Polygenic viral factors enable efficient mosquito-borne transmission of African Zika virus

Torii, Shiho; Lord, Jennifer S.; Lavina, Morgane; Prot, Matthieu; Lecuyer, Alicia; Diagne, Cheikh T.; Faye, Oumar; Faye, Ousmane; Sall, Amadou A.; Bonsall, Michael B.; Simon-Lorière, Etienne; Montagutelli, Xavier; Lambrechts, Louis

doi:10.1038/s41467-025-64627-0

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Published: 30 October 2025

Polygenic viral factors enable efficient mosquito-borne transmission of African Zika virus

Nature Communications volume 16, Article number: 9594 (2025) Cite this article

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Abstract

Zika virus (ZIKV) is a mosquito-borne orthoflavivirus primarily transmitted among humans by Aedes aegypti. Over the past two decades, it has caused significant outbreaks associated with birth defects and neurological disorders. ZIKV consists of two main genotypes: the African and Asian lineages, each exhibiting distinct biological properties. African lineage strains are transmitted more efficiently by mosquitoes, but the genetic basis for this difference has been elusive. Here, we investigate this question by comparing recent African and Asian strains using chimeric viruses with swapped genome segments. Our results show that structural genes from the African strain enhance viral internalization, while non-structural genes improve genome replication and infectious particle production in mosquito cells. In vivo mosquito transmission is most significantly influenced by structural genes, although no single viral gene alone is decisive. We also develop a stochastic model of in vivo viral dynamics that reflects the observed patterns, suggesting the key difference between African and Asian strains lies in their ability to traverse mosquito salivary glands. Our findings imply the polygenic nature of ZIKV transmissibility has hindered Asian strains from achieving the same transmission efficiency as African strains, highlighting the role of lineage-specific adaptive landscapes in ZIKV evolution and emergence.

Introduction

Zika virus (ZIKV) is an emerging mosquito-borne orthoflavivirus of significant global public health concern. It is primarily transmitted among humans by Aedes aegypti mosquitoes, which are found in tropical and sub-tropical regions worldwide^1,2. While most human infections are either asymptomatic or result in mild symptoms, ZIKV can lead to severe neurological disorders, such as Guillain-Barré syndrome, and congenital abnormalities, including microcephaly, in fetuses of infected pregnant women^3,4,5,6,7. Given the absence of specific antiviral treatments or vaccines, understanding ZIKV transmission dynamics is critical for developing effective prevention and control strategies.

First identified in 1947 in a sentinel rhesus monkey from the Ziika Forest in Uganda⁸, ZIKV circulated largely undetected for the next six decades, likely within sylvatic cycles between forest-dwelling mosquitoes and non-human primates, with only 14 sporadic reports of human infections in parts of Africa and Asia^3,9,10. The first documented outbreak occurred in 2007 on the Pacific Island of Yap, Micronesia¹⁰. This was followed by more extensive outbreaks in French Polynesia and other South Pacific islands in 2013–2014. In 2015, ZIKV was first detected in Brazil, where it subsequently attracted widespread international attention due to a major outbreak during 2015–2016. Overall, ZIKV caused more than 1.6 million cases in over 70 countries across the Pacific and the Americas^11,12,13.

Phylogenetically, ZIKV is divided into two main genotypes sharing ~90% nucleotide identity, referred to as the African and Asian lineages, which diverged in the early 19^th century^6,14,15,16. All recorded ZIKV outbreaks, including a recent one in Cape Verde, have been caused by Asian lineage strains, whereas African lineage strains have only been detected in 5 countries in Africa^10,17,18. Furthermore, accumulating evidence suggests that African ZIKV strains, although not implicated in human outbreaks to date, exhibit higher infectivity and pathogenicity compared to Asian strains in various human-derived cells and in vivo animal models^{16,19,20,21,22,23,24,25,26,27}.

The dramatic global emergence of ZIKV in the Pacific and the Americas spurred extensive research into the genetic mutations that may have facilitated its spread in the human population, such as mutations enhancing human tropism, pathogenesis, or transmission by Ae. aegypti²⁸. The ZIKV genome consists of a positive-sense, single-stranded RNA that encodes a single open reading frame, flanked by 5’ and 3’ untranslated regions (UTRs). This open reading frame translates into a polyprotein, which is post-translationally cleaved into three structural proteins (SPs) – capsid (C), precursor membrane (prM), and envelope protein (E) – and eight non-structural proteins (nSPs) – NS1, NS2A, NS2B, NS3, NS4A, 2K, NS4B, and NS5²⁹. Reverse genetics studies comparing the two ZIKV lineages showed that introducing Asian SPs into an African strain reduces viral infectivity in human epithelial and neuronal cells³⁰, while introducing African SPs into an Asian strain increases pathogenicity in mice³¹. Additionally, African strains demonstrate greater transmissibility in Ae. aegypti mosquitoes^{27,32,33,34,35}, yet the specific viral genetic factors contributing to this difference remain unclear. Functional studies have shown that two mutations that arose during ZIKV emergence since 2013 – A188V in NS1 and T106A in C – enhance the replication of Asian strains in Ae. aegypti mosquitoes^36,37. Intriguingly, the same transmission-adaptive variants are also present in sylvatic African strains, suggesting they could be ancestral to the virus³⁸. However, collectively these mutations do not account for the full difference in mosquito transmissibility observed between the Asian and African lineages³⁸, implying that other genetic factors have constrained Asian lineage strains from achieving the same transmissibility as African lineage strains. In addition, the transmission-enhancing effect of the NS1 A188V substitution in Ae. aegypti was not confirmed in a different viral genetic background³⁹, suggesting that this effect is modulated by epistatic interactions.

Here, we conducted a comprehensive comparison of recent African and Asian ZIKV strains to systematically identify the viral genetic factors that influence the efficiency of mosquito-borne transmission. Using chimeric viruses, we assessed the contribution of each region of the viral genome to differences in viral propagation in mosquito cells in vitro and mosquito transmissibility in vivo. Finally, we obtained mechanistic insights into ZIKV transmissibility by integrating experimental data with a stochastic model of in vivo viral dynamics.

Results

Engineered ZIKV strains recapitulate the phenotypes of natural isolates in mosquitoes

To understand the viral genetic factors impacting mosquito transmissibility, we selected, because of their distinct levels in transmission efficiency²⁷, recent ZIKV strains from Senegal and Thailand, representing the African and Asian lineages, respectively. To minimize the impact of natural genetic variability, we used reverse genetics to transform the original virus isolates, iSenegal and iThailand, into engineered strains, which we designated as rSenegal and rThailand, respectively (Supplementary Fig. S1A). To assess whether the strains generated by reverse genetics recapitulated the phenotypes of their natural counterparts, we exposed Ae. aegypti mosquitoes from Colombia to each of these viruses via infectious blood meals (Supplementary Fig. S1B). We chose a mosquito colony from Colombia to ensure consistency with our previous study, in which we found a significant difference in transmission efficiency between the Senegal and Thailand strains using the same mosquito colony²⁷. At 7 and 14 days post blood meal, we measured the prevalence of infection and systemic viral dissemination by RT-PCR and evaluated ZIKV transmission potential by detecting the presence of infectious virus in salivary secretions (Supplementary Fig. S2, Supplementary Table S1). Both rSenegal and rThailand exhibited a similar prevalence of infection, systemic dissemination, and viral presence in saliva compared to their natural counterparts. In line with our earlier study²⁷, both iSenegal and rSenegal demonstrated significantly higher dissemination prevalence and greater transmission efficiency than iThailand and rThailand across the tested time periods (Supplementary Fig. S2B, C). These findings confirm the comparability of the ZIKV strains generated by reverse genetics to the natural isolates in terms of their infection dynamics in mosquitoes. Consequently, we used rSenegal and rThailand as parental strains for all subsequent experiments (Supplementary Fig. S1B).

Superior growth kinetics of the rSenegal strain reflect higher efficiency of viral internalization and genome replication in mosquito cells

We first examined the growth kinetics of rSenegal and rThailand strains in mosquito (C6/36) cells in vitro, due to their ease of manipulation and robustness. Although this cell line is known to be deficient in the RNA interference (RNAi) antiviral response⁴⁰, a previous study has shown a limited impact of the RNAi response on ZIKV infection in Ae. aegypti-derived cells⁴¹. Over the four-day time course, the rSenegal strain consistently showed higher titers compared to rThailand (Fig. 1A). To understand why these strains produced different levels of infectious particles, we performed several functional assays. We evaluated viral attachment by measuring membrane-bound viral RNA levels after one-hour viral attachment on ice. This experiment revealed that the rThailand strain had higher attachment efficiency than the rSenegal strain (Fig. 1B), thus failing to explain the superior growth kinetics of the rSenegal strain. We also analyzed viral internalization efficiency by measuring relative intracellular viral RNA at 3 h post infection (h.p.i.) normalized to the attached virus particles (Fig. 1C). The validity of the assay was verified with Dynasore, an inhibitor of clathrin-mediated endocytosis, which is considered the primary internalization pathway of orthoflaviviruses^42,43,44,45. Internalization efficiency was lower under Dynasore treatment (Fig. 1C, left panel) and was significantly higher for the rSenegal strain than for the rThailand strain (Fig. 1C, right panel), suggesting that differences in internalization efficiency contribute to the higher infectious particle production of the rSenegal strain. Next, we monitored the replication of ZIKV genomic and antigenomic RNA over 24 h using strand-specific RT-qPCR (Fig. 1D). Both strains showed detectable levels of ZIKV antigenomic RNA from 12 h.p.i., with higher replication efficiency in the rSenegal-infected cells detected at 18 h.p.i. (Fig. 1D, left panel). The relative viral genomic RNA level was also significantly higher in rSenegal-infected cells from 15 to 24 h.p.i. (Fig. 1D, right panel). This result indicates that a higher efficiency of viral genome replication by the rSenegal strain may also contribute to the higher production of infectious virus particles. We also explored the ratio of infectious virus titers to viral RNA copies to assess the release of non-infectious immature or defective virus particles^46,47,48,49 (Fig. 1E). Although this ratio increased over time for both strains, it was significantly lower in rSenegal-infected cells at 48 h.p.i., suggesting a slightly higher proportion of immature or defective virus particles produced by the rSenegal strain. By monitoring the decay of infectious virus titers over time at 28 °C, we found that there was no detectable difference in the stability of infectious virus particles between the rSenegal and rThailand strains (Fig. 1F). Finally, we observed that the rSenegal strain significantly reduced cellular ATP levels compared to the rThailand strain at 96 h.p.i. (Fig. 1G). Together, these results indicate that the more efficient virus particle production of the rSenegal strain in mosquito cells reflects enhanced virus internalization and viral genome replication efficiency, despite increased cell toxicity.

