Open Access

Discovery and profiling of small RNAs responsive to stress conditions in the plant pathogen Pectobacterium atrosepticum

  • Stanford Kwenda1,
  • Vladimir Gorshkov2, 3,
  • Aadi Moolam Ramesh1,
  • Sanushka Naidoo4,
  • Enrico Rubagotti5,
  • Paul R. J. Birch6 and
  • Lucy N. Moleleki1Email author
BMC Genomics201617:47

https://doi.org/10.1186/s12864-016-2376-0

Received: 3 September 2015

Accepted: 6 January 2016

Published: 12 January 2016

Abstract

Background

Small RNAs (sRNAs) have emerged as important regulatory molecules and have been studied in several bacteria. However, to date, there have been no whole-transcriptome studies on sRNAs in any of the Soft Rot Enterobacteriaceae (SRE) group of pathogens. Although the main ecological niches for these pathogens are plants, a significant part of their life cycle is undertaken outside their host within adverse soil environment. However, the mechanisms of SRE adaptation to this harsh nutrient-deficient environment are poorly understood.

Results

In the study reported herein, by using strand-specific RNA-seq analysis and in silico sRNA predictions, we describe the sRNA pool of Pectobacterium atrosepticum and reveal numerous sRNA candidates, including those that are induced during starvation-activated stress responses. Consequently, strand-specific RNA-seq enabled detection of 137 sRNAs and sRNA candidates under starvation conditions; 25 of these sRNAs were predicted for this bacterium in silico. Functional annotations were computationally assigned to 68 sRNAs. The expression of sRNAs in P. atrosepticum was compared under growth-promoting and starvation conditions: 68 sRNAs were differentially expressed with 47 sRNAs up-regulated under nutrient-deficient conditions. Conservation analysis using BLAST showed that most of the identified sRNAs are conserved within the SRE. Subsequently, we identified 9 novel sRNAs within the P. atrosepticum genome.

Conclusions

Since many of the identified sRNAs are starvation-induced, the results of our study suggests that sRNAs play key roles in bacterial adaptive response. Finally, this work provides a basis for future experimental characterization and validation of sRNAs in plant pathogens.

Keywords

Small RNAs Strand-specific RNA-seq Pectobacterium atrosepticum in silico prediction Transcriptome Riboswitches 5′ UTR 3′ UTR

Background

The importance of small RNAs (sRNAs) in bacterial gene expression regulation is now broadly appreciated [1, 2]. sRNAs play essential regulatory roles in diverse processes including metabolic reactions, stress response, biofilm formation and pathogenesis [3]. They act as either activators or repressors of proteins and mRNAs. The length of most of the bacterial sRNAs ranges between 50 and 300 but can reach up to 500 nucleotides [4]. The best studied bacterial regulatory sRNAs are those that act through base-pairing interactions with target RNAs, usually modulating gene expression post-transcriptionally by controlling the translation and stability of mRNAs. The majority of these are trans-acting sRNAs found within intergenic regions (IGRs). Trans-acting sRNAs typically regulate mRNAs encoded at different genomic locations on the chromosome in response to changes in environmental conditions [1]. Furthermore, trans-encoded sRNAs tend to have limited complementarity with their target RNAs and require the RNA chaperone Hfq to facilitate their pairing with mRNA targets [4]. In contrast, cis-encoded antisense RNAs (asRNAs), also referred to as naturally occurring RNAs, are expressed on reverse strands opposite to annotated genes and have extensive complementarity with their target mRNAs [4]. Antisense RNAs are thought to play physiological roles such as repression of genes encoding potentially toxic proteins [5]. Additional roles of asRNAs include blocking the translation of mRNA transcripts encoded on the opposite strand and directing their RNAse III-mediated cleavage [4]. Other important classes of sRNAs include 1) riboswitches (leader sequences), which form part of the mRNA they regulate and usually present in the 5′ UTR regions; 2) sRNAs which interact with proteins and modify their activities by mimicking their RNA or DNA targets, and 3) sRNAs with intrinsic regulatory activities [4].

The advent of RNA-seq for the resolution of messenger and structural RNAs has facilitated the analysis of vast numbers of sRNAs with increased sensitivity [6, 7]. An additional benefit of RNA-seq approaches is that information about the direction of transcription can be resolved using directional RNA-seq (strand-specific RNA-seq; ssRNA-seq). This information is important for the detection of non-coding (nc) RNAs as well as 5′ and 3′ untranslated regions (UTRs), antisense transcripts and determination of overlapping features within the genome [6]. Combining deep sequencing with computational (in silico) prediction methods is emerging as an important approach for sRNA detection in bacterial genome sequences [8, 9].

Pectobacterium atrosepticum is an important plant pathogen belonging to the bacterial family Enterobacteriaceae [10]. This pathogen causes major yield losses globally through blackleg disease on potato plants in the field and potato tuber soft rot diseases during post-harvest storage. Most of the information on pectobacteria concerns their interaction with plant hosts, and little is known about how these bacteria spend much of their life outside of the host [11]. However, it is known that P. atrosepticum is able to utilize various adaptive programs that enable bacteria to survive under adverse conditions [12, 13]. In a previous study, we showed that realization of these programs under nutrient-deficient conditions (starvation) is coupled with an increased transcript abundance of stress responsive genes in P. atrosepticum, and bacterial cells undergo morphological and ultrastructural changes [14]. In the current study we have evaluated the possible participation of sRNAs in bacterial starvation-induced stress response.

Few experimental studies on sRNAs have been carried out in P. atrosepticum. A well-known regulatory sRNA in P. atrosepticum is rsmB. This sRNA binds the RsmA protein, which is a homologue of Escherichia coli CrsA, a carbon storage regulator, and modulates its activity. In P. atrosepticum the RsmA/rsmB system regulates the production of virulence factors [1517]. Moreover, a regulatory RNA antisense to the expI gene transcript, which encodes the synthase of mediators of quorum sensing (acyl-homoserine lactones), was found recently in P. atrosepticum [18].

