Phylogenetic inference of Indian malaria vectors from multilocus DNA sequences

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright

Author's personal copy Infection, Genetics and Evolution 10 (2010) 755–763

Contents lists available at ScienceDirect

Infection, Genetics and Evolution journal homepage: www.elsevier.com/locate/meegid

Phylogenetic inference of Indian malaria vectors from multilocus DNA sequences Jyotsana Dixit a, Hemlata Srivastava a, Meenu Sharma a, Manoj K. Das b, O.P. Singh a, K. Raghavendra a, Nutan Nanda a, Aditya P. Dash c, D.N. Saksena d, Aparup Das a,* a

National Institute of Malaria Research (NIMR), Sector 8, Dwarka, New Delhi 110 077, India NIMR Field Unit Ranchi, Jharkhand, India c World Health Organization, Southeast Asian Regional Office, New Delhi, India d School of Studies in Zoology, Jiwaji University, Gwalior, Madhya Pradesh, India b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 14 October 2009 Received in revised form 22 April 2010 Accepted 22 April 2010 Available online 8 May 2010

Inferences on the taxonomic positions, phylogenetic interrelationships and divergence time among closely related species of medical importance is essential to understand evolutionary patterns among species, and based on which, disease control measures could be devised. To this respect, malaria is one of the important mosquito borne diseases of tropical and sub-tropical parts of the globe. Taxonomic status of malaria vectors has been so far documented based on morphological, cytological and few molecular genetic features. However, utilization of multilocus DNA sequences in phylogenetic inferences are still in dearth. India contains one of the richest resources of mosquito species diversity but little molecular taxonomic information is available in Indian malaria vectors. We herewith utilized the whole genome sequence information of An. gambiae to amplify and sequence three orthologous nuclear genetic regions in six Indian malaria vector species (An. culicifacies, An. minimus, An. sundaicus, An. fluviatilis, An. annularis and An. stephensi). Further, we utilized the previously published DNA sequence information on the COII and ITS2 genes in all the six species, making the total number of loci to five. Multilocus molecular phylogenetic study of Indian anophelines and An. gambiae was conducted at each individual genetic region using Neighbour Joining (NJ), Maximum Likelihood (ML), Maximum Parsimony (MP) and Bayesian approaches. Although tree topologies with COII, and ITS2 genes were similar, for no other three genetic regions similar tree topologies were observed. In general, the reconstructed phylogenetic status of Indian malaria vectors follows the pattern based on morphological and cytological classifications that was reconfirmed with COII and ITS2 genetic regions. Further, divergence times based on COII gene sequences were estimated among the seven Anopheles species which corroborate the earlier hypothesis on the radiation of different species of the Anopheles genus during the late Cretaceous period. ß 2010 Elsevier B.V. All rights reserved.

Keywords: Phylogenetics Malaria Anopheles Multilocus DNA India

1. Introduction In recent years, decoding genes and genome sequences of a number of organisms and development of a variety of statistical methods to analyze DNA sequence data have facilitated rapid advancements in molecular phylogenetics to infer taxonomic status and evolutionary interrelationships among various taxa (Blanquer and Uriz, 2007). Using evolutionarily conserved DNA regions, e.g., ribosomal ITS2 gene, it is now possible to reconstruct phylogeny of different species with high accuracy (Barker and Murrel, 2004). However, as genes and genomes diversify during evolutionary timescale, it is unclear as to what extents the genes still retain the true phylogenetic relationships among species. Hence, usage of only one gene for the phylogenetic reconstruction

* Corresponding author. Tel.: +91 11 25307322; fax: +91 11 25307377. E-mail address: [email protected] (A. Das). 1567-1348/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.meegid.2010.04.008

could blur taxonomic status among taxa. This is due to the fact that genes of different functions might be influenced by innumerable factors such as functional constraints and local adaptation of species, which direct the intra-specific diversity and inter-specific divergence pattern of these genes. Furthermore, as selection acts differentially on different regions of the genome, usage of these different regions for phylogenetic interpretations is also a limiting factor, as different genes might have experienced different evolutionary pressures and hence, can represent different phylogenies among species. Moreover, incomplete lineage sorting of ancestral polymorphisms during successive rounds of speciation also results in in-congruency between gene tree and species tree (Nei, 1987; Pamilo and Nei, 1988; Takahata, 1989; Avise, 2000). Thus, in order to reconstruct accurate phylogeny of species, utilization of multilocus genetic approach with genes of different functions and of both coding and non-coding regions has been advocated (Jennings and Edwards, 2005; Liu and Pearl, 2007).

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

756

To this respect, malaria is a deadly infectious disease of the globe. In India this disease is highly endemic, reporting an average of about one million cases and 900 deaths per year (WHO malaria report, 2008). India harbors about 58 anopheline species, out of which a good number of species, i.e., nine species of Anopheles genus belonging to Cellia subgenus have been designated as vectors of human malaria parasites. Among these, six species (An. minimus, An. fluviatilis, An. culicifacies, An. dirus, An. stephensi, An. sundaicus) are considered as the primary vectors, while three (An. annularis, An. nivipes/An. philippinensis and An. varuna) are designated as secondary vectors (Dash et al., 2007). A number of molecular genetic studies have been conducted for identification and species differentiation among these malaria vectors (Garros et al., 2004, 2005; Singh et al., 2004; Chen et al., 2006; Raghavendra et al., 2009). However, very scarce information on the phylogenetic relationships of these species are available, understanding of which could provide important information on the evolutionary history and adaptive abilities of these species, thereby helping in comprehending malaria epidemiology and devising effective vector-mediated malaria control measures. So far almost all molecular phylogenetic studies on Anopheles species of Cellia subgenus are based on single locus genetic markers, e.g., ITS1, ITS2, D3, COI and COII. Nearly for 50 years very few multilocus molecular level phylogenetic studies have been conducted on mosquitoes (Reidenbach et al., 2009). However, the recent availability of whole genome sequence of one of the principal malaria vector of African importance, An. gambiae (Holt et al., 2002), has opened new dimensions for genomic research on malaria vectors (Srivastava et al., 2009, 2010). As a result, studies based on multilocus nuclear genetic markers for inferring the molecular phylogenetic status in anopheline species have now become possible. We herewith present phylogenetic study of Indian malaria vectors using multilocus approach. We have utilized five different DNA fragments (three newly generated and two already reported) to infer phylogenetic relationships and estimated divergence time among six vector species belonging to Cellia subgenus (An. minimus, An. fluviatilis, An. culicifacies, An. stephensi, An. sundaicus, An. annularis) prevalent in India. For comparison, homologous sequences of An. gambiae in all the five loci have been used. The results were discussed in terms of utility of multilocus approach in phylogeny reconstruction in malaria in general and in Anopheles phylogenetics and evolution in particular. 2. Materials and methods 2.1. DNA sequence data retrieval and primer designing We have used the whole genome sequence information of An. gambiae, downloaded from the Ensembl web database

