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Mosquito vector competence for dengue is modulated by insect-specific viruses

Nature Microbiology volume 8, pages 135–149 (2023)Cite this article

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Abstract

Aedes aegypti and A. albopictus mosquitoes are the main vectors for dengue virus (DENV) and other arboviruses, including Zika virus (ZIKV). Understanding the factors that affect transmission of arboviruses from mosquitoes to humans is a priority because it could inform public health and targeted interventions. Reasoning that interactions among viruses in the vector insect might affect transmission, we analysed the viromes of 815 urban Aedes mosquitoes collected from 12 countries worldwide. Two mosquito-specific viruses, Phasi Charoen-like virus (PCLV) and Humaita Tubiacanga virus (HTV), were the most abundant in A. aegypti worldwide. Spatiotemporal analyses of virus circulation in an endemic urban area revealed a 200% increase in chances of having DENV in wild A. aegypti mosquitoes when both HTV and PCLV were present. Using a mouse model in the laboratory, we showed that the presence of HTV and PCLV increased the ability of mosquitoes to transmit DENV and ZIKV to a vertebrate host. By transcriptomic analysis, we found that in DENV-infected mosquitoes, HTV and PCLV block the downregulation of histone H4, which we identify as an important proviral host factor in vivo.

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Fig. 1: The virome of A. aegypti and A. albopictus mosquitoes.
Fig. 2: A highly diverse and distinct collection of viruses in A. aegypti and A. albopictus.
Fig. 3: Concurrent detection of HTV, PCLV and DENV.
Fig. 4: Effects of insect-specific viruses on DENV and ZIKV transmission.
Fig. 5: HTV and PCLV regulate the expression of proviral histone H4 during DENV infection.

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Data availability

Small RNA and transcriptome libraries from this study have been deposited in the Sequence Read Archive (SRA) at NCBI (project accession PRJNA722589). Sequences spanning the RdRP region from newly discovered viruses were deposited in GenBank under accession MZ556103-MZ556111. Accession numbers for small RNA libraries are provided in Supplementary Table 1. Source data are provided with this paper.

Code availability

All scripts used in this work were deposited in GitHub and can be accessed at https://github.com/ericgdp/sRNA-virome code version 1.0.

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Acknowledgements

We thank all members of the ZIKAlliance consortium, especially A.-B. Failloux and A. Kohl for helping with establishing a network of collaborators and contributing fruitful discussions; current and former members of the Marques laboratory and the M3i unit - Insect Models of Innate Immunity, especially S. Blandin and N. Martins for suggestions and discussions. This work of the Interdisciplinary Thematic Institute IMCBio, as part of the ITI 2021-2028 programme of the University of Strasbourg, CNRS and Inserm, was supported by IdEx Unistra (ANR-10-IDEX-0002), by the SFRI-STRAT’US project (ANR 20-SFRI-0012), and EUR IMCBio (IMCBio ANR-17-EURE-0023) under the framework of the French Investments for the Future Program as well as by the previous Labex NetRNA (ANR-10-LABX-0036) to J.T.M and J.-L.I. This work was also supported by grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) to J.T.M. and E.R.G.R.A.; Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG), Rede Mineira de Imunobiológicos (grant no. REDE-00140-16), Rede Mineira de Biomoléculas (grant no. REDE-00125-16), Instituto Nacional de Ciência e Tecnologia de Vacinas (INCTV), Fonds Régional de Coopération pour la Recherche FRCT2020 Région Grand-Est – ViroMod and Institute for Advanced Studies of the University of Strasbourg (USIAS fellowship 2019 to J.T.M.); Google Latin American Research Award (LARA 2019) to J.T.M. and J.P.P.d.A.; FAPESP (Grant No. 13/21719-3) to M.L.N.; the European Union’s Horizon 2020 research and innovation programme under ZIKAlliance grant agreement no. 734548 and Investissement d’Avenir Programs (ANR-10-LABX-0036 and ANR- 11-EQPX-0022) to J.-L.I. and J.T.M.; and ANR PRC TIGERBRIDGE, grant no. 16-CE35-0010-01 to C.P. J.T.M., B.P.D., E.G.K and M.L.N. are CNPq Research Fellows. M.L.N. was partly funded by the Centers for Research in Emerging Infectious Diseases (CREID), the Coordinating Research on Emerging Arboviral Threats Encompassing the Neotropics (CREATE-NEO) grant U01 AI151807 by the National Institutes of Health (NIH/USA). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 to J.T.M. and V.A.-S. R.P.O. received a post-doctoral fellowship from CAPES.

