Ryan A. Lesniewski

Bio: Ryan A. Lesniewski is an academic researcher from University of Southern California. The author has contributed to research in topics: Archaea & Salinity. The author has an hindex of 6, co-authored 6 publications receiving 15267 citations. Previous affiliations of Ryan A. Lesniewski include University of Minnesota & University of Michigan.

Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

The metatranscriptome of a deep-sea hydrothermal plume is dominated by water column methanotrophs and lithotrophs.

Abstract: Microorganisms mediate geochemical processes in deep-sea hydrothermal vent plumes, which are a conduit for transfer of elements and energy from the subsurface to the oceans. Despite this important microbial influence on marine geochemistry, the ecology and activity of microbial communities in hydrothermal plumes is largely unexplored. Here, we use a coordinated metagenomic and metatranscriptomic approach to compare microbial communities in Guaymas Basin hydrothermal plumes to background waters above the plume and in the adjacent Carmen Basin. Despite marked increases in plume total RNA concentrations (3–4 times) and microbially mediated manganese oxidation rates (15–125 times), plume and background metatranscriptomes were dominated by the same groups of methanotrophs and chemolithoautotrophs. Abundant community members of Guaymas Basin seafloor environments (hydrothermal sediments and chimneys) were not prevalent in the plume metatranscriptome. De novo metagenomic assembly was used to reconstruct genomes of abundant populations, including Marine Group I archaea, Methylococcaceae, SAR324 Deltaproteobacteria and SUP05 Gammaproteobacteria. Mapping transcripts to these genomes revealed abundant expression of genes involved in the chemolithotrophic oxidation of ammonia (amo), methane (pmo) and sulfur (sox). Whereas amo and pmo gene transcripts were abundant in both plume and background, transcripts of sox genes for sulfur oxidation from SUP05 groups displayed a 10–20-fold increase in plumes. We conclude that the biogeochemistry of Guaymas Basin hydrothermal plumes is mediated by microorganisms that are derived from seawater rather than from seafloor hydrothermal environments such as chimneys or sediments, and that hydrothermal inputs serve as important electron donors for primary production in the deep Gulf of California.

Genome-enabled transcriptomics reveals archaeal populations that drive nitrification in a deep-sea hydrothermal plume

Abstract: Ammonia-oxidizing Archaea (AOA) are among the most abundant microorganisms in the oceans and have crucial roles in biogeochemical cycling of nitrogen and carbon. To better understand AOA inhabiting the deep sea, we obtained community genomic and transcriptomic data from ammonium-rich hydrothermal plumes in the Guaymas Basin (GB) and from surrounding deep waters of the Gulf of California. Among the most abundant and active lineages in the sequence data were marine group I (MGI) Archaea related to the cultured autotrophic ammonia-oxidizer, Nitrosopumilus maritimus. Assembly of MGI genomic fragments yielded 2.9 Mb of sequence containing seven 16S rRNA genes (95.4–98.4% similar to N. maritimus), including two near-complete genomes and several lower-abundance variants. Equal copy numbers of MGI 16S rRNA genes and ammonia monooxygenase genes and transcription of ammonia oxidation genes indicates that all of these genotypes actively oxidize ammonia. De novo genomic assembly revealed the functional potential of MGI populations and enhanced interpretation of metatranscriptomic data. Physiological distinction from N. maritimus is evident in the transcription of novel genes, including genes for urea utilization, suggesting an alternative source of ammonia. We were also able to determine which genotypes are most active in the plume. Transcripts involved in nitrification were more prominent in the plume and were among the most abundant transcripts in the community. These unique data sets reveal populations of deep-sea AOA thriving in the ammonium-rich GB that are related to surface types, but with key genomic and physiological differences.

Archaeal and bacterial diversity in an arsenic-rich shallow-sea hydrothermal system undergoing phase separation.

Abstract: Phase separation is a ubiquitous process in seafloor hydrothermal vents, creating a large range of salinities. Toxic elements (e.g., arsenic) partition into the vapor phase, and thus can be enriched in both high and low salinity fluids. However, investigations of microbial diversity at sites associated with phase separation are rare. We evaluated prokaryotic diversity in arsenic-rich shallow-sea vents off Milos Island (Greece) by comparative analysis of 16S rRNA clone sequences from two vent sites with similar pH and temperature but marked differences in salinity. Clone sequences were also obtained for aioA-like functional genes (AFGs). Bacteria in the surface sediments (0 to 1.5 cm) at the high salinity site consisted of mainly Epsilonproteobacteria (Arcobacter sp.), which transitioned to almost exclusively Firmicutes (Bacillus sp.) at ~10 cm depth. However, the low salinity site consisted of Bacteroidetes (Flavobacteria) in the surface and Epsilonproteobacteria (Arcobacter sp.) at ~10 cm depth. Archaea in the high salinity surface sediments were dominated by the orders Archaeoglobales and Thermococcales, transitioning to Thermoproteales and Desulfurococcales (Staphylothermus sp.) in the deeper sediments. In contrast, the low salinity site was dominated by Thermoplasmatales in the surface and Thermoproteales at depth. Similarities in gas and redox chemistry suggest that salinity and/or arsenic concentrations may select for microbial communities that can tolerate these parameters. Many of the archaeal 16S rRNA sequences contained inserts, possibly introns, including members of the Euryarchaeota. Clones containing AFGs affiliated with either Alpha- or Betaproteobacteria, although most were only distantly related to published representatives. Most clones (89%) originated from the deeper layer of the low salinity, highest arsenic site. This is the only sample with overlap in 16S rRNA data, suggesting arsenotrophy as an important metabolism in similar environments.

Mineralogy Drives Bacterial Biogeography of Hydrothermally Inactive Seafloor Sulfide Deposits

Abstract: Mid-ocean ridge hydrothermal venting creates sulfide deposits containing gradients in mineralogy, fluid chemistry, and temperature. Even when hydrothermal circulation ceases, sulfides are known to host microbial communities. The relationship between mineralogy and microbial community composition in low-temperature, rock-hosted systems has not been resolved at any spatial scale, local or global. To examine the hypothesis that geochemistry of seafloor deposits is a dominant parameter driving environmental pressure for bacterial communities at low-temperature, the shared community membership, richness, and structure was measured using 16S rRNA gene sequences. The focus of the study was on hydrothermally inactive seafloor deposits from multiple locations within one deposit (e.g., single extinct chimney), within one vent field (intra-vent field), and among globally distributed vent fields from three ocean basins (inter-vent field). Distinct mineral substrates, such as hydrothermally inactive sulfides versus ba...

QIIME allows analysis of high-throughput community sequencing data.

Abstract: 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.

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

Abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

DADA2: High-resolution sample inference from Illumina amplicon data

Abstract: We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

Abstract: Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

Metagenomic biomarker discovery and explanation

Abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.