- Research article
- Open Access
Primary transcriptome map of the hyperthermophilic archaeon Thermococcus kodakarensis
© Jäger et al.; licensee BioMed Central Ltd. 2014
- Received: 28 April 2014
- Accepted: 30 July 2014
- Published: 16 August 2014
Prokaryotes have relatively small genomes, densely-packed with protein-encoding sequences. RNA sequencing has, however, revealed surprisingly complex transcriptomes and here we report the transcripts present in the model hyperthermophilic Archaeon, Thermococcus kodakarensis, under different physiological conditions.
Sequencing cDNA libraries, generated from RNA isolated from cells under different growth and metabolic conditions has identified >2,700 sites of transcription initiation, established a genome-wide map of transcripts, and consensus sequences for transcription initiation and post-transcription regulatory elements. The primary transcription start sites (TSS) upstream of 1,254 annotated genes, plus 644 primary TSS and their promoters within genes, are identified. Most mRNAs have a 5'-untranslated region (5'-UTR) 10 to 50 nt long (median = 16 nt), but ~20% have 5'-UTRs from 50 to 300 nt long and ~14% are leaderless. Approximately 50% of mRNAs contain a consensus ribosome binding sequence. The results identify TSS for 1,018 antisense transcripts, most with sequences complementary to either the 5'- or 3'-region of a sense mRNA, and confirm the presence of transcripts from all three CRISPR loci, the RNase P and 7S RNAs, all tRNAs and rRNAs and 69 predicted snoRNAs. Two putative riboswitch RNAs were present in growing but not in stationary phase cells. The procedure used is designed to identify TSS but, assuming that the number of cDNA reads correlates with transcript abundance, the results also provide a semi-quantitative documentation of the differences in T. kodakarensis genome expression under different growth conditions and confirm previous observations of substrate-dependent specific gene expression. Many previously unanticipated small RNAs have been identified, some with relative low GC contents (≤50%) and sequences that do not fold readily into base-paired secondary structures, contrary to the classical expectations for non-coding RNAs in a hyperthermophile.
The results identify >2,700 TSS, including almost all of the primary sites of transcription initiation upstream of annotated genes, plus many secondary sites, sites within genes and sites resulting in antisense transcripts. The T. kodakarensis genome is small (~2.1 Mbp) and tightly packed with protein-encoding genes, but the transcriptomes established also contain many non-coding RNAs and predict extensive RNA-based regulation in this model Archaeon.
- Antisense RNAs
- Small non-coding RNAs
- Hydrogen regulation
Archaea are prokaryotes, they resemble Bacteria in genome size, genome organization and the absence of a nuclear membrane, but their genetic information storage and expression components are generally more closely related to their eukaryotic than bacterial counterparts . Historically, difficulties in manipulating Archaea genetically limited archaeal research but, with the discovery that Thermococcus kodakarensis is naturally competent for DNA uptake and transformation , genetic tools have been developed and T. kodakarensis established as a readily tractable experimental model for archaeal and hyperthermophile research . As a fermentative heterotroph that grows rapidly, optimally at 85°C on a range of different substrates, T. kodakarensis offers opportunities to investigate archaeal gene regulation and metabolism under a variety of growth conditions and, as a hydrogen-producer, it has also attracted biotechnology attention. By using high-throughput RNA sequencing (RNA-seq), it is now possible to identify essentially all transcripts present in cells [4, 5] and such studies have revealed surprisingly complex transcriptomes in Bacteria, with many previously unanticipated non-coding small (sRNA) and antisense RNAs [6–20]. To add to this database, and specifically to add to the relatively few RNA-seq studies reported to date for Archaea[21–31], we have used differential RNA-seq technology (dRNA-seq) to identify the transcripts present in T. kodakarensis cells growing on different substrates and in stationary-phase cells. An automated analysis  was used to identify the sites at which the transcripts were initiated (transcription start sites; TSS) throughout the genome. Based on the conservation of sequences upstream of the TSS identified, consensus sequences have been identified for the core elements of T. kodakarensis promoters from which the synthesis of primary, secondary, internal and antisense transcripts is initiated.
