Advertisement

Parasites & Vectors

, 12:451 | Cite as

Tick mitochondrial genomes: structural characteristics and phylogenetic implications

  • Tianhong Wang
  • Shiqi Zhang
  • Tingwei Pei
  • Zhijun YuEmail author
  • Jingze LiuEmail author
Open Access
Review

Abstract

Ticks are obligate blood-sucking arachnid ectoparasites from the order Acarina, and many are notorious as vectors of a wide variety of zoonotic pathogens. However, the systematics of ticks in several genera is still controversial. The mitochondrial genome (mt-genome) has been widely used in arthropod phylogeny, molecular evolution and population genetics. With the development of sequencing technologies, an increasing number of tick mt-genomes have been sequenced and annotated. To date, 63 complete tick mt-genomes are available in the NCBI database, and these genomes have become an increasingly important genetic resource and source of molecular markers in phylogenetic studies of ticks in recent years. The present review summarizes all available complete mt-genomes of ticks in the NCBI database and analyses their characteristics, including structure, base composition and gene arrangement. Furthermore, a phylogenetic tree was constructed using mitochondrial protein-coding genes (PCGs) and ribosomal RNA (rRNA) genes from ticks. The results will provide important clues for deciphering new tick mt-genomes and establish a foundation for subsequent taxonomic research.

Keywords

Ticks Mitochondrial genome (mt-genome) Gene structure Phylogeny 

Abbreviations

TBDs

tick-borne diseases

SFTSV

severe fever with thrombocytopenia syndrome virus

TBEV

tick-borne encephalitis virus

ALSV

Alongshan virus

PCGs

protein-coding genes

tRNA

transfer RNA

rRNA

ribosomal RNA

NGS

next-generation sequencing

NCRs

non-coding regions

J strand

majority strand

N strand

minority strand

ML

maximum likelihood

Background

Ticks are obligate blood-sucking arachnid ectoparasites that can feed on a wide range of vertebrates, including mammals, birds and reptiles [1, 2]. Ticks are well-known zoonotic pathogen vectors, and tick-borne diseases (TBDs) are increasingly threatening animal and human health, thereby causing great economic damage [3, 4]. Many important tick-borne pathogens have been characterized from ticks in recent years, including Anaplasma bovis, Babesia ovata, Rickettsia japonica, Chlamydiaceae bacteria and severe fever with thrombocytopenia syndrome virus (SFTSV), which have attracted increasing attention in the field of public health [5, 6, 7, 8, 9]. Recently, a newly segmented virus with a febrile illness similar in its clinical manifestation to tick-borne encephalitis virus (TBEV) was discovered, which was designated as Alongshan virus (ALSV) and confirmed in 86 patients from several provinces in China [10]. Globally, the annual financial losses due to ticks and TBDs are in the billions of dollars [3, 11]. A total of 896 tick species have been described worldwide in three families: Ixodidae (hard ticks, 702 species), Argasidae (soft ticks, 193 species) and Nuttalliellidae (1 species) [12, 13, 14]. Hard ticks possess a sclerotized scutum in all life stages except eggs, have an apically located gnathostoma, usually feed for several days and ingest a large amount of blood [15, 16]. Soft ticks have no sclerotized scutum and mouthparts located anteroventrally. The ticks usually feed and expand the body within minutes to hours [17]. Nuttalliella namaqua is the unique species in the family Nuttalliellidae, and it displays many characteristics associated with hard and soft ticks and can engorge as rapidly as soft ticks [18]. The differences in life history, behaviour, and morphological characteristics are useful for the discrimination of soft ticks and hard ticks, but there are still numerous difficulties among the interspecies taxonomic characterization and geographical origin of ticks, especially for soft ticks [19]. Therefore, the increasing number of characterized mt-genomes has shown considerable potential in tick phylogeny, molecular evolution and population genetics.

The mt-genome is characterized by low molecular weight, high copy quantity and genetic conservation. The mt-genome has been widely used in molecular evolution, phylogeny and genealogy in recent years [20, 21, 22]. Similar to other arthropods, the tick mt-genome has a circular, double-stranded DNA structure with a length of 14–16 kb and a total of 37 genes, including 13 protein-coding genes, 22 transfer RNA genes (tRNAs) and 2 rRNA genes [20, 23]. With the development of next-generation sequencing (NGS) technology, increasing numbers of complete mt-genomes have been sequenced and annotated from various tick species [24]. The complete mt-genome sequences are necessary for advances in areas that are crucial for TBDs study and control [24]. To date, 63 complete tick mt-genomes are available in the NCBI database, and these genomes have become an increasingly important genetic resource and source of molecular markers in phylogenetic studies of ticks in recent years [19, 25]. Hence, in the present study, we used the MITOS online software (http://mitos.bioinf.uni-leipzig.de/index.py/) to annotate the complete mt-genomes of ticks and compare their characteristics, including structure, base composition and gene arrangement. Furthermore, a phylogenetic tree was constructed using PCGs and rRNA genes from ticks. The results will provide important clues for deciphering new tick mt-genomes and provide insights for subsequent taxonomic research.

Present state of research on tick mt-genomes

The first mt-genomes of ticks (Ixodes hexagonus and Rhipicephalus sanguineus) were reported by Black et al. [26] in 1998. As of May 2019, 63 complete tick mt-genomes have been deposited in the NCBI database. Most tick mt-genomes were published in this decade, and are from 3 families and 15 genera, including 35 species in the family Ixodidae: Ixodes (7 species); Amblyomma (7 species); Rhipicephalus (5 species); Rhipicentor (1 species); Dermacentor (4 species); Bothriocroton (2 species); Haemaphysalis (8 species); and Hyalomma (1 species) [26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]; 27 species in the family Argasidae: Argas (8 species); Antricola (1 species); Carios (2 species); Ornithodoros (14 species); Otobius (1 species); and Nothoaspis (1 species) [19, 27, 42, 43, 44]; and 1 Nuttalliella species in family Nuttalliellidae [44] (Table 1). In recent years, phylogenetic studies based on mt-genome sequences have been effectively carried out for many tick species [21, 28, 29, 30, 36, 40]. These achievements are also essential for understanding the genetic differentiation and phylogeny of ticks [31, 32, 33, 34]. However, the genera Anomalohimalaya, Compluriscutula, Margaropus and Nosomma still lack complete mt-genome information, and most species were sampled in a limited geographical area [45]. Complete mt-genome sequences have only been obtained for approximately 7% (63/896) of the tick species, and the general characteristics of most tick mt-genomes remain to be determined.
Table 1

The available tick complete mitochondrial genomes in GenBank

Family

Genus

Species

GenBank ID

Reference

Nuttalliellidae

Nuttalliella

N. namaqua

JQ665719

Mans et al. [44]

Argasidae

Argas

A. africolumbae

KJ133580

Mans et al. [44]

  

A. boueti

KR907234

Mans et al. [Unpublished]a

  

A. brumpti

KR907226

Mans et al. [Unpublished]

  

A. lagenoplastis

KC769587

Burger et al. [27]

  

A. miniatus

KC769590

Burger et al. [27]

  

A. persicus

KJ133581

Mans et al. [Unpublished]

  

A. striatus

KJ133583

Mans et al. [Unpublished]

  

A. walkerae

KJ133585

Mans et al. [Unpublished]

 

Antricola

A. mexicanus

KC769591

Burger et al. [27]

 

Carios

C. capensis

AB075953

Fukunaga et al. [Unpublished]

  

C. faini

KJ133589

Mans et al. [Unpublished]

 

Nothoaspis

N. amazoniensis

KX712088

Lima et al. [Unpublished]

 

Ornithodoros

O. brasiliensis

KC769593

Burger et al. [27]

  

O. compactus

KJ133590

Mans et al. [Unpublished]

  

O. coriaceus

MG593161

Mans et al. [Unpublished]

  

O. costalis

KJ133591

Mans et al. [Unpublished]

  

O. hermsi

MF818032

Mans et al. [Unpublished]

  

O. moubata

AB073679

Fukunaga et al. [43]

  

O. parkeri

MF818029

Mans et al. [Unpublished]

  

O. porcinus

AB105451

Mitani et al. [42]

  

O. rostratus

KC769592

Burger et al. [27]

  

O. savignyi

KJ133604

Mans et al. [Unpublished]

  

O. sonrai

MF818026

Mans et al. [Unpublished]

  

O. tholozani

MF818023

Mans et al. [Unpublished]

  

O. turicata

MF818021

Mans et al. [Unpublished]

  

O. zumpti

KR907257

Mans et al. [Unpublished]

 

Otobius

O. megnini

KC769589

Burger et al. [27]

Ixodidae

Ixodes

I. hexagonus

AF081828

Black et al. [26]

  

I. holocyclus

AB075955

Shao et al. [41]

  

I. pavlovskyi

KJ000060

Mikryukova et al. [Unpublished]

  

I. persulcatus

KU935457

Sui et al. [40]

  

I. ricinus

JN248424

Montagna et al. [39]

  

I. tasmani

MH043269

Burnard et al. [25]

  

I. uriae

AB087746

Shao et al. [37]

 

Amblyomma

A. americanum

KP941755

Williams-Newkirk et al. [36]

  

A. cajennense

JX573118

Burger et al. [29]

