ALGAE

The Korean Society of Phycology (한국조류학회(藻類))
권호 표지
DOI QR코드

DOI QR Code

SoEM: a novel PCR-free biodiversity assessment method based on small-organelles enriched metagenomics

  • Jo, Jihoon (School of Biological Sciences and Technology, College of Natural Sciences, Chonnam National University) ;
  • Lee, Hyun-Gwan (Marine Ecosystem Disturbing and Harmful Organisms (MEDHO) Research Center) ;
  • Kim, Kwang Young (Marine Ecosystem Disturbing and Harmful Organisms (MEDHO) Research Center) ;
  • Park, Chungoo (School of Biological Sciences and Technology, College of Natural Sciences, Chonnam National University)
  • Received : 2018.11.19
  • Accepted : 2019.02.26
  • Published : 2019.03.15
Download PDF
⟨ Previous Next ⟩

Abstract

DNA metabarcoding is currently used for large-scale taxonomic identification to understand the community composition in various marine ecosystems. However, before being widely used in this emerging field, this experimental and analytic approach still has several technical challenges to overcome, such as polymerase chain reaction (PCR) bias, and lack of well-established metabarcoding markers, a task which is difficult but not impossible to achieve. In this study, we present an adapted PCR-free small-organelles enriched metagenomics (SoEM) method for marine biodiversity assessment. To avoid PCR bias and random artefacts, we extracted target DNA sequences without PCR amplification from marine environmental samples enriched with small organelles including mitochondria and plastids because their genome sequences provide a valuable source of molecular markers for phylogenetic analysis. To experimentally enrich small organelles, we performed subcellular fractionation using modified differential centrifugation for marine environmental DNA samples. To validate our SoEM method, two marine environmental samples from the coastal waters were tested the taxonomic capturing capacity against that of traditional DNA metabarcoding method. Results showed that, regardless of taxonomic levels, at least 3-fold greater numbers of taxa were identified in our SoEM method, compared to those identified by the conventional multi-locus DNA metabarcoding method. The SoEM method is thus effective and accurate for identifying taxonomic diversity and presents a useful alternative approach for evaluating biodiversity in the marine environment.

Keywords

Acknowledgement

Grant : Research center for fishery resource management based on the information and communication technology (ICT)

Supported by : Korea Institute of Marine Science and Technology Promotion (KIMST)