(www.ensembl.org version 49–50, accessed from March to July 2008). This website contains the chromosome-wise DNA sequence information of all the annotated genes of An. gambiae. We downloaded DNA sequence information of two genes; Nicotinamide Adenine Dinucleotide Phosphate (NADPH) and Cytochrome P450 (CYP), and designed the EPIC (Exon Primed Intron Crossing Primer) primers (Das A et al., 2004) to amplify DNA fragments for each gene. The gene ID and accession numbers of An. gambiae genes are as follows: CYP 450: AGAP001076–XM 558699.5 and NADPH: AGAP000500-XM_310593. Primers were designed to amplify only the intronic region in CYP, and both intronic as well as the exonic regions in NADPH gene. In order to define the intronexon boundaries for NADPH gene in Indian Anopheles, we have utilized the Eukaryotic ‘GeneMark.hmm’ computer program (http://opal.biology.gatech.edu/GeneMark/eukhmm.cgi). In addition, we have utilized the NCBI web data base (www.ncbi.nlm.nih.gov.) to retrieve sequence information of the whole NOS gene of An. stephensi (accession no. AF130124.1). We have designed primers to amplify a part of the exon 1 of this gene in Indian species of Anopheles. We have also retrieved the homologous DNA fragment of NOS gene for An. gambiae from the Ensembl (Gene ID AGAP0008255) database. The details of all the genes, the primer sequences utilized for PCR amplification and annealing temperatures are listed in Table 1. Furthermore, the published sequences of the mitochondrial COII gene and nuclear ribosomal ITS2 gene of the entire Indian Anopheles species presently studied were retrieved from the NCBI web database. The accession numbers of the two genes (ITS2 and COII) in seven species (six Indian and An. gambiae) are as follows: An. minimus: EF221770 (ITS2) and AY486110 (COII), An. fluviatilis: DQ238490 (ITS2) and AJ512740 (COII), An. culicifacies: AF479315 (ITS2) and EF208913 (COII), An. stephensi: EU359681 (ITS2) and EF208912 (COII), An. annularis: DQ351854 (ITS2) and EU620675 (COII), An. sundaicus: AY768543 (ITS2) and AF417748 (COII) and An. gambiae: AM231290 (ITS2) and AF417742 (COII). 2.2. Mosquito species collection and identification, DNA isolation, PCR amplification and sequencing Mosquitoes belonging to six different species of Anopheles (An. culicifacies, An. fluviatilis, An. minimus, An. stephensi, An. annularis and An. sundaicus), which are considered to be the malaria vectors were collected from different places in India based on their prevalence. The detail of the Anopheles species, taxonomic status, collection and population sites are listed in Table 2. An. culicifacies and An. fluviatilis were utilized from the cyclic colonies maintained at NIMR and these species have been identified morphologically as well as using cytotaxonomy and PCR based diagnostic assays (Subbarao et al., 1988; Singh et al., 2004). An. minimus, An. sundaicus and An. annularis were collected from the fields and were

Table 1 Details of genes and genetic fragments on the basis of An. gambiae sequence and PCR annealing temperature for amplification in Indian Anopheles species. Gene

Gene details

Details of fragments analyzed

Chromosomal location An. gambiae

Function

Coding/non-coding part taken for analysis

Primer sequences 50 -30

Annealing temperatures

CYP

X

Metabolism of endogenous compounds

Non-coding

60 8C

NOS

3L

Signaling and immune responses

Coding

NADPH

X

Cellular oxidation and reduction reactions

Coding + non-coding

COII

Mitochondrial genome

Coding

ITS2

Nuclear genome

Catalyzes transfer of electrons in mitochondrial electron transport chain No function assigned yet

F ctggctgggcaacggacta R ggaaagtaggggcaatcagtttg F atgaggaccaactatcggg R gccttggtgacaatgctc F ggacgcccagacagaaac R gtcgctaagcaaaacacg Sequences retrieved from gene bank. Sequences retrieved from gene bank

Non-coding

50 8C 55 8C Sequences from gene Sequences from gene

retrieved bank retrieved bank

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

757

Table 2 Taxonomic states of Anopheles species and details of collection sites in India. Anopheles species studied

Taxonomic series

Location of samples (place, state)

Population coordinates

Anopheles Anopheles Anopheles Anopheles Anopheles Anopheles

Myzomyia Myzomyia Myzomyia Pyretophorous Neocellia Neocellia

Moregaon, Assam Hardwar, Uttaranchal Bilaspur, Chhattisgarh Car Nicobar, Andaman & Nicobar Islands Hardwar, Uttaranchal Hardwar, Uttaranchal

26850 N 9280 E 298960 N 788160 E 228020 N 828150 E 98100 N 928450 E 298960 N 788160 E 298960 N 788160 E

minimus culicifacies fluviatilis sundaicus stephensi annularis

initially identified to species using classical keys (Christophers, 1933; Nagpal and Sharma, 1995). An. sundaicus was collected from Andaman and Nicobar Islands, where it is the only prevalent malaria vector and morphological identification of An. sundaicus and An. annularis species is quite reliable. In case of An. minimus, apart from the morphological identification, the species were confirmed using PCR based diagnostic assay (Phuc et al., 2003). We have utilized two–three male mosquitoes of each species for genomic DNA extraction and further studies (see below). The genomic DNA from each individual mosquito was separately extracted with DNA isolation kit (DNAeasy Qiagen, Germany). The integrity of the isolated DNA was checked by running the sample on 1% gel and visualizing under UV transilluminator. PCR was conducted using the designed primers (Table 1) for each gene and each individuals of every Indian species of Anopheles. Each 25 ml PCR reaction mixture contained 250 mM concentration of each deoxyribonucleotide (dNTP), 3 pmol of each primer (forward and reverse), 2.5 mM MgCl2, 1U Taq DNA polymerase (Banglore Genei, India) with 1 polymerase buffer and 1 ml template DNA (1/100th of the DNA extracted from a single mosquito). PCR gradient thermal cycling included an initial denaturing step of 5 min at 94 8C, followed by 35 cycles of 30 s at 94 8C, 30 s at 45–65 8C for annealing (gradient) and 1 min at 72 8C for extension, and a final extension cycle of 5 min at 72 8C. Annealing temperatures of each fragment was different as indicated in Table 1. Three microliters of PCR products were run on a 2% agarose gel in TBE buffer, stained with ethidium bromide (EtBr), and visualized under UV illumination. The amplified fragments showing a clear single band without any primer dimmers were subjected to PCR purification using EXOSAP (Fermentas, Life Sciences). One unit each of exonuclease (0.05 ml) and shrimp alkaline phosphatase (1 ml) mixed with 22 ml of PCR products was run in a thermal cycler at 37 8C for 60 min and 85 8C for another 15 min. Further, 1–2 ml Exo-Sap purified products were utilized for preparing sequencing reaction with 6 ml of big dye terminator (BDT) ready reaction mix, and 0.8 pmol of each primer (forward and reverse). Cycle sequencing was performed in a thermal cycler as follows; initial denaturation at 95 8C for 5 min, followed by 25 cycles of final denaturation at 95 8C for 10 s, annealing at 50 8C for 5 s and extension at 60 8C for 4 min. The probes were subjected to automated sequencing from both the directions, i.e. forward and reverse (2 coverage), on an automated DNA analyzer ABI 3730XL (Applied Biosystems), which is an inhouse facility of NIMR. 2.3. DNA sequence editing, and alignment DNA sequences downstream of the sequencer were edited by forming contigs using both the sequenced (forward and reverse) strands of a particular fragment of each individuals of a species using SeqMan module of DNASTAR computer software package (DNAStar, Inc., Madison, WI). In order to confirm that the desired gene fragment has been amplified and sequenced, homology search was performed in the NCBI database with the BLASTN algorithm. DNA fragments showing homology with the corresponding gene of An. gambiae (CYP and NADPH) and with the An. stephensi (NOS) were only considered for further analysis (see