  

A. elaphense

JN863729

Burger et al. [29]

  

A. fimbriatum

JN863730

Burger et al. [28]

  

A. sculptum

KX622791

Lima et al. [31]

  

A. sphenodonti

JN863731

Burger et al. [29]

  

A. triguttatum

AB113317

Fukunaga et al. [Unpublished]

 

Rhipicephalus

R. australis

KC503255

Burger et al. [27]

  

R. geigyi

KC503263

Burger et al. [27]

  

R. microplus

KC503261

Burger et al. [30]

  

R. sanguineus

JX416325

Liu et al. [32]

  

R. turanicus

KY996841

Li et al. [Unpublished]

 

Rhipicentor

R. nuttalli

MF818020

Mans et al. [Unpublished]

 

Dermacentor

D. verestianus

MG986896

Yu et al. [35]

  

D. nitens

KC503258

Burger et al. [27]

  

D. nuttalli

KT764942

Guo et al. [33]

  

D. silvarum

KP258209

Chang et al. [Unpublished]

 

Bothriocroton

B. concolor

JN863727

Burger et al. [28]

  

B. undatum

JN863728

Burger et al. [28]

 

Haemaphysalis

H. bancrofti

MH043268

Burnard et al. [25]

  

H. concinna

KY364906

Fu et al. [38]

  

H. flava

AB075954

Shao et al. [41]

  

H. formosensis

JX573135

Burger et al. [29]

  

H. hystricis

MH510034

Tian et al. [Unpublished]

  

H. japonica

MG253031

Fu et al. [Unpublished]

  

H. longicornis

MG450553

Geng et al. [Unpublished]

  

H. parva

JX573136

Burger et al. [29]

 

Hyalomma

H. asiaticum

MF101817

Liu et al. [34]

aUnpublished here refers to the sequences deposited into GenBank only without paper published

Basic features of tick mt-genomes

The length of the mt-genomes of ticks average 14,633 bp, with the longest reaching 15,227 bp (Ixodes tasmani) and the smallest measuring only 14,307 bp (Argas boueti) (Table 2). Generally, the length of the mt-genomes from hard ticks is slightly longer than that of soft ticks (14,796 and 14,429 bp, respectively). The length differences of the mt-genomes between ticks may be influenced by gene rearrangement and the length of the non-coding regions (NCRs) [46, 47]. MITOS online analysis showed no gene deletion or duplication in tick mt-genomes, which contain 13 PCGs, 2 rRNA genes and 22 tRNA genes. Among the 13 PCGs, 9 PCGs (nad2, cox1, cox2, atp8, atp6, cox3, nad3, nad6, cytb) are located in the majority strand (J strand) and 4 PCGs (nad5, nad4, nad4L, nad1) are located in the minority strand (N strand).
Table 2

The base features of tick mitochondrial genomes

Species

Mitochondrial genome base content

PCGs base content

Length

A + T (%)

A

T

AT-skew

G

C

GC-skew

Length

A + T (%)

