Function annotation of hepatic retinoid x receptor α based on genome-wide DNA binding and transcriptome profiling.

BackgroundRetinoid x receptor α (RXRα) is abundantly expressed in the liver and is essential for the function of other nuclear receptors. Using chromatin immunoprecipitation sequencing and mRNA profiling data generated from wild type and RXRα-null mouse livers, the current study identifies the bona-fide hepatic RXRα targets and biological pathways. In addition, based on binding and motif analysis, the molecular mechanism by which RXRα regulates hepatic genes is elucidated in a high-throughput manner.Principal findingsClose to 80% of hepatic expressed genes were bound by RXRα, while 16% were expressed in an RXRα-dependent manner. Motif analysis predicted direct repeat with a spacer of one nucleotide as the most prevalent RXRα binding site. Many of the 500 strongest binding motifs overlapped with the binding motif of specific protein 1. Biological functional analysis of RXRα-dependent genes revealed that hepatic RXRα deficiency mainly resulted in up-regulation of steroid and cholesterol biosynthesis-related genes and down-regulation of translation- as well as anti-apoptosis-related genes. Furthermore, RXRα bound to many genes that encode nuclear receptors and their cofactors suggesting the central role of RXRα in regulating nuclear receptor-mediated pathways.ConclusionsThis study establishes the relationship between RXRα DNA binding and hepatic gene expression. RXRα binds extensively to the mouse genome. However, DNA binding does not necessarily affect the basal mRNA level. In addition to metabolism, RXRα dictates the expression of genes that regulate RNA processing, translation, and protein folding illustrating the novel roles of hepatic RXRα in post-transcriptional regulation

Distinct transcription kinetics of pluripotent cell states

Mouse embryonic stem cells (mESCs) can adopt naïve, ground, and paused pluripotent states that give rise to unique transcriptomes. Here, we use transient transcriptome sequencing (TT-seq) to define both coding and non-coding transcription units (TUs) in these three pluripotent states and combine TT-seq with RNA polymerase II occupancy profiling to unravel the kinetics of RNA metabolism genome-wide. Compared to the naïve state (serum), RNA synthesis and turnover rates are globally reduced in the ground state (2i) and the paused state (mTORi). The global reduction in RNA synthesis goes along with a genome-wide decrease of polymerase elongation velocity, which is related to epigenomic features and alterations in the Pol II termination window. Our data suggest that transcription activity is the main determinant of steady state mRNA levels in the naïve state and that genome-wide changes in transcription kinetics invoke ground and paused pluripotent states

Transcription kinetics in pluripotent cells : RNA turnover, transcription velocity, and epigenomic regulation

Transcriptional regulation is one of the primary steps in gene expression control. It is now appreciated that a large fraction of coding genome is transcribed in concert of other functional RNAs. A quantitative method for transient transcriptome sequencing (TT-seq) allows profiling of entire transcriptional activities, de novo transcription unit (TU) annotation, and estimation of transcription kinetics from initiation to termination. In Paper I, we showed the establishment of TT-seq method in mouse embryonic stem cells (mESCs) to understand transcriptome plasticity for both coding and non-coding RNAs. With external references in form of a spike-in RNA mix, we were able to estimated RNA synthesis and turnover rates, which consolidated the attenuation under inhibitor-induced pluripotent states (naïve 2i and paused mTORi). We also extended the estimation of transcription velocity to each annotated TU, by integration of RNA polymerase II (Pol II) quantitative profiles from MINUTE-ChIP (quantitative multiplexed ChIP). After explaining transcription velocity with chromatin features, we also evaluated its genome-wide contribution to termination distance. In Paper II, we mapped endogenous genomic G-quadraplex structures (G4) with CUT&Tag in HEK293T and mESCs. We verified the high signal-to-ratio G4 peaks to reflect the DNA motifs of both canonical and trans-strand putative quadraplex sequences (PQS), which enriched on both gene and active enhancer TSSs (transcription start sites). After stabilizing G4 with the small molecule PDS, we observed a genome-wide reduction of RNA synthesis (by TT-seq). The co-occupancy of G4 and R-loop was further verified at transcribed promoters and enhancers. However, promoter G4s could consistently form after transcription inhibition, which suggests an intricate cause-consequence relationship between G4 and transcription activity. In Paper III, we evaluated the regulatory role of repressive histone modifications, H2AK119 ubiquitination and H3K27 tri-methylation. We introduced a rapid H2Aub depletion by BAP1 pulse expression with the amber-suppression system, and observed a wide Polycomb target genes de-repression, especially in the bivalent chromatin state (H3K4me3 + H3K27me3). Further, we observed that H2Aub-mediated repression strength was associated with H3K27me3 occupancy. However, double depletion of H3K27me3 by Ezh2 inhibition with ectopic BAP1 failed to enlarge Polycomb genes de-repression. We also measured transcriptional responses with TT-seq and observed that H2Aub depletion immediately triggered transcription activation before the redistribution of Polycomb proteins and their associated nucleosomes decompaction. Together, our results indicate that H2Aub directly mediates Polycomb integrity and nucleosome barrier that limits early transcription checkpoints

