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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

Inference of gut bacterial microbiome functional content from 16S or shotgun metagenomics libraries. Samples from three healthy individuals (Hthy1-3), the CD and the C. diff samples described in Figure 3, and the three mice samples described in Figure 4 were used here to illustrate metabolic inference of the gut bacteria microbiome from 16S or shotgun metagenomic libraries. High quality sequences were procured as described in Figure 1. (A) Twenty-three KEGG reference pathways known to be present in bacteria are depicted for both types of libraries. (B) Two KEGG pathways are illustrated at the gene (KEGG orthology, KO, groups) level. On top of each heatmap pair, the Pearson correlation coefficient for relative abundance of KOs derived with each method is presented. Inference of the functional content of the 16S metagenome was performed with PICRUSt, while gene content of shotgun metagenomic libraries was determined with MEGAN5. PICRUSt outputs results in number of bacteria cells that encode a gene (KO) while MEGAN5 outputs counts of sequences that mapped to a KO representative sequence. To make results from both methods comparable, counts were normalized by total sum. In both cases, the results represent the abundance of each KO as a fraction of the abundance of all detected KOs in each library. In order to achieve full representation of all values included in each normalized count table, colors in each heatmap were stretched between the minimum and maximum values. Therefore, the intensity (value) of each cell is not comparable between methods (16S of shotgun). Instead the Pearson correlation coefficient is shown as an estimator of the concordance of results provided by both approaches.
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Figure 5: Inference of gut bacterial microbiome functional content from 16S or shotgun metagenomics libraries. Samples from three healthy individuals (Hthy1-3), the CD and the C. diff samples described in Figure 3, and the three mice samples described in Figure 4 were used here to illustrate metabolic inference of the gut bacteria microbiome from 16S or shotgun metagenomic libraries. High quality sequences were procured as described in Figure 1. (A) Twenty-three KEGG reference pathways known to be present in bacteria are depicted for both types of libraries. (B) Two KEGG pathways are illustrated at the gene (KEGG orthology, KO, groups) level. On top of each heatmap pair, the Pearson correlation coefficient for relative abundance of KOs derived with each method is presented. Inference of the functional content of the 16S metagenome was performed with PICRUSt, while gene content of shotgun metagenomic libraries was determined with MEGAN5. PICRUSt outputs results in number of bacteria cells that encode a gene (KO) while MEGAN5 outputs counts of sequences that mapped to a KO representative sequence. To make results from both methods comparable, counts were normalized by total sum. In both cases, the results represent the abundance of each KO as a fraction of the abundance of all detected KOs in each library. In order to achieve full representation of all values included in each normalized count table, colors in each heatmap were stretched between the minimum and maximum values. Therefore, the intensity (value) of each cell is not comparable between methods (16S of shotgun). Instead the Pearson correlation coefficient is shown as an estimator of the concordance of results provided by both approaches.

Mentions: We derived functional profiles from 16S or shotgun libraries with PICRUSt or MEGAN5, respectively. For this analysis, we used stool samples from three healthy individuals, the CD and the C. difficile samples described in Figure 3, and the three mice samples described in Figure 4. Twenty-three KEGG reference pathways were used to compare relative abundance determined from both type of libraries (Figure 5A). The level of concordance between results derived from 16S or from shotgun metagenomics was variable depending on the pathway under consideration. In general both methods recapitulated general patterns of abundance. For example, the metabolic profile of the CD stool sample was clearly distinct from the rest and exhibited the highest gene content related to membrane transport, signal transduction and carbohydrate metabolism and the lowest content related to amino acid metabolism, metabolism of cofactors and vitamins and translation factors, as previously reported for IBD patients (Greenblum et al., 2012; Knights et al., 2013; Kostic et al., 2014). In addition, we show two KEGG reference pathways (at the KO level), which relative abundance was similarly (glycolysis; r = 0.88) or distinctly (fatty acid biosynthesis; r = 0.52) assessed by both programs (Figure 5B). The Pearson correlation coefficient of abundance of KOs detected by at least one of the methods was 0.66.


