1887

Abstract

Automated, high-throughput technologies are becoming increasingly common in microbiome studies to decrease costs and increase efficiency. However, in microbiome studies, small differences in methodology – including storage conditions, wet lab methods, sequencing platforms and data analysis – can influence the reproducibility and comparability of data across studies. There has been limited testing of the effects of high-throughput methods, including microfluidic PCR technologies. In this paper, we compare two extraction methods (the QIAamp DNA Stool Mini Kit and the MoBio PowerSoil DNA Isolation kit), two taq polymerase enzymes (MyTaq HS Red Mix and Accustart II PCR ToughMix), two primer sets (V3–V4 and V4–V5) and two amplification methods (a common two-step PCR protocol and amplicon library preparation on the Fluidigm Access Array system that allows automated multiplexing of primers). Gut microbial community profiles were significantly affected by all variables. While there were no significant differences in alpha diversity measured between the two extraction methods, there was an effect of extraction method on community composition measured by unweighted UniFrac distances. Both amplification method and primers had a significant effect on both alpha diversity and community composition. The relative abundance of was significantly lower when using the MoBio kit or Fluidigm amplification method, and the relative abundance of was lower when using the Qiagen kit. Microbial community profiles based on Fluidigm-generated amplicon libraries were not comparable to those generated with more commonly used methods. Researchers should carefully consider the limitations and biases that different extraction and amplification methods can introduce into their results. Additionally, more thorough benchmarking of automated and multiplexing methods is necessary to determine the magnitude of the potential trade-off between the quality and the quantity of data.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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2019-09-01
2024-12-02
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References