Author information

Author notes

  1. These authors contributed equally: Roenick P. Olmo, Yaovi M. H. Todjro, Eric R. G. R. Aguiar.

Authors and Affiliations

  1. Department of Biochemistry and Immunology, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Roenick P. Olmo, Yaovi M. H. Todjro, Eric R. G. R. Aguiar, João Paulo P. de Almeida, Flávia V. Ferreira, Juliana N. Armache, Isaque J. S. de Faria, Siad C. G. Amadou, Ana Teresa S. Silva, Kátia P. R. de Souza, Ana Paula P. Vilela & João T. Marques

  2. Université de Strasbourg, CNRS UPR9022, INSERM U1257, Strasbourg, France

    Roenick P. Olmo, Antinea Babarit, Eric Marois, Jean-Luc Imler & João T. Marques

  3. Department of Biological Sciences (DCB), Center of Biotechnology and Genetics (CBG), State University of Santa Cruz (UESC), Ilhéus, Brazil

    Eric R. G. R. Aguiar

  4. Mosquitos Vetores: Endossimbiontes e Interação Patógeno-Vetor, Instituto René Rachou-Fiocruz, Belo Horizonte, Minas Gerais, Brazil

    Alvaro G. A. Ferreira

  5. Environmental Health Institute, Vector Biology and Control Division, National Environment Agency, Singapore, Singapore

    Cheong H. Tan

  6. Pôle de Zoologie Médicale, Institut Pasteur de Dakar, Dakar, Senegal

    Mawlouth Diallo & Alioune Gaye

  7. Maladies Infectieuses et Vecteurs: Écologie, Génétique, Évolution et Contrôle (MIVEGEC); Université de Montpellier, Institut de Recherche pour le Développement, CNRS, Montpellier, France

    Christophe Paupy

  8. Laboratoire de Biologie Moléculaire et Cellulaire, Département de Biologie, Université des Sciences et Techniques de Masuku, Franceville, Gabon

    Judicaël Obame-Nkoghe

  9. Écologie des Systèmes Vectoriels, Centre Interdisciplinaire de Recherches Médicales de Franceville, Franceville, Gabon

    Judicaël Obame-Nkoghe

  10. Laboratory of Entomology, Wageningen University and Research, Wageningen, the Netherlands

    Tessa M. Visser & Constantianus J. M. Koenraadt

  11. Central Laboratory of the Bureau of Public Health, Paramaribo, Suriname

    Merril A. Wongsokarijo

  12. Department of Microbiology, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil

    Ana Luiza C. Cruz, Betânia P. Drumond & Erna G. Kroon

  13. Secretaria Municipal de Saúde, Seção de Controle de Vetores, Santos City Hall, Santos, Brazil

    Mariliza T. Prieto

  14. Laboratory of Research in Virology, Faculdade de Medicina de São José do Rio Preto (FAMERP), São José do Rio Preto, Brazil

    Maisa C. P. Parra & Maurício L. Nogueira

  15. Departament of Pathology, University of Texas Medical Branch, Galveston, TX, USA

    Maurício L. Nogueira

  16. Department of Infectious and Parasitic Diseases, Faculdade de Medicina da Universidade de São Paulo (FMUSP), Cerqueira Cesar, Brazil

    Vivian Avelino-Silva

  17. Health Surveillance (Zoonosis Control), Brumadinho City Hall, Brumadinho, Brazil

    Renato N. Mota

  18. Center for Biological and Health Sciences, Universidade Estadual de Montes Claros, Montes Claros, Brazil

    Magno A. Z. Borges

  19. Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, UK

    Mario Recker

  20. Institute of Tropical Medicine, Universitätsklinikum Tübingen, Tübingen, Germany

    Mario Recker

  21. Lancaster Medical School, Lancaster University, Lancaster, UK

    Luigi Sedda

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  1. Roenick P. Olmo
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  2. Yaovi M. H. Todjro
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Contributions

R.P.O., E.R.G.R.A., J.-L.I. and J.T.M conceived the project. R.P.O., Y.M.H.T., E.R.G.R.A., J.P.P.d.A., J.N.A., L.S. and J.T.M. designed the experiments and performed computational analysis. R.P.O., Y.M.H.T., I.J.S.d.F., F.V.F., A.G.A.F., S.C.G.A., A.T.S.S., K.P.R.d.S., A.P.P.V. and A.B. performed experiments. C.H.T., M.D., A.G., C.P., J.O.-N., T.M.V., C.J.M.K., M.A.W., A.L.C.C., M.T.P., M.C.P.P., M.L.N., V.A.-S., R.N.M., M.A.Z.B., B.P.D., E.G.K. and E.M. provided samples and reagents. M.R. performed the mathematical modelling. R.P.O., Y.M.H.T., E.R.G.R.A., J.-L.I. and J.T.M. wrote the manuscript. All authors read and contributed to manuscript editing.