A

T

AT-skew

G

C

GC-skew

Nuttalliella namaqua

14,425

78.59

5864

5472

0.035

1097

1992

− 0.290

10,792

78.64

3756

4731

− 0.115

1150

1155

− 0.002

Argas africolumbae

14,440

73.35

5579

5013

0.053

1311

2537

− 0.319

10,951

72.64

3327

4628

− 0.164

1408

1588

− 0.060

Argas boueti

14,307

76.63

5768

5196

0.052

1152

2191

− 0.311

10,830

76.24

3660

4597

− 0.113

1214

1359

− 0.056

Argas brumpti

14,516

69.91

5094

5054

0.004

1326

3042

− 0.393

10,834

68.42

2926

4487

− 0.211

1571

1850

− 0.082

Argas lagenoplastis

14,478

72.64

5594

4923

0.064

1340

2621

− 0.323

10,864

71.76

3267

4529

− 0.162

1478

1590

− 0.037

Argas miniatus

14,416

74.16

5452

5239

0.020

1252

2473

− 0.328

10,820

73.56

3248

4711

− 0.184

1428

1433

− 0.002

Argas persicus

14,411

72.72

5427

5053

0.036

1264

2667

− 0.357

10,866

71.83

3217

4588

− 0.176

1502

1559

− 0.019

Argas striatus

14,485

76.22

5739

5302

0.040

1167

2277

− 0.322

10,844

75.89

3455

4774

− 0.160

1266

1349

− 0.032

Argas walkerae

14,437

74.36

5488

5247

0.022

1213

2489

− 0.345

10,865

73.65

3313

4689

− 0.172

1377

1486

− 0.038

Antricola mexicanus

14,415

74.60

5706

5047

0.061

1242

2418

− 0.321

10,813

73.80

3547

4433

− 0.111

1422

1410

0.004

Carios capensis

14,418

73.54

5491

5112

0.036

1195

2620

− 0.374

10,875

72.66

3389

4513

− 0.142

1406

1567

− 0.054

Carios faini

14,433

76.68

5902

5165

0.067

1096

2270

− 0.349

10,883

75.97

3677

4591

− 0.111

1259

1356

− 0.037

Ornithodoros brasiliensis

14,489

73.16

5653

4947

0.067

1251

2638

− 0.357

10,843

72.24

3371

4462

− 0.139

1442

1568

− 0.042

Ornithodoros compactus

14,400

72.14

5530

4858

0.065

1265

2747

− 0.369

10,890

71.21

3335

4420

− 0.140

1557

1578

− 0.007

Ornithodoros coriaceus

14,423

69.75

5468

4592

0.087

1295

3068

− 0.406

10,917

67.90

3192

4221

− 0.139

1585

1919

− 0.095

Ornithodoros costalis

14,442

72.32

5343

5101

0.023

1285

2713

− 0.357

10,903

71.26

3277

4493

− 0.156

1460

1673

− 0.068

Ornithodoros hermsi

14,430

71.97

5368

5017

0.034

1348

2697

− 0.333

10,913

71.05

3306

4448

− 0.147

1520

1639

− 0.038

Ornithodoros moubata

14,398

72.26

5548

4856

0.067

1240

2754

− 0.379

10,885

71.36

3344

4423

− 0.139

1542

1576

− 0.011

Ornithodoros parkeri

14,437

74.45

5724

5024

0.065

1262

2427

− 0.316

10,868

73.94

3450

4586

− 0.141

1427

1405

0.008

Ornithodoros porcinus

14,378

70.98

5405

4801

0.059

1346

2826

− 0.355

10,876

70.11

3251

4374

− 0.147

1625

1626

0.000

Ornithodoros rostratus

14,452

72.96

5533

5011

0.050

1304

2604

− 0.333

10,836

72.16

3393

4426

− 0.132

1445

1572

− 0.042

Ornithodoros savignyi

14,401

65.23

5461

3933

0.163

1263

3744

− 0.496

10,889

63.59

3054

3870

− 0.118

1807

2158

− 0.089

Ornithodoros sonrai

14,430

74.02

5383

5298

0.008

1249

2500

− 0.334

10,866

73.23

3300

4657

− 0.171

1413

1496

− 0.029

Ornithodoros tholozani

14,407

69.34

5138

4852

0.029

1425

2992

− 0.355

10,880

67.87

3135

4249

− 0.151

1618

1878

− 0.074

Ornithodoros turicata

14,458

73.27

5653

4941

0.067

1325

2539

− 0.314

10,868

72.41

3398

4472

− 0.136

1461

1537

− 0.025

Ornithodoros zumpti

14,438

69.61

5063

4988

0.007

1452

2935

− 0.338

10,856

68.38

3129

4294

− 0.157

1635

1798

− 0.047

Otobius megnini

14,430

74.85

5609

5192

0.039

1172

2457

− 0.354

10,821

73.83

3408

4581

− 0.147

1355

1477

− 0.043

Nothoaspis amazoniensis

14,416

72.93

5671

4842

0.079

1172

2731

− 0.399

10,851

71.86

3488

4309

− 0.105

1447

1607

− 0.052

Ixodes hexagonus

14,539

72.66

5457

5107

0.033

1260

2715

− 0.366

10,826

71.13

3235

4465

− 0.160

1428

1698

− 0.086

Ixodes holocyclus

15,007

77.38

5728

5884

− 0.013

1266

2129

− 0.254

10,862

76.39

3524

4773

− 0.151

1305

1260

0.018

Ixodes pavlovskyi

14,575

78.09

5529

5852

− 0.028

1177

2017

− 0.263

10,888

77.24

3509

4901

− 0.166

1224

1254

− 0.012

Ixodes persulcatus

14,539

77.35

5496

5750

− 0.023

1202

2091

− 0.270

10,769

76.63

3456

4796

− 0.162

1217

1300

− 0.033

Ixodes ricinus

14,566

78.66

5594

5864

− 0.024

1147

1961

− 0.262

10,813

77.99

3537

4896

− 0.161

1155

1225

− 0.029

Ixodes tasmani

15,227

77.92

5936

5929

0.001

1200

2162

− 0.286

10,765

77.14

3549

4755

− 0.145

1207

1254

− 0.019

Ixodes uriae

15,053

74.79

5667

5591

0.007

1275

2520

− 0.328

10,837

73.75

3439

4553

− 0.139

1386

1459

− 0.026

Amblyomma americanum

14,709

76.78

5478

5816

− 0.030

1458

1957

− 0.146

10,811

76.68

3544

4746

− 0.145

1190

1331

− 0.056

Amblyomma cajennense

14,780

75.96

5444

5783

− 0.030

1488

2064

− 0.162

10,840

75.60

3468

4727

− 0.154

1251

1394

− 0.054

Amblyomma elaphense

14,627

80.45

5696

6072

− 0.032

1234

1625

− 0.137

10,815

80.46

3737

4965

− 0.141

1016

1097

− 0.038

Amblyomma fimbriatum

14,705

77.67

5601

5820

− 0.019

1385

1899

− 0.157

10,874

77.19

3600

4794

− 0.142

1155

1325

− 0.069

Amblyomma sculptum

14,780

76.10

5454

5794

− 0.030

1482

2050

− 0.161

10,840

75.80

3477

4740

− 0.154

1243

1380

− 0.052

Amblyomma sphenodonti

14,772

77.78

5585

5905

− 0.028

1438

1844

− 0.124

10,874

77.67

3595

4851

− 0.149

1169

1259

− 0.037

Amblyomma triguttatum

14,740

78.40

5653

5903

− 0.022

1381

1803

− 0.133

10,876

78.29

3607

4908

− 0.153

1098

1263

− 0.070

Rhipicephalus australis

14,891

79.89

5789

6108

− 0.027

1307

1686

− 0.127

10,828

79.72

3739

4893

− 0.134

1037

1159

− 0.056

Rhipicephalus geigyi

14,948

80.37

5886

6127

− 0.020

1293

1642

− 0.119

10,831

80.47

3828

4888

− 0.122

1023

1092

− 0.033

Rhipicephalus microplus

15,167

79.73

5888

6204

− 0.026

1376

1698

− 0.105

10,824

79.31

3711

4873

− 0.135

1074

1165

− 0.041

Rhipicephalus sanguineus

14,714

77.36

5545

5838

− 0.026

1478

1853

− 0.113

10,814

77.42

3641

4731

− 0.130

1119

1323

− 0.084

Rhipicephalus turanicus

14,717

77.81

5561

5890

− 0.029

1452

1814

− 0.111

10,811

77.88

3666

4754

− 0.129

1108

1283

− 0.073

Rhipicentor nuttalli

14,779

78.27

5581

5987

− 0.035

1380

1831

− 0.140

10,797

78.22

3598

4847

− 0.148

1090

1262

− 0.073

Dermacentor everestianus

15,191

78.80

5806

6165

− 0.030

1436

1784

− 0.108

10,520

78.33

3459

4781

− 0.160

1124

1151

− 0.012

Dermacentor nitens

14,839

77.42

5640

5849

− 0.018

1410

1940

− 0.158

10,520

77.16

3439

4678

− 0.153

1166

1237

− 0.030

Dermacentor nuttalli

15,086

78.93

5871

6036

− 0.014

1324

1855

− 0.167

10,877

78.80

3709

4862

− 0.135

1073

1223

− 0.065

Dermacentor silvarum

14,945

78.78

5812

5961

− 0.013

1336

1836

− 0.158

10,844

78.67

3680

4851

− 0.137

1077

1236

− 0.069

Bothriocroton concolor

14,809

75.14

5443

5685

− 0.022

1607

2704

− 0.254

10,910

74.44

3495

4626

− 0.139

1313

1476

− 0.058

Bothriocroton undatum

14,769

76.90

5464

5893

− 0.038

1540

1872

− 0.097

10,895

76.10

3546

4745

− 0.145

1237

1367

− 0.050

Haemaphysalis bancrofti

14,673

78.35

5687

5810

− 0.011

1381

1795

− 0.130

10,819

78.38

3712

4768

− 0.125

1137

1202

− 0.028

Haemaphysalis concinna

14,675

77.98

5665

5778

− 0.010

1350

1879

− 0.164

10,856

77.92

3692

4767

− 0.127

1129

1268

− 0.058

Haemaphysalis flava

14,689

76.88

5541

5752

− 0.019

1498

1898

− 0.118

10,824

76.62

3601

4692

− 0.132

1213

1318

− 0.041

Haemaphysalis formosensis

14,676

78.29

5667

5823

− 0.014

1369

1817

− 0.141

10,833

78.20

3703

4768

− 0.126

1130

1232

− 0.043

Haemaphysalis hystricis

14,716

77.22

5646

5718

− 0.006

1448

1904

− 0.136

10,820

76.77

3592

4714

− 0.135

1187

1327

− 0.056

Haemaphysalis japonica

14,685

77.58

5605

5788

− 0.016

1435

1845

− 0.125

10,833

77.60

3656

4750

− 0.130

1149

1278

− 0.053

Haemaphysalis longicornis

14,718

77.16

5618

5738

− 0.011

1440

1922

− 0.143

10,795

76.79

3595

4695

− 0.133

1190

1315

− 0.050

Haemaphysalis parva

14,846

78.82

5806

5896

− 0.008

1342

1802

− 0.146

10,822

78.76

3685

4838

− 0.135

1088

1211

− 0.054

Hyalomma asiaticum

14,720

78.18

5600

5908

− 0.027

1374

1838

− 0.144

10,913

78.04

3663

4853

− 0.140

1116

1281

− 0.069

Metazoan mt-genomes usually have a higher adenine–thymine (AT) base content [22, 32, 42]. Analysis of base usage in tick mt-genomes showed that the AT content ranged from 80.45% (Amblyomma elaphense) to 65.23% (Ornithodoros savignyi) with an average content of 75.51% (Table 2). The difference in base usage within the family is generally small [48, 49], but the largest difference in AT content between soft and hard ticks reached 15.22%. This phenomenon may be attributed to the lower AT content in Ornithodoros species, which is 71.65% on average and is considerably lower than the average AT content of ticks. It is possible that the difference in AT content is related to the size of the NCRs, the repeat sequences and the complexity of the gene structure [50, 51, 52]. Additionally, the different living environments and survival strategies of soft and hard ticks influence base usage [53].

The base skew of tick mt-genomes is unique. In general, AT-skew is positive and guanine–cytosine (GC) skew is negative in the metazoan mt-genomes [54, 55], whereas the AT-skew of soft and hard ticks is different. In soft ticks, the AT-skew is positive. In hard ticks, the positive AT-skew is only observed in I. hexagonus and Ixodes uriae, whereas in other hard ticks, the AT skew is negative. In both soft and hard ticks, the average AT-skew is 0.0504 and − 0.0187, respectively, and the average GC-skew is − 0.3532 and − 0.1701, respectively; notably the difference in AT-skew is smaller than that in GC-skew (Table 2).

Protein-coding genes and codon usage

The PCGs in mt-genomes encode several subunits: NADH dehydrogenase subunit, cytochrome c oxidase subunit, ATPase subunit and cytochrome b, which are mainly involved in the oxidative phosphorylation of cells [56]. The average length of mitochondrial PCGs in soft and hard ticks is 10,866 and 10,819 bp, respectively (Table 2). The AT content in PCGs of the soft ticks (71.81%) and hard ticks (77.36%) is also lower than that in the complete mt-genome level. The lowest AT content in PCGs is in Rhipicephalus geigyi (63.59%) and the highest is in Ornithodoros savignyi (80.47%). The base skew in PCGs of ticks is negative, and the skewness characteristics are similar in both soft and hard ticks. No obvious differences have been observed in different genera of ticks, and the level of AT-skew is higher than that of the GC-skew. The mitochondrial PCGs are involved in oxidative phosphorylation and energy production; therefore, the structure is relatively conserved, and the difference in base usage is lower than that of the whole genome. In addition, the higher AT content of tick mt-genomes may be influenced by gene sequences, with there being only a 0.11–1.64% gap between the AT content of PCGs and the whole mt-genome (Table 2).

Similarly to insects, ticks usually adopt the “ATN”-type codon as the initial codon in PCGs [31, 32, 33, 34, 57]. Other codons, including some special initiation codons, can be edited to conventional start codons during transcription [58, 59, 60], which may help reduce the gene spacer region and overlapping region and not affect the normal translation of proteins [61]. The termination codons of ticks are mainly TAA and TAG [31, 34] and sometimes use “T” or “TA”, which may be converted into a complete termination codon by polyadenylation after translation [62, 63].

Transfer RNA and ribosomal RNA genes

The mitochondrial tRNA gene length in ticks ranges from 50 to 90 bp, and most tRNA genes have a complete cloverleaf structure, including four principal structures: amino acid acceptor (AA) arm; TΨC (T) arm; anticodon (AC) arm; and dihydrouridine (DHU) arm [64]. No DHU arm structure exists in trnS1 of the tick mt-genomes; a similar phenomenon is also observed in insects [20, 65, 66]. The distance from the anti-codon to the CCA terminus is hence maintained through the inverted L structure, which helps complete the gene function [67]. Additionally, base mismatches frequently occur in the secondary structure of the tick tRNA genes [68, 69]. The mismatch types are mainly G-U, U-G and U-U, which are similar to those of other insects [62, 70]. These mismatches may be related to the evolutionary mutations and may not affect the function of tRNA genes due to being corrected later [71].

