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Innate immune pathways act synergistically to constrain RNA virus evolution in Drosophila melanogaster

Nature Ecology & Evolution volume 6, pages 565–578 (2022)Cite this article

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Abstract

Host–pathogen interactions impose recurrent selective pressures that lead to constant adaptation and counter-adaptation in both competing species. Here, we sought to study this evolutionary arms-race and assessed the impact of the innate immune system on viral population diversity and evolution, using Drosophila melanogaster as model host and its natural pathogen Drosophila C virus (DCV). We isogenized eight fly genotypes generating animals defective for RNAi, Imd and Toll innate immune pathways as well as pathogen-sensing and gut renewal pathways. Wild-type or mutant flies were then orally infected with DCV and the virus was serially passaged ten times via reinfection in naive flies. Viral population diversity was studied after each viral passage by high-throughput sequencing and infection phenotypes were assessed at the beginning and at the end of the evolution experiment. We found that the absence of any of the various immune pathways studied increased viral genetic diversity while attenuating virulence. Strikingly, these effects were observed in a range of host factors described as having mainly antiviral or antibacterial functions. Together, our results indicate that the innate immune system as a whole and not specific antiviral defence pathways in isolation, generally constrains viral diversity and evolution.

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Fig. 1: Experimental design.
Fig. 2: Viral nucleotide diversity differently evolves in each host genotype.
Fig. 3: Trajectories of DCV variants across passages.
Fig. 4: DCV virulence decreases in the absence of immune pathways.

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Data availability

All raw data from high-throughput sequencing were deposited to NCBI BioProjects under accession number PRJNA782868. Source data are provided with this paper.

Code availability

Scripts are provided in Supplementary Data 1.

References

  1. Morgan, A. D. & Koskella, B. in Genetics and Evolution of Infectious Diseases (ed. Tibayrenc, M.) 115–140 (Elsevier, 2017).

  2. Daugherty, M. D. & Malik, H. S. Rules of engagement: molecular insights from host–virus arms races. Annu. Rev. Genet. 46, 677–700 (2012).

    Article  CAS  PubMed  Google Scholar 

  3. Barreiro, L. B. & Quintana-Murci, L. From evolutionary genetics to human immunology: how selection shapes host defence genes. Nat. Rev. Genet. 11, 17–30 (2010).

  4. Thompson, J. N. & Burdon, J. J. Gene-for-gene coevolution between plants and parasites. 360, 121–125 (1992).

  5. Buckling, A. & Rainey, P. B. Antagonistic coevolution between a bacterium and a bacteriophage. Proc. Biol. Sci. 269, 931–936 (2002).

  6. Masri, L. et al. Host–pathogen coevolution: the selective advantage of Bacillus thuringiensis virulence and its Cry toxin genes. PLoS Biol. 13, e1002169 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Obbard, D. J., Gordon, K. H. J., Buck, A. H. & Jiggins, F. M. The evolution of RNAi as a defence against viruses and transposable elements. Philos. Trans. R. Soc. B 364, 99–115 (2009).

    Article  CAS  Google Scholar 

  8. Lazzaro, B. P. Natural selection on the Drosophila antimicrobial immune system. Curr. Opin. Microbiol. 11, 284–289 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lazzaro, B. P. Molecular population genetics of inducible antibacterial peptide genes in Drosophila melanogaster. Mol. Biol. Evol. 20, 914–923 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Sackton, T. B. et al. Dynamic evolution of the innate immune system in Drosophila. Nat. Genet. 39, 1461–1468 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Brackney, D. E., Beane, J. E. & Ebel, G. D. RNAi targeting of West Nile virus in mosquito midguts promotes virus diversification. PLoS Pathog. 5, e1000502 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Lin, S.-S. et al. Molecular evolution of a viral non-coding sequence under the selective pressure of amiRNA-mediated silencing. PLoS Pathog. 5, e1000312 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Lafforgue, G. et al. Tempo and mode of plant RNA virus escape from RNA interference-mediated resistance. J. Virol. 85, 9686–9695 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Martínez, F. et al. Ultradeep sequencing analysis of population dynamics of virus escape mutants in RNAi-mediated resistant plants. Mol. Biol. Evol. 29, 3297–3307 (2012).

