Additional file 6 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Figure S4. Comparison of dissimilarity measures obtained by k-mer and 3 reference-based methods: BC MetaPhlAn genus, BC MetaPhlAn org and WG UniFrac. For each plot, Y-axis represents k-mer dissimilarity, X-axis - dissimilarity using one of reference-based methods. (PNG 925 kb

Additional file 5 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Figure S3. Correlation between k-mer and taxonomic composition dissimilarity matrices, as well as k-mer dissimilarity matrix computation time with varying values of k. All computations were performed on a compute node with CPU Opteron 6176 2.3 GHz (24 cores) and 64 Gb RAM. (PNG 185 kb

Additional file 1 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Supplementary tables. Supplementary Table S1. Bacterial abundances in Simulation 1 (high-diversity communities). Supplementary Table S2. Bacterial abundances in Simulation 2 (low-diversity communities). Supplementary Table S3. List of genomes in taxonomic catalog for human gut. Supplementary Table S4. Taxonomic composition for real dataset (organism level). Supplementary Table S5. Taxonomic composition for real dataset (genus level). Supplementary Table S6. Functional composition for real dataset (COG). Supplementary Table S7. Taxonomic composition for real dataset by MetaPhlAn (organism level). Supplementary Table S8. Mapped read counts and percentage of mapping on taxonomic and functional catalog and phage genome. (XLS 2693 kb

Additional file 4 of Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Figure S2. Comparison of pairwise dissimilarity measures obtained by k-mer and taxonomic composition for simulated for high- and low-diversity metagenomes. As seen, satisfactory correlation of k-mers with taxonomic composition can be obtained only at relatively high values of k. (PNG 233 kb

Comparison of the graph properties between the microbiomes of the major studies.

<p>Comparison of the graph properties between the microbiomes of the major studies.</p

Dynamics of the system during antibiotic treatment.

<p><b>(A</b>) Change of bacterial quantity over time during antibiotic treatment. Bacteria are mixed. 2 feed backs. (periods of antibiotic gavage are highlighted on a graph). (<b>B</b>) Distribution of density of each bacterial type throughout the artificial gut width (axis y: density of bacterial type, axis x: 0mkm–bottom gut wall; 30000mkm–center of the gut). Bacteria are mixed. (<b>C</b>) Change of bacterial quantity over time during antibiotic treatment. Bacteria are separated into bacterial layers. (<b>D</b>) Distribution of density of each bacterial type throughout the artificial gut width (axis y: density of bacterial type, axis x: 0mkm–bottom gut wall; 30000mkm–center of the gut). Bacteria are separated into bacterial layers.</p

Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

<div><p>Background</p><p>Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.</p><p>Methodology/Principal Findings</p><p>In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.</p><p>Conclusion/Significance</p><p>The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.</p></div

Classification of the community structure after treatment.

<p>Changes of proportion of resistant bacteria (R/(R+S)) over time: (<b>A</b>) resistant strains dominate after treatment; (<b>B</b>) sensitive strains dominate after treatment (periods of antibiotic gavage are highlighted on a graph); (<b>C</b>) constant fluctuations in the ratio of resistant and sensitive strains. (<b>D</b>) Histogram of outcome distributions between 3 classes in percentage. (<b>E</b>) Classification results for the relevant parameters. Class 1 (red)—resistant strains dominate. Class 2 (green)—fluctuations in the ratio of resistant and sensitive strains. Class 3 (blue)—sensitive strains dominate.</p

Set of external parameters describing model.

<p>Set of external parameters describing model.</p

System stability.

<p>(<b>A</b>) Multiple simulations with different initial bacteria numbers (axis 1—number of bacterial type 1, axis 2—number of bacterial type 2, axis 3—frequency of position). Basic schema with toxin-antitoxin systems FB1. One steady state is observed. (<b>B</b>) Multiple simulations with different initial bacteria numbers (axis 1—number of bacterial type 1, axis 2—number of bacterial type 2, axis 3—frequency of position). Basic schema with toxin-antitoxin systems (FB1) and mechanism of "feeding" bacterial type 1 as feedbacks (FB7). Two steady states are observed. (<b>C</b>) Part of artificial gut with FB1 (segregation index by bacterial type 1 0.55). (<b>D</b>) Part of artificial gut with FB1 and FB7 (segregation index by bacterial type 1: in mucin layer nearby gut wall 0.89, in mucin layer nearby gut lumen 0.62; segregation index by bacterial type 2: in mucin layer nearby gut lumen 0.38; in gut lumen 0.96). (<b>E</b>) Part of artificial gut with FB1 and FB7 (segregation index by bacterial type 1 in mucin layer nearby gut wall 0.82, in mucin layer nearby gut lumen 0.54; segregation index by bacterial type 2: in mucin layer nearby gut lumen 0.46; in gut lumen 0.96)</p