Identification Of Transcription Start Sites Research Articles

Transcriptome architecture of the three main lineages of agrobacteria.

Agrobacteria are a diverse, polyphyletic group of prokaryotes with multipartite genomes capable of transferring DNA into the genomes of host plants, making them an essential tool in plant biotechnology. Despite their utility in plant transformation, genome-wide transcriptional regulation is not well understood across the three main lineages of agrobacteria. Transcription start sites (TSSs) are a necessary component of gene expression and regulation. In this study, we used differential RNA-seq and a TSS identification algorithm optimized on manually annotated TSS, then validated with existing TSS to identify thousands of TSS with nucleotide resolution for representatives of each lineage. We extend upon the 356 TSSs previously reported in Agrobacterium fabrum C58 by identifying 1,916 TSSs. In addition, we completed genomes and phenotyping of Rhizobium rhizogenes C16/80 and Allorhizobium vitis T60/94, identifying 2,650 and 2,432 TSSs, respectively. Parameter optimization was crucial for an accurate, high-resolution view of genome and transcriptional dynamics, highlighting the importance of algorithm optimization in genome-wide TSS identification and genomics at large. The optimized algorithm reduced the number of TSSs identified internal and antisense to the coding sequence on average by 90.5% and 91.9%, respectively. Comparison of TSS conservation between orthologs of the three lineages revealed differences in cell cycle regulation of ctrA as well as divergence of transcriptional regulation of chemotaxis-related genes when grown in conditions that simulate the plant environment. These results provide a framework to elucidate the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. IMPORTANCE Transcription start sites (TSSs) are fundamental for understanding gene expression and regulation. Agrobacteria, a group of prokaryotes with the ability to transfer DNA into the genomes of host plants, are widely used in plant biotechnology. However, the genome-wide transcriptional regulation of agrobacteria is not well understood, especially in less-studied lineages. Differential RNA-seq and an optimized algorithm enabled identification of thousands of TSSs with nucleotide resolution for representatives of each lineage. The results of this study provide a framework for elucidating the mechanistic basis and evolution of pathology across the three main lineages of agrobacteria. The optimized algorithm also highlights the importance of parameter optimization in genome-wide TSS identification and genomics at large.

Deep learning and support vector machines for transcription start site identification

Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.

Smar2C2: A Simple and Efficient Protocol for the Identification of Transcription Start Sites.

Promoters and the noncoding sequences that drive their function are fundamental aspects of genes that are critical to their regulation. The transcription preinitiation complex binds and assembles on promoters where it facilitates transcription. The transcription start site (TSS) is located downstream of the promoter sequence and is defined as the location in the genome where polymerase begins transcribing DNA into RNA. Knowing the location of TSSs is useful for annotation of genes, identification of non-coding sequences important to gene regulation, detection of alternative TSSs, and understanding of 5' UTR content. Several existing techniques make it possible to accurately identify TSSs, but are often difficult to perform experimentally, require large amounts of input RNA, or are unable to identify a large number of TSSs from a single sample. Many of these protocols take advantage of template switching reverse transcriptases (TSRTs), which reliably place an adaptor at the 5' end of a first strand synthesis of cDNA. Here, we introduce a protocol that exploits TSRT activity combined with rolling circle amplification to identify TSSs with several unique advantages over existing methods. Sequence adaptors are placed on the 5' and 3' end of the full-length cDNA copy of a transcript. A splint compatible with those adaptors is then used to circularize the full-length cDNA. Linear DNA containing concatemers of the cDNA are generated using rolling circle amplification, and a sequencing library is formed by fragmenting the concatemers. This protocol is straightforward to execute, requiring limited bench time with relatively stable reagents. Using extremely low amounts of RNA input, this protocol produces large numbers of accurate, deduplicated TSSs genome wide. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Splint generation Basic Protocol 2: RNA extraction Basic Protocol 3: cDNA synthesis Basic Protocol 4: cDNA circularization and amplification Basic Protocol 5: Library generation.

DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data

BackgroundThe widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance.ResultsTo evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak.ConclusionsCAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task.This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further.

