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  • Review Article
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Cross-species RNA-seq for deciphering host–microbe interactions

Abstract

The human body is constantly exposed to microorganisms, which entails manifold interactions between human cells and diverse commensal or pathogenic bacteria. The cellular states of the interacting cells are decisive for the outcome of these encounters such as whether bacterial virulence programmes and host defence or tolerance mechanisms are induced. This Review summarizes how next-generation RNA sequencing (RNA-seq) has become a primary technology to study host–microbe interactions with high resolution, improving our understanding of the physiological consequences and the mechanisms at play. We illustrate how the discriminatory power and sensitivity of RNA-seq helps to dissect increasingly complex cellular interactions in time and space down to the single-cell level. We also outline how future transcriptomics may answer currently open questions in host–microbe interactions and inform treatment schemes for microbial disorders.

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Fig. 1: The history of RNA-seq-based infection research.
Fig. 2: Comparison of cellular RNA content between bacteria and mammals.
Fig. 3: The basic steps in commonly used protocols for strand-specific bacterial, mammalian or dual expression profiling.
Fig. 4: Graphical overview of RNA-seq-based approaches to study inter-species interactions in the mammalian intestine.
Fig. 5: Molecular aspects of host–pathogen interactions revealed by transcriptomics.

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References

  1. Bumann, D. Heterogeneous host-pathogen encounters: act locally, think globally. Cell Host Microbe 17, 13–19 (2015).

    CAS  PubMed  Google Scholar 

  2. Sarkar, S. & Heise, M. T. Mouse models as resources for studying infectious diseases. Clin. Ther. 41, 1912–1922 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Barrila, J. et al. Modeling host-pathogen interactions in the context of the microenvironment: three-dimensional cell culture comes of age. Infect. Immun. https://doi.org/10.1128/IAI.00282-18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Colgan, A. M., Cameron, A. D. & Kroger, C. If it transcribes, we can sequence it: mining the complexities of host-pathogen-environment interactions using RNA-seq. Curr. Opin. Microbiol. 36, 37–46 (2017).

    CAS  PubMed  Google Scholar 

  5. Westermann, A. J., Gorski, S. A. & Vogel, J. Dual RNA-seq of pathogen and host. Nat. Rev. Microbiol. 10, 618–630 (2012). A study describing a thought experiment that explores the concept of multi-organismal RNA-seq and coining the term ‘dual RNA-seq’.

    CAS  PubMed  Google Scholar 

  6. Eulalio, A., Schulte, L. & Vogel, J. The mammalian microRNA response to bacterial infections. RNA Biol. 9, 742–750 (2012).

    CAS  PubMed  Google Scholar 

  7. Agliano, F., Rathinam, V. A., Medvedev, A. E., Vanaja, S. K. & Vella, A. T. Long noncoding RNAs in host-pathogen interactions. Trends Immunol. 40, 492–510 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Westermann, A. J. Regulatory RNAs in virulence and host-microbe interactions. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.RWR-0002-2017 (2018).

    Article  PubMed  Google Scholar 

  9. Saliba, A. E., Westermann, A. J., Gorski, S. A. & Vogel, J. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 42, 8845–8860 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Penaranda, C. & Hung, D. T. Single-cell RNA sequencing to understand host-pathogen interactions. ACS Infect. Dis. 5, 336–344 (2019).

    CAS  PubMed  Google Scholar 

  11. Dreyfus, M. & Regnier, P. The poly(A) tail of mRNAs: bodyguard in eukaryotes, scavenger in bacteria. Cell 111, 611–613 (2002).

    CAS  PubMed  Google Scholar 

  12. Wolin, S. L. & Steitz, J. A. Genes for two small cytoplasmic Ro RNAs are adjacent and appear to be single-copy in the human genome. Cell 32, 735–744 (1983).

    CAS  PubMed  Google Scholar 

  13. Sim, S. & Wolin, S. L. Bacterial Y RNAs: gates, tethers, and tRNA mimics. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.RWR-0023-2018 (2018).

    Article  PubMed  Google Scholar 

  14. Lundblad, E. W. & Altman, S. Inhibition of gene expression by RNase P. N. Biotechnol. 27, 212–221 (2010).

    CAS  PubMed  Google Scholar 

  15. Kroger, C. et al. An infection-relevant transcriptomic compendium for Salmonella enterica Serovar Typhimurium. Cell Host Microbe 14, 683–695 (2013).

    CAS  PubMed  Google Scholar 

  16. Hor, J., Matera, G., Vogel, J., Gottesman, S. & Storz, G. Trans-acting small RNAs and their effects on gene expression in Escherichia coli and Salmonella enterica. EcoSal Plus https://doi.org/10.1128/ecosalplus.ESP-0030-2019 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Rion, N. & Ruegg, M. A. LncRNA-encoded peptides: more than translational noise? Cell Res. 27, 604–605 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Yao, R. W., Wang, Y. & Chen, L. L. Cellular functions of long noncoding RNAs. Nat. Cell Biol. 21, 542–551 (2019).

    CAS  PubMed  Google Scholar 

  19. Milo, R. & Phillips, R. Cell Biology by the Number (Garland Science, 2015).

  20. Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).

    CAS  PubMed  Google Scholar 

  21. van Dijk, E. L., Jaszczyszyn, Y., Naquin, D. & Thermes, C. The third revolution in sequencing technology. Trends Genet. 34, 666–681 (2018).

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  23. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Westermann, A. J. & Vogel, J. Host-pathogen transcriptomics by dual RNA-Seq. Methods Mol. Biol. 1737, 59–75 (2018).

    CAS  PubMed  Google Scholar 

  25. Fronicke, L. et al. Toward cell type-specific in vivo dual RNA-Seq. Methods Enzymol. 612, 505–522 (2018).

    CAS  PubMed  Google Scholar 

  26. Avican, K. et al. Reprogramming of Yersinia from virulent to persistent mode revealed by complex in vivo RNA-seq analysis. PLoS Pathog. 11, e1004600 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. Connolly, J. P. R. et al. Host-associated niche metabolism controls enteric infection through fine-tuning the regulation of type 3 secretion. Nat. Commun. 9, 4187 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. Westermann, A. J. et al. Dual RNA-seq unveils noncoding RNA functions in host-pathogen interactions. Nature 529, 496–501 (2016). Comprehensive evaluation of the dual RNA-seq technology for 14 different mammalian host cell types infected with Salmonella and identification of virulence-related non-coding RNAs in this bacterial pathogen.