T. kodakarensistranscripts and transcription start sites (TSS)
5′-untranslated regions (5′-UTRs) and leaderless mRNAs
Archaeal mRNAs typically have short 5′-UTRs and, in some species, many 5′-UTRs are ≤8 nt and, as such, are designated as leaderless and are translated using a distinct initiation mechanism [47–49]. Based on the TSS identified, the majority of the mRNAs in T. kodakarensis have a 5′-UTR between 10 and 50 nt in length, with the median length being 16 nt. A ribosome binding sequence (RBS) that conforms to the consensus GGDGRD is present in ~50% of the predicted mRNAs (Figure 1C). Initially, we identified 179 leaderless mRNAs based on having a 5′-UTR ≤8 nt long, most of which encode proteins with unknown functions although 15 have annotated functions related to RNA processing and modification. To confirm that these were leaderless transcripts, a 100 bp window around each TSS was checked for the presence of a RBS using FIMO and for alternative translation initiating codons. In 28 cases, this revealed evidence against the leaderless mRNA designation (Additional file 4: Table S4). Most often, a GTG codon was annotated as the translation initiating codon but an ATG codon was also present, in-frame, located 2 or 3 codons downstream within the ORF. With this ATG codon designated as the start codon, the 5′-UTR was extended and the transcript no longer conformed to the definition of a leaderless mRNA.
Transcripts with long 5′-untranslated regions (5′-UTRs)
Klein et al. predicted the presence of a regulatory RNA, designated the sscA RNA, located upstream of the gene (TK0308) that encodes the translation elongation factor 1α (aEF1α). Our results confirm that the sscA RNA is present and abundant in growing T. kodakarensis cells and that it is located within the 118 nt 5′-UTR of the TK0308 mRNA (Figure 3C). The function of the sscA RNA remains to be determined, but its abundance increases with sulfur addition, and its location suggests a role in regulating translation. The transcript of a nearby gene (TK0306), that encodes a DEAD-box RNA helicase, was also reported to have a long 158 nt 5′-UTR . Our results confirm the presence of this long 5′-UTR, demonstrate that it is actually 159 nt and reveal that it contains a tRNALys (TKt3) apparently therefore co-transcribed with TK0306 (Figure 3D). A short (~70 nt) antisense RNA is also transcribed from within the TK0306 that is less abundant in cells growing with sulfur.
Internal transcription start sites (iTSS)
For all but three of the remaining iTSS, an appropriately positioned BRE-TATA box sequence is readily apparent consistent with the downstream region of the ORF being transcribed from two structurally separate promoters. In some, but not all cases, there are also sequences that could function as a RBS and translation initiating codon, within the ORF, downstream of the iTSS. In the absence of such translation initiating elements, transcription from the iTSS presumably results in a non-coding RNA as exemplified by the HgcC transcripts. HgcC RNAs were originally identified by bioinformatically and their expression then verified by northern blot analysis . As documented in the RFAM database, homologous sequences are found in 43 Archaea, most are hyperthermophiles, but some are halophiles . The T. kodakarensis genome has sequences encoding seven HgcC transcripts  and the dRNA-seq data confirm that five of these, designated TK HgcC1 to HgcC5, are present in T. kodakarensis cells (Additional file 6: Figure S2). As reported for an HgcC in P. furiosus, TK HgcC1, TK HgcC2 and TK HgcC3 are transcribed from transposase encoding sequences. The P. furiosus HgcC transcript is, however, an antisense transcript relative to the transposase gene whereas TK HgcC1, HgcC2 and HgcC3 are transcribed in the same direction from iTSSs within the transposase genes, TK0298, TK0495 and TK0850, respectively (Additional file 7: Figure S3A-C). TK HgcC4 and TK HgcC5 are not associated with transposase genes. HgcC5 has an iTSS within TK1820 (membrane-associated metalloprotease), and the TSS of HgcC4 is also the pTSS of TK1679 as HgcC4 is encoded within the 5′-region of TK1679 (hypothetical protein) (Additional file 7: Figure S3D-E). An antisense RNA, complementary to part of HgcC4, is also transcribed from the TK1820 region that could function in trans as a regulator of HgcC synthesis and/or function (Additional file 7: Figure S3E).
