Analysis of a native whitefly transcriptome and its sequence divergence with two invasive whitefly species
- Xiao-Wei Wang†1Email author,
- Qiong-Yi Zhao†2,
- Jun-Bo Luan1,
- Yu-Jun Wang1,
- Gen-Hong Yan1 and
- Shu-Sheng Liu1Email author
© Wang et al.; licensee BioMed Central Ltd. 2012
Received: 19 April 2012
Accepted: 28 September 2012
Published: 4 October 2012
Genomic divergence between invasive and native species may provide insight into the molecular basis underlying specific characteristics that drive the invasion and displacement of closely related species. In this study, we sequenced the transcriptome of an indigenous species, Asia II 3, of the Bemisia tabaci complex and compared its genetic divergence with the transcriptomes of two invasive whiteflies species, Middle East Asia Minor 1 (MEAM1) and Mediterranean (MED), respectively.
More than 16 million reads of 74 base pairs in length were obtained for the Asia II 3 species using the Illumina sequencing platform. These reads were assembled into 52,535 distinct sequences (mean size: 466 bp) and 16,596 sequences were annotated with an E-value above 10-5. Protein family comparisons revealed obvious diversification among the transcriptomes of these species suggesting species-specific adaptations during whitefly evolution. On the contrary, substantial conservation of the whitefly transcriptomes was also evident, despite their differences. The overall divergence of coding sequences between the orthologous gene pairs of Asia II 3 and MEAM1 is 1.73%, which is comparable to the average divergence of Asia II 3 and MED transcriptomes (1.84%) and much higher than that of MEAM1 and MED (0.83%). This is consistent with the previous phylogenetic analyses and crossing experiments suggesting these are distinct species. We also identified hundreds of highly diverged genes and compiled sequence identify data into gene functional groups and found the most divergent gene classes are Cytochrome P450, Glutathione metabolism and Oxidative phosphorylation. These results strongly suggest that the divergence of genes related to metabolism might be the driving force of the MEAM1 and Asia II 3 differentiation. We also analyzed single nucleotide polymorphisms within the orthologous gene pairs of indigenous and invasive whiteflies which are helpful for the investigation of association between allelic and phenotypes.
Our data present the most comprehensive sequences for the indigenous whitefly species Asia II 3. The extensive comparisons of Asia II 3, MEAM1 and MED transcriptomes will serve as an invaluable resource for revealing the genetic basis of whitefly invasion and the molecular mechanisms underlying their biological differences.
KeywordsBemisia tabaci Biological invasion Genetic divergence Indigenous species Next generation sequencing Transcriptome Whitefly
The whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a species complex composed of at least 31 morphologically indistinguishable cryptic species (hereafter referred to as "species") [1–6]. These species differ genetically as well as in host range, fecundity, insecticide resistance, mating behavior and ability to transmit begomoviruses [7–12]. While many species within the B. tabaci complex cause no obvious harms to agricultural production; some members of this species complex are highly invasive and cause extensive damage to agricultural, horticultural, and ornamental crops through direct feeding or the transmission of plant viruses [13, 14]. Two species of the B. tabaci complex, Middle East - Asia Minor 1 (previously known as biotype B; hereafter MEAM1) and Mediterranean (previously known as biotype Q; hereafter MED) have risen to international prominence due to their global invasion during the last 20 years [8, 15]. MEAM1 and MED originated from the Middle East Asia Minor and Mediterranean Basin regions respectively, and have invaded many countries around the world [3, 16]. Extensive evidence has indicated that the invasion of MEAM1 and MED are associated with the displacement of their closely related indigenous whitefly species [8, 14].
The invasion of an alien whitefly species and competition between invasive and indigenous species are mediated by many abiotic and biotic factors. Efforts have been made to understand the factors that contribute to the incursion of the two species into new regions and the displacement of indigenous species. For example, the invasion of MEAM1 is assumed to be associated with its high adaptability under various environmental stresses and host plants [9, 10, 17, 18]. Liu et al.  also revealed that the displacement of indigenous whitefly species by MEAM1 is associated with the behavior of mating interference. On the other hand, the spread of MED is closely related to its ability to maintain high levels of resistance to major classes of insecticides [19–22]. Despite these advances, the molecular mechanisms underlying the extraordinary capacity of MEAM1 and MED to spread and ultimately displace the native species remains largely unknown. Furthermore, previous studies have mainly focused on single gene or individual aspect of the B. tabaci biology, a global picture of the genetic factors associated with the invasion of these two whitefly species is still lacking.
