title: Meta-analyses of studies of the human microbiota.
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.
authors.affiliation: Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado, Aurora, Colorado 80045, USA;
authors.forename: Catherine A
authors.initials: CA
authors.lastname: Lozupone
authors.forename: Jesse
authors.initials: J
authors.lastname: Stombaugh
authors.forename: Antonio
authors.initials: A
authors.lastname: Gonzalez
authors.forename: Gail
authors.initials: G
authors.lastname: Ackermann
authors.forename: Doug
authors.initials: D
authors.lastname: Wendel
authors.forename: Yoshiki
authors.initials: Y
authors.lastname: Vázquez-Baeza
authors.forename: Janet K
authors.initials: JK
authors.lastname: Jansson
authors.forename: Jeffrey I
authors.initials: JI
authors.lastname: Gordon
authors.forename: Rob
authors.initials: R
authors.lastname: Knight
mesh_terms: D000328:Adult; D000375:Aging; D001419:Bacteria / Q000145:classification* / Q000235:genetics; D044822:Biodiversity; D003424:Crohn Disease / Q000453:epidemiology / Q000235:genetics / Q000382:microbiology; D004269:DNA, Bacterial / Q000235:genetics*; D005243:Feces / Q000382:microbiology*; D005260:Female; D006801:Humans; D007223:Infant; D054892:Metagenome; D056186:Metagenomics / Q000379:methods*; D064307:Microbiota; D011247:Pregnancy; D011261:Pregnancy Trimester, First; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D017422:Sequence Analysis, DNA
chemical_list: D004269:DNA, Bacterial; D012336:RNA, Ribosomal, 16S