- Email: [email protected]
Elucidation of bacterial translation regulatory networks
Accepted Manuscript Elucidation of bacterial translation regulatory networks Yujin Jeong, Hyeonseok Shin, Sang Woo Seo, Donghyuk Kim, Suhyung Cho, ByungKwan Cho PII:
S2452-3100(17)30034-3
DOI:
10.1016/j.coisb.2017.01.009
Reference:
COISB 28
To appear in:
Current Opinion in Systems Biology
Received Date: 28 October 2016 Revised Date:
25 January 2017
Accepted Date: 27 January 2017
Please cite this article as: Jeong Y, Shin H, Seo SW, Kim D, Cho S, Cho B-K, Elucidation of bacterial translation regulatory networks, Current Opinion in Systems Biology (2017), doi: 10.1016/ j.coisb.2017.01.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Elucidation of bacterial translation regulatory networks
Byung-Kwan Cho1,4,5*
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Yujin Jeong1, Hyeonseok Shin1, Sang Woo Seo2, Donghyuk Kim3, Suhyung Cho1,4, and
Department of Biological Sciences, Korea Advanced Institute of Science and
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Technology, Daejeon 34141, South Korea 2
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School of Chemical and Biological Engineering, Seoul National University, Seoul 151742, South Korea
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Department of Genetic Engineering, College of Life Sciences, Kyung Hee University, Yongin 446-701, South Korea
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KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon
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34141, South Korea
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Intelligent Synthetic Biology Center, Daejeon 34141, South Korea
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Correspondence should be addressed to B.-K.C.
Department of Biological Sciences, College of Life Science and Bioengineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea Tel: +82-42-350-2620, Email: [email protected]
Running title: Bacterial translation regulatory networks
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Abstract The translational regulatory network of bacteria is governed by diverse interaction between target mRNAs, regulatory RNAs, and cognate proteins. Advances in high-
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throughput sequencing techniques has helped in understanding the fundamental
mechanisms behind translational regulation, including identification of a large number of regulatory small RNAs and antisense RNAs, and genome-wide binding locations of
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RNA-binding proteins. In this review, different mechanisms of translational regulation are described in the perspective of methods that allow the detection and generation of
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translational regulatory networks in a genome-wide manner. Furthermore, genome-wide data on translational regulation is the key to fill the gap between transcription and proteins and more importantly, develop more accurate in silico regulatory and metabolic
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network models.
Keywords: Translation regulatory network, Next-generation sequencing, small RNA,
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antisense RNA, RNA-binding protein
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Introduction Bacterial cells operate highly interconnected cellular networks composed of regulatory, signaling, and metabolic networks. These networks are tightly controlled by the flux of
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genetic information from DNA to mRNA and to protein in response to the environmental conditions. In particular, it has been shown that the genome-scale determination of interactions between cis-regulatory elements and trans-acting factors is critical to
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understand and reconstruct the bacterial regulatory networks at the transcriptional level [1-4]. Moreover, recent advances in high-throughput technologies, such as the next-
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generation sequencing (NGS) have accelerated the understanding of transcriptional regulatory networks under different stimuli or genetic manipulation [5,6]. NGS-based high-throughput approaches have also provided the unprecedented platforms to elucidate another regulatory layer at the translational level. This translational regulation
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information is required to formulate mechanistically detailed and data-driven network reconstruction and to perform the integrated analysis of regulatory and metabolic networks. In this review, we first discuss briefly cis- and trans-acting factors in
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translational regulation in bacteria. We then describe recent advances in the genomescale experimental efforts to identify those translational regulatory factors for mapping
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translational regulatory networks. In conclusion, we believe that a reconstruction of holistic network models with translational regulatory networks will facilitate a better understanding of cells at a system level.
