Open Access

Molecular evolution and population genetics of a Gram-negative binding protein gene in the malaria vector Anopheles gambiae (sensu lato)

  • Patrícia Salgueiro1Email author,
  • Ana Sofia Lopes1,
  • Cristina Mendes1,
  • Jacques Derek Charlwood1, 2,
  • Ana Paula Arez1,
  • João Pinto1 and
  • Henrique Silveira1
Parasites & Vectors20169:515

https://doi.org/10.1186/s13071-016-1800-2

Received: 21 March 2016

Accepted: 14 September 2016

Published: 23 September 2016

Abstract

Background

Clarifying the role of the innate immune system of the malaria vector Anopheles gambiae is a potential way to block the development of the Plasmodium parasites. Pathogen recognition is the first step of innate immune response, where pattern recognition proteins like GNBPs play a central role.

Results

We analysed 70 sequences of the protein coding gene GNBPB2 from two species, Anopheles gambiae (s.s.) and An. coluzzii, collected in six African countries. We detected 135 segregating sites defining 63 distinct haplotypes and 30 proteins. Mean nucleotide diversity (π) was 0.014 for both species. We found no significant genetic differentiation between species, but a significant positive correlation between genetic differentiation and geographical distance among populations.

Conclusions

Species status seems to contribute less for the molecular differentiation in GNBPB2 than geographical region in the African continent (West and East). Purifying selection was found to be the most common form of selection, as in many other immunity-related genes. Diversifying selection may be also operating in the GNBPB2 gene.

Keywords

Anopheles gambiae Anopheles coluzzii Gram-negative binding protein gene Glucan binding protein gene Innate immune system

Background

In order to complete their life-cycle, the malaria parasites Plasmodium sp. have to go through important stages within their mosquito vectors Anopheles sp., before being transmitted to human hosts. Malaria control strategies based on obstructing the parasite life-cycle within the mosquito are dependent on an understanding of the mosquito anti-pathogen defence system [1, 2]. This has been facilitated by the availability of the Anopheles gambiae genome sequence [3, 4].

The mosquito innate immune system constitutes a major barrier to infection [5, 6]. The first step of the innate immune response is pathogen recognition, which is activated by pattern recognition receptors (PRRs) that bind to pathogen-associated molecular patterns [7]. One important group of PRRs are the Gram-negative bacteria-binding proteins or glucan-binding proteins (GNBPs). These were initially identified in An. gambiae due to the similarities with GNBPs from other insects and because they are transcriptionally upregulated following infection with bacteria and Plasmodium parasites [8]. Six members of this gene family are expressed in An. gambiae and function as PRRs by binding ß-1,3-glucan and lipopolysaccharide on the surface of pathogens.

GNBPs are divided into two distinct sequence groups: subfamily A, that includes all known fruit fly and moth as well as two mosquito sequences (GNBPA 1 and 2); and subfamily B that is mosquito-specific (GNBPB 1, 2, 3 and 4) and probably a result from gene duplication [7].

Prior work reported that GNBPs are transcribed in multiple tissues (hemocytes, midgut, and salivary glands) and while they are all upregulated following an immune challenge, they vary in their antimicrobial specificities [714]. Specifically, GNBPs have been shown to regulate immune gene expression through the Toll or the IMD (Immune Deficiency) pathways. Certain members are able to mediate Plasmodium oocyst intensities in An. gambiae [13] and one GNBP homologue in An. gambiae was highly expressed in the fat body and salivary glands [8, 15]. On the other hand, in cultured Anopheles cells infected with Wolbachia strains, GNBPB1 gene was downregulated by Wolbachia infection [16]. As for GNBPB2, it has been shown to be induced by challenges with Salmonella typhimurium [13] and Beauveria bassiana [11]. It was also upregulated upon challenge with Escherichia coli [13].

In sub-Saharan Africa, most malaria transmission is sustained by members of the Anopheles gambiae complex. Within the nominal species two molecular forms (denoted M and S) were previously described (see [17] and references therein). Recently, these molecular forms were reclassified as distinct species, and the M-form was named Anopheles coluzzii, while the S form retained the nominotypical name An. gambiae [17].

