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Global Regulatory Functions of the Staphylococcus aureus Endoribonuclease III in Gene Expression

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      Abstract

      RNA turnover plays an important role in both virulence and adaptation to stress in the Gram-positive human pathogen Staphylococcus aureus. However, the molecular players and mechanisms involved in these processes are poorly understood. Here, we explored the functions of S. aureus endoribonuclease III (RNase III), a member of the ubiquitous family of double-strand-specific endoribonucleases. To define genomic transcripts that are bound and processed by RNase III, we performed deep sequencing on cDNA libraries generated from RNAs that were co-immunoprecipitated with wild-type RNase III or two different cleavage-defective mutant variants in vivo. Several newly identified RNase III targets were validated by independent experimental methods. We identified various classes of structured RNAs as RNase III substrates and demonstrated that this enzyme is involved in the maturation of rRNAs and tRNAs, regulates the turnover of mRNAs and non-coding RNAs, and autoregulates its synthesis by cleaving within the coding region of its own mRNA. Moreover, we identified a positive effect of RNase III on protein synthesis based on novel mechanisms. RNase III–mediated cleavage in the 5′ untranslated region (5′UTR) enhanced the stability and translation of cspA mRNA, which encodes the major cold-shock protein. Furthermore, RNase III cleaved overlapping 5′UTRs of divergently transcribed genes to generate leaderless mRNAs, which constitutes a novel way to co-regulate neighboring genes. In agreement with recent findings, low abundance antisense RNAs covering 44% of the annotated genes were captured by co-immunoprecipitation with RNase III mutant proteins. Thus, in addition to gene regulation, RNase III is associated with RNA quality control of pervasive transcription. Overall, this study illustrates the complexity of post-transcriptional regulation mediated by RNase III.

      Author Summary

      Control of mRNA stability is crucial for bacteria to survive and rapidly adapt to environmental changes and stress conditions. The molecular players and the degradation pathways involved in these adaptive processes are poorly understood in Staphylococcus aureus. The universally conserved double-strand-specific endoribonuclease III (RNase III) in S. aureus is known to repress the synthesis of several virulence factors and was recently implicated in genome-wide mRNA processing mediated by antisense transcripts. We present here the first global map of direct RNase III targets in S. aureus. Deep sequencing was used to identify RNAs associated with epitope-tagged wild-type RNase III and two catalytically impaired but binding-competent mutant proteins in vivo. Experimental validation revealed an unexpected variety of structured RNA transcripts as novel RNase III substrates. In addition to rRNA operon maturation, autoregulation, degradation of structured RNAs, and antisense regulation, we propose novel mechanisms by which RNase III increases mRNA translation. Overall, this study shows that RNase III has a broad function in gene regulation of S. aureus. We can now address more specifically the roles of this universally conserved enzyme in gene regulation in response to stress and during host infection.

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      Biogenesis of small RNAs in animals.

      Small RNAs of 20-30 nucleotides can target both chromatin and transcripts, and thereby keep both the genome and the transcriptome under extensive surveillance. Recent progress in high-throughput sequencing has uncovered an astounding landscape of small RNAs in eukaryotic cells. Various small RNAs of distinctive characteristics have been found and can be classified into three classes based on their biogenesis mechanism and the type of Argonaute protein that they are associated with: microRNAs (miRNAs), endogenous small interfering RNAs (endo-siRNAs or esiRNAs) and Piwi-interacting RNAs (piRNAs). This Review summarizes our current knowledge of how these intriguing molecules are generated in animal cells.
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        The Listeria transcriptional landscape from saprophytism to virulence.

        The bacterium Listeria monocytogenes is ubiquitous in the environment and can lead to severe food-borne infections. It has recently emerged as a multifaceted model in pathogenesis. However, how this bacterium switches from a saprophyte to a pathogen is largely unknown. Here, using tiling arrays and RNAs from wild-type and mutant bacteria grown in vitro, ex vivo and in vivo, we have analysed the transcription of its entire genome. We provide the complete Listeria operon map and have uncovered far more diverse types of RNAs than expected: in addition to 50 small RNAs (<500 nucleotides), at least two of which are involved in virulence in mice, we have identified antisense RNAs covering several open-reading frames and long overlapping 5' and 3' untranslated regions. We discovered that riboswitches can act as terminators for upstream genes. When Listeria reaches the host intestinal lumen, an extensive transcriptional reshaping occurs with a SigB-mediated activation of virulence genes. In contrast, in the blood, PrfA controls transcription of virulence genes. Remarkably, several non-coding RNAs absent in the non-pathogenic species Listeria innocua exhibit the same expression patterns as the virulence genes. Together, our data unravel successive and coordinated global transcriptional changes during infection and point to previously unknown regulatory mechanisms in bacteria.
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          Deep Sequencing Analysis of Small Noncoding RNA and mRNA Targets of the Global Post-Transcriptional Regulator, Hfq

          Introduction Until now, global gene expression control studies have generally focussed on the transcriptional regulation exerted by the specific action of DNA binding proteins, and on the post-translational regulation governed by specific protein–protein interactions. In comparison, little is known about how RNA binding proteins facilitate the global control of gene expression at the post-transcriptional level. However, the latest discoveries of many small noncoding RNAs (sRNAs) in both pro- and eukaryotes have shown that the interaction of RNA with proteins plays a prominent role in the regulation of cellular processes. In bacteria, the majority of the sRNAs basepair with target mRNAs to regulate their translation and/or decay [1],[2],[3], and these regulatory events commonly require the bacterial Sm-like protein, Hfq [4],[5]. Hfq is one of the most abundant RNA-binding proteins in bacteria [6],[7],[8]. First identified in Escherichia coli as a host factor required for phage Qβ RNA replication ∼40 years ago [9], Hfq is now known to have an important physiological role in numerous model bacteria [5]. Almost half of all sequenced Gram-negative and Gram-positive species, and at least one archaeon, encode an Hfq homologue [10],[11]. Hfq interacts with regulatory sRNAs and mRNAs, and much of its post-transcriptional function is caused by the facilitation of the generally short and imperfect antisense interactions of sRNAs and their targets [12],[13],[14],[15],[16],[17]. However, Hfq can also act alone as a translational repressor of mRNA [18],[19], and can modulate mRNA decay by stimulating polyadenylation [20],[21]. In addition, roles of Hfq in tRNA biogenesis have recently been described [22],[23]. The pleiotropy of an hfq deletion mutation was first apparent from the multiple stress response-related phenotypes in E. coli [24], and