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Bacterial Regulatory RNA pp 3–30Cite as

Workflow for a Computational Analysis of an sRNA Candidate in Bacteria

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1737))

Abstract

Computational methods can often facilitate the functional characterization of individual sRNAs and furthermore allow high-throughput analysis on large numbers of sRNA candidates. This chapter outlines a potential workflow for computational sRNA analyses and describes in detail methods for homolog detection, target prediction, and functional characterization based on enrichment analysis. The cyanobacterial sRNA IsaR1 is used as a specific example. All methods are available as webservers and easily accessible for nonexpert users.

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References

  1. Wagner EGH, Romby P (2015) Chapter 3. Small RNAs in bacteria and Archaea: who they are, what they do, and how they do it. In: Friedmann T, Dunlap JC, Goodwin SF (eds) Advances in genetics. Academic Press, Waltham, pp 133–208

    Google Scholar 

  2. Barquist L, Vogel J (2015) Accelerating discovery and functional analysis of small RNAs with new technologies. Annu Rev Genet 49:367–394. https://doi.org/10.1146/annurev-genet-112414-054804

    Article  CAS  PubMed  Google Scholar 

  3. Melamed S, Peer A, Faigenbaum-Romm R, Gatt YE, Reiss N, Bar A, Altuvia Y, Argaman L, Margalit H (2016) Global mapping of small RNA-target interactions in bacteria. Mol Cell 63:884–897. https://doi.org/10.1016/j.molcel.2016.07.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Waters SA, McAteer SP, Kudla G, Pang I, Deshpande NP, Amos TG, Leong KW, Wilkins MR, Strugnell R, Gally DL, Tollervey D, Tree JJ (2017) Small RNA interactome of pathogenic E. coli revealed through crosslinking of RNase E. EMBO J 36:374–387. 10.15252/embj.201694639

    Article  CAS  PubMed  Google Scholar 

  5. Storz G, Wolf YI, Ramamurthi KS (2014) Small proteins can no longer be ignored. Annu Rev Biochem 83:753–777. https://doi.org/10.1146/annurev-biochem-070611-102400

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gimpel M, Brantl S (2017) Dual-function small regulatory RNAs in bacteria. Mol Microbiol 103:387–397. https://doi.org/10.1111/mmi.13558

    Article  CAS  PubMed  Google Scholar 

  7. Neuhaus K, Landstorfer R, Simon S, Schober S, Wright PR, Smith C, Backofen R, Wecko R, Keim DA, Scherer S (2017) Differentiation of ncRNAs from small mRNAs in Escherichia coli O157:H7 EDL933 (EHEC) by combined RNAseq and RIBOseq – ryhB encodes the regulatory RNA RyhB and a peptide, RyhP. BMC Genomics 18:216. https://doi.org/10.1186/s12864-017-3586-9

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wadler CS, Vanderpool CK (2007) A dual function for a bacterial small RNA: SgrS performs base pairing-dependent regulation and encodes a functional polypeptide. Proc Natl Acad Sci 104:20454–20459. https://doi.org/10.1073/pnas.0708102104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Updegrove TB, Zhang A, Storz G (2016) Hfq: the flexible RNA matchmaker. Curr Opin Microbiol 30:133–138. https://doi.org/10.1016/j.mib.2016.02.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Olejniczak M, Storz G (2017) ProQ/FinO-domain proteins: another ubiquitous family of RNA matchmakers? Mol Microbiol 104:905–915. https://doi.org/10.1111/mmi.13679

    Article  CAS  PubMed  Google Scholar 

  11. Smirnov A, Förstner KU, Holmqvist E, Otto A, Günster R, Becher D, Reinhardt R, Vogel J (2016) Grad-seq guides the discovery of ProQ as a major small RNA-binding protein. Proc Natl Acad Sci U S A 113:11591–11596. https://doi.org/10.1073/pnas.1609981113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cavanagh AT, Wassarman KM (2014) 6S RNA, a global regulator of transcription in Escherichia coli, Bacillus subtilis, and beyond. Annu Rev Microbiol 68:45–60. https://doi.org/10.1146/annurev-micro-092611-150135

