Image_1_Gut Microbiota in Tibetan Herdsmen Reflects the Degree of Urbanization.PDF

<p>Urbanization is associated with shifts in human lifestyles, thus possibly influencing the diversity, interaction and assembly of gut microbiota. However, the question regarding how human gut microbiota adapts to varying lifestyles remains elusive. To understand the relationship between gut microbiota and urbanization, we compared the diversity, interaction and assembly of gut microbial communities of herdsmen from three regions with different levels of urbanization, namely traditional herdsmen (TH), semi-urban herdsmen (SUH) and urban herdsmen (UH). The relative abundance of Prevotella decreased with the degree of urbanization (from TH to UH), whereas that of Bacteroides, Faecalibacterium, and Blautia showed an opposite trend. Although the alpha diversity measures (observed OTUs and phylogenetic diversity) of gut microbiota were unaffected by urbanization, the beta diversity (Jaccard or Bray–Curtis distances) was significantly influenced by urbanization. Metagenome prediction revealed that the gene functions associated with metabolism (i.e., carbohydrate and lipid metabolism) had significant differences between TH and UH. Network analysis showed that the modularity increased with the degree of urbanization, indicating a high extent of niche differentiation in UH. Meanwhile the trend of network density was opposite, indicating a more complex network in TH. Notably, the relative importance of environmental filtering that governed the community assembly increased with the degree of urbanization, which indicated that deterministic factors (e.g., low-fiber diet) play more important roles than stochastic factors (e.g., stochastic dispersal) in shaping the gut microbiota. A quantification of ecological processes showed a stronger signal of variable selection in UH than TH, implying that different selective pressures cause divergent gut community compositions due to urban lifestyles. Our results suggest that beta diversity, network interactions and ecological processes of gut microbiota may reflect the degree of urbanization, and highlight the adaptation of human gut microbiota to lifestyle changes.</p

Image_2_Gut Microbiota in Tibetan Herdsmen Reflects the Degree of Urbanization.PDF

<p>Urbanization is associated with shifts in human lifestyles, thus possibly influencing the diversity, interaction and assembly of gut microbiota. However, the question regarding how human gut microbiota adapts to varying lifestyles remains elusive. To understand the relationship between gut microbiota and urbanization, we compared the diversity, interaction and assembly of gut microbial communities of herdsmen from three regions with different levels of urbanization, namely traditional herdsmen (TH), semi-urban herdsmen (SUH) and urban herdsmen (UH). The relative abundance of Prevotella decreased with the degree of urbanization (from TH to UH), whereas that of Bacteroides, Faecalibacterium, and Blautia showed an opposite trend. Although the alpha diversity measures (observed OTUs and phylogenetic diversity) of gut microbiota were unaffected by urbanization, the beta diversity (Jaccard or Bray–Curtis distances) was significantly influenced by urbanization. Metagenome prediction revealed that the gene functions associated with metabolism (i.e., carbohydrate and lipid metabolism) had significant differences between TH and UH. Network analysis showed that the modularity increased with the degree of urbanization, indicating a high extent of niche differentiation in UH. Meanwhile the trend of network density was opposite, indicating a more complex network in TH. Notably, the relative importance of environmental filtering that governed the community assembly increased with the degree of urbanization, which indicated that deterministic factors (e.g., low-fiber diet) play more important roles than stochastic factors (e.g., stochastic dispersal) in shaping the gut microbiota. A quantification of ecological processes showed a stronger signal of variable selection in UH than TH, implying that different selective pressures cause divergent gut community compositions due to urban lifestyles. Our results suggest that beta diversity, network interactions and ecological processes of gut microbiota may reflect the degree of urbanization, and highlight the adaptation of human gut microbiota to lifestyle changes.</p

Table_2_Gut Microbiota in Tibetan Herdsmen Reflects the Degree of Urbanization.PDF