below). Sequence data from two-three individuals of each species were aligned and the consensus sequences for each gene fragment for every species were utilized in the analyses. The newly generated sequences of NADPH, CYP and NOS genes for each individual of the six Anopheles species have been deposited in gene bank bearing accession numbers from HM171634 to HM171663. Apart from the three gene fragments, we have also utilized DNA fragments of ITS2 and COII, making the total numbers of loci to five in each species of Anopheles. Thus, the data set for the present study includes five gene sequences of six Indian malaria vectors. In addition we have used homologous DNA sequence of each gene of An. gambiae for the phylogenetic analysis (see below). Multiple sequence alignments of five homologous DNA fragments from seven species of Anopheles were conducted using MegAlign program of DNASTAR following ClustalW algorithm (Thompson et al., 1994). Single Nucleotide Polymorphism (SNPs), were only considered for the analysis, leaving aside the insertion/ deletion polymorphisms, as the evolutionary rate of these two kinds of polymorphisms are considered to be different (Vali et al., 2008), and thus could affect the uniform inference of phylogenetic analysis. 2.4. Phylogenetic tree constructions For each of the five DNA fragments, separate phylogenetic trees were constructed following four different approaches, i.e. neighbor joining (NJ), maximum parsimony (MP), maximum likelihood (ML), and Bayesian. While, NJ trees were constructed using computer program MEGA version 4 (Tamura et al., 2007) (utilizing the maximum composite likelihood model for nucleotide substitution and bootstrapping for testing the phylogeny), for phylogenetic trees estimations with MP, ML, and Bayesian methods, the most appropriate evolutionary model was detected using computer program MODELTEST (Posada and Crandall, 1998). The details of different evolutionary models selected for each gene fragment are provided in Table 4. Parsimony analyses were performed in computer program PAUP 4.0b (Swofford, 2002), using the heuristic search option with TBR branch swapping. Bootstrapping (Felsenstein, 1985) under parsimony utilized 200 pseudoreplicates. Similarly, for Maximum likelihood analyses, the trees were inferred by heuristic searches using PAUP version 4b10 and bootstrapping (Felsenstein, 1985) under the ML criterion was conducted utilizing 200 pseudoreplicates, with TBR branchswapping. Likewise, the Bayesian analyses were performed using the computer programme MrBayes version 3.2 (Huelsenbeck and Ronquist, 2000). The default values for prior probability were used and 10,000 generations of MCMC simulations were performed. The Shimodaira-Hasegawa (SH) test (Shimodaira and Hasegawa, 1999), scores the difference between the best or optimal ML tree and every other tree. In the present study this test was performed in order to detect the incidences of differences between the true or best ML tree and other gene trees. In other words, this test provides information on the tree topologies that are significantly better at explaining their respective datasets than alternate topologies. For this test, we considered the tree constructed on the basis of ITS2 gene sequences as the best or optimal ML tree (since the ITS2 gene tree topology is similar to classifications based on morphological

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

758

characters). The comparison was made between likelihood scores of optimal ML tree and the rest of the gene trees based on four genes (COII, CYP, NADPH and NOS) using the SH test. The likelihood scores of the optimal ML tree and rest of the other trees were determined using the resampling-estimated log-likelihood (RELL) method with 1000 bootstrap replications. The SH test was performed using PAUP version 4b10 (Swofford, 2002). 2.4.1. Estimation of divergence time In order to estimate divergence time in presently studied Anopheles species, we first conducted the relative rate test (Tajima, 1993). This test is based on the molecular clock hypothesis (Morgan, 1998). By running this test one can determine if the gene is evolving at a constant rate following the molecular clock hypothesis. This test was performed separately for each gene using computer program MEGA version 4. Only the COII gene fragment was found to follow the molecular clock hypothesis and thus, only this gene was used for estimating the divergence times. The two Bayesian approach-based computer programmes (i) ‘mcmctree’ which is a part of computer software PAML version 4.2 (Yang, 2007), and (ii) BEAST version 1.5.3 (Drummond and Rambaut, 2007), were used for estimating the divergence dates. Useful internal calibration dates were not available due to poor availability of mosquito fossil records (Poinar et al., 2000; Borkent and Grimaldi, 2004; Grimaldi and Engel, 2005), which is required for ‘mcmctree’ computer programme. Therefore, we used the divergence time of 145–200 MY (using lower 145MY and upper 200MY bound method) between Aedes aegypti and Anopheles which is the only available and rigorously calculated date for the most recent split between the Aedes and the Anopheles genus (Krzywinski et al., 2006). For running the ‘mcmctree’ computer program, we choose the most complex model (HKY 85) and computer simulations were performed by setting the burning period to 10,000, the number of samples to 100,000, and the sample frequency to five, with four independent chains for each analysis. Similarly, for divergence time estimation using another Bayesian MCMC coalescent method that is implemented in computer program BEAST, initial MCMC chains were run for 1,000,000 generations with the scale factors adjusted as suggested by the operator analysis in the computer program. Furthermore, the computer program Tracer, version 1.4 (Rambaut and Drummond, 2007) was used to obtain the posterior probability density distribution and 95% confidence intervals of divergence times. 3. Results 3.1. Ascertainment of multiple DNA fragments The present study focuses on the phylogenetic interrelationships among six Indian malaria vector species utilizing multilocus

approaches. As DNA sequence information is poorly available for Indian vectors, we have utilized the sequence information of An. gambiae to sequence portions of three nuclear genes of different functions in Indian malaria vectors. We have also utilized earlier reported sequence information of two loci, bringing the number of studied loci to five. However, for some species, PCR amplification of some of the gene fragments could not be obtained, and in some cases the sequenced fragments were found to be non-homologous with the An. gambiae reference sequences. For example, in case of CYP gene, while amplification was not obtained for An. annularis, the sequence obtained from An. stephensi and An. culicifacies were not homologous to An. gambiae. For the NADPH gene fragment, PCR amplification was obtained for all the species, but sequence homology could not be obtained for An. annularis and An. culicifacies. These sequences (non-homologous to An. gambiae) were thus, not utilized in phylogenetic tree constructions and further analysis. The details of three gene fragments sequenced in six Indian anophelines are provided in Table 3. For each species, we have sequenced two-three male individuals for each fragment; however, very less polymorphism was detected for each gene, in each species (data not shown). 3.2. Phylogenetic inference with multilocus data The multilocus phylogenetic analysis in seven (six Indian and An. gambiae) Anopheles species revealed several interesting features. Both the congruent and incongruent phylogenies were observed. Interestingly, phylogenetic trees constructed with all the four different methods for a particular gene fragment yielded congruent phylogenetic status of the seven species; meaning no discrepancy in phylogenetic interrelationship based on different algorithms for phylogenetic tree constructions was found. This was revealed to be true for all the five gene fragments. However, trees reconstructed with different genes did not provide congruent results (Fig. 1). Interestingly, results from the COII gene fragment were found to be a mirror-image to the results from ITS2 gene fragment. This is revealed from Fig. 2, which depicts the monophyletic interrelationships between a pair of Anopheles species detected in the present study. It is clear from the figure (Fig. 2) that An. minimus and An. fluviatilis were monophyletic in three gene trees (ITS2, COII and NADPH), while monophyletic relationships in two different genes (COII and ITS2) were obtained for same species pairs (An. minimus – An. culicifacies – An. fluviatilis, An. sundaicus – An. gambiae, An. stephensi – An. annularis). Furthermore, single-gene monophyletic relations for species were obtained only between An. culicifacies – An. annularis and An. fluviatilis – An. sundaicus. Thus, taking into considerations the overall phylogenetic interrelationships from multilocus data, it was quite evident that the tree topologies with COII and ITS2 genes were congruent. This is further evident from the fact that the SH

Table 3 Details of the three gene fragments sequenced in six Indian malaria vector species. S. no.

Species

Length of amplified fragment for CYP

Length of amplified fragment for NOS

Length of amplified fragment for NADPH (base pairs)

1

An. minimus

570 base pairs

246 base pairs

2

An. fluviatilis

490 base pairs

216 base pairs

464 base pairs, intron 1 = 1–155, exon 1 = 156–263, intron 2 = 264–330, exon 2 = 331–402, intron 3 = 403–464 464 base pairs, intron1 = 1–155, exon 1 = 156-263, intron 2 = 264–330, exon 2 = 331–402, intron 3 = 403–464

3 4

An. culicifacies An. stephensi

a

210 base pairs 220 base pairs

a

5 6

An. annularis An. sundaicus

206 base pairs 250 base pairs

a

a b

a

b

599 base pairs

Sequence homology could not be obtained with An. gambiae genome. PCR amplification could not be obtained.