  1. Brown SP, Ferrer A, Dalling JW, Heath KD. Don't put all your eggs in one basket: a cost-effective and powerful method to optimize primer choice for rRNA environmental community analyses using the Fluidigm Access Array. Mol Ecol Resour 2016; 16:946–956 [View Article]
    [Google Scholar]
  2. Menke S, Wasimuddin S, Meier M, Melzheimer JÃrg, Mfune JKE et al. Oligotyping reveals differences between gut microbiomes of free-ranging sympatric Namibian carnivores (Acinonyx jubatus, canis mesomelas) on a bacterial species-like level. Front Microbiol 2014; 5:1–12 [View Article]
    [Google Scholar]
  3. Menke S, Meier M, Mfune JKE, Melzheimer J, Wachter B et al. Effects of host traits and land-use changes on the gut microbiota of the Namibian black-backed jackal (canis mesomelas). FEMS Microbiol Ecol 2017; 93:1–16 [View Article]
    [Google Scholar]
  4. Menke S, Meier M, Sommer S. Shifts in the gut microbiome observed in wildlife faecal samples exposed to natural weather conditions: lessons from time-series analyses using next-generation sequencing for application in field studies. Methods Ecol Evol 2015; 6:1080–1087 [View Article]
    [Google Scholar]
  5. Moonsamy PV, Williams T, Bonella P, Holcomb CL, Höglund BN et al. High throughput HLA genotyping using 454 sequencing and the Fluidigm access Array™ system for simplified amplicon library preparation. Tissue Antigens 2013; 81:141–149 [View Article]
    [Google Scholar]
  6. Mallott EK, Amato KR, Garber PA, Malhi RS. Influence of fruit and invertebrate consumption on the gut microbiota of wild white-faced capuchins (Cebus capucinus). Am J Phys Anthropol 2018; 165:576–588 [View Article]
    [Google Scholar]
  7. Wasimuddin BSD, Tschapka M, Page R, Rasche A et al. Astrovirus infections induce age-dependent dysbiosis in gut microbiomes of bats. Isme J 2018Epub ahead of print
    [Google Scholar]
  8. Venable EB, Bland SD, Holscher HD, Swanson KS. Effects of air travel stress on the canine microbiome: a pilot study. Int J Vet Heal Sci Res 2016; 4:132–139
    [Google Scholar]
  9. Frossard A, Donhauser J, Mestrot A, Gygax S, Bååth E et al. Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry 2018; 120:191–199 [View Article]
    [Google Scholar]
  10. Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018; 16:410–422 [View Article]
    [Google Scholar]
  11. Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet 2012; 13:47–58 [View Article]
    [Google Scholar]
  12. Hamady M, Knight R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res 2009; 19:1141–1152 [View Article]
    [Google Scholar]
  13. Pollock J. The madness of microbiome : attempting to find consensus. Appl Environ Microbiol 2018; 84:e02627–17
    [Google Scholar]
  14. Pickett SB, Bergey CM, Di Fiore A, Fiore ADI. A metagenomic study of primate insect diet diversity. Am J Primatol 2012; 74:622–631 [View Article]
    [Google Scholar]
  15. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010; 7:335–336 [View Article]
    [Google Scholar]
  16. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L et al. Metagenomic biomarker discovery and explanation. Genome Biol 2011; 12:R60 [View Article]
    [Google Scholar]
  17. Peng X, Yu K-Q, Deng G-H, Jiang Y-X, Wang Y et al. Comparison of direct boiling method with commercial kits for extracting fecal microbiome DNA by Illumina sequencing of 16S rRNA tags. J Microbiol Methods 2013; 95:455–462 [View Article]
    [Google Scholar]
  18. Salonen A, Nikkilä J, Jalanka-Tuovinen J, Immonen O, Rajilić-Stojanović M et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J Microbiol Methods 2010; 81:127–134 [View Article]
    [Google Scholar]
  19. Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One 2012; 7:e33865 [View Article]
    [Google Scholar]
  20. Brooks JP, Edwards DJ, Harwich MD, Rivera MC, Fettweis JM et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol 2015; 15:1–14 [View Article]
    [Google Scholar]
  21. Maukonen J, Simões C, Saarela M. The currently used commercial DNA-extraction methods give different results of clostridial and actinobacterial populations derived from human fecal samples. FEMS Microbiol Ecol 2012; 79:697–708 [View Article]
    [Google Scholar]
  22. Wu GD, Lewis JD, Hoffmann C, Chen Y-Y, Knight R et al. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol 2010; 10:206 [View Article]
    [Google Scholar]
  23. Mackenzie BW, Waite DW, Taylor MW. Evaluating variation in human gut microbiota profiles due to DNA extraction method and inter-subject differences. Front Microbiol 2015; 6:1–11
    [Google Scholar]
  24. Wesolowska-Andersen A, Bahl MI, Carvalho V, Kristiansen K, Sicheritz-Pontén T et al. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2014; 2:19–11 [View Article]
    [Google Scholar]
  25. Kennedy NA, Walker AW, Berry SH, Duncan SH, Farquarson FM et al. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLoS One 2014; 9:e88982–88989 [View Article]
    [Google Scholar]
  26. de Boer R, Peters R, Gierveld S, Schuurman T, Kooistra-Smid M et al. Improved detection of microbial DNA after bead-beating before DNA isolation. J Microbiol Methods 2010; 80:209–211 [View Article]
    [Google Scholar]
  27. Wu J-Y, Jiang X-T, Jiang Y-X, Lu S-Y, Zou F et al. Effects of polymerase, template dilution and cycle number on PCR based 16 S rRNA diversity analysis using the deep sequencing method. BMC Microbiol 2010; 10:255 [View Article]
    [Google Scholar]
  28. Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotechnol 2016; 34:942–949 [View Article]
    [Google Scholar]
  29. Ahn JH, Kim BY, Song J, Weon HY. Effects of PCR cycle number and DNA polymerase type on the 16S rRNA gene pyrosequencing analysis of bacterial communities. J Microbiol 2012; 50:1071–1074 [View Article]
    [Google Scholar]
  30. D’Amore R, Ijaz UZ, Schirmer M, Kenny JG, Gregory R et al. A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling. BMC Genomics 2016; 17:
    [Google Scholar]
  31. Pinto AJ, Raskin L. Pcr biases distort bacterial and archaeal community structure in pyrosequencing datasets. PLoS One 2012; 7:e43093 [View Article]
    [Google Scholar]
  32. Kennedy K, Hall MW, Lynch MDJ, Moreno-Hagelsieb G, Neufeld JD. Evaluating bias of illumina-based bacterial 16S rRNA gene profiles. Appl Environ Microbiol 2014; 80:5717–5722 [View Article]
    [Google Scholar]
  33. Fouhy F, Clooney AG, Stanton C, Claesson MJ, Cotter PD. 16S rRNA gene sequencing of mock microbial populations- impact of DNA extraction method, primer choice and sequencing platform. BMC Microbiol 2016; 16:1–13 [View Article]
    [Google Scholar]
  34. Salipante SJ, Kawashima T, Rosenthal C, Hoogestraat DR, Cummings LA et al. Performance comparison of illumina and ion torrent next-generation sequencing platforms for 16S rRNA-based bacterial community profiling. Appl Environ Microbiol 2014; 80:7583–7591 [View Article]
    [Google Scholar]
  35. Schirmer M, Ijaz UZ, D'Amore R, Hall N, Sloan WT et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res 2015; 43:e37 [View Article]
    [Google Scholar]
  36. Nelson MC, Morrison HG, Benjamino J, Grim SL, Graf J. Analysis, optimization and verification of illumina-generated 16S rRNA gene amplicon surveys. PLoS One 2014; 9:e94249 [View Article]
    [Google Scholar]
  37. Whon TW, Chung W-H, Lim MY, Song E-J, Kim PS et al. The effects of sequencing platforms on phylogenetic resolution in 16 S rRNA gene profiling of human feces. Sci Data 2018; 5:1–15 [View Article]
    [Google Scholar]
  38. Fadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2014; 2:6–7 [View Article]
    [Google Scholar]
  39. Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G et al. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 2016; 1:e0009–0015 [View Article]
    [Google Scholar]
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