Antisense transcripts in T. kodakarensis
Before the advent of deep sequencing, most non-coding RNAs were identified through sequence conservation and predictions for conserved RNA folding, with screening for non-coding RNAs generally avoiding ORFs and their complementary antisense regions. However, as transcriptome data have accumulated, it has become increasingly clear that antisense transcription is a major feature of prokaryotic and eukaryotic genome expression . The dRNA-seq data identified 1,018 aTSS, sites at which antisense transcription is initiated on the T. kodakarensis genome. The aTSS are not evenly distributed. Inspection of 150 bp windows around translation start and stop codons revealed that 58% of the aTSS are located near gene termini with 260 and 329 antisense transcripts overlapping the 5′- and 3′-terminus, respectively, of an ORF. A similar enrichment of antisense transcription across gene termini has also been observed in other Archaea, with antisense transcripts often associated with transposase-encoding genes [21, 22, 25, 27, 30, 31, 60–63]. An aTSS is associated with all seven genes (TK0298, TK0495, TK0654, TK0850, TK0931, TK0932, TK1842) annotated as encoding transposases in the T. kodakarensis genome . To identify any additional preferential associations of aTSS with specific functions, the aTSS locations were evaluated relative to the pathways and functions defined in the KEGG database [64, 65]. Antisense transcripts are associated with 19, 14, 13 and 12 ORFs that encode proteins involved in amino acid, purine, pyrimidine and central carbon metabolism, respectively, 12 that participate in ribosome biogenesis and 7 that encode ABC transporters. At least some of these antisense RNAs most likely interact with the complementary sense mRNA but, as yet, there is no direct experimental evidence for such an interaction in vivo. This has been documented experimentally in vitro for an antisense RNA from M. mazei that sequestered the RBS of the target mRNA .
Known or predicted small non-coding RNAs
The RFAM and UCSC databases predict that ~500 small non-coding RNAs are encoded in the T. kodakarensis genome [38, 39]. Most are classified as snoRNAs, archaeal counterparts of eukaryotic snoRNAs (small nucleolar RNAs) that direct 2′-O-methylation (C/D box) and pseudouridylation (H/ACA box) of transfer (tRNA) and ribosomal RNAs (rRNA). We verified the presence of 69 of these small non-coding RNAs (Additional file 8: Table S5); 54 designated as C/D box and 7 as H/ACA box snoRNAs. Of these, 17 are not recognizably linked to annotated genes and so are classified as orphan snoRNAs. There are 11 potential snoRNAs encoded within 5′-UTRs and 12 within the 5′-coding region of ORFs that, presumably, must be released by transcript processing. Tko-sR44, for example, appears to be co-transcribed with a tRNAArg and is then likely released from the co-transcript by RNase Z cleavage, as observed in Nanoarchaeum equitans and in some plants [67, 68]. In some cases, the presence of a K-turn motif near the 5′-terminus, the region predicted to be a snoRNA may, in fact, be a cis-regulatory element rather than a snoRNA. K-turn motifs are important structural elements in riboswitches [69, 70]. Alternatively, as sequences that conform to ribosomal protein L7Ae (TK1311) binding sites are present in several of these transcripts, including those encoding the aNOP56 (TK0184) and Cbf5p (TK1509) components of the snoRNA guide complexes, these transcripts might be processed by complexes containing L7Ae and possibly RNase P . Seventeen of the potential snoRNAs are antisense transcripts, 16 of which are transcribed from DNA that includes the 3′-terminus of the complementary sense gene. In 7 cases, a snoRNA is transcribed convergent to an antisense RNA, a gene organization also documented in S. solfataricus, N. equitans and Pyrobaculum species [22, 25, 28, 62].
