MicroRNAs (miRNAs) are small, conserved, non-coding RNAs that post-transcriptionally regulate gene expression. Bemisia tabaci (Gennadius) B and Q are two invasive and dominant whiteflies, and B. tabaci Q has been displacing B in China. Differences in biological traits (fecundity, host range, resistance to insecticides, etc.) as affected by miRNAs might be involved in the displacement. In this study, we performed high-throughput sequencing to identify miRNAs in B. tabaci B and Q.
We identified 170 conserved miRNAs and 15 novel candidates, and found significant differences in the expression of miRNAs between B. tabaci B and Q.
Citation: Guo Q, Tao Y-L, Chu D (2013) Characterization and Comparative Profiling of miRNAs in Invasive Bemisia tabaci (Gennadius) B and Q. PLoS ONE 8(3): e59884. https://doi.org/10.1371/journal.pone.0059884
Editor: James C. Nelson, Kansas State University, United States of America
Received: November 6, 2012; Accepted: February 19, 2013; Published: March 20, 2013
Copyright: © 2013 Guo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Natural Science Foundation of China (Grant No. 31071747; 31272105), the National Basic Research and Development Program of China (2009CB119200) and the Shandong Provincial Special Fund for “Mount Tai scholars”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
MicroRNAs (miRNAs), which were first discovered in C. elegans , constitute a novel class of non-coding RNA species in viruses, plants, and animals . By post-transcriptional regulation of gene expression, miRNAs play crucial roles in development –, reproduction , stress response , , and in many other important molecular mechanisms and cellular processes –.
In insects, repertoires of miRNAs have been mainly established for those species whose whole genomes have been sequenced. These include 12 Drosophila species , four hymenopterans (Apis mellifera, Nasonia giraulti, N. longicornis, and N. vitripennis) –, three mosquitoes (Aedes aegypti, Anopheles gambiae, and Culex quinquefasciatus) –, the pea aphid (Acyrthosiphon pisum) , the silkworm (Bombyx mori) , , –, the butterfly (Heliconius melpomene) , the migratory locust (Locusta migratoria) , and the flour beetle (Tribolium castaneum) , .
Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is an important agricultural pest worldwide that attacks more than 600 plant species including food, fiber, and ornamental plants under field and greenhouse conditions , . By phloem feeding, contaminating leaves and fruits with honeydew, and transmitting more than 110 kinds of plant viruses, adult and immature instars of B. tabaci cause billions of dollars of annual loss worldwide , . In particular, B. tabaci often causes outbreaks of plant-pathogenic viruses , .
Bemisia tabaci is currently regarded as a cryptic species complex that contains at least 24 morphologically indistinguishable species . Two members of this complex, Middle East-Asia Minor 1 (commonly known as B. tabaci biotype B, herein referred to as B. tabaci B) and Mediterranean (commonly known as B. tabaci biotype Q, herein referred to as B. tabaci Q), have now spread well beyond their home ranges as a consequence of trade in ornamental plants . In China, B. tabaci B and Q are the main whiteflies in agricultural areas and have caused severe damage to many crops –. We previously reported that the ratio of abundances of B. tabaci Q to B. tabaci B has been increasing and that Q is displacing B on cotton, eggplant, and other plants in Shandong Province of China , . Teng et al.  also found that B. tabaci Q has become dominant across China and suggested that B. tabaci Q has been displacing non-Q whiteflies in many regions such as Shanxi, Henan, Hubei, Jiangsu, Zhejiang, Hunan, and Hainan provinces.
As noted earlier, many biological differences between B. tabaci B and Q have been documented. B. tabaci Q had significantly greater reproductive parameters than B in winter weeds , had shorter developmental times than B on sweet pepper at constant temperatures , had better survival than B under low as well as high temperature conditions , and had greater resistance to neonicotinoides and pyriproxyfen insecticides than B –. All of these would seem to at least partially explain the displacement of B by Q . The genetic differences between B and Q have also been studied recently, and several genes involved in metabolism and insecticide resistance were considered as possibly contributing to the divergence of the two whitefly species . Because miRNAs are now recognized as critical regulators of gene expression and animal development, the identification and comparison of miRNAs in B. tabaci B and Q could provide new information about the biological differences in these biotypes. The differences between miRNAs of B and Q might also explain the biological differences because miRNAs play an important role in a wide range of cellular and developmental process including cell proliferation , cell differentiation , , the cell cycle , , metabolism , developmental timing , reproduction , apoptosis , and others .