464 base pairs, intron 1 = 1–26, exon 1 = 27–110, intron 1 = 111–197, exon 2 = 198–405, intron 3 = 406–464 464 base pairs, intron 1 = 1–27, exon 1 = 27–110, intron 2 = 111–209, exon 2 = 210–402, intron 3 = 403–464

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

759

Fig. 1. NJ phylogenetic trees as observed with different gene fragments in seven (six Indian and An. gambiae) malaria vectors: (a) mitochondrial COII, (b) nuclear ITS2, (c) NOS, (d) NADPH and (e) CYP 450. Note that due to the unavailability of sequence information of the NADPH gene in An. annularis and An. culicifacies and for CYP 450 gene fragment in An. annularis, An. culicifacies, and An. stephensi, no phylogenetic information could be retrieved for these species in these gene fragments.

test conducted on ML tree of COII gene obtained likelihood scores not significantly different from the optimal ML tree (Table 4, P > 0.158). To be noted here that, the prior ML tree was based on the ITS2 sequences (see above). It is further reconfirmed through the SH test results that there is in-congruency between the tree topologies based on the other three gene fragments (CYP, NADPH and NOS) among the seven Anopheles species.

3.3. Divergence time estimation Since the COII and ITS2 gene trees were found to be congruent, and the COII gene was evolving under molecular clock hypothesis (as evident from the relative rate test results Table 5), the COII sequences from all the seven species (six Indian and one An. gambiae) were utilized for estimating the species divergence times. We have also considered the sequence of Ae. aegypti COII gene for using as an out-group. Two different methods based on the Bayesian coalescent approach were followed for the estimation of divergence time with the help of two computer programs, ‘mcmctree’ and ‘BEAST’. The results from both the estimates revealed almost similar divergence times between species/clad (Fig. 3). However, in general, the divergence time range with ‘mcmctree’ analysis was found to be slightly higher than the analysis from BEAST. For simplicity, we have indicted the average divergence time, as estimated from both the methods at the point of divergence in the phylogenetic tree (Fig. 3). The split between the two species belonging to pyretophorous series (An. gambiae and An. sundaicus) and two species from Neocellia series (An. annularis and An. stephensi) represents the first basal split among the Cellia species of mosquitoes (45–78 Myr). Also, the time of divergence between An. culicifacies and An. minimus (17–20 Myr) and between An. minimus and An. fluviatilis (9.3–13.5 Myr) seems to be much recent. In general, the multilocus data analyses with different statistical platforms reveal in-congruent phylogenetic status of Indian Anopheles species. However, congruent phylogenetic trees observed between COII and ITS2 and relative rate test for COII

Table 4 Summary of Shimodaira-Hasegawa (SH) test statistics and ModelTest statistics for the five genes analyzed.

Fig. 2. Species interrelationships among seven malaria vectors (six Indian and An. gambiae), based on observed monophyletic positions between a pair of species in different gene fragments as indicated in Fig. 1A bidirectional arrow points to the different species, shows a monophyletic relationships between them.

Tree

ln L

Diff ln L

P

Model selected

ITS2 COII NOS NADPH CYP

1881.94044 1902.81255 2021.16064 1990.56032 2010.46514

(Best) 20.87211 139.22020 108.61988 128.52470

0.158 0.000* 0.000* 0.000*

K80 + G GTR + G JC K80 + G K80

*

P < 0.05.

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

760

Table 5 Results of relative rate test between a pair of Anopheles species at the COII gene sequences in seven anopheline species. The x2 and associated P-values (above-diagonal) and the species considered as the out-group (below diagonal) are indicated for each species pair. Species An. An. An. An. An. An. An.

minimus fluviatilis culicifacies stephensi annularis sundaicus gambiae

An. minimus – An. An. An. An. An. An.

An. fluviatilis 2

culicifacies stephensi sundaicus gambiae stephensi stephensi

x = 0.89 P = 0.345 – An. annularis An. sundaicus An. gambiae An. stephensi An. annularis

An. culicifacies 2

x = 0.00 P = 1.00 x2 = 0.68 P = 0.411 – An. An. An. An.

sundaicus sundaicus stephensi annularis

Fig. 3. Phylogeny and divergence times in seven malaria vectors (six Indian and An. gambiae) as inferred from COII sequences. Values in parentheses indicate the approximate and averaged (from two different estimates ‘mcmctree’ and BEAST) divergence time between two branches of the tree. Divergence time between Anopheles and Ae. aegypti (145–200 MY, Krzywinski et al., 2006) has been taken as the calibration point for estimation of divergence times through ‘mcmctree’.

justify the usability of COII in not only phylogenetic reconstructions of Anopheles species but also to estimate divergence time. 4. Discussion Knowledge on the various aspects of a group or species of medical importance relies upon deep understanding of phylogenetic relationships among members of that group (Morgan et al., 2000). While traditional taxonomic relationships and associated model-based statistical analysis majorly rely upon morphological characters, with the advent of molecular markers, phylogenetic interrelationships among members of closely related organisms are now ascertained more easily and accurately. Thus, molecular phylogenetics, as a method, has been a useful adjunct to traditional taxonomic methods (Godfray and Charls, 2002). However, many such studies relied on limited sequence data such as those from single genes or fragments thereof (de Jong et al., 1981; Miyamoto and Goodman, 1986; Irwin et al., 1991; Springer et al., 1997). The present study is an attempt to use all types of genetic loci in term of functional regions (coding, non-coding), of two different genomes (nuclear and mitochondrial) and of different functions (insecticide resistance, metabolic, immune response, etc.). Since, the evolutionary forces act differentially on both the nuclear and mitochondrial genomes (Saccone et al., 2006); the true phylogenetic relationships of species can be attained by using genes from both the genomes (Saccone et al., 1990; Reyes et al., 2004). Furthermore, phylogenetic inferences from multiple genes could reveal variable signature of gene specific evolutionary forces,

An. stephensi 2

x = 0.07 P = 0.80 x2 = 0.00 P = 1.00 x2 = 0.60 P = 0.438 – An. sundaicus An. gambiae An. culicifacies

An. annularis 2

x = 0.06 P = 0.802 x2 = 0.06 P = 0.802 x2 = 0.67 P = 0.414 x2 = 0.00 P = 1.00 – An. gambiae An. minimus

An. sundaicus 2

x = 0.13 P = 0.257 x2 = 0.08 P = 0.781 x2 = 1.10 P = 0.293 x2 = 0.69 P = 0.405 x2 = 1.23 P = 0.267 – An. stephensi