The mitochondrial rRNA genes display a complex functional structure with a relatively slow evolution rate; these have long been used as population genetics markers [72]. The tick mt-genomes contain two single copy 12S and 16S rRNA genes. In recent years, the mitochondrial 12S and 16S rRNA genes have been extensively used as genetic targets in phylogenetic research of ticks [27, 36, 73]. Due to gene rearrangement, the position of the rRNA genes shifts in ticks, whereas the gene order and the location in the N strand remain unchanged. Previous reports have shown that the average genetic distance of different tick taxa was still very slight even after tens of million years of evolution. Slow nucleotide variation in rRNA genes may be caused by strict structural and functional limitations [27]. Therefore, to this end, using combined PCGs and rRNA genes to reconstruct the phylogenetic relationships and resolve the controversial genealogy of soft ticks may be one of the best methods [19].

Gene rearrangement

The mt-genomes exhibit higher rearrangement potential, but in general, the gene arrangement most likely occurs at a higher taxonomic level, which can provide insights for systematic classification at higher taxa [74, 75]. There are three types of changes in tRNA gene position: shuffling (local rearrangements), translocation (cross-gene displacement) and inversion (change in the encoding or transcriptional direction) [76]. The rearrangements in the tick mt-genomes are mainly divided into two patterns (Fig. 1). The arrangement of the soft ticks and N. namaqua show more similarity with that in the genus Drosophila [77, 78], which represents the ancestral arrangement in insects. In detail, shuffle (minor rearrangement of the gene) is observed only in the trnL2 gene [48], which is moved from cox1–cox2 to nad1–trnL1 with the coding strand changed from the J strand to the N strand, whereas other genes remain unchanged. In hard ticks, a major gene rearrangement is observed in a large gene region (trnF-nad5-trnH-nad4–nad4L-trnT-trnP-cytb-trnS2), which is moved from trnE-nad1 to trnQ-trnM. The major gene rearrangement involves the translocation of three tRNA genes (trnL1, trnL2 and trnC) and the inversion of the trnC gene. The patterns in gene rearrangement might be associated with the rate of molecular evolution, and the different rearrangements between soft and hard ticks may have occurred from a very early period [74, 79].
Fig. 1

Gene rearrangement in the tick mitochondrial genomes

Non-coding regions

In insects, the transcription termination of the mitochondrial NCRs is realized by combining transcription termination factors [80]. In ticks, the mt-genome features a compact structure, which usually contains two conserved site-specific NCRs and several genus-specific conserved NCRs [19, 27, 28, 34, 39]. The larger NCR is located between rrnS–trnI and is approximately 200–400 bp long (Table 3). The length of NCR in soft and hard ticks averages 274 and 261 bp, respectively. The longest NCR is observed in species of the genus Ixodes with an average length of 336 bp. The shortest NCR is only 82 bp in Rhipicentor nuttalli, and the notably short NCR may be attributed to assembly errors. The other conservative NCRs are located between rrnL and trnV, and the length of this region varies greatly. The shortest is only 155 bp in Amblyomma triguttatum, and the longest reaches 565 bp in Argas lagenoplastis. The difference in the average length between the soft and hard ticks is only 1 bp (251 and 252 bp, respectively). The length difference of this type of NCR in ticks is often significant within a genus, except for the genus Haemaphysalis, which shares a similar length of 150 bp. In addition to the abovementioned two NCRs, there is another NCR located between trnL1 and trnC in hard ticks. It is possible that the two related genes (trnL1 and trnC) may be involved in gene rearrangement, and hence the NCRs may act as a fragment insertion and play specific roles during gene transcription [81, 82]. Additionally, some ticks also exhibit other NCRs, such as Dermacentor nitens and A. triguttatum, which display five NCRs. These NCRs may play important roles in protecting gene function during gene rearrangement, and there are currently four hypotheses to explain the formation of these particular NCRs [27, 33, 41, 74].
Table 3

Distribution of NCRs in the tick mitochondrial genomes

Species

Conservative noncoding region

Nonconservative noncoding region

Length

Position

Length

Position

Length

Position

Length

Position

Length

Position

Nuttalliella namaqua

182

rrnL–trnV

229

rrnS–trnI

  

361

trnF-nad5

  

Argas africolumbae

185

rrnL–trnV

293

rrnS–trnI

      

Argas brumpti

184

rrnL–trnV

280

rrnS–trnI

      

Argas boueti

553

rrnL–trnV

279

rrnS–trnI

      

Argas lagenoplastis

565

rrnL–trnV

238

rrnS–trnI

      

Argas miniatus

178

rrnL–trnV

273

rrnS–trnI

      

Argas persicus

179

rrnL–trnV

248

rrnS–trnI

      

Argas striatus

182

rrnL–trnV

295

rrnS–trnI

  

112

nad2-trnW

  

Argas walkerae

177

rrnL–trnV

272

rrnS–trnI

      

Antricola mexicanus

189

rrnL–trnV

264

rrnS–trnI

  

104

nad2-trnW

  

Carios capensis

177

rrnL–trnV

308

rrnS–trnI

      

Carios faini

188

rrnL–trnV

259

rrnS–trnI

      

Nothoaspis amazoniensis

186

rrnL–trnV

264

rrnS–trnI

  

124

trnF-nad5

  

Ornithodoros brasiliensis

193

rrnL–trnV

294

rrnS–trnI

      

Ornithodoros compactus

176

rrnL–trnV

267

rrnS–trnI

      

Ornithodoros coriaceus

189

rrnL–trnV

283

rrnS–trnI

      

Ornithodoros costalis

190

rrnL–trnV

254

rrnS–trnI

      

Ornithodoros hermsi

188

rrnL–trnV

269

rrnS–trnI

      

Ornithodoros moubata

176

rrnL–trnV

283

rrnS–trnI

      

Ornithodoros parkeri

192

rrnL–trnV

257

rrnS–trnI

      

Ornithodoros porcinus

174

rrnL–trnV

265

rrnS–trnI

      

Ornithodoros tratus

190

rrnL–trnV

289

rrnS–trnI

      

Ornithodoros avignyi

181

rrnL–trnV

266

rrnS–trnI

  

125

trnF-nad5

  

Ornithodoros sonrai

563

rrnL–trnV

255

rrnS–trnI

      

Ornithodoros tholozani

554

rrnL–trnV

292

rrnS–trnI

      

Ornithodoros turicata

189

rrnL–trnV

286

rrnS–trnI

  

122

nad4–nad4L

  

Ornithodoros zumpti

564

rrnL–trnV

271

rrnS–trnI

      

Otobius megnini

195

rrnL–trnV

290

rrnS–trnI

      

Ixodes hexagonus

189

rrnL–trnV

268

rrnS–trnI

      

Ixodes holocyclus

335

rrnL–trnV

349

rrnS–trnI

335

trnL1–trnC

    

Ixodes pavlovskyi

193

rrnL–trnV

351

rrnS–trnI

      

Ixodes persulcatus

183

rrnL–trnV

282

rrnS–trnI

  

122

trnH-nad4

  

Ixodes ricinus

197

rrnL–trnV

351

rrnS–trnI

  

107

nad2-trnW

  

Ixodes tasmani

481

rrnL–trnV

366

rrnS–trnI

  

145

nad4–nad4L

  

Ixodes uriae

354

rrnL–trnV

385

rrnS–trnI

354

trnL1–trnC

    

Amblyomma americanum

169

rrnL–trnV

237

rrnS–trnI

306

trnL1–trnC

    

Amblyomma cajennense

172

rrnL–trnV

283

rrnS–trnI

306

trnL1–trnC

    

Amblyomma elaphense

515

rrnL–trnV

238

rrnS–trnI

299

trnL1–trnC

127

nad2-trnW

  

Amblyomma fimbriatum

165

rrnL–trnV

230

rrnS–trnI

274

trnL1–trnC

    

Amblyomma sculptum

172

rrnL–trnV

247

rrnS–trnI

306

trnL1–trnC

    

Amblyommas phenodonti

158

rrnL–trnV

297

rrnS–trnI

328

trnL1–trnC

    

Amblyomma triguttatum

155

rrnL–trnV

264

rrnS–trnI

307

trnL1–trnC

123

nad2-trnW

185

trnF-nad5

Rhipicephalus australis

157

rrnL–trnV

265

rrnS–trnI

305

trnL1–trnC

    

Rhipicephalus geigyi

541

rrnL–trnV

244

rrnS–trnI

303

trnL1–trnC

241

trnE-nad1

  

Rhipicephalus microplus

561

rrnL–trnV

264

rrnS–trnI

307

trnL1–trnC

124

nad2-trnW

  

Rhipicephalus sanguineus

157

rrnL–trnV

233

rrnS–trnI

303

trnL1–trnC

    

Rhipicephalus turanicus

159

rrnL–trnV

240

rrnS–trnI

304

trnL1–trnC

    

Rhipicentor nuttalli

157

rrnL–trnV

82

rrnS–trnI

308

trnL1–trnC

285

trnE-nad1

  

Dermacentor everestianus

569

rrnL–trnV

292

rrnS–trnI

306

trnL1–trnC

322

trnE-nad1

119

trnQ-trnF

Dermacentor nitens

556

rrnL–trnV

235

rrnS–trnI

307

trnL1–trnC

168

trnE-nad1

166

trnQ-trnF

Dermacentor nuttalli

556

rrnL–trnV

235

rrnS–trnI

307

trnL1–trnC

168

trnE-nad1

  

