Advertisement

Experimental and Applied Acarology

, Volume 76, Issue 1, pp 81–97 | Cite as

Enabling large-scale feather mite studies: an Illumina DNA metabarcoding pipeline

  • Antón Vizcaíno
  • Jorge Doña
  • Joaquín Vierna
  • Neus Marí-Mena
  • Rocío Esteban
  • Sergey Mironov
  • Charlotte Urien
  • David Serrano
  • Roger Jovani
Article

Abstract

Feather mites are among the most common and diverse ectosymbionts of birds, yet basic questions such as the nature of their relationship remain largely unanswered. One reason for feather mites being understudied is that their morphological identification is often virtually impossible when using female or young individuals. Even for adult male specimens this task is tedious and requires advanced taxonomic expertise, thus hampering large-scale studies. In addition, molecular-based methods are challenging because the low DNA amounts usually obtained from these tiny mites do not reach the levels required for high-throughput sequencing. This work aims to overcome these issues by using a DNA metabarcoding approach to accurately identify and quantify the feather mite species present in a sample. DNA metabarcoding is a widely used molecular technique that takes advantage of high-throughput sequencing methodologies to assign the taxonomic identity to all the organisms present in a complex sample (i.e., a sample made up of multiple specimens that are hard or impossible to individualise). We present a high-throughput method for feather mite identification using a fragment of the COI gene as marker and Illumina Miseq technology. We tested this method by performing two experiments plus a field test over a total of 11,861 individual mites (5360 of which were also morphologically identified). In the first experiment, we tested the probability of detecting a single feather mite in a heterogeneous pool of non-conspecific individuals. In the second experiment, we made 2 × 2 combinations of species and studied the relationship between the proportion of individuals of a given species in a sample and the proportion of sequences retrieved to test whether DNA metabarcoding can reliably quantify the relative abundance of mites in a sample. Here we also tested the efficacy of degenerate primers (i.e., a mixture of similar primers that differ in one or several bases that are designed to increase the chance of annealing) and investigated the relationship between the number of mismatches and PCR success. Finally, we applied our DNA metabarcoding pipeline to a total of 6501 unidentified and unsorted feather mite individuals sampled from 380 European passerine birds belonging to 10 bird species (field test). Our results show that this proposed pipeline is suitable for correct identification and quantitative estimation of the relative abundance of feather mite species in complex samples, especially when dealing with a moderate number (> 30) of individuals per sample.

Keywords

DNA metabarcoding Feather mites Molecular identification Experimental pipeline High-throughput sequencing 

Notes

Acknowledgements

Funding was provided by the Spanish Ministry of Economy and Competitiveness (Ramón y Cajal research contract RYC-2009-03967 to RJ, research project CGL2011-24466 to RJ, and CGL2015-69650-P to RJ and DS). JD was supported by the Spanish Ministry of Economy and Competitiveness (Severo Ochoa predoctoral contract SVP-2013-067939), and SV was supported by the Russian Foundation for Basic Research (RFBR-6-04-00486). Special thanks for the help in collecting samples to: Carolina Osuna, Alberto Álvarez, Emilio Paganí-Núñez, Carlos Gutiérrez Expósito, Carlos Camacho, David Ochoa, Jaime Potti, José Luis Arroyo, Rubén Rodríguez Olivares, Marina Moreno-García, Pepe Ayala, Jose Luis Garzón, Francisco Jimenez Cazalla and Sociedad Ornitológica de Menorca (SOM).

Supplementary material

10493_2018_288_MOESM1_ESM.xls (269 kb)
Supplementary material 1 (XLS 269 kb)
10493_2018_288_MOESM2_ESM.doc (226 kb)
Supplementary material 2 (DOC 226 kb)
10493_2018_288_MOESM3_ESM.fasta (30 kb)
Supplementary material 3 (FASTA 30 kb)
10493_2018_288_MOESM4_ESM.fasta (23 kb)
Supplementary material 4 (FASTA 24 kb)