An. gambiae

x2 = 0.41 P = 0.522 x2 = 0.60 P = 0.438 x2 = 0.02 P = 0.894 x2 = 0.07 P = 0.785 x2 = 0.07 P = 0.792 x2 = 0.18 P = 0.668 –

which could be important in inferring phylogenetic status of closely related species as a whole. While evolutionarily neutral DNA fragments could unravel demography-mediated divergence of taxa, natural selection might affect divergence patterns in genes differently, which could blur the phylogenetic inferences (Blair et al., 2008). Thus, phylogenetic inferences from a bunch of genes containing both putatively neutral fragments and functional genes could be the best bet in catching the close-to-realistic estimation of phylogenetic inferences. This is important in malaria research, as many characters of pathogens and pathogen carrying vectors are under strong influence of natural selection and thus, limiting phylogenetic inferences from these genes could be unidirectional (natural selection mediated divergence). Further, in case of malaria vectors, where many closely related nascent species are of wide occurrence, detecting small amount of divergence could only be possible if multiple loci are screened and analyzed for phylogenetic inferences. More specifically, the above aspects are important due to (i) presence of a number of cryptic species in each taxon and few phylogenetically informative morphological characters, (ii) several of these species are vectors and others are non-vectors to malaria, and (iii) variable distribution patterns of different species of Anopheles across the globe with different feeding preferences (anthropophagic or zoophagic). Thus, understanding the taxonomic interrelationship among the Anopheles species is not only important for understanding variable adaptive capacity of malaria mosquitoes but it can also serve as a model for other insects of health and agriculture importance (Harbach, 2004). Molecular phylogenetic studies of anophelines are primarily limited to single locus approaches, and many such studies have been reported earlier, such as in species belonging to Anopheles subgenera (Sallum et al., 2002), Neomyzomyia series (Foley et al., 1998), Myzomyia series (Garros et al., 2005), An. (Nyssorhyncus) marajoara (Li and Wilkerson, 2005), and Minimus group species (Sharp et al., 2000). Majority of these studies involve the mitochondrial COII or ribosomal ITS2 genes (Foley et al., 1998; Wilkerson et al., 2005; Marrelli et al., 2006). The present study is therefore a step forward in partially sequencing some of the conserved genes across six malaria vector species and inferring the phylogenetic interrelationships among the members of Cellia subgenus prevalent in India using multilocus approach. 4.1. Concordant phylogeny with COII and ITS2 loci Although mitochondrial genes accumulate sequence changes rapidly and the rate differs considerably between individual genes (Vawter and Brown, 1986; Gissi et al., 2000; Masta, 2000), usage of mitochondrial genes to correctly estimate molecular distances has been proposed (Kumazawa et al., 2004). On the other hand, by virtue of its fast evolution, the ITS2 region (Coleman, 2003; Alvarez and Wendel, 2003) of the nuclear rDNA cistron is used as a reliable marker for taxonomic classification and for phylogenetic reconstructions among recently diverged taxa (Porter and Collins, 1991; Collins and Paskewitz, 1996; Walton et al., 1999). In the present

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

study, both the two markers (COII and ITS2) have provided similar phylogenetic status of the Indian malaria vectors belonging to the Cellia subgenus. Similar to the present results, a recent study on Anopheles species of Cellia subgenus has reported almost similar tree topologies based on the COII and ITS2 genetic regions (Mohanty et al., 2009). Interestingly, phylogenetic positions of seven different species of Anopheles in these two genes follow similar pattern and is in congruence with the classical taxonomic classifications; corroborating the fact that taxonomic relationships among species predominantly reflected by some genes may be very close to the classical phylogeny based on morphology (Pape, 1992). For example, both these genes (ITS and COII) place An. culicifacies, An. minimus and An. fluviatilis in one single major clad and also An. stephensi and An. annularis under a different monophyletic clad (Fig. 1a and b). This molecular phylogenetic classification exactly follows the morphological and cytological classification, according to which, An. minimus and An. fluviatilis fall under the same Minimus subgroup (Chen et al., 2003; Harbach, 2004). An. culicifacies which has a close affinity with the Minimus group was clustered together with these two species (An. minimus and An. fluviatilis) and all the three species belongs to the Myzomyia series (Green, 1982). Further, An. gambiae and An. sundaicus belongs to pyretophorous series and were placed under the same clad as were the two species (An. stephensi and An. annularis) belonging to the Neocellia series (Green and Baimai, 1984; Green et al., 1985). 4.2. Discordant phylogeny with four nuclear DNA fragments It is generally assumed that the gene phylogeny (or gene tree) is isomorphic with the organism phylogeny (or species tree) (Cotton and Page, 2002). However, in the present study, apart from the two (COII and ITS2) genes (see above), all the other three nuclear gene fragments showed incongruent phylogenetic status in the seven species of Anopheles. Incongruence between gene trees and species tree may result due to numerous systematic biases like; horizontal gene transfer, gene duplication, loss and deep coalescence and natural selection (Doolittle, 1999a,b; Eisen, 2000; Jordan et al., 2001; Gu et al., 2002). In the present study, the nuclear genes CYP and NADPH are members of gene families, and thus, the chances of these genes undergoing differential evolutionary changes through gene duplication and lateral gene transfer are plausible and might have resulted in detecting discordant phylogenetic status. Further, single copy nuclear genes in the genome performing important physiological roles are generally subjected to selective constraints (natural selection) that differentially bias the phylogenetic signals away from the correct tree topology (Das J et al., 2004). This argument fits very well to the NOS gene, as being a single copy nuclear gene of anopheline species; it performs crucial roles in the physiology of Anopheles. Furthermore, earlier population genetic studies have provided evidences for the role of natural selection in this gene (Luckhart and Rosenberg, 1999). Apart from all the above facts, it is a well known phenomenon that the mode of evolution of a gene is generally governed by the differential function and the type and amount of evolutionary forces in action (True and Carroll, 2002). This, in turn, might lead to different phylogenetic relationships among species which actually is guided by the evolutionary rate of the studied gene. In order to nullify such effects, nucleotide sequences from different origins (mitochondrial and nuclear), from different parts of the gene (intronic and exonic) and of different functions were considered in this study. For example, the NOS gene has a role in cellular signal transduction mechanisms and immune responses (Lim et al., 2005). Also in An. gambiae and An. stephensi the NOS gene is reported to be involved in inhibiting the parasite development (Luckhart et al., 1998; Oduol et al., 2000; Dana et al., 2006). On the

761

other hand, genes serving somewhat similar functions also showed discordant patterns. For example, two genes (CYP and NADPH) considered in the present study, are reported to be responsible for insecticide resistance, but showed entirely different tree topologies (Fig. 1d and e). This might be due to the fact that, different pathways are involved in insecticide resistance mechanisms and different evolutionary forces might have shaped divergence patterns of these genes across the vector species entirely differently. This implies that the evolutionary patterns of the genes, rather than functions, are important due to which different tree topologies may result between a pair of species. However, such evolutionary patterns might be species-specific, mediated through local adaptation of each of the Anopheles species, as not all the species studied here co-exist in particular locations in India. 4.3. Anopheles species divergence time follow geological records Among the five genetic markers, only the mitochondrial COII gene was found to adhere to molecular clock hypothesis, and thus considered appropriate for estimation of divergence time. Based on several peculiar properties, e.g., the presence of strictly orthologous genes, lack of recombination, and bearing an appropriate nucleotide substitution rate, the mitochondrial genome is considered ‘‘most preferred’’ genetic marker for evolutionary studies including phylogenetic reconstruction and divergence time estimations between closely related species (Gissi et al., 2000). The present study in seven species of Anopheles reconfirms the usage of mitochondrial gene (COII) in phylogenetic and divergence time estimations much more accurately than an array of nuclear genes. Wide geographical distribution of major anopheline groups provides indirect evidence on the evolutionary events leading to the present diversity within the Anopheles genus (Krzywinski et al., 2001). Further, it has been proposed that the members of the Anopheles genus probably have emerged in South America (Krzywinski and Besansky, 2003; Krzywinski et al., 2006), and the two subgenus – Anopheles and Cellia diverged at around 90– 100 Myr (Krzywinski et al., 2006). The present estimate of 45– 79 Myr (Fig. 2) therefore, is somehow consistent with earlier estimation on the evolution and radiation of Cellia subgenus that occurred largely after the break-up of Gondwana land about 100 MA (Million years Ago) (Garros et al., 2005). The time of divergence of An. gambiae and An. funestus (another member of the subgenus Cellia), distributed in similar geographical region (i.e. Africa) has been hypothesized to be about 80 Myr (Krzywinski et al., 2006). The divergence time between An. gambiae and An. sundaicus, i.e. about 45–78 Myr (estimated in this study) (Fig. 2) is also somewhat close to the splitting time between An. gambiae and An. funestus, indicating the fact that An. gambiae might have diverged from other members of Pyretophorous series in around 50 MA. Moreover, the splitting time between An. minimus and An. funestus (both belonging to Myzomyia series) has been proposed to be about 15–20 MA (Garros et al., 2005) which postdated An. gambiae–An. funestus split. Thus, it can be hypothesized that the radiation of species belonging to Myzomyia series had started at about 17–20 MA. The divergence time of about 17–20 Myr between An. culicifacies and An. minimus–An. fluviatilis clad (Fig. 2) accords well with the above hypothesis. Furthermore, the results of one of the recent study (Morgan et al., 2009) on the divergence of species belonging to Neocellia series reveal that, the radiation of species belonging to Annularis group within the Neocellia series has occurred at around 3.2–4.5 Myr. However, the present study shows the divergence time between An. annularis and An. stephensi of about 46–79 Myr (Fig. 2), which is much earlier than the radiation of Annularis group members. Furthermore, An. stephensi has not been placed under any formal

