Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Discovery of Putative Small Non-Coding RNAs from the Obligate Intracellular Bacterium Wolbachia pipientis

  • Megan Woolfit,

    Affiliation School of Biological Sciences, Monash University, Clayton, Victoria, Australia

  • Manjula Algama,

    Affiliation School of Mathematical Sciences, Monash University, Victoria, Australia

  • Jonathan M. Keith,

    Affiliation School of Mathematical Sciences, Monash University, Victoria, Australia

  • Elizabeth A. McGraw,

    Affiliation School of Biological Sciences, Monash University, Clayton, Victoria, Australia

  • Jean Popovici

    jean.o.popovici@gmail.com

    Current address: Jean Popovici, Malaria Molecular Epidemiology Unit, Institut Pasteur in Cambodia, Phnom Penh, Cambodia

    Affiliation School of Biological Sciences, Monash University, Clayton, Victoria, Australia

Discovery of Putative Small Non-Coding RNAs from the Obligate Intracellular Bacterium Wolbachia pipientis

  • Megan Woolfit, 
  • Manjula Algama, 
  • Jonathan M. Keith, 
  • Elizabeth A. McGraw, 
  • Jean Popovici
PLOS
x

Abstract

Wolbachia pipientis is an endosymbiotic bacterium that induces a wide range of effects in its insect hosts, including manipulation of reproduction and protection against pathogens. Little is known of the molecular mechanisms underlying the insect-Wolbachia interaction, though it is likely to be mediated via the secretion of proteins or other factors. There is an increasing amount of evidence that bacteria regulate many cellular processes, including secretion of virulence factors, using small non-coding RNAs (sRNAs), but sRNAs have not previously been described from Wolbachia. We have used two independent approaches, one based on comparative genomics and the other using RNA-Seq data generated for gene expression studies, to identify candidate sRNAs in Wolbachia. We experimentally characterized the expression of one of these candidates in four Wolbachia strains, and showed that it is differentially regulated in different host tissues and sexes. Given the roles played by sRNAs in other host-associated bacteria, the conservation of the candidate sRNAs between different Wolbachia strains, and the sex- and tissue-specific differential regulation we have identified, we hypothesise that sRNAs may play a significant role in the biology of Wolbachia, and in particular in its interactions with its host.

Introduction

Wolbachia pipientis is a vertically transmitted endosymbiotic Alphaproteobacteria that is thought to infect up to 40% of arthropod species [1]. Different Wolbachia strains induce a diverse range of effects in their hosts, including multiple forms of reproductive manipulation that enhance transmission of the endosymbiont to the next host generation [2,3]. More recently it has also been discovered that a number of Wolbachia strains inhibit the replication of viral and other pathogens in both their natural hosts, such as Drosophila melanogaster, and heterologous hosts such as Aedes aegypti [46]. These effects make Wolbachia an attractive biocontrol agent for vector-borne diseases, and field releases of Wolbachia-infected A. aegypti are currently being tested in trials with the aim of reducing dengue virus transmission [7].

The molecular mechanisms by which Wolbachia causes these different host phenotypes remain largely unknown. Recent work has demonstrated that Wolbachia infection modulates expression of mosquito host miRNAs that regulate diverse genetic targets, including host metalloprotease and methylase genes [810]. Wolbachia infection in other taxa has also been shown to affect transcription of host genes involved in iron metabolism and the oxidative stress response [1113]. At least some host responses to Wolbachia infection are likely to be induced by effectors secreted by the endosymbiont. Wolbachia has a conserved and functional type IV secretion system (T4SS) [14], and these systems are known to play a role in infection, survival and proliferation in many other symbiotic and pathogenic intracellular prokaryotes [15]. Wolbachia genomes also contain an unusual number of genes encoding ankyrin domains. Host-interacting ankyrin proteins are secreted via the T4SS in other intracellular Alphaproteobacteria such as Anaplasma phagocytophilum and Ehrlichia chaffeensis, and these proteins are considered the most likely candidates to underlie the molecular dialogue between Wolbachia and its host [1620].

Numerous Wolbachia genes, including those encoding ankyrin domains, show host sex- and tissue-specific expression patterns [21,22], further suggesting that they may be involved in host interaction. The mechanisms by which Wolbachia regulates the expression of these genes are currently unknown. Few transcription factors have been identified in Wolbachia genomes, and these factors have so far been shown to regulate only a small number of genes [23]. Recently, however, numerous other species of facultative or obligate intracellular bacteria have been shown to use small non-coding RNAs (sRNAs) to regulate the expression of genes associated with diverse aspects of host interaction, including iron homeostasis [24], the cell cycle [25], quorum-sensing [26], secretion systems [27] and secreted virulence factors [2830]. These small RNAs are highly variable in sequence and function, and vary in number from a few tens to a few hundreds in many bacterial genomes [31].

There are at least five main classes of sRNAs, which regulate gene expression in several ways [31,32]. Antisense sRNAs are typically 50–500 nt in length, are transcribed from the opposite strand of the genes that they regulate, and act via extensive complementarity with their target mRNAs. Trans-encoded sRNAs, in contrast, are often shorter (around 100 nt), are usually encoded intergenically or with partial overlap of one or more CDSs, may regulate many different mRNAs, and have much more limited complementarity with their targets. Both antisense and trans-encoded sRNAs may interact with mRNA targets to enhance or inhibit translation. A third kind of sRNA, also encoded outside CDSs, are 5' riboswitches, which do not operate as independent transcripts but are part of the mRNA they regulate. Fourth, there are a small number of sRNAs, such as 6S sRNA, that interact with proteins rather than mRNA. Finally, bacteria also encode a number of 'housekeeping' sRNAs that do not pair with mRNAs or regulate proteins; these include the ribozyme RNase P, the 4.5S RNA component of the signal recognition peptide, and tmRNA. Genes encoding tmRNA, 4.5S sRNA, RNase P and 6S sRNA are present in Wolbachia genomes, and the latter two show host tissue-specific expression in filarial nematodes [21]. To our knowledge, however, no antisense or trans-encoded sRNAs have previously been identified in Wolbachia genomes.

The majority of trans-encoded sRNAs described to date are expressed under specific growth conditions [31], and this class of sRNA may therefore be of particular interest in elucidating host sex- and tissue-specific gene regulation in Wolbachia. In this study, two independent methods have allowed the identification of candidate trans-encoded sRNA in several Wolbachia strains. The first method is based on examination of RNA-Seq data from the Wolbachia strains wMelPop, wMelPop-CLA and wMelCS. To investigate the potential utility of RNA-Seq for Wolbachia gene expression studies, we had previously performed a number of trial runs of this sequencing technology. These data were not ideal for detection of sRNAs, as we did not perform strand-specific sequencing and had chosen to sequence DNA fragments of ∼300 nt, which is longer than many known sRNAs. Despite these limitations, however, we serendipitously identified a number of sRNA candidates while analysing the sequencing reads for other purposes. The second method we used to identify candidate sRNAs is bioinformatic, and based on comparative genomics of the strains wMel and wPip.

