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Bioinformatic Application of Fluorescence-Based In Vivo RNA Regional Accessibility Data to Identify Novel sRNA Targets

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RNA Spectroscopy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2113))

Abstract

Data from fluorescence-based methods that measure in vivo hybridization efficacy of unique RNA regions can be used to infer regulatory activity and to identify novel RNA: RNA interactions. Here, we document the step-by-step analysis of fluorescence data collected using an in vivo regional RNA structural sensing system (iRS3) for the purpose of identifying potential functional sites that are likely to be involved in regulatory interactions. We also detail a step-by-step protocol that couples this in vivo accessibility data with computational mRNA target predictions to inform the selection of potentially true targets from long lists of thermodynamic predictions.

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References

  1. Dutta T, Srivastava S (2018) Small RNA-mediated regulation in bacteria: a growing palette of diverse mechanisms. Gene 656:60–72. https://doi.org/10.1016/j.gene.2018.02.068

    Article  CAS  PubMed  Google Scholar 

  2. Gelderman G, Contreras LM (2013) Discovery of posttranscriptional regulatory RNAs using next generation sequencing technologies. In: Alper HS (ed) Systems metabolic engineering: methods and protocols. Humana Press, Totowa, NJ, pp 269–295. https://doi.org/10.1007/978-1-62703-299-5_14

    Chapter  Google Scholar 

  3. Leistra AN, Curtis NC, Contreras LM (2019) Regulatory non-coding sRNAs in bacterial metabolic pathway engineering. Metab Eng 52:190–214. https://doi.org/10.1016/j.ymben.2018.11.013

    Article  CAS  PubMed  Google Scholar 

  4. Carrier M-C, Lalaouna D, Massé E (2016) A game of tag: MAPS catches up on RNA interactomes. RNA Biol 13(5):473–476. https://doi.org/10.1080/15476286.2016.1156830

    Article  PubMed  PubMed Central  Google Scholar 

  5. Figueroa-Bossi N, Bossi L (2018) Sponges and predators in the small RNA world. Microbiol Spectr 6(4). https://doi.org/10.1128/microbiolspec.RWR-0021-2018

  6. Kudla G, Granneman S, Hahn D, Beggs JD, Tollervey D (2011) Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc Natl Acad Sci U S A 108(24):10010–10015. https://doi.org/10.1073/pnas.1017386108

    Article  PubMed  PubMed Central  Google Scholar 

  7. Liu T, Zhang K, Xu S, Wang Z, Fu H, Tian B, Zheng X, Li W (2017) Detecting RNA-RNA interactions in E. coli using a modified CLASH method. BMC Genomics 18(1):343. https://doi.org/10.1186/s12864-017-3725-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Schönberger B, Schaal C, Schäfer R, Voß B (2018) RNA interactomics: recent advances and remaining challenges [version 1; referees: 2 approved]. F1000Res 7(1824). https://doi.org/10.12688/f1000research.16146.1

    Article  Google Scholar 

  9. Melamed S, Peer A, Faigenbaum-Romm R, Gatt Yair E, Reiss N, Bar A, Altuvia Y, Argaman L, Margalit H (2016) Global mapping of small RNA-target interactions in Bacteria. Mol Cell 63(5):884–897. https://doi.org/10.1016/j.molcel.2016.07.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Melamed S, Faigenbaum-Romm R, Peer A, Reiss N, Shechter O, Bar A, Altuvia Y, Argaman L, Margalit H (2017) Mapping the small RNA interactome in bacteria using RIL-seq. Nat Protoc 13:1. https://doi.org/10.1038/nprot.2017.115. https://www.nature.com/articles/nprot.2017.115#supplementary-information

    Article  CAS  PubMed  Google Scholar 

  11. 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(3):374–387. https://doi.org/10.15252/embj.201694639

    Article  CAS  PubMed  Google Scholar 

  12. 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(5):509–513. https://doi.org/10.1080/15476286.2015.1020269

    Article  PubMed  PubMed Central  Google Scholar 

  13. Beisel CL, Storz G (2010) Base pairing small RNAs and their roles in global regulatory networks. FEMS Microbiol Rev 34(5):866–882. https://doi.org/10.1111/j.1574-6976.2010.00241.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Holmqvist E, Vogel J (2018) RNA-binding proteins in bacteria. Nat Rev Microbiol. https://doi.org/10.1038/s41579-018-0049-5

    Article  CAS  Google Scholar 

  15. Peer A, Margalit H (2014) Evolutionary patterns of Escherichia coli small RNAs and their regulatory interactions. RNA (New York, NY) 20(7):994–1003. https://doi.org/10.1261/rna.043133.113

    Article  CAS  Google Scholar 

  16. Sowa SW, Vazquez-Anderson J, Clark CA, De La Peña R, Dunn K, Fung EK, Khoury MJ, Contreras LM (2015) Exploiting post-transcriptional regulation to probe RNA structures in vivo via fluorescence. Nucleic Acids Res 43(2):e13–e13. https://doi.org/10.1093/nar/gku1191