Early genome-wide genotyping studies have shown that most of the genetic divergence between An. gambiae (s.s.) and An. coluzzii is concentrated in three relatively small centromeric regions in X, 2L and 3L [1820]. Differentiation was also detected in immunity genes between the two species [2123] with the most remarkable case being the near fixation in An. coluzzii of an allelic variant of the thioester-containing protein 1 (TEP1) [23]. TEP1 is an important component in the innate immune response of An. gambiae to Plasmodium infection, which targets malaria parasites for destruction during their initial invasion of the body cavity. Several studies have addressed the molecular evolution and genetic diversity of the anti-malaria immune genes of An. gambiae [21, 22, 2433]. However, only a few have focused on GNBP genes [21, 24, 26, 30, 34].

In order to untangle the modes of selection operating in the gene GNBPB2 and better understand its evolution in malaria vectors, patterns of genetic diversity and population differentiation were examined in samples of An. gambiae (s.s.) and An. coluzzii from six sub-Saharan African countries.

Methods

Mosquito sampling

Mosquito samples analysed in this study were collected mainly indoors by various methods of adult sampling during the rainy season in seven localities from six sub-Saharan African countries, within the framework of epidemiological surveys. Details on these collections can be found in Additional file 1: Table S1. After collection, individual specimens were kept in silica gel filled tubes.

DNA extraction, PCR amplification and sequencing

Genomic DNA was extracted from each specimen as described in Collins et al. [35]. Species identification of the members of the An. gambiae complex was carried out by PCR-RFLP as described in Favia et al. [36].

The primers used to amplify the GNBPB2 gene were designed based on the complete An. gambiae genome at Ensembl (sequence annotated AGAP002798). These primers are described in Table 1 and available at NCBI Probe database (Pr032290638).
Table 1

Primer sequences used to amplify the GNBPB2 gene (NCBI Probe database accession number: Pr032290638)

PCR reaction

Primer

Sequence 5′-3′

Product size (bp)

1

GNBPB2-out-F

CACTCCAGCGAACATTTGTG

1902

GNBPB2-out-R

CTTCAGTGTGTGGCGGTTTA

2

GNBPB2-in-3-F

CCCTAAATAAAGCGGCACAC

851

GNBPB2-in-3-R

GCACTCTTGATGGGGTTGAT

3

GNBPB2-in-5-F

GTTCTGGGGATGTGAGCGTA

963

GNBPB2-in-5-R

CAGGGATCTTTTGCGTGATT

4

GNBPB2-centre-F

ACRGGAGAGCTGATCTTTGA

571

GNBPB2-centre -R

GCCWCGRTAGTCCATATTGC

Nested PCR assays were performed in 50 μl reaction volumes with final reagent concentrations of 1× reaction buffer, 3 μM of MgCl2, 4 μM dNTPs, 0.5 μM of each primer (except for the centre primers with 0.1 μM), and 0.05 U/μl of Taq DNA polymerase. PCR cycling conditions consisted in 2 min of initial denaturation at 95 °C, followed by 35 cycles of 1 min at 95 °C, 30 s at 51 °C, 1 min at 72 °C and a final extension step of 5 min at 72 °C. For the primers out, the intermediate step of 72 °C lasted for 2 min. For the primers centre, the annealing temperature was 55 °C. PCR products were purified with SureClean kit (BIOLINE, London, UK) and commercially sequenced by Macrogen, Korea.

Data analysis

Sequences were edited and aligned with BioEdit Sequence Alignment Editor version 7.0.5.2 [37]. In DnaSP version 5.10.01 [38] the different coding and non-coding regions were defined and the translated sequences were obtained.

Summary statistics, including the number of segregating sites (S), number of haplotypes (Hap), Haplotype diversity (Hd), nucleotide diversity (π), and the standard neutrality tests: Tajima’s D [39], Fu and Li’s D* and F*[40] and Fu’s Fs [41] were calculated using DnaSP. This program was also used to compute π between species and the π(a)/π(s) ratios along the gene GNBP2 using the sliding window option (window length = 50 bp; sliding interval = 10 bp).

Additionally, we performed the dN/dS test for detecting selection implemented in the HYPHY program [42]. This test was also executed in a new alignment with the original sequences of the exon 2 obtained in the present study, to which 31 sequences of the same exon (599 bp) of An. gambiae from Cameroon available on GenBank (accession numbers AM774987–AM774989, AM774998–AM775011 [21, 34], AM900863–AM900876 [34]) were added. The same set of exon sequences was analysed for a recombination detection using RDP4 software [43] and no evidence of recombination was found.

Genetic differentiation among populations was quantified by computing pairwise F ST (conventional F-statistics from haplotype frequencies). Slatkin’s linearized F ST estimates were tested for correlation with pairwise measures of the natural logarithm of the geographic distance using Mantel’s test [44]. In order to evaluate if some populations contribute differently than others to the average F ST, population specific F ST indices were also calculated [45].

