It has long been the goal of vector biology to generate genetic knowledge that can be used to “manipulate” natural populations of vectors to eliminate or lessen disease burden. While long in coming, progress towards reaching this goal has been made. Aiming to increase our understanding regarding the interactions between Plasmodium and the Anopheles immune genes, we investigated the patterns of genetic diversity of four anti-Plasmodium genes in the Anopheles gambiae complex of species.
Within a comparative phylogenetic and population genetics framework, the evolutionary history of four innate immunity genes within the An. gambiae complex (including the two most important human malaria vectors, An. gambiae and An. arabiensis) is reconstructed. The effect of natural selection in shaping the genes' diversity is examined.
Introgression and retention of ancestral polymorphisms are relatively rare at all loci. Despite the potential confounding effects of these processes, we could identify sites that exhibited dN/dS ratios greater than 1.
In two of the studied genes, CLIPB14 and FBN8, several sites indicated evolution under positive selection, with CLIPB14 exhibiting the most consistent evidence. Considering only the sites that were consistently identified by all methods, two sites in CLIPB14 are adaptively driven. However, the analysis inferring the lineage -specific evolution of each gene was not in favor of any of the Anopheles lineages evolving under the constraints imposed by positive selection. Nevertheless, the loci and the specific amino acids that were identified as evolving under strong evolutionary pressure merit further investigation for their involvement in the Anopheles defense against microbes in general.
Citation: Parmakelis A, Moustaka M, Poulakakis N, Louis C, Slotman MA, Marshall JC, et al. (2010) Anopheles Immune Genes and Amino Acid Sites Evolving Under the Effect of Positive Selection. PLoS ONE 5(1): e8885. https://doi.org/10.1371/journal.pone.0008885
Editor: Simon Joly, McGill University, Canada
Received: October 21, 2009; Accepted: January 4, 2010; Published: January 26, 2010
Copyright: © 2010 Parmakelis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by the National Institutes of Health grant RO1 A1 046018 to J.R.P. and A.C. A.P. was supported by a Marie Curie Outgoing International Fellowship (Contract No. MOIF-CT-2006-021357) and a “Kapodistrias” Research Grant from the Research Secretariat of the University of Athens. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
It has long been a goal of vector biology to generate genetic knowledge that can be used to “manipulate” natural populations of vectors to ammeliorate the impact of diseases spread by vectors. While long in coming, progress towards reaching this goal has accelerated. Some of the explored methods for generating refractoriness involve using antibodies that kill parasites within the mosquito  and discovering genes that govern refractoriness in natural populations . To this end, a great deal is being discovered about the immune system of mosquitoes , , , leading to the hope that the development of an effective gene construct that reduces the ability of mosquitoes to transmit malaria is not far away.
It is clear that species-specific (on both the mosquito and parasite side) interactions guide the co-evolution of Anopheles and Plasmodium. That An. gambiae has undergone an adaptive response to P. falciparum infection is suggested by several lines of evidence. Both An. gambiae and An. stephensi mosquitoes infected with P. berghei, for which this parasite species are not natural hosts, produce 50–80 oocysts, whereas an infection with P. falciparum results in far fewer oocysts. Furthermore, a specific strain of An. gambiae selected to be refractory to P. cynomolgi (monkey malaria) has very limited refractoriness to strains of P. falciparum isolated in Africa . Genetic crosses between refractory and susceptible strains indicate that different genes are involved in the encapsulation response to different species of Plasmodium , , , . A strain of An. stephensi selected for refractoriness to P. falciparum transmission showed no detectable resistance to other Plasmodium species , . Furthermore, the immune response of An. gambiae, as detected by changes in gene regulation of immune-related genes, is different in response to P. falciparum and P. berghei infections . Finally, a gene silencing assay of three immunity genes of An. gambiae infected with P. falciparum, indicated that the immune response is quite different from that manifested after infection with P. berghei . The results of the latter study highlight one more issue, namely the importance of following up discoveries in laboratory model systems with studies on natural parasite–mosquito interactions.
Presently, few studies , , , , , , ,  have investigated the patterns of genetic diversity and the evolution of the Anopheles innate immunity genes involved in Plasmodium infection. A total number of approximately 65 innate immunity genes have been studied, representing several immunity gene families. Purifying selection was found to be the most common form of selection operating on immune genes , , , , , , , , whereas a single case of positive selection acting on the lineage leading to Anopheles arabiensis was found in LRIM1 . Investigating selection patterns in a species complex of closely related species such as the An. gambiae complex imposes some limitations . These limitations stem mainly from the phylogenetic and population genetic history of the complex , . Researchers are concerned with phylogenetic analysis within the complex, and argue that the use of an appropriate outgroup when investigating patterns of selection in Anopheles immunity genes is of critical importance as is the level of within species recombination , . In an effort to overcome these issues, researchers have applied modifications of well established positive selection methods to Anopheles immunity genes that at least partially circumvent these problems . The recently approved genome sequences  from 13 more species of Anopheles mosquitoes should resolve the outgroup issue. However, until the new Anopheles sequences become available, phylogenetic analysis within the An. gambiae complex will have to use An. melas and An. merus sequences as outgroups. The divergence of these species from the An. gambiae/An. arabiensis clade should be sufficient in the case of genes evolving under strong positive selection .