    CAS  PubMed  Google Scholar 

  29. Westermann, A. J., Barquist, L. & Vogel, J. Resolving host-pathogen interactions by dual RNA-seq. PLoS Pathog. 13, e1006033 (2017).

    PubMed  PubMed Central  Google Scholar 

  30. Jin, D. J., Cagliero, C. & Zhou, Y. N. Growth rate regulation in Escherichia coli. FEMS Microbiol. Rev. 36, 269–287 (2012).

    CAS  PubMed  Google Scholar 

  31. Goodfellow, S. J. & Zomerdijk, J. C. Basic mechanisms in RNA polymerase I transcription of the ribosomal RNA genes. Subcell. Biochem. 61, 211–236 (2013).

    CAS  PubMed  Google Scholar 

  32. Montoya, D. J. et al. Dual RNA-Seq of human leprosy lesions identifies bacterial determinants linked to host immune response. Cell Rep. 26, 3574–3585.e3 (2019). A dual RNA-seq study that measured gene expression of host and pathogen in human leprosy skin lesions, revealing that the human immune response is not primarily shaped by the bacterial dose but by the virulence programmes active in the infecting mycobacteria.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Sintsova, A. et al. Genetically diverse uropathogenic Escherichia coli adopt a common transcriptional program in patients with UTIs. eLife 8, e49748 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. Culviner, P. H., Guegler, C. K. & Laub, M. T. A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies. mBio 11, e00010–20 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Petrova, O. E., Garcia-Alcalde, F., Zampaloni, C. & Sauer, K. Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci Rep. 7, 41114 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Prezza, G. et al. Improved bacterial RNA-seq by Cas9-based depletion of ribosomal RNA reads. RNA 26, 1069–1078 (2020). The study reports the sensitive CRISPR-based depletion of unwanted bacterial sequences to improve targeted next-generation sequencing.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Gu, W. et al. Depletion of abundant sequences by hybridization (DASH): using Cas9 to remove unwanted high-abundance species in sequencing libraries and molecular counting applications. Genome Biol. 17, 41 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Zheng, G. et al. Efficient and quantitative high-throughput tRNA sequencing. Nat. Methods 12, 835–837 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Cozen, A. E. et al. ARM-seq: AlkB-facilitated RNA methylation sequencing reveals a complex landscape of modified tRNA fragments. Nat. Methods 12, 879–884 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Shigematsu, M. et al. YAMAT-seq: an efficient method for high-throughput sequencing of mature transfer RNAs. Nucleic Acids Res. 45, e70 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Haas, B. J., Chin, M., Nusbaum, C., Birren, B. W. & Livny, J. How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? BMC Genomics 13, 734 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014).

    CAS  PubMed  Google Scholar 

  44. Ching, T., Huang, S. & Garmire, L. X. Power analysis and sample size estimation for RNA-Seq differential expression. RNA 20, 1684–1696 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Engstrom, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).

    PubMed  PubMed Central  Google Scholar 

  46. Dillies, M. A. et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinforma. 14, 671–683 (2013).

    CAS  Google Scholar 

  47. Berghoff, B. A., Karlsson, T., Kallman, T., Wagner, E. G. H. & Grabherr, M. G. RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study. BioData Min. 10, 30 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. Munro, S. A. et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat. Commun. 5, 5125 (2014).

    CAS  PubMed  Google Scholar 

  49. Consortium, S. M.-I. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014).

    Google Scholar 

  50. Loven, J. et al. Revisiting global gene expression analysis. Cell 151, 476–482 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  52. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  53. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    PubMed  PubMed Central  Google Scholar 

  54. Garber, M., Grabherr, M. G., Guttman, M. & Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods 8, 469–477 (2011).

    CAS  PubMed  Google Scholar 

  55. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

    Google Scholar 

  56. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    CAS  PubMed  Google Scholar 

  57. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 44, D471–D480 (2016).

    CAS  PubMed  Google Scholar 

  58. Breuer, K. et al. InnateDB: systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic Acids Res. 41, D1228–D1233 (2013).

    CAS  PubMed  Google Scholar 

  59. Sharma, C. M. et al. The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464, 250–255 (2010).

    CAS  PubMed  Google Scholar 

  60. Kroger, C. et al. The transcriptional landscape and small RNAs of Salmonella enterica serovar Typhimurium. Proc. Natl Acad. Sci. USA 109, E1277–E1286 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Slager, J., Aprianto, R. & Veening, J. W. Deep genome annotation of the opportunistic human pathogen Streptococcus pneumoniae D39. Nucleic Acids Res. 46, 9971–9989 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Dar, D. et al. Term-seq reveals abundant ribo-regulation of antibiotics resistance in bacteria. Science 352, aad9822 (2016). The authors established Term-seq to map prokaryotic RNA 3′ ends at the genome-wide scale and, among other uses, applied the method to human oral microbiome samples.

    PubMed  PubMed Central  Google Scholar 

  63. Lalanne, J. B. et al. Evolutionary convergence of pathway-specific enzyme expression stoichiometry. Cell 173, 749–761.e38 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Kroger, C. et al. The primary transcriptome, small RNAs and regulation of antimicrobial resistance in Acinetobacter baumannii ATCC 17978. Nucleic Acids Res. 46, 9684–9698 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Dugar, G. et al. High-resolution transcriptome maps reveal strain-specific regulatory features of multiple Campylobacter jejuni isolates. PLoS Genet. 9, e1003495 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Thomason, M. K. et al. A rhlI 5’ UTR-derived sRNA regulates RhlR-dependent quorum sensing in Pseudomonas aeruginosa. mBio 10, e02253-19 (2019).

    PubMed  PubMed Central  Google Scholar 

  68. Heidrich, N. et al. The primary transcriptome of Neisseria meningitidis and its interaction with the RNA chaperone Hfq. Nucleic Acids Res. 45, 6147–6167 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Thomason, M. K. et al. Global transcriptional start site mapping using differential RNA sequencing reveals novel antisense RNAs in Escherichia coli. J. Bacteriol. 197, 18–28 (2015).

    PubMed  Google Scholar 

  70. Ryan, D., Jenniches, L., Reichardt, S., Barquist, L. & Westermann, A. J. A high-resolution transcriptome map identifies small RNA regulation of metabolism in the gut microbe Bacteroides thetaiotaomicron. Nat. Commun. 11, 3557 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Avican, K. et al. RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection. Preprint at bioRxiv https://doi.org/10.1101/2020.06.29.177147 (2020).

