• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of narLink to Publisher's site
Nucleic Acids Res. Jun 2010; 38(11): 3794–3808.
Published online Mar 26, 2010. doi:  10.1093/nar/gkq032
PMCID: PMC2887942

Genomic SELEX for Hfq-binding RNAs identifies genomic aptamers predominantly in antisense transcripts

Abstract

An unexpectedly high number of regulatory RNAs have been recently discovered that fine-tune the function of genes at all levels of expression. We employed Genomic SELEX, a method to identify protein-binding RNAs encoded in the genome, to search for further regulatory RNAs in Escherichia coli. We used the global regulator protein Hfq as bait, because it can interact with a large number of RNAs, promoting their interaction. The enriched SELEX pool was subjected to deep sequencing, and 8865 sequences were mapped to the E. coli genome. These short sequences represent genomic Hfq-aptamers and are part of potential regulatory elements within RNA molecules. The motif 5′-AAYAAYAA-3′ was enriched in the selected RNAs and confers low-nanomolar affinity to Hfq. The motif was confirmed to bind Hfq by DMS footprinting. The Hfq aptamers are 4-fold more frequent on the antisense strand of protein coding genes than on the sense strand. They were enriched opposite to translation start sites or opposite to intervening sequences between ORFs in operons. These results expand the repertoire of Hfq targets and also suggest that Hfq might regulate the expression of a large number of genes via interaction with cis-antisense RNAs.

INTRODUCTION

Systematic searches for functional RNAs in both prokaryotes and eukaryotes have revealed an astonishingly high number of regulatory RNAs in a variety of organisms. Computational predictions (1–4) and cDNA-based experimental strategies (5–8) have been successfully employed to identify over 100 small RNAs (sRNAs) in Escherichia coli and many other bacteria. Most of these sRNAs are transcribed from intergenic regions in response to environmental stress, act in trans and show only short and partial complementarity to their target mRNAs (9,10). Most sRNAs are regulatory molecules targeting mainly mRNAs, but also protein activities can be regulated (11). Regulation of gene expression by sRNAs occurs mainly by modulation of translation and/or stability of the target mRNAs (11). Many of the E. coli sRNAs require the regulatory protein Hfq for function (12,13).

The E. coli host factor Hfq was originally described as essential factor for the replication of the RNA phage Qß (14). Escherichia coli hfq mutants display a very broad phenotype, because Hfq is involved in regulating the expression of many regulatory genes (15). For example, Hfq regulates the expression of the rpoS gene, which codes for the stationary phase sigma factor σS (16); it controls the stability of several mRNAs (17,18) and small regulatory RNAs (sRNAs) (9,19); and it is involved in the riboregulation of mRNAs (20–22). In addition, Hfq was shown to act as a virulence factor in several bacterial pathogens (23–25). Hfq is an Sm-like protein, which assembles into a ring-shaped homo-hexamer and stimulates RNA–RNA interactions (21,26–29). Hfq was shown, for example, to accelerate the association of the small regulatory RNA DsrA with its target mRNA rpoS (30–33). Because of Hfq’s characteristics and its role in the modulation of small RNA function, it is a perfect candidate for the isolation of novel regulatory RNAs and it has already been used to pull down RNAs via co-immunoprecipitation (6,32,34). Hfq exerts many functions in the cell and is viewed as a general and pleiotropic regulator. However, the functions of Hfq are still elusive in many organisms. Hfq is not essential for growth, but the adaptation to changing environmental conditions is hampered in the absence of Hfq.

RNA-mediated regulation most often concerns down-regulation or complete silencing of genetic elements. If Hfq is controlling, among many other things, the repression of genes, then repressed genes might be targets of Hfq. In order to assess further functions of Hfq, we developed an alternative and complementary approach to search for additional Hfq-binding RNAs, independent of their expression levels. Instead of relying on enriching transcripts from cell extracts, our SELEX-based approach detects regions of the genome that encode RNA domains binding Hfq with high affinity irrespective of their expression. SELEX has been used extensively in the 90’s before whole genomes were sequenced, but today, with so many available genomes and with the development of massive sequencing techniques, Genomic SELEX has the potential to become a major alternative approach for the detection of functional and/or regulatory elements within RNAs, even when these are not transcribed. We have recently evaluated the SELEX procedure aiming at analysing the impact of SELEX-specific non-selective steps on the selected RNAs (35). In that study, we concluded that the SELEX-imposed requirements on the sequences are much weaker in comparison to the enrichment of genomic aptamers, when specific characteristics confer high-affinity interactions.

Using an E. coli genomic library and the global regulator protein Hfq as bait, we performed nine rounds of SELEX and the enriched pool was subjected to 454 sequencing. The vast majority of the identified Hfq aptamers map to the antisense strand opposite of translation initiation sites and in intervening sequences between ORFs in operons. In silico and chemical footprinting analyses revealed sequence motifs that are significantly enriched in antisense strands of the E. coli genome. We demonstrate that Genomic SELEX combined with pyrosequencing and rigorous analysis is a powerful approach to identify regulatory RNAs motifs termed ‘genomic aptamers’. We propose that Hfq might regulate gene expression by targeting cis-antisense RNAs more frequently than hitherto assumed.

MATERIAL AND METHODS

Genomic SELEX

Via random priming, we constructed a representative PCR library of the E. coli genome containing overlapping sequences from 50 to 500 nt in length. Library fragments are flanked by primer annealing sites for amplification and are preceded by a T7 promoter for transcription of the library into RNA (36).

For selection of Hfq-binding RNAs, we incubated Hfq with the respective RNA pool for 30 min at room temperature using near-physiological buffer conditions (150 mM NaCl, 0.8 mM MgCl2, 0.5 mM DTT and 50 mM Tris–HCl pH 7.5). Subsequently, we employed filter binding to separate bound from unbound species, recovered Hfq-binding RNAs via urea-mediated denaturation followed by phenol/chloroform extraction and amplified selected sequences via RT–PCR (35). For the next SELEX cycle, obtained PCR products were again in vitro transcribed into RNA.

Yeast three-hybrid system

We used the yeast three-hybrid system, as described by Bernstein et al. (37), to evaluate Hfq-RNA binding in a cellular environment. Therefore plasmids pIIIa-MS2-2-RNAX, expressing the hybrid MS2-RNA sequences, and pACT2-HFQ, expressing an Hfq-Gal4AD fusion protein, were transformed into the yeast strain YBZ-1. HIS3 and lacZ, encoded by YBZ-1, served as reporter genes to monitor Hfq-RNA binding as well as to estimate the strength of the interaction. Hfq-independent reporter gene activation was assessed by eliminating pACT2-HFQ from YBZ-1, followed by a second β-gal assay.

E. coli strains and growth conditions

In this study, E. coli wild-type strains B and K12 MG1655 were used. Cells were grown at 37°C under the following conditions: exponential phase—growth in LB to OD600 0.4; stationary phase—growth in LB 16–18 h; acidic stress—growth in LB to OD600 0.4, addition of [2-(N-morpholino)ethanesulfonic acid] (MES) to a final concentration of 170 mM, growth for another 40 min; oxidative stress—growth in LB to OD600 0.4, addition of H2O2 to a final concentration of 80 nM, growth for another 15 min; M9: growth in M9 minimal medium supplemented with 0.2% glucose to OD600 0.4; hyperosmotic stress: growth in M9 minimal medium supplemented with 0.4% glycerol to OD600 0.4, addition of NaCl to a final concentration of 300 mM, growth for another 20 min.

