Chapter 13 MicroRNA Target and Gene Validation in Viruses and Bacteria Debora Baroni and Patrizio Arrigo Abstract Noncoding RNAs (ncRNAs) constitute an evolutionary conserved system involved in the regulation of biological functions at posttranscriptional level. The capability to rapidly adapt their metabolism is essential for the survival of organisms. NcRNAs are a valuable means used by cells to rapidly transfer and internalize an external signal. NcRNAs are capable not only to influence the translational phase but also to affect epigenetic processes. They have been identified in almost all kingdoms of life (from archaea to human and plants). In this chapter we outline the currently available resources that could be used for the screening of viral and bacterial ncRNAs. Key words Virus, Prokaryotes, Bioinformatics, ncRNAs
1
Introduction Noncoding RNAs (ncRNAs) are detectable in almost all kingdoms of life. They have been discovered in eukaryotes [1], bacteria [2], and viruses [3]. More recently small RNAs have also been found in archaea, in particular in extremophiles [4]. Beside their involvement in the regulation of gene expression at posttranscriptional level, ncRNAs are involved in the control of a wide range of cellular processes. In fact, a rapid modularly cell response is essential for the adaptation of cells to environmental changes, to stress factors (biotic and abiotic), and to protect against infection. The rapid response to each of these factors requires a highly coordinated system that involves both ncRNAs and protein regulators. Prokaryotes use two basic ways to transfer genetic information (Mobile Genetic Elements): the endosymbiotic gene transfer (EGT) and the horizontal gene transfer (HGT) [5]. Although both processes have been mainly associated with the transfer of DNA elements, some information is now available about the role played by small RNAs in these mechanisms. Endosymbiosis is a process that
Malik Yousef and Jens Allmer (eds.), miRNomics: MicroRNA Biology and Computational Analysis, Methods in Molecular Biology, vol. 1107, DOI 10.1007/978-1-62703-748-8_13, © Springer Science+Business Media New York 2014
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allows free-living prokaryotic cells to reside within another cell that not necessarily belongs to the same phylogenetic kingdom. The transfer of genetic material from an endosymbiotic cell to the recipient one takes advantages of the host’s cellular system. Conversely, HGT implies the exchange of genetic material between two different and separated biological systems that could be evolutionary distant [6]. The interest in HGT between bacteria and higher vertebrates is demonstrated by the activation of the Human Microbiome Project (http://commonfund.nih.gov/hmp/) [7, 8], aimed at unraveling the mechanisms that facilitates the HGT between bacteria and human cells. A human being is exposed not only to bacterial HGT but also to the viral genetic transfer. We could also add to the notion of HGT all the genetic process modifications that allow a virus to control the metabolism of its host. The existence of viral ncRNAs is well established. Viruses are capable of using their ncRNAs to remodulate the host’s metabolism [9]. For instance it is well known that the transition between the lysogenic and the lytic cycle of the Epstein-Barr virus is controlled by several viral miRNAs. It is worth to note that some studies have demonstrated that viral miRNAs show some functional similarity with human ones [10]. Actually, only a relatively limited number of investigations have been carried out aimed at unraveling the potential interference of exogenous (bacterial, viral, and plant) ncRNAs on the host posttranscriptional gene silencing process. From an environmental health perspective, the deep investigation of this kind of interference could be helpful for the comprehension of the onset of complex (multifactorial) diseases. In this regard, the discovery of circulating ncRNAs [11], in particular miRNAs, has pointed out the necessity to reconsider the mechanisms of cell–cell communication and, as a consequence, has underlined, once more, the possibility that exogenous transposable genetic elements could have a deep influence on the metabolism or on the processing of the genetic information of the recipient. From a biotechnological and bioinformatics perspective, the capability to discriminate between endogenous and exogenous ncRNAs is a significant challenge, because this knowledge will permit to predict the role of ncRNAs in the HGT process, in the emergence of drug resistance, or unapparent (latent) infectious diseases. In this chapter we outline the available computational resources, for viral and bacterial ncRNAs, that could support the investigation of the interplay between exogenous and endogenous ncRNA on the host’s posttranscriptional process regulation.
