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The retroelement Lx9 puts a brake on the immune response to virus infection

Abstract

The notion that mobile units of nucleic acid known as transposable elements can operate as genomic controlling elements was put forward over six decades ago1,2. However, it was not until the advancement of genomic sequencing technologies that the abundance and repertoire of transposable elements were revealed, and they are now known to constitute up to two-thirds of mammalian genomes3,4. The presence of DNA regulatory regions including promoters, enhancers and transcription-factor-binding sites within transposable elements5,6,7,8 has led to the hypothesis that transposable elements have been co-opted to regulate mammalian gene expression and cell phenotype8,9,10,11,12,13,14. Mammalian transposable elements include recent acquisitions and ancient transposable elements that have been maintained in the genome over evolutionary time. The presence of ancient conserved transposable elements correlates positively with the likelihood of a regulatory function, but functional validation remains an essential step to identify transposable element insertions that have a positive effect on fitness. Here we show that CRISPR–Cas9-mediated deletion of a transposable element—namely the LINE-1 retrotransposon Lx9c11—in mice results in an exaggerated and lethal immune response to virus infection. Lx9c11 is critical for the neogenesis of a non-coding RNA (Lx9c11-RegoS) that regulates genes of the Schlafen family, reduces the hyperinflammatory phenotype and rescues lethality in virus-infected Lx9c11−/− mice. These findings provide evidence that a transposable element can control the immune system to favour host survival during virus infection.

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Fig. 1: CRISPR–Cas9 deletion of the ancient LINE-1 element Lx9 on chromosome 11 in mice.
Fig. 2: Deletion of Lx9c11 leads to an exaggerated early immune response to virus infection.
Fig. 3: Lx9c11 negatively regulates the immune response to virus infection and hybridizes to its mRNA target.
Fig. 4: Lx9c11-RegoS rescues phenotypic changes in the immune response to CVB4 in Lx9c11−/− mice.

Data availability

RNA-seq, DNA-sequencing and RNA capture sequencing data can be found in the ArrayExpress database under the accession number E-MTAB-8436. Raw reads were mapped to the mouse transcriptome (Gencode release M9, GRCm38.p4) and mouse genome (mm10 assembly). Public data for transcription factor binding in Fig 1 are available in the Gene Expression Omnibus under the accession codes GSE56123 and GSE56121 (ref. 32). Primers and RNA sequences are available in Supplementary Table 3 and other relevant data are available from the corresponding author upon request. In addition to the open-source tools described in details in the Methods and their bash wrapper scripts, a set of custom R (v.4.0.0.) scripts for data analysis are available on GitHub: https://github.com/nbartonicek/lx9c11. For RNA-seq evolutionary analysis, the following datasets were used: for mouse: E-MTAB-6798 (ref. 60), SRP199494 (ref. 61), SRP194004 (ref. 62), SRP093651 (ref. 71), DRP006483 (ref. 72); for rat: E-MTAB-6811 (ref. 60), ERP104234 (ref. 73), SRP057603 (ref. 74); for rabbit: SRP266207 (ref. 75); and for guinea pig and hamster: SRP277304 (ref. 76).

References

  1. McClintock, B. The origin and behavior of mutable loci in maize. Proc. Natl Acad. Sci. USA 36, 344–355 (1950).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. McClintock, B. Chromosome organization and genic expression. Cold Spring Harb. Symp. Quant. Biol. 16, 13–47 (1951).

    CAS  PubMed  Article  Google Scholar 

  3. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    ADS  CAS  PubMed  Article  Google Scholar 

  4. de Koning, A. J., Gu, W., Castoe, T. A., Batzer, M. A. & Pollock, D. D. Repetitive elements may comprise over two-thirds of the human genome. PLoS Genet. 7, e1002384 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  5. Faulkner, G. J. et al. The regulated retrotransposon transcriptome of mammalian cells. Nat. Genet. 41, 563–571 (2009).

    CAS  PubMed  Article  Google Scholar 

  6. Chuong, E. B., Rumi, M. A., Soares, M. J. & Baker, J. C. Endogenous retroviruses function as species-specific enhancer elements in the placenta. Nat. Genet. 45, 325–329 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Elbarbary, R. A., Lucas, B. A. & Maquat, L. E. Retrotransposons as regulators of gene expression. Science 351, aac7247 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  8. Modzelewski, A. J. et al. A mouse-specific retrotransposon drives a conserved Cdk2ap1 isoform essential for development. Cell 184, 5541–5558 (2021).