    Article  PubMed  CAS  Google Scholar 

  15. Das, A. T. et al. Human immunodeficiency virus type 1 escapes from RNA interference-mediated inhibition. J. Virol. 78, 2601–2605 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gitlin, L., Stone, J. K. & Andino, R. Poliovirus escape from RNA interference: short interfering RNA-target recognition and implications for therapeutic approaches. J. Virol. 79, 1027–1035 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mondotte, J. A. & Saleh, M.-C. Antiviral immune response and the route of infection in Drosophila melanogaster. Adv. Virus Res. 100, 247–278 (2017).

  18. Swevers, L., Liu, J. & Smagghe, G. Defense mechanisms against viral infection in Drosophila: RNAi and non-RNAi. Viruses 10, 230 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  19. Galiana-Arnoux, D., Dostert, C., Schneemann, A., Hoffmann, J. A. & Imler, J.-L. Essential function in vivo for Dicer-2 in host defense against RNA viruses in Drosophila. Nat. Immunol. 7, 590–597 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. van Rij, R. P. et al. The RNA silencing endonuclease Argonaute 2 mediates specific antiviral immunity in Drosophila melanogaster. Genes Dev. 20, 2985–2995 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Wang, X.-H. et al. RNA interference directs innate immunity against viruses in adult Drosophila. Science 312, 452–454 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zambon, R. A., Vakharia, V. N. & Wu, L. P. RNAi is an antiviral immune response against a dsRNA virus in Drosophila melanogaster. Cell. Microbiol. 8, 880–889 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Costa, A., Jan, E., Sarnow, P. & Schneider, D. The Imd pathway is involved in antiviral immune responses in Drosophila. PLoS ONE 4, e7436 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Sansone, C. L. et al. Microbiota-dependent priming of antiviral intestinal immunity in Drosophila. Cell Host Microbe 18, 571–581 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ferreira, Á. G. et al. The Toll–Dorsal pathway is required for resistance to viral oral infection in Drosophila. PLoS Pathog. 10, e1004507 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Zambon, R. A., Nandakumar, M., Vakharia, V. N. & Wu, L. P. The Toll pathway is important for an antiviral response in Drosophila. Proc. Natl Acad. Sci. USA 102, 7257–7262 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dostert, C. et al. The Jak-STAT signaling pathway is required but not sufficient for the antiviral response of Drosophila. Nat. Immunol. 6, 946–953 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Merkling, S. H. et al. The epigenetic regulator G9a mediates tolerance to RNA virus infection in Drosophila. PLoS Pathog. 11, e1004692 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Christian, P. D. & Johnson, K. N. The novel genome organization of the insect picorna-like virus Drosophila C virus suggests this virus belongs to a previously undescribed virus family. J. Gen. Virol. 79, 191–203 (1998).

    Article  PubMed  Google Scholar 

  30. Jousset, F. X. & Plus, N. Study of the vertical transmission and horizontal transmission of Drosophila melanogaster and Drosophila immigrans picornavirus (author’s translation). Ann. Microbiol. 126, 231–249 (1975).

    CAS  Google Scholar 

  31. Mondotte, J. A. et al. Immune priming and clearance of orally acquired RNA viruses in Drosophila. Nat. Microbiol. 3, 1394–1403 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Torri, A., Mongelli, V., Mondotte, J. A. & Saleh, M.-C. Viral infection and stress affect protein levels of Dicer 2 and Argonaute 2 in Drosophila melanogaster. Front. Immunol. 11, 362 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Deddouche, S. et al. The DExD/H-box helicase Dicer-2 mediates the induction of antiviral activity in Drosophila. Nat. Immunol. 9, 1425–1432 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Gomariz-Zilber, E., Jeune, B. & Thomas-Orillard, M. Limiting conditions of the horizontal transmission of the Drosophila C virus in its host (D. melanogaster). Acta Oecol. 19, 125–137 (1998).

