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Keywords:

  • Calliphoridae;
  • Chrysomya;
  • blowflies;
  • COI;
  • Diptera;
  • DNA barcoding;
  • forensic entomology;
  • identification;
  • ITS2;
  • myiasis;
  • Australia

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

Abstract The utility of cytochrome oxidase I (COI) DNA barcodes for the identification of nine species of forensically important blowflies of the genus Chrysomya (Diptera: Calliphoridae), from Australia, was tested. A 658-bp fragment of the COI gene was sequenced from 56 specimens, representing all nine Chrysomya species and three calliphorid outgroups. Nucleotide sequence divergences were calculated using the Kimura-two-parameter distance model and a neighbour-joining (NJ) analysis was performed to provide a graphic display of the patterns of divergence among the species. All species were resolved as reciprocally monophyletic on the NJ tree. Mean intraspecific and interspecific sequence divergences were 0.097% (range 0–0.612%, standard error [SE] = 0.119%) and 6.499% (range 0.458–9.254%, SE = 1.864%), respectively. In one case, a specimen that was identified morphologically was recovered with its sister species on the NJ tree. The hybrid status of this specimen was established by sequence analysis of the second ribosomal internal transcribed spacer (ITS2). In another instance, this nuclear region was used to verify four cases of specimen misidentification that had been highlighted by the COI analysis. The COI barcode sequence was found to be suitable for the identification of Chrysomya species from the east coast of Australia.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

It has been suggested that the method and DNA region of choice for the identification of all animals on Earth should be standardized, using the so-called ‘DNA barcoding’ approach (Hebert et al., 2003). This approach utilizes a 658-bp region of the gene encoding the first subunit of mitochondrial cytochrome oxidase (COI), corresponding to nucleotide positions 1490–2198 of the Drosophila yakuba mitochondrial genome (Clary & Wolstenholme, 1985) (Fig. 1). This region has been shown to be suitable for the identification of a range of taxa, including gastropods (Remigio & Hebert, 2003), springtails (Hogg & Hebert, 2004), butterflies (Hebert et al., 2004a), birds (Hebert et al., 2004b), mayflies (Ball et al., 2005) and fish (Ward et al., 2005). The proponents of COI DNA barcoding envisage the construction of a universally accessible, curated database comprising all animal COI sequences, which will then serve as ‘the basis for a global bioidentification system for animals’ (Hebert et al., 2003). The many benefits of DNA barcoding for species identification and discovery have been discussed (e.g. Hebert & Gregory, 2005), although the concept continues to be hotly debated (e.g. Wheeler, 2005). It is believed that a standardized approach to species identification would consolidate global research efforts and be beneficial for the identification of those species with medical, economic or environmental significance (Armstrong & Ball, 2005). In addition to species identification, the construction of a barcode database could expose novel DNA barcodes that may indicate provisional new species (Hebert et al., 2004a).

image

Figure 1. Location of the 658-bp cytochrome oxidase I (COI) barcode region, corresponding to nucleotide positions 1490–2198 of the Drosophila yakuba mitochondrial genome (Clary & Wolstenholme, 1985). The barcode region is represented by Chrysomya semimetallica sequenced in this study; regions of COI sequenced in previous studies are shown for comparison: Ch. semimetallica sequenced by Wallman et al. (2005) and Chrysomya chloropyga sequenced by Wells et al. (2004). Numbers in brackets refer to accession numbers.