partly reflects the reduced efficiency of translation of rpoS mRNA, encoding the major stress sigma factor, σS [25],[26]. However, Hfq clearly impacts on bacterial physiology in a much broader fashion, including the σS-independent control of virulence factors in pathogenic bacteria (e.g., [27],[28],[29],[30],[31],[32],[33]). Specifically, deletion of hfq attenuates the ability of the model pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) to infect mice, to invade epithelial cells, to secrete virulence factors and to survive inside cultured macrophages [32]. Loss of Hfq function also results in a non-motile phenotype for Salmonella and the deregulation of >70 abundant proteins, including the accumulation of outer membrane proteins (OMPs); the latter is accompanied by a chronic activation of the σE (σ24)-mediated envelope stress response [32],[34]. Hfq has also been implicated in the control of Salmonella gene expression changes induced by the low gravity condition experienced during spaceflight [35]. Understanding how Hfq controls Salmonella gene expression at the post-transcriptional level requires the identification of its sRNA and mRNA ligands. In a pioneering global study in E. coli, Zhang et al. (2003) used co-immunoprecipitation (coIP) with Hfq-specific antisera and direct detection of the bound RNAs on genomic high-density oligonucleotide microarrays. Although this method proved highly effective for detecting diverse sRNAs and mRNAs in E. coli, the requirement for high-density microarrays and specialized antibodies has hampered similar studies in other bacteria. An alternate approach identified individual abundant Hfq-associated RNAs by cDNA cloning or direct sequencing [29],[36]; however, these methods are not appropriate for large-scale analyses. To overcome these limitations for the global identification of Hfq targets in Salmonella, we have now used high-throughput pyrosequencing (HTPS, a.k.a. deep sequencing) of RNA associated with an epitope-tagged Hfq protein (Figure 1). We show that this approach recovers Hfq-binding sRNAs with high specificity, and identifies their boundaries with unprecedented resolution. We report the discovery of novel Salmonella sRNA genes, detect the expression of many conserved enterobacterial sRNA genes, and provide a set of potential mRNA targets in this model pathogen. Comparison with the transcriptomic profile of an hfq mutant showed that Hfq mediates its pleiotropic effects by regulating the master transcription factors of complex regulons, and explained how Hfq is required for Salmonella virulence. In microbiology, deep sequencing has been used extensively for genome sequencing, either of individual microbial species [37] or of bacterial communities [38]. This study is the first report that describes the use of deep sequencing to study protein-bound mRNA from bacteria, and to discover bacterial noncoding RNAs. 10.1371/journal.pgen.1000163.g001 Figure 1 Strategy to identify Hfq targets. RNA was co-immunoprecipitated with Hfq in extracts from ESP-grown Salmonella cells (wild-type and chromosomal hfq FLAG strain) using an anti-FLAG antibody. The extracted RNA was converted to 5′ monophosphate RNA, and subsequently into cDNA, followed by direct pyrosequencing. Our approach was validated by hybridization of cDNA to high density oligo microarrays. In addition, total RNA of the wild-type strain and its hfq deletion mutant was used for transcriptomic analysis using Salmonella SALSA microarrays. Results Transcriptomic Profiling Reveals a Large Hfq Regulon in Salmonella To detect genes that are, directly or indirectly, regulated by Hfq the transcriptomic mRNA profile of the Salmonella wild-type and of mutant strain JVS-0255 (Δhfq) was determined. We used two different conditions for the comparison; aerobic growth in L-broth to early stationary phase (ESP; OD600 of 2) was chosen because the hfq mutation causes drastic protein pattern changes in ESP Salmonella [32], and overnight growth in high-salt medium under oxygen limitation (SPI-1-inducing conditions) to specifically activate the Salmonella virulence genes required for host cell invasion [39]. Hfq-dependent mRNAs that showed statistically significant changes (≥2-fold) were identified, and we discovered that 734 genes were differentially expressed in the Δhfq strain grown to ESP (279 up-regulated genes, 455 down-regulated genes, Figure 2 and Table S1). Of the 71 proteins known to be Hfq-dependent (as determined by protein levels on 2D gels; [32]), 50% were regulated by Hfq at the transcriptional level (Table S1). Consequently, Hfq controls the expression of 17% of all Salmonella genes at ESP (based on the 4425 annotated ORFs; [40]). Growth under SPI-1 inducing conditions revealed 164 differentially expressed genes in Δhfq (91 up-, 73 down-regulated; Table S2). 69% of these genes overlapped with the changes seen in ESP. Taken together, Hfq affects at least 785 genes, or 18% of the Salmonella genome. 10.1371/journal.pgen.1000163.g002 Figure 2 Correlation between HTPS, coIP-on-chip and transcriptomic data upon the S. Typhimurium chromosome. The data obtained from transcriptomic, cDNA sequencing and coIP-on-chip analyses of ESP-grown bacteria were mapped onto the Salmonella chromosome for direct comparison. The outer (1st) ring displays changes in gene expression in the Δhfq strain compared to the parental SL1344 strain. Genes that are down-regulated in the Δhfq strain are shown as blue; genes that are up-regulated are shown as red. The next three circles show regions coding for Hfq-associated RNA identified by deep sequencing (2nd ring shows positive strand, and 3rd ring shows negative strand) or coIP-on-chip (4th ring). Ring 5 shows the location of coding sequences on the positive strand (CDS+), on the negative strand (CDS−), and the tRNA and rRNA genes. GC-skew [110] is shown in ring 6; purple and blue regions have a GC skew that is below or above the genomic average, respectively. AT-content is shown in ring 7; blue and red regions have an AT-content that is below or above the genomic average, respectively. Numbers on the inside of the innermost circle are the location relative to position zero measured in millions of base-pairs (Mbp) of the Salmonella LT2 genome. The location of the SPI-1 to SPI-5 is indicated. An invaluable zoomable version of this atlas is available online at http://www.cbs.dtu.dk/services/GenomeAtlas/suppl/zoomatlas/zpidStyphimurium_LT2_Atlas ; click on the region of interest to accurately visualize the data at the level of individual genes. Classification of the genes deregulated at ESP (Table 1) showed that Hfq impacted upon 26 of the 107 functional groups annotated for Salmonella [41]; in seven groups ≥50% of all genes were misregulated. In four of the five major Salmonella pathogenicity islands (i.e., SPI-1, -2, -4, -5), and in the flagellar and chemotaxis pathways, >60% of genes were down-regulated, which explains the previously observed invasion and motility phenotypes of Δhfq [32]. Because Hfq affects the mRNAs of σS (RpoS) and σE (RpoE) [25],[26],[34],[42], two major alternative stress σ factors of enterobacteria, we quantified the expression of these sigma factors in Salmonella at the mRNA level (ESP) and at the protein level (ESP and SPI-1 inducing conditions). σE mRNA and protein levels were strongly elevated in Δhfq under both conditions tested (Figure S1), confirming the previously observed chronic induction of the envelope stress response. Levels of rpoS mRNA were slightly increased, yet RpoS protein levels were strongly decreased. This reflects the poor translation of rpoS mRNA in the absence of Hfq (Figure S1 and [25],[26]). We used published lists of σE- and σS-dependent genes of Salmonella [43],[44] to determine how the Hfq-dependent changes we observed were related to the