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Dual RNA-seq unveils noncoding RNA functions in host–pathogen interactions

Nature volume 529, pages 496–501 (2016)Cite this article

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Abstract

Bacteria express many small RNAs for which the regulatory roles in pathogenesis have remained poorly understood due to a paucity of robust phenotypes in standard virulence assays. Here we use a generic ‘dual RNA-seq’ approach to profile RNA expression simultaneously in pathogen and host during Salmonella enterica serovar Typhimurium infection and reveal the molecular impact of bacterial riboregulators. We identify a PhoP-activated small RNA, PinT, which upon bacterial internalization temporally controls the expression of both invasion-associated effectors and virulence genes required for intracellular survival. This riboregulatory activity causes pervasive changes in coding and noncoding transcripts of the host. Interspecies correlation analysis links PinT to host cell JAK–STAT signalling, and we identify infection-specific alterations in multiple long noncoding RNAs. Our study provides a paradigm for a sensitive RNA-based analysis of intracellular bacterial pathogens and their hosts without physical separation, as well as a new discovery route for hidden functions of pathogen genes.

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Figure 1: Dual RNA-seq captures the full transcript repertoire of infected cells.
Figure 2: A PhoP-dependent Salmonella sRNA induced inside host cells.
Figure 3: PinT temporally controls Salmonella virulence genes.
Figure 4: Effect of PinT activity on the host transcriptome.

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Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

All RNA-seq data has been deposited in NCBI’s Gene Expression Omnibus under accession number GSE60144.

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Acknowledgements

We thank S. Gorski for help with the manuscript, C. Sharma and A. Eulalio for comments on the paper and L. Pfeuffer, B. Plaschke and V. McParland for technical assistance. We further thank S. Hoffmann for discussions about bioinformatic analyses, T. Rudel, K. Tedin, T. Meyer and C. Sharma for eukaryotic cell lines, V. Kozjak-Pavlovic for MitoTracker orange dye, T. Strowig for Ribo-Zero reagents, and W.-D. Hardt, M. Kolbe, H. Aiba and A. Eulalio for providing primary antibodies. A.J.W. was the recipient of an Elite Advancement Ph.D. stipend from the Universität Bayern e.V., Germany. L.B. is supported by a research fellowship from the Alexander von Humboldt Stiftung/Foundation. The Vogel laboratory received funding from the Bavarian BioSysNet program and BMBF (Bundesministerium für Bildung und Forschung) grant “Next-generation transcriptomics of bacterial infections”. This work was further supported by a donation from Baldwin Knauf to the Research Center for Infectious Diseases (ZINF). J.V. and P.F.S. acknowledge support by a joint BMBF grant eBio:RNAsys. This work used the European Grid Infrastructure (EGI) and funding by the EGI-Engage project (Horizon 2020) under grant number 654142.

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Authors and Affiliations

  1. University of Würzburg, RNA Biology Group, Institute for Molecular Infection Biology, Josef-Schneider-Straße 2/D15, Würzburg, D-97080, Germany

    Alexander J. Westermann, Konrad U. Förstner, Lars Barquist, Yanjie Chao, Leon N. Schulte & Jörg Vogel

  2. University of Würzburg, Core Unit Systems Medicine, Josef-Schneider-Straße 2/D15, Würzburg, D-97080, Germany

    Konrad U. Förstner

  3. Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, Leipzig, D-04107, Germany

    Fabian Amman, Lydia Müller & Peter F. Stadler

  4. University of Vienna, Theoretical Biochemistry Group, Institute for Theoretical Chemistry, Währinger Straße 17, Vienna, A-1090, Austria

    Fabian Amman & Peter F. Stadler

  5. Max Planck Genome Centre Cologne, Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, Cologne, D-50829, Germany

    Richard Reinhardt

  6. Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103, Germany

    Peter F. Stadler

  7. Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, 87501, New Mexico, USA

    Peter F. Stadler

  8. Research Centre for Infectious Diseases (ZINF), University of Würzburg, Würzburg, D-97070, Germany

    Jörg Vogel

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  1. Alexander J. Westermann
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Contributions

A.J.W., Y.C., L.N.S. and J.V. designed the research; A.J.W., Y.C. and L.N.S. performed the bench laboratory work; K.U.F., F.A., L.B., L.M. and P.F.S. conducted computational analyses; R.R. carried out part of the sequencing; A.J.W. and J.V. wrote the manuscript, which all co-authors commented on. J.V. supervised the project.