**Fig. 1: Superior growth kinetics of the rSenegal strain are associated with enhanced efficiency of viral internalization and genome replication in mosquito cells.**

Structural genes enhance viral internalization, and non-structural genes increase genome replication of the rSenegal strain relative to the rThailand strain in mosquito cells

To investigate the genetic determinants responsible for differences in mosquito transmissibility between the rSenegal and rThailand strains, we analyzed the full-length genome sequences of these strains (Supplementary Fig. S1C). Since comparative sequence analysis identified 102 amino-acid differences and 1216 nucleotide differences without specific clustering, we constructed a first set of chimeric viruses by swapping SPs, nSPs, and UTRs between the parental strains by reverse genetics (Fig. 2A). We observed significant differences in plaque size on a mammalian (Vero E6) cell monolayer between the chimeric viruses. Adding the rSenegal SPs into the rThailand backbone increased plaque size, whereas adding the rThailand SPs into the rSenegal backbone decreased it (Fig. 2B). The rThailand nSPs introduced into the rSenegal backbone led to smaller plaques but there was no detectable effect of the reciprocal replacement. In contrast, introducing rSenegal UTRs into the rThailand backbone reduced plaque size, while doing so in the opposite direction showed no significant change (Fig. 2B). These results suggest that SPs, nSPs, and UTRs, influence plaque size, although their effects vary depending on the backbone strain. We then assessed the growth kinetics of the chimeric viruses in mosquito cells. Chimeric viruses using the rSenegal strain as a backbone showed that introducing either SPs or nSPs from the rThailand strain significantly reduced virus titers compared to the parental rSenegal strain (Fig. 2C, left panel). For chimeric viruses using the rThailand strain as a backbone, replacement of the nSPs increased virus titers, particularly from 24 to 72 h.p.i. (Fig. 2C, right panel). These results indicate that swapping either SPs or nSPs influence growth kinetics but with a different magnitude depending on the backbone strain, whereas UTRs do not contribute significantly. To assess whether minor ZIKV genetic variants might influence the growth kinetics of chimeric viruses, we performed deep sequencing to estimate the frequency of minor variants in both the viral inoculum and in cells collected at 72 h.p.i. We examined three different chimeric viruses alongside the parental rSenegal strain (Supplementary Table S2). All minor variants detected exhibited similar frequencies in the inoculum and in the cells collected at 72 h.p.i., with less than a 10% change. These results do not support an influence of minor viral variants on the growth kinetics.

**Fig. 2: Structural genes enhance viral internalization, and non-structural genes improve genome replication of rSenegal strain relative to rThailand strain in mosquito cells.**

To identify the genomic regions underlying the distinct growth kinetics of the parental strains in mosquito cells, we examined the internalization efficiency of chimeric viruses. The only replacement that significantly influenced internalization efficiency relative to the parental strain was substituting rThailand SPs in the rSenegal strain (Fig. 2D). We next compared the replication efficiency of chimeric viruses by measuring the production of ZIKV antigenomic and genomic RNAs. Swapping nSPs between parental strains resulted in significant differences in the production of both viral antigenomic RNA (Fig. 2E, left panel) and genomic RNA (Fig. 2E, right panel), indicating that nSPs play a crucial role in viral genome replication. Replacing the SPs from the rThailand strain also decreased the production of viral antigenomic RNA (Fig. 2E, left panel). Finally, we found significant changes in cellular ATP levels following replacement of nSPs in both directions at 96 h.p.i. (Fig. 2F). Taken together, these findings show that differences in growth kinetics between the rSenegal and rThailand strains primarily reflect the effect of SPs on viral internalization and the effect on nSPs on viral genome replication. The nSPs from the rSenegal strain also lead to higher cell toxicity than the nSPs from the rThailand strain.

Both structural and non-structural genes collectively underlie differences in mosquito transmission between rSenegal and rThailand strains

To investigate the viral genetic basis of transmissibility in mosquitoes in vivo, we first assessed the infectivity of the chimeric viruses when delivered through an infectious blood meal. We orally exposed Ae. aegypti mosquitoes from Colombia to differing virus doses as outlined in Supplementary Table S1 and estimated the 50% oral infectious dose (OID₅₀) for each chimeric virus from the dose-response curves (Supplementary Fig. S3). The rSenegal strain had a significantly lower OID₅₀ estimate than the rThailand strain and introducing either the SPs or the nSPs from the rThailand strain into the rSenegal strain increased the OID₅₀ estimates_, whereas the rThailand UTRs did not have a detectable impact (Supplementary Fig. S3B). Conversely, introducing the SPs, nSPs, or UTRs from the rSenegal strain into the rThailand strain did not significantly change the OID₅₀ estimates. These findings suggest that both SPs and nSPs are required to confer the rSenegal strain a higher infectivity in mosquitoes relative to the rThailand strain. They also show that most chimeric viruses achieve 80-100% infection prevalence when the blood meal titer is >10⁶ PFU/ml. Given this, we chose 2 × 10⁶ PFU/ml as the standard oral infectious dose for subsequent experiments.

To assess the mosquito transmissibility of the chimeric viruses, we exposed Ae. aegypti mosquitoes from Colombia to each of the viruses via infectious blood meals containing 2 × 10⁶ PFU/ml. Actual blood meal titers varied from 1.3 to 4.0 × 10⁶ PFU/ml, and this variation was factored into our statistical analysis (Supplementary Table S3). At 7, 10, and 14 days post blood meal, we detected midgut infection and systemic viral dissemination by RT-PCR and evaluated transmission potential by detecting the presence of infectious ZIKV in salivary secretions (Supplementary Fig. S1B). At least 20 mosquitoes per time point were tested for each virus (Supplementary Table S1). We define infection prevalence as the proportion of blood-fed mosquitoes with a virus-positive body, dissemination prevalence as the proportion of infected mosquitoes with a virus-positive head, and transmission prevalence as the proportion of mosquitoes with a virus-positive head releasing infectious virus in their saliva (Supplementary Fig. S1B). Overall transmissibility is encapsulated in transmission efficiency, which is defined as the proportion of blood-fed mosquitoes with infectious saliva. As expected from the infectivity experiment (Supplementary Fig. S3A), the infection prevalence was 80–100% across viruses and time points (Fig. 3A). Dissemination prevalence was also 80–100% across viruses and time points (Fig. 3B). Both infection prevalence and dissemination prevalence were significantly influenced by the virus (Supplementary Table S3), however the magnitude of these differences was modest, ranging on average from 83.3% to 99.0% and from 84.5% to 96.8%, respectively (Fig. 3A, B). Transmission prevalence significantly increased over time and ranged from 0% to ∼50% across viruses and time points (Fig. 3C). It is possible that the observed increase in transmission prevalence over time reflects an increasing proportion of saliva samples exceeding the detection threshold of the assay, but this would remain biologically meaningful because it would indicate an increase in the infectious titer of saliva samples. Notably, the rSenegal strain resulted in significantly higher transmission prevalence than the rThailand strain. Introducing either the SPs or the nSPs from the rThailand strain into the rSenegal strain decreased transmission prevalence, whereas the rThailand UTRs did not have a detectable impact. Conversely, only the SPs from the rSenegal strain increased transmission prevalence when introduced into the rThailand strain. These findings indicate that differences in transmission prevalence between the parental strains are primarily determined by the SPs and, to a lesser extent, the nSPs.

**Fig. 3: Structural and non-structural genes collectively drive differences in mosquito transmission between rSenegal and rThailand strains.**

Individual genes have a limited impact on viral growth in mosquito cells and mosquito transmissibility

To narrow down the genome regions responsible for differences in mosquito transmissibility between the rSenegal and rThailand strains, we constructed a second set of chimeric viruses by substituting each viral gene in the rSenegal strain with the corresponding gene from the rThailand strain (Supplementary Fig. S4A), and a third set by substituting each viral gene in the rThailand strain with the corresponding rSenegal gene (Supplementary Fig. S4B). Introducing specific genes such as prM, E, or NS1 from the rThailand strain into the rSenegal strain resulted in smaller plaques on Vero E6 cells, while replacement of NS1 and NS4 led to larger plaques (Supplementary Fig. S4C). Conversely, all chimeric viruses of the third set formed smaller plaques than the parental rThailand strain, indicating an asymmetric influence on plaque size (Supplementary Fig. S4D). We then compared the growth kinetics of the new sets of chimeric viruses in mosquito cells with that of the parental strains. In the second set, all replacements significantly reduced infectious viral titers except C and NS1 (Supplementary Fig. S4E). In the third set, replacement of C, E, or NS5 transiently increased virus titers at 48 and 72 h.p.i. but resulted in lower titers than rThailand at 96 h.p.i., while replacing NS2 led to a significantly slower growth (Supplementary Fig. S4F). Taken together, these results show that substitutions of E or NS5 most significantly impact virus growth kinetics in mosquito cells, but no single-gene substitution significantly alters the efficiency of infectious particle production in both directions.

To assess how individual viral genes affect mosquito transmissibility, we exposed mosquitoes from Colombia to the second and third sets of chimeric viruses via infectious blood meals containing 2 × 10⁶ PFU/ml (Supplementary Fig. S1B). Actual blood meal titers varied from 0.7 to 2.5 × 10⁶ PFU/ml and from 1.2 to 2.1 × 10⁶ PFU/ml for the second and third sets, respectively, and this variation was factored into our statistical analysis (Supplementary Table S3). At least 10 and 16 mosquitoes per time point were tested for each virus for the second and third sets, respectively (Supplementary Table S1). Although we observed slight differences, likely due to variations in mosquito generations and experimental conditions, we confirmed that the confidence intervals of the parental strains overlapped across all experiments. Virtually all mosquitoes exposed to the second set of chimeric viruses were infected and had a disseminated infection, with no significant variation among viruses (Fig. 3D, E). Transmission prevalence increased over time, reaching 29–71% on day 14 (Fig. 3F), with a marginally significant effect of the virus (Supplementary Table S3). Following exposure to the third set of chimeric viruses, both infection prevalence and dissemination prevalence were significantly influenced by the virus (Supplementary Table S3), however the magnitude of these differences was modest, ranging on average from 78.7% to 98.5% and from 79.6% to 95.5%, respectively (Fig. 3G, H). Transmission prevalence significantly increased over time but was not influenced by the virus (Fig. 3I; Supplementary Table S3). Together, these results show that no single viral gene is solely responsible for the observed variation in transmission prevalence in mosquitoes from Colombia.