    CAS  PubMed  Article  Google Scholar 

  9. Smalheiser, N. R. & Torvik, V. I. Alu elements within human mRNAs are probable microRNA targets. Trends Genet. 22, 532–536 (2006).

    CAS  PubMed  Article  Google Scholar 

  10. Bourque, G. et al. Evolution of the mammalian transcription factor binding repertoire via transposable elements. Genome Res. 18, 1752–1762 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Chuong, E. B., Elde, N. C. & Feschotte, C. Regulatory evolution of innate immunity through co-option of endogenous retroviruses. Science 351, 1083–1087 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Bruno, M., Mahgoub, M. & Macfarlan, T. S. The arms race between KRAB-zinc finger proteins and endogenous retroelements and its impact on mammals. Annu. Rev. Genet. 53, 393–416 (2019).

    CAS  PubMed  Article  Google Scholar 

  14. Marasca, F. et al. LINE1 are spliced in non-canonical transcript variants to regulate T cell quiescence and exhaustion. Nat. Genet. 54, 180–193 (2022).

  15. Boeke, J. D., Garfinkel, D. J., Styles, C. A. & Fink, G. R. Ty elements transpose through an RNA intermediate. Cell 40, 491–500 (1985).

    CAS  PubMed  Article  Google Scholar 

  16. Doucet, A. J. et al. Characterization of LINE-1 ribonucleoprotein particles. PLoS Genet. 6, e1001150 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  18. Howe, K. et al. The zebrafish reference genome sequence and its relationship to the human genome. Nature 496, 498–503 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Sotero-Caio, C. G., Platt, R. N., Suh, A. & Ray, D. A. Evolution and diversity of transposable elements in vertebrate genomes. Genome Biol. Evol. 9, 161–177 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Maksakova, I. A., Mager, D. L. & Reiss, D. Keeping active endogenous retroviral-like elements in check: the epigenetic perspective. Cell. Mol. Life Sci. 65, 3329–3347 (2008).

    CAS  PubMed  Article  Google Scholar 

  21. Schroder, A. R. et al. HIV-1 integration in the human genome favors active genes and local hotspots. Cell 110, 521–529 (2002).

    CAS  PubMed  Article  Google Scholar 

  22. Mitchell, R. S. et al. Retroviral DNA integration: ASLV, HIV, and MLV show distinct target site preferences. PLoS Biol. 2, E234 (2004).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. Pascale, E., Valle, E. & Furano, A. V. Amplification of an ancestral mammalian L1 family of long interspersed repeated DNA occurred just before the murine radiation. Proc. Natl Acad. Sci. USA 87, 9481–9485 (1990).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Pascale, E., Liu, C., Valle, E., Usdin, K. & Furano, A. V. The evolution of long interspersed repeated DNA (L1, LINE 1) as revealed by the analysis of an ancient rodent L1 DNA family. J. Mol. Evol. 36, 9–20 (1993).

    ADS  CAS  PubMed  Article  Google Scholar 

  25. Dotta, F. et al. Coxsackie B4 virus infection of beta cells and natural killer cell insulitis in recent-onset type 1 diabetic patients. Proc. Natl Acad. Sci. USA 104, 5115–5120 (2007).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Schwarz, D. A., Katayama, C. D. & Hedrick, S. M. Schlafen, a new family of growth regulatory genes that affect thymocyte development. Immunity 9, 657–668 (1998).

    CAS  PubMed  Article  Google Scholar 

  28. Brady, G., Boggan, L., Bowie, A. & O’Neill, L. A. Schlafen-1 causes a cell cycle arrest by inhibiting induction of cyclin D1. J. Biol. Chem. 280, 30723–30734 (2005).

    CAS  PubMed  Article  Google Scholar 

  29. Boissinot, S., Davis, J., Entezam, A., Petrov, D. & Furano, A. V. Fitness cost of LINE-1 (L1) activity in humans. Proc. Natl Acad. Sci. USA 103, 9590–9594 (2006).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-3.0 http://www.repeatmasker.org (1996–2010).

