Open Access

Mitochondrial genomes of two phlebotomine sand flies, Phlebotomus chinensis and Phlebotomus papatasi (Diptera: Nematocera), the first representatives from the family Psychodidae

Contributed equally
Parasites & Vectors20158:472

https://doi.org/10.1186/s13071-015-1081-1

Received: 5 May 2015

Accepted: 10 September 2015

Published: 17 September 2015

Abstract

Background

Leishmaniasis is a worldwide but neglected disease of humans and animal transmitted by sand flies, vectors that also transmit other important diseases. Mitochondrial genomes contain abundant information for population genetic and phylogenetic studies, important in disease management. However, the available mitochondrial sequences of these crucial vectors are limited, emphasizing the need for developing more mitochondrial genetic markers.

Methods

The complete mitochondrial genome of Phlebotomus chinensis was amplified in eight fragments and sequenced using primer walking. The mitochondrial genome of Phlebotomus papatasi was reconstructed from whole-genome sequencing data available on Genbank. The phylogenetic relationship of 24 selected representatives of Diptera was deduced from codon positions 1 and 2 for 13 protein coding genes, using Bayesian inference (BI) and maximum likelihood (ML) methods.

Results

We provide the first Phlebotomus (P. chinensis and P. papatasi) mitochondrial genomes. Both genomes contain 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes, and an A + T-rich region. The gene order of Phlebotomus mitochondrial genomes is identical with the ancestral gene order of insect. Phylogenetic analyses demonstrated that Psychodidae and Tanyderidae are sister taxa. Potential markers for population genetic study of Phlebotomus species were also revealed.

Conclusion

The generated mitochondrial genomes of P. chinensis and P. papatasi represent a useful resource for comparative genomic studies and provide valuable future markers for the population genetic study of these important Leishmania vectors. Our results also preliminary demonstrate the phylogenetic placement of Psychodidae based on their mitochondrial genomes.

Keywords

Phlebotomus chinensis Phlebotomus papatasi LeishmaniasisMitochondrial genomePsychodidaePhylogenetic analysis

Background

Phlebotomine sand flies are small insects in the family Psychodidae, and are important vectors of human disease including protozoal parasite, bacteria, and viruses [1] making these insects a global public health concern. Leishmaniasis is one of the world’s most neglected diseases transmitted by phlebotomine sand flies, causing significant mortality and morbidity in more than 80 countries of both the Old and New World. The majority of Old World vector species belong to the genus Phlebotomus (42 vector species) while the New World is dominated by the genus Lutzomyia (56 vector species) [2]. Of Phlebotomus species, two are of particular interest; Phlebotomus chinensis and Phlebotomus papatasi. Phlebotomus chinensis, the main vector of mountainous sub-type of zoonotic visceral leishmaniasis, has wide geographical distribution extending from the Yangtze River to northeast China [35]. In recent years, the number of visceral leishmaniasis (VL) cases and its endemic foci has increased (54.37 % and 41.86 % respectively) compared to that of the 1990s in China. Until now, six provinces/autonomous regions still reported autochthonous cases. The area of mountainous sub-type of zoonotic VL covers four provinces which possess almost half of the total cases [68]. Prevention and control of vector P. chinensis is important to reduce the public health threat of VL in endemic regions. Phlebotomus papatasi is the vector of sand fly fever and zoonotic cutaneous leishmaniasis in Middle East and Mediterranean regions and is also an important model organism used to study sand flies-host-parasite interactions [912].

In recent years, the mitochondrial genome has become increasingly important in phylogenetic analysis, biological identification and population studies, due to its rapid evolutionary rate, low recombination and maternal inheritance [13, 14]. Although microsatellites and individual gene sequences, such as Cytb and ND4, have been used for sand fly studies in the past [1517], the mitochondrial genome of phlebotomine sand flies has gone largely unstudied which is surprising given their pathogenic potential. The complete mitochondrial genome contains important information not available in examining individual genes, including genome-level characteristics for phylogenetic reconstruction. Additionally, due to the varying rates of gene evolution, the mitochondrial genome can also provide various molecular markers for studying phylogenetic relationships at different taxonomic levels, including intraspecies population structure.

Despite these benefits, information on the mitochondrial genomes of Diptera is still limited, especially for representatives of Nematocera. Most of these genomes are sequenced by long PCR with primer walking method. As the widespread application of next-generation sequencing (NGS), long PCR with next-generation sequencing, and direct shotgun sequencing methods has been utilized in mitochondrial genomes determination [18, 19]. Although the Sanger sequencing is still the indispensable method, the NGS method is relatively fast and inexpensive especially for direct shotgun sequencing method. In fact, this method for reconstruction of mitochondrial genomes becomes one of the simplest approaches. In the present study, we determined the complete mitochondrial genome of two important Leishmania vectors, P. chinensis and P. papatasi with long PCR with primer walking method and reconstruction from direct shotgun sequencing data respectively, reporting their genome features and analyzing the overall phylogenetic status of Psychodidae within Diptera. The addition of new mitochondrial genomes from nematoceran species would be of critical importance in understanding the evolution of Nematocera mitochondrial genome and examining the phylogeny in the Nematocera and Diptera.

Methods

Specimen collection and DNA extraction

Specimens of P. chinensis were collected from Wen County (104.25°E, 33.18°N), Gansu province, China. All specimens were preserved in 95 % ethanol and stored at −20 °C until DNA extraction. DNA was extracted from the single adult P. chinensis using the TIANamp Micro DNA Kit (Tiangen Biotech, Beijing, China) according to the manufacturer’s protocol.

