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Published in final edited form as: Adv Exp Med Biol. 2012;954:305–314. doi: 10.1007/978-1-4614-3561-7_38

Global Discovery of Small Noncoding RNAs in Pathogenic Yersinia Species

Jovanka T Koo 1, Wyndham W Lathem 1,✉
PMCID: PMC3522119  NIHMSID: NIHMS424096  PMID: 22782777

38.1 Introduction

Small noncoding RNAs (sRNAs) function as regulatory elements in both eukaryotes and bacteria. Trans-acting bacterial sRNAs posttranscriptionally regulate gene expression by base pairing with target mRNAs, which often leads to changes in translation efficiency and/or stability of the transcript. Bioinformatic search algorithms along with a variety of experimental approaches have become increasingly useful for the discovery of sRNAs and their mRNA targets. Our laboratory and others recently demonstrated that Hfq, a protein chaperone of sRNAs in bacteria, is required for the full virulence of both Yersinia pestis, the bacterium that causes the disease plague, and the genetically related gastrointestinal pathogen Yersinia pseudotuberculosis. This led us to pursue the first global identification and analysis of sRNAs in pathogenic Yersinia species. We have identified 150 previously unannotated sRNAs expressed by Y. pseudotuberculosis when cultured in vitro at either 26°C or 37°C, the majority of which are Yersinia-specific. The deletion of multiple Yersinia-specific sRNAs from either Y. pseudotuberculosis or Y. pestis leads to the attenuation of these pathogens in mouse models of infection. In addition, we have identified the mRNA targets controlled by one of these virulence-associated sRNAs, suggesting potential new virulence determinants in Y. pseudotuberculosis.

38.2 Bacterial Small RNAs (sRNAs): An Introduction

In recent years it has become clear that proteins are not the only modulators of gene expression. Small noncoding RNAs (sRNAs) in both bacteria and eukaryotes are now recognized as major components of diverse regulatory circuits. Bacterial sRNAs are heterogeneous in size (most are 50–500 nucleotides long) and are typically encoded in intergenic regions (IGRs). They are independently transcribed from their own promoters, contain ρ-independent terminators, and are usually not processed (Waters and Storz 2009). The majority of sRNAs control gene expression at the posttranscriptional level by base pairing within the 5′ untranslated region (UTR) of their target mRNAs. This RNA–RNA interaction leads to alterations in mRNA target translation or half-life (Gottesman and Storz 2010; Sharma and Vogel 2009). The predominant outcome of the sRNA–target mRNA interaction is the downregulation of gene expression (Sharma et al. 2007; Urbanowski et al. 2000), but positive regulation by sRNAs has also been described (Tramonti et al. 2008; Vogel and Papenfort 2006). The sRNA contact on the mRNA target is typically short (6–8 contiguous base pairs) and imperfect in part because the sRNA is encoded in trans in a distal genomic location, and in most cases requires the RNA chaperone protein Hfq to presumably stabilize the sRNA–mRNA interaction (McCullen et al. 2010).

38.2.1 Approaches for the Global Discovery of sRNAs

The exact number of sRNAs encoded in the genomes of most bacteria is still not known, although hundreds of sRNAs have recently been discovered in dozens of bacterial species. The identification of sRNAs has been challenging due to the unique features of these RNAs: (1) they are relatively small in size and this makes them resistant to single nucleotide mutagenesis; (2) they typically do not encode proteins and thus cannot be identified by simple searches for open reading frames; (3) the primary sequence of sRNAs is conserved only between closely related bacterial species; and (4) they have been omitted from many genetic screens, such as those using transposon mutagenesis, because they are encoded in the IGRs. The earliest studies relied on computational methods involving homology searches within the IGRs of closely related bacterial species and included the prediction of σ70 promoters and transcription terminators (Livny and Waldor 2007). More recently the use of bioinformatic algorithms that do not rely on primary sequence conservation as a predictive criterion has discovered additional potential sRNAs within the genomes of numerous bacterial species (Livny et al. 2006). However, the majority of the sRNAs identified by this method still warrant experimental validation.