In the present study, identification of sRNAs in the complete genome of P. atrosepticum SCRI1043 was undertaken using in silico prediction and experimental validation via strand-specific RNA-sequencing. Both true (and/or known) and potentially novel sRNA candidates expressed under starvation conditions were identified. Differential expression analysis indicated that many of these sRNAs increase in abundance during exposure of bacteria to starvation compared to rich medium conditions, suggesting an important role of sRNAs in the survival of P. atrosepticum cells during nutrient deficiency induced stress.

Results and discussion

Strand-specific RNA-seq detection of P. atrosepticum sRNAs under starvation-conditions

For experimental detection of sRNAs in P. atrosepticum SCRI1043, we used a combination of in silico and directional whole-transcriptome cDNA sequencing (strand-specific RNA-seq) (Fig. 1). The experimental approach for determination of sRNA in P. atrosepticum is outlined in Fig. 1a. A total of 27.4 and 26.1 million paired-end (PE) reads were obtained from nutrient rich and starvation conditions, respectively. By using SAMtools [19], PE reads mapped to each strand were extracted. Thus, enabling visualization of the sequence (PE) read alignments on the genome in a strand-specific manner. Visual inspections enabled the identification of candidate sRNA transcripts by manually analysing the position of PE reads with respect to annotated protein-coding regions (CDS). This can be a particularly powerful approach to identify sRNAs and resolve their genomic positions because reads that map to intergenic regions may represent previously unannotated transcriptionally active non-coding sRNAs [20]. Only sRNA candidates with a length between 50 to 500 nucleotides were considered to be true positive sRNAs candidates. This technique enabled identification of a total of 137 sRNA candidates expressed under starvation condition (Additional file 1: Table S1). These candidate sRNAs were classified into four distinct sRNA groups based on their position in relation to adjacent CDSs: IGR/ trans-encoded sRNAs, asRNA, 5′ UTR (riboswitches), and 3′ UTR sRNAs (Fig. 2). An in silico approach (described in the section below) was employed to determine the putative transcriptional start sites (TSS) of the identified 137 sRNAs and to resolve their 5′ ends. Only predicted TSS with transcription factor binding sites were considered as bona fide promoters. Thus, using this filter, TSS were identified upstream of 118 sRNA genes (Additional file 2: Table S2).
Fig. 1

Scheme for sRNA identification. a Determination of sRNA using strand-specific RNA seq of P. atrosepticum cultured under starvation conditions. b Comparison of sRNAs identified by strand-specific RNA-seq with sRNA candidates predicted for P. atrosepticum in Rfam database and sRNAs predicted computationally in this study. c Computational (in silico) sRNA prediction

Fig. 2

Classification of sRNAs identified using ssRNA-seq into five classes: These include; IGR/ trans-encoded sRNAs, asRNA, 5′UTR (riboswitches), 3′ UTR and sense sRNAs (seRNAs), based on their proximity and location with regards to CDS regions

Identification of 3′ UTR encoded sRNAs

We identified 15 sRNAs encoded within the 3′ UTR regions of mRNA (referred to in this study as 3′ UTR sRNAs) (Fig. 2). It is now appreciated that sRNAs not only originate from intergenic regions as independent transcripts but are also transcribed from 3′ regions of coding mRNA [21]. These 3′ UTR sRNAs are generated either by means of mRNA transcript processing or as primary transcripts from an internal promoter within the mRNA coding sequence as in the case of dapZ sRNA [22]. Thus, based on how they are produced, 3′ UTR encoded sRNAs can be divided into 2 groups, that are: 1) sRNAs transcribed from an independent promoter located inside the overlapping mRNA gene or 3′ UTR region (Type 1); and 2) sRNAs which are originated from the processing of the parent mRNA (Type 2) [23]. Hence we used our ssRNA-seq data to determine whether the identified 3′ UTR embedded sRNAs are transcribed independently from the parent mRNA. Ten 3′ UTR sRNAs were considered to be independently transcribed based on comparisons of sRNA and parent mRNA RPKM (reads per kilobase of transcript per million mapped reads) values and the presence or absence of an internal promoter (Table 1). To determine the putative 5′ ends and fundamental types of the detected 3′ UTR sRNAs based on their biogenesis, we extracted each sRNA sequence plus 200 nt upstream of the start position of each sRNA and performed promoter predictions using BPROM program (http://www.softberry.com/berry.phtml?topic=bprom&group=programs&subgroup=gfindb). This approach led to the identification of 14 distinct putative promoter sites (transcriptional start sites; TSS) embedded within the coding or 3′ UTR regions of the parent mRNA upstream of each 3′ UTR sRNA gene (Table 1). In addition, transcription factor binding sites were also detected within the predicted promoter regions. Taken together, the presence of putative internal promoter sites upstream of sRNAs TSS and the predicted transcriptional factor binding sites for each promoter, strongly suggests that fourteen 3′ UTR sRNAs are type 1. Nine of which were also differentially expressed compared to their parent mRNAs based on RPKM values, further indicating evidence of independent expression. The remaining sRNA reg_seq13 could be a product of mRNA processing, thus type 2 since no internal promoters supported by transcription binding sites were predicted for this sRNA. Overall, since the sRNA 5′ ends and subsequent TSSs were predicted computationally, we were not able to determine whether these sRNAs possessed the characteristic 5′-triphosphate (5′-PPP) cap common to type 1 sRNAs in this present study.
Table 1

