussian kernel and Scott’s bandwidth [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005896#pcbi.1005896.ref063" target="_blank">63</a>] of the TF binding and histone modification occupancy log₁₀ library size-normalized binned count matrix in control conditions transformed by the first principal component (PC1) for the two largest down-regulated DPGP clusters. (C) Same as (B), but with matrix transformed by PC2 and with the four largest DPGP clusters.</p