Regulatory elements for bacterial translation regulation Bacterial translation is initiated by the direct interaction between the ribosome 30S 3
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subunit, fMet-tRNAfMet and mRNA, which is controlled by translation initiation factors such as IF1, IF2 and IF3. Interaction between the purine-rich Shine-Dalgarno (SD) sequence of mRNA and the pyrimidine-rich anti-SD sequence of 16S rRNA has been
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known to be critical to regulate the translation efficiency (TE) [7]. In addition, the TE is controlled by the distance between SD sequence and translation initiation site, the secondary structure formation of 5′ untranslated region (5′UTR), and the coding
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sequence of a gene of interest [8-11]. In bacteria, the TE is also affected by transcription process because translation occurs co-transcriptionally [7]. For instance, the recent
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observation of transcription pause sites in Escherichia coli genome suggested that RNA polymerase pausing at translation start codon controls folding of the 5ʹUTR region, which influences the ribosome binding site (RBS) accessibility of translation machinery to enhance the rate of translation initiation [12]. Lastly, bacterial translation is affected by
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post-transcriptional mRNA degradation initiated by endonucleolytic cleavage by RNases [13-15]. Bacterial translation is also controlled by a wide array of regulatory factors such as RNA-binding proteins (RBP) and base-pairing interaction of mRNAs with other RNA
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molecules. Those factors include antisense RNAs (asRNA), small RNAs (sRNA), RBP, and ligands (e.g., metabolites) (Fig. 1a). Such regulatory factors are further classified
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into cis-acting and trans-acting factors with their regulatory modes (i.e., translational activation or repression).
Cis-acting translation regulatory factors The regulatory mechanisms of the cis-acting translation regulatory factors encoded nearby or in the opposite strand of the mRNA of target genes are diverse [16]. Often, 4
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asRNA originates by convergent transcription from a promoter on the opposite strand from the target gene, which is capable to form complementary base-pairs with the RBS region of the target gene for translational repression. For example, the formation of a
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symE mRNA-SymR duplex, one of the toxin/antitoxin systems in enterobacteria,
represses the translation initiation of symE gene through inhibiting the interaction
between 30S ribosomal subunit and the RBS region of symE mRNA [17]. The double-
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stranded RNA duplex formation between asRNA and the cognate mRNA facilitates the rapid decay of the mRNA to repress its translation. This indicates that the translation
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regulation may not necessarily include the interaction of cis-encoded asRNA with RBS region or translation start codon. The asRNA-induced mRNA degradation has been also exampled from the interaction between isiA and lsrR RNAs in E. coli (Fig. 1b) [18]. On the other hand, the formation of a double-stranded RNA duplex between
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asRNA and the target mRNA often activates translation through protecting the target mRNA from the selective degradation mediated by RNases (Fig. 1c). For example, in Prochlorococcus sp. MED4, an RNA duplex between a polycistronic mRNA and a 3.5 kb
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long asRNA protects the polycistronic mRNA from RNase E-dependent degradation by masking RNase E recognition site during phase infection [19]. Interestingly, translation
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activation can be mediated by a selective cleavage of an RNA duplex by RNase III. The asRNA GadY in E. coli binds to the intergenic region between gadX and gadW genes, which are transcribed as a polycistronic mRNA, and the mRNA is cut into two stable gadX and gadW mRNAs by RNase III-mediated cleavage in response to the acid stress [20].
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Trans-acting translation regulatory factors The translational regulation by trans-acting elements occurs with the assistance of other molecules by binding to the cis-acting factors or nearby to the SD sequences. As trans-
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acting factors, sRNA and RBP play critical roles in translational activation in bacteria (Fig. 1d). For example, RNA-binding protein HF-I (Hfq) in bacteria mediates the binding of sRNA DsrA to the 5’UTR of the rpoS which stimulates RpoS translation by un-pairing
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the secondary structure of translation initiation region [21]. Some RBPs can protect mRNA from RNase cleavage through sequestration of RNase recognition site in a
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similar manner to the Hfq and ompA example [22,23]. Furthermore, sRNAs also interact with other RBPs. CsrB sRNA activates the translation by binding to CsrA, an RBP which directly binds to SD sequence to inhibit the interaction between 30S ribosomal subunit and SD sequence [24].
