Increased performance of DNA metabarcoding of macroinvertebrates by taxonomic sorting

English version

Autoři: Kevin K. Beentjes ^aff001; Arjen G. C. L. Speksnijder ^aff001; Menno Schilthuizen ^aff001; Marten Hoogeveen ^aff001; Rob Pastoor ^aff001; Berry B. van der Hoorn ^aff001
Působiště autorů: Naturalis Biodiversity Center, Leiden, The Netherlands ^aff001; Institute of Biology Leiden, Leiden University, Leiden, the Netherlands ^aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226527

Souhrn

DNA-based identification through the use of metabarcoding has been proposed as the next step in the monitoring of biological communities, such as those assessed under the Water Framework Directive (WFD). Advances have been made in the field of metabarcoding, but challenges remain when using complex samples. Uneven biomass distributions, preferential amplification and reference database deficiencies can all lead to discrepancies between morphological and DNA-based taxa lists. The effects of different taxonomic groups on these issues remain understudied. By metabarcoding WFD monitoring samples, we analyzed six different taxonomic groups of freshwater organisms, both separately and combined. Identifications based on metabarcoding data were compared directly to morphological assessments performed under the WFD. The diversity of taxa for both morphological and DNA-based assessments was similar, although large differences were observed in some samples. The overlap between the two taxon lists was 56.8% on average across all taxa, and was highest for Crustacea, Heteroptera, and Coleoptera, and lowest for Annelida and Mollusca. Taxonomic sorting in six basic groups before DNA extraction and amplification improved taxon recovery by 46.5%. The impact on ecological quality ratio (EQR) scoring was considerable when replacing morphology with DNA-based identifications, but there was a high correlation when only replacing a single taxonomic group with molecular data. Different taxonomic groups provide their own challenges and benefits. Some groups might benefit from a more consistent and robust method of identification. Others present difficulties in molecular processing, due to uneven biomass distributions, large genetic diversity or shortcomings of the reference database. Sorting samples into basic taxonomic groups that require little taxonomic knowledge greatly improves the recovery of taxa with metabarcoding. Current standards for EQR monitoring may not be easily replaced completely with molecular strategies, but the effectiveness of molecular methods opens up the way for a paradigm shift in biomonitoring.

Klíčová slova:

Taxonomy – Polymerase chain reaction – Sequence databases – DNA extraction – Mites – Molluscs – Annelids – Malacology

Zdroje

1. Hebert PDN, Cywinska A, Ball SL, DeWaard JR. Biological identifications through DNA barcodes. Proc R Soc B Biol Sci. 2003;270: 313–21. doi: 10.1098/rspb.2002.2218 12614582

2. Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol. 2012;21: 2045–50. doi: 10.1111/j.1365-294X.2012.05470.x 22486824

3. Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10: 34. doi: 10.1186/1742-9994-10-34 23767809

4. Pauls SU, Alp M, Bálint M, Bernabò P, Čiampor F, Čiamporová-Zaťovičová Z, et al. Integrating molecular tools into freshwater ecology: developments and opportunities. Freshw Biol. 2014;59: 1559–1576. doi: 10.1111/fwb.12381

5. Pawlowski J, Kelly-Quinn M, Altermatt F, Apothéloz-Perret-Gentil L, Beja P, Boggero A, et al. The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems. Science of the Total Environment. 2018. pp. 1295–1310. doi: 10.1016/j.scitotenv.2018.05.002 29801222

6. European Union. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off J Eur Parliam. 2000;L327: 1–82. doi: 10.1039/ap9842100196

7. Birk S, Bonne W, Borja A, Brucet S, Courrat A, Poikane S, et al. Three hundred ways to assess Europe’s surface waters: An almost complete overview of biological methods to implement the Water Framework Directive. Ecol Indic. 2012;18: 31–41. doi: 10.1016/j.ecolind.2011.10.009

