Skip to main content
Download PDF

Genetic diversity of Nyssorhynchus (Anopheles) darlingi related to biting behavior in western Amazon

Parasites & Vectors volume 12, Article number: 242 (2019) Cite this article

Abstract

Background

In the Amazon Basin, Nyssorhynchus (Anopheles) darlingi is the most aggressive and effective malaria vector. In endemic areas, behavioral aspects of anopheline vectors such as host preference, biting time and resting location post blood meal have a key impact on malaria transmission dynamics and vector control interventions. Nyssorhynchus darlingi presents a range of feeding and resting behaviors throughout its broad distribution.

Methods

To investigate the genetic diversity related to biting behavior, we collected host-seeking Ny. darlingi in two settlement types in Acre, Brazil: Granada (~ 20-year-old, more established, better access by road, few malaria cases) and Remansinho (~ 8-year-old, active logging, poor road access, high numbers malaria cases). Mosquitoes were classified by the location of collection (indoors or outdoors) and time (dusk or dawn).

Results

Genome-wide SNPs, used to assess the degree of genetic divergence and population structure, identified non-random distributions of individuals in the PCA for both location and time analyses. Although genetic diversity related to behavior was confirmed by non-model-based analyses and FST values, model-based STRUCTURE detected considerable admixture of these populations.

Conclusions

To our knowledge, this is the first study to detect genetic markers associated with biting behavior in Ny. darlingi. Additional ecological and genomic studies may help to understand the genetic basis of mosquito behavior and address appropriate surveillance and vector control.

Introduction

Nyssorhynchus (Anopheles) darlingi [1] is the main Neotropical malaria vector in the Amazon Basin due to its role in human Plasmodium transmission [2,3,4]. Following a decrease in the number of malaria cases in the Americas from 2005 to 2014, an increase was observed in the following three years, with Brazil and Venezuela as the countries contributing the largest number of cases [5, 6]. In Brazil, transmission remains entrenched in the Amazon Basin, which accounts for 99.5% of the country’s malaria burden [7]. Human migration to and within this region over the past century has been accompanied by dramatic environmental modification and the promotion of malaria transmission [8,9,10,11]. Deforestation and anthropogenic land changes are known to be associated with increases in Ny. darlingi presence and abundance and thus malaria risk [12,13,14].

Root [15] first described Ny. darlingi based on morphological characters of the egg, fourth-instar larva, pupa, male and female adults. Thenceforth, successive studies reported morphological, behavioral, ecological and genetic heterogeneity throughout its broad distribution from Central to South America [16,17,18,19]. Nevertheless, Ny. darlingi had been considered to be a monophyletic species until Emerson et al. [20] proposed the occurrence of three putative species based on well-supported genetic clusters. In their study, Neotropical biogeographical events explained dispersal of Ny. darlingi populations, at a continental scale, leading to diversification within this species. However, additional genetic diversity at a local scale has been demonstrated for this vector based on ecological differences [21, 22] and seasonality [23].

At least three aspects of mosquito behavior are especially important in determining pathogen transmission rates to humans: anthropophily, endophagy and endophily. The first is the predilection of a vector to blood-feed on humans instead of other animals, the second is a preference for biting inside houses, and the last is indoor resting behavior after a blood meal. These traits are known to vary within and between Anophelinae mosquito species that transmit malaria [24]. Nyssorhynchus darlingi demonstrates high anthropophilic behavior in many areas, sometimes combined with opportunistic zoophilic (non-human) feeding [25, 26]. Depending on location, house type, and host availability, among other environmental factors, it can display exophily/exophagy [27, 28] or endophily/endophagy [29] behavior or both [4, 30, 31]. Variation in peak biting times and patterns is also high [32, 33].

Heterogeneity of vector behavior could be the result of genetically differentiated subpopulations or behavioral plasticity, i.e. individuals with the same genotype adopt different behaviors. Behavioral plasticity seems to be the more likely explanation in malaria vectors. For example, studies on Anopheles farauti in the Solomon Islands identified a single population with individuals which fed indoors and outdoors, and early in the evening as well as late at night [34]. Furthermore, a study of biting time (early evening, dawn) and location (indoor, outdoor) in An. arabiensis analyzed 34 coding SNPs in 8 clock genes, but found no linkage between these phenotypes and the candidate clock genes known to influence behavior in other Diptera [35].

With these findings in mind, and a general dearth of such studies in Neotropical malaria vectors, the present article investigated the extent of population genetic diversity in Ny. darlingi on a microgeographical scale and its association with biting behavior. We tested the hypothesis that there was population genetic structure in this species associated with blood-feeding location (indoors or outdoors) and time (dusk or dawn) using genome-wide SNPs. Understanding the genetic contribution to mosquito biting behavior could lead to the development of molecular tools for more precise vector surveillance and malaria control.

