Abstract
Global gene expression analyses in bacteria have undergone a dramatic transformation. Prior to the development of high-throughput sequencing technologies, real-time PCR or microarray studies were the mainstay of assessing differences in gene expression in bacteria. Real-time PCR remains a critical tool for targeted gene expression analyses. However, microarray studies have given way to the plethora of advantages in RNA sequencing (RNA-seq) for the determination of global gene expression (i.e., transcriptome). Increased accessibility to high-throughput sequencing and user-friendly bioinformatics data analysis software have made RNA-seq technology use more widespread. Here, we provide comprehensive methods to perform RNA sequencing of Streptococcus pyogenes strains grown in vitro in standard laboratory media, including cell growth, RNA extraction, ribosomal RNA depletion, and library construction. Considerations for library sequencing and data analysis are also provided.
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References
Sitkiewicz I, Green NM, Guo N et al (2009) Transcriptome adaptation of group B Streptococcus to growth in human amniotic fluid. PLoS One 4(7):e6114. https://doi.org/10.1371/journal.pone.0006114
Sitkiewicz I, Green NM, Guo N et al (2010) Adaptation of group A Streptococcus to human amniotic fluid. PLoS One 5(3):e9785. https://doi.org/10.1371/journal.pone.0009785
Sitkiewicz I, Musser JM (2006) Expression microarray and mouse virulence analysis of four conserved two-component gene regulatory systems in group A Streptococcus. Infect Immun 74(2):1339–1351. https://doi.org/10.1128/IAI.74.2.1339-1351.2006
Virtaneva K, Porcella SF, Graham MR et al (2005) Longitudinal analysis of the group A Streptococcus transcriptome in experimental pharyngitis in cynomolgus macaques. Proc Natl Acad Sci U S A 102(25):9014–9019
Kachroo P, Eraso JM, Beres SB et al (2019) Integrated analysis of population genomics, transcriptomics and virulence provides novel insights into Streptococcus pyogenes pathogenesis. Nat Genet 51(3):548–559. https://doi.org/10.1038/s41588-018-0343-1
Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. https://doi.org/10.1186/s13059-016-0881-8
Haas BJ, Chin M, Nusbaum C et al (2012) How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? BMC Genomics 13:734. https://doi.org/10.1186/1471-2164-13-734
Flores AR, Jewell BE, Yelamanchili D et al (2015) A single amino acid replacement in the sensor kinase LiaS contributes to a carrier phenotype in group A Streptococcus. Infect Immun 83(11):4237–4246. https://doi.org/10.1128/IAI.00656-15
Flores AR, Olsen RJ, Cantu C et al (2017) Increased pilus production conferred by a naturally occurring mutation alters host-pathogen interaction in favor of carriage in Streptococcus pyogenes. Infect Immun 85(5):e00949–e00916. https://doi.org/10.1128/IAI.00949-16
Chatterjee A, Ahn A, Rodger EJ et al (2018) A guide for designing and analyzing RNA-Seq data. Methods Mol Biol 1783:35–80. https://doi.org/10.1007/978-1-4939-7834-2_3
Johnson BK, Scholz MB, Teal TK, Abramovitch RB (2016) SPARTA: simple program for automated reference-based bacterial RNA-seq Transcriptome analysis. BMC Bioinformatics 17:66. https://doi.org/10.1186/s12859-016-0923-y
Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protoc 7(3):562–578. https://doi.org/10.1038/nprot.2012.016
McClure R, Balasubramanian D, Sun Y et al (2013) Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res 41(14):e140. https://doi.org/10.1093/nar/gkt444
Petrova OE, Garcia-Alcalde F, Zampaloni C, Sauer K (2017) Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci Rep 7:41114. https://doi.org/10.1038/srep41114
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120. https://doi.org/10.1093/bioinformatics/btu170
Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. https://doi.org/10.1186/gb-2009-10-3-r25
Li H, Handsaker B, Wysoker A et al (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
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
Robinson JT, Thorvaldsdottir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26. https://doi.org/10.1038/nbt.1754
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Sanson, M., Flores, A.R. (2020). Group A Streptococcus Transcriptome Analysis. In: Proft, T., Loh, J. (eds) Group A Streptococcus. Methods in Molecular Biology, vol 2136. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0467-0_8
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DOI: https://doi.org/10.1007/978-1-0716-0467-0_8
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