Jesse Stombaugh

QIIME allows analysis of high-throughput community sequencing data

Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.

Diversity, stability and resilience of the human gut microbiota

Trillions of microbes inhabit the human intestine, forming a complex ecological community that influences normal physiology and susceptibility to disease through its collective metabolic activities and host interactions. Understanding the factors that underlie changes in the composition and function of the gut microbiota will aid in the design of therapies that target it. This goal is formidable. The gut microbiota is immensely diverse, varies between individuals and can fluctuate over time — especially during disease and early development. Viewing the microbiota from an ecological perspective could provide insight into how to promote health by targeting this microbial community in clinical treatments.

Succession of microbial consortia in the developing infant gut microbiome

The colonization process of the infant gut microbiome has been called chaotic, but this view could reflect insufficient documentation of the factors affecting the microbiome. We performed a 2.5-y case study of the assembly of the human infant gut microbiome, to relate life events to microbiome composition and function. Sixty fecal samples were collected from a healthy infant along with a diary of diet and health status. Analysis of >300,000 16S rRNA genes indicated that the phylogenetic diversity of the microbiome increased gradually over time and that changes in community composition conformed to a smooth temporal gradient. In contrast, major taxonomic groups showed abrupt shifts in abundance corresponding to changes in diet or health. Community assembly was nonrandom: we observed discrete steps of bacterial succession punctuated by life events. Furthermore, analysis of ≈500,000 DNA metagenomic reads from 12 fecal samples revealed that the earliest microbiome was enriched in genes facilitating lactate utilization, and that functional genes involved in plant polysaccharide metabolism were present before the introduction of solid food, priming the infant gut for an adult diet. However, ingestion of table foods caused a sustained increase in the abundance of Bacteroidetes, elevated fecal short chain fatty acid levels, enrichment of genes associated with carbohydrate utilization, vitamin biosynthesis, and xenobiotic degradation, and a more stable community composition, all of which are characteristic of the adult microbiome. This study revealed that seemingly chaotic shifts in the microbiome are associated with life events; however, additional experiments ought to be conducted to assess how different infants respond to similar life events.

UniFrac: an effective distance metric for microbial community comparison

UniFrac is a β-diversity measure that uses phylogenetic information to compare environmental samples. UniFrac, coupled with standard multivariate statistical techniques including principal coordinates analysis (PCoA), identifies factors explaining differences among microbial communities. A recent simulation study concluded that UniFrac is unsuitable as a distance metric and should not be used for multivariate analysis (Schloss, 2008). We counter this argument by reassessing the data that led to this conclusion and by providing a mathematical proof showing that UniFrac is a distance metric. However, we confirm with actual sequence data that UniFrac values can be influenced by the number of sequences/sample, and recommend sequence jackknifing (that is, determining how often the cluster results are recovered using random subsets of the data) to avoid this issue.

Moving pictures of the human microbiome

BackgroundUnderstanding the normal temporal variation in the human microbiome is critical to developing treatments for putative microbiome-related afflictions such as obesity, Crohns disease, inflammatory bowel disease and malnutrition. Sequencing and computational technologies, however, have been a limiting factor in performing dense time series analysis of the human microbiome. Here, we present the largest human microbiota time series analysis to date, covering two individuals at four body sites over 396 timepoints.ResultsWe find that despite stable differences between body sites and individuals, there is pronounced variability in an individuals microbiota across months, weeks and even days. Additionally, only a small fraction of the total taxa found within a single body site appear to be present across all time points, suggesting that no core temporal microbiome exists at high abundance (although some microbes may be present but drop below the detection threshold). Many more taxa appear to be persistent but non-permanent community members.ConclusionsDNA sequencing and computational advances described here provide the ability to go beyond infrequent snapshots of our human-associated microbial ecology to high-resolution assessments of temporal variations over protracted periods, within and between body habitats and individuals. This capacity will allow us to define normal variation and pathologic states, and assess responses to therapeutic interventions.

Human oral, gut, and plaque microbiota in patients with atherosclerosis

Periodontal disease has been associated with atherosclerosis, suggesting that bacteria from the oral cavity may contribute to the development of atherosclerosis and cardiovascular disease. Furthermore, the gut microbiota may affect obesity, which is associated with atherosclerosis. Using qPCR, we show that bacterial DNA was present in the atherosclerotic plaque and that the amount of DNA correlated with the amount of leukocytes in the atherosclerotic plaque. To investigate the microbial composition of atherosclerotic plaques and test the hypothesis that the oral or gut microbiota may contribute to atherosclerosis in humans, we used 454 pyrosequencing of 16S rRNA genes to survey the bacterial diversity of atherosclerotic plaque, oral, and gut samples of 15 patients with atherosclerosis, and oral and gut samples of healthy controls. We identified Chryseomonas in all atherosclerotic plaque samples, and Veillonella and Streptococcus in the majority. Interestingly, the combined abundances of Veillonella and Streptococcus in atherosclerotic plaques correlated with their abundance in the oral cavity. Moreover, several additional bacterial phylotypes were common to the atherosclerotic plaque and oral or gut samples within the same individual. Interestingly, several bacterial taxa in the oral cavity and the gut correlated with plasma cholesterol levels. Taken together, our findings suggest that bacteria from the oral cavity, and perhaps even the gut, may correlate with disease markers of atherosclerosis.

Microbiota Regulate Intestinal Absorption and Metabolism of Fatty Acids in the Zebrafish

Regulation of intestinal dietary fat absorption is critical to maintaining energy balance. While intestinal microbiota clearly impact the hosts energy balance, their role in intestinal absorption and extraintestinal metabolism of dietary fat is less clear. Using in vivo imaging of fluorescent fatty acid (FA) analogs delivered to gnotobiotic zebrafish hosts, we reveal that microbiota stimulate FA uptake and lipid droplet (LD) formation in the intestinal epithelium and liver. Microbiota increase epithelial LD number in a diet-dependent manner. The presence of food led to the intestinal enrichment of bacteria from the phylum Firmicutes. Diet-enriched Firmicutes and their products were sufficient to increase epithelial LD number, whereas LD size was increased by other bacterial types. Thus, different members of the intestinal microbiota promote FA absorption via distinct mechanisms. Diet-induced alterations in microbiota composition might influence fat absorption, providing mechanistic insight into how microbiota-diet interactions regulate host energy balance.

Using QIIME to analyze 16S rRNA gene sequences from microbial communities.

QIIME (canonically pronounced “chime”) is a software application that performs microbial community analysis. It is an acronym for Quantitative Insights Into Microbial Ecology, and has been used to analyze and interpret nucleic acid sequence data from fungal, viral, bacterial, and archaeal communities. The following protocols describe how to install QIIME on a single computer and use it to analyze microbial 16S sequence data from nine distinct microbial communities. Curr. Protoc. Bioinform. 36:10.7.1‐10.7.20.

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

BackgroundWe present the Biological Observation Matrix (BIOM, pronounced “biome”) format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the “ome-ome”) grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses.FindingsThe BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages.ConclusionsThe BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.

Meta-analyses of studies of the human microbiota

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 Crohns 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.