Abstract P2044: The Cardioprotective Egr-1 And Maladaptive Sp-1 Zinc Finger Transcription Factor Reciprocally Regulates Expression Of The Cardiomyocyte Performance Enhancing Protein S100a1

Background and Hypothesis: Expression of the cardiomyocyte (CM) protein S100A1, which improves contractile performance of the heart, sharply increases during postnatal myocardial maturation but declines rapidly in failing hearts. We therefore hypothesized that CMs are wired with transcriptional factors (TFs) that positively and negatively regulate S100A1’s gene locus activity. Understanding these reciprocal circuits may be relevant for advanced therapeutic modulation of S100A1’s abundance in diseased hearts. Methods and Results: H9C2 rat cardiomyoblasts, an animal-free in vitro tool, displayed a strong concordant rise in S100A1 mRNA and protein levels (8.1+/-1.1 vs. cont.; n=9, p<0.05) amongst other CM markers (i.e., SERCA2a) over a 5-day CM differentiation protocol. Overall TF activity during this process was computationally inferred from TF binding site (TFBS) assessments in promoters of all actively regulated transcripts provided by a time-resolved (undifferentiated, day 0, 2 and 5) transcriptome analysis. From these TFs, EGR1 and SP1 amongst eight others were chosen due to their abundance both in differentiated H9C2-CMs and adult rat hearts and TFBS in the -1000 to +500 bp rat S100A1 gene promoter region due to a 5’ RACE-PCR based transcription start site identification in our model. To capture the relevant TFs from this group, we next co-incubated nuclear extracts from H9C2-CMs with biotinylated fragments of the aforementioned S100A1 promoter region coupled to streptavidin beads. S100A1 promoter-bound TFs were identified by mass spectrometry and only factors with a >2-fold enrichment over control were selected delivering EGR1 and SP1 as top hits. Subsequent siRNA-mediated silencing of both TFs yielded an EGR1 and SP1 knock-down dose-dependent inhibition (80%; p<0.05 vs. contr., n=9) and amplification (126%; p<0.05 vs. contr., n=9) of the rise both in S100A1 mRNA and protein levels in the H9C2-CM model that validated our comp./exp. pipeline and biological relevance of our TF hits, respectively. Conclusion: Our study identified the known cardioprotective EGR1 and maladaptive SP1 as novel positive and negative TF regulators of CM S100A1 expression and targets for therapeutic S100A1 gene locus modulation i.e., by SP1 TFBS gene-editing.

RNA Sequencing Reveals Widespread Transcription of Natural Antisense RNAs in Entamoeba Species.

Entamoeba is a genus of Amoebozoa that includes the intestine-colonizing pathogenic species Entamoeba histolytica. To understand the basis of gene regulation in E. histolytica from an evolutionary perspective, we have profiled the transcriptomes of its closely related species E. dispar, E. moshkovskii and E. invadens. Genome-wide identification of transcription start sites (TSS) and polyadenylation sites (PAS) revealed the similarities and differences of their gene regulatory sequences. In particular, we found the widespread initiation of antisense transcription from within the gene coding sequences is a common feature among all Entamoeba species. Interestingly, we observed the enrichment of antisense transcription in genes involved in several processes that are common to species infecting the human intestine, e.g., the metabolism of phospholipids. These results suggest a potentially conserved and compact gene regulatory system in Entamoeba.

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.

Cap analysis of gene expression (CAGE) and noncoding regulatory elements.

Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.