The genomic divergence between invasive and indigenous species is valuable for determining how phenotypes specific to invasive species have been formed . By examining the divergence of large numbers of genes, a overall picture of genetic differences and invasion mechanisms may be attained . Here, we propose that a global analysis of genomic divergence among the B. tabaci species complex will reveal the molecular mechanisms underlying the biological invasions of MEAM1 and MED. First, the B. tabaci species are reproductively isolated, but retain sufficient genetic similarity for comparative analyses [4, 25, 26]. Second, the whitefly species went through an allopatric divergence process and showed significant differences in survival and reproductive performance [16, 27, 28]. This warrants exploring the interspecies evolutionary processes through the comparison of orthologous genes. Third, at least 31 species have been delineated for the B. tabaci complex including 2 invasive species and 29 indigenous species. The rich diversity of invasive and indigenous species allows extensive cross comparisons of orthologous genes among difference members of the complex, which will facilitate the elucidation of invasive mechanisms.
The transcriptomes of two invasive whitefly species MED and MEAM1 have been sequenced using Illumina sequencing technology [29, 30]. In this study, we sequenced the transcriptome of an indigenous B. tabaci species - Asia II 3 (previously known as biotype ZHJ1) and generated 52,535 distinct sequences. These transcriptome sequences provide a rich molecular resource for functional analysis of the native B. tabaci species. In order to gain further insights on how genes have diverged between the indigenous and invasive whiteflies, we compared the global sequence divergence between the transcriptomes of Asia II 3 and the invasive species MEAM1 and MED. The identification and analysis of divergent sequences between the indigenous and invasive whitefly species opens the door for future investigations on the molecular mechanisms of B. tabaci invasion. The approach described in this manuscript will significantly accelerate the identification of genetic variation underlying adaptation in B. tabaci and other invasive species.
Results and discussion
Illumina sequencing and assembly of Asia II 3 transcriptome
Summary for the Asia II 3 B. tabaci transcriptome
Total number of reads
Total base pairs (bp)
Average read length (bp)
Total number of contigs
Mean length of contigs ( bp)
Total number of scaffolds
Mean length of scaffolds (bp)
Sequences with E-value < 10-5
Functional annotation of Asia II 3 transcriptome
For functional annotation, distinct sequences were searched against the non-redundant (nr) NCBI nucleotide database and a total of 16,596 genes returned an above cut-off BLAST result representing about 31.6% of all distinct sequences (Additional file 2). This proportion is similar to the 20% to 40% of annotated sequences from a traditional Sanger sequenced EST library . To determine the possible functions of assembled Asia II 3 genes, Gene Ontology (GO) assignments were used to classify the distinct sequences. Based on sequence homology, 4,819 sequences could be categorized into functional groups under the “Molecular function”, “Biological process” and “Cellular component” divisions (Additional file 3). The functions of genes cover various biological processes and genes participate in “Cellular process” and “Metabolic process” are the most highly represented. Next, we compared the GO classification of the Asia II 3 transcriptome with that of MEAM1 and MED transcriptomes, respectively [29, 30] and found that the distributions of gene functions from these three species are similar (Additional file 3). These results suggest: i) the functions of genes from Asia II 3, MEAM1 and MED are highly conserved; ii) there is no bias in the construction of the libraries from these B. tabaci species.
Analysis of Asia II 3 gene expression
Highly expressed genes in the transcriptome of Asia II 3
Number of readsa
Tubulin alpha-1 chain
60S ribosomal protein L18a
ADP,ATP carrier protein 2
Elongation factor 1-alpha 2
ATP synthase subunit beta, mitochondrial
Troponin T, skeletal muscle
Elongation factor 1-alpha
40S ribosomal protein S12
40S ribosomal protein S11
60S ribosomal protein L15
40S ribosomal protein S20
60S ribosomal protein L6
Ferritin, middle subunit
Paramyosin, short form
ATP synthase subunit alpha, mitochondrial
40S ribosomal protein S2
Identification and analysis of B. tabaci protein families
Statistically enriched Gene Ontology terms in the “Core protein families”
Number of Core protein family genes mapped to each GO
All transcriptome genes mapped to each GO
Asia II 3
structural molecule activity
amino acid transport
amino acid transmembrane transporter activity
COPI vesicle coat
iron ion binding
cysteine-type peptidase activity
Identification of the orthologous genes between Asia II 3 and MEAM1, Asia II 3 and MED
Sequence divergence of MEAM1/Asia II 3 and MED/Asia II 3
MEAM1/Asia II 3
MED/Asia II 3
Total ortholog pairs:
Total aligned length (kb):
Mean aligned length (bp):
Longest aligned length (bp):
Mean homology (%):
Lowest homology (%):
Highest homology (%):
The sequence divergence between Asia II 3 and MEAM1
Sequence divergence between MEAM1 and Asia II 3
The sequence divergence between Asia II 3 and MED
Analysis of synonymous and non-synonymous sites