In addition to the evolution of biocomputational means for sRNA discovery, there has recently been an explosion of experimental approaches for genome-wide detection of expressed sRNAs. These methodologies include the use of DNA microarrays, RNA-sequencing (RNA-Seq), and co-immunoprecipitation with sRNA-binding proteins (Vogel and Sharma 2005). High-density (tiling) microarrays, which cover both strands of the genome and include the IGRs, have successfully been used for global discovery of sRNAs in Caulobacter crescentus (Landt et al. 2008), Listeria monocytogenes (Toledo-Arana et al. 2009), Mycobacterium leprae (Akama et al. 2009), and Streptococcus pneumoniae (Kumar et al. 2010). The low-density arrays spotted with oligonucleotides or PCR fragments containing a defined set of regions of a particular genome have been useful in validating predicted sRNAs, and examples of these include studies of pathogenesis-relevant sRNAs in Staphylococcus aureus (Pichon and Felden 2005) and the sporulation network of Bacillus subtilis (Silvaggi et al. 2006).

With the advances in high-throughput sequencing techniques, RNA-Seq has been the leading approach for global transcriptomic analysis and sRNA discovery in bacteria. Currently available technologies include 454 pyrosequencing, SOLEXA, and SOLiD, and have all been applied to the identification of new sRNAs (MacLean et al. 2009; Srivatsan et al. 2008). Transcriptome analysis of Burkholderia cenocepacia strains grown under specific environmental conditions using the Illumina-SOLEXA platform resulted in the identification of thirteen sRNAs (Yoder-Himes et al. 2009). The SOLiD platform has been compared to SOLEXA in the transcriptomic profiling of B. anthracis and deemed suitable for sRNA discovery (Passalacqua et al. 2009), while Liu et al. applied the 454 method to Vibrio cholerae, which yielded hundreds of candidate sRNAs (Liu et al. 2009). Most recently, differential RNA-Seq, which is selective for the 5′ end of primary transcripts, has been employed by Sharma et al. to generate the operon map in Helicobacter pylori and has concomitantly allowed for the discovery of 60 previously unidentified sRNAs (Sharma et al. 2010). This approach has also been used in the GC-rich Gram-positive Streptomyces coelicolor and has resulted in the identification of 63 sRNAs, the majority of which are growth phase-dependent for their expression (Vockenhuber et al. 2011).

Lastly, sRNAs have been identified by co-purification with proteins. The sRNA chaperone protein Hfq has most commonly served as bait in these enrichment experiments, including one of the original global studies of sRNAs in E. coli in which interacting sRNAs were identified by co-immunoprecipitation with Hfq followed by tiling microarray hybridization (Zhang et al. 2003). Similar approaches have been successfully used in L. monocytogenes (Christiansen et al. 2004) and Pseudomonas aeruginosa (Sonnleitner et al. 2008). Sittka et al. combined co-immunoprecipitation of sRNAs using a chromosomally encoded, FLAG-tagged Hfq in Salmonella with RNA-Seq to identify not only Hfq-associated sRNAs but also potential mRNA targets (Sittka et al. 2008).

38.2.2 Approaches for sRNA Target Identification and Validation

To fully understand the biological function of a sRNA, identification of the cognate interacting mRNA target is required. Since it is now recognized that many sRNAs regulate multiple targets, a diverse set of tools are available for the genomewide discovery of targets. Several biocomputational approaches, including the programs TargetRNA (Tjaden 2008) and IntaRNA (Busch et al. 2008), have been developed to predict the mRNA targets of sRNAs based on the short and imperfect complementarity required for interaction. “Wet lab” experimental tools, including microarrays and proteomics, rely on the fact that the target regulation results in changes in mRNA and/or protein levels. These approaches are typically coupled to overexpression of sRNAs from a strong promoter or in sRNA-deletion backgrounds. For instance, pulse expression of sRNAs from the tightly controlled, arabinose-inducible PBAD promoter followed by microarray analysis revealed 18 potential targets for the iron starvation regulator RyhB sRNA of E. coli (Masse et al. 2005). A similar approach resulted in the identification of targets for the Salmonella RybB and E. coli OmrAB sRNAs that regulate outer membrane protein-encoding mRNAs (Guillier and Gottesman 2006; Papenfort et al. 2006). Proteomic analysis of strains lacking or overexpressing single sRNAs identified ompA as a MicA target (Rasmussen et al. 2009) and overexpression of the sRNA Spot42 led to a specific decrease in GalK protein levels (Moller et al. 2002). Once the mRNA targets of an sRNA are identified, regulation by the sRNA is usually verified by either chromosomal or plasmid-based mRNA fusions to reporter genes (Mandin and Gottesman 2009; Urban and Vogel 2007) or by direct assessment of target protein levels by immunoblot (or a chromosomal epitope tagging if no specific antibody is available (Koo et al. 2011).