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Viral Noncoding RNA Resources Similar to the human microbiome, it is possible to define a human virome as the viral community that resides, without apparent pathological signs, in the human body [12]. A large number of papers
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have demonstrated the critical role played by ncRNAs in the viral escape from host natural immunity [13]. Viruses possess the capability to bypass host defense by expressing proteins capable of blocking this process in infected cells [14]. The Baltimore viral classification [15] organizes different groups of viruses on the basis of their nucleic acid composition. Among the different classes of viruses, we underline some, such as the Rheovirus, that uses RNAs (both ssRNA and dsRNA) for their replication. Another very important class of viruses is that of Retrovirus [16] (HIV, EpsteinBarr virus (EBV), Herpes simplex virus, etc.) that is nowadays widely accepted being able to move to induce a stable posttranscriptional gene silencing in cultured cells. The genome of a Retrovirus is an RNA capable of producing DNA depending on a reverse transcriptase. It is out of the scope of this chapter to describe in detail the mechanism of viral infection, but it is important to underline that these infective agents are able to produce ncRNAs. The retroviral ncRNAs, and in particular the retroviral miRNAs, have been extensively studied. MicroRNAs are an efficient system, for the virus, to infect the host and to control the switch from the latent phase to virulent phase. In general, viral microRNAs play a multitude of functions inside the host’s cell. They can not only trigger the rise of viral pathologies but also of more complex ones such as cancer. Taking only human cells into account, a viral microRNA can, for example, mimic the function of oncogenic microRNAs [17]. Thus it is possible to state that viral ncRNAs could interfere with host functionality at different levels. We can schematically hypothesize two different possible routes of action for viral miRNAs: (1) they could compete with the host’s miRNAs for the same mRNA target or (2) alternatively they could have a synergistic effect on the host’s target mRNA. In order to underline differences and similarities between viral and human microRNA precursors we show, in Fig. 1, two miRNA precursors (viral and human) which closely resemble each other structurally. The computational screening of viral ncRNAs, in particular of miRNAs, has attracted the interest of many groups. Bioinformatics plays a central role in this search [18]. In order to study viral microRNAs several resources have become available. Table 1 summarizes some of them. The miRBase resource [19] is the more general one. Its organization is well known and it contains also viral microRNAs. Additional and more specific data repositories are available. Vir-mir [20] allows the prediction of potential miRNA coding regions in viral genomes. Another complementary resource is the ViTa database [21]. The authors have developed and implemented a system to predict the targets of viral miRNAs. A recently developed resource is the vHoT database [22] which also contains information to predict the interaction between viral miRNAs
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Fig. 1 Comparison between Viral and human miRNA precursors (Model obtained by MFOLD [36]). (a) Folded structure of EBV bart-14 precursor; (b) Folded structure of human let-7g precursor
Table 1 Viral noncoding RNAs resources Database name
URL
Data accessibility
miRBase
http://www.mirbase.org/
Web Service data, download
Vir-Mir
http://alk.ibms.sinica.edu.tw
Web Service
vHoT
http://best.snu.ac.kr/vhot/
Web Service
RepTar
http://bioinformatics.ekmd.huji.ac.il/reptar/
Web Service data, download
VirSiRNA
http://crdd.osdd.net/servers/virsirnadb/index.php
Web Service
ViTa
http://vita.mbc.nctu.edu.tw/
Web Service, data download
and the host genome. A more comprehensive database is the VIRsiRNAdb [23] (http://crdd.osdd.net/servers/virsirnadb). This repository contains data about siRNA/shRNA that are able to target viral genomes. One challenge, in the investigation of viral function lead by ncRNAs is, without any doubt, the identification of the host’s potential targets and to establish if a specific viral ncRNA could be secreted by infected cells. The suprementoned databases constitute a good bioformetics staiting point to solve this task.
