- Research article
- Open Access
Genome-wide identification of transcriptional start sites in the haloarchaeon Haloferax volcanii based on differential RNA-Seq (dRNA-Seq)
- Julia Babski†1,
- Karina A. Haas†2,
- Daniela Näther-Schindler1,
- Friedhelm Pfeiffer3,
- Konrad U. Förstner4,
- Matthias Hammelmann1,
- Rolf Hilker5,
- Anke Becker6,
- Cynthia M. Sharma4,
- Anita Marchfelder2Email author and
- Jörg Soppa1Email author
© The Author(s). 2016
- Received: 9 March 2016
- Accepted: 7 July 2016
- Published: 12 August 2016
Differential RNA-Seq (dRNA-Seq) is a recently developed method of performing primary transcriptome analyses that allows for the genome-wide mapping of transcriptional start sites (TSSs) and the identification of novel transcripts. Although the transcriptomes of diverse bacterial species have been characterized by dRNA-Seq, the transcriptome analysis of archaeal species is still rather limited. Therefore, we used dRNA-Seq to characterize the primary transcriptome of the model archaeon Haloferax volcanii.
Three independent cultures of Hfx. volcanii grown under optimal conditions to the mid-exponential growth phase were used to determine the primary transcriptome and map the 5′-ends of the transcripts. In total, 4749 potential TSSs were detected. A position weight matrix (PWM) was derived for the promoter predictions, and the results showed that 64 % of the TSSs were preceded by stringent or relaxed basal promoters. Of the identified TSSs, 1851 belonged to protein-coding genes. Thus, fewer than half (46 %) of the 4040 protein-coding genes were expressed under optimal growth conditions. Seventy-two percent of all protein-coding transcripts were leaderless, which emphasized that this pathway is the major pathway for translation initiation in haloarchaea. A total of 2898 of the TSSs belonged to potential non-coding RNAs, which accounted for an unexpectedly high fraction (61 %) of all transcripts. Most of the non-coding TSSs had not been previously described (2792) and represented novel sequences (59 % of all TSSs). A large fraction of the potential novel non-coding transcripts were cis-antisense RNAs (1244 aTSSs). A strong negative correlation between the levels of antisense transcripts and cognate sense mRNAs was found, which suggested that the negative regulation of gene expression via antisense RNAs may play an important role in haloarchaea. The other types of novel non-coding transcripts corresponded to internal transcripts overlapping with mRNAs (1153 iTSSs) and intergenic small RNA (sRNA) candidates (395 TSSs).
This study provides a comprehensive map of the primary transcriptome of Hfx. volcanii grown under optimal conditions. Fewer than half of all protein-coding genes have been transcribed under these conditions. Unexpectedly, more than half of the detected TSSs belonged to several classes of non-coding RNAs. Thus, RNA-based regulation appears to play a more important role in haloarchaea than previously anticipated.
- Haloferax volcanii
- Leaderless transcript
- Non-coding RNA
- Antisense RNA
A transcriptome, by definition, encompasses the complete set of RNA within an organism. Transcriptomes are highly dynamic, especially in prokaryotes, and change in response to varying environmental conditions, growth stages, or developmental states. Transcriptomes were initially thought to be nearly entirely composed of rRNAs, tRNAs, several other housekeeping RNAs, and protein-coding mRNAs. In recent years, research has shown that eukaryotes as well as prokaryotes contain a large number of additional non-coding RNAs. In prokaryotes, these non-coding regulatory RNAs are called “sRNAs” (or “ncRNAs”), and they range in size from approximately 50 to 500 nt [1–4]). The application of high-throughput sequencing for the analysis of cDNA libraries (RNA-Seq) and specialized variants has enabled a comprehensive overview of bacterial and archaeal transcriptomes and led to the discovery of many new sRNAs in all studied species [5–8].
Although a number of RNA-Seq studies of bacterial species have been published in recent years, the number of studies on the transcriptomes of archaea is still limited. Until now, RNA-Seq analyses have only been performed with nine archaeal species: Methanosarcina mazei, Sulfolobus solfataricus, Halobacterium salinarum, Haloferax volcanii, Nanoarchaeum equitans, Methanopyrus kandleri, Pyrococcus abyssi, Thermococcus kodakarensis, and Methanolobus psychrophilus [9–16].
Hfx. volcanii is a model archaeal species used to study many central biological processes, including replication, DNA repair, transcription and transcriptional regulation, translation, protein export, posttranslational protein modification, protein degradation, metabolism, and the CRISPR-Cas system [17–24]. Hfx. volcanii can easily be cultivated in synthetic or complex media under aerobic or anaerobic conditions and has a generation time of approximately 3 h under optimal conditions . The genome of Hfx. volcanii has been sequenced and consists of one major chromosome (2.9 Mbp), three smaller chromosomes (from 89 to 690 kbp), and one smaller plasmid (6.4 kbp) . Functional genomic techniques for analyzing transcriptomes, proteomes, and metabolomes have been established . A transformation protocol was described in 1987 , and since then, many genetic tools and techniques have been developed, such as for the rapid and easy generation of in-frame deletion mutants [29–31].
Several characteristic features of the Hfx. volcanii transcriptome have been analyzed in small-scale studies. One study identified the 5′-ends of 62 haloarchaeal transcripts and determined that the majority did not have 5′-UTRs and were thus leaderless . The first regulatory sRNAs were identified in 2009 using an RNomics approach . Additional sRNAs were identified using bioinformatics predictions . The construction and phenotyping of a set of 27 sRNA gene deletion mutants demonstrated the importance of Hfx. volcanii sRNAs for many biological functions . An RNA-Seq based analysis of the sRNA fraction (17 to 500 nt) increased the number of known Hfx. volcanii sRNAs to nearly 200 . However, because of the limitations of RNA-Seq methodology at that time, the analysis was not comprehensive and only focused on small RNAs without providing a complete overview of the transcriptome. The present study obtained greater transcriptome coverage by combining state-of-the-art technology and the dRNA-Seq approach for analyzing the Hfx. volcanii primary transcriptome.
The dRNA-Seq approach allows for a global identification of transcriptional start sites (TSSs) at a single-nucleotide resolution [8, 36]. This approach is based on the differential sequencing of two cDNA libraries: one generated from untreated RNA and another treated with 5′P-dependent terminator exonuclease (Terminator EXonuclease, TEX). TEX digests RNAs with 5′-monophosphates, whereas primary transcripts with 5′-triphosphates are not degraded. Therefore, the sequencing of TEX−/+ libraries leads to a greater enrichment of primary transcripts in the TEX+ library compared with that of the untreated sample (TEX-), and the corresponding changes in the read patterns can be used to annotate the primary 5′-ends of the transcripts. The dRNA-Seq approach has been applied to various bacterial species  but has only been applied to two archaeal species thus far [9, 15].
dRNA-Seq and classification of TSSs
To globally identify TSSs in Hfx. volcanii, dRNA-Seq was performed using Hfx. volcanii cultures grown to the mid-exponential growth phase under optimal conditions. Three independent cultures were grown, and the results were pooled for data analysis. In total, 200 million reads were obtained by Illumina sequencing and mapped to the genome sequence of Hfx. volcanii. Statistics related to sequencing and mapping are summarized in Additional file 1: Table S1.
In total, 4749 TSSs were identified in at least one dataset, 1493 TSSs were identified in at least two datasets, and 1191 TSSs were identified in all three datasets. The identified TSSs were equally distributed on both strands of the genome. Additional file 2: Table S2 lists all of the identified TSSs for Hfx. volcanii and includes additional information (discussed below), including their genomic localization, associated genes, affiliation with different transcript classes, read coverage, and promoter scores. For each transcript, the table also lists in which of the three experiments the transcript was detected and in which it was predicted to be a primary transcript based on the enrichment after TEX treatment.
Classification of primary transcriptional start sites
Transcriptional start sites (TSSs)
Protein-coding gene TSSs
Novel gene TSSs
Primary transcripts of protein-coding genes
In total, 1851 transcripts for protein-coding genes were detected, which corresponded to 46 % of the 4040 protein-coding genes annotated in the Hfx. volcanii genome. The Hfx. volcanii cultures were grown under optimal conditions; therefore, many functions were not required, such as enzymes involved in many biosynthetic pathways and proteins involved in stress adaptation, biofilm formation and chemotaxis. In addition, because a number of proteins are typically encoded in operons (e.g., ribosomal proteins, ABC transporters, chemotaxis proteins, and heteromeric enzymes), certain TSSs belonged to polycistronic transcripts that encode more than one protein (e.g., five proteins for ABC transporters operons). In a separate study, an in-depth analysis of the Hfx. volcanii proteome under standard growth conditions during the exponential growth phase identified 2360 proteins (Jevtic Z, Stoll B, Sharma K, Urlaub H, Marchfelder A, Lenz, C. and Pfeiffer F, in preparation). Because certain TSSs belonged to polycistronic transcripts, the 1851 TSSs that were identified by dRNA-Seq were consistent with the 2360 proteins that were identified using proteomics.
Wide variations have been observed in the fractions of mRNAs detected in other archaea using (d)RNA-Seq, with 20 % of the transcripts from protein-coding genes detected in M. mazei , 89.5 % detected in Sulfolobus solfataricus , and 92 % detected in Methanolobus psychrophilus . However, the number of predicted TSSs was highly influenced by the growth conditions, the sequencing depth, and the program and parameters used for automatic TSS prediction, among other factors.
Not all archaea contain mostly leaderless transcripts. In fact, archaeal transcriptomes are highly diverse, and obvious phylogenetic signatures have not been observed for archaea that contain mostly leaderless transcripts. For example, the fraction of leaderless transcripts in two species of methanogenic archaea is very low at approximately 11 % in M. mazei  and 16 % in Methanolobus psychrophilus . Similarly, a 12 % fraction of leaderless transcripts has been reported for Thermococcus kodakarensis , whereas leaderless transcripts have been shown to be nearly absent in Pyrococcus abyssi . However, the fraction of leaderless transcripts in the crenarchaeote Sulfolobus solfataricus has been shown to be 69 % , which is similar to that in the euryarchaeote Hfx. volcanii. RNA-Seq studies have not been published for Pyrobaculum aerophilum, although in a small-scale study, all of the transcripts were found to be leaderless , indicating that this archaeon also contains a high fraction of leaderless transcripts.
The fraction of leaderless transcripts also vary widely in bacteria. For example, leaderless transcripts are very scarce in Escherichia coli and might even be confined to specific conditions when a toxin/antitoxin system is induced . In contrast, nearly one-quarter of all Mycobacterium smegmatis transcripts are leaderless , and 60 % of all transcripts in the radio-tolerant bacterium Deinococcus deserti are leaderless . The eukaryotic species Giardia lamblia only contains only leaderless transcripts .
The occurrence of species with high fractions of leaderless transcripts in all three domains of life and the dependence of leaderless translation on a lower number of initiation factors led to the hypothesis that this mechanism of translation initiation might be the most ancient . This hypothesis is supported by the fact that leaderless transcripts can be translated by ribosomes lacking several ribosomal proteins and the 3′-end of 16S rRNA .
Next, the length distribution of the 5′-UTRs of the leadered transcripts of Hfx. volcanii was analyzed, and the results indicated a wide distribution from 6 to 250 nt (Fig. 2b). Surprisingly, the highest number of these transcripts had lengths from 6 to 20 nt (approximately 60 cases), although this value was much lower than the number from 0 to 5 nt in length (1329 cases) discussed above. The presence of 5′-UTR lengths from 6 to 20 nt was unexpected because shortening the length of a 5′-UTR from 20 to 15 nt for one specific transcript significantly decreased the translational efficiency .
The number of transcripts with 5′-UTRs of 21 to 250 nt steadily decreased as the 5′-UTR length increased. Apparently, an optimal 5′-UTR length does not exist in Hfx. volcanii, and our results demonstrated that the previously reported 20 nt average length of 5′-UTRs in Hfx. volcanii  was an underestimation. This conclusion is consistent with the the fact that the number of long 5′-UTRs identified in our study was higher than previously anticipated, although very long UTRs (>150 nucleotides) were infrequent in the present study (only 97 cases).
The lengths of 5′-UTRs differ substantially in other archaea. In M. mazei, 5′-UTRs are typically very long, and the majority of 5′-UTRs are 150 to 200 nt . However, most of the 5′-UTRs from T. kodakarensis are between 10 and 50 nt and present a median length of 16 nt . The median length of the 5′-UTRs from P. abyssi is 37 nt, which is between that of the species mentioned above . The fraction of leadered transcripts in S. solfataricus is so low that 5′-UTR lengths have not been reported . Taken together, the high variability in 5′-UTR length indicates that the mechanisms of translation initiation and the importance of translational regulation via 5′-UTRs (e.g., through riboswitches, RNA thermometers, and regulatory sRNAs) may differ widely among archaea. The scarcity of (long) 5′-UTRs in Hfx. volcanii identified in the present study indicated that translational control requiring structured leaders is not typical in haloarchaea and that alternative mechanisms potentially based on 3′-UTRs might prevail.
Differences between leaderless and leadered transcripts
A considerable difference between the leaderless and the leadered transcripts was found for the second codon (the first three positions in Fig. 3b). The prevalence of A/G at the first codon position was much higher in transcripts with a 5′-UTR than in leaderless transcripts. Thus, the amino acid composition at the second position was more restricted in proteins encoded by leadered transcripts. Because the amino-terminal methionine is frequently removed after translation, this penultimate amino acid becomes the amino-terminal amino acid, and its identity could influence the protein half-life. A very slight difference between the leaderless and leadered transcripts was found for the third codon position. Starting at codon six (nt 18), the leaderless transcripts demonstrated a preference for C (and G to a lesser extent), which was somewhat less prevalent for the leadered transcripts (compare Fig. 3b). This result was consistent with the hypothesis that leaderless transcripts in Hfx. volcanii are enriched in transcripts with high expression levels and exhibit a higher codon preference compared with leadered transcripts.
Start codons of leaderless transcripts
The results from an earlier study indicated that leaderless transcripts exclusively require the start codon AUG, whereas leadered transcripts could also use the start codons GUG and UUG . However, this result was based on mutating the AUG codon of only one leaderless transcript to GUG and UUG, which abolished translation initiation. Another study used a translational fusion between the hsp70 transcript, including a short 4-nt leader with a reporter gene, to analyze the importance of the sequence surrounding the start codon for translation efficiency . Mutating the native AUG codon to a GUG codon led to a decrease in translation efficiency of 83 %. This effect was even more pronounced after removing the short 4-nt 5′-UTR. The translation efficiency of the leaderless version with the GUG start codon was only 2.6 % compared with that of the leaderless version with the AUG start codon, and the authors stated that this low activity could only be quantified because of the high transcript level. Therefore, these studies showed that the translational efficiencies of leaderless transcripts with a GUG start codon were low (2.6 %) or below the detection limit. In each case only one single transcript was used, and neither of these transcripts had native GUG start codons but rather mutated versions of the native AUG start codon.
Importantly, the results of this dRNA-Seq study revealed that a considerable fraction of leaderless transcripts had native GUG start codons (9 % or 118 transcripts); therefore, the results of the two studies described above cannot be generalized to all leaderless transcripts.
Protein-coding genes with alternative promoters
For E. coli, 10 to 20 % of all genes have been identified as having two transcripts with different 5′-ends, which suggests that the trancripts are transcribed from two different promoters . To analyze whether this pattern might also be true for Hfx. volcanii, the possible association of more than one TSS with one gene was examined for the 1851 expressed protein-coding genes. Indeed, 139 protein-coding genes (7.5 %) were associated with two TSSs. Of the 139 genes that were associated with two or three different TSSs, 111 genes had leaderless and leadered transcripts (ten cases had 2 leadered transcripts) and 28 genes had two transcripts (one case had three transcripts) with different 5′-UTR lengths. Interestingly, several transcripts had a leaderless version and an extremely long 5′-UTR version, e.g., HVO_0482 encoding an Hsp20-type chaperone had a leaderless transcript and a transcript with a 5′-UTR of 250 nt. The TSSs of the leaderless transcripts had a high average coverage of 469, whereas the average coverage of the leadered transcripts was considerably lower at 144. Two promoters upstream of one gene may typically have different strengths and different regulatory patterns and thus enhance the regulatory potential of gene expression. One haloarchaeal gene has been long known to be preceded by several promoters, although it is not a protein-coding gene. Namely, the rRNA operon of H. cutirubrum has been shown to be preceded by as many as five promoters, all of which are active under optimal conditions but at different strengths (two are hardly detectable). At least one of these promoters has been shown to exhibit differential growth rate-dependent regulation . For Hfx. volcanii, the dRNA-Seq results showed three TSSs that were 185, 293, and 444 nt upstream of the 5′-ends of both of the 16S rRNA genes (HVO_1728 and HVO_2939). The first and third TSSs had an extremely high average coverage; therefore, their corresponding promoters might be strong. The distances between TSSs and 16S rRNA genes are different in the two species. The strongest promoter, P1, in H. cutirubrum is approximately 750 nt upstream of the 16S rRNA gene. However, this location in Hfx. Volcanii cannot be a promoter because 677 nt upstream of the 16 S rRNA gene is the start site of a protein-coding gene on the other strand.
As discussed below, an alternative explanation for the association of two TSSs with one protein-coding gene might be that the upstream TSS belonged to an unannotated small gene that was expressed independently of the downstream gene. However, because of our extensive search for missing gene calls, this occurrence should have been rare.
Small proteins and uORFs in long 5′-UTRs
In many genomes, the genes encoding small proteins are not annotated to avoid the annotation of many false positives, i.e., sequences that present a certain distance between the start and stop codons but are not real genes because they are neither transcribed nor translated. Often, the lower limit for gene prediction is 100 codons, which results in a systematic disregard for all proteins of less than 11 kDa . However, small proteins do occur in prokaryotes as well as in eukaryotes, and their numbers as well as their importance are heavily underestimated (for reviews, see [52, 53]). For Halobacterium salinarum, experimental approaches to isolate and identify small proteins have been optimized, and the “low-molecular-weight proteome” has been characterized . More than 300 small proteins of less than 10 kDa have been identified, and they account for more than 10 % of all proteins, which is a non-negligible fraction.
In the current study, an extensive search for unannotated genes in the Hfx. volcanii genome was performed (see Methods) using an approach for detecting conserved (very) short proteins. Thus, complete coverage of conserved genes for small proteins should have been achieved. The TSSs for 291 small protein genes (100 amino acids or less) were detected, which represented 16 % of the transcripts of all protein-coding genes. This result indicated that small proteins form a considerable fraction of the proteome of Hfx. volcanii and that a characterization of the biological roles of this neglected class of proteins is urgently required.
Small proteins encoded in long 5′-UTRsa
gene / FC
(prob. low-salt glycan biosyn. flippase)
dppA15 (ABC transport protein)
HTH-10 family transcription regulator
HTH-10 family transcription regulator
SNF family transport protein
(phenylalanine-tRNA ligase alpha subunit)
ABC transport protein
CcbP family protein
ABC transport protein
Previously known non-coding RNAs
In addition to protein-coding genes, the TSSs for previously known RNA genes were also analyzed (Table 1, Additional file 2: Table S2). The TSSs were detected for RNase P RNA and for 7S RNA, which is part of the signal recognition particle. Hfx. volcanii encodes a type I-B CRISPR-Cas system, and TSSs were detected for all three CRISPR RNAs. The previously observed deletion of the CRISPR locus P1 could be confirmed because reads were not obtained for this region [57, 58]. We found reads for all of the other spacers, which again confirmed earlier observations . Consistent with the generation of CRISPR RNAs via the processing of large primary transcripts, their reads were not enriched in the +TEX library and did not constitute TSSs.
The TSSs for both ribosomal operons were found upstream of the 16S rRNA. However, as expected, no TSSs were identified for the mature 5S rRNAs, 16S rRNAs, and 23S rRNAs from the two rRNA operons because these rRNAs were processed from a long primary rRNA operon transcript. The two genes for tRNAAla that were associated with the two rRNA operons and the gene for tRNACys that was encoded in one of the two ribosomal operons were also not associated with a TSS.
All of the tRNAs reads were detected in the –TEX libraries, showing that all of the tRNAs were transcribed, although the TSSs in close proximity to the mature tRNA 5′-end were only found for 39 of the 52 tRNA genes. The lack of a proximal TSS for the remaining 13 tRNA genes could be explained by different mechanisms: 1) in three cases, the tRNA gene was part of an rRNA operon (see above); 2) in four cases, two tRNA genes formed bicistronic operons (HVO_0345- > HVO_0344, HVO0523- > HVO_0524, HVO_2457- > HVO_2458, and HVO_2566- > HVO_2567), and the first but not the second gene was associated with a TSS; 3) one primary tRNA transcript had an exceptionally long leader of 300 nt (tRNAGlyCCC); 4) the tRNA might be part of a multicistronic operon, which could be true for tRNAGlyTCC; and 5) because tRNAs fold into stable structures, the preparation of cDNAs from these molecules was not trivial; therefore, certain TSSs may have been missing because of technical problems that occurred during library preparation.
Previously known sRNAs
In Hfx. volcanii, an earlier RNA-Seq approach identified 145 intergenic sRNAs and 45 cis-antisense sRNAs . Sixty-two of these sRNAs were also associated with TSSs in this dRNA-Seq analysis. The lack of identification of all previously known sRNAs may be explained as follows. First, in the earlier study, the cells were not only grown under optimal conditions, but cultures from six conditions were analyzed; many of the sRNAs were detected solely in cells grown under non-optimal conditions, such as low salt conditions . Second, the sRNAs that were not primary transcripts but that were generated by processing were not included in the TSS analysis of this dRNA-Seq study.
Novel intergenic RNAs
In this study, 395 novel intergenic RNAs that had a minimal distance of at least 250 nt to the start of any known gene were identified. Because UTRs become less frequent with increasing length and the longest previously determined haloarchaeal 5′-UTR had a length of 145 nt , it is unlikely that many of these TSSs were associated with protein-coding genes with extremely long leaders. Because of the limited size of the intergenic regions, most or all of these novel transcripts were most likely small and could be added to the class of intergenic sRNAs, thereby increasing the number of intergenic sRNAs from 145 to 540.
Novel internal RNAs (iTSS)
In total, 1153 novel internal TSSs were detected. In a former study, internal transcripts were not discussed because they might represent meta-stable degradation intermediates without biological functions . However, enrichment in the TEX-treated sample indicated that all of the iTSSs that were detected in this study were valid primary transcripts.
The iTSS class likely contained spurious transcripts that were initiated by the RNA polymerase “by mistake” and that lacked any biological function. An increase in the sequencing depth leads to an increase in the identification of real transcripts but also leads to an increase in the identification of spurious transcripts . However, a considerable fraction of iTSSs were most likely transcripts of biological relevance because 51 % were preceded by a basal promoter and only 49 % were not preceded by a recognizable promoter (promoter motifs are discussed below). Thus, the identified iTSSs probably included a large fraction of transcripts of biological relevance. However, experimental validation is required to determine the importance of individual iTSSs.
iTSSs that are localized close to the 3′-ends of proximal genes on polycistronic transcripts might represent additional transcription initiation sites for distal genes driven by promoters localized within the proximal genes. In the current study, an analysis of the iTSSs located at the 3′-end of annotated genes showed that 144 (12 %) of the 1153 iTSSs had annotated genes downstream (distance 1 to 250 nt); thus, these iTSSs most likely represented promoters for a downstream gene. One example of an internal promoter for a downstream gene was recently reported for Hfx. volcanii . The overlapping gene pair HVO_2723/HVO_2722 was transcribed from a promoter upstream of the first gene, leading to a bi-cistronic mRNA. In addition, the second gene was expressed separately from a promoter within the first gene. A similar example is shown in Fig. 1f. Additionally, for Halobacterium salinarum, a large number of internal transcripts have been described, and they are preceded by transcription factor-binding motifs; thus, they have been proposed as primary transcripts .
A special class of iTSSs accounted for the internal transcripts that overlapped with the 3′-end of transposase genes. The overexpression of one of these internal transcripts in H. salinarum has been shown to result in improved growth, indicating that these 3′-end overlapping internal RNAs have regulatory functions . In the present study, five novel internal RNAs overlapped with the 3′-ends of transposase genes, which indicated that a regulatory function for such RNAs is not confined to Halobacterium. For one of these cases, HVO_0008, the internal overlapping RNA was verified by Northern blot analysis (data not shown). A further 41 novel internal transcripts overlapped with transposase genes, albeit not with the 3′-end. The novel internal transcripts that overlapped with transposase genes might be important for controlling the transposition frequency, which was supported by the extremely low average coverage of the respective transposase transcripts in all but four cases and the lack of TSS detection for these genes in this dRNA-Seq study. Internal transcripts overlapping transposase genes have also been found in S. solfataricus , T. kodakarensis  and Pyrococcus abyssi , which indicates a widespread regulatory mechanism in archaea.
Novel antisense RNAs (aTSS)
The presence of a high fraction of antisense transcripts is not confined to Hfx. volcanii but appears to be widespread among many phylogenetic groups. For example, high fractions of antisense RNAs have been described for higher eukaryotes (reviews: [65–69]). In bacteria, the fractions of antisense transcripts vary widely from 1 to 2 % in Salmonella to more than 50 % in Staphylococcus aureus to approximately 75 % in Prochlorococcus strains (reviews: [6, 70–73]). High levels of antisense transcripts have also been observed in other archaea, e.g., 26 % in Methanolobus psychrophilus  and Pyrococcus abyssi .
In bacteria, antisense transcripts are involved in the regulation of transposition . This involvement appeared to hold true for Hfx. volcanii because 134 of the 1244 antisense transcripts overlapped with transposase genes. The presence of antisense transcripts to transposons has also been reported for other archaea, e.g., T. kodakarensis .
The results of the dRNA-Seq approach employed here showed that during exponential growth under optimal conditions, antisense transcripts were present in approximately 30 % of all of the protein-coding genes.
Distribution of iTSSs and aTSSs within ORFs
Transcription initiation in archaea and transcription initiation in eukaryotes share homologous basal transcription factors and transcription factor-binding motifs. Species of both domains contain the “TATA box binding protein” (TBP) and TATA boxes as well as “transcription factor (II) B” (TFB) and the transcription factor B recognition element (BRE) [76–78]. However, the consensus sequences for the basal promoter elements are not identical between archaea and eukaryotes, and even within the archaea, different groups exhibit different consensus sequences . For haloarchaea, the motifs “TTWT” (W = A or T) for the TATA box, “CGAAA” for the BRE, and “WW” for a −10 element have been proposed . We used the information obtained from the upstream regions of the 4749 experimentally determined TSSs to increase the quality of promoter predictions and develop a “promoter score” as a quantitative value to determine how close each individual upstream region fits with a genome-wide consensus. To this end, the regions upstream of all of the TSSs were analyzed to generate a position weight matrix (PWM) that quantified the non-randomness of a subregion of 28 nt around the known promoter elements BRE and the TATA box. The initial PWM was improved using an iterative approach that utilizes a PWM-Scan routine implemented in the MOODS software package (for details, see the Methods) . The final PWM was used to calculate a promoter score for every TSS and its position relative to the TSS, and this information is included in Additional file 2: Table S2.
Using these definitions, 34 % of all TSSs were preceded by a stringent promoter and 31 % were preceded by a relaxed promoter; however, 55 % of all TSSs were not preceded by any basal promoter elements. Several lines of evidence revealed that the average predictive value of the promoter score was high. For example, the average coverage of genes with stringent promoters was considerably higher than that for genes with relaxed promoters or no promoters (296 versus 137 versus 71, respectively). In addition, the types of promoters upstream of the 10 % of transcripts with the highest and lowest numbers of reads were analyzed. The majority of genes with high transcript levels were preceded by a stringent promoter, whereas most genes with low transcript levels were not preceded by stringent or relaxed promoters (Fig. 8b). Next, the promoter distributions upstream of the different classes of transcripts were analyzed (Fig. 8c). Nearly all of the previously known genes for non-coding RNAs were preceded by a stringent promoter, indicating that their transcription was initiated using the same basal transcription factors as those used for protein-coding genes. Approximately 80 % of all protein-coding genes were preceded by a stringent or relaxed promoter, and this percentage was somewhat higher for leaderless than for leadered transcripts. However, only a small fraction of the novel transcripts were preceded by a stringent promoter, which is consistent with the high sequencing depth of this dRNA-Seq approach that was required for their discovery.
This dRNA-Seq analysis of the Hfx. volcanii transcriptome led to the detection of 4749 TSS candidates, 2792 of which had not been previously observed and thus represented novel transcripts. Only 1851 of the 4749 TSSs belonged to protein-coding genes; therefore more than 60 % of all TSS belonged to transcripts that were likely non-coding. The latter fraction was unexpectedly high, which presented a new understanding of the Hfx. volcanii transcriptome. Seventy-two percent of the protein-coding transcripts were leaderless, which underscored the fact that the leaderless mRNA pathway was the principal method by which translation was initiated in Hfx. volcanii. The 5′-UTRs of the remaining 28 % of protein-coding transcripts had a very broad length distribution, and we could not identify an optimal 5′-UTR length. A significant number of antisense RNAs was identified (1244 aTSSs), and these constituted the highest fraction of non-coding RNAs. A strong negative correlation between the coverage of antisense RNAs and the cognate sense mRNAs was observed, which suggested that the downregulation of gene expression via antisense RNAs may be more important than previously anticipated. In addition, a high number of iTSSs was detected, and a fraction of these iTSSs might represent sub-operonic transcripts of downstream genes in polycistronic operons. The identified iTSSs and aTSSs add to and highly outnumber the as yet best studied class of non-coding RNAs: intergenic sRNAs. In summary, this dRNA-Seq analysis yielded a comprehensive overview of the coding and non-coding fractions of the Hfx. volcanii primary transcriptome.
The Hfx. volcanii strains H26 and H119  were chosen for this study because they are widely used by many laboratories and because targeted mutations can easily be introduced into their genomes. H26 was derived in two steps from the Hfx. volcanii wildtype DS2. H26 was cured from the small plasmid pHV2 and has a deletion in the pyrE gene, which encodes an enzyme essential for uracil biosynthesis. H119 was derived from H26 and has deletions in the two amino acid biosynthesis genes trpA and leuB. None of these mutations were relevant to this dRNA-Seq study because the cultures were grown in complex medium.
Construction of cDNA libraries and Illumina sequencing
Three independent cultures were performed with slight differences in the library preparation. In each case, part of the sample was treated with TEX (+TEX) and part of the sample remained untreated (−TEX). Therefore, six samples were analyzed by high-throughput sequencing.
Replicate 1: Frankfurt −/+TEX
H26 was grown in complex medium under optimal conditions as previously described . Briefly, the optimal conditions included a temperature of 42 °C, a NaCl concentration of 2.2 M, and good aeration (shaking at 250 rpm and 30-ml culture volume in 100-ml Erlenmeyer flasks). Cell growth was monitored spectroscopically (OD600) and by quantifying the cell densities using a Neubauer counting chamber. The cultures were inoculated with exponentially growing pre-cultures and grown to the mid-exponential growth phase (4–5 × 108 cells/ml) before they were harvested and used for the RNA-Seq analysis. The cultures were harvested by centrifugation, and total RNA was isolated using the RNeasy kit according to the manufacturer’s instructions, which included an extensive DNase treatment (Qiagen, Hilden, Germany). The RNA integrity was assessed by analytical gel electrophoresis, the absence of DNA was verified by comparing PCR reactions with and without a prior cDNA step, and the nucleic acid concentration was determined using a NanoDrop 1000 photometer (Thermo Fisher Scientific). Enzymatic treatment with TEX and tobacco acid pyrophosphatase (TAP), RNA adaptor ligation with T4-RNA-ligase, and cDNA synthesis were performed as previously described . Illumina sequencing was performed on a HiSeq 2000 with 100 cycles in single-end mode at the Max Planck Genome Center in Cologne, Germany.