The approaches presented here each have advantages and limitations. Bioinformatic analyses rely on the base-pairing interaction of the sRNA with its mRNA target, which implies complementarity. While this has been the major criterion for target identification using a bioinformatic approach, some of the algorithms that have been developed to identify previously uncharacterized sRNA–mRNA interactions have occasionally failed to detect known pairings. A major hurdle in developing computational methods is our incomplete understanding of the rules that govern the imperfect sRNA–mRNA interactions and the physical constraints that may be involved. The use of microarrays for target identification has been very successful due to the availability of whole genome cDNA and tiling microarrays for many bacterial species. This method, however, makes assumptions about the mechanisms by which the sRNA in question controls its target, as it is biased toward regulatory mechanisms that affect transcript levels. In some cases, sRNAs may alter only target translation and thus would be omitted in this type of analysis. Unfortunately, both microarray and proteomic strategies for target identification are limited by the inability to distinguish between direct and secondary targets. Proteomic analysis, however, has an advantage in that it can determine whether protein abundance is affected by a sRNA that regulates translation without causing a change in transcript level.

38.2.3 sRNA-Mediated Control of Virulence

While the specific functions of many sRNAs are still unknown, it is clear that sRNAs act to integrate extracellular signals that aid bacteria in adjusting to the environment and in the response to a variety of stresses. The control of virulence determinants important for bacterial infections is also coordinated by sRNAs. This includes mechanisms of direct sRNA–mRNA pairing and also through the binding of sRNAs to proteins. The end result of these interactions is the fine tuning of the metabolic requirements of pathogenic bacteria to endure the stress imposed by the host as well as the expression of virulence factors.

For example, a study of S. typhimurium-specific genetic islands led to the identification of 28 candidate sRNAs (Padalon-Brauch et al. 2008). Several of these sRNAs are induced when Salmonella resides within macrophages and one sRNA, IsrJ, was found to affect the translocation efficiency of virulence-associated effector proteins into nonphagocytic cells (Padalon-Brauch et al. 2008).

In S. aureus, RNAIII can both positively and negatively regulate targets that are involved in virulence. RNAIII pairs with the 5′ UTR of the hemolysin gene hla and destabilizes the secondary stem-loop structure that sequesters the ribosome-binding site to activate the translation of the hemolysin (Morfeldt et al. 1995). The same sRNA negatively regulates the synthesis of an adhesin encoded by the spa gene (Huntzinger et al. 2005) as well as the transcriptional regulator RotA (Geisinger et al. 2006). In addition to RNAIII, there are three other sRNAs located on the pathogenicity island of S. aureus that may also be involved in regulation of virulence determinants (Pichon and Felden 2005).

More recent global studies of sRNAs in bacterial pathogens have identified the 6S RNA as a required factor for the optimal intracellular replication of Legionella pneumophila (Faucher et al. 2010) and several sRNAs that allow M. tuberculosis to respond to various stress conditions (Arnvig and Young 2009). Also, Ramirez-Pena et al. have shown that the FasX sRNA in Streptococcus positively regulates the expression of the virulence factor streptokinase (Ramirez-Pena et al. 2010), while the iron-regulated sRNA RyhB regulates pathogenesis of Shigella dysenteriae (Murphy and Payne 2007).

38.2.4 Noncoding RNAs of Yersinia

At the time we initiated the studies described in this report, there were only 15 sRNAs annotated in the genome of Y. pseudotuberculosis strain IP32953 and 7 sRNAs annotated for Y. pestis strain CO92. A computational analysis by Livny et al. predicted that ~1,400 sRNAs may be encoded within the genome of Y. pestis (Livny et al. 2006), although no global experimental examination of sRNAs in Yersiniae had yet been attempted.

In 2003, Delihas predicted the presence of MicF, an sRNA that regulates OmpF in E. coli, within the Y. pestis and Y. enterocolitica genomes. He determined that MicF of Y. pestis shares ~53% conservation with the E. coli ortholog, and also identified additional sequence differences between the Y. pestis and Y. enterocolitica sRNAs (Delihas 2003).