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A DNA barcoding approach may be useful for the identification of taxa for which the use of morphology, or the association of different life stages, is problematic. For these reasons, the utility of the COI barcode sequence for the identification of blowflies of the genus Chrysomya (Diptera: Calliphoridae) was tested. Some members of this genus are causative agents of myiasis in humans and domestic animals (Hall & Wall, 1995). They have also been identified as vectors of disease among people exposed to conditions of poor sanitation and nutrition (Maldonado & Centeno, 2003). Additionally, Chrysomya species constitute a significant proportion of the blowfly species present in cases of murder or suspicious death (e.g. Levot, 2003). The identification of species found in association with a corpse is one of the first steps undertaken by the forensic entomologist in an attempt to estimate the postmortem interval (PMI) (Wells & LaMotte, 2001). The application of blowflies in PMI estimation has been hampered by difficulties associated with correct species identification at all developmental stages (eggs, larvae, pupae and adults) (e.g. Catts, 1992), particularly when only fragments of insect evidence (e.g. puparia, dead or decomposed larvae or adults) are available for analysis (Stevens & Wall, 2001). Blowfly identification requires specialist taxonomic knowledge, often relying upon the recognition of subtle variations in morphological features, such as the cephalopharyngeal skeleton and microtubercles in immatures, and the dissection and inspection of the genitalia in adults (e.g. Wallman, 2001b). Some adult members of the genus Chrysomya can only be reliably distinguished on the basis of subtle morphological differences such as pigmentation. The morphological similarity of Chrysomya species, especially their immatures, makes a DNA-based approach to their identification advantageous. An assortment of molecular methods has been investigated for the identification of blowfly species, utilizing a range of nuclear (e.g. Ratcliffe et al., 2003) and mitochondrial (e.g. Wallman & Donnellan, 2001; Wells & Sperling, 2001) DNA regions. Analysis of the COI barcoding region in Chrysomya will not only offer insight into its utility for the identification of blowflies of forensic and medical importance, but will also further evaluate the capacity of the COI barcode to serve as a global identification system for all animals.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

Specimens

Specimens were collected from locations along the east coast of Australia and identified morphologically by JFW (Table 1; Fig. 2). The specimens were collected directly into absolute alcohol and maintained at 4°C in the Diptera collection at the School of Biological Sciences, University of Wollongong. Where possible, specimens selected for molecular analysis were taken from a range of geographic locations.

Table 1.  Specimen information, collection dates and collection localities for the Australian Chrysomya species studied, and three calliphorid outgroups.
SpeciesVoucher IDGenBank accession no. COI and (ITS2)Collection locality
  1. COI, cytochrome oxidase I; ITS2, second ribosomal internal transcribed space; Qld, Queensland; NSW, New South Wales; ACT, Australian Capital Territory; Vic, Victoria.