In the present work we investigated patterns of genetic diversity of four anti- Plasmodium genes in the Anopheles gambiae complex of species, using a population genetics and phylogenetic framework. The complex is composed of seven species, An. gambiae, An. arabiensis, An. melas, An. merus, An. bwambae and An. quadriannulatus A and B. An. gambiae and An. arabiensis are the two primary African human malaria vectors, whereas An. melas, An. merus and An. bwambae occasionally transmit human malaria locally but do not have sufficiently wide distributions to be considered primary malaria vectors. The species An. quadriannulatus (A and B) are zoophilic and are never or rarely exposed to the human malaria parasite P. falciparum.
Four immunity genes were included in the study (Table 1). These genes are MDL1, MDL2, CLIPB14 and FBN8 and all of them have been implicated to be involved in the defense of Anopheles against malaria. MDL1 is composed of 4 exons and has a transcript length of 750 bp. We amplified a fragment ranging from 618 to 739 bp from all specimens (76 sequences, Table 2). This fragment includes the complete sequence of exons one, two and three, part of exon four, and codes for 151 amino acids (Table S1). MDL2 comprises four exons and has a total length of 3915 bp. The transcript however is only 710 bp long and only 498 bp are translated into amino acids. A total of 49 sequences were obtained from the studied species (Table 2). Sequence length varied from 540 to 868 bp, and included all 498 bp that translate into amino acids (Table S2). CLIPB14 is composed of three exons. We obtained 49 sequences. The fragments amplified in all species was 1364 bp long, with the exception of the fragments amplified from An. bwambae, which were 1271 bp in length. We obtained almost the complete sequence of exon one (except 186 bp in the 5′ end of the exon), and the complete sequence of exon three (Table S3). However, we have not been able to amplify a large fragment (208 bp in An. bwambae and 116 in the remaining five species) of the second exon. The sequences amplified translate into a total of 318 amino acids. For the FBN8 locus we successfully determined 50 sequences from the six species. From this gene, which is not interrupted by an intron (Table 1), we amplified fragments ranging from 512 to 582 bp long. The amplified fragments translate into 194 amino acids (Table S4) out of the 214 residues that constitute the FBN8 protein.
Diversity, Polymorphism and Phylogeny Inference
Out of the 76 sequences of MDL1, 66 different alleles were found (Table 2), and one allele was shared between species (An. bwambae-An. quadriannulatus). The within species nucleotide diversity (Pi) varied from 0.008 to 0.032 and 0.002 to 0.005 in the synonymous and non-synonymous sites, respectively (Table 2). Dxy (average number of nucleotide substitutions per site between species) ranged from 1.10 to 2.83% (Table S5). The phylogenetic tree of MDL1 was not fully resolved and multiple polytomies were present (Fig. 1). Nevertheless, MDL1 was subjected to the maximum likelihood tests for positive selection with PAML and HyPhy.
Numbers on branches are the posterior probabilities of clades, only values above 0.5 are presented. Species names have been abbreviated as follows: ARA: An. arabiensis, BWA: An. bwambae, GAM: An. gambiae, MEL: An. melas, MER: An. merus, and KNP905: An. quadriannulatus. The number following the species abbreviation refers to the individual specimen code, whereas the letters A and B differentiate between the two alleles of a single individual specimen.
Out of the 49 MDL2 sequences 34 different alleles were detectd (Table 2). No alleles were shared between species. Dxy ranged from 1.10 to 3.02% (Table S5). Within species nucleotide diversity (Pi) ranged between 0.000 to 0.014 and 0.000 to 0.015 in the synonymous and non-synonymous sites, respectively (Table 2). With the exception of one allele from An. bwambae and three alleles from An. gambiae, all MDL2 alleles were grouped according to species of origin (Fig. 2).
Numbers on branches are the posterior probabilities of clades, only values above 0.5 are presented. Species names have been abbreviated as follows: ARA: An. arabiensis, BWA: An. bwambae, GAM: An. gambiae, MEL: An. melas, MER: An. merus, and KNP905: An. quadriannulatus. The number following the species abbreviation refers to the individual specimen code, whereas the letters A and B differentiate between the two alleles of a single individual specimen.
Out of the 49 sequences of CLIPB14, 48 alleles were found (Table 2); none were shared between species. Dxy ranged from 1.39 to 3.39% between species (Table S6). The within species nucleotide diversity (Pi) varied from 0.005 to 0.020 in the synonymous sites, and from 0.001 to 0.006 in the non-synonymous sites (Table 2). The alleles of each Anopheles species formed a strongly supported monophyletic clade (Fig. 3), with the exception of An. bwambae, in which two alleles were closely related to the alleles of An. gambiae.
Numbers on branches are the posterior probabilities of clades, only values above 0.5 are presented. Species names have been abbreviated as follows: ARA: An. arabiensis, BWA: An. bwambae, GAM: An. gambiae, MEL: An. melas, MER: An. merus, and KNP905/SQUA: An. quadriannulatus. The number following the species abbreviation refers to the individual specimen code, whereas the letters A and B differentiate between the two alleles of a single individual specimen.
In FBN8, 37 out of the 50 sequences were different alleles (Table 2). Five alleles were shared between species. More specifically, alleles (one in each case) were shared between An. gambiae and An. quadriannulatus, An. gambiae and An. melas, An. arabiensis and An. merus, An. bwambae and An. merus and An. melas and An. merus. Dxy ranged from 2.82 to 5.59% between species. Nucleotide diversity within species (Pi) in the synonymous sites was higher compared to the previous genes and varied from 0.021 to 0.081, whereas in the non-synonymous sites ranged from 0.007 up to 0.019. In the FBN8 tree (Fig. 4) many alleles appear to be more closely related to alleles of species other than their nominal ones. This is particularly the case in An. quadriannulatus in which 3 out of the 8 alleles are scattered across the phylogenetic tree and cluster with alleles of An. merus and An. bwambae. The situation is similar for the An. gambiae alleles that appear closely related to An. quadriannulatus alleles. Finally, some of the An. arabiensis alleles group with An. gambiae and An. merus alleles.