Mitochondrial genome determination

The complete mitochondrial genome of P. chinensis was amplified in eight overlapping PCR fragments from a single adult. First, six fragments were amplified using previously published primers (Table 1). Then, from the generated sequences, two specific primers were designed for amplifying overlapping fragments spanning the whole mitochondrial genome. Short fragments (<2 kb) were amplified using TaKaRa rTaq (not proof-reading; Takara Biotech, Dalian, China; http://www.takara.com.cn) with the following cycling conditions: an initial denaturation for 1 min at 93 °C, followed by 35 cycles of 10 s at 92 °C, 1.5 min at 48–57 °C, 1–2 min at 72 °C, and final extension of 6 min at 72 °C. Long fragments (>2 kb) were amplified using TaKaRa LA Taq (proof-reading; Takara Biotech, Dalian, China; http://www.takara.com.cn) under the following cycling conditions: an initial denaturation for 1 min at 94 °C, followed by 40 cycles of 20 s at 93 °C, 30 s at 48–54 °C, 3–6 min at 68 °C, and final extension of 10 min at 68 °C. After purification with PCR Purification Kit (Sangon Biotech, Shanghai, China), all PCR products were sequenced directly with the PCR primers and internal primers generated by primer walking. The complete mitochondrial genome of P. papatasi was reconstructed from 454 sequencing data publicly available in the Sequence Read Archive (SRA) of GenBank (Accession number: SRX027115). Reconstruction was done by the baiting and iterative mapping approach of [20] using software MITObim v1.7 with default parameters [21, 22]. The mitochondrial genome of P. chinensis as the reference sequence.
Table 1

List of PCR primer combinations used to amplify the mitochondrial genome of Phlebotomus chinensis

Primer name

Gene

Sequence(5′–3′)

Reference

1Fa(SR-J14610)

rrnS

ATAATAGGGTATCTAATCCTAGT

[62]

1Ra(HCO2198)

COI

TAAACTTCAGGGTGACCAAAAAATCA

[63]

2Fa(LCO1490)

COI

GGTCAACAAATCATAAAGATATTGG

[63]

2Ra(C2-N3665)

COII

CCACAAATTTCTGAACATTG

[62]

3F

COII

TTAGATGTCGATAACCGAAT

This study

3R

COIII

AATGTAGTCCTTGAAATGTG

This study

4F(C3-J4792)

COIII

GTTGATTATAGACCWTGRCC

[62]

4R(TF-N6384)

trnF

TATATTTAGAGYATRAYAYTGAAG

[62]

5Fa(TN-J6155)

trnN

TTTAATTGAARCCAAAAAGAGG

[62]

5Ra(N4L-N9629)

ND4L

GTTTGTGAGGGWGYTTTRGG

[62]

6F(N4-J9172)

ND4

CGCTCAGGYTGRTACCCYCA

[62]

6R(CB-N11010)

Cytb

TATCTACAGCRAATCCYCCYCA

[62]

7Fa

Cytb

CTTGATCTATTGGAACATT

This study

7Ra

rrnL

TACCTTAGGGATAACAGCG

This study

8Fa(LR-J12888)

rrnL

CCGGTCTGAACTCARATCATGTA

[62]

8Ra(SR-N14745)

rrnS

GTGCCAGCAGYYGCGGTTANAC

[62]

aThe PCR primers for the long PCR fragment (>2 kb)

Sequence analyses

Contiguous sequence fragments were assembled using Staden Package v1.7.0 [23]. Protein coding genes (PCGs) and ribosomal RNA (rRNA) genes were identified based on homologous regions of other dipteran insects using the Clustal X [24]. Transfer RNAs (tRNA) and their potential cloverleaf structures were identified by tRNAscan-SE 1.21 [25]. The secondary structure of the two rRNA genes was determined mainly by comparison with the published rRNA secondary structures of Drosophila melanogaster and Drosophila virilis [26]. Tandem Repeat Finder v4.07 was used to identify tandem repeats in A + T-rich region [27]. The base composition and codon usage were calculated with MEGA 5.1 [28]. AT and GC skew were calculated according to the formulae: AT skew = (fA − fT) / (fA + fT) and GC skew = (fG − fC) / (fG + fC). Sliding window analyses were performed using DnaSP v5 [29]. A sliding window of 500 bp (in 25 bp overlapping steps) was used to estimate nucleotide diversity Pi (π) across the alignment of P. chinensis, P. papatasi and Lutzomyia umbratilis [30] mitochondrial genomes excluding the A + T-rich region.

Phylogenetic analyses

For the phylogenetic analyses, a total of 24 representative species from Diptera were used to build the alignment (Table 2), with Bittacus pilicornis used as the outgroup (Mecoptera). All 13 PCGs were extracted and translated (excluding the stop codon) using the invertebrate mitochondrial genetic code. We used the Clustal X for alignment of the inferred amino acid sequences. Then the alignments were transferred to the DNA sequences, and third codon positions removed. The best-fit model (GTR + Γ + I) was estimated by the Akaike information criterion in jModelTest [31]. MrBayes ver.3.1.2 [32] and RAxML ver.7.2.8 [33] were used to construct a maximum likelihood (ML) and bayesian inference (BI) phylogeny. For ML analyses, bootstrap analysis was performed with 1,000 replicates. For BI analyses, two sets of four chains were allowed to run simultaneously for 1,000,000 generations. Each set was sampled every 100 generations with a burn-in of 25 %. Stationarity was considered to be reached when the average standard deviation of split frequencies was less than 0.01.
Table 2

The species and their GenBank accession numbers used in our phylogenetic analyses

Species

Family

Accession number

Reference

Phlebotomus chinensis

Psychodidae

KR349297

This study

Phlebotomus papatasi

Psychodidae

KR349298

This study

Lutzomyia umbratilis

Psychodidae

KP702938

[30]

Tipula abdominalis

Tipulidae

JN861743

[34]

Paracladura trichoptera

Trichoceridae

JN861751

[34]

Trichocera bimacula

Trichoceridae

JN861750

[34]

Ptychoptera sp.

