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  • Research article
  • Open Access

Whole genome sequencing of Asia II 1 species of whitefly reveals that genes involved in virus transmission and insecticide resistance have genetic variances between Asia II 1 and MEAM1 species

Contributed equally
BMC Genomics201920:507

https://doi.org/10.1186/s12864-019-5877-9

  • Received: 3 December 2018
  • Accepted: 31 May 2019
  • Published:

Abstract

Background

Whiteflies (Bemisia tabaci) are phloem sap-sucking pests that because of their broad host range and ability to transmit viruses damage crop plants worldwide. B. tabaci are now known to be a complex of cryptic species that differ from each other in many characteristics such as mode of interaction with viruses, invasiveness, and resistance to insecticides. Asia II 1 is an indigenous species found on the Indian sub-continent and south-east Asia while the species named as Middle East Asia Minor 1 (MEAM1), likely originated from the Middle-East and has spread worldwide in recent decades. The purpose of this study is to find genomic differences between these two species.

Results

Sequencing of the nuclear genome of Asia II 1 with Illumina HiSeq and MiSeq generated 198.90 million reads that covers 88% of the reference genome. The sequence comparison with MEAM1 identified 2,327,972 SNPs and 202,479 INDELs. In Total, 1294 genes were detected with high impact variants. The functional analysis revealed that some of the genes are involved in virus transmission including 4 genes in Tomato yellow leaf curl virus (TYLCV) transmission, 96 in Tomato crinivirus (ToCV) transmission, and 14 genes in insecticide resistance.

Conclusions

These genetic differences between Asia II 1 and MEAM1 may underlie the major biological differences between the two species such as virus transmission, insecticide resistance, and range of host plants. The present study provides new genomic data and information resources for Asia II 1 that will not only contribute to the species delimitation of whitefly, but also help in conceiving future research studies to develop more targeted management strategies against whitefly.

Keywords

  • Whitefly
  • Asia II 1
  • MEAM1
  • Sequencing
  • Virus
  • Insecticide

Introduction

Bemisia tabaci (Hemiptera: Aleyrodidae), commonly known as ‘whiteflies’ are phloem sap sucking pests some of which have become a major constraint to important food, fiber and ornamental crops worldwide. The whiteflies can infest as many as 1000 plant species [1] and they damage host plants by infestation, but more importantly by transmitting plant viruses. These whiteflies can potentially vector over 300 plant viruses, mostly viruses in the genus Begomovirus [2]. Major crops affected by B. tabaci-transmitted viruses on a global scale include cotton, cassava, tomato, sweet potato, cucurbits and other crop plant species.

Whiteflies (B. tabaci) are now known to be as a cryptic species complex, based on recent molecular phylogenetic analyses and evidence of reproductive incompatibility [3, 4]. These putative whitefly species differ in many biological aspects such as host range [1], resistance to insecticides [5, 6], specificity and capacity of virus transmission [7, 8] and composition of harbored symbionts [9]. Although the use of ≥3.5% mtCOI divergence as the criterion for species delimitation has been occasionally shown to be inadequate [10], it has been widely used to differentiate species. Based on sequence divergence of mtCOI (≥ 3.5% divergence), B. tabaci has been deduced to include more than 39 cryptic species that are morphologically indistinguishable but genetically distinct [1113].

The long-term association between begomoviruses and whitefly has brought some co-evolved adaptations [14] that allow them to live in equilibrium. Begomoviruses are single-stranded (ss) DNA viruses that are transmitted mostly in a persistent circulative manner. Once ingested through the stylet, these plant viruses move across the mid gut membrane and then via hemolymph translocate to salivary glands and from there these are egested while feeding [15]. In circulation of viruses, mid gut and salivary glands are the main barriers to overcome [16, 17]. Some mid gut proteins and proteins produced by endosymbionts in hemolymph are associated with circulation of viruses in whitefly. These interacting proteins are the main points which lead to the differentiation of cryptic species on the basis of specificity and capacity of virus transmission. The heat shock protein HSP70 is co-localized with Tomato yellow leaf curl virus (TYLCV) coat protein within midgut epithelial cells and inhibits virus transmission [18]. Knottin-1 restricts the virus (TYLCV) amount in whitefly and thus shields the whitefly against its deleterious effects [19]. While cyclophilin B enhances the translocation of virus from mid gut to hemolymph [20]. Another protein peptidoglycan recognition protein (BtPRPG) is involved in whitefly immunity and has a potential binding site for TYLCV. Its co-localization with TYLCV is also reported within the midgut [21]. Endosymbionts which have been living in whitefly for millions of years [22] are also involved in virus transmission. Different cryptic species harbor different endosymbionts. Endosymbionts reside in bacteriocytes and some of them (e.g. Hamiltonella) produce GroEL homologue in the hemolymph which helps in virus circulation in whitefly.