Numbers on branches are the posterior probabilities of clades, only values above 0.5 are presented. Species names have been abbreviated as follows: ARA: An. arabiensis, BWA: An. bwambae, GAM: An. gambiae, MEL: An. melas, MER: An. merus, and QUA: An. quadriannulatus. The number following the species abbreviation refers to the individual specimen code, whereas the letters A and B differentiate between the two alleles of a single individual specimen.
The FST values (permutation tests were significant in all cases) for all studied loci in the majority of the species pairwise comparisons were well above 0.4. In several cases the FST exceeded 0.7. However, there were two cases, one in FBN8 (An. gambiae-An. quadriannulatus) and one in MDL1 (An. gambiae-An. quadriannulatus) where the FST values were lower than 0.25.
The analysis using the RDP software did not detect any statistically significant recombination events in the MDL1 and MDL2 datasets. In total, four recombination events were detected in the CLIPB14 and FBN8 datasets (two events in each locus). The results from the implementation of the GARD module in detecting recombination in the datasets are presented in conjunction with the selection analysis results.
Results of Selection Analyses Using PAML and HyPhy
In MDL1 and MDL2 no sites evolving under positive selection could be inferred, only indications of varying selection pressure (M0 vs. M3) among codons were found (Table 3). The GARD module of HyPhy detected no recombination breakpoints in MDL1 and MDL2, and no sites under positive selection were found by the consecutive analyses of HyPhy as well.
In CLIPB14 multiple sites were indicated as having a dN/dS ratio greater than 1; they were identified both by the naive empirical Bayes (NEB) and the Bayes empirical Bayes (BEB) analyses (see Table 3) of PAML. All the sites suggested by the BEB and the NEB analysis as having a dN/dS ratio greater than 1 (probability >0.99) are presented in Table 3. Three of them (78, 202 and 347: Table S3) were consistently identified by all pair wise tests. The dN/dS ratios in these sites were estimated to be well above 1 in all cases, even when considering the standard error of the estimates. Signs of recombination were detected in CLIPB14 with the GARD module of HyPhy. A single breakpoint was found at position 567 by GARD. Following that, the dataset was partitioned accordingly to take the specific breakpoint into account. At least one of the selection models of the HyPhy analyses, identified sites 78 and 202 as being under positive selection. The estimated dN/dS ratios for these sites by far exceeded those detected by the PAML analysis.
In the FBN8 gene the maximum likelihood tests of PAML indicated several sites as exhibiting values of dN/dS greater than 1. Two sites were repeatedly identified by the likelihood ratio tests. These sites were 34 and 72 (Table 3, Table S4) and their dN/dS values were greater than 1 in all cases (standard error considered). A single recombination breakpoint was detected by the GARD module of HyPhy at position 277. After partitioning the dataset to avoid the results being affected by recombination, a single site (64) was found to be exhibiting a dN/dS (normalized) value of 3.43.
Lineage Specific Positive Selection
The branch-site 2 test (PAML) was applied for all the genes with indications of positive selection by any of the preceding tests. In both schemes applied in the branch-site 2 test, and regardless of which species was designated as the foreground branch, the likelihood ratio tests (PAML) were not in favor of positive selection acting on any of the Anopheles lineages in any of the genes (results not shown).
PAML Analyses and Recombination
The RDP analysis detected two sequences as the result of recombination in each one of the CLIPB14 and FBN8 datasets. Following that, the recombinant sequences were removed from the respective datasets and a new Bayesian tree was inferred for each locus. These trees were used as input trees in a second series of analyses with PAML. The results of these analyses were identical to the initial one, thus neither the phylogenetic trees nor the PAML outputs are presented separately.
As anticipated the within species diversity is higher in the synonymous sites in all loci. The levels of within species diversity both in the synonymous (PiSyn) and the non-synonymous sites (PiNonSyn) are significantly higher in the FBN8 locus in all six species (Fig. S1, S2). The other loci exhibit similar levels of within species diversity in the synonymous and the non-synonymous sites. An exception is the level of non-synonymous diversity within An. arabiensis in MDL2, which matches that of FBN8 (Table 2, Fig. S1, S2). The levels of divergence of MDL1 and MDL2 between the different species (Dxy) are in the same range, having a mean value of 1.96 and 2.00%, respectively (Table S5). The between species divergence is higher in CLIPB14 compared to MDL1 and MDL2 with a mean value of 2.56%, whereas this value is 4.20% for FBN8 (Table S6). The levels of divergence recorded both within and between the species for the four studied loci, are comparable to those that have been estimated for other innate immunity genes of Anopheles , , .