Ptychopteridae

JN861744

[34]

Bittacomorphella fenderiana

Ptychopteridae

JN861745

[34]

Protoplasa fitchii

Tanyderidae

JN861746

[34]

Chironomus tepperi

Chironomidae

JN861749

[34]

Culicoides arakawae

Ceratopogonidae

AB361004

[64]

Culex pipiens

Culicidae

NC_015079

Atyame et al. unpublished data

Anopheles gambiae

Culicidae

NC_002084

[65]

Aedes albopictus

Culicidae

AY072044

Ho et al. unpublished data

Arachnocampa flava

Keroplatidae

JN861748

[34]

Cramptonomyia spenceri

Pachyneuridae

JN861747

[34]

Sylvicola fenestralis

Anisopodidae

JN861752

[34]

Cydistomyia duplonotata

Tabanidae

DQ866052

[39]

Simosyrphus grandicornis

Syrphidae

DQ866050

[39]

Ceratitis capitata

Tephritidae

NC_000857

[66]

Drosophila yakuba

Drosophilidae

NC_001322

[67]

Dermatobia hominis

Oestridae

AY463155

Azeredo-Espin et al. unpublished data

Cochliomyia hominivorax

Calliphoridae

AF260826

[68]

Haematobia irritans

Muscidae

DQ029097

Lessinger et al. unpublished data

Bittacus pilicornis

Bittacidae

NC_015118

[69]

Results and discussion

Genome organization and composition

The circular mitochondrial genome of P. chinensis (GenBank accession number KR349297) is 16,277 bp in size. The complete mitochondrial genome of P. papatasi (GenBank accession number KR349298), 15,557 bp, was assembled from a total of 5579 reads identified as being of mitochondrial origin. An average per base estimated coverage of reconstructed mitochondrial genome of P. papatasi is ~ 209× based on the mean read length. The mitochondrial genome size differential stems mainly from the varying length of the A + T-rich region caused by variability in the number of tandem repeats. Consistent with published dipteran mitochondrial genomes, both Phlebotomus mitochondrial genomes contain 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, two ribosomal RNA (rRNA) genes, and an A + T-rich region (Table 3). The majority-coding strand (J-strand) and the minority-coding strand (N-strand) encode 23 and 14 genes, respectively (Fig. 1). All the 37 genes share the identical arrangement with the hypothesized ancestral pancrustacean mitochondrial genome. The base composition of the Phlebotomus mitochondrial genome is biased toward A + T, with a total A + T content (J-strand) of 79.2 % and 77.5 % for P. chinensis and P. papatasi, respectively. We calculated the AT content, AT- and GC-skew of PCGs, RNAs and the control region of three sand flies (Table 4), and found that these regions also possess high A + T content, in particular the third codon position of PCG and control region is distinctly higher than that of other regions.
Table 3

The organization of the mitochondrial genome of Phlebotomus chinensis and Phlebotomus papatasi

Gene (region)

Strand

Position

Codon

Anticodon

Pc

Pp

Pc

Pp

Start

Stop

Start

Stop

trnI

J

1–65

1–65

    

GAT

trnQ

N

69–137

66–134

    

TTG

trnM

J

147–214

138–205

    

CAT

ND2

J

215–1237

206–1234

ATA

TAA

ATT

TAA

 

trnW

J

1240–1304

1237–1302

    

TCA

trnC

N

1297–1358

1295–1357

    

GCA

trnY

N

1374–1440

1361–1428

    

GTA

COI

J

1439–2977

1427–2965

TCG

TAA

TCG

TAA

 

trnL UUR

J

2973–3037

2961–3025

    

TAA

COII

J

3040–3723

3028–3711

ATG

TAA

ATG

TAA

 

trnK

J

3725–3795

3716–3786

    

CTT

trnD

J

3795–3858

3794–3858

    

GTC

ATP8

J

3859–4020

3859–4020

ATT

TAA

ATT

TAA

 

ATP6

J

4014–4691

4014–4691

ATG

TAA

ATG

TAA

 

COIII

J

4695–5483

4691–5479

ATG

TAA

ATG

TAA

 

trnG

J

5483–5549

5483–5548

    

TCC

ND3

J

5550–5903

5549–5902

ATC

TAA

ATT

TAA

 

trnA

J

5915–5976

5905–5967

    

TGC

trnR

J

5979–6041

5968–6031

    

TCG

trnN

J

6054–6118

6067–6130

    

GTT

trnS AGN

J

6118–6186

6134–6202

    

GCT

trnE

J

6198–6263

6202–6266

    

TTC

trnF

N

6284–6350

6287–6351

    

GAA

ND5

N

6350–8089

6358–8097

ATA

TAA

ATA

TAG

 

trnH

N

8090–8152

8098–8161

    

GTG

ND4

N

8159–9493

8162–9494

ATG

TAA

ATG

T

 

ND4L

N

9493–9780

9494–9781

ATG

TAA

ATG

TAA

 

trnT

J

9783–9844

9784–9847

    

TGT

trnP

N

9845–9908

9848–9911

    

TGG

ND6

J

9911–10438

9914–10438

ATA

TAA

ATA

TAA

 

Cytb

J

10449–11588

10443–11582

ATG

TAG

ATG

TAG

 

trnS UCN

J

11587–11654

11581–11647

    

TGA

ND1

N

11670–12608

11663–12601

ATT

TAA

ATT

TAA

 

trnL CUN

N

12609–12674

12602–12667

    

TAG

rrnL

N

12675–13987

12668–13976

     

trnV

N

13988–14058

13977–14047

    

TAC

rrnS

N

14059–14845

14048–14834

     

A + T-rich region

14846–16277

14835–15557

     

Notes: J and N refer to the majority and minority strand, respectively

Pc Phlebotomus chinensis, Pp Phlebotomus papatasi

Fig. 1

The gene map of the mitochondrial genomes of Phlebotomus chinensis and Phlebotomus papatasi

Table 4

Composition and skewness of mitochondrial genomes of Phlebotomus chinensis, Phlebotomus papatasi and Lutzomyia umbratilis

Region

AT %

AT-skew

GC-skew

Pc

Pp

Lu

Pc

Pp

Lu

Pc

Pp

Lu

Whole genome

79.2

77.5

78.6

−0.014

−0.012

0.003

−0.248

−0.239

−0.209

Protein-coding genes

76.4

75.1

76.5

−0.167

−0.150

−0.125

−0.006

−0.016

0.036

First codon position

70.0

68.6

70.4

−0.088

−0.066

−0.060

0.225

0.209

0.265

Second codon position

67.8

67.5

67.6

−0.386

−0.389

−0.377

−0.159

−0.159

−0.143

Third codon position

91.3

89.0

91.4

−0.072

−0.039

0.008

−0.237

−0.241

−0.081

tRNA genes

80.8

79.8

79.4

−0.003

0.043

0.030

0.161

0.116

0.079

rRNA genes

84.3

83.4

84.1

−0.005

−0.012

−0.015

0.364

0.354

0.385

A + T-rich region

91.1

92.3

90.4

−0.074

−0.061

0.011

−0.938

−0.571

−0.576

Note: Pc Phlebotomus chinensis, Pp Phlebotomus papatasi, and Lu Lutzomyia umbratilis