Chrysomya flavifrons (Aldrich)LN7DQ647333Kuranda, Qld (16°49′S, 145°38′E)
JW47DQ647331Kuranda, Qld (16°49′S, 145°38′E)
JW65DQ647334Tinaroo Falls, Qld (17°10′S, 145°33′E)
JW95DQ647332Mt Stuart, Qld (19°21′S, 146°47′E)
Chrysomya latifrons (Malloch)LN2DQ647347 (DQ310492)Halfway Creek, NSW (30°14′S, 153°06′E)
JW19DQ647345 (EF071960)Halfway Creek, NSW (30°14′S, 153°06′E)
JW19.2DQ647344 (EF071961)Halfway Creek, NSW (30°14′S, 153°06′E)
JW183DQ647382 (EF071962)Mt Keira, NSW (34°20′S, 150°51′E)
JW190DQ647381 (EF071963)Cambewarra, NSW (34°49′S, 150°34′E)
Chrysomya megacephala (Fabricius)LN5DQ647352 (DQ310488)Karuah, NSW (32°38′S, 151°57′E)
JW25DQ647350 (EF071964)Hornsby Heights, NSW (33°39′S, 151°05′E)
JW97DQ647351 (EF071965)Mt Stuart, Qld (19°21′S, 146°47′E)
JW100DQ647353 (EF071966)Kuranda, Qld (16°49′S, 145°38′E)
Chrysomya nigripes AubertinLN6DQ647355Kuranda, Qld (16°49′S, 145°38′E)
JW67DQ647354Tinaroo Falls, Qld (17°10′S, 145°33′E)
JW98DQ647356Mt Stuart, Qld (19°21′S, 146°47′E)
Chrysomya rufifacies (Macquart)LN8DQ647360Kuranda, Qld (16°49′S, 145°38′E)
JW71DQ647358Tinaroo Falls, Qld (17°10′S, 145°33′E)
JW93DQ647359Mt Sampson, Qld (27°24′S, 152°39′E)
JW94DQ647357Mt Stuart, Qld (19°21′S, 146°47′E)
JW161DQ647361Black Mountain, ACT (35°16′S, 149°06′E)
Chrysomya saffranea (Bigot)LN9DQ647367 (DQ310490)Kuranda, Qld (16°49′S, 145°38′E)
JW45DQ647364 (EF071967)Kuranda, Qld (16°49′S, 145°38′E)
JW56DQ647366 (EF071968)Kuranda, Qld (16°49′S, 145°38′E)
JW66DQ647368 (EF071969)Tinaroo Falls, Qld (17°10′S, 145°33′E)
JW96.2DQ647363 (EF071970)Mt Stuart, Qld (19°21′S, 146°47′E)
JW96.3DQ647362 (EF071971)Mt Stuart, Qld (19°21′S, 146°47′E)
JW107DQ647365 (EF071972)Kuranda, Qld (16°49′S, 145°38′E)
Chrysomya semimetallica (Malloch)LN1DQ647373 (DQ310493)Halfway Creek, NSW (30°14′S, 153°06′E)
JW32DQ647372 (EF071973)Halfway Creek, NSW (30°14′S, 153°06′E)
JW42DQ647349 (EF071974)Kuranda, Qld (16°49′S, 145°38′E)
JW42.2DQ647346 (EF071975)Kuranda, Qld (16°49′S, 145°38′E)
JW51DQ647348 (EF071976)Kuranda, Qld (16°49′S, 145°38′E)
JW51.2DQ647343 (EF071977)Kuranda, Qld (16°49′S, 145°38′E)
JW75DQ647369 (EF071978)Kuranda, Qld (16°49′S, 145°38′E)
JW104DQ647374 (EF071979)Kuranda, Qld (16°49′S, 145°38′E)
JW105DQ647371 (EF071980)Kuranda, Qld (16°49′S, 145°38′E)
JW132DQ647370 (EF071981)Coffs Harbour, NSW (30°18′S, 153°07′E)
Chrysomya varipes (Macquart)LN3DQ647379Karuah, NSW (32°38′S, 151°57′E)
JW15DQ647377Myall Lakes National Park, NSW (32°25′S, 152°22′E)
JW92DQ647376Mt Sampson, Qld (27°24′S, 152°39′E)
JW108DQ647380Kuranda, Qld (16°49′S, 145°38′E)
JW136DQ647375Coranderrk Bushland Reserve, Vic (37°40′S, 145°31′E)
JW164DQ647378Black Mountain, ACT (35°16′S, 149°06′E)
Chrysomya incisuralis (Macquart)LN4DQ647342Halfway Creek, NSW (30°14′S, 153°06′E)
JW26DQ647338Halfway Creek, NSW (30°14′S, 153°06′E)
JW133DQ647340Halfway Creek, NSW (30°14′S, 153°06′E)
JW28DQ647339Way Way State Forest, NSW (30°48′S, 152°55′E)
JW29DQ647335Coffs Harbour, NSW (30°18′S, 153°07′E)
JW46DQ647337Kuranda, Qld (16°49′S, 145°38′E)
JW70DQ647336Tinaroo Falls, Qld (17°10′S, 145°33′E)
JW63DQ647341Coranderrk Bushland Reserve, Vic (37°40′S, 145°31′E)
Ch. saffranea/Ch. megacephala hybridJW96Mt Stuart, Qld (19°21′S, 146°47′E)
Calliphora ochracea SchinerJW6DQ647328Halfway Creek, NSW (30°14′S, 153°06′E)
Hemipyrellia fergusoni PattonJW1DQ647329Halfway Creek, NSW (30°14′S, 153°06′E)
Lucilia porphyrina (Walker)JW3DQ647330Halfway Creek, NSW (30°14′S, 153°06′E)
image

Figure 2. Map of locations on the east coast of Australia from which specimens in this study were collected. Qld, Queensland; NSW, New South Wales; ACT, Australian Capital Territory; Vic, Victoria.