    Article  CAS  PubMed  Google Scholar 

  13. Holmqvist E, Wright PR, Li L, Bischler T, Barquist L, Reinhardt R, Backofen R, Vogel J (2016) Global RNA recognition patterns of post-transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo. EMBO J 35:991–1011. 10.15252/embj.201593360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lott S, Schäfer R, Mann M, Hess WR, Voß B, Georg J GLASSgo - Automated and reliable detection of sRNA homologs from a single input sequence. (submitted)

    Google Scholar 

  15. Wright PR, Richter AS, Papenfort K, Mann M, Vogel J, Hess WR, Backofen R, Georg J (2013) Comparative genomics boosts target prediction for bacterial small RNAs. Proc Natl Acad Sci 110:E3487–E3496. https://doi.org/10.1073/pnas.1303248110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wright PR, Georg J, Mann M, Sorescu DA, Richter AS, Lott S, Kleinkauf R, Hess WR, Backofen R (2014) CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains. Nucleic Acids Res 42:W119–W123. https://doi.org/10.1093/nar/gku359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Patrick R. Wright (2016) Predicting small RNA targets in prokaryotes - a challenge beyond the barriers of thermodynamic models. https://freidok.uni-freiburg.de/data/11472

  18. Gruber AR, Findeiß S, Washietl S, Hofacker IL, Stadler PF (2010) RNAz 2.0: improved noncoding RNA detection. Pac Symp Biocomput:69–79

    Google Scholar 

  19. Will S, Joshi T, Hofacker IL, Stadler PF, Backofen R (2012) LocARNA-P: accurate boundary prediction and improved detection of structural RNAs. RNA 18:900–914. https://doi.org/10.1261/rna.029041.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bernhart SH, Hofacker IL, Will S, Gruber AR, Stadler PF (2008) RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics 9:474. https://doi.org/10.1186/1471-2105-9-474

    Article  PubMed  PubMed Central  Google Scholar 

  21. Washietl S, Findeiß S, Müller SA, Kalkhof S, von Bergen M, Hofacker IL, Stadler PF, Goldman N (2011) RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17:578–594. https://doi.org/10.1261/rna.2536111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Busch A, Richter AS, Backofen R (2008) IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions. Bioinformatics 24:2849–2856. https://doi.org/10.1093/bioinformatics/btn544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mann M, Wright PR, Backofen R (2017) IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx279

  24. Menzel P, Gorodkin J, Stadler PF (2009) The tedious task of finding homologous noncoding RNA genes. RNA 15:2075–2082. https://doi.org/10.1261/rna.1556009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nawrocki EP, Burge SW, Bateman A, Daub J, Eberhardt RY, Eddy SR, Floden EW, Gardner PP, Jones TA, Tate J, Finn RD (2015) Rfam 12.0: updates to the RNA families database. Nucleic Acids Res 43:D130–D137. https://doi.org/10.1093/nar/gku1063

    Article  CAS  PubMed  Google Scholar 

  26. Eggenhofer F, Hofacker IL, Höner zu Siederdissen C (2016) RNAlien – unsupervised RNA family model construction. Nucleic Acids Res 44:8433–8441. https://doi.org/10.1093/nar/gkw558

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Katoh K, Toh H (2008) Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework. BMC Bioinformatics 9:212. https://doi.org/10.1186/1471-2105-9-212

    Article  PubMed  PubMed Central  Google Scholar 

  28. Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ (2009) Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 25:1189–1191. https://doi.org/10.1093/bioinformatics/btp033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Morita T, Nishino R, Aiba H (2017) Role of terminator hairpin in biogenesis of functional Hfq-binding sRNAs. RNA 23:1419–1431. https://doi.org/10.1261/rna.060756.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lagares A, Roux I, Valverde C (2016) Phylogenetic distribution and evolutionary pattern of an α-proteobacterial small RNA gene that controls polyhydroxybutyrate accumulation in Sinorhizobium meliloti. Mol Phylogenet Evol 99:182–193. https://doi.org/10.1016/j.ympev.2016.03.026

