Gary L. Andersen

Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

ABSTRACT A 16S rRNA gene database (http://greengenes.lbl.gov ) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.

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. Contact: rob.knight@colorado.edu

An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree’ approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.

Deciphering the rhizosphere microbiome for disease-suppressive bacteria.

A common plant pathogen induces the growth of disease-suppressive microbes in local soil communities. Disease-suppressive soils are exceptional ecosystems in which crop plants suffer less from specific soil-borne pathogens than expected owing to the activities of other soil microorganisms. For most disease-suppressive soils, the microbes and mechanisms involved in pathogen control are unknown. By coupling PhyloChip-based metagenomics of the rhizosphere microbiome with culture-dependent functional analyses, we identified key bacterial taxa and genes involved in suppression of a fungal root pathogen. More than 33,000 bacterial and archaeal species were detected, with Proteobacteria, Firmicutes, and Actinobacteria consistently associated with disease suppression. Members of the γ-Proteobacteria were shown to have disease-suppressive activity governed by nonribosomal peptide synthetases. Our data indicate that upon attack by a fungal root pathogen, plants can exploit microbial consortia from soil for protection against infections.

Urban aerosols harbor diverse and dynamic bacterial populations

Considering the importance of its potential implications for human health, agricultural productivity, and ecosystem stability, surprisingly little is known regarding the composition or dynamics of the atmospheres microbial inhabitants. Using a custom high-density DNA microarray, we detected and monitored bacterial populations in two U.S. cities over 17 weeks. These urban aerosols contained at least 1,800 diverse bacterial types, a richness approaching that of some soil bacterial communities. We also reveal the consistent presence of bacterial families with pathogenic members including environmental relatives of select agents of bioterrorism significance. Finally, using multivariate regression techniques, we demonstrate that temporal and meteorological influences can be stronger factors than location in shaping the biological composition of the air we breathe.

High-Density Universal 16S rRNA Microarray Analysis Reveals Broader Diversity than Typical Clone Library When Sampling the Environment

Molecular approaches aimed at detection of a broad-range of prokaryotes in the environment routinely rely on classifying heterogeneous 16S rRNA genes amplified by polymerase chain reaction (PCR) using primers with broad specificity. The general method of sampling and categorizing DNA has been to clone then sequence the PCR products. However, the number of clones required to adequately catalog the majority of taxa in a sample is unwieldy. Alternatively, hybridizing target sequences to a universal 16S rRNA gene microarray may provide a more rapid and comprehensive view of prokaryotic community composition. This study investigated the breadth and accuracy of a microarray in detecting diverse 16S rRNA gene sequence types compared to clone-and-sequencing using three environmental samples: urban aerosol, subsurface soil, and subsurface water. PCR products generated from universal 16S rRNA gene-targeted primers were classified by using either the clone-and-sequence method or by hybridization to a novel high-density microarray of 297,851 probes complementary to 842 prokaryotic subfamilies. The three clone libraries comprised 1391 high-quality sequences. Approximately 8% of the clones could not be placed into a known subfamily and were considered novel. The microarray results confirmed the majority of clone-detected subfamilies and additionally demonstrated greater amplicon diversity extending into phyla not observed by the cloning method. Sequences matching operational taxonomic units within the phyla Nitrospira, Planctomycetes, and TM7, which were uniquely detected by the array, were verified with specific primers and subsequent amplicon sequencing. Subfamily richness detected by the array corresponded well with nonparametric richness predictions extrapolated from clone libraries except in the water community where clone-based richness predictions were greatly exceeded. It was concluded that although the microarray is unreliable in identifying novel prokaryotic taxa, it reveals greater diversity in environmental samples than sequencing a typically sized clone library. Furthermore, the microarray allowed samples to be rapidly evaluated with replication, a significant advantage in studies of microbial ecology.

Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers

The recent introduction of massively parallel pyrosequencers allows rapid, inexpensive analysis of microbial community composition using 16S ribosomal RNA (rRNA) sequences. However, a major challenge is to design a workflow so that taxonomic information can be accurately and rapidly assigned to each read, so that the composition of each community can be linked back to likely ecological roles played by members of each species, genus, family or phylum. Here, we use three large 16S rRNA datasets to test whether taxonomic information based on the full-length sequences can be recaptured by short reads that simulate the pyrosequencer outputs. We find that different taxonomic assignment methods vary radically in their ability to recapture the taxonomic information in full-length 16S rRNA sequences: most methods are sensitive to the region of the 16S rRNA gene that is targeted for sequencing, but many combinations of methods and rRNA regions produce consistent and accurate results. To process large datasets of partial 16S rRNA sequences obtained from surveys of various microbial communities, including those from human body habitats, we recommend the use of Greengenes or RDP classifier with fragments of at least 250 bases, starting from one of the primers R357, R534, R798, F343 or F517.