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DNA extraction, amplification and sequencing

Genomic DNA was extracted from either the flight muscle or from two legs of each fly using a ‘salting out’ protocol (Sunnucks & Hales, 1996). The DNA was resuspended in 50 μL of sterile distilled water and stored at 4°C. The COI barcoding region was amplified using primers LCO1490-L (5′-GGTCWACWAATCATAAAGATATTGG-3′) and HCO2198-L (5′-TAAACTTCWGGRTGWCCAAARAATCA-3′), which are slightly modified forms of the primers designed by Folmer et al. (1994). In addition, the second ribosomal internal transcribed spacer (ITS2) region was amplified and sequenced from all Chrysomya latifrons (Malloch), Chrysomya semimetallica (Malloch), Chrysomya saffranea (Bigot) and Chrysomya megacephala (Fabricius) specimens using primers L1 (5′-RRCGGTGGATCACTCGGCTC-3′) and 52R (5′-GTTACTTTCTTTTCCTCCCCT-3′ (Ratcliffe et al., 2003). Each 20 μL polymerase chain reaction (PCR) mixture contained 50 mm KCl, 10 mm Tris-HCl (pH 9.0), 0.1% Triton®X-100, deoxynucleotide triphosphates (dNTPs) (25 μm each of dATP, dCTP, dGTP and dTTP), 1.25 mm MgCl2, 0.4 μm of each primer and 0.75 U Taq DNA polymerase (Promega, Madison, WI, U.S.A.). All reaction mixtures contained 0.5 μL DNA extract, except for the negative controls. The PCR temperature cycles, carried out on a Palm-Cycler™ II (Corbett Research, Sydney, Australia), consisted of an initial denaturation step at 94°C for 2 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing across a temperature gradient of 45–65°C for 30 s, and elongation at 72°C for 2 min. The last cycle was followed by 5 min incubation at 72°C to complete any partially synthesized strands. All PCR products were visualized by agarose gel electrophoresis to identify the optimum annealing temperature (generally 45–47°C). These amplicons were treated with ExoSAP-IT® (GE Healthcare, Little Chalfont, U.K.) according to the manufacturer’s instructions, in order to degrade residual primers and unincorporated dNTPs, and stored at 4°C.

Sequencing reactions were performed on ExoSAP-IT®-treated PCR product with the ABI PRISM® BigDye™ Terminator Cycle Sequencing Kit (Version 3.1) (PE Applied Biosystems, Foster City, CA, U.S.A.), using the Palm-Cycler™ II. Primers for PCR were used to initiate the sequencing reactions, which were set up according to the manufacturer’s instructions. Cycling conditions were 30 cycles of 96°C for 30 s, 50°C for 15 s and 60°C for 4 min. Individual reactions (12 μL) were transferred to fresh 1.5-mL tubes containing 74 μL of precipitation buffer (68% ethanol, 3.4 mm ethylenediaminetetraacetic acid (EDTA), pH 8.0 and 81 mm sodium acetate, pH 5.2). The mixtures were incubated at room temperature for 15 min and then spun at 16 000 g for 15 min. The supernatant was discarded and 180 μL ice-cold 70% ethanol pipetted gently onto the pellet. The tubes were spun for a further 5 min, after which the supernatant was discarded completely and the samples air-dried. Sequencing reaction products were then separated using the 3130x Genetic Analyser (PE Applied Biosystems).

DNA sequence analysis

DNA sequences were confirmed and edited manually using BioEdit Sequence Alignment Editor (Version 6.0.7; Hall, 1999) and ChromasPro (Version 1.33; Technelysium Pty Ltd, available online at www.technelysium.com.au/ChromasPro.html). Mitochondrial COI and nuclear ribosomal ITS2 sequences reported in this paper have been submitted to GenBank; their respective accession numbers are indicated in Table 1. Sequences were aligned using CLUSTAL W (Thompson et al., 1994) with the default settings. Some data analysis was undertaken via the workbench of the Barcode of Life Data (BOLD) Systems Management and Analysis System (www.boldsystems.org; Ratnasingham & Hebert, 2006).