To increase the probability that the candidates identified using the methods above are true sRNAs, we have conservatively focused on transcripts that are encoded entirely within intergenic regions rather than overlapping a CDS, and that are transcribed specifically rather than as an intergenic component of a polycistronic mRNA. We identified several candidate sRNAs, and have experimentally confirmed the differential expression of one putative sRNA in four strains of Wolbachia, and in different host sexes and tissues.

Materials and Methods

Fly rearing and cell culture

Drosophila melanogaster (w1118) stock lines stably infected with the wMel, wMelPop, wMelCS and wAu strains of Wolbachia were maintained on standard molasses and cornmeal medium at a constant temperature of 25°C with a 12h light/dark cycle [33,34].

C6/36 cells infected with wMelPop-CLA were routinely passaged in RPMI 1640 medium supplemented with 10% FBS [35].

Sample preparation for RNA-Seq experiment

We performed RNA-Seq sequencing on five trial libraries. Three libraries were created using material from C6/36 cells infected with wMelPop-CLA, and two libraries were created from the heads of flies infected with either wMelPop or wMelCS. In an attempt to minimize the number of experimental manipulations that could affect the transcriptomic profile, we created the two fly libraries without performing either purification of Wolbachia from the host material or depletion of host or Wolbachia rRNA. For each of these libraries, we dissected the heads from 10 flies. wMelCS was obtained from D. melanogaster Canton S virgin female flies at 3 days of age, and wMelPop was obtained from D. melanogaster w1118 virgin female flies at 3 days of age. In each case, total RNA was isolated after homogenization of dissected heads in 100ul of Trizol (Invitrogen). RNA was then purified according to the manufacturer’s instructions and DNase-treated (DNase I recombinant, Roche) before being sent for Illumina sequencing.

The three samples derived from wMelPop-CLA-infected cell culture were each subject to different treatment. For the first, total RNA was isolated from a 175cm2 flask of wMelPop-CLA-infected C6/36 cells at ∼80% confluence using Trizol according to the manufacturer’s instructions, and the RNA was DNase treated and sent for Illumina sequencing. For the second sample, we extracted total RNA from a single flask of cell culture as above, while for the third sample, we purified Wolbachia from the cell culture using the method of Iturbe-Ormaetxe et al [36], then performed total RNA extraction. For both samples, RNA was DNase-treated and then depleted for host and bacterial rRNA using successively the RiboMinus Eukaryote kit (Ambion) and the MicrobExpress bacterial mRNA Enrichment kit (Ambion) according to the manufacturer’s instructions. After depletion, first and second-strand cDNA synthesis was done using SuperScript III Reverse Transcriptase (Life Technologies) and DNA Polymerase I, Klenow Fragment (NEB) according to the manufacturer’s instructions. cDNAs were purified using the MinElute Reaction Cleanup Kit (Qiagen) before being sent for Illumina sequencing.

The second and third cell culture samples (those with rRNA depletion) were indexed on a single lane of Illumina GAII, and sequenced at Micromon (Monash University), with 300 bp size selection and 75 bp paired-end reads. The remaining three samples were sent to Macrogen (South Korea) for library preparation and sequencing indexed on a single lane of HiSeq, with 300 bp size selection and 70 bp paired-end reads.

The RNA-Seq sequence data have been deposited at NCBI under Bioproject PRJNA266744, sample numbers SAMN03174110, SAMN03174111, SAMN03174113, SAMN03174115 and SAMN03174116.

Data analysis and mapping of RNA-Seq reads

We filtered reads for quality using Trimmomatic [37] by removing all trailing bases with quality less than 30, and then discarding (1) reads shorter than 40 nt after trimming and (2) any unpaired reads. We then performed read mapping and downstream analyses using the Nesoni toolset (http://www.vicbioinformatics.com/software.nesoni.shtml). Filtered paired reads were mapped to the reference wMel genome [18] using BWA [38], and then mappings were filtered so that read pairs with multiple equally good alignments were randomly assigned to one of those alignments. We then created a modified wMel gff file that listed intergenic regions as well as the more typical annotation features (CDSs, rRNAs, tRNAs, etc), and used a custom Perl script to count the alignments to each feature.

Because some intergenic regions are smaller than the mean fragment size sequenced, and because there appears to be a substantial amount of polycistronic transcription occuring in wMel, many "intergenic" mapping counts actually reflect transcription of flanking genes. Nonetheless, these counts provided us with a preliminary list of candidate intergenic regions with high transcription levels. We then inspected the read mapping data for these candidate regions in the Artemis genome browser [39]. We identified regions that appeared to have intergenic-specific transcription, based on mapping of read pairs, for further investigation.

Change-point analysis: prediction of conserved candidate sRNAs

We used the program changept to identify a class of segments characterized by a high degree of conservation. The process followed in this analysis is described below.

Sequence and alignment of data.

We used the published complete genome sequences of wMel and wPip (NCBI accession numbers NC_002978.6 and NC_010981.1 respectively) to identify intergenic regions that were highly conserved between these strains. Fragments of the genome may show low levels of divergence between strains for at least two reasons. The first possibility is that they are evolving under selective constraint, and these are the regions we wish to identify. Alternatively, however, genomic regions that were horizontally transferred between wMel and wPip after the divergence of these strains will also be more similar to one another than expected, not due to selection but because they have a more recent common ancestor than the rest of the genome. We took two approaches to exclude these regions. First, we masked prophage regions [40] and a known region of horizontal gene transfer (WD0507-WD0517 [41]) in the genomes before analysis. Secondly, we also performed a post hoc check for horizontal transfer after candidates were identified by extracting their nucleotide sequences from the wMel genome and using them as megablast queries against the NCBI NT database. To attempt to exclude regions that have artifactually low levels of divergence due to recent horizontal transfer between supergroups, we accepted only those candidate regions that had better hits to all available A group genomes than to all available B group genomes. The changept procedure we used to identify these conserved non-CDSs is described below. We aligned the masked genomes using progressive Mauve [42], and used the accessory script stripSubsetLCBs (available from http://gel.ahabs.wisc.edu/mauve/snapshots/) to extract local colinearity blocks (aligned core genome blocks) at least 500 nt in length. We then used a custom script to convert this XMFA output file into Fasta format.

Data transformation.