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3
227
Bacterial Noncoding RNAs Contrary to viruses, less information is available about the potential interference of bacterial ncRNAs with the host cells. Regulatory ncRNAs adjust bacterial physiology in response to environmental cues. ncRNAs can base pair to mRNAs and change their translation efficiency and/or their stability, or they can bind to proteins and modulate their activity. ncRNAs have been discovered in several species throughout the bacterial kingdom [24]. The recent discovery and characterization of bacterial small regulatory RNAs has attracted the interest of many scientists. The acronym sRNA has been defined in the 1960s, to identify soluble RNAs. Nowadays, the abbreviation indicates a large number of relatively small ribonucleic acids that originate from bacteria to generally activate a fast response against environmental stresses. The availability of new high-throughput RNA deep sequencing methods has enhanced the number of screened sRNAs. This discovery has disclosed a new perspective to study the mechanism of gene expression in bacteria [25]. Bacterial sRNAs are longer than eukaryote small ncRNAs. Their length is between 50 and 250 nucleotides and they are highly structured. They do not require a perfect complementarity such as, for instance, in the case of plant miRNAs. The sRNAs seem to be capable of mimicking the functionality of other nucleic acids. The sRNAs are roughly classified into trans-encoded and cis-encoded base-paired RNAs. The cis-encoded sRNAs are transcribed in antisense orientation in respect to the transcription of their target gene. The trans-encoded sRNAs are instead transcribed in the intergenic regions and they are able to target multiple genes. Current knowledge suggests that trans-encoded base-paired RNAs require, for their functionality, the interaction with chaperon proteins. In respect to this, the most extensively studied protein is Hfq, an Escherichia coli host factor essential for replication of the bacteriophage Qβ [26], that is present in Gram-negative bacteria. The regulatory capability of bacteria seems to be associated with some conformational properties of their mRNA, but a relevant role is also played by small RNAs. Research activities are continuously adding new knowledge about the bacterial small RNAs. Figure 2 exemplifies the 2D conformation of a bacterial sRNA. The identification of bacterial and, more generally of prokaryotes and viral, ncRNAs follows the following main guidelines: (1) Identification of structural homology and (2) target prediction. The structural perspective has the objective to identify the conformational determinants conserved during the evolution [27]. This kind of approach embeds two different aspects:
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Fig. 2 An example of Mycobacterium tuberculosis cis-encoded sRNA (BSRD code:smtu1953.1)
1. Identification of structural homologies. This approach aims to determine a consensus structure taking multiple alignments into account. It is important to find the conservation of base pairing of the nucleotide involved in the pairing. 2. Identification of ncRNA genes. In this case genome alignment is required. The alignment allows to predict those gene that have conserved RNA structure. The prediction of bacterial (prokaryotes) ncRNA targets is the most complicated task to be solved. The search for potential targets requires, analogously to eukaryotes, different operational steps. The pivotal phases are briefly summarized below: 1. Identification of complementary regions. The aim of this approach is to identify those nucleotide regions that are mainly involved in ncRNA:mRNA interaction. Bacterial ncRNA:mRNA matching has a hyphen flexibility and thus complexity respect to the eukaryotic miRNA:mRNA interaction. 2. Estimation of duplex characteristics. It is important to estimate the degree of folding conservation in the selected regions. This analysis allows to estimate, even if with many limitations, all the possible RNA–RNA interactions [28]. This analysis allows to determine those conformations that have the highest probability to be functional. 3. Estimation of the best folded local structure in order to obtain a high-ordered co-folded structure. 4. Estimation of site accessibility.
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It is worth to note that structural bioinformatics tools play a pivotal role for the prediction of targets for bacterial sRNAs. These structural approaches are, in any case, dependent on the availability of annotated resources. We briefly describe some of the more relevant resources for bacterial ncRNA screening and analysis. Among the accessible resources that specifically contain information about bacterial ncRNAs we can consider the Bacterial Small Regulatory Database (BSRD) [29]. The BSR database contains the largest number of experimentally validated sRNAs and their known targets. The BSR database also contains information about the combinatorial structure of transcriptional regulatory networks of sRNAs. RNAspace (http://rnaspace.sourceforge.net) is an open source platform that allows to predict the conformation of ncRNAs not only for bacteria but also for eukaryotes and archaea [30]. The sRNAMap is another integrative resource to find information about bacterial sRNAs (http://srnamap.mbc.nctu. edu.tw/) [31]. An interesting tool called nocoRNAc has been developed by Herbig and Nieselt to predict ncRNA gene in bacteria [32]. The sRNAdb [33] is a suite that allows the user to analyze, by alignment, nCRNAs of gram-positive bacteria and to search their potential targets. The Rho element is an important feature of bacterial transcription that could be modulated by some bacterial ncRNAs. RNIE is a suite to predict rho-independent terminators. This information could be valuable to investigate the interaction between ncRNA and transcriptional regulation. Bacterial target identification could be also carried out by sRNATarget [34]. This is a software suite specifically developed to investigate this task. A comprehensive system that contains information about ncRNAs and their function is the BSR database [29]. It contains not only information about nucleotide sequences but also about targets and proteins. A genomic-based system for bacterial target prediction is the program called RNApredator [35]. This system permits to screen the putative sRNAs in bacterial genomes and plasmids. The search can be performed only on a selected specific strain or plasmid. These resources are summarized in Table 2. We underline that we did not include general repositories (Sanger Center, TIGR, or NCBI) because our aim was to highlight the repositories that are more specialized to the screening of bacterial sRNAs. It is important to underline that viral ncRNAs are included in miRBase, but bacterial and archaea ncRNA are developed in an independent manner. Taking our initial considerations about inter-kingdom genetic transfer into account we hope that these different sources will be integrated and become more useful.