In all four loci some alleles were shared between species. This is an indication that introgression and/or retention of ancestral polymorphisms have affected the distribution of the genetic diversity of these immune genes within the Anopheles gambiae complex. This is also evident from the fact that in all studied loci, some alleles are more closely related to alleles other than their nominal ones. To evaluate the effect of introgression and/or retention of ancestral polymorphisms, we have estimated FST values between the species of the complex for each locus separately. The FST values reported herein are within the range reported for other Anopheles innate immunity genes  and indicative of great genetic differentiation between the species of the complex. It is very important to note at this point that allele sharing between species is restricted to a single allele in MDL1, and 5 alleles in FBN8 out of a total of 185 alleles detected at all four loci. Furthermore, no shared alleles were detected between An. gambiae s.s. and An. arabiensis. It seems that allele sharing and retention of ancestral polymorphisms is present only at a very low level, at least for the four genes we studied. Therefore, all the species are significantly differentiated (Tables S5, S6) with no or very little indication of gene flow for these four loci (FST values and Tables S5, S6). This degree of differentiation and isolation minimizes the confounding effects of these processes in the investigation of selection patterns.
In all the studied loci relatively few fixed non-synonymous differences (Tables S5, S6) were found and the McDonald-Kreitman tests did not detect positive selection acting on any of them. We concur with  that this is probably due to the limitations relating to our datasets in conjunction with the inherent properties of the McDonald-Kreitman test.
Regarding MDL1, the result of the McDonald-Kreitman test is corroborated by the maximum likelihood tests implemented in the PAML and HyPhy analysis.
Similarly to MDL1, MDL2 was also found to be evolving under purifying selection. This gene has been shown to exhibit significant induction in the midgut tissue upon P. falciparum ookinete invasion. However, it did not show specificity to P. falciparum infection . At the same time, the maximum likelihood tests of PAML and HyPhy did not detect any branches and/or sites evolving under positive selection.
In CLIPB14, both the PAML and the HyPhy analyses suggest that there may be specific sites of the CLIPB14 protein that exhibit a dN/dS ratio greater than 1. Sites 78 and 202, were identified as exhibiting a dN/dS ratio above 1 in both analyses.
In FBN8 sites 34 and 72 were identified by the BEB method as possibly being under positive selection. However, these sites were not verified by the HyPhy analysis. Site 64, which was identified by the NEB analysis of PAML based on the M3 model (Table 3), was also identified by HyPhy with a dN/dS value of 3.43.
The level of recombination detected in our datasets is below the threshold level suggested by Anisimova et al., (2003), above which recombination may be mistaken for molecular adaptation. Despite this, the PAML analyses for CLIPB14 and FBN8, in which signs of positive selection were indicated, were re-run after the exclusion of the recombinant sequences. The second series of the PAML analyses, after the exclusion of the recombinant sequences, indicated by the RDP software, verified the results of the initial analyses in both genes. We consider this an analysis scheme that significantly reduces the effect of recombination in the inference of selection patterns acting on the datasets.
From the points just made, we conclude that the data collected and methods of analysis implemented, were adequate to detect selection for those genes undergoing positive selection. The analyses we used were capable of overcoming problems posed by evolutionary processes such as introgression, ancestral polymorphisms or recombination. It is not surprising, and it is indeed encouraging, that only a minority of the innate immunity genes that have been studied in this way , ,  exhibit signs of positive selection. The “arsenal” of immunity genes is more than 100  known genes and the fact that the approach we used can eliminate several, and yet detect positive signals for a minority of them is to be expected and gives us hope that these are not false positives. That is, the approach followed here has the power to identify negatives and positives between a set of immunity genes that have multiple roles in the defense of Anopheles against pathogens. Very strong indications exist for their involvement in the immune response of Anopheles against Plasmodium, whereas at the same time they are found to be part of an immunity cascade towards bacteria.
Even though specific codons (two in CLIPB14 and two in FBN8), were found to be exhibiting dN/dS ratios greater than one using PAML, we did not detect positive selection acting on specific lineages involving malaria vectors. This may simply reflect the limitation of the data and the sensitivity of the analytical procedures implemented. Nevertheless,variation at these sites is consistent with an influence on Plasmodium infection.
In conclusion, considering only the sites that were consistently identified by all methods applied, two in the CLIPB14 locus have the most evidence of being adaptively driven. These are sites 78 and 202. Understanding the biological mechanisms underlying this positive selection is beyond the scope of this work. On the other hand, in FBN8 sites 34 and 72 that were identified by the PAML analyses were not verified by the HyPhy results. Therefore, caution should be exerted regarding this locus and the specific amino acid sites. However, we do believe that these sites could also serve as a starting point for geneticists wishing to genetically manipulate Anopheles immunity genes. In most published sequences  positively selected sites inferred by the BEB method of PAML were found to be biologically meaningful following a 3D structure study of the proteins. Perhaps a similar approach could be applied to the CLIPB14 and FBN8 proteins and reveal the role of the positively selected sites in the proteins' structures implying function and their possible involvement in the defense against Plasmodium and/or other microbes.
Materials and Methods
Six species of the An. gambiae complex were used: An. gambiae sensu stricto, An. arabiensis, An. bwambae, An. melas, An. merus and An. quadriannulatus. Details on the origin of specimens and DNA extraction methods are provided by . Species names in figures and tables are abbreviated as follows: An. arabiensis: ARA, An.bwambae: BWA, An. gambiae: GAM, An. melas: MEL, An. merus: MER, An. quadriannulatus: QUA/KPN905/SQUA (depending on location of origin).
Analysis of Immunity Genes
A detailed description of the analyzed loci is given in Table 1. Among the analyzed genes are MDL1 (Ensembl Gene Id: AGAP012352) and MDL2 (Ensembl Gene Id: AGAP002857). Both genes encode an MD-2-like protein and belong to a 13-member gene family . The expression of both was induced in midgut tissue upon P. falciparum ookinete invasion. Furthermore, in RNAi gene silencing assays MDL1 showed specificity in regulating mosquito resistance in P. falciparum but not in P. berghei.