In recent years, high-throughput sequencing technology has been widely used to analyze the characteristics of miRNA within the organisms –. For example, by high-throughput sequencing of miRNA, Marco et al.  characterized 203 miRNAs from the red flour beetle Tribolium castaneum; Liu et al.  analyzed areas of skin where the cashmere grows in anagen and found that the miRNAs that were coexpressed in goat and sheep were located in the same region of the respective chromosomes and may play an essential role in skin and follicle development; Shao et al.  analyzed Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) and found that the accumulation levels of several miRNA*s could be much higher than those of their miRNA partners in certain organs, mutants and/or AGO-associated silencing complexes of both Arabidopsis and rice.
In this study, we performed high-throughput sequencing to identify miRNAs in B. tabaci B and Q, and identified 170 conserved miRNAs and 15 novel candidates. We compared the expression of miRNAs in B and Q to identify differentially expressed miRNAs. The results indicate significant differences in the expression of miRNAs between B. tabaci B and Q.
Materials and Methods
Whitefly Colony and RNA Extraction
Bemisia tabaci B and Q colonies were reared on cotton leaves in growth chambers at 26±1°C and with a 16/8 h light/dark photoperiod. Adult whiteflies (B. tabaci B and Q) were collected and homogenized in Trizol agent RNAiso Plus (TaKaRa, Dalian, China). Total RNA was extracted from B and Q according to the manufacturer’s instructions and was quantified with an Agilent 2100 Bioanalyzer.
Confirmation of B. tabaci B and Q
The identities of B. tabaci B and Q were confirmed based on the cleaved amplified polymorphic sequences (CAPS) of mtCOI (mitochondrial cytochrome oxidase I) with the restriction endonucleases VspI and StuI , . Genomic DNA was extracted from individual adult whiteflies according to the lysis method of Frohlich et al. . The mtCOI fragments were amplified using primers C1-J-2195 (5′-TTGATTTTTTGGTCATCCAGAAGT-3′) and R-BQ-2819 (5′- CTGAATATCGRCGAGGCATTCC -3′) . The 20 µL PCR reaction mixture contained 2 µL of 10×reaction buffer supplemented with 1.5 mM MgCl2, 0.2 µM of each primer, 0.2 mM of each dNTP, 1 unit of Taq DNA polymerase, and 2 µL of each template cDNA. Cycling conditions were as follows: 5 min at 94°C; 35 cycles of 1 min at 94°C, 1 min at 52°C, and 1 min at 72°C; and finally 10 min at 72°C. PCR products were electrophoresed and visualized by ethidium bromide staining. The mtCOI fragment (approximately 620 bp) was first cleaved by VspI , and then the uncut fragment was cleaved by StuI . Specimens whose mtCOI fragments were cut by VspI were identified as B. tabaci Q, whereas specimens whose mtCOI fragments were cut by StuI were identified as B. tabaci B , .
Small RNA Library Preparation and High-throughput Sequencing
For HiSeq deep sequencing, the small RNA samples were prepared as described previously . In brief, RNA fragments with fewer than 40 nt were isolated from total RNA on a 15% Novex TBE-urea PAGE gel. Then, a 5′ adaptor (Illumina, San Diego, CA, USA) was ligated to the purified small RNAs, and the ligation products were purified on a 15% Novex TBE-urea PAGE gel. The 5′ ligation products were then ligated to a 3′ adaptor (Illumina), and products with 5′ and 3′ adaptors were size-fractionated on a 10% Novex TBE-urea PAGE gel. Subsequently, small RNAs ligated with adaptors were reverse transcribed and then subjected to PCR amplification. The amplification products were purified on a 6% Novex TBE PAGE gel. The purified DNA fragments were used for clustering and sequencing by HiSeq high-throughput sequencing technology at the Beijing Genomics Institute, Shenzhen.
Discovery of Conserved miRNAs
The tags under 40 nt sequence from HiSeq sequencing were first subjected to data cleaning, which included removal of the low quality tags and several kinds of contaminants. The distribution of the lengths of the clean tags was then summarized, and the clean tags were assigned to two groups including the summary of unique tags and total tags. The clean tags were annotated into different categories to discard rRNAs, tRNAs, snRNAs, and snoRNAs using Rfam database (version 10.1). Because there was no information concerning miRNAs of B. tabaci in the miRBase v17.0, the remaining small RNA tags were aligned to the miRNA precursors/mature miRNAs of all animals in the miRBase v17.0 , . Sequences in our libraries that were identical to or related to (having four or fewer nucleotide substitutions) sequences from Drosophila melanogaster or other insects (A. aegypti, A. mellifera, B. mori, and T. castaneum) were identified as conserved miRNAs.