Methods

Samples

Collections of Nyssorhynchus darlingi adults were performed outdoors (peridomestic, within 10 m of each house) and indoors in two rural settlements Acre State in April 2011, the older more settled Granada (9°45′S, 67°04′W) and the newer, more recently deforested Remansinho (9°29′S, 66°34′W) (Fig. 1). Collections were performed using human landing catch (HLC) by the authors MC and PEMR, between 18:00–6:00 h. All collected specimens were identified using the key of Consoli & Lourenço-Oliveira [36] based on morphological characters and conserved individually in Eppendorf tubes at – 20 °C; only Ny. darlingi specimens were used for further analysis.

Fig. 1
figure 1

Map of Brazil showing collection points in Brazilian Amazon Basin. Square box: zoom in showing Acre and Amazonas states and the two collection localities, Granada and Remansinho

Full size image

SNP genotyping

DNA was individually extracted using ReliaPrep™ Blood gDNA kit (Promega, Madison, USA) and DNA concentration was estimated using a Qubit fluorometer (Invitrogen, Carlsbad, USA). Double restriction digestion of 200 ng of high-quality genomic DNA with EcoRI-MspI restriction enzymes was performed in a 40 µl reaction volume and then purified with AMPure XP beads following the manufacturer’s protocol (Beckman Coulter, California, USA). Hybridized customized adapters P1 (0.3 µM) and P2 (4.8 µM) were ligated to the digested DNA (T4 DNA Ligase, Promega) as described in Campos et al. [21]. After another purification with AMPure XP beads, DNA was size selected on an agarose gel to 350–400 bp followed by another AMPure XP bead purification. PCR amplification for Nextera® indexing was carried out to generate Illumina sequencing libraries, according to these cycling conditions: an initial denaturation step at 72 °C for 3 min and at 95 °C for 30 s, followed by 16 cycles of 95 °C for 10 s, annealing at 55 °C for 30 s, elongation at 72 °C for 30 s, and a final extension cycle at 72 °C for 5 min, then each PCR product was purified one last time. Samples were individually quantified using the KAPA library quantification kit (KAPA Biosystems, Wilmington, USA) and equimolarly combined to compose a final library. Final libraries were quantified, normalized, denatured, and finally loaded on the Illumina reagent cartridge 150-cycle of paired-end sequencing in a Hiseq 2500 (Genomics and Bioinformatics Core, State University of New York at Buffalo).

SNP data processing

Raw ddRAD reads were processed to identify SNP loci within and between individuals using scripts implemented in Stacks v1.31 [37]. First, sequences were quality filtered using the default parameters of process_radtags script. Then, each individual’s sequence reads were aligned to the Ny. darlingi reference genome [38] using Bowtie2 with default parameters [39]. Aligned reads were input to ref_map.pl Perl script in Stacks, using a minimum of 5 reads (−m) to report a stack and join the catalog. The dataset was corrected using another Stacks script called rxstacks with the following parameters: prune out non-biological haplotypes unlikely to occur in the population (−prune_haplo), minimum log-likelihood required was −10.0 (−lnl_lim). A new catalog was built by cstacks Stacks’ script and each individual was matched against the catalog with sstacks script. We then used the populations script in Stacks to filter loci that were called in at least 50% (−r) of all samples, i.e. the first step was performed without population information to avoid population bias in the SNP selection. This latter step was run with minimum coverage of 5 (−m) and a random single SNP for each RAD locus was selected (write_random_snp). The populations script produced genotype output in several formats (e.g. VCF, Genepop) and summary statistics such as nucleotide diversity, pairwise FST and private alleles.

SNP data analysis

Principal components analysis (PCA) and discriminant analysis of principal components (DAPC) were performed in the R package Adegenet [40]. The former described global diversity overlooking group information whereas DAPC maximizes differences between groups and minimizes variation within clusters. The optimum number of genetic clusters in DAPC was the lowest Bayesian information criterion (BIC) associated with several K-means calculated. Number of retained PCs for DAPC analysis was calculated as described [41]. For model-based method, we used Bayesian clustering algorithm implemented in STRUCTURE (Pritchard et al., 2000), which was run 40 times for each K value that ranged from 1 to 6. STRUCTURE was run with admixture model and correlated allele frequencies, ‛burn-inʼ of 100,000 generations and MCMC chain of 1,000,000 generations. The Evanno method [42] was used to determine the optimal value of K for runs implemented in structureHarvester [43].