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Otherwise, the sRNA-Hfq complex can repress the translation by direct binding to SD sequence or leading to degradation of mRNA (Fig. 1e). These have been exampled from the translational repression of chiA gene in Listeria monocytogenes by
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LhrA sRNA binding to its SD sequence and the degradation of ptsG mRNA by Hfq-SgrS sRNA-RNase E complex, respectively [25,26]. In E. coli, the RNA-binding protein CsrA
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binds to the SD sequence of pgaA gene thereby blocking interaction with the ribosome [27]. As mentioned earlier, the translation of the pgaA is rescued by the interaction between CsrB sRNA and CsrA. On the other hand, the binding of RBP to mRNA can modulate the mRNA secondary structure to expose RNase recognition site, causing RNA degradation as exampled in the case of repression of an RNA chaperone CspA [28]. 6
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High-throughput methods to define translation regulatory networks The transcriptome and the translatome studies have benefit from advances in sequencing technologies where high-resolution and genome-wide investigation of the
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genomic architectures has been enabled. First, the mRNA expression levels are
measured by strand-specific RNA-seq (Fig. 2). The RNA-seq result is mostly obtained by exploiting dUTP incorporation into cDNA followed by the treatment of USER enzyme
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[29]. Consequently, using genome-scale expression profiles, novel transcripts such as sRNA and asRNA can be identified [30,31]. Intensive reviews are available for more in-
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depth discussion on the recent RNA-seq technologies [32-35].
Second, the sites of transcription initiation and termination, which are equivalent with the 5ʹ and 3ʹ end of mRNA, can be identified by the differential RNA-seq (dRNAseq) and the termination sequencing (Term-seq), respectively (Fig. 2) [36,37]. The
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dRNA-seq method sequences primary transcripts by removing transcripts lacking triphosphorylated 5ʹ end and any degraded transcripts by treating with terminator exonuclease [37]. In addition to the promoter information, the identification of TSS
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allows to obtain the genomic positions and sequence information of 5ʹUTR, which is prone to numerous translational regulations as a cognate partner for other cis- and
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trans-acting factors [2,36,38]. Additionally, the existence of sRNA and asRNA can be detected by the small RNA-seq and supported by the site of transcription initiation [2,15,39,40]. On the other hand, Term-seq identifies the 3ʹ end of a transcript by ligating the sequencing adaptor to the 3ʹ end of the transcripts prior to high-throughput sequencing [37]. Interestingly, transcription termination profiles enable the observation of premature transcription termination by riboswitches. Recently, in vivo identification of 7
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riboswitches has been supplemented by other high-throughput sequencing technologies, such as structure-seq, DMS-seq, and Mod-seq [11,41,42]. These methods use dimethyl sulfate (DMS) for determination of in vivo secondary structures of RNA transcripts,
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where the unpaired adenine and cytosine residues are methylated by DMS to identify the positions that form structures. Taken together, not only the sequence but also
structure information on upstream and downstream regions of genes (or transcription
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units) can be obtained through the integrative analyses of the multiple high-throughput data.
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Third, binding of the translation regulatory elements such as sRNA, asRNA and RBP to the target mRNA can be elucidated by using immunoprecipitation-based sequencing, such as RIP-seq or CLIP-seq (Fig. 2). For instance, Hfq and Hfq-bound RNAs were co-immunoprecipitated and the retained RNAs were sequenced, resulting
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that about half of sRNAs expressed in Salmonella enterica serovar Typhimurium were associated with Hfq [43]. Similarly, the CLIP-seq, which was developed to overcome the low stringency and low specificity of RIP-seq by adding UV crosslinking step, was
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applied to Hfq and CsrA protein of S. Typhimurium [44]. With this method, not only the previously known binding sites of Hfq and CsrA, but also the RNA preference and the
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structural constraint when they interact with RNA were precisely identified. Fourth, RNA degradation rate can be measured by calculating RNA half-life
using RNA-seq (Fig. 2) [45,46]. Kristoffersen et al. performed RNA-seq in B. cereus harvested at four time points after treatment with rifampicin, a transcription inhibitor, to calculate global mRNA decay rate [45]. On the other hand, Liu et al. sequenced RNAs of B. subtilis wild type strain and polynucleotide phosphorylase (PNPase) knockout 8
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strain, which is one of the 3ʹ exoribonucleases of B. subtilis [46]. Although not applied to bacterial system, 4-thiouridine-seq (4sU-seq) can be an alternative method to measure
newly synthesized RNAs from overall RNA pools [47].