Deep sequencing identified 107 sense and 215 antisense non-coding RNAs in P. abyssi GE5 . Based on a BLAST search, 68 of these are also encoded in the T. kodakarensis genome, of which 33 are clearly represented in the dRNA-seq libraries. A further 8 orphan small non-coding RNAs predicted and/or documented to be present in Pyrococcus species [23, 31, 56] (Additional file 9: Table S6) and the small non-coding RNAs, designated CRISPR RNAs (crRNA), predicted to be transcribed from the three CRISPR loci in the T. kodakarensis genome are also present in the T. kodakarensis dRNA-seq libraries [37, 56]. Transcription of a CRISPR locus generates a long transcript that is cleaved, first releasing immature crRNAs with 8 nt 5′-extensions and variable 3′-termini. These are then trimmed to produce the mature crRNAs. The TSS identified are fully consistent with the locations predicted for promoters within the CRISPR loci and with this transcript processing .
Orphan small non-coding RNAs
Protein-encoding genes with the highest numbers of reads per ORF in the pyruvate (P exp ) and sulfur (S exp ) cDNA libraries
Tko-sR04 + hypothetical protein
50S ribosomal protein L7 Ae
50S ribosomal protein L7 Ae
DNA/RNA-binding protein A1bA
DNA/RNA-binding protein A1bA
Tko-sR04 + hypothetical protein
50S ribosomal protein L1P
Lrp/AsnC family transcriptional regulator
Lrp-AsnC family transcriptional regulator
Acidic ribosomal protein P0
Tko-sR67 + 7, 8-dihydro-8-oxoguanine-triphosphatase
H/ACA RNA protein complex Garl
50S ribosomal protein L1P
30S ribosomal protein S15
acidic ribosomal protein P0
Tko-sR50 + hypothetical protein
Tko-sR67 + 7, 8-dihydro-8-oxoguanine-triphosphatase
50S ribosomal protein L37 Ae
tRNAs, rRNAs, RNase P and 7S RNAs
All of the tRNAs and rRNAs annotated in the T. kodakarensis genome are present and fully covered in the dRNA-seq libraries (Additional file 11: Table S8). There are also antisense transcripts present complementary to six tRNAs and one tRNAThr is, itself, an antisense transcript of TK1287 (encodes uracil phosphoribosyltransferase). As described above, one tRNAArg appears to be cotranscribed with a snoRNA (Tko-sR44) and a snoRNA (designated Tko-19) encoding sequence is located immediately upstream, and is likely within the 5′-leader region and so cotranscribed with the 16S rRNA-tRNAAla-23S rRNA operon (Additional file 12: Figure S4A)
The dRNA-seq libraries also confirm the presence of the RNase P RNA and 7S (SRP) RNA in T. kodakarensis cells although the 7S RNA is transcribed from the DNA strand opposite to that stated in the genome annotation . The dRNA-seq data are convincing (Additional file 12: Figure S4B) and in agreement with the RFAM database .