XPO1 Inhibition Targets Transcriptional Vulnerability of FLT3-ITD+D835 Double Mutant AML through p53 Accumulation and Inhibition of Oncogenic Transcription Factors: Lesson Learned from Cage Sequencing of Primary AML Cells

Acute myeloid leukemia (AML) with Fms-like tyrosine kinase 3 / the internal tandem duplications (FLT3 /ITD mutations) present a major clinical challenge, because of short remission duration and higher relapse rates. FLT3-targeted therapies using tyrosine kinase inhibitors (TKIs) often promote acquisition of additional point mutations in the tyrosine kinase domains (TKD) mutations, most commonly at D835 within the activation loop.To elucidate the alterations of transcriptome signatures of FLT3 /ITD-D835 mutants in AML, we performed cap analysis of gene expression (CAGE) sequencing of 14 AML patient samples with FLT3 /ITD and 12 patient samples with FLT3 /ITD-D835 mutations. CAGE identifies and quantifies the 5' ends of capped mRNA transcripts, which enables the identification of transcription start sites (TSS) necessary for gene expression. The TSS of genes altered in AML samples with FLT3 /ITD-D835 compared with FLT3 -ITD were mapped. CAGE detected upregulation of 378 and downregulation of 77 genes, respectively (false discovery rate; FDR < 0.05). Ingenuity Pathway Analysis (IPA) highlighted the activation of transcription factors NFKBIA (IκBα), RELA (NFκB subunit), HIF1α, EP300 (co-activator of HIF1α), TP73, PRDM1 (regulator of TP53 activity), NUPR1 (regulator of p21 relocalization), and FOXO3 (post-transcriptional regulators of MYC) in FLT3 /ITD-D835 mutant primary patient samples compared to FLT3 /ITD primary samples (Z score >1.9).To validate these transcriptional changes, we utilized paired isogenic FLT3 /ITD or FLT3 /ITD-D835Y transfected Ba/F3 cell lines. CAGE detected upregulation of 523 and downregulation of 373 genes, respectively, in FLT3 /ITD-D835 compared to FLT3 /ITD cells (FDR < 0.05). IPA indicated activation of HIF1α and MYC and inhibition of TP53 in FLT3 /ITD-D835 compared to FLT3 /ITD cells. Immunoblot analysis confirmed higher expression of c-Myc, IκBα and HIF1α and lower expression of TP53 in FLT3 /ITD-D835 compared to FLT3 /ITD cells. In turn, FLT3 downstream targets phospho- (p-) ERK, p-AKT, and p-STAT5 were significantly lower in FLT3 /ITD-D835 than in FLT3 /ITD cells. These results indicate that secondary FLT3 -D835 mutations activate oncogenic transcription factors c-Myc, IκBα and HIF1α, inhibit tumor suppressor TP53, and downregulate FLT3 downstream signaling pathways ERK, AKT, and STAT in FLT3 /ITD mutated AML cells.Exportin 1 (XPO1) mediates the nucleo-cytoplasmic transport, and is overexpressed in AML cells with FLT3 /ITD mutations (Kojima, Blood, 2013) . TP53 and IκBα are XPO1 cargos, and XPO1 inhibitors are known to upregulate TP53 and block c-MYC and NFκB signaling. We therefore examined the anti-leukemia activity of clinically available XPO1 inhibitor selinexor in FLT3 /ITD and FLT3 /ITD-D835 Ba/F3 cells. As expected, selinexor effectively inhibited cell proliferation in both FLT3 /ITD and FLT3 /ITD-D835 mutated cells at IC50 of 0.3 μM and 0.1 μM, respectively (48 hrs). CAGE detected upregulation of 1,590 and 2,220 genes and downregulation of 1,238 and 2,640 genes, respectively by selinexor treatment in FLT3 /ITD and FLT3 /ITD-D835 Ba/F3 cells, (FDR < 0.05). IPA analysis highlighted TP53, NUPR1 and CDKN2A (ARF; stabilizer of TP53) as the top three activated transcription factors by selinexor both in FLT3 /ITD and FLT3 /ITD-D835 cells. Immunoblot assay detected that selinexor significantly upregulated TP53, and downregulated IκBα and HIF1α both in FLT3 /ITD and FLT3 /ITD-D835 Ba/F3 cells. Selinexor treatment suppressed c-Myc and increased cleaved caspase 3 more prominently in FLT3 /ITD-D835 cells, but also upregulated p-ERK, p-AKT, p-STAT5 and p-STAT3, more notable in FLT3 /ITD cells. MCL1 and mTOR downstream p-4EBP1 and p-pS6 were repressed by selinexor in FLT3 /ITD-D835 but not in FLT3 /ITD cells.Collectively, the primary mechanism underlying the sensitivity to XPO1 inhibition of FLT3 /ITD-D835 cells is intolerance to the accumulation of nuclear TP53 and inhibition of multiple oncogenic transcription factors, including c-Myc, IκBα and HIF1α, which confer pro-survival activity independent of FLT3 downstream signaling. These findings indicate that XPO1 (CRM1) inhibition is a promising therapeutic strategy for AML patients with FLT3 /ITD and secondary acquired D835 mutations. DisclosuresHarada:Celgene: Research Funding; NIPPON SHINYAKU CO.: Speakers Bureau; NOVARTIS: Research Funding. Andreeff:Daiichi Sankyo: Consultancy.

