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Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics.

Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GK - Front Microbiol (2016)

Bottom Line: The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses.To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity).Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, University of Alberta Edmonton, AB, Canada.

ABSTRACT
The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses. Several methods for taxonomic classification of bacterial sequences are discussed. We present simulations to assess the number of sequences that are required to perform reliable appraisals of bacterial community structure. To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity). Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.

No MeSH data available.


Related in: MedlinePlus

Popular techniques for inspection and quantification of beta diversity. (A) Heatmap of normalized counts for the 50 most abundant taxa. On top of the heatmap, group of samples are color-coded. Lilac (Mouse): mutant IL-10−∕− mice that were fed with either high fat (HF), conventional chow (C) or low fat (LF) diet. Yellow (Mock): the three mock bacteria populations described in Figure 1. Light green (Human): samples from two patients suffering Crohn's disease (CD4 and CD11), including resections samples from the terminal ileum at the time of surgery (run in duplicate [A,B]) and biopsies taken 6 months after surgery. (B) Non-metrical multidimensional scaling (NMDS) and Principal Coordinates Analysis (PCoA). Upper panel: Bray-Curtis dissimilarities were ordinated and plotted by either NMDS (i) or PCoA (ii). Lower panel: Unweighted (iii) or weighted (iv) UniFrac distances were analyzed and plotted by PCoA. For unweighted distances, jackknife resampling was performed and the spheres represent the average of such process while semitransparent ellipsoids represent the variance between repeats. Mix1-3 are described in the legend for Figure 1; IL10−∕−C: IL10 deficient mice fed with conventional chow diet; IL10−∕−HF: as previous one, but fed with high fat diet; IL10−∕−LF: as previous one but fed with low fat diet; CD11TxA: Patient 11 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD11TxB: as previous one, repeat (B). CD11Bx: Biopsy from patient 11 colon, 6 months after resection. CD4TxA: Patient 4 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD4TxB: as previous one, repeat (B). CD4Bx: Biopsy from patient 4 colon, 6 months after resection.
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Figure 4: Popular techniques for inspection and quantification of beta diversity. (A) Heatmap of normalized counts for the 50 most abundant taxa. On top of the heatmap, group of samples are color-coded. Lilac (Mouse): mutant IL-10−∕− mice that were fed with either high fat (HF), conventional chow (C) or low fat (LF) diet. Yellow (Mock): the three mock bacteria populations described in Figure 1. Light green (Human): samples from two patients suffering Crohn's disease (CD4 and CD11), including resections samples from the terminal ileum at the time of surgery (run in duplicate [A,B]) and biopsies taken 6 months after surgery. (B) Non-metrical multidimensional scaling (NMDS) and Principal Coordinates Analysis (PCoA). Upper panel: Bray-Curtis dissimilarities were ordinated and plotted by either NMDS (i) or PCoA (ii). Lower panel: Unweighted (iii) or weighted (iv) UniFrac distances were analyzed and plotted by PCoA. For unweighted distances, jackknife resampling was performed and the spheres represent the average of such process while semitransparent ellipsoids represent the variance between repeats. Mix1-3 are described in the legend for Figure 1; IL10−∕−C: IL10 deficient mice fed with conventional chow diet; IL10−∕−HF: as previous one, but fed with high fat diet; IL10−∕−LF: as previous one but fed with low fat diet; CD11TxA: Patient 11 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD11TxB: as previous one, repeat (B). CD11Bx: Biopsy from patient 11 colon, 6 months after resection. CD4TxA: Patient 4 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD4TxB: as previous one, repeat (B). CD4Bx: Biopsy from patient 4 colon, 6 months after resection.

Mentions: Beta (β) diversity considers the difference in bacterial community composition for different environments (Whittaker, 1972; Tuomisto, 2010). To illustrate some ideas and techniques related to beta diversity, we sequenced a set of 16S libraries that constitute three well-defined clusters of samples: three stool samples from mice fed with Chow, high fat or low fat diet; the three mock libraries described in Figure 1; and six ileum samples from two patients affected by Crohn's disease (CD). Users should be aware that clustering of samples that are highly disimilar would be more challenging than the illustrative set of data presented here, and will likely form less well-defined clusters. The analyses shown here are equally applicable to shotgun metagenomics data. Before any comparison can be made, the read counts (reads mapped to each taxon) must be normalized (Dillies et al., 2013; Paulson et al., 2013). In Figure 4A, we illustrate two popular normalization procedures: the total sum and upper quartile normalization. Respectively, for each sample, the normalization factor is the sum of counts of all bacterial taxa detected or the upper quartile value for each sample. In general, normalization procedures attempt to minimize the technical variability between samples, but also accounts for sample-specific dispersion (Dillies et al., 2013). Despite numerous research endeavors in this area, normalization remains a topic under intense debate, without a consensus on which normalization procedure is the most robust one (Paulson et al., 2013).


Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics.

Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GK - Front Microbiol (2016)

Popular techniques for inspection and quantification of beta diversity. (A) Heatmap of normalized counts for the 50 most abundant taxa. On top of the heatmap, group of samples are color-coded. Lilac (Mouse): mutant IL-10−∕− mice that were fed with either high fat (HF), conventional chow (C) or low fat (LF) diet. Yellow (Mock): the three mock bacteria populations described in Figure 1. Light green (Human): samples from two patients suffering Crohn's disease (CD4 and CD11), including resections samples from the terminal ileum at the time of surgery (run in duplicate [A,B]) and biopsies taken 6 months after surgery. (B) Non-metrical multidimensional scaling (NMDS) and Principal Coordinates Analysis (PCoA). Upper panel: Bray-Curtis dissimilarities were ordinated and plotted by either NMDS (i) or PCoA (ii). Lower panel: Unweighted (iii) or weighted (iv) UniFrac distances were analyzed and plotted by PCoA. For unweighted distances, jackknife resampling was performed and the spheres represent the average of such process while semitransparent ellipsoids represent the variance between repeats. Mix1-3 are described in the legend for Figure 1; IL10−∕−C: IL10 deficient mice fed with conventional chow diet; IL10−∕−HF: as previous one, but fed with high fat diet; IL10−∕−LF: as previous one but fed with low fat diet; CD11TxA: Patient 11 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD11TxB: as previous one, repeat (B). CD11Bx: Biopsy from patient 11 colon, 6 months after resection. CD4TxA: Patient 4 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD4TxB: as previous one, repeat (B). CD4Bx: Biopsy from patient 4 colon, 6 months after resection.
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Figure 4: Popular techniques for inspection and quantification of beta diversity. (A) Heatmap of normalized counts for the 50 most abundant taxa. On top of the heatmap, group of samples are color-coded. Lilac (Mouse): mutant IL-10−∕− mice that were fed with either high fat (HF), conventional chow (C) or low fat (LF) diet. Yellow (Mock): the three mock bacteria populations described in Figure 1. Light green (Human): samples from two patients suffering Crohn's disease (CD4 and CD11), including resections samples from the terminal ileum at the time of surgery (run in duplicate [A,B]) and biopsies taken 6 months after surgery. (B) Non-metrical multidimensional scaling (NMDS) and Principal Coordinates Analysis (PCoA). Upper panel: Bray-Curtis dissimilarities were ordinated and plotted by either NMDS (i) or PCoA (ii). Lower panel: Unweighted (iii) or weighted (iv) UniFrac distances were analyzed and plotted by PCoA. For unweighted distances, jackknife resampling was performed and the spheres represent the average of such process while semitransparent ellipsoids represent the variance between repeats. Mix1-3 are described in the legend for Figure 1; IL10−∕−C: IL10 deficient mice fed with conventional chow diet; IL10−∕−HF: as previous one, but fed with high fat diet; IL10−∕−LF: as previous one but fed with low fat diet; CD11TxA: Patient 11 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD11TxB: as previous one, repeat (B). CD11Bx: Biopsy from patient 11 colon, 6 months after resection. CD4TxA: Patient 4 affected with Crohn's disease, tissue sample from ileocolic resection, repeat (A); CD4TxB: as previous one, repeat (B). CD4Bx: Biopsy from patient 4 colon, 6 months after resection.
Mentions: Beta (β) diversity considers the difference in bacterial community composition for different environments (Whittaker, 1972; Tuomisto, 2010). To illustrate some ideas and techniques related to beta diversity, we sequenced a set of 16S libraries that constitute three well-defined clusters of samples: three stool samples from mice fed with Chow, high fat or low fat diet; the three mock libraries described in Figure 1; and six ileum samples from two patients affected by Crohn's disease (CD). Users should be aware that clustering of samples that are highly disimilar would be more challenging than the illustrative set of data presented here, and will likely form less well-defined clusters. The analyses shown here are equally applicable to shotgun metagenomics data. Before any comparison can be made, the read counts (reads mapped to each taxon) must be normalized (Dillies et al., 2013; Paulson et al., 2013). In Figure 4A, we illustrate two popular normalization procedures: the total sum and upper quartile normalization. Respectively, for each sample, the normalization factor is the sum of counts of all bacterial taxa detected or the upper quartile value for each sample. In general, normalization procedures attempt to minimize the technical variability between samples, but also accounts for sample-specific dispersion (Dillies et al., 2013). Despite numerous research endeavors in this area, normalization remains a topic under intense debate, without a consensus on which normalization procedure is the most robust one (Paulson et al., 2013).

Bottom Line: The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses.To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity).Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, University of Alberta Edmonton, AB, Canada.

ABSTRACT
The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses. Several methods for taxonomic classification of bacterial sequences are discussed. We present simulations to assess the number of sequences that are required to perform reliable appraisals of bacterial community structure. To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity). Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.

No MeSH data available.


Related in: MedlinePlus