3′ UTR encoded sRNAs

  

sRNA

RPKMs

Expression

Predicted

sRNA promoter and start site

Transcription Factor

sRNA name

Parent mRNA

Start

end

length

sRNA

mRNA

(based on RPKMs)

promoters

−35

−10

TSS

binding site

fwd_4

rbsB

14355

14537

183

596.8

102.8

Independent

1

14317

14342

14357

rpoD17, cynR, rpoD15, rpoD16, phoB

fwd_6

polA

28634

28755

122

549.8

110.5

Independent

1

28656

28676

28691

cytR, arcA, crp, rpoD15, rpoD17

fwd_15

ECA0044

55039

55291

253

66.8

27.4

Independent

1

55009

55029

55044

rpoD17, fis, rpoD15, rpoD16, phoB

fwd_19

expI

126355

126501

147

338.5

588.1

Co-expression

1

126418

126435

126450

metR, rpoD16

reg_seq13

aldA

139913

140154

242

136.8

30.4

Independent

1

140046

140063

140078

 

reg_seq27

ECA0332

380584

380826

243

1660.5

389.3

Independent

1

380610

380631

380646

metJ

reg_seq31

ECA0449

515673

515910

238

363.3

528.7

Co-expression

1

515790

515813

515828

glpR, ihf, argR2, nagC, argR2, fnr, fis

reg_seq43

mdH

758603

758857

255

1759.1

1895.7

Co-expression

2

758751

758770

758789

glpR, fis, arcA, purR

         

758444

758464

758479

 

reg_seq142

ECA2516

2832294

2832530

237

514.2

77.5

Independent

1

2832185

2832205

2832220

purr, rpoD16

fwd_rfam4

rpiL

255260

255321

62

775.5

1856.1

Independent

    

lrp, hns

comp_seq5

glnA

34992

35234

243

858.8

1209.3

Co-expression

1

35262

35241

35226

rpoD15, rpoD16, phoB

comp_seq11

slmA

164026

164334

309

196.4

454.0

Independent

1

164395

164375

164360

arcA, rpoD17, rpoD15, rpoD16, phoB

comp_seq130

ECA2950

3295111

3295347

237

197.9

222.9

Co-expression

2

3295196

3295176

3295161

rpoD16, argR, arcA, ihf

         

3295506

3295485

3295470

 

rev_rfam22

glpC

4651380

4651485

109

348.2

40.8

Independent

1

4651583

4651562

4651547

Ada, rpoE, tyrR, fur, fur

dapZ

ECA3872 (dapB)

4332178

4332288

111

20.9

110.8

Independent

1

4332302

4332284

4332269

Ihf, argR2, rpoD16, argR,, fis, crp

The 137 sRNAs identified using strand-specific RNA-seq approach were checked against known P. atrosepticum SCRI1043 non-coding RNA descriptions on the Rfam database [24]. For this analysis, all descriptions for tRNAs, rRNAs and CRISPR RNAs were excluded. This also served to assess the efficiency of the strand-specific RNA-seq method in detecting sRNA transcripts. In total 56.6 % (47/83) of the known P. atrosepticum sRNAs in the Rfam database were identified using ssRNA-seq of cells cultured under starvation conditions (Fig. 1b and Additional file 1: Table S1).

Computational prediction of sRNA in the Pectobacterium atrosepticum genome

Even though ssRNA-seq is a powerful tool for identification of sRNAs, it might be subject to some limitations. For example, since the formation of particular sRNAs is highly dependent on culture conditions, it is not possible to unravel the whole pool of sRNAs that is encoded in the genome of the target microorganism within the frameworks of a given experiment. Consequently, a combination of experimental and computational identification of sRNA is often seen as a more comprehensive approach towards identification of sRNAs [25, 26]. Hence, in addition to ssRNA-seq, an in silico sRNA analysis was performed according to computational methods implemented previously [27], with some modifications (see Fig. 1c for a schematic representation of the computational prediction strategy).

An initial step towards in silico sRNA candidate disclosure consisted of identification of predicted rho-independent terminators (RITs) in the P. atrosepticum SCRI1043 genome. Since about 72 % of known sRNAs located within IGRs possess a RIT, computational methods based on prediction of RIT signature sequences have emerged as valuable algorithms for the detection of sRNA molecules [8, 27]. In intergenic and antisense to annotated open reading frames (ORF) in the P. atrosepticum SCRI1043 genome we detected a total of 1598 putative terminators (including both canonical and non-canonical terminators’ candidates) with the ‘Greatest ΔG’ i.e. the most negative ΔG (free Gibbs energy) value. From the 1598 putative sRNA identified, 1165 were filtered out and excluded from further analysis due to the fact that their RITs were located less than 60 nucleotides downstream from stop codons of preceding annotated ORFs within the same strand. This resulted in identification of 433 sRNA candidates of 226–248 nt in length (Additional file 3: Table S3). To be more confident about the accuracy of the rho-independent terminator based prediction strategy used, a second prediction tool (SIPHT) [28] was employed. Herewith, the filtered set of sRNA candidate signatures was compared against sRNA predictions for P. atrosepticum SCRI1043 from the SIPHT web interface by means of BLAST local pairwise alignments using the genomic similarity search tool YASS [29], with standard parameters. Each comparison was made on both regular and complementary strands separately. As a result, a total of 105 and 101 matches (E-value < 0.001) were identified, partially or fully overlapping, for the forward and complementary strands, respectively. This additional filtering step combining comparative genomics with RIT based predictions yielded 206 sRNA candidates in P. atrosepticum SCRI1043 (Additional file 4: Table S4). Similarly to sRNA detected using ssRNA-Seq, predicted sRNAs were further classified into five distinct sRNA groups based on their position in relation to adjacent CDSs (results not included).