Nucleotide sequence divergences were calculated using the Kimura-two-parameter (K2P) distance model (Kimura, 1980). A bootstrap (1000 replicates) neighbour-joining (NJ) analysis (Saitou & Nei, 1987), performed in paup* (Version 4.0b10; Swofford, 2001), provided a graphic display of the patterns of divergence among the species. Three non-Chrysomya species (Calliphora ochracea Schiner, Hemipyrellia fergusoni Patton and Lucilia porphyrina (Walker) [Diptera: Calliphoridae]) were included as outgroups.

To assess whether the phylogenetic analyses were robust to changes in the analytic approach, Bayesian analyses were also performed, using MrBayes (Version 3.1; Huelsenbeck & Ronquist, 2001). In the first analysis the data were unpartitioned. In the second the data were divided into three partitions representing the first, second and third codon positions of COI. For the unpartitioned analysis, the general time reversible model (command: nst = 6) (Tavaré, 1986), with rate variation allowing a proportion of invariable sites (command: rates = propinv) (GTR + I + >; Gu et al., 1995; Waddell & Penny, 1996), was selected as the best-fit model of nucleotide substitution (MrModeltest Version 2.2; Nylander, 2004). For the partitioned analysis, the general time reversible model with variable sites assumed to follow a discrete gamma distribution (command: rates = gamma) (GTR + >; Yang, 1994) was selected as the best-fit model of nucleotide substitution (MrModeltest Version 2.2). Substitution and rate parameters were estimated separately for each partition (command: unlink). For each analysis, four independent runs were performed. Each Markov chain Monte Carlo (MCMC) process was set so that four chains, three heated and one cold, ran simultaneously. We conducted runs for 1 000 000 generations, with trees sampled every 100 generations, yielding a total of 10 000 trees for analysis per run. Independent analyses indicated that ‘stationarity’ (or ‘burnin’: lack of improvement in maximum likelihood scores) was reached at no later than 150 000 generations; thus, the first 1500 trees were discarded from each analysis as the burnin, and the remaining trees were used to generate a 50% majority consensus tree. Posterior probabilities were estimated by counting the proportion of trees that recovered a particular group after the burnin period. The three non-Chrysomya species were included in all Bayesian analyses, with Calliphora ochracea designated as the outgroup.

Results and discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

Amplification and sequencing of the COI barcode region

The COI region proved straightforward to amplify and sequence. A 658-bp fragment of the COI gene was sequenced from all 56 specimens, representing all nine Australian Chrysomya and three calliphorid outgroups. Well defined electropherogram peaks and the absence of stop codons indicated that coamplification of nuclear pseudogenes did not occur (Zhang & Hewitt, 1996). In accordance with previous work (e.g. Hebert et al., 2003), the sequences aligned with ease due to the absence of insertions and deletions. Nucleotide composition showed an AT bias within Chrysomya (mean A = 30.4%, T = 38.1%, C = 15.8%, G = 15.6%).

The mean within- and between-species K2P divergences (0.097% and 6.499%, respectively; Table 2) for Chrysomya are similar to those reported by Smith et al. (2006) for parasitoid flies (Diptera: Tachinidae) (0.170% and 5.781%, respectively) and those reported by Hajibabaei et al. (2006) for three families of Lepidoptera (0.35% and 5.007%, respectively). The highest interspecific sequence divergence for Chrysomya existed between Chrysomya incisuralis (Macquart) and Chrysomya varipes (mean = 9.166%, range 9.078–9.254%). Low COI sequence divergences existed between the species pairs (in descending order of interspecific divergence) Ch. incisuralis and Chrysomya rufifacies (Macquart), Ch. varipes and Chrysomya flavifrons (Aldrich), Chrysomya latifrons and Chrysomya semimetallica, and Chrysomya megacephala and Chrysomya saffranea (Table 3). No intraspecific COI sequence variation was detected in either Ch. latifrons or Ch. megacephala. Mean intraspecific and interspecific COI sequence divergences differed by more than an order of magnitude for Chrysomya (Table 2), although there was overlap in the range of the divergences. This overlap is not due to shared barcode sequences among different species, but rather to intraspecific variation in some parts of the tree exceeding interspecific variation in other parts of the tree: this is attributable to the low sequence divergence (mean = 0.480%) between Ch. megacephala and Ch. saffranea (Table 3), and the high intraspecific sequence divergence encountered for Ch. varipes (Macquart) (mean = 0.295%, range 0–0.612%).

