Review Article | Published:

Dual RNA-seq of pathogen and host

Nature Reviews Microbiology volume 10, pages 618630 (2012) | Download Citation

Abstract

A comprehensive understanding of host–pathogen interactions requires a knowledge of the associated gene expression changes in both the pathogen and the host. Traditional, probe-dependent approaches using microarrays or reverse transcription PCR typically require the pathogen and host cells to be physically separated before gene expression analysis. However, the development of the probe-independent RNA sequencing (RNA-seq) approach has begun to revolutionize transcriptomics. Here, we assess the feasibility of taking transcriptomics one step further by performing 'dual RNA-seq', in which gene expression changes in both the pathogen and the host are analysed simultaneously.

Key points

  • During the infection process, the interaction between pathogen and host results in large-scale changes in gene expression within both organisms.

  • Owing to technical limitations, previous transcriptomic studies using probe- and tag-based approaches often required the separation of the interacting host and pathogen. However, the development of a new technology, RNA sequencing (RNA-seq), now promises to allow the analysis of both partners simultaneously.

  • This dual RNA-seq approach is not without difficulties, owing to the very different nature of bacterial and eukaryotic cells, and especially the properties of the RNAs in these two different domains of life.

  • There are various technical considerations required for the successful determination of the host and pathogen transcriptomes. An analysis of these considerations has led to the proposal that the dual RNA-seq approach is currently becoming feasible and will probably become the gold standard for host–pathogen transcriptomics in the future.

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References

  1. 1.

    & Insights into host responses against pathogens from transcriptional profiling. Nature Rev. Microbiol. 3, 281–294 (2005).

  2. 2.

    , & Host gene expression profiling in pathogen-host interactions. Curr. Opin. Immunol. 18, 422–429 (2006).

  3. 3.

    Pushing the limits of cellular microbiology: microarrays to study bacteria–host cell intimate contacts. Proc. Natl Acad. Sci. USA 97, 13467–13469 (2000).

  4. 4.

    et al. Multiplexed biochemical assays with biological chips. Nature 364, 555–556 (1993).

  5. 5.

    , , & Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

  6. 6.

    , , , & Global RNA half-life analysis in Escherichia coli reveals positional patterns of transcript degradation. Genome Res. 13, 216–223 (2003).

  7. 7.

    et al. Empirical analysis of transcriptional activity in the Arabidopsis genome. Science 302, 842–846 (2003).

  8. 8.

    et al. Global identification of human transcribed sequences with genome tiling arrays. Science 306, 2242–2246 (2004).

  9. 9.

    et al. Host-induced epidemic spread of the cholera bacterium. Nature 417, 642–645 (2002).

  10. 10.

    , & DNA microarray analysis of differential gene expression in Borrelia burgdorferi, the Lyme disease spirochete. Proc. Natl Acad. Sci. USA 99, 1562–1567 (2002).

  11. 11.

    et al. Genomic transcriptional profiling of the developmental cycle of Chlamydia trachomatis. Proc. Natl Acad. Sci. USA 100, 8478–8483 (2003).

  12. 12.

    , , & Gene expression profiles of Chlamydophila pneumoniae during the developmental cycle and iron depletion-mediated persistence. PLoS Pathog. 3, e83 (2007).

  13. 13.

    , , , & Unravelling the biology of macrophage infection by gene expression profiling of intracellular Salmonella enterica. Mol. Microbiol. 47, 103–118 (2003).

  14. 14.

    et al. During infection of epithelial cells Salmonella enterica serovar Typhimurium undergoes a time-dependent transcriptional adaptation that results in simultaneous expression of three type 3 secretion systems. Cell. Microbiol. 10, 958–984 (2008).

  15. 15.

    et al. The Listeria transcriptional landscape from saprophytism to virulence. Nature 459, 950–956 (2009).

  16. 16.

    et al. A genome-wide analysis of small regulatory RNAs in the human pathogen group A Streptococcus. PLoS ONE 4, e7668 (2009).