σE and/or σS regulons. We discovered that 55% (41/75) and 73% (54/74) of σE- and σS-dependent genes were also Hfq-dependent. Therefore, a proportion of the Hfq-dependent gene expression changes observed at ESP and under SPI-1 inducing conditions were indirect effects caused by modulation of σS and σE levels by Hfq. 10.1371/journal.pgen.1000163.t001 Table 1 Pathway clustering of Hfq-dependent genes at ESP. pathwaya genes in pathwayb % upc % downd % genes regulated Flagellar system 53 0 87 87 Chemotaxis 19 0 84 84 Fimbrial proteins 24 0 20 20 SPI1 39 0 90 90 SPI2 40 0 72.5 72.5 SPI3 29 0 14 14 SPI4 6 0 100 100 SPI5 8 0 62.5 62.5 ABC transporter 188 11 7 28 Cyanoamino acid metabolism 10 20 10 30 Cystein metabolism 15 20 0 20 Fatty acid metabolism & biosynthesis 20 15 15 30 Fructose & mannose metabolism 64 2 11 13 Glutamate metabolism 29 7 7 14 Lipopolysaccharidee biosynthesis 28 3.5 3.5 7 Glycerophospholipid metabolism 24 17 12.5 29.5 Glycine, serine & threonine metabolism 35 31.5 3 34.5 Glycolysis/Gluconeogenesis 28 3 21 24 Nitrogen metabolism 33 15 6 21 Pentose phosphate pathway 32 12.5 19 31.5 Purine metabolism 73 11 4 15 Pyrimidine metabolism 49 10 0 10 Pyruvate metabolism 49 12 0 12 Ribosome 78 35 0 35 Selenoamino acid, sulfur metabolism 18 33 17 50 Starch & sucrose metabolism 31 3 26 29 Hfq-dependent genes in ESP-grown Salmonella are shown in Table S1. a Pathway classification according to KEGG (http://www.genome.jp/kegg/; [21]). Pathways in which ≥50% of genes are Hfq-regulated are shadowed. b Number of genes involved in pathway (acc. KEGG). c,d Numbers in percent of genes that were up- or down-regulated in Δhfq compared to wt, (Table S1). The S. Typhimurium genome contains about 444 genes acquired by horizontal gene transfer (HGT; [45]). 122 or 17 of these HGT genes were Hfq-dependent under ESP or SPI-1 inducing conditions, respectively (16 genes being Hfq-dependent under both conditions; Tables S1, S2). In other words, Hfq regulates 28% of the HGT genes, significantly more than the 18% regulated when using the entire Salmonella genome for calculation. This may indicate a role of Hfq in the acquisition of DNA from foreign sources, by regulating expression of newly acquired genes at the RNA level. Deep Sequencing of Hfq-Associated RNAs The variety of transcriptional regulons that showed Hfq-dependent expression patterns could either be mediated by the binding of certain regulatory sRNAs or of specific mRNAs by Hfq. To identify the direct Hfq targets we co-immunoprecipitated RNA with the chromosomally FLAG epitope-tagged Hfq protein expressed by a Salmonella hfq FLAG strain [46]. CoIP was performed in extracts prepared from ESP-grown bacteria. The Hfq-associated RNA was converted to cDNA, and a total of 176,907 cDNAs pooled from two independent biological experiments was then characterised by high-throughput pyrosequencing [37]. The resulting sequences, from here on referred to as “Hfq cDNAs”, ranged in length from 1 to 145 bp, and 92% were ≥18 bp (Figure 3A). Disregarding small cDNAs ( 3000 cDNAs). The lower panel shows a close up of the invR locus and its adjacent genes. Hfq-Dependent sRNAs Are Highly Associated with Hfq Inspection of the cDNA libraries revealed that a major class were derived from sRNA regions. These sRNAs, as well as their enrichment by Hfq coIP, are listed in Tables 2 and S3. The three most abundant sRNAs, according to the numbers of Hfq cDNA sequences are InvR, SraH (a.k.a. RyhA) and SroB (RybC), and are known to be strongly bound by Hfq [17],[46]; coIP of Hfq enriched these three sRNAs by 30- to 57-fold, in comparison to the control reaction. For example, InvR, which binds Hfq with a k D of 10 nM [46], was represented by 3,236 Hfq cDNAs and 113 Control cDNAs (Table 2). In contrast, other sRNAs not expected to be Hfq-dependent were found in equal numbers in the two samples. For example, the CsrB or CsrC sRNAs which target the conserved RNA-binding protein, CsrA [48], were represented by almost equal numbers in the Hfq and Control cDNAs (CsrB, 67/69; CsrC, 63/64; Table 2). Moreover, cDNAs of the abundant yet Hfq-independent 6S RNA [49] were found in smaller numbers in the Hfq than in the control library (451 versus 836; Table 2). 10.1371/journal.pgen.1000163.t002 Table 2 Compilation of expressed Salmonella sRNAs and their enrichment by Hfq coIP. sRNAa Alternative IDs b Identification c Adjacent genes d Orientation e 5′ end f 3′ end f cDNA reads control coIP g cDNA reads Hfq coIP h Enrichment i Northern j sgrS ryaA I yabN/leuD ← → ← 128574 128812 3 61 20.3 isrA - II STM0294.ln/STM0295 → → → 339338 339760 0 0 sroB rybC I ybaK/ybaP ← → ← 556005 556085 27 1530 56.7 sroC - I gltJ/gltI ← ← ← 728913 728761 26 898 34.5 rybB p25 III STM0869/STM0870 → ← ← 942632 942554 3 103 34.3 STnc490 k - IV clpA/tnpA_1 → ← → 1024975 1025165 75 385 5.1 ∼85 nt isrB-1 - II sbcA/STM1010 ← → ← 1104179 1104266 2 4 2.0 STnc500 - IV STM1127/STM1128 ← ← ← 1216157 1216440 7 84 12.0 ∼65 nt STnc150 - V icdA/STM1239 → ← → 1325914 1325649 0 1 ≥1.0 ∼90 nt isrC - II envF/msgA ← → ← 1329145 1329432 0 1 ≥1.0 STnc520 - IV STM1248/STM1249 → ← ← 1332809 1334044 12 100 8.3 ∼80 nt isrD - II STM1261/STM1263 → ← → 1345788 1345738 0 0 ryhB-2 isrE II STM1273/yeaQ → ← → 1352987 1352875 0 0 STnc540 - IV himA/btuC → → → 1419369 1419570 7 23 3.3 ∼85 nt rprA IS083 I ydik/ydil ← ← ← 1444938 1444832 37 286 7.7 rydB tpe7, IS082 I ydiH/STM1368 → → ← 1450415 1450519 4 10 2.5 STnc570 l yneM ORF IV ydeI/ydeE → ← ← 1593723 1594413 2 21 10.5 ∼190 nt STnc560 IV ydeI/ydeE → → ← 1593723 1594413 10 290 29.0 ∼90 nt isrF - II STM1552/STM1554 → ← ← 1630160 1629871 1 0 rydC IS067 I STM1638/cybB → → ← 1729673 1729738 5 245 49.0 micC IS063, tke8 III nifJ/ynaF → ← → 1745786 1745678 0 15 ≥15.0 STnc580 - IV dbpA/STM1656 ← ← ← 1749662 1750147 11 311 28.3 ∼100 nt ryeB tpke79 I STM1871/STM1872 → ← ← 1968155 1968053 24 653 27.2 dsrA - I yodD/yedP → ← → 2068736 2068649 6 149 24.8 rseX - I STM1994/ompS ← → → 2077175 2077269 0 3 ≥3.0 ryeC tp11 I yegD/STM2126 → → → 2213871 2214016 42 72 1.7 cyaR ryeE III yegQ/STM2137 → → → 2231130 2231216 31 659 21.3 isrG - II STM2243/STM2244 ← → → 2344732 2345013 0 0 micF - III ompC/yojN ← → → 2366913 2367005 0 11 ≥11.0 isrH-2 - II glpC/STM2287 → ← → 2394582 2394303 0 0 isrH-1 - II glpC/STM2287 → ← → 2394753 2394303 0 0 STnc250 l ypfM ORF V acrD/yffB → ← → 2596882 2596789 6 24 4.0 ∼220 nt ryfA tp1 I STM2534/sseB → → ← 2674934 2675228 3 6 2.0 glmY tke1, sroF I yfhK/purG ← ← ← 2707847 2707664 20 92 4.6 isrI - II STM2614/STM2616 → ← ← 2761576 2761329 0 2 ≥2.0 isrJ - II STM2614/STM2616 → ← ← 2762031 2761957 1 0 isrK - II STM2616/STM2617 ← ← ← 2762867 2762791 0 0 isrB-2 - II STM2631/sbcA → ← → 2770965 2770872 0 0 isrL - II smpB/STM2690 → ← → 2839399 2839055 0 0 isrM - II STM2762/STM2763 ← → → 2905050 2905378 0 0 isrN - II STM2764/STM2765 ← → ← 2906925 2907067 0 0 micA sraD I luxS/gshA ← → ← 2966853 2966926 1 128 128.0 invR STnc270 III invH/STM 2901 → → → 3044924 3045014 113 3236 28.6 csrB - III yqcC/syd ← ← ← 3117059 3116697 69 67 gcvB IS145 III gcvA/ygdI ← → ← 3135317 3135522 12 402 33.5 omrA rygB III aas/galR ← ← → 3170208 3170122 0 51 ≥51.0 omrB t59, rygA, sraE III aas/galR ← ← → 3170408 3170322 1 52 52.0 STnc290 - V tnpA_4/STM3033 ← ← ← 3194996 3194914 2 72 36.0 ∼85 nt isrO - II STM3038/STM3039 ← → → 3198380 3198580 0 0 ssrS - I ygfE/ygfA → → → 3222098 3222280 836 451 rygC t27 I ygfA/serA → → ← 3222913 3223065 14 17 1.2 rygD tp8, C0730 I yqiK/rfaE → ← ← 3362474 3362327 17 104 6.1 sraF tpk1, IS160 I ygjR/ygjT → → → 3392069 3392261 0 25 ≥25.0 sraH ryhA I yhbL/arcB ← → ← 3490383 3490500 55 2292 41.7 ryhB-1 sraI, IS176 I yhhX/yhhY ← ← → 3715495 3715401 0 2 ≥2.0 istR-1 VI ilvB/emrD ← ← → 3998147 3998018 0 0 ∼75 nt istR-2 VI ilvB/emrD ← ← → 3998147 3998018 0 0 ∼140 nt STnc400 - V STM3844/STM3845 → → → 4051145 4051340 112 42 ∼55 nt