Protein-coding genes and codon usage

All the protein-coding genes of P. chinensis start with the typical ATN codon except for COI (Table 3). In comparison with P. chinensis, only ND2 and ND3 have the different start codon in P. papatasi. The start codon of COI in P. chinensis and P. papatasi is uncommon start codon TCG, which is also reported for COI in some nematoceran mitochondrial gneomes [3436]. The conventional stop codons TAA or TAG were used in all the PCGs of P. chinensis, while ND4 of P. papatasi terminates with the incomplete stop codon T. The conserved 7-bp overlap (ATGATAR) between ATP8 and ATP6 present in all known nematoceran mitochondrial genomes was found in Phlebotomus. However, the typical nematoceran 7-bp overlapping region between ND4 and ND4L was not observed in the mitochondrial genomes of phlebotomine sand flies, in contrast, these two genes overlapped by one nucleotide.

The codon usage patterns of P. chinensis, P. papatasi, and L. umbratilis were summarized and the relative synonymous codon usage (RSCU) values are showed in Fig. 2. In the mitochondrial genome of P. chinensis, three codons ACG (Threonine), AGG (Serine), and UGC (Cysteine) are missing, while in the mitochondrial genome of P. papatasi, only one codon AGG (Serine) is absent. Overall, all unused codons are rich in G/C. For the mitochondrial genome of L. umbratilis, all codons expected codons are present. The significance of an AT-rich genome is reflected in codon usage for mitochondrial proteins. It is clear that codon usage ending with A/T, rather than G/C, is preferred by sand flies. The most frequent amino acids in the PCGs are: Leucine (15.94 %–16.85 %), Isoleucine (10.16 %–10.38 %), Phenylalanine (8.82 %–9.48 %), and Serine (7.39 %–8.80 %). The codons UUA (Leucine), AUU (Isoleucine), UUU (Phenylalanine), and AUA (Methionine) are the most frequently used codons.
Fig. 2

Codon distribution in mitochondrial genomes of Phlebotomus chinensis, Phlebotomus papatasi and Lutzomyia umbratilis. Gray-colored codon indicates codon is not present in the genome

Transfer and Ribosomal RNAs

All typical tRNA genes of metazoan mitochondrial genomes were identified in both Phlebotomus mitochondrial genomes studied. All the 22 tRNAs of P. chinensis, P. papatasi, and L. umbratilis have the common cloverleaf secondary structure, while the DHU arm of trnS AGN is short with only one complementary base pair. All anticodon usage is identical with that described for other nematoceran mitochondrial genomes, except for trnS AGN of L. umbratilis, which uses TCT instead of the common GCT. Considering the codon usage, the RSCU of codon AGA (the corresponding codon to anticodon of trnS AGN) is overwhelmingly higher than those of other three synonymous codons in L. umbratilis. The frequency of AGA is moderate rich in P. chinensis and P. papatasi, however the corresponding codon (AGC) to anticodon (GCT) of their trnS AGN is rarely used. The most conserved tRNAs among P. chinensis, P. papatasi, and L. umbratilis are trnL UUR, trnL CUN, trnS UCN and trnI, however trnA, trnR and trnC exhibit low level of identical nucleotides.

The inferred secondary structure models of small ribosomal subunit (rrnS) and large ribosomal subunit (rrnL) for P. chinensis are shown in Figs. 3 and 4, respectively. The secondary structure of rrnS and rrnL contain three and six domains, respectively. The domain III of rrnL is absent, which was reported in the secondary structure of other arthropodan rrnL [26, 37]. The overall structures of P. chinensis rRNAs resemble that of other insects. Comparative analyses on secondary structures among P. chinensis, P. papatasi, and L. umbratilis manifest uneven distribution of conserved nucleotides, in that domains I and III of the rrnS are more conserved than domain II, and domains I, II, and VI in rrnL have more variable sites. Variable positions of rrnS are largely restricted to H47, H673, H1305 and the region between H577 and H673, and H567 and H769. Domains IV and V of rrnL contain mainly conserved helixes.
Fig. 3

Inferred secondary structure of the mitochondrial rrnS gene for Phlebotomus chinensis (Conserved nucleotides of three Phlebotominae taxa are labelled in blue)

Fig. 4

Inferred secondary structure of the mitochondrial rrnL gene for Phlebotomus chinensis (Conserved nucleotides of three Phlebotominae taxa are labelled in blue)

The A + T-rich region

The A + T-rich regions of P. chinensis and P. papatasi are 1,433 bp and 723 bp respectively, which harbor a high rate of A + T base composition (91.1 % for P. chinensis and 92.3 % for P. papatasi). The A + T-rich regions of P. chinensis contains seven identical tandem repeat units of 159-bp sequence and another shortened tandem repeat unit with only 79-bp. In P. papatasi, there are three tandem repeat units, the first two (162-bp) are nearly identical with one substitution at the 159th position, while the third one is a shortened repeat unit (89-bp). All the tandem repeat sequences of P. chinensis and P. papatasi begin in the rrnS gene, but the tandem repeat sequences (372-bp for repeat unit) of L. umbratilis are located in the central region of A + T-rich region. Additionally, the alignments of tandem repeat units of P. chinensis and P. papatasi show 60.2 % similarity, but there is no evidence for homologous repeat motifs between species of Phlebotomus and L. umbratilis. Abundant microsatellite-like elements occur throughout the region between the tandem repeat sequence and trnI (e.g. (AT)3, (AT)5, (AT)6, (AT)8, (TA)4, and (TA)6 in P. papatasi). These tandem repeat units and microsatellite-like elements are potentially useful markers for the study of geographical population structure [38].

