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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.

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.

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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.

Corresponding authors

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.

Reporting Summary

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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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