Prediction of Novel miRNA Candidates
The characteristic hairpin structure of miRNA precursors can be used to predict novel miRNA candidates. Because there were no completed genome sequences, 27,288 nucleotide sequences of Bemisia tabaci obtained from NCBI (published by Zhejiang University) were used as a reference for novel miRNA prediction. The prediction software Mireap was used to predict novel miRNA candidates by exploring the secondary structure, the Dicer cleavage site, and the minimum free energy of the unannotated small RNA tags that could be mapped to the genome. The rules used to identify novel miRNA candidates were based on those suggested by Allen et al.  and Schwab et al. : (1) novel miRNAs should have no more than four mismatches between sRNA & target (G-U bases count as 0.5 mismatches); (2) novel miRNAs should have no more than two adjacent mismatches in the miRNA/target duplex; (3) novel miRNAs should have no adjacent mismatches in positions 2–12 of the miRNA/target duplex (5′ of miRNA); (4) novel miRNAs should have no mismatches in positions 10–11 of the miRNA/target duplex; (5) novel miRNAs should have no more than 2.5 mismatches in positions 1–12 of the miRNA/target duplex (5′ of miRNA); (6) minimum free energy (MFE) of the miRNA/target duplex should be ≥75% of the MFE of the miRNA bound to its perfect complement.
Comparing the Expression of miRNAs between B. tabaci B and Q
We compared the expression of miRNAs between B. tabaci B and Q to identify differentially expressed miRNAs. The expression of miRNAs in the two libraries was visualized on a scatter plot in which expression of B miRNAs was plotted against expression of Q miRNAs after expression levels were normalized and then transformed into fold-change values (see below). The threshold of a fold change more than 2 was considered significant difference. The procedure had two parts. First, the expression of miRNA in the two libraries (Q as control and B as treatment) was normalized to transcripts per million (TPM). If the normalized expression of a miRNA was zero, it was modified to 0.01 to enable calculation. If the normalized expression of a miRNA was less than 1 in both B and Q libraries, it was ignored to compare for low expression. The normalization formula was:
Normalized expression = Actual miRNA count/Total count of clean reads*1000000. Second, the normalized data were used to calculate fold-change values and P-values, and a scatter plot of the fold-change values was generated. Fold-change was calculated as: Fold-change = log2(B/Q). The P-value was calculated asx represents Q; y represents B; N1 represents the normalized expression of a miRNA in Q library; N2 represents the normalized expression of the same miRNA in B library.
B. tabaci has a Complex Population of Small RNAs
HiSeq high-throughput sequencing technology was used to identify miRNAs in B. tabaci B and Q. Two libraries of small RNAs were constructed, one from B and the other from Q. We obtained 17,953,732 reads from the B library, and 16,448,832 reads from the Q library. Low-quality sequences and those shorter than 18 nt were removed, leaving 17,451,513 reads (2,871,240 unique sequences) in the B library and 15,977,474 reads (3,041,144 unique sequences) in the Q library. The distribution of sequence lengths indicated that both libraries were enriched with small RNAs of 21–23 nt (42.8% and 32.6% of all reads in B and Q libraries, respectively) (Fig. 1), which is considered the standard size of miRNAs. Another type of RNA sequence found in both libraries was 28–30 nt long, corresponding to pi-RNA-like sequences, and represented 28.4% and 49.4% of the reads in B and Q libraries, respectively. In these libraries, sequence length was limited to a maximum of 30 nt.
Subsequent sequence analysis eliminated reads corresponding to rRNAs, tRNAs, snRNAs, and snoRNAs. Another two large fractions of reads were derived from unannotated genome sites (52.2% and 67.3% of high-quality clean reads in B and Q libraries) and miRNAs (37.1% and 25.6%, respectively) (Fig. 2). After successive filtering of these data sets, we identified 52,977 unique miRNA genes comprising 1,504 miRNAs in the B library, and 39,266 unique miRNA genes comprising 1,182 miRNAs in the Q library. Although some miRNAs were very abundant in these data sets, most miRNAs were sequenced only a few times, indicating that the sampled B. tabaci might have a large and complex miRNA population. For example, 37,186 of 52,977 unique miRNA genes were sequenced only one time in the B library. The unique data set with read counts was used to identify conserved and novel miRNAs in B. tabaci.
