Molecules and Cells

, Volume 33, Issue 1, pp 9–17 | Cite as

A DNA barcode library for Korean Chironomidae (Insecta: Diptera) and indexes for defining barcode gap

Article

Abstract

Non-biting midges (Diptera: Chironomidae) are a diverse population that commonly causes respiratory allergies in humans. Chironomid larvae can be used to indicate freshwater pollution, but accurate identification on the basis of morphological characteristics is difficult. In this study, we constructed a mitochondrial cytochrome c oxidase subunit I (COI)-based DNA barcode library for Korean chironomids. This library consists of 211 specimens from 49 species, including adults and unidentified larvae. The interspecies and intraspecies COI sequence variations were analyzed. Sophisticated indexes were developed in order to properly evaluate indistinct barcode gaps that are created by insufficient sampling on both the interspecies and intraspecies levels and by variable mutation rates across taxa. In a variety of insect datasets, these indexes were useful for re-evaluating large barcode datasets and for defining COI barcode gaps. The COI-based DNA barcode library will provide a rapid and reliable tool for the molecular identification of Korean chironomid species. Furthermore, this reverse-taxonomic approach will be improved by the continuous addition of other speceis’ sequences to the library.

Keywords

chironomidae DNA barcode gap DNA barcode library DNA barcoding reverse taxonomy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aagaard, K., Solem, J.O., Bongard, T., and Hanssen, O. (2004). Studies of aquatic insects in the Atna River 1987–2002. Hydrobiologia 521, 87–105.CrossRefGoogle Scholar
  2. Al-Shami, S., Rawi, C.S.M., Nor, S.A.M., Ahmad, A.H., and Ali, A. (2010). Morphological deformities in Chironomus spp. (Diptera: Chironomidae) larvae as a tool for impact assessment of anthropogenic and environmental stresses on three rivers in the Juru River System, Penang, Malaysia. Environ. Entomol. 39, 210–222.PubMedCrossRefGoogle Scholar
  3. Baur, X. (1992). Chironomid midge allergy. Arerugi 41, 81–85.PubMedGoogle Scholar
  4. Bensasson, D., Zhang, D., Hartl, D.L., and Hewitt, G.M. (2001). Mitochondrial pseudogenes: evolution’s misplaced witnesses. Trends. Ecol. Evol. 16, 314–321.PubMedCrossRefGoogle Scholar
  5. Blaxter, M., Mann, J., Chapman, T., Thomas, F., Whitton, C., Floyd, R., and Abebe, E. (2005). Defining operational taxonomic units using DNA barcode data. Phil. Trans. R. Soc. B 360, 1935–1943.PubMedCrossRefGoogle Scholar
  6. Carew, M.E., Pettigrove, V., and Hoffmann, A.A. (2005). The utility of DNA markers in classical taxonomy: using cytochrome oxidase I markers to differentiate Australian Cladopelma (Diptera: Chironomidae) midges. Ann. Entomol. Soc. Am. 98, 587–594.CrossRefGoogle Scholar
  7. Deagle, B.E., Chiaradia, A., McInnes, J., and Jarman, S.N. (2010). Pyrosequencing faecal DNA to determine diet of little penguins: is what goes in what comes out? Conserv. Genet. 11, 2039–2048.CrossRefGoogle Scholar
  8. Edgar, R.C. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797.PubMedCrossRefGoogle Scholar
  9. Ekrem, T., Willassen, E., and Stur, E. (2007). A comprehensive DNA sequence library is essential for identification with DNA barcodes. Mol. Phylogenet. Evol. 43, 530–542.PubMedCrossRefGoogle Scholar
  10. Folmer, O., Black, M., Hoeh, W., Lutz, R., and Vrijenhoek, R. (1994). DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotech. 3, 294–299.Google Scholar