More recently, Horler and Vanderpool identified SgrS, an sRNA that regulates the metabolic stress response, in the genomes of both Y. pestis and Y. pseudotuberculosis (Horler and Vanderpool 2009). Unlike in other Enterobacteriaceae, SgrS in Yersinia is not predicted to encode the small peptide SgrT that facilitates target interaction. Based on these studies, the authors hypothesized that the predicted target-interacting region in the SgrS of Y. pestis and Y. pseudotuberculosis is longer than in other closely related species to presumably facilitate a more stable interaction with the mRNA target. Additionally, Wadler and Vanderpool showed that the Y. pestis SgrS can rescue an E. coli SgrS mutant in base-pairing function, but the lack of SgrT in Y. pestis cannot complement the translation defect of the E. coli SgrS mutant (Wadler and Vanderpool 2009). Therefore, the absence of SgrT in Y. pestis suggests that the plague pathogen may not require this peptide to respond to stress.

The Csr system, including the noncoding RNAs CsrB and CsrC and their cognate RNA-binding protein CsrA, has also been described in Y. pseudotuberculosis. Heroven et al. determined that this posttranscriptional regulatory system is a part of a global pathway that allows Yersinia to adapt to metabolic and environmental stresses (Heroven et al. 2008). The authors found that the Csr system affects the global virulence gene regulator RovA by controlling the synthesis of the LysR-type regulator RovM. The components of the Csr system in Y. pseudotuberculosis appear to be differentially regulated in response to a variety of growth conditions and, much like in other bacterial pathogens, this system plays a role in the host–pathogen interaction.

The unique RNA molecule SsrA, which functions as both a tRNA and an mRNA encoding a short peptide tag, and its chaperone protein SmpB are highly conserved and participate in the quality control of translation (Karzai et al. 2000). Recent studies have identified the SsrA–SmpB system to be critical for the pathogenesis of both Y. pseudotuberculosis (Okan et al. 2006) and Y. pestis (Okan et al. 2010). The attenuation of the ssrA–smpB mutants in both Yersinia species is associated with the reduction in the synthesis and secretion of type III secreted proteins and Okan et al. have presented evidence that immunization of mice with ssrA–smpB deletion strains of Y. pestis leads to protection against a subsequent lethal intranasal challenge with fully virulent Y. pestis (Okan et al. 2010).

Finally, it has been established that the GlmY/GlmZ sRNAs contribute to the regulation of the GlmS enzyme. A recent study determined that while the regulation of GlmZ and GlmY transcription in E. coli is achieved through a σ70 promoter, in Y. pseudotuberculosis σ54 promoters regulate expression of the sRNA (Gopel et al. 2011). The significance of this regulatory difference is not yet understood.

38.2.5 The Small RNA Chaperone Hfq of Yersinia

In most bacteria, canonical trans-acting sRNAs require the chaperone Hfq to mediate and enhance the limited base-pairing interaction with their mRNA targets. In Yersiniae, the gene for Hfq was discovered in a screen for regulators of the heat-stable toxin Yst of Y. enterocolitica and was designated as yrp (Nakao et al. 1995). Deletion of Hfq in Y. enterocolitica and many other bacterial species has pleotropic effects (Meibom et al. 2009; Nakao et al. 1995) and it has been shown that Hfq plays a role in the virulence in a number of bacterial pathogens (Christiansen et al. 2004; Fantappie et al. 2009; Kulesus et al. 2008). Recent work from our laboratory has established that Hfq is critical for the pathogenesis of Y. pseudotuberculosis in the mouse model of Yersiniosis and affects motility, type III secretion, and intracellular survival (Schiano et al. 2010). Additionally, Geng et al. determined that Hfq is required for the full virulence of Y. pestis in the intravenous and subcutaneous models of mouse infection (Geng et al. 2009). This loss of virulence may be due to impaired replication and/or persistence of bacteria within the host macrophages, especially during the initial stage of infection (Geng et al. 2009). This suggests that Hfq, together with the sRNAs it controls, regulates essential virulence determinants in Yersiniae.

38.3 Global Identification of sRNAs Expressed by Y. pseudotuberculosis

As Hfq is required for the full virulence of Yersinia species (and therefore, by association, sRNAs), the goal of our study was to identify all sRNAs expressed by Y. pseudotuberculosis in an unbiased fashion. For this purpose we performed Illumina-SOLEXA-based deep sequencing on sRNA libraries generated from Y. pseudotuberculosis IP32953 grown under multiple conditions (Koo et al. 2011). Our deep sequencing analysis resulted in ~2.5–17 million 36 nt long reads which were categorized into different RNA species. The RNAs corresponding to IGRs were subsequently clustered and analyzed for conserved features such as promoters and ρ-independent terminators, yielding a list of 165 potential sRNAs. This analysis confirmed the expression of the 15 previously annotated regulatory RNAs in the Y. pseudotuberculosis genome and identified 150 previously unannotated sRNAs. This method proved to be extremely sensitive in that it uncovered RNAs whose levels are not detectable by Northern blot (Koo et al. 2011). We refer to the sRNAs we identified in this study as Ysrs (for Yersinia small RNAs).

