A combination of improved differential and global RNA-seq reveals pervasive transcription initiation and events in all stages of the life-cycle of functional RNAs in Propionibacterium acnes, a major contributor to wide-spread human disease
© Lin et al.; licensee BioMed Central Ltd. 2013
Received: 29 May 2013
Accepted: 11 September 2013
Published: 14 September 2013
Sequencing of the genome of Propionibacterium acnes produced a catalogue of genes many of which enable this organism to colonise skin and survive exposure to the elements. Despite this platform, there was little understanding of the gene regulation that gives rise to an organism that has a major impact on human health and wellbeing and causes infections beyond the skin. To address this situation, we have undertaken a genome–wide study of gene regulation using a combination of improved differential and global RNA-sequencing and an analytical approach that takes into account the inherent noise within the data.
We have produced nucleotide-resolution transcriptome maps that identify and differentiate sites of transcription initiation from sites of stable RNA processing and mRNA cleavage. Moreover, analysis of these maps provides strong evidence for ‘pervasive’ transcription and shows that contrary to initial indications it is not biased towards the production of antisense RNAs. In addition, the maps reveal an extensive array of riboswitches, leaderless mRNAs and small non-protein-coding RNAs alongside vegetative promoters and post-transcriptional events, which includes unusual tRNA processing. The identification of such features will inform models of complex gene regulation, as illustrated here for ribonucleotide reductases and a potential quorum-sensing, two-component system.
The approach described here, which is transferable to any bacterial species, has produced a step increase in whole-cell knowledge of gene regulation in P. acnes. Continued expansion of our maps to include transcription associated with different growth conditions and genetic backgrounds will provide a new platform from which to computationally model the gene expression that determines the physiology of P. acnes and its role in human disease.
Propionibacterium acnes is a member of the Actinobacteria, a phylum that includes Streptomyces, Mycobacterium and Corynebacterium. It is renown through its associated with acne vulgaris , the most common of all human skin diseases, as well as life-threatening diseases , such as meningitis  and endocarditis . Most recently, evidence has emerged that P. acnes infection causes chronic back pain following physical injury . More usually, it is a harmless commensal of the skin, residing ubiquitously in sebaceous follicles . To adapt, colonise and survive, P. acnes needs to sense and respond to changes in its natural environment , yet almost nothing is known about how the physiology and growth of P. acnes is shaped by regulated gene expression. The 2.6 Mbp genome of P. acnes strain KPA171202, the first to be sequenced, contains 2,333 annotated genes . However, the organisation and regulation of the transcription units within this clinically important organism remained to be determined experimentally. Indeed, prior to the work reported here, no transcription start sites (TSSs) had been mapped. Other key aspects of gene regulation for which no information was available were mRNA turnover , which ensures translation follows programs of transcription, the generation of RNA components of the translational machinery [10, 11], and the prevalence of small regulatory RNAs . Recently, the importance of understanding P. acnes gene regulation was exemplified by a study that found the ability of different strains of P. acnes to cause disease stems from divergence in the expression as well as the content of their genomes .
In the first part of this report, we describe improved global and differential RNA-sequencing (RNA-seq) approaches (Additional file 1) and their use in the construction of the first nucleotide-resolution transcriptome maps of P. acnes KPA171202, maps that show sites of RNA processing and degradation in addition to sites of transcription initiation. In the second part, we show how analysis of these maps can shed light on many of the key steps in bacterial gene expression and regulation. We describe ‘pervasive’ transcription, riboswitches, leaderless mRNAs, small non-protein-coding RNAs, post-transcriptional control and unusual tRNA processing. Expansion of these maps to incorporate transcriptional landscapes under different conditions and genetic backgrounds will ultimately reveal how regulated gene expression shapes the physiology and growth of P. acnes. As per the original differential (dRNA-seq) approach , we distinguished 5′ ends corresponding to transcription initiation from those generated through RNA processing and degradation on the basis of their 5′-phosphorylation status. This was done here, however, using tobacco acid pyrophosphatase (TAP), an enzyme that converts a 5′ triphosphate to monophosphate , prior to constructing and sequencing cDNA libraries of native 5′-end segments (Additional file 1). The vast majority of primary transcripts are synthesised with a 5′-triphosphate group, while the 5′ ends of most ‘secondary’ transcripts, i.e. those generated through RNA processing and degradation, have a monophosphate group [9, 16, 17]. Thus, an increased number of sequencing reads from a 5′ end following TAP treatment is an identifier of a transcription start site (TSS). The original dRNA-seq approach differentiated 5′ ends using Terminator™ exonuclease (TEX), which removes 5′-monophosphorylated transcripts . To determine 3′ as well as 5′ boundaries and the abundance of transcripts, aliquots of some RNA samples were also analysed using a global RNA-sequencing (gRNA-seq) approach (Additional file 1) that avoids the use of PCR . As indicated above, dRNA-seq identifies native 5′-ends only.
Overview of approach
We analysed the transcriptomes of duplicate cultures of P. acnes strain KPA171202 grown exponentially in Holland’s Synthetic Medium (Additional file 2). This defined medium was chosen as it supports highly reproducible growth, which reduces experimental variation and in turn allows underlying features to be identified with increased certainty. It can also be modified easily to investigate the contribution of individual components to growth and cellular physiology. To assess the sensitivity of our dRNA-seq approach to changes in gene expression, samples were taken following subculture with and without potassium downshift (i.e. removal of potassium sources from the medium). We choose this change because of its long-established relevance to skin, e.g. potassium levels fluctuate as a result of perspiration  and potassium deficiency is associated with dermatoses . Moreover, P. acnes genes inducible by potassium downshift were predictable from prior studies of other bacteria . Thus, the differential approach described here used eight cDNA libraries; 2 replicates × 2 conditions × 2 treatments (minus or plus TAP treatment). 3 to 6 million reads were obtained for each library and mapped onto the P. acnes genome. Next we counted, for each library, the number of times each genome position was the first nucleotide in sequence reads, i.e. associated with a 5′ end in vivo. Then the reads corresponding to minus and plus TAP treatment were compared using M-A scatterplots for each replicate and condition. Samples from one of the cultures were also analysed by 5′ RACE, high-density microarrays and gRNA-seq to allow comparison with more widely used approaches.
Transcription start sites
Processing and degradation sites (PDSs)
Transcription and maturation of stable RNAs
In stark contrast to what has been found for B. subtilis, which along with E. coli is one of the main model systems in which tRNA processing has been studied in detail , none of the P. acnes tRNA genes (Additional file 6) are part of the three rRNA operons. Another striking difference is that most P. acnes tRNA genes are transcribed individually: we only found one example of a tricistronic tRNA operon (Val, GAC; Cys, GCA; Gly, GCC), and two examples of dicistronic operons (Met, CAT; Thr, GGT; and Asp, GTC; Phe, GAA). Thus, co-transcription does not appear to be a major means of regulating stable RNA production in P. acnes, unlike the situation in B. subtilis.