  11. Galtier, N., Nabholz, B., Glemin, S., and Hurst, G.D.D. (2009). Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Mol. Ecol. 18, 4541–4550.PubMedCrossRefGoogle Scholar
  12. Hebert, P.D.N., and Gregory, T.R. (2005). The promise of DNA barcoding for taxonomy. Syst. Biol. 54, 852–859.PubMedCrossRefGoogle Scholar
  13. Hebert, P.D.N., Ratnasingham, S., and deWaard, J.R. (2003). Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. B 270, 96–99.CrossRefGoogle Scholar
  14. Jukes, T.H., and Cantor, C.R. (1969). Evolution of protein molecules. In Mammalian Protein Metabolism. Munro H.H, ed. (New York: Academic Press), pp. 21–132.Google Scholar
  15. Kerr, K.C.R., Stoeckle, M.Y., Dove, C.J., Weigt, L.A., Francis, C.M., and Hebert, P.D.N. (2007). Comprehensive DNA barcode coverage of North American birds. Mol. Ecol. Notes 7, 535–543.PubMedCrossRefGoogle Scholar
  16. Kim, S., Eo, H.S., Koo, H., Choi, J.K., and Kim, W. (2010). DNA Barcode-based molecular identification system for fish species. Mol. Cells 30, 507–512.PubMedCrossRefGoogle Scholar
  17. Kim, S., Kim, C.B., Min, G.S., Suh, Y., Bhak, J., Woo, T., Koo, H., Choi, J.K., Shin, M.k., Jung, J., et al. (2011). Korea Barcode of Life Database System (KBOL). Animal Cells & Systems Published in Proceedings.Google Scholar
  18. Kimura, M. (1980). A simple method for estimating evolutionary rate of base substitution through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120.PubMedCrossRefGoogle Scholar
  19. Lewis, D.J. (1956). Chironomidae as a pest in the Northern Sudan. Acta Trop. 13, 142–158.PubMedGoogle Scholar
  20. Markmann, M., and Tautz, D. (2005). Reverse taxonomy: an approach towards determining the diversity of meiobenthic organisms based on ribosomal RNA signature sequences. Philos. Trans. R. Soc. B Biol. Sci. 360, 1917–1924.CrossRefGoogle Scholar
  21. Martinez, E.A., Moore, B.C., Schaumloffel, J., and Dasgupta, N. (2004). Teratogenic versus mutagenic abnormalities in chironomid larvae exposed to zinc and lead. Arch. Environ. Contam. Toxicol. 47, 193–198.PubMedCrossRefGoogle Scholar
  22. Meier, R., Shiyang, K., Vaidya, G., and Ng, P.K.L. (2006). DNA barcoding and taxonomy in Diptera: a tale of high intraspecific variability and low identification success. Syst. Biol. 55, 715–728.PubMedCrossRefGoogle Scholar
  23. Meier, R., Zhang, G., and Ali, F. (2008). The use of mean instead of smallest interspecific distances exaggerates the size of the “barcoding gap” and leads to misidentification. Syst. Biol. 57, 809–813PubMedCrossRefGoogle Scholar
  24. Meyer, C.P., and Paulay, G. (2005). DNA barcoding: error rates based on comprehensive sampling. PLoS Biol. 3, e422.PubMedCrossRefGoogle Scholar
  25. Nielsen, R., and Matz, M. (2006). Statistical approaches for DNA barcoding. Syst. Biol. 55, 162–169.PubMedCrossRefGoogle Scholar
  26. Park, M.H., Sim, C.J., Baek, J., and Min, G.S. (2007). Identification of genes suitable for DNA barcoding of morphologically indistinguishable Korean Halichondriidae sponges. Mol. Cells 23, 220–227.PubMedGoogle Scholar
  27. Pauls, S.U., Blahnik, R.J., Zhou, X., Wardwell, C.T., and Holzenthal, R.W. (2010). DNA barcode data confirm new species and reveal cryptic diversity in Chilean Smicridea (Trichoptera: Hydropsychidae). J. North Am. Benthol. Soc. 29, 1058–1074.CrossRefGoogle Scholar