  17. 17.

    et al. Identification of novel non-coding small RNAs from Streptococcus pneumoniae TIGR4 using high-resolution genome tiling arrays. BMC Genomics 11, 350 (2010).

  18. 18.

    et al. Identification of genes and genomic islands correlated with high pathogenicity in Streptococcus suis using whole genome tiling microarrays. PLoS ONE 6, e17987 (2011).

  19. 19.

    , , & Identification of genes differentially regulated by interferon α, β, or γ using oligonucleotide arrays. Proc. Natl Acad. Sci. USA 95, 15623–15628 (1998).

  20. 20.

    , , , & Cellular gene expression altered by human cytomegalovirus: global monitoring with oligonucleotide arrays. Proc. Natl Acad. Sci. USA 95, 14470–14475 (1998).

  21. 21.

    , & γδ T cells respond directly to pathogen-associated molecular patterns. J. Immunol. 174, 6045–6053 (2005).

  22. 22.

    , & The distinct response of γδ T cells to the Nod2 agonist muramyl dipeptide. Cell. Immunol. 257, 38–43 (2009).

  23. 23.

    , , , & Global changes in gene expression and synergistic interactions induced by TLR9 and TLR3. Mol. Immunol. 46, 2557–2564 (2009).

  24. 24.

    et al. Simultaneous analysis of host and pathogen interactions during an in vivo infection reveals local induction of host acute phase response proteins, a novel bacterial stress response, and evidence of a host-imposed metal ion limited environment. Cell. Microbiol. 6, 849–865 (2004).

  25. 25.

    , , & Serial analysis of gene expression. Science 270, 484–487 (1995).

  26. 26.

    et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc. Natl Acad. Sci. USA 100, 15776–15781 (2003).

  27. 27.

    , , & SuperSAGE: a modern platform for genome-wide quantitative transcript profiling. Curr. Pharm. Biotechnol. 9, 368–374 (2008).

  28. 28.

    Serial analysis of gene expression in eukaryotic pathogens. Infect. Disord. Drug Targets 6, 281–297 (2006).

  29. 29.

    et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nature Biotech. 24, 1115–1122 (2006).

  30. 30.

    , & RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev. Genet. 10, 57–63 (2009).

  31. 31.

    et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).

  32. 32.

    , , , & RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

  33. 33.

    , , , & Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621–628 (2008).

  34. 34.

    et al. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45, 81–94 (2008).

  35. 35.

    et al. A strand-specific RNA-seq analysis of the transcriptome of the typhoid bacillus Salmonella Typhi. PLoS Genet. 5, e1000569 (2009).

  36. 36.

    et al. Mapping the Burkholderia cenocepacia niche response via high-throughput sequencing. Proc. Natl Acad. Sci. USA 106, 3976–3981 (2009).

  37. 37.

    et al. Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs. BMC Genomics 10, 641 (2009).

  38. 38.

    et al. The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464, 250–255 (2010). A good example of how to use strand-specific RNA-seq of total RNA to analyse gene expression and annotate a bacterial transcriptome with respect to both coding and non-coding information and operon structure.

  39. 39.

    , , & RNA-sequence analysis of human B-cells. Genome Res. 21, 991–998 (2011). An in-depth transcriptomic study on human B cells that defines the coverage requirements for different biological applications such as gene discovery and expression quantification.

  40. 40.

    & RNA-Seq—quantitative measurement of expression through massively parallel RNA-sequencing. Methods 48, 249–257 (2009).

  41. 41.

    et al. Transcriptome analysis of the model protozoan, Tetrahymena thermophila, using deep RNA sequencing. PLoS ONE 7, e30630 (2012).

  42. 42.

    Transcript abundance in yeast varies over six orders of magnitude. J. Biol. Chem. 277, 14363–14366 (2002).

  43. 43.

    et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotech. 28, 511–515 (2010).

  44. 44.

    & Studying bacterial transcriptomes using RNA-seq. Curr. Opin. Microbiol. 13, 619–624 (2010).

