Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays

Genome-wide annotation of transcript boundaries using bacterial Rend-seq datasets

Accurate annotation to single-nucleotide resolution of the transcribed regions in genomes is key to optimally analyse RNA-seq data, understand regulatory events and for the design of experiments. However, currently most genome annotations provided by GenBank generally lack information about untranslated regions. Additionally, information regarding genomic locations of non-coding RNAs, such as sRNAs, or anti-sense RNAs is frequently missing. To provide such information, diverse RNA-seq technologies, such as Rend-seq, have been developed and applied to many bacterial species. However, incorporating this vast amount of information into annotation files has been limited and is bioinformatically challenging, resulting in UTRs and other non-coding elements being overlooked or misrepresented. To overcome this problem, we present pyRAP (python Rend-seq Annotation Pipeline), a software package that analyses Rend-seq datasets to accurately resolve transcript boundaries genome-wide. We report the use of pyRAP to find novel transcripts, transcript isoforms, and RNase-dependent sRNA processing events. In Bacillus subtilis we uncovered 63 novel transcripts and provide genomic coordinates with single-nucleotide resolution for 2218 5'UTRs, 1864 3'UTRs and 161 non-coding RNAs. In Escherichia coli, we report 117 novel transcripts, 2429 5'UTRs, 1619 3'UTRs and 91 non-coding RNAs, and in Staphylococcus aureus, 16 novel transcripts, 664 5'UTRs, 696 3'UTRs, and 81 non-coding RNAs. Finally, we use pyRAP to produce updated annotation files for B. subtilis 168, E. coli K-12 MG1655, and S. aureus 8325 for use in the wider microbial genomics research community.

CRISPR-dCas13a system for programmable small RNAs and polycistronic mRNA repression in bacteria

Bacterial small RNAs (sRNAs) function in post-transcriptional regulatory responses to environmental changes. However, the lack of eukaryotic RNA interference-like machinery in bacteria has limited the systematic engineering of RNA repression. Here, we report the development of clustered regularly interspaced short palindromic repeats (CRISPR)-guided dead CRIPSR-associated protein 13a (dCas13a) ribonucleoprotein that utilizes programmable CRISPR RNAs (crRNAs) to repress trans-acting and cis-acting sRNA as the target, altering regulatory mechanisms and stress-related phenotypes. In addition, we implemented a modular loop engineering of the crRNA to promote modular repression of the target gene with 92% knockdown efficiency and a single base-pair mismatch specificity. With the engineered crRNAs, we achieved targetable single-gene repression in the polycistronic operon. For metabolic application, 102 crRNAs were constructed in the biofoundry and used for screening novel knockdown sRNA targets to improve lycopene (colored antioxidant) production in Escherichia coli. The CRISPR-dCas13a system will assist as a valuable systematic tool for the discovery of novel sRNAs and the fine-tuning of bacterial RNA repression in both scientific and industrial applications.

High-Resolution Small RNAs Landscape Provides Insights into Alkane Adaptation in the Marine Alkane-Degrader Alcanivorax dieselolei B-5

Alkanes are widespread in the ocean, and Alcanivorax is one of the most ubiquitous alkane-degrading bacteria in the marine ecosystem. Small RNAs (sRNAs) are usually at the heart of regulatory pathways, but sRNA-mediated alkane metabolic adaptability still remains largely unknown due to the difficulties of identification. Here, differential RNA sequencing (dRNA-seq) modified with a size selection (~50-nt to 500-nt) strategy was used to generate high-resolution sRNAs profiling in the model species Alcanivorax dieselolei B-5 under alkane (n-hexadecane) and non-alkane (acetate) conditions. As a result, we identified 549 sRNA candidates at single-nucleotide resolution of 5'-ends, 63.4% of which are with transcription start sites (TSSs), and 36.6% of which are with processing sites (PSSs) at the 5'-ends. These sRNAs originate from almost any location in the genome, regardless of intragenic (65.8%), antisense (20.6%) and intergenic (6.2%) regions, and RNase E may function in the maturation of sRNAs. Most sRNAs locally distribute across the 15 reference genomes of Alcanivorax, and only 7.5% of sRNAs are broadly conserved in this genus. Expression responses to the alkane of several core conserved sRNAs, including 6S RNA, M1 RNA and tmRNA, indicate that they may participate in alkane metabolisms and result in more actively global transcription, RNA processing and stresses mitigation. Two novel CsrA-related sRNAs are identified, which may be involved in the translational activation of alkane metabolism-related genes by sequestering the global repressor CsrA. The relationships of sRNAs with the characterized genes of alkane sensing (ompS), chemotaxis (mcp, cheR, cheW2), transporting (ompT1, ompT2, ompT3) and hydroxylation (alkB1, alkB2, almA) were created based on the genome-wide predicted sRNA-mRNA interactions. Overall, the sRNA landscape lays the ground for uncovering cryptic regulations in critical marine bacterium, among which both the core and species-specific sRNAs are implicated in the alkane adaptive metabolisms.

Small RNAs beyond Model Organisms: Have We Only Scratched the Surface?

Small RNAs (sRNAs) are essential regulators in the adaptation of bacteria to environmental changes and act by binding targeted mRNAs through base complementarity. Approximately 550 distinct families of sRNAs have been identified since their initial characterization in the 1980s, accelerated by the emergence of RNA-sequencing. Small RNAs are found in a wide range of bacterial phyla, but they are more prominent in highly researched model organisms compared to the rest of the sequenced bacteria. Indeed, Escherichia coli and Salmonella enterica contain the highest number of sRNAs, with 98 and 118, respectively, with Enterobacteriaceae encoding 145 distinct sRNAs, while other bacteria families have only seven sRNAs on average. Although the past years brought major advances in research on sRNAs, we have perhaps only scratched the surface, even more so considering RNA annotations trail behind gene annotations. A distinctive trend can be observed for genes, whereby their number increases with genome size, but this is not observable for RNAs, although they would be expected to follow the same trend. In this perspective, we aimed at establishing a more accurate representation of the occurrence of sRNAs in bacteria, emphasizing the potential for novel sRNA discoveries.