Structural genes influence virus transmission irrespective of mosquito genetic background

To investigate if the observed differences in transmission prevalence between chimeric viruses (Fig. 3C) were specific to the mosquitoes from Colombia, we tested the first set of chimeric viruses in two other mosquito colonies with different genetic backgrounds and contrasting levels of ZIKV susceptibility. ZIKV susceptibility is known to be significantly higher in globally invasive populations of Ae. aegypti found outside Africa than in native African populations⁵⁰. We chose a ZIKV-resistant mosquito colony from Uganda representative of the native African populations⁵⁰, and a colony from Cape Verde with admixed genetic ancestry and an intermediate level of ZIKV susceptibility¹⁷. We challenged mosquitoes from Uganda and Cape Verde with infectious blood meals containing 1 × 10⁷ PFU/ml and 2 × 10⁶ PFU/ml, respectively, to maximize infection prevalence in these relatively resistant colonies. Actual blood meal titers varied from 3.0 to 8.0 × 10⁶ PFU/ml and from 0.7 to 4.0 × 10⁶ PFU/ml for the mosquitoes from Uganda and Cape Verde, respectively, and this variation was factored into our statistical analysis (Supplementary Table S4). In both mosquito colonies, the virus significantly influenced transmission efficiency (Supplementary Table S4). The rSenegal strain resulted in significantly higher transmission efficiency than the rThailand strain and swapping either SPs or nSPs—but not UTRs—between the parental strains resulted in intermediate transmission efficiencies that were consistent with the patterns previously observed with the mosquitoes from Colombia (Supplementary Fig. S5). Overall, the influence of SPs on transmission efficiency was more pronounced than the influence of nSPs. These results indicate that the viral genetic determinants of ZIKV transmission efficiency are largely independent of the mosquito genetic background.

Relatively simple models explain viral growth in vitro and the dose response of midgut infection

To understand the mechanisms behind the varying mosquito transmissibility of chimeric viruses in mosquitoes, we developed a stochastic model of in vivo viral dynamics that could reproduce the qualitative outcomes of oral exposure to the chimeric viruses, capturing patterns of infection, dissemination, and transmission. Following an infectious blood meal, the virus first infects and replicates within the midgut epithelial cells, then ‘escapes’ from the midgut and disseminates via the hemocoel to other tissues. It ultimately reaches the salivary glands, where it is released in the saliva and transmitted to the next host^51,52,53. We aimed to use the stochastic model to pinpoint the key processes within mosquitoes that are most likely influenced by differences among the chimeric viruses. The output of our logistic model of in vitro viral growth kinetics (Fig. 4A) was consistent with our time-course analyses of infectious particle production using the first set of chimeric viruses when the maximum titer supported by the cell culture (carrying capacity, k) was fixed at 10^8.5 PFU/ml, the starting virus concentration (s) was either 10¹ or 10^1.3 PFU/ml, and the growth rate (r) was either 0.035 or 0.05 h⁻¹, respectively (Supplementary Fig. S6A). We applied this viral growth model to capture in vivo mosquito infection dynamics, incorporating additional parameters: the probability of midgut infection (β), representing the likelihood that the virus successfully infects the midgut; the escape rate (λ), denoting how quickly the virus transfers between different tissues; and the blood meal clearance rate (μ), indicating how rapidly the virus is lost within the ingested blood (Fig. 4B). Our sensitivity analyses showed that within the ranges of the parameter values used, the probability of midgut infection (β) had the strongest effect on the proportion of simulations with midgut infection, followed by the blood meal clearance rate (μ) (Supplementary Fig. S7). In scenarios where the starting virus concentration was 10⁶ PFU/ml, all simulations resulted in successful infections when β ranged from 10^−4.7 to 10^-4 and μ varied between 0.014 and 0.08 h⁻¹. We subsequently used these parameter values in sensitivity analyses to evaluate viral dissemination and potential transmission (Supplementary Fig. S8). We found that by using values of β between 10⁻⁵ and 10⁻³ (Supplementary Fig. S6B), we could simulate the dose-response curves of midgut infection by the first set of chimeric viruses (Supplementary Fig. S3). These results suggest that differences in the dose-response curves of midgut infection across the chimeric viruses likely reflect variations in β, encompassing all processes from viral attachment, internalization, and replication in midgut cells.

**Fig. 4: Modeling ZIKV infection dynamics identifies salivary gland traversal as the primary driver of differences in transmissibility.**

Observed differences in transmission prevalence can be explained by between-mosquito variation in salivary gland infection

When we exposed mosquitoes from Colombia to the first set of chimeric viruses, we observed significant variation in transmission prevalence between viruses (Fig. 3C). Our sensitivity analysis showed that the growth rate (r) was the most influential parameter, at least within the ranges of parameter values used, on viral dissemination in the hemocoel on day 7 (Supplementary Fig. S8A) and transmission potential (salivary gland infection) on day 10 post infectious blood meal (Supplementary Fig. S8B). There was a relatively narrow range of the growth rate (r) values (between 0.05 and 0.1 h⁻¹) where the proportion of simulations went from zero to one for dissemination on day 7, and for transmission potential on day 10. Our analysis of potential causes for the observed differences in transmission dynamics between chimeric viruses focused on variations in r and λ between tissues. We explored alternative model parameterizations for r and λ by using tissue-specific parameter values and by introducing Gamma distributions to account for between-mosquito variation. Of the five hypothesized scenarios (Table 1), only one—featuring a lower hemocoel-to-salivary gland escape rate (λ_H:S) compared with the midgut-to-hemocoel escape rate (λ_M:H) and between-mosquito variation in this parameter—resulted in 100% prevalence of infection and dissemination but less than 60% transmission prevalence (Fig. 4C and scenario five, Table 1). The other scenarios primarily shifted the transmission prevalence curve to the right. Manipulating the variance of the Gamma distribution of λ_H:S reproduced the differences in transmission prevalence observed between the chimeric viruses (Fig. 4D), supporting the conclusion that these differences likely reflect variation in the hemocoel-to-salivary gland escape rate.

Table 1 Model scenarios reflecting different hypotheses concerning how infection and dissemination processes may differ between mosquito tissues

Full size table

Discussion

Previous experimental infections of mosquitoes with ZIKV strains from the African and Asian lineages have demonstrated significant variability in mosquito-borne transmission efficiency²⁷. However, the genetic determinants underlying these differences remain poorly understood. To address this gap, we developed chimeric viruses derived from recent African and Asian ZIKV parental strains, incorporating the numerous amino-acid substitutions and silent mutations distinguishing the parental genomes. Our findings revealed that African SPs enhance viral internalization, while African nSPs significantly improve viral genome replication in mosquito cells in vitro, relative to their Asian counterparts. These observations align with the known roles of the viral M and E proteins, which are key components in forming heterodimers essential for maturing infectious virus particles and facilitating the conformational changes required for membrane fusion in the clathrin-mediated endocytic pathway^54,55, and nSPs in viral RNA replication. Furthermore, our in vivo experiments with the chimeric viruses demonstrated that differences in mosquito-borne transmissibility between African and Asian ZIKV are primarily determined by the SPs and, to a lesser extent, the nSPs. Substitutions in SPs such as E or prM markedly enhanced replication in vitro, and increased transmission prevalence in vivo when introduced from the African to the Asian strain. The consistency of our results across Ae. aegypti populations with varying levels of ZIKV susceptibility highlights the robustness of SPs effects on transmission efficiency and their independence from population-specific mosquito factors. We found that UTRs had no significant effect on viral growth in vitro or transmission efficiency in vivo. Despite the proposed role of non-coding subgenomic flavivirus RNAs derived from the 3’-UTR in mosquito-borne transmission of ZIKV⁵⁶ and other orthoflaviviruses^57,58,59, previous studies have primarily focused on 3’-UTR deletion mutants rather than lineage-specific differences in the 3’-UTR sequence. Moreover, earlier research identified ZIKV lineage-specific variations at the 3’-end but demonstrated that these variations have minimal effects on viral replication and gene expression in mosquito-derived cells⁶⁰. These findings indicate that lineage-specific differences in the ZIKV UTRs do not significantly contribute to variation in mosquito transmissibility.

In general, the chimeric viruses showed intermediate phenotypes, remaining within the range defined by the parental strains. Both SPs and nSPs were necessary to replicate the parental traits, and we found that cumulative effects of multiple genes, rather than any single gene, shape ZIKV transmissibility. These findings suggest that ZIKV transmissibility is governed by complex interactions among multiple viral genes, which act across various stages of virus propagation within mosquito cells. Future studies incorporating targeted combinations of viral gene substitutions, guided by these results, could further elucidate the genetic basis of transmissibility. The observed similarity between engineered and natural ZIKV strains establishes the validity of our reverse genetics system for studying mosquito transmissibility. Such precision in recapitulating phenotypes enables controlled dissection of genetic factors underlying epidemiologically relevant phenotypic differences.

Experimental infections of mosquitoes with viruses often provide only static snapshots of the infection process, typically focusing on individual stages of propagation, or fixed time points post exposure. This limited scope makes it challenging to understand more fully the dynamic processes governing viral propagation in mosquitoes. In this study, we addressed this complexity by integrating experimental data from chimeric viruses into a mathematical model. Consistent with experimental findings, the stochastic model pointed to the hemocoel-to-salivary gland transition as the primary determinant of transmission efficiency in infected mosquitoes. Introducing between-mosquito variability in this transition reproduced differences in transmission prevalence across chimeric viruses, supporting individual-level heterogeneity as a key factor. Together, these results indicate that once a mosquito is infected, ZIKV transmission predominantly depends on the ability of the virus to traverse the mosquito salivary glands, likely involving an infection barrier and/or an escape barrier to viral release into saliva. Unlike midgut infection, which requires the virus to navigate from the apical to the basal side of epithelial cells, secondary infection of salivary glands operates through a distinct mechanism that results in virus release in salivary secretions. Research on the vesicular stomatitis virus glycoprotein G in Drosophila melanogaster suggests that a specific signal motif in the viral envelope protein is critical for viral movement within midgut cells but less so in salivary gland cells, emphasizing the need to investigate the role of ZIKV SPs in polarized trafficking and virus release into saliva⁶¹. Additionally, the dissemination of ZIKV from the hemocoel to the salivary glands warrants further exploration. Hemocytes, known for their antiviral immune roles in mosquitoes, have been observed to assist ZIKV spread to the ovaries and salivary glands after infection⁵³. Future research should focus on tissue-specific mechanisms of virus proliferation, including interactions within the hemolymph and salivary glands.

It is worth noting that most of our experiments used a standardized infectious dose in the blood meals that infected 80–100% of the mosquitoes across the chimeric viruses. However, our initial dose-response experiment showed that chimeric viruses also differed in the ability to infect mosquitoes at lower doses (Supplementary Fig. S3; Supplementary Table S1). Both SPs and nSPs from the African strain contributed to its higher infectivity in mosquitoes compared to the Asian strain. A previous study using in situ immunofluorescence showed that an African ZIKV strain established midgut infections and replicated more efficiently in midgut epithelial cells than an Asian strain⁶². Therefore, it is likely that both superior midgut infectivity and more efficient traversal of the salivary glands contribute to higher transmissibility of African ZIKV strains in a real-world situation.