glmZ k19, ryiA, sraJ I yifK/hemY → → ← 4141650 4141854 20 196 9.8 Spf spf I polA/yihA → → ← 4209066 4209175 2 33 16.5 csrC sraK, ryiB, tpk2 III yihA/yihI ← → → 4210157 4210400 63 64 isrP - II STM4097/STM4098 ← → ← 4306719 4306866 0 2 ≥2.0 oxyS - I argH/oxyR → ← → 4342986 4342866 0 10 ≥10.0 sraL ryjA III soxR/STM4267 → ← → 4505010 4504870 0 0 STnc440 - V STM4310/tnpA_6 → → → 4559193 4559277 9 456 50.7 ∼85 nt isrQ - II STM4508/STM4509 ← → → 4762997 4763158 0 0 a Gene names of Salmonella sRNAs that have been experimentally proven here, and in previous studies. Method of identification is given in the third column. sRNA names follow Salmonella and/or E. coli nomenclature referenced in (Hershberg et al., 2003; Padalon-Brauch et al., 2008; Papenfort et al., 2008), except STnc490, 500, 520, 540, 560, 570, 580, which have been newly predicted in this study (see Supplementary Table S3). b Alternative sRNA IDs. References in (Hershberg et al., 2003; Padalon-Brauch et al., 2008; Papenfort et al., 2008). c Evidence for sRNAs in Salmonella. (I) Conserved sRNA found in Salmonella cDNA libraries, and previously shown to be expressed in E. coli (relevant ref. in (Papenfort et al., 2008); Table 1). (II) sRNA previously predicted and validated on Northern blots in Salmonella by (Padalon-Brauch et al., 2008). (III) sRNA previously validated on Northern blots in Salmonella (Altier et al., 2000; Figueroa-Bossi et al., 2006; Fortune et al., 2006; Papenfort et al., 2006; Papenfort et al., 2008; Pfeiffer et al., 2007; Sharma et al., 2007; Viegas et al., 2007). (IV) sRNA predicted through cDNA sequencing and validated by Northern blot analysis in this study. (V) sRNA previously predicted by (Pfeiffer et al., 2007) is recovered in cDNA sequences and validated by Northern blot analysis in this study. (VI) IstR sRNAs (Vogel et al., 2004) were not recovered in cDNA sequences but their expression in Salmonella validated by Northern blot analysis in this study (Figure S5). d Flanking genes of the intergenic region in which the sRNA candidate is located. e Orientation of sRNA candidate (middle) and flanking genes (→ and ← denote location of a gene on the clockwise or the counterclockwise strand of the Salmonella chromosome). f Genomic location of sRNA candidate gene according to the Salmonella typhimurium LT2 genome. For STnc470 through STnc640 start and end of the entire intergenic region are given. g Out of 145,873 sequences in total. h Out of 122,326 sequences in total. i Enrichment factor calculated by the number of blastable reads from Hfq coIP over control coIP. j Denotes verification on Northern blot in this study for new RNA transcripts; the estimated size is given in nucleotides. k The cDNA reads map antisense internally of the IS200 element. Based on sequence identity they map to all IS200 elements (tnpA_1 to tnpA_6). l STnc250 and STnc570 contain small ORFs annotated as ypfM or yneM, respectively, in E. coli (Wassarman et al., 2001). Figure 5 illustrates the distribution of cDNAs of the three predominant Hfq-bound RNAs and of the Hfq-independent 6S RNA. cDNAs of both the InvR (89 nt; [46]) and SroB (84 nt; [50]) sRNAs mapped along the entire RNA coding sequence from the transcriptional start site to the Rho-independent terminator. SraH, which is transcribed as an unstable 120 nt precursor and processed into an abundant ∼58 nt RNA species (3′ part of SraH; [17],[51]), was almost exclusively recovered as the processed sRNA. Notably, the borders of the cDNA clusters were in perfect agreement with previous 5′ and/or 3′ end mapping data of the four sRNAs [46],[50],[51],[52]. In other words, our cDNA sequencing approach not only detects association with Hfq, but also identifies the termini of expressed sRNAs at nucleotide-level resolution. 10.1371/journal.pgen.1000163.g005 Figure 5 Visualization of the clone distribution of exemplar Hfq dependent and independent sRNAs in Salmonella. Clone distribution for sequences mapped to InvR, SroB, SraH, or 6S sRNAs (red: Hfq coIP, black: control coIP). The vertical axis indicates the number of cDNA sequences that were obtained. A bent arrow indicates each sRNA promoter, a circled “T” its transcriptional terminator. Identification of Expressed Salmonella sRNAs To evaluate the sRNA expression profile of Salmonella more extensively, we analyzed three classes of sRNA candidate loci for coverage by the Hfq and Control cDNAs. First, cDNAs of E. coli sRNA candidate loci with predicted conservation in Salmonella were inspected [17],[47],[49],[50],[51],[53],[54]. Second, we counted cDNAs of Salmonella-specific sRNAs predicted in two recent global screens [46],[55]. Third, we manually inspected cDNAs from a third of the Salmonella chromosome (first 1.6 Mb) and all major five pathogenicity islands for expression patterns of IGRs indicative of new sRNA genes, and for possible enrichment by Hfq coIP. Using criteria similar to [49], our evaluation of these loci considered orphan promoter/terminator signals, and possible conservation in bacteria other than E. coli. Of the latter two classes of candidates (summarized in Table S3), those with an Hfq enrichment factor ≥10 and/or candidates showing strong promoter/terminator predictions were selected for Northern blot analysis. To assess sRNA expression under relevant environmental conditions, we probed RNA from five stages of growth in standard L-broth from exponential to stationary phases, and from two conditions known to strongly induce the expression of the major SPI-1 [39],[56] or SPI-2 [57] virulence regions. The results of this analysis are summarized in Table 2 (the whole set of candidates tested is shown in Table S3); including the 26 previously detected Salmonella sRNAs [34],[46],[55],[58],[59],[60],[61],[62],[63], a total of 64 Salmonella sRNAs can now be considered to be experimentally validated. We used Northern blots to detect 10 of the 31 newly identified Salmonella sRNAs under the environmental conditions that were tested (Figure 6, Tables 2 and S3). These sRNAs yielded stable transcripts, predominantly in the 50 to 100 nt range (Figure 6A and B). Faint bands of larger transcripts were observed for STnc150 (150 nt), and STnc400 (190 nt), resembling certain E. coli sRNAs such as SraH whose precursor is rapidly degraded whilst the processed form accumulates [51]. The STnc150, 400, and 560 sRNAs are almost constitutively expressed, whereas STnc500, 520 and 540 are only expressed in certain environmental conditions. Intriguingly, STnc580 can only be detected under SPI-1 inducing conditions that mimic the environment Salmonella encounters in the host intestine. Generally, only candidates represented by ≥20 cDNAs in a cDNA pool yielded a signal on Northern blots (Tables 2 and S3). While this suggests some correlation between intracellular abundance and cDNA frequency, we note the case of STnc150, for which a single cDNA was recovered yet a strong signal was obtained on Northern blots. In contrast, several candidates with >20 cDNAs failed the Northern blot validation (Table S3). We speculated that the corresponding cDNAs were derived from 5′ or 3′ UTRs of larger mRNA transcripts, and tested this on Northern blots of agarose gels. We tested 14 of such candidates which had the appropriate orientation to flanking mRNA genes to be UTR-derived; six of these showed signals ranging in size from 500 to 2000 nucleotides (STnc180, Stnc190, STnc330, STnc470, STnc610, and STnc640; Figure S2 and Table S3), and are likely to be processed mRNA species. 