The pairwise alignment of wMel and wPip was then converted into a changept input sequence using a 16-character code (A = (a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p)), (Table 1). Insertions and deletions were excluded. The alignment blocks were separated using the ‘#’ symbol; these are considered as fixed change-points by the model. In 16-character representation, characters ‘a’, ‘f’, ‘k’ and ‘p’ represent conserved bases. Although this sequence also contains other biologically significant information, such as the GC content of each species and transition/transversion ratio, we were mainly focused on the different levels of conservation between wMel and wPip.

thumbnail
Table 1. changept 16-character code used for conversion of pairwise alignment of wMel and wPip.

https://doi.org/10.1371/journal.pone.0118595.t001

Model selection.

Changept currently requires the user to specify the number of segment classes. Separate segmentation analyses were performed for models with 1–12 segment classes. Each model was run for 1,000 iterations. Selecting the model with the most appropriate number of classes involved model selection criteria discussed in [43]. In summary, we calculated approximations to three information criteria: AIC, BIC and DIC (Figure A in S1 File). The model with the smallest information criterion is generally considered optimal. However a subjective judgment was made on which model to choose; models containing classes with very low mixture proportions were considered to be over-fitted and thus a model with fewer classes was selected. For this data, we selected the 7-class model. BIC increases with the number of classes, displaying an unusual behaviour (Panel B, Figure A in S1 File); therefore we based our decision on AIC and DICV (Panel A, Figure A in S1 File). The first local minimum of both these criteria occurred at seven classes.

The most conserved class.

To determine the most conserved class of the selected model, the mean proportion of alignment matches was calculated for each iteration of the sampler: where θj; j ∈ A is the frequency of character j in the most conserved segment class.

These values were plotted against iteration number (Figure B in S1 File). This plot was also used as an added check to determine if the model had converged along with the loglikelihood plot (obtained by plotting the log likelihood at each iteration for the 1000 iterations).

Calculating profile values and generating wiggle track.

We used the program readcp (part of the changept package) to calculate profile values for the most conserved segment class. The profile shows the probability that each sequence position belongs to the specified class. These posterior probabilities are estimated using the samples from the post burn-in phase (that is, the first 150 samples were discarded) by Monte Carlo integration. A complete description of the changept and readcp programs can be found in [44,45]. The readcp output is then used to generate a wiggle track (https://cgwb.nci.nih.gov/goldenPath/help/wiggle.html). This file was uploaded to the UCSC browser (http://microbes.ucsc.edu/) for viewing in order to identify highly conserved non-coding segments in wMel and wPip. A section of the wiggle track corresponding to one of the candidate sRNAs (wMel coordinates 1,039,579–1,039,870) predicted by the changept program is shown in Fig. 1. The non-CDs regions longer than 50 nt with profile value ≥ 0.5 are considered as most likely candidates.

thumbnail
Fig 1. The WIG profile of a highly conserved feature.

This profile shows the probability that each position in the region belongs to the most conserved class. The conserved non-coding region is positioned in wMel coordinates 1,039,579–1,039,870. The top 3 bars containing single letter amino acid codes show 3 possible protein translation phases. At the bottom, coding region WD1082 is shown (in light blue with arrows). There are no previously annotated non-coding regions corresponding to this region.

https://doi.org/10.1371/journal.pone.0118595.g001

Validation of 5′ ends of candidate sRNAs by Rapid Amplification of cDNA Ends

A Rapid Amplification of cDNA Ends (RACE) procedure was used to determine the 5’ ends of a selection of candidate sRNAs [46]. The protocol used is based on the procedure described by [47]. Briefly, total RNA was isolated from whole bodies of ∼100 wMel-infected 1-day-old adult male w1118 flies using Trizol, DNAse-treated, and treated with Tobacco Acid Pyrophosphatase (Epicentre) for 30 min at 37°C. T4 RNA ligase (NEB) was used to add a short RNA adaptor on the 5’ ends of RNA. Reverse transcription of RNA was performed using primers complementary for candidate sRNAs and Superscript III (Invitrogen). Primers and adaptor sequences are listed in Table A in S1 File. The resulting cDNA was used as template for PCR using candidate sRNA specific primers and RNA adaptor specific primers. PCR products were run on a 1% agarose gel, gel-extracted using the QIAEX II kit (QIAGEN), and cloned in pGEMTeasy vector (Promega). E. coli DH5α were transformed with the constructs and plasmids were purified using the QIAprep spin miniprep kit (QIAGEN) prior to sequencing using SP6 primer (Micromon, Monash University, Australia).

Verification of transcription of candidate sRNA within intergenic regions

In order to verify that expression in each intergenic region was due to the transcription of the candidate sRNA itself and not the result of transcription together with a downstream gene in a single RNA molecule, a set of RT-PCRs were done using primers overlapping genes downstream of the intergenic regions. These “overlapping RT-PCRs” were done to verify that the 3’ end of a 5’RACE-confirmed candidate sRNA was indeed within the intergenic region. PCR was performed on cDNA from wMel-infected w1118 1 day-old female abdomens and ovaries. Briefly, total RNA was isolated from pools of 5 abdomens or pools of 10 dissected ovaries using Trizol. RNA was further purified according to Trizol instructions and DNase treated. cDNAs were synthesized from 1 μg of total RNA using random primers and SuperScript III, in accordance with the manufacturer's instructions. PCR was performed on cDNA and for every primer pair on DNA controls. PCR products were then visualised on a 2% agarose gel. All primers used are listed in Table A in S1 File.

RNA extraction, cDNA synthesis and quantitative PCR analysis of candidate sRNA

All RNA extractions were performed on 1-day-old flies. Three types of biological material were used for RNA extraction and further qRT-PCR analysis: whole bodies of male flies, abdomens of male and female flies, and dissected tissues (head, carcass, gonads) of male and female flies. Total RNA was isolated from individual whole bodies of male w1118 flies infected with wMel, wMelCS, wMelPop or wAu strains (n = 15 individuals per line) using Trizol. Total RNA was isolated from pools of 5 abdomens of either male or female w1118 flies infected with wMel, wMelCS, wMelPop or wAu strains (n = 15 pools per line) using Trizol. Total RNA was isolated from pools of dissected tissues of 10 male or female w1118 flies infected with wMel (n = 12 pools per tissue per sex). Flies were ice-anaesthetized, then head, gonad and carcass were dissected in ice-cold PBS and immediately transferred into Trizol. All RNA samples were further purified according to Trizol instructions and DNase treated. cDNAs were synthesized from 1 μg of total RNA using random primers and SuperScript III, in accordance with the manufacturer's instructions. Expression of candidate sRNA in all samples was measured by qPCR using the LightCycler480 SYBR Green I Master (Roche) on a LightCycler480 II instrument (Roche) in duplicate on a 2–5 times dilution of the cDNAs. Primers are listed in Table A in S1 File. Wolbachia surface protein wsp expression was used as reference to normalize candidate sRNA expression and account for Wolbachia density [48]. On a subset of samples, to confirm differential expression, we compared the use of wsp as reference gene to Wolbachia 16S and Drosophila melanogaster rps17. Relative quantification of expression was calculated using the LightCycler480 II software. Significant differences in candidate sRNA expression were tested by Mann-Whitney U test using GraphPad Prism 5 software (GraphPad Software, San Diego, California USA).