In order to estimate the total percentage of variance attributable to differences between species and among geographic areas (western and eastern Africa), a standard analysis of molecular variance AMOVA was performed with 5000 permutations [46]. These estimates were obtained with Arlequin version 3.11 [47] using the complete sequence for 70 individuals.

Sequential Bonferroni corrections adjusted critical probability values for multiple tests to minimize type I errors [48].

Results

We obtained 70 sequences of 1335 bp. Twenty-four of the mosquitoes corresponded to An. coluzzii from Angola, Ghana-Okyereko and Guinea-Bissau. The remaining 46 samples corresponded to An. gambiae from Gabon, Ghana-Accra, Ghana-Okyereko, Guinea-Bissau, Mozambique and Tanzania. All sequences are available in the GenBank database under accession numbers: KX620787–KX620856.

Genetic diversity and neutrality tests

The alignment of the 70 sequences resulted in 135 segregating sites defining 63 distinct haplotypes (Additional file 2: Table S2).

Summary diversity statistics are presented in Additional file 3: Table S3 The levels of haplotype diversity were very high and similar among populations and between species (0.911–1.000). The levels of nucleotide diversity compared between species are presented in Fig. 1.
Fig. 1

Nucleotide diversity (π) along the gene GNBP2. Exons are denoted by a grey bold line, introns are denoted by a dashed line. Sliding window was used (window length = 50 bp; sliding interval = 10 bp) adapted from DnaSP graphs

The translation of DNA sequences generated protein sequences with 391 amino acids. In GNBPB2, protein diversity was large. We obtained 17 proteins for An. gambiae, with 13 showing a frequency equal to one. The most common proteins had a frequency of 11, accounting for 24 % of the proteins detected. In An. coluzzii, we obtained 12 proteins, seven of which showed a frequency equal to one. The most common proteins had a frequency of 8, accounting for 24 % of the proteins detected (Fig. 2).
Fig. 2

GNPB2 translated protein variation in An. gambiae (s.s.) and An. coluzzii

For the total sequence, all neutrality tests showed negative results (non-significant, Additional file 3: Table S3). However, significant negative values for Fu and Li’s D* and F* were detected when exon 1 was analysed separately. Furthermore, the D* value for the exon 2 was also significantly negative, over all populations. Considering each population individually, a significant positive value of Tajima’s D test was obtained for the intron section in mosquitoes from Angola.

If most non-synonymous mutations are deleterious, then the rate of non-synonymous evolution will be lower than the neutral rate, resulting in π(a)/π(s) and dN/dS ratios < 1 [49]. Since our data revealed π(a)/π(s) ratio values lower than one, this suggests negative or purifying selection (Fig. 3). This ratio was particularly low in exon 2 (Additional file 3: Table S3).
Fig. 3

π(a)/π(s) ratios along the gene GNBP2. Exons are denoted by a grey bold line, introns are a dashed line. Sliding window was used (window length = 50 bp; sliding interval = 10 bp) adapted from DnaSP graphs

From the dN/dS test performed with HyPhy, in the whole coding region we found two positively selected sites and 45 negatively selected sites, mainly evident in exon 2 (with only one site under positive selection and 40 sites under negative selection). As for exon 1, the selection signature was negligible (one site positively selected and another negatively selected).

When we focused on exon 2 by analysing more sequences from GenBank in a new alignment of 101 sequences, the number of negatively selected sites increased to 60, and with only two sites under positive selection.

Genetic differentiation and population structure

Global F ST among geographic locations was 0.018 (P < 0.003, Table 2) when the complete sequence was analysed, and 0.021 (P < 0.003) when only the coding regions (exon 1 and 2) were considered. The results presented hereafter refer to the whole sequence.
Table 2

Population specific F ST indices for the complete sequence of GNBPB2 gene analysed in Anopheles gambiae (s.s.) and A. coluzii from six African countries

Population

Species

n

F ST

Gabon

An. gambiae

9

0.013

Ghana (Accra)

An. gambiae

7

0.018

Ghana (Okyereko)

An. gambiae

6

0.021

Ghana (Okyereko)

An. coluzzii

6

0.021

Guinea Bissau

An. coluzzii

12

0.009

Guinea Bissau

An. gambiae

7

0.018

Angola

An. coluzzii

6

0.030

Mozambique

An. gambiae

10

0.020

Tanzania

An. gambiae

7

0.024

Global F ST among populations

 

70

0.018

Abbreviations: n number of mosquitoes analysed, F ST fixation index, a measure of population differentiation due to genetic structure

The pairwise differentiation (F ST) estimates ranged from 0 to 0.066, and all comparisons were non-significant (after Bonferroni correction). The same pattern was obtained when the species status was also taken into account, e.g. samples from Guinea Bissau and Ghana (Okyereko) were divided in two, one corresponding to An. coluzzii individuals and the other to An. gambiae (s.s.). The overall F ST between species was 0.006 (P = 0.027).

