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Influenza virus repurposes the antiviral protein IFIT2 to promote translation of viral mRNAs

Nature Microbiology volume 5, pages 1490–1503 (2020)Cite this article

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Abstract

Cells infected by influenza virus mount a large-scale antiviral response and most cells ultimately initiate cell-death pathways in an attempt to suppress viral replication. We performed a CRISPR–Cas9-knockout selection designed to identify host factors required for replication after viral entry. We identified a large class of presumptive antiviral factors that unexpectedly act as important proviral enhancers during influenza virus infection. One of these, IFIT2, is an interferon-stimulated gene with well-established antiviral activity but limited mechanistic understanding. As opposed to suppressing infection, we show in the present study that IFIT2 is instead repurposed by influenza virus to promote viral gene expression. CLIP‐seq demonstrated that IFIT2 binds directly to viral and cellular messenger RNAs in AU‐rich regions, with bound cellular transcripts enriched in interferon‐stimulated mRNAs. Polysome and ribosome profiling revealed that IFIT2 prevents ribosome pausing on bound mRNAs. Together, the data link IFIT2 binding to enhanced translational efficiency for viral and cellular mRNAs and ultimately viral replication. Our findings establish a model for the normal function of IFIT2 as a protein that increases translation of cellular mRNAs to support antiviral responses and explain how influenza virus uses this same activity to redirect a classically antiviral protein into a proviral effector.

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Fig. 1: A CRISPR-knockout screen identifies the antiviral proteins IFIT2 and IFIT3 as proviral regulators of influenza virus infection.
Fig. 2: IFIT2 promotes IAV gene expression and progression through the viral life cycle.
Fig. 3: IFIT2 selectively binds AU-rich regions in viral and host mRNAs.
Fig. 4: IFIT2 associates with active ribosomes and increases the translational efficiency of viral mRNAs.
Fig. 5: IFIT2 modulates translational efficiency by preventing ribosome pausing.

Data availability

Data supporting the findings of the present study are available within the article and the Supplementary Tables, with the exception of raw data for eCLIP and ribosome profiling, which are accessible as BioProject PRJNA633047 containing BioSample accession nos. SAMN14931303 to SAMN14931312 and SRA accession nos. SRR11794832 to SRR11794851 (see Supplementary Table 6). All other data are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Existing code used for data analysis are described above. Customized code and pipelines are described and are available at https://github.com/mehlelab or from the corresponding author upon request.

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Acknowledgements

This work was supported by the National Institutes of Health (NIH, grant nos. R21AI125897 and R01AI125271), the UW2020:WARF Discovery Initiative and a Shaw scientist award to A.M., an NIH National Service Award (no. T32 GM07215) to V.T., National Science Foundation grants (nos. GRFP DGE-1747503 to M.P.L., R01AI104972 to M.S.D and R01AI118938 to A.C.B.), the German Research Foundation (grant no. SFB 1160, project 13) to M.S., the Excellence Initiative of the German Research Foundation (GSC-4, Spemann Graduate School) and the Ministry for Science, Research and Arts of the State of Baden-Wuerttemberg to T.T., no. U19AI106754 to C.B. and no. HHSN272201400008C of the Center for Research on Influenza Pathogenesis, an NIAID-funded Center of Excellence for Influenza Research and Surveillance, and no. U19AI135972 to A.G.-S. A.M. holds an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund. We thank D. Poole for technical assistance, members of the Mehle laboratory, M. Harrison, M. Sheets, C. Fraser, S. Floor and J. D. Sauer for valuable input, and C. Li, N. Reich, P. Friesen, Y. Kawaoka, L. Ristow and R. Welch for sharing reagents. We thank F. Zheng for reagents deposited in Addgene and the University of Wisconsin Biotechnology Center DNA Sequencing Facility for sequencing and IDAA services.

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Author notes

  1. These authors contributed equally: Vy Tran, Mitchell P. Ledwith.

Authors and Affiliations

  1. Medical Microbiology and Immunology, University of Wisconsin Madison, Madison, WI, USA

    Vy Tran, Mitchell P. Ledwith, Christina A. Higgins & Andrew Mehle

  2. Institute of Virology, Medical Center, University of Freiburg, Freiburg, Germany

    Thiprampai Thamamongood & Martin Schwemmle

  3. Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Thiprampai Thamamongood & Martin Schwemmle

  4. Spemann Graduate School of Biology and Medicine, University of Freiburg, Freiburg, Germany

    Thiprampai Thamamongood

  5. Faculty of Biology, University of Freiburg, Freiburg, Germany

    Thiprampai Thamamongood

  6. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Shashank Tripathi & Adolfo García-Sastre