  31. Yang, F. et al. Meiotic failure in male mice lacking an X-linked factor. Genes Dev. 22, 682–691 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Mancino, A. et al. A dual cis-regulatory code links IRF8 to constitutive and inducible gene expression in macrophages. Genes Dev. 29, 394–408 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Mercer, T. R. et al. Targeted sequencing for gene discovery and quantification using RNA CaptureSeq. Nat. Protoc. 9, 989–1009 (2014).

    CAS  PubMed  Article  Google Scholar 

  34. Jaidane, H. et al. Coxsackievirus B4 and type 1 diabetes pathogenesis: contribution of animal models. Diabetes Metab. Res. Rev. 25, 591–603 (2009).

    CAS  PubMed  Article  Google Scholar 

  35. Malmstrom, M. L. et al. Cytokines and organ failure in acute pancreatitis: inflammatory response in acute pancreatitis. Pancreas 41, 271–277 (2012).

    CAS  PubMed  Article  Google Scholar 

  36. Assier, E. et al. NK cells and polymorphonuclear neutrophils are both critical for IL-2-induced pulmonary vascular leak syndrome. J. Immunol. 172, 7661–7668 (2004).

    CAS  PubMed  Article  Google Scholar 

  37. Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31, 3406–3415 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Amodio, N. et al. Drugging the lncRNA MALAT1 via LNA gapmeR ASO inhibits gene expression of proteasome subunits and triggers anti-multiple myeloma activity. Leukemia 32, 1948–1957 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Rubinson, D. A. et al. A lentivirus-based system to functionally silence genes in primary mammalian cells, stem cells and transgenic mice by RNA interference. Nat. Genet. 33, 401–406 (2003).

    CAS  PubMed  Article  Google Scholar 

  40. Huang, J. et al. Lentivirus-mediated RNA interference targeting the long noncoding RNA HOTAIR inhibits proliferation and invasion of endometrial carcinoma cells in vitro and in vivo. Int. J. Gynecol. Cancer 24, 635–642 (2014).

    PubMed  Article  Google Scholar 

  41. Zhang, Z.-K. et al. Long noncoding RNA lncMUMA reverses established skeletal muscle atrophy following mechanical unloading. Mol. Ther. 26, 2669–2680 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Kalfaoglu, B., Almeida-Santos, J., Tye, C. A., Satou, Y. & Ono, M. T-cell hyperactivation and paralysis in severe COVID-19 infection revealed by single-cell analysis. Front. Immunol. 11, 589380 (2020).

  43. Johnson, R. & Guigó, R. The RIDL hypothesis: transposable elements as functional domains of long noncoding RNAs. RNA 20, 959–976 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Carlevaro-Fita, J. et al. Ancient exapted transposable elements promote nuclear enrichment of human long noncoding RNAs. Genome Res. 29, 208–222 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Zucchelli, S. et al. SINEUPs: a new class of natural and synthetic antisense long non-coding RNAs that activate translation. RNA Biol. 12, 771–779 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Wilson, K. D. et al. Endogenous retrovirus-derived lncRNA BANCR promotes cardiomyocyte migration in humans and non-human primates. Dev. Cell 54, 694–709 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Gong, C. & Maquat, L. E. lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements. Nature 470, 284–288 (2011).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Conley, A. B., Miller, W. J. & Jordan, I. K. Human cis natural antisense transcripts initiated by transposable elements. Trends Genet. 24, 53–56 (2008).

    CAS  PubMed  Article  Google Scholar 

  49. Ohno, S. Evolution by Gene Duplication (Springer, 1970).

  50. Yang, L., Yang, J. L., Byrne, S., Pan, J. & Church, G. M. CRISPR/Cas9-directed genome editing of cultured cells. Curr. Protoc. Mol. Biol. 107, 31.1.1–31.1.17 (2014).

    Article  Google Scholar 

  51. Kanno, H., Nose, M., Itoh, J., Taniguchi, Y. & Kyogoku, M. Spontaneous development of pancreatitis in the MRL/Mp strain of mice in autoimmune mechanism. Clin. Exp. Immunol. 89, 68–73 (1992).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Patel, R. K. & Jain, M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 7, e30619 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Article  Google Scholar 

  54. Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  55. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  56. Lee, B. T. et al. The UCSC Genome Browser database: 2022 update. Nucleic Acids Res. 50, D1115–D1122 (2022).