Comparison of RNA-seq results with computational sRNA predictions

The 208 candidate sRNAs identified computationally were compared to the 137 sRNA transcripts identified using ssRNA-seq. Only 25 of the in silico predicted sRNA candidates were also identified by RNA sequencing (Table 2). Such an incomplete overlap between computational sRNA predictions and deep sequencing detection has been noted in previous studies [2, 8, 9, 30]. It is possible that the discrepancy observed here could be largely because experimental detection of sRNAs was restricted to sRNAs expressed under one condition, viz starvation. Hence, it may well be that increasing the number of conditions in which RNA is harvested could lead to bridging the gap between in silico predicted and ssRNAseq identified sRNAs. Lastly, the disparity could be due to the presence of false positive in silico predictions as well as the elimination of sRNAs associated with RITs in close proximity to CDS regions when using RIT identification based in silico predictions. Nonetheless, the lengths of the majority of the in silico predicted sRNA transcripts were comparable to the sizes deduced from the strand-specific RNA-seq sRNA detections for the confirmed sRNA candidates.
Table 2

In silico predicted sRNA candidates confirmed by strand-specific RNA-seq

sRNA candidate

sRNA class

sRNA type

sRNA start

sRNA end

sRNA length

reg_seq3

3′UTR: polA

 

28634

28755

122

reg_seq3b

IGR

spot42 sRNA

28756

28882

127

reg_seq13

3′ UTR: aldA

 

139913

140154

242

reg_seq27

3′ UTR: ECA0332

TPP riboswitch

380584

380826

243

reg_seq27b

5′ UTR: icc

isrH (Hfq binding sRNA)

380825

380917

93

reg_seq31

3′ UTR: ECA0449

 

515673

515910

238

reg_seq34

3′ UTR: topB

STAXI sRNA

601635

601862

228

reg_seq34b

antisense: ECA0527

STAXI sRNA

601860

602087

228

reg_seq43

3′ UTR: mdh

Glycine riboswitch

758603

758857

255

reg_seq67

IGR

 

1185902

1186147

246

reg_seq70

antisense: ECA1096

 

1225748

1226182

435

reg_seq76

5′ UTR: mend

TPP riboswitch

1379050

1379349

300

reg_seq109

antisense: osmB

TPP/ isrH

2217586

2217946

361

reg_seq129

IGR

Trp leader

2602697

2602931

235

reg_seq133

5′ UTR: ansA

RtT and TPP

2651356

2651598

243

reg_seq142

3′ UTR: ECA2516

 

2832294

2832530

237

comp_seq5

3′ UTR: glnL

TPP/ isrH

34992

35234

243

comp_seq11

3′ UTR: slmA

isrH

164026

164334

309

comp_seq16

5′UTR: ECA0353

TPP

403257

403655

399

comp_seq49

IGR

TPP

1218529

1218729

201

comp_seq55

5′ UTR: ECA1196

TPP/ isrH

1358123

1358268

146

comp_seq111

IGR

TPP/ isrH

2881355

2881546

192

comp_seq130

3′ UTR: ECA2950

TPP and RtT

3295111

3295347

237

comp_seq204

5′ UTR: sotB

 

4828158

4828349

192

comp_seq217

5′ UTR: ECA4506

 

5046219

5046427

209

Functional annotation of RNA-seq detected sRNAs

To describe and assign biological functions to the 137 sRNAs detected by strand-specific RNA-seq (including those confirmed by in silico predictions), we used the Rfam database (version 11.0) [31] and the RNAspace platform [32]. The RNAspace platform comprises a suite of ncRNA prediction tools. Similarity searches on the RNAspace platform were restricted to comparative analysis and homology searches using BLAST/ YASS (sequence homology tools) against the Rfam 10.0 seed database and three RNA motif search tools, DARN, ERPIN and INFERNAL. In total, 68 sRNAs representing true (and/or known), previously described sRNA sequences were assigned into 6 functional classes (E-value < 0.001), and these included: 1 ribozyme, 21 riboswitches (consisting of 6 types), 14 RNA elements (10 different types), 30 sRNAs (including 9 Hfq-binding sRNAs), 1 asRNA and 1 tmRNA (Table 3). Amongst these, we characterized 13 sRNA sequences which were previously uncharacterized within the P. atrosepticum genome by means of Blast (e-value < 0.001) and secondary structure predictions using the RNAfold Webserver [33]. No functional classes were assigned to the remaining 69 sRNAs computationally, suggesting that they could be potentially novel sRNA candidates in P. atrosepticum.
Table 3

Functional annotation of the 68 true (and/or known) sRNAs identified by strand-specific RNA-seq

sRNA name

sRNA type

Characterized in this study (RNAspace)

Previously characterized (Rfam)

Total

RNaseP

Ribozyme

 

1

1

tmRNA

  

1

1

Cis-regulators

    
 

Riboswitches

   

TPP riboswitch

 

6

3

9

TPP or isrH

 

8

 

8

glycine riboswitch

 

2

2

4

FMN

  

1

1

lysine riboswitch

 

1

 

1

MOCO_RNA_motif

  

1

1

yybP-ykoY

  

1

1

 

RNA elements

   

alpha_RBS

  

1

1

cspA

  

2

2

greA

  

1

1

his_leader

  

1

1

JUMPStart

  

2

2

leucine operon leader

  

1

1

P26

  

1

1

rne5

  

1

1

RtT

 

1

2

3

trp_leader

  

1

1

trans-encoded sRNA

    
 

sRNA

   

STAXI

  

3

3

6S

  

1

1

crcB

  

1

1

csrB

  

1

1

glmY (tke1)

  

1

1

Rye

 

1

 

1

sraC (ryeA)

  

1

1

STnc240

  

1

1

StyR-44

  

5

5

t44

  

1

1

 

Hfq binding sRNA

   

frnS

  

1

1

isrH

 

2

 

2

glmZ (sraJ)

  

1

1

omrA

  

1

1

rprA

  

1

1

ryhB

  

1

1

ryeB (sdsR)

  

1

1

sgrS

  

1

1

spot 42

  

1

1

antisenseRNA (asRNA)

asRNA

   

