期刊论文详细信息
BMC Microbiology
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies
Gregory A Buck5  Kimberly K Jefferson5  Jerome F Strauss1  Philippe Girerd1  Bernice Huang5  Nihar U Sheth4  Robert A Reris3  Myrna G Serrano5  Jennifer M Fettweis5  Maria C Rivera2  Michael D Harwich5  David J Edwards3  J Paul Brooks4 
[1] Department of Obstetrics and Gynecology, Virginia Commonwealth University, 23284, Richmond, VA, USA;Department of Biology, Virginia Commonwealth University, 23284, Richmond, VA, USA;Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, 23284-3083, Richmond, VA, USA;Center for the Study of Biological Complexity, Virginia Commonwealth University, 23284, Richmond, VA, USA;Department of Microbiology and Immunology, Virginia Commonwealth University, 23284, Richmond, VA, USA
关键词: Next generation sequencing;    Quality control;    PCR bias;    DNA extraction bias;    Assessments of microbial community structure via metagenomics;   
Others  :  1221685
DOI  :  10.1186/s12866-015-0351-6
 received in 2014-09-17, accepted in 2015-01-16,  发布年份 2015
PDF
【 摘 要 】

Background

Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the “true” composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits.

Results

We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions.

Conclusions

Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples.

【 授权许可】

   
2015 Brooks et al.; licensee BioMed Central.

【 预 览 】
附件列表
Files Size Format View
20150803035759139.pdf 1168KB PDF download
Figure 5. 44KB Image download
Figure 4. 53KB Image download
Figure 3. 25KB Image download
Figure 2. 32KB Image download
Figure 1. 62KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】
  • [1]Lagier J-C, Million M, Hugon P, Armougom F, Raoult D. Human gut microbiota: Repertoire and variations. Front Cell Infect Microbiol. 2012; 2:136.
  • [2]Knight R, Jansson J, Field D, Fierer N, Desai N, Fuhrman JA,. Unlocking the potential of metagenomics through replicated experimental design. Nat Biotech. 2012; 30(6):513-20.
  • [3]The Microbiome Quality Control Project (MBQC). [http://www.mbqc.org]
  • [4]Pinto AJ, Raskin L. PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets. PLoS ONE. 2012; 7:43093.
  • [5]Hong SH, Bunge J, Leslin C, Jeon S, Epstein SS. Polymerase chain reaction primers miss half of rRNA microbial diversity. ISME J. 2009; 3:1365-73.
  • [6]Ahn J-H, Kim B-Y, Song J, Weon H-Y. Effects of PCR cycle number and DNA polymerase type on the 16S rRNA gene pyrosequencing analysis of bacterial communities. J Microbiol. 2012; 50:1071-4.
  • [7]Lagier J-C, Armougom F, Million M, Hugon P, Pagnier I, Robert C,. Microbial culturomics: Paradigm shift in the human gut microbiome study. Clin Microbiol Infect. 2012; 18:1185-93.
  • [8]Lee CK, Herbold CW, Polson SW, Wommack KE, Williamson SJ, McDonald IR,. Groundtruthing next-gen sequencing for microbial ecology-biases and errors in community structure estimates from PCR amplicon pyrosequencing. PLoS ONE. 2012; 7:44224.
  • [9]Wu J-Y, Jiang X-T, Jiang Y-X, Lu S-Y, Zou F, Zhou H-W. Effects of polymerase, template dilution and cycle number on PCR based 16S rRNA diversity analysis using the deep sequencing method. BMC Microbiology. 2010; 10:255. BioMed Central Full Text
  • [10]Wu G, Lewis J, Hoffmann C, Chen Y-Y, Knight R, Bittinger K,. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiology. 2010; 10(1):206.
  • [11]Kanagawa T. Bias and artifacts in multitemplate polymerase chain reactions (PCR). J Biosci Bioeng. 2003; 96:317-23.
  • [12]Feinstein LM, Sui WJ, Blackwood CB. Assessment of bias associated with incomplete extraction of microbial DNA from soil. Appl Environ Microbiol. 2009; 75:5428-33.
  • [13]Whitehouse CA, Hottel HE. Comparison of five commercial DNA extraction kits for the recovery of Francisella tularensis DNA from spiked soil samples. Mol Cellular Probes. 2007; 21:92-6.
  • [14]Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB,. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009; 75:7537-41.
  • [15]Quince C, Lanzén A, Curtis TP, Davenport RJ, Hall N, Head IM,. Accurate determination of microbial diversity from 454 pyrosequencing data. Nature Methods. 2009; 6:639-641.
  • [16]Kunin V, Engelbrektson A, Ochman H, Hugenholtz P. Wrinkles in the rare biosphere: Pyrosequencing errors can lead to artificial inflation of diversity estimates. Environmental Microbiology. 2010; 12:118-123.
  • [17]Huse SM, Welch DM, Morrison HG, Sogin ML. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environmental Microbiology. 2010; 12:1889-1898.
  • [18]Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G et al.. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011; 21:494-504.
  • [19]Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Computational Biology. 2012; 8:1002743.