Results

SNP genotyping

A total of 128 individual mosquitoes were sequenced from both settlements, Granada (n = 96) and Remansinho (n = 32). We considered four collection categories: indoor collection at dusk, i.e. 18:00–21:00 h; indoors at dawn, i.e. 3:00–6:00 h; outdoors at dusk and outdoors at dawn (Table 1). Overall collections showed a preference for outdoor biting behavior and a peak of mosquito density between 19:00–21:00 h. From 124,841,110 ddRAD tag sequences, ~ 104 million sequences passed several levels of quality filtering and 34.9% (± 9.8 SD) of this set of reads was aligned to the Ny. darlingi genome [38]. An average (± SD) of 10,107 ± 4,123 ddRAD loci were genotyped per sample, and 25% presented SNPs (Additional file 1: Table S1). Analyses included endophagic (indoor) and exophagic (outdoor) specimens collected at dusk from both settlements, whereas, from Granada, analyses included the dusk and dawn collections, since the number of specimens collected at dawn in Remansinho were very low and were not included in this study. The number of SNPs included in each analysis was determined by including only those loci that were scored in > 50% of all individuals included for that particular analysis. Population assignment was performed after the SNPs data set selection.

Table 1 Number of Nyssorhynchus darlingi genotyped by settlement, location and time
Full size table

Genetic diversity of Nyssorhynchus darlingi associated with biting behavior

Location: indoor vs outdoor

Dataset analyses were conducted to test the hypothesis of population structure among indoor and outdoor samples collected at dusk in Granada and Remansinho. After filtering, 944 SNP-loci were genotyped in at least 50% of all individuals from the four groups. STRUCTURE analysis of the SNP dataset supported 2 genetic clusters, and individuals are admixed among the two clusters, suggesting that all individuals belong to a single panmictic population (Fig. 2d). Principal components analysis (PCA) showed that most of indoor specimens were concisely grouped together regardless of the collection location, whereas outdoor individuals were found in two different areas in the plot (Fig. 2a; PC1 = 14%, PC2 = 6%). Discriminant analysis of principal component (DAPC) showed K = 3 as the best number of genetic clusters i.e. the lowest Bayesian information criterion (BIC) (Fig. 2c). Cluster “1” had only outdoor Ny. darlingi specimens from Granada and cluster “2” had nearly all outdoor individuals from Remansinho, whereas cluster “3” included indoor individuals from both locations (Fig. 2b). Indoor groups showed a higher percentage of polymorphism compared with outdoor ones (Table 2) even when subdivided into dusk and dawn collections (Table 3). Interestingly, the highest pairwise FST was between indoor Remansinho and outdoor Granada samples (FST = 0.177, P < 0.0001), and the lowest was between indoors samples (FST = 0.094, P < 0.0001) (Table 4).

Fig. 2
figure 2

Results of PCA, and clustering by DAPC and STRUCTURE of 944-locus SNP dataset, comparing endophagic and exophagic Nyssorhynchus darlingi. a PCA of outdoor samples (red and yellow dots) and indoor samples (blue and green dots) from Granada and Remansinho. b DAPC ordination of all samples in the three genetic clusters (1–3) in two axes; top right box: number of PCs retained (n = 28). c Bayesian information criterion (BIC) to estimate the appropriate number of genetic clusters, which is the lowest BIC value. d Results of STRUCTURE analysis: each column represents an individual and colors reflect genetic clusters assignment (K = 2 and K = 3)

Full size image
Table 2 Summary statistics for the indoor- and outdoor-collected Nyssorhynchus darlingi
Full size table
Table 3 Summary statistics for the indoor/outdoor- and dusk/dawn-collected Nyssorhynchus darlingi
Full size table
Table 4 Pairwise FST between indoor- and outdoor-collected Nyssorhynchus darlingi
Full size table

Time: dusk vs dawn

Four groups of samples from Granada, indoors and outdoors, collected at dusk and dawn, were analyzed in this set to test the hypothesis of Ny. darlingi population structure between indoor vs outdoor feeding and dawn vs dusk feeding. A total of 589 SNP-loci were genotyped in at least 50% of individuals. STRUCTURE result of the SNP dataset supported a lack of population genetic structure, although the optimal number of genetic clusters was 2 (Fig. 3d). All individuals were admixed for the two clusters, indicating that they belong to a single population. The first axis (15%) in the PCA divided indoor and outdoor samples, whereas the second axis (7%) separated time of collection (Fig. 3a). The lowest BIC for K-means was 4 in the DAPC where essentially the four groups were partitioned (Fig. 3b, c). Samples collected at dawn showed a higher percentage of polymorphic loci compared with those collected at dusk (Table 5). For this dataset, the highest pairwise FST was between outdoor dusk and indoor dawn samples (FST = 0.259, P < 0.0001), and the lowest was between outdoors samples (FST = 0.081, P < 0.0001) (Table 5).