Corresponding author

Correspondence to Jörg Vogel.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Establishment of the infection model with HeLa-S3 cells and constitutively GFP-expressing Salmonella.

a, Rate of infected, apoptotic and cytotoxic HeLa-S3 cells over a range of different m.o.i.s. Left panel: infectivity increases with increasing bacterial doses. The discrepancy between the fractions of infected cells at 4 h and 24 h p.i. results from increasing levels of host cell death over time. Quantification of infectivity was achieved via flow cytometry (FACSCalibur, BD Biosciences) and the Cyflogic software (CyFlo) by gating for the GFP+ and GFP− populations. Middle: apoptosis measurement by annexin V-APC (BD Pharmingen)/propidium iodide (PI) (Sigma) staining followed by flow cytometry using the MACSQuant Analyzer device (Miltenyi Biotec). APC-positive/PI-negative cells were considered apoptotic. Right: lactate dehydrogenase (LDH) release as a proxy for necrosis in infected HeLa-S3 cultures. The colorimetric product was quantified by measuring the absorbance at 490 nm. 100% host cell death was determined by treating the cell culture with lysis buffer before analysis. *P ≤ 0.05; ***P ≤ 0.001 (one-tailed Student’s t-test). b, Intracellular replication of Salmonella inside HeLa-S3 (m.o.i. of 5). Left panel, flow cytometry-based quantification of the increase in GFP intensity per infected host cell over time (see also panel c and Methods). Right: c.f.u. counts. The inset illustrates the relative amount of intracellular bacteria at 4 or 24 h p.i., respectively, as compared to the input. Combination of infectivity (panel a) and c.f.u. data (panel b) allows for the calculation of the average number of intracellular bacteria per invaded cell at distinct time points: 4 h p.i.: ~10 bacteria; 24 h p.i.: ~75 bacteria. c, Representative overlay histogram of flow cytometry data exemplifying the increase in GFP intensity per infected HeLa-S3 cell over time. Plot was generated using the Flowing software (Turku Centre for Biotechnology, Finland). d, Representative FACS plots showing the gating strategy for the separation of invaded and non-invaded HeLa-S3 cells. The signal detected in the phycoerythrin (PE) channel was used as a proxy for a cell’s autofluorescence. The percentage values indicate the relative proportion of GFP+ and GFP− cells before (‘pre-sort’) and after sorting (‘re-analysis’). e, Capillary electrophoresis of total RNA samples from infected cells (4 h p.i.) that were fixed overnight using different reagents. Stop solution refers to 95% EtOH/5% phenol. Where indicated (‘+sucr.’) paraformaldehyde (PFA) was supplemented with 2% sucrose. The band pattern of pure Salmonella RNA is shown to the left. Note that in the infection samples bacterial rRNA bands are not visible due to the overwhelming host RNA background. For gel source data, see Supplementary Fig. 1. f, Influence of different preservatives on FACS-based recovery of invaded host cells through bleaching of the GFP signal. g, Increasing concentrations of RNAlater kill intracellular Salmonella (black line) but do not compromise detection of GFP fluorescence (green bars). h, Extrapolation of the relative representation of Salmonella and human transcriptomes in infection samples. A dilution series of separately isolated Salmonella to HeLa-S3 total RNA was set up and for each ratio, bacterial rfaH (relative to human ACTB) mRNA was quantified by qRT–PCR. The resulting trend-line equation was used to infer the percentage of the Salmonella transcriptome within mixed total RNA samples from infected HeLa-S3 cells at different time points and without (blue) or upon FACS-based enrichment for invaded cells (red). The position of medium control samples (LB, DMEM) is given in grey. i, Normalized read counts for all detected Salmonella or human genes at 4 h p.i. are plotted for 3 biological replicate experiments and the Pearson correlation coefficient (r) is given. Panels a, b, f, g, h show the mean ± s.d. from each 3 biological replicates.

