identifying canadian mosquito species through dna barcodes

Medical and Veterinary Entomology (2006) 20, 413–424

© 2006 The AuthorsJournal compilation © 2006 The Royal Entomological Society 413

Introduction

The increasing loss of biodiversity globally has led to numerous proposals to intensify efforts to produce a census of all biologi-cal diversity and to modernize taxonomy ( Bisby et al. , 2002; Godfray, 2002; Besansky et al. , 2003; Lipscomb et al. , 2003; Mallet & Willmott, 2003; Seberg et al. , 2003; Tautz et al. , 2003 ). Traditional morphology-based taxonomic procedures are time-consuming and not always sufficient for identification to the species level, and therefore a multidisciplinary approach to tax-onomy that includes morphological, molecular and distribu-tional data is essential ( Krzywinski & Besansky, 2003 ).

Hebert et al. (2003a , b) have shown that the analysis of short, standardized genomic regions (DNA barcodes) can discriminate morphologically recognized animal species. In particular, they suggest that the mitochondrial gene cytochrome c oxidase sub-unit 1 (CO1) can serve as a uniform target gene for a bioidenti-fication system.

The ability of DNA barcodes to identify species reliably, quickly and cost-effectively has particular importance in medi-cal entomology, where molecular approaches to species diag-noses are often of great benefit in the identification of all life stages, from eggs to adults. As Besansky et al. (2003) stated: ‘ Nowhere is the gap in taxonomic knowledge more urgent than for medically important pathogens and their invertebrate vec-tors ’ . For example, since the recent arrival of West Nile virus

(WNv) in North America, mosquito identification and assess-ment of vector status has gained renewed significance on this continent. Successful longterm control of WNv will be aided by information on the epidemiological role of mosquitoes and the transmission biology of the virus.

Although biting insects have been studied more extensively than most other animal groups, our taxonomic knowledge of mosquitoes is far from complete. Since Edwards (1932) out-lined the modern system of mosquito classification, the number of described mosquito species has more than doubled from 1400 to almost 3200 ( Zavortink, 1990; Harbach & Kitching, 1998 ) and new species are still being identified.

Several genetic approaches have been applied to the identifica-tion of mosquito species, including protein electrophoresis ( Green et al. , 1992; Foley et al. , 1995; Sukowati et al. , 1999; Van Bortel et al. , 1999 ), hybridization assays ( Beebe et al. , 1996; Crampton & Hill, 1997; Cooper et al. , 2002 ) and polymerase chain reaction (PCR)-based sequence analysis. The latter has the advantage of requiring minute amounts of material for analysis. Methods based on PCR, such as satellite DNA ( Krzywinski et al. , 2005 ), restric-tion fragment length analysis, single-strand conformation shifts, or heteroduplex analysis, have been applied to detect diagnostic differences among PCR products in mosquito species ( Moriais & Severson, 2003; Santomalazza et al. , 2004; Weeto et al. , 2004; Garros et al. , 2005 ; Goswami et al. , 2005 ). Most PCR assays have examined sequence diversity in specific nuclear loci ( Scott et al. ,

Identifying Canadian mosquito species through DNA barcodes

A . C Y W I N S K A 1 , F. F . H U N T E R 1 and P. D . N . H E B E RT 2 1 Department of Biological Sciences, Brock University, St. Catharines, Ontario, Canada and 2 Department of Integrative Biology,

University of Guelph, Guelph, Ontario, Canada

Abstract . A short fragment of mt DNA from the cytochrome c oxidase 1 (CO1) region was used to provide the first CO1 barcodes for 37 species of Canadian mosquitoes (Diptera: Culicidae) from the provinces Ontario and New Brunswick. Sequence varia-tion was analysed in a 617-bp fragment from the 5 ′ end of the CO1 region. Sequences of each mosquito species formed barcode clusters with tight cohesion that were usually clearly distinct from those of allied species. CO1 sequence divergences were, on aver-age, nearly 20 times higher for congeneric species than for members of a species; diver-gences between congeneric species averaged 10.4% (range 0.2 – 17.2%), whereas those for conspecific individuals averaged 0.5% (range 0.0 – 3.9%).

Key words . Barcode of life , CO1 – 5 ′ region , evolution , mitochondrial DNA , molecular taxonomy , mosquitoes , sequence divergence .