    Article  Google Scholar 

  72. Cossart, P., Boquet, P., Normark, S. & Rappuoli, R. Cellular microbiology emerging. Science 271, 315–316 (1996).

    CAS  PubMed  Google Scholar 

  73. Raynaud, S., Le Pabic, H. & Felden, B. Selective recovery of RNAs from bacterial pathogens after their internalization by human host cells. Methods 143, 4–11 (2018).

    CAS  PubMed  Google Scholar 

  74. Hinton, J. C., Hautefort, I., Eriksson, S., Thompson, A. & Rhen, M. Benefits and pitfalls of using microarrays to monitor bacterial gene expression during infection. Curr. Opin. Microbiol. 7, 277–282 (2004).

    CAS  PubMed  Google Scholar 

  75. Szafranska, A. K. et al. High-resolution transcriptomic analysis of the adaptive response of Staphylococcus aureus during acute and chronic phases of osteomyelitis. mBio 5, e01775–14 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Rossi, E., Falcone, M., Molin, S. & Johansen, H. K. High-resolution in situ transcriptomics of Pseudomonas aeruginosa unveils genotype independent patho-phenotypes in cystic fibrosis lungs. Nat. Commun. 9, 3459 (2018).

    PubMed  PubMed Central  Google Scholar 

  77. Mandlik, A. et al. RNA-Seq-based monitoring of infection-linked changes in Vibrio cholerae gene expression. Cell Host Microbe 10, 165–174 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Donaldson, G. P. et al. Spatially distinct physiology of Bacteroides fragilis within the proximal colon of gnotobiotic mice. Nat. Microbiol. 5, 746–756 (2020). This study employed hybrid-selection RNA-seq to enrich Bacteroides fragilis transcripts from host–bacterial RNA mixtures to compare in vivo transcriptome signatures of this gut commensal between different host niches.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Betin, V. et al. Hybridization-based capture of pathogen mRNA enables paired host-pathogen transcriptional analysis. Sci. Rep. 9, 19244 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Bashiardes, S., Zilberman-Schapira, G. & Elinav, E. Use of metatranscriptomics in microbiome research. Bioinforma. Biol. Insights 10, 19–25 (2016).

    CAS  Google Scholar 

  81. Reck, M. et al. Stool metatranscriptomics: a technical guideline for mRNA stabilisation and isolation. BMC Genomics 16, 494 (2015).

    PubMed  PubMed Central  Google Scholar 

  82. Engel, P. & Moran, N. A. The gut microbiota of insects - diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).

    CAS  PubMed  Google Scholar 

  83. Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366 (2018). Comprehensive analysis of the human gut microbiome with a focus on ecological and molecular aspects and defining a ‘core’ and ‘variable’ metatranscriptome across participants.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Yan, Y. et al. Structure of the mucosal and stool microbiome in Lynch syndrome. Cell Host Microbe 27, 585–600.e4 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Mehta, R. S. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. 3, 347–355 (2018). Analysis of the human gut microbiome from several hundred subjects revealed the stability of the metatranscriptome over time, highlighting its potential for diagnostics.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018). This paper describes the launch of HUMAnN2, an algorithm to functionally analyse metatranscriptomic data from host-associated and environmental bacterial communities.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

    PubMed  PubMed Central  Google Scholar 

  89. Forouzan, E., Shariati, P., Mousavi Maleki, M. S., Karkhane, A. A. & Yakhchali, B. Practical evaluation of 11 de novo assemblers in metagenome assembly. J. Microbiol. Methods 151, 99–105 (2018).

    CAS  PubMed  Google Scholar 

  90. Zhang, Y. et al. Compositional and functional differences in the human gut microbiome correlate with clinical outcome following infection with wild-type salmonella enterica serovar typhi. mBio 9, e00686–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Westermann, A. J. et al. The major RNA-binding protein ProQ impacts virulence gene expression in salmonella enterica serovar typhimurium. mBio 10, e02504–18 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Stapels, D. A. C. et al. Salmonella persisters undermine host immune defenses during antibiotic treatment. Science 362, 1156–1160 (2018). Dual RNA-seq of Salmonella-infected mouse macrophages linked the polarization towards the M2-like macrophage phenotype with the secreted bacterial effector protein SteE.

    CAS  PubMed  Google Scholar 

  93. Aprianto, R., Slager, J., Holsappel, S. & Veening, J. W. Time-resolved dual RNA-seq reveals extensive rewiring of lung epithelial and pneumococcal transcriptomes during early infection. Genome Biol. 17, 198 (2016).

    PubMed  PubMed Central  Google Scholar 

  94. Mavromatis, C. H. et al. The co-transcriptome of uropathogenic Escherichia coli-infected mouse macrophages reveals new insights into host-pathogen interactions. Cell Microbiol. 17, 730–746 (2015).

    CAS  PubMed  Google Scholar 

  95. Baddal, B. et al. Dual RNA-seq of nontypeable haemophilus influenzae and host cell transcriptomes reveals novel insights into host-pathogen cross talk. mBio 6, e01765-15 (2015).

    PubMed  PubMed Central  Google Scholar 

  96. Humphrys, M. S. et al. Simultaneous transcriptional profiling of bacteria and their host cells. PLoS ONE 8, e80597 (2013).

    PubMed  PubMed Central  Google Scholar 

  97. Zimmermann, M. et al. Integration of metabolomics and transcriptomics reveals a complex diet of mycobacterium tuberculosis during early macrophage infection. mSystems 2, e00057-17 (2017). The study combined dual RNA-seq with host–pathogen metabolomics for a cell-culture model of M. tuberculosis infection and inferred a system-wide host–pathogen metabolic network.

    PubMed  PubMed Central  Google Scholar 

  98. Rienksma, R. A. et al. Comprehensive insights into transcriptional adaptation of intracellular mycobacteria by microbe-enriched dual RNA sequencing. BMC Genomics 16, 34 (2015).

    PubMed  PubMed Central  Google Scholar 

  99. Mika-Gospodorz, B. et al. Dual RNA-seq of Orientia tsutsugamushi informs on host-pathogen interactions for this neglected intracellular human pathogen. Nat. Commun. 11, 3363 (2020). Dual RNA-seq unveiled differences in the pathogenicity of two strains of the obligate intracellular pathogen O. tsutsugamushi in an infected cell-culture model.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Schulte, L. N. et al. An advanced human intestinal coculture model reveals compartmentalized host and pathogen strategies during salmonella infection. mBio 11, e03348–19 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Hannemann, S., Gao, B. & Galan, J. E. Salmonella modulation of host cell gene expression promotes its intracellular growth. PLoS Pathog. 9, e1003668 (2013).

