Bacterial Transcriptomics What is Beyond the RNA Horiz-ome

8/3/2019 Bacterial Transcriptomics What is Beyond the RNA Horiz-ome

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The regulation of bacterial transcription has been a topicof interest for several decades; the transcriptional regu-latory circuit of the lac operon was described as early as1959 (REF. 1). Bacterial genes can be arranged in operons,which are groups of contiguous genes regulated by a com-mon operator2. Bacterial transcription is carried out by a single RNA polymerase (RNAP) holoenzyme complexthat consists of a core enzymatic machine and a σ-factor.This RNAP holoenzyme binds to different σ-factors thatrecognize different promoters and so control specific setsof genes3. Different bacteria have different numbers of σ-factors, although most have at least a member of thehousekeeping σ-factor family (σ70)4. Transcription fac-tors are additional regulatory factors, although they arenot necessarily part of the holoenzyme. Many transcrip-

tion factors can either promote or repress transcription,depending on the promoter. Escherichia coli encodes 314transcription factors, of which 35% are activators, 43%are repressors and 22% are dual regulators5. Transcriptionis terminated through two different mechanisms: Rho-dependent and Rho-independent termination6. Finally, theinitiation of protein translation requires (in most cases) aShine–Dalgarno motif, which is a short sequence close tothe start codon that recruits the ribosome to the mRNA7.

In addition to this basic regulatory machinery, vari-ous other proteins and regulatory elements increasethe complexity of the events leading from DNA to pro-tein: RNAP-associated proteins affect the processivity

of RNAP8; internal promoters within operons9, smallRNAs (sRNAs)10 and riboswitches (RNAs that regulatetheir own gene activity)11 affect transcription and transla-tion; additional transcription termination regulates thetermination process6; and non-canonical ribosome bind-ing motifs12 and leaderless mRNAs that are translated13 affect protein translation. However, many of the exam-ples of these processes have been considered as ‘oddities’and, until recently, the general view outside the field of microbiology was that bacterial transcription is simpleand well understood.

Over the past 10 years, regulation of gene expres-sion in bacteria has come back into the spotlight, withmany discoveries being made owing to the combinationof classic genetics and biochemical assays with high-

throughput technologies. The large amount of data col-lected has revealed that many of the ‘oddities’ may insteadbe the rule. For instance, sRNAs account in some casesfor 10–20% of the bacterial RNA products and may havean important regulatory role14, and riboswitches affectgene expression upon metabolite binding15. The new evi-dence suggests that the definition of the operon shouldbe redefined, although the new information makes that achallenge. For example, 20% of all Bacillus subtilis genesin polycistronic operons are transcribed from more thanone promoter16. Similarly, almost 6% of the polycistronicoperons contain an internal read-through terminator, atwhich partial continuation of the transcription occurs17.

*Centre for Genomic

Regulation, Universitat

Pompeu Fabra, Av. Dr. Aiguader 88, 08003

Barcelona, Spain.‡ICREA (Institució Catalana de

Recerca i Estudis Avançats),

Passeig Lluís Companys, 23,

08010 Barcelona, Spain.§Present address: Harvard

Medical School, 77 Louis

Pasteur Avenue, Boston,

Massachusetts 02115, USA.||These authors contributed

equally to this work.

Correspondence to L.S.

e-mail: [email protected]

doi:10.1038/nrmicro2620

Bacterial transcriptomics: what isbeyond the RNA horiz-ome?Marc Güell*§ ||, Eva Yus‡ ||, Maria Lluch-Senar* and Luis Serrano*‡

Abstract | Over the past 3 years, bacterial transcriptomics has undergone a massive revolution.

Increased sequencing capacity and novel tools have made it possible to explore the bacterial

transcriptome to an unprecedented depth, which has revealed that the transcriptome is more

complex and dynamic than expected. Alternative transcripts within operons challenge the

classic operon definition, and many small RNAs involved in the regulation of transcription,translation and pathogenesis have been discovered. Furthermore, mRNAs may localize to

specific areas in the cell, and the spatial organization and dynamics of the chromosome have

been shown to be important for transcription. Epigenetic modifications of DNA also affect

transcription, and RNA processing affects translation. Therefore, transcription in bacteria

resembles that in eukaryotes in terms of complexity more closely than was previously thought.

Here we will discuss the contribution of ‘omics’ approaches to these discoveries as well as the

possible impact that they are expected to have in the future.

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658 | SEPTEMBER 2011 | VOLUME 9 www.nature.com/reviews/micro

© 2011 Macmillan Publishers Limited. All rights reserved

mailto:[email protected]

mailto:[email protected]



Transcriptome

The complete set of RNA

molecules produced in a cell.

DNA microarrays

Technology used to carry out

measurements of a large

number of transcript levels

simultaneously. Microarrays

consist of a series of

microscopic spots of DNA

oligonucleotides targeting

specific sequences. Theseprobes hybridize to the target

species (usually cDNA).

Probe–target hybridization is

quantified to determine the

abundance of nucleic acid

sequences in the sample.

Tiling arrays

A subtype of DNA microarray

chips. Tiling arrays differ

according to the nature of the

probes. Probe sequences are

tiled and cover the entire

genome. They are used for

whole-transcriptome profiling.

All of these discoveries have benefited from the tech-nical revolution of the so-called ‘omics’ disciplines. Geneexpression studies have clearly been at the forefront of this revolution, and modern techniques have provideda high-resolution view of various aspects of transcriptome organization. What is now on the horizon? How canwe integrate the newly acquired knowledge? In thisReview, we summarize the quantum leap that bacterialtranscriptomics has taken over the past several years andsuggest areas where the new technologies could have amajor impact.