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Table 2 Bacterial noncoding RNA data repositories Database name
URL
Data accessibility
sRNAMap
http://srnamap.mbc.nctu.edu.tw/
Web Service, Data download
nocoRNAc
http://www-ps.informatik.uni-tuebingen.de
Data download (software)
sRNAdb
http://bioinfo.mikrobio.med.unigiessen.de/sRNAdb/Home
Web Service, data download
RNIE
http://github.com/ppgardne/RNIE
sRNATarget
http://ccb.bmi.ac.cn/srnatarget/index.php
Web Service
BSRD
http://bac-srna.org/BSRD/index.jsp#
Web Service, data download
RNApredator
http://rna.tbi.univie.ac.at/RNApredator
Web Service
4
Conclusion The aim of this chapter is to convey a survey about some of currently available data sources for viral and bacterial ncRNAs. One of the major challenges for ncRNA bioinformatics is the capability to discriminate between endogenous and exogenous ncRNAs in biological samples. This knowledge will allow to improve the design of new diagnostic tools capable, for instance, to detect circulating miRNA associated with diseases with the molecular detect exogenous ncRNAs that can interfere with the molecular diagnostic screening.
References 1. Szymanski M, Barciszewski J (2006) RNA regulation in mammals. Ann N Y Acad Sci 1067: 461–468 2. Repoila F, Darfeuille F (2009) Small regulatory non-coding RNAs in bacteria: physiology and mechanistic aspects. Biol Cell 101:117–131 3. Grundhoff A, Sullivan CS (2011) Virusencoded microRNAs. Virology 411:325–343 4. Marchfelder A, Fischer S, Brendel J et al (2012) Small RNAs for defence and regulation in archaea. Extremophiles 16:685–696 5. Brown JR (2003) Ancient horizontal gene transfer. Nat Rev Genet 4:121–132 6. Syvanen M (2012) Evolutionary implications of horizontal gene transfer. Annu Rev Genet 46:341–358
7. Liu L, Chen X, Skogerbø G et al (2012) The human microbiome: a hot spot of microbial horizontal gene transfer. Genomics 100:265–270 8. Gevers D, Knight R, Petrosino JF et al (2012) The human microbiome project: a community resource for the healthy human microbiome. PLoS Biol 10:e1001377 9. Zhou R, Rana TM (2013) RNA-based mechanisms regulating host-virus interactions. Immunol Rev 253:97–111 10. Yu G, He Q-Y (2011) Functional similarity analysis of human virus-encoded miRNAs. J Clin Bioinforma 1:15 11. Fu Y, Yi Z, Wu X et al (2011) Circulating microRNAs in patients with active pulmonary tuberculosis. J Clin Microbiol 49:4246–4251
Prokaryotes and Viral ncRNA Detection 12. Wylie KM, Weinstock GM, Storch GA (2012) Emerging view of the human virome. Transl Res 160:283–290 13. Cullen BR (2013) MicroRNAs as mediators of viral evasion of the immune system. Nat Immunol 14:205–210 14. Cazalla D, Yario T, Steitz JA et al (2010) Down-regulation of a host microRNA by a Herpesvirus saimiri noncoding RNA. Science 328:1563–1566 15. Baltimore D (1971) Expression of animal virus genomes. Bacteriol Rev 35:235–241 16. Kurth R, Bannert N (2010) Beneficial and detrimental effects of human endogenous retroviruses. Int J Cancer 126:306–314 17. Kincaid RP, Burke JM, Sullivan CS (2012) RNA virus microRNA that mimics a B-cell oncomiR. Proc Natl Acad Sci U S A 109: 3077–3082 18. Grundhoff A (2011) Computational prediction of viral miRNAs. Methods Mol Biol 721: 143–152 19. Griffiths-Jones S, Saini HK, van Dongen S et al (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36:D154–D158 20. Li S-C, Shiau C-K, Lin W-C (2008) Vir-Mir db: prediction of viral microRNA candidate hairpins. Nucleic Acids Res 36:D184–D189 21. Hsu PW-C, Lin L-Z, Hsu S-D et al (2007) ViTa: prediction of host microRNAs targets on viruses. Nucleic Acids Res 35:D381–D385 22. Kim H, Park S, Min H et al (2012) vHoT: a database for predicting interspecies interactions between viral microRNA and host genomes. Arch Virol 157:497–501 23. Thakur N, Qureshi A, Kumar M (2012) VIRsiRNAdb: a curated database of experimentally validated viral siRNA/shRNA. Nucleic Acids Res 40:D230–D236 24. Liu JM, Camilli A (2010) A broadening world of bacterial small RNAs. Curr Opin Microbiol 13:18–23