Correspondence: Alina Cywinska, Department of Biological Sciences, Brock University, St. Catharines, Ontario L2S 3A1, Canada. Tel.: + 1 905 688 5550 (ext. 3879); Fax: + 1 905 688 1855; E-mail: [email protected]

414 A. Cywinska et al.

© 2006 The AuthorsJournal compilation © 2006 The Royal Entomological Society, Medical and Veterinary Entomology, 20, 413–424

1993; Beebe & Saul, 1995; Singh et al. , 1997 ; Koekemoer et al. , 1999; Proft et al. , 1999; Walton et al. , 1999 ; Hackett et al. , 2000; Favia et al. , 2001; Manonmani et al. , 2001; Cohuet et al. , 2003; Kent et al. , 2004; Smith & Fonseca, 2004; Kampen, 2005 ; Marrelli et al. , 2005 ). Other researchers have examined the taxo-nomic insights that can be gained by combining information from two or more genes ( Nguyen et al. , 2000; Krzywinski et al. , 2001; Linton et al. , 2001; Mitchell et al. , 2002; Linton et al. , 2003; Dusfour et al. , 2004; Shaikevitch & Vinogradova, 2004; Cook et al. , 2005 ). Multiplex PCR assays that included both universal (conserved) and species-specific primers were performed by Phuc et al. (2003) . By contrast with the many studies on nuclear genes, little taxonomic work has targeted haploid mitochondrial DNA sequences in mosquitoes and less yet has examined sequence diversity in the CO1 gene ( Rey et al. , 2001; Fairley et al. , 2000 , 2002; Sallum et al., 2002 ), despite its established potential for the diagnosis of biological diversity ( Hebert et al. , 2003a , 2003b, 2004). The CO1 region is present in the hundreds of copies per cell, it generally lacks indels, and, in common with other protein-coding genes, its third position nucleotides show a high incidence of base substitutions. Changes in its amino acid sequence occur more slowly than those in any other mitochon-drial gene, aiding resolution of deeper taxonomic affinities and primer design.

In this study, sequence variation in the barcode region of CO1 was analysed to test its usefulness in the identification of mos-quito species from eastern Canada.

Materials and methods

Mosquito collections

During 2002 and 2003, adults belonging to 37 mosquito spe-cies were collected across Ontario ( Fig. 1, Table 1) as part of the West Nile Virus Surveillance Programme. Mosquitoes were sampled with CO 2 -baited CDC (Center for Disease Control) miniature light traps (BioQuip, Rancho Dominguez, CA, U.S.A.) and were identified using standard taxonomic keys ( Wood et al. , 1979; Darsie & Ward, 2005 ). In addition, individu-als of five mosquito species from New Brunswick ( Table 1 ) were sequenced to provide preliminary information about the degree of geographical variation in sequences within Canada and sequences from 19 species were chosen from GenBank ( Table 1 ) to test for variation across a greater geographical area. These were the only mtDNA sequences for mosquitoes that matched our CO1 barcode region.

Sample preparation, DNA extraction, amplification and sequencing

Nearly half of the samples used for DNA extraction were ob-tained from 100 � L slurries of individual mosquitoes that had been homogenized earlier and pre-treated to test for WNv ( Condotta et al. , 2004 ). DNA extractions for the remaining

Fig. 1. Map of study area showing sampling sites within locations of the Health Units of the Ontario Ministry of Health and the First Nations territo-ries. AL, Algoma; BR, Brant; GB, Bruce Grey Owen Sound; DR, Durham; EG, Elgin – St. Thomas; EO, Eastern Ontario; HK, Haliburton – Kawartha – Pine Ridge; HL, Halton; HM, Hamilton Wentworth; HN, Haldimand – Norfolk; HP, Hastings – Prince Edward; HR, Huron; CK, Kent – Chatham; KG, Kingston – Frontenac – Lennox – Addington; LM, Lambton; LG, Leeds – Grenville – Lanark; MI, Manitouli Island; ML, Middlesex – London; MP, Muskoka – Parry Sound; NB, North Bay and District; NI, Niagara; NW, North-western; OT, Ottawa – Carleton; OX, Oxford; PE, Peel; PC, Porcupine; PD, Perth District Health; PT, Peterborough County; RE, Renfrew County and District; SM, Simcoe; SB, Sudbury; TB, Thunder Bay; TK, Timiskaming; TO, Toronto; WA, Waterloo; WD, Wellington – Dufferin – Guelph; WE, Windsor – Essex County; YK, York. First Nations: CT-FN, Chippewas of the Thames; HI-FN, Hiavatha; MN-FN, Chippewas of Rama; NC-FN, Mississauga of New Credit; SN-FN, Six Nations of Grand River; TY-FN, Mohawks of the Bay of Quinte; WI-FN, Walpole Island.

CO1 barcodes in mosquitoes 415


Table 1. List of mosquito species, collection sites and number of sequences per species used in the study.