10.1371/journal.pgen.1000163.g006 Figure 6 Expression of 10 new Salmonella sRNAs over growth. Total RNA was isolated from Salmonella at seven different growth stages and/or conditions and subjected to Northern blot analysis. (A) Blots showing the detection of stable transcripts for seven new sRNAs. The lanes refer to the following samples (from left to right): aerobic growth of the wild-type strain in L-broth to an OD600 of 0.5, 1 or 2; growth continued after the culture reached OD600 of 2 for 2 or 6 hours, respectively; SPI-1 inducing condition; SPI-2 inducing condition. (B) Northern blots of three sRNAs encoded in close proximity (STnc290, STnc440) or antisense (STnc490) to IS200 elements. A schematic presentation of the position of the sRNAs according to the IS200 element is shown to the right. The upper drawing indicates the two stem-loop structures, start codon, and stop codon of the transposase-encoding mRNA of the IS200 elements. The three detected sRNAs are indicated by black arrows. Growth conditions as Panel A. (C) RNA abundance of selected new sRNAs in wild-type (+) versus hfq mutant (−) Salmonella cells at ESP (OD600 of 2). The enrichment factor of each of these sRNAs in the coIP experiment is given below the blots for comparison. 10.1371/journal.pgen.1000163.g007 Figure 7 Comparison of Hfq and Control coIP cDNA distributions at the ompD and ompA loci. Extract of the screenshot of the Integrated Genome Browser, showing the mapped Hfq and Control cDNAs, and the enrichment curve (ratio of reads of Hfq coIP over control coIP) for (A) the ompD and (B) ompA transcripts. Shown are (from top to bottom) the annotations for the “+” strand (blue), the enrichment curve (grey), the cDNA distributions on the “+” strand for the Hfq coIP (red) and the control coIP (black), the genome coordinates, and the annotations for the “–” strand (blue). In panel A, the annotation of the ompD coding region and the flanking genes, yddG and STM1573, are indicated. For ompA, the CDS, -10 and -35 boxes, as well as the ribosome binding site (RBS) and a CRP binding site are indicated by black arrows. 10.1371/journal.pgen.1000163.g008 Figure 8 Distribution patterns of cDNAs of Hfq-associated mRNA species and confirmation of binding to Hfq. (A) Different mRNAs are shown with marked open reading frame, promoter and terminator (where known). Start and stop codons are indicated. The clone distribution is represented by a stairstep diagram of fold enrichment in Hfq coIP vs control coIP per nucleotide below each mRNA. The vertical axis indicates the enrichment factor in the Hfq coIP calculated over the control coIP. ORF length is given for each gene, for the overlapping ORFs of flhDC, or for the intergenic region in the case of glmUS mRNA. Numbers in parentheses below each gene name denote number of cDNA sequences obtained from Hfq coIP. Promoters and terminators are indicated as above. (B) The binding of Hfq to four mRNA fragments was confirmed by gel mobility shift assay. 32P-labeled RNA fragments of dppA, glmUS, flhD, or hilD, respectively, were incubated with increasing amounts of Hfq protein (concentrations of the hexamer are given in nM above the lanes). The lollipops on the left of the gel panels show the position of the unshifted mRNA fragment. Following 10 minutes incubation at 37°C, samples were resolved on native 6% polyacrylamide gels, autoradiographs of which are shown. 10.1371/journal.pgen.1000163.g009 Figure 9 Rescue of complex Δhfq phenotypes by overexpression of identified Hfq target mRNAs. SDS-PAGE analysis (12% gels stained with Coomassie) of (A) secreted proteins upon overexpression of the SPI-1 transcription factors, HilA and HilD from pCH-112 and pAS-0045 (lanes 3 and 4) in Salmonella Δhfq. Lanes 1 and 2 show the secreted protein profile of Salmonella wild-type and Δhfq bacteria carrying a control vector, pKP8-35. (B) Whole cell and secreted proteins upon overexpression of the flagellar transcription factor, FlhD2C2. The left hand three lanes show total protein samples, and the right hand three lanes show secreted proteins. Genetic background and plasmids are indicated above the lanes; FlhDC was expressed from plasmid pAS-0081. FliC was also analyzed on a Western blot using a specific antibody (lower panel). FliC protein levels are shown (in %), in comparison to wild-type Salmonella, which was set to 100% for either the total protein or secreted protein lanes. Three sRNAs expressing stable transcripts of ∼85 to 90 nts originate from close to, or within, IS200 transposable elements (Figure 6B). STnc290 and STnc440 are expressed just upstream of tnpA_4 and tnpA_6, respectively, whereas STnc490 is antisense to the translational start site of the IS200 transposase ORF. IS200 elements generally posses two stem-loop structures, one of which is a Rho-independent transcription terminator that prevents read-through from genes located upstream of the integration site [64]. Given their location, the STnc290 sRNA could originate from processing of the STM3033 transcripts reading into the tnpA_4 terminator structure; by analogy, STnc440 would be derived from STM4310 transcripts. If so, this would constitute interesting cases in which transposon insertion has created stable sRNAs. The other IS200 stem-loop functions as a translational repressor by sequestering the start codon of the transposon ORF [64]; STnc490 overlaps with this structure on the opposite strand, and by acting as an antisense RNA may function as an additional repressor of IS200. We determined whether 8 of the new Salmonella sRNAs showed an Hfq-dependent pattern of transcript abundance that correlated with Hfq binding (Figure 6C). The STnc290, 440, 490, 520, 540 and 560 sRNAs were all enriched by Hfq coIP (Table 2), by factors up to 51-fold (STnc440). The expression of the four sRNAs with the highest enrichment factors (STnc290, 440, 520, 560) was strongly reduced in Δhfq, and so classified as Hfq-dependent; in contrast, the accumulation of STnc150, STnc490 and STnc540 (≥1.0-, 5.1-, and 3.3-fold enrichment, respectively) was unaffected in the absence of Hfq. STnc500, which is only detected in samples originating from cultures at OD600 of 1, and STnc580, which seems to be specifically expressed under the SPI-1 inducing condition, were not detected on these blots. In addition to the sRNAs listed above, the cDNAs included two loci predicted to encode small peptides, i.e. shorter than the 34 amino acid cut-off used to define ORFs in the current Salmonella genome annotation [40]. These are referred to as STnc250 and STnc570 in Table 2, and correspond to the predicted small ypfM and yneM mRNA-encoding genes of E. coli [49]. Probing of the Salmonella loci yielded signals of stable short mRNAs which are expressed in a growth phase-dependent manner (Figure S3). Hfq-Associated mRNAs To determine which of the 34,136 cDNAs were derived from Hfq-bound mRNAs and represented genuine mRNA targets, a stringent cutoff was used. An mRNA coding region (CDS) was required to be represented by ≥10 cDNAs to be considered significant, which identified 727 Hfq-bound mRNAs (cistrons) for further analysis. Table 3 lists the top 42 mRNAs with at least 100 cDNAs in the Hfq coIP library (Table S4 lists all 727 mRNAs). In the genome browser, many of these enriched mRNAs were readily visible as a distinct cDNA cluster, e.g., the ompD mRNA (encoding the major Salmonella outer membrane protein) shown in Figure 7A. A survey of the transcriptomic data revealed that 33% of the Hfq-bound mRNAs showed an Hfq-dependent pattern of gene expression (Table S1). The reciprocal analysis showed that 32% of the Hfq-dependent mRNAs were bound to Hfq (Table S1). We attribute the observed partial overlap of the Hfq coIP and the transcriptomic data (33%) to three major factors. First, Hfq regulates transcription factors, de-regulation of which alters the expression of downstream genes. In other words, not every gene deregulated in the Δhfq strain is necessarily a “direct” Hfq target, i.e. its mRNA bound by Hfq. Second, there may be a considerable number of Hfq-associated mRNAs below our very stringent cut-off for Hfq-association; increasing sequencing depth will overcome this problem. Third, the precise borders of most 5′/3′ UTRs are unknown in Salmonella (and any other bacterial genome sequence); consequently, calculations of Hfq enrichment were limited to the CDS of an mRNA. As outlined further below (Figure 7B), this can skew the overall enrichment factor. 