    CAS  PubMed  Article  Google Scholar 

  57. Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. Yeo, G. & Burge, C. B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).

    CAS  PubMed  Article  Google Scholar 

  59. Corvelo, A., Hallegger, M., Smith, C. W. & Eyras, E. Genome-wide association between branch point properties and alternative splicing. PLoS Comput. Biol. 6, e1001016 (2010).

    ADS  MathSciNet  PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. The Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    PubMed Central  Article  CAS  Google Scholar 

  62. Huang, Z. et al. The corepressors GPS2 and SMRT control enhancer and silencer remodeling via eRNA transcription during inflammatory activation of macrophages. Mol. Cell 81, 953–968 (2021).

    CAS  PubMed  Article  Google Scholar 

  63. 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  Article  CAS  Google Scholar 

  64. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  67. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Krause, M. et al. tailfindr: alignment-free poly (A) length measurement for Oxford Nanopore RNA and DNA sequencing. RNA 25, 1229–1241 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Pertea, G. & Pertea, M. GFF utilities: GffRead and GffCompare. F1000Res. 9, 304 (2020).

    Article  Google Scholar 

  70. Deblasio, D., Kim, K. & Kingsford, C. More accurate transcript assembly via parameter advising. J. Comput. Biol. 27, 1181–1189 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. Quesnel-Vallières, M. et al. Misregulation of an activity-dependent splicing network as a common mechanism underlying autism spectrum disorders. Mol. Cell 64, 1023–1034 (2016).

    PubMed  Article  CAS  Google Scholar 

  72. Hoshino, D. et al. Trans-omic analysis reveals ROS-dependent pentose phosphate pathway activation after high-frequency electrical stimulation in C2C12 myotubes. iScience 23, 101558 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. Bush, S. J. et al. Species-specificity of transcriptional regulation and the response to lipopolysaccharide in mammalian macrophages. Front. Cell Dev. Biol. 8, 661 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  74. Meng, Q. et al. Traumatic brain injury induces genome-wide transcriptomic, methylomic, and network perturbations in brain and blood predicting neurological disorders. EBioMedicine 16, 184–194 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  75. Jin, L. et al. A pig BodyMap transcriptome reveals diverse tissue physiologies and evolutionary dynamics of transcription. Nat. Commun. 12, 3715 (2021).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Martínez-Pacheco, M. et al. Expression evolution of ancestral XY gametologs across all major groups of placental mammals. Genome Biol. Evol. 12, 2015–2028 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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Acknowledgements

We thank the KCCG Core Sequencing Platform, Garvan Molecular Genetics Facility, Garvan Flow Cytometry Facility, MEGA CRISPR mice facility, Garvan Biological Testing facility and Australian BioResources; we also thank S. Dunbar from Integrated DNA Technologies (IDT) for assistance with Capture probe design and G. Faulkner for expert comments on the manuscript. This work was supported by the Ideas grant 2004306 from the National Health and Medical Research Council (NHMRC) of Australia and DP210103811 from the Australian Research Council (ARC). C.K. is a FRIAS Senior Fellow and Marie Curie Fellow of the EU Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg. R.J.W. was supported by grants from EMBL Australia, the Cancer Council (2003453), NSW Cancer Institute (RG202865) and the ARC (DP200102951).

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Authors and Affiliations

Authors

Contributions

N.B. performed gene expression and RNA capture sequencing analyses, prepared the figures and edited the manuscript. R.R. performed RNA hybridization and lentivirus experiments and edited the manuscript. C.L. analysed data for the selection of transposable elements. J.W., S.W., N.Z., F.L., T.C. and M.Z. performed cellular and molecular experiments. J.B., J.M.H., I.W.D. and A.L.M.R. performed RNA capture sequencing experiments and analyses. R.B. and D.Z. created the CRISPR–Cas9 deletion of Lx9 in C57BL/6J mice. R.J.W. performed bioinformatic analyses with G.S.R., supervised G.S.R. and edited the manuscript. J.S., D.C., T.C., S.G., J.S.M. and M.E.D. provided reagents for experiments and edited the manuscript. C.K. planned and performed experiments, prepared figures and wrote the manuscript.

Corresponding author

Correspondence to Cecile King.

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Extended data figures and tables

Extended Data Fig. 1 Differential expression analyses of immune stimulus-induced genes.