References

  1. Allen JM, Boyd B, Nguyen NP et al (2017) Phylogenomics from whole genome sequences using aTRAM. Syst Biol 66:786–798PubMedGoogle Scholar
  2. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410CrossRefPubMedGoogle Scholar
  3. Andrew S (2010) FastQC, a quality control tool for high throughput sequence data. Retrieved Oct 2015, from http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  4. Arribas P, Andujar C, Hopkins K, Shepherd M, Vogler AP (2016) Metabarcoding and mitochondrial metagenomics of endogean arthropods to unveil the mesofauna of the soil. Methods Ecol Evol 7:1071–1081CrossRefGoogle Scholar
  5. Atyeo WT, Braasch NL (1966) The feather mite genus Proctophyllodes (Sarcoptiformes: Proctophyllodidae). Univ Neb State Mus 5:1–354Google Scholar
  6. Atyeo WT, Gaud J (1970) The feather mite genus Monojourbertia Radford, 1950 (Analgoidea: Proctophyllodidae). Entomologische Mitteilungen aus dem Zoologischen Staatsinstitut und Zoologischen Museum, Hamburg 4:145–155Google Scholar
  7. Baker CC, Bittleston LS, Sanders JG, Pierce NE (2016) Dissecting host-associated communities with DNA barcodes. Philos Trans R Soc B 371:20150328CrossRefGoogle Scholar
  8. Blanco G, Tella J, Potti J, Baz A (2001) Feather mites on birds: costs of parasitism or conditional outcomes? J Avian Biol 32:271–274CrossRefGoogle Scholar
  9. Bolker BM (2008) Ecological models and data in R. Princeton University Press, PrincetonGoogle Scholar
  10. Bushnell B (2014) BBMap: a fast, accurate, splice-aware aligner. Report number: LBNL-7065E, Lawrence Berkeley National Laboratory, Berkeley, CAGoogle Scholar
  11. Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336CrossRefPubMedPubMedCentralGoogle Scholar
  12. Carlsen T, Aas AB, Lindner D, Vrålstad T, Schumacher T, Kauserud H (2012) Don’t make a mista(g)ke: is tag switching an overlooked source of error in amplicon pyrosequencing studies? Fungal Ecol 5:747–749CrossRefGoogle Scholar
  13. Carlson CJ, Burgio KR, Dougherty ER et al (2017) Parasite biodiversity faces extinction and redistribution in a changing climate. Sci Adv 3:e1602422CrossRefPubMedPubMedCentralGoogle Scholar
  14. Dabert J, Ehrnsberger R, Dabert M (2008) Glaucalges tytonis sp. nov. (Analgoidea: Xolalgidae) from the barn owl Tyto alba (Strigiformes: Tytonidae): compiling morphology with DNA barcode data for taxa descriptions in mites (Acari). Zootaxa 1719:41–52Google Scholar
  15. De Tender CA, Devriese LI, Haegeman A, Maes S, Ruttink T, Dawyndt P (2015) Bacterial community profiling of plastic litter in the Belgian part of the North Sea. Environ Sci Technol 49:9629–9638CrossRefPubMedGoogle Scholar
  16. Deagle BE, Jarman SN, Coissac E, Pompanon F, Taberlet P (2014) DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match. Biol Lett 10:20140562CrossRefPubMedPubMedCentralGoogle Scholar
  17. Diaz-Real J, Serrano D, Pérez-Tris J et al (2014) Repeatability of feather mite prevalence and intensity in passerine birds. PLoS ONE 9:e107341CrossRefPubMedPubMedCentralGoogle Scholar
  18. Diaz-Real J, Serrano D, Piriz A, Jovani R (2015) NGS metabarcoding proves successful for quantitative assessment of symbiont abundance: the case of feather mites on birds. Exp Appl Acarol 67:209–218CrossRefPubMedGoogle Scholar
  19. Dobson A, Lafferty K, Kuris A, Hechinger R, Jetz W (2008) Homage to Linnaeus: how many parasites? How many hosts? Proc Natl Acad Sci 105:11482–11489CrossRefPubMedGoogle Scholar