RNA isolation and RT–PCR

For total RNA isolation hot-phenol procedure was used as described by Jahn et al. (38) Alternatively, thaw-freeze protocol was used. Briefly, cells were harvested by centrifugation and resuspended in VD buffer (10 mM Tris/HCl, pH 7.3, 60 mM NH4Cl, 6 mM β-mercaptoethanol, 2 mM MgAc) with the addition of 0.4 mg/ml of lysozyme and 50 U DNaseI (NEB). After incubation on ice for 30 min, three to four cycles of freezing and thawing followed. Lysates were cleared by centrifugation and RNA extracted by conventional phenol–chloroform extraction followed by ethanol precipitation. Total RNA was treated with DNaseI (NEB) as recommended by the manufacturer.

Expression analysis and primer walking were performed using One Step RT-PCR Kit (Qiagen). Manufacturer’s instructions were followed except that 2–4 µg of total RNA was used. Strand specificity of reactions was provided by adding only reverse primer into the RT reaction and forward primer was added immediately prior to PCR. Absence of DNA contamination was confirmed for each reaction by omitting the addition of any primer into the RT step of the reaction. Primers for primer walking were designed to map down- and up-stream from the Hfq binding region in a stepwise manner giving products increasing in size. For reverse transcription primers as-cydD-R1 (5′-GGGTTATGCAGGATGGCCGG-3′), as-cydD-R2 (5′-CTGTTCGCTATTACTGTTGGATG-3′), as-cydD-R3 (5′-GCCAGCGAACAAGAATTACAAG-3′) and as-cydD-R4 (5′-GCGTTTTTATCCTCCGGCATTC-3′). Primers used for subsequent amplification were as-cydD-F1 (5′-CACACTTAATTCCGCGTAACG-3′), as-cydD-F2 (5′-GAGCAGCGTCACAATTGCCAG-3′), as-cydD-F3 (5′-GGTAAAGATGATCGAGCGTATC-3′), as-cydD-F4 (5′-GCACCAAAAATGGTCAGCTCAG-3′) and as-cydD-F5 (5′-CCGTTAAGTCAGAGATACGTAC-3′).

RNA preparation for structural probing

Standard PCR was applied to produce templates from three different loci of E. coli K12 genomic DNA using the following primer sequences: ptsLfow1: 5′-CGTAATACGACTCACTATAGGTGAGCAGTGTCTTACGAAC-3′, ptsLrev1: 5′-CACGGTCAGGTCGCCACC-3′, ccafow1: 5′-CGTAATACGACTCACTATAGGCCACCCACCAGATAAATCTTC-3′, ccaLrev1: 5′-CGCAAACGCAAAGATCGCTG-3′, iclRfow1: 5′-CGTAATACGACTCACTATAGGATTGCATTAGCTAACAATAAAAATG-3′, iclRrev1: 5′-TCTGCCGCGTTTCGCGGG-3′ (Thermo). Underlined nucleotides indicate the T7-promoter sequence. The PCR products were used for in vitro RNA transcription according to the user manual’s guidelines of the RNA MaxX High Yield Kit (Stratagene). The RNAs were purified on an 8% denaturing (7 M Urea) PAGE in 1× TBE (89 mM Tris base, 89 mM Boric acid, 2 mM EDTA pH 8.0), eluted into 1× elution buffer (40 mM MOPS pH 6.0, 1 mM EDTA pH 8.0, 250 mM NaOAc pH 5.0), precipitated and resuspended in ME buffer (10 mM MOPS pH 6.0, 1 mM EDTA pH 8.0) to be stored at −20°C.

In vitro DMS modification

RNA structure determination was performed in the presence of increasing amounts of MgCl2 in a total volume of 50 µl. The RNA (2 pmol) in 10× binding buffer (final concentration: 80 mM Cacodylic acid buffer pH 8.0, 150 mM NaCl) was denatured at 95°C for 1 min and after 2 min at room temperature MgCl2 was added to a final concentration of 0, 1, 10 mM. The RNA was incubated at room temperature for 20 min to ensure correct folding before the treatment with dimethysulphate, (DMS, 21 mM final concentration). The modification reaction was left to proceed for 20 min at room temperature and was stopped by addition of β-mercaptoethanol (57 mM β-ME final concentration). After precipitation, the sample was resuspended in 6 µl ddH2O to be used for reverse transcription. For Hfq footprinting assays the standard protocol was slightly adapted, starting with an input of 4 pmol RNA in 10× binding buffer with DTT (0.5 mM DTT final concentration) but identical denaturing and folding procedures. Then Hfq (final concentration 400 nM or 1.2 µM Hfq6) was added to the sample and incubated for 30 min at room temperature prior to DMS and -mercaptoethanol treatment as described above. The samples were subsequently purified by phenol extraction, ethanol precipitated and resuspended in 6 µl ddH2O for primer extension. DMS (1 pmol) modified or untreated RNA was subjected to reverse transcription. 5′-end labelled oligonucleotides (32P-γ-ATP by Hartmann Analytics; PCR reverse primers were used) were used for primer extension experiments described in ref. (39) and the samples were subsequently separated on 8% denaturing polyacrylamide gel (7 M urea) and detected using the Storm Phosphoimager (Molecular Dynamics) and Image Quant.

Electrophoretic mobility shift assays

In vitro transcribed and gel purified RNAs were 5′-end labelled with γ-32P-ATP. Labelled RNA (0.01 nM) was incubated at room temperature for 30 min in 50 mM Tris–HCl pH 7.5, 150 mM NaCl, 8 mM MgCl2 and 0.5 mM DTT with various amounts of Hfq6 as indicated. RNA–protein complexes were separated on 5% native polyacrylamide gels over-night at 3 V/cm and 4°C. Gels were dried and visualized on a Phosphoimager and quantified using the software ImageQuant1.4. Primers used to clone and transcribe RNA were as for structural probing.

Quantitative RT–PCR

Reverse transcription was performed with 2 µg of total RNA isolated from exponential phase or acidic stress using Omniscript RT Kit, Qiagen. Briefly, RNA was mixed with 1 µM RT-primer, 0.5 mM dNTPs, 10 U RNasin, Promega, 3 U reverse transcriptase and 1× buffer in total volume of 20 µl. Reaction was incubated at 50°C for 30 min followed by enzyme inactivation at 95°C, 15 min. Absence of genomic DNA was confirmed by omitting addition of the enzyme in the RT-reaction. To test absence of unspecific cDNA synthesis, no primer control (NPC) was introduced in which addition of any primer in RT reaction was omitted.

Quantitative PCR was done with Mesa Green qPCR MasterMix Plus for SYBR, Eurogentec on an Abi Prism 7000 machine. Briefly, 1 µl of RT reaction was mixed with 0.1 µM primers and 1× master mix in total volume of 25 µl. All experiments were repeated twice from two independent RNA preparations. All reactions were performed in three technical replicates. RNA copy number was estimated from the standard curve created from series of corresponding PCR products of known starting concentration.

Reverse transcription of sense transcript was obtained using cca-R (5′-TGGCGCAACAGACGATTTT-3′) and htrG-R (5′-TAAGTTGTTTCAACGGGATCCA-3′) primers, while reverse transcription of antisense transcript was preformed using primers cca-F (5′-CGTCGCGATCTGACCATTAA-3′) and htrG-F (5′-CGGCGAGGAAGTGACCTTATT-3′). For subsequent amplification of cca and htrG amplicon primers cca-R and cca-F, and htrG-R and htrG-F were used, respectively. All primers were designed using Primer Express software.