10.1371/journal.pgen.1000163.t003 Table 3 mRNAs represented by ≥100 cDNAs in the pyrosequencing data. STM number Gene namea Number of inserts in control coIPb Number of inserts in Hfq coIPc Enrichmentd Producte STM4261 254 1042 4.1 putative inner membrane protein STM2665 yfiA 72 648 9.0 ribosome stabilization factor STM1377 lpp 168 608 3.6 murein lipoprotein STM4087 glpF 40 570 14.3 glycerol diffusion STM1959 fliC 248 547 2.2 flagellar biosynthesis protein STM2874 prgH 73 415 5.7 needle complex inner membrane protein STM2267 ompC 63 385 6.1 outer membrane protein C precursor STM2882 sipA 36 354 9.8 secreted effector protein STM2885 sipB 126 335 2.7 translocation machinery component STM4326 aspA 79 328 4.2 aspartate ammonia-lyase STM2925 nlpD 30 300 10.0 lipoprotein STM4086 glpK 115 278 2.4 glycerol kinase STM2883 sipD 34 269 7.9 translocation machinery component STM0739 sucD 14 261 18.6 succinyl-CoA synthetase alpha subunit STM1572 ompD 76 246 3.2 putative outer membrane porin precursor STM2898 invG 16 226 14.1 outer membrane secretin precursor STM2879 sicP 6 224 37.3 secretion chaparone STM2283 glpT 30 221 7.4 sn-glycerol-3-phosphate transport protein STM1091 sopB 23 216 9.4 secreted effector protein STM1732 ompW 28 206 7.4 outer membrane protein W precursor STM0451 hupB 14 198 14.1 DNA-binding protein HU-beta STM2871 prgK 46 198 4.3 needle complex inner membrane lipoprotein STM2884 sipC 96 192 2.0 translocation machinery component STM4406.S ytfK 6 191 31.8 putative cytoplasmic protein STM2867 hilC 3 187 62.3 invasion regulatory protein STM2869 orgB 8 182 22.8 needle complex export protein STM2878 sptP 20 177 8.9 protein tyrosine phosphatase/GTPase activating protein STM2894 invC 14 175 12.5 type III secretion system ATPase STM2875 hilD 23 174 7.6 invasion protein regulatory protein STM2284 glpA 57 149 2.6 sn-glycerol-3-phosphate dehydrogenase large subunit STM3526 glpD 39 147 3.8 sn-glycerol-3-phosphate dehydrogenase STM2886 sicA 23 146 6.3 secretion chaperone STM3138 19 143 7.5 putative methyl-accepting chemotaxis protein STM2896 invA 19 142 7.5 needle complex export protein STM0833 ompX 6 137 22.8 outer membrane protein X STM2899 invF 18 129 7.2 invasion regulatory protein STM2924 rpoS 19 129 6.8 RNA polymerase sigma factor STM0629 cspE 9 125 13.9 cold shock protein E STM2285 glpB 33 119 3.6 anaerobic glycerol-3-phosphate dehydrogenase subunit B STM0736 sucA 42 110 2.6 2-oxoglutarate dehydrogenase STM2445 ucpA 5 105 21.0 short chain dehydrogenase STM1070 ompA 77 102 1.3 putative hydrogenase membrane component precurosr a Gene names according to ColiBase (Chaudhuri et al., 2004) b Based on 145,873 sequences c Based on 122,326 sequences d Enrichment factor calculated by the number of blastable reads from Hfq coIP over control coIP. e Product according to KEGG (http://www.genome.jp/kegg/; (Goto et al., 1997)). To validate our cDNA sequencing approach for the detection of Hfq-bound mRNAs by the conventional approach, we hybridized the RNA obtained from Hfq and control coIP to a S. Typhimurium oligonucleotide microarray. Analysis of this coIP-on-Chip experiment with SAM-software (Statistical Analysis of Microarrays; FDR 3,000 cDNAs in the Hfq coIP library), which shows that our approach is not only effective for detecting conserved, but also species-specific sRNAs of recently acquired pathogenicity regions. Horizontal transfer of virulence islands is a driving force in the evolution of bacterial pathogens [82], and knowledge of the functional elements of these islands is key to understanding pathogenesis. Whereas ORF identification in such islands has become routine, island-specific sRNAs are more difficult to recognize by bioinformatic-based approaches. Besides confirming InvR, the present study found evidence for the expression of five of the 47 Salmonella sRNA candidate loci listed by Pfeiffer et al. [46], who predicted orphan promoter/terminator pairs in IGRs (Table S3 and Figure 2). One of these, i.e. STnc250, has turned out as a small mRNA gene (see above). While this study was in progress, others reported the discovery of 18 Salmonella expressed sRNA loci [55]. We recovered cDNAs of 8 of these sRNAs (isrB-1, C, E, I-L, and P; Table 2). The fact that 10 of these sRNAs were not recovered probably reflects their low-level expression under the growth condition used here [55]. This observation suggests an improvement that could be made to our method. RNomics- or microarray-based sRNA discovery methods require sRNAs to be expressed under the chosen assay condition, unlike bioinformatics-aided approaches that score for orphan transcription signals and primary sequence conservation [49],[51],[83],[84] or for conservation of RNA structure [53]. Thus, future studies combining several different growth conditions with increasing sequencing depth are likely to identify even more novel sRNAs. Regarding the sensitivity of our approach, it is remarkable that RyeB sRNA was found in 653 Hfq cDNAs and 24 Control cDNAs (Table 2); RyeB is late stationary phase-specific [49],[50], and barely detectable by probing of Salmonella RNA from the coIP assay condition by Northern blot (unpublished results). Moreover, the 24 cDNAs recovered from the control library, i.e. without Hfq coIP, suggest the exciting possibility that deep sequencing of total RNA, without prior enrichment or size-fractionation, will prove to be a successful approach for sRNA discovery. Like any other global method for RNA identification [85],[86], our approach is likely to show certain biases, e.g., caused by cross-hybridization in the immunoprecipitation step, or from the limited ability of reverse transcriptase to deal with stable RNA structures in cDNA synthesis, and these will need to be studied in more detail. However, it is clear that deep sequencing resolved the termini of many expressed and/or Hfq-bound sRNAs at basepair resolution (Figure 5), which has not been achieved by other methods. The combination of HTPS of co-immunoprecipitated sRNAs and mRNAs with transcriptomics partly explains how Hfq acts as a pleiotropic regulator of Salmonella gene expression. Transcriptome analysis under two different growth conditions suggests that Hfq regulates the expression of nearly a fifth of all Salmonella genes. This proportion of Hfq-dependent genes is similar to Pseudomonas aeruginosa (∼15% of all genes; [87]), but bigger than for E. coli (6.3%; [42]), or Vibrio cholerae (5.6%; [30]). However, the different growth conditions and scoring parameters used for these other organisms preclude a direct comparison with our Salmonella data. Nonetheless, the strong impact of Hfq on the σS and σE stress regulons that we observed is consistent with the findings in E. coli [42] and in part in V. cholerae (σE; [30]), and expands the previous work on Salmonella σS and σE regulated genes [34],[43],[44],[88],[89],[90],[91] to a global level. Importantly, our combined transcriptomic and coIP data revealed that Hfq exerts a direct role in gene expression through the control of specific check-points in other well-defined transcriptional regulons, such as HilD in the SPI-1 virulence regulon, and FlhD2C2 in the flagellar gene expression cascade. Transcriptomic profiling by itself is clearly unable to differentiate between transcriptional and post-transcriptional effects of Hfq. In contrast, enrichment of a regulated mRNA in the Hfq library has successfully hinted at post-transcriptional regulation by sRNAs. For example, the observation