SpeciesCollection origin in Ontario

Collection origin outside Ontario

Number of specimens

Aedes abserratus Central 2 Northern 7 South-western 1

Aedes aegypti GenBank 5 Aedes atropalpus South-western 5

GenBank 1 Aedes aurifer Central 1 Aedes canadensis Central 7

Eastern 4 South-western 3 Northern 2

New Brunswick 4 Aedes cantator South-western 2

New Brunswick 5

Aedes cinereus Central 2 Eastern 1 South-western 1

New Brunswick 3 Aedes communis Northern 5 Aedes dorsalis Central 2

South-western 1 Northern 1

Aedes euedes Central 3 Eastern 4

Aedes excrucians Central 5 Eastern 2

Aedes fi tchii Central 1 Eastern 4

Aedes grossbecki Central 2 South-western 2

Aedes implicatus Central 1 Eastern 6 Northern 3 South-western 1

New Brunswick 2 Aedes intrudens Eastern 1

Northern 1 Aedes japonicus Central 1

Eastern 1 South-western 4

Aedes provocans Central 1 Eastern 5 South-western 1

Aedes riparius Central 7 Aedes sollicitans South-western 5 Aedes stictus Northern 1 Aedes stimulans Central 22

South-western 4 Aedes triserratus Central 3


Aedes trivittatus Central 6 South-western 4

Aedes vexans Central 8 South-western 4 Eastern 1

New Brunswick 8 Anopheles funestus GenBank 1

SpeciesCollection origin in Ontario

Collection origin outside Ontario

Number of specimens

Anopheles earlei Central 1 Northern 3

GenBank 1 Anopheles gambiae GenBank 1 Anopheles maculipennis GenBank 2 Anopheles messeae GenBank 2 Anopheles punctipennis Central 10

South-western 5 Anopheles quadrimaculatus

Central 9

South-western 2 GenBank 1

Anopheles pullus GenBank 2 Anopheles rivulorum GenBank 1 Anopheles sacharovi GenBank 2 Anopheles sinensis GenBank 1 Anopheles stephensi GenBank 1 Anopheles sundaicus GenBank 2 Anopheles walkeri Central 4

Eastern 2 Coquillettidia perturbans

Central 5

Northern 2 Eastern 11 South-western 1

Culex pipiens Central 4 South-western 7

Culex restuans Central 5 South-western 5 Northern 1

Culex salinarius Eastern 5 South-western 1

Culex tarsalis GenBank 1 Culex territans Central 1

Eastern 1 Northern 1

Culiseta impatient GenBank 1 Culiseta inornata South-western 5 Culiseta minnesotae Northern 1

Eastern 1 Culiseta morsitans Central 2

Northern 3 Eastern 1

Orthopodymia alba Central 1 Sabethes cyaneus GenBank 1 Toxorhynchites rutilus GenBank 1 Toxorhynchites sp. GenBank 1 Uranotaenia sapphirina Central 3


Table 1. Continued.



samples were obtained from small amounts of tissue (two to three legs) from frozen pinned mosquitoes.

For each mosquito, 30 � L of total DNA was extracted using the GeneElute TM Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich Co., St. Louis, MO, U.S.A.). The primer pairs LCO1490 and HCO2198 ( Folmer et al. , 1994 ) or LepF (5 ′ -ATTCAACCAATCATAAAGATATTGG-3 ′ ) and HCO2198 were subsequently used to amplify ~ 650 bp fragments of CO1 which were trimmed later to 617 bp in the barcode region of CO1. Each PCR cocktail contained 2.5 � L 10 X PCR buffer, pH 8.3 (10 mM Tris-CH1, pH 8.3 and 50 mM KC1, 0.01% NP-40), 1.5 mM MgCl 2 , 200 � M of each NTP, 1 unit Taq polymerase, 0.3 � M of each primer, 1 – 5 � L of DNA template and the remaining vol-ume of ddH 2 O up to 25 � L. The PCR thermal regime consisted of one cycle of 1 min at 95 °C, 35 cycles of 1 min at 94 °C, 1 min at 55 °C, 1.5 min at 72 °C, and a final cycle of 7 min at 72 °C. All PCR products were subjected to dye terminator cycle sequencing reactions (30 cycles, 55 °C annealing), and

sequenced on ABI 377 or 3730 automated sequencers, using Big Dye vs. 3.1 and LCO1490 primer.

Data analysis

Electropherograms for the CO1 gene were edited and aligned with Sequencher TM Version 4.5 (Gene Codes Corp., Ann Arbor, MI, U.S.A.). Pairwise nucleotide sequence diver-gences were calculated using the Kimura 2-parameter (K2P) model ( Kimura, 1980 ), and neighbour-joining (NJ) analysis ( Saitou & Nei, 1987 ) in mega 2.1 was used to examine rela-tionships among taxa. All sequences obtained in this study have been deposited in GenBank. Collection localities and other specimen information, such as the GenBank submission numbers, will be available in the ‘ Mosquitoes of Canada ’ file in the Completed Project section of the Barcode of Life web-site ( http://www.barcodinglife.org ).

