Identification and characterization of RNA binding sites for (p)ppGpp using RNA-DRaCALA.

Ligand-binding RNAs (RNA aptamers) are widespread in the three domains of life, serving as sensors of metabolites and other small molecules. When aptamers are embedded within RNA transcripts as components of riboswitches, they can regulate gene expression upon binding their ligands. Previous methods for biochemical validation of computationally predicted aptamers are not well-suited for rapid screening of large numbers of RNA aptamers. Therefore, we utilized DRaCALA (Differential Radial Capillary Action of Ligand Assay), a technique designed originally to study protein-ligand interactions, to examine RNA-ligand binding, permitting rapid screening of dozens of RNA aptamer candidates concurrently. Using this method, which we call RNA-DRaCALA, we screened 30 ykkC family subtype 2a RNA aptamers that were computationally predicted to bind (p)ppGpp. Most of the aptamers bound both ppGpp and pppGpp, but some strongly favored only ppGpp or pppGpp, and some bound neither. Expansion of the number of biochemically verified sites allowed construction of more accurate secondary structure models and prediction of key features in the aptamers that distinguish a ppGpp from a pppGpp binding site. To demonstrate that the method works with other ligands, we also used RNA DRaCALA to analyze aptamer binding by thiamine pyrophosphate.

Escherichia coli transcriptome assembly from a compendium of RNA-seq data sets.

Owing to the complexities of bacterial RNA biology, the transcriptomes of even the best studied bacteria are not fully understood. To help elucidate the transcriptional landscape of E. coli, we compiled a compendium of 3,376 RNA-seq data sets composed of more than 7 trillion sequenced bases, which we evaluate with a transcript assembly pipeline. We report expression profiles for all annotated E. coli genes as well as 5,071 other transcripts. Additionally, we observe hundreds of instances of co-transcribed genes that are novel with respect to existing operon databases. By integrating data from a large number of sequencing experiments corresponding to a wide range of conditions, we are able to obtain a comprehensive view of the E. coli transcriptome.

A computational system for identifying operons based on RNA-seq data.

An operon is a set of neighboring genes in a genome that is transcribed as a single polycistronic message. Genes that are part of the same operon often have related functional roles or participate in the same metabolic pathways. The majority of all bacterial genes are co-transcribed with one or more other genes as part of a multi-gene operon. Thus, accurate identification of operons is important in understanding co-regulation of genes and their functional relationships. Here, we present a computational system that uses RNA-seq data to determine operons throughout a genome. The system takes the name of a genome and one or more files of RNA-seq data as input. Our method combines primary genomic sequence information with expression data from the RNA-seq files in a unified probabilistic model in order to identify operons. We assess our method's ability to accurately identify operons in a range of species through comparison to external databases of operons, both experimentally confirmed and computationally predicted, and through focused experiments that confirm new operons identified by our method. Our system is freely available at https://cs.wellesley.edu/~btjaden/Rockhopper/.

Small RNA profiling in Mycobacterium tuberculosis identifies MrsI as necessary for an anticipatory iron sparing response.

One key to the success of Mycobacterium tuberculosis as a pathogen is its ability to reside in the hostile environment of the human macrophage. Bacteria adapt to stress through a variety of mechanisms, including the use of small regulatory RNAs (sRNAs), which posttranscriptionally regulate bacterial gene expression. However, very little is currently known about mycobacterial sRNA-mediated riboregulation. To date, mycobacterial sRNA discovery has been performed primarily in log-phase growth, and no direct interaction between any mycobacterial sRNA and its targets has been validated. Here, we performed large-scale sRNA discovery and expression profiling in M. tuberculosis during exposure to five pathogenically relevant stresses. From these data, we identified a subset of sRNAs that are highly induced in multiple stress conditions. We focused on one of these sRNAs, ncRv11846, here renamed mycobacterial regulatory sRNA in iron (MrsI). We characterized the regulon of MrsI and showed in mycobacteria that it regulates one of its targets, bfrA, through a direct binding interaction. MrsI mediates an iron-sparing response that is required for optimal survival of M. tuberculosis under iron-limiting conditions. However, MrsI is induced by multiple host-like stressors, which appear to trigger MrsI as part of an anticipatory response to impending iron deprivation in the macrophage environment.