A technical revolution

Recent years have witnessed a revolution in the fieldof bacterial transcriptomics (BOX 1; FIG. 1a). PioneeringRNomics studies used cDNA synthesis from RNA sam-ples, cloning and Sanger sequencing 18, but the transcrip-

tomics revolution started when the development of DNAmicroarrays provided a tool to globally quantify geneexpression19. Several years later, technological break-throughs allowed the design of microarrays containinghigh-density tiled probes (termed tiling arrays) that coverthe entire genome of an organism or contiguous regionsof it, and such tiling arrays provided the first compre-hensive transcriptome map for E. coli20 and several otherbacterial species21–23. However, tiling experiments haveonly recently been able to deliver strict strand-specificity and high-resolution cDNA mapping 21 through the useof actinomycin D in the reverse transcription reactionto inhibit DNA polymerase activity 24 (FIG. 1b). The tiling

arrays also revealed that most transcripts are present inlevels that are just above background noise25. Thus, thissystem has a far from ideal signal-to-noise ratio 26 andcannot offer single-based resolution of transcriptionstart sites (TSSs) (FIG. 1c).

In 2008, RNA-seq was introduced, which involves

deep sequencing of cDNA generated from RNA prepa-rations27,28. This technology has overcome some of thedrawbacks of tiling arrays: it provides single-base reso-lution, a better signal-to-noise ratio owing to a reducedbackground and a higher dynamic range29. RNA-seqallows the detection of various transcriptional features,including the 5′ end of all RNAs30,31, and TSSs canbe detected by selecting for primary RNA transcriptsthat have a 5′-triphosphate (processed RNAs have a5′-monophosphate)32. The current sensitivity of ultra-sequencing makes it possible to study unculturablebacteria or bacteria that cannot be isolated (such as endo-symbionts), and has allowed the transcriptomes of micro-bial communities (metatranscriptomes) to be explored,such as those of communities found in marine samples

(see below)33,34. However, metatranscriptomics technol-ogy is not yet standardized and several issues remain tobe solved, such as the unequal coverage of genes, whichcould result in apparent premature transcriptionaltermination for genes with low expression (FIG. 1c).

The operon, a concept under revision

Genes in bacterial genomes are organized into oper-ons, which are defined as functional genomic units thatcontain multiple genes under the control of a singlepromoter. However, this definition no longer stands in

various aspects (FIG. 2). It was thought that this organiza-tion leads to an equal level of expression for all genes, butuneven gene expression within operons was observedseveral years ago35, and recent genome-wide transcrip-tomics studies have revealed that in many bacterial spe-cies, consecutive genes within operons do not have thesame expression level, leading to operon polarity 21,36 (FIG. 2a). Operon polarity could be explained by the pres-ence of internal transcription terminators, by the activity of sRNAs or by riboswitches within operons that block oractivate transcription in response to metabolite binding(see below)37–39. The combinatorial effect of internal pro-moters and terminators would result in the productionof different transcripts, thereby adding plasticity to theoperon. For instance, mapping transcriptional initiationsites in Helicobacter pylori revealed 337 operons, but the

detection of internal TSSs added 192 additional cistrons40 (FIG. 2b), whereas in Mycoplasma pneumoniae, 341 refer-ence operons could be divided into 447 smaller suboper-ons21 (FIG. 2b). Furthermore, transcription initiation may be coupled to termination in some cases. In the E. coligalactose operon, two promoters are separated by 5 bp.Transcription driven from the first promoter is termi-nated earlier than that from the second, suggesting thattranscription termination could depend on transcrip-tion initiation41, possibly as a result of different assem-blies of the RNA transcriptional machinery as dictatedby the promoter. Transcription elongation factors suchas NusA, NusG or GreA can determine the processivity

Box 1 | Computational resources to process transcriptomics data

Computational approaches have dramatically changed with the evolution of

transcriptomics. In 2004, the first DNA ultrasequencing platforms enabled the

simultaneous reading of several hundred thousand DNA fragments with a read length

greater than 100 bases. Today, they produce over 200 million 75–100‑base reads, and

the large throughput of modern sequencing platforms has resulted in them gradually

replacing the array‑based technologies. RNA‑seq28 is probably one of the most complex

next‑generation applications.The first step after the data have been acquired is to align sequences to a reference

genome, which can be done using a growing number of software packages154. The

choice of alignment algorithm is strongly influenced by both the experiment in

question and the details of the sequencing technology used. These algorithms have to

cope with unprecedented amounts of data and specific errors associated with the

specific platform (for example, a tendency for insertion or deletion errors to occur in

homopolymer reads, or an increase in the likelihood of sequence errors towards the end

of the read). Alignment programs use heuristic techniques to quickly identify a small set

of locations in the reference sequence where the best mapping is most likely to be

found. Within this set of putative mapping locations, slower and more accurate

algorithms are used to map the reads. The first tools used hash‑based alignment

methods: examples include SOAP155, MAQ156 and ELAND (Illumina, unpublished).

A second generation of tools has been developed on the basis of the Burrows–Wheeler

transform (BWT): examples include BOWTIE157, BWA158 and SOAP2 (REF. 159). BWT

implementations are much faster than their hash‑based counterparts at the samesensitivity level.

The second step is to represent and analyse the data. Different tools are available, but

some of the most widely used and powerful are within the Bioconductor project: the

ShortRead package provides functionality for the import, quality assurance, visualization

and basic manipulation (such as pile‑up calculation and read property examination) of

‘short‑read’ DNA sequences. In addition, there are packages that address more specific

aspects, such as identifying differentially expressed genes (DEGseq) or providing

alternative base‑calling algorithms (Rolexa).

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Deep sequencingNew sequencing technologies

that make use of massive

parallelization of the

sequencing process. Data

provided by these new

sequencers consist of a large

number of reads (millions) in

each run but with a short read

length (of a few hundred

bases).