Sequencing and computational analysis

The sequences from round 9 of Hfq selection and round 9 of a control neutral selection were tagged and sent to 454 sequencing among other pools as described in Zimmermann et al. (submitted). In total, 9991 and 5894 of these sequences were identified as deriving from round 9 of Hfq and control selection, respectively. Primer and T7 promoter sequences were removed. These sequences were then aligned to the E. coli K12 genome (GenBank accession NC_000913) using vmatch (40), taking only alignments with an E-value of at most 1e−10, leaving 8865 Hfq sequences and 5853 control sequences.

All sequences were analysed for overlap and orientation to known features, based on all available annotations from GenBank (41) and EcoCyc (42). Clusters of reads were computed by grouping all overlapping sequences, including sequences linked by a third sequence, and not overlapping. Base-level enrichment for each type of feature (CDS sense, CDS antisense, etc.) was calculated by summing the number of aligned reads that overlapped the feature at each position of the feature. Start and stop codon positional enrichment was calculated by summing the number of reads overlapping each position up- and down-stream of every translation signal, and dividing the number of reads at each position from the Hfq sequences by the control sequences. The enrichment was visualized using R (43).

For minimum free energy predictions (MFE), we used Zuker’s algorithm (44,45) and the partition function algorithm by McCaskill (46), which calculates base pair probabilities in the thermodynamic ensemble as implemented in version 1.6.5 of Vienna RNA Package RNAfold (47). For visualizations of the secondary structures several options of the Vienna RNA packages were helpful (http://www.tbi.univie.ac.at/~ivo/RNA).

RESULTS

Genomic SELEX for the isolation of RNAs with high affinity to Hfq

We constructed an E. coli genomic library (36,48), which we used to isolate and identify high-affinity Hfq-binding RNAs. This library was transcribed into RNA and used for several consecutive cycles of SELEX for enrichment of Hfq-binding RNAs via filter binding as described in the ‘Materials and Methods’ section (Figure 1A). We selected high affinity Hfq-binding RNAs over 9–10 iterative SELEX cycles, applying increasingly stringent conditions (Figure 1B). Then we cloned and sequenced a total number of 112 RNAs resulting from cycles 8 (minus competitor) and 9 (plus tRNA competitor).

Figure 1.
Genomic SELEX for Hfq-binding aptamers. (A) Schematic outline for our approach to isolate novel Hfq-binding ncRNAs. After construction of a genomic library via random priming, DNA fragments are in vitro transcribed and subjected to multiple rounds of ...

To investigate whether in vitro-selected sequences bind Hfq in a cellular environment, we introduced RNAs resulting from SELEX cycle 8 into the yeast three hybrid system (37) and monitored Hfq-binding via expression of the reporter genes HIS3 and lacZ (Figure 1C). Both tolerance to 3-AT (3-aminotriazole is a competitive inhibitor of the HIS3 gene product) and speed and intensity of colour development in the lacZ assay are indicators of the strength of Hfq–RNA interactions. We picked and sequenced 58 clones, which showed dark blue staining. As all clones investigated were able to activate lacZ in an Hfq-dependent manner, we conclude that in vitro selected RNA sequences do bind to Hfq in a cellular environment.

Initial sequencing of the 170 clones derived from the different three pools enriched from Genomic SELEX and yeast three-hybrid revealed 108 unique RNAs overlapping in all three pools. Most surprising was the observation that the vast majority (85) mapped antisense to protein coding genes, only nine mapped to intergenic regions and 14 within mRNAs. These results clearly show that the sequences were not exhausted and most importantly, known Hfq-binding RNAs were not detected. We therefore performed deep sequencing using the 454 technique. Since some of the sequences were shared among the three pools, we chose only to sequence the SELEX pool of cycle 9.

After eliminating the primers and those reads that did not match to the E. coli genome unambiguously, 8865 reads were mapped to the E. coli genome. Each read was grouped into a cluster based on overlap with other reads. In Figure 1D, the bar demonstrates the number of reads contained in cluster regions of the genome. One thousand and five hundred and twenty-two individual clusters were obtained. Among these clusters, approximately half (775) contain only one read, which we take as an indication that the sequencing is still not exhausted. Table 1 lists those clusters that contained more than 50 reads. The chromosomal location and the function of the gene product are indicated. It is worth mentioning that most of the enriched aptamers are antisense to genes coding for proteins involved in the cell’s interaction with the environment. The most highly enriched aptamers map to the universal stress protein gene (upsG), enterobacterial common antigen (ECA), transporters and membrane proteins.

Table 1.
Clusters of Hfq aptamers with more than 50 reads are listed. Cluster ID: identification number of cluster; Reads: number of obtained sequences matching with indicated cluster; Chromosome starting and ending base number is indicated as well the Gene name ...

Analysis of deep sequenced SELEX pool 9

We evaluated the selection outcome by comparing the selected sequences with known Hfq-binding RNAs, both sRNAs and mRNAs and their targets. We obtained three known sRNAs, Tpk70, OmrA/B and GcvB and the targets of two of these sRNAs, ompT mRNA targeted by OmrA/B and argT and dppA mRNAs targeted by GcvB (49,50). We also obtained reads mapping to two tRNAs, hisR and leuV. Hfq had been reported to bind tRNAs (51). The following known Hfq-binding mRNAs were selected, thrL, yjbJ, glpFK and glgT mRNAs. Also targets of sRNAs known to bind Hfq are present in our selected pool, for example the glmSU mRNAs, which are up-regulated by the sRNAs GlmZ and GlmY (52). In addition, five mRNAs were obtained, which are regulated by sRNAs and hence might also interact with Hfq: tna and gadX mRNAs targeted by the GadY sRNA, rbsD mRNA (DsrA), sdh mRNA (RhyB) and fhlA mRNA (OxyS).

We further compared our sequences with RNAs found to be differentially expressed in Hfq-depleted cells and hence thought to be regulated by Hfq directly or indirectly. Fifty two of our clusters are among the down-regulated and 42 among the up-regulated genes in the microarray analysis of Hfq-depleted cells (53). For example, the mRNA of one of our strongest hits, the ptsL/manX asRNA, is 8-fold down-regulated in the absence of Hfq, whereas the ygiM/cca mRNA is 4-fold up-regulated.

From these results, we conclude that the enrichment of Hfq-binding RNAs was successful, and that we obtained a large but non-exhaustive number of Hfq-binding aptamers, which map precisely to the E. coli genome. Many of the most abundant and best known Hfq-binding small RNAs have not been enriched. This might be due to a lower affinity of these RNAs to Hfq than the low nanomolar affinity of the selected aptamers, or due to secondary-structure elements, which often lead to reverse transcriptase stops.

Identification of Hfq-binding motifs

To discover a possible common motif in the Hfq-binding sequence data, the sequences were subjected to motif recognition. We have excluded all clusters with less than two reads, to ensure that we do not include any artefacts in the motif search. The remaining clusters had an average sequence length of 90bp. For this collection of clusters, MEME (version 4.1.1) (54,55) was applied with the default option, searching the given strand only and a model which assumes zero or more motif occurrences per sequence (TCM). Based on the initial results, we reran MEME again with fixed length 7, 8 and 11 nt (Figure 2, A–C, respectively). The following motifs were determined as most significant: 5′-AYAATA-3′ with an E-value of 7.00e−04 and 5′-AAYAAYAA-3′ with an E-value of 1.8e−05, where Y represents pyrimidines (C or U). Both motifs are included in the 11-mer motif with an E-value of 3.4e−17 (Figure 2). The representation of these related motifs within E. coli was analysed. The 7-mer was found 365 times within protein coding genes and 712 times on strands opposite to protein coding genes. Hence, the factor of the 7-mer motif for asRNAs over mRNAs is 1.95. The antisense/sense ratio for the 8-mer motif is 1.3.

