Comparative metagenomic analysis reveals mechanisms for stress response in hypoliths from extreme hyperarid deserts

Understanding microbial adaptation to environmental stressors is crucial for interpreting broader ecological patterns. In the most extreme hot and cold deserts, cryptic niche communities are thought to play key roles in ecosystem processes and represent excellent model systems for investigating microbial responses to environmental stressors. However, relatively little is known about the genetic diversity underlying such functional processes in climatically extreme desert systems. This study presents the first comparative metagenome analysis of cyanobacteria-dominated hypolithic communities in hot (Namib Desert, Namibia) and cold (Miers Valley, Antarctica) hyperarid deserts. The most abundant phyla in both hypolith metagenomes were Actinobacteria, Proteobacteria, Cyanobacteria and Bacteroidetes with Cyanobacteria dominating in Antarctic hypoliths. However, no significant differences between the twometagenomeswere identified. The Antarctic hypolithicmetagenome displayed a high number of sequences assigned to sigma factors, replication,recombination andrepair, translation, ribosomal structure,andbiogenesis. In contrast, theNamibDesert metagenome showed a high abundance of sequences assigned to carbohydrate transport and metabolism. Metagenome data analysis also revealed significantdivergence inthe geneticdeterminantsof aminoacidandnucleotidemetabolismbetween these two metagenomes and those of soil from other polar deserts, hot deserts, and non-desert soils. Our results suggest extensive niche differentiation in hypolithic microbial communities from these two extreme environments and a high genetic capacity for survival under environmental extremes.Fil: Le, Phuong Thi. University of Pretoria; Sudáfrica. Vlaams Instituut voor Biotechnologie; Bélgica. University of Ghent; BélgicaFil: Makhalanyane, Thulani P.. University of Pretoria; SudáfricaFil: Guerrero, Leandro Demián. University of Pretoria; Sudáfrica. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Vikram, Surendra. University of Pretoria; SudáfricaFil: Van De Peer, Yves. University of Pretoria; Sudáfrica. Vlaams Instituut voor Biotechnologie; Bélgica. University of Ghent; BélgicaFil: Cowan, Don A.. University of Pretoria; Sudáfric

Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome

Function-based metagenomic code. Shown is a plot of the accuracy of identification of an individual’s microbiome sample based on a previously obtained sample from the same individual. The identification was done using functional metagenomic codes of varying sizes (1-20 gene families), based on the gene family abundance profiles normalized with either relative (red) or MUSiCC (cyan) normalization procedures. (PDF 251 kb

Metagenomic biomarker discovery and explanation

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/.National Institute of Dental and Craniofacial Research (U.S.) (grant DE017106)National Institutes of Health (U.S.) (NIH grant AI078942)Burroughs Wellcome FundNational Institutes of Health (U.S.) (NIH 1R01HG005969

Tiny microbes, enormous impacts: what matters in gut microbiome studies?

Many factors affect the microbiomes of humans, mice, and other mammals, but substantial challenges remain in determining which of these factors are of practical importance. Considering the relative effect sizes of both biological and technical covariates can help improve study design and the quality of biological conclusions. Care must be taken to avoid technical bias that can lead to incorrect biological conclusions. The presentation of quantitative effect sizes in addition to P values will improve our ability to perform meta-analysis and to evaluate potentially relevant biological effects. A better consideration of effect size and statistical power will lead to more robust biological conclusions in microbiome studies

Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates.

Dietary habits have been associated with alterations of the human gut resident microorganisms contributing to obesity, diabetes and cancer1. In Western diets, red meat is a frequently eaten food2, but long-term consumption has been associated with increased risk of disease3,4. Red meat is enriched in N-glycolylneuraminic acid (Neu5Gc) that cannot be synthesized by humans5. However, consumption can cause Neu5Gc incorporation into cell surface glycans6, especially in carcinomas4,7. As a consequence, an inflammatory response is triggered when Neu5Gc-containing glycans encounter circulating anti-Neu5Gc antibodies8,9. Although bacteria can use free sialic acids as a nutrient source10-12, it is currently unknown if gut microorganisms contribute to releasing Neu5Gc from food. We found that a Neu5Gc-rich diet induces changes in the gut microbiota, with Bacteroidales and Clostridiales responding the most. Genome assembling of mouse and human shotgun metagenomic sequencing identified bacterial sialidases with previously unobserved substrate preference for Neu5Gc-containing glycans. X-ray crystallography revealed key amino acids potentially contributing to substrate preference. Additionally, we verified that mouse and human sialidases were able to release Neu5Gc from red meat. The release of Neu5Gc from red meat using bacterial sialidases could reduce the risk of inflammatory diseases associated with red meat consumption, including colorectal cancer4 and atherosclerosis13

Analysis of a data matrix and a graph: Metagenomic data and the phylogenetic tree

In biological experiments researchers often have information in the form of a graph that supplements observed numerical data. Incorporating the knowledge contained in these graphs into an analysis of the numerical data is an important and nontrivial task. We look at the example of metagenomic data---data from a genomic survey of the abundance of different species of bacteria in a sample. Here, the graph of interest is a phylogenetic tree depicting the interspecies relationships among the bacteria species. We illustrate that analysis of the data in a nonstandard inner-product space effectively uses this additional graphical information and produces more meaningful results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS402 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org