A. Murat Eren

Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data

Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function are of prime importance. High-throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference data bases or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial data sets. In this paper, we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental data sets through 16S ribosomal RNA gene data by the canonical approaches. Our analysis of two data sets from two different environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance. Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open-source software pipeline for oligotyping.

The Gut Microbiota of Rural Papua New Guineans: Composition, Diversity Patterns, and Ecological Processes

Although recent research revealed an impact of westernization on diversity and composition of the human gut microbiota, the exact consequences on metacommunity characteristics are insufficiently understood, and the underlying ecological mechanisms have not been elucidated. Here, we have compared the fecal microbiota of adults from two non-industrialized regions in Papua New Guinea (PNG) with that of United States (US) residents. Papua New Guineans harbor communities with greater bacterial diversity, lower inter-individual variation, vastly different abundance profiles, and bacterial lineages undetectable in US residents. A quantification of the ecological processes that govern community assembly identified bacterial dispersal as the dominant process that shapes the microbiome in PNG but not in the US. These findings suggest that the microbiome alterations detected in industrialized societies might arise from modern lifestyle factors limiting bacterial dispersal, which has implications for human health and the development of strategies aimed to redress the impact of westernization.

Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences

Molecular microbial ecology investigations often employ large marker gene datasets, for example, ribosomal RNAs, to represent the occurrence of single-cell genomes in microbial communities. Massively parallel DNA sequencing technologies enable extensive surveys of marker gene libraries that sometimes include nearly identical sequences. Computational approaches that rely on pairwise sequence alignments for similarity assessment and de novo clustering with de facto similarity thresholds to partition high-throughput sequencing datasets constrain fine-scale resolution descriptions of microbial communities. Minimum Entropy Decomposition (MED) provides a computationally efficient means to partition marker gene datasets into ‘MED nodes’, which represent homogeneous operational taxonomic units. By employing Shannon entropy, MED uses only the information-rich nucleotide positions across reads and iteratively partitions large datasets while omitting stochastic variation. When applied to analyses of microbiomes from two deep-sea cryptic sponges Hexadella dedritifera and Hexadella cf. dedritifera, MED resolved a key Gammaproteobacteria cluster into multiple MED nodes that are specific to different sponges, and revealed that these closely related sympatric sponge species maintain distinct microbial communities. MED analysis of a previously published human oral microbiome dataset also revealed that taxa separated by less than 1% sequence variation distributed to distinct niches in the oral cavity. The information theory-guided decomposition process behind the MED algorithm enables sensitive discrimination of closely related organisms in marker gene amplicon datasets without relying on extensive computational heuristics and user supervision.

Anvi'o: an advanced analysis and visualization platform for 'omics data.

Advances in high-throughput sequencing and ‘omics technologies are revolutionizing studies of naturally occurring microbial communities. Comprehensive investigations of microbial lifestyles require the ability to interactively organize and visualize genetic information and to incorporate subtle differences that enable greater resolution of complex data. Here we introduce anvi’o, an advanced analysis and visualization platform that offers automated and human-guided characterization of microbial genomes in metagenomic assemblies, with interactive interfaces that can link ‘omics data from multiple sources into a single, intuitive display. Its extensible visualization approach distills multiple dimensions of information about each contig, offering a dynamic and unified work environment for data exploration, manipulation, and reporting. Using anvi’o, we re-analyzed publicly available datasets and explored temporal genomic changes within naturally occurring microbial populations through de novo characterization of single nucleotide variations, and linked cultivar and single-cell genomes with metagenomic and metatranscriptomic data. Anvi’o is an open-source platform that empowers researchers without extensive bioinformatics skills to perform and communicate in-depth analyses on large ‘omics datasets.

Exploring the Diversity of Gardnerella vaginalis in the Genitourinary Tract Microbiota of Monogamous Couples Through Subtle Nucleotide Variation

Background Bacterial vaginosis (BV) is an enigmatic disease of unknown origin that affects a large percentage of women. The vaginal microbiota of women with BV is associated with serious sequelae, including abnormal pregnancies. The etiology of BV is not fully understood, however, it has been suggested that it is transmissible, and that G. vaginalis may be an etiological agent. Studies using enzymatic assays to define G. vaginalis biotypes, as well as more recent genomic comparisons of G. vaginalis isolates from symptomatic and asymptomatic women, suggest that particular G. vaginalis strains may play a key role in the pathogenesis of BV. Methodology/Principal Findings To explore G. vaginalis diversity, distribution and sexual transmission, we developed a Shannon entropy-based method to analyze low-level sequence variation in 65,710 G. vaginalis 16S rRNA gene segments that were PCR-amplified from vaginal samples of 53 monogamous women and from urethral and penile skin samples of their male partners. We observed a high degree of low-level diversity among G. vaginalis sequences with a total of 46 unique sequence variants (oligotypes), and also found strong correlations of these oligotypes between sexual partners. Even though Gram stain-defined normal and some Gram stain-defined intermediate oligotype profiles clustered together in UniFrac analysis, no single G. vaginalis oligotype was found to be specific to BV or normal vaginal samples. Conclusions This study describes a novel method for investigating G. vaginalis diversity at a low level of taxonomic discrimination. The findings support cultivation-based studies that indicate sexual partners harbor the same strains of G. vaginalis. This study also highlights the fact that a few, reproducible nucleotide variations within the 16S rRNA gene can reveal clinical or epidemiological associations that would be missed by genus-level or species-level categorization of 16S rRNA data.

A Filtering Method to Generate High Quality Short Reads Using Illumina Paired-End Technology

Consensus between independent reads improves the accuracy of genome and transcriptome analyses, however lack of consensus between very similar sequences in metagenomic studies can and often does represent natural variation of biological significance. The common use of machine-assigned quality scores on next generation platforms does not necessarily correlate with accuracy. Here, we describe using the overlap of paired-end, short sequence reads to identify error-prone reads in marker gene analyses and their contribution to spurious OTUs following clustering analysis using QIIME. Our approach can also reduce error in shotgun sequencing data generated from libraries with small, tightly constrained insert sizes. The open-source implementation of this algorithm in Python programming language with user instructions can be obtained from https://github.com/meren/illumina-utils.

Oligotyping analysis of the human oral microbiome

Significance The human body, including the mouth, is home to a diverse assemblage of microbial organisms. Although high-throughput sequencing of 16S rRNA genes provides enormous amounts of census data, accurate identification of taxa in these large datasets remains problematic because widely used computational approaches do not resolve closely related but distinct organisms. We used a computational approach that relies on information theory to reanalyze the human oral microbiome. This analysis revealed organisms differing by as little as a single rRNA nucleotide, with dramatically different distributions across habitats or individuals. Our information theory-based approach in combination with habitat analysis demonstrates the potential to deconstruct entire microbiomes, detect previously unrecognized diversity, and provide deep insight into microbial communities in health and disease. The Human Microbiome Project provided a census of bacterial populations in healthy individuals, but an understanding of the biomedical significance of this census has been hindered by limited taxonomic resolution. A high-resolution method termed oligotyping overcomes this limitation by evaluating individual nucleotide positions using Shannon entropy to identify the most information-rich nucleotide positions, which then define oligotypes. We have applied this method to comprehensively analyze the oral microbiome. Using Human Microbiome Project 16S rRNA gene sequence data for the nine sites in the oral cavity, we identified 493 oligotypes from the V1-V3 data and 360 oligotypes from the V3-V5 data. We associated these oligotypes with species-level taxon names by comparison with the Human Oral Microbiome Database. We discovered closely related oligotypes, differing sometimes by as little as a single nucleotide, that showed dramatically different distributions among oral sites and among individuals. We also detected potentially pathogenic taxa in high abundance in individual samples. Numerous oligotypes were preferentially located in plaque, others in keratinized gingiva or buccal mucosa, and some oligotypes were characteristic of habitat groupings such as throat, tonsils, tongue dorsum, hard palate, and saliva. The differing habitat distributions of closely related oligotypes suggest a level of ecological and functional biodiversity not previously recognized. We conclude that the Shannon entropy approach of oligotyping has the capacity to analyze entire microbiomes, discriminate between closely related but distinct taxa and, in combination with habitat analysis, provide deep insight into the microbial communities in health and disease.