Figure 2.
Hfq-binding motif(s). Predicted Hfq-binding motifs determined by MEME (52,53) as most significant on a remaining cluster set with at least three reads in one cluster. The height of a letter at the given position (x-axis) indicates the frequencies scaled ...

Determination of the Hfq-binding motif by DMS footprinting

To confirm that the predicted Hfq-binding motif is indeed the binding site for Hfq, we determined the interaction site of Hfq in the aptamers by dimethylsulphate (DMS) footprinting. This chemical reagent methylates the N1 position of adenosine and the N3 position of cytosine, in case they are accessible. DMS structural probing was performed for the three selected sequences from the cca asRNA, ptsL/manX asRNA and icIR mRNA. First, the secondary structures of the Hfq-binding aptamers were analysed by computational prediction via calculation of the MFE structure and the base pairing probability matrix to take suboptimal structures into account (RNAfold program of the Vienna RNA package, 5).

Figure 3A shows as an example of the DMS footprinting data for Hfq binding for the cca asRNA. Acrylamide gels were quantified (Figure 3B) and protected bases are marked by green dots, whereas enhancements of DMS modification upon Hfq binding are indicated in orange. The secondary-structures probed by DMS were plotted to the secondary-structure predictions of RNAfold (Figures 3C–E). The DMS pattern of all RNAs shows distinct differences upon incubation with Hfq, both protections and enhancements could be observed. The cca asRNA, although highly structured harbouring two extended loop regions, has one discrete cluster of ten nucleotides A56–C66 (labelled green in Figure 3C) residing in the otherwise very accessible loop, that are protected from methylation in the presence of Hfq. Furthermore, induced fit upon binding of Hfq was observed, detectable by weak enhancements (labelled orange throughout Figure 3) in P1 and P3 regions. Also further weak enhancements at single-stranded cytosines showed higher accessibility upon protein interaction.

Figure 3.
DMS footprinting of Hfq-binding motifs. (A) Footprinting data of the Hfq-binding site in the cca asRNA. A representative region of an 8% denaturing gel shows the cluster of protections (green dots) and one enhancement (orange diamond) upon Hfq addition ...

Figure 3D shows the footprinting results for the ptsL/manX asRNA. Strongly modified and hence accessible nucleotides are located between A24 and A30 and from A45 to C58, the latter representing the stretch of highest accessibility in this RNA, which maps perfectly to the secondary-structure prediction. A similar change of modification pattern upon Hfq binding as seen for the cca asRNA could also be observed for this RNA. We detected a region of eleven successive protections in the loop J4a/4b (A45–A57) and enhancements in P3, again indicating an induced fit upon Hfq binding.

For the third tested aptamer, located in the iclR mRNA, again a large accessible loop was detected, which is protected upon Hfq binding between position A13 and A23 (Figure 3E). In addition, a second accessible domain between positions A41 and A52 is protected. Either the sequences harbour a second Hfq binding site or a stabilization of the suboptimal secondary-structure could occur. However, the latter possibility is rather unlikely since most of the affected nucleotides are predicted to be accessible even if the stem-loop conformation forms.

From the DMS footprinting data, we conclude that the predicted enriched motif 5′-AAYAAYAA-3′, is indeed an interaction site for Hfq, because in all three tested RNAs, the motif is present and protected from DMS modification after incubation with Hfq. However, we are aware that the predicted motifs may change if regions with a different coverage (e.g. clusters of size <2) are included in our motif analysis.

The binding affinities for four selected RNAs (as-cca, as-ptsL, ig-proX/ygaX and as-metH/icIR) were determined via electrophoresis mobility shift assays. In vitro transcribed 5′ γ-32P labelled RNAs (0.01 nM) were incubated with increasing amounts of Hfq6 as indicated in Figure 4. K1/2 values ranged from 0.2 nM for as-metH/icIR to 11 nM for ig-proX/ygaX. The Hill coefficients suggest that more than one Hfq6 complex binds to the RNAs. These affinities are indeed stronger than those of known Hfq-binding small RNAs. For example, the well-studied DsrA RNA binds with a Kd of 18 nM Hfq12 (or 36 nM Hfq6) and the rpoS mRNA 146 Hfq6 (30). The RNA with the highest affinity to Hfq known is the sodB mRNA, which has a Kd of 2 nM (56). The 5′ UTR of this mRNA has the exact Hfq-binding motif we identified here.

Figure 4.
Determination of binding affinities of Hfq aptamers. Binding properties of Hfq aptamers were determined via electrophoretic mobility shift assays. γ-32P labeled RNAs (0.01 nM) (as indicated in the figure) were incubated at room temperature for ...

Expression profile of cis-antisense RNAs

As the identified cis-antisense RNAs originate from a genomic DNA library, we next asked under which conditions they are expressed, if at all. We isolated total RNA from cells exposed to different growth conditions and studied expression patterns via RT–PCR. We analysed 11 transcripts (cca, ptsL, ydbJ, ybdQ, mtlA, ptsI, pqi5, sprT, phoR, ybjJ and ftsQA asRNAs) and found all of them to be expressed (Figure 5A). While they all show individual expression profiles, we repeatedly found that expression in stationary phase is lower than during exponential growth (9 out of 11 RNAs). For the remaining two RNAs (ptsL and ptsI asRNAs), expression is equally low both in stationary and exponential phase. Most are much higher expressed under stress than under optimal conditions. Since we observed expression for all transcripts investigated, we assume that most, if not all, of the cis-antisense RNAs identified in our screen are expressed likewise, suggesting that transcription from the antisense strand of protein coding genes might be much more prevalent than hitherto presumed. In addition, genome-wide low level transcription from opposite protein coding regions has been observed in a tiling array study of E. coli antisense genes, although expression of detected transcripts was not examined further (57).

Figure 5.
Characterization of cis-antisense transcripts. (A) Strand-specific RT–PCR analysis of cis-encoded antisense RNAs (ybjJ, cca, ybdQ, phoR, sprT, ydbJ, ptsI, ftsQA, mltA, pqi5, ptsL asRNAs) and 5S rRNA, using 4 µg total RNA isolated from ...

A strong argument that the selected Hfq aptamers are indeed part of expressed RNAs that bind Hfq also in vivo comes from a recent report on a global study of Hfq-binding RNAs in Salmonella typhimurium. A high throughput approach was performed to identify Hfq target RNAs by co-immunoprecipitation of Hfq followed by deep sequencing (32). With this approach, a small set of antisense transcripts was detected. We compared the E. coli genes containing genomic Hfq aptamers on the antisense strand with the Salmonella antisense RNAs and found 93 antisense RNAs to overlap in both screens. Note, that many Salmonella genes cannot be annotated to E. coli genes.

We further estimated the approximate size of the cis-antisense RNAs, which contained Hfq aptamers by strand specific RT–PCR and primer walking. As a typical example we show in Figure 5B the result for the cydD asRNA. The cydD asRNA is at least 1073 base pairs long (Figure 5C). We further tested three additional asRNAs (cca, tldD and yjeH) and all are at least 1 kb long (data not shown).