Extended Data Figure 2 Establishment of an rRNA-depletion step for dual RNA-seq.

a, Experimental workflow. b, Comparative mapping statistics of dual RNA-seq samples without (upper panel) or upon the joint depletion of bacterial and eukaryotic rRNA using the Ribo-Zero Gold (Epidemiology) kit (lower panel). c, Gene-wise correlation between read coverages without or upon rRNA removal for Salmonella (left) and human (right) data subsets. The Pearson’s r is given. The red dots in squares represent the rRNA transcripts that had zero reads in the rRNA-depleted sample.

Extended Data Figure 3 PinT is induced during infection via PhoP binding to its promoter region.

a, Scheme of the comparative high-resolution time-course analysed by dual RNA-seq. For both the Salmonella strains, five individual time points post-invasion of HeLa-S3 cells were sampled and enriched for the fraction of invaded (GFP+) host cells. Mock-treated cells were used as a host, and extracellular Salmonella in DMEM medium (0 h) as a bacterial reference control. Together this resulted in 17 different conditions which were sampled as biological triplicates. b, Average RPKM distribution over individual Salmonella or human transcript classes from the wild-type infection time-course. c, PinT activation during invasion of HeLa-S3 cells as revealed by dual RNA-seq (red graph) can be reproduced by qRT–PCR measurements (black dots; each dot represents a single out of 4 (2; 8; 16 h), 5 (4 h) or 6 biological replicate experiments (24 h)). Normalization was achieved using the constitutively expressed gfp mRNA as a reference. d, Northern blot detection of PinT in the Salmonella wild-type and various mutant backgrounds in which the indicated global regulators were deleted. For gel source data, see Supplementary Fig. 1. e, Mutational analysis identifies PhoP as a direct transcriptional activator of the pinT promoter. A transcriptional gfp fusion construct containing the pinT upstream promoter region (−41 bp to +5 bp) was analysed in the wild-type, phoP deletion or phoP complementation backgrounds. A non-fluorescent (‘no gfp’) or unrelated ompC promoter reporter served as negative controls and a phoP promoter reporter as a positive control (as PhoP is known to auto-regulate its own expression83). Two-nucleotide exchanges (T−27/−28→A−27/−28) in the predicted PhoP binding site (see alignment in panel f) are sufficient to abrogate PhoP responsiveness of PinT expression. Error bars indicate the s.d. from the mean from biological triplicates. f, Sequence alignment shows the conservation of PinT sRNA within the genus Salmonella. “STY”: S. Typhi, “SEN”: S. Enteritidis, “SGA”: S. Gallinarum, “SAR”: S. arizonae, “SBG”: S. bongori. Perfectly conserved ribonucleobases are labelled in red, less conserved bases are shown in blue. The numbers indicate the position relative to the 5′ end of PinT (+1 position). Black lines and sequence motif below the alignment highlight a PhoP binding site (the asterisks denote thymines that were converted into adenines for mutational analysis in panel e). Asterisks below the seed sequence (position ~30–40) mark two guanines that were mutated to cytosines in Fig. 3c and Extended Data Fig. 6. g, h, Infection rate (g) and intracellular replication kinetics (h) of the indicated Salmonella strains in HeLa-S3 cells (m.o.i. of 5). Data are derived from flow cytometry measurements as described for Extended Data Fig. 1a, b, and refer to the mean ± s.d. from 3 biological replicates.

Extended Data Figure 4 PinT is strongly induced upon the invasion of diverse host cell types while its deletion only has slight effects in cell culture models.