Dermacentor silvarum

556

rrnL–trnV

232

rrnS–trnI

307

trnL1–trnC

167

trnE-nad1

  

Bothriocroton concolor

162

rrnL–trnV

247

rrnS–trnI

311

trnL1–trnC

    

Bothriocroton undatum

157

rrnL–trnV

230

rrnS–trnI

310

trnL1–trnC

113

nad4–nad4L

  

Haemaphysalis bancrofti

163

rrnL–trnV

262

rrnS–trnI

307

trnL1–trnC

    

Haemaphysalis concinna

161

rrnL–trnV

230

rrnS–trnI

311

trnL1–trnC

    

Haemaphysalis flava

158

rrnL–trnV

228

rrnS–trnI

311

trnL1–trnC

    

Haemaphysalis formosensis

160

rrnL–trnV

265

rrnS–trnI

311

trnL1–trnC

    

Haemaphysalis hystricis

162

rrnL–trnV

228

rrnS–trnI

309

trnL1–trnC

    

Haemaphysalis japonica

156

rrnL–trnV

229

rrnS–trnI

310

trnL1–trnC

    

Haemaphysalis longicornis

159

rrnL–trnV

240

rrnS–trnI

309

trnL1–trnC

    

Haemaphysalis parva

158

rrnL–trnV

252

rrnS–trnI

318

trnL1–trnC

211

trnE-nad1

  

Hyalomma asiaticum

160

rrnL–trnV

287

rrnS–trnI

307

trnL1–trnC

    

It is noteworthy that a common marker sequence is found in the NCRs of the tick mt-genomes, which are formed by degeneration during evolution and named the “Tick-box” [39]. This conserved sequence is located at the boundary of two gene rearrangement regions in the tick mt-genomes, which may be affected by the arrangement of mitochondrial genes in ticks [27, 36]. However, this sequence is not discarded during long-term evolution and likely functions as a transcriptional maturation or termination signal. Annotation of these sequences can help identify hidden molecular functions, which is useful for genetic analysis of higher taxa [39].

Mt-genome phylogeny

The mt-genomes play an important role in the molecular systematics and origin of ticks. In the present study, 13 PCGs and 2 rRNA genes from the MITOS analysis results of all available tick complete mt-genomes were used to construct a phylogenetic tree through the maximum likelihood method (ML) [83]. MEGA v.6.0 for Windows (https://www.megasoftware.net/) was first used for alignment and splicing, and then the IQ-Tree online server (http://iqtree.cibiv.univie.ac.at/) was used for establishment of the phylogenetic tree with 1000 bootstrap replications [84, 85]. The phylogenetic tree was constructed using the nucleotide sequences (12,150 bp) of 63 tick species. Limulus polyphemus (NC003057) was used as the outgroup and the percentage of the bootstrap support is given at each node.

In soft ticks, some species in Argas and Ornithodoros have previously been phylogenetically analyzed using 10 mitochondrial genes [27]. Recently, several new mt-genomes have become available for the genus Argas including Ar. boueti, Ar. brumpti, Ar. persicus, Ar. striatus and Ar. walkerae, and for the genus Ornithodoros including O. compactus, O. coriaceus, O. costalis, O. hermsi, O. parkeri, O. sonrai, O. tholozani, O. turicata and O. zumpti. These were incorporated into the present phylogenetic analysis using 13 PCGs and 2 rRNA genes. Results yielded ambiguous species delimitation and phylogenetic relationships of these two genera (Fig. 2), which are complicated with the existing of monophyly, paraphyly, or polyphyly phenomena. Possibly, the concatenation of present genes with other informative genes help a better phylogenetic resolution. The tick Ar. boueti was clustered within the subfamily Ornithodorinae with a minimum bootstrap of 51%. This clustering may influence the location of other genera, including Antricola, Nothoaspis and Carios. Additionally, the tick Carios faini was clustered first with Antricola mexicanus and Nothoaspis amazoniensis, as well as with C. capensis. Subsequently, the incongruence was apparent between phylogenetic configurations and morphological characterizations, which requires further evidential confirmation.
Fig. 2

The phylogenetic tree shows the evolutionary relationships among tick species based on the complete mt-genome (13 PCGs and 2 rRNA). The tree was constructed using ML analysis of the 13 PCGs and 2 rRNA nucleotide sequences (12,150 bp) of 63 tick species. Limulus polyphemus (NC003057) is the outgroup. In the phylogenetic tree, the scale-bar represents the number of expected changes per site. Percentage of the bootstrap support is given at each node. The gray, red and green areas indicate species of Nuttalliellidae, Argasidae and Ixodidae, respectively. GenBank accession numbers are listed in Table 1

In hard ticks, Rhipicentor nuttalli was clustered with species within the genus Rhipicephalus, which provided corroborative evidence for their close relationship. Although most clades among the hard ticks in different genera showed moderate support and the clustering of the tick lineages were similar to previous studies [25], some particular species including Amblyomma elaphense, Am. spnenodonti and Hylomma asiaticum require total evidence support. The only tick in the family Nuttalliellidae, Nuttalliella namaqua, is the sister group of the family Ixodidae, which is similar to the previous mt-genome phylogenetic analysis [27].

ML analysis of mitochondrial genes is widely used in the molecular systematics of ticks [19, 29, 34]. Although there were some changes in our results, the phylogenetic branching results were similar to those obtained based on ten PCGs [27]. This finding suggests that the combination of more mitochondrial genes may provide more robust evidence for tick taxonomy. Different mitochondrial genes or sites usually have different evolutionary rates, which may affect the topological structure and lower the support rate of the phylogenetic tree, thereby affecting the reliability of phylogenetic results [86, 87]. When the data matrix is partitioned according to both genes and coding sites, the phylogenetic calculation will be difficult to converge, which prevents phylogenetic analysis using a large number of mitochondrial genes simultaneously [88]. Thus, most studies usually adopt different PCGs or gene loci with proper partition, and the calculation can be optimized by modifying gene loci and selecting appropriate phylogenetic tree methods [89, 90]. Previous research based on morphological and nuclear rRNA data supported the cladistic results of Klompen et al. [19, 91]. The results obtained by combining multiple mitochondrial PCGs are partly different from those obtained using nuclear rRNA alone. Although some genera clades may change with the increasing number of mt-genomes, most genera remain clustered in the same clades [31, 32, 33, 34] (Fig. 2). Molecular evidence based on the mt-genomes largely does not disagree with the recognized phylogenetic status of many tick species [12]. The description of new species and the characterization of new genetic markers will serve to systematically classify ticks [92].

Perspectives and future directions

Ticks and mites of the subphylum Chelicerata account for 53% of parasitic arthropods, which cause substantial losses in agriculture and human health [93]. In recent years, the mt-genomes have shown significant advantages and have been widely used in taxonomic and phylogenetic research [19, 36, 94]. However, challenges still exist in systematic investigations on the tick mt-genomes. The number of available mt-genomes remains limited, as only 63 complete tick mt-genomes are presently available in the NCBI database; the complete mt-genomes of approximately 93% of tick species remain unexplored. The absence of complete tick mt-genomes, especially for some soft ticks with geographical and taxonomic bias will undoubtedly hinder the reliability of the cladistics (phylogenetic) of the species within subclass Acari, order Ixodida. The different evolution rates of mitochondrial genes may lead to variation in gene length of many species, and different sequences. It should be mentioned that the annotation methods would be also able to affect the sequence assembly [94, 95]. Furthermore, the mitochondrion is essential for energy metabolism and temperature regulation in metazoans [96]. Previous studies have shown that the mitochondrial genes have significantly different transcriptional activities during the freezing or anoxia adaptation and organism development [97, 98, 99, 100]. The differential expression of specific functional genes may attribute to adaptive evolution [101]. Finally, no genes are encoded by the NCRs; therefore, NCRs receive less selection pressure during the process of evolution and are prone to base mutations [102]. NCRs can regulate gene expression and have many multiple tandem repeats and complex structures; hence, NCRs are more difficult to sequence [18, 102]. The tick mt-genomes are characterized by two typical conserved NCRs, but there are significant differences in the length, number, and location among the different species.

Due to the above challenges, several important directions for future research on the tick mt-genomes were prospected. First, more complete mt-genome sequences, combing with morphological characteristics and nucleus sequences, are required to integrately illuminate the phylogenetic relationships within Ixodida. Secondly, through extensive practices, mt-genome annotation methods are constantly improving [94]. However, annotation of a genome is still challenging, as different annotation methods may result in annotation bias or errors [102]. Hence, it is important to use unified annotation methods to help reduce or eliminate incorrect sequencing errors, and more attention should be given to NCRs. Thirdly, the functions and physiological relevance of the tick mitochondrial genes, including mitochondrial transcription, proteomics analysis of mitochondrial proteins, and epigenetic regulation in mitochondria under environmental or physiological stress, warrant further investigation. Finally, it is of considerable practical and theoretical interest to determine whether insecticides and acaricides can act on tick mitochondrial PCGs, which have been previously proved in mites [103, 104]. This knowledge may provide new molecular biology information to further understand the genetic diversity of ticks, and shed light on novel strategies to control TBDs damage.