Processing within mRNA
We next analysed the sequences upstream of the 1106 TSSs associated with step increases in transcription, i.e. the 5′ boundaries of obvious transcription units. This revealed that the vast majority had appropriately positioned sequences matching the −10 box consensus. For example, using MEME, we identified 872 (79%) that matched the single most common sequence variant (TAnnnT). As an aside, this finding reinforces the fact that the combined RNA-seq approach described here identifies bona fide TSSs. Computational predictions of TSSs in Propionibacterium and related genera that utilise the promoters identified here as a learning set will be presented elsewhere. Consistent with the analysis of the promoters of rRNA, r-protein and tRNA genes (Additional file 7 and Figure 7), the overall level of sequence conservation at the −35 position was considerably lower. Nevertheless, promoter sequences were identified that also matched the single most common sequence variant of the −35 box consensus (GnTTnG). In addition to the promoters of rRNA, r-proteins and tRNA genes, this included the promoters of the genes of translation factors EF-Tu (PPA1873), IF-2 (PPA1493) and IF-3 (PPA1414) and central metabolism enzymes, e.g. alanine dehydrogenase (PPA2274), dihydrolipoamide acyltransferase (PPA0693), uridylate kinase (PPA1519), 3-oxoacyl-(acyl-carrier-protein) reductase (PPA1533), cytochrome d ubiquinol oxidase subunit I (PPA0176), fructose-1,6-bisphosphate aldolase (PPA2024), isopentenyl diphosphate delta isomerase (PPA2115), nitric-oxide reductase subunit B (PPA1975), and polynucleotide phosphorylase (PPA1471). Moreover, these promoters were associated with some of the highest transcript levels (data not shown), consistent with the well-established finding that promoters with matches to a consensus tend to be ‘strong’ .
Uncovering multiple layers of regulation
We also found evidence of post-transcriptional control: much of the transcription of the nrdAB operon (encoding subunits of a RNR) appears to terminate before the first structural gene (Figure 8). Consistent with this interpretation, the 5′ UTR region of nrdAB is annotated as containing a cobalamin riboswitch , a cis-regulatory element that is widely distributed in the 5′ UTRs of cobalamin (vitamin B12)-related genes in bacteria [79–82]. Interestingly, the 5′ UTR of nrdDG (encoding an anaerobic RNR and activating protein), but not the nrdRJ operon is also annotated as containing a cobalamin riboswitch. Furthermore, the activity of the RNR encoded by nrdJ, which is co-transcribed with nrdR, is cobalamin dependent , and we detected expression of cobalamin biosynthetic genes (data not shown). The above suggests that cobalamin is present at sufficient levels to activate the riboswitches, which in down regulates the expression of nrdAB and nrdDG. The result is that much of the RNR production is via nrdJ.
Our results also lead us to propose that the NrdR repressor is active under the conditions used for this study. The bulk of the transcription of nrdRJ appears to initiate at the distal promoter, not the proximal promoter overlapped by a pair of nrd-boxes, and we did not detect transcription initiation in the immediate vicinity of the pair of nrd-boxes located upstream of nrdDG. We did detect relatively high levels of transcription from an nrdAB promoter, but this is overlapped by only a single nrd-box. We speculate that should a promoter exist in the vicinity of the nrd-boxes located upstream of nrdDG inactivation of NrdR will produce a transcript lacking a functional riboswitch, thereby removing the cobalamin regulation. The RNR encoded by the nrdDG operon is thought to function under anaerobic conditions.
Comparison with standard 5′ RACE
Identification of potential sRNAs
Re-annotation of protein-coding genes and operon structures
We also noticed that a significant proportion of TSSs associated with significant step increases in transcription were internal to the 5′ half of annotated genes (for examples, see Figure 11, panels C & D) suggesting that the actual gene might be shorter. Consistent with this notion, we have been able to find RBS along with appropriately spaced start codons downstream of many of these TSSs (Additional file 10) and homologous genes that lack sequences matching the 5′ end of the original gene annotation (panels C and D). Our combined RNA-seq approach also revealed many examples of operon structures that differ significantly from bioinformatics predictions (for example, see panel E). This was not particular surprising; it is known that even the best bioinformatic approaches are not completely accurate . Nevertheless, achieving accurate information on gene and operon structures is essential for gene expression and regulation to be modelled  at the level of the whole cell . Our transcriptome approach and data should hasten the achievement of this goal for P. acnes.
Here we describe a number of mechanistic insights gained from an improved dRNA-seq approach that distinguished sites of transcription initiation without erasing the secondary transcriptome using TEX, an enzyme that in our hands can degrade a substantial proportion of 5′-triphosphorylated RNA under the conditions recommended by the vendor (Additional file 11). With the advent of sequencing techniques that can provide in excess of 100 million reads (e.g. Illumina Solexa) there is now no need to erase the secondary transcriptome in order to detect transcription start sites. We simply used TAP  to distinguish tri- from mono-phosphorylated 5′ ends. This enzyme was used in earlier dRNA-seq approaches, but to facilitate the cloning of 5′-fragments remaining after TEX treatment [14, 95]. Other improvements were to fragment the RNA after the addition of the 5′ adaptor to improve the efficient cloning of 5′ ends from large transcripts, and to combine with a gRNA-seq approach that does not require an amplification step . The latter – used here for the first time to study bacterial transcription - allowed us to identify the 3′, as well as 5′, boundaries of transcripts. It should be particularly beneficial for the study of organisms with a high GC content in their genomes, as the amplification of segments of such genomes (or transcriptomes) is prone to PCR bias and artefacts . Moreover, as we included biological replicates in our dRNA-seq approach and incorporated a statistical method that is ideally suited to the analysis of dRNA-seq data, we are confident that our analyses are not dominated by false positives. This is particularly relevant with regard to our finding that the majority of TSSs in P. acnes are not associated with step increases in transcription that continued into annotated genes or produced discrete RNAs of high abundance relative to flanking regions (Additional file 3).
Evidence for pervasive transcription on a genome scale has previously been obtained for several bacteria [14, 32–46, 100]. However, this has largely been in the form of the identification of transcripts antisense to annotated genes . By including biological replicates and mapping TSSs in addition to transcripts, our study shows that pervasive transcription in P. acnes stems as much from transcription of the coding strand as the non-coding strand of annotated genes. Of the thousands of TSSs identified within annotated genes (and not associated with obvious transcription of a flanking gene), approximately half produced transcripts sense to the coding strand. The only strong bias we have identified so far is for the leading strand of replication (data not shown). Our interpretation is that clashes with oncoming DNA replication hinder pervasive transcription initiation on the lagging strand.
The initiation of pervasive transcription is not random. MEME analysis of sequences immediately upstream of TSSs associated with pervasive transcription revealed that a high proportion (61%) matched the single most common sequence variant (TAnnnT) of the −10 box of P. acnes vegetative promoters (data not shown). It is our view that much of the pervasive transcription observed in bacteria is a consequence of the ability of RNA polymerases to recognise a range of sequences, which means that these enzymes can initiate transcription from sub-optimal sites albeit at reduced frequency. Just because a region is transcribed (i.e. active) does not mean that it is has a function . Nevertheless, pervasive transcription may be of evolutionary significance by facilitating the transcription of genes acquired horizontally and in turn the production of products that could confer a selective advantage. Pervasive transcription might also have been the source of bona fide sRNA regulation. Through spontaneous mutation of promoter regions upstream of TSSs or the acquisition of mobile elements such as REP sequences, which are known to stabilise transcripts , small RNAs could increase in abundance and their likelihood of being subject to selection.
Very recent work suggests that the folding architecture of the chromosome influences the location of pervasive transcription . This is consistent with a model in which pervasive transcription is largely driven by the ability of RNA polymerases, given access, to initiate transcription from sub-optimal sites albeit at reduced frequency. Nevertheless, the growing evidence for pervasive transcription in bacteria challenges the dogma that transcription starts and ends just before and after a gene. Moreover, it seems likely that mechanisms exist to minimise the accumulation of pervasive transcripts. One means of controlling pervasive transcripts is likely to be RNA degradation. It is well established that RNA turnover is particularly rapid in the absence of protection by RNA-binding proteins or translating ribosomes and appropriately located structures [9, 17, 103, 104]. In other words, the transcriptional landscape may be defined by the stability of bacterial transcripts more than is currently appreciated.