Novel Oxidative Phosphorylation Inhibitor IACS-010759 Induces AMPK-Dependent Apoptosis of AML Cells

Acute myeloid leukemia (AML) cells frequently adjust to increased energy/substrate demand under stress conditions in the bone marrow microenvironment. Recent studies indicate that AML cells including leukemia-initiating cells are highly dependent on oxidative phosphorylation (OxPhos) for survival, while normal hematopoietic stem cells predominantly utilize glycolysis for energy homeostasis.The aim of this study is to assess the biomarkers of anti-leukemia efficacy of the first-in-class oxidative phosphorylation inhibitor (OxPhosi) IACS-010759. IACS-010759 at nanomolar concentrations blocks cellular respiration through inhibition of complex I of the electron transport chain (Molina et al., AACR 2016), is orally bioavailable and is currently in Phase 1 clinical trial in AML (NCT #02882321).We utilized primary AML samples from patients with newly diagnosed or relapsed/refractory AML (11 sensitive and 3 resistant to IACS-010759) as well as OxPhosi-sensitive OCI-AML3 and -resistant MOLM13 AML cell lines . To identify transcript abundances for the transcriptome profiling, Cap Analysis of Gene Expression (CAGE)-Seq analysis has been conducted. CAGE-Seq identifies and quantifies the 5' ends of capped mRNA transcripts, which enables the identification of transcription start sites (TSS) and allows investigating promoter structures necessary for gene expression. CAGE-Seq analysis identified recurrent baseline differences of TSS gene expression between IACS-010759-sensitive and -resistant primary AML samples; 4 genes were higher and 124 genes were lower in sensitive samples compared to resistant samples (false positive rate; FDR<0.05). In IACS-010759 sensitive OCI-AML3 cells the expression levels of 1,623 genes were higher and 1,821 were lower than in resistant MOLM13 cells (triplicate, FDR<0.05). Based on these transcriptome differences, the Ingenuity Pathway Analysis (IPA) bioinformatics platform highlighted consistent overexpression of 4 factors in IACS-010759-resistant AML primary samples and cell line compared to the sensitive AML. These include mammalian target of rapamycin (mTOR) kinase, a positive regulator of cell metabolism; Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) that enhances kinase-mediated activation; erythropoietin (EPO), a positive regulator of Jak-Stat pathway; and IL3, a potent growth-promoting cytokine. These findings indicate that the basal metabolic energetic capacity might affect the sensitivity of AML cells to IACS-010759. AMPK is a crucial cellular energy sensor and regulates energy metabolism through mTOR inhibition. We therefore studied the role of energy metabolism regulator AMP-activated protein kinase (AMPK) and of mTOR in anti-leukemia efficacy. To validate the role of AMPK, we utilized AMPK knockdown (sh AMPK) OCI-AML3 cells, and examined the sensitivity against IACS-010759, comparing to parental shC OCI-AML3 cells. By immunoblotting, IACS-010759 induced significant increase of p-AMPK in parental OCI-AML3 cells along with decrease of mTOR downstream target phospho- (p-) 4EBP1. OCI-AML3 stably infected with sh AMPK predictably overexpressed p-4EBP1 which was not modulated by IACS-010759. sh AMPK OCI-AML3 cells were more than 10-fold less sensitive against IACS-010759 compared to shC OCI-AML3 (IC50; 8.6±4.2 nM vs 0.5±0.1 nM, respectively), albeit remained in low nM concentrations. Interestingly, baseline level of p-AMPK expression in parental OCI-AML3 cells was markedly suppressed under physiologic hypoxic (1% oxygen) conditions, which caused activation of mTOR signaling (upregulation of p-4EBP1) and decreased sensitivity to IACS-010759 (IC50; 14.5±7.1 nM).Taken together, these results indicate that the responsiveness of AML to IACS-010759 depend at least in part on the AMPK-mTOR signaling, the gauge of cellular energy metabolic capacity. In sensitive AML cells IACS-010759 induces AMPK activation leading to mTOR suppression which results in cell growth inhibition in AML cells. In contrast, in cells with lower levels of AMPK, or in highly proliferating cells (such as stimulated by IL-3), mTOR is constitutively activated conferring resistance to Oxphosi, possibly through glycolytic adaptation. Altogether, these findings highlight AMPK and mTOR as putative biomarkers of anti-leukemia activity of the novel OxPhosi IACS-010759. DisclosuresAndreeff:Daiichi Sankyo: Consultancy.