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the RNA half-life [47]. This method uses short metabolic labeling with 4sU to distinguish
Lastly, TE of each mRNA, which is defined by the ratio between levels of
ribosome-protected fragments (RPF) of mRNAs and individual mRNA, can be analyzed
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by the integrated approach of ribosome profiling (Ribo-seq) and RNA-seq (Fig. 2)
[48,49]. Briefly, the Ribo-seq method analyzes only RPF of mRNAs that are being
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translated and the abundances of ribosomes bound to those regions. The comprehensive analyses of transcriptome and translatome in S. coelicolor [50], for example, revealed that the specific regulators of antibiotics gene clusters were identified to be under translational regulation at the transition growth phase. In addition, the
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comparison of 5ʹUTR sequences with TE showed that the 5ʹUTR sequences of efficiently translated genes have lower G+C content and higher free energy than the 5ʹUTR of lowly translated genes, which means the lower probability to form secondary
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structures at the 5ʹUTR. This means, more importantly, that the TE can be measured as a translational regulatory output. Codon bias in the translation elongation step can be
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also calculated by Ribo-seq data [51].
Perspectives and conclusion Along with the regulatory elements in translation level, we have reviewed the highthroughput methods for genome-wide detection and investigation of translational regulation. Our level of understanding of the translational regulation is far from being 9
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complete. However, importantly, the data generation of the translational regulation has led to the identification of several biological findings such as novel riboswitches, regulatory RNAs, and translational regulation sites [37,52]. With the increasing amount
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of information on regulatory interactions, development of in silico models has advanced to the reconstruction at the whole cell level. In particular, the metabolic and gene
expression coupled model (ME-Model) have led to integration of various data such as
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protein localization and enzyme structures to predict reaction fluxes from gene
expression level [5,6,53-55]. In addition, genome-scale model with protein structure
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(GEM-PRO) which integrates protein structural information with genome-scale MEmodel of E. coli improves predictive capacity of metabolic network for protein thermostability [55].
Interestingly, integration of translational regulation data with the genome-scale
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models could be similar with case of transcriptional regulation data, which composed of transcriptional regulator and cognate gene as inputs, and gene expression level as output. For example, the riboswitches, sRNAs, and RBPs can be integrated to networks
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as translational regulatory inputs and Ribo-seq profiles can be used as regulatory outputs to the networks by using similar algorithms made using the transcription factors
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that models the transcription activation and repression [56]. Also, Ribo-seq profiles and RNA-seq expression data are similar in that, they are both data acquired from the RNA, which means that the dynamic range of their expression at transcription and translation levels. Thus, integration of transcription and translation regulatory information to network analysis such as metabolic reconstruction can be an important step that improves the current network models. With increasing availability of the multiple-omics 10
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data on translation, use of these data in current in silico models and regulatory networks would greatly benefit by reducing the discrepancy between gene expression and protein level. From the systems biology perspective, translational regulation data provide
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another layer of information towards the development of more comprehensive and accurate in silico models and networks.
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Conflicts of interest
The authors declare no financial interest or conflicts of interest with regard to this
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manuscript.
Financial support
This work was supported by the Intelligent Synthetic Biology Center of Global Frontier
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Project (2011-0031957 to B.K.C.), the Basic Core Technology Development Program for the Oceans and the Polar Regions (2016M1A5A1027458 to B.K.C.), and the Basic Science Research Program (NRF-2015R1C1A2A01053505 to S.C.) through the
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National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT,
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and Future Planning.
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Figure legends Figure 1. Cis- and trans-acting elements in the bacterial translational regulation. a. Diverse mechanisms for translation regulation. b. Translational activation and
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repression by cis- and trans-acting regulatory factors. The examples for each mechanism are indicated in brackets.
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Figure 2. High-throughput sequencing techniques to understand genome-wide
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translational regulation in bacteria.
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Highlights in references
** Waters LS, Storz G: Regulatory RNAs in bacteria. Cell 2009, 136:615-628.
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24.
Critical review on a wide range of regulatory roles of bacterial RNA molecules on the
36.
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modulation of transcription, translation, and cell metabolism.