Fig. 2. Neighbour-joining analysis of Kimura 2-parameter (K2P) distances of CO1 mosquito sequences from Ontario and New Brunswick. Labels indicate mosquitoes collected in New Brunswick.

MSQ011-04|JE011|Aedes cantator MSQ012-04|JE012|Aedes cantator


193-10 Aedes cantator MSQ018-04|JE018|Aedes cantator

MSQ013-04|JE013|Aedes canadensis 173-20 Aedes canadensis

MSQ006-04|JE006|Aedes canadensis MSQ020-04|JE020|Aedes canadensis

MSQ027-04|JE027|Aedes canadensis 18-20 Aedes intrudens

193-9 Aedes dorsalis 21-43 Aedes riparius 35.03 Aedes euedes

23.03 Aedes stimulans 30.03 Aedes excrucians

281-45 Aedes trivittatus SL6 Aedes sollicitans

176-16 Aedes triseriatus 32-14 Aedes aurifer

CO73 Aedes communis AB55 Aedes abserratus MSQ007-04|JE007|Aedes implicatus AB56 Aedes implicatus MSQ028-04|JE028|Aedes implicatus

PR64 Aedes provocans 32-18 Aedes fitchii

20-19 Aedes grossbecki ST11 Aedes stictus

JP9 Aedes japonicus AP20 Aedes atropalpus

MSQ008-04|JE008|Aedes vexans MSQ023-04|JE023|Aedes vexans MSQ015-04|JE015|Aedes vexans

MSQ016-04|JE016|Aedes vexans MSQ002-04|JE002|Aedes vexans


34.03 Aedes vexans MSQ009-04|JE009|Aedes vexans

MSQ010-04|JE010|Aedes cinereus MSQ024-04|JE024|Aedes cinereus

CN46 Aedes cireneus MSQ001-04|JE001|Aedes cinereus

185-15 Culiseta inornata I1-40h1LT Orthopodymia alba

193-64 Uranotaenia sapphirina TT41 Culex territans

PP10 Culex pipiens SN38 Culex salinarius

RN26 Culex restuans 184-70 Culiseta morsitans 22-52 Culiseta minnesotae

Cq79 Coquillettidia perturbans C1-23 Anopheles quadrimaculatus

N1-34 Anopheles earlei 43FN Anopheles punctipennis

C1-14 Anopheles walkeri SLB-2003 Tipula sp. (outgroup)

Mosquito sequences from New Brunswick MSQ011-04|JE011|Aedes cantator

MSQ013-04|JE013|Aedes canadensis MSQ018-04|JE018|Aedes cantator


MSQ012-04|JE012|Aedes cantator

MSQ007-04|JE007|Aedes implicatus

MSQ027-04|JE027|Aedes canadensisMSQ020-04|JE020|Aedes canadensis

MSQ006-04|JE006|Aedes canadensis

MSQ023-04|JE023|Aedes vexansMSQ008-04|JE008|Aedes vexans

MSQ028-04|JE028|Aedes implicatus


MSQ002-04|JE002|Aedes vexans

MSQ009-04|JE009|Aedes vexans


MSQ001-04|JE001|Aedes cinereus

MSQ024-04|JE024|Aedes cinereus MSQ010-04|JE010|Aedes cinereus

0.02



Fig. 3. Neighbour-joining analysis of Kimura 2-parameter (K2P) distances of CO1 mosquito sequences from Ontario and from GenBank. Labels indicate sequences obtained from GenBank.

23.03 Aedes stimulans 30.03 Aedes excrucians

35.03 Aedes euedes 21-43 Aedes riparius

18-20 Aedes intrudens 193-9 Aedes dorsalis

193-10 Aedes cantator 173-20 Aedes canadensis

PR64 Aedes provocans 32-18 Aedes fitchii

20-19 Aedes grossbecki 281-45 Aedes trivittatus SL6 Aedes sollicitans

176-16 Aedes triseriatus 32-14 Aedes aurifer

CO73 Aedes communis AB55 Aedes abserratus AB56 Aedes implicatus

ST11 Aedes stictus JP9 Aedes japonicus

AP20 Aedes atropalpus AF425845 Aedes atropalpus

34.03 Aedes vexans CN46 Aedes cireneus

AF390098 Aedes aegypti AY056596 Aedes aegypti

AY056597 Aedes aegypti AF380835 Aedes aegypti AF425846 Aedes aegypti

185-15 Culiseta inornata AF425848 Culiseta impatiens

184-70 Culiseta morsitans 22-52 Culiseta minnesotae

Cq79 Coquillettidia perturbans TT41 Culex territans PP10 Culex pipiens SN38 Culex salinarius