  20. Doña J, Diaz-Real J, Mironov S, Bazaga P, Serrano D, Jovani R (2015a) DNA barcoding and mini-barcoding as a powerful tool for feather mite studies. Mol Ecol Resour 15:1216–1225CrossRefPubMedGoogle Scholar
  21. Doña J, Moreno-García M, Criscione CD, Serrano D, Jovani R (2015b) Species mtDNA genetic diversity explained by infrapopulation size in a host-symbiont system. Ecol Evolut 5:5801–5809CrossRefGoogle Scholar
  22. Doña J, Proctor H, Mironov S, Serrano D, Jovani R (2016) Global associations between birds and vane-dwelling feather mites. Ecology 97:3242CrossRefGoogle Scholar
  23. Doña J, Potti J, De la Hera I, Blanco G, Frías O, Jovani R (2017a) Vertical transmission in feather mites: insights into its adaptive value. Ecol Entomol 42:492–499CrossRefGoogle Scholar
  24. Doña J, Sweet AD, Johnson KP, Serrano D, Mironov S, Jovani R (2017b) Cophylogenetic analyses reveal extensive host-shift speciation in a highly specialized and host-specific symbiont system. Mol Phylogenet Evol 115:190–196CrossRefPubMedGoogle Scholar
  25. Doña J, Proctor H, Serrano D et al (2018) Feather mites play a role in cleaning host feathers: new insights from DNA metabarcoding and microscopy. Mol Ecol.  https://doi.org/10.1111/mec.14581 CrossRefPubMedGoogle Scholar
  26. Dubinin VB (1951) Feather mites (Analgesoidea). Part 1. Introduction to their study. Fauna USSR 6:1–363 (in Russian) Google Scholar
  27. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.  https://doi.org/10.1093/bioinformatics/btq461 CrossRefPubMedGoogle Scholar
  28. Elbrecht V, Leese F (2017) PrimerMiner: an R package for development and in silico validation of DNA metabarcoding primers. Methods Ecol Evol 8:622–626CrossRefGoogle Scholar
  29. Elbrecht V, Vamos EE, Meissner K, Aroviita J, Leese F (2017) Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol Evol 8:1265–1275CrossRefGoogle Scholar
  30. Esling P, Lejzerowicz F, Pawlowski J (2015) Accurate multiplexing and filtering for high-throughput amplicon-sequencing. Nucl Acids Res 43:2513–2524CrossRefPubMedGoogle Scholar
  31. Ferrari S, Cribari-Neto F (2004) Beta regression for modelling rates and proportions. J Appl Stat 31:799–815CrossRefGoogle Scholar
  32. Ficetola G, Pansu J, Bonin A et al (2015) Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol Ecol Resour 15:543–556CrossRefPubMedGoogle Scholar
  33. Gaud J, Atyeo WT (1996) Feather mites of the World (Acarina, Astigmata): the supraspecific taxa. Annales du Musee Royale de L’Afrique Centrale, Sciences Zoologiques, 277, 1–193 (Pt. 1, text), 1–436 (Pt. 2, illustrations)Google Scholar
  34. Geisen S, Laros I, Vizcaíno A, Bonkowski M, de Groot GA (2015) Not all are free-living: high-throughput DNA metabarcoding reveals a diverse community of protists parasitizing soil metazoa. Mol Ecol 24:4556–4569CrossRefPubMedGoogle Scholar
  35. Hawkins TL, O’Connor-Morin T, Roy A, Santillan C (1994) DNA purification and isolation using a solid-phase. Nucl Acids Res 22:4543–4544CrossRefPubMedGoogle Scholar
  36. Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proc R Soc Lond B 270:313–321CrossRefGoogle Scholar
  37. Jousselin E, Clamens AL, Galan M et al (2016) Assessment of a 16S rRNA amplicon illumina sequencing procedure for studying the microbiome of a symbiont-rich aphid genus. Mol Ecol Resour 16:628–640CrossRefPubMedGoogle Scholar
  38. Kearse M, Moir R, Wilson A et al (2012) Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649CrossRefPubMedPubMedCentralGoogle Scholar