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25. Storz G, Vogel J, Wassarman KM (2011) Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell 43:880–891 26. Brennan RG, Link TM (2007) Hfq structure, function and ligand binding. Curr Opin Microbiol 10:125–133 27. Backofen R (2012) Bioinformatics of bacterial sRNAs and their targets. In: Hess WR, Marchfelder A (eds) Regulatory RNAs in prokaryotes. Springer, Vienna, pp 221–239 28. Salari R, Backofen R, Sahinalp SC (2010) Fast prediction of RNA-RNA interaction. Algorithms Mol Biol 5:5 29. Li L, Huang D, Cheung MK et al (2013) BSRD: a repository for bacterial small regulatory RNA. Nucleic Acids Res 41:D233–D238 30. Cros M-J, de Monte A, Mariette J et al (2011) RNAspace.org: an integrated environment for the prediction, annotation, and analysis of ncRNA. RNA 17:1947–1956 31. Huang H-Y, Chang H-Y, Chou C-H et al (2009) sRNAMap: genomic maps for small non-coding RNAs, their regulators and their targets in microbial genomes. Nucleic Acids Res 37:D150–D154 32. Herbig A, Nieselt K (2011) nocoRNAc: characterization of non-coding RNAs in prokaryotes. BMC Bioinformatics 12:40 33. Gardner PP, Barquist L, Bateman A et al (2011) RNIE: genome-wide prediction of bacterial intrinsic terminators. Nucleic Acids Res 39: 5845–5852 34. Cao Y, Zhao Y, Cha L et al (2009) sRNATarget: a web server for prediction of bacterial sRNA targets. Bioinformation 3:364–366 35. Eggenhofer F, Tafer H, Stadler PF et al (2011) RNApredator: fast accessibility-based prediction of sRNA targets. Nucleic Acids Res 39: W149–W154 36. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415
1
Introduction Noncoding RNAs (ncRNAs) are detectable in almost all kingdoms of life. They have been discovered in eukaryotes [1], bacteria [2], and viruses [3]. More recently small RNAs have also been found in archaea, in particular in extremophiles [4]. Beside their involvement in the regulation of gene expression at posttranscriptional level, ncRNAs are involved in the control of a wide range of cellular processes. In fact, a rapid modularly cell response is essential for the adaptation of cells to environmental changes, to stress factors (biotic and abiotic), and to protect against infection. The rapid response to each of these factors requires a highly coordinated system that involves both ncRNAs and protein regulators. Prokaryotes use two basic ways to transfer genetic information (Mobile Genetic Elements): the endosymbiotic gene transfer (EGT) and the horizontal gene transfer (HGT) [5]. Although both processes have been mainly associated with the transfer of DNA elements, some information is now available about the role played by small RNAs in these mechanisms. Endosymbiosis is a process that
Malik Yousef and Jens Allmer (eds.), miRNomics: MicroRNA Biology and Computational Analysis, Methods in Molecular Biology, vol. 1107, DOI 10.1007/978-1-62703-748-8_13, © Springer Science+Business Media New York 2014
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allows free-living prokaryotic cells to reside within another cell that not necessarily belongs to the same phylogenetic kingdom. The transfer of genetic material from an endosymbiotic cell to the recipient one takes advantages of the host’s cellular system. Conversely, HGT implies the exchange of genetic material between two different and separated biological systems that could be evolutionary distant [6]. The interest in HGT between bacteria and higher vertebrates is demonstrated by the activation of the Human Microbiome Project (http://commonfund.nih.gov/hmp/) [7, 8], aimed at unraveling the mechanisms that facilitates the HGT between bacteria and human cells. A human being is exposed not only to bacterial HGT but also to the viral genetic transfer. We could also add to the notion of HGT all the genetic process modifications that allow a virus to control the metabolism of its host. The existence of viral ncRNAs is well established. Viruses are capable of using their ncRNAs to remodulate the host’s metabolism [9]. For instance it is well known that the transition between the lysogenic and the lytic cycle of the Epstein-Barr virus is controlled by several viral miRNAs. It is worth to note that some studies have demonstrated that viral miRNAs show some functional similarity with human ones [10]. Actually, only a relatively limited number of investigations have been carried out aimed at unraveling the potential interference of exogenous (bacterial, viral, and plant) ncRNAs on the host posttranscriptional gene silencing process. From an environmental health perspective, the deep investigation of this kind of interference could be helpful for the comprehension of the onset of complex (multifactorial) diseases. In this regard, the discovery of circulating ncRNAs [11], in particular miRNAs, has pointed out the necessity to reconsider the mechanisms of cell–cell communication and, as a consequence, has underlined, once more, the possibility that exogenous transposable genetic elements could have a deep influence on the metabolism or on the processing of the genetic information of the recipient. From a biotechnological and bioinformatics perspective, the capability to discriminate between endogenous and exogenous ncRNAs is a significant challenge, because this knowledge will permit to predict the role of ncRNAs in the HGT process, in the emergence of drug resistance, or unapparent (latent) infectious diseases. In this chapter we outline the available computational resources, for viral and bacterial ncRNAs, that could support the investigation of the interplay between exogenous and endogenous ncRNA on the host’s posttranscriptional process regulation.