Corresponding author

Correspondence to João T. Marques.

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The authors declare no competing interests.

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Nature Microbiology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Co-occurrence of 139 viral contig clusters identified in A. aegypti and A. albopictus mosquitoes.

Heatmap represents the small RNA abundance for each of the 139 viral contigs in our 91 small RNA libraries from A. aegypti and A. albopictus mosquitoes. White indicates absence of small RNAs mapping to that contig. Contig clusters were defined using the dendrogram shown on the heatmap. Clusters that had a RdRp sequence were classified as a putative virus. Virus presence was considered if >50% of contigs belonging to a cluster were represented.

Source data

Extended Data Fig. 2 Phylogeny of viruses identified in A. aegypti mosquitoes.

Phylogenetic trees were generated using the RdRp amino acid (aa) or nucleotide (nt) sequences and the substitution models as indicated: a, Aslam narnavirus (aa - LG + G); b, Nyamuk partiti-like virus (aa - BLOSUM 62); c, Orbis virgavirus (aa - BLOSUM62 + F); d, Bahianus rhabdovirus (aa - BLOSUM62); e, Lactea totivirus (nt - Tamura-Nei 93). Bootstrap confidence is shown close to each clade and values under 60% were omitted.

Source data

Extended Data Fig. 3 Virus-derived small RNA profiles in mosquitoes.

Small RNA size distribution and 5’ base preference is shown on the left while the density of small RNAs (coverage) is shown on the right for representative contig(s) of each of the 12 viruses identified in this study.

Source data

Extended Data Fig. 4 Burden of viruses in mosquitoes from different collection sites.

a, Abundance of small RNA sequences in pooled libraries from each location. Each dot represents the small RNA abundance in a contig, and violin plots represent contig clusters (see Extended Data Fig. 1) at different locations with colors matching the mosquito species. Error bars represent the standard deviation of the mean for each location. The number of contigs analyzed per location is indicated above each graph. b, Detection of representative contigs of newly detected viruses by RT-qPCR (black bars) in comparison to the detection of small RNAs (20-30 nt length) to the same given contig (gray bars). RT-qPCR detection is normalized against the endogenous constitutive gene RpL32. *, indicates detection by conventional RT-PCR. c, viral contig detection by conventional RT-PCR using independent sets of primers pairs. Conventional PCR and qPCR were repeated twice on the same samples. The expected size of viral contigs is shown. ns indicates a non-specific band. d, Sequence variation between viral contigs of Orbis virgavirus in RNA samples originated from Suriname along the region that is complementary to RT-qPCR primers. e, Ratio between relative RT-qPCR and small RNA abundance for each virus. The number of independent mosquito samples analyzed per virus is indicated above each graph. f, Combined incidence of DENV and ZIKV for each mosquito capture location in the previous, current, and subsequent years of collection (represented by −1, 0, and +1, respectively). Data were obtained from public sources for each location.

Source data

Extended Data Fig. 5 Characterization of HTV and PCLV infection in wild and laboratory mosquitoes.

a, geographic distribution of mosquitoes carrying HTV and PCLV in the city of Caratinga, Brazil. Maps show the density of adult A. aegypti mosquitoes captured from July 2010 until August 2011 estimated from the number of mosquitoes captured in individual traps. All mosquitoes, HTV positive, PCLV-positive and double positive individuals are shown. Virus detection was performed by RT-qPCR. Map source: OpenStreetMap. b,c tissue tropism of HTV and PCLV upon natural and artificial infections in A. aegypti mosquitoes. b, Scheme of mosquito dissection and tissues tested for virus infection by RT-qPCR. Pie charts show the prevalence of HTV and PCLV infection, assessed in tissues of naturally infected wild mosquitoes or laboratory mosquitoes injected with HTV and PCLV. Individual tissues were tested for virus presence upon dissection at 2-, 4-, 6- and 8-days post injection (d.p.i.) by RT-qPCR. c, Detection of HTV and PCLV in eggs by RT-PCR. Eggs were either rinsed with distilled water (no treatment group) or washed with bleach (2,5% active chlorine) prior to RNA extraction. The endogenous constitutive gene RpL32 was used as amplification control. Results are representative of two independent experiments. d-e, HTV and PCLV do not grow in mammalian cell culture. VERO cells were exposed to mosquito extracts containing HTV (d) and PCLV (e), and supernatants were collected at 1-, 3- and 5-days post exposure. A spike containing 105 pfu of vesicular stomatitis virus (VSV) was added prior RNA extraction and used to normalize the quantification of HTV and PCLV in the supernatant. No statistically significant difference was observed in HTV and PCLV levels at 1-, 3- and 5-days post infection as determined by two-sided one-way ANOVA with Dunns’ correction for multiple comparisons. Dots and error bars indicate the mean and the standard error of the mean, respectively. n indicates the number of independent tissue culture wells tested for each virus at each time point.