In addition to providing additional evidence for pervasive transcription, we have identified over a thousand TSSs and associated transcripts encompassing all classes of functional RNA, mono- and poly-cistronic mRNA, ribosomal RNA, transfer RNA, and ubiquitous small RNAs (Figure 2), This alone is an important milestone along the route to understanding the cellular workings of P. acnes. In addition, we have identified changes in gene expression resulting from potassium downshift (Figure 3), post-transcriptional steps (Figures 5 and 6) including unusual 3′ processing of tRNA (Figure 4), features of vegetative promoters (Figure 7), potential transcription factor-binding sites (Figures 8 and 9), functioning riboswitches as well as a number of sRNAs (Figure 10), and an abundance of leaderless mRNAs (Figure 11). We have also shown how knowledge of the above can be used to build models of gene regulation that inform experimental investigation (Figures 8 and 9). Simply knowing the position of a TSS narrows the search area for transcription factor-binding sites. For example, inspection of the promoter of the kdp operon identified a direct repeat overlapping the −35 region (Figure 3, inset), which is characteristic of the binding sites of the response regulators of two-component systems .
The prevalence of leaderless mRNAs in P. acnes is in stark contrast to the situation in E. coli, the main bacterial system in which the translation of leaderless mRNA has been studied [106, 107]. Indeed, only two examples of E. coli leaderless mRNA have been widely reported, the cI repressor of bacteriophage lambda  and the tetR repressor of transposon Tn1721 . The association between leaderless mRNA and repressors within mobile genetic elements in E. coli has been extended to the repressors of the Rac, e14 and Qin prophages by our own deep RNA-seq analysis of the E. coli transcriptome (unpubl. result). We speculate that some aspect of the translation of these leaderless mRNAs may be important in linking the mobilisation of the corresponding genetic elements and the physiological of their host. Recently, it has been shown that stress induces the production of specialised ribosomes that selectively translate a group of mRNAs made leaderless by MazF , an endoribonuclease of a toxin-antitoxin (TA) module. Like their mRNA targets, the specialised ribosomes are produced by MazF cleavage, which removes 43 nt from the 3′ end of E. coli 16S rRNA . Intriguingly, we have mapped a processing site 53 nt from the 3′ end of P. acnes 16S rRNA (data not shown). This raises the possibility that specialised ribosomes, similar to those generated by MazF in E. coli, could mediate much of the translation in P. acnes. However, unlike the situation described for E. coli[110–112], the translation of leaderless mRNA in P. acnes does not appear to require that the start codon is AUG. As described above, a significant proportion of the leaderless mRNA in P. acnes have GUG (29%) or UUG (3%) start codons in place of AUG (68%) (Additional file 9).
Regardless of the actual mechanism by which leaderless mRNAs are translated, our study adds to a growing body of evidence that leaderless mRNAs are prevalent outside E. coli and its closest relatives and the notion that the mechanism of their translation may represent an ancient milestone in the evolution of gene expression [106, 107]. A gene ontology analysis of leaderless mRNA in P. acnes revealed a wide distribution of cellular roles (data not shown). Thus, since the emergence of translation mediated by a Shine-Dalgarno interaction, there does not seem to have been divergence in terms of cellular functions that are dependent on leaderless translation in P. acnes. It will be interesting to establish for P. acnes whether there is a correlation between leaderless translation and the level of gene expression, as measured by protein levels, or the response of genes under conditions of stress or both.
We estimate that around 250 reads (gRNA-seq) correspond to 1 transcript per cell, assuming that the levels of RNase P and tmRNA are similar to those in E. coli[113–116]. This appears to be reasonable; our estimate yields 60,000 ribosomes per cell, which is within the range reported for E. coli and other bacteria, when applied to the 15 million reads for P. acnes 5S rRNA. Many of the mRNAs we identified were associated with less than 250 reads. This does mean necessarily that not every cell expresses the corresponding gene, only that it was expressed for a proportion of the P. acnes cell cycle. Moreover, many mRNAs will not need to be continuously present to ensure translation produces sufficient protein for the daughter cell at division, given that the doubling time is relatively long (~6 hours). We estimate that transcription associated with pervasive initiation corresponds on average to 0.06 transcripts per cell (15 reads). This relatively low level is perhaps consistent with a background of sporadic transcription.
In summary, by combining the differential and global RNA-sequencing approaches used here to analyse P. acnes, it is clear that major step increases in both knowledge and understanding of gene organisation and regulation can be obtained for any bacterial species, which in turn would inform experimental investigation, as illustrated using the nrdR operon and pqs locus, as well as further computational approaches. With regard to the latter, our data can be mined further, for example, to identify cis-regulatory signals that control transcription termination, initiation from the promoters of genes with shared cellular function, RNA processing and degradation, and to predict the potential structures and targets of small RNAs. Our RNA-seq approach can also be used to identify and compare genes expressed under particular conditions. There is excellent agreement between the microarray and gRNA-seq data  and we are currently comparing the expression profiles obtained here for cells growth in liquid culture with those of cells grown as a biofilm (Lin and McDowall, unpublished data) as well as cells grown by others in a rich complex medium . With continuing annotation, it should be possible eventually to computationally model the P. acnes cell and its interaction with the environment and animal hosts .
P. acnes and its cultivation in defined media
Propionibacterium acnes strain KPA171202 was obtained from Ulm University, Göttingen, Germany  and cultivated in an anaerobic workstation (MACS-MG-1000, Don Whitely Scientific) at 34°C under 80% [v/v] N2, 10% [v/v] CO2, and 10% [v/v] H2. All analyses were done using cells cultivated without shaking in 100 mL of modified Holland Synthetic Medium (HSM) [84, 118] in a 250-mL Erlenmeyer flask. Inocula were prepared in two stages. First a single colony isolated from the surface of reinforced clostridial agar  was used to inoculate 10 mL of tryptone-yeast extract-glucose (TYG) broth  in a 30 mL plastic universal bottle. After growing to stationary phase, an aliquot was used to inoculate 100 mL of TYG broth to an OD600 of 0.2. The culture was then incubated to an OD600 of 1.0, after which cells were harvested by centrifugation (3,000 × g for 20 min) and washed by first resuspending in 10 mL of HSM (pre-warmed to 34°C) and then repeating the harvesting step. Finally, the cells were resuspended in 10 mL of pre-warmed HSM and an appropriate aliquot was used to inoculate 100 mL of pre-warmed HSM to an OD600 of 0.2. To study the effects of a potassium downshift (depletion from growth media), a 100 mL culture of P. acnes was prepared as above and grown to an OD600 of 1.0, after which the culture was separated into two equal halves and cells harvested as described above. One half was washed using standard HSM, then used to inoculate 100 mL of fresh HSM and reincubated. The other half was processed in the same way, except the HSM lacked potassium (KH2PO4 and K2HPO4). After 1 h of reincubation, 12.5 mL of stop solution (95% [v/v] ethanol; 5% [v/v] phenol) was added to inhibit cell metabolism , and the cells were harvested by centrifugation. When necessary, cell pellets were stored frozen at −80°C.
Isolation of bacterial RNA
Cell pellets of P. acnes were resuspended in Kirby mix , 1 OD600 unit per 100 μL, and transferred to Lysing Matrix B tubes containing fine silica beads (MP Biomedical). Tubes were then placed in a high-speed benchtop homogenizer (Fastprep-24, MP Biomedical; set at 6.5 M/s). Cells were lysed by three cycles of homogenisation for 1 min with cooling between each cycle in an ice-water bath for 1 min. Lysates were extracted using an equal volume of acidic phenol: chloroform: isoamyl alcohol (50: 50: 1) and then chloroform: isoamyl alcohol (49: 1). Nucleic acid in the aqueous phase was precipitated by adding NaCl to 150 mM and 2.5 × volumes of 100% [v/v] ethanol, chilling at −20°C for 1 h, and then harvested by centrifugation (13,000 × g for 30 min at 4°C). Nucleic acid pellets were washed twice with 700 μL of 70% [v/v] ethanol, air dried for 5 min and resuspended in RNase-free water. To remove contaminating DNA, samples were treated with DNase I using conditions described by the vendor (Ambion) and extracted with phenol: chloroform as described above. The concentration and integrity of RNA samples were determined using a NanoDrop™ 1000 spectrophotometer (Thermo Fisher Scientific) and agarose gel electrophoresis , respectively.