Results

We used two approaches to identify candidate novel sRNAs in Wolbachia. For the first approach, we extracted RNA from D. melanogaster flies or Aedes albopictus C6/36 cell lines infected with Wolbachia, performed RNA-Seq, and mapped the resulting reads to the wMel and host genomes (Table 2). As these sequencing runs were exploratory trials and our treatments (rRNA depletion and Wolbachia purification) were not replicated, we cannot draw any firm conclusions about the effects of each treatment on the quality or content of the resulting sequence data. It is clear, however, that some kind of purification, rRNA and/or host RNA depletion is necessary to obtain reasonable coverage of the Wolbachia transcriptome without wasted sequencing effort as shown in other studies [49,50]. Even though our RNA-Seq experiments were not designed for this purpose, we observed reads mapping to intergenic regions of Wolbachia (S1 Data). Given the variable coverage of the Wolbachia genome we obtained from the different RNA-Seq experiments, we have focused our work using the dataset with the highest number of reads mapping to Wolbachia: RNA extracted from Wolbachia in cell culture.

RNA-Seq reveals transcription from multiple Wolbachia intergenic regions

A large number of RNA-Seq reads from each sequencing library mapped to intergenic regions of the wMel reference genome, as listed in S1 Data. However, many of these reads may derive not from independent intergenic transcripts, but rather from 5' or 3' UTRs, or the intergenic regions of polycistronic transcripts. We identified candidate sRNAs by selecting intergenic regions with high transcription levels, and then inspecting the mapping of paired reads in these regions. We excluded from further analysis transcribed intergenic regions in which one read of any pair mapped to the intergenic region and the other mapped to a flanking CDS, and focused only on those regions in which both ends of all read pairs mapped within the intergenic region. Candidate sRNAs presented here were identified using the data obtained from wMelPop-CLA in C6/36 cells.

Bioinformatic prediction of candidate conserved intergenic sRNAs

Our second, independent approach to identifying candidate sRNAs was based on comparative genomics. Previous work has demonstrated that, while some sRNAs are specific to a single bacterial strain or species, others show conservation of sequence across broader taxonomic distances [51,52]. If conserved intergenic sRNAs are undergoing purifying selection to maintain functionality, we would expect them to evolve more slowly than other intergenic regions that are not functionally constrained in this way. To search for such regions, we aligned the published genome sequences of the moderately divergent Wolbachia strains wMel and wPip (from the A and B supergroups of Wolbachia, respectively [18,53]), and used the program changept to identify highly conserved non-CDS regions. A full description of the changept model can be found in previous papers [5456]. In summary, the algorithm takes as input a sequence of characters (which may represent pairwise or multiple alignments) and estimates positions (called change-points) that delineate homogeneous segments. Using the changept program, we identified a class of segments (Class 4 of the 7-Class model, Figure B in S1 File) characterized by the highest degree of conservation (∼95% between wMel and wPip). Non-coding segments longer than 50 nt and with ≥ 0.5 probability of belonging to this class form the focus of our analysis. There are 42 non-CDS regions in the most conserved class (Table B in S1 File). These included the 16S, 23S and 5S rRNA genes, 17 tRNA genes, a recent pseudogene (WD0002), and the housekeeping sRNAs RNase P and tmRNA. We also identified 19 highly conserved intergenic regions (Table 3) with no previous annotation, which represented a preliminary list of candidate conserved sRNAs.

thumbnail
Table 3. Intergenic regions predicted by changept to be highly conserved.

https://doi.org/10.1371/journal.pone.0118595.t003

We then checked the read pairs mapping to these regions in the wMelPop-CLA RNA-Seq data, as described above. Reads were mapped to all but one of these intergenic regions, indicating that they were transcribed in this strain. However, only one of these intergenic regions showed evidence of specific transcription, rather than transcription as part of a unit with one or both flanking genes. This region (wMel coordinates 1,039,579–1,039,870) was therefore selected for experimental validation together with the other candidate sRNAs identified using RNA-Seq data above.

This approach is limited to the comparison of strains with an intermediate level of divergence. We repeated the changept analysis comparing the genomes of two more closely related Wolbachia strains, wMel and wRi. These genomes have undergone only limited divergence, and we found that over 70% of the genome was assigned to the most highly conserved class of segments, greatly reducing the predictive power of the method. All highly conserved non-CDS regions identified in the wMel-wPip comparison were, however, also identified in the wMel-wRi comparison. At the other end of the taxonomic scale, we also attempted to repeat this analysis comparing the genomes of wMel or wPip with the more distantly related D group strain wBm. Unfortunately, the genomes of these strains have undergone extensive rearrangement since their divergence, and too few genomic regions with conserved synteny and of sufficient length could be identified to perform the analysis.

Experimental validation of candidate sRNAs

For all subsequent experiments to investigate expression of our candidate sRNAs, we used Wolbachia-infected insects, rather than cell culture, to ensure that our results reflect the natural biology of the symbiont. As an initial step, we tested whether the candidate sRNAs identified by our two methods were transcribed specifically, rather than as part of a single transcript with a flanking gene, in wMel in D. melanogaster. We first used a 5’ RACE procedure to identify the 5’ end of those candidate sRNA transcripts for which it was possible to design a combination of RACE primers specific to the intergenic region. In many cases this was not possible, due to the high levels of repetitive sequence in the wMel genome. These transcripts were discarded as candidates for further analysis.

We performed the 5'RACE procedure on 13 candidate sRNAs. Of these, five amplified successfully, and sequencing of the resulting plasmid showed that the 5' end of each RNA was indeed within the intergenic region (Table 4). The sequences of the plasmids are provided in Table C in S1 File. The other regions did not amplify, which could be due to multiple factors: no expression in the given biological conditions (age or tissue for example), expression below our detection limit, or expression from the opposite DNA strand as we designed all the primers on the positive strand only for this preliminary analysis. These non-amplifying regions were not considered further as candidate sRNAs.

thumbnail
Table 4. Intergenic regions (IGR) selected for 5’RACE experimentsa and name and position of the two putativeWolbachia small non-coding RNAs we identified.

https://doi.org/10.1371/journal.pone.0118595.t004

In addition to performing 5'RACE, 'overlapping RT-PCR' was done to verify that the 3' end of each of the candidate sRNAs was also within the intergenic region. Of the five regions with a 5' end confirmed by 5'RACE to be within an intergenic region, it was possible to design specific RT primers for four. Two regions could be amplified using a forward intergenic primer and reverse downstream gene primer, indicating that the transcription of these regions occurred as a single RNA molecule with a flanking gene. These two intergenic regions might contain sRNAs transcribed as part of an operon, but could alternatively be 5' UTR regions, and so were excluded from further analysis. However, two regions showed no amplification when RT-PCR was performed using reverse primers in the downstream flanking gene, while amplification occurred using forward and reverse primers that both bound to the intergenic region. These results demonstrate that the transcription of RNA from these regions begins and ends in the intergenic region, and these two RNA molecules can consequently be considered to be intergenic sRNAs. We labeled them non-coding RNA Wolbachia wMel 01 and 02 (ncrwmel01 and ncrwmel02). The putative sRNA ncrwmel02 is a highly conserved intergenic region that was predicted by both RNA-Seq and comparative genomics approaches (Table 4).