a, The given cell types were infected with wild-type Salmonella for the indicated time periods and total or rRNA-depleted RNA (as indicated in Supplementary Table 1) was sequenced. THP-1 cells either were differentiated by treating them with phorbol myristate acetate before infection (‘+PMA’) or were kept monocytic (‘−PMA’). For all but HeLa-S3 cells and porcine cell types infection was established at an m.o.i. of 10. IPEC-J2 and 3D4/31 cells were infected at an m.o.i. of 20 and HeLa-S3 at an m.o.i. of 5. Shown are detected (≥10 reads in each replicate; light grey) and regulated sRNAs (adjusted P value < 0.05; dark grey). The data was derived from 3 biological replicates for HeLa-S3 and 3D4/31 and 2 biological replicates for the other cell types. PinT expression (red line) was significantly upregulated in all cell types. b, qRT–PCR validation of the induction of PinT in five selected host cell types. qRT–PCR data from 3 biological replicates were drawn (solid lines). Normalization was against gfp mRNA. The data from HeLa-S3 is the same as in Extended Data Fig. 3c. For comparison, in each case the RNA-seq-based expression data of PinT (as shown in panel a) are re-plotted (dashed curves). c, Invasion assays for wild-type, ΔpinT and pinT+ Salmonella with HeLa-S3 (m.o.i. 5), CaCo-2 and undifferentiated THP-1 (both m.o.i. 10), as well as IPEC-J2 and 3D4/31 (both m.o.i. 20). In all cases, the invasion rate was profiled 10 min p.i. by flow cytometry. d, Intracellular replication kinetics for the same strains and host cell types. The increase in GFP intensity of infected cells over time was monitored by flow cytometry and expressed as fold change compared to the t0 time point (see Methods). The asterisk denotes a significantly different replication rate between the wild-type and ΔpinT strain (P < 0.05; two-tailed Mann–Whitney U-test). e, Host cell cytotoxicity measurements for the same host cell types upon infection with the indicated Salmonella strains. At the respective time points p.i., LDH activity in the supernatant of the infected cultures was quantified and the increase over time was with respect to the LDH activity measured in supernatants of mock-infected cells. The data in panels c–e represent the mean ± s.d. from 3 biological replicates.

Extended Data Figure 5 Salmonella PinT sRNA represses SPI-2 expression during the infection of HeLa-S3 cells and pig macrophages.

a, The heat map shows the result from gene set enrichment analyses of Salmonella gene expression data from the comparative dual RNA-seq time-course experiments with HeLa-S3 cells and porcine 3D4/31 macrophages, and from the comparative RNA-seq experiment of Salmonella grown for 1 h under SPI-2-inducing in vitro conditions. It reveals the de-repression of SPI-2 genes in the absence of PinT (ΔpinT) at several time points—a specific effect as SPI-2 expression reverts to wild-type levels upon trans-complementation of PinT (pinT+) in the in vitro assay. b, qRT–PCR measurements during the early stages of HeLa-S3 infection validates the de-repression of Salmonella SPI-2 genes in the ΔpinT background (red) as compared to both wild-type (black) and pinT+ (grey) strains. Normalization was performed using gfp mRNA. Dots represent individual biological replicate experiments (five for hilD, sseA, ssrB; four for the other mRNAs) and the solid lines indicate their mean. c, qRT–PCR validation of de-repressed SPI-2 genes 6 h after the invasion of pig macrophages. Porcine GAPDH mRNA was used for normalization. The data represent the results from biological triplicate measurements. The asterisks in panels b and c denote significantly increased transcript levels in ΔpinT compared to wild-type Salmonella (P < 0.05; one-tailed Mann–Whitney U-test).

Extended Data Figure 6 PinT directly targets Salmonella sopE/E2, grxA and crp mRNAs.

