COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems

English version

Autoři: Mehrdad Hajibabaei ^aff001; Teresita M. Porter ^aff001; Michael Wright ^aff001; Josip Rudar ^aff001
Působiště autorů: Centre for Biodiversity Genomics at Biodiversity Institute of Ontario and Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada ^aff001; Natural Resources Canada, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada ^aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0220953

Souhrn

Mixed community or environmental DNA marker gene sequencing has become a commonly used technique for biodiversity analyses in freshwater systems. Many cytochrome c oxidase subunit I (COI) primer sets are now available for such work. The purpose of this study is to test whether COI primer choice affects the recovery of arthropod richness, beta diversity, and recovery of target assemblages in the benthos kick-net samples typically used in freshwater biomonitoring. We examine six commonly used COI primer sets on samples collected from six freshwater sites. Biodiversity analyses show that richness is sensitive to primer choice and the combined use of multiple COI amplicons recovers higher richness. Thus, to recover maximum richness, multiple primer sets should be used with COI metabarcoding. In ordination analyses based on community dissimilarity, samples consistently cluster by site regardless of amplicon choice or PCR replicate. Thus, for broadscale community analyses, overall beta diversity patterns are robust to COI marker choice. Recovery of traditional freshwater bioindicator assemblages such as Ephemeroptera, Trichoptera, Plectoptera, and Chironomidae as well as Arthropoda site indicators were differentially detected by each amplicon tested. This work will help future biodiversity and biomonitoring studies develop not just standardized, but optimized workflows that either maximize taxon-detection or the selection of amplicons for water quality or Arthropoda site indicators.

Klíčová slova:

Biology and life sciences – Genetics – Genomics – Genome analysis – Organisms – Eukaryota – Computational biology – Research and analysis methods – Molecular biology – Sequence assembly tools – Database and informatics methods – Bioinformatics – Sequence analysis – Animals – Invertebrates – Arthropoda – Molecular biology techniques – Computer and information sciences – Taxonomy – Data management – Artificial gene amplification and extension – Polymerase chain reaction – Earth sciences – Ecology and environmental sciences – Ecology – Marine and aquatic sciences – Sequence databases – Biological databases – Aquatic environments – Freshwater environments – Biodiversity – Fresh water – Water quality

Zdroje

1. 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

2. Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E. Towards next-generation biodiversity assessment using DNA metabarcoding. Molecular ecology. 2012;21: 2045–2050. doi: 10.1111/j.1365-294X.2012.05470.x 22486824

3. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME Journal. 2017;11: 2639–2643. doi: 10.1038/ismej.2017.119 28731476

4. Porter TM, Hajibabaei M. Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis. Molecular Ecology. 2018;27: 313–338. doi: 10.1111/mec.14478 29292539

5. Baird DJ, Hajibabaei M. Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Molecular ecology. 2012;21: 2039–2044. 22590728

6. Leese F, Bouchez A, Abarenkov K, Altermatt F, Borja Á, Bruce K, et al. Why We Need Sustainable Networks Bridging Countries, Disciplines, Cultures and Generations for Aquatic Biomonitoring 2.0: A Perspective Derived From the DNAqua-Net COST Action. Advances in Ecological Research. Elsevier; 2018. pp. 63–99. doi: 10.1016/bs.aecr.2018.01.001

7. Suzuki MT, Giovannoni SJ. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Applied and environmental microbiology. 1996;62: 625–630. 8593063

8. Polz MF, Cavanaugh CM. Bias in template-to-product ratios in multitemplate PCR. Applied and environmental Microbiology. 1998;64: 3724–3730. 9758791

9. Clarke LJ, Soubrier J, Weyrich LS, Cooper A. Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias. Molecular Ecology Resources. 2014;14: 1160–1170. doi: 10.1111/1755-0998.12265 24751203

10. Emilson CE, Thompson DG, Venier LA, Porter TM, Swystun T, Chartrand D, et al. DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient. Scientific Reports. 2017;7. doi: 10.1038/s41598-017-13157-x 28986575

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

12. Braukmann TW, Ivanova NV, Prosser SW, Elbrecht V, Steinke D, Ratnasingham S, et al. Revealing the Complexities of Metabarcoding with a Diverse Arthropod Mock Community. 2018;

13. 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. PNAS. 2014;111: 8007–8012. doi: 10.1073/pnas.1406468111 24808136

14. Buss DF, Baptista DF, Silveira MP, Nessimian JL, Dorvillé LF. Influence of water chemistry and environmental degradation on macroinvertebrate assemblages in a river basin in south-east Brazil. Hydrobiologia. 2002;481: 125–136.