(A) GO-terms of genes associated with LINE-1 elements based on GREAT analysis (http://great.stanford.edu/). GO-terms filtered by REVIGO (B) GO-term of intergenic LINE-1 elements differentially expressed (adj-p-value < 0.05, DESeq2) using GREAT analysis. (C) Heat map showing differentially expressed genes in the pancreas 4 days after infection of C57BL/6J mice with CVB4. Mock-infected C57BL/6J (B6) (n = 3 individual mice group) are compared with C57BL/6J CVB4-infected mice (n = 3); a total of 4115 DE genes with FC > 2 and FDR = 0.0001. (D) Gene expression pathway enrichment in pancreatic tissue 4 days following infection of C57BL/6 with CVB4 (n = 3 mice per group) showing the top 10 most significantly upregulated (left) and downregulated (right) KEGG pathways. Differentially expressed genes from the linear model fit (FDR < 0.05) were used to calculate enrichment of KEGG pathways with function “kegga” with default parameters. Circle size denotes the number of genes in a pathway, and the colour intensity denotes the percentage of overexpressed genes per pathway as shown (E) Heat map showing differentially expressed Slfn family genes in mock-infected C57BL/6J (B6) (n = 3) compared with C57BL/6J CVB4-infected pancreases (n = 3) (FDR < 0.05). Data relate to Fig. 1.

Extended Data Fig. 2 Gene expression and transcription factor analyses after CVB4 infection.

(A) Violin plot showing the distance of differentially expressed transposable elements and non-differentially expressed transposable elements from the transcriptional start site of the 500 most-upregulated genes (shown in red) and 500 most-downregulated genes (black line) in the pancreas 4 days after CVB4 infection of C57BL/6J mice relative to mock-infected C57BL/6J mice (n = 3/group). Statistical analyses; Mann-Whitney test, p-value was 0, or too small to represent. (B) Enrichment of transcription factors in promoters of the top 500 differentially expressed genes in pancreas tissue 4 days after infection with CVB4. Enrichr analysis performed with gene set Transcription factor PPIs. Distance of repetitive elements from transcription start site (TSS) of the top 25 DE genes. Transcription factor score denotes overlap with interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) motif families - grey for none, orange for one, red for both. (C) Differentially expressed LINE retrotransposons in the pancreas on day 4 of CVB4 infection, data compares gene expression in mock-infected and CVB4-infected C57BL/6J mice (n = 3 mice per group). (D) A bubble plot displaying enrichment of family-specific LINE-1 elements based on conservation status. Size of bubble represents adjusted p-value (sher exact test) and colour represents the number of expressed events. Distal promoter was arbitrarily defined as the region 10 kb upstream of the transcription start site. (E) Expression of Tex11 determined by RNA-seq of pancreases from day 4 CVB4-infected and mock-infected Lx9c11−/− and WT female mice, aged 8–10 weeks of age. Data are shown as 3 individual mice per group means SD, there was no Tex11 expression detected in 11/12 individual female mice. (F) DNA sequence of Lx9 showing transcription factor consensus binding motifs for interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT). JASPAR motif analyses with a default relative profile score threshold of 90% was used to scan Lx9c11 and 500bp either side of Lx9c11. Data relate to Fig. 1.

Extended Data Fig. 3 RNA-seq analyses of Lx9c11 and L1_Mus3.

(A) Representative RNA-seq reads across Lx9c11 and L1_Mus3 in mock-infected WT, day 4 CVB4-infected WT, mock-infected Lx9c11−/− and day 4 CVB4-infected Lx9c11−/− pancreases as shown in Fig. 1, using single mapping. (B) Quantitative PCR analyses of the expression of Lx9c11 and L1_Mus3 in WT and Lx9c11−/− splenocytes stimulated for 24 hours with polyI:C or (C) splenocytes stimulated for 4 h with LPS. For panels B and C; fold modulation in stimulated (treated) or untreated cells is relative to housekeeping gene RPL19, data are shown as n = 5 individual mice/group and mean ± SD from 2 independent experiments, statistical significance determined by 2-tailed, unpaired students T-test. (D) Schematic showing CRISPR–Cas9 targeted deletion of Lx9c11 and RNA capture nanopore sequencing defined transcripts for Lx9c11-RegoS and L1_Mus3 between Slfn2 and Slfn4 on chromosome 11 showing collapsed repeats (locus) and condensed repeats (transcripts) (see Fig. 1).