Replicate 2: Würzburg −/+TEX
H26 cultures were grown, and total RNA was isolated in Frankfurt as described above. Libraries for Illumina cDNA sequencing were prepared by Vertis Biotechnology AG, Germany (http://www.vertis-biotech.com/) as previously described for eukaryotic microRNAs  but without the RNA size-fractionation step prior to the cDNA synthesis. The transcripts were not fragmented to obtain sequencing reads from the 5′-transcript ends. Equal amounts of RNA samples were poly(A)-tailed using poly(A) polymerase. Then, the 5′-triphosphates were removed by applying tobacco acid pyrophosphatase (TAP), which resulted in 5′-monophosphates. Afterwards, an RNA adapter was ligated to the 5′-phosphate of the RNA. First-strand cDNA was synthesized using an oligo(dT)-adapter primer and M-MLV reverse transcriptase. In a PCR-based amplification step using a high-fidelity DNA polymerase, the cDNA concentration increased to 20 to 30 ng/μl. A library-specific barcode for multiplex sequencing was part of a 3′-sequencing adapter. The following adapter sequences flanked the cDNA inserts:
TrueSeq Sense primer
TrueSeq Antisense NNNNNN primer (NNNNNN = 6n barcode for multiplexing)
The resulting cDNA libraries were sequenced using a HiSeq 2000 machine (Illumina) in single-read mode and running 100 cycles.
Replicate 3: Ulm −/+TEX
Haloferax cultures (H119) were grown in the Marchfelder Laboratory in Ulm in Hv-YPC medium  to an OD600 of 0.8. RNA was isolated and divided into two RNA fractions: fractions 1 and 2. From fraction 2, the rRNA was removed using the RiboZero kit (Epicentre). Both RNA fractions were subsequently sent to Vertis (Vertis Biotechnologie AG, Martinsried, Germany) for cDNA preparation and high-throughput sequencing. Here, RNA fraction 1 was incubated with TEX to remove the RNA species that carried a 5′-mono-phosphate. Thus, this RNA fraction was enriched for primary transcripts (fraction +TEX). For cDNA synthesis, both RNA fractions were poly(A)-tailed using poly(A) polymerase and then treated with TAP to remove the 5′-PPP structures. Afterwards, an RNA adapter was ligated to the 5′-monophosphates of the RNAs. First-strand cDNA synthesis was performed using an oligo(dT)-adapter primer and M-MLV reverse transcriptase. The resulting cDNAs were PCR-amplified to approximately 10 to 20 ng/μl using a high-fidelity DNA polymerase. Barcode sequences, which were part of the 3′-sequencing adapter, were added as the PCR cycles completed. The cDNAs were purified using the Agencourt AMPure XP kit (Beckman Coulter Genomics) and then analyzed by capillary electrophoresis. For Illumina sequencing, the cDNAs were pooled in approximately equimolar amounts. The cDNA pool was eluted in the size range of 200 to 500 bp from a preparative agarose gel. An aliquot of the size-fractionated cDNA pool was analyzed by capillary electrophoresis. The cDNA pool was sequenced on an Illumina HiSeq 2000 machine with a 100-bp read length.
Reference genome reannotation
At an early stage of the analysis, we systematically analyzed all of the TSSs that were internal to protein-coding genes but close to start codons (within 250 bp). The corresponding proteins were subjected to homology-based start codon checking . In several cases, this manual curation effort allowed us to detect and correct start codon assignment errors. Commonly, the TSS was upstream of the corrected start codon. This analysis was completed prior to the final evaluation of the TSS data.
In the context of the current manuscript, an additional approach was developed to identify missing gene calls. In particular, even very short protein-coding genes could be included in the genome annotation after completing this procedure. This analysis was also completed prior to final TSS mapping. All of the intergenic regions (defined as not-overlapping protein-coding genes, stable RNAs or transposons) were identified, sorted by length, and concatenated using a 12-mer linker (TTAATTAATTAA). This linker introduced stop codons in all six reading frames. Chunks of this concatenated sequence (2–3 kb) were analyzed against UniProtKB (i.e., SwissProt plus TrEMBL) using the blast service at UniProt (which executes BlastX with nucleotide sequences). All of the subregions with strong blast matches were manually evaluated. Several previously missing gene calls were identified by this approach. A large number of strong blast matches were false positives because of invalid start codon assignments (database ORFs were too long) or the presence in the draft genome sequences of pseudogenes, which frequently have aberrant C-terminal sequences. A large number of Haloferax genomes have been sequenced [86, 87], and some are extremely similar to Hfx. volcanii at the DNA sequence level. By the approach described here, all of the ORF calls produced during those genome sequencing projects were automatically reconciled with the Hfx. volcanii reference genome. All of the previously determined sRNAs genes [11, 33, 34] were added to the annotation.
Read mapping and prediction of transcription start sites (TSSs)
The Illumina reads in FASTQ format were trimmed with a cut-off Phred score of 20 by the program fastq_quality_trimmer from the FASTX toolkit version 0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit/). The following analysis steps were performed using the subcommand “create”, “align” and “coverage” of the tool READemption version 0.3.5 . The poly(A)-tail sequences were removed, and a size-filtering step was applied in which sequences shorter than 12 nt were discarded. The collections of the remaining reads were mapped to the reference genome sequences using segemehl and its remapper lack version 0.2.0 . Mapping statistics (input, aligned, uniquely aligned reads, etc.) can be found in Additional file 1: Table S1. Coverage plots in wiggle format representing the number of aligned reads per nucleotide were generated based on the mapping files and visualized in the Integrated Genome Browser . Each graph was normalized to the total number of reads that could be aligned from the respective library. To restore the original data range, each graph was then multiplied by the minimum number of mapped reads calculated among all libraries.
The TSSs were predicted based on the normalized coverage files obtained from TSSpredator version 1.06  using the “super specific” parameter set but with a minimal enrichment factor of 2.5 instead of 3.0.
The read mapping data underlying the detected TSSs were visually inspected using the read mapping viewer ReadXplorer .
The TSSs of all of the replicons were loaded into one Excel table with accompanying information, such as the replicon identity, localization, coverage in the six datasets, associated genes, etc. Very closely spaced TSSs with a distance of less than 10 nt were considered one TSS. The TSSs were sorted into different classes using the following parameter: all of the TSS were annotated as “novel genes” when the next annotated gene start site was more than 250 nt away. If the TSS was found within an annotated gene, the transcripts were classified as antisense transcripts or internal transcripts depending on the strand of TSS and gene. For known RNAs (stable RNAs and sRNAs), a distance between −3 (retraction by 3) and +250 nt was allowed. A small retraction was assigned for a few sRNAs that seemed to be annotated as slightly too long. For protein-coding genes, TSSs with an annotated gene start 0 to 5 nt downstream were classified as “leaderless transcripts” and TSSs with an annotated gene start 6 to 250 nt downstream were classified as “leadered transcripts”.
Analysis of iTSS and aTSS distributions within open reading frames
Annotated genes for which an iTSS was detected were split into 10 equal-sized sections, where the first section covered the first 10 % of the gene, the second section covered the second 10 %, etc. The number of iTSSs for each section was determined for all genes.