Middle East-Asia Minor 1 (MEAM1, formerly known as “biotype B”) and Mediterranean (MED, formerly “Q biotype”) are globally important cryptic species of whitefly [23, 24] because of their invasiveness and broad host range. The two species originated in the Middle East regions, but are now reported from many regions of the world, and its presence has also been well reported in the southern Sindh region of Pakistan [25, 26]. Asia 1 and Asia II 1 are two species of whitefly indigenous to Pakistan, with Asia II 1 being the most prevalent whitefly in the central region of the country [26]. The different species of whitefly recorded from Pakistan have been shown to differ in many aspects including virus transmission, insecticide resistance, and host range. For example, MEAM1 is more efficient than Asia II 1 in transmitting Tomato yellow leaf curl virus (TYLCV) [27]. In a study in Vietnam where Asia II 1 is indigenous, Asia II 1 is reported to be more efficient in transmitting Tomato leaf curl Hainan virus (ToLCHnV) than that of TYLCV, while MEAM1 is more efficient in transmitting TYLCV than ToLCHnV [28]. Asia II 1 has been reported to be the most abundant species of whitefly in areas of high incidence of cotton leaf curl disease (CLCuD) in Pakistan and the western region of India. Two recent studies in China [17, 29] directly compared the transmission efficiency of begomoviruses by MEAM1, Asia II 1 and two more species, and showed that among these species Asia II 1 is the most efficient in transmitting both Cotton leaf curl Multan virus (CLCuMuV) and Tobacco curly shoot virus (TbCSV). Apart from differences in transmission efficiency of viruses, these species of whiteflies also differ in insecticide resistance [30] and host plant preference [31]. However, the physiological and molecular mechanisms underlying the differences between species of whitefly are yet poorly known.

Over the past several years, next generation sequencing (NGS) technology has emerged as an innovative approach to high-throughput sequencing [32], and the rapid development of this modern technology provides us an unprecedented opportunity to understand and explore numerous genetic findings, which can help to improve our research on the physiology and molecular biology of the whiteflies. These results can also provide new knowledge and concepts for the development of novel strategies and technology to manage whitefly pests and the viral disease agents they vector. In this study, our aim is to unravel some genetic information from Asia II 1 and MEAM1, the two major whitefly pests in Pakistan. First, with access to the data of MEAM1 [4], we performed high throughput sequencing of Asia II 1 and aligned with that of MEAM1, to identify major genomic differences between the two species. We detected some high impact variants in genes (which were previously reported as differentially expressed genes) that have been predicted to be associated with virus transmission and insecticide resistance.

Results

Mapping summary of nuclear genome

Genome sequencing of Asia II 1 with Illumina HiSeq and MiSeq generated a total of 31.15 Gb of data comprising 198.90 million reads with read size 100 and 300 bp (the summary of raw data generated from each of seven libraries is given in Additional file 1). Approximately 91% of the reads passed the quality control criteria and 82 to 86% of these reads were mapped correctly to the reference genome. The available sequence from the reference genome is 615 Mb [4] of the assessed total genome size of ~ 680–690 Mb as estimated in a previous study [33] using both flow cytometry and Kmer analysis. These reads covered 88% of the reference genome. The mean read length was 159 bp. The summary of the sequencing and mapping is shown in Table 1. The average depth of coverage of genome after filtration was 34X. Total length of the coding region of the reference genome is 44.43 Mb, 51% of which was covered with more than 5X depth of coverage, and 53% of the number of coding regions with 100% of length have at least 5X depth of coverage. The mean coverage of the coding region is 32X. Figure 1 displays the different number of coding regions with different lengths having at least 5X depth of coverage. Approximately 8366 coding regions have at least 5X coverage with full length genes.
Table 1

Mapping Summary

Total NGS Library

7

Total Insert Size

550

Sequencer

IlluminaHiSeq2500 &MiSeq

Total Raw Data Generate

(HiSeq: 14GB)

(MiSeq: 16 Gb)

Total: 31.15 Gb

Average Coverage

47.34 X

Average Coverage After Filtration

34.52 X

Total No of Reads Generate

HiSeq: 142605246

MiSeq: 56300942

Total: 198906188

Total No of Reads Quality Passed

181,434,767

Total No of Reads Mapped

156,293,812 (86%)

Total No of Reads Mapped Properly

149,439,368 (82%)

Reference Genome Covered

88%

Mean Read Length

159 bp

Fig. 1
Fig. 1

Total coding regions are 15,664. All the coding regions with less than 10% each of their length are covered with at least 5X coverage, 53% of coding regions (8366) with full length are covered with at least 5X coverage

Variant statistics

After variant calling and two times filtration with Genome Analysis Tool Kit (GATK), total number of 2,530,451 high quality variants were discovered. Variant annotations and effect prediction through SnpEff resulted in 3,504,011 effects. Effects are greater in number than number of variants as one variant could have more than one effect. For example, one variant could be non-synonymous for one gene while being downstream to another. A variant statistics summary is given in Table 2 (raw variant calling data and the data after each filtration is provided in Additional file 2). Approximately 2,327,972 SNPs and 202,479 INDELs were detected. In eight amplified regions ranging in size from 500 to 600 bp, there are 96 SNPs which were all validated through Sanger sequencing. The primers pairs list and validated SNPs positions are given in Additional file 4. The initial average variant rate was 1/20 bp, but that was decreased to 1/235 bp after filtration (when depth of read coverage at a variant point was increased to 30X in variant calling criterion). Variant rate also varied in different regions, the maximum variant rate recorded was 1/27 bp and minimum variant rate was 1/32,808 bp. Transition to transversion ratio is 1.71 and heterozygous to homozygous variant ratio is 0.05. In this study, insertions and deletions ranging from 1 to 100 bp were considered as INDELs. The maximum number of INDELs were 1 bp in length while lowest number of INDELs were of 14, 15, 20, 21, 23, 28, 33, 69, 89 or 100 bp in length. The distribution and types of variant effects in the whole genome are given in Table 3. According to functional effects of variants, these were distributed into three classes; silent (69.94%), missense (29.77%) and nonsense (0.29%).
Table 2