HPnc0260

  

1

1

Total

 

21

47

68

Most of the detected riboswitches in this study corresponded to thiamine pyrophosphate (TPP) (Thi-box) riboswitches (Table 3). Bacterial riboswitches are embedded within the leader sequences (5′ UTR regions) of numerous metabolic genes and act by repressing or activating their cognate genes at the translational level in gram-negative bacteria [34]. Most thiamin-regulated genes encode transporters in different bacterial organisms [35]. For example, TPP riboswitches identified are present upstream of genes involved in potassium transport (trkD), amino acid biosynthesis (argG), and genes related to the biosynthesis of secondary metabolites (menD). Generally, TPP riboswitches are found upstream (5′ UTR regions) of many genes key in metabolic processes which use TPP as a cofactor [35]. In this study, we also detected other riboswitches other than TPP-type riboswitches, that include Flavin mononucleotide (FMN), glycine, lysine, yybP-ykoy and MOCO RNA motif riboswitches.

Some of the detected RNA elements (leader sequences) were located upstream of operons or genes involved in biosynthesis of amino acids including leucine, histidine and tryptophan biosynthesis; and polysaccharide synthesis (Additional file 1: Table S1). It therefore seems plausible that most of the detected cis-regulatory elements are engaged in regulating processes involving substrate transport and biosynthesis in P. atrosepticum.

Conservation analysis of predicted sRNAs

The vast majority of known sRNAs are typically highly conserved across genera [36]. We therefore analysed the conservation of identified sRNAs in P. atrosepticum SCRI1043 in five soft rot Enterobacteriaceae species whose complete genome sequences are available on GenBank. The 68 true/ known sRNA sequences with assigned functional classes were used for the conservation analysis. BLASTn analysis (E-value < 0.0001) using the YASS tool against P. carotovorum subsp. carotovorum PC1, P. wasabiae WPP163, Pectobacterium spp. SCC3193, Dickeya dadantii Ech703 and D. zeae Ech1591 complete genome sequences revealed that most sRNAs are conserved within the soft rot bacterial species with 42 sRNAs (including 13 trans-encoded sRNAs, 18 riboswitches, 10 RNA elements, and 1 asRNAs) being present in all five SRE species (Fig. 3 and Additional file 5: Table S5). The high conservation of sRNAs within the SRE species emphasizes their regulatory importance in these bacteria. Six IGR sRNAs were conserved only in the Pectobacterium genus and belonged to two RNA families; namely styR-44, and crcB RNA motif (fluoride riboswitch) sensing fluoride ions and regulating the crcB gene (hypothetical protein) which possibly encodes a protein that functions by removing excess fluoride ions from the cell.
Fig. 3

Summary of sRNAs conserved in soft rot Enterobacterieace

To be more confident with the 69 potentially novel sRNA candidates detected by ssRNA-seq, we filtered and screened them by checking their conservation within the five representative SRE strains using sequence similarity analysis. Nine of these candidate sRNAs had high sequence conservation (100 % identity and coverage) within SRE strains and only single hits from the BLAST analysis and therefore were considered as novel sRNAs (Table 4). To validate the expression and lengths of the nine novel sRNAs, reverse transcription PCR (RT-PCR) was performed on cDNA of bacteria cells cultured under starvation conditions (Fig. 4). For each of the cDNA samples, a single amplicon that corresponded to the sRNA transcript size identified by ssRNA-seq was observed. As an additional validation step, the nucleotide bases of observed amplicons were confirmed by sequencing and alignment to respective sRNA sequences (Additional file 6: Table S6).
Table 4

Novel sRNA candidates obtained using conservation analysis

sRNA Name

Strand

Length

sRNA Class

rev_11

-

420

asRNA: ECA0328

rev_13

-

354

asRNA: ECA0388

rev_24

-

489

asRNA: rcsC

rev_39

-

300

5′ UTR: ilvG

rev_41

-

480

IGR/ 5′ UTR: bcsB

fwd_6

+

122

3′ UTR: ECA3097

fwd_42

+

480

5′ UTR: zipA

fwd_44

+

336

asRNA: ECA0910

fwd_72

+

426

asRNA: gudP

Fig. 4

Validation of novel sRNA expression by RT-PCR: Agarose gel electrophoresis of the PCR amplicon fragments of the 9 novel sRNAs. Lane 1. rev_41, Lane 2. rev_13, Lane 3. fwd_6, Lane 4. rev_11, Lane 5. fwd_72, Lane 6. fwd_44, Lane 7. fwd_42, Lane 8. rev_24, Lane 9. rev_39, Lane 10. No reverse transcriptase control, Lane 11. 100 bp DNA Ladder

Differential expression of sRNAs under nutrient-rich and starvation conditions

Application of strand-specific RNA-seq to study the transcriptome of P. atrosepticum uncovered an abundance of sRNAs including antisense transcripts, intergenic sRNAs and cis-encoded regulatory elements. The number of RNA-seq reads mapping to individual sRNA sequences provides a realistic assessment of relative transcript abundance [37], thus enabling quantification of differential expression of the sRNA transcripts in P. atrosepticum cells existing under nutrient-rich and nutrient-deficient (starvation) conditions. The differential expression of sRNAs when growth conditions are changed could suggest potential functions and clarify conditions that induce or repress formation of specific sRNAs [38]. Hence, in order to understand the expression profiles of sRNAs in response to carbon and phosphorus starvation, we compared expression patterns of P. atrosepticum cells under nutrient-rich and nutrient-deficient (starvation) conditions. Based on the combined statistics of edgeR package [39] (dispersion = 0.04; q-value < 0.1), and Gfold algorithm (v.1.1.4) [40], which uses a posterior distribution of log fold change for determining expression changes in experiments with single biological replication, thus, overcoming the shortcomings of relying on statistics based on p-value when biological replication is lacking [40]. Subsequently, only sRNAs with significant differential expression from edgeR and Gfold analyses were considered. Thus, a total of 68 sRNA candidates were differentially expressed (Additional file 7: Table S7). Of these, 47 sRNAs were up-regulated under nutrient-deficient conditions (Additional file 7: Table S7) suggesting that they are likely involved in regulatory mechanisms of stress response or adaptation in P. atrosepticum. To validate expression profiles identified by ssRNA-seq, we performed reverse transcription quantitative PCR (RT-qPCR) using three biological replicates, on eight selected sRNAs that were differentially expressed under nutrient-rich and starvation conditions. The RT-qPCR results confirmed expression patterns of these eight sRNA transcripts and validated our RNA-seq data (Fig. 5). Selected examples are discussed below.
Fig. 5