  7. Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Shashank Tripathi & Adolfo García-Sastre

  8. Department of Medicine, University of California, San Diego, San Diego, CA, USA

    Max W. Chang & Christopher Benner

  9. Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Adolfo García-Sastre

  10. Departments of Medicine, Molecular Microbiology, and Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Adrianus C. M. Boon & Michael S. Diamond

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  1. Vy Tran
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Contributions

V.T. performed the CRISPR–Cas9 screen with WSN, MaGECK analysis and validation experiments. M.P.L. performed IFIT2 CLIP-seq and validation, polysome profiling, ribosome sequencing and analysis of CLIP-seq and ribosome-sequencing data. T.T. performed the CRISPR–Cas9 screen with SC35M. C.A.H. performed IFIT2 reporter assays. S.T. contributed methodology. M.W.C. performed the analysis of the screen with SC35M. C.B. supervised the SC35M analysis and contributed HOMER analysis. A.G.-S. and M.S. supervised the study. A.C.M.B. and M.S.D. contributed conceptualization and reagents. A.M. performed biochemistry and supervised the study.

Corresponding author

Correspondence to Andrew Mehle.

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Extended data

Extended Data Fig. 1 Enrichment of IFIT2 and IFIT3 over sequential influenza virus selections.

a, Schematic for hit analysis and selection. b-c, Frequency distribution of sgRNAs present in the parental libraries and those following selection with WSN (b) or SC35M (c). d–f, sgRNA enrichment trajectories following (d) sequential IAV-G selections, (e) IAV-G followed by bona fide influenza virus (WSN) infection or (f) selection with SC35M-GFP. The abundance of each sgRNA targeting the indicated gene is shown. g, Overlap between top hits from each screen. A conservative cut-off of RRA<10-5 was used to define top hits.

Extended Data Fig. 2 Characterization of replication and protein expression in the presence or absence of IFIT2 or IFIT3.

a, Two IFIT2-/- clones were created in A549 cells by CRISPR/Cas9 mutagenesis using distinct sgRNAs. Homozygous knockouts were identified by IDAA (indel detection by amplicon analysis). Chromatographs are shown for WT and mutant amplicons from IFIT2 knockout line #1 targeting AGAACGCCATTGACCCTCTG and IFIT2 knockout line #2 targeting GGCCAGTAGGTTGCACATTG. Size standards appear in red and amplicon peaks are shown in blue with calculated sizes in nucleotides listed above each peak. b, Ifit2-/- MEFS survive infection. WT and knockout cells were challenged with WSN or IAV stably encoding VSV-G (IAV-G). Surviving cells were visualized by staining with crystal violet. c, Kinetics of IFIT2 and NP expression were determined by infecting A549 cells with WSN (MOI = 0.2) and measuring protein expression by western blotting samples acquired at the indicated times post-infection. d, Clonal WT or IFIT2-/- 293 cells were infected with an IAV (WSN) reporter virus at an MOI = 0.01 and viral gene expression was measured 8 hpi. Each point represents mean gene expression (n = 4) from 4 independent clonal knockout lines. The mean of all lines is shown as a gray bar ± SEM. e, IFIT2 was expressed in WT 293 cells (clonal line 1B8) or IFIT2-/- knockout 293 cells (clonal line 3B9) for 12 hr followed by infection with an IAV (WSN) reporter virus (MOI = 0.1) for 8 hr. n = 2 independent infections. f, Two homozygous IFIT3-/- clones were identified by IDAA. Chromatographs are shown for WT and mutant amplicons from IFIT3 knockout lines #1 and #2 targeting CACTGCGGAGGACATCTGTT. Size standards appear in red and amplicon peaks are shown in blue with calculated sizes in nucleotides listed above each peak. g, Infection-induced IFIT3 expression was monitored in WT and IFIT3 knockout cells by western blotting. h, Multi-cycle replication of WSN in wildtype or IFIT3 knockout A549 human lung cells. Data represent mean ± standard deviation (n = 3 independent infections). i, Viral gene expression in wildtype or IFIT3 knockout cells infected with an influenza reporter virus for the indicated times. Data represent mean + sd (n = 4 independent infections). (*** p < 0.001; two-tailed Student’s t-test for pairwise comparisons between control and knockout clonal lines in d; * p < 0.05; ** p < 0.01 one-way ANOVA with post hoc Tukey’s HSD for e, h and i).

