Culture-independent methods have granted the possibility to study microbial diversity in great detail, but technical issues pose a threat to the accuracy of new findings. Biases introduced during DNA extraction can result in erroneous representations of the microbial community, particularly in samples with low microbial biomass. We evaluated the DNA extraction method, initial sample biomass, and reagent contamination on the assessment of the human gut microbiota. Fecal samples of 200 mg were subjected to 1:10 serial dilutions; total DNA was obtained using two commercial kits and the microbiota assessed by 16S ribosomal RNA (rRNA) gene sequencing. In addition, we sequenced multiple technical controls. The two kits were efficient in extracting DNA from samples with as low as 2 mg of feces. However, in instances of lower biomass, only one kit performed well. The number of reads from negative controls was negligible. Both DNA extraction kits allowed inferring microbial consortia with similar membership but different abundances. Furthermore, we found differences in the taxonomic profile of the microbial community. Unexpectedly, the effect of sample dilution was moderate and did not introduce severe bias into the microbial inference. Indeed, the microbiota inferred from fecal samples was distinguishable from that of negative controls. In most cases, samples as low as 2 mg did not result in a dissimilar representation of the microbial community compared with the undiluted sample. Our results indicate that the gut microbiota inference is not much affected by contamination with laboratory reagents but largely impacted by the protocol to extract DNA.
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Alivisatos AP, Blaser MJ, Brodie EL, Chun M, Dangl JL, Donohue TJ, Dorrestein PC, Gilbert JA, Green JL, Jansson JK, Knight R, Maxon ME, McFall-Ngai MJ, Miller JF, Pollard KS, Ruby EG, Taha SA (2015) A unified initiative to harness Earth’s microbiomes. Science 350:507–508. https://doi.org/10.1126/science.aac8480
Ariefdjohan MW, Savaiano DA, Nakatsu CH (2010) Comparison of DNA extraction kits for PCR-DGGE analysis of human intestinal microbial communities from fecal specimens. Nutr J 9:23. https://doi.org/10.1186/1475-2891-9-23
Blaser MJ, Cardon ZG, Cho MK, Dangl JL, Donohue TJ, Green JL, Knight R, Maxon ME, Northen TR, Pollard KS, Brodie EL (2016) Toward a predictive understanding of Earth’s microbiomes to address 21st century challenges. Am Soc Microbiol 7:1–16. https://doi.org/10.1128/mBio.00714-16
Brazelton WJ, Ludwig K a, Sogin ML, Andreishcheva EN, Kelley DS, Shen CC, Edwards RL, Baross J a (2010) Archaea and bacteria with surprising microdiversity show shifts in dominance over 1,000-year time scales in hydrothermal chimneys. Proc Natl Acad Sci U S A 107:1612–1617. https://doi.org/10.1073/pnas.0905369107
Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H (2012) Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28:2106–2113. https://doi.org/10.1093/bioinformatics/bts342
Corless CE, Guiver M, Borrow R, Edwards-Jones V, Kaczmarski EB, Fox AJ (2000) Contamination and sensitivity issues with a real-time universal 16s rRNA PCR. J Clin Microbiol 38:1747–1752
Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R (2009) Bacterial community variation in human body habitats across space and time. Science 326:1694–1697. https://doi.org/10.1126/science.1177486
de la Cuesta-Zuluaga J, Escobar JS (2016) Considerations for optimizing microbiome analysis using a marker gene. Front Nutr 3:26. https://doi.org/10.3389/fnut.2016.00026
de Vos WM, De Vos Eaj (2012) Role of the intestinal microbiome in health and disease: from correlation to causation. Nutr, Rev 70:S45–S56. https://doi.org/10.1111/j.1753-4887.2012.00505.x
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200. https://doi.org/10.1093/bioinformatics/btr381
Evans J, Sheneman L, Foster J (2006) Relaxed neighbor joining: a fast distance-based phylogenetic tree construction method. J Mol Evol 62:785–792. https://doi.org/10.1007/s00239-005-0176-2
Feinstein LM, Woo JS, Blackwood CB (2009) Assessment of bias associated with incomplete extraction of microbial DNA from soil. Appl Environ Microbiol 75:5428–5433. https://doi.org/10.1128/AEM.00120-09
Grahn N, Olofsson M, Ellnebo-Svedlund K, Monstein HJ, Jonasson J (2003) Identification of mixed bacterial DNA contamination in broad-range PCR amplification of 16S rDNA V1 and V3 variable regions by pyrosequencing of cloned amplicons. FEMS Microbiol Lett 219:87–91. https://doi.org/10.1016/S0378-1097(02)01190-4