2
Viral Noncoding RNA Resources Similar to the human microbiome, it is possible to define a human virome as the viral community that resides, without apparent pathological signs, in the human body [12]. A large number of papers
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have demonstrated the critical role played by ncRNAs in the viral escape from host natural immunity [13]. Viruses possess the capability to bypass host defense by expressing proteins capable of blocking this process in infected cells [14]. The Baltimore viral classification [15] organizes different groups of viruses on the basis of their nucleic acid composition. Among the different classes of viruses, we underline some, such as the Rheovirus, that uses RNAs (both ssRNA and dsRNA) for their replication. Another very important class of viruses is that of Retrovirus [16] (HIV, EpsteinBarr virus (EBV), Herpes simplex virus, etc.) that is nowadays widely accepted being able to move to induce a stable posttranscriptional gene silencing in cultured cells. The genome of a Retrovirus is an RNA capable of producing DNA depending on a reverse transcriptase. It is out of the scope of this chapter to describe in detail the mechanism of viral infection, but it is important to underline that these infective agents are able to produce ncRNAs. The retroviral ncRNAs, and in particular the retroviral miRNAs, have been extensively studied. MicroRNAs are an efficient system, for the virus, to infect the host and to control the switch from the latent phase to virulent phase. In general, viral microRNAs play a multitude of functions inside the host’s cell. They can not only trigger the rise of viral pathologies but also of more complex ones such as cancer. Taking only human cells into account, a viral microRNA can, for example, mimic the function of oncogenic microRNAs [17]. Thus it is possible to state that viral ncRNAs could interfere with host functionality at different levels. We can schematically hypothesize two different possible routes of action for viral miRNAs: (1) they could compete with the host’s miRNAs for the same mRNA target or (2) alternatively they could have a synergistic effect on the host’s target mRNA. In order to underline differences and similarities between viral and human microRNA precursors we show, in Fig. 1, two miRNA precursors (viral and human) which closely resemble each other structurally. The computational screening of viral ncRNAs, in particular of miRNAs, has attracted the interest of many groups. Bioinformatics plays a central role in this search [18]. In order to study viral microRNAs several resources have become available. Table 1 summarizes some of them. The miRBase resource [19] is the more general one. Its organization is well known and it contains also viral microRNAs. Additional and more specific data repositories are available. Vir-mir [20] allows the prediction of potential miRNA coding regions in viral genomes. Another complementary resource is the ViTa database [21]. The authors have developed and implemented a system to predict the targets of viral miRNAs. A recently developed resource is the vHoT database [22] which also contains information to predict the interaction between viral miRNAs
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Fig. 1 Comparison between Viral and human miRNA precursors (Model obtained by MFOLD [36]). (a) Folded structure of EBV bart-14 precursor; (b) Folded structure of human let-7g precursor
Table 1 Viral noncoding RNAs resources Database name
URL
Data accessibility
miRBase
http://www.mirbase.org/
Web Service data, download
Vir-Mir
http://alk.ibms.sinica.edu.tw
Web Service
vHoT
http://best.snu.ac.kr/vhot/
Web Service
RepTar
http://bioinformatics.ekmd.huji.ac.il/reptar/
Web Service data, download
VirSiRNA
http://crdd.osdd.net/servers/virsirnadb/index.php
Web Service
ViTa
http://vita.mbc.nctu.edu.tw/
Web Service, data download