The differential RNA-seq data was generated by vertis Biotechnologie AG (Germany) as a service that included the construction of cDNA libraries before and after treatment with TAP (Additional file 1), sequencing of libraries using an Illumina HiSeq platform (single end, 50-bp read length), and the alignment of sequences to the genome (NCBI, accession number AE017283). It should be noted that the 5′-sequencing adaptor was ligated to transcripts prior to fragmentation, thereby allowing the 5′ ends of both long and short transcripts to be detected. RNA was fragmented using a Bioruptor® Next Gen UCD-300™ sonication system (Diagenode), then tailed at the 3′ end using poly(A) polymerase (New England BioLabs), copied into cDNA using M-MLV reverse transcriptase (RNase H minus, AffinityScript, Agilent) and an oligo-dT primer, amplified by PCR and fractioned using gel electrophoresis. Fragments of 250–500 bp were selected for Illumina sequencing. Reads were trimmed of 5′ adapter and poly(A) sequences and mapped using the CLC Genomics Workbench and standard settings. Prior to differential RNA-seq, samples were enriched for mRNA using MICROBE xpress™-Bacteria beads, as described by the manufacturer (Ambion).
Global transcriptome sequencing was performed by Dr Lira Mamanova (Welcome Trust, Sanger Centre, Cambridge, UK) using a published methodology  on samples that were enriched for mRNA. RNA sequences from the global analysis were processed in-house using Galaxy . Adapter sequences were removed and reads trimmed for quality before being mapped to the genome using Bowtie 1.0  with custom parameter: -l 28 for read1, -l 20 for read2, and -y -a --best --strata. Pairs of datasets were compared using M-A (ratio-intensity) scatterplots, where M is Log2 (reads plus/minus TAP treatment), and A is (log2 plus + log2 minus)/2. For each value of A, we calculated the average (μ) and standard deviation (σ) of M in a moving window of 5,000 pairs that were sorted in ascending order of A. Upper and lower envelopes were defined by the equation: μ ± Xσ, and positions outside the envelope recorded, as described previously [22, 23]. The value of X was user defined (see text below for details). Microarray data was collected by Roche NimbleGen (Iceland) as a service using 4 × 72 k format arrays. RNA samples were analysed from duplicate cultures of P. acnes following subculture with and without potassium downshift.
5′ RACE and RT-PCR analysis
The mapping of the 5′ ends of specific transcripts was done using a 2nd generation 5′/3′ RACE kit as described by the vendor (Roche Applied Science), except an aliquot of each RNA sample was treated with Terminator™ 5′-Phosphate-Dependent Exonuclease (TEX), as described by the vendor (Epicentre® Biotechnologies), prior to starting the mapping protocol. The sequences of specific primers are indicated in the legends to relevant figures. Primers were designed with the assistance of Primer 3 software  and purchased from Eurogentec. For the Reverse-transcription polymerase chain reaction (RT-PCR), cDNA was synthesised using SuperScript® RT III (Invitrogen) with random hexamers (100 nM) and 200 ng of RNA template, the rest of the protocol was carried out as stated by manufacturer with no modifications. The PCR reaction was carried out using GoTaq® DNA polymerase (Promega) according to the vendor’s instruction using cDNA diluted with RNase-free water as the template.
Availability of supporting data
Both raw and processed RNA-seq data has been submitted to NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/). Accession: GSE46883 and GSE46810.
This work was supported in part by a grant from the Leeds Foundation for Dermatological Research to R.A. Bojar. Y-fL gratefully acknowledges a White Rose PhD Studentship awarded to K. Stephenson as part of the MICROSYST network. Y-fL and SG thank K. Stephenson, K.T. Holland and R.A. Bojar for guidance during their PhD studies. The global RNA-sequencing was done at The Wellcome Trust Sanger Institute. The McDowall laboratory is supported by funding from the BBSRC.
- Kurokawa I, Danby FW, Ju Q, Wang XL, Xiang LF, Xia LQ, Chen WC, Nagy I, Picardo M, Suh DH: New developments in our understanding of acne pathogenesis and treatment. Exp Dermatol. 2009, 18 (10): 821-832. 10.1111/j.1600-0625.2009.00890.x.PubMedGoogle Scholar
- Perry A, Lambert P: Propionibacterium acnes: infection beyond the skin. Exp Rev Anti-infe. 2011, 9 (12): 1149-1156. 10.1586/eri.11.137.Google Scholar
- Brook I: Meningitis and shunt infection caused by anaerobic bacteria in children. Pediatr Neurol. 2002, 26 (2): 99-105. 10.1016/S0887-8994(01)00330-7.PubMedGoogle Scholar
- Clayton JJ, Baig W, Reynolds GW, Sandoe JA: Endocarditis caused by Propionibacterium species: a report of three cases and a review of clinical features and diagnostic difficulties. J Med Microbiol. 2006, 55 (Pt 8): 981-987.PubMedGoogle Scholar
- Albert HB, Sorensen JS, Christensen BS, Manniche C: Antibiotic treatment in patients with chronic low back pain and vertebral bone edema (Modic type 1 changes): a double-blind randomized clinical controlled trial of efficacy. Eur Spine J. 2013, 22 (4): 697-707. 10.1007/s00586-013-2675-y.PubMed CentralPubMedGoogle Scholar
- Cogen AL, Nizet V, Gallo RL: Skin microbiota: a source of disease or defence?. Br J Dermatol. 2008, 158 (3): 442-455. 10.1111/j.1365-2133.2008.08437.x.PubMed CentralPubMedGoogle Scholar
- Bojar RA, Holland KT: Review: the human cutaneous microflora and factors controlling colonisation. World J Microb Biot. 2002, 18 (9): 889-903. 10.1023/A:1021271028979.Google Scholar
- Bruggemann H, Henne A, Hoster F, Liesegang H, Wiezer A, Strittmatter A, Hujer S, Durre P, Gottschalk G: The complete genome sequence of Propionibacterium acnes, a commensal of human skin. Science. 2004, 305 (5684): 671-673. 10.1126/science.1100330.PubMedGoogle Scholar
- Carpousis AJ, Luisi BF, McDowall KJ: Endonucleolytic Initiation of mRNA Decay in Escherichia coli. Molecular Biology of RNA Processing and Decay in Prokaryotes. Edited by: Condon C. 2009, London, UK: Academic Press, 91-135.Google Scholar
- Deutscher MP: Maturation and degradation of ribosomal RNA in bacteria. Molecular Biology of RNA Processing and Decay in Prokaryotes. Edited by: Condon C. 2009, London, UK: Academic press, 369-391.Google Scholar
- Hartmann RK, Gossringer M, Spath B, Fischer S, Marchfelder A: The Making of tRNAs and More - RNase P and tRNase Z. Molecular Biology of RNA Processing and Decay in Prokaryotes. Edited by: Condon C. 2009, London, UK: Academic Press, 319-368.Google Scholar
- Storz G, Vogel J, Wassarman KM: Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell. 2011, 43 (6): 880-891. 10.1016/j.molcel.2011.08.022.PubMed CentralPubMedGoogle Scholar
- Brzuszkiewicz E, Weiner J, Wollherr A, Thurmer A, Hupeden J, Lomholt HB, Kilian M, Gottschalk G, Daniel R, Mollenkopf HJ: Comparative genomics and transcriptomics of Propionibacterium acnes. Plos One. 2011, 6 (6): e21581-10.1371/journal.pone.0021581.PubMed CentralPubMedGoogle Scholar
- Sharma CM, Hoffmann S, Darfeuille F, Reignier J, Findeiss S, Sittka A, Chabas S, Reiche K, Hackermuller J, Reinhardt R: The primary transcriptome of the major human pathogen Helicobacter pylori. Nature. 2010, 464 (7286): 250-255. 10.1038/nature08756.PubMedGoogle Scholar
- Breter HJ, Rhoads RE: Analysis of NaIO4-oxidized/NaBH4-reduced mRNA cap analogs by high-performance liquid anion-exchange chromatography and tobacco acid pyrophosphatase (EC 188.8.131.52). H-S Z Physiol Chem. 1979, 360 (3): 240-240.Google Scholar
- Bechhofer DH: Messenger RNA Decay and Maturation in Bacillus subtilis. Molecular Biology of RNA Processing and Decay in Prokaryotes. Edited by: Condon C. 2009, London, UK: Academic Press, 231-273.Google Scholar
- Belasco JG: All things must pass: contrasts and commonalities in eukaryotic and bacterial mRNA decay. Nat Rev Mol Cell Biol. 2010, 11 (7): 467-478. 10.1038/nrm2917.PubMed CentralPubMedGoogle Scholar
- Mamanova L, Andrews RM, James KD, Sheridan EM, Ellis PD, Langford CF, Ost TWB, Collins JE, Turner DJ: FRT-seq: amplification-free, strand-specific transcriptome sequencing. Nat Methods. 2010, 7 (2): 130-U163. 10.1038/nmeth.1417.PubMed CentralPubMedGoogle Scholar
- Berenson CS, Burch GE: A study of the Na, K, Cl content of thermal sweat of man collected from small isolated areas of the skin. J Lab Clin Med. 1953, 42: 58-77.PubMedGoogle Scholar
- Borelli F: Research on quantitative variations of potassium of the blood and skin in normal and pathological conditions. Derm Sifilografo. 1932, 7: 353-Google Scholar
- Ballal A, Basu B, Apte SK: The Kdp-ATPase system and its regulation. J Biosci. 2007, 32 (3): 559-568. 10.1007/s12038-007-0055-7.PubMedGoogle Scholar
- Hovatta I, Kimppa K, Lehmussola A, Pasanen T, Saarela J, Saarikko I, Saharinen J, Tiikkainen P, Toivanen T, Tolvanen M: DNA Microarray Data Analysis. 2005, Helsinki: CSC - Scientific Computing Ltd, 2Google Scholar
- Marincs F, Manfield IW, Stead JA, McDowall KJ, Stockley PG: Transcript analysis reveals an extended regulon and the importance of protein-protein co-operativity for the Escherichia coli methionine repressor. Biochem J. 2006, 396 (2): 227-234. 10.1042/BJ20060021.PubMed CentralPubMedGoogle Scholar
- Salgado H, Gama-Castro S, Peralta-Gil M, Diaz-Peredo E, Sanchez-Solano F, Santos-Zavaleta A, Martinez-Flores I, Jimenez-Jacinto V, Bonavides-Martinez C, Segura-Salazar J: RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 2006, 34: D394-D397. 10.1093/nar/gkj156.PubMed CentralPubMedGoogle Scholar
- Breitling R, Armengaud P, Amtmann A, Herzyk P: Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004, 573 (1–3): 83-92.PubMedGoogle Scholar
- Laing E, Smith CP: RankProdIt: A web-interactive Rank Products analysis tool. BMC Res Notes. 2010, 3: 221-10.1186/1756-0500-3-221.PubMed CentralPubMedGoogle Scholar
- Chan PP, Holmes AD, Smith AM, Tran D, Lowe TM: The UCSC Archaeal Genome Browser: 2012 update. Nucleic Acids Res. 2012, 40: D646-652. 10.1093/nar/gkr990.PubMed CentralPubMedGoogle Scholar
- Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW: GenBank. Nucleic Acids Res. 2013, 41: D36-42. 10.1093/nar/gks1195.PubMed CentralPubMedGoogle Scholar
- Schneider KL, Pollard KS, Baertsch R, Pohl A, Lowe TM: The UCSC Archaeal Genome Browser. Nucleic Acids Res. 2005, 34: D407-410.PubMed CentralGoogle Scholar
- Jacquier A: The complex eukaryotic transcriptome: unexpected pervasive transcription and novel small RNAs. Nat Rev Genet. 2009, 10 (12): 833-844.PubMedGoogle Scholar
- Marguerat S, Bahler J: RNA-seq: from technology to biology. Cell Mol Life Sci. 2010, 67 (4): 569-579. 10.1007/s00018-009-0180-6.PubMed CentralPubMedGoogle Scholar
- Albrecht M, Sharma CM, Reinhardt R, Vogel J, Rudel T: Deep sequencing-based discovery of the Chlamydia trachomatis transcriptome. Nucleic Acids Res. 2010, 38 (3): 868-877. 10.1093/nar/gkp1032.PubMed CentralPubMedGoogle Scholar
- Beaume M, Hernandez D, Farinelli L, Deluen C, Linder P, Gaspin C, Romby P, Schrenzel J, Francois P: Cartography of methicillin-resistant S. aureus transcripts: detection, orientation and temporal expression during growth phase and stress conditions. Plos One. 2010, 5 (5): e10725-10.1371/journal.pone.0010725.PubMed CentralPubMedGoogle Scholar
- Cho BK, Zengler K, Qiu Y, Park YS, Knight EM, Barrett CL, Gao Y, Palsson BO: The transcription unit architecture of the Escherichia coli genome. Nat Biotechnol. 2009, 27 (11): 1043-1049. 10.1038/nbt.1582.PubMedGoogle Scholar
- Dornenburg JE, DeVita AM, Palumbo MJ, Wade JT: Widespread antisense transcription in Escherichia coli. Mbio. 2010, 1 (1): e00024-10.PubMed CentralPubMedGoogle Scholar
- Filiatrault MJ, Stodghill PV, Bronstein PA, Moll S, Lindeberg M, Grills G, Schweitzer P, Wang W, Schroth GP, Luo SJ: Transcriptome analysis of Pseudomonas syringae identifies new genes, noncoding RNAs, and antisense activity. J Bacteriol. 2010, 192 (9): 2359-2372. 10.1128/JB.01445-09.PubMed CentralPubMedGoogle Scholar
- Georg J, Voss B, Scholz I, Mitschke J, Wilde A, Hess WR: Evidence for a major role of antisense RNAs in cyanobacterial gene regulation. Mol Syst Biol. 2009, 5: doi:10.1038/msb.2009.63Google Scholar
- Guell M, van Noort V, Yus E, Chen WH, Leigh-Bell J, Michalodimitrakis K, Yamada T, Arumugam M, Doerks T, Kuhner S: Transcriptome complexity in a genome-reduced bacterium. Science. 2009, 326 (5957): 1268-1271. 10.1126/science.1176951.PubMedGoogle Scholar
- Jager D, Sharma CM, Thomsen J, Ehlers C, Vogel J, Schmitz RA: Deep sequencing analysis of the Methanosarcina mazei Go1 transcriptome in response to nitrogen availability. Proc Natl Acad Sci USA. 2009, 106 (51): 21878-21882. 10.1073/pnas.0909051106.PubMed CentralPubMedGoogle Scholar