Fig. 3
figure 3

Results of PCA, and clustering by DAPC and STRUCTURE of 589-locus SNP dataset, comparing dusk/dawn endophagic and exophagic Nyssorhynchus darlingi. a PCA of dusk samples (red and blue dots) and dawn samples (purple and orange dots) from Granada. b DAPC ordination of all samples in the four genetic clusters (1–4) in two axes; bottom right box: number of PCs retained (n = 23). c Bayesian information criterion (BIC) to estimate the appropriate number of genetic clusters, which is the lowest BIC value. d STRUCTURE result: each column represents an individual and colors reflect genetic clusters assignment (best K = 2)

Full size image
Table 5 Pairwise FST between indoor/outdoor- and dusk/dawn-collected Nyssorhynchus darlingi
Full size table

Discussion

Heterogenous biting behavior is quite characteristic of Ny. darlingi throughout the species’ range in Central and South America [2, 25, 28, 44]. Early studies reported its tendency for indoor blood-feeding behavior, i.e. endophagy (reviewed in [4]), but over time a variety of endo:exophagy and endo:exophily ratios have been reported and correlated with different local environmental variables. One example is the shift towards increased exophagy after a vector control intervention such as long-lasting insecticide nets (LLINs) and/or indoors residual spraying (IRS) described in Ny. darlingi [45] and other anophelines [46,47,48].

Despite numerous entomological studies investigating the different aspects of Ny. darlingi behavior, few studies focus on the genetic basis of these phenotypic traits. In fact, this study is the first to detect genetic markers associated with exophagy and endophagy as well as biting times (dusk/dawn) in Ny. darlingi females. Previous studies with other species such as An. gambiae (s.l.) and An. funestus showed genetic differentiation regarding divergent behavior using chromosomal inversion karyotypes frequencies [49,50,51]. Moreover, recently, a genetic component was detected for host choice for blood-feeding in An. arabiensis linked to two paracentromeric chromosome inversions [52]. However, there is still a lack of studies at such a fine scale, which may be due to the relatively high cost of whole genome sequencing and the complexity of association mapping studies. To achieve high power in genome-wide association studies prior genomic knowledge is required, i.e. linkage disequilibrium, which the current version of the Ny. darlingi genome assembly does not provide. Thus, we are aware of the limitations of the present study in identifying the precise location of the causal genetic variants.

Reduced representation genome-sequencing methods, such as ddRAD, have proven to be powerful tools for the assessment of a large number of SNPs on a genome-wide scale, with considerably lower library construction and sequencing effort [53, 54]. However, these approaches suffer from sampling biases, i.e. allele dropout (ADO), due to the absence or polymorphism within a restriction site [55]. Here, in order to minimize any bias from allele dropout or from other sources, we applied two strict filters for all sets of analysis: no prior information of population and only loci present in more than half of all individuals were selected.

Our results, using 994 genome-wide SNPs, showed a lack of population structuring related to indoor and outdoor biting behavior. On the other hand, PCA and DAPC analysis showed that the same markers are present in indoor mosquitoes collected at both Granada and Remansinho (Fig. 2). Although, overall FST values are significant between these groups, and a consistently lower value was detected for endophagic populations, no different clustering assignment were found using STRUCTURE analysis. The single endophagic group could be explained by similar selection of Ny. darlingi females related to environmental conditions within houses such as temperature, shelter and blood meal availability. In fact, Paaijmans & Thomas [56] showed that indoor environments are warmer than outdoor ones and usually present less daily variation. Besides temperature, indoor residual spraying could be an important driver in the selection of mosquito females. In contrast to the present study, PCA and STRUCTURE analysis of SNPs of Ny. darlingi from two localities in Loreto, Peru, Cahuide and Lupuna, detected no genetic differentiation between endophagic and exophagic specimens, nor between specimens collected at dusk vs dawn [36]. However, Cahuide and Lupuna in Loreto are both older riverine communities, with similar microsatellite profiles and levels of deforestation [21]. We hypothesize that the contrast and difference in outdoor environments between the two Acre populations of Granada and Remansinho is the main driving force for the genetic diversity detected in the present study.

In terms of the time of biting behavior in Ny. darlingi, the present study showed a non-random distribution of individuals in the PCA and clustering in DAPC, besides lack of population structuring. To avoid confounding effects of microgeographical population structure, only one collection point was used in this analysis. As shown in the first set of analysis, PC1 segregated endophagic and exophagic Ny. darlingi females (Fig. 3). In addition, PC2 segregated dusk and dawn individuals. In fact, DAPC presented four genetic clusters containing samples from each category. Across its broad distribution, Ny. darlingi populations present a range of peak biting times and patterns, i.e. unimodal, bimodal, trimodal [4, 33]. Biting time variation of Ny. darlingi has been associated with seasonality [23, 57] and local and ecological factors [58]. Additional sampling could help to determine whether the differentiation detected in this study is consistent between populations of Ny. darlingi showing similar behavior patterns.