The nonsynonymous (Ka) and synonymous (Ks) substitution rates have been widely used to measure the intensity of gene evolution. To identify genes undergoing purifying or positive selections, we estimated Ka and Ks rates of orthologous gene pairs. Among the 2,966 pairs of CDS between Asia II 3 and MEAM1, both a Ka and a Ks rate could be calculated for 1,373 orthologs (Additional file 7). The 1,373 sequence pairs had mean values of Ka, Ks and Ka/Ks of 0.0094, 0.0623 and 0.198. These ratios are similar to the average Ka/Ks ratio of MEAM1 & MED (0.225), Ka/Ks ratio of rat & mouse (0.19) and human & chimpanzee (0.22) [30, 38, 39]. The distribution of the Ka/Ks ratio showed that the majority of genes (98.2%, 1348/1373) have Ka/Ks ratios less than 1, indicating the strong purifying selection for these genes. In addition, Fisher's exact test reports that nearly 56% of the genes are statistically significant (P<0.01) (Additional file 7). In this set of data, 25 orthologous gene pairs had a Ka/Ks value > 1 suggesting that strong positive selection acts on these genes. Among the sequences with Ka/Ks values > 1, a number of genes are involved in protein metabolism, such as peptide deformylase, cathepsin, cysteine proteinase and metalloendopeptidase, suggesting this process is critical for the differentiation of the B. tabaci species complex. Between Asia II 3 and MED, a total of 1,221 orthologs had Ka and Ks rates and the mean values of Ka, Ks and Ka/Ks were 0.0092, 0.0729 and 0.201. The list of Asia II 3 and MED genes with Ka/Ks and functional annotation is presented in Additional file 8. The distribution of Ka/Ks between Asia II 3 and MED is similar to that of Asia II 3 and MEAM1, in which 7.9% of the genes had a high Ka/Ks>0.5, 44.8% of the genes were highly conserved (Ka/Ks<0.1) and 25 genes had a Ka/Ks >1. Even though a number of genes under positive selection have been identified in our study, the simple Ka/Ks calculations are quite conservative and may fail to detect positive selection even when it exists . Further studies using more sophisticated site and branch specific models are needed for estimating Ka/Ks.
Sequences with very weak amino-acid similarity
Mean identity of the orthologous gene pairs by KEGG classification
Number of genes
Metabolism of xenobiotics by cytochrome P450
Drug metabolism - cytochrome P450
Ascorbate and aldarate metabolism
Pentose and glucuronate interconversions
Ubiquinone and terpenoid-quinone biosynthesis
Glyoxylate and dicarboxylate metabolism
Other types of O-glycan biosynthesis
Steroid hormone biosynthesis
MAPK signaling pathway - yeast
Cardiac muscle contraction
Insect hormone biosynthesis
Cytokine-cytokine receptor interaction
Analysis of SNP
To further understand the mechanism of divergence, we analyzed the potential SNP sites in CDS of the orthologous gene pairs between Asia II 3 and MEAM1. For Asia II 3, a total of 138 SNPs were identified within the 1433.63 kb aligned regions, about 1 every ten thousands bp. The complete list of SNPs with annotation can be found in Additional file 10. Of the 138 SNPs, 96 (69.6%) were synonymous and 42 (30.4%) were non-synonymous. This percentage of SNPs is much lower than those obtained in previous analyses in other insects [41, 42]. The possible reason is that our B. tabaci populations were established from a pair of B. tabaci and the samples were collected within five generations. For MEAM1, a slightly higher number of SNPs (248, about 1.7 every ten thousands bp) were found in the orthologous gene pairs with 196 synonymous and 52 non-synonymous (Additional file 10). Compared with the average divergence between Asia II 3 and MEAM1 in CDS (1.83%), the percentage of SNPs is more than 100 times lower. Next, the potential SNP sites in the CDS of orthologous gene pairs between Asia II 3 and MED were analyzed. Our results showed that a total of 56 SNPs in Asia II 3 and 627 SNPs in MED were identified within the 1,071 kb aligned regions (Additional file 11). The large variation between the numbers of SNPs in Asia II 3 and MED is probably due to the difference in the sequencing among of Asia II 3 (1G) and MED (3G) (Additional file 1). Some of the SNPs in Asia II 3 might have been filtered out because only SNP sites with the minimum read depth of 10 reads were selected. Thses results are consistent with previous findings, in which overall number of SNPs decreases at lower coverage levels . While the SNPs we have identified here are suitable for future research, more rigorous statistical tests are required to confirm the current results as well as to detect specific codons undergoing adaptive changes. In addition, further studies of the SNPs on population samples are warranted as our data were generated from inbred lab colonies.
In summary, this study dramatically increases the number of genes from the native Asia II 3 B. tabaci species. Together with the previously available MEAM1 and MED transcriptomes, this study is the first globally comparative analyses of the genetic differences between the invasive and indigenous B. tabaci species. Based on sequence homology, a group of 3,023 protein families conserved among the Asia II 3, MEAM1 and MED species were identified. These protein families might be responsible for core cellular and physiological functions of the B. tabaci complex. Sequence comparisons of all orthologous gene pairs revealed that the average genetic divergences between Asia II 3 and invasive MEAM1 are nearly twice of that between MEAM1 and MED, in accordance with previous genetic studies. The divergent genes identified in this study will be an invaluable resource to reveal the possible mechanisms of B. tabaci invasion, displacement and speciation.