    Article  CAS  PubMed  Google Scholar 

  31. Georg J, Kostova G, Vuorijoki L, Schön V, Kadowaki T, Huokko T, Baumgartner D, Müller M, Klähn S, Allahverdiyeva Y, Hihara Y, Futschik ME, Aro E-M, Hess WR (2017) Acclimation of oxygenic photosynthesis to iron starvation is controlled by the sRNA IsaR1. Curr Biol. https://doi.org/10.1016/j.cub.2017.04.010

  32. Kery MB, Feldman M, Livny J, Tjaden B (2014) TargetRNA2: identifying targets of small regulatory RNAs in bacteria. Nucleic Acids Res 42:W124–W129. https://doi.org/10.1093/nar/gku317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Eggenhofer F, Tafer H, Stadler PF, Hofacker IL (2011) RNApredator: fast accessibility-based prediction of sRNA targets. Nucleic Acids Res 39:W149–W154. https://doi.org/10.1093/nar/gkr467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pain A, Ott A, Amine H, Rochat T, Bouloc P, Gautheret D (2015) An assessment of bacterial small RNA target prediction programs. RNA Biol 12:509–513. https://doi.org/10.1080/15476286.2015.1020269

    Article  PubMed  PubMed Central  Google Scholar 

  35. Umu SU, Gardner PP (2017) A comprehensive benchmark of RNA–RNA interaction prediction tools for all domains of life. Bioinformatics 33:988–996. https://doi.org/10.1093/bioinformatics/btw728

    PubMed  Google Scholar 

  36. Babski J, Maier L-K, Heyer R, Jaschinski K, Prasse D, Jäger D, Randau L, Schmitz RA, Marchfelder A, Soppa J (2014) Small regulatory RNAs in Archaea. RNA Biol 11:484–493. https://doi.org/10.4161/rna.28452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jiao X, Sherman BT, Huang DW, Stephens R, Baseler MW, Lane HC, Lempicki RA (2012) DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics 28:1805–1806. https://doi.org/10.1093/bioinformatics/bts251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Robledo M, Frage B, Wright PR, Becker A (2015) A stress-induced small RNA modulates alpha-rhizobial cell cycle progression. PLoS Genet 11:e1005153. https://doi.org/10.1371/journal.pgen.1005153

    Article  PubMed  PubMed Central  Google Scholar 

  39. Robledo M, Peregrina A, Millán V, García-Tomsig NI, Torres-Quesada O, Mateos PF, Becker A, Jiménez-Zurdo JI (2017) A conserved α-proteobacterial small RNA contributes to osmoadaptation and symbiotic efficiency of rhizobia on legume roots. Environ Microbiol 9:2661–2680. https://doi.org/10.1111/1462-2920.13757

    Article  Google Scholar 

  40. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M, Balakrishnan R, Cherry JM, Christie KR, Costanzo MC, Dwight SS, Engel S, Fisk DG, Hirschman JE, Hong EL, Nash RS, Sethuraman A, Theesfeld CL, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee SY, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz EM, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, de la Cruz N, Tonellato P, Jaiswal P, Seigfried T, White R, Gene Ontology Consortium (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32:D258–D261. https://doi.org/10.1093/nar/gkh036

    Article  CAS  PubMed  Google Scholar 

  41. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13. https://doi.org/10.1093/nar/gkn923

    Article  Google Scholar 

  43. Hernández-Prieto MA, Schön V, Georg J, Barreira L, Varela J, Hess WR, Futschik ME (2012) Iron deprivation in Synechocystis: inference of pathways, non-coding RNAs, and regulatory elements from comprehensive expression profiling. G3 2:1475–1495. https://doi.org/10.1534/g3.112.003863