The absence of recorded outbreaks caused by ZIKV strains of the African lineage, despite their higher transmissibility in experimental studies, presents a paradox. The lower ZIKV susceptibility of African Ae. aegypti populations may have played a role in preventing ZIKV outbreaks on the continent⁵⁰. Alternatively, the lower mosquito transmissibility of Asian ZIKV strains might be compensated by a potential fitness advantage within human hosts. The epidemiological fitness of ZIKV depends not only on the efficiency of mosquito-borne transmission but also on the level of human infectiousness to mosquitoes, influenced by viremia, disease pathology and immune responses. In mouse models, African ZIKV strains typically exhibit higher viremia peaks compared to Asian strains²⁷, but the relative magnitude and duration of infectiousness to mosquitoes remain unclear. Theoretical models indicate that while high viremia may lead to a rapid decline in titer, a lower yet more sustained viremia could be more advantageous for viral transmission^63,64. Future research should investigate ZIKV lineage-specific differences in both magnitude and duration of human infectiousness to mosquitoes.

Epidemic strains of mosquito-borne viruses frequently harbor mutations that facilitate human transmission and consequently, intensify outbreaks⁶⁵. Previous studies identified mutations that enhance the replication of Asian lineage ZIKV strains in Ae. aegypti mosquitoes, which arose just prior to ZIKV emergence in the Americas^36,37. These transmission-adaptive variants are thought to be ancestral because they are also found in African lineage ZIKV strains³⁸. However, individually, these mutations do not recapitulate the level of transmissibility observed in African lineage ZIKV strains³⁸. Our findings indicate that the greater transmissibility of African lineage ZIKV strains compared to their Asian counterparts involves multiple genetic factors rather than single mutations. Thus, the polygenic nature of ZIKV transmissibility may have prevented Asian lineage strains from achieving the same transmission efficiency as African lineage strains. While recombination could bridge this gap, it is unlikely due to the limited co-circulation of ZIKV lineages. African ZIKV strains primarily circulate in a sylvatic cycle, whereas Asian strains, even when introduced into Africa, have only been detected in a human transmission cycle.

Our results underscore the complexity of viral adaptation, which is often limited by lineage-specific adaptive landscapes (i.e., unique sets of evolutionary possibilities and constraints that dictate how each viral lineage adapts over time). These adaptive landscapes are shaped by the specific genetic background, ecological context, and host interactions of each viral lineage. They explain why closely related lineages such as the Asian and African ZIKV lineages can follow divergent mutation pathways resulting in differing transmissibility. Lineage-specific adaptive landscapes have been previously documented for other mosquito-borne viruses. For example, the replacement in Southeast Asia of endemic Asian strains of chikungunya virus by a new lineage of African origin, adapted to the Aedes albopictus vector, was shown to result from lineage-specific epistatic constraints in the E1 glycoprotein⁶⁶. Similarly, the existence of distinct epidemic and sylvatic dengue virus lineages may reflect lineage-specific adaptive landscapes driven by different viral ecologies⁶⁷. Elucidating the complex viral genetic basis of mosquito-borne transmission is critical for understanding viral evolution and improving our ability to track and monitor new, more transmissible strains and effectively manage epidemic situations.

Methods

Ethics and regulatory information

All experiments were conducted in compliance with national guidelines and with European Commission Directive 2000/54/EC of 18 September 2000 on the protection of workers from risks related to exposure to biological agents at work, and with Directives 2009/41/EC of 6 May 2009 and 98/81/EC of 12 June 1989 on the contained use of genetically modified micro-organisms. The generation of chimeric viruses and their use in experimental infections of mosquitoes were approved by the French Ministry of Higher Education, Research, and Technology (authorization number 8933, 4 August 2021) and the Institut Pasteur Dual Use Liaison Group (DURC 2021-03, 8 March 2022). Wild mosquito eggs were collected and exported with permission from local institutions and/or governments as required (Uganda: permit 2014-12-134, 2014; Cape Verde: authorization No 988, 2020).

Cells

Huh-7 and Vero E6 cells were maintained at 37 °C under 5% CO₂ in high-glucose Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal bovine serum (FBS; Gibco Thermo Fisher Scientific) and 1% penicillin/streptomycin (pen/strep; Gibco Thermo Fisher Scientific). C6/36 cells were maintained at 28 °C in Leibovitz’s L-15 medium (L15; Gibco Thermo Fisher Scientific) containing 10% FBS, 2% tryptose phosphate broth (TPB; Gibco Thermo Fisher Scientific), 1× non-essential amino acids (NEAA; Gibco Thermo Fisher Scientific), and 1% pen/strep.

Wild-type viruses

Wild-type ZIKV strain Kedougou2011, referred to as the iSenegal strain in this study, was isolated near Kedougou in 2011 from a pool of wild-caught mosquitoes⁶⁸. Wild-type ZIKV strain THA/2014/SV0127-14, referred to as the iThailand strain in this study, was isolated from a human serum sample⁶⁹. Virus stocks were prepared in C6/36 cells and the viral genome sequences were obtained by high-throughput sequencing as previously described^27,70. Wild-type ZIKV strains iSenegal and iThailand were converted by reverse genetics into rSenegal and rThailand strains, respectively, as described below for chimeric viruses.

Chimeric viruses

Parental and chimeric ZIKV strains were generated by circular polymerase extension reaction (CPER) following a published method⁷¹ with modifications. For each virus, a total of six ZIKV complementary DNA (cDNA) fragments covering the full-length viral genome were amplified utilizing PrimeSTAR GXL DNA Polymerase (TaKaRa Bio) and cloned into the pMW118 vector (listed in the Reagent Table). A CMV linker for ZIKV was then inserted into the pCR-Blunt II-TOPO vector, encoding sequences of the HDVr, Late SV40 pA signal, and CMV promoter. All DNA inserts were verified through Sanger sequencing. Following the cloning process (with some optimizations shown in Supplementary Fig. S1A), infectious cDNA was generated by assembling DNA fragments F1-F6, which encompass the entire ZIKV genome, and fragment F7, encoding HDVr, SV40 pA signal, and CMV promoter. The fragments were amplified, and the chimeric viral genomes were introduced using cloning plasmids as templates along with specific primer sets detailed in Supplementary Table S5. All the DNA fragments were designed to have complementary ends with a 49- to 95-nucleotide overlap. Equimolar amounts (0.1 pmol each) of the resulting DNA fragments F1-F7 were mixed in 50-μl reaction volumes of PrimeStar GXL with 2 μl of DNA polymerase. CPER was carried out with an initial 2 min of denaturation at 98 °C; 20 cycles of 10 s at 98 °C, 15 s at 55 °C, and 12 min at 68 °C; and a final extension for 12 min at 68 °C. The resulting CPER products, encoding the CMV promoter, full-length ZIKV genome sequence, followed by HDVr and SV40 poly(A) signal, were directly transfected into Huh-7 cells using Trans IT LT-1 (Mirus) following the manufacturer’s protocol. The culture supernatants from Huh-7 cells were collected and inoculated onto C6/36 cells. After three passages in C6/36 cells, the virus stocks were centrifuged to remove cell debris and stored at −80 °C for future use. Virus stock titers in plaque-forming units/ml (PFU/ml) were determined by plaque assay and full-genome sequences were confirmed by high-throughput sequencing as described below.

Viral genome sequencing

The full-length sequence of parental and chimeric ZIKV genomes was determined by Illumina sequencing as previously described⁷². Briefly, total RNA was extracted from a virus stock using QIAamp Viral RNA Mini Kit (Qiagen) and treated with Turbo DNase (Invitrogen). After the RNA purification with RNAClean XP beads (Beckman Coulter), cDNA was synthesized using M-MLV Reverse Transcriptase (Invitrogen), random hexameric primers (Roche) and RNaseOUT Recombinant Ribonuclease Inhibitor (Thermo Fisher Scientific) according to the manufacturer’s protocol. Double-stranded DNA (dsDNA) was produced with Second-Strand Synthesis Buffer (New England Biolabs), E. coli DNA ligase (New England Biolabs), E. coli DNA polymerase I (New England Biolabs) and E. coli RNase H (New England Biolabs), followed by DNA purification with AMPure XP beads (Beckman Coulter). The dsDNA was quantified by Qubit dsDNA HS assay kit (Invitrogen) and used for library preparation with a Nextera XT Library preparation kit (Illumina) according to the manufacturer’s instructions. The final libraries were checked with Bioanalyzer high sensitivity DNA analysis (Agilent) and sequenced on an Ilumina NextSeq 500 instrument (150 cycles, paired ends) with NextSeq 500/550 v2.0 Kit (Illumina). Adapters and low-quality sequences of raw reads were removed using Trimmomatic v0.39⁷³. The trimmed reads were assembled using megahit v1.2.9⁷⁴ with default parameters. The contigs were queried against the NCBI non-redundant protein database using DIAMOND v2.0.4⁷⁵, to look for potential contaminants in addition to the detected ZIKV genome. ZIKV scaffolds were constructed using the longest assembled contig and a viral sequence obtained in the previous study²⁷. The scaffolds were used to map the trimmed reads, using clc-assembly-cell v5.1.0. The consensus sequence generation and intrasample variant analysis were performed with ivar v1.0⁷⁶ using a minimum of 5× read depth of coverage for the consensus, and 500× with a 2% minimum threshold for minor variants. Samtools v1.10⁷⁷ was used to sort the aligned BAM files and generate alignment statistics. The mapping data was visually checked to confirm the accuracy of the obtained genomes using Geneious Prime 2023 (www.geneious.com). The raw reads were deposited in the Sequence Read Archive under bioproject PRJNA1199883, and the full-length consensus genome sequences of the viruses were deposited in GenBank under accession numbers PQ869243-PQ869266.

Plaque assay

Vero E6 cells were seeded in 24-well plates one day prior to virus inoculation. Ten-fold serial dilutions of the samples were prepared in DMEM and inoculated onto the confluent Vero E6 cells after removing the cell-culture supernatant. After 1-h incubation at 37 °C, the inoculum was replaced by DMEM containing 1.0% carboxymethylcellulose (Avantor, VWR), 1% FBS, and 1% pen/strep and the cells were incubated for 7 days at 37 °C. To count plaque numbers, the cells were fixed with 4% formaldehyde solution (Sigma) and stained with 0.2% crystal violet (Sigma).

Viral growth kinetics in vitro

C6/36 cells were seeded in 6-well plates one day prior to virus inoculation. After removing the cell-culture supernatant, the confluent cells were inoculated with ZIKV at a multiplicity of infection (MOI) of 0.01 for 1 h. The virus inoculum was replaced by fresh L15 medium containing 2% FBS, TPB, NEAA, and 1% pen/strep (2% FBS-L15 medium) and the cells were incubated at 28 °C. At 24, 48, 72, and 96 h post infection (h.p.i.), culture supernatants were collected, and infectious titers were determined by plaque assay as described above.

Virus attachment assay

C6/36 cells were seeded in 24-well plates one day before virus inoculation. The confluent cells were pre-chilled on ice for 15 min before the start of the assay. After removing the cell-culture supernatant, ZIKV was added to the cells at an MOI of 1 to allow virus attachment. After 1-h incubation on ice, the supernatant was collected for RNA extraction with QIAamp Viral RNA Mini Kit (Qiagen). The cells were rinsed twice with pre-chilled PBS before being lysed using TRIzol (Invitrogen). Total RNA was then extracted following the manufacturer’s protocol. Viral RNA was quantified by quantitative reverse transcription PCR (RT-qPCR) for ZIKV on total RNA using GoTaq Probe 1-step RT-qPCR kit (Promega), following the manufacturer’s protocol. The primers, probes, and gBlocks utilized in the RT-qPCR for ZIKV are listed in Supplementary Table S6.