RT-qPCR validation of RNA-seq expression analysis. Relative expression changes of sRNAs were determined using the 2-ΔΔct method by comparing expression in starvation conditions to nutrient-rich. Error bars indicate the standard error of three independent biological replicates. Asterisks represent significant difference at p < 0.05 (Students t-test)

We noticed that rprA was up-regulated (~1.5-fold) in P. atrosepticum under carbon-starvation conditions (Fig. 5 and Additional file 7: Table S7). The sRNA rprA acts by increasing (positively regulating) the translation of rpoS gene transcript [41, 42]. RpoS is a sigma factor that controls the expression of stress responsive genes in bacteria during adverse conditions and stationary phase. We observed that the expression of rpoS gene in P. atrosepticum was higher during starvation than under nutrient-rich conditions (data not shown). This observation is consistent with previous data demonstrating that RpoS is a principal regulator of the general stress response in bacteria allowing cells to survive environmental challenges as well as prepare for subsequent stresses [43]. This is also consistent with our previous observations, demonstrating that rpoS gene expression increases significantly in P. atrosepticum under stress conditions [14]. Generally, the regulation of rpoS gene expression is known to be modulated at the translational level by at least four sRNA, namely, arcZ, dsrA, rprA, and oxyS in response to temperature, osmotic shock, oxidative stress and nutrient deprivation in E. coli [41, 44]. Hence, increased rprA expression observed in our study in P. atrosepticum under nutrient starvation conditions is likely to promote the enhanced translation of rpoS mRNA during adaptation of bacteria to starvation conditions.

ryhB2, a 106 nucleotide paralogue of ryhB sRNA, was up-regulated by a 15-fold magnitude in P. atrosepticum under nutrient-starvation conditions (Fig. 5). Generally, ryhB, regulates iron metabolism, including its acquisition and assimilation. ryhB acts by down-regulating expression of genes encoding iron-storage and iron-using proteins when iron is in limited supply. The main target genes for ryhB include the sdhCDAB operon encoding succinate dehydrogenase and sodB which encodes the iron-dependent superoxide dismutase [45]. ryhB expression level is usually inversely correlated with expression levels of the mRNA for the sdhCDAB operon [46]. This is consistent with our observations for P. atrosepticum: the transcription of the sdhCDAB operon was reduced under starvation conditions compared to the growth-promoting ones (Fig. 6).
Fig. 6

The expression of the sdhCDAB operon is relatively lower under starvation compared to growth promoting conditions. Reads mapped from the nutrient-rich condition are represented by the red line. The blue line represents mapped reads from the starvation conditions. Annotated features are labelled below the plot in blue blocks. The y-axis shows the read coverage per coding region (CDS)

Starvation conditions also induced the expression of glmZ (~2-fold increase) and glmY (5-fold increase) sRNAs in P. atrosepticum (Fig. 5). In enteric bacteria, these two sRNAs regulate amino sugar metabolism by activating the expression of glmUS operon which encodes the glucosamine-6-phosphate synthase, an essential enzyme in amino sugar metabolism [47]. The regulation by these two sRNAs modulates the transitions between carbon storage and carbon metabolism [48]. The level of glmY is increased in the absence of glucosamine-6-phosphate leading to stabilization of glmZ. The latter, in turn, activates glmS gene expression in an anti-antisense mechanism [48]. GlmS enables cells to utilize the intermediates of glycolytic pathway including the fructose-6-phosphate for production of amino sugars. The glucosamine-6-phosphate is an essential precursor for biosynthesis of essential components of the cell envelope such as peptidoglycan and lipopolysaccharide in gram-negative bacteria. Thus, induction of glmY and glmZ expression in P. atrosepticum under starvation conditions likely indicates the important role of the amino sugar metabolism in adaptive response on this bacterium.

In summary, we have shown that several sRNAs are induced under nutrient-deficient compared to nutrient-rich conditions. We have also shown that induction of these sRNA leads to induction of various genes that potentially play a role in the survival of P. atrosepticum. In other members of the Enterobacteriaceae family including E. coli and Salmonella, sRNAs have also been shown to play an important role in adaptation to nutrient limited condition [49]. In these bacteria, sRNAs provide a signal that triggers production of extracellular polysaccharides (EPS) which in turn are involved in biofilm formation [50]. Although P. atrosepticum does not readily form biofilms in vitro, the overexpression of a diguanylate cyclase (PleD*), induced formation of biofilms suggesting that biofilm formation in this pathogen is cryptic and can be activated under optimum conditions [51]. Part of the pathogenesis of P. atrosepticum is in xylem tissue (when causing black leg disease of potato stems). The xylem is typified by limited nutrients and as such a harsh environment that requires well defined methods of survival. Hence, it is not surprising that many xylem dwelling phytopathogens such as Xanthomonas, Clavibacter, Ralstonia and Xylella form biofilms in xylem tissues of their respective hosts. Thus, it is possible that sRNA are extensively involved in the adaptation of P. atrosepticum and survival in stem vasculature. Identification of this suite of sRNA will allow us to study the role that these play in survival of this phytopathogen during stem colonisation.

