Genome-wide approaches to explore transcriptional regulation in eukaryotes

Transcriptional regulation is a complicated process controlled by numerous factors such as transcription factors (TFs), chromatin remodeling enzymes, nucleosomes, post-transcriptional machineries, and cis-acting DNA sequence. I explored the complex transcriptional regulation in eukaryotes through three distinct studies to comprehensively understand the functional genomics at various steps. Although a variety of high throughput approaches have been developed to understand this complex system on a genome wide scale with high resolution, a lack of accurate and comprehensive annotation transcription start sites (TSS) and polyadenylation sites (PAS) has hindered precise analyses even in Saccharomyces cerevisiae, one of the simplest eukaryotes. We developed Simultaneous Mapping Of RNA Ends by sequencing (SMORE-seq) and identified the strongest TSS and PAS of over 90% of yeast genes with single nucleotide resolution. Owing to the high accuracy of TSS identified by SMORE-seq, we detected possibly mis-annotated 150 genes that have a TSS downstream of the annotated start codon. Furthermore, SMORE-seq showed that 5’-capped non-coding RNAs were highly transcribed divergently from TATA-less promoters in wild-type cells under normal conditions. Mapping of DNA-protein interactions is essential to understanding the role of TFs in transcriptional regulation. ChIP-seq is the most widely used method for this purpose. However, careful attention has not been given to technical bias reflected in final target calling due to many experimental steps of ChIP-seq including fixation and shearing of chromatin, immunoprecipitation, sequencing library construction, and computational analysis. While analyzing large-scale ChIP-seq data, we observed that unrelated proteins appeared to bind to the gene bodies of highly transcribed genes across datasets. Control experiments including input, IgG ChIP in untagged cells, and the Golgi factor Mnn10 ChIP also showed the strong binding at the same loci, indicating that the signals were obviously derived from bias that is devoid of biological meaning. In addition, the appearance of nucleosomal periodicity in ChIP-seq data for proteins localizing to gene bodies is another bias that can be mistaken for false interactions with nucleosomes. We alleviated these biases by correcting data with proper negative controls, but the biases could not be completely removed. Therefore, caution is warranted in interpreting the results from ChIP-seq. Nucleosome positioning is another critical mechanism of transcriptional regulation. Global mapping of nucleosome occupancy in S. cerevisiae strains deleted for chromatin remodeling complexes has elucidated the role of these complexes on a genome wide scale. In this study, loss of chromodomain helicase DNA binding protein 1 (Chd1) resulted in severe disorganization of nucleosome positioning. Despite the difficulties of performing ChIP-seq for chromatin remodeling complexes due to their transient and dynamic localization on chromatin, we successfully mapped the genome-wide occupancy of Chd1 and quantitatively showed that Chd1 co-localizes with early transcription elongation factors, but not late transcription elongation factors. Interestingly, Chd1 occupancy was independent of the methylation levels at H3K36, indicating the necessity of a new working model describing Chd1 localization.Cellular and Molecular Biolog

Chd1 co-localizes with early transcription elongation factors independently of H3K36 methylation and releases stalled RNA polymerase II at introns

BACKGROUND: Chromatin consists of ordered nucleosomal arrays that are controlled by highly conserved adenosine triphosphate (ATP)-dependent chromatin remodeling complexes. One such remodeler, chromodomain helicase DNA binding protein 1 (Chd1), is believed to play an integral role in nucleosomal organization, as the loss of Chd1 is known to disrupt chromatin. However, the specificity and basis for the functional and physical localization of Chd1 on chromatin remains largely unknown. RESULTS: Using genome-wide approaches, we found that the loss of Chd1 significantly disrupted nucleosome arrays within the gene bodies of highly transcribed genes. We also found that Chd1 is physically recruited to gene bodies, and that its occupancy specifically corresponds to that of the early elongating form of RNA polymerase, RNAPII Ser 5-P. Conversely, RNAPII Ser 5-P occupancy was affected by the loss of Chd1, suggesting that Chd1 is associated with early transcription elongation. Surprisingly, the occupancy of RNAPII Ser 5-P was affected by the loss of Chd1 specifically at intron-containing genes. Nucleosome turnover was also affected at these sites in the absence of Chd1. We also found that deletion of the histone methyltransferase for H3K36 (SET2) did not affect either Chd1 occupancy or nucleosome organization genome-wide. CONCLUSIONS: Chd1 is specifically recruited onto the gene bodies of highly transcribed genes in an elongation-dependent but H3K36me3-independent manner. Chd1 co-localizes with the early elongating form of RNA polymerase, and affects the occupancy of RNAPII only at genes containing introns, suggesting a role in relieving splicing-related pausing of RNAPII. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1756-8935-7-32) contains supplementary material, which is available to authorized users