Virus internalization assay

C6/36 cells were seeded in 24-well plates and pre-chilled on ice for 15 min before the start of the assay. As controls, cells were pre-treated with Dynasore (Sigma) at a final concentration of 100 μM in 2% FBS-L15 medium or with an equivalent dilution of DMSO for 1 h before chilling. After removing the cell-culture supernatant, ZIKV was added to the cells at an MOI of 1 and incubated on ice for 1 h to allow virus attachment. The cells were washed with PBS to remove unbound virus particles, and fresh 2% FBS-L15 medium containing the same concentrations of Dynasore or DMSO was added. The virus was then allowed to internalize into the cells for 3 h at 28 °C. After this period, the cells were washed with PBS and treated with protease E (5 mg/ml, Sigma) for 15 min on ice to remove any viruses remaining on the cell surface. The cells were washed 3 times with PBS and subsequently lysed with TRIzol (Invitrogen) for RNA extraction. The levels of viral RNA were quantified in total RNA by RT-qPCR using GoTaq 1-step RT-qPCR kit (Promega). Actin expression was assessed as an internal control using the same RT-qPCR method and the primer set listed in Supplementary Table S6. The efficiency of virus internalization, measured at 3 h.p.i, was determined by calculating the ratio of intracellular viral RNA to Actin expression. This ratio was normalized to the ratio of viral RNA to Actin expression from cells sampled at 0 h.p.i. without protease E treatment.

Quantification of ZIKV genomic and antigenomic RNA

C6/36 cells were seeded in 24-well plates and pre-chilled on ice for 15 min before the start of the assay. The confluent cells were inoculated with ZIKV at an MOI of 1 for one hour on ice to allow virus attachment. After one hour, the cells were washed with PBS and fresh 2% FBS-L15 medium was added. The cells were then incubated at 28 °C, and samples were collected at 0, 3, 6, 9, 12, 15, 18, 21, and 24 h.p.i. Each time point involved washing the cells with PBS, treating them with 5 mg/ml protease E for 15 min on ice, and lysing in TRIzol for total RNA extraction. Viral genomic and antigenomic RNAs were quantified using strand-specific RT-qPCR as previously described^78,79, with minor optimizations. The standard curve for antigenomic RNA was constructed by amplifying qPCR targets using primers listed in Supplementary Table S6, followed by reverse transcription using the MEGAscript T7 Transcription kit (Invitrogen) and purification using the MEGAclear Transcription Clean-Up kit (Invitrogen) as per the manufacturer’s guidelines. RNA concentrations were measured using a Nanodrop spectrophotometer, and 10-fold serial dilutions were made for standard curves. For specific incorporation of the tag sequence into cDNA, total RNA extracted from ZIKV-infected cells was reverse transcribed using M-MLV Reverse Transcriptase (Invitrogen) with RT-specific primers (Supplementary Table S6). Quantification of cDNA was then performed using the GoTaq probe qPCR Master Mix (Promega) and a set of strand-specific and tag-specific primers listed in Supplementary Table S6. At each time point, the ratio of viral antigenomic or genomic RNA to Actin expression was calculated as described above. To assess replication efficiency, relative viral antigenomic RNA was normalized against the ratio at 12 h.p.i., and relative genomic RNA against the ratio at 0 h.p.i.

Infectious titer/viral genome ratio

C6/36 cells were seeded in 24-well plates one day prior to virus inoculation. The confluent cells were inoculated with ZIKV at an MOI of 1 for one hour at 28 °C. After one hour, the inoculum was replaced by fresh 2% FBS-L15 medium. Cell-culture supernatant was sampled at 24, 48, 72, and 96 h.p.i. to determine the infectious titer by plaque assay and the concentration of viral genomic RNA by RT-qPCR, as described above.

Virus decay

ZIKV was prepared at a titer of 10⁶ PFU/ml and incubated at 28 °C for 96 h. Samples were collected after 0, 6, 12, 24, 48, 72, and 96 h, to determine infectious titer by plaque assay, as described above.

Cell viability assay

C6/36 cells were seeded in 24-well plates one day prior to virus inoculation. The confluent cells were inoculated with ZIKV at a MOI of 0.01 for 1 h at 28 °C. After the 1-h incubation, the inoculum was replaced by fresh 2% FBS-L15 medium. At the designated time points, the cell-culture supernatant was removed and CellTiter-Glo reagent (Promega) was added to the cells. Following a 10-min incubation at room temperature (20–25 °C), luminescence was measured using a GloMax 96 microplate luminometer (Promega) to quantify ATP, which is indicative of cell viability. For each viral infection, the cellular ATP levels at the indicated time points were normalized to the ATP levels of cells sampled at 0 h.p.i.

Mosquitoes

Ae. aegypti colonies were originally established from wild specimens caught in Colombia in 2017, Uganda in 2015, and Cape Verde in 2020, as previously described^17,50. Mosquitoes were reared under controlled insectary conditions (28° ± 1 °C, 12-h light/dark cycle and 70% relative humidity). Prior to performing the experiments, their eggs were hatched synchronously in a vacuum chamber for one hour. Larvae were reared in dechlorinated tap water supplemented with a standard diet of TetraMin fish food (Tetra). Adults were kept in 30 × 30 × 30-cm BugDorm-1 insect cages (BugDorm) with permanent access to 10% sucrose solution. Mosquito experimental infections were performed with the 16^th-19^th, 23^rd, and 9^th laboratory generations of the colonies from Colombia, Uganda, and Cape Verde, respectively.

Mosquito oral exposure to ZIKV

Mosquitoes were orally exposed to ZIKV by membrane feeding in a biosafety level 3 containment facility. Briefly, 5- to 7-day-old female mosquitoes were starved for one day prior to the oral challenge. The infectious blood meal comprised a 2:1 mixture of washed rabbit erythrocytes (BCL) and ZIKV suspension, supplemented with 10 mM adenosine triphosphate (Sigma) and 0.1% sodium bicarbonate (Sigma). Mosquitoes were allowed to feed on the infectious blood meal for 15 min via a membrane-feeding apparatus (Hemotek Ltd.) with porcine intestine serving as the membrane. After feeding, fully engorged females were sorted on ice, transferred to 1-pint cardboard containers, and maintained under controlled conditions (28 ° ± 1 °C, 12-h light/dark cycle with 70% relative humidity) within a climatic chamber, with permanent access to 10% sucrose solution. The infectious titer of the blood meal was verified by plaque assay as described above.

Salivation assay

Mosquitoes were paralyzed with triethylamine (Sigma) for 5 min at 7, 10, and 14 days post infectious blood meal to collect saliva in vitro as previously described²⁷. Briefly, after removal of all legs, each mosquito’s proboscis was inserted into a 20-μl pipet tip containing 10 μl of FBS. The mosquitoes were allowed to salivate into this medium for 30 min. The saliva-containing FBS was then collected, combined with 40 μl of 2% FBS-DMEM supplemented with 4% Antibiotic-Antimycotic 100× (Life Technologies), and stored at −80 °C for subsequent titration by focus-forming assay, as described below. After salivation, the heads and bodies of each mosquito were dissected and individually transferred to 300 μl of squash buffer, composed of 10 mM Tris pH 8.0, 50 mM NaCl, and 1.27 mM EDTA pH 8.0 (all from Invitrogen), supplemented with 0.35 mg/ml proteinase K (Eurobio Scientific). Head and body samples were stored at −80 °C for subsequent testing by RT-PCR, as described below.

Focus-forming assay

Vero E6 cells were seeded in 96-well plates one day prior to virus inoculation. Serial 10-fold dilutions of the samples were prepared in DMEM (except for saliva samples that were used undiluted) and inoculated onto the confluent cells after removal of the cell-culture supernatant. Following an incubation period at 37 °C for 1 h, the inoculum was replaced with DMEM containing 1.0% carboxymethylcellulose, 1% FBS, 1% pen/strep, and 4% Antibiotic-Antimycotic 100×. The cells were further incubated for 5 days at 37 °C before being fixed with a 4% formaldehyde solution. For immunostaining, the fixed cells were permeabilized with 0.1% Triton X-100 in PBS (Sigma) for 10 min, blocked with 1% bovine serum albumin (Sigma) in PBS, and then incubated with a 1:1000 dilution of mouse anti-flavivirus group antigen monoclonal antibody clone D1-4G2-4-15 (Merck) in PBS for 1 h at room temperature (20–25 °C). Following three washes with PBS, the cells were incubated with Alexa Fluor 488-conjugated goat anti-mouse IgG (Life Technologies) at a 1:1000 dilution in PBS for 1 h at room temperature. Fluorescent foci were visualized using an EVOS FL fluorescence microscope (Thermo Fisher Scientific) equipped with appropriate barrier and excitation filters.

Detection of viral RNA by qualitative RT-PCR

To assess the presence of viral RNA in mosquito heads and bodies, the samples were homogenized for 30 s at 6000 rotations per minute in a Precellys 24 grinder (Bertin Technologies). A 100-μl aliquot of the homogenate was transferred to a PCR plate and subjected to crude RNA extraction by incubation for 5 min at 56 °C followed by 10 min at 98 °C. Total RNA was then used to synthesize cDNA using M-MLV Reverse Transcriptase, RNaseOUT Recombinant Ribonuclease Inhibitor, and random hexameric primers, following the manufacturer’s instructions. The resulting cDNA was amplified by PCR utilizing DreamTaq DNA polymerase (Thermo Fisher Scientific) with two sets of primers: pair 1 comprised ZIKV-PCR-F (5’-GTATGGAATGGAGATAAGGCCCA-3’) and ZIKV-PCR-R (5’-ACCAGCACTGCCATTGATGTGC-3’), and pair 2 consisting of ZIKV-PCR-F and ZIKV-PCR-R1 (5’-TCGTATTGCCAACCAGGCCAAAGC-3’). PCR cycling conditions were set as follows: an initial denaturation for 2 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 55 °C, and 90 s at 72 °C, concluding with a final extension of 7 min at 72 °C. The PCR products were subsequently visualized via electrophoresis on a 1.5% agarose gel.

Mosquito dose-response curves

To estimate the 50% oral infectious dose (OID₅₀) of mosquitoes, dose-response curves were generated by preparing infectious blood meals containing 10⁴, 10⁵, and 10⁶ PFU/ml of ZIKV. Mosquitoes were orally exposed to ZIKV as described above. At 3- and 7-days post blood feeding, whole mosquito bodies were individually homogenized in 300 µl of 2% FBS-L15 supplemented with 4% Antibiotic-Antimycotic 100×. From each homogenate, 150 µl were used for total RNA extraction using the NucleoSpin96 kit (Macherey-Nagel), following the manufacturer’s protocol. Viral RNA detection was performed by qualitative RT-PCR as described above. The remaining homogenates were stored at −80 °C for subsequent titration by focus-forming assay, as described above. The OID₅₀ estimate was calculated from the dose-response curves using the drc package in R v.4.2.2 (www.r-project.org).