Variant Statistics

Number of variants

2,530,451

Number of effects

3,504,011

Variant rate

1 /235 bases

SNP

2,327,972

INS

103,960

DEL

98,519

Missense / Silent

0.4257

Ts/Tv ratio

1.7147

Heterozygous

122,045

Homozygous

2,349,906

Heterozygous/Homozygous

0.05193612

Table 3

Classification of effects and their number in the whole genome

Type of Effects

No of Effects

 

Count

Percent

High Effect

Total

1821

0.052

 Splice acceptor variant

96

0.003

 Splice donor variant

135

0.004

 Start loss

56

0.002

 Stop gain

371

0.003

 Stop lost

96

0.003

 Frame shift

1102

0.031

Moderate Effect

Total

35,583

2.724

 Conservative inframe deletion

49

0.001

 Conservative inframe insertion

83

0.002

 Disruptive inframe deletion

98

0.003

 Disruptive inframe insertion

74

0.002

 Missense variant

35,285

1.004

Low Effect

Total

95,439

1.015

 5′ UTR premature start gain

3020

0.086

 Splice region variant

10,980

0.312

 Stop retained

106

0.003

 Synonymous variant

83,150

2.366

 Initiator codon/ non syn start

15

0

Modifier Effects

Total

3,371,168

96.209

 3′ UTR

174,811

4.974

 5′ UTR

23,577

0.671

 Downstream

485,837

13.823

 Upstream

421,908

12.041

 Non-coding transcript

470

0.013

 Intron variant

1,479,087

42.082

 Intergenic regions

794,375

22.67

Among the total estimated genes in whitefly MEAM1 (15,664), 1294 genes were found to have high impact variants in this data. These genes were selected for further analysis of ontology. The distribution and number of variants and their effects in different genic regions are given in Fig. 2. The number of genes in each class of high impact variants are also provided in Table 4.
Fig. 2
Fig. 2

Distribution of variants in different genic regions

Table 4

Number of variant genes in each sub-class of high effects. One gene may have more than one effect and same gene may count in more than one category of high effects

Type of High Effects

No of Genes

Splice acceptor variant

92

Splice donor variant

129

Start Loss

55

Stop gain

346

Stop lost

91

Frame shift

765

Total

1294

Gene ontology

Coding regions that have high impact variants (1294 genes) were selected for gene ontology analysis. The Blast2GO results are shown in Fig. 3. The functions of these were classified into three groups: biological process (BP), molecular function (MF), and cellular components (CC). The greatest number of genes were associated with the BP category. IDs of genes associated with each sub category of these three functional classes are given in Additional file 3. Additional file 7 shows the associated pathways for the genes (with high impact variants), which were predicted by Blast2GO.
Fig. 3
Fig. 3

Histogram representation of GO classification of genes with high impact variants. These genes are classified into CC: cellular component, BP: biological process and MF: molecular function. In the supplementary data, genes are listed, that belong to each of sub class of these three categories

Genes involved in virus transmission and insecticide resistance

Fourteen genes of MEAM1 that were reported for potential involvement in insecticide resistance [4] and 96 genes which were reported to be associated with virus transmission [6] were expected to be high impact variants between Asia II 1 and MEAM1. In the present study, there were 15 high impact variants found in 14 genes which could potentially be involved in insecticide resistance. High impact variants include frame shift, start loss, stop gain, splice acceptor and splice donor. These lead to truncated or modified proteins with partial or complete loss of function. There is also a chance that because of these mutations some of the proteins may gain more efficiency rather than to be dis-functional. These 14 genes belong to 4 gene families: acetylcholinesterase like protein, cathepsin (B, F, cathepsin L like), Cytochrome P450, and phosphatidylethanolamine-binding protein 1. A list of these insecticide resistance gene IDs is shown in Table 5 and those for virus transmission in Table 6 (TYLCV) and 7 (ToCV). All the genes described in Table 5 are reported for the potential involvement in insecticide resistance by Chen et al., [4], and those for virus transmission in Table 6 and Table 7 are reported by Hasegawa et al., [34] and Kaur et al., [6] respectively.
Table 5

Genes potentially involved in insecticide resistance with variants between Asia II 1 and MEAM1