Stock culture of the Asia II 3 (mitochondrial cytochrome oxidase I gene GenBank accession no: AJ867556) was maintained on cotton, Gossypium hirsutum (Malvaceae) cv. Zhe-Mian 1793 in a climate chamber (see conditions below). The purity of the culture was monitored using the random amplified polymorphic DNA-PCR technique with the primer H16 (5’-TCTCAGCTGG-3’) . For sample preparation, a pair of virgin adults of B. tabaci Asia II 3 were released onto a cotton plant to oviposit and develop for five generations at 27 ± 1°C, a photoperiod of 14 h light:10 h darkness and 70 ± 10% relative humidity . The same protocols were used to raise the MEAM1 and MED whiteflies for sample collection and subsequent transcriptome data generation.
Sample preparation and RNA isolation
In order to get an overall picture of the Asia II 3 whitefly transcriptome, we collected the samples from four different developing stages: 1) egg & nymph (the eggs are extremely small, therefore a mixture of eggs and first to third instar nymphs were collected as one sample); 2) pupa; 3) female adult and 4) male adult. To ensure that the whitefly adults are in the same developmental stage, only newly emerged adults were collected. Previously, samples from MEAM1 and MED have been collected using the same strategy [29, 30]. Total RNA was isolated from the four samples using SV total RNA isolation system (Promega) according to the manufacturer’s protocol . RNA integrity was confirmed using the 2100 Bioanalyzer (Agilent Technologies) with a minimum RNA integrated number value of 8. Then, equal amount of RNA from egg & nymph, pupa, female adult and male adult were mixed, and mRNA was purified from the mixture using oligo (dT) magnetic beads.
Library preparation and Illumina sequencing
For transcriptome sequencing, a 200 bp cDNA library was prepared using Illumina’s kit as previously described . The library was not normalized because we intend to use the resulting sequence reads to analyze the level of gene expression. The cDNA library was sequenced for both ends on the GAII Illumina sequencing platform (a single lane) at The Beijing Genome Institute (Shenzhen, China). The total sequencing amount was 1G. The raw reads were filtered by removing adaptor sequences, empty reads and low quality sequences (reads with unknown sequences 'N') . Next, the reads were randomly clipped into 21 bp K-mers for assembly using SOAPdenovo software because the 21-mer provided the best result for transcriptome assembly. Small K-mers made the graph very complex; while large K-mers have poor overlap in low sequencing depth regions . For alleles, the nucleotides with the highest frequency were selected. The resultant contigs were joined into scaffolds based on the mate pairs information and the scaffolds were clustered using TGI Clustering tools . Assembled genes were used for subsequent analyses and are referred to as “distinct sequences”.
The data sets of Illumina sequencing are available at the NCBI Short Read Archive (SRA) database with the accession number: SRR062575. The assembled sequences were deposited in the NCBI Transcriptome Shotgun Assembly (TSA) database under the accession number of HP777244 to HP823074 and can be searched using the GeneID listed in Additional file 2, Supporting information.
Functional annotation and gene expression analysis
Distinct sequences were annotated by Blast search against the NCBI nr database with a cut-off E-value of 10-5. GO annotation was analyzed by Blast2GO software . The GO terms were retrieved from Blast hits with an E-value threshold of 10-5. Comparisons of the distribution of GO terms among the Asia II, MEAM1 and MED transcriptomes were done using the Web Gene Ontology Annotation Plot (WEGO) . Pathway annotation was performed using Blastall software against the KEGG database. Based on the number of reads for a gene, gene expression levels can be estimated from Illumina sequencing with great accuracy [50, 51]. To estimate the level of gene expression, the number of reads mapped to each distinct sequence was extracted. Since read mapping is sensitive to the size of the target reference sequence and sequencing amount, we adjusted the raw read count by the total number of reads mapped and the length of the gene by calculating Reads Per Kilobase per Million mapped reads (RPKM) .
Analysis of protein families
To reveal the functional differences among Asia II 3, MEAM1 and MED transcriptomes, we analyzed their protein families. Using Blastx (E-value <10-5), the translated region of each gene was identified by aligning the sequence to the Swissprot database. The longest translated protein sequence for each gene was then extracted and sequences less than 200 bp were removed. To identify protein families among the three transcriptomes, an all-against-all Blastp was performed for all the translated genes from the three transcriptomes. Blastp results were further analyzed by a Markov Cluster Algorithm (MCL) with an inflation factor of 1.6. The protein families belonging to all the three transcriptomes were referred to as “Core protein families”. Based on the GO annotation of Asia II 3, MEAM1 and MED transcriptomes, we calculated the total number of genes under each GO term in the “Core protein families” and the three transcriptomes. For each GO term, its enrichment in the “Core protein families” was measured using the hypergeometric test with an cut-off p value of 10-5.