Conclusions

In conclusion, in this study we have used a combination of strand-specific RNA-sequencing and in silico approaches to detect and analyse sRNAs in P. atrosepticum SCRI1043. We demonstrated the efficiency of ssRNA-seq in detecting sRNAs and determining the sRNA expression levels in response to specific bacterial growth conditions. A total of 137 sRNAs and sRNA candidates were experimentally detected in this study. We successfully determined sRNAs (that are riboswitches, trans-encoded sRNAs, 3′ UTR sRNAs and asRNAs) that may play key roles in regulating stress responses. Most of the identified sRNAs in P. atrosepticum are conserved within the soft rot enterobacteria (SRE) species suggesting their importance in physiological responses for the SRE species. To our knowledge, this study constitutes the first genome/ transcriptome-wide analysis aimed at the discovery of sRNAs responsive to nutrient-deficiency (starvation) in bacteria. A significant fraction of the unravelled sRNAs appeared to be starvation responsive indicative of their importance in adaptation of bacteria to stress conditions. Determining the biological roles of these sRNAs will broaden our understanding of the diverse regulatory mechanisms they provide in modulating gene expression in P. atrosepticum and other SRE species during adaptation to changing environments.

Methods

Strains, culture conditions and strand-specific RNA-seq

Bacterial strains, media and culture conditions

A strain of P. atrosepticum SCRI1043 [10], was used in this study. sRNA profile was analysed in bacterial cells existing under growth-promoting and starvation conditions. The cultures with inoculation titer of 2–3 × 106 CFU (colony forming units) per ml were grown in Luria-Bertani medium [52], with aeration (200 r.p.m.) at 28 °C for 16 h (growth-promoting conditions). Aliquots of these cultures were used for total RNA extraction. The remaining cells were transferred (after double wash) to carbon and phosphorus deficient AB medium containing 1 g l−1 NH4Cl; 0.62 g l−1 MgSO4 × 7H2O; 0.15 g l−1 KCl; 0.013 g l−1 CaCl2 × 2H2O and 0.005 g l−1 FeSO4 × 7H2O, pH 7.5 and incubated under starvation conditions with initial cell density of 5.4 × 108 ± 6.1 × 107 CFU per ml in glass vials without aeration at 28 °C [53]. Total RNA was extracted from 24 h starving cells.

Total RNA preparation

Total RNA was isolated from bacterial cells using the RNeasy Protect Bacteria Mini Kit (Qiagen, USA), according to the manufacturer’s instructions. Contaminating DNA was removed from the samples by DNAse (Qiagen) treatment. RNA was quantified using a Qubit fluorometer (Invitrogen, USA).

cDNA library construction and bacteria strand-specific RNA sequencing

Library construction and strand-specific sequencing were carried out at the Beijing Genomics Institute (BGI-Shenzhen, China; http://www.genomics.cn/en/index), following the manufacturer’s protocols. Briefly, the rRNA was depleted from 1 microgram of total RNA using the Ribo-Zero Magnetic Gold Kit (Epicenter). TruSeq RNA Sample Prep Kit v2 (Illumina) was used for library construction. RNA was fragmented into small pieces using Elute Prime Fragment Mix. First-strand cDNA was synthesized with First Strand Master Mix and Super Script II (Invitrogen) reverse transcription (25 °C for 10 min; 42 °C for 50 min; 70 °C for 15 min). After product purification (Agencourt RNAClean XP Beads, AGENCOURT) the second-strand cDNA library was synthesized using Second Strand Master Mix and dATP, dGTP, dCTP, dUTP mix (1 h at 16 °C). Purified fragmented cDNA was end repaired (30 min at 30 °C) and purified with AMPureXP Beads (AGENCOURT). Addition of the poly (A) tail was done with A-tailing Mix (30 min at 37 °C) prior to ligating sequencing adapters (10 min at 30 °C). The second-strand cDNA was degraded using the Uracil-N-Glycosylase (UNG) enzyme (10 min at 37 °C) and the product purified by AMPureXP Beads (AGENCOURT). Several rounds of PCR amplification with PCR Primer Cocktail were performed to enrich the cDNA fragments and the PCR products were purified with AMPureXP Beads (AGENCOURT). Sequencing was performed using the Illumina HiSeq™ 2000 platform with pair-end 90 base reads.

Sequence read processing and experimental detection of sRNAs

Prior to analyzing the sequencing reads, adaptors were removed and the Illumina pair-end reads were quality checked using FASTQC: Read QC and trimmed using Trim sequences (version 1.0.0) implemented within the Galaxy software [5456]. Quality trimmed reads were mapped to the P. atrosepticum SCRI1043 genome (http://www.ncbi.nlm.nih.gov/nuccore/50118965?report=fasta) using Bowtie2 [57]. The mapped reads in SAM format were converted to sorted and indexed BAM files using SAMtools version 0.1.18 [19]. Each BAM file was split into two separate forward and reverse strand alignments using SAMtools to obtain transcriptional direction. For visualization of the data in a strand-specific manner, the genome browser Artemis [58], was used. The strand-specific RNA Sequencing data from this study have been deposited in NCBI’s Gene Expression Omnibus (GEO) with the accession number GSE68547.

