Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling

Autoři: Ching Jian aff001;  Panu Luukkonen aff002;  Hannele Yki-Järvinen aff002;  Anne Salonen aff001;  Katri Korpela aff001
Působiště autorů: Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland aff001;  Minerva Foundation Institute for Medical Research, Helsinki, Finland aff002;  Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227285


The use of relative abundance data from next generation sequencing (NGS) can lead to misinterpretations of microbial community structures, as the increase of one taxon leads to the concurrent decrease of the other(s) in compositional data. Although different DNA- and cell-based methods as well as statistical approaches have been developed to overcome the compositionality problem, and the biological relevance of absolute bacterial abundances has been demonstrated, the human microbiome research has not yet adopted these methods, likely due to feasibility issues. Here, we describe how quantitative PCR (qPCR) done in parallel to NGS library preparation provides an accurate estimation of absolute taxon abundances from NGS data and hence provides an attainable solution to compositionality in high-throughput microbiome analyses. The advantages and potential challenges of the method are also discussed.

Klíčová slova:

Bacteria – DNA extraction – Flow cytometry – Microbiome – Next-generation sequencing – Polymerase chain reaction – Ribosomal RNA – Statistical data


1. Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, et al. Best practices for analysing microbiomes. Nature reviews Microbiology. 2018;16(7):410–22. Epub 2018/05/26. doi: 10.1038/s41579-018-0029-9 29795328.

2. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Frontiers in microbiology. 2017;8:2224. Epub 2017/12/01. doi: 10.3389/fmicb.2017.02224 29187837; PubMed Central PMCID: PMC5695134.

3. Tsilimigras MC, Fodor AA. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Annals of epidemiology. 2016;26(5):330–5. Epub 2016/06/04. doi: 10.1016/j.annepidem.2016.03.002 27255738.

4. Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, et al. Establishing microbial composition measurement standards with reference frames. Nat Commun. 2019;10(1):2719. Epub 2019/06/22. doi: 10.1038/s41467-019-10656-5 31222023; PubMed Central PMCID: PMC6586903.

5. Contijoch EJ, Britton GJ, Yang C, Mogno I, Li Z, Ng R, et al. Gut microbiota density influences host physiology and is shaped by host and microbial factors. eLife. 2019;8. Epub 2019/01/23. doi: 10.7554/eLife.40553 30666957; PubMed Central PMCID: PMC6342524.

6. Vandeputte D, Kathagen G, D'Hoe K, Vieira-Silva S, Valles-Colomer M, Sabino J, et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature. 2017;551(7681):507–11. Epub 2017/11/17. doi: 10.1038/nature24460 29143816.

7. Liu CM, Hungate BA, Tobian AA, Ravel J, Prodger JL, Serwadda D, et al. Penile Microbiota and Female Partner Bacterial Vaginosis in Rakai, Uganda. mBio. 2015;6(3):e00589. Epub 2015/06/18. doi: 10.1128/mBio.00589-15 26081632; PubMed Central PMCID: PMC4471566.

8. Lewis ZT, Totten SM, Smilowitz JT, Popovic M, Parker E, Lemay DG, et al. Maternal fucosyltransferase 2 status affects the gut bifidobacterial communities of breastfed infants. Microbiome. 2015;3:13. Epub 2015/04/30. doi: 10.1186/s40168-015-0071-z 25922665; PubMed Central PMCID: PMC4412032.

9. Props R, Kerckhof FM, Rubbens P, De Vrieze J, Hernandez Sanabria E, Waegeman W, et al. Absolute quantification of microbial taxon abundances. The ISME journal. 2017;11(2):584–7. Epub 2016/09/10. doi: 10.1038/ismej.2016.117 27612291; PubMed Central PMCID: PMC5270559.

10. Stammler F, Glasner J, Hiergeist A, Holler E, Weber D, Oefner PJ, et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome. 2016;4(1):28. Epub 2016/06/23. doi: 10.1186/s40168-016-0175-0 27329048; PubMed Central PMCID: PMC4915049.

11. Tkacz A, Hortala M, Poole PS. Absolute quantitation of microbiota abundance in environmental samples. Microbiome. 2018;6(1):110. Epub 2018/06/21. doi: 10.1186/s40168-018-0491-7 29921326; PubMed Central PMCID: PMC6009823.

12. Dannemiller KC, Lang-Yona N, Yamamoto N, Rudich Y, Peccia J. Combining real-time PCR and next-generation DNA sequencing to provide quantitative comparisons of fungal aerosol populations. Atmospheric Environment. 2014;84:113–21. doi: https://doi.org/10.1016/j.atmosenv.2013.11.036.

13. Korpela K, Blakstad EW, Moltu SJ, Strommen K, Nakstad B, Ronnestad AE, et al. Intestinal microbiota development and gestational age in preterm neonates. Sci Rep. 2018;8(1):2453. Epub 2018/02/08. doi: 10.1038/s41598-018-20827-x 29410448; PubMed Central PMCID: PMC5802739.

14. Luukkonen PK, Sadevirta S, Zhou Y, Kayser B, Ali A, Ahonen L, et al. Saturated Fat Is More Metabolically Harmful for the Human Liver Than Unsaturated Fat or Simple Sugars. Diabetes care. 2018;41(8):1732–9. Epub 2018/05/31. doi: 10.2337/dc18-0071 29844096.

15. Salonen A, Nikkila J, Jalanka-Tuovinen J, Immonen O, Rajilic-Stojanovic M, Kekkonen RA, et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. Journal of microbiological methods. 2010;81(2):127–34. Epub 2010/02/23. doi: 10.1016/j.mimet.2010.02.007 20171997.