  45. 45.

    Progress in prokaryotic transcriptomics. Curr. Opin. Microbiol. 14, 579–586 (2011).

  46. 46.

    et al. Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq. Genome Res. 20, 1451–1458 (2010).

  47. 47.

    , , , & Genome-wide analysis of mRNA abundance in two life-cycle stages of Trypanosoma brucei and identification of splicing and polyadenylation sites. Nucleic Acids Res. 38, 4946–4957 (2010).

  48. 48.

    et al. The transcriptome of the human pathogen Trypanosoma brucei at single-nucleotide resolution. PLoS Pathog. 6, e1001090 (2010).

  49. 49.

    , & RNA-Seq analysis of splicing in Plasmodium falciparum uncovers new splice junctions, alternative splicing and splicing of antisense transcripts. Nucleic Acids Res. 39, 3820–3835 (2011).

  50. 50.

    et al. Comparative transcriptomics of pathogenic and non-pathogenic Listeria species. Mol. Syst. Biol. 8, 583 (2012). A pioneering study that uses RNA-seq to explore differences and similarities between related bacterial transcriptomes.

  51. 51.

    , , , & Deep sequencing-based discovery of the Chlamydia trachomatis transcriptome. Nucleic Acids Res. 38, 868–877 (2010).

  52. 52.

    et al. The transcriptional landscape of Chlamydia pneumoniae. Genome Biol. 12, R98 (2011).

  53. 53.

    et al. RNA-seq-based monitoring of infection-linked changes in Vibrio cholerae gene expression. Cell Host Microbe 10, 165–174 (2011). A description of the pathogen transcriptomes from bacteria isolated from either rabbit or mouse hosts.

  54. 54.

    et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods 5, 613–619 (2008).

  55. 55.

    et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).

  56. 56.

    , , , & Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genet. 40, 1413–1415 (2008).

  57. 57.

    et al. Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Nature Biotech. 30, 253–260 (2012).

  58. 58.

    , , , & Analysis of the host microRNA response to Salmonella uncovers the control of major cytokines by the let-7 family. EMBO J. 30, 1977–1989 (2011).

  59. 59.

    et al. Dual organism transcriptomics of airway epithelial cells interacting with conidia of Aspergillus fumigatus. PLoS ONE 6, e20527 (2011).

  60. 60.

    et al. Simultaneous host and parasite expression profiling identifies tissue-specific transcriptional programs associated with susceptibility or resistance to experimental cerebral malaria. BMC Genomics 7, 295 (2006).

  61. 61.

    et al. Simultaneous gene expression profiling in human macrophages infected with Leishmania major parasites using SAGE. BMC Genomics 9, 238 (2008).

  62. 62.

    et al. Gene expression analysis of plant host–pathogen interactions by SuperSAGE. Proc. Natl Acad. Sci. USA 100, 15718–15723 (2003).

  63. 63.

    et al. An interspecies regulatory network inferred from simultaneous RNA-seq of Candida albicans invading innate immune cells. Front. Microbiol. 3, 85 (2012). The first study to describe the parallel analysis of a eukaryotic host and a eukaryotic pathogen via RNA-seq.

  64. 64.

    et al. Investigating fixative-induced changes in RNA quality and utility by microarray analysis. Exp. Mol. Pathol. 84, 156–172 (2008).

  65. 65.

    , & The mammalian microRNA response to bacterial infections. RNA Biol 9, 742–750 (2012).

  66. 66.

    & Molecular mechanisms of long noncoding RNAs. Mol. Cell 43, 904–914 (2011).

  67. 67.

    & snRNA 3′ end formation: the dawn of the Integrator complex. Biochem. Soc. Trans. 38, 1082–1087 (2010).

  68. 68.

    & Biology and applications of small nucleolar RNAs. Cell. Mol. Life Sci. 68, 3843–3851 (2011).

  69. 69.

    & Regulatory RNA in bacterial pathogens. Cell Host Microbe 8, 116–127 (2010).