Putative sRNA shows sequence conservation but differential transcript levels across Wolbachia strains

We then used qPCR to test for differences in the expression of ncrwmel02 in four different Wolbachia strains (wMel, wMelPop, wMelCS and wAu) in the whole body of 1 day-old male D. melanogaster flies. We selected this putative sRNA because its sequence is conserved (Figure C in S1 File), and we were able to design specific qPCR primers for the intended template and successfully amplify cDNA from all strains, while we could not for ncrwmel01.

The expression of ncrwmel02 was normalized against the expression of the Wolbachia Surface Protein encoding gene, wsp, to account for differences in Wolbachia density between the strains. ncrwmel02 was expressed at a relatively low level in wMelCS and wMelPop, but approximately twice as highly in wMel, and seven-fold more highly in wAu (Fig. 2).

thumbnail
Fig 2. Expression of ncrwmel02 in four Wolbachia strains from the whole body of 1-day-old male D. melanogaster.

Expression (mean ± 95% CI) normalized to wsp expression (Mann-Whitney U test ** p < 0.01, **** p < 0.0001)

https://doi.org/10.1371/journal.pone.0118595.g002

Wolbachia putative sRNA expression is differentially regulated in host tissues and sexes

In order to assess whether the expression of this Wolbachia putative sRNA is constitutive or regulated, we analysed its expression in different tissues of male and female flies. First, ncrwmel02 expression was compared in the abdomens of male and female 1-day-old flies for the four Wolbachia strains (Fig. 3). We observed only one significant difference in ncrwmel02 expression in these tissues: ncrwmel02 in wMel was more highly expressed in male than in female abdomens, demonstrating that in some conditions its expression is differentially regulated.

thumbnail
Fig 3. Expression of ncrwmel02 in four Wolbachia strains from abdomens of 1-day old male (black) or female (red) D. melanogaster.

Expression (mean ± 95% CI) normalized to wsp expression (Mann-Whitney U test, ** p < 0.01)

https://doi.org/10.1371/journal.pone.0118595.g003

Because ncrwmel02 expression in whole abdomens might not reflect the level of regulation occurring in specific Wolbachia-infected tissues, we performed a second experiment in which its expression was analyzed in dissected tissues (gonads, head and carcasses) of male and female flies infected with the wMel strain (Fig. 4). In contrast to our observation of generally stable ncrwmel02 expression in male and female whole abdomens, its expression in dissected tissues showed clear evidence of differential expression.

thumbnail
Fig 4. Expression of ncrwmel02 in the wMel strain from gonads, heads and carcasses of 1-day-old male (black) or female (red) D. melanogaster.

Expression (mean ± 95% CI) normalized to wsp expression (Mann-Whitney U test, ** p < 0.01 *** p < 0.001)

https://doi.org/10.1371/journal.pone.0118595.g004

The greatest differences were observed between gonads (ovaries and testes), which have very different physiological activity and regulation. Expression was significantly upregulated in testes compared to ovaries with more than ten-fold difference. For body parts that are expected to be more similar in terms of regulation and activity between males and females, such as heads and carcasses, no differential regulation of ncrwmel02 in different host sexes was observed (Fig. 4). In addition, we also observed differential expression of ncrwmel02 when comparing different tissues in the same sex. For example, expression was upregulated in female carcasses compared to female gonads, and upregulated in male gonads compared to male heads. To validate the differential expression we observed on those dissected tissues we compared the use of wsp, Wolbachia 16S and Drosophila melanogaster rps17 as reference genes. Whatever the gene used as reference, all the patterns of expression remain the same and all differential expression remains significant (Figure D in S1 File).

In total, these expression experiments show that the Wolbachia putative sRNA ncrwme02 is expressed in the four Wolbachia strains in a regulated pattern that differs according to the sex of the host and the tissue in which the bacterium is localized.

Discussion

Recent research has begun to uncover the critical roles played by small RNAs in the regulation of cellular processes ranging from highly conserved housekeeping functions to rapid responses to environmental or host cues. Most research on sRNAs to date has focused on free-living or facultatively intracellular bacteria. Yet we might expect that obligately host-associated bacteria would rely on sRNAs at least as much as, if not more than, free-living bacteria, for two reasons. First, sRNAs offer bacteria a flexible and rapidly adaptable mode of gene regulation that may be ideally suited to the constantly changing co-adaptive interplay between host and symbiont. Secondly, many endosymbiotic bacteria, including Wolbachia, have undergone at least some degree of genome reduction, often resulting in the loss of genes encoding canonical transcriptional regulatory proteins [18]. Intergenic regulatory regions associated with sRNAs show evidence of retention and conservation even in some of the most reduced endosymbiotic genomes [57], indicating that sRNA-based regulation may remain necessary and be under sufficient selection to resist loss via genome reduction.

We have identified two novel putative sRNAs in Wolbachia genomes, using two independent methods that are likely to detect different subsets of sRNAs. The comparative genomics approach we used could detect sRNA candidates that are conserved at the nucleotide level across strains from A and B supergroups of Wolbachia, regardless of the conditions under which they are expressed. It would not, however, identify candidates that are not present in both strains, have originated since the divergence of the supergroups, or are conserved at the level of secondary structure rather than nucleotide sequence. In contrast, RNA-Seq data could be used to detect these latter classes of sRNAs, but would not be able to identify sRNAs if they were not expressed under the experimental conditions used to generate the data. The two putative sRNAs characterized here were identified by our analysis of wMelPop-CLA RNA-Seq data, while only ncrwmel02 was predicted using comparative genomics. This probably reflects the level of sequence conservation of these putative sRNAs: when used as a blastN query against the NCBI NT and WGS databases, ncrwmel02 has longer hits, with higher percentage sequence identity, to a broader range of other Wolbachia strains (from the A, B, C and D supergroups), than ncrwmel01.