References

  1. Andujar, C., Arribas, P., Ruzicka, F., Crampton-Platt, A., Timmermans, M. J. T. N. & Vogler, A. P. 2015. Phylogenetic community ecology of soil biodiversity using mitochondrial metagenomics. Mol. Ecol. 24:3603-3617. https://doi.org/10.1111/mec.13195
  2. Andujar, C., Arribas, P., Yu, D. W., Vogler, A. P. & Emerson, B. C. 2018. Why the COI barcode should be the community DNA metabarcode for the metazoa. Mol. Ecol. 27:3968-3975. https://doi.org/10.1111/mec.14844
  3. Ansorge, W. J. 2009. Next-generation DNA sequencing techniques. N. Biotechnol. 25:195-203. https://doi.org/10.1016/j.nbt.2008.12.009
  4. Appeltans, W., Ahyong, S. T., Anderson, G., Angel, M. V., Artois, T., Bailly, N., Bamber, R., Barber, A., Bartsch, I., Berta, A., Blazewicz-Paszkowycz, M., Bock, P., Boxshall, G., Boyko, C. B., Brandāo, S. N., Bray, R. A., Bruce, N. L., Cairns, S. D., Chan, T. -Y., Cheng, L., Collins, A. G., Cribb, T., Curini-Galletti, M., Dahdouh-Guebas, F., Davie, P. J. F., Dawson, M. N., De Clerck, O., Decock, W., De Grave, S., de Voogd, N. J., Domning, D. P., Emig, C. C., Erseus, C., Eschmeyer, W., Fauchald, K., Fautin, D. G., Feist, S. W., Fransen, C. H. J. M., Furuya, H., Garcia-Alvarez, O., Gerken, S., Gibson, D., Gittenberger, A., Gofas, S., Gomez-Daglio, L., Gordon, D. P., Guiry, M. D., Hernandez, F., Hoeksema, B. W., Hopcroft, R. R., Jaume, D., Kirk, P., Koedam, N., Koenemann, S., Kolb, J. B., Kristensen, R. M., Kroh, A., Lambert, G., Lazarus, D. B., Lemaitre, R., Longshaw, M., Lowry, J., Macpherson, E., Madin, L. P., Mah, C., Mapstone, G., McLaughlin, P. A., Mees, J., Meland, K., Messing, C. G., Mills, C. E., Molodtsova, T. N., Mooi, R., Neuhaus, B., Ng, P. K. L., Nielsen, C., Norenburg, J., Opresko, D. M., Osawa, M., Paulay, G., Perrin, W., Pilger, J. F., Poore, G. C. B., Pugh, P., Read, G. B., Reimer, J. D., Rius, M., Rocha, R. M., Saiz-Salinas, J. I., Scarabino, V., Schierwater, B., Schmidt-Rhaesa, A., Schnabel, K. E., Schotte, M., Schuchert, P., Schwabe, E., Segers, H., Self-Sullivan, C., Shenkar, N., Siegel, V., Sterrer, W., Stohr, S., Swalla, B., Tasker, M. L., Thuesen, E. V., Timm, T., Todaro, M. A., Turon, X., Tyler, S., Uetz, P., van der Land, J., Vanhoorne, B., van Ofwegen, L. P., van Soest, R. W. M., Vanaverbeke, J., Walker-Smith, G., Walter, T. C., Warren, A., Williams, G. C., Wilson, S. P. & Costello, M. J. 2012. The magnitude of global marine species diversity. Curr. Biol. 22:2189-2202. https://doi.org/10.1016/j.cub.2012.09.036
  5. Bellemain, E., Carlsen, T., Brochmann, C., Coissac, E., Taberlet, P. & Kauserud, H. 2010. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol. 10:189. https://doi.org/10.1186/1471-2180-10-189
  6. Bik, H. M., Fournier, D., Sung, W., Bergeron, R. D. & Thomas, W. K. 2013. Intra-genomic variation in the ribosomal repeats of nematodes. PLoS ONE 8:e78230. https://doi.org/10.1371/journal.pone.0078230
  7. Bolger, A. M., Lohse, M. & Usadel, B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114-2120. https://doi.org/10.1093/bioinformatics/btu170
  8. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Pena, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J., Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko, T., Zaneveld, J. & Knight, R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7:335-336. https://doi.org/10.1038/nmeth.f.303
  9. Cha, R. S. & Thilly, W. G. 1993. Specificity, efficiency, and fidelity of PCR. PCR Methods Appl. 3:S18-S29. https://doi.org/10.1101/gr.3.3.S18
  10. Chamberlain, S. A. & Szocs, E. 2013. taxize: taxonomic search and retrieval in R. F1000Res 2:191. https://doi.org/10.12688/f1000research.2-191.v2
  11. Crampton-Platt, A., Timmermans, M. J., Gimmel, M. L., Kutty, S. N., Cockerill, T. D., Vun Khen, C. & Vogler, A. P. 2015. Soup to tree: the phylogeny of beetles inferred by mitochondrial metagenomics of a Bornean rainforest sample. Mol. Biol. Evol. 32:2302-2316. https://doi.org/10.1093/molbev/msv111