Sharma CM, Hoffmann S, Darfeuille F, Reignier J, Findeiss S, Sittka A, Chabas S,
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Reiche K, Hackermuller J, Reinhardt R, et al.: The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 2010, 464:250-255. This work introduces the first high-throughput method (differential RNA-seq) to identify the site of transcription initiation at a genome-scale. This study reports comprehensive
43.
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identification of sRNA from Helicobacter pylori.
Sittka A, Lucchini S, Papenfort K, Sharma CM, Rolle K, Binnewies TT, Hinton JC,
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Vogel J: Deep sequencing analysis of small noncoding RNA and mRNA targets of the global post-transcriptional regulator, Hfq. PLoS Genet 2008,
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S2452-3100(17)30034-3
DOI:
10.1016/j.coisb.2017.01.009
Reference:
COISB 28
To appear in:
Current Opinion in Systems Biology
Received Date: 28 October 2016 Revised Date:
25 January 2017
Accepted Date: 27 January 2017
Please cite this article as: Jeong Y, Shin H, Seo SW, Kim D, Cho S, Cho B-K, Elucidation of bacterial translation regulatory networks, Current Opinion in Systems Biology (2017), doi: 10.1016/ j.coisb.2017.01.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Elucidation of bacterial translation regulatory networks
Byung-Kwan Cho1,4,5*
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Yujin Jeong1, Hyeonseok Shin1, Sang Woo Seo2, Donghyuk Kim3, Suhyung Cho1,4, and
Department of Biological Sciences, Korea Advanced Institute of Science and
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Technology, Daejeon 34141, South Korea 2
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School of Chemical and Biological Engineering, Seoul National University, Seoul 151742, South Korea
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Department of Genetic Engineering, College of Life Sciences, Kyung Hee University, Yongin 446-701, South Korea
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KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon
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34141, South Korea
*
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Intelligent Synthetic Biology Center, Daejeon 34141, South Korea
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Correspondence should be addressed to B.-K.C.
Department of Biological Sciences, College of Life Science and Bioengineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea Tel: +82-42-350-2620, Email: [email protected]
Running title: Bacterial translation regulatory networks
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Abstract The translational regulatory network of bacteria is governed by diverse interaction between target mRNAs, regulatory RNAs, and cognate proteins. Advances in high-
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throughput sequencing techniques has helped in understanding the fundamental
mechanisms behind translational regulation, including identification of a large number of regulatory small RNAs and antisense RNAs, and genome-wide binding locations of
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RNA-binding proteins. In this review, different mechanisms of translational regulation are described in the perspective of methods that allow the detection and generation of
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translational regulatory networks in a genome-wide manner. Furthermore, genome-wide data on translational regulation is the key to fill the gap between transcription and proteins and more importantly, develop more accurate in silico regulatory and metabolic
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network models.
Keywords: Translation regulatory network, Next-generation sequencing, small RNA,
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antisense RNA, RNA-binding protein
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Introduction Bacterial cells operate highly interconnected cellular networks composed of regulatory, signaling, and metabolic networks. These networks are tightly controlled by the flux of
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genetic information from DNA to mRNA and to protein in response to the environmental conditions. In particular, it has been shown that the genome-scale determination of interactions between cis-regulatory elements and trans-acting factors is critical to
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understand and reconstruct the bacterial regulatory networks at the transcriptional level [1-4]. Moreover, recent advances in high-throughput technologies, such as the next-
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generation sequencing (NGS) have accelerated the understanding of transcriptional regulatory networks under different stimuli or genetic manipulation [5,6]. NGS-based high-throughput approaches have also provided the unprecedented platforms to elucidate another regulatory layer at the translational level. This translational regulation
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information is required to formulate mechanistically detailed and data-driven network reconstruction and to perform the integrated analysis of regulatory and metabolic networks. In this review, we first discuss briefly cis- and trans-acting factors in
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translational regulation in bacteria. We then describe recent advances in the genomescale experimental efforts to identify those translational regulatory factors for mapping
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translational regulatory networks. In conclusion, we believe that a reconstruction of holistic network models with translational regulatory networks will facilitate a better understanding of cells at a system level.