Integrative transcription start site identification with iTiSS.

Many experimental approaches have been developed to identify transcription start sites (TSS) from genomic scale data. However, experiment specific biases lead to large numbers of false-positive calls. Here, we present our integrative approach iTiSS, which is an accurate and generic TSS caller for any TSS profiling experiment in eukaryotes, and substantially reduces the number of false positives by a joint analysis of several complementary datasets. iTiSS is platform independent and implemented in Java (v1.8) and is freely available at https://www.erhard-lab.de/software and https://github.com/erhard-lab/iTiSS. Supplementary data are available at Bioinformatics online.

A two-stream convolutional neural network for microRNA transcription start site feature integration and identification

MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is the lack of condition-specific annotation of miRNA transcription start sites (TSSs). Unlike protein-coding genes, miRNA TSSs can be tens of thousands of nucleotides away from the precursor miRNAs and they are hard to be detected by conventional RNA-Seq experiments. A number of studies have been attempted to computationally predict miRNA TSSs. However, high-resolution condition-specific miRNA TSS prediction remains a challenging problem. Recently, deep learning models have been successfully applied to various bioinformatics problems but have not been effectively created for condition-specific miRNA TSS prediction. Here we created a two-stream deep learning model called D-miRT for computational prediction of condition-specific miRNA TSSs (http://hulab.ucf.edu/research/projects/DmiRT/). D-miRT is a natural fit for the integration of low-resolution epigenetic features (DNase-Seq and histone modification data) and high-resolution sequence features. Compared with alternative computational models on different sets of training data, D-miRT outperformed all baseline models and demonstrated high accuracy for condition-specific miRNA TSS prediction tasks. Comparing with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance.

Genome-Wide Identification of Transcription Start Sites in Two Alphaproteobacteria, Rhodobacter sphaeroides 2.4.1 and Novosphingobium aromaticivorans DSM 12444.

Here, we report the genome-wide identification of transcription start sites (TSSs) from two Alphaproteobacteria grown under conditions that result in significant changes in gene expression. TSSs that were identified as present in one condition or both will be an important resource for future studies of these, and possibly other, Alphaproteobacteria.

Genome-Wide Transcription Start Site Mapping and Promoter Assignments to a Sigma Factor in the Human Enteropathogen Clostridioides difficile.