of OmpX overproduction in Salmonella Δhfq, combined with ompX mRNA enrichment by Hfq coIP in E. coli [17], led to the prediction that OmpX synthesis is repressed by an Hfq-dependent antisense sRNA; this sRNA was subsequently identified as CyaR in Salmonella [63]. Tables 2 and 3 confirm that both ompX mRNA and CyaR strongly associate with Salmonella Hfq (22.8-fold and 21.2-fold enrichment, respectively). Our current data set comprises several hundred such candidate mRNAs (Table S4); this catalogue contains many experimentally confirmed targets of Salmonella sRNAs, e.g., the dppA, fadL, ompD, or oppA mRNAs [34],[46],[58],[59]. Integrating the score for Hfq-association deduced from our experiments, and–where applicable–from the available E. coli data [17] into available algorithms such as TargetRNA [92] could significantly improve target predictions for the large class of Hfq-dependent sRNAs. Such predictions bring new understanding to the pleiotropic phenotypes caused by the absence of Hfq in Salmonella [32]. The fact that the Salmonella hfq mutant is attenuated for virulence can now be explained by the requirement of Hfq for the expression of all but one key pathogenicity islands of Salmonella (SPI-3). In the SPI-1 invasion gene island, HilD acts at the top of a transcription factor cascade to activate SPI-1 genes, and to mediate secretion of effector proteins by the SPI-1 type III secretion system (reviewed in [67],[93]). The levels of hilD mRNA were sevenfold reduced in Δhfq, but the unchanged activity of a hilD promoter fusion in this background (unpublished data) argues against direct transcriptional control by Hfq. Rather, the 7.5-fold enrichment of hilD cDNAs by Hfq coIP (Table S4) suggests that hilD is post-transcriptionally activated in a Hfq-dependent process, presumably involving an unknown sRNA. Our demonstration that SPI-1 virulence factor secretion is fully restored by HilD overproduction in Δhfq raises the exciting possibility that post-transcriptional hilD activation could be key event in Salmonella invasion of epithelial cells. We expect Hfq to have further roles in SPI-1 expression since the protein seems to bind to many mRNAs encoded by this pathogenicity island (Figures 4 and S4). Interestingly, SPI-1 has a significantly higher AT content than the rest of the S. Typhimurium chromosome [40], predicting that SPI-1 mRNAs are AU-rich. Coincidently, Hfq primarily recognizes AU-rich single-stranded regions in RNAs [12],[94],[95],[96]. This type of sequence is also recognized by the major endoribonuclease, RNase E, and Hfq has been shown to protect certain RNAs by competitive binding to RNase E sites [97],[98]. It is tempting to speculate that Hfq could reduce the impact of DNA from foreign sources by controlling expression of newly acquired AT-rich genes at the RNA level, similar to the role of the H-NS DNA-binding protein in controlling such genes at the DNA level [99],[100],[101]. Collectively, the present study provides the first picture of the impact of Hfq on Salmonella gene expression at both the transcriptional and post-transcriptional level. We believe that more detailed inspection of this freely available data set, in particular of the remaining ∼60% of the chromosome that remains to be fully analyzed, as well as sampling under different growth conditions, will expand the gamut of Salmonella small mRNA and noncoding RNA genes. In addition, the available data sets should help to discover whether Hfq controls the expression of cis-antisense sRNAs that overlap with mRNA coding regions [54], or whether certain Salmonella tRNAs are selectively associated with this protein [22],[23]. Bacterial genomes encode a large number of RNA binding proteins [102], including globally acting proteins such as the CsrA/RsmA [48] and Csp families [103]. Our generic method will identify the RNA targets of these proteins in any genetically tractable bacterium. Materials and Methods Bacterial Strains, Plasmids, and Oligodeoxynucleotides The Salmonella enterica serovar Typhimurium strains used in this study were: JVS-0255 (Δhfq::CmR, [32]), JVS-1338 (hfq FLAG, [46]), and the isogenic wild-type strain SL1344 [104]. Plasmid pKP8-35 [59] served as a pBAD control plasmid. The SPI-1 transcription factor, HilA, was expressed from pCH-112 [105], and HilD from plasmid pAS-0045 (which carries a hilD PCR fragment obtained with primer pair JVO-686/-687 amplified from Salmonella DNA, inserted into plasmid pLS-119 [106] by NcoI/EcoRI cloning). The FlhDC expression plasmid, pAS-0081, was constructed by inserting a PCR fragment obtained with primers JVS-2152/-2153 into plasmid pBAD/Myc-His A (Invitrogen) by NcoI/XhoI cloning. All cloning procedures where carried out in E. coli strain Top10 (Invitrogen). Table S6 lists the sequences of oligodeoxynucleotides used in this study for cloning and T7 transcript generation. Bacterial Growth and L-arabinose Induction Growth in Lennox (L) broth (220 rpm, 37°C) or on L-plates at 37°C was used throughout this study. Antibiotics (where appropriate) were used at the following concentrations: 50 µg/ml ampicillin, 30 µg/ml chloramphenicol. For early stationary phase (ESP) cultures, 30 ml L-broth in 100 ml flasks were inoculated 1/100 from overnight cultures and incubated at 37°C, 220 rpm to an optical density of 2. For SPI-1 induced cultures, 5 ml L-broth containing NaCl (final concentration 0.3 M) was inoculated from single colonies; incubation was carried out for 12 hours at 37°C, 220 rpm in tightly closed 15 ml Falcon tubes. For SPI-2 induced cultures, 70 ml SPI-2 medium [107] in 250 ml flasks were inoculated 1/100 from overnight cultures grown in the same medium. Bacteria were grown at 37°C, 220 rpm until the culture reached an OD of 0.3. For HilA, HilD, and FlhDC expression from pBAD-derived plasmids, growth media were supplemented with 0.1% L-arabinose. Transcriptomic Experiments Strain SL1344 and JVS-0255 (Δhfq) were grown in L-broth either to an OD600 of 2 (ESP aerobic growth), or for 12 hours under SPI-1 inducing conditions. RNA extraction and data generation were carried out as described with SALSA microarrays [59]. The complete dataset is available at GEO under accession number GSE8985. SDS PAGE and Western Blot for Protein Quantification Proteins were resolved by SDS PAGE (12% gels). For Coomassie stain or Western analysis, proteins equivalent to 0.1 OD or 0.05 OD, respectively, were loaded per lane. For FliC detection, strains SL1344 and JVS-0255 carrying the indicated plasmids were grown to an OD of 1, and induced with L-arabinose. Growth continued for one hour, and whole cell and secreted protein fractions were analyzed as described in [32]. FliC was detected using a monoclonal FliC antibody (BioLegend). RNA Isolation and Northern Blot Analysis RNA was prepared by hot phenol extraction [108], followed by DNase I treatment. After separation on 5% polyacrylamide (PAA) gels containing 8.3 M Urea, or agarose gels, respectively, RNA was transferred onto Hybond-XL membrane (Amersham). 