It is widely accepted that behavioral aspects of anopheline species such as host preference, local and time of biting and resting location after a blood meal have a major impact on malaria transmission dynamics in endemic areas [59, 60]. The apparent discrepancy between the lack of population structuring and the non-aleatory distribution of markers related to behavior in our samples could be due to the lack of association between behavior and mating-related genes. Probably, at the time of host seeking, the female is already inseminated. From an evolutionary point of view, it is tempting to speculate that this behavioural diversity of related genes of Ny. darlingi contributed to its dispersion in different niches within the Amazon region. Together with recent events directly attributable to human presence, such as deforestation and land use changes on a massive scale [4, 11, 12] we hypothesize that these processes could select different traits of Ny. darlingi and may explain the behavior heterogeneity observed in these different studies. A better understanding of the genetic diversity in Ny. darlingi regarding its behavior may help to predict and improve vector control methods and effectiveness of malaria frontline interventions.

Conclusions

In this study, we provided evidence of genetic diversity associated with biting behavior in the major Neotropical malaria vector Ny. darlingi and showed that this association is not due to population structuring. Mosquito behavior is one of the multiple factors directly influencing in the impact of measures of vector control. Molecular tools, such as presented in this study, could help to address appropriate vector surveillance and control on a local-scale perspective. It is emphasized that additional ecological and genomic studies may help to understand the genetic basis of Ny. darlingi behavior by the identification of relevant genes and/or genomic regions.

Availability of data and materials

Data supporting the conclusions of this article are included within the article. The dataset analyzed are available in the NCBI SRA BioProject PRJNA29824, BioSample: SAMN04202372–SAMN04202499.

Abbreviations

BIC:

Bayesian information criterion

DAPC:

discriminant analysis of principal components

ddRADseq:

double digestion restriction-site associated DNA sequencing

PCA:

principal components analysis

SNP:

single nucleotide polymorphism

References

  1. Foster PG, de Oliveira TMP, Bergo ES, Conn JE, Sant’Ana DC, Nagaki SS, et al. Phylogeny of Anophelinae using mitochondrial protein coding genes. R Soc Open Sci. 2017;4:170758.

    Article  Google Scholar 

  2. Deane L, Deane MP. Notas sobre a distribuição e a biologia dos anofelinos das regiões nordestina e amazônica do Brasil. Revista do Serviço Especial de Saúde Pública. 1948;1:827–965.

    Google Scholar 

  3. Tadei WP, Dutary Thatcher B. Malaria vectors in the Brazilian amazon: Anopheles of the subgenus Nyssorhynchus. Rev Inst Med Trop Sao Paulo. 2000;42:87–94.

    Article  CAS  Google Scholar 

  4. Hiwat H, Bretas G. Ecology of Anopheles darlingi root with respect to vector importance: a review. Parasites Vectors. 2011;4:177.

    Article  Google Scholar 

  5. World Health Organization. World malaria report 2017. 2017. http://www.who.int/malaria/publications/world-malaria-report-2017/report/en/. Accessed Nov 2018.

  6. Pan American Health Organization/World Health Organization. Epidemiological Alert: Increase of malaria in the Americas. 2018. https://www.paho.org/hq/index.php?option=com_docman&task=doc_view&Itemid=270&gid=43434&lang=en. Accessed Nov 2018.

  7. Ferreira MU, Castro MC. Challenges for malaria elimination in Brazil. Malar J. 2016;15:284.

    Article  Google Scholar 

  8. Cruz Marques A. Human migration and the spread of malaria in Brazil. Parasitol Today. 1987;3:166–70.

    Article  CAS  Google Scholar 

  9. Deane LM, Daniel Ribeiro C, Lourenço de Oliveira R, Oliveira-Ferreira J, Guimarães AE. Study on the natural history of malaria in areas of the Rondonia State-Brazil and problems related to its control. Rev Inst Med Trop Sao Paulo. 1988;30:153–6.

    Article  CAS  Google Scholar 

  10. de Castro MC, Monte-Mór RL, Sawyer DO, Singer BH. Malaria risk on the Amazon frontier. Proc Natl Acad Sci USA. 2006;103:2452–7.

    Article  Google Scholar 

  11. Chaves LSM, Conn JE, López RVM, Sallum MAM. Abundance of impacted forest patches less than 5 km is a key driver of the incidence of malaria in Amazonian Brazil. Sci Rep. 2018;8:7077.

    Article  Google Scholar 

  12. Vittor AY, Gilman RH, Tielsch J, Glass G, Shields T, Lozano WS, et al. The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of falciparum malaria in the Peruvian Amazon. Am J Trop Med Hyg. 2006;74:3–11.

    Article  Google Scholar 

  13. Vittor AY, Pan W, Gilman RH, Tielsch J, Glass G, Shields T, et al. Linking deforestation to malaria in the Amazon: characterization of the breeding habitat of the principal malaria vector, Anopheles darlingi. Am J Trop Med Hyg. 2009;81:5–12.