Statistical analyses

The prevalence of ZIKV infection, dissemination, and transmission in mosquitoes was analyzed by logistic regression as a function of experiment, ZIKV strain, and time. The initial statistical model included all their interactions, which were removed from the final model if their effect was non-significant (p > 0.05). Time was considered a continuous variable. To account for small, uncontrolled differences in virus concentration, the log₁₀-transformed blood meal titer was also included in the initial model as a covariate and removed from the final model if non-significant (p > 0.05). For in vitro assays, statistical significance was determined by two-tailed Student’s t test or one-way analysis of variance (ANOVA) followed by Dunnett’s test. Statistical analyses were performed in JMP v.14.0.0 (www.jmp.com) and R v.4.2.2 (www.r-project.org).

Model of in vitro viral dynamics

A logistic growth curve was employed to establish a model for predicting infectious viral titer (V_t) in cell culture at time t post infection defined as:

$${V}_{t}={sk}{e}^{{rt}}/(\left(k-s\right)+s{e}^{{rt}})$$

(1)

where s is the starting virus concentration, r is the growth rate, and k is the carrying capacity. Empirical data of viral growth kinetics in mosquito cells infected with the first set of chimeric viruses was used to approximate values for s, r, and k, enabling replication of dynamics observed in the laboratory experiments (Fig. 2C). To simplify the modeling process and avoid complex statistical fitting, s and k were approximated based on the viral titer at the start and end of the experiments, respectively. The growth rate r was adjusted to ensure a reasonable match between model predictions and empirical growth curves.

Model of in vivo viral dynamics

Following a previous study⁸⁰, the in vitro model of Eq. (1) was extended to simulate virus propagation within the mosquito midgut, hemocoel, and salivary glands. A probability of midgut infection (β) was incorporated to reflect the fact that only a few virions typically initiate infection out of thousands in the infectious blood meal^81,82. This approach introduces a stochastic element into the model to reflect demographic variability observed in oral experimental infections of mosquitoes. The model entails six probabilistic processes (Table 2) affecting three key stages: i) initial probability (β) of midgut infection from virus in the blood meal, before viral clearance from the blood meal (µ); ii) viral replication (r), constrained by a carrying capacity (k) in the midgut and hemocoel; and iii) viral ‘escape’ (λ) from the midgut to the hemocoel (M:H) and from the hemocoel to the salivary glands (H:S). G_v, M_v, H_v, and S_v represent the virus levels in the blood meal, midgut, hemocoel, and salivary glands, respectively. The stochastic dynamics were simulated using the tau-leap version of the Gillespie algorithm⁸³. In the simplest model scenario (Table 1), the parameters for r, k, and λ were assumed to be the same across tissues.

Table 2 Transitions and reaction rates in the stochastic model of in vivo mosquito infection dynamics

Full size table

Sensitivity analysis of the in vivo model

A sensitivity analysis was conducted to explore the effects of varying parameter values on the probability of midgut infection as a function of starting virus concentration, and of the probability of systemic dissemination and transmission as a function of time. Empirical dose-response curves were used to inform on ranges of parameter values that could potentially produce results qualitatively to match those observed in the experiments. The stochastic model was run 30 times for each subset of parameters to simulate viral dynamics in 30 individual mosquitoes. Midgut infection simulations ran for 124 hourly time steps, whereas dissemination and transmission simulations ran for 360 hourly time steps. For midgut infection, Latin hypercube sampling generated 1000 parameter sets within the following ranges: μ from 1/72 to 1/12 h⁻¹; β from 0 to 0.0001; r from 0.001 to 0.1 h⁻¹; and k from 10³ to 10²⁰ PFU/ml. The proportion of mosquitoes developing a midgut infection was determined as the proportion of simulations where virus levels in the midgut exceeded zero (M_v > 0) at the end of the 124 hourly time steps. G_v was set at 10⁶ PFU/ml, adjusted by multiplying by 0.003, reflecting the average mosquito blood meal volume⁸⁴. Using the same sampling approach, 1000 parameter sets were generated for viral dissemination (hemocoel) and transmission potential (salivary glands), adopting the maximal and minimal values of μ and β that resulted in successful midgut infections from earlier simulations. Parameter ranges for r and k remained unchanged. λ was set between 10⁻⁸ and 10⁻⁵ h⁻¹. The outcome was determined as the proportion of simulations showing established infections in the hemocoel (H_v ≥ 1), or salivary glands (S_v ≥ 1), over intervals of 24 time steps (equivalent to per day), taking the last output for each daily time step. Evaluation threshold was set at 1000 PFU/ml to confirm infection establishment.

Simulating the in vivo data with the model

Initially, the model was used to reproduce, qualitatively, the dose-response curves of ZIKV infection prevalence observed experimentally. By adjusting β of at least one virion infecting the midgut epithelium, the model was calibrated to match empirical observations with the first set of chimeric viruses. Each value of β was tested over 124 hourly time steps in 100 simulations across varying G_v values from 10² and 10⁸ PFU/ml to quantify the proportion of established midgut infections, as described above. Following the results from the sensitivity analyses, μ of 1/72 h⁻¹, r of 0.04 h⁻¹, k of 10²⁰ PFU/ml, and λ of 0.00005 h⁻¹ were kept constant. Next, parameter values of β derived from dose-response curves in vivo and of r estimated from growth kinetics in mosquito cells were combined to simulate virus dissemination to the hemocoel and salivary glands. Simulations aimed to explore five different scenarios to understand patterns of virus spread within mosquitoes and to provide biologically plausible hypotheses to explain the experimental data (Table 1). Of note, the process of salivary gland infection and virus escape into the saliva were collapsed into S_v in the simulations, whereas the experiments detected virus presence in expectorated saliva. The baseline model (scenario 1, Table 1) was used as a simplistic framework where uniform infection and growth parameters were assumed to enable comparisons against four more elaborate scenarios. Differences in the infection processes are expected, such as variations in r across different cell types⁸⁵, cell-to-cell viral spread within the midgut, and virus propagation via freely moving hemocytes in the hemocoel^52,53, and distinct anatomical barriers between the midgut, hemocoel, and salivary glands^86,87. Additional model parameterizations were explored to represent the observed tissue-specific infection patterns more accurately. These modifications were aimed at modeling widespread infection across simulations consistently, though not necessarily achieving transmission in every single mosquito or each simulation (Fig. 4). The four more complex scenarios were designed to reflect variations in processes between tissues that could be consistent with the observed data. Parameterizations were specifically targeted to result in a disseminated infection across all simulations but not necessarily leading to salivary gland infection in every instance (Fig. 4C). From the baseline scenario, the impact of varying parameterizations was demonstrated by running the model 100 times with G_v at 10⁶ PFU/ml. Throughout the scenarios, μ at 1/72 h⁻¹, β at 10⁻³, the midgut growth rate (r_M) at 0.04 h⁻¹, the escape rate from the midgut to the hemocoel (λ_M:H) at 0.0005 h⁻¹, and k of all tissues at 10²⁰ PFU/ml, were kept constant. For scenarios two through five, where the value of one parameter was reduced by a tenth to illustrate the impact on model outputs, variations were introduced in the hemocoel growth rate (r_H) and the escape rate from hemocoel to salivary glands (λ_H:S), using Gamma distributions. For r_H, a shape (α) of 400 and a scale (θ) of 0.025 were used, resulting in an average of 0.04/10 and a variance of 0.00001. For λ_H:S, α of 2500 and θ of 0.0004 were utilized, achieving an average of 0.0005/10 and a variance of 0.00000002. In scenarios three and five, parameter values were randomly selected from these distributions for each simulation. Additionally, in scenario five, the variance of the Gamma distribution was varied, with simulations run using a mean of 0.00008 and variances of 10^−7.5, 10^−7.3, 10⁻⁷, and 10^−6.5. The five model scenarios were compared to identify the qualitative differences in model outputs among the biologically relevant model structures and to determine which scenario was qualitatively most similar to the observed results. Consequently, a qualitative comparison between observed data and modeled outputs was conducted visually and by assessing the relative differences between the proportion of mosquitoes with a disseminated infection and the proportion capable of transmitting.

Biosafety statement

All work on ZIKV was performed in biosafety level 3 (BSL3) facilities at Institut Pasteur in accordance with the European and French legislation. All experiments were conducted in compliance with national guidelines and with European Commission Directive 2000/54/EC of 18 September 2000 on the protection of workers from risks related to exposure to biological agents at work. All personnel working with ZIKV were trained with relevant safety and protocol-specific procedures.

Risk-benefit analysis

This study was undertaken to enhance understanding of ZIKV, which is an ongoing threat to global public health. This work included the generation of chimeric viruses by substituting the segments of parental ZIKV genomes and their use in experimental infections of mosquitoes. Such basic investigations are widely accepted to benefit public health by increasing our understanding of the mechanisms of transmissibility. The generation of chimeric viruses and their use in experimental infections of mosquitoes were approved by the French Ministry of Higher Education, Research, and Technology (authorization number 8933, 4 August 2021) and the Institut Pasteur Dual Use Liaison Group (DURC 2021-03, 8 March 2022).

The construction of chimeric ZIKV from parental strains is used to study how segments of the viral genome affect viral growth and in vivo transmissibility. Such chimeric viruses have been constructed for many different arboviruses over the last 10+ years and are generally accepted to be safe and low risk because the chimeric viruses do not become more dangerous than the natural isolates. The genomes of RNA viruses like ZIKV are highly optimized due to their ability to efficiently adapt to animal hosts and mosquito vectors. Natural selection has favored viral variants that are best suited to their ecological niches, balancing infectivity, pathogenicity, and transmissibility. As a result of this evolutionary fine-tuning, the likelihood that constructing chimeric ZIKV strains in the laboratory will yield a version of the virus with dramatically enhanced characteristics is low. Such changes would likely have been naturally selected for if they conferred a significant advantage, meaning that most potentially advantageous mutations have already been explored and either retained or discarded by evolutionary pressures.

Most studies using chimeric arboviruses reported to date reveal in-between or inferior phenotypes than their parental strains. In general, chimeric viruses with reduced infectivity and pathogenicity in animals and lower transmissibility in mosquitoes are unlikely to become endemic in the wild. While our work offers significant new insights into the genetic basis of mosquito-borne transmission of ZIKV, it does not provide a road map for creating a form of the virus that is more infectious, pathogenic, or transmissible than what already exists in nature.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw data of Figs. 1, 2 and S4 are provided in the corresponding Source Data files. The raw data of Fig. 3, S2, S3 and S5 are provided in the Supporting Information. The RNA-seq data were deposited in the Sequence Read Archive under bioproject PRJNA1199883, and the full-length consensus genome sequences of the viruses were deposited in GenBank under accession numbers PQ869243-PQ869266. Source data are provided with this paper.