    Article  Google Scholar 

  35. Stevanovic, A. L. & Johnson, K. N. Infectivity of Drosophila C virus following oral delivery in Drosophila larvae. J. Gen. Virol. 96, 1490–1496 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Royet, J. Epithelial homeostasis and the underlying molecular mechanisms in the gut of the insect model Drosophila melanogaster. Cell. Mol. Life Sci. 68, 3651–3660 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Buchon, N., Broderick, N. A., Poidevin, M., Pradervand, S. & Lemaitre, B. Drosophila intestinal response to bacterial infection: activation of host defense and stem cell proliferation. Cell Host Microbe 5, 200–211 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Buchon, N., Broderick, N. A., Chakrabarti, S. & Lemaitre, B. Invasive and indigenous microbiota impact intestinal stem cell activity through multiple pathways in Drosophila. Genes Dev. 23, 2333–2344 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Buchon, N., Broderick, N. A., Kuraishi, T. & Lemaitre, B. Drosophila EGFR pathway coordinates stem cell proliferation and gut remodeling following infection. BMC Biol. 8, 152 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Xu, J. et al. ERK signaling couples nutrient status to antiviral defense in the insect gut. Proc. Natl Acad. Sci. USA 110, 15025–15030 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lauring, A. S. & Andino, R. Quasispecies theory and the behavior of RNA viruses. PLoS Pathog. 6, e1001005 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Isakov, O. et al. Deep sequencing analysis of viral infection and evolution allows rapid and detailed characterization of viral mutant spectrum. Bioinformatics 31, 2141–2150 (2015).

  43. Hartl, D. L. & Clark, A. G. Principles of Population Genetics 4th edn (Sinauer Associates, 2006).

  44. Travisano, M., Mongold, J. A., Bennett, A. F. & Lenski, R. E. Experimental tests of the roles of adaptation, chance, and history in evolution. Science 267, 87–90 (1995).

    Article  CAS  PubMed  Google Scholar 

  45. Desai, M. M. & Fisher, D. S. Beneficial mutation–selection balance and the effect of linkage on positive selection. Genetics 176, 1759–1798 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Miralles, R. Clonal interference and the evolution of RNA viruses. Science 285, 1745–1747 (1999).

    Article  CAS  PubMed  Google Scholar 

  47. Miralles, R., Moya, A. & Elena, S. F. Diminishing returns of population size in the rate of RNA virus adaptation. J. Virol. 74, 3566–3571 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pepin, K. M. & Wichman, H. A. Experimental evolution and genome sequencing reveal variation in levels of clonal interference in large populations of bacteriophage φX174. BMC Evol. Biol. 8, 85 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Navarro, R., Ambrós, S., Martínez, F. & Elena, S. F. Diminishing returns of inoculum size on the rate of a plant RNA virus evolution. Europhys. Lett. 120, 38001 (2017).

    Article  CAS  Google Scholar 

  50. Pandit, A. & de Boer, R. J. Reliable reconstruction of HIV-1 whole genome haplotypes reveals clonal interference and genetic hitchhiking among immune escape variants. Retrovirology 11, 56 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Strelkowa, N. & Lässig, M. Clonal interference in the evolution of influenza. Genetics 192, 671–682 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gerrish, P. J. & Lenski, R. E. in Mutation and Evolution (eds Woodruff, R. C. & Thompson, J. N.) 127–144 (Springer, 1998).

  53. Held, T., Klemmer, D. & Lässig, M. Survival of the simplest in microbial evolution. Nat. Commun. 10, 2472 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Novella, I. S., Zárate, S., Metzgar, D. & Ebendick-Corpus, B. E. Positive selection of synonymous mutations in vesicular stomatitis virus. J. Mol. Biol. 342, 1415–1421 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. Zanini, F. & Neher, R. A. Quantifying selection against synonymous mutations in HIV-1 env evolution. J. Virol. 87, 11843–11850 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Martínez, M. A., Jordan-Paiz, A., Franco, S. & Nevot, M. Synonymous virus genome recoding as a tool to impact viral fitness. Trends Microbiol. 24, 134–147 (2016).

    Article  PubMed  CAS  Google Scholar 

  57. Kieft, J. S., Zhou, K., Jubin, R. & Doudna, J. A. Mechanism of ribosome recruitment by hepatitis C IRES RNA. RNA 7, 194–206 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhang, L. et al. lncRNA sensing of a viral suppressor of RNAi activates non-canonical innate immune signaling in Drosophila. Cell Host Microbe 27, 115–128 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Navarro, R. et al. Defects in plant immunity modulate the rates and patterns of RNA virus evolution. Preprint at bioRxiv https://doi.org/10.1101/2020.10.13.337402 (2021).