Table 2.  Summary of genetic divergences (using K2P model) of nine species (52 sequences) within the genus Chrysomya.
Chrysomya speciesDistance (%)
MinimumMeanMaximumSE
  1. SE, standard error.

Ch. flavifrons0.0000.0760.1520.076
Ch. incisuralis0.0000.0650.1520.075
Ch. latifrons0.0000.0000.0000.000
Ch. megacephala0.0000.0000.0000.000
Ch. nigripes0.0000.1010.1520.072
Ch. rufifacies0.0000.0910.1520.075
Ch. saffranea0.0000.0430.1520.069
Ch. semimetallica0.0000.1150.3050.097
Ch. varipes0.0000.2950.6120.162
Comparisons within: 
Species0.0000.0970.6120.119
Genus0.4586.4999.2541.864
Table 3.  Percentage sequence divergences (K2P) between selected sister Chrysomya species for the cytochrome oxidase I (COI) barcode region.
 MinimumMeanMaximumSE
  1. SE, standard error.

Ch. saffranea vs. Ch. megacephala0.4580.4800.6120.167
Ch. latifrons vs. Ch. semimetallica1.2311.3871.5430.099
Ch. flavifrons vs. Ch. varipes5.0585.2385.5580.135
Ch. incisuralis vs. Ch. rufifacies6.6966.7646.8650.083

Extremely low sequence divergences between sister species and among species complexes are believed to be indicative of their recent origin (Funk & Omland, 2003; Tautz et al., 2003). Instances of low sequence divergence between closely related species pose significant challenges for barcoding, especially when ancestral polymorphisms are retained, which can lead to the occurrence of shared barcode sequences (e.g. Hajibabaei et al., 2006). If the level of barcode variation is not taken into account in such instances, confidence in identification could be undermined (Armstrong & Ball, 2005). We are confident, due to the number of specimens included in the analysis, that we adequately sampled the levels of intraspecific variation for Chrysomya from the east coast of Australia. Therefore, the overlap between intraspecific and interspecific sequence divergences should not be problematic for the identification of unknown specimens in this case, as they should allocate reliably to their correct species on the tree (Meyer & Paulay, 2005). Where closely related species cannot be separated due to shared barcode sequences and morphological similarity, additional analysis of other genes, possibly of nuclear origin, would be required (Hebert et al., 2003).

Comprehensive taxonomic sampling is particularly essential for taxa with an extensive geographic range. There did not appear to be a correlation between geographic location and COI sequence divergences for the specimens examined in this study. Indeed, no intraspecific divergence was detected for Ch. megacephala, although specimens originated from geographically distinct populations at distances ranging from approximately 306 km to 1870 km. Varying levels of intraspecific divergence were encountered within other species, but these did not appear to reflect the geographic proximity of populations from which specimens were sampled. Although this study did not find a convincing relationship between geographic distance and sequence divergence, this has been noted in the past (e.g. Ball et al., 2005) and further investigations will be required for species with transcontinental distributions.

Neighbour-joining analysis of COI barcode sequences

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

The purpose of this study was to investigate whether the COI barcode provided sufficient resolution to identify blowflies of the genus Chrysomya. The NJ analysis showed that this was the case, and supported the results of previous studies that have found the COI barcode to be an effective tool for identification purposes (e.g. Hebert et al., 2003). All Chrysomya species were resolved as reciprocally monophyletic groups, despite low COI divergences between some sister species (Fig. 3). Although the COI barcode region alone is not intended to be used to resolve taxonomic relationships, it appears to contain enough phylogenetic signal to delineate close relationships within Chrysomya from the east coast of Australia (e.g. Ball et al., 2005). Some of the relationships denoted by the COI barcode region were consistent with a more extensive analysis of mitochondrial ND4−ND4L, COI and COII sequences reported by Wallman et al. (2005) for the same group of taxa. These included the close genetic relationships between Ch. megacephala and Ch. saffranea, Ch. latifrons and Ch. semimetallica, Ch. rufifacies and Ch. incisuralis, and Ch. varipes and Ch. flavifrons. The close genetic relationships between the species pairs Ch. megacephala and Ch. saffranea, and Ch. latifrons and Ch. semimetallica are supported by studies that have examined morphological features of members of this genus (Wells & Kurahashi, 1996; Wallman, 2001a). The NJ analysis of the COI barcode region placed Chrysomya nigripes Aubertin as sister to Ch. saffranea and Ch. megacephala, whereas the more comprehensive analysis of Wallman et al. (2005) placed it as sister to a clade comprising Ch. varipes, Ch. flavifrons, Ch. megacephala and Ch. saffranea.