RN26 Culex restuans AF425847 Culex tarsalis

C1-14 Anopheles walkeri AY423060 Anopheles rivulorum

AF368140 Anopheles sundaicus AF368117 Anopheles sundaicus

AY423059 Anopheles funestus AF425844 Anopheles stephensi NC000084 Anopheles gambiae

AY444348 Anopheles pullus AY444349 Anopheles pullus

AY444351 Anopheles sinensis C1-23 Anopheles quadrimaculatus NC000875 Anopheles quadrimaculatus

43FN Anopheles punctipennis N1-34 Anopheles earlei AF425843 Anopheles earlei

AY135695 Anopheles sacharovi AY135697 Anopheles sacharovi

AY258169 Anopheles messeae AY258170 Anopheles messeae

AF342719 Anopheles maculipennis AF491733 Anopheles maculipennis

AF425840 Sabethes cyaneus I1-40LT Orthopodymia alba

AF425849 Toxorhynchites rutilus AF425850 Toxorhynchites sp.

193-64 Uranotaenia sapphirina SLB-2003 Tipula sp. (outgroup)

AF425846 Aedes aegyptiAF380835 Aedes aegypti AY056597 Aedes aegypti

AF425845 Aedes atropalpus

AY056596 Aedes aegyptiAF390098 Aedes aegypti

AY423059 Anopheles funestus AF368117 Anopheles sundaicusAF368140 Anopheles sundaicus

AY423060 Anopheles rivulorum

AF425847 Culex tarsalis

AF425848 Culiseta impatiens

AY444351 Anopheles sinensisAY444349 Anopheles pullusAY444348 Anopheles pullus

NC000084 Anopheles gambiaeAF425844 Anopheles stephensi

AY258169 Anopheles messeae AY135697 Anopheles sacharovi AY135695 Anopheles sacharovi

AF425843 Anopheles earlei

NC000875 Anopheles quadrimaculatus

AF425850 Toxorhynchites sp. AF425849 Toxorhynchites rutilus

AF425840 Sabethes cyaneus AF491733 Anopheles maculipennis

AF342719 Anopheles maculipennisAY258170 Anopheles messeae

Results

Sequences for 37 mosquito species from Ontario were compared with those for five species from New Brunswick ( Fig. 2) and 19 species from GenBank ( Fig. 3), producing sequence records for 53 mosquito species belonging to nine genera and three sub-families (Anophelinae, Culicinae and Toxorhynchitinae). Individual species were represented by one to 26 individuals, for a total of 302 CO1 sequences. Because these sequences contained no indels, alignments were straightforward. The CO1 sequences had a strong A + T bias (average 67% for all codons), especially, at third codon positions (91%) ( Table 2). The pattern was gener-ally consistent across genera, with the A + T content ranging from 64% ( Coquillettidia sp.) to 70% ( Uranotaenia sp.).

All but one of the sequences lacked nonsense or stop codons, supporting their origin from the mitochondrial gene. One prob-

able pseudogene, a CO1 sequence from an individual of Culex restuans (Theobald), contained one stop codon and 15 amino acid substitutions in comparison with the consensus sequence for other mosquitoes. In addition, it showed substantial sequence divergence (3.9 − 4.4% nt) from the other representa-tives of its species.

Individuals of a single species always grouped closely to-gether, regardless of where they were collected ( Figs 2, 3 and 4). All species also possessed a distinctive set of CO1 sequences, most of which showed low intraspecific divergences. Conspecific K2P divergence averaged 0.5% (range 0 – 3.9%), whereas se-quence divergences between congeneric species averaged 10.4% (range 0.2 – 17.2%) ( Fig. 5a, b). Sequence divergences were even higher among species in different genera, averaging 16.0% (range 7.2 – 26.3%; Fig. 5c ). Most conspecific sequences (98%) showed < 2% divergence, including those between two



Tab

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A +

TA

TC

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TC

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17.0

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030

.037

.616

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53.

389

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829

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.826

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53.

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67.7

28.7

26.9

15.7

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55.6

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42.7

26.7

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56.4

45.4

45.9

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56.5

15.0

43.4

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58.4

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48.1

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Fig. 4. Neighbour-joining analysis of the Kimura 2-parameter (K2P) distances of CO1 sequences from mosquitoes collected in Ontario. Labels indi-cate the collection areas in Ontario.