Growth and media-dependent transcription
Based on previous studies of P. furiosus[32, 33] and T. kodakarensis[34, 35], we generated cDNA libraries from cells growing in media with sulfur (Sexp) or pyruvate (Pexp) to increase the number of TSS likely identified. We also generated cDNA libraries from cells grown with sulfur to stationary phase (Sstat) and from cells growing with pyruvate but with sulfur added 20 min before RNA isolation (PS). The RNA preparations were subjected to TEX digestion before cDNA synthesis and, given in vivo transcript processing and in vitro fragmentation during purification, the resulting cDNA libraries were, as expected, enriched for 5′-terminal sequences. This facilitates the identification of TSS but, assuming that the number of cDNA reads correlates with transcript abundance, the dRNA-seq data also provide a semi-quantitative overview of global genome expression and are consistent with previous observations of substrate-dependent specific gene expression. Based on the number of cDNA reads, there is little or no transcription from ~35% of transcriptional units (TUs) in cells growing in pyruvate medium, of ~28% of the TUs in cells growing in sulfur medium, and of ~87% of TUs in stationary phase cells. Transcript abundances vary substantially, but <2.5% are present >1000-fold above the minimal detectable level (Additional file 13: Table S9). Consistent with constitutively high expression and/or transcript stability, 18 of the 25 most abundant transcripts were the same in RNA preparations from cells growing in sulfur or pyruvate medium (Table 1)
The reductant needed to generate H2 and/or H2S is most likely supplied by a reduced ferredoxin [34, 35] but there are three candidate ferredoxins encoded in the T. kodakarensis genome . Based on cDNA reads, transcripts of TK1694 (encodes ferredoxin-1) are very abundant under all of the growth conditions investigated (Table 1 and Additional file 10: Table S7), indicative of ferredoxin-1 participating in many metabolic pathways, although there is a ~2-fold decrease after sulfur addition to pyruvate growing cells. Ferredoxin-2 is encoded by TK1087, the middle gene in a three gene operon (TK1086-TK1088) that also encodes SurR (TK1086), a redox-responsive transcription regulator of many genes involved in sulfur metabolism . The extent of ferredoxin-2 reduction could provide redox-state information to SurR, and so modulate SurR activity, but sulfur addition had little effect on the abundance of TK1086-TK1088 transcripts (Additional file 10: Table S7). In contrast, there was a ~6-fold decrease in TK2012 transcripts (encodes ferredoxin-3) following sulfur addition to pyruvate growing cells, arguing that ferredoxin-3 is likely the predominant electron donor for H2 production by the mbh encoded hydrogenase (Additional file 10: Table S7). Intriguingly, an antisense transcript is also generated from the TK2012 region that has increased expression in the presence of sulfur (Additional file 14: Figure S5), and a SurR binding site overlaps the TATA box of the promoter for this antisense transcript. The antisense RNA is therefore likely part of the SurR regulon, and TK2012 expression and so ferredoxin-3 synthesis may be indirectly subject to SurR regulation through post-transcription regulation by the antisense RNA.
The results obtained establish that transcription initiation occurs at >2,700 sites around the T. kodakarensis genome (Additional file 2) and recognizable BRE-TATA-box promoter elements are appropriately located upstream of ~78% of all the T. kodakarensis transcription units identified. As reported for other Archaea[22, 25, 51, 56, 76], many promoters and TSS are embedded within ORFs, and antisense transcription occurs extensively throughout the T. kodakarensis genome adding significantly to the genome complexity, and predicting a major involvement of antisense transcripts in regulating gene expression. T. kodakarensis cells contain many small non-coding RNAs, some previously identified or predicted including two candidate riboswitches [23, 31, 51, 56] but also many previously unanticipated RNAs (Additional file 8: Table S5, Additional file 9: Table S6 and Additional file 10: Table S7). As in P. abyssi, some have relatively AU-rich sequences, in contrast to the high GC content of T. kodakarensis tRNAs and rRNAs, and contrary to the expectation [56, 73] that small non-coding RNAs in hyperthermophiles would be GC-rich to stabilize secondary structures. The sequences of most of these small AU-rich RNAs do not readily fold into canonical base-paired secondary structures, and may function as unstructured molecules , but could have secondary and tertiary structures stabilized in vivo by nucleic acid and protein interactions . Most 5′-UTRs in T. kodakarensis are short and some are leaderless (Figure 1). But, as in other Archaea[21, 22, 32], there are also mRNAs with long 5′-UTRs that are consistent and predictive of cis-regulatory elements, although there is no direct experimental support to date for regulation in vivo by attenuators or riboswitches in Archaea.