Identification and Characterization of Serotype-Specific Variation in Group A Streptococcus Pilus Expression.

Isolates of a given bacterial pathogen often display phenotypic variation, and this can negatively impact public health, for example, by reducing the efficacy of preventative measures. Here, we identify that the human pathogen group A Streptococcus (GAS; Streptococcus pyogenes) expresses pili on its cell surface in a serotype-specific manner. Specifically, we show that serotype M3 GAS isolates, which are nonrandomly associated with causing particularly severe and lethal invasive infections, produce negligible amounts of pili relative to serotype M1 and M49 isolates. Performance of an interserotype transcriptome comparison (serotype M1 versus serotype M3) was instrumental in this discovery. We also identified that the transcriptional regulator Nra positively regulates pilus expression in M3 GAS isolates and that the low level of pilus expression of these isolates correlates with a low level of nra transcription. Finally, we discovered that the phenotypic consequences of low levels of pilus expression by M3 GAS isolates are a reduced ability to adhere to host cells and an increased ability to survive and proliferate in human blood. We propose that an enhanced ability to survive in human blood, in part due to reduced pilus expression, is a contributing factor in the association of serotype M3 isolates with highly invasive infections. In conclusion, our data show that GAS isolates express pili in a serotype-dependent manner and may inform vaccine development, given that pilus proteins are being discussed as possible GAS vaccine antigens.

Probing the sRNA regulatory landscape of P. aeruginosa: post-transcriptional control of determinants of pathogenicity and antibiotic susceptibility.

During environmental adaptation bacteria use small regulatory RNAs (sRNAs) to repress or activate expression of a large fraction of their proteome. We extended the use of the in vivo RNA proximity ligation method toward probing global sRNA interactions with their targets in Pseudomonas aeruginosa and verified the method with a known regulon controlled by the PrrF1 sRNA. We also identified two sRNAs (Sr0161 and ErsA) that interact with the mRNA encoding the major porin OprD responsible for the uptake of carbapenem antibiotics. These two sRNAs base pair with the 5' UTR of oprD leading to increase in resistance of the bacteria to meropenem. Additional proximity ligation experiments and enrichment for Sr0161 targets identified the mRNA for the regulator of type III secretion system. Interaction between the exsA mRNA and Sr0161 leads to a block in the synthesis of a component of the T3SS apparatus and an effector. Another sRNA, Sr006, positively regulates, without Hfq, the expression of PagL, an enzyme responsible for deacylation of lipid A, reducing its pro-inflammatory property and resulting in polymyxin resistance. Therefore, an analysis of global sRNA-mRNA interactions can lead to discoveries of novel pathways controlling gene expression that are likely integrated into larger regulatory networks.

TargetRNA2: identifying targets of small regulatory RNAs in bacteria.

Many small, noncoding RNAs (sRNAs) in bacteria act as posttranscriptional regulators of messenger RNAs. TargetRNA2 is a web server that identifies mRNA targets of sRNA regulatory action in bacteria. As input, TargetRNA2 takes the sequence of an sRNA and the name of a sequenced bacterial replicon. When searching for targets of RNA regulation, TargetRNA2 uses a variety of features, including conservation of the sRNA in other bacteria, the secondary structure of the sRNA, the secondary structure of each candidate mRNA target and the hybridization energy between the sRNA and each candidate mRNA target. TargetRNA2 outputs a ranked list of likely regulatory targets for the input sRNA. When evaluated on a comprehensive set of sRNA-target interactions, TargetRNA2 was found to be both accurate and efficient in identifying targets of sRNA regulatory action. Furthermore, TargetRNA2 has the ability to integrate RNA-seq data, if available. If an sRNA is differentially expressed in two or more RNA-seq experiments, TargetRNA2 considers co-differential gene expression when searching for regulatory targets, significantly improving the accuracy of target identifications. The TargetRNA2 web server is freely available for use at http://cs.wellesley.edu/∼btjaden/TargetRNA2.

Computational analysis of bacterial RNA-Seq data.

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system's ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.

Assessing computational tools for the discovery of small RNA genes in bacteria.

