Mycobacterial HelD connects RNA polymerase recycling with transcription initiation.

Mycobacterial HelD is a transcription factor that recycles stalled RNAP by dissociating it from nucleic acids and, if present, from the antibiotic rifampicin. The rescued RNAP, however, must disengage from HelD to participate in subsequent rounds of transcription. The mechanism of release is unknown. We show that HelD from Mycobacterium smegmatis forms a complex with RNAP associated with the primary sigma factor σA and transcription factor RbpA but not CarD. We solve several structures of RNAP-σA-RbpA-HelD without and with promoter DNA. These snapshots capture HelD during transcription initiation, describing mechanistic aspects of HelD release from RNAP and its protective effect against rifampicin. Biochemical evidence supports these findings, defines the role of ATP binding and hydrolysis by HelD in the process, and confirms the rifampicin-protective effect of HelD. Collectively, these results show that when HelD is present during transcription initiation, the process is protected from rifampicin until the last possible moment.

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria.

Bacteria have evolved structured RNAs that can associate with RNA polymerase (RNAP). Two of them have been known so far-6S RNA and Ms1 RNA but it is unclear if any other types of RNAs binding to RNAP exist in bacteria. To identify all RNAs interacting with RNAP and the primary σ factors, we have established and performed native RIP-seq in Bacillus subtilis, Corynebacterium glutamicum, Streptomyces coelicolor, Mycobacterium smegmatis and the pathogenic Mycobacterium tuberculosis. Besides known 6S RNAs in B. subtilis and Ms1 in M. smegmatis, we detected MTS2823, a homologue of Ms1, on RNAP in M. tuberculosis. In C. glutamicum, we discovered novel types of structured RNAs that associate with RNAP. Furthermore, we identified other species-specific RNAs including full-length mRNAs, revealing a previously unknown landscape of RNAs interacting with the bacterial transcription machinery.

σE of Streptomyces coelicolor can function both as a direct activator or repressor of transcription.

σ factors are considered as positive regulators of gene expression. Here we reveal the opposite, inhibitory role of these proteins. We used a combination of molecular biology methods and computational modeling to analyze the regulatory activity of the extracytoplasmic σE factor from Streptomyces coelicolor. The direct activator/repressor function of σE was then explored by experimental analysis of selected promoter regions in vivo. Additionally, the σE interactome was defined. Taken together, the results characterize σE, its regulation, regulon, and suggest its direct inhibitory function (as a repressor) in gene expression, a phenomenon that may be common also to other σ factors and organisms.

Ms1 RNA Interacts With the RNA Polymerase Core in Streptomyces coelicolor and Was Identified in Majority of Actinobacteria Using a Linguistic Gene Synteny Search.

Bacteria employ small non-coding RNAs (sRNAs) to regulate gene expression. Ms1 is an sRNA that binds to the RNA polymerase (RNAP) core and affects the intracellular level of this essential enzyme. Ms1 is structurally related to 6S RNA that binds to a different form of RNAP, the holoenzyme bearing the primary sigma factor. 6S RNAs are widespread in the bacterial kingdom except for the industrially and medicinally important Actinobacteria. While Ms1 RNA was identified in Mycobacterium, it is not clear whether Ms1 RNA is present also in other Actinobacteria species. Here, using a computational search based on secondary structure similarities combined with a linguistic gene synteny approach, we identified Ms1 RNA in Streptomyces. In S. coelicolor, Ms1 RNA overlaps with the previously annotated scr3559 sRNA with an unknown function. We experimentally confirmed that Ms1 RNA/scr3559 associates with the RNAP core without the primary sigma factor HrdB in vivo. Subsequently, we applied the computational approach to other Actinobacteria and identified Ms1 RNA candidates in 824 Actinobacteria species, revealing Ms1 RNA as a widespread class of RNAP binding sRNAs, and demonstrating the ability of our multifactorial computational approach to identify weakly conserved sRNAs in evolutionarily distant genomes.

rboAnalyzer webserver: web service for non-coding RNA characterization from NCBI BLAST output.

SUMMARY: We present a web service for improving characterization of non-coding RNAs (ncRNAs) from NCBI BLAST outputs, based on a command-line application rboAnalyzer. Briefly, the application extends subject sequences of selected high scoring pairs (HSPs) in BLAST output to their plausible full length, and predicts their homology and secondary structures. The aim of the application is to aid to characterize subject RNAs in HSPs that come uncharacterized in BLAST output. The main advantages of the web-server are easy use and interactive analysis with search, filtering and data export options. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at rboanalyzer.elixir-czech.cz. The website frontend is implemented in Elm, while backend is implemented in Python and served by Apache.

Kinetic Modeling and Meta-Analysis of the Bacillus subtilis SigB Regulon during Spore Germination and Outgrowth.