  60. Merkling, S. H. & van Rij, R. P. Analysis of resistance and tolerance to virus infection in Drosophila. Nat. Protoc. 10, 1084–1097 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Lee, Y. S. et al. Distinct roles for Drosophila Dicer-1 and Dicer-2 in the siRNA/miRNA silencing pathways. Cell 117, 69–81 (2004).

    Article  CAS  PubMed  Google Scholar 

  62. Okamura, K. Distinct roles for Argonaute proteins in small RNA-directed RNA cleavage pathways. Genes Dev. 18, 1655–1666 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Levashina, E. A., Ohresser, S., Lemaitre, B. & Imler, J. L. Two distinct pathways can control expression of the gene encoding the Drosophila antimicrobial peptide metchnikowin. J. Mol. Biol. 278, 515–527 (1998).

    Article  CAS  PubMed  Google Scholar 

  64. Rutschmann, S. et al. The Rel protein DIF mediates the antifungal but not the antibacterial host defense in Drosophila. Immunity 12, 569–580 (2000).

    Article  CAS  PubMed  Google Scholar 

  65. Hedengren, M. et al. Relish, a central factor in the control of humoral but not cellular immunity in Drosophila. Mol. Cell 4, 827–837 (1999).

    Article  CAS  PubMed  Google Scholar 

  66. Díaz-Benjumea, F. J. & García-Bellido, A. Behaviour of cells mutant for an EGF receptor homologue of Drosophila in genetic mosaics. Proc. Biol. Sci. 242, 36–44 (1990).

    Article  PubMed  Google Scholar 

  67. Reed, L. J. & Muench, H. A simple method of estimating fifty percent endpoints. Am. J. Epidemiol. 27, 493–497 (1938).

    Article  Google Scholar 

  68. Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

    Article  PubMed  Google Scholar 

  69. Fox, J. & Weisberg S. An R Companion to Applied Regression 3rd edn (Sage, 2019).

  70. Lenth, R. V. et al. Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.5 (2021).

  71. Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Article  Google Scholar 

  72. Cornman, R. S. et al. Population-genomic variation within RNA viruses of the western honey bee, Apis mellifera, inferred from deep sequencing. BMC Genomics 14, 154 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lequime, S., Fontaine, A., Ar Gouilh, M., Moltini-Conclois, I. & Lambrechts, L. Genetic drift, purifying selection and vector genotype shape Dengue virus intra-host genetic diversity in mosquitoes. PLoS Genet. 12, e1006111 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  75. Myers, M. A., Satas, G. & Raphael, B. J. CALDER: inferring phylogenetic trees from longitudinal tumor samples. Cell Syst. 8, 514–522 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Smith, M. A. et al. E-scapescape: interactive visualization of single-cell phylogenetics and cancer evolution. Nat. Methods 14, 549–550 (2017).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank members of the Saleh Lab, M. Vignuzzi and J. Pfeiffer for fruitful discussions. We thank C. Meignin for RelE20 and VagoΔM10 flies. This work was supported by the European Research Council (FP7/2013–2019 ERC CoG 615220) and the French Government’s Investissement d’Avenir programme, Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (grant no. ANR-10-LABX-62-IBEID) to M.-C.S. Work in S.F.E.’s laboratory was supported by grant nos BFU2015-65037-P and PID2019-103998GB-I00 (Spain Agencia Estatal de Investigación—FEDER) and PROMETEU2019/012 (Generalitat Valenciana).