Source data

Extended Data Fig. 3 Human IFIT2 and murine Ifit2 promote virus-induced apoptosis.

a, Caspase 3/7 activity was measured in wildtype, Ifit2-/-, and Bak/Bax-/- MEFs infected with WSN at an MOI of 0.01 for the indicated times. n = 4 independent infections ± standard deviation. b, Expression of cleaved (C-PARP) versus full-length (FL-PARP) PARP-1 in IAV infected A549 and IFIT2 knockout cell lines. Protein bands were quantified and the percent C-PARP of total PARP signal normalized to a tubulin loading control is indicated below. c, Viability of Ifit2-/- cells during infection with WSN assessed by CellTiter Glo Assay at the indicated time points. n = 4 independent infections ± standard deviation. d, Multicycle replication of WSN reporter virus in in WT, Ifit2-/-, and Bak/Bax-/- MEFs. n = 3 independent infections ± standard deviation. e, Single-cycle WSN reporter virus gene expression at early time points post-infection in WT, Ifit2-/-, and Bak/Bax-/- MEFs. n = 4 independent infections ± standard deviation. f, WSN reporter virus gene expression in Bak/Bax-/- cells ectopically expressing an empty vector, hIFIT2, or hIFIT2-ΔRNA. n = 3 independent infections ± standard deviation. *p < 0.05, **p < 0.01; *** p < 0.001; Two-tailed Student’s T-test for pairwise comparisons (c) or one-way ANOVA with post hoc Tukey’s HSD for multiple comparisons to WT cells (a, d, e and f). All data are plotted as mean ± sd.

Source data

Extended Data Fig. 4 Analysis of IFIT2 CLIP-Seq data.

a, Experimental workflow for eCLIP of IFIT2 from IAV WSN-infected A549 cells. RNA-protein adducts were formed in infected cells by UV cross-linking. Lysates were prepared and RNA was partially digested with RNase. A fraction of lysate was removed to serve as the size-matched input control (SM input). IFIT2 was immunopurified and bound RNAs were processed via eCLIP followed by sequencing and analysis. b, CLIP-Seq was performed in duplicate and compared for IFIT2 CLIP or size-matched input (SMI) controls. Pearson’s correlation coefficient is shown as a heatmap. c, Concordance between ranked peaks in two independent biological replicates of IFIT2 CLIP-Seq was assessed by calculating an irreproducible discover rate (IDR). A conservative threshold of 5% was used to identify significant peaks. d, Abundance distribution in the size-matched input control for all transcripts and those with IFIT2 CLIP peaks. Populations were compared by a two-sided Mann-Whitney U-test. e, Meta-analysis of GC content for peaks identified in the 5’ UTR and coding sequence (CDS) of bound transcripts. IFIT2-bound RNAs were compared to all expressed 5’ UTRs or CDS via a two-sided Mann-Whitney U-test. f, A degenerate UAGnnUAU motif was found in ~20% of IFIT2 CLIP peaks (p = 10-279 compared to background).

Extended Data Fig. 5 IFIT2 binds viral mRNA but not genomic vRNA.

a, IFIT2 CLIP-Seq was performed on WSN infected cells. Reads were mapped to viral mRNA (top) or genomic vRNA (bottom) and analyzed with CLIPper. No CLIP peaks were identified on the genomic RNA. Data from biological duplicates of size-matched input controls or IFIT2 CLIP are plotted as mean and standard deviation using dark and light shades of the same color, respectively. BPM = bins per million. b, Western blot of immunoprecipitated IFIT2 or IgG controls used for RIP-qRT-PCR shown in Fig. 3e. c, Recombinant HisGST-IFIT2 used for EMSA analysis in Fig. 3f was expressed and purified from E. coli. Protein integrity and purity was verified by gel electrophoresis and Coomassie staining. IFIT2∆RNA = R212E/K410E mutant.

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Extended Data Fig. 6 IFIT2 enhances the translational efficiency of influenza virus NP mRNA.

a–c, Full dataset underlying results presented in Fig. 4d. Polysome profiling was performed on WT and IFIT2-/- A549 cells infected with WSN. a, The polysome profile is shown again for clarity. b, qRT-PCR quantification of β-actin or c, NP mRNA in each fraction. The sum of less efficiently translated mRNA present in “light” polysome fractions (5 or less ribosomes per message) or efficiently translated mRNA in “heavy” polysome fractions (>5 ribosomes per message) is shown under each trace. Loss of IFIT2 shifts NP mRNA towards the top of the gradient, indicating less efficient translation than in WT cells. d, IFIT2-/- 293 cells or two clones of WT cells were transfected with an IFIT2 expression vector or an empty vector control prior to infection with WSN. Proteins were detected by western blot.