  12. Crampton-Platt, A., Yu, D. W., Zhou, X. & Vogler, A. P. 2016. Mitochondrial metagenomics: letting the genes out of the bottle. Gigascience 5:15. https://doi.org/10.1186/s13742-016-0120-y
  13. Davenport, C. F. & Tümmler, B. 2013. Advances in computational analysis of metagenome sequences. Environ. Microbiol. 15:1-5. https://doi.org/10.1111/j.1462-2920.2012.02843.x
  14. Decelle, J., Probert, I., Bittner, L., Desdevises, Y., Colin, S., de Vargas, C., Galí, M., Simo, R. & Not, F. 2012. An original mode of symbiosis in open ocean plankton. Proc. Natl. Acad. Sci. U. S. A. 109:18000-18005. https://doi.org/10.1073/pnas.1212303109
  15. Dodsworth, S. 2015. Genome skimming for next-generation biodiversity analysis. Trends Plant Sci. 20:525-527. https://doi.org/10.1016/j.tplants.2015.06.012
  16. Drummond, A. J., Newcomb, R. D., Buckley, T. R., Xie, D., Dopheide, A., Potter, B. C. M., Heled, J., Ross, H. A., Tooman, L., Grosser, S., Park, D., Demetras, N. J., Stevens, M. I., Russell, J. C., Anderson, S. H., Carter, A. & Nelson, N. 2015. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4:46. https://doi.org/10.1186/s13742-015-0086-1
  17. Eberhardt, U. 2012. Methods for DNA barcoding of fungi. Methods Mol. Biol. 858:183-205. https://doi.org/10.1007/978-1-61779-591-6_9
  18. Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10:996-998. https://doi.org/10.1038/nmeth.2604
  19. Elbrecht, V. & Leese, F. 2015. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass: sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10:e0130324. https://doi.org/10.1371/journal.pone.0130324
  20. Federhen, S. 2012. The NCBI Taxonomy database. Nucleic Acids Res. 40(Database issue):D136-D143. https://doi.org/10.1093/nar/gkr1178
  21. Fenchel, T. 1988. Marine plankton food chains. Annu. Rev. Ecol. Syst. 19:19-38. https://doi.org/10.1146/annurev.es.19.110188.000315
  22. Fontaneto, D., Kaya, M., Herniou, E. A. & Barraclough, T. G. 2009. Extreme levels of hidden diversity in microscopic animals (Rotifera) revealed by DNA taxonomy. Mol. Phylogenet. Evol. 53:182-189. https://doi.org/10.1016/j.ympev.2009.04.011
  23. Greshake, B., Zehr, S., Dal Grande, F., Meiser, A., Schmitt, I. & Ebersberger, I. 2016. Potential and pitfalls of eukaryotic metagenome skimming: a test case for lichens. Mol. Ecol. Resour. 16:511-523. https://doi.org/10.1111/1755-0998.12463
  24. Haider, B., Ahn, T. -H., Bushnell, B., Chai, J., Copeland, A. & Pan, C. 2014. Omega: an overlap-graph de novo assembler for metagenomics. Bioinformatics 30:2717-2722. https://doi.org/10.1093/bioinformatics/btu395
  25. Hajibabaei, M., Shokralla, S., Zhou, X., Singer, G. A. C. & Baird, D. J. 2011. Environmental barcoding: a next-generation sequencing approach for biomonitoring applications using river benthos. PLoS ONE 6:e17497. https://doi.org/10.1371/journal.pone.0017497
  26. Hays, G. C., Richardson, A. J. & Robinson, C. 2005. Climate change and marine plankton. Trends Ecol. Evol. 20:337-344. https://doi.org/10.1016/j.tree.2005.03.004
  27. Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. 2003a. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270:313-321. https://doi.org/10.1098/rspb.2002.2218
  28. Hebert, P. D. N., Penton, E. H., Burns, J. M., Janzen, D. H. & Hallwachs, W. 2004. Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc. Natl. Acad. Sci. U. S. A. 101:14812-14817. https://doi.org/10.1073/pnas.0406166101
  29. Hebert, P. D. N., Ratnasingham, S. & deWaard, J. R. 2003b. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. Biol. Sci. 270(Suppl. 1):S96-S99.
  30. Hillmann, B., Al-Ghalith, G. A., Shields-Cutler, R. R., Zhu, Q., Gohl, D. M., Beckman, K. B., Knight, R. & Knights, D. 2018. Evaluating the information content of shallow shotgun metagenomics. mSystems 3:e00069-18.
  31. Huson, D. H., Mitra, S., Ruscheweyh, H. -J., Weber, N. & Schuster, S. C. 2011. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 21:1552-1560. https://doi.org/10.1101/gr.120618.111