Author's personal copy 762

J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763

group in Neocellia series; therefore, it can be supposed that An. stephensi might have diverged much earlier from the other members of Annularis group species. Overall, the present findings signify that the species belonging to the Neocellia series have diverged much earlier than the species of Myzomyia series. Putting together the results of the present study with the previous reports, it can be summarized that the radiation of species belonging to Myzomyia has occurred much recently as compared to Neocellia and Pyretophorous series. The present study on multilocus phylogenetic inference of Indian malaria vectors also fits well with classical taxonomy as revealed with morphological and cytological approaches, and also with divergence times as estimated earlier (Garros et al., 2005; Krzywinski et al., 2006). For Indian malaria vectors it is important, since a vast number of species are prevalent in India, and an ample amount of speciation events might have occurred within the lineages, which is implied by the fact that out of the six Indian Anopheles malaria vector species included in the study, four species form complexes of isomorphic sibling species (Subbarao et al., 1994; Singh et al., 2009). The members of these sibling species complexes differ not only in distribution patterns but also in feeding preferences and disease transmission potential. Further, different gene-specific phylogenetic status of Anopheline species signifies that the evolutionary patterns of these genes might be species- and population-specific and population genetic studies in local species are needed for detail evolutionary genetic understanding. The estimation of divergence time, however, may be taken cautiously, as there are inherent reported pitfalls in using molecular clocks to date divergence events (Arbogast et al., 2002). In the present study, the problem was compounded by only one available calibration point, and the usage of a single locus (COII). However, we have followed rigorous analytical approaches to calculate dates of divergence of Indian malaria vector lineages and to add new important temporal dimension to the knowledge on mosquito evolution. Like the previous study (Krzywinski et al., 2006), the divergence time estimates differ broadly between data partitions, and thus, should be regarded with caution. However, since the present study is an initiation of such estimates for Cellia subgenus, further studies (by denser sampling of mosquitoes belonging to Cellia subgenus and sequencing of more genetic loci) should provide useful information on the evolution of Cellia subgenus of Anopheles genus in general and Indian species of malaria vectors, in particular. Acknowledgements AD thanks the Indian Council of Medical Research (ICMR), New Delhi for intramural funding under the North-Eastern task force grant. JD, HS and MS are Senior Research Fellows of the ICMR, New Delhi. We thank Drs. Vas Dev, Arun Sharma, S. P. Singh, and field workers and technical staff of the NIMR field units for all necessary help. We also thank the two anonymous reviewers for critical and constructive comments on an earlier version of the manuscript. References Alvarez, I., Wendel, J.F., 2003. Ribosomal ITS sequences and plant phylogenetic inference. Mol. Phylogenet. Evol. 29, 417–434. Arbogast, B.S., Edwards, S.V., Wakeley, J., Beerli, P., Slowinski, J.B., 2002. Estimating divergence times from molecular data on phylogenetic and population genetic timescales. Annu. Rev. Ecol. Syst. 33, 707–740. Avise, J.C., 2000. Phylogeography: The History and Formation of Species. Harvard University Press, Cambridge, MA. Barker, S.C., Murrel, A., 2004. Systematics and evolution of ticks with a list of valid genus and species names. Parasitology 129, 15–36. Blair, J.E., Coffey, M.D., Park, S.Y., Geiser, D.M., Kang, S., 2008. A multi-locus phylogeny for Phytophthora utilizing markers derived from complete genome sequences. Fungal Genet. Biol. 45, 266–277.