The emerging human enteropathogen Clostridioides difficile is the main cause of diarrhea associated with antibiotherapy. Regulatory pathways underlying the adaptive responses remain understudied and the global view of C. difficile promoter structure is still missing. In the genome of C. difficile 630, 22 genes encoding sigma factors are present suggesting a complex pattern of transcription in this bacterium. We present here the first transcriptional map of the C. difficile genome resulting from the identification of transcriptional start sites (TSS), promoter motifs and operon structures. By 5′-end RNA-seq approach, we mapped more than 1000 TSS upstream of genes. In addition to these primary TSS, this analysis revealed complex structure of transcriptional units such as alternative and internal promoters, potential RNA processing events and 5′ untranslated regions. By following an in silico iterative strategy that used as an input previously published consensus sequences and transcriptomic analysis, we identified candidate promoters upstream of most of protein-coding and non-coding RNAs genes. This strategy also led to refine consensus sequences of promoters recognized by major sigma factors of C. difficile. Detailed analysis focuses on the transcription in the pathogenicity locus and regulatory genes, as well as regulons of transition phase and sporulation sigma factors as important components of C. difficile regulatory network governing toxin gene expression and spore formation. Among the still uncharacterized regulons of the major sigma factors of C. difficile, we defined the SigL regulon by combining transcriptome and in silico analyses. We showed that the SigL regulon is largely involved in amino-acid degradation, a metabolism crucial for C. difficile gut colonization. Finally, we combined our TSS mapping, in silico identification of promoters and RNA-seq data to improve gene annotation and to suggest operon organization in C. difficile. These data will considerably improve our knowledge of global regulatory circuits controlling gene expression in C. difficile and will serve as a useful rich resource for scientific community both for the detailed analysis of specific genes and systems biology studies.

Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data

Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides highly accurate experimentally derived TSSs on a genome-wide scale. It has been specifically designed for flexibility and ease-of-use by only requiring aligned CAGE data and the underlying genomic sequence. When compared to existing algorithms, ADAPT-CAGE exhibits improved performance on every benchmark that we designed based on both annotation- and experimentally-driven strategies. This performance boost brings ADAPT-CAGE in the spotlight as a computational framework that is able to assist in the refinement of gene regulatory networks, the incorporation of accurate information of gene expression regulators and alternative promoter usage in both physiological and pathological conditions.

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.

Whole-genome Identification of Transcriptional Start Sites by Differential RNA-seq in Bacteria.

Gene transcription in bacteria often starts some nucleotides upstream of the start codon. Identifying the specific Transcriptional Start Site (TSS) is essential for genetic manipulation, as in many cases upstream of the start codon there are sequence elements that are involved in gene expression regulation. Taken into account the classical gene structure, we are able to identify two kinds of transcriptional start site: primary and secondary. A primary transcriptional start site is located some nucleotides upstream of the translational start site, while a secondary transcriptional start site is located within the gene encoding sequence. Here, we present a step by step protocol for genome-wide transcriptional start sites determination by differential RNA-sequencing (dRNA-seq) using the enteric pathogen Shigella flexneri serotype 5a strain M90T as model. However, this method can be employed in any other bacterial species of choice. In the first steps, total RNA is purified from bacterial cultures using the hot phenol method. Ribosomal RNA (rRNA) is specifically depleted via hybridization probes using a commercial kit. A 5'-monophosphate-dependent exonuclease (TEX)-treated RNA library enriched in primary transcripts is then prepared for comparison with a library that has not undergone TEX-treatment, followed by ligation of an RNA linker adaptor of known sequence allowing the determination of TSS with single nucleotide precision. Finally, the RNA is processed for Illumina sequencing library preparation and sequenced as purchased service. TSS are identified by in-house bioinformatic analysis. Our protocol is cost-effective as it minimizes the use of commercial kits and employs freely available software.

Next Generation DNA-Seq and Differential RNA-Seq Allow Re-annotation of the Pyrococcus furiosus DSM 3638 Genome and Provide Insights Into Archaeal Antisense Transcription.