15. Bonada N, Prat N, Resh VH, Statzner B. DEVELOPMENTS IN AQUATIC INSECT BIOMONITORING: A Comparative Analysis of Recent Approaches. Annual Review of Entomology. 2006;51: 495–523. doi: 10.1146/annurev.ento.51.110104.151124 16332221

16. Elbrecht V, Leese F. Validation and Development of COI Metabarcoding Primers for Freshwater Macroinvertebrate Bioassessment. Frontiers in Environmental Science. 2017;5: 11. doi: 10.3389/fenvs.2017.00011

17. De Cáceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90: 3566–3574. doi: 10.1890/08-1823.1 20120823

18. Gibson J, 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

19. Erdozain M, Thompson DG, Porter TM, Kidd KA, Kreutzweiser DP, Sibley PK, et al. Metabarcoding of storage ethanol vs. conventional morphometric identification in relation to the use of stream macroinvertebrates as ecological indicators in forest management. Ecological Indicators. 2019;101: 173–184. doi: 10.1016/j.ecolind.2019.01.014

20. Jones C, Somers KM, Craig B, Reynoldson TB. Ontario Benthos Biomonitoring Network: Protocol Manual. Toronto: Queens Printer for Ontario; 2007.

21. Maddison WP, Maddison DR. Mesquite [Internet]. 2015. http://mesquiteproject.org

22. Hajibabaei M, Spall JL, Shokralla S, van Konynenburg S. Assessing biodiversity of a freshwater benthic macroinvertebrate community through non-destructive environmental barcoding of DNA from preservative ethanol. BMC Ecology. 2012;12: 28. doi: 10.1186/1472-6785-12-28 23259585

23. Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular marine biology and biotechnology. 1994;3: 294–299. 7881515

24. 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. Frontiers in Zoology. 2013;10: 34. doi: 10.1186/1742-9994-10-34 23767809

25. Geller J, Meyer C, Parker M, Hawk H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol Ecol Resour. 2013;13: 851–861. doi: 10.1111/1755-0998.12138 23848937

26. Vamos E, Elbrecht V, Leese F. Short COI markers for freshwater macroinvertebrate metabarcoding. Metabarcoding and Metagenomics. 2017;1: e14625. doi: 10.3897/mbmg.1.14625

27. Tange O. GNU Parallel—The Command-Line Power Tool.; login: The USENIX Magazine. 2011;February: 42–47.

28. St. John J. SeqPrep [Internet]. Downloaded 2016. https://github.com/jstjohn/SeqPrep/releases

29. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet journal. 2011;17: pp–10.

30. 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

31. Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. 2016;

32. Brown SP, Veach AM, Rigdon-Huss AR, Grond K, Lickteig SK, Lothamer K, et al. Scraping the bottom of the barrel: are rare high throughput sequences artifacts? Fungal Ecology. 2015;13: 221–225. doi: 10.1016/j.funeco.2014.08.006

33. Tedersoo L, Nilsson RH, Abarenkov K, Jairus T, Sadam A, Saar I, et al. 454 Pyrosequencing and Sanger sequencing of tropical mycorrhizal fungi provide similar results but reveal substantial methodological biases. New Phytologist. 2010;188: 291–301. doi: 10.1111/j.1469-8137.2010.03373.x 20636324

34. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and Environmental Microbiology. 2007;73: 5261–5267. doi: 10.1128/AEM.00062-07 17586664

35. Porter TM, Gibson JF, Shokralla S, Baird DJ, Golding GB, Hajibabaei M. Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier. Mol Ecol Resour. 2014;14: 929–942. doi: 10.1111/1755-0998.12240

36. Porter TM, Hajibabaei M. Automated high throughput animal CO1 metabarcode classification. Scientific Reports. 2018;8: 4226. doi: 10.1038/s41598-018-22505-4 29523803

37. RStudio Team. RStudio: Integrated Development for R [Internet]. 2016. http://www.rstudio.com/

38. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. 2017. https://www.R-project.org/

39. Oksanen J, Blanchet GF, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.5–2. [Internet]. 2018. https://CRAN.R-project.org/package=vegan

40. Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5: 27. doi: 10.1186/s40168-017-0237-y 28253908

41. Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. New York: Springer-Verlag; 2009. http://ggplot2.org