- Lasa I, Toledo-Arana A, Dobin A, Villanueva M, Vergara-Irigaray M, Segura V, Fagegaltier D, Penades JR, Valle J, de los Mozos IR: Genome-wide antisense transcription drives mRNA processing in bacteria. Proc Natl Acad Sci USA. 2011, 108 (50): 20172-20177. 10.1073/pnas.1113521108.PubMed CentralPubMedGoogle Scholar
- Martin J, Zhu WH, Passalacqua KD, Bergman N, Borodovsky M: Bacillus anthracis genome organization in light of whole transcriptome sequencing. Bmc Bioinformatics. 2010, 11: doi:10.1186/1471-2105-11-S3-S10Google Scholar
- Mendoza-Vargas A, Olvera L, Olvera M, Grande R, Vega-Alvarado L, Taboada B, Jimenez-Jacinto V, Salgado H, Juarez K, Contreras-Moreira B: Genome-wide identification of transcription start sites, promoters and transcription factor binding sites in E. coli. Plos One. 2009, 4 (10): e7526-10.1371/journal.pone.0007526.PubMed CentralPubMedGoogle Scholar
- Mitschke J, Georg J, Scholz I, Sharma CM, Dienst D, Bantscheff J, Voss B, Steglich C, Wilde A, Vogel J: An experimentally anchored map of transcriptional start sites in the model cyanobacterium Synechocystis sp PCC6803. Proc Natl Acad Sci USA. 2011, 108 (5): 2124-2129. 10.1073/pnas.1015154108.PubMed CentralPubMedGoogle Scholar
- Rasmussen S, Nielsen HB, Jarmer H: The transcriptionally active regions in the genome of Bacillus subtilis. Mol Microbiol. 2009, 73 (6): 1043-1057. 10.1111/j.1365-2958.2009.06830.x.PubMed CentralPubMedGoogle Scholar
- Toledo-Arana A, Dussurget O, Nikitas G, Sesto N, Guet-Revillet H, Balestrino D, Loh E, Gripenland J, Tiensuu T, Vaitkevicius K: The Listeria transcriptional landscape from saprophytism to virulence. Nature. 2009, 459 (7249): 950-956. 10.1038/nature08080.PubMedGoogle Scholar
- Wurtzel O, Sapra R, Chen F, Zhu YW, Simmons BA, Sorek R: A single-base resolution map of an archaeal transcriptome. Genome Res. 2010, 20 (1): 133-141. 10.1101/gr.100396.109.PubMed CentralPubMedGoogle Scholar
- Ghora BK, Apirion D: Structural analysis and in vitro processing to P5 rRNA of a 9S RNA molecule isolated from an rne mutant of E. coli. Cell. 1978, 15 (3): 1055-1066. 10.1016/0092-8674(78)90289-1.PubMedGoogle Scholar
- Shahbabian K, Jamalli A, Zig L, Putzer H: RNase Y, a novel endoribonuclease, initiates riboswitch turnover in Bacillus subtilis. EMBO J. 2009, 28 (22): 3523-3533. 10.1038/emboj.2009.283.PubMed CentralPubMedGoogle Scholar
- Robertson HD, Webster RE, Zinder ND: Purification and properties of ribonuclease III from Escherichia coli. J Biol Chem. 1968, 243 (1): 82-91.PubMedGoogle Scholar
- Even S, Pellegrini O, Zig L, Labas V, Vinh J, Brechemmier-Baey D, Putzer H: Ribonucleases J1 and J2: two novel endoribonucleases in B. subtilis with functional homology to E. coli RNase E. Nucleic Acids Res. 2005, 33 (7): 2141-2152. 10.1093/nar/gki505.PubMed CentralPubMedGoogle Scholar
- Mathy N, Benard L, Pellegrini O, Daou R, Wen TY, Condon C: 5′-to-3′ exoribonuclease activity in bacteria: role of RNase J1 in rRNA maturation and 5′ stability of mRNA. Cell. 2007, 129 (4): 681-692. 10.1016/j.cell.2007.02.051.PubMedGoogle Scholar
- Lin Y: Genome-wide analysis of Propionibacterium acnes gene regulation. 2013, Leeds, UK: University of LeedsGoogle Scholar
- Dittmar KA, Mobley EM, Radek AJ, Pan T: Exploring the regulation of tRNA distribution on the genomic scale. J Mol Biol. 2004, 337 (1): 31-47. 10.1016/j.jmb.2004.01.024.PubMedGoogle Scholar
- Betat H, Rammelt C, Morl M: tRNA nucleotidyltransferases: ancient catalysts with an unusual mechanism of polymerization. Cell Mol Life Sci. 2010, 67 (9): 1447-1463. 10.1007/s00018-010-0271-4.PubMedGoogle Scholar
- McDowall KJ, Lin-Chao S, Cohen SN: A + U content rather than a particular nucleotide order determines the specificity of RNase E cleavage. J Biol Chem. 1994, 269 (14): 10790-10796.PubMedGoogle Scholar
- de Boer HA, Gilbert SF, Nomura M: DNA sequences of promoter regions for rRNA operons rrnE and rrnA in Escherichia coli. Cell. 1979, 17 (1): 201-209. 10.1016/0092-8674(79)90308-8.PubMedGoogle Scholar
- Gilbert SF, de Boer HA, Nomura M: Identification of initiation sites for the in vitro transcription of rRNA operons rrnE and rrnA in Escherichia coli. Cell. 1979, 17 (1): 211-224. 10.1016/0092-8674(79)90309-X.PubMedGoogle Scholar
- Young RA, Steitz JA: Tandem promoters direct Escherichia coli rRNA synthesis. Cell. 1979, 17 (1): 225-234. 10.1016/0092-8674(79)90310-6.PubMedGoogle Scholar
- Stewart GC, Bott KF: DNA sequence of the tandem rRNA promoter for B subtilis operon rrnB. Nucleic Acids Res. 1983, 11 (18): 6289-6300. 10.1093/nar/11.18.6289.PubMed CentralPubMedGoogle Scholar
- Nicholson AW: The ribonuclease III superfamily: forms and functions in RNA maturation, decay, and gene silencing. RNAi: A Guide to Gene Silencing. Edited by: Hannon GJ. 2003, Cold Spring Harbor, NY: Cold Spring Harbor Laboratory PressGoogle Scholar
- Jacob AI, Kohrer C, Davies BW, Rajbhandary UL, Walker GC: Conserved Bacterial RNase YbeY Plays Key Roles in 70S Ribosome Quality Control and 16S rRNA Maturation. Mol Cell. 2013, 49 (3): 427-438. 10.1016/j.molcel.2012.11.025.PubMed CentralPubMedGoogle Scholar
- Gausing K: Regulation of ribosome production in Escherichia coli: synthesis and stability of ribosomal RNA and of ribosomal protein messenger RNA at different growth rates. J Mol Biol. 1977, 115 (3): 335-354. 10.1016/0022-2836(77)90158-9.PubMedGoogle Scholar
- Norris TE, Koch AL: Effect of growth rate on the relative rates of synthesis of messenger, ribosomal and transfer RNA in Escherichia coli. J Mol Biol. 1972, 64 (3): 633-649. 10.1016/0022-2836(72)90088-5.PubMedGoogle Scholar
- Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31 (13): 3406-3415. 10.1093/nar/gkg595.PubMed CentralPubMedGoogle Scholar
- Jarrige AC, Mathy N, Portier C: PNPase autocontrols its expression by degrading a double-stranded structure in the pnp mRNA leader. EMBO J. 2001, 20 (23): 6845-6855. 10.1093/emboj/20.23.6845.PubMed CentralPubMedGoogle Scholar
- Portier C, Robert-Le meur M: Escherichia coli polynucleotide phosphorylase expression is autoregulated through an RNase III-dependent mechanism. EMBO J. 1992, 11 (7): 2633-2641.PubMed CentralPubMedGoogle Scholar
- Portier C, Robert-Le meur M: Polynucleotide phosphorylase of Escherichia coli induces the degradation of its RNase III-processed messenger by preventing its translation. Nucleic Acids Res. 1994, 22 (3): 397-403. 10.1093/nar/22.3.397.PubMed CentralPubMedGoogle Scholar