Conclusions

This study summarizes the basic features, including genomic structure, base difference and gene arrangement, of the tick mt-genomes available in the NCBI database. Research on tick mt-genomes has lagged behind that conducted in insects. Fortunately, an increasing number of mt-genomes have been published in recent years, and these have become important molecular markers for the phylogeny of ticks. Our study constructed a phylogenetic tree by maximum likelihood using 13 PCGs and 2 rRNA genes, and the results further supported the phylogenetic status of many tick species. Undoubtedly, the application of polygenic joint analysis and appropriate software will be widely applied in solving the phylogenetic and genetic evolution of diverse taxa of ticks, which will be of profound significance for the rapid identification of tick species.

Notes

Acknowledgements

We are very grateful to Dr Abolfazl Masoudi and Yankai Zhang from our laboratory for reviewing the manuscript and providing valuable comments.

Authors’ contributions

ZY and JL conceived the study. TW drafted the manuscript. JL revised the manuscript. SZ and TP participated in data collection and helped to revise the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (31672365), the Youth Top Talent Support Program of Hebei Province to ZY, the Natural Science Foundation of Hebei Province (C2019205064), the Natural Science Research Programmes of the Educational Department of Hebei Province (BJ2016032), the Financial Assistance for the Introduction of Overseas Researchers (C20190350) and the Science Foundation of Hebei Normal University (L2018J04).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    Kaufman WR. Ticks: physiological aspects with implications for pathogen transmission. Ticks Tick Borne Dis. 2010;1:11–22.CrossRefGoogle Scholar
  2. 2.
    Islam MS, You MJ. Expression patterns of host inflammatory cytokine genes during infestation with Haemaphysalis longicornis, a zoonotic vector, in blood-sucking periods. Korean J Parasitol. 2018;56:53–9.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Jongejan F, Uilenberg G. The global importance of ticks. Parasitology. 2004;129:S3–14.PubMedCrossRefPubMedCentralGoogle Scholar
  4. 4.
    Ros-Garcã- A, M’Ghirbi Y, Hurtado A, Bouattour A. Prevalence and genetic diversity of piroplasm species in horses and ticks from Tunisia. Infect Genet Evol. 2013;17:33–7.CrossRefGoogle Scholar
  5. 5.
    Parola P, Paddock CD, Socolovschi C, Labruna MB, Mediannikov O, Kernif T, et al. Update on tick-borne rickettsioses around the world: a geographic approach. Clin Microbiol Rev. 2013;26:657–702.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Takahashi T, Maeda K, Suzuki T, Ishido A. The first identification and retrospective study of severe fever with thrombocytopenia syndrome in Japan. J Infect Dis. 2014;209:816–27.PubMedCrossRefPubMedCentralGoogle Scholar
  7. 7.
    Qin XR, Han FJ, Luo LM, Zhao FM, Han HJ, Zhang ZT, et al. Anaplasma species detected in Haemaphysalis longicornis tick from china. Ticks Tick Borne Dis. 2018;9:840–3.PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Zhang RL, Huang ZD, Yu GF, Zhang Z. Characterization of microbiota diversity of field-collected Haemaphysalis longicornis (Acari: Ixodidae) with regard to sex and blood meals. J Basic Microbiol. 2019;59:215–23.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Burnard D, Weaver H, Gillett A, Loader J, Flanagan C, Polkinghorne A. Novel Chlamydiales genotypes identified in ticks from australian wildlife. Parasit Vectors. 2017;10:46.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Wang ZD, Wang B, Wei F, Han SZ, Zhang L, Yang ZT, et al. A new segmented virus associated with human febrile illness in China. N Engl J Med. 2019;380:2116–25.PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Scott JD, Foley JE. Detection of Borrelia americana in the avian coastal tick, Ixodes auritulus (Acari: Ixodidae), collected from a bird captured in Canada. J Anim Sci. 2016;6:207–16.Google Scholar
  12. 12.
    Guglielmone AA, Robbins RG, Apanaskevich DA, Petney TN, Barker SC. The Argasidae, Ixodidae and Nuttalliellidae (Acari: Ixodida) of the world: a list of valid species names. Zootaxa. 2010;2528:1–28.CrossRefGoogle Scholar
  13. 13.
    Chen Z, Yang X, Bu F, Yang XH, Yang XL, Liu JZ. Ticks (Acari: Ixodoidea: Argasidae, Ixodidae) of China. Exp Appl Acarol. 2010;51:393–404.PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Fernandes KK, Bittencourt VP, Roberts DW. Perspectives on the potential of entomopathogenic fungi in biological control of ticks. Exp Parasitol. 2012;130:300–5.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    McKeever DJ. Bovine immunity-a driver for diversity in Theileria parasites? Trends Parasitol. 2009;25:269–76.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Nava S, Beati L, Labruna MB, Cáceres AG, Mangold AJ, Guglielmone AA. Reassessment of the taxonomic status of Amblyomma cajennense, with the description of three new species, Amblyomma tonelliae n. sp. Amblyomma interandinum n. sp. and Amblyomma patinoi n. sp. and reinstatement of Amblyomma mixtum, and Amblyomma sculptum. Ticks Tick Borne Dis. 2014;5:252–76.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Nuttall GHF. Notes on ticks II. Parasitology. 1912;5:50–60.CrossRefGoogle Scholar
  18. 18.
    Mans BJ, Neitz AWH. Adaptation of ticks to a blood-feeding environment: evolution from a functional perspective. Insect Biochem Mol Biol. 2004;34:1–17.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Mans BJ, Featherston J, Kvas M, Pillay KA, Klerk DG, Pienaar R, et al. Argasid and ixodid systematics: implications for soft tick evolution and systematics, with a new argasid species list. Ticks Tick Borne Dis. 2019;10:219–40.PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Cameron SL. Insect mitochondrial genomics: implications for evolution and phylogeny. Annu Rev Entomol. 2014;59:95–117.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Simon S, Hadrys H. A comparative analysis of complete mitochondrial genomes among Hexapoda. Mol Phylogenet Evol. 2013;69:393–403.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Li K, Liang AP. Hemiptera mitochondrial control region: new sights into the structural organization, phylogenetic utility, and roles of tandem repetitions of the noncoding segment. Int J Mol Sci. 2018;19:1292.PubMedCentralCrossRefGoogle Scholar
  23. 23.
    Simonsen TJ, Zakharov EV, Djernaes M, Cotton AM, Vane-Wright RI, Sperling FAH. Phylogenetics and divergence times of Papilioninae (Lepidoptera) with special reference to the enigmatic genera Teinopalpus and Meandrusa. Cladistics. 2011;27:113–37.CrossRefGoogle Scholar
  24. 24.
    Ramakodi MP, Singh B, Wells JD, Guerrero F, Ray DA. A 454 sequencing approach to dipteran mitochondrial genome research. Genomics. 2015;105:53–60.PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Burnard D, Shao R. Mitochondrial genome analysis reveals intraspecific variation within Australian hard tick species. Ticks Tick Borne Dis. 2019;10:677–81.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Black WC, Roehrdanz RL. Mitochondrial gene order is not conserved in arthropods: prostriate and metastriate tick mitochondrial genomes. Mol Biol Evol. 1998;15:1772–85.PubMedCrossRefPubMedCentralGoogle Scholar
  27. 27.
    Burger TD, Shao R, Labruna MB, Barker SC. Molecular phylogeny of soft ticks (Ixodida: Argasidae) inferred from mitochondrial genome and nuclear rRNA sequences. Ticks Tick Borne Dis. 2014;5:195–207.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Burger TD, Shao R, Beati L, Miller H, Barker SC. Phylogenetic analysis of ticks (Acari: Ixodida) using mitochondrial genomes and nuclear rRNA genes indicates that the genus Amblyomma is polyphyletic. Mol Phylogenet Evol. 2012;64:45–55.PubMedCrossRefPubMedCentralGoogle Scholar
  29. 29.