14.03 20.03

12.03 10.03

32-19 184-38 EX16 FT59 14-19 44-69

18.03 15.03

184-8 1.03

17.03 23.03 24-17 32-13 16.03 19.03 31.03 33.03

2.03 13.03 21.03

24-78 168-78 EX1

30.03 EX3 EX70 25.03 EX2

32-2 32-52 35.03 EU17 EU43 FT60 X-9

RP4 21-43 RP1 RP3 RP5 21-44 RP2

18-20 37-61

184-50 X-37

193-9 184-56

173-12 193-10 CD10

173-7 CD3 CD9

CD1 X-7 CD4 44-26 173-20

CD6 CD11

CD2 CD5

CD7 CD8

CD12 FT61

FT39 32-18

FT44 FT40

20-19 101-62 GB43

PR60 PR61 PR65 PR64 PR62 14-24

PR63 281-70 281-74

281-18 281-15 281-45 TV25

176-9 176-21

TV32mA5 TV28

SL7 SL5

SL6 SI67 SI68

52-18 172-41

176-12 TS14

58-9 176-16 184-35 TS68 172-64 TS67

32-14 17-23 CO73 CO74 CO76

CO75 32-60

ST12 AB57 AB55 AB23 32-26 16-17 21-16

ST10 AB72

AB22 IP50

32-3 IP52 AB56

AB77 AB24 IP51 21-51

32-6 42-44

ST11 AP21

AP70 AP20 AP71 AP72

191-57 Z-65

JP12 JP9 JP10 JP11

CN47 CN48

CN46 28.03

54-29 VN48

178-22 16-8 VN50 T1-10

VN49 T1-2 34.03

E1-72 VN51 281-27 J1-36

Eastern Ontario

Northern Ontario

Central Ontario

Southwestern Ontario

Aedes stimulans

Aedes excrucians

Aedes euedes

Aedes riparius

Aedes intrudens

Aedes dorsalis

Aedes cantator

Aedes canadensis

Aedes grossbecki

Aedes fitchii

Aedes provocans

Aedes trivittatus

Aedes sollicitans

Aedes triserratus

Aedes aurifer

Aedes communis

Aedes abserratus

Aedes implicatus

Aedes vexans

Aedes stictus

Aedes atropalpus

Aedes japonicus

Aedes cinereus

14.03 20.03

12.03 10.03

32-19 184-38

FT59

44-69

18.03 15.03

184-8 1.03

17.03 23.03

32-13 16.03 19.03 31.03 33.032.0313.03 21.03

24-1724 17

24-7824 78

EX16

14-1944 6944 69

EX3EX70

168-78 EX130.03

25.03EX2

32-232-52

EU17EU43

35.03

FT60X-9

RP4 21-43 RP1 RP3 RP5

21-44RP2

18-20 37-61

184-50 X-37

184-56193-9

173-12193-10

CD3

CD4

CD7

CD12

CD1X-7

44-26

CD6 CD11

CD2 CD5

173-7

173-20

CD9

CD8

281-18

281-45 TV25

176-9176-21

TV28

281-70 281-74

281-15

TV32mA5 176 21

SL7 SL5

SL6 SI67 SI68

52-18172-41

176-12

58-9 TS14

176-16

172-64

184-35 TS68

TS67 32-14

17-23 CO73 CO74 CO76CO75

32-26 16-17

ST12 AB57 AB55 AB23

21-16 ST10

AB72

32-60

AB22

32-3

AB77 AB24

32-642-44

AB56

IP50

IP52

IP51 21-51

PR61 PR65 PR64 PR62 14-24

PR60

PR63

CD10

54-29 VN48

VN50

VN49 T1-2 34.03E1-72 VN51 281-27

178-2216-8

T1-10

J1-36

AP21AP70 AP20 AP71AP72

ST11

191-57 Z-65

JP12 JP9 JP10 JP11

CN47

28.03

CN48 CN46

FT61FT39

FT44 FT40

32-18

101-6220-19

GB43



Fig. 4. Continued.

CN47 CN48

CN46 28.03

54-29 VN48

178-22 16-8 VN50 T1-10

VN49 T1-2 34.03

E1-72 VN51 281-27 J1-36

Q1-52 SP18

SP30 193-64 190-57 SP19

I1-40 TT40

TT41 184-42

27-27 IN65

IN64 185-15 179-66 281-11

PP5 PP3 PP6 174-12 PP2 PP10 PP7 PP4 PP9

174-9 SN33 SN34 SN35

SN38 191-29 SN37

RN22 RN29 281-37 176-50 RN24 RN25 RN19 RN26 RN27 RN23 RN20

RN28 22-52

MN55 281-81 184-54 184-70 70-3

MS28 W1-2

44.03 42.03 Cq75

F-3 43.03 Cq73 Cq78

40.03 Cq79

29.03 37.03 Cq71 Cq77 Cq74

176-18

39.03 41.03

176-15 Cq72

26.03 184-14 36.03 bl31 184-11 32.03

Q1-56 C1-23 27.03

bl77 bl74

184-51 EA38

N1-34 184-3

30FN 182-59 31FN

44FN 42FN

43FN 34FN

37FN 182-18

36FN 32FN F-52

28TOR 45FN 281-33

182-15 182-41 C1-14

WK19 WK21

WK22

SLB-2003 Tipula sp. (outgroup)