The accurate estimation of length and number of repeats and assembly of A + T-rich region are often difficult, particularly for including various complex repeat regions. For obtaining the accurate A + T-rich region of P. chinensis, Sanger sequencing with paired ends can cover the length of repeat region (approximate 1.2 kb), and agarose gel electrophoresis for amplified control region was used to determine the correct size and number of the length of repeat region. In control region of P. papatasi, we reconstructed the similar pattern of architecture for P. chinensis. The high coverage and comparatively long read length also make sequence accurate.

Nucleotide diversity of mitochondrial genome among Phlebotomus chinensis, P. papatasi and Lutzomyia umbratilis

A sliding window analysis was performed to estimate nucleotide diversity Pi (π) across the mitochondrial genomes of P. chinensis, P. papatasi and L. umbratilis, excluding the A + T-rich region (Fig. 5). The sliding window indicated that the most variable coding regions were within ND5 gene suggesting that these regions are under accelerated evolution and few selective constraints, and can be used as effective markers to investigate population structure and potentially resolve the phylogenetic relationship of closely related species. Not unexpectedly, the overall sequence variability of the rRNA regions is lower than that of other regions. The most conserved fragments were found in the rrnL region. Amongst PCGs, COI and ND1 were the most conserved. By contrast, ND6, ATP8 and ND3 displayed the high variability.
Fig. 5

Sliding window analyses of the alignment among Phlebotomus chinensis, Phlebotomus papatasi and Lutzomyia umbratilis mitochondrial genomes. The line shows the value of nucleotide diversity (π) in a sliding window analysis of window size 500 bp with step size 25 bp, the value is inserted at its mid-point

Phylogenetic analyses

Diptera is a megadiverse group of extant insects. Historically, Diptera was divided into two suborders, Nematocera and Brachycera. Brachycera was confirmed as a monophyletic group with robust phylogenetic analyses, but Nematocera is generally accepted as a paraphyletic group and Brachycera is derived from part of these lineages. The mitochondrial genome contains much information and has been used to resolve the phylogenetic relationships of Diptera, especially that of Brachycera [3942]. In the present study, the phylogenetic relationships inferred from ML analyses and BI analyses using only first and second codon positions of 13 PCGs share similar topologies (Fig. 6). Consistent with previous results, Brachycera formed a monophyletic group and clustered with Bibionomorpha as the sister group [43, 44]. Surprisingly, Psychodidae species clustered with Protoplasa fitchii, the lone representative of Tanyderidae with high support, which is the first time this relationship has been elucidated by mitochondrial data (bootstrap value of 98 % in ML analyses and Bayesian posterior probabilities (Bpp) of 1 in BI analyses) and identical to results of other molecular datasets [43, 44]. This clade was derived from Culicomorpha but the node was weakly supported (<50 % for bootstrap value and 0.7 for Bpp) suggesting the relationship between this branch and Culicomorpha is still ambiguous. However, the close relationship within this large clade was confirmed by moderate node support (72 % for bootstrap value and 0.99 for Bpp), which is in accordance with previous studies using multiple markers [43]. The traditional basal branch comprised of Tipulidae and Trichoceridae (Tipulomorpha) was not grouped as a monophyletic clade, instead Tipulidae was an early split in the phylogeny of Diptera. While the families Ptychopteridae and Trichoceridae formed a branch that clustered with all remaining groups as the sister group. This arrangement of basal branches is identical with 13PCG12 (third codon sites removed) + rRNAs dataset, however 5PCG12 (COI-III, Cytb, and ATP6) + rRNAs dataset shows a different topology [34]. However, using different phylogenetic hypotheses caused the topology to change, with Tipulomorpha containing Tipulidae or Tipulidae + Trichoceridae [4446], therefore we can conclude that the basal placement of Tipulomorpha in the phylogeny of Diptera is stable. Phylogenetic analyses in this study were based only on mitochondrial data, so we believe it is still indispensable to combine nuclear and mitochondrial data with a broader taxon sample to provide an even more robust phylogenetic analyses depicting the evolution of the Diptera.
Fig. 6

Mitochondrial phylogenetic relationship of representative members of Diptera. First values at the branches correspond to ML bootstrap support in percentages while the second values indicate Bayesian posterior probabilities (ML bootstrap values < 50 % are not shown)

Implications

Low flight capacity, a preference to remain close to area of emergence, geographic barriers and variability in climate across their distribution has led to genetically structured populations of phlebotomine sand flies, with cryptic species also being recorded [47, 48]. Genetically distinct species and populations have demonstrated a varying ability to both transmit Leishmania and resist insecticides [4951] highlighting the need to quantify their population structure and delineate cryptic species. The sliding window analysis presented in this study provides a useful comparison of the evolutionary rates of each gene, allowing future researchers to design population genetic and large-scale phylogenetic studies utilizing the most appropriate marker for their task. One immediate use for such data will be the exploration of the relationships between P. chinensis and another disputed and close relative vector species Phlebotomus sichuanensis or ‘large type of P. chinensis’ [5254]. It is debated whether these two nominal species are in fact distinct or if they are different populations of the same species occupying different altitudes [16, 53, 55].

NGS technology has been routinely used in genomic research with Illumina and 454 platforms. Although, these sequencing technology have been verified to obtain mitochondrial genomes for insects, the A + T-rich region is still difficult to assemble owing to various complex repeat regions [56, 57]. Ramakodi et al. [56] reported that the coverage may not have the crucial factors for reconstruction of control region using 454 reads, and known repeat sequences can help to reconstruct the full length of control region. In the present study, we successfully retrieved the complete mitochondrial genome with entire A + T rich region using P. chinensis as the reference. Both these control regions contain a similar pattern of repeat sequences, and the repeat units also hold 60.2 % similarity suggesting control region (or repeat sequences) of closely related species may contributes to the reconstruction of a new control region. Furthermore, the results also indicate that mitochondrial genome of closely related species as reference are more appropriate than shot target sequences for reconstruction of the full length of control region, in particular to that including complex repeat sequences. In other words, it suggests the reference species and sequence must be carefully selected when using the same approach. These first Phlebotomus mitochondrial genomes will make it easier to generate additional mitochondrial genomes data including control region from different population and species which will provide insight into the speciation, distribution pattern, evolution and divergence times of sand flies at the genome-level [5861].