CTCF cooperates with CtIP to drive homologous recombination repair of double-strand breaks

The pleiotropic CCCTC-binding factor (CTCF) plays a role in homologous recombination (HR) repair of DNA double-strand breaks (DSBs). However, the precise mechanistic role of CTCF in HR remains largely unclear. Here, we show that CTCF engages in DNA end resection, which is the initial, crucial step in HR, through its interactions with MRE11 and CtIP. Depletion of CTCF profoundly impairs HR and attenuates CtIP recruitment at DSBs. CTCF physically interacts with MRE11 and CtIP and promotes CtIP recruitment to sites of DNA damage. Subsequently, CTCF facilitates DNA end resection to allow HR, in conjunction with MRE11-CtIP. Notably, the zinc finger domain of CTCF binds to both MRE11 and CtIP and enables proficient CtIP recruitment, DNA end resection and HR. The N-terminus of CTCF is able to bind to only MRE11 and its C-terminus is incapable of binding to MRE11 and CtIP, thereby resulting in compromised CtIP recruitment, DSB resection and HR. Overall, this suggests an important function of CTCF in DNA end resection through the recruitment of CtIP at DSBs. Collectively, our findings identify a critical role of CTCF at the first control point in selecting the HR repair pathway

Molecular understanding of the serum antibody repertoires after seasonal influenza vaccination among different age cohorts

Numerous influenza vaccination studies based on bulk serology have indicated that the antibody responses to the vaccine markedly decrease in the elderly. However, whether such decline results from the changes in the overall quantity or the quality of the circulating antibodies in serum remains unknown. Utilizing novel antibody repertoire profiling technologies, combining tandem mass spectrometry (LC-MS/MS) and high-throughput sequencing, we investigated the influenza-specific serological repertoires of 10 donors ranging from 26 to 70 years old vaccinated with Fluzone® 2013-2014 and/or 2014-2015. In particular, we determined the serum antibodies that are specific to the H1 or H3 component of the vaccine or cross-reactive between the two (H1+H3) and examined their relative quantitative distributions. Our data indicate that the proportion of H1+H3 antibodies significantly increases in the elderly and that the somatic hypermutation rates of the influenza-specific antibodies are higher in the elderly. These results suggest that the repeated exposure to the different virus subtypes could have led to the prolonged selection of H1+H3 antibodies targeting highly conserved epitopes. To evaluate the potency of the antibodies circulating in different age groups, we recombinantly expressed a number of representative monoclonal antibodies isolated from the donors in different age groups for further characterizations. Overall, our analysis suggests that the influenza-specific repertoire in the elderly may converge toward shared epitopes but the quality of the antibodies can be superior in terms of cross-reactivity. However, because the antibody repertoire “shrinks” as we age while targeting more conserved epitopes across different influenza subtypes, it is possible that the elderly is particularly susceptible to significantly altered strains. Collectively, profiling vaccine induced serological repertoires among different age cohorts can provide unprecedented insights regarding humoral immunity associated with age and a potential explanation for the vulnerability of the elderly