Each TSS was associated with a sequence of 71 bases, and the last base corresponded to the first base of the transcript. We used a fragment of 28 bases centered around the TATA box and preceding the BRE element to calculate a position weight matrix (PWM). Each 71 mer was scored against this PWM using the search function from the MOODS package . The parameters were threshold = 0.001, threshold_from_p = True, convert_log_odds = True, and both_strands = False. The background base composition was calculated from the complete genome and not from the collection of 71-mers. The MOODS search reported hits above the applied threshold, which we considered rather restrictive, and their match position. A prominent match position maximum was also observed (47 % of all positives assigned to that position and 78 % if the adjacent position on each side was also considered). There was a degree of flexibility, albeit restricted, for the distance between the TATA box and the transcription start; therefore, the TATA boxes may have been misaligned when sequence phasing was based on the transcription start site. An iteration was started where only the search hits above the threshold were retained and a slight deviation (4 bases) from the mean position was allowed. Sequences with match positions only few nucleotides from the peak positions were shifted accordingly. A new PWM was calculated from this subset and re-analyzed by a MOODS search against the complete set of 71-mers. As the PWM evolved, previously MOODS-negative upstream sequences became positive and vice versa. After 43 iterations, we obtained complete convergence. The main convergence occurred by the 25th iteration, and thereafter, fewer than 10 sequences changed between subsequent iterations. The resulting 1302 sequences were used to generate an RNA structure logo .
The final PWM was used for a MOODS search with threshold of 0.001 against the original non-shifted 71-mers, and the score and match position were recorded. This process resulted in “stringent matches”. Then, the MOODS search was repeated with a relaxed threshold of 0.01 to calculate the “relaxed matches”.
dRNA-SeQ, differential RNA-Seq; PWM, position weight matrix; TEX, 5’-P-dependent terminal exonuclease; UTR, untranslated region
This study was supported by the German Research Council (Deutsche Forschungsgemeinschaft, DFG) through grants MA1538/11-2 und SO264/14-2 in the framework of the Priority Program SPP1258 “Sensory and regulatory RNAs in Prokaryotes” and by grants MA1538/18-1 and SO264/21-1 in the framework of the “Normalverfahren”, both of which were collaborative efforts of Anita Marchfelder and Jörg Soppa.
Availability of data and material
The RNA-Seq data discussed in this publication were deposited in NCBI’s Gene Expression Omnibus  and are accessible through GEO Series accession number GSE82206 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82206).
DN-S, KAH, CMS, AM, and JS designed the study. KAH, DN-S, and MH performed the experiments. FP, KUF, RH, and AB performed the bioinformatic analyses. JB, FP, AM, and JS analyzed the data. JB, FP, CMS, AM, and JS wrote the manuscript with the help of the other authors. All of the authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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.
- Durand S, Tomasini A, Braun F, Condon C, Romby P. sRNA and mRNA turnover in Gram-positive bacteria. FEMS Microbiol Rev. 2015;39:316–30. doi:10.1093/femsre/fuv007.View ArticlePubMedGoogle Scholar
- Babski J, Maier LK, Heyer R, Jaschinski K, Prasse D, Jäger D, et al. Small regulatory RNAs in Archaea. RNA Biol. 2014;11:484–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Michaux C, Verneuil N, Hartke A, Giard J. Physiological roles of small RNA molecules. Microbiology. 2014;160:1007–19.View ArticlePubMedGoogle Scholar
- Storz G, Vogel J, Wassarman KM. Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell. 2011;43:880–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Barquist L, Vogel J. Accelerating discovery and functional analysis of small RNAs with new technologies. Annu Rev Genet. 2015;49:367–94.View ArticlePubMedGoogle Scholar
- Wagner EG, Romby P. Small RNAs in bacteria and archaea: who they are, what they do, and how they do it. Adv Genet. 2015;90:133–208.View ArticlePubMedGoogle Scholar
- Creecy JP, Conway T. Quantitative bacterial transcriptomics with RNA-seq. Curr Opin Microbiol. 2015;23:133–40.View ArticlePubMedGoogle Scholar
- Sharma CM, Vogel J. Differential RNA-seq: the approach behind and the biological insight gained. Curr Opin Microbiol. 2014;19:97–105.View ArticlePubMedGoogle Scholar
- Jäger D, Sharma CM, Thomsen J, Ehlers C, Vogel J, Schmitz RA. Deep sequencing analysis of the Methanosarcina mazei Gö1 transcriptome in response to nitrogen availability. Proc Natl Acad Sci U S A. 2009;106:21878–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Wurtzel O, Sapra R, Chen F, Zhu Y, Simmons BA, Sorek R. A single-base resolution map of an archaeal transcriptome. Genome Res. 2010;20:133–41.View ArticlePubMedPubMed CentralGoogle Scholar
- Heyer R, Dörr M, Jellen-Ritter A, Späth B, Babski J, Jaschinski K, et al. High throughput sequencing reveals a plethora of small RNAs including tRNA derived fragments in Haloferax volcanii. RNA Biol. 2012;9:1011–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Randau L. RNA processing in the minimal organism Nanoarchaeum equitans. Genome Biol. 2012;13:R63.View ArticlePubMedPubMed CentralGoogle Scholar
- Su AA, Tripp V, Randau L. RNA-Seq analyses reveal the order of tRNA processing events and the maturation of C/D box and CRISPR RNAs in the hyperthermophile Methanopyrus kandleri. Nucleic Acids Res. 2013;41:6250–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Toffano-Nioche C, Ott A, Crozat E, Nguyen AN, Zytnicki M, Leclerc F, et al. RNA at 92 °C: the non-coding transcriptome of the hyperthermophilic archaeon Pyrococcus abyssi. RNA Biol. 2013;10:1211–20.View ArticlePubMed CentralGoogle Scholar
- Jäger D, Förstner KU, Sharma CM, Santangelo TJ, Reeve JN. Primary transcriptome map of the hyperthermophilic archaeon Thermococcus kodakarensis. BMC Genomics. 2014;15:684.View ArticlePubMedPubMed CentralGoogle Scholar
- Li J, Qi L, Guo Y, Yue L, Li Y, Ge W, et al. Global mapping transcriptional start sites revealed both transcriptional and post-transcriptional regulation of cold adaptation in the methanogenic archaeon Methanolobus psychrophilus. Sci Rep. 2015;5:9209.View ArticlePubMedGoogle Scholar
- Maier LK, Dyall-Smith M, Marchfelder A. The adaptive immune system of Haloferax volcanii. Life (Basel). 2015;5:521–37.Google Scholar
- Giroux X, MacNeill SA. Molecular genetic methods to study DNA replication protein function in Haloferax volcanii, a model archaeal organism. Methods Mol Biol. 2015;1300:187–218.View ArticlePubMedGoogle Scholar
- Pickl A, Schönheit P. The oxidative pentose phosphate pathway in the haloarchaeon Haloferax volcanii involves a novel type of glucose-6-phosphate dehydrogenase--the archaeal Zwischenferment. FEBS Lett. 2015;589:1105–11.View ArticlePubMedGoogle Scholar
- Pohlschroder M, Esquivel RN. Archaeal type IV pili and their involvement in biofilm formation. Front Microbiol. 2015;6:190.View ArticlePubMedPubMed CentralGoogle Scholar
- Kramer P, Gäbel K, Pfeiffer F, Soppa J. Haloferax volcanii, a prokaryotic species that does not use the Shine Dalgarno mechanism for translation initiation at 5′-UTRs. PLoS One. 2014;9:e94979.View ArticlePubMedPubMed CentralGoogle Scholar
