Brian Tjaden

Global analysis of small RNA and mRNA targets of Hfq

Hfq, a bacterial member of the Sm family of RNA‐binding proteins, is required for the action of many small regulatory RNAs that act by basepairing with target mRNAs. Hfq binds this family of small RNAs efficiently. We have used co‐immunoprecipitation with Hfq and direct detection of the bound RNAs on genomic microarrays to identify members of this small RNA family. This approach was extremely sensitive; even Hfq‐binding small RNAs expressed at low levels were readily detected. At least 15 of 46 known small RNAs in E. coli interact with Hfq. In addition, high signals in other intergenic regions suggested up to 20 previously unidentified small RNAs bind Hfq; five were confirmed by Northern analysis. Strong signals within genes and operons also were detected, some of which correspond to known Hfq targets. Within the argX‐hisR‐leuT‐proM operon, Hfq appears to compete with RNase E and modulate RNA processing and degradation. Thus Hfq immunoprecipitation followed by microarray analysis is a highly effective method for detecting a major class of small RNAs as well as identifying new Hfq functions.

De novo assembly of bacterial transcriptomes from RNA-seq data

Transcriptome assays are increasingly being performed by high-throughput RNA sequencing (RNA-seq). For organisms whose genomes have not been sequenced and annotated, transcriptomes must be assembled de novo from the RNA-seq data. Here, we present novel algorithms, specific to bacterial gene structures and transcriptomes, for analysis of bacterial RNA-seq data and de novo transcriptome assembly. The algorithms are implemented in an open source software system called Rockhopper 2. We find that Rockhopper 2 outperforms other de novo transcriptome assemblers and offers accurate and efficient analysis of bacterial RNA-seq data. Rockhopper 2 is available at http://cs.wellesley.edu/~btjaden/Rockhopper.

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.

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.

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.

The Entomopathogenic Bacterial Endosymbionts Xenorhabdus and Photorhabdus: Convergent Lifestyles from Divergent Genomes

Members of the genus Xenorhabdus are entomopathogenic bacteria that associate with nematodes. The nematode-bacteria pair infects and kills insects, with both partners contributing to insect pathogenesis and the bacteria providing nutrition to the nematode from available insect-derived nutrients. The nematode provides the bacteria with protection from predators, access to nutrients, and a mechanism of dispersal. Members of the bacterial genus Photorhabdus also associate with nematodes to kill insects, and both genera of bacteria provide similar services to their different nematode hosts through unique physiological and metabolic mechanisms. We posited that these differences would be reflected in their respective genomes. To test this, we sequenced to completion the genomes of Xenorhabdus nematophila ATCC 19061 and Xenorhabdus bovienii SS-2004. As expected, both Xenorhabdus genomes encode many anti-insecticidal compounds, commensurate with their entomopathogenic lifestyle. Despite the similarities in lifestyle between Xenorhabdus and Photorhabdus bacteria, a comparative analysis of the Xenorhabdus, Photorhabdus luminescens, and P. asymbiotica genomes suggests genomic divergence. These findings indicate that evolutionary changes shaped by symbiotic interactions can follow different routes to achieve similar end points.

A Novel Fur- and Iron-Regulated Small RNA, NrrF, Is Required for Indirect Fur-Mediated Regulation of the sdhA and sdhC Genes in Neisseria meningitidis

Iron is both essential for bacterial growth and toxic at higher concentrations; thus, iron homeostasis is tightly regulated. In Neisseria meningitidis the majority of iron-responsive gene regulation is mediated by the ferric uptake regulator protein (Fur), a protein classically defined as a transcriptional repressor. Recently, however, microarray studies have identified a number of genes in N. meningitidis that are iron and Fur activated, demonstrating a new role for Fur as a transcriptional activator. Since Fur has been shown to indirectly activate gene transcription through the repression of small regulatory RNA molecules in other organisms, we hypothesized that a similar mechanism could account for Fur-dependent, iron-activated gene transcription in N. meningitidis. In this study, we used a bioinformatics approach to screen for the presence of Fur-regulated small RNA molecules in N. meningitidis MC58. This screen identified one small RNA, herein named NrrF (for neisserial regulatory RNA responsive to iron [Fe]), which was demonstrated to be both iron responsive and Fur regulated and which has a well-conserved orthologue in N. gonorrhoeae. In addition, this screen identified a number of other likely, novel small RNA transcripts. Lastly, we utilized a new bioinformatics approach to predict regulatory targets of the NrrF small RNA. This analysis led to the identification of the sdhA and sdhC genes, which were subsequently demonstrated to be under NrrF regulation in an nrrF mutant. This study is the first report of small RNAs in N. meningitidis and the first to use a bioinformatics approach to identify, a priori, regulatory targets of a small RNA.

Mutations in Interaction Surfaces Differentially Impact E. coli Hfq Association with Small RNAs and Their mRNA Targets

The RNA chaperone protein Hfq is required for the function of all small RNAs (sRNAs) that regulate mRNA stability or translation by limited base pairing in Escherichia coli. While there have been numerous in vitro studies to characterize Hfq activity and the importance of specific residues, there has been only limited characterization of Hfq mutants in vivo. Here, we use a set of reporters as well as co-immunoprecipitation to examine 14 Hfq mutants expressed from the E. coli chromosome. The majority of the proximal face residues, as expected, were important for the function of sRNAs. The failure of sRNAs to regulate target mRNAs in these mutants can be explained by reduced sRNA accumulation. Two of the proximal mutants, D9A and F39A, acted differently from the others in that they had mixed effects on different sRNA/mRNA pairs and, in the case of F39A, showed differential sRNA accumulation. Mutations of charged residues at the rim of Hfq interfered with positive regulation and gave mixed effects for negative regulation. Some, but not all, sRNAs accumulated to lower levels in rim mutants, suggesting qualitative differences in how individual sRNAs are affected by Hfq. The distal face mutants were expected to disrupt binding of ARN motifs found in mRNAs. They were more defective for positive regulation than negative regulation at low mRNA expression, but the defects could be suppressed by higher levels of mRNA expression. We discuss the implications of these observations for Hfq binding to RNA and mechanisms of action.

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.

Initial Proteome Analysis of Model Microorganism Haemophilus influenzae Strain Rd KW20

The proteome of Haemophilus influenzae strain Rd KW20 was analyzed by liquid chromatography (LC) coupled with ion trap tandem mass spectrometry (MS/MS). This approach does not require a gel electrophoresis step and provides a rapidly developed snapshot of the proteome. In order to gain insight into the central metabolism of H. influenzae, cells were grown microaerobically and anaerobically in a rich medium and soluble and membrane proteins of strain Rd KW20 were proteolyzed with trypsin and directly examined by LC-MS/MS. Several different experimental and computational approaches were utilized to optimize the proteome coverage and to ensure statistically valid protein identification. Approximately 25% of all predicted proteins (open reading frames) of H. influenzae strain Rd KW20 were identified with high confidence, as their component peptides were unambiguously assigned to tandem mass spectra. Approximately 80% of the predicted ribosomal proteins were identified with high confidence, compared to the 33% of the predicted ribosomal proteins detected by previous two-dimensional gel electrophoresis studies. The results obtained in this study are generally consistent with those obtained from computational genome analysis, two-dimensional gel electrophoresis, and whole-genome transposon mutagenesis studies. At least 15 genes originally annotated as conserved hypothetical were found to encode expressed proteins. Two more proteins, previously annotated as predicted coding regions, were detected with high confidence; these proteins also have close homologs in related bacteria. The direct proteomics approach to studying protein expression in vivo reported here is a powerful method that is applicable to proteome analysis of any (micro)organism.