    Article  PubMed  PubMed Central  Google Scholar 

  44. Georg J, Dienst D, Schürgers N, Wallner T, Kopp D, Stazic D, Kuchmina E, Klähn S, Lokstein H, Hess WR, Wilde A (2014) The small regulatory RNA SyR1/PsrR1 controls photosynthetic functions in cyanobacteria. Plant Cell 26:3661–3679. https://doi.org/10.1105/tpc.114.129767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Guo MS, Updegrove TB, Gogol EB, Shabalina SA, Gross CA, Storz G (2014) MicL, a new σE-dependent sRNA, combats envelope stress by repressing synthesis of Lpp, the major outer membrane lipoprotein. Genes Dev 28:1620–1634. https://doi.org/10.1101/gad.243485.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Klähn S, Schaal C, Georg J, Baumgartner D, Knippen G, Hagemann M, Muro-Pastor AM, Hess WR (2015) The sRNA NsiR4 is involved in nitrogen assimilation control in cyanobacteria by targeting glutamine synthetase inactivating factor IF7. Proc Natl Acad Sci U S A 112:E6243–E6252. https://doi.org/10.1073/pnas.1508412112

    Article  PubMed  PubMed Central  Google Scholar 

  47. Desnoyers G, Massé E (2012) Noncanonical repression of translation initiation through small RNA recruitment of the RNA chaperone Hfq. Genes Dev 26:726–739. https://doi.org/10.1101/gad.182493.111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Corcoran CP, Podkaminski D, Papenfort K, Urban JH, Hinton JCD, Vogel J (2012) Superfolder GFP reporters validate diverse new mRNA targets of the classic porin regulator, MicF RNA. Mol Microbiol 84:428–445. https://doi.org/10.1111/j.1365-2958.2012.08031.x

    Article  CAS  PubMed  Google Scholar 

  49. Sharma CM, Darfeuille F, Plantinga TH, Vogel J (2007) A small RNA regulates multiple ABC transporter mRNAs by targeting C/A-rich elements inside and upstream of ribosome-binding sites. Genes Dev 21:2804–2817. https://doi.org/10.1101/gad.447207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Pfeiffer V, Papenfort K, Lucchini S, Hinton JCD, Vogel J (2009) Coding sequence targeting by MicC RNA reveals bacterial mRNA silencing downstream of translational initiation. Nat Struct Mol Biol 16:840–846. https://doi.org/10.1038/nsmb.1631

    Article  CAS  PubMed  Google Scholar 

  51. Papenfort K, Said N, Welsink T, Lucchini S, Hinton JCD, Vogel J (2009) Specific and pleiotropic patterns of mRNA regulation by ArcZ, a conserved, Hfq-dependent small RNA. Mol Microbiol 74:139–158. https://doi.org/10.1111/j.1365-2958.2009.06857.x

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany

    Patrick R. Wright

  2. Genetics and Experimental Bioinformatics, Faculty of Biology, Institute of Biology III, University of Freiburg, Freiburg im Breisgau, Germany

    Jens Georg

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  1. Patrick R. Wright
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  2. Jens Georg
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Correspondence to Jens Georg .

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Editors and Affiliations

  1. Université Paris Diderot & LLB/CEA/CNRS, Gif-sur-Yvette, France

    Véronique Arluison

  2. Laboratorio de Bioquímica, Microbiología e Interacciones, Biológicas en el Suelo, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes-CONICET, Bernal, Argentina

    Claudio Valverde

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Wright, P.R., Georg, J. (2018). Workflow for a Computational Analysis of an sRNA Candidate in Bacteria. In: Arluison, V., Valverde, C. (eds) Bacterial Regulatory RNA. Methods in Molecular Biology, vol 1737. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7634-8_1

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  • DOI: https://doi.org/10.1007/978-1-4939-7634-8_1

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