16. Costea PI, Zeller G, Sunagawa S, Pelletier E, Alberti A, Levenez F, et al. Towards standards for human fecal sample processing in metagenomic studies. Nature biotechnology. 2017;35(11):1069–76. Epub 2017/10/03. doi: 10.1038/nbt.3960 28967887.

17. Raju SC, Lagstrom S, Ellonen P, de Vos WM, Eriksson JG, Weiderpass E, et al. Reproducibility and repeatability of six high-throughput 16S rDNA sequencing protocols for microbiota profiling. Journal of microbiological methods. 2018;147:76–86. Epub 2018/03/23. doi: 10.1016/j.mimet.2018.03.003 29563060.

18. Korpela K. mare: Microbiota Analysis in R Easily2016. Available from: https://github.com/katrikorpela/mare.

19. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England). 2010;26(19):2460–1. Epub 2010/08/17. doi: 10.1093/bioinformatics/btq461 20709691.

20. Tikhonov M, Leach RW, Wingreen NS. Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution. The ISME journal. 2015;9(1):68–80. Epub 2014/07/12. doi: 10.1038/ismej.2014.117 25012900; PubMed Central PMCID: PMC4274427.

21. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research. 2013;41(Database issue):D590–6. Epub 2012/11/30. doi: 10.1093/nar/gks1219 23193283; PubMed Central PMCID: PMC3531112.

22. Korpela K, Salonen A, Vepsalainen O, Suomalainen M, Kolmeder C, Varjosalo M, et al. Probiotic supplementation restores normal microbiota composition and function in antibiotic-treated and in caesarean-born infants. Microbiome. 2018;6(1):182. Epub 2018/10/18. doi: 10.1186/s40168-018-0567-4 30326954; PubMed Central PMCID: PMC6192119.

23. Rinttila T, Kassinen A, Malinen E, Krogius L, Palva A. Development of an extensive set of 16S rDNA-targeted primers for quantification of pathogenic and indigenous bacteria in faecal samples by real-time PCR. Journal of applied microbiology. 2004;97(6):1166–77. Epub 2004/11/18. doi: 10.1111/j.1365-2672.2004.02409.x 15546407.

24. Louis P, Flint HJ. Development of a semiquantitative degenerate real-time pcr-based assay for estimation of numbers of butyryl-coenzyme A (CoA) CoA transferase genes in complex bacterial samples. Applied and environmental microbiology. 2007;73(6):2009–12. Epub 2007/01/30. doi: 10.1128/AEM.02561-06 17259367; PubMed Central PMCID: PMC1828812.

25. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C(T) method. Nature protocols. 2008;3(6):1101–8. Epub 2008/06/13. doi: 10.1038/nprot.2008.73 18546601.

26. Louis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19(1):29–41. Epub 2016/12/09. doi: 10.1111/1462-2920.13589 27928878.

27. Reichardt N, Vollmer M, Holtrop G, Farquharson FM, Wefers D, Bunzel M, et al. Specific substrate-driven changes in human faecal microbiota composition contrast with functional redundancy in short-chain fatty acid production. The ISME journal. 2018;12(2):610–22. Epub 2017/12/02. doi: 10.1038/ismej.2017.196 29192904; PubMed Central PMCID: PMC5776475.

28. Stoddard SF, Smith BJ, Hein R, Roller BR, Schmidt TM. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic acids research. 2015;43(Database issue):D593–8. Epub 2014/11/22. doi: 10.1093/nar/gku1201 25414355; PubMed Central PMCID: PMC4383981.

29. Nadkarni MA, Martin FE, Jacques NA, Hunter N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology (Reading, England). 2002;148(Pt 1):257–66. Epub 2002/01/10. doi: 10.1099/00221287-148-1-257 11782518.

30. Gonzalez JM, Portillo MC, Belda-Ferre P, Mira A. Amplification by PCR artificially reduces the proportion of the rare biosphere in microbial communities. PLoS ONE. 2012;7(1):e29973. Epub 2012/01/19. doi: 10.1371/journal.pone.0029973 22253843; PubMed Central PMCID: PMC3256211.

31. Czechowska K, Johnson DR, van der Meer JR. Use of flow cytometric methods for single-cell analysis in environmental microbiology. Current opinion in microbiology. 2008;11(3):205–12. Epub 2008/06/20. doi: 10.1016/j.mib.2008.04.006 18562243.

32. Frossard A, Hammes F, Gessner MO. Flow Cytometric Assessment of Bacterial Abundance in Soils, Sediments and Sludge. Frontiers in microbiology. 2016;7:903. Epub 2016/07/06. doi: 10.3389/fmicb.2016.00903 27379043; PubMed Central PMCID: PMC4905975.

33. Qi C, Li Y, Yu RQ, Zhou SL, Wang XG, Le GW, et al. Composition and immuno-stimulatory properties of extracellular DNA from mouse gut flora. World Journal of Gastroenterology. 2017;23(44):7830–9. doi: 10.3748/wjg.v23.i44.7830 29209124; PubMed Central PMCID: PMC5703912.

34. Bonk F, Popp D, Harms H, Centler F. PCR-based quantification of taxa-specific abundances in microbial communities: Quantifying and avoiding common pitfalls. Journal of microbiological methods. 2018;153:139–47. Epub 2018/09/30. doi: 10.1016/j.mimet.2018.09.015 30267718.

35. von Wintzingerode F, Gobel UB, Stackebrandt E. Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS microbiology reviews. 1997;21(3):213–29. Epub 1998/02/06. doi: 10.1111/j.1574-6976.1997.tb00351.x 9451814.

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