and the host genome. A more comprehensive database is the VIRsiRNAdb [23] (http://crdd.osdd.net/servers/virsirnadb). This repository contains data about siRNA/shRNA that are able to target viral genomes. One challenge, in the investigation of viral function lead by ncRNAs is, without any doubt, the identification of the host’s potential targets and to establish if a specific viral ncRNA could be secreted by infected cells. The suprementoned databases constitute a good bioformetics staiting point to solve this task.
Prokaryotes and Viral ncRNA Detection
3
227
Bacterial Noncoding RNAs Contrary to viruses, less information is available about the potential interference of bacterial ncRNAs with the host cells. Regulatory ncRNAs adjust bacterial physiology in response to environmental cues. ncRNAs can base pair to mRNAs and change their translation efficiency and/or their stability, or they can bind to proteins and modulate their activity. ncRNAs have been discovered in several species throughout the bacterial kingdom [24]. The recent discovery and characterization of bacterial small regulatory RNAs has attracted the interest of many scientists. The acronym sRNA has been defined in the 1960s, to identify soluble RNAs. Nowadays, the abbreviation indicates a large number of relatively small ribonucleic acids that originate from bacteria to generally activate a fast response against environmental stresses. The availability of new high-throughput RNA deep sequencing methods has enhanced the number of screened sRNAs. This discovery has disclosed a new perspective to study the mechanism of gene expression in bacteria [25]. Bacterial sRNAs are longer than eukaryote small ncRNAs. Their length is between 50 and 250 nucleotides and they are highly structured. They do not require a perfect complementarity such as, for instance, in the case of plant miRNAs. The sRNAs seem to be capable of mimicking the functionality of other nucleic acids. The sRNAs are roughly classified into trans-encoded and cis-encoded base-paired RNAs. The cis-encoded sRNAs are transcribed in antisense orientation in respect to the transcription of their target gene. The trans-encoded sRNAs are instead transcribed in the intergenic regions and they are able to target multiple genes. Current knowledge suggests that trans-encoded base-paired RNAs require, for their functionality, the interaction with chaperon proteins. In respect to this, the most extensively studied protein is Hfq, an Escherichia coli host factor essential for replication of the bacteriophage Qβ [26], that is present in Gram-negative bacteria. The regulatory capability of bacteria seems to be associated with some conformational properties of their mRNA, but a relevant role is also played by small RNAs. Research activities are continuously adding new knowledge about the bacterial small RNAs. Figure 2 exemplifies the 2D conformation of a bacterial sRNA. The identification of bacterial and, more generally of prokaryotes and viral, ncRNAs follows the following main guidelines: (1) Identification of structural homology and (2) target prediction. The structural perspective has the objective to identify the conformational determinants conserved during the evolution [27]. This kind of approach embeds two different aspects:
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Fig. 2 An example of Mycobacterium tuberculosis cis-encoded sRNA (BSRD code:smtu1953.1)
1. Identification of structural homologies. This approach aims to determine a consensus structure taking multiple alignments into account. It is important to find the conservation of base pairing of the nucleotide involved in the pairing. 2. Identification of ncRNA genes. In this case genome alignment is required. The alignment allows to predict those gene that have conserved RNA structure. The prediction of bacterial (prokaryotes) ncRNA targets is the most complicated task to be solved. The search for potential targets requires, analogously to eukaryotes, different operational steps. The pivotal phases are briefly summarized below: 1. Identification of complementary regions. The aim of this approach is to identify those nucleotide regions that are mainly involved in ncRNA:mRNA interaction. Bacterial ncRNA:mRNA matching has a hyphen flexibility and thus complexity respect to the eukaryotic miRNA:mRNA interaction. 2. Estimation of duplex characteristics. It is important to estimate the degree of folding conservation in the selected regions. This analysis allows to estimate, even if with many limitations, all the possible RNA–RNA interactions [28]. This analysis allows to determine those conformations that have the highest probability to be functional. 3. Estimation of the best folded local structure in order to obtain a high-ordered co-folded structure. 4. Estimation of site accessibility.