VAMPS: a website for visualization and analysis of microbial population structures

BackgroundThe advent of next-generation DNA sequencing platforms has revolutionized molecular microbial ecology by making the detailed analysis of complex communities over time and space a tractable research pursuit for small research groups. However, the ability to generate 105–108 reads with relative ease brings with it many downstream complications. Beyond the computational resources and skills needed to process and analyze data, it is difficult to compare datasets in an intuitive and interactive manner that leads to hypothesis generation and testing.ResultsWe developed the free web service VAMPS (Visualization and Analysis of Microbial Population Structures, http://vamps.mbl.edu) to address these challenges and to facilitate research by individuals or collaborating groups working on projects with large-scale sequencing data. Users can upload marker gene sequences and associated metadata; reads are quality filtered and assigned to both taxonomic structures and to taxonomy-independent clusters. A simple point-and-click interface allows users to select for analysis any combination of their own or their collaborators’ private data and data from public projects, filter these by their choice of taxonomic and/or abundance criteria, and then explore these data using a wide range of analytic methods and visualizations. Each result is extensively hyperlinked to other analysis and visualization options, promoting data exploration and leading to a greater understanding of data relationships.ConclusionsVAMPS allows researchers using marker gene sequence data to analyze the diversity of microbial communities and the relationships between communities, to explore these analyses in an intuitive visual context, and to download data, results, and images for publication. VAMPS obviates the need for individual research groups to make the considerable investment in computational infrastructure and bioinformatic support otherwise necessary to process, analyze, and interpret massive amounts of next-generation sequence data. Any web-capable device can be used to upload, process, explore, and extract data and results from VAMPS. VAMPS encourages researchers to share sequence and metadata, and fosters collaboration between researchers of disparate biomes who recognize common patterns in shared data.

Host-specificity among abundant and rare taxa in the sponge microbiome.

Microbial communities have a key role in the physiology of the sponge host, and it is therefore essential to understand the stability and specificity of sponge–symbiont associations. Host-specific bacterial associations spanning large geographic distance are widely acknowledged in sponges. However, the full spectrum of specificity remains unclear. In particular, it is not known whether closely related sponges host similar or very different microbiota over wide bathymetric and geographic gradients, and whether specific associations extend to the rare members of the sponge microbiome. Using the ultra-deep Illumina sequencing technology, we conducted a comparison of sponge bacterial communities in seven closely related Hexadella species with a well-resolved host phylogeny, as well as of a distantly related sponge Mycale. These samples spanned unprecedentedly large bathymetric (15–960 m) gradients and varying European locations. In addition, this study included a bacterial community analysis of the local background seawater for both Mycale and the widespread deep-sea taxa Hexadella cf. dedritifera. We observed a striking diversity of microbes associated with the sponges, spanning 47 bacterial phyla. The data did not reveal any Hexadella microbiota co-speciation pattern, but confirmed sponge-specific and species-specific host–bacteria associations, even within extremely low abundant taxa. Oligotyping analysis also revealed differential enrichment preferences of closely related Nitrospira members in closely related sponges species. Overall, these results demonstrate highly diverse, remarkably specific and stable sponge–bacteria associations that extend to members of the rare biosphere at a very fine phylogenetic scale, over significant geographic and bathymetric gradients.

Extensive modulation of the fecal metagenome in children With Crohn’s disease during exclusive enteral nutrition

OBJECTIVES:Exploring associations between the gut microbiota and colonic inflammation and assessing sequential changes during exclusive enteral nutrition (EEN) may offer clues into the microbial origins of Crohn’s disease (CD).METHODS:Fecal samples (n=117) were collected from 23 CD and 21 healthy children. From CD children fecal samples were collected before, during EEN, and when patients returned to their habitual diets. Microbiota composition and functional capacity were characterized using sequencing of the 16S rRNA gene and shotgun metagenomics.RESULTS:Microbial diversity was lower in CD than controls before EEN (P=0.006); differences were observed in 36 genera, 141 operational taxonomic units (OTUs), and 44 oligotypes. During EEN, the microbial diversity of CD children further decreased, and the community structure became even more dissimilar than that of controls. Every 10 days on EEN, 0.6 genus diversity equivalents were lost; 34 genera decreased and one increased during EEN. Fecal calprotectin correlated with 35 OTUs, 14 of which accounted for 78% of its variation. OTUs that correlated positively or negatively with calprotectin decreased during EEN. The microbiota of CD patients had a broader functional capacity than healthy controls, but diversity decreased with EEN. Genes involved in membrane transport, sulfur reduction, and nutrient biosynthesis differed between patients and controls. The abundance of genes involved in biotin (P=0.005) and thiamine biosynthesis decreased (P=0.017), whereas those involved in spermidine/putrescine biosynthesis (P=0.031), or the shikimate pathway (P=0.058), increased during EEN.CONCLUSIONS:Disease improvement following treatment with EEN is associated with extensive modulation of the gut microbiome.