Combined analysis of microbial metagenomic and metatranscriptomic sequencing data to assess in situ physiological conditions in the premature infant gut

Microbes alter their transcriptomic profiles in response to the environment. The physiological conditions experienced by a microbial community can thus be inferred using meta-transcriptomic sequencing by comparing transcription levels of specifically chosen genes. However, this analysis requires accurate reference genomes to identify the specific genes from which RNA reads originate. In addition, such an analysis should avoid biases in transcript counts related to differences in organism abundance. In this study we describe an approach to address these difficulties. Sample-specific meta-genomic assembled genomes (MAGs) were used as reference genomes to accurately identify the origin of RNA reads, and transcript ratios of genes with opposite transcription responses were compared to eliminate biases related to differences in organismal abundance, an approach hereafter named the "diametric ratio" method. We used this approach to probe the environmental conditions experienced by Escherichia spp. in the gut of 4 premature infants, 2 of whom developed necrotizing enterocolitis (NEC), a severe inflammatory intestinal disease. We analyzed twenty fecal samples taken from four premature infants (4-6 time points from each infant), and found significantly higher diametric ratios of genes associated with low oxygen levels in samples of infants later diagnosed with NEC than in samples without NEC. We also show this method can be used for examining other physiological conditions, such as exposure to nitric oxide and osmotic pressure. These study results should be treated with caution, due to the presence of confounding factors that might also distinguish between NEC and control infants. Nevertheless, together with benchmarking analyses, we show here that the diametric ratio approach can be applied for evaluating the physiological conditions experienced by microbes in situ. Results from similar studies can be further applied for designing diagnostic methods to detect NEC in its early developmental stages.

Metabolic remodeling in Escherichia coli MG1655. A prophage e14-encoded small RNA, co293, post-transcriptionally regulates transcription factors HcaR and FadR

In previous studies, we have shown the existence of metabolic remodeling in glucose-grown Escherichia coli MG1655 cells expressing the esterase Orf306 from the opd island of Sphingobium fuliginis. We now show that Orf306-dependent metabolic remodeling is due to regulation of a novel small RNA (sRNA). Endogenous propionate, produced due to the esterase/lipase activity of Orf306, repressed expression of a novel E. coli sRNA, co293. This sRNA post-transcriptionally regulates expression of the transcription factors HcaR and FadR either by inhibiting translation or by destabilizing their transcripts. Hence, repression of co293 expression elevates the levels of HcaR and FadR with consequent activation of alternative carbon catabolic pathways. HcaR activates the hca and MHP operons leading to upregulation of the phenyl propionate and hydroxy phenyl propionate (HPP) degradation pathways. Similarly, FadR stimulates the expression of the transcription factor IclR which negatively regulates the glyoxylate bypass pathway genes, aceBAK.

Reprogramming of gene expression in Escherichia coli cultured on pyruvate versus glucose

Previous studies revealed important roles of small RNAs (sRNAs) in regulation of bacterial metabolism, stress responses and virulence. However, only a minor fraction of sRNAs is well characterized with respect to the spectra of their targets, conditional expression profiles and actual mechanisms they use to regulate gene expression to control particular biological pathways. To learn more about the specific contribution of sRNAs to the global regulatory network controlling the Escherichia coli central carbon metabolism (CCM), we employed microarray analysis and compared transcriptome profiles of E. coli cells grown on two alternative minimal media supplemented with either pyruvate or glucose, respectively. Microarray analysis revealed that utilization of these alternative carbon sources led to profound differences in gene expression affecting all major gene clusters associated with CCM as well as expression of several known (CyaR, RyhB, GcvB and RyeA) and putative (C0652) sRNAs. To assess the impact of transcriptional reprogramming of gene expression on E. coli protein abundance, we also employed two-dimensional protein gel electrophoresis. Our experimental data made it possible to determine the major pathways for pyruvate assimilation when it is used as a sole carbon source and reveal the impact of other key processes (i.e., energy production, molecular transport and cell resistance to stress) associated with the CCM in E. coli. Moreover, some of these processes were apparently controlled by GcvB, RyhB and CyaR at the post-transcriptional level, thus indicating the complexity and interconnection of the regulatory networks that control CCM in bacteria.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides

Antimicrobial peptides are promising alternative antimicrobial agents. However, little is known about whether resistance to small-molecule antibiotics leads to cross-resistance (decreased sensitivity) or collateral sensitivity (increased sensitivity) to antimicrobial peptides. We systematically addressed this question by studying the susceptibilities of a comprehensive set of 60 antibiotic-resistant Escherichia coli strains towards 24 antimicrobial peptides. Strikingly, antibiotic-resistant bacteria show a high frequency of collateral sensitivity to antimicrobial peptides, whereas cross-resistance is relatively rare. We identify clinically relevant multidrug-resistance mutations that increase bacterial sensitivity to antimicrobial peptides. Collateral sensitivity in multidrug-resistant bacteria arises partly through regulatory changes shaping the lipopolysaccharide composition of the bacterial outer membrane. These advances allow the identification of antimicrobial peptide-antibiotic combinations that enhance antibiotic activity against multidrug-resistant bacteria and slow down de novo evolution of resistance. In particular, when co-administered as an adjuvant, the antimicrobial peptide glycine-leucine-amide caused up to 30-fold decrease in the antibiotic resistance level of resistant bacteria. Our work provides guidelines for the development of efficient peptide-based therapies of antibiotic-resistant infections.

Systems Metabolic Engineering of Escherichia coli

Systems metabolic engineering, which recently emerged as metabolic engineering integrated with systems biology, synthetic biology, and evolutionary engineering, allows engineering of microorganisms on a systemic level for the production of valuable chemicals far beyond its native capabilities. Here, we review the strategies for systems metabolic engineering and particularly its applications in Escherichia coli. First, we cover the various tools developed for genetic manipulation in E. coli to increase the production titers of desired chemicals. Next, we detail the strategies for systems metabolic engineering in E. coli, covering the engineering of the native metabolism, the expansion of metabolism with synthetic pathways, and the process engineering aspects undertaken to achieve higher production titers of desired chemicals. Finally, we examine a couple of notable products as case studies produced in E. coli strains developed by systems metabolic engineering. The large portfolio of chemical products successfully produced by engineered E. coli listed here demonstrates the sheer capacity of what can be envisioned and achieved with respect to microbial production of chemicals. Systems metabolic engineering is no longer in its infancy; it is now widely employed and is also positioned to further embrace next-generation interdisciplinary principles and innovation for its upgrade. Systems metabolic engineering will play increasingly important roles in developing industrial strains including E. coli that are capable of efficiently producing natural and nonnatural chemicals and materials from renewable nonfood biomass.

Genomic and transcriptomic landscape of Escherichia coli BL21(DE3)

Escherichia coli BL21(DE3) has long served as a model organism for scientific research, as well as a workhorse for biotechnology. Here we present the most current genome annotation of E. coli BL21(DE3) based on the transcriptome structure of the strain that was determined for the first time. The genome was annotated using multiple automated pipelines and compared to the current genome annotation of the closely related strain, E. coli K-12. High-resolution tiling array data of E. coli BL21(DE3) from several different stages of cell growth in rich and minimal media were analyzed to characterize the transcriptome structure and to provide supporting evidence for open reading frames. This new integrated analysis of the genomic and transcriptomic structure of E. coli BL21(DE3) has led to the correction of translation initiation sites for 88 coding DNA sequences and provided updated information for most genes. Additionally, 37 putative genes and 66 putative non-coding RNAs were also identified. The panoramic landscape of the genome and transcriptome of E. coli BL21(DE3) revealed here will allow us to better understand the fundamental biology of the strain and also advance biotechnological applications in industry.