Conclusion

The present study determined the mitochondrial genomes of P. chinensis and P. papatasi, and conducted a comparative analysis of three sand fly mitochondrial genomes. We present the first examination of the phylogenetic status of the Psychodidae and, based on all mitochondrial PCGs, provide stable support that families Psychodidae and Tanyderidae are sister taxa. We confirmed the known sequences in control region of closely related species facilitate the reconstruction of uncharted control region using the similar approach. Our results also provide a source of genetic markers for future studies on the population biology and molecular phylogeny of these important vectors.

Notes

Declarations

Acknowledgments

This research was supported by the National Natural Science Foundation of China (31372158).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Co-Innovation Center for Qinba regions’ sustainable development, College of Life Science, Shaanxi Normal University
(2)
Department of Biology, Dalhousie University

References

  1. Maroli M, Feliciangeli MD, Bichaud L, Charrel RN, Gradoni L. Phlebotomine sandflies and the spreading of leishmaniases and other diseases of public health concern. Med Vet Entomol. 2013;27:123–47.View ArticlePubMedGoogle Scholar
  2. World Health Organization (WHO). Control of the leishmaniasis: Report of a Meeting of the WHO Expert Committee on the Control of Leishmaniasis, Geneva, 22–26 March 2010. WHO Tech Rep Ser. 2010;949:xii–xiii. 1–186.Google Scholar
  3. Leng YJ, Zhang LM. Check list and geographical distribution of phlebotomine sandflies in China. Ann Trop Med Parasitol. 1993;87:83–94.PubMedGoogle Scholar
  4. Zhang LM, Leng YJ. Eighty-year research of phlebotomine sandflies (Diptera: Psychodidae) in China (1915-1995). II. Phlebotomine vectors of leishmaniasis in China. Parasite. 1997;4:299–306.View ArticlePubMedGoogle Scholar
  5. Wei F, Shang L, Jin H, Lian H, Liu W, Li Z, et al. Molecular detection and genetic diversity of Leishmania donovani in naturally infected Phlebotomus chinensis from southwestern China. Vector Borne Zoonotic Dis. 2011;11:849–52.View ArticlePubMedGoogle Scholar
  6. Wang JY, Cui G, Chen HT, Zhou XN, Gao CH, Yang YT. Current epidemiological profile and features of visceral leishmaniasis in People’s Republic of China. Parasit Vectors. 2012;5:31.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Fu Q, Li SZ, Wu WP, Hou YY, Zhang S, Feng Y, et al. Endemic characteristics of infantile visceral leishmaniasis in the People’s Republic of China. Parasit Vectors. 2013;6:143.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Guan LR, Shen WX. Recent advances in visceral leishmaniasis in China. Southeast Asian J Trop Med Public Health. 1991;22:291–8.PubMedGoogle Scholar
  9. Chelbi I, Kaabi B, Bejaoui M, Derbali M, Zhioua E. Spatial correlation between Phlebotomus papatasi Scopoli (Diptera: Psychodidae) and incidence of zoonotic cutaneous leishmaniasis in Tunisia. J Med Entomol. 2009;46:400–2.View ArticlePubMedGoogle Scholar
  10. Tesh RB, Saidi S, Gajdamovič SJ, Rodhain F, Vesenjak-Hirjan J. Serological studies on the epidemiology of sandfly fever in the Old World. Bull World Health Organ. 1976;54:663–74.PubMed CentralPubMedGoogle Scholar
  11. Wasserberg G, Yarom I, Warburg A. Seasonal abundance patterns of the sandfly Phlebotomus papatasi in climatically distinct foci of cutaneous leishmaniasis in Israeli deserts. Med Vet Entomol. 2003;17:452–6.View ArticlePubMedGoogle Scholar
  12. Hamarsheh O. Distribution of Leishmania major zymodemes in relation to populations of Phlebotomus papatasi sand flies. Parasit Vectors. 2011;4:9.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Gissi C, Iannelli F, Pesole G. Evolution of the mitochondrial genome of Metazoa as exemplified by comparison of congeneric species. Heredity. 2008;101:301–20.View ArticlePubMedGoogle Scholar
  14. Boore JL. Animal mitochondrial genomes. Nucleic Acids Res. 1999;27:1767–80.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Hamarsheh O, Presber W, Abdeen Z, Sawalha S, Al-Lahem A, Schönian G. Genetic structure of Mediterranean populations of the sandfly Phlebotomus papatasi by mitochondrial cytochrome b haplotype analysis. Med Vet Entomol. 2007;21:270–7.View ArticlePubMedGoogle Scholar
  16. Zhang L, Ma Y, Xu J. Genetic differentiation between sandfly populations of Phlebotomus chinensis and Phlebotomus sichuanensis (Diptera: Psychodidae) in China inferred by microsatellites. Parasit Vectors. 2013;6:115.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Depaquit J, Lienard E, Verzeaux-Griffon A, Ferté H, Bounamous A, Gantier JC, et al. Molecular homogeneity in diverse geographical populations of Phlebotomus papatasi (Diptera, Psychodidae) inferred from ND4 mtDNA and ITS2 rDNA epidemiological consequences. Infect Genet Evol. 2008;8:159–70.View ArticlePubMedGoogle Scholar
  18. Tang M, Tan M, Meng G, Yang S, Su X, Liu S, et al. Multiplex sequencing of pooled mitochondrial genomes––a crucial step toward biodiversity analysis using mito-metagenomics. Nucleic Acids Res. 2014;42:e166.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Cameron SL. How to sequence and annotate insect mitochondrial genomes for systematic and comparative genomics research. Syst Entomol. 2014;39:400–11.View ArticleGoogle Scholar