Application of a High-Density Oligonucleotide Microarray Approach To Study Bacterial Population Dynamics during Uranium Reduction and Reoxidation

ABSTRACT Reduction of soluble uranium U(VI) to less-soluble uranium U(IV) is a promising approach to minimize migration from contaminated aquifers. It is generally assumed that, under constant reducing conditions, U(IV) is stable and immobile; however, in a previous study, we documented reoxidation of U(IV) under continuous reducing conditions (Wan et al., Environ. Sci. Technol. 2005, 39:6162-6169). To determine if changes in microbial community composition were a factor in U(IV) reoxidation, we employed a high-density phylogenetic DNA microarray (16S microarray) containing 500,000 probes to monitor changes in bacterial populations during this remediation process. Comparison of the 16S microarray with clone libraries demonstrated successful detection and classification of most clone groups. Analysis of the most dynamic groups of 16S rRNA gene amplicons detected by the 16S microarray identified five clusters of bacterial subfamilies responding in a similar manner. This approach demonstrated that amplicons of known metal-reducing bacteria such as Geothrix fermentans (confirmed by quantitative PCR) and those within the Geobacteraceae were abundant during U(VI) reduction and did not decline during the U(IV) reoxidation phase. Significantly, it appears that the observed reoxidation of uranium under reducing conditions occurred despite elevated microbial activity and the consistent presence of metal-reducing bacteria. High-density phylogenetic microarrays constitute a powerful tool, enabling the detection and monitoring of a substantial portion of the microbial population in a routine, accurate, and reproducible manner.

High-Density Microarray of Small-Subunit Ribosomal DNA Probes

ABSTRACT Ribosomal DNA sequence analysis, originally conceived as a way to provide a universal phylogeny for life forms, has proven useful in many areas of biological research. Some of the most promising applications of this approach are presently limited by the rate at which sequences can be analyzed. As a step toward overcoming this limitation, we have investigated the use of photolithography chip technology to perform sequence analyses on amplified small-subunit rRNA genes. The GeneChip (Affymetrix Corporation) contained 31,179 20-mer oligonucleotides that were complementary to a subalignment of sequences in the Ribosomal Database Project (RDP) (B. L. Maidak et al., Nucleic Acids Res. 29:173-174, 2001). The chip and standard Affymetrix software were able to correctly match small-subunit ribosomal DNA amplicons with the corresponding sequences in the RDP database for 15 of 17 bacterial species grown in pure culture. When bacteria collected from an air sample were tested, the method compared favorably with cloning and sequencing amplicons in determining the presence of phylogenetic groups. However, the method could not resolve the individual sequences comprising a complex mixed sample. Given these results and the potential for future enhancement of this technology, it may become widely useful.

Chronic kidney disease alters intestinal microbial flora

The population of microbes (microbiome) in the intestine is a symbiotic ecosystem conferring trophic and protective functions. Since the biochemical environment shapes the structure and function of the microbiome, we tested whether uremia and/or dietary and pharmacologic interventions in chronic kidney disease alters the microbiome. To identify different microbial populations, microbial DNA was isolated from the stools of 24 patients with end-stage renal disease (ESRD) and 12 healthy persons, and analyzed by phylogenetic microarray. There were marked differences in the abundance of 190 bacterial operational taxonomic units (OTUs) between the ESRD and control groups. OTUs from Brachybacterium, Catenibacterium, Enterobacteriaceae, Halomonadaceae, Moraxellaceae, Nesterenkonia, Polyangiaceae, Pseudomonadaceae, and Thiothrix families were markedly increased in patients with ESRD. To isolate the effect of uremia from inter-individual variations, comorbid conditions, and dietary and medicinal interventions, rats were studied 8 weeks post 5/6 nephrectomy or sham operation. This showed a significant difference in the abundance of 175 bacterial OTUs between the uremic and control animals, most notably as decreases in the Lactobacillaceae and Prevotellaceae families. Thus, uremia profoundly alters the composition of the gut microbiome. The biological impact of this phenomenon is unknown and awaits further investigation.