Small RNAs in Bacterial Virulence and Communication

Bacterial pathogens must endure or adapt to different environments and stresses during transmission and infection. Posttranscriptional gene expression control by regulatory RNAs, such as small RNAs and riboswitches, is now considered central to adaptation in many bacteria, including pathogens. The study of RNA-based regulation (riboregulation) in pathogenic species has provided novel insight into how these bacteria regulate virulence gene expression. It has also uncovered diverse mechanisms by which bacterial small RNAs, in general, globally control gene expression. Riboregulators as well as their targets may also prove to be alternative targets or provide new strategies for antimicrobials. In this article, we present an overview of the general mechanisms that bacteria use to regulate with RNA, focusing on examples from pathogens. In addition, we also briefly review how deep sequencing approaches have aided in opening new perspectives in small RNA identification and the study of their functions. Finally, we discuss examples of riboregulators in two model pathogens that control virulence factor expression or survival-associated phenotypes, such as stress tolerance, biofilm formation, or cell-cell communication, to illustrate how riboregulation factors into regulatory networks in bacterial pathogens.

Deep sequencing approaches for the analysis of prokaryotic transcriptional boundaries and dynamics

The identification of the protein-coding regions of a genome is straightforward due to the universality of start and stop codons. However, the boundaries of the transcribed regions, conditional operon structures, non-coding RNAs and the dynamics of transcription, such as pausing of elongation, are non-trivial to identify, even in the comparatively simple genomes of prokaryotes. Traditional methods for the study of these areas, such as tiling arrays, are noisy, labour-intensive and lack the resolution required for densely-packed bacterial genomes. Recently, deep sequencing has become increasingly popular for the study of the transcriptome due to its lower costs, higher accuracy and single nucleotide resolution. These methods have revolutionised our understanding of prokaryotic transcriptional dynamics. Here, we review the deep sequencing and data analysis techniques that are available for the study of transcription in prokaryotes, and discuss the bioinformatic considerations of these analyses.

The Small RNA GcvB Promotes Mutagenic Break Repair by Opposing the Membrane Stress Response

Microbes and human cells possess mechanisms of mutagenesis activated by stress responses. Stress-inducible mutagenesis mechanisms may provide important models for mutagenesis that drives host-pathogen interactions, antibiotic resistance, and possibly much of evolution generally. In Escherichia coli, repair of DNA double-strand breaks is switched to a mutagenic mode, using error-prone DNA polymerases, via the SOS DNA damage and general (σ^S) stress responses. We investigated small RNA (sRNA) clients of Hfq, an RNA chaperone that promotes mutagenic break repair (MBR), and found that GcvB promotes MBR by allowing a robust σ^S response, achieved via opposing the membrane stress (σ^E) response. Cells that lack gcvB were MBR deficient and displayed reduced σ^S-dependent transcription but not reduced σ^S protein levels. The defects in MBR and σ^S-dependent transcription in ΔgcvB cells were alleviated by artificially increasing σ^S levels, implying that GcvB promotes mutagenesis by allowing a normal σ^S response. ΔgcvB cells were highly induced for the σ^E response, and blocking σ^E response induction restored both mutagenesis and σ^S-promoted transcription. We suggest that GcvB may promote the σ^S response and mutagenesis indirectly, by promoting membrane integrity, which keeps σ^E levels lower. At high levels, σ^E might outcompete σ^S for binding RNA polymerase and so reduce the σ^S response and mutagenesis. The data show the delicate balance of stress response modulation of mutagenesis.

IMPORTANCE

Mutagenesis mechanisms upregulated by stress responses promote de novo antibiotic resistance and cross-resistance in bacteria, antifungal drug resistance in yeasts, and genome instability in cancer cells under hypoxic stress. This paper describes the role of a small RNA (sRNA) in promoting a stress-inducible-mutagenesis mechanism, mutagenic DNA break repair in Escherichia coli The roles of many sRNAs in E. coli remain unknown. This study shows that ΔgcvB cells, which lack the GcvB sRNA, display a hyperactivated membrane stress response and reduced general stress response, possibly because of sigma factor competition for RNA polymerase. This results in a mutagenic break repair defect. The data illuminate a function of GcvB sRNA in opposing the membrane stress response, and thus indirectly upregulating mutagenesis.

A new custom microarray for sRNA profiling in Escherichia coli

Bacterial small RNAs (sRNAs) play essential roles in the post-transcriptional control of gene expression. To improve their detection by conventional microarrays, we designed a custom microarray containing a group of probes targeting known and some putative Escherichia coli sRNAs. To assess its potential in detection of sRNAs, RNA profiling experiments were performed with total RNA extracted from E. coli MG1655 cells exponentially grown in rich (Luria-Bertani) and minimal (M9/glucose) media. We found that many sRNAs could yield reasonably strong and statistically significant signals corresponding to nearly all sRNAs annotated in the EcoCyc database. Besides differential expression of two sRNAs (GcvB and RydB), expression of other sRNAs was less affected by the composition of the growth media. Other examples of the differentially expressed sRNAs were revealed by comparing gene expression of the wild-type strain and its isogenic mutant lacking functional poly(A) polymerase I (pcnB). Further, northern blot analysis was employed to validate these data and to assess the existence of new putative sRNAs. Our results suggest that the use of custom microarrays with improved capacities for detection of sRNAs can offer an attractive opportunity for efficient gene expression profiling of sRNAs and their target mRNAs at the whole transcriptome level.

A Transcriptome Map of Actinobacillus pleuropneumoniae at Single-Nucleotide Resolution Using Deep RNA-Seq

Actinobacillus pleuropneumoniae is the pathogen of porcine contagious pleuropneumoniae, a highly contagious respiratory disease of swine. Although the genome of A. pleuropneumoniae was sequenced several years ago, limited information is available on the genome-wide transcriptional analysis to accurately annotate the gene structures and regulatory elements. High-throughput RNA sequencing (RNA-seq) has been applied to study the transcriptional landscape of bacteria, which can efficiently and accurately identify gene expression regions and unknown transcriptional units, especially small non-coding RNAs (sRNAs), UTRs and regulatory regions. The aim of this study is to comprehensively analyze the transcriptome of A. pleuropneumoniae by RNA-seq in order to improve the existing genome annotation and promote our understanding of A. pleuropneumoniae gene structures and RNA-based regulation. In this study, we utilized RNA-seq to construct a single nucleotide resolution transcriptome map of A. pleuropneumoniae. More than 3.8 million high-quality reads (average length ~90 bp) from a cDNA library were generated and aligned to the reference genome. We identified 32 open reading frames encoding novel proteins that were mis-annotated in the previous genome annotations. The start sites for 35 genes based on the current genome annotation were corrected. Furthermore, 51 sRNAs in the A. pleuropneumoniae genome were discovered, of which 40 sRNAs were never reported in previous studies. The transcriptome map also enabled visualization of 5'- and 3'-UTR regions, in which contained 11 sRNAs. In addition, 351 operons covering 1230 genes throughout the whole genome were identified. The RNA-Seq based transcriptome map validated annotated genes and corrected annotations of open reading frames in the genome, and led to the identification of many functional elements (e.g. regions encoding novel proteins, non-coding sRNAs and operon structures). The transcriptional units described in this study provide a foundation for future studies concerning the gene functions and the transcriptional regulatory architectures of this pathogen.