  32. Krehenwinkel, H., Wolf, M., Lim, J. Y., Rominger, A. J., Simison, W. B. & Gillespie, R. G. 2017. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci. Rep. 7:17668. https://doi.org/10.1038/s41598-017-17333-x
  33. Kress, W. J. & Erickson, D. L. 2008. DNA barcodes: genes, genomics, and bioinformatics. Proc. Natl. Acad. Sci. U. S. A. 105:2761-2762. https://doi.org/10.1073/pnas.0800476105
  34. Kress, W. J., Garcia-Robledo, C., Uriarte, M. & Erickson, D. L. 2015. DNA barcodes for ecology, evolution, and conservation. Trends Ecol. Evol. 30:25-35. https://doi.org/10.1016/j.tree.2014.10.008
  35. Kroger, N. & Poulsen, N. 2008. Diatoms-from cell wall biogenesis to nanotechnology. Annu. Rev. Genet. 42:83-107. https://doi.org/10.1146/annurev.genet.41.110306.130109
  36. Kunin, V., Copeland, A., Lapidus, A., Mavromatis, K. & Hugenholtz, P. 2008. A bioinformatician's guide to metagenomics. Microbiol. Mol. Biol. Rev. 72:557-578. https://doi.org/10.1128/MMBR.00009-08
  37. Leasi, F. & Todaro, M. A. 2009. Meiofaunal cryptic species revealed by confocal microscopy: the case of Xenotrichula intermedia (Gastrotricha). Mar. Biol. 156:1335-1346. https://doi.org/10.1007/s00227-009-1175-4
  38. Lee, H. -G., Kim, H. M., Min, J., Kim, K., Park, M. G., Jeong, H. J. & Kim, K. Y. 2017. An advanced tool, droplet digital PCR (ddPCR), for absolute quantification of the red-tide dinoflagellate, Cochlodinium polykrikoides Margalef (Dinophyceae). Algae 32:189-197. https://doi.org/10.4490/algae.2017.32.9.10
  39. Linard, B., Crampton-Platt, A., Gillett, C. P., Timmermans, M. J. & Vogler, A. P. 2015. Metagenome skimming of insect specimen pools: potential for comparative genomics. Genome Biol. Evol. 7:1474-1489. https://doi.org/10.1093/gbe/evv086
  40. Liu, S., Wang, X., Xie, L., Tan, M., Li, Z., Su, X., Zhang, H., Misof, B., Kjer, K. M., Tang, M., Niehuis, O., Jiang, H. & Zhou, X. 2016. Mitochondrial capture enriches mito-DNA 100 fold, enabling PCR-free mitogenomics biodiversity analysis. Mol. Ecol. Resour. 16:470-479. https://doi.org/10.1111/1755-0998.12472
  41. Losos, J. B. 2010. Adaptive radiation, ecological opportunity, and evolutionary determinism. American Society of Naturalists E. O. Wilson award address. Am. Nat. 175:623-639. https://doi.org/10.1086/652433
  42. Luo, R., Liu, B., Xie, Y., Li, Z., Huang, W., Yuan, J., He, G., Chen, Y., Pan, Q., Liu, Y., Tang, J., Wu, G., Zhang, H., Shi, Y., Liu, Y., Yu, C., Wang, B., Lu, Y., Han, C., Cheung, D. W., Yiu, S. -M., Peng, S., Xiaoqian, Z., Liu, G., Liao, X., Li, Y., Yang, H., Wang, J., Lam, T. -W. & Wang, J. 2012. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1:18. https://doi.org/10.1186/2047-217X-1-18
  43. Martin, M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17:10-12. https://doi.org/10.14806/ej.17.1.200
  44. McManus, G. B. & Katz, L. A. 2009. Molecular and morphological methods for identifying plankton: what makes a successful marriage? J. Plankton Res. 31:1119-1129. https://doi.org/10.1093/plankt/fbp061
  45. Nilakanta, H., Drews, K. L., Firrell, S., Foulkes, M. A. & Jablonski, K. A. 2014. A review of software for analyzing molecular sequences. BMC Res. Notes 7:830. https://doi.org/10.1186/1756-0500-7-830
  46. Pawlowski, J., Audic, S., Adl, S., Bass, D., Belbahri, L., Berney, C., Bowser, S. S., Cepicka, I., Decelle, J., Dunthorn, M., Fiore-Donno, A. M., Gile, G. H., Holzmann, M., Jahn, R., Jirku, M., Keeling, P. J., Kostka, M., Kudryavtsev, A., Lara, E., Lukes, J., Mann, D. G., Mitchell, E. A. D., Nitsche, F., Romeralo, M., Saunders, G. W., Simpson, A. G. B., Smirnov, A. V., Spouge, J. L., Stern, R. F., Stoeck, T., Zim-mermann, J., Schindel, D. & de Vargas, C. 2012. CBOL Protist Working Group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS Biol. 10:e1001419. https://doi.org/10.1371/journal.pbio.1001419