CLIPB14, a gene encoding a clip domain serine protease (Ensemble Gene Id: AGAP010833) was also included in the study. This gene is expressed in mosquito hemocytes and is transcriptionally induced by both bacterial and Plasmodium challenges . Functional studies applying RNA interference revealed that CLIPB14 is involved in the elimination of Plasmodium ookinetes in An. gambiae . Finally, we included a member of the fibrinogen-related protein (FREP) genes, FBN8 (Ensemble Gene Id: AGAP011223). The fibrinogen-like (FBG) domains in members of this protein family, are predicted to recognize carbohydrates and their derivatives on the surface of microorganisms during the innate immune response . As pointed out by , the ability of mosquitoes to recognize parasites in innate immunity and physiologies associated with blood feeding, is probably correlated with the structure of the FBG domains.
Besides the above mentioned reasons for selecting the specific set of immunity genes, we have to note that these loci were also investigated by  as well, and scored relatively high regarding their specific involvement in the defense against Plasmodium.
Multiple primers were designed for each of the targeted loci based on the An. gambiae genome . Primers were manually designed and their characteristics were estimated using FastPCR . This software was also used to investigate potential primer-pair incompatibilities. A nested-PCR protocol was used to amplify MDL1, MDL2 and CLIPB14. The sequences of the primers used in the amplification of each locus are shown in Table 1. PCR products were separated electrophoretically on a 1–2% agarose gel, purified using commercially available kits, and were sequenced in both directions in a 3730 ABI capillary sequencer. All individuals that were found to be heterozygous for two or more positions were re-amplified purified and cloned using the TOPO-TA cloning kit for sequencing (Invitrogen). From each individual, a minimum of three transformed colonies were selected, and the size of the DNA insert was determined by PCR using the T3/T7 primer pair of the TOPO-TA vector. In cases where the size of PCR product indicated the presence of the correct insert, this product was sequenced in both directions. To ensure a minimal number of miss-incorporations, Platinum High Fidelity Taq (Invitrogen) was used in all amplifications. Sequence chromatograms were inspected by eye to confirm differences between alleles of the same individual, or within and between species. Sequences were viewed, edited, assembled and aligned using CodonCode Aligner (v.1.6.3 CodonCode Corporation, Dedham, MA, USA). All sequences were blasted using the BLAST tool of VectorBase (http://www.vectorbase.org/Tools/BLAST) against the An. gambiae genome to verify homology to the respective loci. The sequences have been submitted to GenBank under the accession numbers GU432776 to GU432999.
Polymorphism and Divergence
Basic analyses of polymorphism and divergence were performed using the computer program DNAsp . Estimated parameters included the within-species pairwise diversity (Pi) at synonymous and non-synonymous sites, and the average number of nucleotide substitutions per site between species (Dxy). Introgression and/or retention of ancestral polymorphisms have resulted in the sharing of variation between the An. gambiae complex members , , ,  and the calculation of FST between the members of the complex therefore becomes meaningful . We used DNAsp  to calculate FST values between the six species. FST values measure the genetic differentiation as a proportion of total diversity that is due to between-group differences.The permutation test (5000 replicates) as implemented in DNAsp, was applied to address the question of whether the observed FST values are significantly greater than zero.
Detecting Genes Affected by Positive Selection Using PAML
Phylogenies were constructed using both coding and non-coding regions for each of the four genes with MrBayes 3.1 , using partitioned or non-partitioned data depending on the dataset. In partitioned datasets a different substitution model was applied to the introns, the first, second, and third codon positions. The substitution models were suggested by Modeltest 3.7  according to the Akaike Information Criterion .
These Bayesian trees were used to implement the maximum likelihood methods of the PAML v.4. package  aimed at detecting codons that show signs of adaptive evolution. For each locus, datasets were initially analyzed using the M0 (one-ratio) model implemented in the “codeml” program. The M0 model assumes a constant ω value (dN/dS ratio) along all branches in the tree and among all codon sites in the gene . At least two runs of the M0 model were performed on each alignment to check the consistency of the log-likelihood values between the multiple runs. Runs that were not consistent were rerun until the values converged. In the subsequent calculations of the log-likelihood of each tree under the M1a, M2a, M7 and M8 models of PAML, the initial branch lengths were those estimated under the M0 model. Model M1a is a neutral model which divides the codon sites into two categories, one having conserved sites with ω0 = 0 and the other involving neutral sites with ω1 = 1. Model M2a allows an additional category of sites with ω2 estimated from the data, thus accommodating positively selected sites. Models M7 and M8 assume that ω follows a beta distribution with the shape parameters estimated in the interval (0, 1), and M8 includes one additional category to account for positively selected sites . In all these models the rate of synonymous substitutions (dS) is constant among sites, while the rate of non-synonymous substitutions (dN) is variable . According to , the site model pairs that appear to be particularly useful for real data analysis, are M1a versus M2a, and M7 versus M8. The significance of positive selection was calculated by comparing twice the log likelihood difference in a chi-square test with two degrees of freedom.
