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Meta-Analysis
. 2013 Oct;23(10):1704-14.
doi: 10.1101/gr.151803.112. Epub 2013 Jul 16.

Meta-analyses of studies of the human microbiota

Catherine A Lozupone  1 , Jesse Stombaugh, Antonio Gonzalez, Gail Ackermann, Doug Wendel, Yoshiki Vázquez-Baeza, Janet K Jansson, Jeffrey I Gordon, Rob Knight
Affiliations
Meta-Analysis

Meta-analyses of studies of the human microbiota

Catherine A Lozupone et al. Genome Res. 2013 Oct.

Abstract

Our body habitat-associated microbial communities are of intense research interest because of their influence on human health. Because many studies of the microbiota are based on the same bacterial 16S ribosomal RNA (rRNA) gene target, they can, in principle, be compared to determine the relative importance of different disease/physiologic/developmental states. However, differences in experimental protocols used may produce variation that outweighs biological differences. By comparing 16S rRNA gene sequences generated from diverse studies of the human microbiota using the QIIME database, we found that variation in composition of the microbiota across different body sites was consistently larger than technical variability across studies. However, samples from different studies of the Western adult fecal microbiota generally clustered by study, and the 16S rRNA target region, DNA extraction technique, and sequencing platform produced systematic biases in observed diversity that could obscure biologically meaningful compositional differences. In contrast, systematic compositional differences in the fecal microbiota that occurred with age and between Western and more agrarian cultures were great enough to outweigh technical variation. Furthermore, individuals with ileal Crohn's disease and in their third trimester of pregnancy often resembled infants from different studies more than controls from the same study, indicating parallel compositional attributes of these distinct developmental/physiological/disease states. Together, these results show that cross-study comparisons of human microbiota are valuable when the studied parameter has a large effect size, but studies of more subtle effects on the human microbiota require carefully selected control populations and standardized protocols.

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Figures

Figure 1.
Figure 1.
Unweighted UniFrac PCoA plot illustrating that samples from the human microbiome cluster primarily by body site. Each point represents a sample from one of the studies detailed in Table 1. Samples were classified broadly as from the Gut (mostly feces but also colon, ileum, and rectum), vagina, oral cavity (e.g., saliva, tongue, cheek), and skin and other (diverse skin sites, hair, nostril, and urine). Gut samples from individuals older than 2½ yr are colored brown and from individuals ages 0 to 2½ yr are colored across a dark purple (0 yr) to light purple (2½ yr) spectrum. Samples from one infant sampled repeatedly over the first 2½ yr of life are joined together with a purple line with a decreasingly dark hue with age. The infant samples are also shown in the inset. The most abundant bacterial families are superimposed on the same PCoA plot in the lower panel in purple. The size of the sphere representing a taxon is proportional to the mean relative abundance of the taxon across all samples.
Figure 2.
Figure 2.
Unweighted UniFrac PCoA plots illustrating the relative degree to which age, cultural/geographic stratification, systematic differences in the collection of samples, and sequencing method affect the observed diversity of the gut microbiota. (A–C) Data from three different studies with age gradients from culturally diverse populations (Global_gut, US_infant_timeseries, and Italy/Burkina Faso) (Table 1). Points are colored by age gradient in A or by county in B. C plots the most abundant bacterial families as a weighted average of the coordinates of all samples in purple, where the weights are the relative abundances of the taxon in the samples. The size of the sphere representing a taxon is proportional to the mean relative abundance of the taxon across all samples.
Figure 3.
Figure 3.
Unweighted UniFrac PCoA plots illustrating a strong study effect when comparing fecal samples of Western adults. (A) Studies conducted with Western adult populations. (B) Clustering of the fecal samples from IBD_twins (Table 1) colored by disease state. (ICD) Ileal Crohn's disease, (CCD) Colonic Crohn's Disease, (UC) Ulcerative Colitis. (C,D) Same as in A but with the axes rotated to maximize clustering by study. D shows just the bacterial orders as a weighted average of the coordinates of all samples, where the weights are the relative abundances of the taxon in the samples. The size of the sphere representing a taxon is proportional to the mean relative abundance of the taxon across all samples. Gram-positive bacterial orders are labeled in red text and Gram-negative in blue.
Figure 4.
Figure 4.
Unweighted UniFrac PCoA plots illustrating the relationship between the bacterial diversity in fecal samples from different disease/physiologic states in adults and the infant microbiome. (A) Compares samples from same studies as in Figure 3A, but with the US_infant_timeseries, the US infants and children from Global_gut, and adults from a study of pregnancy (Pregnant_adults) (Table 1) added in addition. (A–D) The same plot, except that different subsets of the samples are shown or are colored differently. (A) Points colored by study. (B) Points colored by an age gradient. The samples from Pregnant_adults are not shown because the age of study participants was not available. (C) Samples from pregnant women in their first and third trimesters and 1 mo post-delivery (from Pregnant_adults) (Table 1) (D) Healthy individuals and individuals with ileal Crohn's disease from IBD_twins. (E) Bacterial families are plotted as a weighted average of the coordinates of all samples where the weights are the relative abundances of the taxon in the samples (purple circles). Gram-negative bacterial orders are in blue text, Gram-positive in red.
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