    Burger TD, Shao R, Barker SC. Phylogenetic analysis of the mitochondrial genomes and nuclear rRNA genes of ticks reveals a deep phylogenetic structure within the genus Haemaphysalis and further elucidates the polyphyly of the genus Amblyomma with respect to Amblyomma sphenodonti and Amblyomma elaphense. Ticks Tick Borne Dis. 2013;4:265–74.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Burger TD, Shao R, Barker SC. Phylogenetic analysis of mitochondrial genome sequences indicates that the cattle tick, Rhipicephalus (Boophilus) microplus, contains a cryptic species. Mol Phylogenet Evol. 2014;76:241–53.PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    de Lima PHC, Barcelos RM, Klein RC, Vidiga PMP, Montandon CE, Fabres-Klein MH, et al. Sequencing and comparative analysis of the Amblyomma sculptum mitogenome. Vet Parasitol. 2017;247:121–8.PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    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.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Guo DH, Zhang Y, Fu X, Gao Y, Liu YT, Qiu JH, et al. Complete mitochondrial genomes of Dermacentor silvarum and comparative analyses with another hard tick Dermacentor nitens. Exp Parasitol. 2016;169:22–7.PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Liu ZQ, Liu YF, Kuermanali N, Wang DF, Chen SJ, Guo HL, et al. Sequencing of complete mitochondrial genomes confirms synonymization of Hyalomma asiaticum asiaticum and kozlovi, and advances phylogenetic hypotheses for the Ixodidae. PLoS One. 2018;13:e0197524.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Yu ZJ, Zhang SQ, Wang TH, Yang XL, Wang H, Liu JZ. The mitochondrial genome and phylogenetic analysis of the tick Dermacentor everestianus Hirst, 1926 (Acari: Ixodidae). Syst Appl Acarol. 2018;23:1313–21.Google Scholar
  36. 36.
    Williams-Newkirk AJ, Burroughs M, Changayil SS, Dasch GA. The mitochondrial genome of the lone star tick (Amblyomma americanum). Ticks Tick Borne Dis. 2015;6:793–801.PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Shao R, Barker SC, Mitani H, Aoki Y, Fukunaga M. Evolution of duplicate control regions in the mitochondrial genomes of metazoa: a case study with australasian ixodes ticks. Mol Biol Evol. 2005;22:620–9.PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Chang QC, Fu X, Song CL, Liu HB, Sun Y, Jia N, et al. The complete mitochondrial genome of Haemaphysalis concinna (ixodida: ixodidae). Mitochondrial DNA B. 2018;3:348–9.CrossRefGoogle Scholar
  39. 39.
    Montagna M, Sassera D, Griggio F, Epis S, Bandi C, Gissi CJ. Tick-box for 3′-end formation of mitochondrial transcripts in Ixodida, basal Chelicerates and Drosophila. PLoS One. 2012;7:e47538.PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Sui S, Yang Y, Fang ZQ, Wang JC, Wang J, Fu YQ, et al. Complete mitochondrial genome and phylogenetic analysis of Ixodes persulcatus (taiga tick). Mitochondrial DNA B. 2017;2:3–4.CrossRefGoogle Scholar
  41. 41.
    Shao R, Aoki Y, Mitani H, Tabuchi N, Barker SC, Fukunaga M. The mitochondrial genomes of soft ticks have an arrangement of genes that has remained unchanged for over 400 million years. Insect Mol Biol. 2004;13:219–24.PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Mitani H, Talbert A, Fukunaga M. New World relapsing fever Borrelia found in Ornithodoros porcinus ticks in central Tanzania. Microbiol Immunol. 2013;48:501–5.CrossRefGoogle Scholar
  43. 43.
    Fukunaga M, Ushijima Y, Aoki Y, Talbert A. Detection of Borrelia duttonii, a tick-borne relapsing fever agent in central Tanzania, within ticks by flagellin gene-based nested polymerase chain reaction. Vector Borne Zoonot. 2001;1:331–8.CrossRefGoogle Scholar
  44. 44.
    Mans BJ, Klerk D, Pienaar R, Castro MH, Latif AA. The mitochondrial genomes of Nuttalliella namaqua (Ixodoidea: Nuttalliellidae) and Argas africolumbae (Ixodoidae: Argasidae): estimation of divergence dates for the major tick lineages and reconstruction of ancestral blood-feeding characters. PLoS One. 2012;7:e49461.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Mans BJ, Klerk D, Pienaar R, Castro MH, Latif AA. Next-generation sequencing as means to retrieve tick systematic markers, with the focus on Nuttalliella namaqua (Ixodoidea: Nuttalliellidae). Ticks Tick Borne Dis. 2015;6:450–62.PubMedCrossRefPubMedCentralGoogle Scholar
  46. 46.
    Boore JL, Brown WM. Mitochondrial genomes of Galathealinum, Helobdella, and Platynereis: SEQUENCE and gene arrangement comparisons indicate that Pogonophora is not a phylum and Annelida and Arthropoda are not sister taxa. Mol Biol Evol. 2000;17:87–106.PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Cameron SL, Beckenbach AT, Dowton M, Whiting MF. Evidence from mitochondrial genomics on inter-ordinal relationships in insects. Arthropod Syst Phylo. 2006;64:27–34.Google Scholar
  48. 48.
    Cameron SL, Johnson KP, Whiting MF. The mitochondrial genome of the screamer louse Bothriometopus (Phthiraptera: Ischnocera): effects of extensive gene rearrangements on the evolution of the genome. J Mol Evol. 2007;65:589–604.PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Salvato P, Simonato M, Battisti A, Negrisolo E. The complete mitochondrial genome of the bag-shelter moth Ochrogaster lunifer (Lepidoptera, Notodontidae). BMC Genomics. 2008;9:331.PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    Hua J, Li M, Dong PZ, Cui Y, Bu WJ. Comparative and phylogenomic studies on the mitochondrial genomes of Pentatomomorpha (Insecta: Hemiptera: Heteroptera). BMC Genomics. 2008;9:610.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Li H, Liu HY, Song F, Shi AM, Zhou XG, Cai WZ. Comparative mitogenomic analysis of damsel bugs representing three tribes in the family Nabidae (Insecta: Hemiptera). PLoS ONE. 2012;7:e45925.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Yuan ML, Zhang QL, Guo ZL, Wang J, Shen YY. Comparative mitogenomic analysis of the superfamily Pentatomoidea (Insecta: Hemiptera: Heteroptera) and phylogenetic implications. BMC Genomics. 2015;16:460.PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Zhang YK, Zhang XY, Liu JZ. Ticks (Acari: Ixodoidea) in China: geographical distribution, host diversity, and specificity. Arch Insect Biochem. 2019.  https://doi.org/10.1002/arch.21544.CrossRefGoogle Scholar
  54. 54.
    Hassanin A, Nelly L, Deutsch J. Evidence for multiple reversals of asymmetric mutational constraints during the evolution of the mitochondrial genome of metazoa, and consequences for phylogenetic inferences. Syst Biol. 2005;54:277–98.PubMedCrossRefPubMedCentralGoogle Scholar
  55. 55.
    Kilpert F, Podsiadlowski L. The complete mitochondrial genome of the common sea slater, Ligia oceanica (Crustacea, Isopoda) bears a novel gene order and unusual control region features. BMC Genomics. 2006;7:241.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Boore JL. Survey and summary animal mitochondrial genomes. Nucleic Acids Res. 1999;27:1767–80.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Xin ZZ, Liu Y, Zhang DZ, Wang ZF, Liu QN. Comparative mitochondrial genome analysis of Spilarctia subcarnea and other noctuid insects. Int J Biol Macromol. 2018;107:121–8.PubMedCrossRefPubMedCentralGoogle Scholar
  58. 58.
    Weigl S, Testini G, Parisi A, Dantas-Torres F, Traversa D, Colwell DD, et al. The mitochondrial genome of the common cattle grub, Hypoderma lineatum. Med Vet Entomol. 2010;24:329–35.PubMedPubMedCentralGoogle Scholar
  59. 59.
    Behura SK, Lobo NF, Haas B, Debruyn B, Lovin DD, Shumway MF, et al. Complete sequences of mitochondria genomes of Aedes aegypti and Culex quinquefasciatus and comparative analysis of mitochondrial DNA fragments inserted in the nuclear genomes. Insect Biochem Mol Biol. 2011;41:770–7.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Sorokina SY, Andrianov BV, Mitrofanov VG. Complete mitochondrial genome sequence of Drosophila littoralis (Diptera: Drosophilidae). Comparative analysis of mitochondrial genomes in the Drosophila virilis group. Moscow Univ Biol Sci Bull. 2010;65:224–6.CrossRefGoogle Scholar
  61. 61.
    Clary DO, Wolstenholme DR. The mitochondrial DNA molecule of Drosophila yakuba: nucleotide sequence, gene organization, and genetic code. J Mol Evol. 1985;22:252–71.PubMedCrossRefPubMedCentralGoogle Scholar
  62. 62.
    Ojala D, Montoya J, Attardi G. tRNA punctuation model of RNA processing in human mitochondria. Nature. 1981;290:470–4.PubMedCrossRefPubMedCentralGoogle Scholar
  63. 63.
    Yokobori SI, Pääbo S. Polyadenylation creates the discriminator nucleotide of chicken mitochondrial tRNATyr. J Mol Biol. 1997;265:95–9.PubMedCrossRefPubMedCentralGoogle Scholar
  64. 64.
    Zhang M, Nie XP, Cao TW, Wang JP, Li T, Zhang XN, et al. The complete mitochondrial genome of the butterfly Apatura metis (Lepidoptera: Nymphalidae). Mol Biol Rep. 2012;39:6529–36.PubMedCrossRefPubMedCentralGoogle Scholar
  65. 65.
    Fang Y, Liang AP. The complete mitochondrial genome of Ugyops sp. (Hemiptera: Delphacidae). J Insect Sci. 2018;18:1–13.CrossRefGoogle Scholar
  66. 66.
    Wang Y, Cao JJ, Li WH. Complete mitochondrial genome of Suwallia teleckojensis (Plecoptera: Chloroperlidae) and implications for the higher phylogeny of stoneflies. Int J Mol Sci. 2018;19:680.PubMedCentralCrossRefGoogle Scholar
