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. 2020 Mar 11;9:e50240. doi: 10.7554/eLife.50240

Figure 1. Age influences microbiome composition as well as microbiome-disease signatures.

(A) Bar plots showing the effect (denoted by R2 values computed using PERMANOVA after adjusting for the DNA extraction technique as the confounder) of host factors with microbiome composition in the ExperimentHub repository. Only metadata available for at least 30% of the samples are shown. The p-values for the significance of association are also indicated as ****: p<0.0001; ***: p<0.001, **: p<0.01, *: p<0.05. (B) Principal Co-ordinate Analysis (PCoA) plots of the species profiles of the ‘control’ samples grouped into three age ranges, Young (20–39 years), Middle (40–59 years) and Elderly (60 years and above). The significance (p-value) of the differences between the three groups, computed using PERMANOVA (adonis) after considering the country-specific differences and the DNA extraction technique, is also indicated. The boxplots on the top show the variation of the top three PCoA coordinates for the samples belonging to the three age-groups. The elderly harboured a significantly different microbiome compared to the young/middle-aged. (C) Barplots of PERMANOVA R2 values showing the variation of microbiome with disease (adjusting for age-group) and age-group (adjusting for disease status) in the five disease cohorts. The Cohort-specific analyses ensured that the variations observed were not due to country-specific regional differences in microbiome composition. However, within each cohort, there were skews in the representation of diseased and control samples from different age-groups (as seen in Table 1). Furthermore, in four out of the eight cohorts, there were significant differences in the age variation of control and diseased individuals, as shown by the beanplots in D.

Figure 1.

Figure 1—figure supplement 1. Effect of median read length and DNA extraction techniques on the microbiome variation.

Figure 1—figure supplement 1.

(A) PCoA Plot showing the relatedness of the microbiome profiles of the ExperimentHub datasets of different median read length ranges. The different read length categories (into which the datasets were grouped) were’ 30 to 90’ (base pairs) and ‘Greater than 90’ (base pairs). The R-squared value and P-value of the association obtained using bootstrapped envfit iterations (sub-sample size = 200 and number of iterations: 25) are indicated. (B) PCoA analysis of the effects of the DNA extraction methods on the microbiome profiles, indicating that the samples extracted using the method tagged as ‘Illuminakit’ (shown in Green) (used by SchirmerC_201635), had a profile significantly different from those used by other methods (‘Gnome’, ‘Mobio’ and ‘Qiagen’) (bootstrapped envfit median R-squared: 0.13 and median p-value<0.001). Removing these samples in (C) indicated that the rest of the samples had only a marginal effect on the profiles (p<0.08; R-squared = 0.019).
Figure 1—figure supplement 2. Pictorial summary describing the workflow used for preparing a core set of around 2564 gut metagenomic datasets derived from the publicly available datasets (curatedMetagenomicData9 and Franzosa et al 20188) and the ELDERMET repository.

Figure 1—figure supplement 2.

While the datasets used in the core-analysis are highlighted in blue, the validation cohorts including the ELDERMET are highlighted in brown.
Figure 1—figure supplement 3. Number of control and diseased individuals belonging to the different age-groups present in (A) country-specific and (B) continent-specific groups pertaining to each disease.

Figure 1—figure supplement 3.

Age-groups where the number of control/diseased samples are less than 15 are highlighted in red. The shortened notations for the different country used are ESP: Spain; USA: United States, CHN: China, SWE: Sweden, AUT: Austria, FRA: France (C) Boxplots comparing the PERMANOVA -log P-values obtained for the effects of the geographical factors, country and continent, by taking repeated subsets of control samples (n = 25, subset size = 20%). The overall R2 value obtained for the PERMANOVA is also indicated. While R2 was higher for country, the p-values obtained for continent was significantly lower as compared to country, indicating that the effect of continent is much more significant than the country. The results indicated that country and continent had similar effects on the microbiome. (D) The country and continent specific cohorts within which the analyses were restricted for each disease, to take into account the regional variations.