  20. Hahn C, Bachmann L, Chevreux B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach. Nucl Acids Res. 2013;41:e129.PubMed CentralView ArticlePubMedGoogle Scholar
  21. Dietrich C, Brune A. The complete mitogenomes of six higher termite species reconstructed from metagenomic datasets (Cornitermes sp., Cubitermes ugandensis, Microcerotermes parvus, Nasutitermes corniger, Neocapritermes taracua, and Termes hospes). Mitochondrial DNA. 2014. doi:10.3109/19401736.2014.987257.PubMedGoogle Scholar
  22. Zhang D, Huang J, Zhou F, Gong F, Jiang S. The complete mitochondrial genome of banana shrimp Fenneropenaeus merguiensis with phylogenetic consideration. Mitochondrial DNA. 2015. doi:10.3109/19401736.2015.1041116.Google Scholar
  23. Staden R, Beal KF, Bonfield JK. The Staden package, 1998. Methods in Molecular Biology. 2000;132:115–30.PubMedGoogle Scholar
  24. Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG. The CLUSTAL_X Windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 1997;25:4876–82.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Cannone JJ, Subramanian S, Schnare MN, Collett JR, D’Souza LM, Du Y, et al. The Comparative RNA Web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinformatics. 2002;3:2.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27:573–80.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28:2731–9.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25:1451–2.View ArticlePubMedGoogle Scholar
  30. Kocher A, Gantier JC, Holota H, Jeziorski C, Coissac E, Bañuls AL, et al. Complete mitochondrial genome of Lutzomyia (Nyssomyia) umbratilis (Diptera: Psychodidae), the main vector of Leishmania guyanensis. Mitochondrial DNA. 2015. doi:10.3109/19401736.2015.1022748.PubMedGoogle Scholar
  31. Posada D. jModelTest: phylogenetic model averaging. Mol Biol Evol. 2008;25:1253–6.View ArticlePubMedGoogle Scholar
  32. Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003;19:1572–4.View ArticlePubMedGoogle Scholar
  33. Stamatakis A, Ludwig T, Meier H. RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics. 2005;21:456–63.View ArticlePubMedGoogle Scholar
  34. Beckenbach AT. Mitochondrial genome sequences of Nematocera (lower Diptera): evidence of rearrangement following a complete genome duplication in a winter crane fly. Genome Biol Evol. 2012;4:89–101.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Mitchell SE, Cockburn AF, Seawright JA. The mitochondrial genome of Anopheles quadrimaculatus species A: complete nucleotide sequence and gene organization. Genome. 1993;36:1058–73.View ArticlePubMedGoogle Scholar
  36. Logue K, Chan ER, Phipps T, Small ST, Reimer L, Henry-Halldin C, et al. Mitochondrial genome sequences reveal deep divergences among Anopheles punctulatus sibling species in Papua New Guinea. Malar J. 2013;12:64.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Pons J, Bauzà-Ribot MM, Jaume D, Juan C. Next-generation sequencing, phylogenetic signal and comparative mitogenomic analyses in Metacrangonyctidae (Amphipoda: Crustacea). BMC Genomics. 2014;15:566.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Zhang KJ, Zhu WC, Rong X, Zhang YK, Ding XL, Liu J, et al. The complete mitochondrial genomes of two rice planthoppers, Nilaparvata lugens and Laodelphax striatellus: conserved genome rearrangement in Delphacidae and discovery of new characteristics of atp8 and tRNA genes. BMC Genomics. 2013;14:417.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Cameron SL, Lambkin CL, Barker SC, Whiting MF. A mitochondrial genome phylogeny of Diptera: whole genome sequence data accurately resolve relationships over broad timescales with high precision. Syst Entomol. 2007;32:40–59.View ArticleGoogle Scholar
  40. Zhao Z, Su T, Chesters D, Wang S, Ho SYW, Zhu C, et al. The mitochondrial genome of Elodia flavipalpis Aldrich (Diptera: Tachinidae) and the evolutionary timescale of tachinid flies. PLoS ONE. 2013;8:e61814.PubMed CentralView ArticlePubMedGoogle Scholar
  41. Nelson LA, Lambkin CL, Batterham P, Wallman JF, Dowton M, Whiting MF, et al. Beyond barcoding: A mitochondrial genomics approach to molecular phylogenetics and diagnostics of blowflies (Diptera: Calliphoridae). Gene. 2012;511:131–42.View ArticlePubMedGoogle Scholar
  42. Nardi F, Carapelli A, Boore JL, Roderick GK, Dallai R, Frati F. Domestication of olive fly through a multi-regional host shift to cultivated olives: Comparative dating using complete mitochondrial genomes. Mol Phylogenet Evol. 2010;57:678–86.View ArticlePubMedGoogle Scholar
  43. Wiegmann BM, Trautwein MD, Winkler IS, Barr NB, Kim JW, Lambkin C, et al. Episodic radiations in the fly tree of life. Proc Natl Acad Sci USA. 2011;108:5690–5.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Bertone MA, Courtney GW, Wiegmann BM. Phylogenetics and temporal diversification of the earliest true flies (Insecta: Diptera) based on multiple nuclear genes. Syst Entomol. 2008;33:668–87.View ArticleGoogle Scholar
  45. Hennig W. Ordnung Diptera (Zweiflügler). In: Helmcke JG, Starck D, Wermuth H, editors. Handbuch der Zoologie, Bd. 4: Arthropoda, 2.Hälfter: Insecta, 2. Aufl., 2 Teil Spezielles. Berlin: Walter de Gruyter; 1973. p. 1–377.Google Scholar
  46. Wood DM, Borkent A. Phylogeny and classification of the Nematocera. In: McAlpine JF, Wood DM, editors. Manual of Nearctic Diptera, vol. 3. Ottawa: Agriculture Canada Research Branch; 1989. p. 1333–70.Google Scholar