  67. 67.
    Hanada T, Suzuki T, Watanabe K. Translation activity of mitochondrial tRNA with unusual secondary structure. Nucleic Acids Symp Ser. 2000;44:249–50.CrossRefGoogle Scholar
  68. 68.
    Bae JS, Kim I, Sohn HD, Jin BR. The mitochondrial genome of the firefly, Pyrocoelia rufa: complete DNA sequence, genome organization, and phylogenetic analysis with other insects. Mol Phylogenet Evol. 2004;32:978–85.PubMedCrossRefPubMedCentralGoogle Scholar
  69. 69.
    Jühling F, Pütz J, Bernt M, Donath A, Middendorf M, Florentz C, et al. Improved systematic tRNA gene annotation allows new insights into the evolution of mitochondrial tRNA structures and into the mechanisms of mitochondrial genome rearrangements. Nucleic Acids Res. 2012;40:2833–45.PubMedCrossRefPubMedCentralGoogle Scholar
  70. 70.
    Breinholt JW, Kawahara AY. Phylotranscriptomics: saturated third codon positions radically influence the estimation of trees based on next-gen data. Genome Biol Evol. 2013;5:2082–92.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Watanabe YI, Kawai G, Yokogawa T, Hayashi N, Kumazawa Y, Ueda T, et al. Higher-order structure of bovine mitochondrial tRNA (SerUGA): chemical modification and computer modeling. Nucleic Acids Res. 1994;22:5378–84.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Araya-Anchetta A, Busch JD, Scoles GA, Wagner DM. Thirty years of tick population genetics: a comprehensive review. Infect Genet Evol. 2015;29:164–79.PubMedCrossRefPubMedCentralGoogle Scholar
  73. 73.
    Mixson TR, Lydy SL, Dasch GA, Real LA. Inferring the population structure and demographic history of the tick, Amblyomma americanum Linnaeus. J Vector Ecol. 2006;31:181–92.PubMedCrossRefPubMedCentralGoogle Scholar
  74. 74.
    Shao R, Barker SC, Mitani H, Takahashi M, Fukunaga M. Molecular mechanisms for the variation of mitochondrial gene content and gene arrangement among chigger mites of the genus Leptotrombidium (Acari: Acariformes). J Mol Evol. 2006;63:251–61.PubMedCrossRefPubMedCentralGoogle Scholar
  75. 75.
    Boore JL. Big trees from little genomes: mitochondrial gene order as a phylogenetic tool. Curr Opin Genet Dev. 1998;8:668–74.PubMedCrossRefPubMedCentralGoogle Scholar
  76. 76.
    Dowton M, Austin AD. Evolutionary dynamics of a mitochondrial rearrangement “hot spot” in the Hymenoptera. Mol Biol Evol. 1999;16:298–309.PubMedCrossRefPubMedCentralGoogle Scholar
  77. 77.
    Dowton M, Castro LR, Austin AD. Mitochondrial gene rearrangements as phylogenetic characters in the invertebrates: the examination of genome ‛morphologyʼ. Invertebr Syst. 2002;16:345–56.CrossRefGoogle Scholar
  78. 78.
    Cameron SL, Sullivan J, Song H, Miller KB, Whiting MF. Amitochondrial genome phylogeny of the Neuropterida (lace-wings, alderfliesand snakeflies) and their relationship to the other holometabolous insect orders. Zool Scr. 2009;38:575–90.CrossRefGoogle Scholar
  79. 79.
    Xu W, Jameson D, Tang B, Higgs PG. The relationship between the rate of molecular evolution and the rate of genome rearrangement in animal mitochondrial genomes. J Mol Evol. 2006;63:375–92.PubMedCrossRefPubMedCentralGoogle Scholar
  80. 80.
    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.PubMedCrossRefPubMedCentralGoogle Scholar
  81. 81.
    Cameron SL, Whiting MF. The complete mitochondrial genome of the tobacco hornworm, Manduca sexta, (Insecta: Lepidoptera: Sphingidae), and an examination of mitochondrial gene variability within butterflies and moths. Gene. 2008;408:112–23.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Mccooke JK, Guerrero FD, Barrero RA, Black M, Hunter A, Bell C, et al. The mitochondrial genome of a Texas outbreak strain of the cattle tick, Rhipicephalus (Boophilus) microplus, derived from whole genome sequencing pacific biosciences and Illumina reads. Gene. 2015;571:135–41.PubMedCrossRefPubMedCentralGoogle Scholar
  83. 83.
    Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.PubMedCrossRefPubMedCentralGoogle Scholar
  84. 84.
    Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30:1188–95.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Caterino MS, Reed RD, Kuo MM, et al. A partitioned likelihood analysis of swallowtail butterfly phylogeny (Lepidoptera: Papilionidae). Syst Biol. 2001;50:106–27.PubMedCrossRefPubMedCentralGoogle Scholar
  87. 87.
    Megens HJ. Molecular phylogeny of the oriental butterfly genus Arhopala (Lycaenidae, Theclinae) inferred from mitochondrial and nuclear genes. Syst Entomol. 2004;29:115–31.CrossRefGoogle Scholar
  88. 88.
    Castro LR, Dowton M. Mitochondrial genomes in the Hymenoptera and their utility as phylogenetic markers. Syst Entomol. 2007;32:60–9.CrossRefGoogle Scholar
  89. 89.
    Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007;7:e214.CrossRefGoogle Scholar
  90. 90.
    Shi QH, Sun XY, Wang YL, Hao JS, Yang Q. Morphological characters are compatible with mitogenomic data in resolving the phylogeny of nymphalid butterflies (Lepidoptera: Papilionoidea: Nymphalidae). PLoS ONE. 2015;10:e0124349.PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Klompen JSH, Oliver JH. Systematic relationships in the soft ticks (Acari: Ixodida: Argasidae). Syst Entomol. 1993;18:313–31.CrossRefGoogle Scholar
  92. 92.
    Mans BJ, De Castro MH, Pienaar R, De Klerk D, Gaven P, Genu S, et al. Ancestral reconstruction of tick lineages. Ticks Tick Borne Dis. 2016;7:509–35.PubMedCrossRefPubMedCentralGoogle Scholar
  93. 93.
    Shao R, Barker SC. Mitochondrial genomes of parasitic arthropods: implications for studies of population genetics and evolution. Parasitology. 2007;134:153–67.PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Cameron SL. How to sequence and annotate insect mitochondrial genomes for systematic and comparative genomics research. Syst Entomol. 2014;39:400–11.CrossRefGoogle Scholar
  95. 95.
    Sheffield NC, Song H, Cameron SL, Whiting MF. Nonstationary evolution and compositional heterogeneity in beetle mitochondrial phylogenomics. Syst Biol. 2009;58:381–94.PubMedCrossRefPubMedCentralGoogle Scholar
  96. 96.
    Detmer SA, Chan DC. Functions and dysfunctions of dynamics. Nat Rev Mol Cell Bio. 2007;8:870–9.CrossRefGoogle Scholar
  97. 97.
    Levin DB, Danks HV, Barber SA. Variations in mitochondrial DNA and gene transcription in freezing-tolerant larvae of Eurosta solidaginis (Diptera: Tephritidae) and Gynaephora groenlandica (Lepidoptera: Lymantriidae). Insect Mol Biol. 2010;12:281–9.CrossRefGoogle Scholar
  98. 98.
    Jain S, Al-Hasan Y, Thompson L. 231: prenatal hypoxia programs increased hepatic mitochondrial gene expression in guinea pig (GP) offspring. Am J Obstet Gynecol. 2013;208:S106.CrossRefGoogle Scholar
  99. 99.
    Zhang JY, Lu BE, Yu DN, Zhang LP, Al-Attar R, Storey KB. The complete mitochondrial genome of Dryophytes versicolor: phylogenetic relationship among hylidae and mitochondrial protein-coding gene expression in response to freezing and anoxia. Int J Biol Macromol. 2019;132:461–9.PubMedCrossRefPubMedCentralGoogle Scholar
  100. 100.
    Wang TH, Zhang SQ, Pei TW, Yu ZJ, Liu JZ. The complete mitochondrial genome and expression profile of mitochondrial protein-coding genes in the bisexual and parthenogenetic Haemaphysalis longicornis. Front Physiol. 2019;10:982.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Ballard JWO, Pichaud N. Mitochondrial DNA: more than an evolutionary bystander. Funct Ecol. 2014;28:218–31.CrossRefGoogle Scholar
  102. 102.
    Beckenbach AT, Joy JB. Evolution of the mitochondrial genomes of gall midges (Diptera: Cecidomyiidae): rearrangement and severe truncation of tRNA genes. Genome Biol Evol. 2009;1:278–87.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Jewess PJ. Insecticides and acaricides which act at the rotenone-binding site of mitochondrial NADH: ubiquinone oxidoreductase; competitive displacement studies using a 3H-labelled rotenone analogue. Biochem Soc T. 1994;22:247–51.CrossRefGoogle Scholar
  104. 104.
    Motoba K, Suzuki T, Uchida M. Effect of a new acaricide, fenpyroximate, on energy metabolism and mitochondrial morphology in adult female Tetranychus urticae (two-spotted spider mite). Pestic Biochem Phys. 1992;43:37–44.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

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 and Affiliations

  1. 1.Hebei Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology, College of Life SciencesHebei Normal UniversityShijiazhuangChina

Personalised recommendations