Next, we aimed to quantify expression of cca asRNA more precisely using qRT–PCR. In addition, this approach allowed us to quantify previously reported unspecific cDNA synthesis (58) and assess the portion to which it influences target signal. Unspecific cDNA synthesis can be a consequence of back-looping or other internal structure of the target RNA. Otherwise, it can be an effect in which short degradation products of either RNA or DNA serve as random oligonucleotides which lead to general reverse transcription which then compromises strand specificity. To monitor this artefact, we introduced a novel control—NPC in which addition of any primer during the RT is omitted.

We quantified levels of sense and antisense cca transcript in two different growth conditions; exponential phase of growth and under acidic stress. Based on size of cca asRNA derived from primer walking (data not shown), two primer pairs were designed giving amplicons on each end of both opposing transcripts. Resulting cca and htrG/ygiM amplicons lie towards the 5′- and 3′-ends of the antisense transcript, respectively, and inversely in case of sense transcript. Resulting expression level of sense transcript is considerably high and ranges from 10 × 106 to 27 × 106 copies while antisense transcript level is ~2×105 copies per microgram of total RNA (Figure 6). Most importantly, unspecific cDNA synthesis gives rise to only 4000 copies per microgram and hence does not influence detection of endogenous transcripts.

Figure 6.
Quantitative strand specific RT–PCR of sense-antisense RNA pair. Copy number of sense and antisense htrG/ygiM-cca transcript was determined using qRT–PCR. Each transcript is represented by two independent amplicons (cca and htrG). Expression ...

Considerably similar levels of cca asRNA obtained from both amplicons (Figure 6) imply that indeed they belong to the same endogenous transcript. This serves as a verification of long antisense transcripts, existence of which we are reporting here.

Location of Hfq aptamers within E. coli genes

From the total reads shown in Figure 1D, 84% are located within annotated E. coli genes, whereby 15% map to the sense strand of mRNAs and 69% map to the antisense strand. Thus, Hfq aptamers are 4-fold more frequent on the antisense strand than the sense strand. Reads (9.9%) were in regions that are not annotated and only 0.5% within non-coding RNAs.

We further looked more closely asking whether Hfq aptamers were evenly distributed within genes or whether there are positions where they are enriched. Figure 7A shows the relative enrichment of Hfq genomic aptamers as compared a control ‘neutral’ selection (35) relative to their distance to the translation start and stop sites. Most interesting is that coding regions are unenriched, whereas there is a peak surrounding the start site and reaching into a few codons into the coding region. There is a strong enrichment of Hfq aptamers antisense to the translation start site. Figure 7B shows the distance of Hfq aptamers relative to the translation termination site. Here again the enrichment in the antisense strand is remarkable when compared to the sense strand, suggesting that codon regions should be devoid of Hfq aptamers. There is a slight increase of aptamers mapping downstream of the stop codon. However, when looking closer into this phenomenon, we observed that most of these are located opposite to intervening sequences between two ORFs in polycistronic mRNAs.

Figure 7.
Enrichment of genomic Hfq aptamers surrounding start and stop codons. Positional fold enrichment of genomic aptamers as compared to a control ‘neutral’ selection surrounding translation signal codons. The upper part, in green, of the diagram ...

A typical example of such an Hfq aptamer is the ycbU/ycbV asRNA (Figure 8A). This aptamer lies opposite to a 4 kb polycistronic RNA coding for the ycb operon containing genes ycbS, T, U, V and F and the aptamer lies opposite to the intervening sequence between ycbU and V. A homologue of this large antisense RNA has recently been detected in a tiling array screen in Listeria monocytogenes (59). It’s mRNA codes for a predicted fimbrial-like adhesin protein.

Figure 8.
Examples of mapping sites of genomic Hfq aptamers in the E. coli genome. In this figure, selected regions of the E. coli K-12 genome where genomic aptamers were detected are shown in a genome browser window. Features are drawn as thick, coloured rectangles, ...

Another typical example of the location of Hfq aptamers is the cca aptamer located opposite to the intervening sequence between the htrG/ygiM and the cca genes (Figure 8B). This antisense RNA was also pulled down in Salmonella (32) and the mRNA is up-regulated in the absence of Hfq in E. coli (53).

A third example is the wzzE asRNA located antisense to the 5′-end of the coding sequence. wzzE codes for the modulator of the polysaccharide chain length of the Enterobacteria common antigen (ECA) and lies in a 13-kb-long polycistronic operon coding for 12 genes. These genes code for proteins involved in the biosynthesis of the ECA and most of the antigens interact with hosts. Interestingly, we obtained 11 genomic Hfq aptamers within this operon, most antisense but for example there is one position where we obtained genomic aptamers on both strands just close to the 3′-end of the wzxE gene (Figure 8C).

DISCUSSION

Genomic SELEX is a powerful tool to identify genomic aptamers

The first small regulatory RNAs were initially discovered by chance or due to their high abundance (60). Their relevance was only appreciated later when systematic computational searches looking for secondary structure, sequence conservation and orphan promoters and terminators in intergenic regions revealed a large number of small RNAs in many different bacteria (61). More recently, small RNAs have been discovered by cDNA cloning (RNomics) (7), by co-immunoprecipitation coupled to microarray analyses (6) and more lately by deep sequencing of total RNA (32). Here we describe an alternative and complementary approach to detect regulatory domains within RNAs, Genomic SELEX. While computational prediction is a knowledge-based approach, cloning and deep sequencing is dependent on the expression levels of the RNAs, limiting the discovery to those RNAs that have already known properties or are expressed at the analysed conditions. Genomic SELEX overcomes both limitations, as the initial library pool used to select RNAs is derived from genomic DNA, and no predictions have to be made. The selection procedure does, however, favour the selection of sequences with lower structural stability and specific nucleotide content, but the positive selection forces exceed the biases imposed by the SELEX procedure (35). The key step in Genomic SELEX is the selection of the protein that serves as bait. We chose the regulator protein Hfq, due to its properties. Hfq displays chaperone activities (27,29,30,62) and binds a very large number of quite different RNAs with variable affinities (13,18,63). We enriched the SELEX pool aiming at isolating high affinity binding RNAs in the low nanomolar range. Deep sequencing of the enriched SELEX pool revealed 1522 individual aptamers with varying representation in the pool. The most enriched sequences were present over 100 times, and 775 were obtained only once. We assume that by further sequencing more sequences will be obtained.

Therefore, we conclude that genomic SELEX is a valuable approach to identify regulatory domains within RNAs provided the choice of the bait is appropriate. We performed in parallel to the Hfq selection, a selection for high affinity binders to another E. coli protein with RNA chaperone activity, StpA, which does not bind RNAs specifically and no RNAs were enriched (64). This indicates that only specific binders survive the procedure and are enriched.

Genomic aptamers

RNAs enriched via Genomic SELEX do not represent bona fide RNA molecules, but represent domains within putative RNAs that provide high affinity to the ligand used as bait. We therefore define the term ‘genomic aptamer’ as a domain within RNAs that recognizes specific ligands and as a consequence might act as regulatory element. The aptamer domains within riboswitches are typical examples of genomic aptamers that bind the metabolite of choice and induce a regulatory switch (65). The outcome of our Genomic SELEX against the regulator protein Hfq is a collection of short sequences that bind with high affinity to Hfq and have the potential to act as regulatory elements.

