A Core Human Microbiome as Viewed through 16S rRNA Sequence Clusters

We explore the microbiota of 18 body sites in over 200 individuals using sequences amplified V1–V3 and the V3–V5 small subunit ribosomal RNA (16S) hypervariable regions as part of the NIH Common Fund Human Microbiome Project. The body sites with the greatest number of core OTUs, defined as OTUs shared amongst 95% or more of the individuals, were the oral sites (saliva, tongue, cheek, gums, and throat) followed by the nose, stool, and skin, while the vaginal sites had the fewest number of OTUs shared across subjects. We found that commonalities between samples based on taxonomy could sometimes belie variability at the sub-genus OTU level. This was particularly apparent in the mouth where a given genus can be present in many different oral sites, but the sub-genus OTUs show very distinct site selection, and in the vaginal sites, which are consistently dominated by the Lactobacillus genus but have distinctly different sub-genus V1–V3 OTU populations across subjects. Different body sites show approximately a ten-fold difference in estimated microbial richness, with stool samples having the highest estimated richness, followed by the mouth, throat and gums, then by the skin, nasal and vaginal sites. Richness as measured by the V1–V3 primers was consistently higher than richness measured by V3–V5. We also show that when such a large cohort is analyzed at the genus level, most subjects fit the stool “enterotype” profile, but other subjects are intermediate, blurring the distinction between the enterotypes. When analyzed at the finer-scale, OTU level, there was little or no segregation into stool enterotypes, but in the vagina distinct biotypes were apparent. Finally, we note that even OTUs present in nearly every subject, or that dominate in some samples, showed orders of magnitude variation in relative abundance emphasizing the highly variable nature across individuals

Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.Comment: 20 pages, 5 figures accepted for publication in Plos Comp Bio. Supplementary video and information availabl

A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma.

© 2014 Haider et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options

Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community

Assessment of Metagenomic Assembly Using Simulated Next Generation Sequencing Data

Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available

S100A14 Stimulates Cell Proliferation and Induces Cell Apoptosis at Different Concentrations via Receptor for Advanced Glycation End Products (RAGE)

S100A14 is an EF-hand containing calcium-binding protein of the S100 protein family that exerts its biological effects on different types of cells. However, exact extracellular roles of S100A14 have not been clarified yet. Here we investigated the effects of S100A14 on esophageal squamous cell carcinoma (ESCC) cell lines. Results demonstrated that low doses of extracellular S100A14 stimulate cell proliferation and promote survival in KYSE180 cells through activating ERK1/2 MAPK and NF-κB signaling pathways. Immunoprecipitation assay showed that S100A14 binds to receptor for advanced glycation end products (RAGE) in KYSE180 cells. Inhibition of RAGE signaling by different approaches including siRNA for RAGE, overexpression of a dominant-negative RAGE construct or a RAGE antagonist peptide (AmphP) significantly blocked S100A14-induced effects, suggesting that S100A14 acts via RAGE ligation. Furthermore, mutation of the N-EF hand of S100A14 (E39A, E45A) virtually reduced 10 µg/ml S100A14-induced cell proliferation and ERK1/2 activation. However, high dose (80 µg/ml) of S100A14 causes apoptosis via the mitochondrial pathway with activation of caspase-3, caspase-9, and poly(ADP-ribose) polymerase. High dose S100A14 induces cell apoptosis is partially in a RAGE-dependent manner. This is the first study to demonstrate that S100A14 binds to RAGE and stimulates RAGE-dependent signaling cascades, promoting cell proliferation or triggering cell apoptosis at different doses

Genomic variation landscape of the human gut microbiome

While large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the latter is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 fecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short indels, and 1,051 structural variants. The average ratio of non-synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This implies that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake

Diet rapidly and reproducibly alters the human gut microbiome

Long-term diet influences the structure and activity of the trillions of microorganisms residing in the human gut1–5, but it remains unclear how rapidly and reproducibly the human gut microbiome responds to short-term macronutrient change. Here, we show that the short-term consumption of diets composed entirely of animal or plant products alters microbial community structure and overwhelms inter-individual differences in microbial gene expression. The animal-based diet increased the abundance of bile-tolerant microorganisms (Alistipes, Bilophila, and Bacteroides) and decreased the levels of Firmicutes that metabolize dietary plant polysaccharides (Roseburia, Eubacterium rectale, and Ruminococcus bromii). Microbial activity mirrored differences between herbivorous and carnivorous mammals2, reflecting trade-offs between carbohydrate and protein fermentation. Foodborne microbes from both diets transiently colonized the gut, including bacteria, fungi, and even viruses. Finally, increases in the abundance and activity of Bilophila wadsworthia on the animal-based diet support a link between dietary fat, bile acids, and the outgrowth of microorganisms capable of triggering inflammatory bowel disease6. In concert, these results demonstrate that the gut microbiome can rapidly respond to altered diet, potentially facilitating the diversity of human dietary lifestyles