Code availability

Code and data used for the mathematical modeling are openly available on GitHub (https://github.com/jenniesuz/ZIKVintraMozDyn)⁸⁸.

References

Boyer, S., Calvez, E., Chouin-Carneiro, T., Diallo, D. & Failloux, A. B. An overview of mosquito vectors of Zika virus. Microbes Infect. 20, 646–660 (2018).
Article PubMed Google Scholar
Gutierrez-Bugallo, G. et al. Vector-borne transmission and evolution of Zika virus. Nat. Ecol. Evol. 3, 561–569 (2019).
Article PubMed PubMed Central Google Scholar
Musso, D. & Gubler, D. J. Zika virus. Clin. Microbiol. Rev. 29, 487–524 (2016).
Article CAS PubMed PubMed Central Google Scholar
Petersen, L. R., Jamieson, D. J., Powers, A. M. & Honein, M. A. Zika virus. N. Engl. J. Med. 374, 1552–1563 (2016).
Article CAS PubMed Google Scholar
Baud, D., Gubler, D. J., Schaub, B., Lanteri, M. C. & Musso, D. An update on Zika virus infection. Lancet 390, 2099–2109 (2017).
Article PubMed Google Scholar
Musso, D., Ko, A. I. & Baud, D. Zika virus infection - after the pandemic. N. Engl. J. Med. 381, 1444–1457 (2019).
Article PubMed Google Scholar
Mlakar, J. et al. Zika virus associated with microcephaly. N. Engl. J. Med. 374, 951–958 (2016).
Article CAS PubMed Google Scholar
Dick, G. W., Kitchen, S. F. & Haddow, A. J. Zika virus. I. Isolations and serological specificity. Trans. R. Soc. Trop. Med. Hyg. 46, 509–520 (1952).
Kindhauser, M. K., Allen, T., Frank, V., Santhana, R. S. & Dye, C. Zika: the origin and spread of a mosquito-borne virus. Bull. World Health Organ. 94, 675–686C (2016).
Article PubMed PubMed Central Google Scholar
Duffy, M. R. et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. N. Engl. J. Med. 360, 2536–2543 (2009).
Article CAS PubMed Google Scholar
Gyawali, N., Bradbury, R. S. & Taylor-Robinson, A. W. The global spread of Zika virus: is public and media concern justified in regions currently unaffected?. Infect. Dis. Poverty 5, 37 (2016).
Article PubMed PubMed Central Google Scholar
Krauer, F. et al. Zika virus infection as a cause of congenital brain abnormalities and Guillain-Barré syndrome: systematic review. PLoS Med. 14, e1002203 (2017).
Article PubMed PubMed Central Google Scholar
Basu, R. & Tumban, E. Zika virus on a Spreading Spree: what we now know that was unknown in the 1950’s. Virol. J. 13, 165 (2016).
Article PubMed PubMed Central Google Scholar
Pettersson, J. H. et al. How did Zika virus emerge in the Pacific Islands and Latin America? mBio 7, e01239-16 (2016).
Haddow, A. D. et al. Genetic characterization of Zika virus strains: geographic expansion of the Asian lineage. PLoS Negl. Trop. Dis. 6, e1477 (2012).
Article CAS PubMed PubMed Central Google Scholar
Smith, D. R. et al. African and Asian Zika virus isolates display phenotypic differences both in vitro and in vivo. Am. J. Trop. Med. Hyg. 98, 432–444 (2018).
Article CAS PubMed Google Scholar
Rose, N. H. et al. Enhanced mosquito vectorial capacity underlies the Cape Verde Zika epidemic. PLoS Biol. 20, e3001864 (2022).
Article CAS PubMed PubMed Central Google Scholar
Hill, S. C. et al. Emergence of the Asian lineage of Zika virus in Angola: an outbreak investigation. Lancet Infect. Dis. 19, 1138–1147 (2019).
Article CAS PubMed PubMed Central Google Scholar
Anfasa, F. et al. Phenotypic differences between Asian and African lineage Zika viruses in human neural progenitor cells. mSphere 2, e00292-17 (2017).
Hamel, R. et al. African and Asian Zika virus strains differentially induce early antiviral responses in primary human astrocytes. Infect. Genet. Evol. 49, 134–137 (2017).
Article CAS PubMed Google Scholar
Tripathi, S. et al. A novel Zika virus mouse model reveals strain specific differences in virus pathogenesis and host inflammatory immune responses. PLoS Pathog. 13, e1006258 (2017).
Article MathSciNet PubMed PubMed Central Google Scholar
Shao, Q. et al. The African Zika virus MR-766 is more virulent and causes more severe brain damage than current Asian lineage and dengue virus. Development 144, 4114–4124 (2017).
CAS PubMed PubMed Central Google Scholar
Jaeger, A. S. et al. Zika viruses of African and Asian lineages cause fetal harm in a mouse model of vertical transmission. PLoS Negl. Trop. Dis. 13, e0007343 (2019).
Article PubMed PubMed Central Google Scholar
Goodfellow, F. T. et al. Strain-dependent consequences of Zika virus infection and differential impact on neural development. Viruses 10, 550 (2018).
Willard, K. A. et al. Zika virus exhibits lineage-specific phenotypes in cell culture, in Aedes aegypti mosquitoes, and in an embryo model. Viruses 9, 383 (2017).
Duggal, N. K. et al. Differential neurovirulence of African and Asian genotype Zika virus isolates in outbred immunocompetent mice. Am. J. Trop. Med. Hyg. 97, 1410–1417 (2017).
Article CAS PubMed PubMed Central Google Scholar
Aubry, F. et al. Recent African strains of Zika virus display higher transmissibility and fetal pathogenicity than Asian strains. Nat. Commun. 12, 916 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Weaver, S. C. Emergence of epidemic Zika virus transmission and congenital Zika syndrome: are recently evolved traits to blame? mBio 8, e02063-16 (2017).
Kuno, G. & Chang, G. J. Full-length sequencing and genomic characterization of Bagaza, Kedougou, and Zika viruses. Arch. Virol. 152, 687–696 (2007).
Article CAS PubMed Google Scholar
Bos, S. et al. The structural proteins of epidemic and historical strains of Zika virus differ in their ability to initiate viral infection in human host cells. Virology 516, 265–273 (2018).
Article CAS PubMed Google Scholar
Nunes, B. T. D. et al. Zika structural genes determine the virulence of African and Asian lineages. Emerg. Microbes Infect. 9, 1023–1033 (2020).
Article CAS PubMed PubMed Central Google Scholar
Pompon, J. et al. A Zika virus from America is more efficiently transmitted than an Asian virus by Aedes aegypti mosquitoes from Asia. Sci. Rep. 7, 1215 (2017).
Article ADS PubMed PubMed Central Google Scholar
Weger-Lucarelli, J. et al. Vector competence of American mosquitoes for three strains of Zika virus. PLoS Negl. Trop. Dis. 10, e0005101 (2016).
Article PubMed PubMed Central Google Scholar
Roundy, C. M. et al. Variation in Aedes aegypti mosquito competence for Zika virus transmission. Emerg. Infect. Dis. 23, 625–632 (2017).
Article PubMed PubMed Central Google Scholar
Calvez, E. et al. Differential transmission of Asian and African Zika virus lineages by Aedes aegypti from New Caledonia. Emerg. Microbes Infect. 7, 159 (2018).
Article PubMed PubMed Central Google Scholar
Liu, Y. et al. Evolutionary enhancement of Zika virus infectivity in Aedes aegypti mosquitoes. Nature 545, 482–486 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Yu, X. et al. A mutation-mediated evolutionary adaptation of Zika virus in mosquito and mammalian host. Proc. Natl Acad. Sci. USA 118, e2113015118 (2021).
Liu, J. et al. Role of mutational reversions and fitness restoration in Zika virus spread to the Americas. Nat. Commun. 12, 595 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Kuo, L. et al. Reversion to ancestral Zika virus NS1 residues increases competence of Aedes albopictus. PLoS Pathog. 16, e1008951 (2020).
Article CAS PubMed PubMed Central Google Scholar
Brackney, D. E. et al. C6/36 Aedes albopictus cells have a dysfunctional antiviral RNA interference response. PLoS Negl. Trop. Dis. 4, e856 (2010).
Article PubMed PubMed Central Google Scholar
Varjak, M. et al. Characterization of the Zika virus induced small RNA response in Aedes aegypti cells. PLoS Negl. Trop. Dis. 11, e0006010 (2017).
Article PubMed PubMed Central Google Scholar
Agrelli, A., de Moura, R. R., Crovella, S. & Brandão, L. A. C. ZIKA virus entry mechanisms in human cells. Infect. Genet. Evol. 69, 22–29 (2019).
Article CAS PubMed Google Scholar
Li, M. et al. Characterization of Zika virus endocytic pathways in human glioblastoma cells. Front. Microbiol. 11, 242 (2020).
Article PubMed PubMed Central Google Scholar
Mosso, C., Galván-Mendoza, I. J., Ludert, J. E. & del Angel, R. M. Endocytic pathway followed by dengue virus to infect the mosquito cell line C6/36 HT. Virology 378, 193–199 (2008).
Article CAS PubMed Google Scholar
Rinkenberger, N. & Schoggins, J. W. Comparative analysis of viral entry for Asian and African lineages of Zika virus. Virology 533, 59–67 (2019).
Article CAS PubMed Google Scholar
Murray, J. M., Aaskov, J. G. & Wright, P. J. Processing of the dengue virus type 2 proteins prM and C-prM. J. Gen. Virol. 74, 175–182 (1993).
Article CAS PubMed Google Scholar
Wang, S., He, R. & Anderson, R. PrM- and cell-binding domains of the dengue virus E protein. J. Virol. 73, 2547–2551 (1999).
Article CAS PubMed PubMed Central Google Scholar
Bae, H. G., Nitsche, A., Teichmann, A., Biel, S. S. & Niedrig, M. Detection of yellow fever virus: a comparison of quantitative real-time PCR and plaque assay. J. Virol. Methods 110, 185–191 (2003).
Article CAS PubMed Google Scholar
van der Schaar, H. M. et al. Characterization of the early events in dengue virus cell entry by biochemical assays and single-virus tracking. J. Virol. 81, 12019–12028 (2007).
Article PubMed PubMed Central Google Scholar
Aubry, F. et al. Enhanced Zika virus susceptibility of globally invasive Aedes aegypti populations. Science 370, 991–996 (2020).
Article ADS CAS PubMed Google Scholar
Cui, Y., Grant, D. G., Lin, J., Yu, X. & Franz, A. W. E. Zika virus dissemination from the midgut of Aedes aegypti is facilitated by bloodmeal-mediated structural modification of the midgut basal lamina. Viruses 11, 1056 (2019).
Leite, T. H. J. F., Ferreira, Á, Imler, J. L. & Marques, J. T. Distinct roles of hemocytes at different stages of infection by dengue and Zika viruses in Aedes aegypti mosquitoes. Front. Immunol. 12, 660873 (2021).
Article CAS PubMed PubMed Central Google Scholar
Hall, D. R. et al. Mosquito immune cells enhance dengue and Zika virus dissemination in Aedes aegypti. Nat. Commun. 16, 5891 (2025).
Article ADS PubMed PubMed Central Google Scholar
Sevvana, M. et al. Refinement and analysis of the mature Zika virus cryo-EM structure at 3.1 Å resolution. Structure 26, 1169–1177.e1163 (2018).
Article CAS PubMed PubMed Central Google Scholar
DiNunno, N. M. et al. Identification of a pocket factor that is critical to Zika virus assembly. Nat. Commun. 11, 4953 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Göertz, G. P. et al. Subgenomic flavivirus RNA binds the mosquito DEAD/H-box helicase ME31B and determines Zika virus transmission by Aedes aegypti. Proc. Natl. Acad. Sci. USA 116, 19136–19144 (2019).
Article ADS PubMed PubMed Central Google Scholar
Göertz, G. P. et al. Noncoding subgenomic flavivirus RNA is processed by the mosquito RNA interference machinery and determines West Nile virus transmission by Culex pipiens mosquitoes. J. Virol. 90, 10145–10159 (2016).
Article PubMed PubMed Central Google Scholar
Pompon, J. et al. Dengue subgenomic flaviviral RNA disrupts immunity in mosquito salivary glands to increase virus transmission. PLoS Pathog. 13, e1006535 (2017).
Article PubMed PubMed Central Google Scholar
Bellone, R. et al. Experimental adaptation of dengue virus 1 to Aedes albopictus mosquitoes by in vivo selection. Sci. Rep. 10, 18404 (2020).
Article CAS PubMed PubMed Central Google Scholar
Jung, H. G. et al. Influence of Zika virus 3’-end sequence and nonstructural protein evolution on the viral replication competence and virulence. Emerg. Microbes Infect. 11, 2447–2465 (2022).
Article CAS PubMed PubMed Central Google Scholar
Hodgson, J. J., Chen, R. Y., Blissard, G. W. & Buchon, N. Viral and cellular determinants of polarized trafficking of viral envelope proteins from insect-specific and insect-vectored viruses in insect midgut and salivary gland cells. J. Virol. 98, e0054024 (2024).
Article PubMed Google Scholar
Phengchat, R. et al. Differential intra-host infection kinetics in Aedes aegypti underlie superior transmissibility of African relative to Asian Zika virus. mSphere 8, e0054523 (2023).
Article PubMed Google Scholar
Althouse, B. M. & Hanley, K. A. The tortoise or the hare? Impacts of within-host dynamics on transmission success of arthropod-borne viruses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140299 (2015).
Hanley, K. A. et al. Trade-offs shaping transmission of sylvatic dengue and Zika viruses in monkey hosts. Nat. Commun. 15, 2682 (2024).
Article ADS CAS PubMed PubMed Central Google Scholar
Lambrechts, L. Does arbovirus emergence in humans require adaptation to domestic mosquitoes?. Curr. Opin. Virol. 60, 101315 (2023).
Article PubMed Google Scholar
Tsetsarkin, K. A. et al. Chikungunya virus emergence is constrained in Asia by lineage-specific adaptive landscapes. Proc. Natl. Acad. Sci. USA 108, 7872–7877 (2011).
Article ADS CAS PubMed PubMed Central Google Scholar
Vasilakis, N. et al. Genetic and phenotypic characterization of sylvatic dengue virus type 2 strains. Virology 377, 296–307 (2008).
Article CAS PubMed Google Scholar
Diallo, D. et al. Zika virus emergence in mosquitoes in southeastern Senegal, 2011. PLoS One 9, e109442 (2014).
Article ADS PubMed PubMed Central Google Scholar
Ellison, D. W. et al. Complete genome sequences of Zika virus strains isolated from the blood of patients in Thailand in 2014 and the Philippines in 2012. Genome Announc. 4, e00359-16 (2016).
Fontaine, A., Jiolle, D., Moltini-Conclois, I., Lequime, S. & Lambrechts, L. Excretion of dengue virus RNA by Aedes aegypti allows non-destructive monitoring of viral dissemination in individual mosquitoes. Sci. Rep. 6, 24885 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Edmonds, J. et al. A novel bacterium-free method for generation of flavivirus infectious DNA by circular polymerase extension reaction allows accurate recapitulation of viral heterogeneity. J. Virol. 87, 2367–2372 (2013).
Article CAS PubMed PubMed Central Google Scholar
Faye, O. et al. Genomic epidemiology of 2015-2016 Zika virus outbreak in Cape Verde. Emerg. Infect. Dis. 26, 1084–1090 (2020).
Article CAS PubMed PubMed Central Google Scholar
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Article CAS PubMed PubMed Central Google Scholar
Li, D. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).
Article CAS PubMed Google Scholar
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Article CAS PubMed Google Scholar
Grubaugh, N. D. et al. An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar. Genome Biol. 20, 8 (2019).
Article PubMed PubMed Central Google Scholar
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Article PubMed PubMed Central Google Scholar
Raquin, V. & Lambrechts, L. Dengue virus replicates and accumulates in Aedes aegypti salivary glands. Virology 507, 75–81 (2017).
Article CAS PubMed Google Scholar
Plaskon, N. E., Adelman, Z. N. & Myles, K. M. Accurate strand-specific quantification of viral RNA. PLoS One 4, e7468 (2009).
Article ADS PubMed PubMed Central Google Scholar
Lord, J. S. & Bonsall, M. B. Mechanistic modelling of within-mosquito viral dynamics: insights into infection and dissemination patterns. PLoS Comput. Biol. 19, e1011520 (2023).
Article ADS CAS PubMed PubMed Central Google Scholar
Smith, D. R., Adams, A. P., Kenney, J. L., Wang, E. & Weaver, S. C. Venezuelan equine encephalitis virus in the mosquito vector Aedes taeniorhynchus: infection initiated by a small number of susceptible epithelial cells and a population bottleneck. Virology 372, 176–186 (2008).
Article CAS PubMed Google Scholar
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 Google Scholar
Gillespie, D. T. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22, 403–434 (1976).
Article ADS MathSciNet CAS Google Scholar
Graumans, W. et al. A mosquito feeding assay to examine Plasmodium transmission to mosquitoes using small blood volumes in 3D printed nano-feeders. Parasit. Vectors 13, 401 (2020).
Article CAS PubMed PubMed Central Google Scholar
Ramos da Silva, S., Cheng, F., Huang, I. C., Jung, J. U. & Gao, S. J. Efficiencies and kinetics of infection in different cell types/lines by African and Asian strains of Zika virus. J. Med. Virol. 91, 179–189 (2019).
Article PubMed Google Scholar
Franz, A. W., Kantor, A. M., Passarelli, A. L. & Clem, R. J. Tissue barriers to arbovirus infection in mosquitoes. Viruses 7, 3741–3767 (2015).
Article CAS PubMed PubMed Central Google Scholar
Black, W. C. et al. Flavivirus susceptibility in Aedes aegypti. Arch. Med. Res. 33, 379–388 (2002).
Article ADS CAS PubMed Google Scholar
Torii, S. et al. Github repository jenniesuz/ZIKVintraMozDyn. https://doi.org/10.5281/zenodo.16941173 (2025).