Identification of Conserved miRNAs
To identify conserved miRNAs in B. tabaci, we searched all small RNA sequences against the currently miRNAs of all animals in miRBase v17.0 using BLASTn , . In total, 1,504 miRNAs were found in the B library, and 1,182 miRNAs were found in the Q library. Sequences in our libraries that were identical to or related to (having four or fewer nucleotide substitutions) sequences from D. melanogaster or other insects (A. aegypti, A. mellifera, B. mori, and T. castaneum) were identified as conserved miRNAs. After BLASTn searches and further sequence analysis, a total of 170 conserved miRNAs identified from the miRNAs were found in both B and Q libraries (Table 1). In these conserved miRNAs, there were nine miRNA families containing five or more miRNAs (Table 1). The identified miRNA families are conserved in a variety of animal species. For example, let-7 , miR-9 , , miR-10 , miR-133 , , and miR-263  have been found in 100, 63, 46, 89, and 74 animal species, respectively.
Identification of Candidate Novel miRNA Candidates
In addition to the identification of conserved miRNAs, we identified 15 potential novel miRNA candidates in both B and Q libraries (Table 2). The length of the 15 predicted novel miRNA candidates ranged from 21 to 24 nt. The free energy of folding for these hairpin structures ranged from −32.22 kcal/mol to −18.6 kcal/mol. The read number for each novel miRNA was lower than that of the conserved miRNAs, which was consistent with previous studies. To investigate the conservation of these novel miRNA candidates in a wide range of animal species, we used these miRNAs as query sequences to perform BLASTn searches against all nucleotide sequences in miRBase v17.0 databases. No homologs were found in any animal species, suggesting that these newly identified miRNAs are all whitefly-specific.
Comparing the Expression of miRNAs between B and Q
To explore their difference in miRNAs, we compared the expression of miRNAs between B and Q (Fig. 3 and Table S1). The expression of 342 miRNAs was higher in the Q library than in the B library, and 198 miRNAs were found only in the Q library. The expression of 474 miRNAs was lower in the Q library than in the B library, and 303 miRNAs were found only in the B library. About 579 miRNAs were found in both B and Q libraries; among these, the expression of 144 miRNAs was higher in the Q library than in the B library, and the expression of 171 miRNAs was lower in the Q library than in the B library.
miRNAs post-transcriptionally regulate gene expression by targeting the 3′ untranslated region of specific messenger RNAs and causing mRNA degradation or translational repression , . In this study, the expression of the most conserved miRNAs was 1.1 to 2.5 times greater in B. tabaci B than in Q (Table 1). Because miRNAs could recognize the target mRNAs based on sequence complementarity and cause mRNA degradation, the expression of some functional proteins should be lower in B. tabaci B than in Q, which possibly contributing to the biological differences between B and Q.
We found substantial differences between B. tabaci B and Q in the expression of miRNAs. For example, miR-139*, miR-1468, miR-4496, miR-1566, and miR-2993 were found only in B. tabaci Q, while miR-2687, miR-989b, miR-3178, miR-615, and miR-3070a were found only in B. The expression levels of these 10 miRNAs were very high, especially for miR-2687, which had 168,454 counts in B. In the 1080 miRNAs listed in the Table S1, only 268 miRNAs had similar normalized expression levels in B and Q libraries. Among the miRNAs, about 75.2% had substantially different expression levels in B. tabaci Q vs. B. Differences in the expression levels of these miRNAs could influence development, reproduction, insecticide resistance, apoptosis, etc., which might contribute to the displacement of B by Q.
B. tabaci Q has shown greater resistance to neonicotinoid and pyriproxyfen insecticides than B –. Accumulating evidence indicates an important role of miRNAs in drug resistance, and miRNA expression profiling is correlated with the development of drug resistance –. Although many studies have reported the involvement of miRNAs in drug resistance, few of these have concerned insects. We therefore analyzed the miRNAs possibly related to drug resistance on the basis of the studies in humans. In the present study, the normalized expression of miR-638 was 19,471 times higher in B. tabaci B than in Q (Table S1), suggesting a much lower level of drug resistance in B than in Q. Haenisch et al.  found that miR-379 increased (maximally 4.10±1.33-fold) in HepG2 cells after 48 h of treatment with 5 µM rifampicin. In our study, the normalized expression of miR-379 was 58 times higher in B. tabaci Q than in B (Table S1), again indicating a much greater drug resistance. Additional research is needed to determine whether miRNAs are involved in insecticide resistance.