Genomic Hfq aptamers are highly enriched in the antisense strand of protein coding genes

The most unexpected outcome of our Hfq aptamer analysis is their mapping to the antisense strand of protein coding genes. Until recently, most antisense RNAs in E. coli were thought to act in trans and to have only limited complementarity to their target RNAs. Mainly plasmid-encoded small RNAs were transcribed from cis-antisense strands resulting in transcripts with potentially extensive complementarity. Recently, a few examples have been reported for chromosomally encoded homologues of the plasmid hok/sok and hok/sok-like systems. These antisense RNAs are called ‘antitoxin’ RNAs, as they inhibit the expression of short toxic peptides (66). They act by base pairing across the ribosome binding site blocking initiation of translation and often activating cleavage and leading to degradation of the whole mRNA. Two examples of cis-acting antisense RNAs are the SymR RNA, which is complementary to the Shine-Dalgarno (SD) sequence and extends into the coding region of the symE mRNA across the AUG start codon (67) and the sok RNA, which is complementary to the SD sequence of the mok mRNA, which needs to be translated to mediate translation of the hok mRNA (68,69). Further examples of antitoxin RNAs are the Sib and OhsC RNAs (70) and the IstR RNA(71), which act by competing with the ribosome by base pairing to the ribosome standby site ~100 nucleotides upstream of the SD site (72). These antitoxin RNAs, are from their site of expression and mode of action, clearly cis-acting antisense RNAs reminiscent of the Hfq-aptamers identified in this study. Like the majority of the Hfq aptamers, they map antisense to protein coding genes just opposite of the translation initiation site spanning the SD sequence and the AUG start codon. It is, however, not known whether Hfq plays a role in promoting the interaction of the antitoxin RNAs with their target RNAs. However, it is conceivable that these RNAs need some feature to make base pairing more efficient. For example, some of these RNAs are highly structured and their base pairing is facilitated by loop–loop interactions, which might contain a U-turn loop structure (73). Alternatively, for less structured RNAs, which cannot present a loop to facilitate base pairing, a Hfq binding site might be able to accelerate the interaction of the mRNA with its antisense RNA.

In line with this idea is the recent finding that a short motif 5′-AAYAA-3′ within the rpoS mRNA leader greatly affects the annealing rates of the sRNA DsrA to its target rpoS mRNA. Hfq accelerated the annealing of DsrA to the rpoS mRNA more than 50-fold. Deletion of the 5′-AAYAA-3′ motif from the rpoS leader mRNA had only a slight effect on the binding affinity to Hfq, but it practically abolished Hfq’s capacity to accelerate DsrA/rpoS annealing (31). The 5′-AAYAA-3′ motif is contained within the identified motif of the genomic Hfq aptamers. This would indicate that these motifs could be involved in promoting annealing of the antisense RNAs with their target sequences.

Enrichment of Hfq aptamers opposite to translation start sites and to intervening sequences between ORFs of polycistronic transcripts

From the analysis of the position of Hfq-aptamers relative to start and stop codons three interesting observations become apparent. (i) Significantly few Hfq-aptamers map to the coding regions on the sense strand compared to the antisense strand. This suggests that there might be a reason for coding regions to be devoid of Hfq-binding sites. This is in line with a very recent report that the MicC sRNA targets the ompD mRNA downstream of its start codon with a 12 base pair complementary region in codons 23–26. This sRNA–mRNA interaction does not inhibit translation initiation but leads to an RNase E-dependent degradation of the mRNA (74). Thus, the presence of Hfq-binding sites within codon regions might be a destabilizing factor that mRNAs should avoid. (ii) The genomic aptamers are enriched antisense of the SD and start codons suggesting that they might be able to regulate translation. This is reminiscent of the cis-encoded antitoxin RNAs (66,67,69), and finally (iii) there is an enrichment opposite to intervening sequences between ORFs in operons, suggesting that Hfq aptamers might influence the processing and individual expression of genes within operons. There is one example of a small cis-antisense RNA, GadY, where its pairing to the region between the gadXW mRNAs leads to cleavage between the two ORFs and to stabilization of gadX mRNA (75,76).

The physiological roles of Hfq are still not completely understood. It is clear that Hfq serves as an accelerator of RNA annealing between regulatory RNAs and their targets, but for example in Staphylococcus aureus, Hfq lacks the C-terminal domain required for interaction with A-rich domains of mRNAs and it is not required for the function of regulatory RNAs. Our results suggest that Hfq might be involved in establishing a balance between pairs of sense/antisense transcripts. Because antisense transcripts are much less abundant than sense RNAs, we must assume that their role can only manifest when their expression is induced or when their cognate mRNA is repressed. A possible role for these antisense transcripts containing Hfq aptamers might be to inhibit translation of their cognate mRNAs, when their concentration is not higher than that of their cognate antisense pair. Our study expands the repertoire of Hfq targets to a new class of molecules, large cis-antisense transcripts. Their functions remain to be revealed, but our results point to a potential regulatory function in translation and in the differential expression of individual genes within operons.

FUNDING

Austrian Science Fund project Z-72 and by the European Commission STREP program BAC-RNA FP6-2004-LIFESCIHEALTH-5 No 018618 to RS, by the Austrian Ministry for Science and Research GEN-AU Bioinformatics Integration Network III grant to A.v.H. T.G. is supported by the Wiener Wissenschafts Forschungs- und Technologiefonds (WWTF). Funding for open access charge: Austrian Science Found FWF.

Conflict of interest statement. None declared.

ACKNOWLEDGEMENTS

The authors are grateful to A. Feig for the gift of Hfq used for the SELEX experiments and to Branislav Vecerek and Udo Bläsi for Hfq used for binding assays. They thank M. Wickens for the three-hybrid system strain and plasmids.