    PubMed  PubMed Central  Google Scholar 

  102. Thanert, R., Goldmann, O., Beineke, A. & Medina, E. Host-inherent variability influences the transcriptional response of Staphylococcus aureus during in vivo infection. Nat. Commun. 8, 14268 (2017).

    PubMed  PubMed Central  Google Scholar 

  103. Nuss, A. M. et al. Tissue dual RNA-seq allows fast discovery of infection-specific functions and riboregulators shaping host-pathogen transcriptomes. Proc. Natl Acad. Sci. USA 114, E791–E800 (2017). One of the first applications of dual RNA-seq to an in vivo setting, analysing host and bacterial gene expression during the infection of mouse Peyer’s patches with Y. pseudotuberculosis.

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Damron, F. H., Oglesby-Sherrouse, A. G., Wilks, A. & Barbier, M. Dual-seq transcriptomics reveals the battle for iron during Pseudomonas aeruginosa acute murine pneumonia. Sci. Rep. 6, 39172 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Minhas, V. et al. In vivo dual RNA-seq reveals that neutrophil recruitment underlies differential tissue tropism of Streptococcus pneumoniae. Commun. Biol. 3, 293 (2020). The authors devised a comparative dual RNA-seq approach to identify the impact of a SNP in a Streptococcus raffinose utilization regulator gene on infection outcome in a mouse model.

    PubMed  PubMed Central  Google Scholar 

  106. Griesenauer, B. et al. Determination of an interaction network between an extracellular bacterial pathogen and the human host. mBio 10, e01193–19 (2019). Combined dual RNA-seq and metabolomics study of punch biopsy samples from human volunteers inoculated with the bacterial skin pathogen H. ducreyi.

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Thanert, R. et al. Molecular profiling of tissue biopsies reveals unique signatures associated with streptococcal necrotizing soft tissue infections. Nat. Commun. 10, 3846 (2019). Comprehensive host–bacterial transcriptomic analysis of necrotizing soft-tissue biopsy samples from human patients with either S. pyogenes monomicrobial or polymicrobial infections.

    PubMed  PubMed Central  Google Scholar 

  108. Pisu, D., Huang, L., Grenier, J. K. & Russell, D. G. Dual RNA-Seq of Mtb-infected macrophages in vivo reveals ontologically distinct host-pathogen interactions. Cell Rep. 30, 335–350.e4 (2020). In vivo dual RNA-seq of mouse lungs infected with M. tuberculosis that revealed the segregation of host–pathogen transcriptomes dependent on macrophage phenotype.

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Jew, B. et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat. Commun. 11, 1971 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Zaitsev, K., Bambouskova, M., Swain, A. & Artyomov, M. N. Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures. Nat. Commun. 10, 2209 (2019).

    PubMed  PubMed Central  Google Scholar 

  111. Qiu, J. et al. Mixed-species RNA-seq for elucidation of non-cell-autonomous control of gene transcription. Nat. Protoc. 13, 2176–2199 (2018).

    CAS  PubMed  Google Scholar 

  112. Seelbinder, B. et al. Triple RNA-seq reveals synergy in a human virus-fungus co-infection model. Cell Rep. 33, 108389 (2020). First (and thus far only) triple RNA-seq study. It revealed the molecular basis of synergistic virulence strategies of two frequently co-occurring pulmonary pathogens.

    CAS  PubMed  Google Scholar 

  113. Le-Bury, G. & Niedergang, F. Defective phagocytic properties of HIV-infected macrophages: how might they be implicated in the development of invasive salmonella typhimurium? Front. Immunol. 9, 531 (2018).

    PubMed  PubMed Central  Google Scholar 

  114. Gordon, M. A. Salmonella infections in immunocompromised adults. J. Infect. 56, 413–422 (2008).

    PubMed  Google Scholar 

  115. Chertow, D. S. & Memoli, M. J. Bacterial coinfection in influenza a grand rounds review. J. Am. Med. Assoc. 309, 275–282 (2013).

    CAS  Google Scholar 

  116. Steben, M. & Duarte-Franco, E. Human papillomavirus infection: epidemiology and pathophysiology. Gynecol. Oncol. 107 (Suppl. 1), S2–S5 (2007).

    CAS  PubMed  Google Scholar 

  117. Perez-Losada, M., Castro-Nallar, E., Bendall, M. L., Freishtat, R. J. & Crandall, K. A. Dual transcriptomic profiling of host and microbiota during health and disease in pediatric asthma. PLoS ONE 10, e0131819 (2015).

    PubMed  PubMed Central  Google Scholar 

  118. Ren, L. et al. Transcriptionally active lung microbiome and its association with bacterial biomass and host inflammatory status. mSystems 3, e00199–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Bordenstein, S. R. & Theis, K. R. Host biology in light of the microbiome: ten principles of holobionts and hologenomes. PLoS Biol. 13, e1002226 (2015).

    PubMed  PubMed Central  Google Scholar 

  120. Birnbaum, K. D. Power in numbers: single-cell RNA-Seq strategies to dissect complex tissues. Annu. Rev. Genet. 52, 203–221 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Hautefort, I., Proenca, M. J. & Hinton, J. C. Single-copy green fluorescent protein gene fusions allow accurate measurement of Salmonella gene expression in vitro and during infection of mammalian cells. Appl. Env. Microbiol. 69, 7480–7491 (2003).

    CAS  Google Scholar 

  122. Tsai, C. N. & Coombes, B. K. The role of the host in driving phenotypic heterogeneity in Salmonella. Trends Microbiol. 27, 508–523 (2019).

    CAS  PubMed  Google Scholar 

  123. Ackermann, M. et al. Self-destructive cooperation mediated by phenotypic noise. Nature 454, 987–990 (2008).

    CAS  PubMed  Google Scholar 

  124. Rivera-Chavez, F. et al. Salmonella uses energy taxis to benefit from intestinal inflammation. PLoS Pathog. 9, e1003267 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Vieth, B., Parekh, S., Ziegenhain, C., Enard, W. & Hellmann, I. A systematic evaluation of single cell RNA-seq analysis pipelines. Nat. Commun. 10, 4667 (2019).