Extended Data Fig. 4 RNA-seq confirms altered splice sites observed in Nanopore long-read sequencing of RNA.

(A) Nanopore and RNA-seq analyses of CVB4-infected (day 4) and mock-infected C57BL/6 (WT) mice showing Slfn1 isoforms and heat map showing differentially expressed Slfn1 isoforms. Slfn1 isoforms were detected by nanopore reads that overlap canonical Slfn1 exons, RNA-seq reads were aligned with STAR and quantified with RSEM. The transcript isoform with the highest percentage (45.2%) TCONS_00000008 contained a novel exon followed by the canonical Slfn1 isoforms TCONS_00000028 with 36.6% and TCONS_00000027 with 12.5%. (B) RNA-seq analyses of public datasets for equivalent tissues (liver, brain WBC, testis, kidney) across identified organisms. Reads were mapped with STAR and expression levels (TPM) calculated for the 500nt region upstream of Slfn1 (equivalent to area overlapping Lx9c11-RegoS). Each dot in the plot represents a tissue sample with colours associated with each species. (C) Schematic showing multiexonic Lx9c11-RegoS transcripts (in black) identified by Nanopore long-read sequencing of RNA from CVB4-infected (day 4) and mock-infected C57BL/6 (WT) mice, and the truncated variant observed in Lx9c11−/− mice determined from analyses of contiguous reads spanning the Lx9 excision site shown by a grey vertical line (n = 2–4 mice per group). Uniquely mapped RNA-seq spliced reads over transcripts that overlap the Lx9 excision site are shown in red (negative strand) with the number indicating normalised counts (CPMs). Blue lines indicate splice sites overlapping the Slfn1 locus (positive strand) (see Fig. 1).

Extended Data Fig. 5 Flow cytometric analyses of the spleen and BALF after CVB4 infection.

Flow cytometry and FACS analyses following CVB4 infection of Lx9c11−/− and C57BL/6j (WT) mice. (A) Representative dot plots from flow cytometric analyses showing gating strategy for NK cells in the spleens of WT and Lx9c11−/− mice on day 4 of CVB4 infection, also noted in the Methods section. Percentages (B) and numbers (C) of NK cells in the spleen of WT and Lx9c11−/− mice on day 1 following CVB4 infection. Percentages (D) and numbers (E) of NK cells in the spleen of WT and Lx9c11−/− mice on day 4 of CVB4 infection. For panels B–E; data are shown as n = 4–5 individual mice/group and mean ± SD from 2 independent experiments. Statistical significance was determined by 2-tailed unpaired t-test. (F) CD11b+ F480+ macrophages, (G) CD11b+ Ly6G+ neutrophils (H) CD8+ CD44hi CD3+ T cells (I), CD4+ CD44hi CD3+ T cells in the spleen on day 4 of infection. Quantitation of cells in WT and Lx9c11−/− BALF on day 7 of CVB4 infection, gating strategy shown in Fig. 2q and in the Methods section. Numbers of (J) CD64+ monocytes/macrophages, (K) Ly6C+CD64+ monocyte subset, (L) Ly6CCD64+ macrophage subset, (M) CD69+ CD8+ T cells, (N) activated/memory phenotype CD44hi+ CD8+ T cells, (O) Activated/memory phenotype CD44hi CD4+ T cells. For panels F-O; data are extended from Fig. 2 and shown as individual mice and mean ± SD n = 4–5 individual mice/group, from 2 independent experiments, and statistical significance was determined by one-way ANOVA.

Extended Data Fig. 6 Transcriptomic analyses of Lx9c11−/− mice.

(A) Heat map from RNA-seq analyses shown in Fig. 3, depicting the top 50 upregulated genes in the pancreas 4 days following infection of Lx9c11−/− and C57BL/6J (WT) mice with CVB4 relative to mock-infected mice (n = 3/group). (B) Volcano plot for day 4 CVB4-induced gene expression relative to mock-infected controls in pancreases of Lx9c11−/− mice compared with WT mice, showing differential expression of Slfn genes, Ifnar1 and little to no differential expression of Kalrn (related to Fig. 3 and SuplementaryTable 2).

Extended Data Fig. 7 Hybridization of LINE-1 elements and target RNA.