Gene ID

Annotation

Type of Variant

Variant Position

Bta08717

Acetylcholinesterase-like protein

Frame Shift

Scaffold325:2419087

Bta12286

Cathepsin B

start lost

Scaffold562:2252138

Bta06690

Cathepsin F

stop gain

Scaffold2605:1316025

Bta07152

Cathepsin L-like protease

Frame Shift

Scaffold2737:56518

Bta02560

Cathepsin L-like protease

Frame Shift

Scaffold132:3684567

Bta04696

Cytochrome P450

Splice acceptor

Scaffold1685:811440

Bta06044

Cytochrome P450

Stop lost

Scaffold231:1494714

Bta01556

Phosphatidylethanolamine-binding protein

Frame Shift

Scaffold1224:613594

Bta01355

Phosphatidylethanolamine-binding protein 1

start lost, splice acceptor variant

Scaffold1195:116803, Scaffold1195:118926

Bta15207

Phosphatidylethanolamine-binding protein 1

Start lost

Scaffold923:587527

Bta07891

Phosphatidylethanolamine-binding protein 1

splice donor

Scaffold300:6735496

Bta12136

Phosphatidylethanolamine-binding protein 1

Frame Shift

Scaffold545:18333

Bta13188

Phosphatidylethanolamine-binding protein 1

Splice acceptor

Scaffold637:1563358

Bta02907

Phosphatidylethanolamine-binding protein, putative

Frame Shift

Scaffold14:2449776

Table 6

List of gene IDs which are potentially involved in TYLCV virus transmission and have genetic variants between Asia II 1 and MEAM1

Gene ID

Annotation

Type of Variant

Variant Position

Bta10341

Aldo-keto reductase

Frame Shift

Scaffold 403:3624744

Bta04072

Elicitin-like protein 6

Frame Shift

Scaffold161:5952976

Bta02276

Ubiquitin carboxyl-terminal hydrolase

Frame Shift

Scaffold130:858376

Bta14634

Unknown protein

Frame Shift, Splice Donor Variant

scaffold811: 176696, Scaffold811:176710

Table 7

List of gene IDs which are potentially involved in ToCV virus transmission and have genetic variants between Asia II 1 and MEAM 1

