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

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

Data_Sheet_1.docx

<p>Ecological understandings of soil bacterial community succession and assembly mechanism along elevational gradients in mountains remain not well understood. Here, by employing the high-throughput sequencing technique, we systematically examined soil bacterial diversity patterns, the driving factors, and community assembly mechanisms along the elevational gradients of 1800–4100 m on Gongga Mountain in China. Soil bacterial diversity showed an extraordinary stair-step pattern along the elevational gradients. There was an abrupt decrease of bacterial diversity between 2600 and 2800 m, while no significant change at either lower (1800–2600 m) or higher (2800–4100 m) elevations, which coincided with the variation in soil pH. In addition, the community structure differed significantly between the lower and higher elevations, which could be primarily attributed to shifts in soil pH and vegetation types. Although there was no direct effect of MAP and MAT on bacterial community structure, our partial least squares path modeling analysis indicated that bacterial communities were indirectly influenced by climate via the effect on vegetation and the derived effect on soil properties. As for bacterial community assembly mechanisms, the null model analysis suggested that environmental filtering played an overwhelming role in the assembly of bacterial communities in this region. In addition, variation partition analysis indicated that, at lower elevations, environmental attributes explained much larger fraction of the β-deviation than spatial attributes, while spatial attributes increased their contributions at higher elevations. Our results highlight the importance of environmental filtering, as well as elevation-related spatial attributes in structuring soil bacterial communities in mountain ecosystems.</p

Functional Potential of Soil Microbial Communities in the Maize Rhizosphere

<div><p>Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Here, we identified important functional genes that characterize the rhizosphere microbial community to understand metabolic capabilities in the maize rhizosphere using the GeoChip-based functional gene array method. Significant differences in functional gene structure were apparent between rhizosphere and bulk soil microbial communities. Approximately half of the detected gene families were significantly (p<0.05) increased in the rhizosphere. Based on the detected <i>gyrB</i> genes, Gammaproteobacteria, Betaproteobacteria, Firmicutes, Bacteroidetes and Cyanobacteria were most enriched in the rhizosphere compared to those in the bulk soil. The rhizosphere niche also supported greater functional diversity in catabolic pathways. The maize rhizosphere had significantly enriched genes involved in carbon fixation and degradation (especially for hemicelluloses, aromatics and lignin), nitrogen fixation, ammonification, denitrification, polyphosphate biosynthesis and degradation, sulfur reduction and oxidation. This research demonstrates that the maize rhizosphere is a hotspot of genes, mostly originating from dominant soil microbial groups such as Proteobacteria, providing functional capacity for the transformation of labile and recalcitrant organic C, N, P and S compounds.</p></div

Normalized signal intensity of genes involved in (a) carbon fixation, (b) carbon degradation pathways in the rhizosphere and bulk soil.

<p>All data are presented as means ± standard errors (n = 3). *p<i><</i>0.05, and **p<i><</i>0.01.</p

Differences in the abundance of N cycling genes in the rhizosphere and bulk soil.

<p>The numbers in brackets indicate the percentage difference of a functional gene signal intensity between rhizosphere and bulk soil samples relative to the normalized signal intensity in the bulk soil sample. The gray-colored genes were not detected by GeoChip 3.0. *p<i><</i>0.05, and **p<i><</i>0.01.</p

Soil carbon and nitrogen content (%) and microbial diversity indices based on 454-pyrosequencing and GeoChip data in the rhizosphere and bulk soils.

<p>a) Pyrosequencing data are from Li et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112609#pone.0112609-Li1" target="_blank">[5]</a>. Samples were collected from the same field site in different years.</p><p>b) The value is the average of triplicate samples ± standard deviation.</p><p>*p<0.05,</p><p>**p<0.01.</p><p>Soil carbon and nitrogen content (%) and microbial diversity indices based on 454-pyrosequencing and GeoChip data in the rhizosphere and bulk soils.</p

Normalized signal intensity of antibiotic resistance genes in the rhizosphere and bulk soil.

<p>All data are presented as means ± standard errors (n = 3). *p<i><</i>0.05, and **p<i><</i>0.01.</p

Normalized signal intensity of <i>gyrB</i> genes derived from different phylogenetic groups in the rhizosphere and bulk soil.

<p>All data are presented as means ± standard errors (n = 3). *p<i><</i>0.05, and **p<i><</i>0.01.</p

Phylogenetic tree of the putative GH5 proteins encoded by genes from the BAC library.

<p>The tree was constructed from 300 amino acid sequences using Mage 3.0 software. ACX75523 (GH45), an endoglucanase from <i>Fibrobacter succinogenes</i>, was used as the outgroup. Names of the organisms from which sequences are derived are given. Sequences with an “Uc” prefix refer to uncultured clones, and those with “contig” are from this study. Proteins with sequences in boldface were overexpressed and characterized in this study. Cluster affiliation of glycoside hydrolase families are given on the right, and the GenBank accession numbers follow the sequence names. Numbers at the cluster nodes are the supporting percentages of bootstrap evaluation. Bar, 20% sequence divergence.</p