Transcription Start Site Data Research Articles

Position-dependent function of human sequence-specific transcription factors

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1–3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF’s distinct functional profiles—many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.

TE-TSS: an integrated data resource of human and mouse transposable element (TE)-derived transcription start site (TSS).

Transposable elements (TEs) are abundant in the genome and serve as crucial regulatory elements. Some TEs function as epigenetically regulated promoters, and these TE-derived transcription start sites (TSSs) play a crucial role in regulating genes associated with specific functions, such as cancer and embryogenesis. However, the lack of an accessible database that systematically gathers TE-derived TSS data is a current research gap. To address this, we established TE-TSS, an integrated data resource of human and mouse TE-derived TSSs (http://xozhanglab.com/TETSS). TE-TSS has compiled 2681 RNA sequencing datasets, spanning various tissues, cell linesand developmental stages. From these, we identified 5768 human TE-derived TSSs and 2797 mouse TE-derived TSSs, with 47% and 38% being experimentally validated, respectively. TE-TSS enables comprehensive exploration of TSS usage in diverse samples, providing insights into tissue-specific gene expression patterns and transcriptional regulatory elements. Furthermore, TE-TSS compares TE-derived TSS regions across 15 mammalian species, enhancing our understanding of their evolutionary and functional aspects. The establishment of TE-TSS facilitates further investigations into the roles of TEs in shaping the transcriptomic landscape and offers valuable resources for comprehending their involvement in diverse biological processes.

TSSr: an R package for comprehensive analyses of TSS sequencing data.

Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5′end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr.

Extensive Reannotation of the Genome of the Model Streptomycete Streptomyces lividans TK24 Based on Transcriptome and Proteome Information

Streptomyces lividans TK24 is a relevant Gram-positive soil inhabiting bacterium and one of the model organisms of the genus Streptomyces. It is known for its potential to produce secondary metabolites, antibiotics, and other industrially relevant products. S. lividans TK24 is the plasmid-free derivative of S. lividans 66 and a close genetic relative of the strain Streptomyces coelicolor A3(2). In this study, we used transcriptome and proteome data to improve the annotation of the S. lividans TK24 genome. The RNA-seq data of primary 5′-ends of transcripts were used to determine transcription start sites (TSS) in the genome. We identified 5,424 TSS, of which 4,664 were assigned to annotated CDS and ncRNAs, 687 to antisense transcripts distributed between 606 CDS and their UTRs, 67 to tRNAs, and 108 to novel transcripts and CDS. Using the TSS data, the promoter regions and their motifs were analyzed in detail, revealing a conserved -10 (TAnnnT) and a weakly conserved -35 region (nTGACn). The analysis of the 5′ untranslated region (UTRs) of S. lividans TK24 revealed 17% leaderless transcripts. Several cis-regulatory elements, like riboswitches or attenuator structures could be detected in the 5′-UTRs. The S. lividans TK24 transcriptome contains at least 929 operons. The genome harbors 27 secondary metabolite gene clusters of which 26 could be shown to be transcribed under at least one of the applied conditions. Comparison of the reannotated genome with that of the strain Streptomyces coelicolor A3(2) revealed a high degree of similarity. This study presents an extensive reannotation of the S. lividans TK24 genome based on transcriptome and proteome analyses. The analysis of TSS data revealed insights into the promoter structure, 5′-UTRs, cis-regulatory elements, attenuator structures and novel transcripts, like small RNAs. Finally, the repertoire of secondary metabolite gene clusters was examined. These data provide a basis for future studies regarding gene characterization, transcriptional regulatory networks, and usage as a secondary metabolite producing strain.

PD-1 Imposes Qualitative Control of Cellular Transcriptomes in Response to T Cell Activation.

Targeted blockade of programmed cell death 1 (PD-1), an immune-checkpoint receptor that inhibits Tcell activation, provides clinical benefits in various cancers. However, how PD-1 modulates gene expression in Tcells remains enigmatic. Here weinvestigated how PD-1 affects transcriptome changes induced by Tcell receptor (TCR) activation. Intriguingly, we identified a huge variance in PD-1 sensitivity among TCR-inducible genes. When we quantified the half maximal effective concentration (EC50) as the relationship between change in gene expression and TCR signal strength, we found that genes associated with survival and proliferation were efficiently expressed upon TCR activation and resistant to PD-1-mediated inhibition. Conversely, genes encoding cytokines and effector molecules were expressed less efficiently and sensitive to PD-1-mediated inhibition. We further demonstrated that transcription factor binding motifs and CpG frequency in the promoter region affect EC50 and thus the PD-1 sensitivity of genes. Our findings explain how PD-1, dependent on the TCR signal strength, calibrates cellular transcriptomes to shape functional properties of Tcell populations.