Identification of orthologous genes and prediction of coding and untranslated regions
The orthologous genes between Asia II 3 and MEAM1 and those between Asia II 3 and MED were identified respectively according to the previous description using MegaBLAST . Briefly, pairs of sequences that were reciprocally a best hit and with a minimum length of 200 bp were retained as putative orthologs. To remove potential paralogs, only pairs of sequences unambiguously mapped to the same protein in Swissprot database with an E-value <1×10-5 were selected. The CDS of the orthologous genes were determined by BLASTx the Swissprot database with an E value<1×10-5. The start codon was determined by examination of the in-frame ATG codon of the aligned reference protein. The stop codon position was determined by examination of in-frame TAA, TAG and TGA motifs present within 30 bp of the stop codon of the reference protein. The 5’UTR and 3’UTR regions were defined based on the position of start codon, stop codon and predicted CDS. To prevent false positive results, only UTR pairs with an E-value<1×10-30 were selected for further analyses. CDS containing unexpected stop codon(s) and shorter than 150 bp were removed.
Sequence divergence analyses and estimation of substitution rates
The 5’UTR, CDS and 3’UTR regions were separately extracted from each pair of orthologs. The CDS and UTR regions were aligned separately to each other with a MegaBlast algorithm and checked manually for errors. Only the homologous regions of each gene pair were extracted for sequence comparison. Sequence divergence between the homologous regions of each gene pair was calculated by dividing the number of substitutions by the number of base pairs compared. The average divergence between transcriptomes was determined by dividing the total number of substitutions by the total number of base pairs compared. The sequence divergence at nondegenerate (nd), fourfold degenerate (4d), CpG and non-CpG regions was determined respectively according to the previous descriptions . The ratio of transition over transversion (ts/tv) was determined for the 5’UTR, CDS and 3’UTR as well. Using the KaKs Calculator, we also estimated the substitution rates for non-synonymous sites (Ka) and synonymous (Ks) sites (YN method) [52, 53].
Phylogeny of Asia II 3, MEAM1 and MED
The orthologous genes among Asia II 3, MEAM1 and MED were selected for sequence alignment using MUSCLE  and the aligned fragments were extracted for phylogenetic analysis using MEGA 5 . The evolutionary history was inferred using the neighbor-joining method . All positions containing gaps and missing data were eliminated and the within population polymorphisms were not included for divergence estimation. The analysis involved a total of 686,101 positions in the final dataset. The evolutionary distances were computed using the Maximum Composite Likelihood method  and are in the units of the number of base substitutions per site.
To reveal the mechanism of divergence between Asia II 3 and MEAM1, we analyzed the potential SNP sites in CDS of the orthologous gene pairs between the invasive and indigenous whitefly species. The orthologous gene pairs of Asia II 3 & MEAM1 and Asia II 3 & MED were subjected to SNP analysis according to the previous description with slight modifications . In short, the Illumina sequencing reads were mapped to the orthologous CDS regions of each gene using TopHat (V1.2.0) with the following parameters: -g1 -r 200 --mate-std-dev 20 -I 10000 . All possible SNP sites with the minimum read depth of 10 reads were then identified by SAMTools (V0.1.13) based on aligned outcomes . The analyses of amino acid mutation and functional annotation were performed by a custom-written algorithm.
Financial support for this study was provided by the National Basic Research Program of China (Project 2009CB119203), and the National Natural Science Foundation of China (Project 31021003, 31071686). We thank Laura Boykin for editing the manuscript.
- Dinsdale A, Cook L, Riginos C, Buckley Y, De Barro PJ: Refined global analysis of Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodoidea) mitochondrial CO1 to identify species level genetic boundaries. Ann Entomol Soc Am. 2010, 103: 196-208. 10.1603/AN09061.View ArticleGoogle Scholar
- De Barro PJ, Liu SS, Boykin LM, Dinsdale AB: Bemisia tabaci: a statement of species status. Annu Rev Entomol. 2011, 56: 1-19. 10.1146/annurev-ento-112408-085504.View ArticlePubMedGoogle Scholar
- Hu J, De Barro PJ, Zhao H, Wang J, Nardi F, Liu SS: An extensive field survey combined with a phylogenetic analysis reveals rapid and widespread invasion of two alien whiteflies in china. PLoS One. 2011, 6: e16061-10.1371/journal.pone.0016061.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu SS, Colvin J, De Barro PJ: Species concepts as applied to the whitefly Bemisia tabaci systematics: how many species are there?. J Integr Agri. 2012, 11: 176-186.View ArticleGoogle Scholar
- Alemandri V, De Barro P, Bejerman N, Arguello Caro EB, Dumon AD, Mattio MF, Rodriguez SM, Truoli G: Species within the Bemisia tabaci (Hemiptera: Aleyrodidae) complex in soybean and bean crops in Argentina. J Econ Entomol. 2012, 105: 48-53. 10.1603/EC11161.View ArticlePubMedGoogle Scholar
- Parrella G, Scassillo L, Giorgini M: Evidence for a new genetic variant in the Bemisia tabaci species complex and the prevalence of the biotype Q in southern Italy. J Pest Sci. 2012, 85: 227-238. 10.1007/s10340-012-0417-2.View ArticleGoogle Scholar
- Brown JK: Molecular markers for the identification and global tracking of whitefly vector-Begomovirus complexes. Virus Res. 2000, 71: 233-260. 10.1016/S0168-1702(00)00221-5.View ArticlePubMedGoogle Scholar
- Liu SS, De Barro PJ, Xu J, Luan JB, Zang LS, Ruan YM, Wan FH: Asymmetric mating interactions drive widespread invasion and displacement in a whitefly. Science. 2007, 318: 1769-1772. 10.1126/science.1149887.View ArticlePubMedGoogle Scholar
- Jiu M, Zhou XP, Tong L, Xu J, Yang X, Wan FH, Liu SS: Vector-virus mutualism accelerates population increase of an invasive whitefly. PLoS One. 2007, 2: e182-10.1371/journal.pone.0000182.PubMed CentralView ArticlePubMedGoogle Scholar
- Crowder DW, Horowitz AR, De Barro PJ: Mating behaviour, life history and adaptation to insecticides determine species exclusion between whiteflies. J Anim Ecol. 2010, 79: 563-570. 10.1111/j.1365-2656.2010.01666.x.View ArticlePubMedGoogle Scholar
- Gorman K, Slater R, Blande JD, Clarke A, Wren J, McCaffery A, Denholm I: Cross-resistance relationships between neonicotinoids and pymetrozine in Bemisia tabaci (Hemiptera: Aleyrodidae). Pest Manag Sci. 2010, 66: 1186-1190. 10.1002/ps.1989.View ArticlePubMedGoogle Scholar
- Czosnek H, Ghanim M, Ghanim M: The circulative pathway of begomoviruses in the whitefly vector Bemisia tabaci; insights from studies with Tomato yellow leaf curl virus. Ann Appl Biol. 2002, 140: 215-231. 10.1111/j.1744-7348.2002.tb00175.x.View ArticleGoogle Scholar
- Dalton R: Whitefly infestations: the Christmas Invasion. Nature. 2006, 443: 898-900. 10.1038/443898a.View ArticlePubMedGoogle Scholar
- Naranjo SE, Castle SJ, De Barro PJ, Liu SS: Population Dynamics, Demography, Dispersal and Spread of Bemisia tabaci. Bemisia: Bionomics and Management of a Global Pest. Edited by: Stansly PA, Naranjo SE. 2010, Heidelberg: Springer, 185-226.Google Scholar
- Brown JK, Frohlich DR, Rosell RC: The sweetpotato or silverleaf whiteflies: biotypes of Bemisia tabaci or a species complex?. Annu Rev Entomol. 1995, 40: 511-534. 10.1146/annurev.en.40.010195.002455.View ArticleGoogle Scholar
- Boykin LM, Shatters RG, Rosell RC, McKenzie CL, Bagnall RA, De Barro PJ, Frohlich DR: Global relationships of Bemisia tabaci (Hemiptera: Aleyrodidae) revealed using Bayesian analysis of mitochondrial COI DNA sequences. Mol Phylogenet Evol. 2007, 44: 1306-1319. 10.1016/j.ympev.2007.04.020.View ArticlePubMedGoogle Scholar
- Luan JB, Xu J, Lin KK, Zalucki MP, Shu-sheng L: Species exclusion between an invasive and an indigenous whitefly on host plants with differential levels of suitability. J Integr Agri. 2012, 11: 215-224.View ArticleGoogle Scholar
- Muñiz M, Nombela G: Differential variation in development of the B- and Q-biotypes of Bemisia tabaci (Homoptera: Aleyrodidae) on sweet pepper at constant temperatures. Environ Entomol. 2001, 30: 720-727. 10.1603/0046-225X-30.4.720.View ArticleGoogle Scholar
- Nauen R, Stumpf N, Elbert A: Toxicological and mechanistic studies on neonicotinoid cross resistance in Q-type Bemisia tabaci (Hemiptera: Aleyrodidae). Pest Manag Sci. 2002, 58: 868-875. 10.1002/ps.557.View ArticlePubMedGoogle Scholar
- Horowitz AR, Kontsedalov S, Khasdan V, Ishaaya I: Biotypes B and Q of Bemisia tabaci and their relevance to neonicotinoid and pyriproxyfen resistance. Arch Insect Biochem Physiol. 2005, 58: 216-225. 10.1002/arch.20044.View ArticlePubMedGoogle Scholar