    PubMed  PubMed Central  Google Scholar 

  126. Attar, M. et al. A practical solution for preserving single cells for RNA sequencing. Sci. Rep. 8, 2151 (2018).

    PubMed  PubMed Central  Google Scholar 

  127. Avraham, R. et al. Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell 162, 1309–1321 (2015). Combined eukaryotic single-cell and dual RNA-seq study that associated heterogeneity in Salmonella PhoP activity with interferon signalling in infected mouse macrophages.

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Saliba, A. E. et al. Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella. Nat. Microbiol. 2, 16206 (2016). One of the first applications of eukaryotic single-cell RNA-seq to an infection model; revealed Salmonella-infected macrophages to differentiate into divergently polarized phenotypes with consequences for the intracellular behaviour of this pathogen.

    CAS  PubMed  Google Scholar 

  129. Helaine, S. et al. Internalization of Salmonella by macrophages induces formation of nonreplicating persisters. Science 343, 204–208 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Claudi, B. et al. Phenotypic variation of salmonella in host tissues delays eradication by antimicrobial chemotherapy. Cell 158, 722–733 (2014).

    CAS  PubMed  Google Scholar 

  131. Panagi, I. et al. Salmonella effector SteE converts the mammalian serine/threonine kinase GSK3 into a tyrosine kinase to direct macrophage polarization. Cell Host Microbe 27, 41-53.e6 (2020).

    PubMed  PubMed Central  Google Scholar 

  132. Ben-Moshe, N. B. et al. Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells. Nat. Commun. 10, 3266 (2019).

    Google Scholar 

  133. Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017). This study introduced NICHE-seq, which combines eukaryotic gene expression analysis at single-cell resolution with spatial information to dissect cellular and molecular aspects of infection niches.

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Hagemann-Jensen, M., Abdullayev, I., Sandberg, R. & Faridani, O. R. Small-seq for single-cell small-RNA sequencing. Nat. Protoc. 13, 2407–2424 (2018).

    CAS  PubMed  Google Scholar 

  136. Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

    CAS  PubMed  Google Scholar 

  137. Xiao, Z. et al. Holo-Seq: single-cell sequencing of holo-transcriptome. Genome Biol. 19, 163 (2018).

    PubMed  PubMed Central  Google Scholar 

  138. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Blattman, S. B., Jiang, W., Oikonomou, P. & Tavazoie, S. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201 (2020). Pioneering study of bacterial scRNA-seq using a SPLiT-seq-related protocol termed ‘PETRI-seq’ for individual E. coli and S. aureus cells.

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A.-E. & Vogel, J. Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat. Microbiol. 5, 1202–1206 (2020). Pioneering study of bacterial scRNA-seq that applied MATQ-seq to individual S. typhimurium and P. aeruginosa cells.

    CAS  PubMed  Google Scholar 

  141. Sheng, K. et al. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat. Methods 14, 267–270 (2017).

    CAS  PubMed  Google Scholar 

  142. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Bartholomaus, A. et al. Bacteria differently regulate mRNA abundance to specifically respond to various stresses. Philos. Trans. A Math. Phys. Eng. Sci. 374, 20150069 (2016).

    PubMed  Google Scholar 

  144. Kuchina, A. et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science https://doi.org/10.1126/science.aba5257 (2020). Pioneering study of bacterial scRNA-seq via ‘micro-SPLiT’ that was used to detect the transcriptome of >25,000 individual B. subtilis cells.

  145. Haque, A., Engel, J., Teichmann, S. A. & Lonnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).

    PubMed  PubMed Central  Google Scholar 

  146. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed  PubMed Central  Google Scholar 

  147. Fisher, R. A., Gollan, B. & Helaine, S. Persistent bacterial infections and persister cells. Nat. Rev. Microbiol. 15, 453–464 (2017).

    CAS  PubMed  Google Scholar 

  148. Gomez, J. A. et al. The NeST long ncRNA controls microbial susceptibility and epigenetic activation of the interferon-gamma locus. Cell 152, 743–754 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Imamura, K. et al. Diminished nuclear RNA decay upon Salmonella infection upregulates antibacterial noncoding RNAs. EMBO J. 37, e97723 (2018).

    PubMed  PubMed Central  Google Scholar 

  150. Schulte, L. N., Eulalio, A., Mollenkopf, H. J., Reinhardt, R. & Vogel, J. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Schulte, L. N., Westermann, A. J. & Vogel, J. Differential activation and functional specialization of miR-146 and miR-155 in innate immune sensing. Nucleic Acids Res. 41, 542–553 (2013).

    CAS  PubMed  Google Scholar 

  152. Maudet et al. Functional high-throughput screening identifies the miR-15 microRNA family as cellular restriction factors for Salmonella infection. Nat. Commun. 5, 4718 (2014).

    CAS  PubMed  Google Scholar 

  153. Rodriguez, A. et al. Requirement of bic/microRNA-155 for normal immune function. Science 316, 608–611 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Aguilar, C. et al. Functional screenings reveal different requirements for host microRNAs in Salmonella and Shigella infection. Nat. Microbiol. 5, 192–205 (2020).

    CAS  PubMed  Google Scholar 

  155. Gong, H. et al. A Salmonella small non-coding RNA facilitates bacterial invasion and intracellular replication by modulating the expression of virulence factors. PLoS Pathog. 7, e1002120 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Lee, E. J. & Groisman, E. A. An antisense RNA that governs the expression kinetics of a multifunctional virulence gene. Mol. Microbiol. 76, 1020–1033 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Padalon-Brauch, G. et al. Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence. Nucleic Acids Res. 36, 1913–1927 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Ellis, M. J. et al. Silent but deadly: IS200 promotes pathogenicity in Salmonella Typhimurium. RNA Biol. 15, 176–181 (2018).

    PubMed  Google Scholar 

  159. Hofer, K. & Jaschke, A. Epitranscriptomics: RNA modifications in bacteria and archaea. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.RWR-0015-2017 (2018).

    Article  PubMed  Google Scholar 

  160. Frye, M., Jaffrey, S. R., Pan, T., Rechavi, G. & Suzuki, T. RNA modifications: what have we learned and where are we headed? Nat. Rev. Genet. 17, 365–372 (2016).

    CAS  PubMed  Google Scholar 

  161. Rimbach, K., Kaiser, S., Helm, M., Dalpke, A. H. & Eigenbrod, T. 2’-O-methylation within bacterial RNA acts as suppressor of TLR7/TLR8 activation in human innate immune cells. J. Innate Immun. 7, 482–493 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Shippy, D. C. & Fadl, A. A. tRNA modification enzymes GidA and MnmE: potential role in virulence of bacterial pathogens. Int. J. Mol. Sci. 15, 18267–18280 (2014).