Genome-wide RNA pol II initiation and pausing in neural progenitors of the rat

BackgroundGlobal RNA sequencing technologies have revealed widespread RNA polymerase II (Pol II) transcription outside of gene promoters. Small 5′-capped RNA sequencing (Start-seq) originally developed for the detection of promoter-proximal Pol II pausing has helped improve annotation of Transcription Start Sites (TSSs) of genes as well as identification of non-genic regulatory elements. However, apart from the most well studied genomes of human and mouse, mammalian transcription has not been profiled with sufficiently high precision.ResultsWe prepared and sequenced Start-seq libraries from rat (Rattus norgevicus) primary neural progenitor cells. Over 48 million uniquely mappable reads from two independent biological replicates allowed us to define the TSSs of 7365 known genes in the rn6 genome, reannotating 2503 TSSs by more than 5 base pairs, characterize promoter-associated antisense transcription, and profile Pol II pausing. By combining TSS data with polyA-selected RNA sequencing, we also identified thousands of potential new genes producing stable RNA as well as non-genic transcripts representing possible regulatory elements.ConclusionsOur study has produced the first Start-seq dataset for the rat. Apart from profiling transcription initiation, our data reaffirm the prevalence of Pol II pausing across the rat genome and indicate conservation of pausing mechanisms across metazoan genomes. We suggest that pausing location, at least in mammals, is constrained by a distance from initiation of transcription, whether it occurs at or outside of a gene promoter. Abundant antisense transcription initiation around protein coding genes indicates that Pol II recruited to the vicinity of a promoter is distributed to available start sites of transcription at either DNA strand. Transcriptome profiling of neural progenitors presented here will facilitate further studies of other rat cell types as well as other organisms.

Transcriptome sequencing of the human pathogen Corynebacterium diphtheriae NCTC 13129 provides detailed insights into its transcriptional landscape and into DtxR-mediated transcriptional regulation

BackgroundThe human pathogen Corynebacterium diphtheriae is the causative agent of diphtheria. In the 1990s a large diphtheria outbreak in Eastern Europe was caused by the strain C. diphtheriae NCTC 13129. Although the genome was sequenced more than a decade ago, not much is known about its transcriptome. Our aim was to use transcriptome sequencing (RNA-Seq) to close this knowledge gap and gain insights into the transcriptional landscape of a C. diphtheriae tox+ strain.ResultsWe applied two different RNA-Seq techniques, one to retrieve 5′-ends of primary transcripts and the other to characterize the whole transcriptional landscape in order to gain insights into various features of the C. diphtheriae NCTC 13129 transcriptome. By examining the data we identified 1656 transcription start sites (TSS), of which 1202 were assigned to genes and 454 to putative novel transcripts. By using the TSS data promoter regions recognized by the housekeeping sigma factor σA and its motifs were analyzed in detail, revealing a well conserved −10 but an only weakly conserved −35 motif, respectively. Furthermore, with the TSS data 5’-UTR lengths were explored. The observed 5’-UTRs range from zero length (leaderless transcripts), which make up 20% of all genes, up to over 450 nt long leaders, which may harbor regulatory functions. The C. diphtheriae transcriptome consists of 471 operons which are further divided into 167 sub-operon structures. In a differential expression analysis approach, we discovered that genetic disruption of the iron-sensing transcription regulator DtxR, which controls expression of diphtheria toxin (DT), causes a strong influence on general gene expression. Nearly 15% of the genome is differentially transcribed, indicating that DtxR might have other regulatory functions in addition to regulation of iron metabolism and DT. Furthermore, our findings shed light on the transcriptional landscape of the DT encoding gene tox and present evidence for two tox antisense RNAs, which point to a new way of transcriptional regulation of toxin production.ConclusionsThis study presents extensive insights into the transcriptome of C. diphtheriae and provides a basis for future studies regarding gene characterization, transcriptional regulatory networks, and regulation of the tox gene in particular.

OverGeneDB: a database of 5' end protein coding overlapping genes in human and mouse genomes.