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Extended Data Fig. 7 Loss of IFIT2 results in ribosomal pausing that decreases translation and increases pausing of IFIT2-bound mRNAs.

Ribosome dynamics were monitored in cells infected with IAV WSN. a, The translational efficiency of IFIT2-bound ISG mRNAs is decreased in the absence of IFIT2. Bound transcripts were compared to unbound transcripts via a two-sided Mann-Whitney U-test. b-c, Accumulation of paused ribosomes in the absence of IFIT2. Normalized read density for total RNA (top) and ribosome-protected fragments (RPFs) (bottom) mapping to IAV mRNAs (b) or GAPDH (c) in infected WT and IFIT2-/-cells. A close-up view of NP is shown in Fig. 5c. Data from replicate experiments are plotted as mean and standard deviation using dark and light shades of the same color, respectively. Pause sites are shown below. Pause sites enriched in IFIT2-/- cells >1.5-fold are filled in. High coverage at the 5’ and 3’ end of PB2, PB1 and PA is consistent with low levels of defective-interfering (DI) particles commonly found in laboratory viral stocks. Note that there are no IFIT2-dependent pause sites identified on GAPDH mRNA. BPM = bins per million. d, Ribosome pausing increases during infection of IFIT2-/- cells. Changes in pause intensity (pauseI) between the maximum and minimum pauseI sites on each transcript were calculated during infection of WT and IFIT2-/- cells. e, Transcripts contain multiple pause sites. The pause sites with the minimum, median and maximum pauseI were identified. The effect of IFIT2 on pausing for each of these classes was determined by comparing IFIT2-bound transcripts to unbound transcripts. In the absence of IFIT2, pauseI increases at the strongest pause sites (maximum) while it is reduced at the weakest pause sites (minimum). The median pause sites are unchanged. f, Increased pausing and decreased translation are correlated for IFIT2-bound mRNAs. The relationship between pausing and translation efficiency was assessed by calculating a pause-TE index (pauseImax/TE) for each transcript. Changes in the pause-TE index were assessed for IFIT2-bound mRNAs (left), IAV mRNAs (middle), and ISG mRNAs (right). Comparisons in (d–f) were performed via a two-sided Mann-Whitney U-test.

Extended Data Fig. 8 Sequence-dependent enhancement of NP translation in the presence of IFIT2.

a, Diagram of NP-2A-GFP polyprotein. For cell-based assays in Fig. 5f, NP was expressed as a polyprotein where NP was separated from GFP by the 2A cleavage sequence from porcine teschovirus. NP is shaded to represent the codon juggling that introduced silent mutations to NPjug. See Methods for details. b, NP was expressed in 293T cells from a gene encoding WT or juggled codons for only NP, and not a polyprotein. IFIT2-V5 was co-expressed where indicated. Expression was detected by western blot, including Hsp90 as a loading control.

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Extended Data Fig. 9 Classic “antiviral” proteins can be re-purposed during infection into pro-viral effectors.

A model for the dichotomous functions of IFIT2. IFIT2 enhances translation of host mRNAs, including ISGs, to exert antiviral activity to diverse viruses. Yet, as shown here, IFIT2 is re-purposed during influenza virus infection to increase translation of viral mRNAs and shift the balance resulting in a net increase in viral mRNA translation and viral replication.

Extended Data Fig. 10 Replication kinetics of recombinant B/Brisbane/60/2008 and the reporter virus B/Brisbane/60/2008-PASTN.

An influenza B reporter virus encoding NanoLuc in the PA gene segment (see Methods for details). Infections were performed in MDCK cells (MOI = 0.01) at 33 ˚C and viral titers were determined by plaque assay at the indicated time points. Data are mean of n = 3 independent infection ± standard deviation.

Supplementary information

Reporting summary

Peer review file

Supplementary Table 1

MaGECK analysis of cells selected with WSN.

Supplementary Table 2

MaGECK analysis of cells selected with SC35M.

Supplementary Table 3

IFIT2-bound transcripts.

Supplementary Table 4

Changes in ribosome occupancy in IFIT2-knockout cells.

Supplementary Table 5

IFIT2-dependent ribosomal pausing.

Supplementary Table 6

Statistics and accession nos. for sequencing data.

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Unprocessed western blots for Extended Data Fig. 8.

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Tran, V., Ledwith, M.P., Thamamongood, T. et al. Influenza virus repurposes the antiviral protein IFIT2 to promote translation of viral mRNAs. Nat Microbiol 5, 1490–1503 (2020). https://doi.org/10.1038/s41564-020-0778-x

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