Cistrons

Segments of DNA that have

the information to produce a

polypeptide chain.

of the polymerase42. Moreover, other proteins (such asSpx and Dsk), the 6S sRNA or even small molecules(including (p)ppGpp or NTPs) can bind to the initialRNAP complex independently of DNA and change theproperties of the holoenzyme, although binding dependson environmental conditions42. It is tempting to imag-ine that promoters could engage various transcriptioncomplexes that could produce different RNA outputs.Almost half of the polycistronic operons in M. pneumo-

niae show natural polarity and transcriptional attenu-ation (optional use of terminator signals)21 (FIG. 2a).Transcriptome mapping data for M. pneumoniae haverevealed that, within an operon, transcription attenua-tion generally coincides with the stop codon21. Possibly,as a result of transcriptional–translational coupling, theribosome induces transcriptional termination in stopcodons by an as-yet-unknown mechanism. Indeed, theα-subunit of M. pneumoniae RNAP associates withthe ribosomal protein RpsD, suggesting a crosstalkbetween the transcription and translation machineries43.Furthermore, the first ribosome bound to the mRNAcooperates with the RNAP in transcription elongation44.

These data not only support a coupling between tran-scription and translation but also offer an explanationfor transcriptional attenuation based on an increasedprobability of RNAP release if the ribosome dissociateswhen it encounters a stop codon. The complexity of bacterial operons can thus be compared to that of alter-native splicing in eukaryotes. However, the regulatory mechanisms of this alternative transcription remain tobe unveiled. Deep sequencing technologies now allow

TSSs to be identified to a single-base precision, thereforemaking it possible to map all internal promoters withinan operon32. However, lacking still is a technology thatcould identify the termination site with the same preci-sion, although it is expected that long reads provided by single-molecule DNA sequencing45,46 could cover thatgap. This knowledge is essential to understand the rulesgoverning RNAP release and transcriptional termina-tion at internal operon sites. Combining ultrasequenc-ing and chromatin immunoprecipitation followed by sequencing (ChIP–seq) to target all proteins associatedwith RNAP could also offer insight into which transcrip-tional complexes are assembled at each promoter, as well

Figure 1 | Methodological transcriptomics tool kit. a | Various methods that have been recently developed to measure

different transcriptome features by mapping transcripts, antisense transcripts, transcription start sites (TSSs) and

protein-binding sites. b | Strand specificity in RNA-seq studies. Second-strand cDNA synthesis during reverse transcription

reactions can by prevented with actinomycin D (bottom panel).c | Comparison of the advantages of transcriptome

mapping based on either deep sequencing (RNA-seq) or tiling arrays. These are representative loci of the Mycoplasma

pneumoniae chromosome; the gene name is indicated above the dotted line. ChIP–chip, chromatin immunoprecipitation

followed by microarray; RNAP, RNA polymerase.

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′ ′

as the relationship between the assembly of these andthe processivity and recognition of transcriptional ter-mination signals. Native elongating transcript sequenc-ing (NET-seq) is a new method that visualizes RNAsin vivo as they are produced47. This technique utilizesthe high stability of the RNAP−DNA−RNA complexthat allows it to be affinity purified without crosslink-ing, so that bound RNAs can be subsequently submit-ted to deep sequencing. This method could be applied

to bacteria, allowing nascent transcripts to be identifiedand both transcription elongation and RNAP pausingto be monitored.

Regulations at the operon level

Like eukaryotic promoters, bacterial promoters can beregulated by more than one signal or transcription fac-tor48. In Caulobacter crescentus, 6% of the genes are tran-scribed from multiple TSSs22, indicating that various

Figure 2 | Recent discoveries in bacterial transcriptomics. a | Intra-operon decaying expression. Almost half of theconsecutive genes within operons in Mycoplasma pneumoniae show ‘staircase’ behaviour21. In general, the steps tend to

occur in the proximity of the stop codons at the end of every gene. b | Alternative transcripts in operons. Three alternative

transcripts are present in the cag25–cag18 primary operon in Helicobacter pylori40. c | Abundance of antisense RNA. The

number of antisense RNAs in various species as a percentage of the number of open reading frames (ORFs). d | Leaderless

mRNAs. Transcripts starting at the translation initiation codon are preferentially bound by the 70S ribosome, whereas

those with a Shine–Dalgarno sequence are preferentially recognized by the 30S ribosomal subunit.e | mRNA intrinsic

regulation. A lysine riboswitch is located upstream of a lysine transporter gene (lmo0795)60. In the presence of lysine

lmo0795, transcription is prevented by a Rho-independent terminator. Functional elements present on mRNA molecules

are depicted. f | Overlapping untranslated regions (UTRs). The lmo0649 mRNA (red) can be transcribed from two

promoters. Transcription from the distal promoter generates a long UTR that overlaps with the coding region of lmo0648

(blue)60. g | Chromosome structure. Bacterial chromatin exhibits different degrees of compaction and supercoiling

depending on the growth phase (it is more compacted in the exponential phase compared to the stationary phase) and

on the transcription status (not shown). h | Epigenetic modifications. Methylation (Me) states of bases (in this case a

guanosine (G)) that affect transcription can be inherited. Binding of a repressor to an unmethylated promoter prevents

transcription of the repressor protein. Perturbations that lead to promoter methylation prevent the repressor from bindingto the promoter; this methylated state is inherited and a new stable state is created. TSS, transcription start site.