<p>Urbanization is associated with shifts in human lifestyles, thus possibly influencing the diversity, interaction and assembly of gut microbiota. However, the question regarding how human gut microbiota adapts to varying lifestyles remains elusive. To understand the relationship between gut microbiota and urbanization, we compared the diversity, interaction and assembly of gut microbial communities of herdsmen from three regions with different levels of urbanization, namely traditional herdsmen (TH), semi-urban herdsmen (SUH) and urban herdsmen (UH). The relative abundance of Prevotella decreased with the degree of urbanization (from TH to UH), whereas that of Bacteroides, Faecalibacterium, and Blautia showed an opposite trend. Although the alpha diversity measures (observed OTUs and phylogenetic diversity) of gut microbiota were unaffected by urbanization, the beta diversity (Jaccard or Bray–Curtis distances) was significantly influenced by urbanization. Metagenome prediction revealed that the gene functions associated with metabolism (i.e., carbohydrate and lipid metabolism) had significant differences between TH and UH. Network analysis showed that the modularity increased with the degree of urbanization, indicating a high extent of niche differentiation in UH. Meanwhile the trend of network density was opposite, indicating a more complex network in TH. Notably, the relative importance of environmental filtering that governed the community assembly increased with the degree of urbanization, which indicated that deterministic factors (e.g., low-fiber diet) play more important roles than stochastic factors (e.g., stochastic dispersal) in shaping the gut microbiota. A quantification of ecological processes showed a stronger signal of variable selection in UH than TH, implying that different selective pressures cause divergent gut community compositions due to urban lifestyles. Our results suggest that beta diversity, network interactions and ecological processes of gut microbiota may reflect the degree of urbanization, and highlight the adaptation of human gut microbiota to lifestyle changes.</p

Table_1_Gut Microbiota in Tibetan Herdsmen Reflects the Degree of Urbanization.PDF

<p>Urbanization is associated with shifts in human lifestyles, thus possibly influencing the diversity, interaction and assembly of gut microbiota. However, the question regarding how human gut microbiota adapts to varying lifestyles remains elusive. To understand the relationship between gut microbiota and urbanization, we compared the diversity, interaction and assembly of gut microbial communities of herdsmen from three regions with different levels of urbanization, namely traditional herdsmen (TH), semi-urban herdsmen (SUH) and urban herdsmen (UH). The relative abundance of Prevotella decreased with the degree of urbanization (from TH to UH), whereas that of Bacteroides, Faecalibacterium, and Blautia showed an opposite trend. Although the alpha diversity measures (observed OTUs and phylogenetic diversity) of gut microbiota were unaffected by urbanization, the beta diversity (Jaccard or Bray–Curtis distances) was significantly influenced by urbanization. Metagenome prediction revealed that the gene functions associated with metabolism (i.e., carbohydrate and lipid metabolism) had significant differences between TH and UH. Network analysis showed that the modularity increased with the degree of urbanization, indicating a high extent of niche differentiation in UH. Meanwhile the trend of network density was opposite, indicating a more complex network in TH. Notably, the relative importance of environmental filtering that governed the community assembly increased with the degree of urbanization, which indicated that deterministic factors (e.g., low-fiber diet) play more important roles than stochastic factors (e.g., stochastic dispersal) in shaping the gut microbiota. A quantification of ecological processes showed a stronger signal of variable selection in UH than TH, implying that different selective pressures cause divergent gut community compositions due to urban lifestyles. Our results suggest that beta diversity, network interactions and ecological processes of gut microbiota may reflect the degree of urbanization, and highlight the adaptation of human gut microbiota to lifestyle changes.</p

MOESM1 of The core populations and co-occurrence patterns of prokaryotic communities in household biogas digesters

Additional file 1: Figure S1. PCoA score plot based on weighted UniFrac metrics colored by (A) locations, and (B) substrates. P: swine manure; B: cattle manure; H: human manure; C: poultry manure; E: donkey manure; G: grass. Figure S2. Rarefaction curve of observed OTUs before re-sampling. Figure S3. Relationships between (A) NH4 +-N concentration and the relative abundance of Euryarchaeota in Cluster II, (B) the relative abundance of Clostridium and that of Euryarchaeota, and (C) the relative abundance of Bacteroidetes and that of Spirochaetes in all samples. Figure S4. Networks of co-occurring prokaryotic OTUs in (A) Cluster I and (B) Cluster II based on correlation analysis. OTUs were colored by modularity class with labeled genera names. A connection stands for a strong (Spearman’s ρ > 0.6) and significant (p < 0.01) correlation. For each panel, the size of each node is proportional to the number of connections (degree); the thickness of each connection between two nodes (edge) is proportional to the value of Spearman’s correlation coefficients ranging from 0.60 to 0.95. Ca.: Candidatus. Figure S5. Number of shared nodes (OTUs) among networks AS, C1, and C2. Figure S6. Relationships among functional modules of prokaryotic communities of (A) Cluster I and (B) Cluster II. The shapes of each module represent the main function of the module. The color of each module represents the correlation between the module and NH4 +-N concentration: black, positive correlation (p < 0.05); white, negative correlation (p < 0.05); grey, no significant correlation. The thickness of each solid line between modules is proportional to the sum of positive Spearman’s ρ between them in the networks (C1 and C2) ranging from 0.6 to 18.5; a dotted line represents over 10 couples of OTUs with significant negative correlations (Spearman’s ρ < −0.6, p < 0.01) between modules