Gene ID

Gene Name

Type of Variants

Scaffold:Snp Position

Bta08892

70 kDa heat shock protein

Frame shift

Scaffold3328:264318

Bta01665

AAA-ATPase-like domain-containing protein

frame shift

Scaffold1224:5022892

Bta12603

AAA-ATPase-like domain-containing protein

stop gain

Scaffold597:2078628

Bta05346

Afadin, putative

stop lost

Scaffold199:1272506

Bta11978

Alpha-glucosidase

Frame shift

Scaffold521:859550

Bta01804

Ankyrin repeat and LEM domain-containing protein

Frame shift

Scaffold123:4405622

Bta01772

Cathepsin B

Frame shift

Scaffold123:2832350

Bta07402

Cathepsin B

Frame shift

Scaffold2816:1342943

Bta02120

Cathepsin L-like protease

Frame shift

Scaffold1261:554552

Bta02560

Cathepsin L-like protease

Frame shift

Scaffold132:3684567

Bta07152

Cathepsin L-like protease

Frame shift

Scaffold2737:56518

Bta06739

Cation transport regulator-like protein 1

Frame shift

Scaffold2605:2569958

Bta08022

CG13675, isoform D

Frame shift

Scaffold3040:3058531

Bta04412

CG14375

Frame shift

Scaffold165:195426

Bta03710

CG17612, isoform A

splice acceptor variant

Scaffold155:194033

Bta11746

CG7120, isoform F

splice donor, frame shift

Scaffold52:4009995

Bta10928

Chromodomain Y-like protein 2

Frame shift

Scaffold477:1214758

Bta12891

Citron Rho-interacting kinase

Frame shift

Scaffold613:2332964

Bta02184

Cystatin

frame shift

Scaffold128:1309680

Bta07162

DDB1-and CUL4-associated factor

start loss,stop gain,

Scaffold2737:410077, Scaffold2737:419749

Bta07434

DNA-directed RNA polymerase, omega subunit family protein

stop gain, frame shift

Scaffold2890:209245

Bta14689

Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit STT3B

splice acceptor variant

Scaffold811:2521172

Bta15680

E3 ubiquitin-protein ligase TTC3

Frame shift

Scaffold988:3252798

Bta03681

Eukaryotic translation initiation factor 3 subunit A

Frame shift

Scaffold1512:1481746

Bta14560

Galectin

Frame shift

Scaffold809:3964391

Bta10009

General transcription factor 3C polypeptide 2

Frame shift

Scaffold382:2610001

Bta04387

GH16255p

splice acceptor variant

Scaffold1647:2597569

Bta00770

GK11989

stop lost, frame shift

Scaffold1103:753116

Bta01833

Klarsicht, isoform E

stop gain, frame shift

Scaffold123:5600056

Bta09051

Laminin subunit beta-1

stop gain

Scaffold338:1218247

Bta01704

Loquacious

Frame shift

Scaffold123:255863

Bta03800

Lysosomal-trafficking regulator

Frame shift

Scaffold155:4253425

Bta05467

Major royal jelly-related protein

stop lost

Scaffold199:6871677

Bta05773

NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 12

stop gain

Scaffold2229:151223

Bta15454

Neuroendocrine convertase 1

Frame shift

Scaffold959:3096344

Bta10191

Nidogen-2

stop gain

Scaffold3978:2723

Bta13257

Protein patched

Frame shift

Scaffold641:259689

Bta15368

Protein phosphatase 1 L

Frame shift

Scaffold959:15812

Bta13589

Protein unc-45-like protein A

Frame shift

Scaffold651:2187902

Bta02051

Regucalcin

start loss

Scaffold1240:111531

Bta10926

Replication factor-a protein 1

Frame shift

Scaffold477:1149858

Bta12190

Sortilin-related receptor

Frame shift

Scaffold545:2426274

Bta02847

Sulfotransferase

stop gain

Scaffold14:67127

Bta08229

Symplekin

splice acceptor variant

Scaffold317:609334

Bta07946

Terribly reduced optic lobes, isoform AN

splice acceptor

Scaffold3040:556936

Bta05242

Transcriptional protein SWT1

Frame shift

Scaffold1898:321532

Bta09856

Trehalase

stop gain

Scaffold374:3016858

Bta03298

Trypsin-like serine protease

stop lost

Scaffold147:7182519

Bta09090

Tudor domain protein

stop lost

Scaffold338:1990664

Bta08596

Tudor domain-containing protein 1

Frame shift

Scaffold322:4722919

Bta03892

Ubiquitin carboxyl-terminal hydrolase

start lost

Scaffold1580:568946

Bta01518

Unknown protein

stop gain

Scaffold1214:734963

Bta01571

Unknown protein

Frame shift

Scaffold1224:1139169

Bta01615

Unknown protein

Frame shift

Scaffold1224:3224397

Bta02665

Unknown protein

Frame shift

Scaffold1339:520464

Bta02767

Unknown protein

Frame shift

Scaffold137:1379435

Bta02836

Unknown protein

Frame shift

Scaffold139:1098948

Bta02920

Unknown protein

Frame shift

Scaffold14:3202002

Bta03301

Unknown protein

stop gain, frame shift

Scaffold147:7328434

Bta03426

Unknown protein

stop gain

Scaffold1496:690294

Bta03435

Unknown protein

Frame shift

Scaffold1496:1047497

Bta04551

Unknown protein

Frame shift

Scaffold165:5163918

Bta04829

Unknown protein

Frame shift

Scaffold17:652047

Bta04921

Unknown protein

Frame shift

Scaffold17:652047

Bta05143

Unknown protein

stop gain

Scaffold18461:1072084

Bta05268

Unknown protein

stop gain, frame shift

Scaffold1971:32055

Bta05546

Unknown protein

Frame shift

Scaffold2013:237841

Bta05683

Unknown protein

Frame shift

Scaffold2124:427571

Bta05758

Unknown protein

Frame shift

Scaffold2225:1204179

Bta05761

Unknown protein

stop gain

Scaffold2225:1258041

Bta05893

Unknown protein

stop gain

Scaffold226:1397519

Bta06123

Unknown protein

splice donor

Scaffold231:3876649

Bta07727

Unknown protein

Frame shift

Scaffold300:708005

Bta07839

Unknown protein

stop gain

Scaffold300:4527825

Bta08000

Unknown protein

stop gain, frame shift

Scaffold3040:2567504

Bta08242

Unknown protein

Frame shift

Scaffold317:1074159

Bta08287

Unknown protein

stop gain

Scaffold320:265593

Bta08375

Unknown protein

stop gain

Scaffold320:3813827

Bta08462

Unknown protein

Frame shift

Scaffold322:385722

Bta08745

Unknown protein

Frame shift

Scaffold325:3471439

Bta10862

Unknown protein

splice acceptor variant

Scaffold471:791307

Bta11840

Unknown protein

Frame shift

Scaffold52:7764853

Bta12278

Unknown protein

stop gain

Scaffold562:2009445

Bta12668

Unknown protein

start lost

Scaffold607:1307735

Bta12727

Unknown protein

Frame shift

Scaffold607:2833985

Bta13235

Unknown protein

Frame shift

Scaffold64:63239

Bta13327

Unknown protein

splice donor

Scaffold641:3718364

Bta13745

Unknown protein

Frame shift

Scaffold657:1097200

Bta13859

Unknown protein

stop gain

Scaffold67:1393372

Bta13954

Unknown protein

Frame shift

Scaffold699:810303

Bta15302

Unknown protein

Frame shift

Scaffold942:1732675

Bta15415

Unknown protein

Frame shift

Scaffold959:1270849

Bta07758

Zinc finger protein

Frame shift

Scaffold300:1778386

Bta06175

Zinc finger protein 227

stop gain

Scaffold232:1822927

Bta08766

Zinc finger protein 34

Frame shift

Scaffold325:3972542

Bta11305

Zinc finger protein 845

Frame shift

Scaffold493:2884873

Structural variants

Structural variants were predicted through CNVnator in which the method of detection of structural variants is based on assessing the read of depth of the mapping genome. With CNVnator, among all the structural variants (duplications, deletions, insertions, inversions and translocations), some duplications were detected in the present study. Duplications with more than 1.5 cnv value are enlisted in Table 8 with their positions on the scaffolds and included genes in them. Functional annotations of these genes are presented in Additional file 6. Copy number variations were detected by CNVkit, which are described in Additional file 5. The structural variants in this study is not a comprehensive data and it is necessary to mention that reference genome is a draft genome that is about 90% of total estimated genome (~ 680–690 Mb) and in present study, 88% of this draft genome was covered with mapping reads. When the complete reference genome would be used to detect the structural variants, the results may include some more structural variants.
Table 8