42. Shapiro SS, Wilk MB. An Analysis of Variance Test for Normality (Complete Samples). Biometrika. 1965;52: 591–611.

43. 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

44. Wei T, Simko V. R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84). [Internet]. 2017. https://github.com/taiyun/corrplot

45. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research [Internet]. 2018. https://CRAN.R-project.org/package=psych

46. Holm S. A Simple Sequentially Rejective Multiple Test Procedure. Scand J Statist. 1979;6: 65–70.

47. 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? Freshwater Science. 2018;37: 178–189. doi: 10.1086/696613

48. Porter TM, Hajibabaei M. Over 2.5 million COI sequences in GenBank and growing. PLoS ONE. 2018;13: e0200177. doi: 10.1371/journal.pone.0200177 30192752

49. Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecological Monographs. 2013;83: 557–574. doi: 10.1890/12-2010.1

50. Bik HM, Porazinska DL, Creer S, Caporaso JG, Knight R, Thomas WK. Sequencing our way towards understanding global eukaryotic biodiversity. Trends in Ecology & Evolution. 2012;27: 233–243. doi: 10.1016/j.tree.2011.11.010 22244672

51. Yu DW, Ji Y, Emerson BC, Wang X, Ye C, Yang C, et al. Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring: Biodiversity soup. Methods in Ecology and Evolution. 2012;3: 613–623.

52. Creer S, Deiner K, Frey S, Porazinska D, Taberlet P, Thomas WK, et al. The ecologist’s field guide to sequence-based identification of biodiversity. Methods in Ecology and Evolution. 2016;7: 1008–1018. doi: 10.1111/2041-210X.12574

53. Leese F, Altermatt F, Bouchez A, Ekrem T, Hering D, Meissner K, et al. DNAqua-Net: Developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe. Research Ideas and Outcomes. 2016;2: e11321. doi: 10.3897/rio.2.e11321

54. Bush A, Sollmann R, Wilting A, Bohmann K, Cole B, Balzter H, et al. Connecting Earth observation to high-throughput biodiversity data. Nature Ecology & Evolution. 2017;1: 0176. doi: 10.1038/s41559-017-0176

55. Bush A, Compson Z, Monk W, Porter TM, Steeves R, Emilson E, et al. Studying ecosystems with DNA metabarcoding: lessons from aquatic biomonitoring. bioRxiv. 2019;

56. Clarke J, Wu H-C, Jayasinghe L, Patel A, Reid S, Bayley H. Continuous base identification for single-molecule nanopore DNA sequencing. Nat Nano. 2009;4: 265–270. doi: 10.1038/nnano.2009.12

57. Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H avard. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC microbiology. 2010;10: 189. doi: 10.1186/1471-2180-10-189 20618939

58. Glassman SI, Martiny JB. Ecological patterns are robust to use of exact sequence variants versus operational taxonomic units. mSphere. 2018;3: e00148–18. doi: 10.1128/mSphere.00148-18 30021874

59. Drummond AJ, Newcomb RD, Buckley TR, Xie D, Dopheide A, Potter BC, et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. GigaSci. 2015;4: 46. doi: 10.1186/s13742-015-0086-1 26445670

60. Grey EK, Bernatchez L, Cassey P, Deiner K, Deveney M, Howland KL, et al. Effects of sampling effort on biodiversity patterns estimated from environmental DNA metabarcoding surveys. Scientific Reports. 2018;8. doi: 10.1038/s41598-018-27048-2 29891968

61. Haegeman B, Hamelin J, Moriarty J, Neal P, Dushoff J, Weitz JS. Robust estimation of microbial diversity in theory and in practice. ISME J. 2013;7: 1092–1101. doi: 10.1038/ismej.2013.10 23407313

62. Singer GAC, Fahner NA, Barnes JG, McCarthy A, Hajibabaei M. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: a case study of eDNA metabarcoding seawater. Sci Rep. 2019;9: 5991. doi: 10.1038/s41598-019-42455-9 30979963

63. McMurdie PJ, Holmes S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLOS Comput Biol. 2014;10: e1003531. doi: 10.1371/journal.pcbi.1003531 24699258

64. Weiss SJ, Xu Z, Amir A, Peddada S, Bittinger K, Gonzalez A, et al. Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data. doi: 10.7287/peerj.preprints.1157v1

65. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Frontiers in Microbiology. 2017;8. doi: 10.3389/fmicb.2017.02224 29187837

COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz