Associating microbiome composition with environmental covariates using generalized UniFrac distances

Motivation: The human microbiome plays an important role in human disease and health. Identification of factors that affect the microbiome composition can provide insights into disease mechanism as well as suggest ways to modulate the microbiome composition for therapeutical purposes. Distance-based statistical tests have been applied to test the association of microbiome composition with environmental or biological covariates. The unweighted and weighted UniFrac distances are the most widely used distance measures. However, these two measures assign too much weight either to rare lineages or to most abundant lineages, which can lead to loss of power when the important composition change occurs in moderately abundant lineages.

Results: We develop generalized UniFrac distances that extend the weighted and unweighted UniFrac distances for detecting a much wider range of biologically relevant changes. We evaluate the use of generalized UniFrac distances in associating microbiome composition with environmental covariates using extensive Monte Carlo simulations. Our results show that tests using the unweighted and weighted UniFrac distances are less powerful in detecting abundance change in moderately abundant lineages. In contrast, the generalized UniFrac distance is most powerful in detecting such changes, yet it retains nearly all its power for detecting rare and highly abundant lineages. The generalized UniFrac distance also has an overall better power than the joint use of unweighted/weighted UniFrac distances. Application to two real microbiome datasets has demonstrated gains in power in testing the associations between human microbiome and diet intakes and habitual smoking.

Availability:http://cran.r-project.org/web/packages/GUniFrac

Contact:hongzhe@upenn.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Linking long-term dietary patterns with gut microbial enterotypes

Diet strongly affects human health, partly by modulating gut microbiome composition. We used diet inventories and 16S rDNA sequencing to characterize fecal samples from 98 individuals. Fecal communities clustered into enterotypes distinguished primarily by levels of Bacteroides and Prevotella. Enterotypes were strongly associated with long-term diets, particularly protein and animal fat (Bacteroides) versus carbohydrates (Prevotella). A controlled-feeding study of 10 subjects showed that microbiome composition changed detectably within 24 hours of initiating a high-fat/low-fiber or low-fat/high-fiber diet, but that enterotype identity remained stable during the 10-day study. Thus, alternative enterotype states are associated with long-term diet.

Topographical continuity of bacterial populations in the healthy human respiratory tract

RATIONALE

Defining the biogeography of bacterial populations in human body habitats is a high priority for understanding microbial-host relationships in health and disease. The healthy lung was traditionally considered sterile, but this notion has been challenged by emerging molecular approaches that enable comprehensive examination of microbial communities. However, studies of the lung are challenging due to difficulties in working with low biomass samples.

OBJECTIVES

Our goal was to use molecular methods to define the bacterial microbiota present in the lungs of healthy individuals and assess its relationship to upper airway populations.

METHODS

We sampled respiratory flora intensively at multiple sites in six healthy individuals. The upper tract was sampled by oral wash and oro-/nasopharyngeal swabs. Two bronchoscopes were used to collect samples up to the glottis, followed by serial bronchoalveolar lavage and lower airway protected brush. Bacterial abundance and composition were analyzed by 16S rDNA Q-PCR and deep sequencing.

MEASUREMENTS AND MAIN RESULTS

Bacterial communities from the lung displayed composition indistinguishable from the upper airways, but were 2 to 4 logs lower in biomass. Lung-specific sequences were rare and not shared among individuals. There was no unique lung microbiome.

CONCLUSIONS

In contrast to other organ systems, the respiratory tract harbors a homogenous microbiota that decreases in biomass from upper to lower tract. The healthy lung does not contain a consistent distinct microbiome, but instead contains low levels of bacterial sequences largely indistinguishable from upper respiratory flora. These findings establish baseline data for healthy subjects and sampling approaches for sequence-based analysis of diseases.

Disordered microbial communities in the upper respiratory tract of cigarette smokers

Cigarette smokers have an increased risk of infectious diseases involving the respiratory tract. Some effects of smoking on specific respiratory tract bacteria have been described, but the consequences for global airway microbial community composition have not been determined. Here, we used culture-independent high-density sequencing to analyze the microbiota from the right and left nasopharynx and oropharynx of 29 smoking and 33 nonsmoking healthy asymptomatic adults to assess microbial composition and effects of cigarette smoking. Bacterial communities were profiled using 454 pyrosequencing of 16S sequence tags (803,391 total reads), aligned to 16S rRNA databases, and communities compared using the UniFrac distance metric. A Random Forest machine-learning algorithm was used to predict smoking status and identify taxa that best distinguished between smokers and nonsmokers. Community composition was primarily determined by airway site, with individuals exhibiting minimal side-of-body or temporal variation. Within airway habitats, microbiota from smokers were significantly more diverse than nonsmokers and clustered separately. The distributions of several genera were systematically altered by smoking in both the oro- and nasopharynx, and there was an enrichment of anaerobic lineages associated with periodontal disease in the oropharynx. These results indicate that distinct regions of the human upper respiratory tract contain characteristic microbial communities that exhibit disordered patterns in cigarette smokers, both in individual components and global structure, which may contribute to the prevalence of respiratory tract complications in this population.

Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags

Intense interest centers on the role of the human gut microbiome in health and disease, but optimal methods for analysis are still under development. Here we present a study of methods for surveying bacterial communities in human feces using 454/Roche pyrosequencing of 16S rRNA gene tags. We analyzed fecal samples from 10 individuals and compared methods for storage, DNA purification and sequence acquisition. To assess reproducibility, we compared samples one cm apart on a single stool specimen for each individual. To analyze storage methods, we compared 1) immediate freezing at -80°C, 2) storage on ice for 24 or 3) 48 hours. For DNA purification methods, we tested three commercial kits and bead beating in hot phenol. Variations due to the different methodologies were compared to variation among individuals using two approaches--one based on presence-absence information for bacterial taxa (unweighted UniFrac) and the other taking into account their relative abundance (weighted UniFrac). In the unweighted analysis relatively little variation was associated with the different analytical procedures, and variation between individuals predominated. In the weighted analysis considerable variation was associated with the purification methods. Particularly notable was improved recovery of Firmicutes sequences using the hot phenol method. We also carried out surveys of the effects of different 454 sequencing methods (FLX versus Titanium) and amplification of different 16S rRNA variable gene segments. Based on our findings we present recommendations for protocols to collect, process and sequence bacterial 16S rDNA from fecal samples--some major points are 1) if feasible, bead-beating in hot phenol or use of the PSP kit improves recovery; 2) storage methods can be adjusted based on experimental convenience; 3) unweighted (presence-absence) comparisons are less affected by lysis method.

PyNAST: a flexible tool for aligning sequences to a template alignment

MOTIVATION

The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API).

RESULTS

The availability of PyNAST will make the popular NAST algorithm more portable and thereby applicable to datasets orders of magnitude larger by allowing users to install PyNAST on their own hardware. Additionally because users can align to arbitrary template alignments, a feature not available via the original NAST web interface, the NAST algorithm will be readily applicable to novel tasks outside of microbial community analysis.

AVAILABILITY

PyNAST is available at http://pynast.sourceforge.net.