Image_1_Microbial Community Structure and Functional Potential Along a Hypersaline Gradient.TIF

<p>Salinity is one of the strongest environmental drivers of microbial evolution and community composition. Here we aimed to determine the impact of salt concentrations (2.5, 7.5, and 33.2%) on the microbial community structure of reclaimed saltern ponds near San Francisco, California, and to discover prospective enzymes with potential biotechnological applications. Community compositions were determined by 16S rRNA amplicon sequencing revealing both higher richness and evenness in the pond sediments compared to the water columns. Co-occurrence network analysis additionally uncovered the presence of microbial seed bank communities, potentially primed to respond to rapid changes in salinity. In addition, functional annotation of shotgun metagenomic DNA showed different capabilities if the microbial communities at different salinities for methanogenesis, amino acid metabolism, and carbohydrate-active enzymes. There was an overall shift with increasing salinity in the functional potential for starch degradation, and a decrease in degradation of cellulose and other oligosaccharides. Further, many carbohydrate-active enzymes identified have acidic isoelectric points that have potential biotechnological applications, including deconstruction of biofuel feedstocks under high ionic conditions. Metagenome-assembled genomes (MAGs) of individual halotolerant and halophilic microbes were binned revealing a variety of carbohydrate-degrading potential of individual pond inhabitants.</p

Clustering of distal gut metaproteomes according to disease.

<p>Non-metric multidimensional scaling (nMDS) of distal gut metaproteomes from CD twin cohort. The different colored square symbols represent the metaproteomic profiles for each sample (Blue  =  CCD, Grey  =  Healthy, Red  =  ICD). The numbers beside the symbols refer to the specific patient ID from Dicksved et al., 2008 (proteomes were run in technical duplicates). The axes are dimensionless: the coefficients of determination for the correlations between ordination distances and distances in the original n-dimensional space are. 472 and. 831 for Axis 1 and 2, respectively. A matrix of normalized spectral counts per protein (HMRG database search) from each duplicate metaproteome was imported into PCORD v5 software. nMDS was performed using the Bray-Curtis distance measure A three-dimensional solution was found after 119 iterations. The final stress for the nMDS was 6.47458. The white spots with grey shading correspond to individual proteins identified using HMRG database. Arrows indicate strength of correlation of specific bacterial strains to ordinated data. Pearson correlation coefficients for <i>Faecalibacterium prausnitzii, Anaerofustis stercorihominis, Clostridium leptum, Bacteroides ovatus</i>, <i>Bacteroides sp. 4_3</i>, and <i>Bacteroides sp. 3_1</i> were −0.875, −0.851, 0.784, 0.8, 0.788, and 0.817, respectively.</p

Metabolic Pathways that Differentiate Healthy and ICD phenotypes.

<p>(<b>A</b>) Metabolic pathways differentiating between healthy and ICD according to metabolic module analysis (p<0.05; 5% FDR). All pathways are less abundant in ICD compared to healthy except for <i>Bacteroides</i> membrane proteins (upper left box) that are more abundant in ICD. The colors reflect their phylogenetic origin that was determined using the lowest common ancestor of their HMRG mappings. Grey highlighted areas discussed in the main text: (1) butyrate production; (2) membrane proteins. (<b>B</b>) Observed metabolic module abundance shift versus its expected value based on the abundance of the host species. To separate out modules whose fold change is higher/lower than expected by the difference in its species abundance, we used the prediction interval of a fitted linear model (blue lines). The grey symbols are (species-separated) modules that are not significantly different between ICD and H (Wilcoxon rank-sum test; 5% FDR). They could have a high median fold change, but this is not always significant (eg when interpersonal variation is high). The colored symbols are (species-separated) modules that are significant between ICD and H (Wilcoxon rank-sum test; 5% FDR). Colored symbols inside the interval are significantly different but are in line with what would be expected from the species difference. Colored symbols outside the blue lines are higher/lower than expected. Specific <i>Faecalibacterium</i> proteins that are down regulated in the butyrate module (green squares) include the following: butyryl-CoA dehydrogenase (EC 1.3.99.2), 3-hydroxyacyl-CoA dehydrogenase (EC 1.1.1.35), enoyl-CoA hydratase/carnithine racemase, and acetyl-CoA acetyltransferases; as well as the module for lysine fermentation to acetate and butyrate (pink square). Specific <i>Bacteroides</i> proteins that are down regulated in the DNA-directed RNA polymerase module are the following (red X's): alpha and beta subunits (EC 2.7.7.6).</p

Specific genes and proteins that differ in relative amounts according to disease state.