a, Volcano plot showing Salmonella mRNA levels at 5 min after the pulse-expression of PinT under SPI-1-inducing conditions (LB medium, OD600 of 2.0). The data are derived from two biological replicates. Candidates that were confirmed to be directly targeted by PinT are coloured. Red: targets regulated predominantly under SPI-1 conditions; blue: targets regulated (also) under SPI-2 conditions (see panel e). For the full list of changes in gene expression after the PinT pulse see Supplementary Table 1. b, RNA duplex formation between PinT and the sopE and sopE2 leaders as predicted by the RNA-hybrid program84. Point mutations introduced for compensatory base-pair exchange experiments are indicated. The ribosome binding site and start codon are marked in blue or red, respectively. c, Validation of the base-pair interactions as shown in panel b using translational sopE::gfp and sopE2::gfp reporter gene fusions by compensatory base-pair exchanges. Salmonella strains containing both a gfp reporter plasmid and an sRNA overexpression vector were grown overnight in LB and analysed by flow cytometry. The error bars indicate s.d. from the mean from biological triplicates. d, In vivo pulse-expression establishment. GFP-expressing Salmonella strains harbouring PinT sRNA under an l-arabinose-inducible promoter on a plasmid or corresponding control strains, respectively, were used to infect HeLa-S3 cells. At 4 h p.i., l-arabinose was added to the cell medium. Samples were taken over a time-course of 20 min after the pulse and enriched for Salmonella transcripts (see Methods section). PinT sRNA levels were measured by qRT–PCR in the resulting RNA samples and are plotted (mean ± s.d. from technical triplicates). e, Pulse-expression of PinT under in vivo(-like) conditions (the full data are in Supplementary Table 1). PinT was transiently overexpressed under SPI-2-inducing conditions in vitro or 4 h after HeLa-S3 infection (see panel d). In either case, 5 min after induction total RNA samples were taken and analysed by RNA-seq (each two biological replicates). Axes represent fold changes in mRNA abundance between strains harbouring the empty and the PinT-containing plasmid. The two targets validated in panel f are labelled in blue. The celB (cellobiose-specific permease IIC component) and ecnB (entericidin B precursor) mRNAs might be further targets of PinT (see also panel a), but were not followed up here. f, Validation of direct targeting of grxA and crp mRNAs by the seed region of PinT using translational grxA::gfp and crp::gfp reporter gene fusions as described for panel c.

Extended Data Figure 7 Host expression data for the comparative infection with wild-type or ΔpinT Salmonella.

a, Heat maps showing differentially expressed mRNAs (upper panels), lncRNAs (middle) or miRNAs (lower) between the infection of HeLa-S3 cells with either one of the two indicated Salmonella strains and mock-infected controls. Plotted are all genes that were significantly differentially expressed (adjusted P value < 0.05; 3 biological replicates) between the indicated conditions for at least one time point during infection. Numbers to the right refer to detected transcripts and transcripts differentially expressed after wild-type infection vs mock, or after ΔpinT infection vs mock, respectively. b, PinT affects the expression of coding and noncoding transcripts of the human host. Venn diagrams indicate HeLa-S3 transcripts commonly or specifically regulated compared to mock for the respective infection strain. c, Infection with ΔpinT Salmonella leads to increased SOCS3 expression in porcine macrophages. qRT–PCR data (mean ± s.d.) from biological triplicate experiments of the infection of 3D4/31 cells with Salmonella wild-type, ΔpinT, or pinT+. Total RNA samples were taken at 6 h p.i. Porcine GAPDH mRNA was used for normalization. d, qRT–PCR measurement of human IL8 mRNA in total RNA samples isolated from HeLa-S3 cells at 16 h after either wild-type, ΔpinT or pinT+ infection and each sorted into the fractions of invaded (GFP+) and non-invaded (GFP−) cells. U6 snRNA was used for normalization. The data represent the mean ± s.d. from 3 biological replicates. Note that differential gene expression was not caused by different bacterial loads as judged from the ratio of gfp mRNA to U6 snRNA (‘bacterial load control’). e, Enzyme-linked immunosorbent assay for human IL-8 protein in supernatant samples from HeLa-S3 cells at 20 h after their infection with the indicated Salmonella strains. Data refer to the mean ± s.d. from biological triplicate measurements. f, Karyogram plot displaying the individual human (female) chromosomes and the genomic position of differentially expressed lncRNA candidates. lncRNAs differentially regulated (adjusted P value < 0.05; 3 biological replicates) in response to wild-type infection compared to mock-treated HeLa-S3 cells are indicated as grey bars and candidates differentially expressed between wild-type- and ΔpinT-infected cells as red bars. The position of the bars relative to the respective chromosome indicates the direction of regulation (above the chromosome: upregulation; below: downregulation at the earliest time point when regulation was observed). Panels c–e, asterisks denote significantly different transcript (c, d) or protein (e) levels between wild-type- and ΔpinT-infected cells (P < 0.05; one-tailed Mann–Whitney U-test).