Source data

Extended Data Fig. 6 HTV and PCLV facilitate systemic ZIKV infection in mosquitoes.

a-c, Strategy to evaluate the interference of HTV and PCLV for ZIKV infection and replication in natural populations of mosquitoes. (a) HTV/PCLV-infected and virus-free wild mosquito populations were infected with ZIKV by intrathoracic injection. Viral loads and prevalence of infection were measured in the (b) midgut and (c) carcass of mosquitoes at 2-, 4- and 8-days post feeding. The prevalence of infection in each group is shown below plots. d-f, Laboratory mosquitoes (d) were infected artificially with HTV and PCLV and 7 days later were fed on ZIKV-infected mice. Viral loads and prevalence of infection were measured in the (e) midgut and (f) carcass of mosquitoes at 4- and 8-days post injection. The prevalence of infection in each group is shown below the plots. g-i, Laboratory mosquitoes were infected artificially with HTV and PCLV or control (mock) and 7 days later infected with ZIKV by intrathoracic injection. Viral loads and prevalence of infection were measured in the (h) midgut and (i) carcass of mosquitoes at 2-, 4- and 8-days post injection. The prevalence of infection in each group is shown below plots. j-n, Wild mosquito populations naturally infected with HTV and PCLV were allowed to feed in mice infected with ZIKV or mock-infected controls. Viral loads of HTV and PCLV were measured in the midgut (k,l) and in the carcass (m,n) of mosquitoes at 4-, 8- and 14-days post feeding. o-p, HTV/PCLV-infected or control mosquitoes were exposed to ZIKV-infected mice (o). Viral loads and prevalence of infection were measured in salivary glands (p) of mosquitoes at the indicated time points. Pie charts below each group indicate the prevalence of ZIKV infection. d.p.f. – days post feeding, d.p.i. – days post injection, NS – non-significant. In box plots of b, c, e, f, h, i, k, l, m, n, and p, boxes show the second and third interquartile ranges divided by the median while whiskers represent maximum and minimum values. Statistical significance was determined by two-tailed Mann–Whitney U-test. Numbers of infected samples over the total number tested are indicated above each column. Each dot represents an individual sample. Statistical significance of prevalence was determined by two-tailed Fisher’s exact test.

Source data

Extended Data Fig. 7 Differential gene expression in wild mosquitoes carrying HTV and PCLV.

a, Differential gene expression in the carcass of wild mosquitoes carrying HTV and PCLV or non-infected siblings during DENV infection at 4, 8 and 14 days post feeding. b, number of up or down regulated genes regarding the infection with HTV and PCLV at each time point as shown in a. Common genes across time points are shown. c, Immune genes regulated during infection with HTV and PCLV in comparison to virus-free siblings at different times after DENV infection. d-e, Intrathoracic injection of ZIKV in wild mosquitoes carrying HTV and PCLV or virus free siblings (d). Histone H4 levels were quantified in the midgut of mosquitoes at 2, 4, and 8 days post injection with ZIKV (e). Error bars represent mean and standard deviations of the mean, and statistical significance was determined by two-sided one-way ANOVA with Tukey’s correction for multiple comparisons. f-g, Artificial infection of laboratory mosquitoes with HTV and PCLV does not modulate levels of histone H4. g, laboratory mosquitoes were artificially infected with HTV and PCLV and histone H4 levels were analyzed at different time points. In box plots of e and g, boxes show the second and third interquartile ranges divided by the median while whiskers represent maximum and minimum values. Statistics were performed using two-sided one-way ANOVA with Tukey’s correction for multiple comparisons. Each dot represents an individual sample. CPM – counts per million, d.p.i. – days post infection, NS – non-significant.