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Stationary phase

A stage of bacterial growth in

which the growth rate slows as

a result of nutrient depletion

and accumulation of toxic

products.

activators and/or repressors can regulate transcription.Thus, an operon could respond to two or more differentinputs, enabling higher regulatory plasticity and respon-siveness to stimuli. In some cases, converging signalsregulate the promoter by cooperatively binding to theDNA, thus increasing the robustness of the response49.Cooperative binding can result in either bimodal or all-or-none expression and can turn on gene expression afteran input has crossed the threshold required for activa-tion48; an example of this is the famous phage λ switch inE. coli50. Complex regulatory mechanisms can likewise beobserved when there are bidirectional promoters, whichare also common in eukaryotes; 66% of divergent genepairs in yeast51 and 10% of all promoters in the humangenome show such regulation52. Depending on the overlapbetween the two promoters, they might be co-expressed53,co-expressed with a particular hierarchy 54 or anti-regulatedon each strand. They might also share transcription fac-tor target sites, which would be consistent with the factthat divergent genes tend to have related functions inhigher eukaryotes55. To achieve a global view of such

complex regulatory control in bacteria, all transcriptionfactor binding sites will have to be mapped and all TSSsprecisely determined.

In the case of the gene that encodes Rns, a transcrip-tional regulator from the AraC family, the binding sitefor activators is located downstream of the core pro-moter but, surprisingly, Rns activates its own expres-sion by binding both upstream and downstream of thepromoter56 in a manner that is reminiscent of enhancers in downstream regions in eukaryotes. A recent study onE. coli that examined 600 combinations of promoter andcoding regions from various genes found evidence thatthe regulatory information stored in the coding regionsof genes strongly affects gene expression levels, indicatingthat this could be a more general phenomenon57.

DNA looping, which was discovered in the ara and gal operons and subsequently found in other bacterialregulatory regions58, is used in some bacteria to regulatetranscription. In several E. coli promoters, including thehdeABp promoter, H-NS represses RNAP complexescarrying the σ70 subunit. However, σ38 RNAP com-plexes are not repressed. Atomic force microscopy andother techniques revealed that DNA traps σ70, but not σ38,by completely wrapping it in cooperation with H-NS59.The increase in resolution of experiments that addressthe three-dimensional (3D) structure of chromatin (thatis, chromosome conformation capture (3C) experiments),

in combination with ChIP–seq experiments, will allowa complete analysis of all DNA looping regions andopen the way to a comprehensive study of their role intranscriptional regulation.

sRNAs galore

Although it was known that bacteria express sRNAs of 50–500 bases, the sheer abundance of such sRNAs, andespecially antisense RNAs (some of which are thoughtto have regulatory roles), was unexpected21,40,60,61(FIG. 2c).These RNAs can work in cis by targeting a gene withinthe same locus62 or in trans63,64 by targeting loci elsewherein the genome. sRNAs are involved in regulating various

processes, such as transcriptional interference, transcrip-tional activation, translational control and regulation of mRNA half-life65. sRNAs may be a part of ribonucleo-protein complexes (and thus modulate protein activity,as is the case for 4.5S RNA and transfer mRNA (tmRNA;also known as SsrA)66), sequester proteins (as exempli-fied by 6S RNA, which binds RNAP and induces changesin gene expression at the transition to the stationary

phase67) or directly regulate mRNA translation and/orstability 68. sRNAs could also indirectly regulate tran-scription of neighbouring genes by promoting changesin DNA supercoiling69,70 as a result of transcription of thesRNA gene. For example, in M. pneumoniae, genes withoverlapping antisense transcripts have lower expressionlevels21. Trans-encoded sRNAs typically interact withmultiple mRNAs71, as these sRNAs contact their targetmRNAs in discontinuous patches. Thus, a single RNAcan globally modulate particular physiological responsesand networks in a manner similar to a transcriptionfactor but at the post-transcriptional level72–75 and with

varying degrees of stringency and outcomes76.

The high number of sRNAs indicates that they musthave an important role in bacterial physiology. Unveilingthe functions of these sRNAs in gene regulation shouldbe addressed by high-throughput experiments afteroverexpression or knockout of trans sRNAs or after genesilencing of cis RNAs by complementary antisense RNA.

It has been suggested that the number of antisensetranscripts roughly anticorrelates with the genome size77 based on the finding that in the E. coli genome, whichcontains ~4,000 genes, antisense RNAs represent 2.4%of the total number of genes, whereas in the M. pneumo-niae genome, which is much smaller than the genomesof most other bacteria and contains only 689 protein-coding genes, including only a handful of transcriptionfactors21, ~12% of the genes encode antisense RNAs.Such a disproportionately large antisense RNA popu-lation could be the biological consequence of genomereduction, with transcriptional regulation taken overby RNA-mediated control. However, the use of ultrase-quencing technology in E. coli has led to the percentageof antisense RNAs being revised upward from 2.4% to20% of the genes78. Similarly, in H. pylori, 27% of genesencode for antisense RNAs32, and manual examinationof M. pneumoniae transcripts raised the total to closeto 20%. These data suggest that the proportion of anti-sense RNA could be ~10–20% of the genes in bacteria,and that the small numbers previously observed were

a result of low-sensitivity detection technologies. Thesenumbers are similar to those found in certain eukaryotes(with ~20% in humans79 and plants80) and slightly higherthan in yeast (7%)51. Recent metatranscriptomic stud-ies of complex samples containing thousands of bacte-rial sequences have also corroborated the abundance of sRNAs within bacterial communities; for example, over40,000 new sRNAs were identified in a single study 33.

Aside from the challenge of determining the functionof most of these sRNAs, there are also technical issuesthat remain to be solved. For example, the heterogene-ity that arises from the diverse protocols and samplingmethods used in the different laboratories leads to an

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overall poor reproducibility in sRNA determination;the results of independent transcriptomics studies inBurkholderia cenocepacia did not overlap81, and stud-ies in Salmonella enterica subsp. enterica serovar Typhiand S. enterica subsp. enterica serovar Typhimuriumdetected only partial overlap, with an overlap of 42 outof 82 annotated sRNAs in one data set 82 and of 5 out of 52 in another data set83. Strand specificity needs to beensured when sequencing RNA transcripts, as artefac-tual sRNAs could be found opposite to coding genes24.Thus, complementary high-throughput approaches21,84 or validation by either quantitative PCR or RACE85 arerequired to confirm all newly discovered sRNAs beforestarting functional studies. These technical issues not-withstanding, the use of omics technologies has allowedthe discovery of conserved sRNAs that could not beidentified by genome sequence analyses86, as well asnew mechanisms, such as the binding of the 6S RNA toRNAP to produce short RNA products87.