image

Figure 3. Neighbour-joining tree of Kimura-two-parameter (K2P) distances for 55 cytochrome oxidase I (COI) sequences from all nine Australian Chrysomya and three calliphorid outgroups. Numbers above branches refer to bootstrap proportions among 1000 bootstrap replicates. Specimen voucher codes referred to in Table 1 are shown in parentheses following species names. Collection locations are summarized to state, abbreviated as follows: Qld, Queensland; NSW, New South Wales; ACT, Australian Capital Territory; Vic, Victoria.

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Comparison of methods of COI barcode analysis

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

Although the COI barcode is not designed to resolve phylogenetic relationships, it must reliably distinguish reciprocally monophyletic groups in order to delineate species.

The NJ method has been promoted as the analysis tool of choice for the construction of barcoding databases, due to its advantage of speed and its performance when sequence divergences are low (Hebert et al., 2003; Ball et al., 2005). However, a comparison of tree-building methods is vital during the development of the barcoding method, particularly as the suitability of this method for species delineation has been questioned in the past (e.g. DeSalle et al., 2005). In some cases, an oversimplified or inadequate phylogenetic analysis may fail to distinguish reciprocally monophyletic groups, whereas an analysis that more realistically models the history of molecular evolution for the COI gene may perform better (e.g. Whelan et al., 2001; Sullivan & Joyce, 2005). To assess this, we compared the NJ tree with trees generated from Bayesian analyses of the COI data. Both tree-building methods recovered each Chrysomya species as a monophyletic group (Bayesian data not shown).

ITS2 sequence analysis for selected species

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

Two complications were encountered early in this study. In the first, four specimens which had been preliminarily identified in the field as Ch. latifrons (JW42, JW42.2, JW51 and JW51.2, from Kuranda, Queensland) were recovered with Ch. semimetallica in the COI NJ tree. The second complication concerned a specimen (JW96) identified morphologically as Ch. saffranea, but recovered with its closest relative, Ch. megacephala, in the NJ tree. These issues raised concerns about (a) inadequate phylogenetic analysis; (b) an inability of COI to resolve these species, and (c) incorrect specimen identification, due to the significant morphological similarities shared by each set of sister species. Because of these close relationships, the possibility of high levels of intraspecific variation, perhaps due to retained ancestral polymorphisms, and hybridization, were considered (Hajibabaei et al., 2006). To investigate these concerns, the COI data were reassessed using a Bayesian analysis, the ITS2 region was analysed from all Ch. latifrons, Ch. semimetallica, Ch. megacephala and Ch. saffranea specimens, and further morphological examinations were undertaken. The ITS2 region was shown previously to distinguish members of the genus Chrysomya, the results of which will appear in a separate publication.

The Bayesian analyses of the COI dataset did not alter or improve the delineation of species boundaries established by the NJ tree. Detailed morphological examination, coupled with ITS2 and COI analysis, confirmed that the four Ch. semimetallica specimens had been misidentified as Ch. latifrons, and that this was the cause of the apparent misplacement of the specimens in the NJ tree. Distinguishing these sister species is particularly challenging due to their close morphological similarity. The identifications of the above-mentioned specimens were modified accordingly for the final analysis. The ITS2 sequence analysis revealed no intraspecific sequence variation for Ch. latifrons and Ch. semimetallica, and an interspecific divergence of 1.345%. The ITS2 interspecific divergence is comparable with that for COI (mean = 1.387%). The detection of misidentified specimens reinforces the utility and sensitivity of a DNA-based identification system in circumstances where morphological characters are either unreliable or require specialized taxonomic scrutiny.