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)

Inference of gut bacterial microbiome functional content from 16S or shotgun metagenomics libraries. Samples from three healthy individuals (Hthy1-3), the CD and the C. diff samples described in Figure 3, and the three mice samples described in Figure 4 were used here to illustrate metabolic inference of the gut bacteria microbiome from 16S or shotgun metagenomic libraries. High quality sequences were procured as described in Figure 1. (A) Twenty-three KEGG reference pathways known to be present in bacteria are depicted for both types of libraries. (B) Two KEGG pathways are illustrated at the gene (KEGG orthology, KO, groups) level. On top of each heatmap pair, the Pearson correlation coefficient for relative abundance of KOs derived with each method is presented. Inference of the functional content of the 16S metagenome was performed with PICRUSt, while gene content of shotgun metagenomic libraries was determined with MEGAN5. PICRUSt outputs results in number of bacteria cells that encode a gene (KO) while MEGAN5 outputs counts of sequences that mapped to a KO representative sequence. To make results from both methods comparable, counts were normalized by total sum. In both cases, the results represent the abundance of each KO as a fraction of the abundance of all detected KOs in each library. In order to achieve full representation of all values included in each normalized count table, colors in each heatmap were stretched between the minimum and maximum values. Therefore, the intensity (value) of each cell is not comparable between methods (16S of shotgun). Instead the Pearson correlation coefficient is shown as an estimator of the concordance of results provided by both approaches.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4837688&req=5

Figure 5: Inference of gut bacterial microbiome functional content from 16S or shotgun metagenomics libraries. Samples from three healthy individuals (Hthy1-3), the CD and the C. diff samples described in Figure 3, and the three mice samples described in Figure 4 were used here to illustrate metabolic inference of the gut bacteria microbiome from 16S or shotgun metagenomic libraries. High quality sequences were procured as described in Figure 1. (A) Twenty-three KEGG reference pathways known to be present in bacteria are depicted for both types of libraries. (B) Two KEGG pathways are illustrated at the gene (KEGG orthology, KO, groups) level. On top of each heatmap pair, the Pearson correlation coefficient for relative abundance of KOs derived with each method is presented. Inference of the functional content of the 16S metagenome was performed with PICRUSt, while gene content of shotgun metagenomic libraries was determined with MEGAN5. PICRUSt outputs results in number of bacteria cells that encode a gene (KO) while MEGAN5 outputs counts of sequences that mapped to a KO representative sequence. To make results from both methods comparable, counts were normalized by total sum. In both cases, the results represent the abundance of each KO as a fraction of the abundance of all detected KOs in each library. In order to achieve full representation of all values included in each normalized count table, colors in each heatmap were stretched between the minimum and maximum values. Therefore, the intensity (value) of each cell is not comparable between methods (16S of shotgun). Instead the Pearson correlation coefficient is shown as an estimator of the concordance of results provided by both approaches.
Mentions: We derived functional profiles from 16S or shotgun libraries with PICRUSt or MEGAN5, respectively. For this analysis, we used stool samples from three healthy individuals, the CD and the C. difficile samples described in Figure 3, and the three mice samples described in Figure 4. Twenty-three KEGG reference pathways were used to compare relative abundance determined from both type of libraries (Figure 5A). The level of concordance between results derived from 16S or from shotgun metagenomics was variable depending on the pathway under consideration. In general both methods recapitulated general patterns of abundance. For example, the metabolic profile of the CD stool sample was clearly distinct from the rest and exhibited the highest gene content related to membrane transport, signal transduction and carbohydrate metabolism and the lowest content related to amino acid metabolism, metabolism of cofactors and vitamins and translation factors, as previously reported for IBD patients (Greenblum et al., 2012; Knights et al., 2013; Kostic et al., 2014). In addition, we show two KEGG reference pathways (at the KO level), which relative abundance was similarly (glycolysis; r = 0.88) or distinctly (fatty acid biosynthesis; r = 0.52) assessed by both programs (Figure 5B). The Pearson correlation coefficient of abundance of KOs detected by at least one of the methods was 0.66.

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