Source data

Extended Data Fig. 8 Complexity of histone genes in the genome of A. aegypti.

a, Histone H4 gene copies in the A. aegypti genome (Vectorbase version 52) were reannotated using BLAST similarity search with further confirmation of RNA-seq reads mapping to each gene copy. Along with other histone genes currently annotated in Vectorbase, the number of copies in chromosomes or supercontigs are shown and the largest cluster of genes highlighted. b, Organization of the largest cluster of histone genes on chromosome 3 as indicated by the gray box. c, Weblogo showing the conservation of the amino acid sequence of histone H4 open reading frames, which only varied at the positions 36 and 98, indicated by a circle and an asterisk, respectively. The number of amino acid changes in each position is indicated. d, Histone H4 genes organized by nucleotide sequence similarity according to the dendrogram with the expression indicated by the heatmap in different A. aegypti tissues. Bootstrap values over 60 are shown. Histone H4 genes positioned in the cluster at chromosome 3 are indicated by gray boxes and the presence of a polyadenylation signal is indicated. e, Histone H4 gene expression in wild mosquito populations carrying HTV and PCLV or virus free siblings infected with DENV, quantified by RT-qPCR from cDNAs synthesized with random primers (hexamers) or anchored oligo dT22. d.p.i., days post injection. In d, SRA accession numbers in same order as shown in the heatmap: Female whole body (non-BF SRR1585314, SRR1585315, SRR1585316; 48 h post-BF SRR1532683, SRR1532684, SRR1532685, SRR1532693, SRR1532694, SRR1532695); Female brain (48 h post-BF SRR1166497; 96 h post-BF SRR1167481); Male (brain SRR1167543); Female salivary glands (SRR2659965, SRR2659966); Female midgut (SRR5288077, SRR5288080, SRR5288082, SRR5288087, SRR5288093, SRR5288100); Female malp. Tubules (non-BF SRR3680433, SRR3680434); Female carcass (12 h post-BF SRR923823; 24 h post-BF SRR923830; 36 h post-BF SRR923835; 48 h post-BF SRR923841; 60 h post-BF SRR923847; 72 h post-BF SRR923736); Fem. Carcass (no ovaries) 24 h post-BF (SRR388683); Fem. Low reprod. Tract (0 h post-mating SRR3213863, SRR3213864; 6 h post-mating SRR3213865, SRR3213866; 24 h post-mating SRR3213867, SRR3213868); Male sperm (early SRR3554588; late SRR3554589); Male testis (SRR6311395, SRR6311396); Embryo (4-8 h SRR1578254, SRR1578255, SRR1578256); 1 day old female ovaries (SRR388680); Female ovaries (non-BF SRR1167515, SRR1167516, SRR1167517, SRR1167518, SRR1167519, SRR1167520; 24 h post-BF SRR388682; 96 h post-BF SRR1167538, SRR1167539). f-h, silencing of histone H4 by RNA interference in adult mosquitoes. f, strategy for dsRNA mediated gene silencing in adult mosquitoes. g-h, Histone H4 levels in the midgut of ISV free laboratory mosquitoes (g) or wild mosquitoes carrying HTV and PCLV (h) injected with dsRNA targeting GFP (dsGFP) as control or histone H4 (dsH4) at 4 days post feeding. I, AGO2 levels in the midgut of mosquitoes carrying HTV and PCLV injected with dsRNA targeting GFP (dsGFP) as control or Ago2 (dsAGO2). Each dot represents an individual sample. In box plots of e, g, h, and i, boxes show interquartile ranges divided by the median while whiskers represent maximum and minimum values. Statistical significance was determined using two-sided one-way ANOVA with Tukey’s correction for multiple comparisons.

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Supplementary information

Reporting Summary

Supplementary Table

Supplementary Table 1Overview of small RNA libraries. Metadata of small RNA libraries used in our study (SRA deposit ID, species, mosquito capture location, number of mosquitoes per pool, RNA treatment, and sequencing method). Supplementary Table 2Small RNA assembly metrics. Detailed information of assembled contigs with length >50 nt and length >199 nt per small RNA library. Supplementary Table 3Overview of contigs with similarity to viral sequences deposited in GenBank. Detailed information of BLASTn or BLASTp hits from contigs matching viral sequences (length > 199 nt) per small RNA library. Supplementary Table 4Overview of CD-Hit clusters. Number of contigs that compose each CD-Hit cluster. Supplementary Table 5List of oligonucleotides used in this study. Supplementary Table 6Parameters used to model DENV transmission.

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Source Data Extended Data Fig./Table 1

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Source data in fasta and Newick formats.

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Source data of coverage plots in BED formats and bar plots in Excel table.

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Statistical source data, unprocessed gel image.

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Statistical source data, unprocessed gel image.

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Source Data Extended Data Fig./Table 8

Statistical source data, annotation of histone genes in gff3 format.

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Olmo, R.P., Todjro, Y.M.H., Aguiar, E.R.G.R. et al. Mosquito vector competence for dengue is modulated by insect-specific viruses. Nat Microbiol 8, 135–149 (2023). https://doi.org/10.1038/s41564-022-01289-4

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