Blanquer, A., Uriz, M.J., 2007. Cryptic speciation in marine sponges evidenced by mitochondrial and nuclear genes: a phylogenetic approach. Mol. Phylogenet. Evol. 45, 392–397. Borkent, A., Grimaldi, D.A., 2004. The earliest fossil mosquito (Diptera: Culicidae), in Mid-Cretaceous Burmese amber. Ann. Entomol. Soc. Am. 97, 882–888. Chen, B., Butlin, R.K., Harbach, R.E., 2003. Molecular phylogenetics of the oriental members of Myzomyia series of Anopheles subgenus Cellia (Diptera: Culicidae) infered from nuclear and mitochondrial DNA sequences. Syst. Entomol. 28, 57– 69. Chen, B., Butlin, R.K., Pedro, P.M., Wang, X.Z., Harbach, R.E., 2006. Molecular variation, systematics and distribution of the Anopheles fluviatilis complex in southern Asia. Med. Vet. Entomol 20, 33–43. Christophers, S.R., 1933. The Fauna of British India, Including Ceylon and Burma, Diptera, volume IV, family Culicidae, tribe Anophelini. Taylor and Francis, Red Lion Court, Fleet Street, London. Coleman, A.W., 2003. ITS2 is a double-edged tool for eukaryote evolutionary comparisons. Trends Genet. 19, 370–375. Collins, F.H., Paskewitz, S.M., 1996. A review of the use of ribosomal DNA (rDNA) to differentiate among cryptic Anopheles species. Insect Mol. Biol. 5, 1–9. Cotton, J.A., Page, R.D.M., 2002. Going nuclear: gene family evolution and vertebrate phylogeny reconciled. Proc. R. Soc. Lond. B 269, 1555–1561. Dana, A.N., Hillenmeyer, M.E., Lobo, N.F., Kern, M.K., Romans, P.A., Collins, F.H., 2006. Differential gene expression in abdomens of the malaria vector mosquito, Anopheles gambiae, after sugar feeding, blood feeding and Plasmodium berghei infection. BMC Genomics 7, 119. Das, A., Mohanty, S., Stephan, W., 2004. Inferring population structure and demography of Drosophila ananassae from multilocus data. Genetics 168, 1975–1985. Das, J., Stephen, T., Miller, S.T., Stern, D.L., 2004. Comparison of diverse protein sequences of the nuclear-encoded subunits of Cytochrome oxidase suggests conservation of structure underlies evolving functional sites. Mol. Biol. Evol. 21, 1572–1582. Dash, A.P., Adak, T., Raghavendra, K., Singh, O.P., 2007. The biology and control of malaria vectors in India. Curr. Sci. 92, 1571–1578. de Jong, W.W., Zweers, A., Goodman, M., 1981. Relationship of aardvark to elephants, hyraxes and sea cows from a-crystallin sequences. Nature 292, 538– 540. Doolittle, W.F., 1999a. Phylogenetic classification and the universal tree. Science 284, 2124–2129. Doolittle, W.F., 1999b. Technical comments (response) on Doolittle (1999a). Science 286, 1443. Drummond, A.J., Rambaut, A., 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214. Eisen, J.A., 2000. Horizontal gene transfer among microbial genomes: new insights from complete genome analysis. Curr. Opin. Genet. Dev. 10, 606–611. Felsenstein, J., 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791. Foley, D.H., Bryan, J.H., Yeates, D., Saul, A., 1998. Evolution and systematics of Anopheles: insights from a molecular phylogeny of Australasian mosquitoes. Mol. Phylogenet. Evol. 9, 262–275. Garros, C.L., Koekemoer, L., Coetzee, M., Coosemans, M., Manguin, S., 2004. A single multiplex assay to identify major malaria vectors within the African Anopheles funestus and the Oriental An. minimus groups. Am. J. Trop. Med. Hyg. 70, 583– 590. Garros, C., Harbach, R.E., Manguin, S., 2005. Systematics and biogeographical implications of the phylogenetic relationships between members of the Funestus and Minimus Groups of Anopheles (Diptera: Culicidae). J. Med. Entomol. 42, 7–18. Gissi, C., Reyes, A., Pesole, G., Saccone, C., 2000. Lineage-specific evolutionary rate in mammalian mitochondrial DNA. Mol. Biol. Evol. 17, 1022–1031. Godfray, J., Charls, H., 2002. Challenges for taxonomy. Nature 417, 17–19. Green, C.A., 1982. Cladistic analysis of mosquito chromosome data (Anopheles (Cellia) Myzomyia). J. Hered. 73, 2–11. Green, C.A., Baimai, V., 1984. Polytene chromosomes and their use in species studies of malaria vectors as exemplified by the Anophelems aculatus complex. In: Chopra, V.L., et al. (Eds.), Genetics: New Frontiers. Proceedings of the XV Intemational Congress of Genetics, vol. III, Genetics and Health. Oxford Publishing Co., Oxford, pp. 89–97. Green, C.A., Harrison, B.A., Klein, T., Baimai, V., 1985. Cladistic analysis of polytene chromosome rearrangements in anopheline mosquitoes, subgenus Cellia, series Neocellia. Can. J. Genet. Cytol. 27, 123–133. Grimaldi, D., Engel, M.S., 2005. Evolution of the Insects. Cambridge University Press, Cambridge, UK. Gu, X., Wang, Y., Gu, J., 2002. Age-distribution of human gene families showing equal roles of large and small scale duplications in vertebrate evolution. Nat. Genet. 31, 205–209. Harbach, R.E., 2004. The classification of genus Anopheles (Diptera: Culicidae): a working hypothesis of phylogenetic relationships. Bull. Entomol. Res. 94, 537– 553. Holt, R.A., Subramanian, G.M., Halpern, A., Sutton, G.G., Charlab, R., Nusskern, D.R., Wincker, P., Clark, A.G., Ribeiro, J.M.C., Wides, R., Salzberg, S.L., Loftus, B., Yandell, M., Majoros, W.H., et al., 2002. The genome sequence of the malaria mosquito Anopheles gambiae. Science 298, 129–148. Huelsenbeck, J.P., Ronquist, F.R., 2000. MRBAYES: Bayesian inference of phylogenetic trees. Bioinform. Appl. Note 17, 754–755. Irwin, D.M., Kocher, T.D., Wilson, A.C., 1991. Evolution of the cytochrome b gene of mammals. J. Mol. Evol. 32, 128–144.

Author's personal copy J. Dixit et al. / Infection, Genetics and Evolution 10 (2010) 755–763 Jennings, B.W., Edwards, S.V., 2005. Speciational history of Australian grass finches (Poephila) inferred from thirty gene trees. Evolution 59, 2033–2047. Jordan, I.K., Makarova, K.S., Spouge, J.L., Wolf, Y.I., Koonin, E.V., 2001. Lineagespecific gene expansions in bacterial and archaeal genomes. Genome Res. 11, 555–565. Krzywinski, J., Besansky, N.J., 2003. Molecular systematics of Anopheles: from subgenera to subpopulations. Annu. Rev. Entomol. 48, 111–139. Krzywinski, J., Wilkerson, R.C., Besansky, N.J., 2001. Towards understanding Anophelinae (Diptera, Culicidae) Phylogeny: insights from nuclear single-copy genes and the weight of evidence. Syst. Biol. 50, 540–556. Krzywinski, J., Grushko, O.G., Besansky, N.J., 2006. Analysis of the complete mitochondrial DNA from Anopheles funestus: An improved dipteran mitochondrial genome annotation and a temporal dimension of mosquito evolution. Mol. Phylogenet. Evol. 39, 417–423. Kumazawa, Y., Azuma, Y., Nishida, M., 2004. Tempo of mitochondrial gene evolution: can mitochondrial DNA be used to date old divergences? Endocytobiosis Cell Res. 15, 136–142. Li, C., Wilkerson, R.C., 2005. Identification of Anopheles (Nyssorhynchus) albitarsis complex species (Diptera: Culicidae) using rDNA internal transcribed spacer 2based polymerase chain reaction primers. Mem. Inst. Oswaldo Cruz 100, 495– 500. Lim, J., Gawda, D.C., Krishnegowda, G., Luckhart, S., 2005. Induction of nitric oxide synthase in Anopheles stephensi by Plasmodium falciparum: mechanism of signaling and the role of parasite glycosylphosphatidylinositols. Infect. Immun. 73, 2778–3289. Liu, L., Pearl, D.K., 2007. Species trees from gene trees: reconstructing Bayesian posterior distributions of a species phylogeny using estimated gene tree distributions. Syst. Biol. 56, 504–514. Luckhart, S., Rosenberg, R., 1999. Gene structure and polymorphism of an invertebrate nitric oxide synthase gene. Gene 232, 25–34. Luckhart, S., Vodovotz, Y., Cui, L., Rosenberg, R., 1998. The mosquito Anopheles stephensi limits malaria parasite development with inducible synthesis of nitric oxide. Proc. Natl. Acad. Sci. U.S.A. 95, 5700–5705. Marrelli, M.T., Sallum, M.A.M., Marinotti, O., 2006. The second internal transcribed spacer of nuclear ribosomal DNA as a tool for latin American anopheline taxonomy—a critical review. Mem. Inst. Oswaldo Cruz 101, 817–832. Masta, S.E., 2000. Mitochondrial sequence evolution in spiders: intraspecific variation in tRNAs lacking the TcC Arm. Mol. Biol. Evol. 17, 1091–1100. Miyamoto, M.M., Goodman, M., 1986. Biomolecular systematics of eutherian mammals: phylogenetic patterns and classification. Syst. Zool. 35, 230–240. Mohanty, A., Swain, S., Kar, S.K., Hazra, R.K., 2009. Analysis of the phylogenetic relationship of Anopheles species, subgenus Cellia (Diptera: Culicidae) and using it to define the relationship of morphologically similar species. Infect. Genet. Evol. 9, 1204–1224. Morgan, G.J., 1998. Emile Zuckerkandl, Linus Pauling, and the molecular evolutionary clock. J. Hist. Biol. 31, 155–178. Morgan, U.M., Xiao, L., Monis, P., Sulaiman, I., Pavlasek, I., Blagburn, B., Olson, M., Upton, S.J., Khramtsov, N.V., Lal, A., Elliot, A., Thompson, R.C.A., 2000. Molecular and phylogenetic analysis of Cryptosporidium muris from various hosts. Parasitology 120, 457–464. Morgan, K., Samantha, M.O., Mun-Yik, F., Linton, Y.M., Somboon, P., Min, S., Htun, P.T., Nambanya, S., Weerasinghe, I., Sochantha, T., Prakash, A., Walton, C., 2009. Molecular phylogenetics and biogeography of the Neocellia Series of Anopheles mosquitoes in the Oriental Region. Mol. Phylogenet. Evol. 52, 588–601. Nagpal, B.N., Sharma, V.P., 1995. Indian Anophelines. Oxford and IBH publishing Co. Pvt. Ltd., New Delhi. Nei, M., 1987. Molecular Evolutionary Genetics. Columbia University Press, New York. Oduol, F., Jiannong, X., Niare, O., Natarajan, R., Vernick, K.D., 2000. Genes identified by an expression screen of the vector mosquito Anopheles gambiae display differential molecular immune response to malaria parasites and bacteria. Proc. Natl. Acad. Sci. U.S.A. 21, 11397–11402. Pamilo, P., Nei, M., 1988. Relationships between gene trees and species trees. Mol. Biol. Evol. 5, 568–583. Pape, T., 1992. Cladistic analysis of mosquito chromosome data in Anopheles subgenus Cellia (Diptera:Culicidae). Mosq. Systematics 24, 1–11. Phuc, H.K., Ball, A.J., Son, L., Hanh, N.V., Tu, N.D., Lien, N.G., Vevardi, A., Townson, H., 2003. Multiplex PCR assay for malaria vector Anopheles minimus and four related species in the Myzomyia series from South East Asia. Med. Vet. Entomol. 17, 423–428. Poinar, G.O., Zavortink, T.J., Pike, T., Johnson, P.A., 2000. Paleoculicis minutus (Diptera: Culicidae) n. gen., n. sp., from Cretaceous Canadian amber, with a summary of described fossil mosquitoes. Acta. Geol. Hisp. 35, 119–128. Porter, C.H., Collins, F.H., 1991. Species-diagnostic differences in a ribosomal DNA internal transcribed spacer from the sibling species Anopheles freeborni and Anopheles hermsi (Diptera: Culicidae). Am. J. Trop. Med. Hyg. 45, 271–279. Posada, D., Crandall, K.A., 1998. Modeltest: testing the model of DNA substitution. Bioinformatics 14, 817–818.