Regulatory elements for bacterial translation regulation Bacterial translation is initiated by the direct interaction between the ribosome 30S 3
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subunit, fMet-tRNAfMet and mRNA, which is controlled by translation initiation factors such as IF1, IF2 and IF3. Interaction between the purine-rich Shine-Dalgarno (SD) sequence of mRNA and the pyrimidine-rich anti-SD sequence of 16S rRNA has been
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known to be critical to regulate the translation efficiency (TE) [7]. In addition, the TE is controlled by the distance between SD sequence and translation initiation site, the secondary structure formation of 5′ untranslated region (5′UTR), and the coding
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sequence of a gene of interest [8-11]. In bacteria, the TE is also affected by transcription process because translation occurs co-transcriptionally [7]. For instance, the recent
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observation of transcription pause sites in Escherichia coli genome suggested that RNA polymerase pausing at translation start codon controls folding of the 5ʹUTR region, which influences the ribosome binding site (RBS) accessibility of translation machinery to enhance the rate of translation initiation [12]. Lastly, bacterial translation is affected by
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post-transcriptional mRNA degradation initiated by endonucleolytic cleavage by RNases [13-15]. Bacterial translation is also controlled by a wide array of regulatory factors such as RNA-binding proteins (RBP) and base-pairing interaction of mRNAs with other RNA
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molecules. Those factors include antisense RNAs (asRNA), small RNAs (sRNA), RBP, and ligands (e.g., metabolites) (Fig. 1a). Such regulatory factors are further classified
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into cis-acting and trans-acting factors with their regulatory modes (i.e., translational activation or repression).
Cis-acting translation regulatory factors The regulatory mechanisms of the cis-acting translation regulatory factors encoded nearby or in the opposite strand of the mRNA of target genes are diverse [16]. Often, 4
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asRNA originates by convergent transcription from a promoter on the opposite strand from the target gene, which is capable to form complementary base-pairs with the RBS region of the target gene for translational repression. For example, the formation of a
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symE mRNA-SymR duplex, one of the toxin/antitoxin systems in enterobacteria,
represses the translation initiation of symE gene through inhibiting the interaction
between 30S ribosomal subunit and the RBS region of symE mRNA [17]. The double-
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stranded RNA duplex formation between asRNA and the cognate mRNA facilitates the rapid decay of the mRNA to repress its translation. This indicates that the translation
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regulation may not necessarily include the interaction of cis-encoded asRNA with RBS region or translation start codon. The asRNA-induced mRNA degradation has been also exampled from the interaction between isiA and lsrR RNAs in E. coli (Fig. 1b) [18]. On the other hand, the formation of a double-stranded RNA duplex between
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asRNA and the target mRNA often activates translation through protecting the target mRNA from the selective degradation mediated by RNases (Fig. 1c). For example, in Prochlorococcus sp. MED4, an RNA duplex between a polycistronic mRNA and a 3.5 kb
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long asRNA protects the polycistronic mRNA from RNase E-dependent degradation by masking RNase E recognition site during phase infection [19]. Interestingly, translation
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activation can be mediated by a selective cleavage of an RNA duplex by RNase III. The asRNA GadY in E. coli binds to the intergenic region between gadX and gadW genes, which are transcribed as a polycistronic mRNA, and the mRNA is cut into two stable gadX and gadW mRNAs by RNase III-mediated cleavage in response to the acid stress [20].
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Trans-acting translation regulatory factors The translational regulation by trans-acting elements occurs with the assistance of other molecules by binding to the cis-acting factors or nearby to the SD sequences. As trans-
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acting factors, sRNA and RBP play critical roles in translational activation in bacteria (Fig. 1d). For example, RNA-binding protein HF-I (Hfq) in bacteria mediates the binding of sRNA DsrA to the 5’UTR of the rpoS which stimulates RpoS translation by un-pairing
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the secondary structure of translation initiation region [21]. Some RBPs can protect mRNA from RNase cleavage through sequestration of RNase recognition site in a
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similar manner to the Hfq and ompA example [22,23]. Furthermore, sRNAs also interact with other RBPs. CsrB sRNA activates the translation by binding to CsrA, an RBP which directly binds to SD sequence to inhibit the interaction between 30S ribosomal subunit and SD sequence [24].