REFERENCES

1. Argaman L, Hershberg R, Vogel J, Bejerano G, Wagner EG, Margalit H, Altuvia S. Novel small RNA-encoding genes in the intergenic regions of Escherichia coli. Curr. Biol. 2001;11:941–950. [PubMed]
2. Chen S, Lesnik EA, Hall TA, Sampath R, Griffey RH, Ecker DJ, Blyn LB. A bioinformatics based approach to discover small RNA genes in the Escherichia coli genome. Biosystems. 2002;65:157–177. [PubMed]
3. Rivas E, Klein RJ, Jones TA, Eddy SR. Computational identification of noncoding RNAs in E. coli by comparative genomics. Curr. Biol. 2001;11:1369–1373. [PubMed]
4. Wassarman KM, Repoila F, Rosenow C, Storz G, Gottesman S. Identification of novel small RNAs using comparative genomics and microarrays. Genes Dev. 2001;15:1637–1651. [PMC free article] [PubMed]
5. Tjaden B, Saxena RM, Stolyar S, Haynor DR, Kolker E, Rosenow C. Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays. Nucleic Acids Res. 2002;30:3732–3738. [PMC free article] [PubMed]
6. Zhang A, Wassarman KM, Rosenow C, Tjaden BC, Storz G, Gottesman S. Global analysis of small RNA and mRNA targets of Hfq. Mol. Microbiol. 2003;50:1111–1124. [PubMed]
7. Vogel J, Bartels V, Tang TH, Churakov G, Slagter-Jager JG, Huttenhofer A, Wagner EG. RNomics in Escherichia coli detects new sRNA species and indicates parallel transcriptional output in bacteria. Nucleic Acids Res. 2003;31:6435–6443. [PMC free article] [PubMed]
8. Kawano M, Storz G, Rao BS, Rosner JL, Martin RG. Detection of low-level promoter activity within open reading frame sequences of Escherichia coli. Nucleic Acids Res. 2005;33:6268–6276. [PMC free article] [PubMed]
9. Gottesman S. The small RNA regulators of Escherichia coli: roles and mechanisms. Annu. Rev. Microbiol. 2004 [PubMed]
10. Storz G, Altuvia S, Wassarman KM. An abundance of RNA regulators. Annu. Rev. Biochem. 2005;74:199–217. [PubMed]
11. Waters LS, Storz G. Regulatory RNAs in bacteria. Cell. 2009;136:615–628. [PMC free article] [PubMed]
12. Valentin-Hansen P, Eriksen M, Udesen C. The bacterial Sm-like protein Hfq: a key player in RNA transactions. Mol. Microbiol. 2004;51:1525–1533. [PubMed]
13. Brennan RG, Link TM. Hfq structure, function and ligand binding. Curr. Opin. Microbiol. 2007;10:125–133. [PubMed]
14. Franze de Fernandez MT, Eoyang L, August JT. Factor fraction required for the synthesis of bacteriophage Qbeta-RNA. Nature. 1968;219:588–590. [PubMed]
15. Tsui HC, Leung HC, Winkler ME. Characterization of broadly pleiotropic phenotypes caused by an hfq insertion mutation in Escherichia coli K-12. Mol. Microbiol. 1994;13:35–49. [PubMed]
16. Muffler A, Fischer D, Hengge-Aronis R. The RNA-binding protein HF-I, known as a host factor for phage Qbeta RNA replication, is essential for rpoS translation in Escherichia coli. Genes Dev. 1996;10:1143–1151. [PubMed]
17. Vytvytska O, Moll I, Kaberdin VR, von Gabain A, Blasi U. Hfq (HF1) stimulates ompA mRNA decay by interfering with ribosome binding. Genes Dev. 2000;14:1109–1118. [PMC free article] [PubMed]
18. Folichon M, Arluison V, Pellegrini O, Huntzinger E, Regnier P, Hajnsdorf E. The poly(A) binding protein Hfq protects RNA from RNase E and exoribonucleolytic degradation. Nucleic Acids Res. 2003;31:7302–7310. [PMC free article] [PubMed]
19. Sledjeski DD, Whitman C, Zhang A. Hfq is necessary for regulation by the untranslated RNA DsrA. J. Bacteriol. 2001;183:1997–2005. [PMC free article] [PubMed]
20. Masse E, Gottesman S. A small RNA regulates the expression of genes involved in iron metabolism in Escherichia coli. Proc. Natl Acad. Sci. USA. 2002;99:4620–4625. [PMC free article] [PubMed]
21. Zhang A, Wassarman KM, Ortega J, Steven AC, Storz G. The Sm-like Hfq Protein Increases OxyS RNA Interaction with Target mRNAs. Mol. Cell. 2002;9:11–22. [PubMed]
22. Masse E, Escorcia FE, Gottesman S. Coupled degradation of a small regulatory RNA and its mRNA targets in Escherichia coli. Genes Dev. 2003;17:2374–2383. [PMC free article] [PubMed]
23. Robertson GT, Roop R.M., Jr The Brucella abortus host factor I (HF-I) protein contributes to stress resistance during stationary phase and is a major determinant of virulence in mice. Mol. Microbiol. 1999;34:690–700. [PubMed]
24. Sonnleitner E, Hagens S, Rosenau F, Wilhelm S, Habel A, Jager KE, Blasi U. Reduced virulence of a hfq mutant of Pseudomonas aeruginosa O1. Microb. Pathog. 2003;35:217–228. [PubMed]
25. Christiansen JK, Larsen MH, Ingmer H, Sogaard-Andersen L, Kallipolitis BH. The RNA-binding protein Hfq of Listeria monocytogenes: role in stress tolerance and virulence. J. Bacteriol. 2004;186:3355–3362. [PMC free article] [PubMed]
26. Schumacher MA, Pearson RF, Moller T, Valentin-Hansen P, Brennan RG. Structures of the pleiotropic translational regulator Hfq and an Hfq-RNA complex: a bacterial Sm-like protein. EMBO J. 2002;21:3546–3556. [PMC free article] [PubMed]
27. Moller T, Franch T, Hojrup P, Keene DR, Bachinger HP, Brennan RG, Valentin-Hansen P. Hfq. A Bacterial Sm-like Protein that Mediates RNA-RNA Interaction. Mol. Cell. 2002;9:23–30. [PubMed]
28. Kawamoto H, Koide Y, Morita T, Aiba H. Base-pairing requirement for RNA silencing by a bacterial small RNA and acceleration of duplex formation by Hfq. Mol. Microbiol. 2006;61:1013–1022. [PubMed]
29. Rajkowitsch L, Schroeder R. Dissecting RNA chaperone activity. RNA. 2007;13:2053–2060. [PMC free article] [PubMed]
30. Lease RA, Woodson SA. Cycling of the Sm-like protein Hfq on the DsrA small regulatory RNA. J. Mol. Biol. 2004;344:1211–1223. [PubMed]
31. Soper TJ, Woodson SA. The rpoS mRNA leader recruits Hfq to facilitate annealing with DsrA sRNA. Rna. 2008;14:1907–1917. [PMC free article] [PubMed]
32. Sittka A, Lucchini S, Papenfort K, Sharma CM, Rolle K, Binnewies TT, Hinton JC, Vogel J. Deep sequencing analysis of small noncoding RNA and mRNA targets of the global post-transcriptional regulator, Hfq. PLoS Genet. 2008;4:e1000163. [PMC free article] [PubMed]
33. Sittka A, Vogel J. A glimpse at the evolution of virulence control. Cell Host Microbe. 2008;4:310–312. [PubMed]
34. Sittka A, Sharma CM, Rolle K, Vogel J. Deep sequencing of Salmonella RNA associated with heterologous Hfq proteins in vivo reveals small RNAs as a major target class and identifies RNA processing phenotypes. RNA Biol. 2009;6:266–275. [PubMed]
35. Zimmermann B, Gesell T, Chen D, Lorenz C, Schroder R. Monitoring Genomic Sequences During SELEX Using High-Throughput Sequencing: Neutral SELEX. PLoSONE. 2010;5:e9169. [PMC free article] [PubMed]
36. Lorenz C, F. VP, Schroeder R. Genomic systematic evolution of ligands by exponential enrichment (genomic SELEX) for the identification of protein-binding RNAs independent of their expression level. Nature Protocols. 2006;1:2204–2212. [PubMed]
37. Bernstein DS, Buter N, Stumpf C, Wickens M. Analyzing mRNA-protein complexes using a yeast three-hybrid system. Methods. 2002;26:123–141. [PubMed]
38. Jahn CE, Charkowski AO, Willis DK. Evaluation of isolation methods and RNA integrity for bacterial RNA quantitation. J Microbiol Methods. 2008;75:318–324. [PubMed]