  47. Alexander B, Young DG. Dispersal of phlebotomine sand flies (Diptera: Psychodidae) in a Colombian focus of Leishmania (Viannia) braziliensis. Mem Inst Oswaldo Cruz. 1992;87:397–403.View ArticlePubMedGoogle Scholar
  48. Dujardin JP, Le Pont F, Cruz M, Leon R, Tarrieu LF, Guderian R, et al. Cryptic speciation in Lutzomyia (Nyssomyia) trapidoi (Fairchild & Hertig) (Diptera: Psychodidae) detected by multilocus enzyme electrophoresis. Am J Trop Med Hyg. 1996;54:42–5.PubMedGoogle Scholar
  49. Lanzaro GC, Ostrovska K, Herrero MV, Lawyer PG, Warburg A. Lutzomyia longipalpis is a species complex: genetic divergence and interspecific hybrid sterility among three populations. Am J Trop Med Hyg. 1993;48:839–47.PubMedGoogle Scholar
  50. Hassan MM, Widaa SO, Osman OM, Numiary MSM, Ibrahim MA, Abushama HM. Insecticide resistance in the sand fly, Phlebotomus papatasi from Khartoum State, Sudan. Parasit Vectors. 2012;5:46.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Alexander B, Maroli M. Control of phlebotomine sandflies. Med Vet Entomol. 2003;17:1–18.View ArticlePubMedGoogle Scholar
  52. Leng YJ, Yin ZC. The taxonomy of phlebotomine sandflies (Diptera: Psychodidae) of Sichuan Province, China, with descriptions of two species, Phlebotomus (Adlerius) sichuanensis sp. n. and Sergentomyia (Neophlebotomus) zhengjiani sp. n. Ann Trop Med Parasitol. 1983;77:421–31.PubMedGoogle Scholar
  53. Xiong GH, Jin CF, Hong YM. A preliminary investigation on the types of Phlebotomus chinensis in relation to longitudinal altitude distribution in southern Gansu and northern Sichuan. Endemic Disease Bulletin. 1988;3:48–56.Google Scholar
  54. Xiong GH, Jin CF. Studies on the longitudinal distribution of sandfly Phlebotomus chinensis and its relation Kala-azar in southern Gansu and northern Sichuan. Endemic Disease Bulletin. 1989;4:15–21.Google Scholar
  55. Zhang L, Ma Y. Identification of Phlebotomus chinensis (Diptera: Psychodidae) inferred by morphological characters and molecular markers. Entomotaxonomia. 2012;34:71–80.Google Scholar
  56. Ramakodi MP, Singh B, Wells JD, Guerrero F, Ray DA. A 454 sequencing approach to dipteran mitochondrial genome research. Genomics. 2015;105:53–60.View ArticlePubMedGoogle Scholar
  57. Rasmussen DA, Noor MAF. What can you do with 0.1× genome coverage? A casestudy based on a genome survey of the scuttle fly Megaselia scalaris (Phoridae). BMC Genomics. 2009;10:382.PubMed CentralView ArticlePubMedGoogle Scholar
  58. Ma C, Yang P, Jiang F, Chapuis MP, Shali Y, Sword GA, et al. Mitochondrial genomes reveal the global phylogeography and dispersal routes of the migratory locust. Mol Ecol. 2012;21:4344–58.View ArticlePubMedGoogle Scholar
  59. Gu XB, Liu GH, Song HQ, Liu TY, Yang GY, Zhu XQ. The complete mitochondrial genome of the scab mite Psoroptes cuniculi (Arthropoda: Arachnida) provides insights into Acari phylogeny. Parasit Vectors. 2014;7:340.Google Scholar
  60. Timmermans MJTN, Dodsworth S, Culverwell CL, Bocak L, Ahrens D, Littlewood DTJ, et al. Why barcode? High-throughput multiplex sequencing of mitochondrial genomes for molecular systematics. Nucleic Acids Res. 2010;38:e197.PubMed CentralView ArticlePubMedGoogle Scholar
  61. Liu GH, Chen F, Chen YZ, Song HQ, Lin RQ, Zhou DH, et al. Complete mitochondrial genome sequence data provides genetic evidence that the brown dog tick Rhipicephalus sanguineus (Acari: Ixodidae) represents a species complex. Int J Biol Sci. 2013;9:361–9.PubMed CentralView ArticlePubMedGoogle Scholar
  62. Simon C, Buckley TR, Frati F, Stewart JB, Beckenbach AT. Incorporating molecular evolution into phylogenetic analysis, and a new compilation of conserved polymerase chain reaction primers for animal mitochondrial DNA. Annu Rev Ecol Evol Syst. 2006;37:545–79.View ArticleGoogle Scholar
  63. Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol Mar Biol Biotech. 1994;3:294–9.Google Scholar
  64. Matsumoto Y, Yanase T, Tsuda T, Noda H. Species-specific mitochondrial gene rearrangements in biting midges and vector species identification. Med Vet Entomol. 2009;23:47–55.View ArticlePubMedGoogle Scholar
  65. Beard CB, Hamm DM, Collins FC. The mitochondrial genome of the mosquito Anopheles gambiae: DNA sequence, genome organization, and comparisons with mitochondrial sequences of other insects. Insect Mol Biol. 1993;2:103–24.View ArticlePubMedGoogle Scholar
  66. Spanos L, Koutroumbas G, Kotsyfakis M, Louis C. The mitochondrial genome of the Mediterranean fruitfly Ceratitis capitata. Insect Mol Biol. 2000;9:139–44.View ArticlePubMedGoogle Scholar
  67. Clary DO, Goddard JM, Martin SC, Fauron CM, Wolstenholme DR. Drosophila mitochondrial DNA: a novel gene order. Nucleic Acids Res. 1982;10:6619–37.PubMed CentralView ArticlePubMedGoogle Scholar
  68. Lessinger AC, Martins Junqueira AC, Lemos TA, Kemper EL, Da Silva FR, Vettore AL, et al. The mitochondrial genome of the primary screwworm fly Cochliomyia hominivorax (Diptera: Calliphoridae). Insect Mol Biol. 2000;9:521–9.View ArticlePubMedGoogle Scholar
  69. Beckenbach AT. Mitochondrial genome sequences of representatives of three families of scorpionflies (Order Mecoptera) and evolution in a major duplication of coding sequence. Genome. 2011;54:368–76.View ArticlePubMedGoogle Scholar

Copyright

© Ye et al. 2015

Comments

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

Advertisement