Extended Data Figure 8 Impact of PinT on host mitochondria.

a, KEGG Pathview representation of oxidative phosphorylation in mitochondria. Dual RNA-seq data of infected HeLa-S3 cells (3 biological replicates) were plotted on top of the pathway map. Individual boxes represent the different time points sampled. Coloured boxes: at the respective time point the given gene(-cluster) was hyper-activated (red) or suppressed (blue) in ΔpinT-infected compared to wild-type-infected cells. b, Northern blot detection of mitochondrial tRNAs (shown in Fig. 4e) in HeLa-S3 cells infected for 16 h with the indicated Salmonella strains and sorted for GFP+ and GFP− fractions. Salmonella 5S rRNA serves as a bacterial and human U6 snRNA as a host control. The ‘Salmonella only’ sample demonstrates specificity of the probes against the respective human (but not bacterial) tRNAs. For gel source data, see Supplementary Fig. 1. c, Elevated mitochondrial expression in response to ΔpinT infection is accompanied by the sub-cellular re-localization of mitochondria in invaded hosts. Mitochondria of infected HeLa-S3 cells were stained using the MitoTracker Orange dye (Life Technologies) and nuclei with Hoechst (Invitrogen). The scale bar indicates 15 μm. The white arrowhead marks a prominent cluster of re-localized mitochondria.

Extended Data Figure 9 Comparative dual RNA-seq experiments with further sRNA mutant Salmonella.

a, Northern blot detection of additional sRNAs induced upon host cell invasion (see Fig. 2a). ‘Inoculum’ refers to bacteria in DMEM. Gel source data in Supplementary Fig. 1. b, HeLa-S3 invasion efficiency of the three indicated sRNA double mutants and wild-type Salmonella (m.o.i. of 5). c, Intracellular replication kinetics of the same strains inside HeLa-S3 (m.o.i. of 5). Data in panels b and c are derived from flow cytometry measurements as described for Extended Data Fig. 1a, b, and refer to the mean ± s.d. from 3 biological replicates. d, Dual RNA-seq data of the infection of HeLa-S3 cells with the indicated Salmonella strains at 0 h, 4 h, and 16 h p.i. The Venn diagram shows the number of significantly (adjusted P value < 0.05) differentially expressed Salmonella genes (combined for all three time points) between wild-type Salmonella and the respective sRNA double mutant strain. e, Heat map of bacterial mRNA and sRNA expression changes for ΔomrA/B, ΔryhB/ΔisrE and ΔyrlA/B as compared to wild-type Salmonella at 0 h, 4 h and 16 h p.i. of HeLa-S3 cells. Plotted are all genes (except the respectively deleted sRNAs) that were significantly differentially expressed (adjusted P value < 0.05) for at least one of the indicated conditions. The gene for a putative oxidoreductase (SL1344_3511) was specifically upregulated in intracellular ΔryhB/ΔisrE mutants compared to wild-type Salmonella. Many genes for ribosomal subunits were downregulated in all three mutant strains compared to the wild-type. f, Venn diagram for the number of commonly or specifically affected human pathways between Salmonella wild-type infection and that of the three mutant strains (further specified in panel g). g, Infection of HeLa-S3 with three sRNA double mutant strains affects distinct sets of host pathways as compared to wild-type infection. Shown are normalized enrichment scores (norm. ES) from a human gene set enrichment analysis (adjusted P value < 0.05). The dual RNA-seq data in panels d–g were derived from 3 biological replicate experiments.

Supplementary information

Supplementary Figure 1

This file contains gel source data. (PDF 620 kb)

Supplementary Table 1

This file contains information about the individual cDNA libraries sequenced, Salmonella pathway/regulon compositions, fold-changes in Salmonella gene expression upon PinT pulse-expression followed by sequencing, list of regulated host genes during the high-resolution comparative time-course Dual RNA-seq experiment, gene set and enriched GO-terms of the inter-species correlation analysis, strains, plasmids, oligonucleotides and antibodies used in this study. (XLSX 899 kb)

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Westermann, A., Förstner, K., Amman, F. et al. Dual RNA-seq unveils noncoding RNA functions in host–pathogen interactions. Nature 529, 496–501 (2016). https://doi.org/10.1038/nature16547

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