The Impact of Different DNA Extraction Kits and Laboratories upon the Assessment of Human Gut Microbiota Composition by 16S rRNA Gene Sequencing

Kennedy, Nicholas A; Walker, Alan W.; Berry, Susan H.; Duncan, Sylvia H.; Farquarson, Freda M.; Louis, Petra; Thomson, John; Satsangi, Jack; Flint, Harry J.; Parkhill, Julian; Lees, Charlie W; Hold, Georgina L.

doi:10.1371/journal.pone.0088982

Cited by 262 publications

(219 citation statements)

References 41 publications

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“…Our replicate data sets were limited to only two different platforms and two hospitals, and the betweenhospital replicate data set was an unbalanced subset of the original cohort and rather small. Finally, since DNA extraction has been shown to be an important factor in microbiota composition variation (28,29), our replicate data sets did not include reisolation of the DNA, rather than reprocessing of the same DNA isolates. The DNA isolates were therefore stored until the two replicates were analyzed, which may also introduce some degree of variation.…”

Section: Figmentioning

confidence: 99%

Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease

et al. 2017

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Strong evidence suggests that the gut microbiota is altered in inflammatory bowel disease (IBD), indicating its potential role in noninvasive diagnostics. However, no clinical applications are currently used for routine patient care. The main obstacle to implementing a gut microbiota test for IBD is the lack of standardization, which leads to high interlaboratory variation. We studied the between-hospital and between-platform batch effects and their effects on predictive accuracy for IBD. Fecal samples from 91 pediatric IBD patients and 58 healthy children were collected. IS-pro, a standardized technique designed for routine microbiota profiling in clinical settings, was used for microbiota composition characterization. Additionally, a large synthetic data set was used to simulate various perturbations and study their effects on the accuracy of different classifiers. Perturbations were validated in two replicate data sets, one processed in another laboratory and the other with a different analysis platform. The type of perturbation determined its effect on predictive accuracy. Real-life perturbations induced by between-platform variation were significantly greater than those caused by between-laboratory variation. Random forest was found to be robust to both simulated and observed perturbations, even when these perturbations had a dramatic effect on other classifiers. It achieved high accuracy both when cross-validated within the same data set and when using data sets analyzed in different laboratories. Robust clinical predictions based on the gut microbiota can be performed even when samples are processed in different hospitals. This study contributes to the effort to develop a universal IBD test that would enable simple diagnostics and disease activity monitoring.

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Section: Figmentioning

confidence: 99%

Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease

et al. 2017

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“…In the first DNA extraction step (EM), only a mild mechanical lysis using bead beating was applied. In the second extraction step (FP), the pellet from the first extraction was treated using the FastPrep instrument, which has been shown to crack even very recalcitrant soil bacteria and to retrieve up to 20-fold more DNA from gut samples than other methods (32). Unexpectedly, the abundances of all but three classes of bacteria were the same in the EM and FP DNA preparations.…”

Section: Resultsmentioning

confidence: 99%

High-Resolution Taxonomic Profiling of the Subgingival Microbiome for Biomarker Discovery and Periodontitis Diagnosis

Szafrański

Wos‐Oxley

Vílchez-Vargas

et al. 2015

Appl Environ Microbiol

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fThe oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health-and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis.

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“…The recovery of high-quality, high-purity representative DNA from environmental samples highlights the importance of the extraction efficacy of the microbial DNA from the initial samples (Fang et al 2014;Kennedy et al 2014;Mirsepasi et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Sponge-associated actinobacterial diversity: validation of the methods of actinobacterial DNA extraction and optimization of 16S rRNA gene amplification

Yang

Franco

Zhang

2015

Appl Microbiol Biotechnol

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Experiments were designed to validate the two common DNA extraction protocols (CTAB-based method and DNeasy Blood & Tissue Kit) used to effectively recover actinobacterial DNA from sponge samples in order to study the sponge-associated actinobacterial diversity. This was done by artificially spiking sponge samples with actinobacteria (spores, mycelia and a combination of the two). Our results demonstrated that both DNA extraction methods were effective in obtaining DNA from the sponge samples as well as the sponge samples spiked with different amounts of actinobacteria. However, it was noted that in the presence of the sponge, the bacterial 16S rRNA gene could not be amplified unless the combined DNA template was diluted. To test the hypothesis that the extracted sponge DNA contained inhibitors, dilutions of the DNA extracts were tested for six sponge species representing five orders. The results suggested that the inhibitors were co-extracted with the sponge DNA, and a high dilution of this DNA was required for the successful PCR amplification for most of the samples. The optimized PCR conditions, including primer selection, PCR reaction system and program optimization, further improved the PCR performance. However, no single PCR condition was found to be suitable for the diverse sponge samples using various primer sets. These results highlight for the first time that the DNA extraction methods used are effective in obtaining actinobacterial DNA and that the presence of inhibitors in the sponge DNA requires high dilution coupled with fine tuning of the PCR conditions to achieve success in the study of sponge-associated actinobacterial diversity.

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The Impact of Different DNA Extraction Kits and Laboratories upon the Assessment of Human Gut Microbiota Composition by 16S rRNA Gene Sequencing

Cited by 262 publications

References 41 publications

Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease

Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease

High-Resolution Taxonomic Profiling of the Subgingival Microbiome for Biomarker Discovery and Periodontitis Diagnosis

Sponge-associated actinobacterial diversity: validation of the methods of actinobacterial DNA extraction and optimization of 16S rRNA gene amplification

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