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It is worth to note that structural bioinformatics tools play a pivotal role for the prediction of targets for bacterial sRNAs. These structural approaches are, in any case, dependent on the availability of annotated resources. We briefly describe some of the more relevant resources for bacterial ncRNA screening and analysis. Among the accessible resources that specifically contain information about bacterial ncRNAs we can consider the Bacterial Small Regulatory Database (BSRD) [29]. The BSR database contains the largest number of experimentally validated sRNAs and their known targets. The BSR database also contains information about the combinatorial structure of transcriptional regulatory networks of sRNAs. RNAspace (http://rnaspace.sourceforge.net) is an open source platform that allows to predict the conformation of ncRNAs not only for bacteria but also for eukaryotes and archaea [30]. The sRNAMap is another integrative resource to find information about bacterial sRNAs (http://srnamap.mbc.nctu. edu.tw/) [31]. An interesting tool called nocoRNAc has been developed by Herbig and Nieselt to predict ncRNA gene in bacteria [32]. The sRNAdb [33] is a suite that allows the user to analyze, by alignment, nCRNAs of gram-positive bacteria and to search their potential targets. The Rho element is an important feature of bacterial transcription that could be modulated by some bacterial ncRNAs. RNIE is a suite to predict rho-independent terminators. This information could be valuable to investigate the interaction between ncRNA and transcriptional regulation. Bacterial target identification could be also carried out by sRNATarget [34]. This is a software suite specifically developed to investigate this task. A comprehensive system that contains information about ncRNAs and their function is the BSR database [29]. It contains not only information about nucleotide sequences but also about targets and proteins. A genomic-based system for bacterial target prediction is the program called RNApredator [35]. This system permits to screen the putative sRNAs in bacterial genomes and plasmids. The search can be performed only on a selected specific strain or plasmid. These resources are summarized in Table 2. We underline that we did not include general repositories (Sanger Center, TIGR, or NCBI) because our aim was to highlight the repositories that are more specialized to the screening of bacterial sRNAs. It is important to underline that viral ncRNAs are included in miRBase, but bacterial and archaea ncRNA are developed in an independent manner. Taking our initial considerations about inter-kingdom genetic transfer into account we hope that these different sources will be integrated and become more useful.
Debora Baroni and Patrizio Arrigo
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Table 2 Bacterial noncoding RNA data repositories Database name
URL
Data accessibility
sRNAMap
http://srnamap.mbc.nctu.edu.tw/
Web Service, Data download
nocoRNAc
http://www-ps.informatik.uni-tuebingen.de
Data download (software)
sRNAdb
http://bioinfo.mikrobio.med.unigiessen.de/sRNAdb/Home
Web Service, data download
RNIE
http://github.com/ppgardne/RNIE
sRNATarget
http://ccb.bmi.ac.cn/srnatarget/index.php
Web Service
BSRD
http://bac-srna.org/BSRD/index.jsp#
Web Service, data download
RNApredator
http://rna.tbi.univie.ac.at/RNApredator
Web Service
4
Conclusion The aim of this chapter is to convey a survey about some of currently available data sources for viral and bacterial ncRNAs. One of the major challenges for ncRNA bioinformatics is the capability to discriminate between endogenous and exogenous ncRNAs in biological samples. This knowledge will allow to improve the design of new diagnostic tools capable, for instance, to detect circulating miRNA associated with diseases with the molecular detect exogenous ncRNAs that can interfere with the molecular diagnostic screening.
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