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. 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(Web Server issue):W119–W123. https://doi.org/10.1093/nar/gku359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mann M, Wright PR, Backofen R (2017) IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions. Nucleic Acids Res 45(Web Server issue):W435–W439. https://doi.org/10.1093/nar/gkx279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Leistra AN, Mihailovic MK, Contreras LM (2018) Fluorescence-based methods for characterizing RNA interactions in vivo. Methods in Mol Biol 1737:129–164. https://doi.org/10.1007/978-1-4939-7634-8_9

    Article  CAS  Google Scholar 

  21. Mihailovic MK, Vazquez-Anderson J, Li Y, Fry V, Vimalathas P, Herrera D, Lease RA, Powell WB, Contreras LM (2018) High-throughput in vivo mapping of RNA accessible interfaces to identify functional sRNA binding sites. Nat Commun 9(1):4084. https://doi.org/10.1038/s41467-018-06207-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gruber CC, Sperandio V (2015) Global analysis of posttranscriptional regulation by GlmY and GlmZ in Enterohemorrhagic Escherichia coli O157:H7. Infect Immun 83(4):1286–1295. https://doi.org/10.1128/IAI.02918-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  24. Müller K, Wickham H, François R, Henry L (2018) dplyr: A Grammar of Data Manipulation

    Google Scholar 

  25. H. Pagès P Aboyoun, R. Gentleman and S. DebRoy (2018) Biostrings: Efficient manipulation of biological strings

    Google Scholar 

  26. Wickham H (2018) stringr: Simple, Consistent Wrappers for Common String Operations

    Google Scholar 

  27. Anaconda Software Distribution (2017) Computer software. Vers. 2–2.4.0. Anaconda, Inc.

    Google Scholar 

  28. McKinney W (2011) pandas: a Foundational Python Library for Data Analysis and Statistics

    Google Scholar 

  29. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192. https://doi.org/10.1093/bib/bbs017

    Article  CAS  PubMed  Google Scholar 

  30. Vazquez-Anderson J, Mihailovic MK, Baldridge KC, Reyes KG, Haning K, Cho SH, Amador P, Powell WB, Contreras LM (2017) Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions. Nucleic Acids Res 45(9):5523–5538. https://doi.org/10.1093/nar/gkx115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  CAS  Google Scholar 

  32. Leistra AN, Amador P, Buvanendiran A, Moon-Walker A, Contreras LM (2017) Rational modular RNA engineering based on in vivo profiling of structural accessibility. ACS Synth Biol. https://doi.org/10.1021/acssynbio.7b00185

    Article  CAS  Google Scholar 

  33. Green AA, Silver PA, Collins JJ, Yin P (2014) Toehold switches: De-novo-designed regulators of gene expression. Cell 159(4):925–939. https://doi.org/10.1016/j.cell.2014.10.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zadeh JN, Steenberg CD, Bois JS, Wolfe BR, Pierce MB, Khan AR, Dirks RM, Pierce NA (2011) NUPACK: analysis and design of nucleic acid systems. J Comput Chem 32(1):170–173. https://doi.org/10.1002/jcc.21596

    Article  CAS  PubMed  Google Scholar 

  35. Gama-Castro S, Salgado H, Santos-Zavaleta A, Ledezma-Tejeida D, Muñiz-Rascado L, García-Sotelo JS, Alquicira-Hernández K, Martínez-Flores I, Pannier L, Castro-Mondragón JA, Medina-Rivera A, Solano-Lira H, Bonavides-Martínez C, Pérez-Rueda E, Alquicira-Hernández S, Porrón-Sotelo L, López-Fuentes A, Hernández-Koutoucheva A, Del Moral-Chávez V, Rinaldi F, Collado-Vides J (2016) RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res 44(D1):D133–D143. https://doi.org/10.1093/nar/gkv1156

    Article  CAS  Google Scholar 

  36. (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45(D1):D158–D169. https://doi.org/10.1093/nar/gkw1099

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Acknowledgments

This work is supported by the Welch Foundation (Grant F-1756 to LMC) and the National Science Foundation (Grant MCB 1716777 to LMC and DGE-1610403 to MKM).

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Authors and Affiliations

  1. Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA

    Emily K. Bowman, Mia K. Mihailovic, Bridget Li & Lydia M. Contreras

Authors
  1. Emily K. Bowman
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  2. Mia K. Mihailovic
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  3. Bridget Li
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  4. Lydia M. Contreras
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Corresponding author

Correspondence to Lydia M. Contreras .

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Editors and Affiliations

  1. Université de Paris, Paris, France, Laboratoire Léon Brillouin LLB, CEA, CNRS UMR12, Université Paris Saclay, Gif-sur-Yvette, France

    Véronique Arluison

  2. Synchrotron SOLEIL L’Orme des Merisiers Saint Aubin, Gif-sur-Yvette, France

    Frank Wien

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Bowman, E.K., Mihailovic, M.K., Li, B., Contreras, L.M. (2020). Bioinformatic Application of Fluorescence-Based In Vivo RNA Regional Accessibility Data to Identify Novel sRNA Targets. In: Arluison, V., Wien, F. (eds) RNA Spectroscopy. Methods in Molecular Biology, vol 2113. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0278-2_5

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  • DOI: https://doi.org/10.1007/978-1-0716-0278-2_5

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0277-5

  • Online ISBN: 978-1-0716-0278-2

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