Bacterial gene regulation by sRNAs has gained a lot of attention in recent years, because it plays an important role in many cellular processes, including response to environmental changes, growth, and pathogenesis. There is an intriguingly large diversity of regulatory mechanisms, including cis- and trans-acting sRNAs, untranslated regions, and riboswitches. Some sRNA molecules act as repressors of translation and destabilize mRNA transcripts, but others act by activating and stabilizing target mRNAs
[30–32]. One of the best characterized sRNAs in GAS is FASX, which is involved in virulence-related gene regulation
[17, 33]. Knock-out mutants of fasX show a reduced expression of secreted virulence factors such as streptokinase and streptolysin S. The mechanism for streptokinase gene (ska) expression control is the stabilization of the ska transcript
. Lack of FASX-ska-mRNA-interaction in the fasX deletion mutant decreased transcript levels, and consequently decreased streptokinase protein abundance.
A second example of a regulatory RNA in GAS is the untranslated mRNA of the streptococcal pleiotropic effect locus (pel), which contains sagA, the structural gene for streptolysin S. This region was described as a positive regulator of important streptococcal virulence factors, including M-protein, Sic, and SpeB
. Strain specificity of PEL function is indicated by the fact that emm transcription was not affected in a sagA-deficient mutant with a M6 background
. Similar results have been obtained in GAS M1 and M18 Tn916 sagA mutant strains
. Additionally, pel deletion mutant analysis of four M1T1 GAS isolates did not identify any regulatory function for the pel sRNA in this serotype
Another, more recently described untranslated RNA with influence on streptococcal virulence is the 4.5S RNA, a component of the bacterial signal recognition particle (SRP)
. While the 4.5S RNA gene is not essential, mutation impairs bacterial growth, lowers virulence factor secretion, and reduces virulence in a mouse infection model.
Recently, several whole genome sRNA screens in Gram-positive bacteria, employing either tiling array or next generation sequencing approaches, revealed an unexpected number of potential sRNAs in several pathogenic species
[38–42]. In this context, it is likely that GAS expresses more sRNAs responsible for virulence gene expression control. One whole-genome intergenic tiling array screen of GAS M1T1 identified approximately 40 sRNAs that were expressed during the exponential growth phase in cells cultivated in THY complex medium
. The GAS M49 sRNAome in the present study was determined using cells grown in CDM. From 55 putative sRNAs in GAS M49, only 12 were detected previously in the GAS M1T1 screen (Figure
3B). This result is in accord with the concept that sRNA expression is serotype-dependent and regulated by environmental stimuli. Consequently, we detected media- and growth-phase-dependent sRNA gene regulation in the tiling array expression analysis, or by qRT-PCR of selected candidate genes. It would be interesting to monitor sRNA gene expression regulation under infection-relevant conditions.
Clustered, regularly interspaced, short palindromic repeat (CRISPR) loci represent an adaptive RNA-based immune system that protects bacteria and archaea from horizontal transfer of phage and plasmid DNA
. Among the putative sRNA genes detected in GAS M49 by the tiling array approach, two sequences were categorised by the Rfam prediction program as CRISPR-related RNAs (Table
1). sRNASpy490822 and sRNASpy490827 are encoded by the system II (Nmeni/CASS4 subtype)
 CRISPR/Cas locus, which was characterized recently by differential RNA sequencing in GAS SF370 (M1 serotype)
. Our data suggest that this locus is also active in GAS M49. Expression of sRNASpy490822 was confirmed by RT-PCR on the opposite strand of the CRISPR-associated genes under all conditions tested in this study. This transcript corresponds to the trans-activating CRISPR RNA (tracrRNA), which is responsible for the maturation of CRISPR RNA in concert with RNase III and the CRISPR-associated Csn1 protein
. A third CRISPR-related RNA detected in our expression screen, sRNASpy491206c, is encoded in the system I-C (Dvulg/CASS subtype)
 CRISPR/Cas locus, which is also conserved in streptococcal genomes. In contrast to our array data, this locus appeared to be silent in GAS SF370, where no expression was detected in the differential RNA sequencing approach
. Even though the CRISPR loci are conserved throughout GAS genomes, the activity of different CRISPR subtypes appears to be serotype-specific.
In the early years of sRNA research, many bioinformatic prediction tools were developed. One of the most prominent programs was the SIPHT tool, which has been used for many bacterial species
[45, 46]. However, comparison of the prediction results with the actual in vivo expression of sRNAs often revealed very little overlap between the different screening methods
[20, 41, 47]. The reasons for this discrepancy may be the limitations of the prediction programs as well as the fact that not all sRNAs are expressed under all conditions. The development of sRNA prediction software with improved properties is on-going. We compared our tiling array data with the prediction results of two recently published bioinformatics tools, sRNAScanner
 and MOSES
. As depicted in Figure
3A, the overlap between the tiling array expression data and the sRNA predictions was low. From the 20 most probable candidates of the MOSES analysis, 25% were expressed in GAS M49, whereas 8% of the predicted sRNAScanner predictions were found in the array analysis. Even the overlap between the two bioinformatics data sets was low. The only sRNA that was detected in all three screens was the previously characterized sRNA FASX
. These results strongly suggest that a comprehensive analysis of bacterial genomes requires the combination of mathematical predictions with the collection of expression data. In the long term, testing of different conditions, especially mimicking in vivo situations by employing infection models, might lead to an increased overlap of expression detection and bioinformatics analyses.