Deep sequencing in combination with pulldowns of Hfq proteins in bacteria have identified sRNAs as the

major target of these proteins83. Complete catalogues of sRNAs from several species will allow the identificationof conserved sRNAs, which could point to functionality,whereas anticorrelated expression patterns could suggesta mechanism of regulation of mRNA transcription by sRNAs and identify new targets for functional studies.

sRNAs and small peptides

Some sRNAs are multifunctional as they encode func-tional peptides. Probably the most paradigmatic exam-ple of this is RNAIII in Staphylococcus aureus, which wasone of the first cis-acting RNAs to be described and isinvolved in quorum sensing. RNAIII encodes a 26-amino-acid peptide that may be involved in biofilm integrity 88.Other cases have been documented in E. coli 89,90 andin Listeria monocytogenes60. For example, SgrS causestranslational repression when pre-annealed with ptsGin E. coli91. Interestingly, the 5′ region of SgrS contains a43-amino-acid open reading frame (ORF) termed sgrT ,which is translated during glucose-phosphate stress.Downregulation of ptsG mRNA does not require SgrT,and SgrT by itself has no effect on ptsG mRNA stability.However, cells expressing SgrT alone have a defectiveglucose uptake, even when they exhibit nearly wild-typelevels of PtsG. Together, these data suggest that SgrS isa bifunctional sRNA that encodes physiologically redun-dant but mechanistically distinct functions that contrib-

ute to the same stress response90. This list is expected toexpand substantially as small peptides are difficultto detect using standard proteomics approaches andrequire appropriate isolation and fractionation techniquesthat specifically enrich small proteins92. The combinationof deep sequencing, translation in the three phases of theidentified sRNAs and the guided search for all ORFs witha translation initiation codon using mass spectrometry could reveal a new layer of regulation in bacteria.

RNA processing

It is generally assumed that post-transcriptional regula-tion of RNA in eukaryotes is extremely important and

complex, whereas it is either minimal or non-existent inbacteria. Thus, nascent mRNA in bacteria is thought tobe bound to ribosomes and degraded after some min-utes. However, recent studies have shown the existenceof mechanisms that affect mRNA stability in bacteria(FIG. 3a), as well as different types of RNA processing.RNA degradation is usually triggered by an endonucleo-lytic cleavage, followed by a rapid exonucleolytic activity.Different ribonucleases (RNases) are responsible for thefirst step, depending on the species, and some proteinscan even cleave their own mRNA and thereby autoregu-late their expression93. Several factors affect the stability of a given mRNA, including RNA-binding proteins andsRNA94. For example, removal of the 5′-triphosphate by the enzyme RppH triggers mRNA degradation in E. coli95.Furthermore, RNA stability is species dependent and can

vary depending on the environmental conditions96.There are several examples of RNA processing in

which internal cleavage occurs in a regulated manner 97.It is expected that differential identification of primary RNAs with a 5′-triphosphate and processed RNAs with

a 5′-monophosphate32 will help to identify in vivo mRNAprocessing sites at the genome level and under differentconditions. Contrary to the common view, polyadenyla-tion (addition of untemplated adenosine residues tothe 3′ end of transcripts) is also present in bacteria andcellular organelles of bacterial origin (TABLE 1). In factbacteria perform two types of polyadenylation: classicpoly adenylation catalysed by poly(A) polymerase I, whichadds exclusively adenosines, and addition of a mixture of different bases with about 50% adenosines98 catalysed by polynucleotide phosphorylase (PNPase). Genome-widestudies using poly(T) oligonucleotides to enrich thesetranscripts indicated that around 0.01–0.2% of the tran-scripts have poly(A) tails, but this low number could berelated to the fact that they are degraded very rapidly 99;in B. subtilis, around 20% of the total RNA was found tobe polyadenylated100 and a recent genome-wide study of polyadenylated RNA in E. coli revealed that around 72%of all RNAs that contain a Rho-independent terminationsignal are polyadenylated101. Thus, polyadenylation is acommon modification and, moreover, can vary with thegrowth phases102. Several possible roles for polyadenyla-tion in bacteria have been suggested; it was reported thatpolyadenylation targets transcripts for degradation by thedegradosome103 and that polyadenylation is involved inquality control for transcriptional and processing errors104.

Furthermore, RNA editing can alter the genetic

information by mutation, deletion or insertion of a base.This process has been well documented for eukaryoticendosymbionts, in which RNA editing is promotedby guide RNA, which is another type of sRNA. In themitochondrion of Trypanosoma brucei, for instance,many mRNAs cannot be translated owing to multipleframeshifts; in this case, guide RNAs serve as templatesfor correcting such mistakes at the mRNA level with theaid of ribonucleoprotein complexes105,106.

Finally, RNA splicing has been reported in endo-symbionts107 and bacteriophages108, indicating that thisfeature is also not exclusive to eukaryotes (TABLE 1). Thesefindings highlight the ancient role of RNA and ribozymes

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′ ′

′

′ ′

and provide insight into putative functions of some of the still uncharacterized sRNAs. Group II self-splicingintrons are large catalytic molecules that are consideredto be related to the progenitor of spliceosome introns.

They were initially identified in the organelles of lowereukaryotes and plants and are also present in bacteria,although their frequency is unknown109. Genome-wideRNA-seq analysis could be used to obtain a global viewof all of these post-transcriptional processes, althoughthis would involve a modification of existing standardmapping methods to allow for the identification of added poly-base tails that are not present in the genomeor of the fusion of non-contiguous DNA sequences.