    PubMed  PubMed Central  Google Scholar 

  163. Helm, M. & Motorin, Y. Detecting RNA modifications in the epitranscriptome: predict and validate. Nat. Rev. Genet. 18, 275–291 (2017).

    CAS  PubMed  Google Scholar 

  164. Marbaniang, C. N. & Vogel, J. Emerging roles of RNA modifications in bacteria. Curr. Opin. Microbiol. 30, 50–57 (2016).

    CAS  PubMed  Google Scholar 

  165. Soneson, C. et al. A comprehensive examination of nanopore native RNA sequencing for characterization of complex transcriptomes. Nat. Commun. 10, 3359 (2019).

    PubMed  PubMed Central  Google Scholar 

  166. Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).

    CAS  PubMed  Google Scholar 

  167. Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Nat. Methods 16, 1297–1305 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Tsatsaronis, J. A., Franch-Arroyo, S., Resch, U. & Charpentier, E. Extracellular vesicle RNA: a universal mediator of microbial communication? Trends Microbiol. 26, 401–410 (2018).

    CAS  PubMed  Google Scholar 

  169. Brody, H. Extracellular RNA. Nature https://doi.org/10.1038/d41586-020-01762-2 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  170. Das, S. et al. The extracellular RNA communication consortium: establishing foundational knowledge and technologies for extracellular RNA research. Cell 177, 231–242 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Lecrivain, A. L. & Beckmann, B. M. Bacterial RNA in extracellular vesicles: A new regulator of host-pathogen interactions? Biochim. Biophys. Acta Gene Regul. Mech. 1863, 194519 (2020).

    CAS  PubMed  Google Scholar 

  172. Baptista, M. A. P. & Dolken, L. RNA dynamics revealed by metabolic RNA labeling and biochemical nucleoside conversions. Nat. Methods 15, 171–172 (2018).

    CAS  PubMed  Google Scholar 

  173. Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).

    CAS  PubMed  Google Scholar 

  174. Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).

    CAS  PubMed  Google Scholar 

  175. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  176. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    CAS  PubMed  Google Scholar 

  177. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    PubMed  PubMed Central  Google Scholar 

  178. Liao, J., Lu, X., Shao, X., Zhu, L. & Fan, X. Uncovering an Organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol. 39, 43–58 (2021).

    CAS  PubMed  Google Scholar 

  179. Wilbrey-Clark, A., Roberts, K. & Teichmann, S. A. Cell Atlas technologies and insights into tissue architecture. Biochem. J. 477, 1427–1442 (2020).

    CAS  PubMed  Google Scholar 

  180. Avital, G. et al. scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing. Genome Biol. 18, 200 (2017).

    PubMed  PubMed Central  Google Scholar 

  181. Sano, T., Smith, C. L. & Cantor, C. R. Immuno-PCR: very sensitive antigen detection by means of specific antibody-DNA conjugates. Science 258, 120–122 (1992).

    CAS  PubMed  Google Scholar 

  182. Ravikumar, V., Jers, C. & Mijakovic, I. Elucidating host-pathogen interactions based on post-translational modifications using proteomics approaches. Front. Microbiol. 6, 1313 (2015).

    PubMed  PubMed Central  Google Scholar 

  183. Baddal, B. Next-generation technologies for studying host-pathogen interactions: a focus on dual transcriptomics, CRISPR/Cas9 screening and organs-on-chips. Pathog. Dis. 77, ftz060 (2019).

    CAS  PubMed  Google Scholar 

  184. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  187. Guell, M. et al. Transcriptome complexity in a genome-reduced bacterium. Science 326, 1268–1271 (2009).

    PubMed  Google Scholar 

  188. Passalacqua, K. D. et al. Structure and complexity of a bacterial transcriptome. J. Bacteriol. 191, 3203–3211 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  189. Perkins, T. T. et al. A strand-specific RNA-Seq analysis of the transcriptome of the typhoid bacillus Salmonella typhi. PLoS Genet. 5, e1000569 (2009).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  191. Wurtzel, O. et al. A single-base resolution map of an archaeal transcriptome. Genome Res. 20, 133–141 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Srikumar, S. et al. RNA-seq brings new insights to the intra-macrophage transcriptome of salmonella typhimurium. PLoS Pathog. 11, e1005262 (2015).

    PubMed  PubMed Central  Google Scholar 

  193. Sittka, A. et al. Deep sequencing analysis of small noncoding RNA and mRNA targets of the global post-transcriptional regulator, Hfq. PLoS Genet. 4, e1000163 (2008).

    PubMed  PubMed Central  Google Scholar 

  194. McNulty, N. P. et al. The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Sci. Transl Med. 3, 106ra106 (2011).

    PubMed  PubMed Central  Google Scholar 

  195. Livny, J. et al. Comparative RNA-Seq based dissection of the regulatory networks and environmental stimuli underlying Vibrio parahaemolyticus gene expression during infection. Nucleic Acids Res. 42, 12212–12223 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  196. Galvez, E. J. C. et al. Distinct polysaccharide utilization determines interspecies competition between intestinal Prevotella spp. Cell Host Microbe 28, 838–852.e6 (2020).

    CAS  PubMed  Google Scholar 

  197. Ryan, D., Prezza, G. & Westermann, A. J. An RNA-centric view on gut Bacteroidetes. Biol. Chem. https://doi.org/10.1515/hsz-2020-0230 (2020).

    Article  PubMed  Google Scholar 

  198. Wilson, D. J. Insights from genomics into bacterial pathogen populations. PLoS Pathog. 8, e1002874 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  199. Sheppard, S. K., Guttman, D. S. & Fitzgerald, R. Population genomics of bacterial host adaptation. Nat. Rev. Genet. 19, 549–565 (2018).

    CAS  PubMed  Google Scholar 

  200. Moustafa, A. M., Lal, A. & Planet, P. J. Comparative genomics in infectious disease. Curr. Opin. Microbiol. 53, 61–70 (2020).

    CAS  PubMed  Google Scholar 

  201. Kellam, P. & Weiss, R. A. Infectogenomics: insights from the host genome into infectious diseases. Cell 124, 695–697 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  202. Khor, C. C. & Hibberd, M. L. Host-pathogen interactions revealed by human genome-wide surveys. Trends Genet. 28, 233–243 (2012).