- Prunetti L, Reuter CJ, Hepowit NL, Wu Y, Barrueto L, Miranda HV, et al. Structural and biochemical properties of an extreme ‘salt-loving’ proteasome activating nucleotidase from the archaeon Haloferax volcanii. Extremophiles. 2014;18:283–93.View ArticlePubMedGoogle Scholar
- Eichler J, Arbiv A, Cohen-Rosenzweig C, Kaminski L, Kandiba L, Konrad Z. N-glycosylation in Haloferax volcanii: adjusting the sweetness. Front Microbiol. 2013;4:403.View ArticlePubMedPubMed CentralGoogle Scholar
- Hawkins M, Malla S, Blythe MJ, Nieduszynski CA, Allers T. Accelerated growth in the absence of DNA replication origins. Nature. 2013;503:544–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Jantzer K, Zerulla K, Soppa J. Phenotyping in the archaea: optimization of growth parameters and analysis of mutants of Haloferax volcanii. FEMS Microbiol Lett. 2011;322:123–30.View ArticlePubMedGoogle Scholar
- Hartman AL, Norais C, Badger JH, Delmas S, Haldenby S, Madupu R, et al. The complete genome sequence of Haloferax volcanii DS2, a model archaeon. PLoS One. 2010;5:e9605.View ArticlePubMedPubMed CentralGoogle Scholar
- Soppa J. Functional genomic and advanced genetic studies reveal novel insights into the metabolism, regulation, and biology of Haloferax volcanii. Archaea. 2011;2011:602408.View ArticlePubMedPubMed CentralGoogle Scholar
- Charlebois RL, Lam WL, Cline SW, Doolittle WF. Characterization of pHV2 from Halobacterium volcanii and its use in demonstrating transformation of an archaebacterium. Proc Natl Acad Sci U S A. 1987;84:8530–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Allers T, Barak S, Liddell S, Wardell K, Mevarech M. Improved strains and plasmid vectors for conditional overexpression of His-tagged proteins in Haloferax volcanii. Appl Environ Microbiol. 2010;76:1759–69.View ArticlePubMedPubMed CentralGoogle Scholar
- Large A, Stamme C, Lange C, Duan Z, Allers T, Soppa J, et al. Characterization of a tightly controlled promoter of the halophilic archaeon Haloferax volcanii and its use in the analysis of the essential cct1 gene. Mol Microbiol. 2007;66:1092–106.View ArticlePubMedGoogle Scholar
- Allers T, Ngo HP, Mevarech M, Lloyd RG. Development of additional selectable markers for the halophilic archaeon Haloferax volcanii based on the leuB and trpA genes. Appl Environ Microbiol. 2004;70:943–53.View ArticlePubMedPubMed CentralGoogle Scholar
- Brenneis M, Hering O, Lange C, Soppa J. Experimental characterization of Cis-acting elements important for translation and transcription in halophilic archaea. PLoS Genet. 2007;3:e229.View ArticlePubMedPubMed CentralGoogle Scholar
- Straub J, Brenneis M, Jellen-Ritter A, Heyer R, Soppa J, Marchfelder A. Small RNAs in haloarchaea: identification, differential expression and biological function. RNA Biol. 2009;6:281–92.View ArticlePubMedGoogle Scholar
- Babski J, Tjaden B, Voss B, Jellen-Ritter A, Marchfelder A, Hess WR, et al. Bioinformatic prediction and experimental verification of sRNAs in the haloarchaeon Haloferax volcanii. RNA Biol. 2011;8:806–16.View ArticlePubMedGoogle Scholar
- Jaschinski K, Babski J, Lehr M, Burmester A, Benz J, Heyer R, et al. Generation and phenotyping of a collection of sRNA gene deletion mutants of the haloarchaeon Haloferax volcanii. PLoS One. 2014;9:e90763.View ArticlePubMedPubMed CentralGoogle Scholar
- Sharma CM, Hoffmann S, Darfeuille F, Reignier J, Findeiss S, Sittka A, et al. The primary transcriptome of the major human pathogen Helicobacter pylori. Nature. 2010;464:250–5.View ArticlePubMedGoogle Scholar
- Stazic D, Voß B. The complexity of bacterial transcriptomes. J Biotechnol. 2015;1656:30147–54.Google Scholar
- Hering O, Brenneis M, Beer J, Suess B, Soppa J. A novel mechanism for translation initiation operates in haloarchaea. Mol Microbiol. 2009;71:1451–63.View ArticlePubMedGoogle Scholar
- Chen Z, Yu H, Li L, Hu S, Dong X. The genome and transcriptome of a newly described psychrophilic archaeon, Methanolobus psychrophilus R15, reveal its cold adaptive characteristics. Environ Microbiol Rep. 2012;4:633–41.PubMedGoogle Scholar
- Slupska MM, King AG, Fitz-Gibbon S, Besemer J, Borodovsky M, Miller JH. Leaderless transcripts of the crenarchaeal hyperthermophile Pyrobaculum aerophilum. J Mol Biol. 2001;309:347–60.View ArticlePubMedGoogle Scholar
- Vesper O, Amitai S, Belitsky M, Byrgazov K, Kaberdina AC, Engelberg-Kulka H, et al. Selective translation of leaderless mRNAs by specialized ribosomes generated by MazF in Escherichia coli. Cell. 2011;147:147–57.View ArticlePubMedPubMed CentralGoogle Scholar
- Shell SS, Wang J, Lapierre P, Mir M, Chase MR, Pyle MM, et al. Leaderless transcripts and small proteins are common features of the mycobacterial translational landscape. PLoS Genet. 2015;11:e1005641.View ArticlePubMedPubMed CentralGoogle Scholar
- de Groot A de, Roche D, Fernandez B, Ludanyi M, Cruveiller S, Pignol D, et al. RNA sequencing and proteogenomics reveal the importance of leaderless mRNAs in the radiation-tolerant bacterium Deinococcus deserti. Genome Biol Evol. 2014;6:932–48.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Wang CC. Capped mRNA with a single nucleotide leader is optimally translated in a primitive eukaryote, Giardia lamblia. J Biol Chem. 2004;279:14656–64.View ArticlePubMedGoogle Scholar
- Londei P. Evolution of translational initiation: new insights from the archaea. FEMS Microbiol Rev. 2005;29:185–200.View ArticlePubMedGoogle Scholar
- Kaberdina AC, Szaflarski W, Nierhaus KH, Moll I. An unexpected type of ribosomes induced by kasugamycin: a look into ancestral times of protein synthesis? Mol Cell. 2009;33:227–36.View ArticlePubMedPubMed CentralGoogle Scholar
- Gorodkin J, Heyer LJ, Brunak S, Stormo GD. Displaying the information contents of structural RNA alignments: the structure logos. Comput Appl Biosci. 1997;13:583–6.PubMedGoogle Scholar
- Chen W, Yang G, He Y, Zhang S, Chen H, Shen P, et al. Nucleotides flanking the start codon in hsp70 mRNAs with very short 5. PLoS One. 2015;10:0138473.Google Scholar
- Mendoza-Vargas A, Olvera L, Olvera M, Grande R, Vega-Alvarado L, Taboada B, et al. Genome-wide identification of transcription start sites, promoters and transcription factor binding sites in E. coli. PLoS One. 2009;4:e7526.View ArticlePubMedPubMed CentralGoogle Scholar
- Dennis PP. Multiple promoters for the transcription of the ribosomal RNA gene cluster in Halobacterium cutirubrum. J Mol Biol. 1985;186:457–61.View ArticlePubMedGoogle Scholar
- Su M, Ling Y, Yu J, Wu J, Xiao J. Small proteins: untapped area of potential biological importance. Front Genet. 2013;4:286.View ArticlePubMedPubMed CentralGoogle Scholar
- Storz G, Wolf YI, Ramamurthi KS. Small proteins can no longer be ignored. Annu Rev Biochem. 2014;83:753–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Landry CR, Zhong X, Nielly-Thibault L, Roucou X. Found in translation: functions and evolution of a recently discovered alternative proteome. Curr Opin Struct Biol. 2015;32:74–80.View ArticlePubMedGoogle Scholar
- Klein C, Aivaliotis M, Olsen JV, Falb M, Besir H, Scheffer B, et al. The low molecular weight proteome of Halobacterium salinarum. J Proteome Res. 2007;6:1510–8.View ArticlePubMedGoogle Scholar
- Wethmar K. The regulatory potential of upstream open reading frames in eukaryotic gene expression. Wiley Interdiscip Rev RNA. 2014;5:765–78.View ArticlePubMedGoogle Scholar
- Barbosa C, Peixeiro I, Romão L. Gene expression regulation by upstream open reading frames and human disease. PLoS Genet. 2013;9:e1003529.View ArticlePubMedPubMed CentralGoogle Scholar