Structural Variants

Type

scaffold

start

end

length

CNV

Genes*

duplication

Scaffold112

2,190,001

2,470,000

280,000

1.59861

 

duplication

Scaffold130

2,120,001

2,590,000

470,000

1.50412

Bta02314 Bta02317 Bta02318 Bta02321 Bta02311 Bta02319 Bta02313 Bta02315 Bta02320 Bta02322 Bta02312 Bta02316

duplication

Scaffold310

2,080,001

2,870,000

790,000

1.51561

Bta08154 Bta08157 Bta08159 Bta08153 Bta08161 Bta08158 Bta08160 Bta08155 Bta08156

duplication

Scaffold343

3,950,001

4,160,000

210,000

2.19297

Bta09326

duplication

Scaffold403

2,470,001

2,980,000

510,000

1.55897

Bta10316 Bta10315 Bta10317 Bta10318 Bta10319

*Annotation of genes are described in Additional file 6

Discussion

Whitefly divergence into different distinct genotypes initiates the question whether the divergence results in a complex of different biotypes or it is a complex of different species! In order to resolve the divergence of whitefly question, it would be helpful to set criterion for sorting the different biotypes of whitefly and set a limit above which the difference is sufficient to declare new species status. Biological features e.g. virus transmission capacity, gut microbe diversity, host range, capacity to induce physiological changes in host plants, intermating capabilities, and capacity to spread widely have been used to differentiate cryptic species. Some of the genetic groups share common biological characters and some of the characters also show within group variability. Thus, most of the differences are uninformative or unable to resolve the cryptic species of whitefly. Molecular markers (such as AFLP, RAPD, 16S, CAPS, SCAR and mtCOI) have been used to show genetic differences between genotypes. The 3.5% genetic difference in terms of mtCOI sequences, differentiates almost all reproductively isolated groups according to available biological data. But some reports show disagreements with the outcomes using partial sequences of mitogenome. For example, a recent study [35], using genome wide analysis, suggested that MEAM2 might not be a separate genetic group but fall entirely into the MEAM1 group, whereas previously it was considered as a separate genetic group using mitochondrial genes. A similar phenomenon was observed in a recent study [36], where a combined analysis of experimental biological data with mitogenome sequences proposed that the African silverleafing (ASL) genotype, formerly treated as MED, may form a separate cryptic species. Thus, in view of these reports, species delimitation across the B. tabaci species complex requires data in addition to sequence divergence of mtCOI. Many recent reports show that species delimitation of a cryptic species complex requires a multi-method approach that integrates genetic differentiation, biological character, DNA barcoding, molecular phylogenetic analysis and possibly other biological features. In this regard, our study provides whole genome nuclear variants data, which will be useful to improve species delimitation of the B. tabaci species complex. It is also necessary to mention that although we detected all these variants between the two species Asia II 1 and MEAM1, but it may also possible that some of the variants may segregate within the same species.

In this study, we have sequenced the genome of the Asia II 1 species of whitefly and have used published transcriptomic data to infer biological differences between Asia II 1 and MEAM1. The sequencing of Asia II 1 not only provided new genomic resources for Asia II 1, but its comparative study with MEAM1 also provided insight into the comprehensive genetic differences between them.

With Blast2GO analysis, high impact variant genes were analyzed to identify the involvement of these genes in molecular pathways. The goal was to find out how genetic variances may alter or affect pathways which may then help in understanding the biological differences between the two species. Signal transduction pathways were considered as one of main points where gene alterations might help the whitefly to deal with any changes in the environment or inside the whitefly cells. Phosphatase and kinases are well-known enzymes in signal transduction pathways [37] as they activate or deactivate the functional proteins by either phosphorylation or dephosphorylation. Kinase and phosphatase functions in antagonistic ways as kinase initiates the phosphorylation and phosphatase removes the phosphate group from its substrate protein. In Additional file 7, it is noticeable that most of the genes are encoding phosphatase and kinase in different pathways e.g. phosphatase in T cell receptor signaling pathways, purine and thymine metabolism, and kinase in drug metabolism (important for pesticide resistance) and phosphatidylinositol signaling pathways. Genetic variants of these genes may alter their systematic regulatory role in biological functions.

Another prominent group of genes comprised “oxidase, dehydrogenase and reductase” enzymes performing functions in oxidative phosphorylation, amino acid (glycine, serine, threonine, valine, isoleucine, arginine and proline) metabolism, steroid degradation and biosynthesis, and biosynthesis of antibiotics. The robustness of a phloem sap sucking pest depends on the amino acid and carbohydrate contents of phloem sap of their host [38] as well as on their processing power of amino acids. For example, a Florida strain of whitefly processes more phloem sap that allows it to have more expanded host range [39]. Phloem sap lacks some essential amino acids and vitamins, so phloem sap sucking pests rely heavily on endosymbionts for some essential amino acids. There are number of genes which are present in more than one pathway for example Bta13274 encodes an oxidase involved in biosynthesis of antibiotics as well as arginine and proline metabolism, indirectly contributes to environmental fitness. A previous study reported that MEAM1 performed better than Asia II 1 on many commonly cultivated crops in China [40], and in another study MEAM1 showed the ability to adapt to unsuitable hosts [41]. Genetic variants in these genes may provide clues to the differential capacity of Asia II 1 and MEAM1 to adapt to changing environments.