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Otherwise, the sRNA-Hfq complex can repress the translation by direct binding to SD sequence or leading to degradation of mRNA (Fig. 1e). These have been exampled from the translational repression of chiA gene in Listeria monocytogenes by
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LhrA sRNA binding to its SD sequence and the degradation of ptsG mRNA by Hfq-SgrS sRNA-RNase E complex, respectively [25,26]. In E. coli, the RNA-binding protein CsrA
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binds to the SD sequence of pgaA gene thereby blocking interaction with the ribosome [27]. As mentioned earlier, the translation of the pgaA is rescued by the interaction between CsrB sRNA and CsrA. On the other hand, the binding of RBP to mRNA can modulate the mRNA secondary structure to expose RNase recognition site, causing RNA degradation as exampled in the case of repression of an RNA chaperone CspA [28]. 6
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High-throughput methods to define translation regulatory networks The transcriptome and the translatome studies have benefit from advances in sequencing technologies where high-resolution and genome-wide investigation of the
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genomic architectures has been enabled. First, the mRNA expression levels are
measured by strand-specific RNA-seq (Fig. 2). The RNA-seq result is mostly obtained by exploiting dUTP incorporation into cDNA followed by the treatment of USER enzyme
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[29]. Consequently, using genome-scale expression profiles, novel transcripts such as sRNA and asRNA can be identified [30,31]. Intensive reviews are available for more in-
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depth discussion on the recent RNA-seq technologies [32-35].
Second, the sites of transcription initiation and termination, which are equivalent with the 5ʹ and 3ʹ end of mRNA, can be identified by the differential RNA-seq (dRNAseq) and the termination sequencing (Term-seq), respectively (Fig. 2) [36,37]. The
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dRNA-seq method sequences primary transcripts by removing transcripts lacking triphosphorylated 5ʹ end and any degraded transcripts by treating with terminator exonuclease [37]. In addition to the promoter information, the identification of TSS
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allows to obtain the genomic positions and sequence information of 5ʹUTR, which is prone to numerous translational regulations as a cognate partner for other cis- and
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trans-acting factors [2,36,38]. Additionally, the existence of sRNA and asRNA can be detected by the small RNA-seq and supported by the site of transcription initiation [2,15,39,40]. On the other hand, Term-seq identifies the 3ʹ end of a transcript by ligating the sequencing adaptor to the 3ʹ end of the transcripts prior to high-throughput sequencing [37]. Interestingly, transcription termination profiles enable the observation of premature transcription termination by riboswitches. Recently, in vivo identification of 7
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riboswitches has been supplemented by other high-throughput sequencing technologies, such as structure-seq, DMS-seq, and Mod-seq [11,41,42]. These methods use dimethyl sulfate (DMS) for determination of in vivo secondary structures of RNA transcripts,
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where the unpaired adenine and cytosine residues are methylated by DMS to identify the positions that form structures. Taken together, not only the sequence but also
structure information on upstream and downstream regions of genes (or transcription
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units) can be obtained through the integrative analyses of the multiple high-throughput data.
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Third, binding of the translation regulatory elements such as sRNA, asRNA and RBP to the target mRNA can be elucidated by using immunoprecipitation-based sequencing, such as RIP-seq or CLIP-seq (Fig. 2). For instance, Hfq and Hfq-bound RNAs were co-immunoprecipitated and the retained RNAs were sequenced, resulting
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that about half of sRNAs expressed in Salmonella enterica serovar Typhimurium were associated with Hfq [43]. Similarly, the CLIP-seq, which was developed to overcome the low stringency and low specificity of RIP-seq by adding UV crosslinking step, was
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applied to Hfq and CsrA protein of S. Typhimurium [44]. With this method, not only the previously known binding sites of Hfq and CsrA, but also the RNA preference and the
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structural constraint when they interact with RNA were precisely identified. Fourth, RNA degradation rate can be measured by calculating RNA half-life
using RNA-seq (Fig. 2) [45,46]. Kristoffersen et al. performed RNA-seq in B. cereus harvested at four time points after treatment with rifampicin, a transcription inhibitor, to calculate global mRNA decay rate [45]. On the other hand, Liu et al. sequenced RNAs of B. subtilis wild type strain and polynucleotide phosphorylase (PNPase) knockout 8
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strain, which is one of the 3ʹ exoribonucleases of B. subtilis [46]. Although not applied to bacterial system, 4-thiouridine-seq (4sU-seq) can be an alternative method to measure
newly synthesized RNAs from overall RNA pools [47].