Differential expression of small RNAs under chemical stress and fed-batch fermentation in E. coli

Background

Bacterial small RNAs (sRNAs) are recognized as posttranscriptional regulators involved in the control of bacterial lifestyle and adaptation to stressful conditions. Although chemical stress due to the toxicity of precursor and product compounds is frequently encountered in microbial bioprocessing applications, the involvement of sRNAs in this process is not well understood. We have used RNA sequencing to map sRNA expression in E. coli under chemical stress and high cell density fermentation conditions with the aim of identifying sRNAs involved in the transcriptional response and those with potential roles in stress tolerance.

Results

RNA sequencing libraries were prepared from RNA isolated from E. coli K-12 MG1655 cells grown under high cell density fermentation conditions or subjected to chemical stress with twelve compounds including four organic solvent-like compounds, four organic acids, two amino acids, geraniol and decanoic acid. We have discovered 253 novel intergenic transcripts with this approach, adding to the roughly 200 intergenic sRNAs previously reported in E. coli. There are eighty-four differentially expressed sRNAs during fermentation, of which the majority are novel, supporting possible regulatory roles for these transcripts in adaptation during different fermentation stages. There are a total of 139 differentially expressed sRNAs under chemical stress conditions, where twenty-nine exhibit significant expression changes in multiple tested conditions, suggesting that they may be involved in a more general chemical stress response. Among those with known functions are sRNAs involved in regulation of outer membrane proteins, iron availability, maintaining envelope homeostasis, as well as sRNAs incorporated into complex networks controlling motility and biofilm formation.

Conclusions

This study has used deep sequencing to reveal a wealth of hitherto undescribed sRNAs in E. coli and provides an atlas of sRNA expression during seventeen different growth and stress conditions. Although the number of novel sRNAs with regulatory functions is unknown, several exhibit specific expression patterns during high cell density fermentation and are differentially expressed in the presence of multiple chemicals, suggesting they may play regulatory roles during these stress conditions. These novel sRNAs, together with specific known sRNAs, are candidates for improving stress tolerance and our understanding of the E. coli regulatory network during fed-batch fermentation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2231-8) contains supplementary material, which is available to authorized users.

Identification of novel sRNAs involved in biofilm formation, motility, and fimbriae formation in Escherichia coli

Bacterial small RNAs (sRNAs) are known regulators in many physiological processes. In Escherichia coli, a large number of sRNAs have been predicted, among which only about a hundred are experimentally validated. Despite considerable research, the majority of their functions remain uncovered. Therefore, collective analysis of the roles of sRNAs in specific cellular processes may provide an effective approach to identify their functions. Here, we constructed a collection of plasmids overexpressing 99 individual sRNAs, and analyzed their effects on biofilm formation and related phenotypes. Thirty-three sRNAs significantly affecting these cellular processes were identified. No consistent correlations were observed, except that all five sRNAs suppressing type I fimbriae inhibited biofilm formation. Interestingly, IS118, yet to be characterized, suppressed all the processes. Our data not only reveal potentially critical functions of individual sRNAs in biofilm formation and other phenotypes but also highlight the unexpected complexity of sRNA-mediated metabolic pathways leading to these processes.

Accelerating Discovery and Functional Analysis of Small RNAs with New Technologies

Over the past decade, bacterial small RNAs (sRNAs) have gone from a biological curiosity to being recognized as a major class of regulatory molecules. High-throughput methods for sampling the transcriptional output of bacterial cells demonstrate that sRNAs are universal features of bacterial transcriptomes, are plentiful, and appear to vary extensively over evolutionary time. With ever more bacteria coming under study, the question becomes how can we accelerate the discovery and functional characterization of sRNAs in diverse organisms. New technologies built on high-throughput sequencing are emerging that can rapidly provide global insight into the numbers and functions of sRNAs in bacteria of interest, providing information that can shape hypotheses and guide research. In this review, we describe recent developments in transcriptomics (RNA-seq) and functional genomics that we expect to help us develop an integrated, systems-level view of sRNA biology in bacteria.

Cis-Antisense Transcription Gives Rise to Tunable Genetic Switch Behavior: A Mathematical Modeling Approach

Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.

Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors

As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the key regulatory elements, which recruit the transcriptional machinery through binding to a variety of regulatory proteins (known as sigma factors). The identification of the promoter regions is very challenging because these regions do not adhere to specific sequence patterns or motifs and are difficult to determine experimentally. Machine learning represents a promising and cost-effective approach for computational identification of prokaryotic promoter regions. However, the quality of the predictors depends on several factors including: i) training data; ii) data representation; iii) classification algorithms; iv) evaluation procedures. In this work, we create several variants of E. coli promoter data sets and utilize them to experimentally examine the effect of these factors on the predictive performance of E. coli σ70 promoter models. Our results suggest that under some combinations of the first three criteria, a prediction model might perform very well on cross-validation experiments while its performance on independent test data is drastically very poor. This emphasizes the importance of evaluating promoter region predictors using independent test data, which corrects for the over-optimistic performance that might be estimated using the cross-validation procedure. Our analysis of the tested models shows that good prediction models often perform well despite how the non-promoter data was obtained. On the other hand, poor prediction models seems to be more sensitive to the choice of non-promoter sequences. Interestingly, the best performing sequence-based classifiers outperform the best performing structure-based classifiers on both cross-validation and independent test performance evaluation experiments. Finally, we propose a meta-predictor method combining two top performing sequence-based and structure-based classifiers and compare its performance with some of the state-of-the-art E. coli σ70 promoter prediction methods.

Genome rearrangements can make and break small RNA genes

Small RNAs (sRNAs) are short, transcribed regulatory elements that are typically encoded in the intergenic regions (IGRs) of bacterial genomes. Several sRNAs, first recognized in Escherichia coli, are conserved among enteric bacteria, but because of the regulatory roles of sRNAs, differences in sRNA repertoires might be responsible for features that differentiate closely related species. We scanned the E. coli MG1655 and Salmonella enterica Typhimurium genomes for nonsyntenic IGRs as a potential source of uncharacterized, species-specific sRNAs and found that genome rearrangements have reconfigured several IGRs causing the disruption and formation of sRNAs. Within an IGR that is present in E. coli but was disrupted in Salmonella by a translocation event is an sRNA that is associated with the FNR/CRP global regulators and influences E. coli biofilm formation. A Salmonella-specific sRNA evolved de novo through point mutations that generated a σ⁷⁰ promoter sequence in an IGR that arose through genome rearrangement events. The differences in the sRNA pools among bacterial species have previously been ascribed to duplication, deletion, or horizontal acquisition. Here, we show that genomic rearrangements also contribute to this process by either disrupting sRNA-containing IGRs or creating IGRs in which novel sRNAs may evolve.