Gene overlap plays various regulatory functions on transcriptional and post-transcriptional levels. Most current studies focus on protein-coding genes overlapping with non-protein-coding counterparts, the so called natural antisense transcripts. Considerably less is known about the role of gene overlap in the case of two protein-coding genes. Here, we provide OverGeneDB, a database of human and mouse 5′ end protein-coding overlapping genes. The database contains 582 human and 113 mouse gene pairs that are transcribed using overlapping promoters in at least one analyzed library. Gene pairs were identified based on the analysis of the transcription start site (TSS) coordinates in 73 human and 10 mouse organs, tissues and cell lines. Beside TSS data, resources for 26 human lung adenocarcinoma cell lines also contain RNA-Seq and ChIP-Seq data for seven histone modifications and RNA Polymerase II activity. The collected data revealed that the overlap region is rarely conserved between the studied species and tissues. In ∼50% of the overlapping genes, transcription started explicitly in the overlap regions. In the remaining half of overlapping genes, transcription was initiated both from overlapping and non-overlapping TSSs. OverGeneDB is accessible at http://overgenedb.amu.edu.pl.

CAGEr: precise TSS data retrieval and high-resolution promoterome mining for integrative analyses.

Cap analysis of gene expression (CAGE) is a high-throughput method for transcriptome analysis that provides a single base-pair resolution map of transcription start sites (TSS) and their relative usage. Despite their high resolution and functional significance, published CAGE data are still underused in promoter analysis due to the absence of tools that enable its efficient manipulation and integration with other genome data types. Here we present CAGEr, an R implementation of novel methods for the analysis of differential TSS usage and promoter dynamics, integrated with CAGE data processing and promoterome mining into a first comprehensive CAGE toolbox on a common analysis platform. Crucially, we provide collections of TSSs derived from most published CAGE datasets, as well as direct access to FANTOM5 resource of TSSs for numerous human and mouse cell/tissue types from within R, greatly increasing the accessibility of precise context-specific TSS data for integrative analyses. The CAGEr package is freely available from Bioconductor at http://www.bioconductor.org/packages/release/bioc/html/CAGEr.html.

Analysis of changes in transcription start site distribution by a classification approach

Change in transcription start site (TSS) usage is an important mechanism for the control of transcription process, and has a significant effect on the isoforms being transcribed. One of the goals in the study of TSS is the understanding of how and why their usage differs in different tissues or under different conditions. In light of recent efforts in the mapping of transcription start site landscape using high-throughput sequencing approaches, a quantitative and automated method is needed to process all the data that are being produced. In this work we propose a statistical approach that will classify changes in TSS distribution between different samples into several categories of changes that may have biological significance. Genes selected by the classifiers can then be analyzed together with additional supporting data to determine their biological significance. We use a set of time-course TSS data from mouse dendritic cells stimulated with lipopolysaccharide (LPS) to demonstrate the usefulness of our method.

Ppdb: plant promoter database version 3.0

ppdb (http://ppdb.agr.gifu-u.ac.jp) is a plant promoter database that provides information on transcription start sites (TSSs), core promoter structure (TATA boxes, Initiators, Y Patches, GA and CA elements) and regulatory element groups (REGs) as putative and comprehensive transcriptional regulatory elements. Since the last report in this journal, the database has been updated in three areas to version 3.0. First, new genomes have been included in the database, and now ppdb provides information on Arabidopsis thaliana, rice, Physcomitrella patens and poplar. Second, new TSS tag data (34 million) from A. thaliana, determined by a high throughput sequencer, has been added to give a ∼200-fold increase in TSS data compared with version 1.0. This results in a much higher coverage of ∼27 000 A. thaliana genes and finer positioning of promoters even for genes with low expression levels. Third, microarray data-based predictions have been appended as REG annotations which inform their putative physiological roles.

Mapping of transcription start sites in Saccharomyces cerevisiae using 5′ SAGE

A minimally addressed area in Saccharomyces cerevisiae research is the mapping of transcription start sites (TSS). Mapping of TSS in S.cerevisiae has the potential to contribute to our understanding of gene regulation, transcription, mRNA stability and aspects of RNA biology. Here, we use 5′ SAGE to map 5′ TSS in S.cerevisiae. Tags identifying the first 15–17 bases of the transcripts are created, ligated to form ditags, amplified, concatemerized and ligated into a vector to create a library. Each clone sequenced from this library identifies 10–20 TSS. We have identified 13 746 unique, unambiguous sequence tags from 2231 S.cerevisiae genes. TSS identified in this study are consistent with published results, with primer extension results described here, and are consistent with expectations based on previous work on transcription initiation. We have aligned the sequence flanking 4637 TSS to identify the consensus sequence A(Arich)5NPyA(A/T)NN(Arich)6, which confirms and expands the previous reported PyA(A/T)Pu consensus pattern. The TSS data allowed the identification of a previously unrecognized gene, uncovered errors in previous annotation, and identified potential regulatory RNAs and upstream open reading frames in 5′-untranslated region.