RNA polymerase V targets transcriptional silencing components to promoters of protein‐coding genes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/1/tpj12034-sup-0010-TableS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/2/tpj12034-sup-0006-FigureS4.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/3/tpj12034-sup-0007-FigureS5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/4/tpj12034-sup-0003-FigureS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/5/tpj12034-sup-0008-FigureS6.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/6/tpj12034-sup-0005-FigureS3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/7/tpj12034-sup-0004-FigureS2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/8/tpj12034-sup-0009-FigureS7.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/9/tpj12034.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/96338/10/tpj12034-sup-0002-MethodsS1.pd

The scent of genome complexity: exploring genomic instability in mouse Olfactory Epithelium

In the olfactory epithelium (OE) the detection of volatile compounds (odors) is accomplished by a large family of olfactory receptors (ORs), located on the surface of the cilia of olfactory sensory neurons (OSNs). These represent the major sensory component of the OE and reside in the nasal cavity. The extraordinary chemical diversity of olfactory ligands is matched in the mouse genome by a collection of more than 1200 mouse and 350 human active OR genes encoding for G-protein-coupled receptors (GPCRs). Each mature OSN in the OE is thought to express only one allele of a single OR gene (monoallelic and monogenic expression). A given OR gene is expressed in a mosaic or punctate pattern of OSNs within a characteristic zone of the OE. The transcriptional mechanisms that underlie this extraordinarily tight regulation of gene expression remain unclear. I hypothesize that OR expression choice can be influenced by somatic LINE-1- associated genomic variations. Indeed, it is now well established that active LINE-1s can create genomic rearrangements at insertional and post-insertional stages. Besides promoting genome plasticity and diversification during evolution, somatic variations can contribute to gene expression regulation for those genes that are characterized by a stochastic and monoallelic expression.Under this hypothesis, I expect the genomic sequence around the expressed ORs to be different with respect to that around the same ORs in non-expressing cells, for the presence of variations able to activate chromatin and promote ORs transcription. I first showed high LINE-1 expression and retrotransposition in OE. Then I investigated the presence and involvement of LINE-1-associated variations with OR expression, comparing the genomic sequence around an active and an inactive OR locus. In particular, I analyzed a genomic region of 50 kb around the Olfr2 TSS taking advantage of a GFP knock-in mouse. In these mice, the OSNs naturally expressing Olfr2 co-express also GFP. Targeted sequencing of Olfr2 locus revealed hundreds of heterozygous structural variants (insertions, deletions, inversions and duplications) in the vicinity of the locus. Deletions were the most abundant variation category.By end point PCR I validated six LINE-1 associated deletions potentially involved in Olfr2 expression. Nevertheless, functional validation experiments in vivo will be performed to prove their effective role in Olfr2 choice. Looking at the putative mechanisms supporting the deletions, I started investigating a possible involvement of DSBs. With this aim, I performed a chromatin immunoprecipitation and sequencing (ChIP-Seq) analysis for endogenous gamma-H2AX (an early response marker for DNA-DSBs) in mouse OE and liver. I performed a general characterization of endogenous gamma-H2AX in normal tissues. In both tissues analyzed, gamma- H2AX signal was not randomly distributed in the genome but preferentially localized within transcribed and regulatory regions. Overall, gamma-H2AX peaks were depleted in the OR clusters. Interestingly, an exception was given by a peak located within the Olfr2 locus, in close proximity to two validated deleted regions

Transcriptional regulatory logic of the diurnal cycle in the mouse liver.

Many organisms exhibit temporal rhythms in gene expression that propel diurnal cycles in physiology. In the liver of mammals, these rhythms are controlled by transcription-translation feedback loops of the core circadian clock and by feeding-fasting cycles. To better understand the regulatory interplay between the circadian clock and feeding rhythms, we mapped DNase I hypersensitive sites (DHSs) in the mouse liver during a diurnal cycle. The intensity of DNase I cleavages cycled at a substantial fraction of all DHSs, suggesting that DHSs harbor regulatory elements that control rhythmic transcription. Using chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq), we found that hypersensitivity cycled in phase with RNA polymerase II (Pol II) loading and H3K27ac histone marks. We then combined the DHSs with temporal Pol II profiles in wild-type (WT) and Bmal1-/- livers to computationally identify transcription factors through which the core clock and feeding-fasting cycles control diurnal rhythms in transcription. While a similar number of mRNAs accumulated rhythmically in Bmal1-/- compared to WT livers, the amplitudes in Bmal1-/- were generally lower. The residual rhythms in Bmal1-/- reflected transcriptional regulators mediating feeding-fasting responses as well as responses to rhythmic systemic signals. Finally, the analysis of DNase I cuts at nucleotide resolution showed dynamically changing footprints consistent with dynamic binding of CLOCK:BMAL1 complexes. Structural modeling suggested that these footprints are driven by a transient heterotetramer binding configuration at peak activity. Together, our temporal DNase I mappings allowed us to decipher the global regulation of diurnal transcription rhythms in the mouse liver