Leaderless mRNAs and regulation by UTRs

The Shine–Dalgarno ribosomal recognition sequencewas long considered to be essential for ribosome

attachment and efficient translation of mRNA. The lat-est transcriptomics studies have now demonstrated thatseveral RNA transcripts are leaderless: that is, that thesetranscripts start with one start codon (FIG. 2d) or have

non-canonical ribosome-binding sites110. The mecha-nism by which leaderless transcripts with no or a very short 5′ untranslated region (UTR) could be recog-nized by the ribosome has long been unclear. However,in E. coli, it was shown that the ATG initiation codoncan become the ribosome-binding sequence111 and thatleaderless transcripts can bind 70S ribosomes rather than30S ribosomal subunits, suggesting a different pathway of translation for these mRNA112. Genome-wide tran-scriptional studies have revealed that there are 26 leader-less RNAs in H. pylori 40 and 25 in S. Typhimurium82.These transcripts may be preferentially translatedwhen bulk mRNAs cannot be translated, inducing a

Figure 3 | Perspectives in transcriptomics. a | RNA modifications. There is growing evidence that RNA in bacteria can

undergo a number of post-transcriptional modifications that alter its stability and functionality. New ultrasequencing

methods coupled with classical biochemical approaches should allow comprehensive studies on these modifications and

reveal their function. b | Spatial organization of transcription and translation. The colocalization in space of functionally

related genes could drive the assembly of newly synthesized proteins into complexes143. The implementation of new

methodologies, such as chromosome conformation capture (3C) in bacteria129, will make it possible to obtain a

three-dimensional view of bacterial chromatin. c | Full-length transcripts. New sequencing technologies could provideoperon-long reads. The assembly-free sequencing of transcripts will allow the characterization of alternative transcript

termination and provide the chance to study the relationship between transcription initiation and termination. d | RNA–

protein interactions. The identification of RNA-binding proteins will be another means of gaining insight into the roles of

newly discovered RNAs. e | RNA secondary structure. High-throughput RNA structure determination should increase our

understanding of how non-coding RNAs, such as untranslated regions and small RNAs (sRNAs), function.f | Single-cell

transcriptomes. A substantial percentage of the transcripts are in the stochastic range. Studies in individual bacteria will

provide new biological insights into population heterogeneity and regulation. RNAP, RNA polymerase.

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non-transcriptional shift between proteins producedin non-optimal conditions, such as starvation or station-ary phase, when 70S monosomes prevail113. Consistentwith this, in E. coli, leaderless mRNAs are poorly trans-lated during the exponential phase and are only translatedwhen genes containing a canonical ribosome binding siteare not translated114; this exemplifies another regulatory mechanism at the level of translation.

Transcriptome studies have also revealed that many mRNAs contain a 5′ UTR of up to several hundred bases(the average length in M. pneumoniae is ~60 nucleo-tides)21. These regions may be regulating translationthrough secondary structure. Some of these long UTRsencode proteins or peptides; a classic example is theE. coli trp operon leader peptide, which controls pre-mature transcriptional termination depending ontryptophan availability 115. Alternatively, they can alsocontain riboswitches, which are regulatory regions thataffect mRNA stability and translation efficiency 40,60.Riboswitches are regulated by binding small moleculesthat act as environmental signals. Several classes of riboswitches that could regulate translation in responseto metabolites have been described17 (FIG. 2e). For exam-ple, the S-adenosylmethionine (SAM)-III riboswitch isa SAM-binding element that is found in the 5′ UTR of the metK gene, which encodes the SAM synthetase, in

Lactobacillus spp.116. SAM binding to SAM-III resultsin sequestration of part of the ribosome binding sitesequence of the metK mRNA, thus inhibiting ribo-some binding and leading to repression of translationinitiation116,117. Recent experiments have revealed thatSAM-III is a reversible riboswitch that can differentially regulate the transcript at various stages of the lifetime of the transcript, thus allowing the cell to respond rapidly to SAM fluctuations by regulating metK expression118.

There are other roles for UTRs. For instance, mogRmRNA in L. monocytogenes can be transcribed from twodifferent promoters. Transcription from the first pro-moter produces a long isoform that is induced only in the

stationary phase and generates a long 5′ UTR that over-laps with three genes. This 5′ UTR binds to the mRNAof the overlapping genes, leading to their degradation60 (FIG. 2f).

The abundance of leaderless mRNAs, as well asthe presence of mRNAs with long 5′ UTRs, suggests theexistence of a hidden and complex layer of regulationby the UTRs. Determination of the 5′ end of all mRNAscould elucidate different mechanisms of translationalregulation and guide further studies. As determiningthe structure of RNA is key to understanding its func-tion, another promising technique is the use of RNasesto determine the specific secondary structures of RNAs,which can be performed genome-wide. This allows base-paired regions within RNAs to be identified, revealingpotential riboswitches and other relevant structures.This method has been successfully applied to yeast119 and mouse transcriptomes120 and is valuable for bothUTRs and sRNAs.

Bacterial DNA is more than a random bundle

Like eukaryotes, bacteria contain nucleoid proteins thatbind DNA in a sequence-specific or non-sequence-specific manner, thereby promoting nucleoid compac-tion and domain formation and varying gene expressionlevels in response to environmental perturbations108.

E. coli contains around 450 nucleoid structure domains,which have an average length of around 10 kbp, with

variable boundaries and a random distribution along thechromosome121,122. Nucleoid compaction and domainformation of DNA in bacteria are expected to have aneffect on transcriptional regulation14,123. Because nucleoidstructure can be altered by changes in DNA supercoil-ing or changes in expression of histone-like proteins,it has been proposed that nucleoid reorganization anddynamics in E. coli could assume the role of a ‘transcrip-tion factor’ (REF. 124). For example, changes in DNAsupercoiling in E. coli induced by osmotic stress resultin major changes in gene expression118.