- Fernandez E, Gravalos C, Haro PJ, Cifuentes D, Bielza P: Insecticide resistance status of Bemisia tabaci Q-biotype in south-eastern Spain. Pest Manag Sci. 2009, 65: 885-891. 10.1002/ps.1769.View ArticlePubMedGoogle Scholar
- Ghanim M, Kontsedalov S: Gene expression in pyriproxyfen-resistant Bemisia tabaci Q biotype. Pest Manag Sci. 2007, 63: 776-783. 10.1002/ps.1410.View ArticlePubMedGoogle Scholar
- Stewart CNJ: Weedy and Invasive Plant Genomics. 2009, New Jersey: Wiley-BlackwellView ArticleGoogle Scholar
- Charlesworth D, Charlesworth B, McVean GA: Genome sequences and evolutionary biology, a two-way interaction. Trends Ecol Evol. 2001, 16: 235-242. 10.1016/S0169-5347(01)02126-7.View ArticlePubMedGoogle Scholar
- Wang P, Sun DB, Qiu BL, Liu SS: The presence of six putative species of the whitefly Bemisia tabaci complex in China as revealed by crossing experiments. Insect Sci. 2011, 18: 67-77. 10.1111/j.1744-7917.2010.01381.x.View ArticleGoogle Scholar
- Sun DB, Xu J, Luan JB, Liu SS: Reproductive incompatibility between the B and Q biotypes of the whitefly Bemisia tabaci: genetic and behavioural evidence. Bull Entomol Res. 2011, 101: 211-220. 10.1017/S0007485310000416.View ArticlePubMedGoogle Scholar
- Moya A, Guirao P, Cifuentes D, Beitia F, Cenis JL: Genetic diversity of Iberian populations of Bemisia tabaci (Hemiptera: Aleyrodidae) based on random amplified polymorphic DNA-polymerase chain reaction. Mol Ecol. 2001, 10: 891-897. 10.1046/j.1365-294X.2001.01221.x.View ArticlePubMedGoogle Scholar
- Elbaz M, Weiser M, Morin S: Asymmetry in thermal tolerance trade-offs between the B and Q sibling species of Bemisia tabaci (Hemiptera: Aleyrodidae). J Evol Biol. 2011, 24: 1099-1109. 10.1111/j.1420-9101.2011.02241.x.View ArticlePubMedGoogle Scholar
- Wang XW, Luan JB, Li JM, Bao YY, Zhang CX, Liu SS: De novo characterization of a whitefly transcriptome and analysis of its gene expression during development. BMC Genomics. 2010, 11: 400-10.1186/1471-2164-11-400.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang XW, Luan JB, Li JM, Su YL, Xia J, Liu SS: Transcriptome analysis and comparison reveal divergence between two invasive whitefly cryptic species. BMC Genomics. 2011, 12: 458-10.1186/1471-2164-12-458.PubMed CentralView ArticlePubMedGoogle Scholar
- Li R, Zhu H, Ruan J, Qian W, Fang X, Shi Z, Li Y, Li S, Shan G, Kristiansen K, Li S, Yang H, Wang J, Wang J: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010, 20: 265-272. 10.1101/gr.097261.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Elmer KR, Fan S, Gunter HM, Jones JC, Boekhoff S, Kuraku S, Meyer A: Rapid evolution and selection inferred from the transcriptomes of sympatric crater lake cichlid fishes. Mol Ecol. 2010, 19 (Suppl 1): 197-211.View ArticlePubMedGoogle Scholar
- Yin Y, Martin J, Abubucker S, Scott AL, McCarter JP, Wilson RK, Jasmer DP, Mitreva M: Intestinal transcriptomes of nematodes: comparison of the parasites Ascaris suum and Haemonchus contortus with the free-living Caenorhabditis elegans. PLoS Negl Trop Dis. 2008, 2: e269-10.1371/journal.pntd.0000269.PubMed CentralView ArticlePubMedGoogle Scholar
- Zang LS, Chen WQ, Liu SS: Comparison of performance on different host plants between the B biotype and a non-B biotype of Bemisia tabaci from Zhejiang, China. Entomol Exp Appl. 2006, 121: 221-227. 10.1111/j.1570-8703.2006.00482.x.View ArticleGoogle Scholar
- Yang Z, Yoder AD: Estimation of the transition/transversion rate bias and species sampling. J Mol Evol. 1999, 48: 274-283. 10.1007/PL00006470.View ArticlePubMedGoogle Scholar
- Shen JC, Rideout WM, Jones PA: The rate of hydrolytic deamination of 5-methylcytosine in double-stranded DNA. Nucleic Acids Res. 1994, 22: 972-976. 10.1093/nar/22.6.972.PubMed CentralView ArticlePubMedGoogle Scholar
- Xu J, De Barro PJ, Liu SS: Reproductive incompatibility among genetic groups of Bemisia tabaci supports the proposition that the whitefly is a cryptic species complex. Bull Entomol Res. 2010, 100: 359-366. 10.1017/S0007485310000015.View ArticlePubMedGoogle Scholar
- Makalowski W, Boguski MS: Evolutionary parameters of the transcribed mammalian genome: an analysis of 2,820 orthologous rodent and human sequences. Proc Nat Acad Sci USA. 1998, 95: 9407-9412.PubMed CentralView ArticlePubMedGoogle Scholar