The most exciting and experimentally-challenging conclusion from this, and from all other archaeal transcriptome studies to date, is that archaeal cells contain many different, often abundant and apparently non-coding small RNAs. T. kodakarensis appears typical; it has a very small genome (~2.1 Mbp) tightly packed with ORFs but also with genes that encode non-translated RNAs and so likely has widespread RNA-based regulation. Historically, RNA secondary structure was sought and equated with non-coding RNA function but it is now clear that such structure is not mandatory. For example eukaryotic siRNAs and lncRNAs apparently bind their RNA and protein targets, and carry out their regulatory functions, without extensive structure [77, 78]. Given that gene expression in Archaea, and particularly transcription-related features are simpler but have many features in common with their eukaryotic counterparts, it seems likely that investigating RNA-based regulation in Archaea, with T. kodakarensis providing a model system , will generate results that are valuable and legitimately extrapolated into eukaryotic gene expression.
T. kodakarensis cultures were grown anaerobically at 85°C in nutrient-rich artificial sea water medium that contained 5 g/l yeast extract, 5 g/l tryptone (ASW-YT), the required trace minerals and vitamins [34, 79], and either 2 g S°/l (Sulfur medium) or 5 g sodium pyruvate/l (Pyr medium). The growth of cultures was followed by optical density measurements at 600 nm (OD600) and, in most experiments, aliquots (50-500 ml) were removed for RNA isolation when the OD600 reached 0.2. In experiments where sulfur was added (final concentrations of 2 g/l) to cultures growing in Pyr medium, the addition occurred when the culture reached an OD600 of 0.2, and a 500 ml aliquot was removed for RNA isolation after a further 20 min incubation at 85°C.
Cells were removed from suspension by centrifugation (4000 g; 30 min) at 4°C, the resulting cell pellet immediately resuspended in TRIzol (Invitrogen), instantly frozen in liquid nitrogen and stored at -70°C. After thawing, total RNA was extracted using the TRIzol manufacturer’s protocol, then incubated at 37°C for 1 h with DNAse I (Thermo Fisher Scientific Inc) and an aliquot subjected to agarose gel electrophoresis and visualized by staining to determine the size-profile of the RNA molecules present. The concentration of the RNA solution was determined using a NanoDrop 1000 spectrophotometer (Thermo Fischer Scientific Inc).
Construction of cDNA libraries and Illumina sequencing
The cDNA libraries were constructed as previously described [7, 15]. For Illumina sequencing (HiSeq) of cDNA molecules, the libraries were constructed by vertis Biotechnology AG, Germany, as described previously for eukaryotic microRNA libraries  but without a RNA size-fractionation step before the cDNA synthesis. The cDNA libraries were sequenced using a HiSeq 2000 machine (Illumina) in single-read mode and 100 cycles. The raw, de-multiplexed reads and coverage files have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO; ) with accession code GSE56262. Detailed descriptions of procedures used for read mapping, expression graph construction and normalization of expression graphs have been published . For graph visualization the Integrative Genomics Viewer (IGV version 2.3.27) was used .