<p>Relative Abundance of mucin-desulfating sulfatase (Mds), RagB and SusC/D, Outer Membrane Protein A (OmpA), TonB, Short-Chain Fatty Acid production (SCFA) and Butyrate production in (<b>A</b>) metagenomes and (<b>B</b>) MM metaproteomes. Error bars in (A) and (B) represent the standard error of the mean of the samples from Healthy (3 MG, 4 MP), ICD (5 MG, 6 MP) and CCD (2 MG/MP). (<b>C</b>) Specific outer membrane proteins and proteins involved in SCFA pathway that differed between disease categories. Protein abundances were calculated as normalized spectral abundance using the HMRG database search. The presence-absence heatmap indicates which of the 51 bacterial strains each protein matched to in the HMRG database search: black  =  species present, white  =  species absent. Grey  =  Healthy, Blue  =  CCD, Red  =  ICD.</p

Metaproteome differences between mean Healthy and mean ICD COG frequencies.

<p>To determine statistically significant differences between categories, White's non-parametric t-test was used with bootstrapping and Storey FDR multiple test correction. 95% upper and lower confidence intervals are shown. Red and grey bars indicate COG categories that are higher in ICD or Healthy metaproteomes, respectively; Asterisks indicate COG categories that were significantly different between ICD and healthy (q-value<0.05).</p

Comparison of protein expression levels across disease categories.

<p>(<b>A</b>) Boxplots depicting the distribution of the fraction of the metagenomes with PSMs. Boxes indicate 25^th, 50^th and 75^th percentile, with whiskers representing 10^th and 90^th percentile points. (<b>B</b>) Gene family richness as measured by the number of KEGG Orthologous group (KO) matches in the metagenomic dataset. Grey  =  Healthy, Blue  =  CCD, Red  =  ICD.</p

Numbers of Archaea and Bacteria.

1<p><a href="http://www.arb-silva.de" target="_blank">http://www.arb-silva.de</a>;</p>2<p><a href="http://wdcm.org" target="_blank">http://wdcm.org</a>;</p>3<p><a href="http://services.namesforlife.com/home" target="_blank">http://services.namesforlife.com/home</a>;</p>4<p><a href="http://www.bacterio.cict.fr" target="_blank">http://www.bacterio.cict.fr</a>;</p>5<p><a href="http://genomesonline.org/" target="_blank">http://genomesonline.org/</a>.</p

Genome project coverage of bacterial and archaeal type strains.

<p>From a total of approximately 11,000 bacterial and archaeal type strains, 3,285 (30%) have a publicly known genome project.</p

Interactive map based on the NamesforLife (N4L) taxonomic information of the type strains.

<p>Each leaf represents a type strain. Colors denote strains with or without genome projects. Lighter colored nodes denote higher taxonomic ranks. Branch lengths are not meaningful.</p

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Placing changes in the microbiome in the context of the American Gut. We accumulated samples over sequencing runs to demonstrate the structural consistency in the data. We demonstrate that while the ICU dataset (https://www.ncbi.nlm.nih.gov/pubmed/27602409) falls within the American Gut samples, they do not fall close to most samples at any of the body sites. We then highlight samples from the United Kingdom, Australia, the United States and other countries to show that nationality does not overcome the variation in body site. We then highlight the utility of the American Gut in meta-analysis by reproducing results from (https://www.ncbi.nlm.nih.gov/pubmed/20668239) and (https://www.ncbi.nlm.nih.gov/pubmed/23861384), using the AGP dataset as the context for dynamic microbiome changes instead of the HMP dataset. We show rapid, complete recovery of C. diff patients following fecal material transplantation and also contextualized the change in an infant gut over time until it settles into an adult state. This demonstrates the power of the American Gut dataset, both as a cohesive study and as a context for other investigations