8. Sweeney BW, Battle JM, Jackson JK, Dapkey T. Can DNA barcodes of stream macroinvertebrates improve descriptions of community structure and water quality? J North Am Benthol Soc. 2011;30: 195–216. doi: 10.1899/10-016.1

9. Haase P, Murray-Bligh J, Lohse S, Pauls S, Sundermann A, Gunn R, et al. Assessing the impact of errors in sorting and identifying macroinvertebrate samples. Hydrobiologia. 2006;566: 505–521. doi: 10.1007/s10750-006-0075-6

10. Stribling JB, Pavlik KL, Holdsworth SM, Leppo EW. Data quality, performance, and uncertainty in taxonomic identification for biological assessments. J North Am Benthol Soc. 2008;27: 906–919. doi: 10.1899/07-175.1

11. Darling JA, Mahon AR. From molecules to management: adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ Res. 2011;111: 978–88. doi: 10.1016/j.envres.2011.02.001 21353670

12. Stein ED, Martinez MC, Stiles S, Miller PE, Zakharov E V. Is DNA barcoding actually cheaper and faster than traditional morphological methods: results from a survey of freshwater bioassessment efforts in the United States? PLoS One. 2014;9: e95525. doi: 10.1371/journal.pone.0095525 24755838

13. Marshall JC, Steward AL, Harch BD. Taxonomic resolution and quantification of freshwater macroinvertebrate samples from an Australian dryland river: the benefits and costs of using species abundance data. Hydrobiologia. 2006;572: 171–194. doi: 10.1007/s10750-005-9007-0

14. Gibson J, Shokralla S, Porter TM, King I, van Konynenburg S, Janzen DH, et al. Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics. Proc Natl Acad Sci. 2014;111: 8007–12. doi: 10.1073/pnas.1406468111 24808136

15. Aylagas E, Borja Á, Irigoien X, Rodríguez-Ezpeleta N. Benchmarking DNA metabarcoding for biodiversity-based monitoring and assessment. Front Mar Sci. 2016;3: 96. doi: 10.3389/fmars.2016.00096

16. Elbrecht V, Vamos EE, Meissner K, Aroviita J, Leese F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol Evol. 2017;8: 1265–1275. doi: 10.1111/2041-210X.12789

17. Aylagas E, Borja Á, Muxika I, Rodríguez-Ezpeleta N. Adapting metabarcoding-based benthic biomonitoring into routine marine ecological status assessment networks. Ecol Indic. 2018;95: 194–202. doi: 10.1016/j.ecolind.2018.07.044

18. Bush A, Compson Z, Monk W, Porter T, Steeves R, Emilson E, et al. Studying ecosystems with DNA metabarcoding: lessons from aquatic biomonitoring. bioRxiv. 2019; 578591. doi: 10.1101/578591

19. Hajibabaei M, Shokralla S, Zhou X, Singer GAC, Baird DJ. Environmental barcoding: a next-generation sequencing approach for biomonitoring applications using river benthos. PLoS One. 2011;6: e17497. doi: 10.1371/journal.pone.0017497 21533287

20. Carew ME, Pettigrove VJ, Metzeling L, Hoffmann AA. Environmental monitoring using next generation sequencing: rapid identification of macroinvertebrate bioindicator species. Front Zool. 2013;10: 45. doi: 10.1186/1742-9994-10-45 23919569

21. Bista I, Carvalho GR, Tang M, Walsh K, Zhou X, Hajibabaei M, et al. Performance of amplicon and shotgun sequencing for accurate biomass estimation in invertebrate community samples. Molecular Ecology Resources. 21 May 2018. doi: 10.1111/1755-0998.12888 29667329

22. Elbrecht V, Leese F. Validation and Development of COI Metabarcoding Primers for Freshwater Macroinvertebrate Bioassessment. Front Environ Sci. 2017;5: 11. doi: 10.3389/fenvs.2017.00011