The exponential increase in the number of conducted studies combined with the development of sequencing methods have led to an enormous accumulation of partially processed experimental data in the past two decades. Here, we present an approach using literature-mined data complemented with gene expression kinetic modeling and promoter sequence analysis. This approach allowed us to identify the regulon of Bacillus subtilis sigma factor SigB of RNA polymerase (RNAP) specifically expressed during germination and outgrowth. SigB is critical for the cell's response to general stress but is also expressed during spore germination and outgrowth, and this specific regulon is not known. This approach allowed us to (i) define a subset of the known SigB regulon controlled by SigB specifically during spore germination and outgrowth, (ii) identify the influence of the promoter sequence binding motif organization on the expression of the SigB-regulated genes, and (iii) suggest additional sigma factors co-controlling other SigB-dependent genes. Experiments then validated promoter sequence characteristics necessary for direct RNAP-SigB binding. In summary, this work documents the potential of computational approaches to unravel new information even for a well-studied system; moreover, the study specifically identifies the subset of the SigB regulon, which is activated during germination and outgrowth.

rboAnalyzer: A Software to Improve Characterization of Non-coding RNAs From Sequence Database Search Output.

Searching for similar sequences in a database via BLAST or a similar tool is one of the most common bioinformatics tasks applied in general, and to non-coding RNAs in particular. However, the results of the search might be difficult to interpret due to the presence of partial matches to the database subject sequences. Here, we present rboAnalyzer - a tool that helps with interpreting sequence search result by (1) extending partial matches into plausible full-length subject sequences, (2) predicting homology of RNAs represented by full-length subject sequences to the query RNA, (3) pooling information across homologous RNAs found in the search results and public databases such as Rfam to predict more reliable secondary structures for all matches, and (4) contextualizing the matches by providing the prediction results and other relevant information in a rich graphical output. Using predicted full-length matches improves secondary structure prediction and makes rboAnalyzer robust with regards to identification of homology. The output of the tool should help the user to reliably characterize non-coding RNAs in BLAST output. The usefulness of the rboAnalyzer and its ability to correctly extend partial matches to full-length is demonstrated on known homologous RNAs. To allow the user to use custom databases and search options, rboAnalyzer accepts any search results as a text file in the BLAST format. The main output is an interactive HTML page displaying the computed characteristics and other context of the matches. The output can also be exported in an appropriate sequence and/or secondary structure formats.

DNA mapping and kinetic modeling of the HrdB regulon in Streptomyces coelicolor.

HrdB in streptomycetes is a principal sigma factor whose deletion is lethal. This is also the reason why its regulon has not been investigated so far. To overcome experimental obstacles, for investigating the HrdB regulon, we constructed a strain whose HrdB protein was tagged by an HA epitope. ChIP-seq experiment, done in 3 repeats, identified 2137 protein-coding genes organized in 337 operons, 75 small RNAs, 62 tRNAs, 6 rRNAs and 3 miscellaneous RNAs. Subsequent kinetic modeling of regulation of protein-coding genes with HrdB alone and with a complex of HrdB and a transcriptional cofactor RbpA, using gene expression time series, identified 1694 genes that were under their direct control. When using the HrdB-RbpA complex in the model, an increase of the model fidelity was found for 322 genes. Functional analysis revealed that HrdB controls the majority of gene groups essential for the primary metabolism and the vegetative growth. Particularly, almost all ribosomal protein-coding genes were found in the HrdB regulon. Analysis of promoter binding sites revealed binding motif at the -10 region and suggested the possible role of mono- or di-nucleotides upstream of the -10 element.

σI from Bacillus subtilis: Impact on Gene Expression and Characterization of σI-Dependent Transcription That Requires New Types of Promoters with Extended -35 and -10 Elements.

The σI sigma factor from Bacillus subtilis is a σ factor associated with RNA polymerase (RNAP) that was previously implicated in adaptation of the cell to elevated temperature. Here, we provide a comprehensive characterization of this transcriptional regulator. By transcriptome sequencing (RNA-seq) of wild-type (wt) and σI-null strains at 37°C and 52°C, we identified ∼130 genes affected by the absence of σI Further analysis revealed that the majority of these genes were affected indirectly by σI The σI regulon, i.e., the genes directly regulated by σI, consists of 16 genes, of which eight (the dhb and yku operons) are involved in iron metabolism. The involvement of σI in iron metabolism was confirmed phenotypically. Next, we set up an in vitro transcription system and defined and experimentally validated the promoter sequence logo that, in addition to -35 and -10 regions, also contains extended -35 and -10 motifs. Thus, σI-dependent promoters are relatively information rich in comparison with most other promoters. In summary, this study supplies information about the least-explored σ factor from the industrially important model organism B. subtilisIMPORTANCE In bacteria, σ factors are essential for transcription initiation. Knowledge about their regulons (i.e., genes transcribed from promoters dependent on these σ factors) is the key for understanding how bacteria cope with the changing environment and could be instrumental for biotechnologically motivated rewiring of gene expression. Here, we characterize the σI regulon from the industrially important model Gram-positive bacterium Bacillus subtilis We reveal that σI affects expression of ∼130 genes, of which 16 are directly regulated by σI, including genes encoding proteins involved in iron homeostasis. Detailed analysis of promoter elements then identifies unique sequences important for σI-dependent transcription. This study thus provides a comprehensive view on this underexplored component of the B. subtilis transcription machinery.

An Algorithm for Template-Based Prediction of Secondary Structures of Individual RNA Sequences.

While understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template. Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable. The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.