Abstract
A detailed knowledge about virulence-relevant genes, as well as where and when they are expressed during the course of an infection is required to obtain a comprehensive understanding of the complex host–pathogen interactions. The development of unbiased probe-independent RNA sequencing (RNA-seq) approaches has dramatically changed transcriptomics. It allows simultaneous monitoring of genome-wide, infection-linked transcriptional alterations of the host tissue and colonizing pathogens. Here, we provide a detailed protocol for the preparation and analysis of lymphatic tissue infected with the mainly extracellularly growing pathogen Yersinia pseudotuberculosis. This method can be used as a powerful tool for the discovery of Yersinia-induced host responses, colonization and persistence strategies of the pathogen, and underlying regulatory processes. Furthermore, we describe computational methods with which we analyzed obtained datasets.
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References
Nuss AM, Beckstette M, Pimenova M, Schmühl C, Opitz W, Pisano F, Heroven A, Dersch P (2017) Tissue dual RNA-seq: a fast discovery path for infection-specific functions and riboregulators shaping host-pathogen transcriptomes. Proc Natl Acad Sci U S A 114(5):E791–E800
Avican K, Fahlgren A, Huss M, Heroven AK, Beckstette M, Dersch P, Fallman M (2015) Reprogramming of Yersinia from virulent to persistent mode revealed by complex in vivo RNA-seq analysis. PLoS Pathog 11(1):e1004600. https://doi.org/10.1371/journal.ppat.1004600
Heine W, Beckstette M, Heroven AK, Thiemann S, Heise U, Nuss AM, Pisano F, Strowig T, Dersch P (2018) Loss of CNFY toxin-induced inflammation drives Yersinia pseudotuberculosis into persistency. PLoS Pathog 14(2):e1006858. https://doi.org/10.1371/journal.ppat.1006858
Chain PS, Carniel E, Larimer FW, Lamerdin J, Stoutland PO, Regala WM, Georgescu AM, Vergez LM, Land ML, Motin VL, Brubaker RR, Fowler J, Hinnebusch J, Marceau M, Medigue C, Simonet M, Chenal-Francisque V, Souza B, Dacheux D, Elliott JM, Derbise A, Hauser LJ, Garcia E (2004) Insights into the evolution of Yersinia pestis through whole-genome comparison with Yersinia pseudotuberculosis. Proc Natl Acad Sci U S A 101(38):13826–13831
Dotsch A, Eckweiler D, Schniederjans M, Zimmermann A, Jensen V, Scharfe M, Geffers R, Haussler S (2012) The Pseudomonas aeruginosa transcriptome in planktonic cultures and static biofilms using RNA sequencing. PLoS One 7(2):e31092. https://doi.org/10.1371/journal.pone.0031092
Aronesty E (2011) ea-utils: Command-line tools for processing biological data
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with bowtie 2. Nat Methods 9(4):357–359. https://doi.org/10.1038/nmeth.1923
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14(4):R36. https://doi.org/10.1186/gb-2013-14-4-r36
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Anders S, Pyl PT, Huber W (2015) HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/bioinformatics/btu638
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https://doi.org/10.1186/s13059-014-0550-8
Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42(Database issue):D199–D205. https://doi.org/10.1093/nar/gkt1076
Falcon S, Gentleman R (2007) Using GOstats to test gene lists for GO term association. Bioinformatics 23(2):257–258. https://doi.org/10.1093/bioinformatics/btl567
Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim JS, Kim CJ, Kusanovic JP, Romero R (2009) A novel signaling pathway impact analysis. Bioinformatics 25(1):75–82. https://doi.org/10.1093/bioinformatics/btn577
Amman F, Wolfinger MT, Lorenz R, Hofacker IL, Stadler PF, Findeiss S (2014) TSSAR: TSS annotation regime for dRNA-seq data. BMC Bioinformatics 15:89. https://doi.org/10.1186/1471-2105-15-89
Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14(6):1188–1190. https://doi.org/10.1101/gr.849004
Bailey TL, Elkan C (1994) Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol 2:28–36
Nicol JW, Helt GA, Blanchard SG Jr, Raja A, Loraine AE (2009) The integrated genome browser: free software for distribution and exploration of genome-scale datasets. Bioinformatics 25(20):2730–2731. https://doi.org/10.1093/bioinformatics/btp472
Sambrook J (2001) Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratories, Cold Spring Harbor, NY
Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21(9):1543–1551. https://doi.org/10.1101/gr.121095.111
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. https://doi.org/10.1093/bioinformatics/btp616
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oles AK, Pages H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015) Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 12(2):115–121. https://doi.org/10.1038/nmeth.3252
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635
Westermann AJ, Forstner KU, Amman F, Barquist L, Chao Y, Schulte LN, Muller L, Reinhardt R, Stadler PF, Vogel J (2016) Dual RNA-seq unveils noncoding RNA functions in host-pathogen interactions. Nature 529(7587):496–501. https://doi.org/10.1038/nature16547
Acknowledgments
We are grateful to M. Fenner for discussions and Robert Geffers and Michael Jarek from the Department of Genome Analytics for Illumina sequencing. This work was supported from grants of the German Research Foundation (DE616/4, DE616/6, SPP1617-young investigator startup funding for A.M. Nuss), and a stipend of the Helmholtz Center for Infection Research Graduate School for M. Kusmierek. P. Dersch is supported by the German Center for Infection Research.
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Kusmierek, M., Heroven, A.K., Beckstette, M., Nuss, A.M., Dersch, P. (2019). Discovering Yersinia–Host Interactions by Tissue Dual RNA-Seq. In: Vadyvaloo, V., Lawrenz, M. (eds) Pathogenic Yersinia. Methods in Molecular Biology, vol 2010. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9541-7_8
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DOI: https://doi.org/10.1007/978-1-4939-9541-7_8
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