Widespread misinterpretable ChIP-seq bias in yeast.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to detect genome-wide interactions between a protein of interest and DNA in vivo. Loci showing strong enrichment over adjacent background regions are typically considered to be sites of binding. Insufficient attention has been given to systematic artifacts inherent to the ChIP-seq procedure that might generate a misleading picture of protein binding to certain loci. We show here that unrelated transcription factors appear to consistently bind to the gene bodies of highly transcribed genes in yeast. Strikingly, several types of negative control experiments, including a protein that is not expected to bind chromatin, also showed similar patterns of strong binding within gene bodies. These false positive signals were evident across sequencing platforms and immunoprecipitation protocols, as well as in previously published datasets from other labs. We show that these false positive signals derive from high rates of transcription, and are inherent to the ChIP procedure, although they are exacerbated by sequencing library construction procedures. This expression bias is strong enough that a known transcriptional repressor like Tup1 can erroneously appear to be an activator. Another type of background bias stems from the inherent nucleosomal structure of chromatin, and can potentially make it seem like certain factors bind nucleosomes even when they don't. Our analysis suggests that a mock ChIP sample offers a better normalization control for the expression bias, whereas the ChIP input is more appropriate for the nucleosomal periodicity bias. While these controls alleviate the effect of the biases to some extent, they are unable to eliminate it completely. Caution is therefore warranted regarding the interpretation of data that seemingly show the association of various transcription and chromatin factors with highly transcribed genes in yeast

Example of high background signal across multiple datasets.

<p>Sequencing datasets from different factors, controls, epitope tags, transcription factors and growth conditions as indicated are represented in a browser view. Based on the read counts normalized by transcript lengths from RNA-seq data [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083506#B44" target="_blank">44</a>], PHO84 is the 82^nd most highly expressed gene under normal conditions in WT yeast.</p

Deep RNA-Seq analysis reveals unexpected features of human prostate basal epithelial cells

Prostate cancer is the second leading cause of cancer-related deaths among American men [1]. The prostate gland mainly contains basal and luminal cells, which are constructed as a pseudostratified epithelium. Annotation of prostate epithelial transcriptomes provides a foundation for discoveries that can impact disease understanding and treatment. Here, for the first time, we describe a whole-genome transcriptome analysis of human benign prostatic basal and luminal populations by using deep RNA sequencing (GSE67070) [2]. Combined with comprehensive molecular and biological characterizations, we show that the differential gene expression profiles account for their distinct functional phenotypes. Strikingly, in contrast to luminal cells, basal cells preferentially express gene categories associated with stem cells, neural and neuronal development, and RNA processing. Of clinical relevance, the treatment failed castration-resistant and anaplastic prostate cancers molecularly resemble a basal-like phenotype. We also identified genes associated with patient clinical outcome. Therefore, we provide a gene expression resource for understanding human prostate epithelial lineages, and link the cell-type specific gene signatures to subtypes of prostate cancer development. Keywords: Prostate epithelial cells, Basal cells, Luminal cells, RNA-se

Genes with high transcription rates (TR) have high average read counts within gene bodies in ChIP-seq and control experiments.

<p>Lines show average read counts in 10 bp bins for the indicated groups of genes, which are either the 100 most highly transcribed genes based on RNAPII Ser5P occupancy as described in the text (High TR genes, red line) or all the other genes (All ORFs, blue line). The shaded bands represent the 95% confidence interval of the data. All ChIP samples in this figure were sequenced using the Illumina platform. (A) Under normal growth conditions (30°C in YPD), mock ChIP had comparable bias to Swi6 ChIP. (B) Both SWI6 (an activator) and TUP1 (a repressor) show comparable high levels of the expression bias at high TR genes. (C) Input has a lower expression bias than mock ChIP. For (B) and (C) cells were treated with DMSO, which was a control for rapamycin treatment.</p

Uncorrected Tup1 differential binding targets misleadingly indicate that Tup1 is primarily a transcriptional activator.

<p>Scatter plots show the differential transcriptional activation after rapamycin treatment as blue points. Differential RNAPII Ser5P occupancy before and after rapamycin treatment was measured by ChIP-seq and plotted on the X-axis. Differential mRNA expression levels in the same cultures were measured using microarrays and plotted on the Y-axis, in scatter plots showing 4929 genes. We used MACS to identify differential binding targets (DBTs) of Tup1 as described in the text and plotted them on the same plots in red. (A) The top 100 DBT peaks ranked by fold change were assigned to 55 ORFs, which are plotted in red. (B) The top 500 DBT peaks were assigned to 295 ORFs, which are plotted in red. Tup1 DBT ORFs tended to be upregulated genes in response to rapamycin.</p