Author information

Authors and Affiliations

  1. Viruses and RNA Interference Unit, Institut Pasteur, CNRS, Paris, France

    Vanesa Mongelli, Valérie Gausson, Hervé Blanc, Jared Nigg & Maria-Carla Saleh

  2. Cluster of Microbial Ecology, Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, the Netherlands

    Sebastian Lequime

  3. Human Evolutionary Genetic Unit, Institut Pasteur, CNRS, Paris, France

    Athanasios Kousathanas & Lluis Quintana-Murci

  4. Human Genomics and Evolution, Collège de France, Paris, France

    Lluis Quintana-Murci

  5. Instituto de Biología Integrativa de Sistemas (CSIC—Universitat de València), València, Spain

    Santiago F. Elena

  6. The Santa Fe Institute, Santa Fe, NM, USA

    Santiago F. Elena

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  1. Vanesa Mongelli
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Contributions

V.M. and M.-C.S. conceived the study. V.M., M.-C.S., A.K. and L.Q.-M. established the experimental design. V.M., V.G., H.B and J.N. performed the investigations. S.L. and S.F.E. performed the formal analyses. V.M., S.F.E. and M.-C.S. wrote the paper and acquired funding.

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Correspondence to Santiago F. Elena or Maria-Carla Saleh.

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Nature Ecology & Evolution thanks Guan-Zhu Han and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Viral load and prevalence across the DCV evolution experiment.

Viral load of 10 individual flies coming from DCV inoculated cages and four individual flies coming from mock inoculated cages was determined by TCID50. a) Prevalence, calculated as the percentage of flies positive by TCID50. b) Viral load determined by TCID50 in each genotype across the 10 DCV passages. c) DCV replication assessed by negative-strand RT–qPCR. Left panel: standard curve produced from a tenfold dilution series over a range from 108 to 103 copies per reaction of in vitro transcribed RNA corresponding to a portion of the full-length negative-strand DCV RNA (slope = −3.644, R2 = 0.990, efficiency = 88.25%). Right panel: amount of negative-strand DCV RNA present in the viral stocks produced from each fly genotype in P10, S2 DCV stock and DCV stock. Mock-infected flies were added as controls. LOD: Limit of detection of DCV negative stranded RNA. d) Average viral loads per individual fly of each genotype estimated from the GLM fitted to the data shown in panel b. Error bars represent ±1 SD.

Source data

Extended Data Fig. 2 Grouping of DCV population swarms by similarity and increasing nucleotide diversity (π).

Viral nucleotide diversity (π) was determined in each condition and grouped using a post hoc Bonferroni test based on the pairwise comparisons from Supplementary Table 1. SE: standard error. asymp.LCL: asymptomatic lower confidence level; asymp.UCL: asymptomatic upper confidence level.

Source data

Extended Data Fig. 3 Evolution of DCV variants.

a) Trajectories of DCV variants across passages, N: total number of SPNs found above the estimated frequency threshold (≥ 0.0028). Trajectories of viral variants found significant after FDR correction are show in green (p ≤ 0.006) and yellow (0,047 ≤ p ≤ 0.006) (based on data from Table 2). b) to k) Heatmaps showing the Pearson correlation coefficients between mutations’ frequencies along evolutionary time, ranging from blue, where no linkage between the SNPs was found, to red, where the SNPs were linked in a same viral haplotype.

Source data

Extended Data Fig. 4 SNPs on the DCV genome with significant estimates of fitness effects.

Green triangles represent synonymous mutations, pink triangles non-synonymous mutations and grey triangles mutations in non-coding sequences. Cases significant after FDR correction (p ≤ 0.006) are marked with an asterisk (based on data from Table 2).

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Tables 1 and 2.

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Supplementary Data 1

R scripts used to estimate mutational fitness effects and their linkage (measured as correlation coefficients along the time-series data and as LVAFFP).

Source data

Source Data Fig. 2

Contains the DCV variants detected above the allele frequency threshold of 0.0028 determined.

Source Data Fig. 3

Contains the DCV variants detected above the allele frequency threshold of 0.0028 determined.

Source Data Fig. 4

Contains the raw data of the survival curves of the flies after DCV infection.

Source Data Extended Data Fig. 1

Contains the viral load and prevalence values, as well as qPCR results.

Source Data Extended Data Fig. 2

Contains the DCV variants detected above the allele frequency threshold of 0.0028 determined.

Source Data Extended Data Fig. 3

Contains the DCV variants detected above the allele frequency threshold of 0.0028 determined.

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Mongelli, V., Lequime, S., Kousathanas, A. et al. Innate immune pathways act synergistically to constrain RNA virus evolution in Drosophila melanogaster. Nat Ecol Evol 6, 565–578 (2022). https://doi.org/10.1038/s41559-022-01697-z

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