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Acknowledgements

We thank Catherine Lallemand for assistance with mosquito rearing. We thank Caroline Manet and all members of the Lambrechts lab for their input during the project. We are grateful to Claudia Romero-Vivas and Silvânia da Veiga Leal, who helped establish the mosquito colonies from Colombia and Cape Verde, respectively. We thank John-Paul Mutebi, Noah Rose, and Lindy McBride for initially providing the colony from Uganda. We thank Rick Jarman for providing the ZIKV strain from Thailand. During the preparation of this work, the authors used ChatGPT-4 (OpenAI) to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. This work was supported by MSDAVENIR (grant INTRANZIGEANT to X.M. and L.L.), the France 2030 initiative through state funds managed by ANRS-MIE (grant ANRS-23-PEPR-MIE-0004 to L.L.), the French Government’s Investissement d’Avenir programme Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (grant ANR-10-LABX-62-IBEID to E.S.-L., X.M., and L.L.), the French Government’s Investissement d’Avenir programme INCEPTION (grant ANR-16-CONV-0005 to E.S.-L.), the iXcore foundation for research (grant to E.S.-L.), the HERA project DURABLE (grant No 101102733 to E.S.-L.) and the National Institutes of Health PICREID (grant No U01AI151758 to E.S.-L.). This project also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions grant agreement No 101066146 (ZIKVMosTransmit, Postdoctoral Fellowship to S.T.), and the UK Medical Research Council (Fellowship MR/W017059/1 to J.S.L.).

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Authors and Affiliations

Institut Pasteur, Université Paris Cité, CNRS UMR2000, Insect-Virus Interactions Unit, Paris, France
Shiho Torii, Morgane Lavina, Alicia Lecuyer & Louis Lambrechts
Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
Jennifer S. Lord
Institut Pasteur, Université Paris Cité, Paris, Evolutionary Genomics of RNA Viruses Unit, Paris, France
Matthieu Prot & Etienne Simon-Lorière
Arbovirus and Viral Hemorrhagic Fevers Unit, Institut Pasteur de Dakar, Dakar, Senegal
Cheikh T. Diagne, Oumar Faye, Ousmane Faye & Amadou A. Sall
Department of Biology, University of Oxford, Oxford, United Kingdom
Michael B. Bonsall
Institut Pasteur, Université Paris Cité, Mouse Genetics Laboratory, Paris, France
Xavier Montagutelli

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Shiho Torii
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Jennifer S. Lord
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Contributions

Conceptualization: S.T., X.M., L.L. Methodology: J.S.L., M.B.B. Formal analysis: S.T., J.S.L., M.B.B., E.S.-L., L.L. Investigation: S.T., M.L., M.P., A.L. Resources: C.T.D., Oum.F., Ous.F., A.A.S. Data curation: E.S.-L. Writing – original draft: S.T., L.L. Writing – review & editing: S.T., J.S.L., M.B.B., E.S.-L., X.M., L.L. Visualization: S.T., J.S.L. Supervision: L.L. Funding acquisition: S.T., J.S.L., E.S.-L., X.M., L.L.

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Correspondence to Louis Lambrechts.

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Torii, S., Lord, J.S., Lavina, M. et al. Polygenic viral factors enable efficient mosquito-borne transmission of African Zika virus. Nat Commun 16, 9594 (2025). https://doi.org/10.1038/s41467-025-64627-0

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Received: 07 February 2025
Accepted: 22 September 2025
Published: 30 October 2025
Version of record: 30 October 2025
DOI: https://doi.org/10.1038/s41467-025-64627-0