Abstract
Technical variation in metagenomic analysis must be minimized to confidently assess the contributions of microbiota to human health. Here we tested 21 representative DNA extraction protocols on the same fecal samples and quantified differences in observed microbial community composition. We compared them with differences due to library preparation and sample storage, which we contrasted with observed biological variation within the same specimen or within an individual over time. We found that DNA extraction had the largest effect on the outcome of metagenomic analysis. To rank DNA extraction protocols, we considered resulting DNA quantity and quality, and we ascertained biases in estimates of community diversity and the ratio between Gram-positive and Gram-negative bacteria. We recommend a standardized DNA extraction method for human fecal samples, for which transferability across labs was established and which was further benchmarked using a mock community of known composition. Its adoption will improve comparability of human gut microbiome studies and facilitate meta-analyses.
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Acknowledgements
We thank S. Burz and K. Weizer for editing and web-posting the SOPs. We thank D. Ordonez and N.P. Gabrielli Lopez for advice on flow cytometry, which was provided by the Flow Cytometry Core Facility, EMBL. This study was funded by the European Community's Seventh Framework Programme via International Human Microbiome Standards (HEALTH-F4-2010-261376) grant. We also received support from Scottish Government Rural and Environmental Science and Analytical Services as well as from EMBL.
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P.I.C., S.S. and G.Z. analyzed data and drafted and finalized the manuscript. E.P. and A.A. analyzed data, sequenced samples and wrote the manuscript. F.L., J.R.K., M.R.H., L.P.C. and E.A.-V. analyzed data and wrote the manuscript. M.T., M. Driessen, R.H., F.-E.J. and K.R.P. created and quantified the mock community. M.B., J.R.M.B., L.B., T.C., S.C.-P., M. Derrien, A.D., M. Daigneault, R.A.L., W.M.d.V., B.B.F., H.J.F., F.G., M.H., H.H., J.v.H.V., J.J., I.K., P.L., E.L.C., V.M., C. Manichanh, J.C.M., C. Mery, H.M., C.O., P.W.O., J.P., S.P., N.P., M.P., A.S., D.S., K.P.S., B.S., K.S., P.V., J.V., L.Z. and E.G.Z. extracted samples and wrote the manuscript. S.D.E., J.D. and P.B. designed the study and wrote the manuscript.
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Integrated supplementary information
Supplementary Figure 1 Inter-individual distance dependence on study.
Similar to Figure 3, we show the estimated effect sizes of different parameters in the context of inter-individual distance assessed within the different studies used. It is clear that while small, there are clear differences in the median distance within studies, with HMP samples appearing to be more homogenous that MetaHIT ones.
Supplementary Figure 2 Extraction bias across the two samples.
Extraction bias is consistent across the two samples, independent of the distance measure that was used. (a) shows a PCoA projection of the species abundances for each sample, independently, using a Spearman ranked correlation as well as a Euclidean distance. Most of the variation is captured by the first two principal coordinates and the clustering of extraction methods is easily observable. (b) shows a PCoA projection of the functional distance, both Spearman ranked and Euclidean.
Supplementary Figure 3 Lysis of Gram-positive bacteria positively correlates with Shannon diversity.
Recovery of Gram-positive bacteria correlates with overall Shannon diversity. Considering only the top 20 most abundant species within each sample, ratios were computed between all Gram-positive and Gram-negative bacteria as well as Gram-negative to Gram-negative bacteria. The top panel shows the correlation of these ratios with the Shannon diversity index, while the lower panel exemplifies this correlation on the most abundant Gram-positive and Gram-negative bacteria that are common to both samples A and B, indicating the strong positive relation between recovery of Gram-positive bacteria and observed Shannon diversity.
Supplementary Figure 4 Shannon diversity of sample composition.
Observed Shannon diversity is consistently influenced by extraction method, as illustrated in both samples. Furthermore, there is a considerable difference in diversity between the two samples, which is not overwritten by extraction bias.
Supplementary Figure 5 Extraction bias of best performing protocols considered in Phase II.
Extraction variation is the same in Phase II replicates as that of Phase I (bars 1 and 2, respectively). Furthermore, the three protocols that have been merged into protocol Q for Phase II, namely 6, 9 and 15 produce similar results and present extraction bias below the biological replicate variation. The tree Phase II protocols (H, W and Q), when applied in different laboratories, with no previous experience in the particular protocol used, produce comparable abundance estimates, with errors below the level of biological variation within one specimen.
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Supplementary Figures 1–5 (PDF 693 kb)
Supplementary Methods
Supplementary Methods (PDF 1766 kb)
Supplementary Data 1
Protocol descriptors (XLSX 19 kb)
Supplementary Data 2
Members and composition of mock community (XLSX 13 kb)
Supplementary Data 3
Sample description (XLSX 15 kb)
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Costea, P., Zeller, G., Sunagawa, S. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat Biotechnol 35, 1069–1076 (2017). https://doi.org/10.1038/nbt.3960
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DOI: https://doi.org/10.1038/nbt.3960
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