0.02

Aedes vexans

Culiseta morsitans

Culiseta minnesotae

Culiseta inornata

Culex territans

Uranotaenia sapphirina

Orthopodymia alba

Aedes cinereus

Culex pipiens

Culex salinarius

Culex restuans

Coquillettidia perturbans

Anopheles quadrimaculatus

Anopheles earlei

Anopheles punctipennis

Anopheles walkeri

Tipula sp. (outgroup)

pseudogene

54-29 VN48

VN50

VN49 T1-2 34.03

E1-72 VN51 281-27

178-2216-8VN50 T1-10

J1-36

CN47

28.03

CN48 CN46

SP30 193-64 190-57

Q1-52

SP19

SP18SP18

I1-40 TT40

TT41 184-42

174-12

PP7PP4

174-9

281-11 PP5 PP3PP6PP6

PP2PP10

PP9 PP9

27-27 IN65

IN64 185-15179-66

SN33 SN34 SN35

SN38

SN37 191-29

281-37 176-50

RN25

RN27 RN23

RN26

RN24

RN19

RN20 RN28

RN29

44.03 42.03 Cq75

F-3 43.03 Cq73 Cq78

40.03 Cq79

29.03 37.03

Cq74 176-18

39.03 41.03

176-15Cq72Cq72

Cq71 Cq77

MS28

MN55

W1-2

184-70 70-3

281-81 184-54

22-52

26.03 Cq72

36.03 bl31 184-11 32.03

C1-23 27.03

bl77 bl74

184-14

Q1-56

N1-34 184-3

184-51 EA38

30FN 182-59 31FN

34FN

182-18 36FN

32FN F-52

28TOR

281-33

44FN42FN

43FN

37FN

45FN

WK19WK21

182-15 182-41 C1-14

WK22

sp (outgroup)

*



Fig. 5. Pairwise comparisons between CO1 sequences among mosquito species separated into three categories: (a) intraspecific; (b) intra-genic, and (c) intergenic differences between individuals.

aWithin species mean dist=0.56% n=1464

89%

2%9%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

b Within genus mean dist.=10.31% n=12080

1%1%9%17%

19%

17%

14%

5%5%2%3%1%1%1% 2%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

c

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Within family mean dist.=15.44% n=34661

1% 5% 14% 25%

30%

19%

3%

Fig. 6. Observed numbers of transitions (ts) and transversions (tv) for the CO1 gene plotted against sequence divergence. The ts saturation begins to level off at around 7.5% sequence di-vergence. Tv increases steadily from the conspe-cific level of ~ 7% divergence, then, after a sudden jump, continues to grow more rapidly among more distantly related taxa.

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30Sequence divergence %

Su

bs

titu

tio

ns

(T

s, T

v)

TvTs

morphologically distinctive subspecies of Aedes vexans (Ae. vexans vexans (Meigen) and Ae. vexans nipponi (Theobald), 0 − 1.11%) , but deeper divergences were detected in two species. One specimen of Aedes fitchii (Felt & Young) showed 3.6 − 3.9% divergence from other conspecific individuals, and specimens of Aedes abserratus (Felt & Young) were separated into three clusters showing 2.6 – 3.2% sequence divergence. A single case involving Ae. fitchii and Aedes grossbecki Dyar & Knab was detected where the three individuals of Ae. grossbecki were closer to some individuals of Ae. fitchii than to some of the lat-ter ’ s own conspecifics, but they still possessed distinct barcodes. There was generally high bootstrap support (90 − 100%) for the terminal branches at the species level, with the exception of a few records for Anopheles species, obtained from GenBank and represented by only one or two individuals.

Plots of the total number of transitions (ts) + transversions (tv) at all sites against the sequence divergence ( Fig. 6) showed a rapid increase in transitions at the conspecific level and partially at the congeneric levels to a max of 30 – 40 ts substitu-tions at around 7% divergence. The incidence of transitions lev-elled off at the border between the congeneric and the intergeneric

levels. By contrast, transversions increased steadily from zero substitutions at the conspecific level to < 10 substitutions at around 7% divergence at the congeneric level, and then, after a sudden jump to 20 substitutions, grew rapidly to a max of ~ 60 substitutions among more distantly related sequences.

The neighbour-joining analysis of nucleotide and amino acid sequences showed that most mosquito species separated into distinct clusters ( Figs 2, 3 and 4 ). Species in genera represented by more than one taxon usually formed cohesive assemblages.