This miRNA of miR-146b, which is highly homologous to miR-146a, was much more abundant in B. tabaci Q than B, and miR-146c was found only in Q (Table S1). The normalized expression of miR-215 was lower in B. tabaci Q than in B (Table S1), indicating lower apoptosis in Q. Apoptosis is considered a vital component of various processes including normal cell turnover, embryonic development, and chemical-induced cell death . To date, several miRNAs have been identified that regulate the complex networks of apoptotic pathways . Experimental evidence in human has demonstrated that miR-146a modulates activation-induced cell death (AICD), acting as an anti-apoptotic factor, and that was associated death domain (FADD) is a target of miR-146a . Differences in the expression of these miRNAs might also explain difference in survival among B. tabaci biotypes. The tumor suppressor p53 acts as a major defense against cancer and can elicit both apoptotic death and cell cycle arrest . miR-215 was identified as a p53-regulated miRNA , and induced cell cycle arrest , . miR-215 which had pro-apoptotic function was detected at high levels in normal human colon tissue but at low levels in many human colon cancer samples. Once again, differences in expression of this miRNA in B. tabaci Q and B might contribute to biological differences in developmental time, reproduction, and survival.
High-throughput sequencing enabled the study of miRNAs in B. tabaci, which is an important pest worldwide. We identified 170 conserved miRNAs and 15 novel miRNA candidates in B and Q. We found significant differences in the expression of miRNAs between B and Q, which might contribute to the displacement of B by Q. To date, little is known about the functions of these miRNAs in insects, especially in B. tabaci. Further analysis of the expression and function of these miRNAs could increase our understanding of regulatory networks in the insect and lead to novel approaches to its control.
Conceived and designed the experiments: DC QG. Performed the experiments: QG YLT. Analyzed the data: QG. Contributed reagents/materials/analysis tools: DC. Wrote the paper: QG DC.
- 1. Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75: 843–854.
- 2. He L, Hannon GJ (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 5: 522–531.
- 3. Neilson JR, Zheng GX, Burge CB, Sharp PA (2007) Dynamic regulation of miRNA expression in ordered stages of cellular development. Genes Dev 21: 578–589.
- 4. Yu X, Zhou Q, Li SC, Luo Q, Cai Y, et al. (2008) The Silkworm (Bombyx mori) microRNAs and their expressions in multiple developmental stages. PLoS ONE 3: e2997.
- 5. Zhang L, Hammell M, Kudlow BA, Ambros V, Han M (2009) Systematic analysis of dynamic miRNA-target interactions during C. elegans development. Development 136: 3043–3055.
- 6. Jagadeeswaran G, Zheng Y, Sumathipala N, Jiang H, Arrese EL, et al. (2010) Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development. BMC Genomics 11: 52.
- 7. Behura SK, Haugen M, Flannery E, Sarro J, Tessier CR, et al. (2011) Comparative genomic analysis of Drosophila melanogaster and vector mosquito developmental genes. PLoS ONE 6: e21504.
- 8. Hu SJ, Ren G, Liu JL, Zhao ZA, Yu YS, et al. (2008) MicroRNA expression and regulation in mouse uterus during embryo implantation. J Biol Chem 283: 23473–23484.
- 9. Ben Amor B, Wirth S, Merchan F, Laporte P, d’Aubenton-Carafa Y, et al. (2009) Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses. Genome Res 19: 57–69.
- 10. Cordes KR, Srivastava D (2009) MicroRNA regulation of cardiovascular development. Circ Res 104: 724–732.
- 11. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136: 215–233.
- 12. Hu H, Gatti RA (2011) MicroRNAs: new players in the DNA damage response. J Mol Cell Biol 3: 151–158.
- 13. Yi R, Fuchs E (2011) MicroRNAs and their roles in mammalian stem cells. J Cell Sci 124: 1775–1783.
- 14. Stark A, Kheradpour P, Parts L, Brennecke J, Hodges E, et al. (2007) Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes. Genome Res 17: 1865–1879.
- 15. Weaver DB, Anzola JM, Evans JD, Reid JG, Reese JT, et al. (2007) Computational and transcriptional evidence for microRNAs in the honey bee genome. Genome Biol 8: R97.
- 16. Behura SK, Whitfield CW (2010) Correlated expression patterns of microRNA genes with age-dependent behavioural changes in honeybee. Insect Mol Biol 19: 431–439.
- 17. Chen X, Yu X, Cai Y, Zheng H, Yu D, et al. (2010) Next-generation small RNA sequencing for microRNAs profiling in the honey bee Apis mellifera. Insect Mol Biol 19: 799–805.
- 18. Winter F, Edaye S, Hüttenhofer A, Brunel C (2007) Anopheles gambiae miRNAs as actors of defence reaction against Plasmodium invasion. Nucleic Acids Res 35: 6953–6962.
- 19. Li S, Mead EA, Liang S, Tu Z (2009) Direct sequencing and expression analysis of a large number of miRNAs in Aedes aegypti and a multi-species survey of novel mosquito miRNAs. BMC Genomics 10: 581.
- 20. Skalsky R, Vanlandingham D, Scholle F, Higgs S, Cullen B (2010) Identification of microRNAs expressed in two mosquito vectors, Aedes albopictus and Culex quinquefasciatus. BMC Genomics 11: 119.