A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels

RNA Polymerase II Pausing Downstream of Core Histone Genes Is Different from Genes Producing Polyadenylated Transcripts

Recent genome-wide chromatin immunoprecipitation coupled high throughput sequencing (ChIP-seq) analyses performed in various eukaryotic organisms, analysed RNA Polymerase II (Pol II) pausing around the transcription start sites of genes. In this study we have further investigated genome-wide binding of Pol II downstream of the 3′ end of the annotated genes (EAGs) by ChIP-seq in human cells. At almost all expressed genes we observed Pol II occupancy downstream of the EAGs suggesting that Pol II pausing 3′ from the transcription units is a rather common phenomenon. Downstream of EAGs Pol II transcripts can also be detected by global run-on and sequencing, suggesting the presence of functionally active Pol II. Based on Pol II occupancy downstream of EAGs we could distinguish distinct clusters of Pol II pause patterns. On core histone genes, coding for non-polyadenylated transcripts, Pol II occupancy is quickly dropping after the EAG. In contrast, on genes, whose transcripts undergo polyA tail addition [poly(A)+], Pol II occupancy downstream of the EAGs can be detected up to 4–6 kb. Inhibition of polyadenylation significantly increased Pol II occupancy downstream of EAGs at poly(A)+ genes, but not at the EAGs of core histone genes. The differential genome-wide Pol II occupancy profiles 3′ of the EAGs have also been confirmed in mouse embryonic stem (mES) cells, indicating that Pol II pauses genome-wide downstream of the EAGs in mammalian cells. Moreover, in mES cells the sharp drop of Pol II signal at the EAG of core histone genes seems to be independent of the phosphorylation status of the C-terminal domain of the large subunit of Pol II. Thus, our study uncovers a potential link between different mRNA 3′ end processing mechanisms and consequent Pol II transcription termination processes

Bioinformatics analysis of multi-omics data elucidates U2 snRNP function in transcription

Transcription by RNA polymerase II (Pol II) is an important step in cell function and regulation. Pol II transcription has been shown to be coupled to pre-mRNA splicing, but the underlying mechanisms remain poorly understood. Co-transcriptional splicing requires the assembly of a functional spliceosome on nascent pre-mRNA, but whether and how this influences Pol II transcription remains unclear. To investigate this, we used a human erythroleukemic cell line and performed transient transcriptome sequencing (TT-seq) and mammalian native elongating transcript sequencing (mNET-seq) upon fast inhibition of U2 snRNP function. We further studied how the positive transcription elongation factor b (P-TEFb) recruitment is related to the Pol II pause duration, using chromatin immunoprecipitation and sequencing (ChIP-seq) of the PTEF-b kinase cyclin T1 (CycT1) upon U2 snRNP inhibition. I performed a bioinformatics analysis of the different datasets generated for this study and two additional published datasets. I also conducted a multiomics analysis combining TT-seq and mNET-seq data to calculate and quantify transcription kinetic parameters such as Pol II productive initiation frequency, pause duration and elongation velocity. Here we show that inhibition of pre-mRNA branch site recognition by the spliceosome component U2 snRNP leads to a widespread and strong decrease in new RNA synthesis from human genes. We further show that inhibition of U2 snRNP function increases the duration of Pol II pausing in the promoter-proximal region, impairs recruitment of the pause release factor P-TEFb, and reduces Pol II elongation velocity at the beginning of genes. Our results indicate that efficient release of paused Pol II into active transcription elongation requires the formation of functional spliceosomes and that eukaryotic mRNA biogenesis relies on positive feedback from the splicing machinery to the transcription machinery. We further show that the fast U2 snRNP inhibition affects the expression of genes related to RNA synthesis and it is not related to stress response genes. Our new multi-omics approach for the calculation of Pol II elongation velocity can be applied to further study the impact on Pol II kinetics regarding different splicing and transcription factors. This is of great importance to unravel the mechanisms behind Pol II transcription and splicing and understand how the disruption of this regulation leads to several pathological cell phenotypes and diseases.2021-08-2

Genome-wide mapping of RNA Pol-II promoter usage in mouse tissues by ChIP-seq

Alternative promoters that are differentially used in various cellular contexts and tissue types add to the transcriptional complexity in mammalian genome. Identification of alternative promoters and the annotation of their activity in different tissues is one of the major challenges in understanding the transcriptional regulation of the mammalian genes and their isoforms. To determine the use of alternative promoters in different tissues, we performed ChIP-seq experiments using antibody against RNA Pol-II, in five adult mouse tissues (brain, liver, lung, spleen and kidney). Our analysis identified 38 639 Pol-II promoters, including 12 270 novel promoters, for both protein coding and non-coding mouse genes. Of these, 6384 promoters are tissue specific which are CpG poor and we find that only 34% of the novel promoters are located in CpG-rich regions, suggesting that novel promoters are mostly tissue specific. By identifying the Pol-II bound promoter(s) of each annotated gene in a given tissue, we found that 37% of the protein coding genes use alternative promoters in the five mouse tissues. The promoter annotations and ChIP-seq data presented here will aid ongoing efforts of characterizing gene regulatory regions in mammalian genomes