Elena Deych

Bio: Elena Deych is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 25, co-authored 63 publications receiving 18697 citations.

Structure, function and diversity of the healthy human microbiome

Abstract: The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

Structure, function and diversity of the healthy human microbiome

Abstract: Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.

A framework for human microbiome research

Abstract: A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.

Use of Pharmacogenetic and Clinical Factors to Predict the Therapeutic Dose of Warfarin

Abstract: Initiation of warfarin therapy using trial-and-error dosing is problematic. Our goal was to develop and validate a pharmacogenetic algorithm. In the derivation cohort of 1,015 participants, the independent predictors of therapeutic dose were: VKORC1 polymorphism -1639/3673 G>A (-28% per allele), body surface area (BSA) (+11% per 0.25 m(2)), CYP2C9(*)3 (-33% per allele), CYP2C9(*)2 (-19% per allele), age (-7% per decade), target international normalized ratio (INR) (+11% per 0.5 unit increase), amiodarone use (-22%), smoker status (+10%), race (-9%), and current thrombosis (+7%). This pharmacogenetic equation explained 53-54% of the variability in the warfarin dose in the derivation and validation (N= 292) cohorts. For comparison, a clinical equation explained only 17-22% of the dose variability (P < 0.001). In the validation cohort, we prospectively used the pharmacogenetic-dosing algorithm in patients initiating warfarin therapy, two of whom had a major hemorrhage. To facilitate use of these pharmacogenetic and clinical algorithms, we developed a nonprofit website, http://www.WarfarinDosing.org.

Patterned Progression of Bacterial Populations in the Premature Infant Gut

Abstract: In the weeks after birth, the gut acquires a nascent microbiome, and starts its transition to bacterial population equilibrium. This early-in-life microbial population quite likely influences later-in-life host biology. However, we know little about the governance of community development: does the gut serve as a passive incubator where the first organisms randomly encountered gain entry and predominate, or is there an orderly progression of members joining the community of bacteria? We used fine interval enumeration of microbes in stools from multiple subjects to answer this question. We demonstrate via 16S rRNA gene pyrosequencing of 922 specimens from 58 subjects that the gut microbiota of premature infants residing in a tightly controlled microbial environment progresses through a choreographed succession of bacterial classes from Bacilli to Gammaproteobacteria to Clostridia, interrupted by abrupt population changes. As infants approach 33–36 wk postconceptional age (corresponding to the third to the twelfth weeks of life depending on gestational age at birth), the gut is well colonized by anaerobes. Antibiotics, vaginal vs. Caesarian birth, diet, and age of the infants when sampled influence the pace, but not the sequence, of progression. Our results suggest that in infants in a microbiologically constrained ecosphere of a neonatal intensive care unit, gut bacterial communities have an overall nonrandom assembly that is punctuated by microbial population abruptions. The possibility that the pace of this assembly depends more on host biology (chiefly gestational age at birth) than identifiable exogenous factors warrants further consideration.

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.

UPARSE: highly accurate OTU sequences from microbial amplicon reads

Abstract: Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.

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.

Structure, function and diversity of the healthy human microbiome

Abstract: The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

Abstract: Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community's functional capabilities. Here we describe PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.