- Gatewood ML, Bralley P, Jones GH: RNase III-dependent expression of the rpsO-pnp operon of Streptomyces coelicolor. J Bacteriol. 2011, 193 (17): 4371-4379. 10.1128/JB.00452-11.PubMed CentralPubMedGoogle Scholar
- Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS: MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009, 37: W202-208. 10.1093/nar/gkp335.PubMed CentralPubMedGoogle Scholar
- Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: A sequence logo generator. Genome Res. 2004, 14 (6): 1188-1190. 10.1101/gr.849004.PubMed CentralPubMedGoogle Scholar
- Harley CB, Reynolds RP: analysis of Escherichia coli promoter sequences. Nucleic Acids Res. 1987, 15 (5): 2343-2361. 10.1093/nar/15.5.2343.PubMed CentralPubMedGoogle Scholar
- Lisser S, Margalit H: Compilation of Escherichia coli mRNA promoter sequences. Nucleic Acids Res. 1993, 21 (7): 1507-1516. 10.1093/nar/21.7.1507.PubMed CentralPubMedGoogle Scholar
- Mulligan ME, Hawley DK, Entriken R, McClure WR: Escherichia coli promoter sequences predict in vitro RNA polymerase selectivity. Nucleic Acids Res. 1984, 12 (1 Pt 2): 789-800.PubMed CentralPubMedGoogle Scholar
- Borovok I, Gorovitz B, Yanku M, Schreiber R, Gust B, Chater K, Aharonowitz Y, Cohen G: Alternative oxygen-dependent and oxygen-independent ribonucleotide reductases in Streptomyces: cross-regulation and physiological role in response to oxygen limitation. Mol Microbiol. 2004, 54 (4): 1022-1035. 10.1111/j.1365-2958.2004.04325.x.PubMedGoogle Scholar
- Torrents E, Grinberg I, Gorovitz-Harris B, Lundstrom H, Borovok I, Aharonowitz Y, Sjoberg BM, Cohen G: NrdR controls differential expression of the Escherichia coli ribonucleotide reductase genes. J Bacteriol. 2007, 189 (14): 5012-5021. 10.1128/JB.00440-07.PubMed CentralPubMedGoogle Scholar
- Herrick J, Sclavi B: Ribonucleotide reductase and the regulation of DNA replication: an old story and an ancient heritage. Mol Microbiol. 2007, 63 (1): 22-34. 10.1111/j.1365-2958.2006.05493.x.PubMedGoogle Scholar
- Hiard S, Maree R, Colson S, Hoskisson PA, Titgemeyer F, van Wezel GP, Joris B, Wehenkel L, Rigali S: PREDetector: a new tool to identify regulatory elements in bacterial genomes. Biochem Biophys Res Commun. 2007, 357 (4): 861-864. 10.1016/j.bbrc.2007.03.180.PubMedGoogle Scholar
- Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A: Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005, 33: D121-D124.PubMed CentralPubMedGoogle Scholar
- Barrick JE, Breaker RR: The distributions, mechanisms, and structures of metabolite-binding riboswitches. Genome Biol. 2007, 8 (11): R239-10.1186/gb-2007-8-11-r239.PubMed CentralPubMedGoogle Scholar
- Franklund CV, Kadner RJ: Multiple transcribed elements control expression of the Escherichia coli btuB gene. J Bacteriol. 1997, 179 (12): 4039-4042.PubMed CentralPubMedGoogle Scholar
- Nahvi A, Sudarsan N, Ebert MS, Zou X, Brown KL, Breaker RR: Genetic control by a metabolite binding mRNA. Chem Biol. 2002, 9 (9): 1043-1049. 10.1016/S1074-5521(02)00224-7.PubMedGoogle Scholar
- Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS: Regulation of the vitamin B12 metabolism and transport in bacteria by a conserved RNA structural element. RNA. 2003, 9 (9): 1084-1097. 10.1261/rna.5710303.PubMed CentralPubMedGoogle Scholar
- Nordlund N, Reichard P: Ribonucleotide reductases. Annu Rev Biochem. 2006, 75: 681-706. 10.1146/annurev.biochem.75.103004.142443.PubMedGoogle Scholar
- Guan S: A novel two-component signal transduction system in Propionibacterium acnes and its association with a putative extracellular signalling peptide. 2011, Leeds: University of LeedsGoogle Scholar
- Breaker RR: Riboswitches and the RNA world. Cold Spring Harb Perspect Biol. 2012, 4 (2): a003566-10.1101/cshperspect.a003566.PubMed CentralPubMedGoogle Scholar
- Henkin TM, Yanofsky C: Regulation by transcription attenuation in bacteria: how RNA provides instructions for transcription termination/antitermination decisions. Bioessays. 2002, 24 (8): 700-707. 10.1002/bies.10125.PubMedGoogle Scholar
- Higgins CF, McLaren RS, Newbury SF: Repetitive extragenic palindromic sequences, mRNA stability and gene expression: evolution by gene conversion? A review. Gene. 1988, 72 (1–2): 3-14.PubMedGoogle Scholar
- Bandyra KJ, Said N, Pfeiffer V, Gorna MW, Vogel J, Luisi BF: The seed region of a small RNA drives the controlled destruction of the target mRNA by the endoribonuclease RNase E. Mol Cell. 2012, 47 (6): 943-953. 10.1016/j.molcel.2012.07.015.PubMed CentralPubMedGoogle Scholar
- Pruitt KD, Tatusova T, Maglott DR: NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007, 35: D61-D65. 10.1093/nar/gkl842.PubMed CentralPubMedGoogle Scholar
- Shine J, Dalgarno L: 3′-terminal sequence of Escherichia coli 16S rRNA: possible role in initiation and termination of protein synthesis. P Aust Biochem Soc. 1974, 7: 72-72.Google Scholar
- Shine J, Dalgarno L: Terminal sequence analysis of bacterial rRNA: correlation between 3′-terminal polypyrimidine sequence of 16S RNA and translational specificity of ribosome. Eur J Biochem. 1975, 57 (1): 221-230. 10.1111/j.1432-1033.1975.tb02294.x.PubMedGoogle Scholar
- Suzek BE, Ermolaeva MD, Schreiber M, Salzberg SL: A probabilistic, method for identifying start codons in bacterial genomes. Bioinformatics. 2001, 17 (12): 1123-1130. 10.1093/bioinformatics/17.12.1123.PubMedGoogle Scholar
- Janssen GR: Eubacterial, archaebacterial, and eukaryotic genes that encode leaderless mRNA. Industrial Microorganisms: Basic and Applied Molecular Genetics. Edited by: Baltz RH, Hegeman G. 1993, Washington, D.C, United States: ASM Press, 59-67.Google Scholar
- Price MN, Huang KH, Alm EJ, Arkin AP: A novel method for accurate operon predictions in all sequenced prokaryotes. Nucleic Acids Res. 2005, 33 (3): 880-892. 10.1093/nar/gki232.PubMed CentralPubMedGoogle Scholar
- Vockenhuber MP, Sharma CM, Statt MG, Schmidt D, Xu ZJ, Dietrich S, Liesegang H, Mathews DH, Suess B: Deep sequencing-based identification of small non-coding RNAs in Streptomyces coelicolor. RNA Biol. 2011, 8 (3): 468-477. 10.4161/rna.8.3.14421.PubMed CentralPubMedGoogle Scholar
- Vesper O, Amitai S, Belitsky M, Byrgazov K, Kaberdina AC, Engelberg-Kulka H, Moll I: Selective translation of leaderless mRNAs by specialized ribosomes generated by MazF in Escherichia coli. Cell. 2011, 147 (1): 147-157. 10.1016/j.cell.2011.07.047.PubMedGoogle Scholar