- Maier LK, Lange SJ, Stoll B, Haas KA, Fischer S, Fischer E, et al. Essential requirements for the detection and degradation of invaders by the Haloferax volcanii CRISPR/Cas system I-B. RNA Biol. 2013;10:865–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Fischer S, Maier LK, Stoll B, Brendel J, Fischer E, Pfeiffer F, et al. An archaeal immune system can detect multiple protospacer adjacent motifs (PAMs) to target invader DNA. J Biol Chem. 2012;287:33351–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Haas BJ, Chin M, Nusbaum C, Birren BW, Livny J. How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? BMC Genomics. 2012;13:734.View ArticlePubMedPubMed CentralGoogle Scholar
- Maier LK, Benz J, Fischer S, Alstetter M, Jaschinski K, Hilker R, et al. Deletion of the Sm1 encoding motif in the lsm gene results in distinct changes in the transcriptome and enhanced swarming activity of Haloferax cells. Biochimie. 2015;117:129–37.View ArticlePubMedGoogle Scholar
- Koide T, Reiss DJ, Bare JC, Pang WL, Facciotti MT, Schmid AK, et al. Prevalence of transcription promoters within archaeal operons and coding sequences. Mol Syst Biol. 2009;5:285.View ArticlePubMedPubMed CentralGoogle Scholar
- Gomes-Filho JV, Zaramela LS, Italiani VC, Baliga NS, Vêncio RZ, Koide T. Sense overlapping transcripts in IS1341-type transposase genes are functional non-coding RNAs in archaea. RNA Biol. 2015;12:490–500.View ArticlePubMedPubMed CentralGoogle Scholar
- Zago MA, Dennis PP, Omer AD. The expanding world of small RNAs in the hyperthermophilic archaeon Sulfolobus solfataricus. Mol Microbiol. 2005;55:1812–28.View ArticlePubMedGoogle Scholar
- Phok K, Moisan A, Rinaldi D, Brucato N, Carpousis AJ, Gaspin C, et al. Identification of CRISPR and riboswitch related RNAs among novel noncoding RNAs of the euryarchaeon Pyrococcus abyssi. BMC Genomics. 2011;12:312.View ArticlePubMedPubMed CentralGoogle Scholar
- Khorkova O, Myers AJ, Hsiao J, Wahlestedt C. Natural antisense transcripts. Hum Mol Genet. 2014;23:R54–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Villegas VE, Zaphiropoulos PG. Neighboring gene regulation by antisense long non-coding RNAs. Int J Mol Sci. 2015;16:3251–66.View ArticlePubMedPubMed CentralGoogle Scholar
- Nishizawa M, Ikeya Y, Okumura T, Kimura T. Post-transcriptional inducible gene regulation by natural antisense RNA. Front Biosci (Landmark Ed). 2015;20:1–36.View ArticleGoogle Scholar
- Ietswaart R, Wu Z, Dean C. Flowering time control: another window to the connection between antisense RNA and chromatin. Trends Genet. 2012;28:445–53.View ArticlePubMedGoogle Scholar
- Chen HM, Neiman AM. A conserved regulatory role for antisense RNA in meiotic gene expression in yeast. Curr Opin Microbiol. 2011;14:655–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Heueis N, Vockenhuber MP, Suess B. Small non-coding RNAs in streptomycetes. RNA Biol. 2014;11:464–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Georg J, Hess WR. cis-antisense RNA, another level of gene regulation in bacteria. Microbiol Mol Biol Rev. 2011;75:286–300.View ArticlePubMedPubMed CentralGoogle Scholar
- Becker A, Overlöper A, Schlüter JP, Reinkensmeier J, Robledo M, Giegerich R, et al. Riboregulation in plant-associated alpha-proteobacteria. RNA Biol. 2014;11:550–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Brantl S. Regulatory mechanisms employed by cis-encoded antisense RNAs. Curr Opin Microbiol. 2007;10:102–9.View ArticlePubMedGoogle Scholar
- Thomason MK, Bischler T, Eisenbart SK, Förstner KU, Zhang A, Herbig A, et al. Global transcriptional start site mapping using differential RNA sequencing reveals novel antisense RNAs in Escherichia coli. J Bacteriol. 2015;197:18–28.View ArticlePubMedGoogle Scholar
- Shao W, Price MN, Deutschbauer AM, Romine MF, Arkin AP. Conservation of transcription start sites within genes across a bacterial genus. MBio. 2014;5:e01398-14.View ArticlePubMedPubMed CentralGoogle Scholar
- Bell SD, Magill CP, Jackson SP. Basal and regulated transcription in Archaea. Biochem Soc Trans. 2001;29:392–5.View ArticlePubMedGoogle Scholar
- Soppa J. Transcription initiation in Archaea: facts, factors and future aspects. Mol Microbiol. 1999;31:1295–305.View ArticlePubMedGoogle Scholar
- Reiter WD, Palm P, Zillig W. Analysis of transcription in the archaebacterium Sulfolobus indicates that archaebacterial promoters are homologous to eukaryotic pol II promoters. Nucleic Acids Res. 1988;16:1–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Soppa J. Normalized nucleotide frequencies allow the definition of archaeal promoter elements for different archaeal groups and reveal base-specific TFB contacts upstream of the TATA box. Mol Microbiol. 1999;31:1589–92.View ArticlePubMedGoogle Scholar
- Korhonen J, Martinmäki P, Pizzi C, Rastas P, Ukkonen E. MOODS: fast search for position weight matrix matches in DNA sequences. BioInformatics. 2009;25:3181–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Dambeck M, Soppa J. Characterization of a Haloferax volcanii member of the enolase superfamily: deletion mutant construction, expression analysis, and transcriptome comparison. Arch Microbiol. 2008;190:341–53.View ArticlePubMedGoogle Scholar
- Dugar G, Herbig A, Förstner KU, Heidrich N, Reinhardt R, Nieselt K, et al. High-resolution transcriptome maps reveal strain-specific regulatory features of multiple Campylobacter jejuni isolates. PLoS Genet. 2013;9:e1003495.View ArticlePubMedPubMed CentralGoogle Scholar
- Berezikov E, van Tetering G, Verheul M, van de Belt J, van Laake L, Vos J, et al. Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Res. 2006;16:1289–98.View ArticlePubMedPubMed CentralGoogle Scholar
- Pfeiffer F, Oesterhelt D. A manual curation strategy to improve genome annotation: application to a set of haloarchael genomes. Life (Basel). 2015;5:1427–44.Google Scholar
- Pfeiffer F, Broicher A, Gillich T, Klee K, Mejía J, Rampp M, et al. Genome information management and integrated data analysis with HaloLex. Arch Microbiol. 2008;190:281–99.View ArticlePubMedPubMed CentralGoogle Scholar
- Lynch EA, Langille MG, Darling A, Wilbanks EG, Haltiner C, Shao KS, et al. Sequencing of seven haloarchaeal genomes reveals patterns of genomic flux. PLoS One. 2012;7:e41389.View ArticlePubMedPubMed CentralGoogle Scholar
- Becker EA, Seitzer PM, Tritt A, Larsen D, Krusor M, Yao AI, et al. Phylogenetically driven sequencing of extremely halophilic archaea reveals strategies for static and dynamic osmo-response. PLoS Genet. 2014;10:e1004784.View ArticlePubMedPubMed CentralGoogle Scholar
- Förstner KU, Vogel J, Sharma CM. READemption-a tool for the computational analysis of deep-sequencing-based transcriptome data. BioInformatics. 2014;30:3421–3.View ArticlePubMedGoogle Scholar
- Hoffmann S, Otto C, Kurtz S, Sharma CM, Khaitovich P, Vogel J, et al. Fast mapping of shorcit sequences with mismatches, insertions and deletions using index structures. PLoS Comput Biol. 2009;5:e10000502.View ArticleGoogle Scholar
- Nicol JW, Helt GA, Blanchard Jr SG, Raja A, Loraine AE. The integrated genome browser: free software for distribution and exploration of genome-scale datasets. BioInformatics. 2009;25:2730–1.View ArticlePubMedPubMed CentralGoogle Scholar
- Hilker R, Stadermann KB, Doppmeier D, Kalinowski J, Stoye J, Straube J, et al. ReadXplorer–visualization and analysis of mapped sequences. BioInformatics. 2014;30:2247–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10.View ArticlePubMedPubMed CentralGoogle Scholar