MOESM1 of Phylogeny-structured carbohydrate metabolism across microbiomes collected from different units in wastewater treatment process

Additional file 1: Table S1. 16S/18S rRNA annotation efficiency of sludge samples collected from different process of WWTP. Table S2. Phylogenetic and functional correlation between technical and biological replicates (Pearson’s correlation coefficient). Table S3. All CAG families detected in the sludge samples. CAGs are quantified based on the number of ORFs containing the particular CAG domain. CAG families are sorted alphabetically according to their names. Table S4. Comparison of CAGs involves in lignocellulose hydrolysis between sludge system and other four plant feeding microbiota. Glycoside hydrolase (GH) families are assigned to enzyme categories based on the classification previously published [3]. Table S5. Quantification of 46 major CAG families detected based on ORFs containing the particular CAG domain. CAG families are sorted descending according to their average relative abundance across sludge samples. Table S6. Quantification of 40 major orders within the compared sludge samples based on 16S rRNA gene sequences. Orders are sorted descending according to their average relative abundance across sludge samples. Table S7. Topological properties of the co-occurrence network (positive network) of 46 major CAG families and 40 prevalent phylogenetic orders. Table S8. Observed and random co-occurring incidence within network modules. Table S9. Information of the metagenomic libraries of sludge samples and technical/biological replicates. Table S10. Statistics of assembled scaffolds from metagenome of sludge samples and technical/biological replicates. Figure S1. Illustration of the experimental design of metagenomes used for this study. Frames of technical and biological replicates are respectively filled with blue and green color. Figure S2. Rarefaction analysis of the sludge metagenomes. Figure S3. Phylogenetic orders showed significant variation (P-value< 0.05 and proportion difference > 1%) between biological replicates. Figure S4. Similarity distribution of GH-encoding ORFs to their best BLASTN hit against NCBI nr database. Left and right figure respectively shows the ORFs counts and accumulative abundance of GH-encoding ORFs from different sludge microbiomes. Figure S5. Heatmap of the most prevalent phylogenetic groups (order level) determined by the CAGs-encoding genes (a) and 16S rRNA gene sequences (b). Figure S6. Major CAG families showed significant variation (p-value < 0.05 by one-way ANOVA analysis) among sludge samples with different dissolved oxygen (left), temperature (middle) and salinity (right). Abbreviations in the figures: M: Mesophilic; A: Ambient temperature; T: Thermophilic. Figure S7. Whole network among 46 major CAG families and 40 prevalent phylogenetic orders. Nodes representing either CAG families or phylogenetic orders, are colored according to the network modules (that is clusters) determined by multi-level aggregation method (Louvain algorithm [18]). Each edge represents a strong (Spearman’s rank correlation coefficient r2 > 0.6) and significant (p-value < 0.01) correlation between node-pairs. Edges are colored according to the value of r2 with red stands for positive correlation; blue represents negative correlation. The size of each node and the font size of label is proportion to the number of connections (that is degree) of that node. And the thickness of edge is proportion to the correlation coefficient between nodes. Figure S8. Co-exclusion network (that is the negative network) among 46 major CAG families and 40 prevalent phylogenetic orders. Nodes representing either CAG families or phylogenetic orders, are colored according to the network modules (that is clusters) determined by multi-level aggregation method (Louvain algorithm [18]). Each edge representing a strong (Spearman’s rank correlation coefficient r2 > 0.6) and significant (p-value < 0.01) correlation between node-pairs, are in the same color with its source node. The size of each node and the font size of label is proportion to the number of connections (that is degree) of that node. And the thickness of edge is proportion to the correlation coefficient between nodes

Genome Reconstruction and Gene Expression of “<i>Candidatus</i> Accumulibacter phosphatis” Clade IB Performing Biological Phosphorus Removal

We report the first integrated metatranscriptomic and metagenomic analysis of enhanced biological phosphorus removal (EBPR) sludge. A draft genome of <i>Candidatus</i> Accumulibacter spp. strain HKU-1, a member of Clade IB, was retrieved. It was estimated to be ∼90% complete and shared average nucleotide identities of 83% and 88% with the finished genome CAP IIA UW-1 and the draft genome CAP IA UW-2, respectively. Different from CAP IIA UW-1, the phosphotransferase (<i>pap</i>) in polyphosphate metabolism and <i>V-ATPase</i> in orthophosphate transport were absent from CAP IB HKU-1. Additionally, unlike CAP IA UW-2, CAP IB HKU-1 carried the genes for carbon fixation and nitrogen fixation. Despite these differences, the key genes required for acetate uptake, glycolysis and polyhydroxyalkanoate (PHA) synthesis were conserved in all these Accumulibacter genomes. The preliminary metatranscriptomic results revealed that the most significantly up-regulated genes of CAP IB HKU-1 from the anaerobic to the aerobic phase were responsible for assimilatory sulfate reduction, genetic information processing and phosphorus absorption, while the down-regulated genes were related to N₂O reduction, PHA synthesis and acetyl-CoA formation. This study yielded another important Accumulibacter genome, revealed the functional difference within the Accumulibacter Type I, and uncovered the genetic responses to EBPR stimuli at a higher resolution

Structure, Variation, and Co-occurrence of Soil Microbial Communities in Abandoned Sites of a Rare Earth Elements Mine

Mining activity for rare earth elements (REEs) has caused serious environmental pollution, particularly for soil ecosystems. However, the effects of REEs on soil microbiota are still poorly understood. In this study, soils were collected from abandoned sites of a REEs mine, and the structure, diversity, and co-occurrence patterns of soil microbiota were evaluated by Illumina high-throughput sequencing targeting 16S rRNA genes. Although microbiota developed significantly along with the natural restoration, the microbial structure on the site abandoned for 10 years still significantly differed from that on the unmined site. Potential plant growth promoting bacteria (PGPB) were identified by comparing 16S sequences against a self-constructed PGPB database via BLAST, and it was found that siderophore-producing and phosphorus-solubilizing bacteria were more abundant in the studied soils than in reference soils. Canonical correspondence analysis indicated that species richness of plant community was the prime factor affecting microbial structure, followed by limiting nutrients (total carbon and total nitrogen) and REEs content. Further co-occurring network analysis revealed nonrandom assembly patterns of microbiota in the studied soils. These results increase our understanding of microbial variation and assembly pattern during natural restoration in REE contaminated soils