5 or 10 µg (PAA gels) or 20 µg (agarose gels) RNA was loaded per sample. For detection of new transcripts γ-ATP end-labeled oligodeoxyribonucleotides were used (see Table S7). Gel Mobility Shift Assay of In Vitro RNA DNA templates carrying a T7 promoter sequence were generated by PCR using genomic DNA and primers as listed in Table S6. For dppA oligonucleotides JVO-1034/1035 (the fragment covers the dppA region from positions −163 to +73 relative to the start codon) were used. For the PCR of the intergenic region of glmUS primer JVO-2471/2472 were used, resulting in a product starting 38 nucleotides upstream of the glmU stop codon and extending to nucleotide 113 in the intergenic region. For flhD, oligonucleotides JVO-2284/-2285 were used, to yield a fragment that covers flhD from position −59 to +38 relative to the start codon. The hilD fragment (oligonucleotides JVO-2286/-2287) spans region +400 to +600 relative to the start codon. In vitro transcription was performed using the MEGAscript High Yield Transcription Kit (Ambion, #1333), followed by DNase I digestion (1 unit, 15 min, 37°C). Following extraction with phenol:chloroform:isopropanol (25∶24∶1 v/v), the RNA was precipitated overnight at -20°C with 1 vol of isopropanol. RNA integrity was checked on a denaturing polyacrylamide gel. RNA was 5′ end-labeled and purified as described in [59]. Gel mobility shift assays were carried out as described in [32]. In brief, labeled RNA was used in 10 µl reactions at a final concentration of 4 nM. Hfq was added to a final concentration in the range of 1.25 to 150 nM of the hexamer. After incubation for 10 min at 37°C complexes were separated on 6% native PAA gels at 4°C. Signals were detected with a Fuji PhosphorImager. coIP and Sequence Analysis Strains SL1344 and JVS-1338 (hfq FLAG) were grown in L-broth under normal aeration at 37°C to ESP. Co-immunoprecipitation was carried out using the protocol published in [46]. For pyrosequencing and coIP-on-Chip experiments, samples of two independent pull down experiments were used. cDNA cloning and pyrosequencing was performed as described for the identification of eukaryotic microRNA [109] but omitting size-fractionation of RNA prior to cDNA synthesis. Microarrays used for the coIP-on-Chip experiments were designed and produced by Oxford Gene Technology (Kidlington, UK). They consist of 21,939 60-mer oligonucleotides tiled throughout the S. Typhimurium SL1344 NCTC13347 genome and 636 control oligonucleotides. The SL1344 sequence was obtained from the Sanger Institute (Hinxton, UK) website (http://www.sanger.ac.uk/Projects/Salmonella/). As this genome is not yet fully annotated, the oligonucleotides were associated with corresponding S. Typhimurium LT2 genes or intergenic regions, if conserved in both organisms. Full description of the microarray and protocols used for generating and analysing the data are associated with the dataset deposited in the GEO data repository (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE10149. For detailed description of data analysis using the Integrated Genome Browser see the Supplementary Text S1. In brief, cDNA reads ≥18 nt were mapped to the Salmonella chromosome and hits per nucleotide were calculated along the entire genome. To calculate enrichment factors for Hfq coIP, the Hfq cDNA number was divided by Control cDNA number at each position of the genome, following normalization to the total number of mapped reads. Upon upload of the Salmonella genome sequence and annotation from Genbank (NC_003197.fna and NC_003197.gff), the two graphs for each library were loaded into the Integrated Genome Browser (IGB) of Affymetrix (version IGB-4.56), which can be directly launched by Java Web Start at http://www.affymetrix.com/support/developer/tools/download_igb.affx or downloaded from http://genoviz.sourceforge.net/. Supporting Information Figure S1 Expression levels of RpoE and RpoS in wild-type and Δhfq cells. Samples were taken from wild-type and Δhfq strains grown under standard conditions to early stationary phase (OD600 of 2) or for 12 hours under SPI-1 inducing conditions, respectively. (A) Analysis of mRNA level by real time PCR for rpoE, degP, and rpoS mRNA. (B) Whole cell proteins were separated by 12% SDS PAGE and sigma factors detected via Western blot. Expression levels of each protein were determined by densitometry and are given as a percentage of the wild-type level of expression below each gel. (0.29 MB TIF) Click here for additional data file. Figure S2 Northern detection of Hfq bound mRNAs. Total RNA was isolated from Salmonella at OD600 of 2. Northern blots based on agarose gel for detection of long transcripts showing the detection of six mRNAs. (1.29 MB TIF) Click here for additional data file. Figure S3 Expression levels of small peptide encoding mRNAs in Salmonella. RNA samples were either taken from wild-type or hfq mutant Salmonella at different growth stages (as in Figure 6 in the main manuscript), and probed for STnc250 and STnc570 over growth (A) or at early stationary phase (B). (0.99 MB TIF) Click here for additional data file. Figure S4 Hfq binds significantly to a few but not all mRNAs of the SPI-1 and the flagellar regulon. Shown are all genes belonging to the SPI-1 and the flagellar regulon. The level of Hfq-dependent gene regulation is shown as fold-change below each gene (taken from the transcriptomic dataset; Table S1). Representation of cDNAs in pyrosequencing is indicated by different colours (green: 1–10 clones, turquoise: 11–100 clones, orange: 101–500 clones, magenta: ≥501 clones). (0.41 MB TIF) Click here for additional data file. Figure S5 Expression of IstR-1 and IstR-2 in Salmonella. Northern analysis of istR transcripts. Total RNA was extracted from of E. coli K12 and Salmonella Typhimurium SL1344 cells grown to an OD600 of 2, exposed to Mitomycin C (0.5 µg/ml) for 30 min as described by [2]. Length is indicated according to marker sizes in nt. Full-length IstR-1 and IstR-2 are indicated by arrows. (0.28 MB TIF) Click here for additional data file. Table S1 Deregulated genes in Δhfq at ESP. (0.95 MB DOC) Click here for additional data file. Table S2 Deregulated genes in Δhfq after 12 hrs SPI-inducing conditions. (0.21 MB DOC) Click here for additional data file. Table S3 Coverage of known and candidate Salmonella sRNA loci in pyrosequencing data. (0.26 MB DOC) Click here for additional data file. Table S4 mRNAs in Hfq CoIP identified by ≥10 of 170,000 inserts in pyrosequencing data. (0.81 MB DOC) Click here for additional data file. Table S5 Genes that were significantly enriched in coIP-on-Chip and were identified by pyrosequencing. (0.32 MB DOC) Click here for additional data file. Table S6 Oligodeoxynucleotides used in this study. (0.06 MB DOC) Click here for additional data file. Table S7 Oligodeoxynucleotides used for Northern detection. (0.05 MB DOC) Click here for additional data file. Text S1 Supplementary material and methods. (0.31 MB DOC) Click here for additional data file.
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            Author and article information

            Affiliations
            [1 ]Architecture et Réactivité de l′ARN, Université de Strasbourg, CNRS, IBMC, Strasbourg, France
            [2 ]Zentrum für Infektionsforschung (ZINF), Würzburg, Germany
            [3 ]Inserm U851, Centre National de Référence des Staphylocoques, Université de Lyon, Lyon, France
            [4 ]Institut für Molekulare Infektionsbiologie, Würzburg, Germany
            Uppsala University, Sweden
            Author notes

            Conceived and designed the experiments: PR EL JV. Performed the experiments: EL CMS IC A-CH PF. Analyzed the data: PR EL IC CMS FV JV. Contributed reagents/materials/analysis tools: PR FV JV. Wrote the paper: PR EL CMS JV.

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            1553-7390
            1553-7404
            June 2012
            June 2012
            28 June 2012
            : 8
            : 6
            3386247
            22761586
            PGENETICS-D-11-02374
            10.1371/journal.pgen.1002782
            (Editor)
            Lioliou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 21
            Categories
            Research Article
            Biology
            Genetics
            Gene Expression
            RNA processing
            RNA stability
            Microbiology
            Microbial Growth and Development
            Model Organisms
            Prokaryotic Models
            Bacillus Subtilis

            Genetics

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