Table 1 | Bacterial transcriptome features that recall eukaryotic complexity

Feature Present in eukaryotes? Present in bacteria?

Small RNAs Yes Yes

Complex promoter regulation Yes, eukaryotes have many transcription factorsand other proteins

Yes, but simpler than in eukaryotes

Alternative transcripts Yes Yes, alternative promoters and terminators arepresent

RNA processing Yes Yes, but mainly related to the regulation of degradation, with few examples of specific processing

RNA splicing Yes Yes, but mainly restricted to organelles (mitochondriaand chloroplasts), tRNAs and ribosomal RNAs, andthere are few examples

Polyadenylation Yes, polyadenylation stabilizes RNA Yes, polyadenylation destabilizes RNA

Localized translation Yes, many mRNAs are transported to specific siteswhere they are translated

Yes, recent data show that mRNAs in bacteria donot diffuse and are translated on specific nucleoidlocalizations

Epigenetic modifications Yes Yes, but little information available

Impact of chromatin and nucleoidstructure on transcriptional regulation

Yes, supercoiling and chromatin domains have amajor impact on transcription

Yes, several examples have been found

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Occupancy studies in E. coli have shown the presenceof regions in the chromosome with contiguous proteinbinding of around 1.6 kbp in length, some of which cor-respond to active transcriptional regions, such as theribosomal RNA operon125. Interestingly, most E. coli orSalmonella spp. genes are expressed at a low level — onaverage, the mRNA/DNA ratio is less than one103 — andwould thus have little or no impact on nucleoid structure(FIG. 2g). In agreement with this observation, only very few loci have a high concentration of RNAP, as observedby electron microscopy 126. When cells are in the station-ary phase, the transcription factories disassemble, RNAPis distributed throughout the chromosome and the 3Dstructure becomes less compact (FIG. 2g). However,other regions with contiguous protein occupancy correspond to transcriptionally silent areas and could beinvolved in establishing chromatin domains125. Locally,chromatin domains induced by RNAP supercoiling canbe consolidated if the encoded protein is a membraneprotein127,128. This is owing to the fact that insertion of a protein into the membrane, or periplasm, during co-

transcriptional translation will tether its mRNA to thecell membrane, and this will enhance topological effectson the DNA127,128. Once established, chromatin domainscan induce a type of topological memory generating lin-eages with different expression states, thereby creatingcellular subpopulations that have the same genome butare primed to respond to various environments128.

High-throughput techniques have been applied to thedetermination of long-range DNA interactions and 3DDNA structure in vivo in eukaryotes: namely 3C129 and thelater variations circularized chromosome conformationcapture (4C), carbon-copy chromosome conforma-tion capture (5C)130 and Hi-C131. These techniques rely on crosslinking, restriction digestion and re-ligation of DNA regions that are close in space but not in the pri-mary sequence132. They have been applied to regions of the human genome133,134 and in a genome-wide study inyeast135. Similar studies in bacteria would help to unveilthe structure and dynamics of the bacterial chromosome,as well as its role in transcription regulation.

Epigenetics: memory of the bacterial nucleoid

Until recently, methylation of bacterial DNA was con-sidered to be used primarily to distinguish foreign DNA.However, DNA methylation has important roles in bac-teria, such as during chromosome partitioning, DNAreplication, DNA repair, timing of transposition and

conjugation136. In bacteria, cytosine can be methylated atN4 (m4C) or N5 (m5C) and adenine can be methylatedat N6 (m6A). It has been suggested that m4C is relatedto epigenetic transcriptional regulation136, and severalstudies have revealed that individual bacterial cells canhave a particular DNA methylation status that changestranscription of some operons in a specific, heritablemanner137,138. This occurs through a competition formethylation sites between a methylase and the proteinthat recognizes the unmethylated state (FIG. 2h); bindingof the protein that recognizes the unmethylated state willprevent access of the cognate methylase to the site, whichaffects transcription of the downstream genes. This effect

can be enhanced if the gene downstream encodes for theprotein that binds the unmethylated site and if its tran-scription requires a non-methylated status. If changesin environmental conditions result in degradation orinhibition of the protein protecting the methylation site,then the site will be methylated, blocking further tran-scription; this status will be inherited by the subsequentgenerations. A typical example is the pap pilin phase

variation125. The pap operon has two leucine-responsiveprotein (Lrp) binding sites. Binding of Lrp to the proxi-mal site blocks transcription and permits methylation of the distal site, which prevents Lrp from binding there. If poly(A) polymerase I is expressed, it dimerizes with Lrpand binds at the distal site, allowing methylation of theproximal site and therefore transcription of the operon.It is estimated that the methylation status of only a smallfraction of the approximately 20,000 methylated sites inthe E. coli chromosome can change when subjected todifferent conditions (for a review, see REF. 136).