Over the past decade, a number of biocomputational tools have been developed to predict small RNA (sRNA) genes in bacterial genomes. In this study, several of the leading biocomputational tools, which use different methodologies, were investigated. The performance of the tools, both individually and in combination, was evaluated on ten sets of benchmark data, including data from a novel RNA-seq experiment conducted in this study. The results of this study offer insight into the utility as well as the limitations of the leading biocomputational tools for sRNA identification and provide practical guidance for users of the tools.

TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning.

Small regulatory RNAs pervade prokaryotes, with the best-studied family of these non-coding genes corresponding to trans-acting regulators that bind via base pairing to their message targets. Given the increasing frequency with which these genes are being identified, it is important that methods for illuminating their regulatory targets keep pace. Using a machine learning approach, we investigate thousands of interactions between small RNAs and their targets, and we interrogate more than a hundred features indicative of these interactions. We present a new method, TargetRNA3, for predicting targets of small RNA regulators and show that it outperforms existing approaches. TargetRNA3 is available at https://cs.wellesley.edu/~btjaden/TargetRNA3 .

MLpronto: A tool for democratizing machine learning.

The democratization of machine learning is a popular and growing movement. In a world with a wealth of publicly available data, it is important that algorithms for analysis of data are accessible and usable by everyone. We present MLpronto, a system for machine learning analysis that is designed to be easy to use so as to facilitate engagement with machine learning algorithms. With its web interface, MLpronto requires no computer programming or machine learning background, and it normally returns results in a matter of seconds. As input, MLpronto takes a file of data to be analyzed. MLpronto then executes some of the more commonly used supervised machine learning algorithms on the data and reports the results of the analyses. As part of its execution, MLpronto generates computer programming code corresponding to its machine learning analysis, which it also supplies as output. Thus, MLpronto can be used as a no-code solution for citizen data scientists with no machine learning or programming background, as an educational tool for those learning about machine learning, and as a first step for those who prefer to engage with programming code in order to facilitate rapid development of machine learning projects. MLpronto is freely available for use at https://mlpronto.org/.

Bioinformatic prediction and experimental verification of sRNAs in the haloarchaeon Haloferax volcanii.

Recently a small-scale RNomics study led to the experimental identification of 21 intergenic and 18 antisense sRNA genes in the haloarchaeon Haloferax volcanii. To broaden the knowledge about sRNAs in haloarchaea, two bioinformatic approaches were used to predict sRNA genes in the genome of H. volcanii. More than 120 putative intergenic sRNA genes were identified by these comparative genomic approaches. The expression of 61 of the predicted genes was analyzed using DNA microarrays, and 37 were found to be expressed under at least one of three conditions tested. Using the results of Northern blot analyses and of a high throughput sequencing study the number of expressed genes was raised to 54 and the small size was verified for 26 predicted sRNAs. An analysis of the coding capacity revealed that the set of predicted sRNAs most likely does not encode proteins or peptides. In two cases it turned out that the predictions had not identified bona fide sRNAs but conserved regions in UTRs of large protein-encoding transcripts. Taken together, the combination of bioinformatic prediction and experimental verification has more than tripled the number of known haloarchaeal sRNAs, underscoring the importance of regulatory RNAs in the third domain of life, the archaea. Further analyses of the biological functions of selected sRNAs, including the construction of deletion mutants, are currently under way.

TargetRNA: a tool for predicting targets of small RNA action in bacteria.

Many small RNA (sRNA) genes in bacteria act as posttranscriptional regulators of target messenger RNAs. Here, we present TargetRNA, a web tool for predicting mRNA targets of sRNA action in bacteria. TargetRNA takes as input a genomic sequence that may correspond to an sRNA gene. TargetRNA then uses a dynamic programming algorithm to search each annotated message in a specified genome for mRNAs that evince basepair-binding potential to the input sRNA sequence. Based on the calculated basepair-binding potential of each message with the given sRNA regulator, TargetRNA outputs a ranked list of candidate mRNA targets along with the predicted basepairing interaction of each target to the sRNA. The predictive performance of TargetRNA has been validated experimentally in several bacterial organisms. TargetRNA is freely available at http://snowwhite.wellesley.edu/targetRNA.

Small non-coding RNAs in Streptomyces coelicolor.