  47. Pawluczyk, M., Weiss, J., Links, M. G., Egana Aranguren, M., Wilkinson, M. D. & Egea-Cortines, M. 2015. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal. Bioanal. Chem. 407:1841-1848. https://doi.org/10.1007/s00216-014-8435-y
  48. Peng, Y., Leung, H. C., Yiu, S. M. & Chin, F. Y. 2012. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28:1420-1428. https://doi.org/10.1093/bioinformatics/bts174
  49. Porazinska, D. L., Giblin-Davis, R. M., Faller, L., Farmerie, W., Kanzaki, N., Morris, K., Powers, T. O., Tucker, A. E., Sung, W. & Thomas, W. K. 2009. Evaluating high-throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol. Ecol. Resour. 9:1439-1450. https://doi.org/10.1111/j.1755-0998.2009.02611.x
  50. Porter, T. M. & Hajibabaei, M. 2018. Scaling up: a guide to high-throughput genomic approaches for biodiversity analysis. Mol. Ecol. 27:313-338. https://doi.org/10.1111/mec.14478
  51. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J. & Glockner, F. O. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41(Database issue):D590-D596.
  52. Roy, K. & Foote, M. 1997. Morphological approaches to measuring biodiversity. Trends Ecol. Evol. 12:277-281.
  53. Saunders, G. W. & McDevit, D. C. 2012. Methods for DNA barcoding photosynthetic protists emphasizing the macroalgae and diatoms. Methods Mol. Biol. 858:207-222. https://doi.org/10.1007/978-1-61779-591-6_10
  54. Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J. & Weber, C. F. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:7537-7541. https://doi.org/10.1128/AEM.01541-09
  55. Schuster, S. C. 2007. Next-generation sequencing transforms today's biology. Nat. Methods 5:16-18. https://doi.org/10.1038/nmeth1156
  56. Scotland, R. W., Olmstead, R. G. & Bennett, J. R. 2003. Phylogeny reconstruction: the role of morphology. Syst. Biol. 52:539-548. https://doi.org/10.1080/10635150390223613
  57. Straub, S. C., Parks, M., Weitemier, K., Fishbein, M., Cronn, R. C. & Liston, A. 2012. Navigating the tip of the genomic iceberg: next-generation sequencing for plant systematics. Am. J. Bot. 99:349-364. https://doi.org/10.3732/ajb.1100335
  58. Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. 2012. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21:2045-2050. https://doi.org/10.1111/j.1365-294X.2012.05470.x
  59. Tang, M., Tan, M., Meng, G., Yang, S., Su, X., Liu, S., Song, W., Li, Y., Wu, Q., Zhang, A. & Zhou, X. 2014. Multiplex sequencing of pooled mitochondrial genomes: a crucial step toward biodiversity analysis using mito-metagenomics. Nucleic Acids Res. 42:e166. https://doi.org/10.1093/nar/gku917
  60. Walker, B. H. 1992. Biodiversity and ecological redundancy. Conserv. Biol. 6:18-23. https://doi.org/10.1046/j.1523-1739.1992.610018.x
  61. Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. 2014. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30:614-620. https://doi.org/10.1093/bioinformatics/btt593
  62. Zhou, J., Bruns, M. A. & Tiedje, J. M. 1996. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62:316-322. https://doi.org/10.1128/AEM.62.2.316-322.1996
  63. Zhou, X., Adamowicz, S. J., Jacobus, L. M., Dewalt, R. E. & 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. https://doi.org/10.1186/1742-9994-6-30
  64. Zhou, X., Li, Y., Liu, S., Yang, Q., Su, X., Zhou, L., Tang, M., Fu, R., Li, J. & Huang, Q. 2013. Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification. Gigascience 2:4. https://doi.org/10.1186/2047-217X-2-4

Cited by

  1. Epibionts associated with floating Sargassum horneri in the Korea Strait vol.34, pp.4, 2019, https://doi.org/10.4490/algae.2019.34.12.10
  2. Comparing diversity levels in environmental samples: DNA sequence capture and metabarcoding approaches using 18S and COI genes vol.20, pp.5, 2019, https://doi.org/10.1111/1755-0998.13201
  3. An assessment of the taxonomic reliability of DNA barcode sequences in publicly available databases vol.35, pp.3, 2019, https://doi.org/10.4490/algae.2020.35.9.4