We showed ncrwmel02 was present and transcribed in the four A group strains we used to experimentally characterize the expression of this putative sRNA. The strains wMel, wMelCS and wMelPop are closely related [58], naturally infect D. melanogaster, and all induce host cytoplasmic incompatibility (CI), the most frequently observed type of reproductive manipulation caused by Wolbachia. wMel and wMelCS are otherwise benign, but wMelPop is pathogenic, causing its adult hosts to die prematurely. In contrast, wAu is somewhat more distantly related [59], infects D. simulans, and does not cause CI. All four strains were placed into the same genetic background (D. melanogaster w1118) for these analyses, to limit the effects of different host species on sRNA expression patterns.

Almost all pairwise strain comparisons are significantly different at the whole body level for ncrwmel02 expression; most strikingly, it is substantially more highly expressed in wAu than in the three other strains. Host sex-specific differences in expression in wMel become apparent at the tissue level. ncrwmel02 is more highly expressed in testes than in ovaries, but does not show evidence of male-specific upregulation in other tissues. The significant upregulation of ncrwmel02 in testes compared to ovaries suggests that sRNAs might possibly be involved in some aspect of host reproductive manipulation, although the data we provide here only suggest this hypothesis and future experimental demonstration would be required on a range of CI and non-CI inducing strains. CI involves Wolbachia-induced modification of sperm in infected hosts [2], and increased transcription of sRNAs may play a role in that process. More generally, given the regulatory roles of sRNAs in other bacterial species, differential expression of sRNA could contribute to a range of phenotypic differences between strains.

Developing a full understanding of the roles of sRNA in Wolbachia will require not only searching for additional sRNAs and characterizing their expression in different strains and host tissues, but identifying the targets of these molecules. Many of these targets, whether genes, mRNA or proteins, are expected to be of Wolbachia origin, but it is also possible that Wolbachia sRNAs could directly target host gene expression. Although secretion of functional bacterial sRNAs into eukaryotic host cells has not been observed, it is a possibility worth considering [60]. Viral sRNAs are known to target host genes [61], and bacterial sRNAs may have the same ability. In addition, it has already been shown that interplay occurs between Wolbachia and its insect host via eukaryote miRNA [8,9], and that the virus-blocking phenotype induced by Wolbachia involves changes in the expression of host miRNA [10]. It is possible that Wolbachia-induced phenotypes such as dengue inhibition may occur as part of a molecular dialogue between the bacterial endosymbiont and its eukaryotic host involving uni- or bi-directional gene regulation by small non-coding RNAs.

Conclusions

Considering (a) the fundamental roles played by sRNA in other bacteria, especially in quorum-sensing, pathogenesis and virulence, (b) the conservation of ncrwmel02 between different Wolbachia strains, and (c) the strain-, sex- and tissue-specific differential regulation of ncrwmel02 expression, we hypothesize that sRNAs may play significant roles in the biology of Wolbachia. The analyses described here are preliminary and had limited power, and the two putative sRNAs we have identified are likely to represent only the largest, most highly expressed and/or conserved sRNAs in Wolbachia genomes. Additional RNA-Seq experiments with different size selection of RNA, bacterial purification, host and rRNA depletion [49,50], and strand-specific library preparation might allow the identification of many more of these molecules, and further research will be required to assess the roles of sRNAs in the insect-Wolbachia interaction. Nonetheless, the descriptive work presented here opens a new path in understanding the molecular mechanisms underlying the complex and diverse range of phenotypes induced by Wolbachia within its host.

Supporting Information

S1 Data. RNA-Seq reads mapping to wMel features.

Reads mapping to CDS and intergenic regions are indicated for all 5 RNA-Seq experiments.

https://doi.org/10.1371/journal.pone.0118595.s001

(XLS)

S1 File. Figure A, Selection of optimal number of classes.

We used approximations to the well-known information criteria AIC, BIC and DIC to identify the number of distinct classes of conservation levels. Generally, a lower value of the information criteria indicates a better model. BIC favoured a 1-class model, which is inappropriate. We therefore based our judgement on AIC and DICV and selected the 7-class model as the first local minimum of AIC and DICV has occurred at seven classes. Figure B, Identifying the most conserved class. The mean proportion of alignment matches was plotted against each iteration of the sampler to identify the class that contains the most conserved segments in wMel and wPip (Class 4). The different colours represent different classes in the 7-class model. Figure C, Sequence alignment of the ncrwmel02 amplicon from the published genome data of wMel [18], wMelCS, wMelPop [58] and wAu [62]. Figure D, Validation of ncrwmel02 differential expression observed using wsp as reference gene in dissected tissues of wMel-infected male (black) or female (red) D. melanogaster. ncrwmel02 expression calculated using wsp, 16S or rps17 is represented for the three significant differential expression observed using wsp. Expression (mean ± 95% CI) normalized to wsp, 16S or rps17 expression (Mann-Whitney U test, * p < 0.1, ** p < 0.01 *** p < 0.001). Panel A: ncrwmel02 expression in male and female gonads. Panel B: ncrwmel02 expression in female dissected tissues. Panel C: ncrwmel02 expression in male dissected tissues. Table A, Oligonucleotides used in this study. Table B, Highly conserved non-coding region predicted by changept. Thresholds used: 1. Conservation = 0.95 (conservation level of the most conserved class-Class 4); 2. Profile value ≥0.5 (probability that each position in the conserved feature belongs to Class 4); 3. Length >50 nt (length of the conserved feature). Table C, 5’ RACE of intergenic regions (IGR) plasmid sequences. Insert in pGEMTeasy in bold.

https://doi.org/10.1371/journal.pone.0118595.s002

(DOC)

Acknowledgments

We thank Thomas Walker and Iñaki Iturbe-Ormaetxe for helpful discussion about the manuscript, Jyotika Taneja de Bruyne for technical help with fly rearing, and Yi Dong and Alison Carrasco for laboratory assistance.

Author Contributions

Conceived and designed the experiments: MW JK JP. Performed the experiments: MA JP. Analyzed the data: MW MA JK EAM JP. Contributed reagents/materials/analysis tools: MW JK EAM. Wrote the paper: MW MA JK EAM JP.