Transcriptional start site (TSS) annotation and expression analysis
The pooled sequence reads were de-multiplexed and the adapter sequences were removed. The reads in Fastq format were then quality trimmed using fastq_quality_trimmer (FastX suite version 0.0.13 ) with a cut-off Phred score of 20 and converted to Fasta format using fastq_to_fasta (FastX suite). The read processing [including poly(A) removal, size filtering (min 12 nt length), statistics generation, coverage calculation and normalization] was performed using the RNA-analysis pipeline READemption version 0.1.6  with default parameters which used segemehl version 0.1.4 . An automated pipeline (TSSpredator) was used to identify the TSS . The software was provided with the T. kodakarensis genome annotation  extended by entries of known and predicted RNAs taken from the RFAM database  and the UCSC archaeal genome browser . TSS were first identified in the cDNA libraries (Sexp and C), generated with and without TEX digestion, and the remaining libraries were then manually checked to confirm these TSS and for additional TSS. As illustrated (Figure 1A), the TSS were defined and grouped as primary (pTSS), secondary (sTSS), internal (iTSS) and/or antisense (aTSS) transcription start sites, depending on their location relative to an annotated gene. Based on the location of a translation start codon, the distribution of the lengths predicted for 5′-UTRs was visualized using RStudio (RStudio, Inc.) and the ggplot2 package . The bioconductor package DEseq was used to measure expression with the results listed in Additional file 10: Table S7. A heatmap comparison (Figure 6) of dRNA-seq data from T. kodakarensis and microarray expression data for P. furiosus taken from the NCBI GEO database (GPL4688) was generated using heatmap.plus. The organization of genes into transcription units (operons) in the T. kodakarensis genome was taken from the DOOR2 database , and to calculate transcript abundances, all ORFs in an operon were grouped and the normalized average reads per gene (Additional file 10: Table S7) were summed (Additional file 13: Table S9).
Promoter and RBS motifs detection and data visualization
To identify promoter motifs, the sequences from 50 bp upstream of each TSS to the TSS were scanned by MEME version 4.8.1  using standard parameters, but searching only the sense strand. Ribosome binding sites (RBS) were located in mRNAs by MEME and potential RBS in previously unrecognized transcripts were sought by FIMO using standard settings  and the MEME generated position weight matrix (PWM) as input (Additional file 15). When a TSS indicated a leaderless mRNAs, a 100 bp window around the TSS was scanned with FIMO and all alternative translation initiation sites so detected were manually inspected. When deemed likely, a start codon was reassigned (Additional file 4: Table S4) and the TSS then, as necessary, re-categorized.
Conservation of small non-coding RNAs
The RFAM 11.0 database  was screened using cmsearch of the INFERNAL package version 1.1 with standard settings  to detect all known sRNAs. snoScan and snoReport were applied to identify additional potential snoRNAs within the previously unrecognized sRNAs. To identify sRNA homologues, the NCBI nucleotide database restricted to the domain Archaea was searched using blastn (part of BLAST+, version 2.2.28 ). The word-size parameter was set to 10 nt, an empirical filter used to identify blastn alignments with an expected value (e-value) < 0.06, and all potential homologues were then manually inspected. The number of identical nucleotides in sRNA alignments was divided by the total number of nucleotides in the query sRNA, and multiplied by 100 to obtain a percentage conservation value. Only conservation values ≥40% were retained for further analysis (Additional file 9: Table S6). The extent of conservation, determined by a BLAST analysis, is given as the closest common taxonomic level. RNA secondary structure predictions were performed using RNAfold (ViennaRNA package version 2.1.g ). Orphan transcripts were screened for ORFs and all putative polypeptides containing at least 20 amino acid residues were used as query proteins in blastp analyses (part of BLAST+, version 2.2.28 ) with default parameters. Only homology pairs with an e-value < 10-3 were further considered.
DJ is supported by a DFG postdoctoral fellowship (JA 2309/1-1). This work was supported by National Institutes of Health grants R01-GM100329 to TJS and R24-GM098176 to JNR and TJS. CMS is supported by the ZINF Young Investigator program at the Research Center for Infectious Diseases (ZINF) in Würzburg, the Young Fellow program of the Bavarian Academy of Sciences, and the Daimler and Benz Foundation. We thank Professor Ruth Schmitz-Streit (Christian-Albrechts University, Kiel) for additional support and Professor Richard Reinhardt (Max Planck Genome Center, Cologne) for help with the deep sequencing.
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