- 21. Legeai F, Rizk G, Walsh T, Edwards O, Gordon K, et al. (2010) Bioinformatic prediction, deep sequencing of microRNAs and expression analysis during phenotypic plasticity in the pea aphid, Acyrthosiphon pisum. BMC Genomics 11: 281.
- 22. He PA, Nie Z, Chen J, Lv Z, Sheng Q, et al. (2008) Identification and characteristics of microRNAs from Bombyx mori. BMC Genomics 9: 248.
- 23. Liu S, Zhang L, Li Q, Zhao P, Duan J, et al. (2009) MicroRNA expression profiling during the life cycle of the silkworm (Bombyx mori). BMC Genomics 10: 455.
- 24. Yu X, Zhou Q, Cai Y, Luo Q, Lin H, et al. (2009) A discovery of novel microRNAs in the silkworm (Bombyx mori) genome. Genomics 94: 438–444.
- 25. Zhang Y, Zhou X, Ge X, Jiang J, Li M, et al. (2009) Insect-specific microRNA involved in the development of the silkworm Bombyx mori. PLoS ONE 4: e4677.
- 26. Liu S, Gao S, Zhang D, Yin J, Xiang Z, et al. (2010) MicroRNAs show diverse and dynamic expression patterns in multiple tissues of Bombyx mori. BMC Genomics 11: 85.
- 27. Liu S, Li D, Li Q, Zhao P, Xiang Z, et al. (2010) MicroRNAs of Bombyx mori identified by Solexa sequencing. BMC Genomics 11: 148.
- 28. Surridge AK, Lopez-Gomollon S, Moxon S, Maroja LS, Rathjen T, et al. (2011) Characterisation and expression of microRNAs in developing wings of the neotropical butterfly Heliconius melpomene. BMC Genomics 12: 62.
- 29. Wei Y, Chen S, Yang P, Ma Z, Kang L (2009) Characterization and comparative profiling of the small RNA transcriptomes in two phases of locust. Genome Biol 10: R6.
- 30. Luo Q, Zhou Q, Yu X, Lin H, Hu S, et al. (2008) Genome-wide mapping of conserved microRNAs and their host transcripts in Tribolium castaneum. J Genet Genomics 35: 349–355.
- 31. Singh J, Nagaraju J (2008) In silico prediction and characterization of microRNAs from red flour beetle (Tribolium castaneum). Insect Mol Biol 17: 427–436.
- 32. Brown JK, Frohlich DR, Rosell RC (1995) The sweetpotato or silverleaf whiteflies: biotypes of Bemisia tabaci or a species complex? Annu Rev Entomol 40: 511–534.
- 33. Oliveira MRV, Henneberry TJ, Anderson P (2001) History, current status, and collaborative research projects for Bemisia tabaci. Crop Prot 20: 709–723.
- 34. Jiu M, Zhou XP, Tong L, Xu J, Yang X, et al. (2007) Vector-virus mutualism accelerates population increase of an invasive whitefly. PLoS ONE 2: e182.
- 35. Liu SS, De Barro PJ, Xu J, Luan JB, Zang LS, et al. (2007) Asymmetric mating interactions drive widespread invasion and displacement in a whitefly. Science 318: 1769–1772.
- 36. Jones DR (2003) Plant viruses transmitted by whiteflies. Eur J Plant Pathol 109: 195–219.
- 37. Navas-Castillo J, Fiallo-Olive E, Sanchez-Campos S (2011) Emerging virus diseases transmitted by whiteflies. Annu Rev Phytopathol 49: 219–248.
- 38. De Barro PJ, Liu SS, Boykin LM, Dinsdale AB (2011) Bemisia tabaci: a statement of species status. Annu Rev Entomol 56: 1–19.
- 39. Chu D, Jiang T, Liu GX, Jiang DF, Tao YL, et al. (2007) Biotype status and distribution of Bemisia tabaci (Hemiptera: Aleyrodidae) in Shandong province of China based on mitochondrial DNA markers. Environ Entomol 36: 1290–1295.
- 40. Teng X, Wan F-H, Chu D (2010) Bemisia tabaci biotype Q dominates other biotypes across China. Fla Entomol 93: 363–368.
- 41. Hu J, De Barro P, Zhao H, Wang J, Nardi F, et al. (2011) An extensive field survey combined with a phylogenetic analysis reveals rapid and widespread invasion of two alien whiteflies in China. PLoS ONE 6: e16061.