    CAS  PubMed  Google Scholar 

  203. de Monerri, N. C. S. & Kim, K. Pathogens hijack the epigenome a new twist on host-pathogen interactions. Am. J. Pathol. 184, 897–911 (2014).

    Google Scholar 

  204. Gomez-Diaz, E., Jorda, M., Peinado, M. A. & Rivero, A. Epigenetics of host-pathogen interactions: the road ahead and the road behind. PLoS Pathog. 8, e1003007 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  205. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  206. Fremin, B. J., Sberro, H. & Bhatt, A. S. MetaRibo-Seq measures translation in microbiomes. Nat. Commun. 11, 3268 (2020). A sensitive protocol for the performance of MetaRibo-seq to globally profile bacterial translation in complex consortia such as the human faecal microbiota.

    CAS  PubMed  PubMed Central  Google Scholar 

  207. Stern-Ginossar, N. Decoding viral infection by ribosome profiling. J. Virol. 89, 6164–6166 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  208. Holmes, M. J., Shah, P., Wek, R. C. & Sullivan, W. J. Jr. Simultaneous ribosome profiling of human host cells infected with toxoplasma gondii. mSphere 4, e00292–19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  209. Leary, D. H., Hervey, W. J. T., Deschamps, J. R., Kusterbeck, A. W. & Vora, G. J. Which metaproteome? The impact of protein extraction bias on metaproteomic analyses. Mol. Cell. Probes 27, 193–199 (2013).

    CAS  PubMed  Google Scholar 

  210. Wilmes, P., Heintz-Buschart, A. & Bond, P. L. A decade of metaproteomics: where we stand and what the future holds. Proteomics 15, 3409–3417 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  211. Wang, Z., Han, Q. Q., Zhou, M. T., Chen, X. & Guo, L. Protein turnover analysis in Salmonella Typhimurium during infection by dynamic SILAC, Topograph, and quantitative proteomics. J. Basic Microbiol. 56, 801–811 (2016).

    CAS  PubMed  Google Scholar 

  212. Kaloyanova, D., Vogels, M., van Balkom, B. W. & Helms, J. B. Quantitative proteomic identification of host factors involved in the Salmonella typhimurium infection cycle. Methods Mol. Biol. 1225, 29–45 (2015).

    CAS  PubMed  Google Scholar 

  213. Selkrig, J. et al. Spatiotemporal proteomics uncovers cathepsin-dependent macrophage cell death during Salmonella infection. Nat. Microbiol. 5, 1119–1133 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  214. Auweter, S. D. et al. Quantitative mass spectrometry catalogues Salmonella pathogenicity island-2 effectors and identifies their cognate host binding partners. J. Biol. Chem. 286, 24023–24035 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  215. Jean Beltran, P. M., Federspiel, J. D., Sheng, X. & Cristea, I. M. Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases. Mol. Syst. Biol. 13, 922 (2017).

    PubMed  PubMed Central  Google Scholar 

  216. Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  217. Seger, C. & Salzmann, L. After another decade: LC-MS/MS became routine in clinical diagnostics. Clin. Biochem. 82, 2–11 (2020).

    CAS  PubMed  Google Scholar 

  218. Hatzios, S. K. et al. Chemoproteomic profiling of host and pathogen enzymes active in cholera. Nat. Chem. Biol. 12, 268–274 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  219. Newsom, S. N. & McCall, L. I. Metabolomics: eavesdropping on silent conversations between hosts and their unwelcome guests. PLoS Pathog. 14, e1006926 (2018).

    PubMed  PubMed Central  Google Scholar 

  220. Kentner, D. et al. Shigella reroutes host cell central metabolism to obtain high-flux nutrient supply for vigorous intracellular growth. Proc. Natl Acad. Sci. USA 111, 9929–9934 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  221. Beste, D. J. et al. 13C-flux spectral analysis of host-pathogen metabolism reveals a mixed diet for intracellular Mycobacterium tuberculosis. Chem. Biol. 20, 1012–1021 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  222. Garg, N. et al. Three-dimensional microbiome and metabolome cartography of a diseased human lung. Cell Host Microbe 22, 705–716.e4 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  223. Deatherage Kaiser, B. L. et al. A multi-omic view of host-pathogen-commensal interplay in salmonella-mediated intestinal infection. PLoS ONE 8, e67155 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  224. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  225. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  226. Shishkin, A. A. et al. Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  227. Wade, J. T. Where to begin? Mapping transcription start sites genome-wide in Escherichia coli. J. Bacteriol. 197, 4–6 (2015).

    PubMed  Google Scholar 

  228. Adiconis, X. et al. Comprehensive comparative analysis of 5’-end RNA-sequencing methods. Nat. Methods 15, 505–511 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  229. Tian, B. & Manley, J. L. Alternative cleavage and polyadenylation: the long and short of it. Trends Biochem. Sci. 38, 312–320 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  230. Ju, X., Li, D. & Liu, S. Full-length RNA profiling reveals pervasive bidirectional transcription terminators in bacteria. Nat. Microbio. 4, 1907–1918 (2019).

    CAS  Google Scholar 

  231. Grünberger, F. et al. Exploring prokaryotic transcription, operon structures, rRNA maturation and modifications using Nanopore-based native RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/2019.12.18.880849 (2019).

    Article  Google Scholar 

  232. Byrne, A., Cole, C., Volden, R. & Vollmers, C. Realizing the potential of full-length transcriptome sequencing. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20190097 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  233. Cain, A. K. et al. A decade of advances in transposon-insertion sequencing. Nat. Rev. Genet. 21, 526–540 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  234. Gawronski, J. D., Wong, S. M., Giannoukos, G., Ward, D. V. & Akerley, B. J. Tracking insertion mutants within libraries by deep sequencing and a genome-wide screen for Haemophilus genes required in the lung. Proc. Natl Acad. Sci. USA 106, 16422–16427 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  235. Gonyar, L. A. et al. In vivo gene essentiality and metabolism in Bordetella pertussis. mSphere 4, e00694–18 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  236. Sternon, J. F. et al. Transposon sequencing of brucella abortus uncovers essential genes for growth in vitro and inside macrophages. Infect. Immun. 86, e00312–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  237. Zhang, X. et al. RNA-seq and Tn-seq reveal fitness determinants of vancomycin-resistant Enterococcus faecium during growth in human serum. BMC Genomics 18, 893 (2017).

