proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms

A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data

In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.

RegulonDB v12.0: a comprehensive resource of transcriptional regulation in E. coli K-12

RegulonDB is a database that contains the most comprehensive corpus of knowledge of the regulation of transcription initiation of Escherichia coli K-12, including data from both classical molecular biology and high-throughput methodologies. Here, we describe biological advances since our last NAR paper of 2019. We explain the changes to satisfy FAIR requirements. We also present a full reconstruction of the RegulonDB computational infrastructure, which has significantly improved data storage, retrieval and accessibility and thus supports a more intuitive and user-friendly experience. The integration of graphical tools provides clear visual representations of genetic regulation data, facilitating data interpretation and knowledge integration. RegulonDB version 12.0 can be accessed at https://regulondb.ccg.unam.mx.

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users' specific needs.

RfaH Counter-Silences Inhibition of Transcript Elongation by H-NS-StpA Nucleoprotein Filaments in Pathogenic Escherichia coli

Expression of virulence genes in pathogenic Escherichia coli is controlled in part by the transcription silencer H-NS and its paralogs (e.g., StpA), which sequester DNA in multi-kb nucleoprotein filaments to inhibit transcription initiation, elongation, or both. Some activators counter-silence initiation by displacing H-NS from promoters, but how H-NS inhibition of elongation is overcome is not understood. In uropathogenic E. coli (UPEC), elongation regulator RfaH aids expression of some H-NS-silenced pathogenicity operons (e.g., hlyCABD encoding hemolysin). RfaH associates with elongation complexes (ECs) via direct contacts to a transiently exposed, nontemplate DNA strand sequence called operon polarity suppressor (ops). RfaH-ops interactions establish long-lived RfaH-EC contacts that allow RfaH to recruit ribosomes to the nascent mRNA and to suppress transcriptional pausing and termination. Using ChIP-seq, we mapped the genome-scale distributions of RfaH, H-NS, StpA, RNA polymerase (RNAP), and σ⁷⁰ in the UPEC strain CFT073. We identify eight RfaH-activated operons, all of which were bound by H-NS and StpA. Four are new additions to the RfaH regulon. Deletion of RfaH caused premature termination, whereas deletion of H-NS and StpA allowed elongation without RfaH. Thus, RfaH is an elongation counter-silencer of H-NS. Consistent with elongation counter-silencing, deletion of StpA alone decreased the effect of RfaH. StpA increases DNA bridging, which inhibits transcript elongation via topological constraints on RNAP. Residual RfaH effect when both H-NS and StpA were deleted was attributable to targeting of RfaH-regulated operons by a minor H-NS paralog, Hfp. These operons have evolved higher levels of H-NS-binding features, explaining minor-paralog targeting. IMPORTANCE Bacterial pathogens adapt to hosts and host defenses by reprogramming gene expression, including by H-NS counter-silencing. Counter-silencing turns on transcription initiation when regulators bind to promoters and rearrange repressive H-NS nucleoprotein filaments that ordinarily block transcription. The specialized NusG paralog RfaH also reprograms virulence genes but regulates transcription elongation. To understand how elongation regulators might affect genes silenced by H-NS, we mapped H-NS, StpA (an H-NS paralog), RfaH, σ⁷⁰, and RNA polymerase (RNAP) locations on DNA in the uropathogenic E. coli strain CFT073. Although H-NS-StpA filaments bind only 18% of the CFT073 genome, all loci at which RfaH binds RNAP are also bound by H-NS-StpA and are silenced when RfaH is absent. Thus, RfaH represents a distinct class of counter-silencer that acts on elongating RNAP to enable transcription through repressive nucleoprotein filaments. Our findings define a new mechanism of elongation counter-silencing and explain how RfaH functions as a virulence regulator.

Exploring the mono-/bistability range of positively autoregulated signaling systems in the presence of competing transcription factor binding sites

Binding of transcription factor (TF) proteins to regulatory DNA sites is key to accurate control of gene expression in response to environmental stimuli. Theoretical modeling of transcription regulation is often focused on a limited set of genes of interest, while binding of the TF to other genomic sites is seldom considered. The total number of TF binding sites (TFBSs) affects the availability of TF protein molecules and sequestration of a TF by TFBSs can promote bistability. For many signaling systems where a graded response is desirable for continuous control over the input range, biochemical parameters of the regulatory proteins need be tuned to avoid bistability. Here we analyze the mono-/bistable parameter range for positively autoregulated two-component systems (TCSs) in the presence of different numbers of competing TFBSs. TCS signaling, one of the major bacterial signaling strategies, couples signal perception with output responses via protein phosphorylation. For bistability, competition for TF proteins by TFBSs lowers the requirement for high fold change of the autoregulated transcription but demands high phosphorylation activities of TCS proteins. We show that bistability can be avoided with a low phosphorylation capacity of TCSs, a high TF affinity for the autoregulated promoter or a low fold change in signaling protein levels upon induction. These may represent general design rules for TCSs to ensure uniform graded responses. Examining the mono-/bistability parameter range allows qualitative prediction of steady-state responses, which are experimentally validated in the E. coli CusRS system.

RegulonDB 11.0: Comprehensive high-throughput datasets on transcriptional regulation in Escherichia coli K-12

Genomics has set the basis for a variety of methodologies that produce high-throughput datasets identifying the different players that define gene regulation, particularly regulation of transcription initiation and operon organization. These datasets are available in public repositories, such as the Gene Expression Omnibus, or ArrayExpress. However, accessing and navigating such a wealth of data is not straightforward. No resource currently exists that offers all available high and low-throughput data on transcriptional regulation in Escherichia coli K-12 to easily use both as whole datasets, or as individual interactions and regulatory elements. RegulonDB (https://regulondb.ccg.unam.mx) began gathering high-throughput dataset collections in 2009, starting with transcription start sites, then adding ChIP-seq and gSELEX in 2012, with up to 99 different experimental high-throughput datasets available in 2019. In this paper we present a radical upgrade to more than 2000 high-throughput datasets, processed to facilitate their comparison, introducing up-to-date collections of transcription termination sites, transcription units, as well as transcription factor binding interactions derived from ChIP-seq, ChIP-exo, gSELEX and DAP-seq experiments, besides expression profiles derived from RNA-seq experiments. For ChIP-seq experiments we offer both the data as presented by the authors, as well as data uniformly processed in-house, enhancing their comparability, as well as the traceability of the methods and reproducibility of the results. Furthermore, we have expanded the tools available for browsing and visualization across and within datasets. We include comparisons against previously existing knowledge in RegulonDB from classic experiments, a nucleotide-resolution genome viewer, and an interface that enables users to browse datasets by querying their metadata. A particular effort was made to automatically extract detailed experimental growth conditions by implementing an assisted curation strategy applying Natural language processing and machine learning. We provide summaries with the total number of interactions found in each experiment, as well as tools to identify common results among different experiments. This is a long-awaited resource to make use of such wealth of knowledge and advance our understanding of the biology of the model bacterium E. coli K-12.