Although DNA methylation is the best-known mech-anism involved in epigenetic regulation in bacteria, for-

mation of a possible feedback loop by other mechanismsalso permits inheritance of epigenetic states139. The latestultrasequencing technologies that sequence single DNAmolecules in real time have made it possible to deter-mine which bases are methylated, as these bases inducea delay in the polymerization process140. This opens thepossibility of complete genome-wide analyses of DNAmethylation at adenine and cytosine, which could bedetermined under various environmental perturbationsand thus provide insights into the role of epigenetics intranscriptional regulation140.

mRNA localization in bacteria

Organisms from all three domains of life can local-ize proteins to specific regions within a cell, either by targeting the protein with a signal that directs it to itsdestination or by locating the mRNA that encodes theprotein to the appropriate region. Both mechanismsoperate in eukaryotes, whereas only the first mecha-nism has been reported in bacteria141. Originally, track-ing single RNA molecules in E. coli showed localizedmotion consistent with Brownian motion of a polymerbound to the DNA and free diffusion142, but recent stud-ies are challenging the notion that mRNA in bacteria israndomly distributed. Analyses of mRNA localizationand diffusion in C. crescentus and E. coli have shownthat mRNA diffuses two orders of magnitude less than

would be expected in solution and that it restricts ribo-somal mobility 143. As a consequence, translation couldbe spatially organized by using the chromosome layoutas a template (FIG. 3b) and proteins encoded by neigh-bouring genes could be produced in close proximity.Therefore, spatial co-expression could promote imme-diate protein interactions and macromolecular complexformation143. If this proves to be a general phenomenon,it would justify the existence of operons encoding pro-tein complexes, as well as of chromosomal 3D domainsharbouring genes involved in related functions. Such3D clusters that include genes that are co-regulatedby a given transcription factor have been described in

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mammals, in which the term ‘transcriptional factories’has been proposed144.

Thus, the influence of chromatin structure on geneexpression and on the organization of RNA synthesis inspace is not an exclusive feature of eukaryotes. Recently,it was found that certain mRNAs in E. coli can migrateto the destination of their encoded proteins. This processis controlled by sequences within the RNA transmem-brane-coding sequence. Although the molecular mecha-nism is unknown, the data suggest an active transportof the mRNA to its translation location, as occurs ineukaryotes145. Other studies have shown that complexesinvolved in mRNA degradation show specific localization(for a recent review, see REF. 146).

If applied to all RNAs in a bacterium, technologiesthat allow the visualization of single RNA molecules inthe cell147,148 could elucidate whether mRNA localiza-tion is a widespread phenomenon. Determining dis-tinct RNA localizations could allow the identification of specific sequences that guide this localization, thereby opening a new research area.

Perspectives

Future directions should involve integrating our knowl-edge of transcription with the physiology of the micro-organism. Therefore, data should be complementedwith other levels of regulation. Technology based onsingle-molecule sequencing46 could represent the nextimportant advance in transcriptomics, as it should becapable of delivering reads with lengths up to tens of thousands of bases, allowing full-length polycistronictranscripts to be sequenced and providing directassociation of promoters and terminators (FIG. 3c).Currently, transcriptomes are constructed by usingshort-read libraries in combination with TSS detectionmethods30,40; this approach can provide information oninternal promoters but not on terminators, and neithercan it provide a complete list of the alternative tran-scripts that compose an operon. Genome-wide map-pings with long reads will not only identify the different5′ UTRs and 3′ UTRs but will also deconvolve the exactmolecular nature of the operon by sequencing full-length transcripts. Additionally, technologies based onsingle-molecule sequencing will substantially enhancethe speed at which data are acquired. Current Illuminaand Roche technologies measure the incorporatedbases by stopping the sequencing reaction. By contrast,new single-cell sequencing technologies can monitor

base incorporation in real time, thereby decreasingsequencing run length from days to hours46.

The number of known sRNAs is increasing every day,but the functions of many of these remain unclear. Newhigh-throughput methods for detecting RNA–proteininteractions are expected to reveal important biological

insights into RNA function (FIG. 3d) and the regulationof protein activity by RNAs. Despite the important rolethat the 3D structure of RNA can have in its function,relatively few RNA structures have been determinedexperimentally. For instance, in several trans-actingsRNAs, only a part of the sequence is involved in basepairing with the target. Based on a 3D structure, it is pos-sible to hypothesize which residues are available to basepair. Parallel analysis of RNA structure (PARS), a newmethod for studying RNA secondary structure genome-wide, obtains information about the secondary structureof RNA by treating RNA with structure-specific enzymesand then subjecting the fragments to deep sequenc-ing. PARS was initially developed for mRNAs, but themethod could also be used on bacterial sRNA fractionsand even entire transcriptomes119 (FIG. 3e).

Transcript levels for the majority of the RNAs aregenerally low (the abundance of mRNA for genes withmore than 100 protein copies per cell ranges from 0.05to 5 mRNAs per cell149). This, together with the fact thattranscription is a stochastic process (as it deals with

very few sites in a large chromosome, and transcriptionfactors are bound nonspecifically to DNA and diffusealong DNA for most of the time150) results in large tem-poral variations among cells149 (FIG. 3f). Gene expres-sion heterogeneity within individual cells is essentialfor various processes151. The single-cell transcriptomicsdata that are needed to understand this stochasticity ineukaryotes are already available152, so a major challengeis to provide similar single-cell data sets for bacteria.

In addition to these future technical prospects,integration of data that combine different sourcesof information is needed to provide a global picture of transcription.

As mentioned above, chromatin organization and epi-genetics in bacteria could play a major part in transcrip-tion and translation. Determining the role of genomicarchitecture and dynamics, together with understand-ing the function of the massive amount of sRNAs,presents the next important challenge in decipheringthe mechanisms of regulation of gene expression at thetranscriptional and translational levels. Therefore,the evidence that non-canonical DNA-binding proteinscould have a role in shaping the nucleoid architecturethat links metabolism and transcription is highly relevant.In this scenario153, obtaining a global overview of DNA-binding proteins, and disentangling their effects on tran-scription and metabolism, would greatly enhance the

design of the multilayer models required to understandthe complexity of a biological system.

Note added in proof

Recently, a paper describing single-cell RNA analysis inbacteria was published160.

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AcknowledgementsWe would like to thank M. Isalan and B. Lehner for critical

reading of the manuscript. This work was supported by the

Consolider programme of the Spanish Ministry of Research,

the Fundación Marcelino Botín and the European Research

Council.

Competing interests statementThe authors declare no competing financial interests.

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