We detected a significant correlation between genetic distance (Slatkin’s linearized FST) and geographic distance (Mantel test: r = 0.46, P = 0.02). Population specific FST indices for An. coluzzii ranged between 0.009 (Guinea Bissau) and 0.030 (Angola), while for An. gambiae specific FST values ranged between 0.013 (Gabon) and 0.024 (Tanzania) (Table 2).

The partition of molecular variance was 0.14 % of total variance (P ≥ 0.05) between species and 1.75 % (P < 0.01 in Table 3) among sample sites. On the other hand, when the groups were defined based on geographic areas: (E) East Africa (Mozambique and Tanzania) and (W) West Africa (all the rest), independent of species status, the molecular variance among groups increased to 2.26 % (P = 0.03), and the variance among sample sites was reduced to 0.85 % (P ≥ 0.05). Furthermore, the combination with the maximum variation among groups (2.75 %, P < 0.01) was the one comprising three groups: Angola, East Africa and West Africa without Angola (Table 3).
Table 3

Partition of genetic variation based on conventional F-statistic from haplotype frequencies (AMOVA) for Anopheles gambiae (s.s.) and A. coluzzii over six African countries

Tested groups

Source of variation

Degrees of freedom

% of variation

P-value

Two species [An. coluzzii & An. gambiae (s.s.)]

Among groups

1

0.14

ns*

Among populations within groups

7

1.75

0.006

Within populations

61

98.11

0.001

Two geographical regions (W & E)

Among groups

1

2.26

0.030

Among populations within groups

7

0.86

ns

Within populations

61

96.89

0.002

Three geographical regions (W & E & Angola)

Among groups

2

2.75

0.004

Among populations within groups

6

0.29

ns

Within populations

61

96.96

0.002

*P-values ≥0.05 were considered non-significant (ns); W West Africa, E East Africa

Discussion

The levels of nucleotide diversity for GNBPB2 detected in this study are comparable with the GNBP gene reported by Lehmann et al. [30] and other immune-related genes in An. gambiae [24, 27, 32] and Drosophila melanogaster [50, 51]. As an exception, Morlais et al. [24] found levels of nucleotide diversity for GNBPB1 ten times smaller (Π = 0.0016) than the values of GNBPB2, but this work was based on the analysis of laboratory strains rather than wild populations so that comparisons may not be straightforward. The patterns of high protein variation in GNBPB2, and high haplotype diversity in exon 2 may be consistent with diversifying selection, a mode of selection that maintains high levels of diversity (e.g. MHC genes in mammals [52]) and fits well with the role of immune recognition [53]. This selection for hyper-variability was not excluded for the GNBP gene in the study from Lehman et al. [30] that also presented large protein diversity.

Our study showed very low levels of genetic differentiation between species (F ST = 0.006, P = 0.027), when compared with values obtained in other population studies in several immune-related genes [21, 22]. Unlike these studies where only samples from one village were used (from Cameroon in [21] and Burkina Faso in [22]), we sampled mosquitoes from nine countries, ranging from Guinea Bissau in western Africa to Mozambique in the Southeast of the continent. Overall, the effect of geographical distance among populations was more decisive in our study than that of species status. Indeed, the genetic discontinuity between West and East Africa accounts for 2.2 % (P = 0.03) of the total variance (Table 3), while the hierarchical genetic diversity analysis revealed that 0.14 % (P > 0.05) of the total variance arose from differences between the two species. Furthermore, we also detected a pattern of isolation by distance made evident by the significant positive correlation between genetic differentiation and geographical distance. This scenario has already been reported for An. gambiae in an extensive analysis of neutral markers on a large geographic scale by [54].