  39. Lafferty KD, Dobson A, Kuris AM (2006) Parasites dominate food web links. Proc Natl Acad Sci 103:11211–11216CrossRefPubMedGoogle Scholar
  40. Lange V, Böhme I, Hofman J et al (2014) Cost-efficient high-throughput HLA typing by MiSeq amplicon sequencing. BMC Genom 15:63CrossRefGoogle Scholar
  41. Linard B, Arribas P, Andújar C, Crampton-Platt A, Vogler AP (2016) Lessons from genome skimming of arthropod-preserving ethanol. Mol Ecol Resour 16:1365–1377CrossRefPubMedGoogle Scholar
  42. Meléndez L, Laiolo P, Mironov S, García M, Magaña O, Jovani R (2014) Climate-driven variation in the intensity of a host-symbiont animal interaction along a broad elevation gradient. PLoS ONE 9:e101942CrossRefPubMedPubMedCentralGoogle Scholar
  43. Mironov SV, Galloway TD (2006) New and little-known species of the feather mites (Acari: Analgoidea: Pteronyssidae) from birds in North America. Can Entomol 138:165–188CrossRefGoogle Scholar
  44. Mironov SV, Wauthy G (2006) Systematic review of feather mites of the genus Sturnotrogus Mironov, 1989 (Astigmata: Pteronyssidae) from starlings (Passeriformes: Sturnidae) in Africa and Europe. Bulletin de l’Institut Royal des Sciences naturelles de Belgique, Entomogie 76:55–81Google Scholar
  45. Mironov SV, Dabert J, Dabert M (2012) A new feather mite species of the genus Proctophyllodes Robin, 1877 (Astigmata: Proctophyllodidae) from the Long-tailed Tit Aegithalos caudatus (Passeriformes: Aegithalidae)—morphological description with DNA barcode data. Zootaxa 3253:54–61Google Scholar
  46. Mironov SV, Doña J, Jovani R (2015) A new feather mite of the genus Dolichodectes (Astigmata: Proctophyllodidae) from Hippolais polyglotta (Passeriformes: Acrocephalidae) in Spain. Folia Parasitol 62:032CrossRefGoogle Scholar
  47. Navarro-Noya YE, Valenzuela-Encinas C, Sandoval-Yuriar A, Jiménez-Bueno NG, Marsch R, Dendooven L (2015) Archaeal communities in a heterogeneous hypersaline-alkaline soil. Archaea 2015:11CrossRefGoogle Scholar
  48. Owens GL, Todesco M, Drummond EB, Yeaman S, Rieseberg LH (2018) A novel post hoc method for detecting index switching finds no evidence for increased switching on the Illumina HiSeq X. Mol Ecol Resour 18:169–175CrossRefPubMedGoogle Scholar
  49. Pap P, Vágási C, Osváth G, Mureşan C, Barta Z (2010) Seasonality in the uropygial gland size and feather mite abundance in house sparrows Passer domesticus: natural covariation and an experiment. J Avian Biol 41:653–661CrossRefGoogle Scholar
  50. Park CK, Atyeo WT (1971) A generic revision of the Pterodectinae, a new subfamily of feather mites (Sarcoptiformes: Analgoidea). Bull Univ Neb State Mus 9:39–88Google Scholar
  51. Pornon A, Escaravage N, Burrus M et al (2016) Using metabarcoding to reveal and quantify plant–pollinator interactions. Sci Rep 6:27282CrossRefPubMedPubMedCentralGoogle Scholar
  52. Poulin R (2014) Parasite biodiversity revisited: frontiers and constraints. Int J Parasitol 44:581–589CrossRefPubMedGoogle Scholar
  53. Proctor H (2003) Feather mites (Acari: Astigmata): ecology, behavior, and evolution. Annu Rev Entomol 48:185–209CrossRefPubMedGoogle Scholar
  54. Reva ON, Zaets IE, Ovcharenko LP et al (2015) Metabarcoding of the kombucha microbial community grown in different microenvironments. AMB Express 5:124CrossRefPubMedGoogle Scholar
  55. Riaz T, Shehzad W, Viari A, Pompanon F, Taberlet P, Coissac E (2011) ecoPrimers: inference of new DNA barcode markers from whole genome sequence analysis. Nucl Acids Res 39:e145–e145CrossRefPubMedGoogle Scholar