- Hellmann I, Zollner S, Enard W, Ebersberger I, Nickel B, Paabo S: Selection on human genes as revealed by comparisons to chimpanzee cDNA. Genome Res. 2003, 13: 831-837. 10.1101/gr.944903.PubMed CentralView ArticlePubMedGoogle Scholar
- Nielsen R, Yang Z: Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics. 1998, 148: 929-936.PubMed CentralPubMedGoogle Scholar
- Wondji CS, Hemingway J, Ranson H: Identification and analysis of single nucleotide polymorphisms (SNPs) in the mosquito Anopheles funestus, malaria vector. BMC Genomics. 2007, 8: 5-10.1186/1471-2164-8-5.PubMed CentralView ArticlePubMedGoogle Scholar
- Morlais I, Poncon N, Simard F, Cohuet A, Fontenille D: Intraspecific nucleotide variation in Anopheles gambiae: new insights into the biology of malaria vectors. Am J Trop Med Hyg. 2004, 71: 795-802.PubMedGoogle Scholar
- Kim SY, Lohmueller KE, Albrechtsen A, Li Y, Korneliussen T, Tian G, Grarup N, Jiang T, Andersen G, Witte D, Jorgensen T, Hansen T, Pedersen O, Wang J, Nielsen R: Estimation of allele frequency and association mapping using next-generation sequencing data. BMC Bioinformatics. 2011, 12: 231-10.1186/1471-2105-12-231.PubMed CentralView ArticlePubMedGoogle Scholar
- Luo C, Jones CM, Devine G, Zhang F, Denholm I, Gorman K: Insecticide resistance in Bemisia tabaci biotype Q (Hemiptera: Aleyrodidae) from China. Crop Prot. 2010, 29: 429-434. 10.1016/j.cropro.2009.10.001.View ArticleGoogle Scholar
- Li FF, Xia J, Li JM, Liu SS, Wang XW: p38 MAPK is a component of the signal transduction pathway triggering cold stress response in the MED cryptic species of Bemisia tabaci. J Integr Agri. 2012, 11: 302-311.Google Scholar
- Li JM, Ruan YM, Li FF, Liu SS, Wang XW: Gene expression profiling of the whitefly (Bemisia tabaci) Middle East – Asia Minor 1 feeding on healthy and Tomato yellow leaf curl China virus-infected tobacco. Insect Sci. 2011, 18: 11-22. 10.1111/j.1744-7917.2010.01386.x.View ArticleGoogle Scholar
- Pertea G, Huang X, Liang F, Antonescu V, Sultana R, Karamycheva S, Lee Y, White J, Cheung F, Parvizi B, Tsai J, Quackenbush J: TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics. 2003, 19: 651-652. 10.1093/bioinformatics/btg034.View ArticlePubMedGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21: 3674-3676. 10.1093/bioinformatics/bti610.View ArticlePubMedGoogle Scholar
- Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, Wang J, Li S, Li R, Bolund L, Wang J: WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006, 34: W293-W297. 10.1093/nar/gkl031.PubMed CentralView ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5: 621-628. 10.1038/nmeth.1226.View ArticlePubMedGoogle Scholar
- Wolf JB, Bayer T, Haubold B, Schilhabel M, Rosenstiel P, Tautz D: Nucleotide divergence vs. gene expression differentiation: comparative transcriptome sequencing in natural isolates from the carrion crow and its hybrid zone with the hooded crow. Mol Ecol. 2010, 19 (Suppl 1): 162-175.View ArticlePubMedGoogle Scholar
- Yang Z, Nielsen R: Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol. 2000, 17: 32-43. 10.1093/oxfordjournals.molbev.a026236.View ArticlePubMedGoogle Scholar
- Zhang Z, Li J, Zhao XQ, Wang J, Wong GK, Yu J: KaKs_Calculator: calculating Ka and Ks through model selection and model averaging. Genomics Proteomics Bioinformatics. 2006, 4: 259-263. 10.1016/S1672-0229(07)60007-2.View ArticlePubMedGoogle Scholar
- Edgar RC: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32: 1792-1797. 10.1093/nar/gkh340.PubMed CentralView ArticlePubMedGoogle Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011, 28: 2731-2739. 10.1093/molbev/msr121.PubMed CentralView ArticlePubMedGoogle Scholar
- Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987, 4: 406-425.PubMedGoogle Scholar
- Tamura K, Nei M, Kumar S: Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc Natl Acad Sci USA. 2004, 101: 11030-11035. 10.1073/pnas.0404206101.PubMed CentralView ArticlePubMedGoogle Scholar
- You FM, Huo N, Deal KR, Gu YQ, Luo MC, McGuire PE, Dvorak J, Anderson OD: Annotation-based genome-wide SNP discovery in the large and complex Aegilops tauschii genome using next-generation sequencing without a reference genome sequence. BMC Genomics. 2011, 12: 59-10.1186/1471-2164-12-59.PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120.PubMed CentralView ArticlePubMedGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.PubMed CentralView ArticlePubMedGoogle Scholar