References

  1. 1. Zug R, Hammerstein P (2012) Still a host of hosts for Wolbachia: analysis of recent data suggests that 40% of terrestrial arthropod species are infected. PLoS One 7: e38544. pmid:22685581
  2. 2. Werren JH, Baldo L, Clark ME (2008) Wolbachia: master manipulators of invertebrate biology. Nature Reviews Microbiology 6: 741–751. pmid:18794912
  3. 3. Hoffmann A, Turelli M (1997) Cytoplasmic incompatibility in insects. In: O'Neill SL, Hoffmann A, Werren JH, editors. Influential passengers Inherited microorganisms and arthropod reproduction. Oxford: Oxford University Press. pp. 42–80.
  4. 4. Hedges LM, Brownlie JC, O'Neill SL, Johnson KN (2008) Wolbachia and Virus Protection in Insects. Science 322: 702–702. pmid:18974344
  5. 5. Teixeira L, Ferreira A, Ashburner M (2008) The Bacterial Symbiont Wolbachia Induces Resistance to RNA Viral Infections in Drosophila melanogaster. Plos Biology 6: 2753–2763.
  6. 6. Moreira LA, Iturbe-Ormaetxe I, Jeffery JAL, Lu G, Pyke AT, et al. (2009) A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya and Plasmodium Cell 139: 1268–1278. pmid:20064373
  7. 7. Hoffmann A, Montgomery B, Popovici J, Iturbe-Ormaetxe I, Johnson P, et al. (2011) Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature 476: 454–457. pmid:21866160
  8. 8. Osei-Amo S, Hussain M, O’Neill SL, Asgari S (2012) Wolbachia-Induced aae-miR-12 miRNA Negatively Regulates the Expression of MCT1 and MCM6 Genes in Wolbachia-Infected Mosquito Cell Line. PLoS One 7: e50049. pmid:23166816
  9. 9. Hussain M, Frentiu FD, Moreira LA, O'Neill SL, Asgari S (2011) Wolbachia uses host microRNAs to manipulate host gene expression and facilitate colonization of the dengue vector Aedes aegypti. Proceedings of the National Academy of Sciences 108: 9250–9255. pmid:21576469
  10. 10. Zhang G, Hussain M, O’Neill SL, Asgari S (2013) Wolbachia uses a host microRNA to regulate transcripts of a methyltransferase, contributing to dengue virus inhibition in Aedes aegypti. Proceedings of the National Academy of Sciences 110: 10276–10281. pmid:23733960
  11. 11. Brennan LJ, Keddie BA, Braig HR, Harris HL (2008) The Endosymbiont <italic>Wolbachia pipientis</italic> Induces the Expression of Host Antioxidant Proteins in an <italic>Aedes albopictus</italic> Cell Line. PLoS ONE 3: e2083. pmid:18461124
  12. 12. Kremer N, Voronin D, Charif D, Mavingui P, Mollereau B, et al. (2009) Wolbachia Interferes with Ferritin Expression and Iron Metabolism in Insects. PLoS Pathog 5: e1000630. pmid:19851452
  13. 13. Kremer N, Charif D, Henri H, Gavory F, Wincker P, et al. (2012) Influence of Wolbachia on host gene expression in an obligatory symbiosis. BMC Microbiology 12: S7. pmid:22376153
  14. 14. Rances E, Voronin D, Tran-Van V, Mavingui P (2008) Genetic and functional characterization of the type IV secretion system in Wolbachia. Journal of Bacteriology 190: 5020–5030. pmid:18502862
  15. 15. Voth DE, Broederdorf LJ, Graham JG (2012) Bacterial Type IV secretion systems: versatile virulence machines. Future microbiology 7: 241–257. pmid:22324993
  16. 16. Siozios S, Ioannidis P, Klasson L, Andersson SG, Braig HR, et al. (2013) The Diversity and Evolution of Wolbachia Ankyrin Repeat Domain Genes. PLoS One 8: e55390. pmid:23390535
  17. 17. Walker T, Klasson L, Sebaihia M, Sanders MJ, Thomson NR, et al. (2007) Ankyrin repeat domain-encoding genes in the wPip strain of Wolbachia from the Culex pipiens group. Bmc Biology 5.
  18. 18. Wu M, Sun LV, Vamathevan J, Riegler M, Deboy R, et al. (2004) Phylogenomics of the reproductive parasite Wolbachia pipientis wMel: A streamlined genome overrun by mobile genetic elements. Plos Biology 2: 327–341.
  19. 19. Iturbe-Ormaetxe I, Burke GR, Riegler M, O'Neill SL (2005) Distribution, expression, and motif variability of ankyrin domain genes in Wolbachia pipientis. Journal of Bacteriology 187: 5136–5145. pmid:16030207
  20. 20. Rikihisa Y, Lin M, Niu H (2010) Microreview: Type IV secretion in the obligatory intracellular bacterium Anaplasma phagocytophilum. Cellular Microbiology 12: 1213–1221. pmid:20670295
  21. 21. Darby AC, Armstrong SD, Bah GS, Kaur G, Hughes MA, et al. (2012) Analysis of gene expression from the Wolbachia genome of a filarial nematode supports both metabolic and defensive roles within the symbiosis. Genome Research 22: 2467–2477. pmid:22919073
  22. 22. Papafotiou G, Oehler S, Savakis C, Bourtzis K (2011) Regulation of Wolbachia ankyrin domain encoding genes in Drosophila gonads. Research in Microbiology 162: 764–772. pmid:21726632
  23. 23. Li Z, Carlow CKS (2012) Characterization of Transcription Factors That Regulate the Type IV Secretion System and Riboflavin Biosynthesis in <italic>Wolbachia</italic> of <italic>Brugia malayi</italic>. PLoS ONE 7: e51597. pmid:23251587
  24. 24. Boughammoura A, Matzanke BF, Böttger L, Reverchon S, Lesuisse E, et al. (2008) Differential role of ferritins in iron metabolism and virulence of the plant-pathogenic bacterium Erwinia chrysanthemi 3937. Journal of Bacteriology 190: 1518–1530. pmid:18165304
  25. 25. Grieshaber NA, Grieshaber SS, Fischer ER, Hackstadt T (2006) A small RNA inhibits translation of the histone-like protein Hc1 in Chlamydia trachomatis. Molecular Microbiology 59: 541–550. pmid:16390448
  26. 26. Bejerano-Sagie M, Xavier KB (2007) The role of small RNAs in quorum sensing. Current Opinion in Microbiology 10: 189–198. pmid:17387037
  27. 27. Murphy ER, Payne SM (2007) RyhB, an Iron-Responsive Small RNA Molecule, Regulates Shigella dysenteriae Virulence. Infection and Immunity 75: 3470–3477. pmid:17438026
  28. 28. Podkaminski D, Vogel J (2010) Small RNAs promote mRNA stability to activate the synthesis of virulence factors. Molecular Microbiology 78: 1327–1331. pmid:21143308
  29. 29. Bradley ES, Bodi K, Ismail AM, Camilli A (2011) A genome-wide approach to discovery of small RNAs involved in regulation of virulence in Vibrio cholerae. Plos Pathogens 7: e1002126. pmid:21779167
  30. 30. Koo JT, Alleyne TM, Schiano CA, Jafari N, Lathem WW (2011) Global discovery of small RNAs in Yersinia pseudotuberculosis identifies Yersinia-specific small, noncoding RNAs required for virulence. Proceedings of the National Academy of Sciences 108: E709–E717. pmid:21876162