Discussion

An effective DNA-based identification system requires the sat-isfaction of three conditions: (a) it must be possible to recover the target DNA from all species; (b) the sequence information must be easily analysed, and (c) the information content of the target sequence must be sufficient to enable species-level iden-tification. All three of these requirements were met in this study. We were able to recover and align the targeted CO1 fragment from all mosquito species we examined. Furthermore, although



we amplified CO1 from total genomic DNA, we detected only a single nuclear pseudogene. In addition, specimens of all species formed distinctive clusters and, with the exception of a single species pair, barcode divergences were relatively large between taxa. Finally, species boundaries were congruent with those es-tablished by morphological taxonomic work.

DNA-based species identification systems depend on the ability to distinguish intraspecific from interspecific variation. In the present study, CO1 sequence differences among conge-neric species were, on average, almost 20 times higher than the average differences within species. The average conspecific K2P divergence for mosquito species (0.55%) in this study is slightly higher than those earlier reported for North American birds (0.27%; Hebert et al. , 2004 ) and moths (0.25%; Hebert et al. , 2003a ). However, this difference reflects our detection of deep intraspecific divergence in two taxa ( Ae. fitchii, Ae. abserratus ), instances that may indicate overlooked sibling species.

Interestingly, two morphologically distinctive subspecies, Ae. vexans vexans and Ae. vexans nipponi (dissimilar coloration of scales on the abdominal sternites) show barcode congruence. The latter subspecies was brought to the U.S.A. in 1999, prob-ably from Korea.

We detected one case of low interspecific sequence diver-gence, involving the Ae. fitchii/Ae. grossbecki complex. Ae. grossbecki is rare in Ontario, although common in nearby north-western Ohio ( Venard & Mead, 1953 ). Adults of this species were collected from the Windsor − London area, in the region of its first recorded presence in Canada ( Helson et al. , 1978 ), and specimens of Ae. fitchii were collected further to the north-east. The latter individuals showed morphological evidence of hybridization in that two types of scales were present on the wings of single individuals: large triangular scales, typical of Ae. grossbecki , were observed on the anterior half of their wings and elongate scales, typical of Ae. fitchii, occurred poste-riorly. In cases such as this, indicating possible hybridization, as well as in those cases characterized by incomplete sorting of mitochondrial lineages, more detailed morphological and ge-netic examinations will be required. The examination of faster evolving mitochondrial genes, such as the control region or ND4, as well as analysis of nuclear regions, such as internal transcribed spacers (ITS), may aid in establishing species boundaries in at least some of the cases that cannot be resolved though CO1.

The effective application of DNA sequence data to molecular diagnostics depends on patterns of nucleotide substitution and the rate of variation among sites ( Blouin et al. , 1998 ). The CO1 region in mosquitoes is characterized by a high rate of transi-tional saturation along the sequence divergence axis, particu-larly at silent sites. The ts saturation begins to level off at around 7.5% sequence divergence, suggesting caution in the interpreta-tion of pairwise comparisons at the congeneric and intergeneric levels, unless silent sites are excluded from analysis.

Although our studies on the mosquito fauna of eastern Canada provide an early indication of the patterns of CO1 sequence diver-gence within and among species, GenBank sequences for Anopheles earlei Vargas from South Africa and Anopheles quad-rimaculatus Say from the U.S.A. grouped closely with other indi-

viduals of their species from Ontario. Moreover, other GenBank sequences from ‘ exotic ’ species, in the genera Culex, Culiseta and Anopheles , grouped with allied taxa in our NJ analysis, but formed distinct, tight sequence clusters. On this basis, we antici-pate that further growth in taxon and geographical coverage will not seriously alter the conclusions drawn from this study. In short, we expect that congeneric species will regularly show sequence di-vergences in the CO1 region averaging ~ 10% and that divergence values for conspecific individuals will usually fall below 0.5%.

In summary, this study has provided the first CO1 barcodes for Canadian mosquitoes and has established their effectiveness in discriminating species of mosquitoes recognized through prior taxonomic work. Specimens of single species formed barcode clusters with tight cohesion that were usually clearly distinct from those of allied species. Sequence divergences were, on av-erage, nearly 20 times higher for congeneric species than for members of a species, ensuring that species identifications within this local species assemblage were robust. As an effort has now been launched to gather DNA barcodes for all known mosquito species, a full evaluation of the effectiveness of DNA barcoding for members of the family Culicidae should soon be available.

Acknowledgements

This work was supported by grants from NSERC to F. F. Hunter and P. D. N. Hebert. We are grateful to Aynsley Thielman and Jim Edsall for mosquito identifications and to Monty Wood and Richard Darsie for verifying taxonomic assignments. We thank Angela Hollis for assistance with DNA sequencing, Jim Edsall of the New Brunswick Museum, Trudy Stanfield of Health Canada-First Nation & Inuit Health Branch, various units of the Ontario Ministry of Health and the West Nile Virus Surveillance Programme for providing some specimens examined in this study.

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Accepted 13 September 2006

identifying canadian mosquito species through dna barcodes

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