    Article  Google Scholar 

  14. Hahn MB, Gangnon RE, Barcellos C, Asner GP, Patz JA. Influence of deforestation, logging, and fire on malaria in the Brazilian Amazon. PLoS ONE. 2014;9:e85725.

    Article  Google Scholar 

  15. Root FM. Studies on Brazilian mosquitoes. I. The Anophelines of the Nyssorhynchus group. Am J Hyg. 1926;6:684–717.

    Google Scholar 

  16. Lourenço-de-Oliveira R, da Guimarães AEG, Arlé M, da Silva TF, Castro MG, Motta MA, et al. Anopheline species, some of their habits and relation to malaria in endemic areas of Rondonia State, Amazon region of Brazil. Mem Inst Oswaldo Cruz. 1989;84:501–14.

    Article  Google Scholar 

  17. Rosa-Freitas MG, Broomfield G, Priestman A, Milligan PJ, Momen H, Molyneux DH. Cuticular hydrocarbons, isoenzymes and behavior of three populations of Anopheles darlingi from Brazil. J Am Mosq Control Assoc. 1992;8:357–66.

    CAS  PubMed  Google Scholar 

  18. Sinka ME, Rubio-Palis Y, Manguin S, Patil AP, Temperley WH, Gething PW, et al. The dominant Anopheles vectors of human malaria in the Americas: occurrence data, distribution maps and bionomic précis. Parasites Vectors. 2010;3:72.

    Article  Google Scholar 

  19. Motoki MT, Suesdek L, Bergo ES, Sallum MAM. Wing geometry of Anopheles darlingi root (Diptera: Culicidae) in five major Brazilian ecoregions. Infect Genet Evol. 2012;12:1246–52.

    Article  Google Scholar 

  20. Emerson KJ, Conn JE, Bergo ES, Randel MA, Sallum MAM. Brazilian Anopheles darlingi root (Diptera: Culicidae) clusters by major biogeographical region. PLoS ONE. 2015;10:e0130773.

    Article  Google Scholar 

  21. Campos M, Conn JE, Alonso DP, Vinetz JM, Emerson KJ, Ribolla PEM. Microgeographical structure in the major Neotropical malaria vector Anopheles darlingi using microsatellites and SNP markers. Parasites Vectors. 2017;10:76.

    Article  Google Scholar 

  22. Lainhart W, Bickersmith SA, Nadler KJ, Moreno M, Saavedra MP, Chu VM, et al. Evidence for temporal population replacement and the signature of ecological adaptation in a major Neotropical malaria vector in Amazonian Peru. Malar J. 2015;14:375.

    Article  Google Scholar 

  23. Angêlla AF, Salgueiro P, Gil LHS, Vicente JL, Pinto J, Ribolla PEM. Seasonal genetic partitioning in the Neotropical malaria vector, Anopheles darlingi. Malar J. 2014;13:203.

    Article  Google Scholar 

  24. Pates H, Curtis C. Mosquito behavior and vector control. Annu Rev Entomol. 2005;50:53–70.

    Article  CAS  Google Scholar 

  25. Barbosa LMC, Souto RNP, Dos Anjos Ferreira RM, Scarpassa VM. Behavioral patterns, parity rate and natural infection analysis in anopheline species involved in the transmission of malaria in the northeastern Brazilian Amazon region. Acta Trop. 2016;164:216–25.

    Article  Google Scholar 

  26. Moreno M, Saavedra MP, Bickersmith SA, Prussing C, Michalski A, Tong Rios C, et al. Intensive trapping of blood-fed Anopheles darlingi in Amazonian Peru reveals unexpectedly high proportions of avian blood-meals. PLoS Negl Trop Dis. 2017;11:e0005337.

    Article  Google Scholar 

  27. Forattini OP. Comportamento exófilo de Anopheles darlingi Root, em região meridional do Brasil. Rev Saúde Públ. 1987;21:291–304.

    Article  CAS  Google Scholar 

  28. Moreno M, Saavedra MP, Bickersmith SA, Lainhart W, Tong C, Alava F, et al. Implications for changes in Anopheles darlingi biting behaviour in three communities in the peri-Iquitos region of Amazonian Peru. Malar J. 2015;14:290.

    Article  Google Scholar 

  29. Montoya-Lerma J, Solarte YA, Giraldo-Calderón GI, Quiñones ML, Ruiz-López F, Wilkerson RC, et al. Malaria vector species in Colombia: a review. Mem Inst Oswaldo Cruz. 2011;106(Suppl. 1):223–38.