  56. Rocha CFD, Bergallo HG, Bittencourt EB (2016) More than just invisible inhabitants: parasites are important but neglected components of the biodiversity. Zoologia (Curitiba) 33:e20150198CrossRefGoogle Scholar
  57. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/. Accessed July 2017
  58. Santana F (1976) A review of the genus Trouessartia: (Analgoidea: Alloptidae). J Med Entomol 13:1–125CrossRefGoogle Scholar
  59. Schnell IB, Bohmann K, Gilbert MT (2015) Tag jumps illuminated: reducing sequence-to-sample misidentifications in metabarcoding studies. Mol Ecol Resour 15:1289–1303CrossRefPubMedGoogle Scholar
  60. Schrader C, Schielke A, Ellerbroek L, Johne R (2012) PCR inhibitors: occurrence, properties and removal. J Appl Microbiol 113:1014–1026CrossRefPubMedGoogle Scholar
  61. Sinha R, Stanley G, Gulati G et al (2017) Index switching causes “spreading-of-signal” among multiplexed samples in Illumina HiSeq 4000 DNA sequencing. bioRxiv.  https://doi.org/10.1101/125724 CrossRefGoogle Scholar
  62. Sipos R, Székely A, Palatinszky M, Révész S, Márialigeti K, Nikolausz M (2007) Effect of primer mismatch, annealing temperature and PCR cycle number on 16S rRNA gene-targetting bacterial community analysis. FEMS Microbiol Ecol 60:341–350CrossRefPubMedGoogle Scholar
  63. Soininen EM, Zinger L, Gielly L et al (2013) Shedding new light on the diet of Norwegian lemmings: DNA metabarcoding of stomach content. Polar Biol 36:1069–1076CrossRefGoogle Scholar
  64. Stephens ZD, Lee SY, Faghri F et al (2015) Big data: astronomical or genomical? PLoS Biol 13:1–11CrossRefGoogle Scholar
  65. Taberlet P, Coissac E, Hajibabaei M, Rieseberg LH (2012a) Environmental DNA. Mol Ecol 21:1789–1793CrossRefPubMedGoogle Scholar
  66. Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E (2012b) Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol 21:2045–2050CrossRefPubMedGoogle Scholar
  67. Thomas CD, Cameron A, Green RE et al (2004) Extinction risk from climate change. Nature 427:145–148CrossRefPubMedGoogle Scholar
  68. Tripp E, Zhang N, Schneider H et al (2017) Reshaping Darwin’s tree: impact of the symbiome. Trends Ecol Evol 32:552–555CrossRefPubMedGoogle Scholar
  69. Truett GE, Heeger P, Mynatt RL, Truett AA, Walker JA, Warman ML (2000) Preparation of PCR-quality mouse genomic DNA with hot sodium hydroxide and tris (HotSHOT). Biotechniques 29:52–54CrossRefPubMedGoogle Scholar
  70. Vierna J, Doña J, Vizcaíno A, Serrano D, Jovani R (2017) PCR cycles above routine numbers do not compromise high-throughput DNA barcoding results. Genome 60:868–873CrossRefPubMedGoogle Scholar
  71. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267CrossRefPubMedPubMedCentralGoogle Scholar
  72. Wolak ME, Fairbairn DJ, Paulsen YR (2012) Guidelines for estimating repeatability. Methods Ecol Evol 3:129–137CrossRefGoogle Scholar
  73. Zeileis A, Cribari-Neto F, Gruen B, Kosmidis I (2012) Package ‘betareg’. https://cran.r-project.org/web/packages/betareg/betareg.pdf

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.AllGenetics & Biology SLA CoruñaSpain
  2. 2.Department of Evolutionary EcologyEstación Biológica de Doñana (CSIC)SevilleSpain
  3. 3.Zoological InstituteRussian Academy of SciencesSaint PetersburgRussia
  4. 4.Department of Conservation BiologyEstación Biológica de Doñana (CSIC)SevilleSpain
  5. 5.Illinois Natural History Survey, Prairie Research InstituteUniversity of Illinois at Urbana-ChampaignChampaignUSA

Personalised recommendations