Genome-wide analyses in bacteria show small-RNA enrichment for long and conserved intergenic regions

Interest in finding small RNAs (sRNAs) in bacteria has significantly increased in recent years due to their regulatory functions. Development of high-throughput methods and more sophisticated computational algorithms has allowed rapid identification of sRNA candidates in different species. However, given their various sizes (50 to 500 nucleotides [nt]) and their potential genomic locations in the 5' and 3' untranslated regions as well as in intergenic regions, identification and validation of true sRNAs have been challenging. In addition, the evolution of bacterial sRNAs across different species continues to be puzzling, given that they can exert similar functions with various sequences and structures. In this study, we analyzed the enrichment patterns of sRNAs in 13 well-annotated bacterial species using existing transcriptome and experimental data. All intergenic regions were analyzed by WU-BLAST to examine conservation levels relative to species within or outside their genus. In total, more than 900 validated bacterial sRNAs and 23,000 intergenic regions were analyzed. The results indicate that sRNAs are enriched in intergenic regions, which are longer and more conserved than the average intergenic regions in the corresponding bacterial genome. We also found that sRNA-coding regions have different conservation levels relative to their flanking regions. This work provides a way to analyze how noncoding RNAs are distributed in bacterial genomes and also shows conserved features of intergenic regions that encode sRNAs. These results also provide insight into the functions of regions surrounding sRNAs and into optimization of RNA search algorithms.

High-density tiling microarray analysis of the full transcriptional activity of yeast

Understanding the relationship between DNA sequence variation and phenotypic variation in complex or quantitative traits is one of the major challenges in modern biology. We are witnessing a deluge of DNA sequence information and association studies of genetic polymorphisms with phenotypes of interest in families and populations. In addition, it has become clear that large portions of eukaryotic genomes beyond protein-coding genes are transcribed, generating numerous noncoding RNA (ncRNA) molecules whose functions remain mostly unknown.DNA oligonucleotide microarrays constitute a powerful technology for studying the expression of genes in different organisms. The Saccharomyces cerevisiae tiling array presents a significant advance over previous array-based platforms. It has a high density of overlapping probes that start on average every 8 bp along each strand of the genome, enabling precise definition of transcript structure. Furthermore, the array includes probes specific for the polymorphic positions of another, distantly related yeast strain, allowing accurate measurement of allele-specific expression in a hybrid of the two strains. This technology thus allows high-resolution, quantitative, strand- and allele-specific measurements of transcription from a full eukaryotic genome. In this chapter, we describe the methods for extracting RNA, synthesizing first-strand cDNA, fragmenting, and labeling of samples for hybridization to the tiling array. Combining genome-wide information on variation in DNA sequence with variation in transcript structure and levels promises to increase our understanding of the genotype-to-phenotype relationship.

Tiling array study of MNNG treated Escherichia coli reveals a widespread transcriptional response

The alkylating agent N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) is known to trigger the adaptive response by inducing the ada-regulon – consisting of three DNA repair enzymes Ada, AlkB, AlkA and the enigmatic AidB. We have applied custom designed tiling arrays to study transcriptional changes in Escherichia coli following a MNNG challenge. Along with the expected upregulation of the adaptive response genes (ada, alkA and alkB), we identified a number of differentially expressed transcripts, both novel and annotated. This indicates a wider regulatory response than previously documented. There were 250 differentially-expressed and 2275 similarly-expressed unannotated transcripts. We found novel upregulation of several stress-induced transcripts, including the SOS inducible genes recN and tisAB, indicating a novel role for these genes in alkylation repair. Furthermore, the ada-regulon A and B boxes were found to be insufficient to explain the regulation of the adaptive response genes after MNNG exposure, suggesting that additional regulatory elements must be involved.

Anaerobic expression of the gadE-mdtEF multidrug efflux operon is primarily regulated by the two-component system ArcBA through antagonizing the H-NS mediated repression

The gadE-mdtEF operon encodes a central acid resistance regulator GadE and two multidrug efflux proteins MdtEF. Although transcriptional regulation of gadE in the context of acid resistance under the aerobic growth environment of Escherichia coli has been extensively studied, regulation of the operon under the physiologically relevant environment of anaerobic growth and its effect on the expression of the multidrug efflux proteins MdtEF in the operon has not been disclosed. Our previous study revealed that anaerobic induction of the operon was dependent on ArcA, the response regulator of the ArcBA two-component system, in the M9 glucose minimal medium. However, the detailed regulatory mechanism remains unknown. In this study, we showed that anaerobic activation of mdtEF was driven by the 798 bp unusually long gadE promoter. Deletion of evgA, ydeO, rpoS, and gadX which has been shown to activate the gadE expression during acid stresses under aerobic condition did not have a significant effect on the anaerobic activation of the operon. Rather, anaerobic activation of the operon was largely dependent on the global regulator ArcA and a GTPase MnmE. Under aerobic condition, transcription of gadE was repressed by the global DNA silencer H-NS in M9 minimal medium. Interestingly, under anaerobic condition, while ΔarcA almost completely abolished transcription of gadE-mdtEF, further deletion of hns in ΔarcA mutant restored the transcription of the full-length PgadE-lacZ, and P1- and P3-lacZ fusions, suggesting an antagonistic effect of ArcA on the H-NS mediated repression. Taken together, we conclude that the anaerobic activation of the gadE-mdtEF was primarily mediated by the two-component system ArcBA through antagonizing the H-NS mediated repression.

Most RNAs regulating ribosomal protein biosynthesis in Escherichia coli are narrowly distributed to Gammaproteobacteria

In Escherichia coli, 12 distinct RNA structures within the transcripts encoding ribosomal proteins interact with specific ribosomal proteins to allow autogenous regulation of expression from large multi-gene operons, thus coordinating ribosomal protein biosynthesis across multiple operons. However, these RNA structures are typically not represented in the RNA Families Database or annotated in genomic sequences databases, and their phylogenetic distribution is largely unknown. To investigate the extent to which these RNA structures are conserved across eubacterial phyla, we created multiple sequence alignments representing 10 of these messenger RNA (mRNA) structures in E. coli. We find that while three RNA structures are widely distributed across many phyla of bacteria, seven of the RNAs are narrowly distributed to a few orders of Gammaproteobacteria. To experimentally validate our computational predictions, we biochemically confirmed dual L1-binding sites identified in many Firmicute species. This work reveals that RNA-based regulation of ribosomal protein biosynthesis is used in nearly all eubacterial phyla, but the specific RNA structures that regulate ribosomal protein biosynthesis in E. coli are narrowly distributed. These results highlight the limits of our knowledge regarding ribosomal protein biosynthesis regulation outside of E. coli, and the potential for alternative RNA structures responsible for regulating ribosomal proteins in other eubacteria.

A comparison of dense transposon insertion libraries in the Salmonella serovars Typhi and Typhimurium

Salmonella Typhi and Typhimurium diverged only ∼50 000 years ago, yet have very different host ranges and pathogenicity. Despite the availability of multiple whole-genome sequences, the genetic differences that have driven these changes in phenotype are only beginning to be understood. In this study, we use transposon-directed insertion-site sequencing to probe differences in gene requirements for competitive growth in rich media between these two closely related serovars. We identify a conserved core of 281 genes that are required for growth in both serovars, 228 of which are essential in Escherichia coli. We are able to identify active prophage elements through the requirement for their repressors. We also find distinct differences in requirements for genes involved in cell surface structure biogenesis and iron utilization. Finally, we demonstrate that transposon-directed insertion-site sequencing is not only applicable to the protein-coding content of the cell but also has sufficient resolution to generate hypotheses regarding the functions of non-coding RNAs (ncRNAs) as well. We are able to assign probable functions to a number of cis-regulatory ncRNA elements, as well as to infer likely differences in trans-acting ncRNA regulatory networks.

Evidence classification of high-throughput protocols and confidence integration in RegulonDB

RegulonDB provides curated information on the transcriptional regulatory network of Escherichia coli and contains both experimental data and computationally predicted objects. To account for the heterogeneity of these data, we introduced in version 6.0, a two-tier rating system for the strength of evidence, classifying evidence as either ‘weak’ or ‘strong’ (Gama-Castro,S., Jimenez-Jacinto,V., Peralta-Gil,M. et al. RegulonDB (Version 6.0): gene regulation model of Escherichia Coli K-12 beyond transcription, active (experimental) annotated promoters and textpresso navigation. Nucleic Acids Res., 2008;36:D120–D124.). We now add to our classification scheme the classification of high-throughput evidence, including chromatin immunoprecipitation (ChIP) and RNA-seq technologies. To integrate these data into RegulonDB, we present two strategies for the evaluation of confidence, statistical validation and independent cross-validation. Statistical validation involves verification of ChIP data for transcription factor-binding sites, using tools for motif discovery and quality assessment of the discovered matrices. Independent cross-validation combines independent evidence with the intention to mutually exclude false positives. Both statistical validation and cross-validation allow to upgrade subsets of data that are supported by weak evidence to a higher confidence level. Likewise, cross-validation of strong confidence data extends our two-tier rating system to a three-tier system by introducing a third confidence score ‘confirmed’.

Database URL:http://regulondb.ccg.unam.mx/

Transcriptomic profiling of the oyster pathogen Vibrio splendidus opens a window on the evolutionary dynamics of the small RNA repertoire in the Vibrio genus

Work in recent years has led to the recognition of the importance of small regulatory RNAs (sRNAs) in bacterial regulation networks. New high-throughput sequencing technologies are paving the way to the exploration of an expanding sRNA world in nonmodel bacteria. In the Vibrio genus, compared to the enterobacteriaceae, still a limited number of sRNAs have been characterized, mostly in Vibrio cholerae, where they have been shown to be important for virulence, as well as in Vibrio harveyi. In addition, genome-wide approaches in V. cholerae have led to the discovery of hundreds of potential new sRNAs. Vibrio splendidus is an oyster pathogen that has been recently associated with massive mortality episodes in the French oyster growing industry. Here, we report the first RNA-seq study in a Vibrio outside of the V. cholerae species. We have uncovered hundreds of candidate regulatory RNAs, be it cis-regulatory elements, antisense RNAs, and trans-encoded sRNAs. Conservation studies showed the majority of them to be specific to V. splendidus. However, several novel sRNAs, previously unidentified, are also present in V. cholerae. Finally, we identified 28 trans sRNAs that are conserved in all the Vibrio genus species for which a complete genome sequence is available, possibly forming a Vibrio "sRNA core."

Genome-wide prediction and validation of sigma70 promoters in Lactobacillus plantarum WCFS1

BACKGROUND

In prokaryotes, sigma factors are essential for directing the transcription machinery towards promoters. Various sigma factors have been described that recognize, and bind to specific DNA sequence motifs in promoter sequences. The canonical sigma factor σ(70) is commonly involved in transcription of the cell's housekeeping genes, which is mediated by the conserved σ(70) promoter sequence motifs. In this study the σ(70)-promoter sequences in Lactobacillus plantarum WCFS1 were predicted using a genome-wide analysis. The accuracy of the transcriptionally-active part of this promoter prediction was subsequently evaluated by correlating locations of predicted promoters with transcription start sites inferred from the 5'-ends of transcripts detected by high-resolution tiling array transcriptome datasets.

RESULTS

To identify σ(70)-related promoter sequences, we performed a genome-wide sequence motif scan of the L. plantarum WCFS1 genome focussing on the regions upstream of protein-encoding genes. We obtained several highly conserved motifs including those resembling the conserved σ(70)-promoter consensus. Position weight matrices-based models of the recovered σ(70)-promoter sequence motif were employed to identify 3874 motifs with significant similarity (p-value<10(-4)) to the model-motif in the L. plantarum genome. Genome-wide transcript information deduced from whole genome tiling-array transcriptome datasets, was used to infer transcription start sites (TSSs) from the 5'-end of transcripts. By this procedure, 1167 putative TSSs were identified that were used to corroborate the transcriptionally active fraction of these predicted promoters. In total, 568 predicted promoters were found in proximity (≤ 40 nucleotides) of the putative TSSs, showing a highly significant co-occurrence of predicted promoter and TSS (p-value<10(-263)).

CONCLUSIONS

High-resolution tiling arrays provide a suitable source to infer TSSs at a genome-wide level, and allow experimental verification of in silico predicted promoter sequence motifs.

An atlas of Hfq-bound transcripts reveals 3' UTRs as a genomic reservoir of regulatory small RNAs

The small RNAs associated with the protein Hfq constitute one of the largest classes of post-transcriptional regulators known to date. Most previously investigated members of this class are encoded by conserved free-standing genes. Here, deep sequencing of Hfq-bound transcripts from multiple stages of growth of Salmonella typhimurium revealed a plethora of new small RNA species from within mRNA loci, including DapZ, which overlaps with the 3' region of the biosynthetic gene, dapB. Synthesis of the DapZ small RNA is independent of DapB protein synthesis, and is controlled by HilD, the master regulator of Salmonella invasion genes. DapZ carries a short G/U-rich domain similar to that of the globally acting GcvB small RNA, and uses GcvB-like seed pairing to repress translation of the major ABC transporters, DppA and OppA. This exemplifies double functional output from an mRNA locus by the production of both a protein and an Hfq-dependent trans-acting RNA. Our atlas of Hfq targets suggests that the 3' regions of mRNA genes constitute a rich reservoir that provides the Hfq network with new regulatory small RNAs.

Features for computational operon prediction in prokaryotes

Accurate prediction of operons can improve the functional annotation and application of genes within operons in prokaryotes. Here, we review several features: (i) intergenic distance, (ii) metabolic pathways, (iii) homologous genes, (iv) promoters and terminators, (v) gene order conservation, (vi) microarray, (vii) clusters of orthologous groups, (viii) gene length ratio, (ix) phylogenetic profiles, (x) operon length/size and (xi) STRING database scores, as well as some other features, which have been applied in recent operon prediction methods in prokaryotes in the literature. Based on a comparison of the prediction performances of these features, we conclude that other, as yet undiscovered features, or feature selection with a receiver operating characteristic analysis before algorithm processing can improve operon prediction in prokaryotes.

ARF-TSS: an alternative method for identification of transcription start site in bacteria

Current methods for identifying transcription start sites (TSSs) of specific genes in bacteria usually require adaptors or radioactive labeling. These approaches can be technically demanding and environmentally unfriendly. Here we present a method for identifying TSS called ARF-TSS, which is based on cDNA generation, circularization, PCR amplification, and DNA sequencing to determine the 5'-end of transcripts, thus circumventing the need for adaptors and radioactive labeling. We validated the method using the gene lasI from the bacterial pathogen Pseudomonas aeruginosa. Our results show that ARF-TSS could be a good alternative to traditional methods for bacterial TSS analysis.

Esre: a novel essential non-coding RNA in Escherichia coli

YigP gene (GeneID: 948915) locates between ubiquinone biosynthetic genes ubiE and ubiB in Escherichia coli. GeneBank annotates yigP as a putative protein-coding gene. In this study, we found a new essential sRNA gene, esre, locates within the region of yigP. The E. coli strain with inactive esre must rely on a complementary plasmid to survive. Moreover, RACE experiments showed esre encodes an RNA molecule of 252 nt. Further experiments revealed esre gene is immune to frame shift mutations and the function of esre depends mostly on the RNA secondary structure, which are typical traits of sRNA. Since it is difficult to predict the target of an essential sRNA, more research is needed to reveal the function and mechanism of esre.

RNA-seq based transcriptional map of bovine respiratory disease pathogen "Histophilus somni 2336"

Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify “novel” genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.

The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

Metatranscriptomic analysis of microbes in an Oceanfront deep-subsurface hot spring reveals novel small RNAs and type-specific tRNA degradation

Studies of small noncoding RNAs (sRNAs) have been conducted predominantly using culturable organisms, and the acquisition of further information about sRNAs from global environments containing uncultured organisms now is very important. In this study, hot spring water (57°C, pH 8.1) was collected directly from the underground environment at depths of 250 to 1,000 m in Yunohama, Japan, and small RNA sequences obtained from the environment were analyzed. A phylogenetic analysis of both archaeal and bacterial 16S rRNA gene sequences was conducted, and the results suggested the presence of unique species in the environment, corresponding to the Archaeal Richmond Mine Acidophilic Nanoorganisms (ARMAN) group and three new Betaproteobacteria. A metatranscriptomic analysis identified 64,194 (20,057 nonredundant) cDNA sequences. Of these cDNAs, 90% were either tRNAs, tRNA fragments, rRNAs, or rRNA fragments, whereas 2,181 reads (10%) were classified as previously uncharacterized putative candidate sRNAs. Among these, 15 were particularly abundant, 14 of which showed no sequence similarity to any known noncoding RNA, and at least six of which form very stable RNA secondary structures. The analysis of a large number of tRNA fragments suggested that unique relationships exist between the anticodons of the tRNAs and the sites of tRNA degradation. Previous bacterial tRNA degradation studies have been limited to specific organisms, such as Escherichia coli and Streptomyces coelicolor, and the current results suggest that specific tRNA decay occurs more frequently than previously expected.

An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.

Deep sequencing reveals as-yet-undiscovered small RNAs in Escherichia coli

BACKGROUND

In Escherichia coli, approximately 100 regulatory small RNAs (sRNAs) have been identified experimentally and many more have been predicted by various methods. To provide a comprehensive overview of sRNAs, we analysed the low-molecular-weight RNAs (< 200 nt) of E. coli with deep sequencing, because the regulatory RNAs in bacteria are usually 50-200 nt in length.

RESULTS

We discovered 229 novel candidate sRNAs (≥ 50 nt) with computational or experimental evidence of transcription initiation. Among them, the expression of seven intergenic sRNAs and three cis-antisense sRNAs was detected by northern blot analysis. Interestingly, five novel sRNAs are expressed from prophage regions and we note that these sRNAs have several specific characteristics. Furthermore, we conducted an evolutionary conservation analysis of the candidate sRNAs and summarised the data among closely related bacterial strains.

CONCLUSIONS

This comprehensive screen for E. coli sRNAs using a deep sequencing approach has shown that many as-yet-undiscovered sRNAs are potentially encoded in the E. coli genome. We constructed the Escherichia coli Small RNA Browser (ECSBrowser; http://rna.iab.keio.ac.jp/), which integrates the data for previously identified sRNAs and the novel sRNAs found in this study.

cis-antisense RNA, another level of gene regulation in bacteria

A substantial amount of antisense transcription is a hallmark of gene expression in eukaryotes. However, antisense transcription was first demonstrated in bacteria almost 50 years ago. The transcriptomes of bacteria as different as Helicobacter pylori, Bacillus subtilis, Escherichia coli, Synechocystis sp. strain PCC6803, Mycoplasma pneumoniae, Sinorhizobium meliloti, Geobacter sulfurreducens, Vibrio cholerae, Chlamydia trachomatis, Pseudomonas syringae, and Staphylococcus aureus have now been reported to contain antisense RNA (asRNA) transcripts for a high percentage of genes. Bacterial asRNAs share functional similarities with trans-acting regulatory RNAs, but in addition, they use their own distinct mechanisms. Among their confirmed functional roles are transcription termination, codegradation, control of translation, transcriptional interference, and enhanced stability of their respective target transcripts. Here, we review recent publications indicating that asRNAs occur as frequently in simple unicellular bacteria as they do in higher organisms, and we provide a comprehensive overview of the experimentally confirmed characteristics of asRNA actions and intimately linked quantitative aspects. Emerging functional data suggest that asRNAs in bacteria mediate a plethora of effects and are involved in far more processes than were previously anticipated. Thus, the functional impact of asRNAs should be considered when developing new strategies against pathogenic bacteria and when optimizing bacterial strains for biotechnology.

Gene discovery by genome-wide CDS re-prediction and microarray-based transcriptional analysis in phytopathogen Xanthomonas campestris

Background

One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward.

Results

This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of confirmed that Xcc_CDS002 encoding a novel SIR2-like domain protein is involved in virulence and Xcc_CDS1553 encoding a ArsR family transcription factor is involved in arsenate resistance.

Conclusions

Despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.

An integrated approach for finding overlooked genes in Shigella

BACKGROUND

The completion of numerous genome sequences introduced an era of whole-genome study. However, many genes are missed during genome annotation, including small RNAs (sRNAs) and small open reading frames (sORFs). In order to improve genome annotation, we aimed to identify novel sRNAs and sORFs in Shigella, the principal etiologic agents of bacillary dysentery.

METHODOLOGY/PRINCIPAL FINDINGS

We identified 64 sRNAs in Shigella, which were experimentally validated in other bacteria based on sequence conservation. We employed computer-based and tiling array-based methods to search for sRNAs, followed by RT-PCR and northern blots, to identify nine sRNAs in Shigella flexneri strain 301 (Sf301) and 256 regions containing possible sRNA genes. We found 29 candidate sORFs using bioinformatic prediction, array hybridization and RT-PCR verification. We experimentally validated 557 (57.9%) DOOR operon predictions in the chromosomes of Sf301 and 46 (76.7%) in virulence plasmid.We found 40 additional co-expressed gene pairs that were not predicted by DOOR.

CONCLUSIONS/SIGNIFICANCE

We provide an updated and comprehensive annotation of the Shigella genome. Our study increased the expected numbers of sORFs and sRNAs, which will impact on future functional genomics and proteomics studies. Our method can be used for large scale reannotation of sRNAs and sORFs in any microbe with a known genome sequence.

Genome-wide identification of transcription start sites yields a novel thermosensing RNA and new cyclic AMP receptor protein-regulated genes in Escherichia coli

Intergenic regions often contain regulatory elements that control the expression of flanking genes. Using a deep-sequencing approach, we identified numerous new transcription start sites in Escherichia coli, yielding a new thermosensing regulatory RNA and seven genes previously unknown to be under the control of the global regulator CRP.

RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data

With the availability of genome-wide transcription data and massive comparative sequencing, the discrimination of coding from noncoding RNAs and the assessment of coding potential in evolutionarily conserved regions arose as a core analysis task. Here we present RNAcode, a program to detect coding regions in multiple sequence alignments that is optimized for emerging applications not covered by current protein gene-finding software. Our algorithm combines information from nucleotide substitution and gap patterns in a unified framework and also deals with real-life issues such as alignment and sequencing errors. It uses an explicit statistical model with no machine learning component and can therefore be applied "out of the box," without any training, to data from all domains of life. We describe the RNAcode method and apply it in combination with mass spectrometry experiments to predict and confirm seven novel short peptides in Escherichia coli and to analyze the coding potential of RNAs previously annotated as "noncoding." RNAcode is open source software and available for all major platforms at http://wash.github.com/rnacode.

Genome-wide transcriptome analysis in yeast using high-density tiling arrays

In the last decade, it became clear that transcription goes far beyond that of protein-coding genes. Most RNA molecules are transcribed from intergenic regions or introns and exhibit much variability in size, expression level, secondary structure, and evolutionary conservation. While for several types of non-coding RNAs some cellular functions have been reported, like for micro-RNAs and small nucleolar RNAs, for most others no indications of function or regulation have so far been found. Therefore, the RNA population inside a cell is diverse and cryptic and, thus, demands powerful methods to study its composition, abundance, and structure. DNA oligonucleotide microarrays have proven to be of great utility to study transcription of genes in various organisms. Recently, due to advancement in microarray technology, tiling microarrays that extend transcription measurement to genomic regions beyond protein-coding genes were designed for several species. The Saccharomyces cerevisiae yeast tiling array contains overlapping probes across the full genomic sequence, with consecutive probes starting every 8 bp on average on each strand, enabling strand-specific measurement of transcription from a full eukaryotic genome. Here, we describe the methods used to extract yeast RNA, convert it into first-strand cDNA, fragment, and label it for hybridization to the tiling array. This protocol will enable researchers not only to study which genes are expressed and to what levels, but also to identify non-coding RNAs and to study the structure of transcripts including their untranslated regions, alternative start, stop, and processing sites. This information will allow understanding their roles inside cells.

Tiling array analysis of UV treated Escherichia coli predicts novel differentially expressed small peptides

Background

Despite comprehensive investigation, the Escherichia coli SOS response system is not yet fully understood. We have applied custom designed whole genome tiling arrays to measure UV invoked transcriptional changes in E. coli. This study provides a more complete insight into the transcriptome and the UV irradiation response of this microorganism.

Results

We detected a number of novel differentially expressed transcripts in addition to the expected SOS response genes (such as sulA, recN, uvrA, lexA, umuC and umuD) in the UV treated cells. Several of the differentially expressed transcripts might play important roles in regulation of the cellular response to UV damage. We have predicted 23 novel small peptides from our set of detected non-gene transcripts. Further, three of the predicted peptides were cloned into protein expression vectors to test the biological activity. All three constructs expressed the predicted peptides, in which two of them were highly toxic to the cell. Additionally, a remarkably high overlap with previously in-silico predicted non-coding RNAs (ncRNAs) was detected. Generally we detected a far higher transcriptional activity than the annotation suggests, and these findings correspond with previous transcription mappings from E. coli and other organisms.

Conclusions

Here we demonstrate that the E. coli transcriptome consists of far more transcripts than the present annotation suggests, of which many transcripts seem important to the bacterial stress response. Sequence alignment of promoter regions suggest novel regulatory consensus sequences for some of the upregulated genes. Finally, several of the novel transcripts identified in this study encode putative small peptides, which are biologically active.

Analysis of Escherichia coli RNase E and RNase III activity in vivo using tiling microarrays

Tiling microarrays have proven to be a valuable tool for gaining insights into the transcriptomes of microbial organisms grown under various nutritional or stress conditions. Here, we describe the use of such an array, constructed at the level of 20 nt resolution for the Escherichia coli MG1655 genome, to observe genome-wide changes in the steady-state RNA levels in mutants defective in either RNase E or RNase III. The array data were validated by comparison to previously published results for a variety of specific transcripts as well as independent northern analysis of additional mRNAs and sRNAs. In the absence of RNase E, 60% of the annotated coding sequences showed either increases or decreases in their steady-state levels. In contrast, only 12% of the coding sequences were affected in the absence of RNase III. Unexpectedly, many coding sequences showed decreased abundance in the RNase E mutant, while more than half of the annotated sRNAs showed changes in abundance. Furthermore, the steady-state levels of many transcripts showed overlapping effects of both ribonucleases. Data are also presented demonstrating how the arrays were used to identify potential new genes, RNase III cleavage sites and the direct or indirect control of specific biological pathways.