Jens Reeder

QIIME allows analysis of high-throughput community sequencing data

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.

Human gut microbiome viewed across age and geography

Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.

Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions

We developed a fast method for denoising pyrosequencing for community 16S rRNA analysis. We observe a 2–4 fold reduction in the number of observed OTUs (operational taxonomic units) comparing denoised with non-denoised data. ~50,000 sequences can be denoised on a laptop within an hour, two orders of magnitude faster than published techniques. We demonstrate the effects of denoising on alpha and beta diversity of large 16S rRNA datasets.

Reshaping the gut microbiome with bacterial transplantation and antibiotic intake

The intestinal microbiota consists of over 1000 species, which play key roles in gut physiology and homeostasis. Imbalances in the composition of this bacterial community can lead to transient intestinal dysfunctions and chronic disease states. Understanding how to manipulate this ecosystem is thus essential for treating many disorders. In this study, we took advantage of recently developed tools for deep sequencing and phylogenetic clustering to examine the long-term effects of exogenous microbiota transplantation combined with and without an antibiotic pretreatment. In our rat model, deep sequencing revealed an intestinal bacterial diversity exceeding that of the human gut by a factor of two to three. The transplantation produced a marked increase in the microbial diversity of the recipients, which stemmed from both capture of new phylotypes and increase in abundance of others. However, when transplantation was performed after antibiotic intake, the resulting state simply combined the reshaping effects of the individual treatments (including the reduced diversity from antibiotic treatment alone). Therefore, lowering the recipient bacterial load by antibiotic intake prior to transplantation did not increase establishment of the donor phylotypes, although some dominant lineages still transferred successfully. Remarkably, all of these effects were observed after 1 mo of treatment and persisted after 3 mo. Overall, our results indicate that the indigenous gut microbial composition is more plastic that previously anticipated. However, since antibiotic pretreatment counterintuitively interferes with the establishment of an exogenous community, such plasticity is likely conditioned more by the altered microbiome gut homeostasis caused by antibiotics than by the primary bacterial loss.

The 'rare biosphere': a reality check

Methods for error correction and classification of metagenomic datasets suggest that the rare biosphere is not as large as previously assumed.

Extrapolation of Urn Models via Poissonization: Accurate Measurements of the Microbial Unknown

The availability of high-throughput parallel methods for sequencing microbial communities is increasing our knowledge of the microbial world at an unprecedented rate. Though most attention has focused on determining lower-bounds on the -diversity i.e. the total number of different species present in the environment, tight bounds on this quantity may be highly uncertain because a small fraction of the environment could be composed of a vast number of different species. To better assess what remains unknown, we propose instead to predict the fraction of the environment that belongs to unsampled classes. Modeling samples as draws with replacement of colored balls from an urn with an unknown composition, and under the sole assumption that there are still undiscovered species, we show that conditionally unbiased predictors and exact prediction intervals (of constant length in logarithmic scale) are possible for the fraction of the environment that belongs to unsampled classes. Our predictions are based on a Poissonization argument, which we have implemented in what we call the Embedding algorithm. In fixed i.e. non-randomized sample sizes, the algorithm leads to very accurate predictions on a sub-sample of the original sample. We quantify the effect of fixed sample sizes on our prediction intervals and test our methods and others found in the literature against simulated environments, which we devise taking into account datasets from a human-gut and -hand microbiota. Our methodology applies to any dataset that can be conceptualized as a sample with replacement from an urn. In particular, it could be applied, for example, to quantify the proportion of all the unseen solutions to a binding site problem in a random RNA pool, or to reassess the surveillance of a certain terrorist group, predicting the conditional probability that it deploys a new tactic in a next attack.

Nucleotides that are essential but not conserved; a sufficient L-tryptophan site in RNA

Conservation is often used to define essential sequences within RNA sites. However, conservation finds only invariant sequence elements that are necessary for function, rather than finding a set of sequence elements sufficient for function. Biochemical studies in several systems-including the hammerhead ribozyme and the purine riboswitch-find additional elements, such as loop-loop interactions, required for function yet not phylogenetically conserved. Here we define a critical test of sufficiency: We embed a minimal, apparently sufficient motif for binding the amino acid tryptophan in a random-sequence background and ask whether we obtain functional molecules. After a negative result, we use a combination of three-dimensional structural modeling, selection, designed mutations, high-throughput sequencing, and bioinformatics to explore functional insufficiency. This reveals an essential unpaired G in a diverse structural context, varied sequence, and flexible distance from the invariant internal loop binding site identified previously. Addition of the new element yields a sufficient binding site by the insertion criterion, binding tryptophan in 22 out of 23 tries. Random insertion testing for site sufficiency seems likely to be broadly revealing.

RNA Secondary Structure Analysis Using The RNAshapes Package

This unit shows how to use the RNAshapes package for the prediction of the secondary structure of a single RNA sequence using either minimum free energy methods or weighted ensemble information. It also includes a protocol for the consensus prediction of a set of related sequences. Curr. Protoc. Bioinform. 26:12.8.1‐12.8.17.