In bacteria, small RNAs (sRNAs) make important regulatory contributions to an ever increasing number of cellular processes. To expand the repertoire of known sRNAs, we sought to identify novel sRNAs in the differentiating, multicellular bacterium Streptomyces coelicolor. We describe a combined bioinformatic and experimental approach that enabled the identification and characterization of nine novel sRNAs in S. coelicolor, including a cis-encoded antisense sRNA. We examined sRNA expression throughout the S. coelicolor developmental cycle, which progresses from vegetative mycelium formation, to aerial mycelium formation and finally sporulation. We further determined the effects of growth medium composition (rich versus minimal medium) on sRNA gene expression, and compared wild-type sRNA expression profiles with those of four developmental mutants. All but two of the sRNAs exhibited some degree of medium dependence, with three sRNAs being expressed exclusively during growth on one medium type. Unlike most sRNAs characterized thus far, several sRNA genes in S. coelicolor were expressed constitutively (apart from during late sporulation), suggesting a possible housekeeping role for these transcripts. Others were expressed at specific developmental stages, and their expression profiles were altered in response to developmental mutations. Expression of one sRNA in particular was dependent upon the sporulation-specific sigma factor sigma(WhiG).

Small RNAs of the halophilic archaeon Haloferax volcanii.

In recent years, sRNAs (small non-coding RNAs) have been found to be abundant in eukaryotes and bacteria and have been recognized as a novel class of gene expression regulators. In contrast, much less is known about sRNAs in archaea, except for snoRNAs (small nucleolar RNAs) that are involved in the modification of bases in stable RNAs. Therefore bioinformatic and experimental RNomics approaches were undertaken to search for the presence of sRNAs in the model archaeon Haloferax volcanii, resulting in more than 150 putative sRNA genes being identified. Northern blot analyses were used to study (differential) expression of sRNA genes. Several chromosomal deletion mutants of sRNA genes were generated and compared with the wild-type. It turned out that two sRNAs are essential for growth at low salt concentrations and high temperatures respectively, and one is involved in the regulation of carbon metabolism. Taken together, it could be shown that sRNAs are as abundant in H. volcanii as they are in well-studied bacterial species and that they fulfil important biological roles under specific conditions.

Target prediction for small, noncoding RNAs in bacteria.

Many small, noncoding RNAs in bacteria act as post-transcriptional regulators by basepairing with target mRNAs. While the number of characterized small RNAs (sRNAs) has steadily increased, only a limited number of the corresponding mRNA targets have been identified. Here we present a program, TargetRNA, that predicts the targets of these bacterial RNA regulators. The program was evaluated by assessing whether previously known targets could be identified. The program was then used to predict targets for the Escherichia coli RNAs RyhB, OmrA, OmrB and OxyS, and the predictions were compared with changes in whole genome expression patterns observed upon expression of the sRNAs. Our results show that TargetRNA is a useful tool for finding mRNA targets of sRNAs, although its rate of success varies between sRNAs.

Transcriptome Analysis of Neisseria gonorrhoeae during Natural Infection Reveals Differential Expression of Antibiotic Resistance Determinants between Men and Women.

Neisseria gonorrhoeae is a bacterial pathogen responsible for the sexually transmitted infection gonorrhea. Emergence of antimicrobial resistance (AMR) of N. gonorrhoeae worldwide has resulted in limited therapeutic choices for this infection. Men who seek treatment often have symptomatic urethritis; in contrast, gonococcal cervicitis in women is usually minimally symptomatic, but may progress to pelvic inflammatory disease. Previously, we reported the first analysis of gonococcal transcriptome expression determined in secretions from women with cervical infection. Here, we defined gonococcal global transcriptional responses in urethral specimens from men with symptomatic urethritis and compared these with transcriptional responses in specimens obtained from women with cervical infections and in vitro-grown N. gonorrhoeae isolates. This is the first comprehensive comparison of gonococcal gene expression in infected men and women. RNA sequencing analysis revealed that 9.4% of gonococcal genes showed increased expression exclusively in men and included genes involved in host immune cell interactions, while 4.3% showed increased expression exclusively in women and included phage-associated genes. Infected men and women displayed comparable antibiotic-resistant genotypes and in vitro phenotypes, but a 4-fold higher expression of the Mtr efflux pump-related genes was observed in men. These results suggest that expression of AMR genes is programed genotypically and also driven by sex-specific environments. Collectively, our results indicate that distinct N. gonorrhoeae gene expression signatures are detected during genital infection in men and women. We propose that therapeutic strategies could target sex-specific differences in expression of antibiotic resistance genes.IMPORTANCE Recent emergence of antimicrobial resistance of Neisseria gonorrhoeae worldwide has resulted in limited therapeutic choices for treatment of infections caused by this organism. We performed global transcriptomic analysis of N. gonorrhoeae in subjects with gonorrhea who attended a Nanjing, China, sexually transmitted infection (STI) clinic, where antimicrobial resistance of N. gonorrhoeae is high and increasing. We found that N. gonorrhoeae transcriptional responses to infection differed in genital specimens taken from men and women, particularly antibiotic resistance gene expression, which was increased in men. These sex-specific findings may provide a new approach to guide therapeutic interventions and preventive measures that are also sex specific while providing additional insight to address antimicrobial resistance of N. gonorrhoeae.