    Article  Google Scholar 

  30. Dusfour I, Carinci R, Gaborit P, Issaly J, Girod R. Evaluation of four methods for collecting malaria vectors in French Guiana. J Econ Entomol. 2010;103:973–6.

    Article  CAS  Google Scholar 

  31. Vezenegho SB, Adde A, de Santi VP, Issaly J, Carinci R, Gaborit P, et al. High malaria transmission in a forested malaria focus in French Guiana: how can exophagic Anopheles darlingi thwart vector control and prevention measures? Mem Inst Oswaldo Cruz. 2016;111:561–9.

    Article  CAS  Google Scholar 

  32. Moutinho PR, Gil LHS, Cruz RB, Ribolla PEM. Population dynamics, structure and behavior of Anopheles darlingi in a rural settlement in the Amazon rainforest of Acre, Brazil. Malar J. 2011;10:174.

    Article  Google Scholar 

  33. Zimmerman RH, Lounibos LP, Nishimura N, Galardo AKR, Galardo CD, Arruda ME. Nightly biting cycles of malaria vectors in a heterogeneous transmission area of eastern Amazonian Brazil. Malar J. 2013;12:262.

    Article  Google Scholar 

  34. Russell TL, Beebe NW, Bugoro H, Apairamo A, Collins FH, Cooper RD, et al. Anopheles farauti is a homogeneous population that blood feeds early and outdoors in the Solomon Islands. Malar J. 2016;15:151.

    Article  Google Scholar 

  35. Maliti DV, Marsden CD, Main BJ, Govella NJ, Yamasaki Y, Collier TC, et al. Investigating associations between biting time in the malaria vector Anopheles arabiensis Patton and single nucleotide polymorphisms in circadian clock genes: support for sub-structure among An. arabiensis in the Kilombero valley of Tanzania. Parasites Vectors. 2016;9:109.

    Article  Google Scholar 

  36. Consoli RAGB, Rotraut AG, de Oliveira RL. Principais mosquitos de importância sanitária no Brasil. Rio de Janeiro: FIOCRUZ; 1994.

    Book  Google Scholar 

  37. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013;22:3124–40.

    Article  Google Scholar 

  38. Marinotti O, Cerqueira GC, de Almeida LGP, Ferro MIT, da Loreto ELS, Zaha A, et al. The genome of Anopheles darlingi, the main neotropical malaria vector. Nucleic Acids Res. 2013;41:7387–400.

    Article  CAS  Google Scholar 

  39. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    Article  CAS  Google Scholar 

  40. Jombart T, Ahmed I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. 2011;27:3070–1.

    Article  CAS  Google Scholar 

  41. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 2010;11:94.

    Article  Google Scholar 

  42. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14:2611–20.

    Article  CAS  Google Scholar 

  43. Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4:359–61.

    Article  Google Scholar 

  44. Moreno M, Salgueiro P, Vicente JL, Cano J, Berzosa PJ, de Lucio A, et al. Genetic population structure of Anopheles gambiae in Equatorial Guinea. Malar J. 2007;6:137.

    Article  Google Scholar 

  45. Prussing C, Moreno M, Saavedra MP, Bickersmith SA, Gamboa D, Alava F, et al. Decreasing proportion of Anopheles darlingi biting outdoors between long-lasting insecticidal net distributions in peri-Iquitos, Amazonian Peru. Malar J. 2018;17:86.

    Article  Google Scholar 

  46. Russell TL, Govella NJ, Azizi S, Drakeley CJ, Kachur SP, Killeen GF. Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. Malar J. 2011;10:80.

    Article  Google Scholar 

  47. Moiroux N, Gomez MB, Pennetier C, Elanga E, Djènontin A, Chandre F, et al. Changes in Anopheles funestus biting behavior following universal coverage of long-lasting insecticidal nets in Benin. J Infect Dis. 2012;206:1622–9.

    Article  CAS  Google Scholar 

  48. Meyers JI, Pathikonda S, Popkin-Hall ZR, Medeiros MC, Fuseini G, Matias A, et al. Increasing outdoor host-seeking in Anopheles gambiae over 6 years of vector control on Bioko Island. Malar J. 2016;15:239.

    Article  Google Scholar 

  49. Coluzzi M, Sabatini A, Petrarca V, Di Deco MA. Behavioural divergences between mosquitoes with different inversion karyotypes in polymorphic populations of the Anopheles gambiae complex. Nature. 1977;266:832–3.

    Article  CAS  Google Scholar 

  50. Coluzzi M, Sabatini A, Petrarca V, Di Deco MA. Chromosomal differentiation and adaptation to human environments in the Anopheles gambiae complex. Trans R Soc Trop Med Hyg. 1979;73:483–97.

    Article  CAS  Google Scholar 

  51. Guelbeogo WM, Sagnon NF, Liu F, Besansky NJ, Costantini C. Behavioural divergence of sympatric Anopheles funestus populations in Burkina Faso. Malar J. 2014;13:65.