- Chuang LY, Chang HW, Tsai JH, Yang CH: Features for computational operon prediction in prokaryotes. Brief Funct Genomics. 2012, 11 (4): 291-299. 10.1093/bfgp/els024.PubMedGoogle Scholar
- Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW: A whole-cell computational model predicts phenotype from genotype. Cell. 2012, 150 (2): 389-401. 10.1016/j.cell.2012.05.044.PubMed CentralPubMedGoogle Scholar
- McDowell DG, Burns NA, Parkes HC: Localised sequence regions possessing high melting temperatures prevent the amplification of a DNA mimic in competitive PCR. Nucleic Acids Res. 1998, 26 (14): 3340-3347. 10.1093/nar/26.14.3340.PubMed CentralPubMedGoogle Scholar
- Liu JM, Livny J, Lawrence MS, Kimball MD, Waldor MK, Camilli A: Experimental discovery of sRNAs in Vibrio cholerae by direct cloning, 5S/tRNA depletion and parallel sequencing. Nucleic Acids Res. 2009, 37 (6): e46-10.1093/nar/gkp080.PubMed CentralPubMedGoogle Scholar
- Graur D, Zheng Y, Price N, Azevedo RB, Zufall RA, Elhaik E: On the immortality of television sets: “function” in the human genome according to the evolution-free gospel of ENCODE. Genome Biol Evol. 2013, 5 (3): 578-590. 10.1093/gbe/evt028.PubMed CentralPubMedGoogle Scholar
- Chintakayala K, Singh SS, Rossiter AE, Shahapure R, Dame RT, Grainger DC: E. coli Fis protein insulates the cbpA gene from uncontrolled transcription. Plos Genetics. 2013, 9 (1): e1003152-10.1371/journal.pgen.1003152.PubMed CentralPubMedGoogle Scholar
- Dreyfus M: Killer and protective ribosomes. Molecular Biology of Rna Processing and Decay in Prokaryotes. 2009, San Diego: Elsevier Academic Press Inc, 85: 423-466.Google Scholar
- Andrade JM, Pobre V, Silva IJ, Domingues S, Arraiano CM: The role of 3′-5′ exoribonucleases in RNA degradation. Molecular Biology of RNA Processing and Decay in Prokaryotes. Edited by: Condon C. 2009, London, UK: Academic Press, 187-229.Google Scholar
- Jung K, Fried L, Behr S, Heermann R: Histidine kinases and response regulators in networks. Curr Opin Microbiol. 2012, 15 (2): 118-124. 10.1016/j.mib.2011.11.009.PubMedGoogle Scholar
- Moll I, Grill S, Gualerzi CO, Blasi U: Leaderless mRNAs in bacteria: surprises in ribosomal recruitment and translational control. Mol Microbiol. 2002, 43 (1): 239-246. 10.1046/j.1365-2958.2002.02739.x.PubMedGoogle Scholar
- Malys N, McCarthy JEG: Translation initiation: variations in the mechanism can be anticipated. Cell Mol Life Sci. 2011, 68 (6): 991-1003. 10.1007/s00018-010-0588-z.PubMedGoogle Scholar
- Walz A, Pirrotta V, Ineichen K: Lambda repressor regulates switch between PR and PRM promoters. Nature. 1976, 262 (5570): 665-669. 10.1038/262665a0.PubMedGoogle Scholar
- Baumeister R, Flache P, Melefors O, Vongabain A, Hillen W: Lack of a 5′ noncoding region in Tn1721-encoded tetR mRNA is associated with a low efficiency of translation and a short half-life in Escherichia coli. Nucleic Acids Res. 1991, 19 (17): 4595-4600. 10.1093/nar/19.17.4595.PubMed CentralPubMedGoogle Scholar
- Van Etten WJ, Janssen GR: An AUG initiation codon, not codon-anticodon complementarity, is required for the translation of unleadered mRNA in Escherichia coli. Mol Microbiol. 1998, 27 (5): 987-1001. 10.1046/j.1365-2958.1998.00744.x.PubMedGoogle Scholar
- O’Donnell SA, Janssen GR: Leaderless mRNAs bind 70S ribosomes more strongly than 30S ribosomal subunits in Escherichia coli. J Bacteriol. 2002, 184 (23): 6730-6733. 10.1128/JB.184.23.6730-6733.2002.PubMed CentralPubMedGoogle Scholar
- Brock JE, Pourshahian S, Giliberti J, Limbach PA, Janssen GR: Ribosomes bind leaderless mRNA in Escherichia coli through recognition of their 5 ′-terminal AUG. RNA. 2008, 14 (10): 2159-2169. 10.1261/rna.1089208.PubMed CentralPubMedGoogle Scholar
- Vioque A, Arnez J, Altman S: Protein-RNA interactions in the RNase P holoenzyme from Escherichia coli. J Mol Biol. 1988, 202 (4): 835-848. 10.1016/0022-2836(88)90562-1.PubMedGoogle Scholar
- Chauhan AK, Apirion D: The gene for a small stable RNA (10Sa RNA) of Escherichia coli. Mol Microbiol. 1989, 3 (11): 1481-1485. 10.1111/j.1365-2958.1989.tb00133.x.PubMedGoogle Scholar
- Lee SY, Bailey SC, Apirion D: Small stable RNAs from Escherichia coli: evidence for existence of new molecules and for a new ribonucleoprotein particle containing 6S RNA. J Bacteriol. 1978, 133 (2): 1015-1023.PubMed CentralPubMedGoogle Scholar
- Glynn B, Lacey K, Palta P, Kaplinski L, Remm M, Barry T, Smith T, Maher M: Demonstration of the application of the tmRNA transcript of the bacterial ssrA gene as a molecular diagnostic target using a combination of NASBA and BiaCore technologies. Int J Antimicrob Ag. 2007, 29: S392-S392.Google Scholar
- Bremer H, Dennis PP: Modulation of chemical composition and other parameters of the cell by growth rate. Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology. Edited by: Neidhardt FC. 1996, Washington, DC: ASM Press, 1553-1569. 2Google Scholar
- Holland KT, Greenman J, Cunliffe WJ: Growth of cutaneous Propionibacteria on synthetic medium - growth yields and exoenzyme production. J Appl Bacteriol. 1979, 47 (3): 383-394. 10.1111/j.1365-2672.1979.tb01198.x.PubMedGoogle Scholar
- Hirsch A, Grinstead E: Methods for the growth and enumeration of anaerobic spore-formers from cheese, with observations on the effect of nisin. J Dairy Res. 1954, 21 (1): 101-110. 10.1017/S0022029900007196.Google Scholar
- Kim J, Naylor HB: Spore production by Bacillus stearothermophilus. Appl Microbiol. 1966, 14 (4): 690-691.PubMed CentralPubMedGoogle Scholar
- Kime L, Jourdan SS, McDowall KJ: Identifying and characterizing substrates of the RNase E/G family of enzyme. RNA Turnover in Bacteria, Archaea and Organelles. Edited by: Maquat LE, Arraiano CM. 2008, San Diego, California, USA: Academic Press, 215-241.Google Scholar
- Kieser T, Bibb MJ, Buttner MJ, Chater KF, Hopwood DA: Practical Streptomyces Genetics. 2000, Norwich: The John Innes FoundationGoogle Scholar
- Goecks J, Nekrutenko A, Taylor J, Team G: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010, 11 (8): R86-10.1186/gb-2010-11-8-r86.PubMed CentralPubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10 (3): R25-10.1186/gb-2009-10-3-r25.PubMed CentralPubMedGoogle Scholar
- Rozen S, Skaletsky HJ: Primer3 on the WWW for general users and for biologist programmers. Bioinformatics Methods and Protocols: Methods in Molecular Biology. Edited by: Krawetz S, Misener S. 2000, Totowa, NJ: Humana Press, 365-386.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.