In terms of genetic structure, the sample of An. coluzzii from Angola stands out in this study. The variance among groups was maximized when three groups of samples were defined: West Africa, East Africa and Angola (2.76 % of variation, P = 0.01, Table 3). The population from Angola is the most differentiated population with the highest specific F ST index (0.030, Table 2). This value is 1.7 times the global F ST among populations, and is higher than the indices calculated for the most eastern populations (Mozambique and Tanzania). Such differential contribution to the average F ST may suggest special evolutionary constraints in the population [55]. In fact, in Angola a significant positive value of the D Tajima’s neutrality test [39] in the intron section of the GNBPB2 (Additional file 3: Table S3) was detected, which signifies an excess of intermediate frequency polymorphisms, indicating a possible decrease in population size.

Overall our findings may reflect a population structuring associated with different African biomes as reported by Pinto et al. [56]. These authors analysed An. gambiae samples from 12 African countries with 13 microsatellite loci and reported a strong population structuring within An. coluzzii, which was divided into three distinct genetic clusters (west, central, and southern Africa). These clusters were associated with the central African rainforest belt and northern and southern savannah biomes, suggesting limited gene flow between them. Furthermore, a study based on sequence analysis of an X-linked locus revealed that the majority of An. coluzzii individuals in Angola had a 16-bp insertion that was fixed in An. gambiae but absent in An. coluzzii individuals from west and central Africa [57], a finding that suggests interspecific introgression may have occurred in this geographical region.

The results of neutrality tests were generally variable and non-significant. Because both, selective events or demographic changes can produce similar deviations from neutrality in these tests [58] we used also the dN/dS ratio test that is not sensitive to demographic events, to help in the detection of selection effects. In both exons, dN/dS was <1, which signifies a rate of non-synonymous evolution lower than neutral rate, due to most non-synonymous mutations being deleterious (i.e. purifying selection) [49]. Indeed, a strong signature of purifying selection was detected essentially in exon 2, and further confirmed by a joint analysis of other sequences of exon 2 of GNBPB2 available from previous studies [21, 34]. This is concordant with the majority of Anopheles immune related genes, which are also under purifying selection (e.g. [29, 30, 32]). Overall, this suggests functional constraints possibly associated with the immunoregulatory role of this gene.

Conclusions

The present paper expands our limited knowledge about the gene GNBPB2 with a population genetics approach in the two main malaria vectors, An. gambiae (s.s.) and An. coluzzii, over a wide geographic area in Africa. Our study showed that GNBPB2 is similar in the two species. On the other hand, the variability of the gene is differentiated according to the geographic distance of different populations in the African continent. Generally, the selection tests results are consistent with most of the studies that have addressed questions regarding the evolution and genetic diversity of Anopheles sp. innate immunity genes involved in Plasmodium infection. Purifying selection was found to be the most common form of selection operating on these genes [21, 2534], but diversifying selection should not be excluded. Specifically, Lehmann et al. [30] confirmed similar selective effects on the GNBP gene on a contemporary time scale.

Declarations

Acknowledgements

Advice given by Ana Abecasis has been of great help in the bioinformatics analyses.

Funding

This work was supported by the Fundação para a Ciência e a Tecnologia (FCT), through research funds from the projects POCI/SAU-IMI/59489/2004; PTDC/SAU-MII/102596/2008 and PEst-OE/SAU/LA0018/2011 - Strategic Project LA0018 2013/2014. CM holds an FCT PhD grant (SRFH/BD/41473/2007). PS has been supported by the FCT postdoctoral fellowship SFRH/BPD/72532/2010, which is part of the EDCTP2 Programme supported by the European Union.

Availability of data and material

The datasets supporting the conclusions of this article are included within the article and its additional files. All sequences are available at GenBank under accession numbers KX620787–KX620856.

Authors’ contributions

ASL and CM performed the experiments; ASL, CM, PS, JP and HS analysed and interpreted the data; APA, JDC and JP contributed with samples. PS and HS drafted the paper. HS conceived and designed the experiments. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis 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.