Some recent studies report genes showing differential expression upon treatment of insecticide or virus infection. In our data, we identified high impact variants in 14 genes associated with insecticide resistance, 4 genes involved in TYLCV transmission, and 96 genes involved in ToCV transmission. The cathepsin gene family is involved in both insecticide resistance and ToCV transmission. Our results identified high impact variants in cathepsin B (3 genes), cathepsin F (1 gene) and cathepsin L-like genes (3 genes) that are involved in insecticide resistance and ToCV transmission. Cathepsins are proteases involved in many biological functions such as protein degradation, apoptosis, and signaling, and their activity in lysosomes has been broadly connected to virus transmission. The cathepsin B family is expanded in B. tabaci and also a novel clade of cathepsin L-like genes is identified in comparison to 15 other arthropods [4] which lead to the prediction of a possible contribution of cathepsin in virus acquisition or other responses that are involved in whitefly-virus interactions. Another important family in which genetic variants were found, associated with insecticide resistance is cytochrome P450 [42]. Two high impact variants were identified in two CYP 450 genes (Bta04696 and Bta06044). Chen et al., [4] inferred the involvement of these genes in insecticide resistance in MEAM1 on the basis of their differential expression upon treatment with insecticides. Another gene that encodes a heat shock protein known to be involved in virus transmission [18] has frame shift variant in Asia II 1. Three genes which have high impact variants and are linked to ToCV transmission are associated with three KEGG pathways: oxidative phosphorylation (Bta05773), T cell receptor signaling pathway (Bta15368), and sucrose and starch metabolism (Bta09856). Bta09856 encodes trehalase a glycosidase which convert trehalose (major sugar reserve in insects play a vital role as an instant source of energy and in dealing with abiotic stresses) into glucose in sucrose and starch metabolism. The inhibition of trehalase causes abnormal growth and unsuccessful stress recovery [43]. Inhibition of trehalase provides promising area towards formulating strategy for insect control. There are also some genes with unknown functions, associated with transmission of ToCV [6]. We reported the genetic variants between Asia II 1 and MEAM1 for these genes, and future annotation of these unknown genes may provide further clues about the mechanism through which whitefly interact with a virus. This comprehensive data set of variations between indigenous and invasive species provide insights into the variations in mechanisms which give different attributes to whitefly species. Based on all these results we conclude that the MEAM1 species is more invasive due to its genetic variations.

Conclusion

In present study, whole genome wide variants between Asia II 1 (indigenous to the Indian sub-continent and south-east Asia) and MEAM1 (originated in the Middle East but has spread worldwide in recent decades) are presented with their detailed annotations and impact. Variants detection in some important genes such as genes associated with virus transmission and insecticide resistance will help in conceiving future research towards targeted management strategies against whitefly. Furthermore, this study provides a genomic resource of Asia II 1 that will contribute to resolving species delimitation of whitefly.

Methods

Colony maintenance and confirmation of cryptic species

The source of whitefly (Asia II-1) population collected from NIBGE, Faisalabad in 2016. An isogenic population was established and maintained in aired glass confinement on cotton (Gossypium hirsutum) plants at 32 °C. The universal mtCOI primers C1-J-2195 (5′-TTGATTTTTTGGTCATCCAGAAGT-3′) and TL2-N-3014 (5′-TCCAATGCACTAATCTGCCATATTA-3′ were used to confirm the cryptic species (Asia II 1) [44]. PCR amplifications were performed in 20 μL reactions using DreamTaq Green PCR Master Mix (Thermo Fisher Scientific). The polymerase chain reaction (PCR) cycling parameters were one denaturation cycle of 94 °C for 5 min, followed by 35 cycles of 94 °C for 1 min, 45 °C for 1 min, and 72 °C for 1 min, followed by a final extension of 72 °C for 7 min. PCR products were visualized on a 1% agarose gel. Sanger sequencing [45] confirmed the Asia II 1 culture.

Genomic DNA extraction and library preparation

DNA extraction was done with “ISOLATE II Genomic DNA Kit” (Bioline Cat No. BIO-52066). Eight libraries with 550 bp insert size were prepared by the Illumina NeoPrep automation system with the library kit, Illumina #NP-101-1001, “TruSeq Nano DNA Library Kit for NeoPrep”, which includes the adapter set “TruSeq LT” (adapter sequences: adapter read1 AGATCGGAAGAGCACACGTCTGAACTCCAGTCA, adapter read2 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT). The target insert size selection was performed by the “Illumina NeoPrep Liberary Prep System”. Actual insert size ranges were calculated by CLC Genomics Workbench (v. 8.5.1).