  28. Pfenninger, M., Nowak, C., Kley, C., Steinke, D., and Streit, B. (2007). Utility of DNA taxonomy and barcoding for the inference of larval community structure in morphologically cryptic Chironomus (Diptera) species. Mol. Ecol. 16, 1957–1968.PubMedCrossRefGoogle Scholar
  29. Radulovici, A.E., Archambault, P., and Dufresne, F. (2010). DNA barcodes for marine biodiversity: moving fast forward? Diversity 2, 450–472.CrossRefGoogle Scholar
  30. Ree, H.I. (2009). One new and six unrecorded species of chironomidae (Insecta: Diptera) in Korea. Korean J. Syst. Zool. 25, 49–59.CrossRefGoogle Scholar
  31. Song, H., Buhay, J.E., Whiting, M.F., and Crandall, K.A. (2008). Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proc. Natl. Acad. Sci. USA 105, 13486–13491.PubMedCrossRefGoogle Scholar
  32. Steinke, D., Vences, M., Salzburger, W., and Meyer, A. (2005). TaxI: a software tool for DNA barcoding using distance methods. Phil. Trans. R. Soc. B 360, 1975–1980.PubMedCrossRefGoogle Scholar
  33. Tamura, K. (1992). Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G+ Ccontent biases. Mol. Biol. Evol. 9, 678–687.PubMedGoogle Scholar
  34. Tamura, K., and Nei, M. (1993). Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526.PubMedGoogle Scholar
  35. Tamura, K., Dudley, J., Nei, M., and Kumar, S. (2007). MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24, 1596–1599.PubMedCrossRefGoogle Scholar
  36. Tautz, D., Arctander, P., Minelli, A., Thomas, R.H., and Vogler, A.P. (2002). DNA points the way ahead in taxonomy. Nature 418, 479–479.PubMedCrossRefGoogle Scholar
  37. Velle, G., Brooks, S.J., Birks, H.J.B., and Willassen, E. (2005). Chironomids as a tool for inferring Holocene climate: an assessment based on six sites in southern Scandinavia. Quaternary Sci. Rev. 24, 1429–1462.CrossRefGoogle Scholar
  38. Wiemers, M., and Fiedler, K. (2007). Does the DNA barcoding gap exist? a case study in blue butterflies (Lepidoptera: Lycaenidae). Front. Zool. 4, 8–16.PubMedCrossRefGoogle Scholar
  39. Wright, J.F. (1984). The chironomid larvae of a small chalk stream in Berkshire, England. Ecol. Entomol. 9, 231–238.CrossRefGoogle Scholar
  40. Yoo, H.S., Eah, J.Y., Kim, J.S., Kim, Y.J., Min, M.S., Paek, W.K., Lee, H., and Kim, C.B. (2006). DNA barcoding Korean birds. Mol. Cells 22, 323–327.PubMedGoogle Scholar
  41. Zeale, M.R.K., Butlin, R.K., Barker, G.L.A., Lees, D.C., and Jones, G. (2010). Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Notes 11, 236–244.Google Scholar
  42. Zhou, X., Adamowicz, S.J., Jacobus, L.M., DeWalt, R.E., and Hebert, P.D.N. (2009). Towards a comprehensive barcode library for arctic life-Ephemeroptera, Plecoptera, and Trichoptera of Churchill, Manitoba, Canada. Front. Zool. 6, 30.PubMedCrossRefGoogle Scholar

Copyright information

© The Korean Society for Molecular and Cellular Biology and Springer Netherlands 2012

Authors and Affiliations

  • Sungmin Kim
    • 1
  • Kyo-Hong Song
    • 2
  • Han-Il Ree
    • 3
  • Won Kim
    • 4
  1. 1.Interdisciplinary Program in BioinformaticsSeoul National UniversitySeoulKorea
  2. 2.Wildlife Genetic Resources CenterNational Institute of Biological ResourcesIncheonKorea
  3. 3.Department of Environmental Medical Biology, Institute of Tropical Medicine, and Korean National Arthropods of Medical Importance Resource BankYonsei University College of MedicineSeoulKorea
  4. 4.School of Biological SciencesSeoul National UniversitySeoulKorea

Personalised recommendations