Authors’ Affiliations

(1)
Global Health and Tropical Medicine Centre (GHTM), Unidade de Parasitologia Médica, Instituto de Higiene e Medicina Tropical (IHMT), Universidade Nova de Lisboa
(2)
London School of Hygiene and Tropical Medicine

References

  1. Osta MA, Christophides GK, Vlachou D, Kafatos FC. Innate immunity in the malaria vector Anopheles gambiae: comparative and functional genomics. J Exp Biol. 2004;207:2551–63.View ArticlePubMedGoogle Scholar
  2. Meister S, Kanzok SM, Zheng XL, Luna C, Li TR, Hoa NT, et al. Immune signaling pathways regulating bacterial and malaria parasite infection of the mosquito Anopheles gambiae. Proc Natl Acad Sci U S A. 2005;102(32):11420–5.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Holt R, Subramanian G, Halpern A, Sutton G, Charlab R, Nusskern D, et al. The genome sequence of the malaria mosquito Anopheles gambiae. Science. 2002;298:129–49.View ArticlePubMedGoogle Scholar
  4. Mongin E, Louis C, Holt RA, Birney E, Collins FH. The Anopheles gambiae genome: an update. Trends Parasitol. 2004;20(2):49–52.View ArticlePubMedGoogle Scholar
  5. Cirimotich CM, Dong Y, Garver LS, Sim S, Dimopoulos G. Mosquito immune defenses against Plasmodium infection. Dev Comp Immunol. 2010;34(4):387–95.View ArticlePubMedGoogle Scholar
  6. Yassine H, Osta MA. Anopheles gambiae innate immunity. Cell Microbiol. 2010;12(1):1–9.View ArticlePubMedGoogle Scholar
  7. Christophides G, Zdobnov E, Barillas-Mury C, Birney E, Blandin S, Blass C, et al. Immunity-related genes and gene families in Anopheles gambiae. Science. 2002;298(5591):159–65.View ArticlePubMedGoogle Scholar
  8. Dimopoulos G, Richman A, Müller H-M, Kafatos FC. Molecular immune responses of the mosquito Anopheles gambiae to bacteria and malaria parasites. Proc Natl Acad Sci U S A. 1997;94(21):11508–13.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Richman AM. Plasmodium activates the innate immune response of Anopheles gambiae mosquitoes. Embo J. 1997;16:6114–9.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Tahar R, Boudin C, Thiery I, Bourgouin C. Immune response of Anopheles gambiae to the early sporogonic stages of the human malaria parasite Plasmodium falciparum. Embo J. 2002;21(24):6673–80.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Aguilar R, Jedlicka AE, Mintz M, Mahairaki V, Scott AL, Dimopoulos G. Global gene expression analysis of Anopheles gambiae responses to microbial challenge. Insect Biochem Molec Biol. 2005;35(7):709–19.View ArticleGoogle Scholar
  12. Dong Y, Aguilar R, Xi Z, Warr E, Mongin E, Dimopoulos G. Anopheles gambiae immune responses to human and rodent Plasmodium parasite species. PLoS Pathog. 2006;2:e52.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Warr E, Das S, Dong Y, Dimopoulos G. The Gram-negative bacteria-binding protein gene family: its role in the innate immune system of Anopheles gambiae and in anti-Plasmodium defence. Insect Mol Biol. 2008;17(1):39–51.View ArticlePubMedGoogle Scholar
  14. Hillyer JF. Mosquito immunity. In: Söderhäll K, editor. Invertebrate Immunity. Chapter 12: Landes Bioscience and Springer Science+Business Media; 2010. p. 218–238Google Scholar
  15. Dimopoulos G, Seeley D, Wolf A, Kafatos FC. Malaria infection of the mosquito Anopheles gambiae activates immune-responsive genes during critical transition stages of the parasite life cycle. Embo J. 1998;17(21):6115–23.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Hughes GL, Ren X, Ramirez JL, Sakamoto JM, Bailey JA, Jedlicka AE, Rasgon JL. Wolbachia infections in Anopheles gambiae cells: transcriptomic characterization of a novel host-symbiont interaction. PLoS Pathog. 2011;7(2):e1001296.Google Scholar
  17. Coetzee M, Hunt RH, Wilkerson R, della Torre A, Coulibaly MB, Besansky NJ. Anopheles coluzzii and Anopheles amharicus, new members of the Anopheles gambiae complex. Zootaxa. 2013;3619:246-74.Google Scholar
  18. Turner T, Hahn M, Nuzhdin S. Genomic islands of speciation in Anopheles gambiae. PLoS Biol. 2005;3(9):e285.View ArticlePubMedPubMed CentralGoogle Scholar