    PubMed  PubMed Central  Google Scholar 

  238. Fu, Y., Waldor, M. K. & Mekalanos, J. J. Tn-Seq analysis of Vibrio cholerae intestinal colonization reveals a role for T6SS-mediated antibacterial activity in the host. Cell Host Microbe 14, 652–663 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  239. Chaudhuri, R. R. et al. Comprehensive assignment of roles for Salmonella typhimurium genes in intestinal colonization of food-producing animals. PLoS Genet. 9, e1003456 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  240. Vohra, P. et al. Retrospective application of transposon-directed insertion-site sequencing to investigate niche-specific virulence of Salmonella Typhimurium in cattle. BMC Genomics 20, 20 (2019).

    PubMed  PubMed Central  Google Scholar 

  241. Shames, S. R. et al. Multiple Legionella pneumophila effector virulence phenotypes revealed through high-throughput analysis of targeted mutant libraries. Proc. Natl Acad. Sci. USA 114, E10446–E10454 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  242. Zhu, L. et al. Novel genes required for the fitness of streptococcus pyogenes in human saliva. mSphere 2, e00460–17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  243. Subashchandrabose, S. et al. Acinetobacter baumannii genes required for bacterial survival during bloodstream infection. mSphere 1, e00013–15 (2015).

    PubMed  PubMed Central  Google Scholar 

  244. Wang, H. et al. Hypermutation-induced in vivo oxidative stress resistance enhances Vibrio cholerae host adaptation. PLoS Pathog. 14, e1007413 (2018).

    PubMed  PubMed Central  Google Scholar 

  245. Crabill, E., Schofield, W. B., Newton, H. J., Goodman, A. L. & Roy, C. R. Dot/Icm-translocated proteins important for biogenesis of the Coxiella burnetii-containing vacuole identified by screening of an effector mutant sublibrary. Infect. Immun. 86, e00758–17 (2018).

    PubMed  PubMed Central  Google Scholar 

  246. Mann, B. et al. Control of virulence by small RNAs in Streptococcus pneumoniae. PLoS Pathog. 8, e1002788 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  247. Capel, E. et al. Comprehensive Identification of meningococcal genes and small noncoding RNAs required for host cell colonization. mBio 7, e01173–16 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  248. Carette, J. E. et al. Global gene disruption in human cells to assign genes to phenotypes by deep sequencing. Nat. Biotechnol. 29, 542–546 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  249. Friedrich, M. J. et al. Genome-wide transposon screening and quantitative insertion site sequencing for cancer gene discovery in mice. Nat. Protoc. 12, 289–309 (2017).

    CAS  PubMed  Google Scholar 

  250. Franceschini, A. et al. Specific inhibition of diverse pathogens in human cells by synthetic microRNA-like oligonucleotides inferred from RNAi screens. Proc. Natl Acad. Sci. USA 111, 4548–4553 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  251. Strich, J. R. & Chertow, D. S. CRISPR-cas biology and its application to infectious diseases. J. Clin. Microbiol. 57, e01307-18 (2019).

    PubMed  PubMed Central  Google Scholar 

  252. Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    CAS  PubMed  Google Scholar 

  253. Pacheco, A. R. et al. CRISPR screen reveals that EHEC’s T3SS and shiga toxin rely on shared host factors for infection. mBio 9, e01003–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  254. Blondel, C. J. et al. CRISPR/Cas9 screens reveal requirements for host cell sulfation and fucosylation in bacterial type III secretion system-mediated cytotoxicity. Cell Host Microbe 20, 226–237 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  255. Chang, S. J., Jin, S. C., Jiao, X. & Galan, J. E. Unique features in the intracellular transport of typhoid toxin revealed by a genome-wide screen. PLoS Pathog. 15, e1007704 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  256. Tromp, A. T. et al. Human CD45 is an F-component-specific receptor for the staphylococcal toxin Panton-Valentine leukocidin. Nat. Microbiol. 3, 708–717 (2018).

    CAS  PubMed  Google Scholar 

  257. Tao, L. et al. Frizzled proteins are colonic epithelial receptors for C. difficile toxin B. Nature 538, 350–355 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  258. Vigouroux, A. & Bikard, D. CRISPR tools to control gene expression in bacteria. Microbiol. Mol. Biol. Rev. 84, e00077–19 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  259. Qu, J. et al. Modulating pathogenesis with mobile-CRISPRi. J. Bacteriol. 201, e00304–19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  260. Liu, X. et al. Exploration of bacterial bottlenecks and Streptococcus pneumoniae pathogenesis by CRISPRi-Seq. Cell Host Microbe https://doi.org/10.1016/j.chom.2020.10.001 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  261. Storz, G., Wolf, Y. I. & Ramamurthi, K. S. Small proteins can no longer be ignored. Annu. Rev. Biochem. 83, 753-777 (2014).

    PubMed Central  Google Scholar 

  262. Duval, M. & Cossart, P. Small bacterial and phagic proteins: an updated view on a rapidly moving field. Curr. Opin. Microbiol. 39, 81–88 (2017).

    CAS  PubMed  Google Scholar 

  263. Vogel, J. An RNA biology perspective on species‐specific programmable RNA antibiotics. Mol. Microbiol. 113, 550–559 (2020).

    CAS  PubMed  Google Scholar 

  264. Bikard, D. et al. Exploiting CRISPR-Cas nucleases to produce sequence-specific antimicrobials. Nat. Biotechnol. 32, 1146–1150 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  265. Citorik, R. J., Mimee, M. & Lu, T. K. Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases. Nat. Biotechnol. 32, 1141–1145 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  266. Beisel, C. L., Gomaa, A. A. & Barrangou, R. A CRISPR design for next-generation antimicrobials. Genome Biol. 15, 516 (2014).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge financial support from the German Research Foundation (DFG): Individual Research Grant We6689/1-1 (to A.J.W.) and Leibnitz Award Vo875/18 (to J.V.).

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Glossary

Axenic cultures

Describes cultures comprised of only a single, defined bacterial species or strain.

Gnotobiotic

Pertaining to ‘gnotobiosis’, which is Greek for ‘known life’. The term generally describes biological systems wherein all present organisms can be accounted for. In the present context, the term refers to ex-germ-free mice that were inoculated and colonized with a defined bacterial species or consortium.

Metatranscriptomics

Methods to detect and quantify steady-state transcript levels from multiple bacterial species within a community present in a given environmental or host-derived sample.

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Westermann, A.J., Vogel, J. Cross-species RNA-seq for deciphering host–microbe interactions. Nat Rev Genet 22, 361–378 (2021). https://doi.org/10.1038/s41576-021-00326-y

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