  31. 31. Gottesman S, Storz G (2011) Bacterial Small RNA Regulators: Versatile Roles and Rapidly Evolving Variations. Cold Spring Harbor Perspectives in Biology 3.
  32. 32. Storz G, Vogel J, Wassarman Karen M Regulation by Small RNAs in Bacteria: Expanding Frontiers. Molecular Cell 43: 880–891. pmid:21925377
  33. 33. Yamada R, Iturbe-Ormaetxe I, Brownlie JC, O'Neill SL (2011) Functional test of the influence of Wolbachia genes on cytoplasmic incompatibility expression in Drosophila melanogaster. Insect Molecular Biology 20: 75–85. pmid:20854481
  34. 34. Min KT, Benzer S (1997) Wolbachia, normally a symbiont of Drosophila, can be virulent, causing degeneration and early death. Proceedings of the National Academy of Sciences of the United States of America 94: 10792–10796. pmid:9380712
  35. 35. Frentiu FD, Robinson J, Young PR, McGraw EA, O'Neill SL (2010) Wolbachia-Mediated Resistance to Dengue Virus Infection and Death at the Cellular Level. PLoS One 5: e13398. pmid:20976219
  36. 36. Iturbe-Ormaetxe I, Woolfit M, Rances E, Duplouy A, O'Neill SL (2011) A simple protocol to obtain highly pure Wolbachia endosymbiont DNA for genome sequencing. J Microbiol Methods 84: 134–136. pmid:21047535
  37. 37. Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, et al. (2012) RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Research 40: W622–W627. pmid:22684630
  38. 38. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25: 1754–1760. pmid:19451168
  39. 39. Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA (2012) Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28: 464–469. pmid:22199388
  40. 40. Shelby KS, Popham HJR (2007) Increased plasma selenium levels correlate with elevated resistance of Heliothis virescens larvae against baculovirus infection. Journal of Invertebrate Pathology 95: 77–83. pmid:17316679
  41. 41. Woolfit M, Iturbe-Ormaetxe I, McGraw EA, O'Neill SL (2009) An Ancient Horizontal Gene Transfer between Mosquito and the Endosymbiotic Bacterium Wolbachia pipientis. Molecular Biology and Evolution 26: 367–374. pmid:18988686
  42. 42. Darling ACE, Mau B, Blattner FR, Perna NT (2004) Mauve: Multiple Alignment of Conserved Genomic Sequence With Rearrangements. Genome Research 14: 1394–1403. pmid:15231754
  43. 43. Oldmeadow C, Keith JM (2011) Model selection in Bayesian segmentation of multiple DNA alignments. Bioinformatics 27: 604–610. pmid:21208984
  44. 44. Keith J (2008) Sequence Segmentation. In: Keith J, editor. Bioinformatics: Humana Press. pp. 207–229.
  45. 45. Algama M, Keith JM (2014) Investigating genomic structure using changept: A Bayesian segmentation model. Computational and Structural Biotechnology Journal 10: 107–115. pmid:25349679
  46. 46. Frohman MA, Dush MK, Martin GR (1988) Rapid production of full-length cDNAs from rare transcripts: amplification using a single gene-specific oligonucleotide primer. Proceedings of the National Academy of Sciences 85: 8998–9002. pmid:2461560
  47. 47. Gerhart E, Wagner H, Vogel J (2008) Approaches to Identify Novel Non-messenger RNAs in Bacteria and to Investigate their Biological Functions: Functional Analysis of Identified Non-mRNAs. Handbook of RNA biochemistry: 614–642.
  48. 48. Walker T, Klasson L, Sebaihia M, Sanders MJ, Thomson NR, et al. (2007) Ankyrin repeat domain-encoding genes in the wPip strain of Wolbachia from the Culex pipiens group. BMC Biol 5: 39. pmid:17883830
  49. 49. Darby AC, Christina Gill A, Armstrong SD, Hartley CS, Xia D, et al. (2014) Integrated transcriptomic and proteomic analysis of the global response of Wolbachia to doxycycline-induced stress. ISME J 8: 925–937. pmid:24152719
  50. 50. Kumar N, Creasy T, Sun Y, Flowers M, Tallon LJ, et al. (2012) Efficient subtraction of insect rRNA prior to transcriptome analysis of Wolbachia-Drosophila lateral gene transfer. BMC research notes 5: 230. pmid:22583543
  51. 51. Albrecht M, Sharma CM, Dittrich MT, Muller T, Reinhardt R, et al. (2011) The transcriptional landscape of Chlamydia pneumoniae. Genome Biol 12: R98. pmid:21989159
  52. 52. Skippington E, Ragan MA (2012) Evolutionary Dynamics of Small RNAs in 27 Escherichia coli and Shigella Genomes. Genome Biology and Evolution 4: 330–345. pmid:22223756
  53. 53. Klasson L, Walker T, Sebaihia M, Sanders MJ, Quail MA, et al. (2008) Genome evolution of Wolbachia strain wPip from the Culex pipiens group. Molecular Biology and Evolution 25: 1877–1887. pmid:18550617
  54. 54. Keith J, Kroese D, Bryant D (2004) A Generalized Markov Sampler. Methodology And Computing In Applied Probability 6: 29–53.
  55. 55. Keith JM, Adams P, Stephen S, Mattick JS (2008) Delineating slowly and rapidly evolving fractions of the Drosophila genome. Journal of Computational Biology 15: 407–430. pmid:18435570
  56. 56. Keith JM (2006) Segmenting eukaryotic genomes with the generalized Gibbs sampler. Journal of Computational Biology 13: 1369–1383. pmid:17037964
  57. 57. Degnan PH, Ochman H, Moran NA (2011) Sequence conservation and functional constraint on intergenic spacers in reduced genomes of the obligate symbiont Buchnera. PLoS genetics 7: e1002252. pmid:21912528
  58. 58. Woolfit M, Iturbe-Ormaetxe I, Brownlie JC, Walker T, Riegler M, et al. (2013) Genomic evolution of the pathogenic Wolbachia strain, wMelPop. Genome Biol Evol 5: 2189–2204. pmid:24190075
  59. 59. Paraskevopoulos C, Bordenstein SR, Wernegreen JJ, Werren JH, Bourtzis K (2006) Toward a Wolbachia multilocus sequence typing system: Discrimination of Wolbachia strains present in Drosophila species. Current Microbiology 53: 388–395. pmid:17036209
  60. 60. Storz G, Vogel J, Wassarman KM (2011) Regulation by small RNAs in bacteria: expanding frontiers. Molecular Cell 43: 880–891. pmid:21925377
  61. 61. Smith NA, Eamens AL, Wang M-B (2011) Viral small interfering RNAs target host genes to mediate disease symptoms in plants. PLoS pathogens 7: e1002022. pmid:21573142
  62. 62. Sutton E, Harris S, Parkhill J, Sinkins S (2014) Comparative genome analysis of Wolbachia strain wAu. BMC Genomics 15: 928. pmid:25341639