23. Elbrecht V, Peinert B, Leese F. Sorting things out: Assessing effects of unequal specimen biomass on DNA metabarcoding. Ecol Evol. 2017;7: 6918–6926. doi: 10.1002/ece3.3192 28904771

24. Lobo J, Shokralla S, Costa MH, Hajibabaei M, Costa FO. DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities. Sci Rep. 2017;7: 15618. doi: 10.1038/s41598-017-15823-6 29142319

25. Gibson JF, Shokralla S, Curry C, Baird DJ, Monk WA, King I, et al. Large-scale biomonitoring of remote and threatened ecosystems via high-throughput sequencing. PLoS One. 2015;10: e0138432. doi: 10.1371/journal.pone.0138432 26488407

26. Pawluczyk M, Weiss J, Links MG, Egaña Aranguren M, Wilkinson MD, Egea-Cortines M, et al. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal Bioanal Chem. 2015;407: 1841–1848. doi: 10.1007/s00216-014-8435-y 25577362

27. Creedy TJ, Ng WS, Vogler AP. Toward accurate species-level metabarcoding of arthropod communities from the tropical forest canopy. Ecol Evol. 2019;9: 3105–3116. doi: 10.1002/ece3.4839 30962884

28. Elbrecht V, Leese F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS One. 2015;10: e0130324. doi: 10.1371/journal.pone.0130324 26154168

29. Beentjes KK, Speksnijder AGCL, Schilthuizen M, Schaub BEM, van der Hoorn BB. The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands. Metabarcoding and Metagenomics. 2018;2: e26744. doi: 10.3897/mbmg.2.26744

30. Bijkerk R, editor. Handboek Hydrobiologie. Biologisch onderzoek voor de ecologische beoordeling van Nederlandse zoete en brakke oppervlaktewateren. Rapport 2014–02 [Internet]. 2014. Available: https://www.stowa.nl/publicaties/handboek-hydrobiologie

31. Afgan E, Baker D, Batut B, Van Den Beek M, Bouvier D, Ech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46: W537–W544. doi: 10.1093/nar/gky379 29790989

32. Magoč T, Salzberg SL. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27: 2957–2963. doi: 10.1093/bioinformatics/btr507 21903629

33. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17: 10. doi: 10.14806/ej.17.1.200

34. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27: 863–864. doi: 10.1093/bioinformatics/btr026 21278185

35. Elbrecht V, Hebert PDN, Steinke D. Slippage of degenerate primers can cause variation in amplicon length. Sci Rep. 2018;8: 10999. doi: 10.1038/s41598-018-29364-z 30030475

36. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4: e2584. doi: 10.7717/peerj.2584 27781170

37. Alberdi A, Aizpurua O, Gilbert MTP, Bohmann K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Mahon A, editor. Methods Ecol Evol. 2018;9: 134–147. doi: 10.1111/2041-210X.12849

38. Beentjes KK, Speksnijder AGCL, van der Hoorn BB, van Tol J. DNA barcoding program at Naturalis Biodiversity Center, the Netherlands. Genome. 2015;58: 193. doi: 10.1139/gen-2015-0087

39. Ratnasingham S, Hebert PDN. BOLD: the Barcode of Life Data System (www.barcodinglife.org). Mol Ecol Notes. 2007;7: 355–364. doi: 10.1111/j.1471-8286.2007.01678.x 18784790

40. Chamberlain S. bold: Interface to Bold Systems ‘API’. R package version 0.5.0. [Internet]. 2017. Available: https://cran.r-project.org/package=bold

41. RStudio. RStudio: Integrated development environment for R (Version 0.99.902) [Internet]. Boston, MA; 2015. Available: http://www.rstudio.com/

42. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res. 2005;33: D34–8. doi: 10.1093/nar/gki063 15608212

43. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17: 377–86. doi: 10.1101/gr.5969107 17255551

44. Pot R. QBWat, programma voor beoordeling van de biologische waterkwaliteit volgens de Nederlandse maatlatten voor de Kaderrichtlijn Water, versie 5.33. [Internet]. 2015. Available: http://www.roelfpot.nl/qbwat

45. Haase P, Pauls SU, Schindehütte K, Sundermann A. First audit of macroinvertebrate samples from an EU Water Framework Directive monitoring program: human error greatly lowers precision of assessment results. J North Am Benthol Soc. 2010;29: 1279–1291.