    Article  Google Scholar 

  52. Main BJ, Lee Y, Ferguson HM, Kreppel KS, Kihonda A, Govella NJ, et al. The genetic basis of host preference and resting behavior in the major African malaria vector, Anopheles arabiensis. PLoS Genet. 2016;12:e1006303.

    Article  Google Scholar 

  53. Catchen JM, Hohenlohe PA, Bernatchez L, Chris Funk W, Andrews KR, Allendorf FW. Unbroken: RADseq remains a powerful tool for understanding the genetics of adaptation in natural populations. Mol Ecol Resour. 2017;17:362–5.

    Article  CAS  Google Scholar 

  54. Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet. 2016;17:81–92.

    Article  CAS  Google Scholar 

  55. Gautier M, Gharbi K, Cezard T, Foucaud J, Kerdelhué C, Pudlo P, et al. The effect of RAD allele dropout on the estimation of genetic variation within and between populations. Mol Ecol. 2013;22:3165–78.

    Article  CAS  Google Scholar 

  56. Paaijmans KP, Thomas MB. The influence of mosquito resting behaviour and associated microclimate for malaria risk. Malar J. 2011;10:183.

    Article  Google Scholar 

  57. Jiménez IP, Jiménez IP, Conn JE, Brochero H. Preliminary biological studies on larvae and adult Anopheles mosquitoes (Diptera: Culicidae) in Miraflores, a malaria endemic locality in Guaviare department, Amazonian Colombia. J Med Entomol. 2014;51:1002–9.

    Article  Google Scholar 

  58. Charlwood JD. Biological variation in Anopheles darlingi root. Mem Inst Oswaldo Cruz. 1996;91:391–8.

    Article  CAS  Google Scholar 

  59. Russell TL, Lwetoijera DW, Knols BGJ, Takken W, Killeen GF, Ferguson HM. Linking individual phenotype to density-dependent population growth: the influence of body size on the population dynamics of malaria vectors. Proc Biol Sci. 2011;278:3142–51.

    Article  Google Scholar 

  60. Gatton ML, Chitnis N, Churcher T, Donnelly MJ, Ghani AC, Godfray HCJ, et al. The importance of mosquito behavioural adaptations to malaria control in Africa. Evolution. 2013;67:1218–30.

    Article  Google Scholar 

Download references

Acknowledgements

The authors want to acknowledge the residents of Granada and Remansinho who allowed us to conduct the field work in and around their houses.

Funding

MC was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). JMV received funding from the National Institutes of Health, USA, International Centers for Excellence in Malaria Research (grant U19AI089681). JEC received funding from National Institutes of Health, USA, grant R01 AI110112. KJE received funding St. Mary’s College of Maryland. PEMR has a Conselho Nacional de Pesquisa (CNPq) fellowship.

Author information

Authors and Affiliations

  1. Biotechnology Institute (IBTEC) & Biosciences Institute at Botucatu (IBB), Sao Paulo State University (UNESP), Sao Paulo, Brazil

    Melina Campos, Diego Peres Alonso & Paulo Eduardo Martins Ribolla

  2. Department of Biomedical Sciences, School of Public Health, University at Albany (State University of New York), Albany, NY, USA

    Jan E. Conn

  3. Wadsworth Center, New York State Department of Health, Albany, NY, USA

    Jan E. Conn

  4. Division of Infectious Diseases, Department of Medicine, University of California, San Diego, La Jolla, California, USA

    Joseph M. Vinetz

  5. Laboratorio de Investigación y Desarrollo, Departamento de Ciencias Celulares y Moleculares, Instituto de Medicina Tropical “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima, Peru

    Joseph M. Vinetz

  6. Biology Department, St. Mary’s College of Maryland, St. Mary’s City, MD, USA

    Kevin J. Emerson

Authors
  1. Melina Campos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diego Peres Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan E. Conn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph M. Vinetz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kevin J. Emerson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paulo Eduardo Martins Ribolla
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PEMR and MC designed the field and laboratory work. MC and DPA performed the laboratory research. PEMR, MC and KJE analyzed data. All authors actively contributed to the interpretation of the findings. PEMR, MC, DPA wrote, JEC, KJE and JMV revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Paulo Eduardo Martins Ribolla.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional file

Additional file 1.

Per-individual An. darlingi detail of the number of sequence reads and unique stacks genotyped.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Campos, M., Alonso, D.P., Conn, J.E. et al. Genetic diversity of Nyssorhynchus (Anopheles) darlingi related to biting behavior in western Amazon. Parasites Vectors 12, 242 (2019). https://doi.org/10.1186/s13071-019-3498-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13071-019-3498-4

Keywords

Parasites & Vectors

ISSN: 1756-3305

Contact us