  70. 70.

    et al. Genome-wide analysis of long noncoding RNA stability. Genome Res. 22, 885–898 (2012).

  71. 71.

    et al. Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Genome Res. 13, 1863–1872 (2003).

  72. 72.

    et al. Digital transcriptome profiling using selective hexamer priming for cDNA synthesis. Nature Methods 6, 647–649 (2009).

  73. 73.

    & Ribosomal RNA depletion for massively parallel bacterial RNA-sequencing applications. Methods Mol. Biol. 733, 93–103 (2011).

  74. 74.

    et al. An RNA-Seq strategy to detect the complete coding and non-coding transcriptome including full-length imprinted macro ncRNAs. PLoS ONE 6, e27288 (2011).

  75. 75.

    et al. Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol. 13, R23 (2012).

  76. 76.

    When you can't trust the DNA: RNA editing changes transcript sequences. Cell. Mol. Life Sci. 68, 567–586 (2011).

  77. 77.

    et al. The RNA Modification Database, RNAMDB: 2011 update. Nucleic Acids Res. 39, D195–D201 (2011).

  78. 78.

    , & Structure, dynamics, and function of RNA modification enzymes. Curr. Opin. Struct. Biol. 18, 330–339 (2008).

  79. 79.

    , , & Identification of modified residues in RNAs by reverse transcription-based methods. Methods Enzymol. 425, 21–53 (2007).

  80. 80.

    , , & Traces of post-transcriptional RNA modifications in deep sequencing data. Biol. Chem. 392, 305–313 (2011).

  81. 81.

    , & Bioinformatics analysis suggests base modifications of tRNAs and miRNAs in Arabidopsis thaliana. BMC Genomics 10, 155 (2009).

  82. 82.

    et al. Meta-analysis of small RNA-sequencing errors reveals ubiquitous post-transcriptional RNA modifications. Nucleic Acids Res. 37, 2461–2470 (2009).

  83. 83.

    et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nature Methods 7, 709–715 (2010). A detailed overview of the current approaches used to ensure the maintenance of strand-specific information in RNA-seq experiments.

  84. 84.

    et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123 (2009).

  85. 85.

    , & A strand-specific library preparation protocol for RNA sequencing. Methods Enzymol. 500, 79–98 (2011).

  86. 86.

    et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic Acids Res. 37, e148 (2009).

  87. 87.

    & Optimization of enzymatic reaction conditions for generating representative pools of cDNA from small RNA. RNA 16, 2537–2552 (2010).

  88. 88.

    , , , & Differential expression in RNA-seq: a matter of depth. Genome Res. 21, 2213–2223 (2011). An exploration of the sequencing depths that are required for accurate gene expression profiling in mammals.

  89. 89.

    et al. RNA-seq: technical variability and sampling. BMC Genomics 12, 293 (2011).

  90. 90.

    et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

  91. 91.

    , , & Computational methods for transcriptome annotation and quantification using RNA-seq. Nature Methods 8, 469–477 (2011).

  92. 92.

    & Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. 9, 34 (2011).

  93. 93.

    Sequencing technologies — the next generation. Nature Rev. Genet. 11, 31–46 (2010). A review of the working principles, performances and costs of popular NGS platforms, with an outlook on third-generation sequencing techniques.

  94. 94.

    , , & The impact of next-generation sequencing on genomics. J. Genet. Genomics 38, 95–109 (2011).

  95. 95.

    et al. An integrated semiconductor device enabling non-optical genome sequencing. Nature 475, 348–352 (2011).

  96. 96.

    et al. A comparison of single molecule and amplification based sequencing of cancer transcriptomes. PLoS ONE 6, e17305 (2011).

  97. 97.

    et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nature Biotech. 30, 693–700 (2012).

  98. 98.

    et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nature Nanotechnol. 4, 265–270 (2009).

  99. 99.

    , & Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes. Genes Dev. 23, 1379–1386 (2009).

  100. 100.