(A) mFold structures for predicted Lx9c11 and L1_Mus3 RNA hybrids, related to Fig. 3. PAGE, showing the full gels from Fig. 3 of RNA after hybridization; IRD-probe labelled Lx9c11-RegoS combined with unlabelled (B) Slfn1 nc_exon 1 (C), Slfn1 exon 2-3’UTR or negative-control RNA and IRD-probe labelled L1_Mus3 combined with unlabelled (D) Slfn9, (E) Ifnar1 or negative-control RNA.

Extended Data Fig. 8 LNA knockdown of Lx9c11 and L1_Mus3.

Splenocytes and enzymatically dispersed pancreases from C57BL/6j mice were transfected for 24 h with single-stranded gapmer locked nucleic acid (LNA)-modified DNA oligonucleotides (LNA gapmers) designed to silence expression of Lx9c11 and L1_Mus3 or with LNA gapmer negative controls. Splenocytes were subsequently stimulated with polyI:C for 24 h or enzymatically dispersed pancreases were infected with CVB4 for 24 h in vitro. RNA was extracted for qPCR and the levels of RNA were normalized to the housekeeping gene RPL19. Expression of Lx9c11 (A) and L1_Mus3 (B) in splenocytes and pancreas shown as fold modulation relative to either splenocytes stimulated with polyI:C alone or pancreases infected with CVB4 alone. Data are shown for individual mice (n = 5 mice per group) from 2 experiments with similar findings. Fold modulation of slfn1, slfn9 and Ifnar1 in (C) Splenocytes or (D) pancreases transfected with Lx9c11 LNA gapmer and (E) Splenocytes or (F) pancreases transfected with L1_Mus3 LNA gapmer prior to stimulation relative to LNA Gapmer negative control. For panels A–F; data are shown as individual mice (n = 4–7 mice per group) and mean ± SD from 4 independent experiments. Statistical significance was assessed by 2-tailed unpaired Student’s t-test.

Extended Data Fig. 9 Lentivirus replacement of Lx9c11-RegoS and L1_Mus3 transcripts.

(A) Splenocytes from Lx9c11−/− mice were transfected with Lx9c11-RegoS, L1_Mus3 or empty control pLL3.7 lentiviral constructs for 24 h then stimulated with polyI:C for a further 24 h. RNA was extracted for qPCR and the levels of RNA were normalized to the housekeeping gene RPL19. The expression of Slfn1, Slfn9 and Ifnar1 are shown as fold modulation relative to spleen cells cultured with empty pLL3.7 vector from individual mice (n = 5, 2 experimental repeats) with bars showing mean values ± SD. Statistical significance was assessed by 2-tailed unpaired Student’s t-test. C57BL/6j (WT) mice were injected with both Lx9c11-RegoS lentivirus and L1_Mus3 lentivirus, each LINE-1 construct alone or empty vector (pLL3.7). Percentage (B) and numbers (C) of NK cells in the spleen, IFNβ (D) and IL-6 (E) proteins detected in sera and (F) pulmonary oedema measured by lung water content, shown as weight (mg), of C57BL/6J (WT) recipients of RegoS-Lx9c11 pLL3.7 plus L1_Mus3 pLL3.7 vectors, RegoS-Lx9c11 pLL3.7 vector, L1_Mus3 pLL3.7 vector, empty pLL3.7 vector on day 4 of CVB4 infection. Data shown are WT recipients related to Fig. 4 and are shown for individual mice with bars showing mean values ± SD n = 5 mice/group, 2 experiments. Panels B–F: Statistical significance was assessed by one-way ANOVA using Bonferroni’s multiple comparisons test.

Extended Data Table 1 BLAST alignments of Lx9c11-RegoS and L1_Mus3

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1–3: Supplementary Table 1 shows whole Genomic DNA-sequencing analyses of Lx9c11-/- mice using HiSeqX platform (KAPA PCR-Free v2.1); Supplementary Table 2 lists the top 100 hits for L1_Mus3 determined in murine genomic and transcript databases by megaBLAST and Supplementary Table 3 lists primers and RNA sequences.

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Bartonicek, N., Rouet, R., Warren, J. et al. The retroelement Lx9 puts a brake on the immune response to virus infection. Nature 608, 757–765 (2022). https://doi.org/10.1038/s41586-022-05054-9

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