  19. White BJ, Cheng C, Simard F, Costantini C, Besansky NJ. Genetic association of physically unlinked islands of genomic divergence in incipient species of Anopheles gambiae. Mol Ecol. 2010;19(5):925–39.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Weetman D, Wilding CS, Steen K, Pinto J, Donnelly MJ. Gene flow-dependent genomic divergence between Anopheles gambiae M and S forms. Mol Biol Evol. 2012;29(1):279–91.View ArticlePubMedGoogle Scholar
  21. Cohuet A, Krishnakumar S, Simard F, Morlais I, Koutsos A, Fontenille D, et al. SNP discovery and molecular evolution in Anopheles gambiae, with special emphasis on innate immune system. BMC Genomics. 2008;9:227.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Crawford JE, Bischoff E, Garnier T, Gneme A, Eiglmeier K, Holm I, et al. Evidence for population-specific positive selection on immune genes of Anopheles gambiae. G3. 2012;2(12):1505–19.View ArticlePubMedPubMed CentralGoogle Scholar
  23. White BJ, Lawniczak MKN, Cheng C, Coulibaly MB, Wilson MD, Sagnon NF, et al. Adaptive divergence between incipient species of Anopheles gambiae increases resistance to Plasmodium. Proc Natl Acad Sci U S A. 2011;108(1):244–9.View ArticlePubMedGoogle Scholar
  24. Morlais I, Poncon N, Simard F, Cohuet A, Fontenille D. Intraspecific nucleotide variation in Anopheles gambiae: new insights into the biology of malaria vectors. Am J Trop Med Hyg. 2004;71(6):795–802.PubMedGoogle Scholar
  25. Obbard DJ, Linton Y-M, Jiggins FM, Yan G, Little TJ. Population genetics of Plasmodium resistance genes in Anopheles gambiae: no evidence for strong selection. Mol Ecol. 2007;16(16):3497–510.View ArticlePubMedGoogle Scholar
  26. Slotman MA, Parmakelis A, Marshall JC, Awono-Ambene PH, Antonio-Nkondjo C, Simard F, et al. Patterns of selection in anti-malarial immune genes in malaria vectors: evidence for adaptive evolution in LRIM1 in Anopheles arabiensis. PLoS One. 2007;2(8):e793.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Simard F, Licht M, Besansky N, Lehmann T. Polymorphism at the defensin gene in the Anopheles gambiae complex: testing different selection hypotheses. Infect Genet Evol. 2007;7:285–92.View ArticlePubMedGoogle Scholar
  28. Obbard DJ, Callister DM, Jiggins FM, Soares DC, Yan G, J T, Little TJ. The evolution of TEP1, an exceptionally polymorphic immunity gene in Anopheles gambiae. BMC Evol Biol. 2008;8(274)Google Scholar
  29. Parmakelis A, Slotman M, Marshall J, Awono-Ambene P, Antonio-Nkondjio C, Simard F, Caccone A, Powell J. The molecular evolution of four anti-malarial immune genes in the Anopheles gambiae species complex. BMC Evol Biol. 2008;8(1):79.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Lehmann T, Hume JC, Licht M, Burns CS, Wollenberg K, Simard F, Ribeiro JM. Molecular evolution of immune genes in the malaria mosquito Anopheles gambiae. PLoS One. 2009;4:e4549.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Obbard D, Welch J, Little T. Inferring selection in the Anopheles gambiae species complex: an example from immune-related serine protease inhibitors. Malar J. 2009;8(1):117.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Mendes C, Felix R, Sousa A-M, Lamego J, Charlwood D, do Rosario V, et al. Molecular evolution of the three short PGRPs of the malaria vectors Anopheles gambiae and Anopheles arabiensis in East Africa. BMC Evol Biol. 2010;10(1):9.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Parmakelis A, Moustaka M, Poulakakis N, Louis C, Slotman MA, Marshall JC, et al. Anopheles immune genes and amino acid sites evolving under the effect of positive selection. PLoS One. 2010;5(1):e8885.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Mendes AM, Schlegelmilch T, Cohuet A, Awono-Ambene P, De Iorio M, Fontenille D, et al. Conserved mosquito/parasite interactions affect development of Plasmodium falciparum in Africa. PLoS Pathog. 2008;4(5):e1000069.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Collins F, Finnerty V, Petrarca V. Ribosomal DNA-probes differentiate five cryptic species in the Anopheles gambiae complex. Parassitologia. 1988;30:231–40.PubMedGoogle Scholar
  36. Favia G, della Torre A, Bagayoko M, Lanfrancotti A, Sagnon N, Toure YT, Coluzzi M. Molecular identification of sympatric chromosomal forms of Anopheles gambiae and further evidence of their reproductive isolation. Insect Mol Biol. 1997;6:377–83.View ArticlePubMedGoogle Scholar