BlastN analysis of Ysrs determined that 32 sRNAs encoded in the Y. pseudotuberculosis genome are represented by orthologous sequences in the E. coli and S. typhimurium genomes (Fig. 38.1, light gray) and these include many previously characterized sRNAs such as MicA, FnrS/Stnc520, RprA, GcvB, RybB, RhyB, GlmY, GlmZ, and OmrA/B (Coornaert et al. 2010). On the other hand, 75% of the Ysrs we identified are specific to Y. pseudotuberculosis and Y. pestis in that they do not show sequence conservation with other bacterial species (Fig. 38.1, dark gray). In addition, we identified 6 Ysrs that are Y. pseudotuberculosis-specific (for which there are no homologous sequences in the genome of Y. pestis) (Fig. 38.1, black). The Yersinia-specific sRNA group contains 63 Ysrs encoded by both Y. pseudotuberculosis and Y. pestis with single or multiple differences in sequence between the species (i.e. mismatches, deletions, insertions - Fig. 38.1, right panel). These may be significant in that a single nucleotide mismatch between an sRNA and its target can abolish or alter the regulatory effect. Additionally, Northern blot analysis revealed a difference in timing, temperature, and Hfq requirement for the expression of a subset of RNAs that are conserved between Y. pestis and Y. pseudotuberculosis (Koo et al. 2011). This suggests that evolutionary changes in posttranscriptional regulation between these species have led to a distinct temporal regulation of potentially conserved target mRNAs (including virulence determinants).

Fig. 38.1.

Fig. 38.1

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Newly discovered Yersinia small RNAs (Ysrs). Yersinia-specific sRNAs, comprising 75% of the total, are shown in dark gray. Ysrs with orthologs in E. coli and Salmonella, as determined by BlastN analysis, are shown in light gray. Y. pseudotuberculosis-specific Ysrs (4%) are represented in black. The right panel shows that 56% of Yersinia-specific sRNAs contain sequence differences between Y. pseudotuberculosis and Y. pestis

38.4 Contribution of Newly Identified Yersinia sRNAs to Virulence

To determine if any of the sRNAs we identified by deep sequencing contribute to the virulence of Y. pseudotuberculosis, we generated bacterial mutants deleted for the sRNAs Ysr29, a Y. pseudotuberculosis-specific sRNA, Ysr35, which is conserved between Y. pestis and Y. pseudotuberculosis, and RybB, a sRNA found in many Gram-negative bacteria. While the deletions of these RNAs did not affect bacterial growth or the expression of neighboring genes (Koo et al. 2011), deletions of Ysr29 and Ysr35 resulted in a significant attenuation of bacteria in a mouse model of Yersiniosis (Fig. 38.2a). These results implicate Ysr29 and Ysr35 as potential regulators of virulence determinants. On the other hand, deletion of RybB did not significantly affect the virulence of Y. pseudotuberculosis.

Fig. 38.2.

Fig. 38.2

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Contribution of Ysrs to the virulence of Y. pseudotuberculosis and Y. pestis. (a) Groups of 10 mice were orally inoculated with Y. pseudotuberculosis wild-type, ΔYsr29, ΔYsr35, and ΔRybB strains (~2.0 × 105 CFU). Survival of mice was monitored over 21 days. P-values were determined by Mantel–Cox survival analysis log-rank test. *, P = 0.0202 for ΔYsr29 vs. wild type; ***, P = 0.0002 for ΔYsr35 vs. wild type; P = 0.9154 for ΔRybB vs. wild type (not significant). Data are representative of three independent experiments. (b) Groups of 10 mice were inoculated via the intranasal route with Y. pestis wild type, ΔYsr35 and ΔRybB strains (~1.0 × 104 CFU). Survival of mice was monitored over 7 days. ***, P < 0.0001 for ΔYsr35 vs. wild type; P = 0.0946 for ΔRybB vs. wild type (not significant). Data are representative of two independent experiments

In addition, we found that the deletion of Ysr35 from the genome of Y. pestis also attenuated the pathogen in a mouse model of pneumonic plague (Fig. 38.2b). This suggests that Y. pseudotuberculosis and Y. pestis encode at least one conserved RNA that controls virulence, but it is not yet known whether the targets of this sRNA are also conserved between the two species, or whether Y. pestis has acquired and/or lost targets specifically regulated by this RNA.