Pyrococcus furiosus DSM 3638 is a model organism for hyperthermophilic archaea with an optimal growth temperature near 100°C. The genome was sequenced about 18 years ago. However, some publications suggest that in contrast to other Pyrococcus species, the genome of P. furiosus DSM 3638 is prone to genomic rearrangements. Therefore, we re-sequenced the genome using third generation sequencing techniques. The new de novo assembled genome is 1,889,914 bp in size and exhibits high sequence identity to the published sequence. However, two major deviations were detected: (1) The genome is 18,342 bp smaller than the NCBI reference genome due to a recently described deletion. (2) The region between PF0349 and PF0388 is inverted most likely due an assembly problem for the original sequence. In addition, numerous minor variations, ranging from single nucleotide exchanges, deletions or insertions were identified. The total number of insertion sequence (IS) elements is also reduced from 30 to 24 in the new sequence. Re-sequencing of a 2-year-old “lab culture” using Nanopore sequencing confirmed the overall stability of the P. furiosus DSM 3638 genome even under normal lab conditions without taking any special care. To improve genome annotation, the updated DNA sequence was combined with an RNA sequencing approach. Here, RNAs from eight different growth conditions were pooled to increase the number of detected transcripts. Furthermore, a differential RNA-Seq approach was employed for the identification of transcription start sites (TSSs). In total, 2515 TSSs were detected and classified into 834 primary (pTSS), 797 antisense (aTSS), 739 internal and 145 secondary TSSs. Our analysis of the upstream regions revealed a well conserved archaeal promoter structure. Interrogation of the distances between pTSSs and aTSSs revealed a significant number of antisense transcripts, which are a result of bidirectional transcription from the same TATA box. This mechanism of antisense transcript production could be further confirmed by in vitro transcription experiments. We assume that bidirectional transcription gives rise to non-functional antisense RNAs and that this is a widespread phenomenon in archaea due to the architecture of the TATA element and the symmetric structure of the TATA-binding protein.

The landscape of transcription initiation across latent and lytic KSHV genomes.

Precise promoter annotation is required for understanding the mechanistic basis of transcription initiation. In the context of complex genomes, such as herpesviruses where there is extensive genic overlap, identification of transcription start sites (TSSs) is particularly problematic and cannot be comprehensively accessed by standard RNA sequencing approaches. Kaposi's sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of Kaposi’s sarcoma and the B cell lymphoma primary effusion lymphoma (PEL). Here, we leverage RNA annotation and mapping of promoters for analysis of gene expression (RAMPAGE) and define KSHV TSSs transcriptome-wide and at nucleotide resolution in two widely used models of KSHV infection, namely iSLK.219 cells and the PEL cell line TREx-BCBL1-RTA. By mapping TSSs over a 96 h time course of reactivation we confirm 48 of 50 previously identified TSSs. Moreover, we identify over 100 novel transcription start site clusters (TSCs) in each cell line. Our analyses identified cell-type specific differences in TSC positions as well as promoter strength, and defined motifs within viral core promoters. Collectively, by defining TSSs at high resolution we have greatly expanded the transcriptional landscape of the KSHV genome and identified transcriptional control mechanisms at play during KSHV lytic reactivation.

NanoPARE: parallel analysis of RNA 5' ends from low-input RNA.

Diverse RNA 5′ ends are generated through both transcriptional and post-transcriptional processes. These important modes of gene regulation often vary across cell types and can contribute to the diversification of transcriptomes and thus cellular differentiation. Therefore, the identification of primary and processed 5′ ends of RNAs is important for their functional characterization. Methods have been developed to profile either RNA 5′ ends from primary transcripts or the products of RNA degradation genome-wide. However, these approaches either require high amounts of starting RNA or are performed in the absence of paired gene-body mRNA-seq data. This limits current efforts in RNA 5′ end annotation to whole tissues and can prevent accurate RNA 5′ end classification due to biases in the data sets. To enable the accurate identification and precise classification of RNA 5′ ends from standard and low-input RNA, we developed a next-generation sequencing-based method called nanoPARE and associated software. By integrating RNA 5′ end information from nanoPARE with gene-body mRNA-seq data from the same RNA sample, our method enables the identification of transcription start sites at single-nucleotide resolution from single-cell levels of total RNA, as well as small RNA-mediated cleavage events from at least 10,000-fold less total RNA compared to conventional approaches. NanoPARE can therefore be used to accurately profile transcription start sites, noncapped RNA 5′ ends, and small RNA targeting events from individual tissue types. As a proof-of-principle, we utilized nanoPARE to improve Arabidopsis thaliana RNA 5′ end annotations and quantify microRNA-mediated cleavage events across five different flower tissues.