Additional file 2: Table S1. of The process-related dynamics of microbial community during a simulated fermentation of Chinese strong-flavored liquor

Good coverages of prokaryotic Sequencing (16S rRNA gene). Table S2. Good coverages of eukaryotic Sequencing (ITS gene). Table S3. The OTU BLAST result based on 16S rRNA gene. Table S4. The OTU BLAST result based on ITS region. (PDF 309 kb

MOESM2 of The core populations and co-occurrence patterns of prokaryotic communities in household biogas digesters

Additional file 2: Table S1. Prokaryotic diversity indices based on 97 % identity of 16S rRNA gene sequences and 2230 reads per sample. Table S2. The relative contributions (R square value) of each environmental factor to OTUs in all samples, Cluster I and II based on RDA analysis. Table S3. Relative abundances of core genera/OTUs in all 43 samples and sub-core genera/OTUs in Cluster I and II, and their correlation to environmental factors. Table S4. Relative abundances of the abundant phyla (average relative abundance >0.1%) and genera (average relative abundance >0.05 %). Table S5. Pearson’s correlation of abundant phyla and genera to environmental factors in Cluster I and II. Table S6. Pearson’s correlation of abundant methanogens to environmental factors in all samples. Table S7. Topological properties of co-occurring networks AS (43 samples), C1 (27 samples in Cluster I), and C2 (16 samples in Cluster II), generated with Gephi software. Table S8. Node information of 103 cosmopolitan OTUs in the network C1 (Cluster I). Table S9. Positive and negative interactions among modules in network AS, C1, and C2. Table S10. Node information of 206 cosmopolitan OTUs in the network C2 (Cluster II). Table S11. Node information of 110 cosmopolitan OTUs in the network AS (all samples). Table S12. Fermentation conditions and chemical properties in biogas digesters

PM10 concentration comparison between observation and simulation at different heights from 11:13 MDT to 13:13 MDT on April 1, 2008 (MDT: Mountain Daylight Time).

<p>The concentration of the simulation at each height was calculated for the downwind plume area that intersected with the concentration of observation. Heights were from 200 to 550 m. The lables on the graph are heights following number of compared data points (e.g., 350 m, 14: The number of compared data points (number of corresponding observed data points to a compared simulation data point), was 6 at 450 m).</p

MOESM1 of Improvement of n-caproic acid production with Ruminococcaceae bacterium CPB6: selection of electron acceptors and carbon sources and optimization of the culture medium

Additional file 1: Table S1. Full factorial experimental design matrix and results. Table S2. Modle coefficients on CA production estimated by fractional factorial experimental design. Table S3. Steepest ascent experimental design matrix and results. Table S4. Analysis of variance (ANOVA) of the Box–Behnken design on CA production

MOESM2 of Production of high-concentration n-caproic acid from lactate through fermentation using a newly isolated Ruminococcaceae bacterium CPB6

Additional file 2: Figure S1. SEM image of Ruminococcaceae bacterium CPB6. Figure S2. Effects of pH and temperature on strain CPB6 growth and its influence on caproate production rate at 96 h: growth A (pH) and B (temperature); caproate production rate: C (pH) and D (temperature). Figure S3. pH values before and after the reaction. Figure S4. Caproic acid (CA) recovery from lactate-containing wastewater by strain CPB6 under sterilized conditions. The fermentation was started using the mixed wastewater as described in the Materials and Methods section. When the wastewater was depleted, additional wastewater was added to support the further CA production. Figure S5. Fermentation profiles of strain CPB6 when acetate (A) and butyrate (B) was used as the sole carbon source (without lactate provided)