Sequencing and mapping with reference genome

Sequencing was performed on the Illumina MiSeq and HiSeq2500 with Rapid v2 chemistry, 2x100bp, across 2 flow cell lanes. The Illumina bcl2fastq v2.16 software was used to convert raw basecalls (.bcl) to fastq.gz, and demultiplex the sequenced pool of libraries by the TruSeq LT indices in the NeoPrep process. The bcl2fastq script was set to automatically trim the adapters, if present. All duplicated reads, low quality regions (phred score less than threshold value) and reads containing N were trimmed. Cleaned reads were mapped onto the total reference genome of whitefly. Reference genome was accessed through ftp://www.whiteflygenomics.org/pub/MEAM1/MEAM1/ [4]. Mapping was done using BWA V0.7.12 with MEM algorithm using CLC Genomics Workbench 7.5. Raw data was visualized and analyzed to pass through quality control steps. Variant calling was performed by Haplotype caller GATK (using ‘ERC GVCF-variant_index_type LINEAR -variant_index_parameter 128,000). Variant filtration was performed two times using parameters (filtration1: DP > 20 & QD > 25.0 & FS < 5.00, filtration 2: DP > 30 & QD > 30.0 & FS = 0.00).

Analysis of variants

SnpEff [46] was used to annotate variants and effect prediction, and to classify the effects of variants by ‘functional classes’ (missense, nonsense and silent), by ‘impact’ (high, moderate, low and modifier), and by ‘type and region’ (downstream, exon, intergenic, intron, splice site acceptor, splice site donor, splice site region, transcript, upstream, UTR 3′, and UTR 5′). Then all genes that had “high impact variants” were analyzed with “Blast2GO Pro” (trial version) software [47] for gene ontology and to categorize gene functions into three classes: biological process, cellular components and molecular function. With Blast2GO Pro, KEGG pathways of these genes were also developed to analyze their function. All the mapped reads were evaluated to find structural variants. CNVnator [48] was used in the present study to find structural variants. CNVnator analyzes the “read of depth” from alignment to predict the structural variants. Copy number variations were detected by CNVkit [49].

SNPs validation

Some SNPs were randomly selected for the validation. Eight primer pairs were designed to amplify the regions (each with 500-600 bp length) which have a total of 96 SNPs. DNA was extracted from single whiteflies by the CTAB method [50]. Each region was amplified using DNA extracted from a single whitefly. PCR were performed in 50 μL reactions using DreamTaq Green PCR Master Mix (Thermo Fisher Scientific). PCR cycling parameters were one denaturation cycle of 94 °C for 5 min, followed by 35 cycles of 94 °C for 1 min, 54 °C for 30s, and 72 °C for 40 s, followed by a final extension of 72 °C for 7 min. PCR products were visualized on a 1% agarose gel. Amplified products were purified by “AxyPrep PCR Clean-up Kit” and then these were sequenced by the Sanger method [45]. The sequenced reads were aligned with MEAM1 sequences by DNAStar software to validate the predicted SNPs.

We also analyzed the previously published transcriptomic data of MEAM1 [4, 6, 34]. They reported some genes that were associated with virus transmission (TYLCV and ToCV) and insecticide resistance. In our data we identified genes which had high impact variants and as well as genes previously reported as differentially expressed under virus or insecticide treatment.

Notes

Abbreviations

BP: 

Biological Process

CC: 

Cellular Component

CTAB: 

Cetyl Trimethyl Ammonium Bromide

GATK: 

Genome Analysis Tool Kit

INDELS: 

Insertions Deletions

MEAM1: 

Middle East Asia Minor 1

MF: 

Molecular Function

mtCOI: 

Mitochondrial Cytochrome Oxidase Subunit I

SNP: 

Singe Nucleotide Polymorphism

ToCV: 

Tomato crinivirus

ToLCHnV: 

Tomato leaf curl Hainan virus

TYLCV: 

Tomato yellow leaf curl virus

Declarations

Acknowledgements

We thank Atiq Ur Rehman, Imran Rauf, Muhammad Hamza Rana, Mariyam Masood and Nasim Ahmed for technical assistance.

Funding

This work is supported by the ‘Pak-US cotton productivity enhancement program’ of the International Center for Agricultural Research in the Dry Areas (ICARDA) funded by United States Department of Agriculture (USDA), Agricultural Research Service (ARS), under agreement 58–6402-0-178F. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the USDA or ICARDA.

Authors’ contributions

SH and MF has equal contribution in this study. SH performed functional analysis (Blast2GO), SNPs validation and contributed in detection of structural variants. SH also drafted the manuscript. MF performed the genome mapping, variant calling, variant analysis (SnpEff) and detection of structural variants. HJM maintained the whitefly isogenic culture and performed the confirmation of species (Asia II 1) of whitefly. IA contributed in wet lab experiments and computational analysis, and reviewed the manuscript. BS performed the sequencing. SL and JS reviewed and edited the manuscript. SM designed the study and contributed in writing the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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)
National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan
(2)
Department of Biotechnology, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
(3)
USDA-ARS, Genomics and Bioinformatics Research Unit, 141 Experiment Station Rd., Stoneville, MS 38776, USA
(4)
USDA-ARS, Crop Genetics Research Unit, 141 Experiment Station Rd, Stoneville, MS 38776, USA
(5)
Institute of Insect Sciences, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China

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Copyright

© The Author(s). 2019

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