    & Gene expression in mouse oocytes by RNA-Seq. Methods Mol. Biol. 825, 237–251 (2012).

  101. 101.

    et al. mRNA-sequencing whole transcriptome analysis of a single cell on the SOLiD system. J. Biomol. Tech. 20, 266–271 (2009). The first study to successfully combine single-cell analysis with RNA-seq.

  102. 102.

    et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nature Protoc. 5, 516–535 (2010).

  103. 103.

    , & Development and applications of single-cell transcriptome analysis. Nature Methods 8, S6–S11 (2011). An overview of the requirements for single-cell RNA-seq.

  104. 104.

    et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6, 468–478 (2010).

  105. 105.

    et al. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS ONE 6, e21208 (2011).

  106. 106.

    et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

  107. 107.

    et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotech. 22 Jul 2012 (doi: 10.1038/nbt.2282).

  108. 108.

    et al. Transcript amplification from single bacterium for transcriptome analysis. Genome Res. 21, 925–935 (2011). The first study to monitor differential gene expression in a single bacterial cell by Sanger sequencing.

  109. 109.

    Droplet microfluidics for single-cell analysis. Methods Mol. Biol. 853, 105–139 (2012).

  110. 110.

    et al. Probing prokaryotic social behaviors with bacterial “lobster traps”. mBio 1, e00202-10 (2010).

  111. 111.

    , , & Laser capture microdissection: methods and applications. Methods Mol. Biol. 755, 1–15 (2011).

  112. 112.

    et al. Amplification-free digital gene expression profiling from minute cell quantities. Nature Methods 7, 619–621 (2010).

  113. 113.

    Recent advances in DNA sequencing methods - general principles of sample preparation. Exp. Cell Res. 316, 1339–1343 (2010).

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Acknowledgements

The authors acknowledge support from the German Research Foundation (DFG) Priority Program SPP1258 (DFG grant Vo875/4-2) and from the German Ministry of Education and Research (BMBF) (grant 01GS0806). A.J.W. is the recipient of an Elite Advancement Ph.D. stipend from the Universität Bayern e.V., Germany.

Author information

Affiliations

  1. Institute for Molecular Infection Biology, University of Würzburg, D-97080, Germany

    • Alexander J. Westermann
    • , Stanislaw A. Gorski
    •  & Jörg Vogel

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jörg Vogel.

Supplementary information

PDF files

  1. 1.

    Supplementary Table S1

    Selection of (pro- and eukaryotic) organisms to which full-genome RNA-seq has been applied to

  2. 2.

    Supplementary Table S2

    Steady increase in sequencing depth from early to current RNA-seq studies in both bacteria and mammals

  3. 3.

    Supplementary Table S3

    Selection of reported values of copy numbers of different bacterial species per host cell

Glossary

Pathogen-associated molecular patterns

(PAMPs).General small molecular motifs that are present on microorganisms and engage host innate immune receptors, in particular Toll-like receptors. Examples of PAMPs include lipopolysaccharide, peptidoglycan and flagellin.

Tiling arrays

DNA microarray chips on which probe sequences are tiled (overlapping) and comprise a subset of, or the whole, genome at high resolution.

Small non-coding RNA

A short transcript (50–500 nucleotides) that regulates gene expression in bacteria, often by base-pairing with mRNAs.

microRNA

A short (22 nucleotide) processed RNA that guides post-transcriptional repression of mRNAs in animals and plants.

Long non-coding RNAs

Heterogeneous non-coding RNAs (>200 nucleotides) that lack protein-coding capability and are found in eukaryotes.

Small nuclear RNAs

Short RNAs that are involved in precursor mRNA processing.

Small nucleolar RNAs

RNAs that typically guide ribose methylation and pseudouridylation in other RNA molecules.

Bar-code sequences

Short unique sequence tags (4–6 nucleotides) that are incorporated into cDNA fragments and used to tag a specific sequence as belonging to a particular cDNA library.

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DOI

https://doi.org/10.1038/nrmicro2852

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