The branch-site 2 test ,  as implemented in PAML was used to test for positive selection along specific branches. We tested each of the Anopheles clades on the species phylogeny, treating each in turn as the foreground clade. The alternative branch-site model has four codon site categories; two for sites evolving under purifying and neutral selection along branches, and two for sites under positive selection along the foreground branch. The null model restricts sites on the foreground lineage to evolve neutrally. Each branch-site model was run multiple times to ensure convergence of log-likelihood values. The significance of positive selection was calculated by comparing twice the log-likelihood difference in a chi-square test with 1 degree of freedom. Since some species appeared as non-monophyletic in the phylogenetic analyses, two different running schemes were implemented for the branch-site 2 test. In the first scheme all alleles, even those of a different species (non-species alleles), that clustered within a specific Anopheles species clade were assigned to the foreground branch. In the second scheme, the non-species alleles were not assigned to the foreground branch.
In PAML the NEB method ,  and the BEB method  are used to identify sites under positive selection. BEB is implemented under models M2a and M8 only. As suggested in the PAML manual only the results of the BEB method should be considered robust in the identification of sites under selection.
Implementing HyPhy in the Detection of Positive Selection and GARD to Screen for Recombination
To verify whether the codon sites inferred by PAML to be under positive selection are identified by other methods as well, the program HyPhy  was implemented using Datamonkey , the Web interface of HyPhy. Similarly to PAML, the likelihood methods in HyPhy are based on a codon substitution model . Three different codon-based maximum likelihood methods, SLAC, FEL and REL, can be used to estimate the dN/dS (ω) ratio at every codon in the alignment. A detailed discussion of each approach can be found in Pond & Frost .
In HyPhy all methods can take recombination into account, provided that prior to the selection analysis a screening of the sequences for recombination breakpoints is performed. This is done by using the GARD module . In the case that recombination breakpoints have been detected the dataset is partitioned and each partition is allowed to have its own phylogenetic tree. Following that, the selection analysis is performed separately on each tree. The HyPhy approach allowed us to investigate whether the presence of recombination in the datasets was producing false positives in the PAML analyses. In contrast to codeml models, HypHy also estimates the rate of synonymous substitutions (dS) at each codon site, thus taking into account potential synonymous rate variation among sites . Furthermore, the PARRIS method  of HyPhy, that extends traditional codon-based likelihood ratio tests to detect if a proportion of sites in the alignment evolve with dN/dS>1, was also applied to the datasets. The PARRIS method also takes recombination and synonymous rate variation into account.
The starting tree of each locus that served as the basis for the HyPhy analyses was inferred by the program itself.
McDonald-Kreitman Test and Positive Selection
A different approach to detect signs of selection involved the McDonald-Kreitman test . This test was applied to our datasets using DNAsp . The McDonald-Kreitman test compares the ratios of fixed to polymorphic substitutions of non-synonymous and synonymous substitutions between species. Under neutrality, the fixation rate of synonymous and non-synonymous substitutions is expected to be equal, but positive selection would increase the rate of fixation in non-synonymous sites. In contrast to the phylogeny-based tests mentioned above, the McDonald-Kreitman test allows the detection of selection on a whole protein, is bound to be quite conservative in detecting selection  and lacks the power of a site by site analysis.
Addressing the Recombination Issue
As already mentioned, within-population recombination can have a confounding effect in the inference of adaptive evolution , . Patterns of genetic variability created by recombination can closely resemble the effects of molecular adaptation (e.g. ). Current codon models of heterogeneous dN/dS ratios among sites assume no recombination, raising concerns about the possibility that the likelihood ratio tests (LRTs) can mistakenly interpret the effects of recombination as evidence for positive selection. However, as  have shown using simulated data, when the recombination events are maintained at a low level (fewer than three recombination events in the history of a sample of ten sequences) the positive selection inferring tests, including the LRT, are quite robust. Furthermore, according to the same authors identification of sites under positive selection by the BEB method  appears to be less affected than the LRT by recombination.
In this study, two different approaches were applied to account for the effects of recombination. In the first approach the datasets were analyzed (see section above) using the Web interface of HyPhy, namely Datamonkey  and applying the GARD algorithm . In the second approach, the datasets were scanned for recombination using the software RDP  and implementing all seven tests included in the package. The settings of the scans in RDP were adjusted according to the software's manual (http://darwin.uvigo.es/rdp/rdp.html). Only the statistically significant recombination events detected by any of the tests of RDP, were considered in the consecutive phylogeny-based analyses.
MacDonald-Kreitman tests on MDL1 and MDL2 and between species divergence (Dxy)
(0.05 MB DOC)
MacDonald-Kreitman tests on CLIPB14 and FBN8 and between species divergence (Dxy)
(0.05 MB DOC)
Within species diversity (coding sequence) in synonymous sites (PiSyn) in each locus
(0.20 MB EPS)
We are very grateful to Drs. Anton Cornel, Ralph Harbach and David O'Brochta for providing Anopheles specimens. We also wish to express our gratitude to Dr. George Dimopoulos for suggestions on issues relating to the choice of immune genes to be studied. We wish to express our appreciation to three anonymous reviewers that provided comments that greatly improved the manuscript. Furthermore, we would like to thank Dr Sergei L. Kosakovsky Pond that granted us permission to analyze a sequence dataset that exceeded the custom size accepted by the Datamonkey server.
Conceived and designed the experiments: AP AC JRP. Performed the experiments: AP MM NP. Analyzed the data: AP. Contributed reagents/materials/analysis tools: CL MAS JCM PHAA CAN FS. Wrote the paper: AP. Co-ordinated the project and helped draft the manuscript: AC, JRP.