38.5 Proteomic Analysis for Target Identification

Considering the uniqueness of Ysr29 to Y. pseudotuberculosis and its contribution to virulence, we performed a proteomic analysis using 2D differential gel electrophoresis (2D-DIGE) to determine the regulated targets of this sRNA. Were compared protein profiles from whole-cell lysates of wild-type Y. pseudotuberculosis to those of the ΔYsr29 strain grown to stationary phase at 26°C, the time point at which this sRNA is most abundant. A comparison of protein profiles between the wild-type and ΔYsr29 strains showed 16 spots with 1.5-fold or more difference in fluorescence intensity (Fig. 38.3) and identified 8 proteins regulated by Ysr29 using MALDI-TOF mass spectroscopy (Table 38.1). Significantly, each of these potential targets could be involved in virulence since all are required for the proper response of bacteria to a variety of stresses (Allocati et al. 2009; Rowley et al. 2006).

Fig. 38.3.

Fig. 38.3

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Proteomic comparison of Y. pseudotuberculosis wild type and ΔYsr29 strains by 2D-DIGE. The graphic representation of the gel image shows that majority of the spots were within the 1.5× differences in spot volume ratio (vertical lines flanking the curve). Sixteen marked spots (spots outside the two vertical lines) showed differences 1.5× or greater between the two strains. Spots to the left of the left 1.5× marker represent protein spots with increased expression in the wild-type strain. Spots to the right of the right 1.5× marker correspond to the proteins with increased expression in ΔYsr29 strain. Left Y-axis: number of protein spots; right Y-axis: maximum protein spot volume; X-axis: Cy3 (wild-type)/Cy5 (ΔYsr29) spot intensity ratio

Table 38.1.

Proteins identified in proteomic analysis of Ysr29 mutant and wild-type Y. pseudotuberculosis cell lysates by 2D-DIGE/mass spectrometric analysis

Protein Fold change
(mutant vs. wild-type)
DnaK −7.9
RpsA −6.2
UreC   1.5
GroEL −7.0
OmpA −6.6
AhpC   1.7
GST   2.1
RRF   2.0
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We verified the effects of Ysr29 at the posttranscriptional level by generating chromosomal in-frame fusions of the GST, RpsA, OmpA, and GroEL coding regions with the HA-epitope tag in both wild-type and ΔYsr29 strains. Levels of fusion proteins were measured by western blot analysis using an anti-HA antibody and we confirmed that GST is more abundant in the ΔYsr29 strain than in the wild-type background, while RpsA, OmpA, and GroEL are elevated in the wild type as compared to the ΔYsr29 strain, demonstrating posttranscriptional regulation by this sRNA (Koo et al. 2011).

38.6 Conclusions

Small noncoding RNAs have been recognized as critical regulators of gene expression in bacteria. In recent years there has been an abundance of studies that have used global approaches to discover sRNAs and their mRNA targets in many bacterial species. Prior to our work, however, knowledge about Yersinia sRNAs has been limited. By using RNA-Seq, we have identified the global set of sRNAs expressed by Y. pseudotuberculosis under in vitro conditions. The Y. pseudotuberculosis sRNA-ome appears to be distinct from other enteric bacteria and also from the closely related species Y. pestis. We have determined that multiple sRNAs are required for the full virulence of Y. pseudotuberculosis and that one of these shared RNAs is also required for the full virulence of Y. pestis. In addition, we determined that one of the virulence-associated RNAs that is unique to Y. pseudotuberculosis controls the abundance of at least eight protein targets. Our study provides new insight into how sRNAs contribute to the pathogenesis of bacteria by regulating the expression of virulence determinants, particularly in pathogenic Yersinia species. Additional studies will determine if the gain, loss, or sequence divergence of sRNAs has contributed to the evolution and changing virulence potential of these species.

Acknowledgements

We thank Trevis Alleyne for assistance with bioinformatics analysis of the deep sequencing data, Chelsea Schiano for contributing reagents, and Lauren Bellows for technical assistance. This work was sponsored by the Northwestern University Feinberg School of Medicine and the NIH/NIAID Regional Center of Excellence for Bio-defense and Emerging Infectious Diseases Research (RCE) Program. We also acknowledge membership within and support from the Region V “Great Lakes” RCE (NIH award U54 AI057153).

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