  • [20]Dubourg G, Lagier J-C, Armougom F, Robert C, Hamad I, Brouqui P,. The gut microbiota of a patient with resistant tuberculosis is more comprehensively studied by culturomics than by metagenomics. Eur. J. Clin. Microbiol. Infect. Dis. 2013; 32:637-645.
  • [21]Paulson JN, Stine OS, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods. 2013; 10:1200-1202.
  • [22]Bergmann GT, Bates ST, Eilers KG, Lauber CL, Caporaso JG, Walters WA,. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biology & Biochemistry. 2011; 43:1450-1455.
  • [23]Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Applied and Environmental Microbiology. 2009; 75:5111-5120.
  • [24]Andreson R, Mols T, Remm M. Predicting failure rate of PCR in large genomes. Nucleic Acids Res. 2008; 36:66.
  • [25]Shinoda N, Yoshida T, Kusama T, Takagi M, Hayakawa T, Onodera T,. High GC contents of primer 5’-end increases reaction efficiency in polymerase chain reaction. Nucleosides, Nucleotides, and Nucleic Acids. 2009; 28:324-330.
  • [26]Kiviharju K, Leisola M, Eerikäinen T. Optimization of Streptomyces peucetius var. caesius n47 cultivation and ε-rhodomycinone production using experimental designs and response surface methods. Journal of Industrial Microbiology and Biotechnology. 2004; 31:475-481.
  • [27]Rispoli FJ, Shah V. Mixture design as a first step for optimization of fermentation medium for cutinase production from Ceolletotrichum lindemutianum. Journal of Industrial Microbiology and Biotechnology. 2007; 5:349-355.
  • [28]Bautista-Gallego J, Arroyo-López FN, Chiesa A, Duráin-Quintana MC, Garrido-Fernández A. Use of a D-optimal design with constrains to quantify the effects of the mixture of sodium, potassium, calcium and magnesium chloride salts on the growth parameters of Saccharomyces cerevisiae. Journal of Industrial Microbiology and Biotechnology. 2008; 35:889-900.
  • [29]Harbi B, Chaieb K, Jabeur C, Mahdouani K, Bakhrouf A. PCR detection of nitrite reductase genes (nirk and nirs) and use of active consortia of constructed ternary adherent staphylococcal cultures via mixture design for a denitrification process. World Journal of Microbiology and Biotechnology. 2010; 31:473-480.
  • [30]Arroyo-López FN, Bautista-Gallego J, Chiesa A, Durán-Quintana MC, Garrido-Fernández A. Use of a D-optimal mixture design to estimate the effects of diverse chloride salts on the growth parameters of Lactobacillus pentosus. Food Microbiology. 2009; 26:396-403.
  • [31]Evaluation of 16S rDNA-based community profiling for human microbiome research. PLoS ONE. 2012; 7(6):39315.
  • [32]Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology. 2007; 73(16):5261-5267.
  • [33]Polz MF, Cavanaugh CM. Bias in template-to-product ratios in multitemplate PCR. Applied and Environmental Microbiolology. 1998; 64:3724-3730.
  • [34]Huber JA, Morrison HG, Huse SM, Neal PR, Sogin ML, Welch DBM. Effect of PCR amplicon size on assessments of clone library microbial diversity and community structure. Environmental microbiology. 2009; 11(5):1292-1302.
  • [35]Cornell J. Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data. Wiley, New York; 2002.
  • [36]Scheffé H. Experiments with mixtures. Journal of the Royal Statistical Society, Series B. 1958; 20:344-366.
  • [37]Goos P, Jones B. Optimal Design of Experiments: A Case Study Approach. Wiley, New York; 2011.
  • [38]Fettweis JM, Serrano MG, Sheth NU, Mayer CM, Glascock AL, Brooks JP,. Species-level classification of the vaginal microbiome. BMC Genomics. 2012; 13:17. BioMed Central Full Text
  • [39]Structure, function and diversity of the healthy human microbiome. Nature. 2012; 486:207-214.
  • [40]Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SSK, McCulle SL,. Vaginal microbiome of reproductive-age women. Proceedings of the National Academy of Sciences. 2011; 108 Supplement 1:4680-4687.
  • [41]National Center for Biotechnology Information (NCBI). [http://www.ncbi.nlm.nih.gov]
  • [42]Harwich MD Jr., Serrano MG, Fettweis JM, Alves JM, Reimers MA et al.. Genomic sequence analysis and characterization of Sneathia amnii sp. nov. BMC Genomics. 2012; 13 Suppl 8:4-21641384201217. BioMed Central Full Text
  • [43]Fettweis JM, Alves JP, Borzelleca JF, Brooks JP, Friedline CJ, Gao Y, et al. The vaginal microbiome: Disease, genetics and the environment. Nature Precedings, 10–1038201151502 (2011).
  • [44]Leisch F. boostrap: Functions for the Book “An Introduction to the Bootstrap”. [http://cran.rproject.org/web/packages/bootstrap/index.html]
  • [45]Wickham H. Ggplot2: Elegant Graphics for Data Analysis. Springer, New York; 2009.
  • [46]JMP. [http://www.jmp.com]
  • [47]Liaw A, Wiener M. Classification and regression by randomforest. R News. 2002; 2:18-22.
  文献评价指标  
  下载次数:18次 浏览次数:3次