An approach for clustering gene expression data with error information.

BACKGROUND: Clustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated genes. Even the best clustering methods, however, are unlikely to provide meaningful results if too much of the data is unreliable. With the maturation of microarray technology, a wealth of research on statistical analysis of gene expression data has encouraged researchers to consider error and uncertainty in their microarray experiments, so that experiments are being performed increasingly with repeat spots per gene per chip and with repeat experiments. One of the challenges is to incorporate the measurement error information into downstream analyses of gene expression data, such as traditional clustering techniques. RESULTS: In this study, a clustering approach is presented which incorporates both gene expression values and error information about the expression measurements. Using repeat expression measurements, the error of each gene expression measurement in each experiment condition is estimated, and this measurement error information is incorporated directly into the clustering algorithm. The algorithm, CORE (Clustering Of Repeat Expression data), is presented and its performance is validated using statistical measures. By using error information about gene expression measurements, the clustering approach is less sensitive to noise in the underlying data and it is able to achieve more accurate clusterings. Results are described for both synthetic expression data as well as real gene expression data from Escherichia coli and Saccharomyces cerevisiae. CONCLUSION: The additional information provided by replicate gene expression measurements is a valuable asset in effective clustering. Gene expression profiles with high errors, as determined from repeat measurements, may be unreliable and may associate with different clusters, whereas gene expression profiles with low errors can be clustered with higher specificity. Results indicate that including error information from repeat gene expression measurements can lead to significant improvements in clustering accuracy.

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

Microarrays traditionally have been used to analyze the expression behavior of large numbers of coding transcripts. Here we present a comprehensive approach for high-throughput transcript discovery in Escherichia coli focused mainly on intergenic regions which, together with analysis of coding transcripts, provides us with a more complete insight into the organism's transcriptome. Using a whole genome array, we detected expression for 4052 coding transcripts and identified 1102 additional transcripts in the intergenic regions of the E.coli genome. Further classification reveals 317 novel transcripts with unknown function. Our results show that, despite sophisticated approaches to genome annotation, many cellular transcripts remain unidentified. Through the experimental identification of all RNAs expressed under a specific condition, we gain a more thorough understanding of all cellular processes.

GRIL-seq provides a method for identifying direct targets of bacterial small regulatory RNA by in vivo proximity ligation.

The first step in the post-transcriptional regulatory function of most bacterial small non-coding RNAs (sRNAs) is base pairing with partially complementary sequences of targeted transcripts. We present a simple method for identifying sRNA targets in vivo and defining processing sites of the regulated transcripts. The technique, referred to as global small non-coding RNA target identification by ligation and sequencing (GRIL-seq), is based on preferential ligation of sRNAs to the ends of base-paired targets in bacteria co-expressing T4 RNA ligase, followed by sequencing to identify the chimaeras. In addition to the RNA chaperone Hfq, the GRIL-seq method depends on the activity of the pyrophosphorylase RppH. Using PrrF1, an iron-regulated sRNA in Pseudomonas aeruginosa, we demonstrated that direct regulatory targets of this sRNA can readily be identified. Therefore, GRIL-seq represents a powerful tool not only for identifying direct targets of sRNAs in a variety of environments, but also for uncovering novel roles for sRNAs and their targets in complex regulatory networks.