- 42. Chu D, Wan FH, Zhang YJ, Brown JK (2010) Change in the biotype composition of Bemisia tabaci in Shandong Province of China from 2005 to 2008. Environ Entomol 39: 1028–1036.
- 43. Chu D, Zhang YJ, Wan FH (2010) Cryptic invasion of the exotic Bemisia tabaci biotype Q occurred widespread in Shandong Province of China. Fla Entomol 93: 203–207.
- 44. Muñiz M (2000) Host suitability of two biotypes of Bemisia tabaci on some common weeds. Entomo Exp Appl 95: 63–70.
- 45. Muñiz M, Nombela G (2001) Differential variation in development of the B- and Q-biotypes of Bemisia tabaci (Homoptera: Aleyrodidae) on sweet pepper at constant temperatures. Environ Entomol 30: 720–727.
- 46. Mahadav A, Kontsedalov S, Czosnek H, Ghanim M (2009) Thermotolerance and gene expression following heat stress in the whitefly Bemisia tabaci B and Q biotypes. Insect Biochem Mol Biol 39: 668–676.
- 47. Nauen R, Stumpf N, Elbert A (2002) Toxicological and mechanistic studies on neonicotinoid cross resistance in Q-type Bemisia tabaci (Hemiptera: Aleyrodidae). Pest Manag Sci 58: 868–875.
- 48. Horowitz AR, Kontsedalov S, Khasdan V, Ishaaya I (2005) Biotypes B and Q of Bemisia tabaci and their relevance to neonicotinoid and pyriproxyfen resistance. Arch Insect Biochem Physiol 58: 216–225.
- 49. Luo C, Jones CM, Devine G, Zhang F, Denholm I, et al. (2010) Insecticide resistance in Bemisia tabaci biotype Q (Hemiptera: Aleyrodidae) from China. Crop Prot 29: 429–434.
- 50. Crowder DW, Horowitz AR, De Barro PJ, Liu SS, Showalter AM, et al. (2010) Mating behaviour, life history and adaptation to insecticides determine species exclusion between whiteflies. J Anim Ecol 79: 563–570.
- 51. Wang XW, Luan JB, Li JM, Su YL, Xia J, et al. (2011) Transcriptome analysis and comparison reveal divergence between two invasive whitefly cryptic species. BMC Genomics 12: 458.
- 52. Becam I, Rafel N, Hong X, Cohen SM, Milan M (2011) Notch-mediated repression of bantam miRNA contributes to boundary formation in the Drosophila wing. Development 138: 3781–3789.
- 53. Tarantino C, Paolella G, Cozzuto L, Minopoli G, Pastore L, et al. (2010) miRNA 34a, 100, and 137 modulate differentiation of mouse embryonic stem cells. FASEB J 24: 3255–3263.
- 54. Eskildsen T, Taipaleenmaki H, Stenvang J, Abdallah BM, Ditzel N, et al. (2011) MicroRNA-138 regulates osteogenic differentiation of human stromal (mesenchymal) stem cells in vivo. Proc Natl Acad Sci U S A 108: 6139–6144.
- 55. Georges SA, Biery MC, Kim SY, Schelter JM, Guo J, et al. (2008) Coordinated regulation of cell cycle transcripts by p53-Inducible microRNAs, miR-192 and miR-215. Cancer Res 68: 10105–10112.
- 56. Yu JY, Reynolds SH, Hatfield SD, Shcherbata HR, Fischer KA, et al. (2009) Dicer-1-dependent Dacapo suppression acts downstream of Insulin receptor in regulating cell division of Drosophila germline stem cells. Development 136: 1497–1507.
- 57. Rayner KJ, Suarez Y, Davalos A, Parathath S, Fitzgerald ML, et al. (2010) MiR-33 contributes to the regulation of cholesterol homeostasis. Science 328: 1570–1573.
- 58. Bracht JR, Van Wynsberghe PM, Mondol V, Pasquinelli AE (2010) Regulation of lin-4 miRNA expression, organismal growth and development by a conserved RNA binding protein in C. elegans. Dev Biol 348: 210–221.
- 59. Brennecke J, Hipfner DR, Stark A, Russell RB, Cohen SM (2003) bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113: 25–36.
- 60. Marco A, Hui JH, Ronshaugen M, Griffiths-Jones S (2010) Functional shifts in insect microRNA evolution. Genome Biol Evol 2: 686–696.
- 61. Liu Z, Xiao H, Li H, Zhao Y, Lai S, et al. (2012) Identification of Conserved and Novel microRNAs in Cashmere Goat Skin by Deep Sequencing. PLoS ONE 7: e50001.
- 62. Shao C, Ma X, Xu X, Meng Y (2013) Identification of the highly accumulated microRNA*s in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Gene 515: 123–127.