Guo F, Zhang T (2013) Biases during DNA extraction of activated sludge samples revealed by high throughput sequencing. Appl Microbiol Biotechnol 97:4607–4616. https://doi.org/10.1007/s00253-012-4244-4
Henderson G, Cox F, Kittelmann S, Miri VH, Zethof M, Noel SJ, Waghorn GC, Janssen PH (2013) Effect of DNA extraction methods and sampling techniques on the apparent structure of cow and sheep rumen microbial communities. PLoS One 8:e74787. https://doi.org/10.1371/journal.pone.0074787
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq illumina sequencing platform. Appl Environ Microbiol 79:5112–5120. https://doi.org/10.1128/AEM.01043-13
Kulakov LA, McAlister MB, Ogden KL, Larkin MJ, O’Hanlon JF (2002) Analysis of bacteria contaminating ultrapure water in industrial systems. Appl Environ Microbiol 68:1548–1555. https://doi.org/10.1128/AEM.68.4.1548-1555.2002
Mcalister M, Kulakov L, O’hanlon J, Larkin M, Ogden K (2002) Survival and nutritional requirements of three bacteria isolated from ultrapure water. J Ind Microbiol Biotechnol 29:75–82. https://doi.org/10.1038/sj.jim.7000273
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2015) Vegan: community ecology package. R Packag version:2.3–2.1. https://doi.org/10.4135/9781412971874.n145
Pflughoeft KJ, Versalovic J (2012) Human microbiome in health and disease. Annu Rev Pathol 7:99–122. https://doi.org/10.1146/annurev-pathol-011811-132421
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41. https://doi.org/10.1093/nar/gks1219
Rand KH, Houck H (1990) Taq polymerase contains bacterial DNA of unknown origin. Mol Cell Probes 4:445–450. https://doi.org/10.1016/0890-8508(90)90003-I
Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, Turner P, Parkhill J, Loman NJ, Walker AW (2014) Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 12:87. https://doi.org/10.1186/s12915-014-0087-z
Schloss PD (2008) Evaluating different approaches that test whether microbial communities have the same structure. Isme J 2:265–275. https://doi.org/10.1038/Ismej.2008.5
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Ryan A, Oakley BB, Parks DH, Courtney J, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF, Lesniewski RA, Robinson CJ (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
Shen H, Rogelj S, Kieft TL (2006) Sensitive, real-time PCR detects low-levels of contamination by Legionella pneumophila in commercial reagents. Mol Cell Probes 20:147–153. https://doi.org/10.1016/j.colsurfa.2005.08.010
Smith B, Li N, Andersen AS, Slotved HC, Krogfelt KA (2011) Optimising bacterial DNA extraction from faecal samples: comparison of three methods. Open Microbiol J 5:14–17. https://doi.org/10.2174/1874285801105010014
Tanner MA, Goebel BM, Dojka MA, Pace NR (1998) Specific ribosomal DNA sequences from diverse environmental settings correlate with experimental contaminants. Appl Environ Microbiol 64:3110–3113
The Human Microbiome Project Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature 486:207–214. https://doi.org/10.1038/nature11234
Wesolowska-Andersen A, Bahl MI, Carvalho V, Kristiansen K, Sicheritz-Pontén T, Gupta R, Licht TR (2014) Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2:19. https://doi.org/10.1186/2049-2618-2-19
We thank participants who agreed to donate samples, the APOLO Scientific Computing Center at EAFIT University for hosting part of the bioinformatic resources employed in analyses, and the University of Michigan Medical School Host Microbiome Initiative for sequencing support.
This work was funded by Grupo Empresarial Nutresa. The funder has not had any role in designing or conducting the study; in the analysis or interpretation of the data; in the writing, review, or approval of the manuscript; and in the decision to submit the manuscript for publication.
Conflict of interest
The authors declare that they have no conflict of interest.
This study was conducted in accordance with the principles of the Declaration of Helsinki and had minimal risk according to the Colombian Ministry of Health (Resolution 008430 of 1993). This study was conducted with approval from the Bioethics Committee of SIU—University of Antioquia (approbation act 14-24-588 dated May 28, 2014).
The participants were assured of anonymity and confidentiality. Written informed consent was obtained from them before beginning the study.
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Velásquez-Mejía, E.P., de la Cuesta-Zuluaga, J. & Escobar, J.S. Impact of DNA extraction, sample dilution, and reagent contamination on 16S rRNA gene sequencing of human feces. Appl Microbiol Biotechnol 102, 403–411 (2018). https://doi.org/10.1007/s00253-017-8583-z
- DNA reads
- Sample biomass
- Gut microbiota