39. Waldsich C, Schroeder R. In: Handbook of RNA Biochemistry. Hartman RK, Bindereif A, Schoen A, Westhof E, editors. Vol. 1. Weinheim: Wiley-VCH Verlag GmbH; 2005. pp. 229–237.
40. Abouelhoda M, Kurtz S, Ohlebusch E. Replacing suffix trees with enhanced suffix arrays. J. Discrete Algorithms. 2004
41. Benson D, Lipman DJ, Ostell J. GenBank. Nucleic Acids Res. 1993;21:2963–2965. [PMC free article] [PubMed]
42. Karp PD, Riley M, Paley SM, Pellegrini-Toole A, Krummenacker M. Eco Cyc: encyclopedia of Escherichia coli genes and metabolism. Nucleic Acids Res. 1999;27:55–58. [PMC free article] [PubMed]
43. R Development Core Team. Vienna, Austria: 2008.
44. Zuker M, Stiegler P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 1981;9:133–148. [PMC free article] [PubMed]
45. Zuker M. Calculating nucleic acid secondary structure. Curr. Opin. Struct. Biol. 2000;10:303–310. [PubMed]
46. McCaskill JS. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers. 1990;29:1105–1119. [PubMed]
47. Hofacker IL, Fontana W, Stadler PF, Bonhoeffer S, Tacker M, Schuster P. Fast folding and comparison of RNA secondary structures. Monatsh. Chem. 1994;125:167–188.
48. Singer B, Shtatland T, Brown D, Gold L. Libraries for genomic SELEX. Nucleic Acids Res. 1997;25:781–786. [PMC free article] [PubMed]
49. Urbanowski ML, Stauffer LT, Stauffer GV. The gcvB gene encodes a small untranslated RNA involved in expression of the dipeptide and oligopeptide transport systems in Escherichia coli. Mol. Microbiol. 2000;37:856–868. [PubMed]
50. Guillier M, Gottesman S. Remodelling of the Escherichia coli outer membrane by two small regulatory RNAs. Mol. Microbiol. 2006;59:231–247. [PubMed]
51. Lee T, Feig AL. The RNA binding protein Hfq interacts specifically with tRNAs. Rna. 2008;14:514–523. [PMC free article] [PubMed]
52. Urban JH, Papenfort K, Thomsen J, Schmitz RA, Vogel J. A conserved small RNA promotes discoordinate expression of the glmUS operon mRNA to activate GlmS synthesis. J. Mol. Biol. 2007;373:521–528. [PubMed]
53. Guisbert E, Rhodius VA, Ahuja N, Witkin E, Gross CA. Hfq modulates the sigmaE-mediated envelope stress response and the sigma32-mediated cytoplasmic stress response in Escherichia coli. J. Bacteriol. 2007;189:1963–1973. [PMC free article] [PubMed]
54. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1994;2:28–36. [PubMed]
55. Bailey TL, Williams N, Misleh C, Li WW. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 2006;34:W369–W373. [PMC free article] [PubMed]
56. Geissmann TA, Touati D. Hfq, a new chaperoning role: binding to messenger RNA determines access for small RNA regulator. EMBO J. 2004;23:396–405. [PMC free article] [PubMed]
57. Selinger DW, Cheung KJ, Mei R, Johansson EM, Richmond CS, Blattner FR, Lockhart DJ, Church GM. RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nat. Biotechnol. 2000;18:1262–1268. [PubMed]
58. Haddad F, Qin AX, Giger JM, Guo H, Baldwin KM. Potential pitfalls in the accuracy of analysis of natural sense-antisense RNA pairs by reverse transcription-PCR. BMC Biotechnol. 2007;7:21. [PMC free article] [PubMed]
59. Toledo-Arana A, Dussurget O, Nikitas G, Sesto N, Guet-Revillet H, Balestrino D, Loh E, Gripenland J, Tiensuu T, Vaitkevicius K, et al. The Listeria transcriptional landscape from saprophytism to virulence. Nature. 2009;459:950–956. [PubMed]
60. Wassarman KM, Zhang A, Storz G. Small RNAs in Escherichia coli. Trends Microbiol. 1999;7:37–45. [PubMed]
61. Livny J, Waldor MK. Identification of small RNAs in diverse bacterial species. Curr. Opin. Microbiol. 2007;10:96–101. [PubMed]
62. Moll I, Leitsch D, Steinhauser T, Blasi U. RNA chaperone activity of the Sm-like Hfq protein. EMBO Rep. 2003;4:284–289. [PMC free article] [PubMed]
63. Mikulecky PJ, Kaw MK, Brescia CC, Takach JC, Sledjeski DD, Feig AL. Escherichia coli Hfq has distinct interaction surfaces for DsrA, rpoS and poly(A) RNAs. Nat. Struct. Mol. Biol. 2004;11:1206–1214. [PMC free article] [PubMed]
64. Mayer O, Lorenz C, Schroeder R. Transient interactions rather than tight binding determines the RNA chaperone activity of the E. coli protein StpA. Nucleic Acids Res. 2006;35:1257–1269. [PMC free article] [PubMed]
65. Wakeman CA, Winkler WC, Dann C.E., 3rd Structural features of metabolite-sensing riboswitches. Trends Biochem. Sci. 2007;32:415–424. [PMC free article] [PubMed]
66. Fozo EM, Hemm MR, Storz G. Small toxic proteins and the antisense RNAs that repress them. Microbiol. Mol. Biol. Rev. 2008;72:579–589. table of contents. [PMC free article] [PubMed]
67. Kawano M, Aravind L, Storz G. An antisense RNA controls synthesis of an SOS-induced toxin evolved from an antitoxin. Mol. Microbiol. 2007;64:738–754. [PMC free article] [PubMed]
68. Gerdes K, Nielsen A, Thorsted P, Wagner EG. Mechanism of killer gene activation. Antisense RNA-dependent RNase III cleavage ensures rapid turn-over of the stable hok, srnB and pndA effector messenger RNAs. J. Mol. Biol. 1992;226:637–649. [PubMed]
69. Gerdes K, Wagner EG. RNA antitoxins. Curr. Opin. Microbiol. 2007;10:117–124. [PubMed]
70. Fozo EM, Kawano M, Fontaine F, Kaya Y, Mendieta KS, Jones KL, Ocampo A, Rudd KE, Storz G. Repression of small toxic protein synthesis by the Sib and OhsC small RNAs. Mol. Microbiol. 2008;70:1076–1093. [PMC free article] [PubMed]
71. Vogel J, Argaman L, Wagner EG, Altuvia S. The small RNA IstR inhibits synthesis of an SOS-induced toxic peptide. Curr. Biol. 2004;14:2271–2276. [PubMed]
72. Darfeuille F, Unoson C, Vogel J, Wagner EG. An antisense RNA inhibits translation by competing with standby ribosomes. Mol. Cell. 2007;26:381–392. [PubMed]
73. Franch T, Petersen M, Wagner EG, Jacobsen JP, Gerdes K. Antisense RNA regulation in prokaryotes: rapid RNA/RNA interaction facilitated by a general U-turn loop structure. J. Mol. Biol. 1999;294:1115–1125. [PubMed]
74. Pfeiffer V, Papenfort K, Lucchini S, Hinton JC, Vogel J. Coding sequence targeting by MicC RNA reveals bacterial mRNA silencing downstream of translational initiation. Nat. Struct. Mol. Biol. 2009;16:840–846. [PubMed]
75. Opdyke JA, Kang JG, Storz G. GadY, a small-RNA regulator of acid response genes in Escherichia coli. J. Bacteriol. 2004;186:6698–6705. [PMC free article] [PubMed]
76. Tramonti A, De Canio M, De Biase D. GadX/GadW-dependent regulation of the Escherichia coli acid fitness island: transcriptional control at the gadY-gadW divergent promoters and identification of four novel 42 bp GadX/GadW-specific binding sites. Mol. Microbiol. 2008;70:965–982. [PubMed]
77. Vecerek B, Rajkowitsch L, Sonnleitner E, Schroeder R, Blasi U. The C-terminal domain of Escherichia coli Hfq is required for regulation. Nucleic Acids Res. 2008;36:133–143. [PMC free article] [PubMed]
78. Hofacker IL. Vienna RNA secondary structure server. Nucleic Acids Res. 2003;31:3429–3431. [PMC free article] [PubMed]

Articles from Nucleic Acids Research are provided here courtesy of Oxford University Press

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...