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Meta-Analysis
. 2017 Dec 5;8(1):1784.
doi: 10.1038/s41467-017-01973-8.

Meta-analysis of gut microbiome studies identifies disease-specific and shared responses (VSports注册入口)

Claire Duvallet  1   2 , Sean M Gibbons  1   2   3 , Thomas Gurry  1   2   3 , Rafael A Irizarry  4   5 , Eric J Alm  6   7   8
Affiliations
Meta-Analysis

Meta-analysis of gut microbiome studies identifies disease-specific and shared responses

Claire Duvallet et al. Nat Commun. .

Abstract

Hundreds of clinical studies have demonstrated associations between the human microbiome and disease, yet fundamental questions remain on how we can generalize this knowledge. Results from individual studies can be inconsistent, and comparing published data is further complicated by a lack of standard processing and analysis methods. Here we introduce the MicrobiomeHD database, which includes 28 published case-control gut microbiome studies spanning ten diseases. We perform a cross-disease meta-analysis of these studies using standardized methods. We find consistent patterns characterizing disease-associated microbiome changes. Some diseases are associated with over 50 genera, while most show only 10-15 genus-level changes. Some diseases are marked by the presence of potentially pathogenic microbes, whereas others are characterized by a depletion of health-associated bacteria. Furthermore, we show that about half of genera associated with individual studies are bacteria that respond to more than one disease. Thus, many associations found in case-control studies are likely not disease-specific but rather part of a non-specific, shared response to health and disease. VSports手机版.

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Conflict of interest statement

E. A. is on the Board of Directors of OpenBiome, a non-profit stool bank. E V体育安卓版. A. is a co-founder of Finch Therapeutics, which aims to develop microbiome-based therapeutics. The remaining authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
Most diseases show microbiome alterations, and consistent disease-associated shifts differ in their extent and direction. a Left: Total sample size for each study included in these analyses. Additional information about each data set can be found in Table 1. Studies on the y-axis are grouped by disease and ordered by decreasing sample size (top to bottom). Right: Area under the ROC curve (AUC) for genus-level random forest classifiers. X-axis starts at 0.5, the expected value for a classifier which assigns labels randomly, and AUCs less than 0.5 are not shown. ROC curves for all data sets are in Supplementary Fig. 1. Note that Youngster et al. had only four distinct control patients was excluded from the random forest analysis. b Left: Number of genera with q < 0.05 (Kruskal–Wallis (KW) test, Benjamini–Hochberg FDR correction) for each data set. If a study has no significant associations, no point is shown. Right: Direction of the microbiome shift, i.e., the percent of total associated genera which were enriched in diseased patients. In data sets on the leftmost blue line, 100% of associated (q < 0.05, FDR KW test) genera are health-associated (i.e., depleted in patients relative to controls). In data sets on the rightmost red line, 100% of associated (q < 0.05, FDR KW test) genera are disease-associated (i.e., enriched in patients relative to controls). Supplementary Figs. 14 and 15 show q values and effects for each genus in each study
Fig. 2
Fig. 2
Comparing results from multiple studies of the same disease reveals patterns in disease-associated microbiome alterations. Heat maps showing log10(q values) for each disease (KW test, Benjamini–Hochberg FDR correction). Rows include all genera which were significant in at least one data set within each disease, columns are data sets. q values are colored by direction of the effect, where red indicates higher mean abundance in disease patients and blue indicates higher mean abundance in controls. Opacity ranges from q = 0.05–1, where q values less than 0.05 are the most opaque and q values close to 1 are gray. White indicates that the genus was not present in that data set. Within each heat map, rows are ordered from most disease-associated (top) to most health-associated (bottom) (i.e., by the sum across rows of the log10(q values), signed according to directionality of the effect). The extent of a disease-associated microbiome shift can be visualized by the number of rows in each disease heat map; the directionality of a shift can be seen in the ratio of red rows to blue rows within each disease. See Supplementary Fig. 2 for genus (row) labels
Fig. 3
Fig. 3
The majority of disease-associated microbiome associations overlap with a non-specific microbial response to disease. a Non-specific and disease-associated genera. Genera are in columns, arranged phylogenetically according to a PhyloT tree built from genus-level NCBI IDs (http://phylot.biobyte.de). Non-specific genera are associated with health (or disease) in at least two different diseases (q < 0.05, KW test, Benjamini–Hochberg FDR correction). Disease-specific genera are significant in the same direction in at least two studies of the same disease (q < 0.05, FDR KW test). As in Fig. 2, blue indicates higher mean abundance in controls and red indicates higher mean abundance in patients. Black bars indicate mixed genera which were associated with health in two diseases and also associated with disease in two diseases. Disease-specific genera are shown for diseases with at least three studies. Phyla, left to right: Euryarchaeota (brown), Verrucomicrobia Subdivision 5 (gray), Candidatus Saccharibacteria (gray), Bacteroidetes (blue), Proteobacteria (red), Synergistetes (pink), Actinobacteria (green), Firmicutes (purple), Verrucomicrobia (gray), Lentisphaerae (pink), Fusobacteria (orange). See Supplementary Fig. 3 for genus labels. b The percent of each study’s genus-level associations which overlap with the shared response (q < 0.05, FDR KW test). Only data sets with at least one significant association are shown. c Overall, abundance and ubiquity of non-specific genera across all patients in all data sets. Non-specific genera on the x-axis are as defined above
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