RT-PCR validation of novel sRNA candidates

For RT-PCR, first-strand cDNA was synthesized from 1 μg of total RNA using Superscript™ III First-Strand cDNA Synthesis SuperMix kit according to the manufacturer’s protocol (Invitrogen, USA). The first-strand cDNA samples were used for RT-PCR, which was performed on Bio-RAD T100TM Thermal Cycler conventional PCR (Bio-RAD, USA). To check for genomic DNA contamination, a non reverse-transcriptase control was included. The sRNA primers were designed online using Primer3 (Additional file 8: Table S8). PCR was performed in a 25 μl reaction mix containing 1 μl of template cDNA (~40 ng), Taq DNA Polymerase, 10× Taq Buffer (New England Biolabs, UK), 2.5 mM dNTPs each and 0. 5 μM of forward and reverse primer each. Thermal cycling conditions were: 95 °C for 2 min; 30 cycles of 95 °C for 30 sec, 57 °C for 30 sec, 72 °C for 60 sec, and the final extension at 72 °C for 5 min. The PCR products were analysed on 1.5 % agarose gel including 100 bp DNA molecular weight ladder (NEB, UK).

Differential expression analysis of sRNAs

Artemis genome browser was used to create features of the discovered sRNAs on the P. atrosepticum reference genome and to make read counts for reads aligning to each strand under each growth condition. The read counts were used as input for the sRNA differential expression analysis using edgeR [39]. sRNA transcripts were considered differentially expressed provided that the p-value was < 0.05 and q-value < 0.1.

RT-qPCR validation of RNA-seq data

First strand cDNA synthesis was performed individually from total RNA samples from each of three biological replicates per condition using Superscript III First-Strand cDNA Synthesis SuperMix kit (Invitrogen, USA). For RT-qPCR, 2 μl of sample was added to 8 μl of Applied Biosystems SYBR Green Master Mix including each primer at a concentration of 0.4 μM and the reaction performed in the QuantStudio 12 K Flex Real-Time PCR system (Life Technologies, USA). The following cycling conditions were used: an initial denaturation at 50 °C for 5 min and 95 °C for 2 min, followed by 45 cycles of 95 °C for 15 s and 60 °C for 1 min. Each sample was run in triplicate. Relative expression was measured using the comparative 2-ΔΔct method [59] after normalizing the samples to recA as the reference gene. Primers were designed using Primer3Plus (http://primer3plus.com/cgi-bin/dev/primer3plus.cgi) (Additional file 9: Table S9).

Computational (in silico) sRNA prediction

Soft rot bacteria genome sequences

The genome sequences of six soft rot bacteria species (Pectobacterium atrosepticum SCRI1043, P. carotovorum subsp. carotovorum PC1, P. wasabiae WPP163, Pectobacterium sp. SCC3193, D. dadantii Ech703, and Dickeya zeae Ech1591 were obtained from the European Nucleotide Archive (http://www.ebi.ac.uk/ena/).

Identification of RITs

The WebGeSTer DB [60], database was used in this study to predict Rho-independent terminators (RITS) in P. atrosepticum SCRI1043 using default parameters. Briefly, no more than three mismatches were permitted within the stem structure and only RIT candidates with the highest ∆G score (∆G < = − 12.0 kcal/mol) were considered. Coordinates for putative RITs were obtained from the WebGeSTer DB, and java scripts were used to extract the sequences 200 nt upstream of the terminators (including the stem loop and tail sequences of the terminator). These sequences were considered as putative sRNA candidates and used in downstream sRNA prediction analysis. Additionally, known sRNAs within the P. atrosepticum SCRI1043 genome sequence were searched in the SIPHT web interface (published annotations) [28].

sRNA conservation analysis

The conservation of sRNA sequences detected using deep sequencing was determined by similarity analysis against sequences of complete genomes of five soft rot Enterobacteriaceae species using YASS [29], a sequence similarity search tool, with standard parameters.

Classification of sRNA

Following the model implemented for Escherichia coli [27], custom scripts written in java were used to classify the predicted sRNA candidates into five non-coding RNA groups based on their position in relation to adjacent CDSs. Briefly, the first nucleotide in each RIT was used as the representative position of each sRNA candidate. To determine asRNA, the reference nucleotide on the opposite DNA strand had to be at least +15 nt relative to the ATG codon to − 50 nt with respect to the stop codon. For 5′ UTR, sRNA candidates had to be on the same DNA strand as the CDS and in a distance of < −100 nt upstream the ATG codon and for 3′ UTR between +60 and +200 nt downstream of the stop codon. The rest of the remaining putative sRNAs were considered as IGR candidates if they were outside a CDS.

Abbreviations

3′ UTR: 

3′ untranslated regions

5′ UTR: 

5′ untranslated regions

asRNAs: 

antisense RNAs

BLAST: 

basic local alignment search tool

CDSs: 

protein-coding regions

CFU: 

colony forming units

EPS: 

extracellular polysaccharides

IGR: 

intergenic regions

mRNA: 

messenger RNA

ORF: 

open reading frame

RITs: 

rho-independent terminators

RPKM: 

reads per kilobase of transcript per Million mapped reads

SRE: 

soft rot enterobacteriaceae

SRNA: 

small RNA

ssRNA-seq: 

strand-specific RNA sequencing

TPP: 

thiamine pyrophosphate

TSS: 

transcriptional start sites

Declarations

Acknowledgements

The authors would like to acknowledge funding from The National Research Foundation (NRF) Thuthuka Grant UID: 69362; UID BFG 93685 and NRF South Africa Russia Bilateral Grant UID: 75252 and from the Russian Foundation for Basic Research (Research Project No. 15-04-02380). The University of Pretoria is acknowledged for providing a studentship for S. Kwenda. V. Gorshkov is supported by The Russian Science Foundation (Project No. 15-14-10022).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Microbiology and Plant Pathology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria
(2)
Kazan Institute of Biochemistry and Biophysics, Kazan Scientific Center, Russian Academy of Sciences
(3)
Department of Botany and Plant Physiology, Kazan Federal University
(4)
Department of Genetics, Forestry and Agricultural Biotechnology (FABI), University of Pretoria
(5)
Genomics Research Institute, Centre for Microbial Ecology and Genomics (CMEG), University of Pretoria
(6)
Division of Plant Sciences, College of Life Sciences, University of Dundee (at The James Hutton Institute)

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