- 63. Chu D, Gao CS, De Barro P, Zhang YJ, Wan FH, et al. (2011) Further insights into the strange role of bacterial endosymbionts in whitefly, Bemisia tabaci: Comparison of secondary symbionts from biotypes B and Q in China. Bull Entomol Res 101: 477–486.
- 64. Frohlich DR, Torres-Jerez II, Bedford ID, Markham PG, Brown JK (1999) A phylogeographical analysis of the bemisia tabaci species complex based on mitochondrial DNA markers. Mol Ecol 8: 1683–1691.
- 65. Khasdan V, Levin I, Rosner A, Morin S, Kontsedalov S, et al. (2005) DNA markers for identifying biotypes B and Q of Bemisia tabaci (Hemiptera: Aleyrodidae) and studying population dynamics. Bull Entomol Res 95: 605–613.
- 66. Ueda S (2006) Simple and rapid detection by mtCOI PCR-RFLP to distinguish the Q biotype of Bemisia tabaci. Kyushu Pl Prot Res 52: 44–48.
- 67. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36: D154–158.
- 68. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–157.
- 69. Allen E, Xie Z, Gustafson AM, Carrington JC (2005) microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121: 207–221.
- 70. Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, et al. (2005) Specific effects of microRNAs on the plant transcriptome. Dev Cell 8: 517–527.
- 71. Johnson SM, Lin S-Y, Slack FJ (2003) The time of appearance of the C. elegans let-7 microRNA is transcriptionally controlled utilizing a temporal regulatory element in its promoter. Dev Biol 259: 364–379.
- 72. Plaisance V, Abderrahmani A, Perret-Menoud V, Jacquemin P, Lemaigre F, et al. (2006) MicroRNA-9 controls the expression of Granuphilin/Slp4 and the secretory response of insulin-producing cells. J Biol Chem 281: 26932–26942.
- 73. Shigehara K, Yokomuro S, Ishibashi O, Mizuguchi Y, Arima Y, et al. (2011) Real-time PCR-based analysis of the human bile microRNAome identifies miR-9 as a potential diagnostic biomarker for biliary tract cancer. PLoS ONE 6: e23584.
- 74. Woltering JM, Durston AJ (2008) MiR-10 represses HoxB1a and HoxB3a in zebrafish. PLoS ONE 3: e1396.
- 75. Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE, et al. (2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat Genet 38: 228–233.
- 76. Torella D, Iaconetti C, Catalucci D, Ellison GM, Leone A, et al. (2011) MicroRNA-133 controls vascular smooth muscle cell phenotypic switch in vitro and vascular remodeling in vivo. Circ Res 109: 880–893.
- 77. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297.
- 78. Carthew RW, Sontheimer EJ (2009) Origins and mechanisms of miRNAs and siRNAs. Cell 136: 642–655.
- 79. Ma J, Dong C, Ji C (2010) MicroRNA and drug resistance. Cancer Gene Ther 17: 523–531.
- 80. Zheng T, Wang J, Chen X, Liu L (2010) Role of microRNA in anticancer drug resistance. Int J Cancer 126: 2–10.
- 81. Xin F, Li M, Balch C, Thomson M, Fan M, et al. (2009) Computational analysis of microRNA profiles and their target genes suggests significant involvement in breast cancer antiestrogen resistance. Bioinformatics 25: 430–434.
- 82. Haenisch S, Laechelt S, Bruckmueller H, Werk A, Noack A, et al. (2011) Down-regulation of ATP-binding cassette C2 protein expression in HepG2 cells after rifampicin treatment is mediated by microRNA-379. Mol Pharmacol 80: 314–320.
- 83. Elmore S (2007) Apoptosis: a review of programmed cell death. Toxicol Pathol 35: 495–516.
- 84. Mezzanzanica D, Canevari S, Cecco LD, Bagnoli M (2011) miRNA control of apoptotic programs: focus on ovarian cancer. Expert Rev Mol Diagn 11: 277–286.
- 85. Curtale G, Citarella F, Carissimi C, Goldoni M, Carucci N, et al. (2010) An emerging player in the adaptive immune response: microRNA-146a is a modulator of IL-2 expression and activation-induced cell death in T lymphocytes. Blood 115: 265–273.
- 86. Aylon Y, Oren M (2007) Living with p53, dying of p53. Cell 130: 597–600.
- 87. Braun CJ, Zhang X, Savelyeva I, Wolff S, Moll UM, et al. (2008) p53-Responsive microRNAs 192 and 215 are capable of inducing cell cycle arrest. Cancer Res 68: 10094–10104.