The ITS2 sequence of specimen JW96 was the same as those of five of the seven Ch. saffranea specimens, but differed from Ch. megacephala ITS2 by two base changes and three indels. The ITS2 sequence of specimen JW96 therefore agreed with the morphological identification and placed specimen JW96 as Ch. saffranea. Together with the morphological evidence, the conflicting species assignment by the nuclear and mitochondrial DNA suggested that the specimen was a hybrid resulting from mating between a female Ch. megacephala and a male Ch. saffranea, due to the maternal contribution of mitochondrial DNA. Due to the suspected hybrid status of this specimen, it was excluded from further investigations. The ITS2 sequence analysis revealed a lack of variation within this region for Ch. megacephala, and a low level of sequence variation for Ch. saffranea (mean = 0.107%, range 0–0.464%). Overall, the mean interspecific ITS2 sequence divergence between Ch. megacephala and Ch. saffranea was 0.462% (range 0.461–0.465%), which is comparable with the COI interspecific divergence (mean = 0.480%).

Although considered less likely, an alternative explanation for the disagreement between the nuclear and mitochondrial sequences for JW96 could be the retention of a shared ancestral polymorphism by this specimen (e.g. Funk & Omland, 2003). However, hybridization is believed to be possible between such closely related species as Ch. megacephala and Ch. saffranea, which exhibited the lowest interspecific COI divergence within Chrysomya (mean COI divergence 0.480%, excluding Ch. saffranea specimen JW96). Wallman et al. (2005) found a similarly low (0.403%) mitochondrial DNA sequence divergence between these species. Furthermore, ITS2 sequence analysis of the Ch. megacephala and Ch. saffranea specimens in this study confirmed their close genetic relationship (mean interspecific ITS2 divergence = 0.462%, excluding specimen JW96). The suspected occurrence of hybridization among flies was also noted by Smith et al. (2006) for parasitoid tachinids (Diptera: Tachinidae), based on sequence analysis of the COI barcode region and the first ribosomal internal transcribed spacer (ITS1). The discovery of hybrid specimens draws attention to a limitation of any identification system based on a single character. Were it not for the combined molecular evidence given by COI and ITS2, along with morphological examination, barcode analysis would have identified the specimen as Ch. megacephala. The inclusion of sequence data from more than one DNA region would therefore enhance confidence in DNA-based identifications (Matz & Nielsen, 2005). The need for re-examination of the misplaced specimens in this study highlights the importance of a voucher collection for all members of a barcode database.

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

This study found that a COI barcode identification system would be suitable for the identification of Chrysomya species from the east coast of Australia. The COI barcode region proved straightforward to amplify and sequence, which would facilitate the rapid generation of a barcode database and subsequent identification of specimens. Although the COI barcode provided adequate resolution to separate Australian Chrysomya species, this study demonstrated the use of a nuclear gene, ITS2, to provide extra information in cases of uncertain specimen identification. The multitude of fly species potentially encountered in forensic cases represents a substantial obstacle to streamlined PMI estimation. A technique that could aid the prompt and accurate identification of specimens of all life stages, or fragments thereof, would be enormously beneficial in the application of forensic insect evidence. Based on the results for Chrysomya, it is foreseeable that DNA barcoding could be effective in the identification of other blowfly taxa. Further investigations should confirm this feasibility and establish the reliability of the technique for routine application in forensic cases and other circumstances featuring flies of applied importance.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References

The authors acknowledge the Forensic Services Group of the NSW Police and the Australian Research Council for financial support of this work. We are grateful to Dr M.S. Archer for supplying the Victorian Chrysomya incisuralis specimen, and to Mr A.P. Johnson for assistance with map generation.

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  6. Neighbour-joining analysis of COI barcode sequences
  7. Comparison of methods of COI barcode analysis
  8. ITS2 sequence analysis for selected species
  9. Conclusions
  10. Acknowledgements
  11. References
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