- 1. Capurro MD, Coleman J, Beerntsen BT, Myles KM, Olson KE, et al. (2000) Virus-expressed, recombinant single-chain antibody blocks sporozoite infection of salivary glands in Plasmodium gallinaceum-infected Aedes aegypti. American Journal of Tropical Medicine and Hygiene 62: 427–433.
- 2. Riehle MM, Markianos K, Niare O, Xu JN, Li J, et al. (2006) Natural malaria infection in Anopheles gambiae is regulated by a single genomic control region. Science 312: 577–579.
- 3. Dong YM, Aguilar R, Xi ZY, Warr E, Mongin E, et al. (2006) Anopheles gambiae immune responses to human and rodent Plasmodium parasite species. PLoS Pathogens 2: 513–525.
- 4. Dong YM, Taylor HE, Dimopoulos G (2006) AgDscam, a hypervariable immunoglobulin domain-containing receptor of the Anopheles gambiae innate immune system. PLoS Biology 4: 1137–1146.
- 5. Waterhouse RM, Kriventseva EV, Meister S, Xi Z, Alvarez KS, et al. (2007) Evolutionary dynamics of immune-related genes and pathways in disease-vector mosquitoes. Science 316: 1738–1743.
- 6. Collins FH, Sakai RK, Vernick KD, Paskewitz S, Seeley DC, et al. (1986) Genetic selection of a Plasmodium-Refractory strain of the malaria Vector Anopheles gambiae. Science 234: 607–610.
- 7. Vernick KD, Collins FH (1989) Association of a Plasmodium-refractory phenotype with an esterase locus in Anopheles-gambiae. American Journal of Tropical Medicine and Hygiene 40: 593–597.
- 8. Vernick KD, Collins FH, Gwadz RW (1989) A general system of resistance to malaria infection in Anopheles gambiae controlled by 2 main genetic loci. American Journal of Tropical Medicine and Hygiene 40: 585–592.
- 9. Zheng LB, Cornel AJ, Wang R, Erfle H, Voss H, et al. (1997) Quantitative trait loci for refractoriness of Anopheles gambiae to Plasmodium cynomolgi B. Science 276: 425–428.
- 10. Zheng LB, Wang S, Romans P, Zhao HY, Luna C, et al. (2003) Quantitative trait loci in Anopheles gambiae controlling the encapsulation response against Plasmodium cynomolgi Ceylon. BMC Genetics 4: 16.
- 11. Feldmann AM, Ponnudurai T (1989) Selection of Anopheles stephensi for refractoriness and susceptibility to Plasmodium falciparum. Medical and Veterinary Entomology 3: 41–52.
- 12. Feldmann AM, Ponnudurai T, Meuwissen JHET (1986) The selection of Anopheles stephensi for refractoriness and susceptibility to Plasmodium falciparum. Tropical and Geographical Medicine 38: 317–318.
- 13. Tahar R, Boudin C, Thiery I, Bourgouin C (2002) Immune response of Anopheles gambiae to the early sporogonic stages of the human malaria parasite Plasmodium falciparum. EMBO Journal 21: 6673–6680.
- 14. Cohuet A, Osta MA, Morlais I, Awono-Ambene PH, Michel K, et al. (2006) Anopheles and Plasmodium: from laboratory models to natural systems in the field. EMBO Reports 7: 1285–1289.
- 15. Cohuet A, Krishnakumar S, Simard F, Morlais I, Koutsos A, et al. (2008) SNP discovery and molecular evolution in Anopheles gambiae, with special emphasis on innate immune system. BMC Genomics 9: 227.
- 16. Lehmann T, Hume JCC, Licht M, Burns CS, Wollenberg K, et al. (2009) Molecular evolution of immune genes in the malaria mosquito Anopheles gambiae. PLoS ONE 4: e4549.
- 17. Obbard D, Callister D, Jiggins F, Soares D, Yan G, et al. (2008) The evolution of TEP1, an exceptionally polymorphic immunity gene in Anopheles gambiae. BMC Evolutionary Biology 8: 274.
- 18. Obbard D, Welch J, Little T (2009) Inferring selection in the Anopheles gambiae species complex: an example from immune-related serine protease inhibitors. Malaria Journal 8: 117.
- 19. Obbard DJ, Linton YM, Jiggins FM, Yan G, Little TJ (2007) Population genetics of Plasmodium resistance genes in Anopheles gambiae: no evidence for strong selection. Molecular Ecology 16: 3497–3510.
- 20. Parmakelis A, Slotman M, Marshall J, Awono-Ambene P, Antonio-Nkondjio C, et al. (2008) The molecular evolution of four anti-malarial immune genes in the Anopheles gambiae species complex. BMC Evolutionary Biology 8: 79.
- 21. Simard F, Licht M, Besansky NJ, Lehmann T (2007) Polymorphism at the defensin gene in the Anopheles gambiae complex: testing different selection hypotheses. Infection Genetics and Evolution 7: 285–292.
- 22. Slotman M, Parmakelis A, Marshall J, Awono-Ambene P, Antonio-Nkondjo C, et al. (2007) Patterns of selection in anti-malarial immune genes in malaria vectors: evidence for adaptive evolution in LRIM1 in Anopheles arabiensis. PLoS One 2: e793.
- 23. Besansky NJ (2008) White Paper: Genome analysis of vectorial capacity in major Anopheles vectors of malaria parasites. NIH, National Human Genome Research Institute.