46. Jones FC. Taxonomic sufficiency: The influence of taxonomic resolution on freshwater bioassessments using benthic macroinvertebrates. Environ Rev. 2008;16: 45–69. doi: 10.1139/A07-010

47. Huemer P, Mutanen M, Sefc KM, Hebert PDN. Testing DNA barcode performance in 1000 species of European Lepidoptera: Large geographic distances have small genetic impacts. PLoS One. 2014;9: e115774. doi: 10.1371/journal.pone.0115774 25541991

48. Weigand H, Beermann AJ, Čiampor F, Costa FO, Csabai Z, Duarte S, et al. DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work. bioRxiv. 2019; 576553. doi: 10.1101/576553

49. Wangensteen OS, Palacín C, Guardiola M, Turon X. DNA metabarcoding of littoral hard-bottom communities: high diversity and database gaps revealed by two molecular markers. PeerJ. 2018;6: e4705. doi: 10.7717/peerj.4705 29740514

50. Kvist S. Barcoding in the dark?: A critical view of the sufficiency of zoological DNA barcoding databases and a plea for broader integration of taxonomic knowledge. Mol Phylogenet Evol. 2013;69: 39–45. doi: 10.1016/j.ympev.2013.05.012 23721749

51. Elbrecht V, Vamos EE, Steinke D, Leese F. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ. 2018;6: e4644. doi: 10.7717/peerj.4644 29666773

52. Brown EA, Chain FJJ, Crease TJ, MacIsaac HJ, Cristescu ME. Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities? Ecol Evol. 2015;5: 2234–2251. doi: 10.1002/ece3.1485 26078859

53. Song H, Buhay JE, Whiting MF, Crandall KA. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proc Natl Acad Sci. 2008;105: 13486–13491. doi: 10.1073/pnas.0803076105 18757756

54. Hebert PDN, Ratnasingham S, Zakharov E V., Telfer AC, Levesque-Beaudin V, Milton MA, et al. Counting animal species with DNA barcodes: Canadian insects. Philos Trans R Soc B Biol Sci. 2016;371: 20150333. doi: 10.1098/rstb.2015.0333 27481785

55. Macher JN, Salis RK, Blakemore KS, Tollrian R, Matthaei CD, Leese F. Multiple-stressor effects on stream invertebrates: DNA barcoding reveals contrasting responses of cryptic mayfly species. Ecol Indic. 2016;61: 159–169. doi: 10.1016/j.ecolind.2015.08.024

56. Beermann AJ, Zizka VMA, Elbrecht V, Baranov V, Leese F. DNA metabarcoding reveals the complex and hidden responses of chironomids to multiple stressors. Environ Sci Eur. 2018;30: 26. doi: 10.1186/s12302-018-0157-x

57. Curry CJ, Gibson JF, Shokralla S, Hajibabaei M, Baird DJ. Identifying North American freshwater invertebrates using DNA barcodes: are existing COI sequence libraries fit for purpose? Freshw Sci. 2018;37: 178–189. doi: 10.1086/696613

58. Porter TM, Hajibabaei M. Automated high throughput animal CO1 metabarcode classification. Sci Rep. 2018;8: 219675. doi: 10.1038/s41598-018-22505-4 29523803

59. Van der Molen D, Pot R, Evers C, Van Herpen F, Van Nieuwerburgh L. Referenties en maatlatten voor natuurlijke watertypen voor de KRW 2015–2021. STOWA rapportnummer 2012–31. 2016.

Increased performance of DNA metabarcoding of macroinvertebrates by taxonomic sorting

Souhrn

Klíčová slova:

Zdroje

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