  37. Hall T. BioEdit: a user friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser. 1999;42:95–8.View ArticleGoogle Scholar
  38. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25:1451–2.View ArticlePubMedGoogle Scholar
  39. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123(3):585–95.PubMedPubMed CentralGoogle Scholar
  40. Fu YX, Li WH. Statistical tests of neutrality of mutations. Genetics. 1993;133:693–709.PubMedPubMed CentralGoogle Scholar
  41. Fu Y. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics. 1997;147:915–25.PubMedPubMed CentralGoogle Scholar
  42. Pond SLK, Muse SV. HyPhy: hypothesis testing using phylogenies. In: Gail M, Krickeberg K, Samet J, Tsiatis A, Wong W, editors. Statistical methods in molecular evolution. New York: Springer; 2005. 125-181-181.Google Scholar
  43. Martin DP, Murrell B, Golden M, Khoosal A, Muhire B. RDP4: detection and analysis of recombination patterns in virus genomes. Virus Evol. 2015;1(1):vev003.View ArticleGoogle Scholar
  44. Smouse PE, Long JC, Sokal RR. Multiple regression and correlation extensions of the Mantel Test of matrix correspondence. Syst Zool. 1986;35:627–32.View ArticleGoogle Scholar
  45. Weir B, Cockerham C. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38:1358–70.View ArticleGoogle Scholar
  46. Excoffier L, Smouse P, Quattro J. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992;131:479–91.PubMedPubMed CentralGoogle Scholar
  47. Excoffier L, Laval G, Schneider S. Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online. 2005;1:47–50.Google Scholar
  48. Rice WR. Analysis tables of statistical tests. Evolution. 1989;43:223–5.View ArticleGoogle Scholar
  49. Ford M. Applications of selective neutrality tests to molecular ecology. Mol Ecol. 2002;11(8):1245–62.View ArticlePubMedGoogle Scholar
  50. Labate JA, Biermann CH, Eanes WF. Nucleotide variation at the runt locus in Drosophila melanogaster and Drosophila simulans. Mol Biol Evol. 1999;16(6):724–31.View ArticlePubMedGoogle Scholar
  51. Jiggins F, Hurst G. The evolution of parasite recognition genes in the innate immune system: purifying selection on Drosophila melanogaster peptidoglycan recognition proteins. J Mol Evol. 2003;57(5):598–605.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Hughes AL, Nei M. Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection. Nature. 1988;335(6186):167–70.View ArticlePubMedGoogle Scholar
  53. Gilbert SC, Plebanski M, Gupta S, Morris J, Cox M, Aidoo M, et al. Association of malaria parasite population structure, HLA, and immunological antagonism. Science. 1998;279(5354):1173–7.View ArticlePubMedGoogle Scholar
  54. Lehmann T, Licht M, Elissa N, Maega B, Chimumbwa J, Watsenga F, et al. Population structure of Anopheles gambiae in Africa. J Heredity. 2003;94:133–47.View ArticleGoogle Scholar
  55. Weir BS, Hill WG. Estimating F-statistics. Annu Rev Genet. 2002;36:721–50.View ArticlePubMedGoogle Scholar
  56. Pinto J, Egyir-Yawson A, Vicente J, Gomes B, Santolamazza F, Moreno M, et al. Geographic population structure of the African malaria vector Anopheles gambiae suggests a role for the forest-savannah biome transition as a barrier to gene flow. Evol Appl. 2013;6(6):910–24.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Choi KS, Townson H. Evidence for X-linked introgression between molecular forms of Anopheles gambiae from Angola. Med Vet Entomol. 2012;26(2):218–27.View ArticlePubMedGoogle Scholar
  58. Ramirez-Soriano A, Ramos-Onsins SE, Rozas J, Calafell F, Navarro A. Statistical power analysis of neutrality tests under demographic expansions, contractions and bottlenecks with recombination. Genetics. 2008;179(1):555–67.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Arez AP, Pinto J, Pålsson K, Snounou G, Jaenson TGT, Rosário V. Transmission of mixed Plasmodium species and Plasmodium falciparum genotypes. Am J Trop Med Hyg. 2003;68(2):161–8.PubMedGoogle Scholar
  60. Drakeley C, Schellenberg D, Kihonda J, Sousa CA, Arez AP, Lopes D, et al. An estimation of the entomological inoculation rate for Ifakara: a semi-urban area in a region of intense malaria transmission in Tanzania. Trop Med Int Health. 2003;8:767–74.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2016

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Advertisement