- 24. Christophides GK, Vlachou D, Kafatos FC (2004) Comparative and functional genomics of the innate immune system in the malaria vector Anopheles gambiae. Immunological Reviews 198: 127–148.
- 25. Yang ZH, Swanson WJ, Vacquier VD (2000) Maximum-likelihood analysis of molecular adaptation in abalone sperm lysin reveals variable selective pressures among lineages and sites. Molecular Biology and Evolution 17: 1446–1455.
- 26. Volz J, Osta MA, Kafatos FC, Muller HM (2005) The roles of two clip domain serine proteases in innate immune responses of the malaria vector Anopheles gambiae. Journal of Biological Chemistry 280: 40161–40168.
- 27. Wang X, Zhao Q, Christensen B (2005) Identification and characterization of the fibrinogen-like domain of fibrinogen-related proteins in the mosquito, Anopheles gambiae, and the fruitfly, Drosophila melanogaster, genomes. BMC Genomics 6: 114.
- 28. Holt RA, Subramanian GM, Halpern A, Sutton GG, Charlab R, et al. (2002) The genome sequence of the malaria mosquito Anopheles gambiae. Science 298: 129–149.
- 29. Kalendar R (2007) FastPCR: a PCR primer and probe design and repeat sequence searching software with additional tools for the manipulation and analysis of DNA and protein. Helsinki, Finland.
- 30. Librado P, Rozas J (2009) DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25: 1451–1452.
- 31. Besansky NJ, Krzywinski J, Lehmann T, Simard F, Kern M, et al. (2003) Semipermeable species boundaries between Anopheles gambiae and Anopheles arabiensis: Evidence from multilocus DNA sequence variation. Proceedings of the National Academy of Sciences of the United States of America 100: 10818–10823.
- 32. Donnelly MJ, Pinto J, Girod R, Besansky NJ, Lehmann T (2004) Revisiting the role of introgression vs shared ancestral polymorphisms as key processes shaping genetic diversity in the recently separated sibling species of the Anopheles gambiae complex. Heredity 92: 61–68.
- 33. Parmakelis A, Russello MA, Caccone A, Marcondes CB, Costa J, et al. (2008) Historical Analysis of a Near Disaster: Anopheles gambiae in Brazil. American Journal of Tropical Medicine and Hygiene 78: 176–178.
- 34. Ronquist F, Huelsenbeck JP (2003) MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19: 1572–1574.
- 35. Posada D, Crandall KA (1998) MODELTEST: testing the model of DNA substitution. Bioinformatics 14: 817–818.
- 36. Akaike H (1974) New look at statistical-model identification. IEEE Transactions on Automatic Control Ac19: 716–723.
- 37. Yang ZH (2007) PAML 4: Phylogenetic analysis by maximum likelihood. Molecular Biology and Evolution 24: 1586–1591.
- 38. Yang ZH, Nielsen R, Goldman N, Pedersen AMK (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155: 431–449.
- 39. Nielsen R, Yang ZH (1998) Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148: 929–936.
- 40. Yang ZH, Nielsen R (2002) Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Molecular Biology and Evolution 19: 908–917.
- 41. Zhang JZ, Nielsen R, Yang ZH (2005) Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Molecular Biology and Evolution 22: 2472–2479.
- 42. Yang ZH, Wong WSW, Nielsen R (2005) Bayes empirical Bayes inference of amino acid sites under positive selection. Molecular Biology and Evolution 22: 1107–1118.
- 43. Pond SLK, Frost SDW, Muse SV (2005) HyPhy: hypothesis testing using phylogenies. Bioinformatics 21: 676–679.
- 44. Pond SLK, Frost SDW (2005) Datamonkey: rapid detection of selective pressure on individual sites of codon alignments. Bioinformatics 21: 2531–2533.
- 45. Muse SV, Gaut BS (1994) A Likelihood Approach for Comparing Synonymous and Nonsynonymous Nucleotide Substitution Rates, with Application to the Chloroplast Genome. Molecular Biology and Evolution 11: 715–724.
- 46. Pond SLK, Frost SDW (2005) Not so different after all: a comparison of methods for detecting amino acid sites under selection. Molecular Biology and Evolution 22: 1208–1222.
- 47. Pond SLK, Posada D, Gravenor MB, Woelk CH, Frost SDW (2006) Automated phylogenetic detection of recombination using a genetic algorithm. Molecular Biology and Evolution 23: 1891–1901.
- 48. Pond SK, Muse SV (2005) Site-to-site variation of synonymous substitution rates. Molecular Biology and Evolution 22: 2375–2385.
- 49. Scheffler K, Martin DP, Seoighe C (2006) Robust inference of positive selection from recombining coding sequences. Bioinformatics 22: 2493–2499.
- 50. McDonald JH, Kreitman M (1991) Adaptive protein evolution at the adh locus in Drosophila. Nature 351: 652–654.
- 51. Nielsen R (2001) Statistical tests of selective neutrality in the age of genomics. Heredity 86: 641–647.
- 52. McVean G, Spencer CCA (2006) Scanning the human genome for signals of selection. Current Opinion in Genetics & Development 16: 624–629.
- 53. Anisimova M, Nielsen R, Yang ZH (2003) Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics 164: 1229–1236.
- 54. McVean GAT (2001) What do patterns of genetic variability reveal about mitochondrial recombination? Heredity 87: 613–620.
- 55. Martin DP, Williamson C, Posada D (2005) RDP2: recombination detection and analysis from sequence alignments. Bioinformatics 21: 260–262.