763

Raghavendra, K., Cornel, A.J., Reddy, B.P.N., Collins, F.H., Nanda, N., Chandra, D., Verma, V., Dash, A.P., Subbarao, S.K., 2009. Multiplex PCR assay and phylogenetic analysis of sequences derived from D2 domain of 28S rDNA distinguished members of the Anopheles culicifacies complex into two groups, A/D and B/C/E. Infect. Genet. Evol. 9, 271–277. Rambaut, A., Drummond, A.J., 2007. Tracer v1.4. Available from: . Reidenbach, K.R., Cook, S., Bertone, M.A., Harbach, R.E., Weigman, B.M., Besansky, N.j., 2009. Phylogenetic analysis and temporal diversification of mosquitoes (Diptera: Culicidae) based on nuclear genes and morphology. BMC Evol. Biol. 9, 298. Reyes, A., Gissi, C., Catzeflis, F., Nevo, E., Pesole, G., Saccone, C., 2004. Congruent mammalian trees from mitochondrial and nuclear genes using Bayesian methods. Mol. Biol. Evol. 21, 397–403. Saccone, C., Lanave, C., Pesole, G., Preparata, G., 1990. Influence of base composition on quantitative estimates of gene evolution. Methods Enzymol. 183, 570–583. Saccone, C., Lanave, C., Grassi, A.D., 2006. Metazoan OXPHOS gene families: evolutionary forces at the level of mitochondrial and nuclear genomes. Biochim. Biophys. Acta 1757, 1171–1178. Sallum, M.A.M., Schultz, T.R., Foster, P.G., Aronstein, K., Wirtz, R.A., Wilkerson, R.C., 2002. Phylogeny of anophelinae (Diptera: Culicidae) based on nuclear ribosomal and mitochondrial DNA sequences. Syst. Entomol. 27, 361–382. Sharp, R.G., Harbach, R., Butlin, R.K., 2000. Molecular variation and phylogeny of members of the minimus group of Anopheles subgenus Cellia (Diptera:Culicidae). Syst. Entomol. 25, 263–272. Shimodaira, H., Hasegawa, M., 1999. Multiple comparisons of Log-Likelihoods with applications to phylogenetic inference. Mol. Biol. Evol. 16, 1114–1116. Singh, O.P., Chandra, D., Nanda, N., Raghavendra, K., Sunil, S., Sharma, S.K., Dua, V.K., Subbarao, S.K., 2004. Differentiation of members of the Anopheles fluviatilis species complex by an allele specific polymerase chain reaction based on 28S ribosomal DNA sequences. Am. J. Trop. Med. Hyg. 70, 27–32. Singh, V., Mishra, N., Awasthi, G., Dash, A.P., Das, A., 2009. Why is it important to study malaria epidemiology in India? Trends Parasitol. 25, 452–457. Springer, M.S., Cleven, G.C., Madsen, O., de Jong, W.W., Waddell, V.G., Amrine, H.M., Stanhope, M.J., 1997. Endemic African mammals shake the phylogenetic tree. Nature 388, 61–64. Srivastava, H., Dixit, J., Dash, A.P., Das, A., 2009. Fine-scale evolutionary insights into Anopheles gambiae X chromosome. J. Biomed. Sci. Eng. 2, 304–311. Srivastava, H., Sharma, M., Dixit, J., Das A., 2010. Evolutionary insights into insecticide resistance gene families of Anopheles gambiae. Infect. Genet. Evol. 10, doi:10.1016/j.meegid.2010.04.002. Subbarao, S.K., Vasantha, K., Raghavendra, K., Sharma, V.P., Sharma, G.K., 1988. Anopheles culicifacies: sibling species composition and its relationship to malaria incidence. J Am. Mosq. Contr. Assoc. 4, 29–33. Subbarao, S.K., Nanda, N., Vasantha, K., Dua, V.K., Malhotra, M.S., Yadav, R.S., Sharma, V.P., 1994. Cytogenetic evidence for three sibling species in Anopheles fluviatilis (Diptera: Culicidae). Ann. Entomol. Soc. Am. 87, 116–121. Swofford, D.L., 2002. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. Sinauer Associates, Sunderland Massachusetts. Tajima, F., 1993. Simple methods for testing the molecular evolutionary clock hypothesis. Genetics 135, 599–607. Takahata, N., 1989. Gene genealogy in three related populations: consistency probability between gene and population trees. Genetics 122, 957–966. Tamura, K., Dudley, J., Nei, M., Kumar, S., 2007. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24, 1596– 1599. Thompson, J.D., Higgins, D.G., Gibson, T.J., 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680. True, J.R., Carroll, S.B., 2002. Gene co-option in physiological and morphological evolution. Ann. Rev. Cell Dev. Biol. 18, 53–80. Vali, U., Brandstrom, M., Johansson, M., Ellegren, H., 2008. Insertion deletion polymorphisms (indels) as genetic markers in natural populations. BMC Genet. 9, 8. Vawter, L., Brown, W.M., 1986. Nuclear and mitochondrial DNA comparisons reveal extreme rate variation in the molecular clock. Science 234, 194–196. Walton, C., Handley, J.M., Kuvangkadilok, C., Collins, F.H., Harbach, R.E., Baimai, V., Butlin, R.K., 1999. Identification of five species of the Anopheles dirus complex from Thailand, using allele-specific polymerase chain reaction. Med. Vet. Entomol. 13, 24–32. WHO malaria report, 2008. http://apps.who.int/malaria/wmr2008/malaria2008.pdf. Wilkerson, R.C., Foster, P.G., Li, C., Sallum, M.A.M., 2005. Molecular phylogeny of neotropical Anopheles (Nyssorhynchus) Albitarsis Species complex (Diptera: Culicidae). Ann. Entomol. Soc. Am. 98, 918–925. Yang, Z., 2007. PAML 4: a program package for phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591.