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the RNA half-life [47]. This method uses short metabolic labeling with 4sU to distinguish
Lastly, TE of each mRNA, which is defined by the ratio between levels of
ribosome-protected fragments (RPF) of mRNAs and individual mRNA, can be analyzed
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by the integrated approach of ribosome profiling (Ribo-seq) and RNA-seq (Fig. 2)
[48,49]. Briefly, the Ribo-seq method analyzes only RPF of mRNAs that are being
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translated and the abundances of ribosomes bound to those regions. The comprehensive analyses of transcriptome and translatome in S. coelicolor [50], for example, revealed that the specific regulators of antibiotics gene clusters were identified to be under translational regulation at the transition growth phase. In addition, the
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comparison of 5ʹUTR sequences with TE showed that the 5ʹUTR sequences of efficiently translated genes have lower G+C content and higher free energy than the 5ʹUTR of lowly translated genes, which means the lower probability to form secondary
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structures at the 5ʹUTR. This means, more importantly, that the TE can be measured as a translational regulatory output. Codon bias in the translation elongation step can be
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also calculated by Ribo-seq data [51].
Perspectives and conclusion Along with the regulatory elements in translation level, we have reviewed the highthroughput methods for genome-wide detection and investigation of translational regulation. Our level of understanding of the translational regulation is far from being 9
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complete. However, importantly, the data generation of the translational regulation has led to the identification of several biological findings such as novel riboswitches, regulatory RNAs, and translational regulation sites [37,52]. With the increasing amount
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of information on regulatory interactions, development of in silico models has advanced to the reconstruction at the whole cell level. In particular, the metabolic and gene
expression coupled model (ME-Model) have led to integration of various data such as
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protein localization and enzyme structures to predict reaction fluxes from gene
expression level [5,6,53-55]. In addition, genome-scale model with protein structure
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(GEM-PRO) which integrates protein structural information with genome-scale MEmodel of E. coli improves predictive capacity of metabolic network for protein thermostability [55].
Interestingly, integration of translational regulation data with the genome-scale
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models could be similar with case of transcriptional regulation data, which composed of transcriptional regulator and cognate gene as inputs, and gene expression level as output. For example, the riboswitches, sRNAs, and RBPs can be integrated to networks
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as translational regulatory inputs and Ribo-seq profiles can be used as regulatory outputs to the networks by using similar algorithms made using the transcription factors
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that models the transcription activation and repression [56]. Also, Ribo-seq profiles and RNA-seq expression data are similar in that, they are both data acquired from the RNA, which means that the dynamic range of their expression at transcription and translation levels. Thus, integration of transcription and translation regulatory information to network analysis such as metabolic reconstruction can be an important step that improves the current network models. With increasing availability of the multiple-omics 10
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data on translation, use of these data in current in silico models and regulatory networks would greatly benefit by reducing the discrepancy between gene expression and protein level. From the systems biology perspective, translational regulation data provide
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another layer of information towards the development of more comprehensive and accurate in silico models and networks.
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Conflicts of interest
The authors declare no financial interest or conflicts of interest with regard to this
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manuscript.
Financial support
This work was supported by the Intelligent Synthetic Biology Center of Global Frontier
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Project (2011-0031957 to B.K.C.), the Basic Core Technology Development Program for the Oceans and the Polar Regions (2016M1A5A1027458 to B.K.C.), and the Basic Science Research Program (NRF-2015R1C1A2A01053505 to S.C.) through the
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National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT,
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and Future Planning.
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Figure legends Figure 1. Cis- and trans-acting elements in the bacterial translational regulation. a. Diverse mechanisms for translation regulation. b. Translational activation and
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repression by cis- and trans-acting regulatory factors. The examples for each mechanism are indicated in brackets.
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Figure 2. High-throughput sequencing techniques to understand genome-wide
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translational regulation in bacteria.
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