Microbial Community Shift and Role of Bacteria in Rapid Granulation By Using Diatomite

Aerobic granular sludge (AGS) technology is a promising biological method for modern wastewater treatment. However, granulation time have become a major issue for the application of AGS technology especially in low strength wastewater. Recent studies on granulation are focusing towards rapid start-up granulation process. Diatomite, a friable light-coloured sedimentary rock was introduces in this study to enhanced the granules formation. This study highlight the effect of diatomite towards the microbial community during the transformation of seed sludge until development of granules. DNA extraction and Metagenomic analysis was conducted with three samples (seed sludge, control AGS, AGS diatomite) to compare the microbial community. The microbial community analysis revealed the alpha diversity, phylum and class level, and the abundance of EPS producing bacteria of each bacteria samples respectively. Diatomite has a significant influence towards the microbial diversity (High Shannon index alpha diversity). Also, diatomite promotes the abundance of functional bacteria especially EPS producing bacteria, which seen as a crucial elements in granulation process.

Coastal Hypoxia Drives Microbial Diversity: Elucidation through 16S rRNA Amplicon Sequencing

The formation of oxygen-depleted zones in the bottom waters is one of the most widespread phenomena in coastal areas. Upwelling episodes occurring along the west coast of India due to the southwest monsoon lead to an increase in biological productivity which further lowers the dissolved oxygen in the upwelled waters, which intensifies annually between June and October. Here, we have determined the changes in the microbial community in response to the varying oxygen levels and other physicochemical parameters at the Candolim Time Series Station using high-throughput sequencing. Amplicon Sequence Variants across all the samples collected in different seasons were mostly affiliated to the phyla Proteobacteria, Actinobacteria, Bacteroidetes, Verrucomicrobia, Chloroflexi, Firmicutes and Planctomycetes, with the most dominant being Proteobacteria (21-41%). Statistical analysis revealed that microbial diversity differed significantly with changing DO, ammonia, nitrate and nitrite concentrations during different seasons. The microbial community shift due to seasonal hypoxia results in the differential biogeochemical cycling of essential nutrients with certain years seeing redox conditions up to sulphate reduction, while certain years seeing only nitrogen loss. Future scenario of global warming will serve as a big challenge for understanding the role of microbial diversity and its implications in the cycling of natural elements.

Rhizobium Symbiotic Capacity Shapes Root-Associated Microbiomes in Soybean

Root-microbiome interactions are of central importance for plant performance and yield. A distinctive feature of legumes is that they engage in symbiosis with N2-fixing rhizobia. If and how the rhizobial symbiotic capacity modulates root-associated microbiomes are still not yet well understood. We determined root-associated microbiomes of soybean inoculated with wild type (WT) or a noeI mutant of Bradyrhizobium diazoefficiens USDA 110 by amplicon sequencing. UPLC-MS/MS was used to analyze root exudates. The noeI gene is responsible for fucose-methylation of Nod factor secreted by USDA 110 WT strain. Soybean roots inoculated with the noeI mutant showed a significant decrease in nodulation and root-flavonoid exudation compared to roots inoculated with WT strain. The noeI mutant-inoculated roots exhibited strong changes in microbiome assembly in the rhizosphere and rhizoplane, including reduced diversity, changed co-occurrence interactions and a substantial depletion of root microbes. Root exudates and soil physiochemical properties were significantly correlated with microbial community shift in the rhizosphere between different rhizobial treatments. These results illustrate that rhizobial symbiotic capacity dramatically alters root-associated microbiomes, in which root exudation and edaphic patterns play a vital role. This study has important implications for understanding the evolution of plant-microbiome interactions.

Soil eukaryote community shift but not composition is consistently recovered by different OTU inference methods applied to long read metabarcoding data

Long amplicon metabarcoding has opened the door for phylogenetic analysis of the largely unknown communites of microeukaryotes in soil. Here, we amplified and sequenced the ITS and LSU regions of the rDNA operon (around 1500 bp) from grassland soils using PacBio SMRT sequencing and evaluated the performance of three different methods for generation of operational taxonomic units (OTUs). The field site at Kungsängen Nature Reserve has drawn frequent visitors since Linnaeus’s time, and its species rich vegetation includes the largest population of Fritillaria meleagris in Sweden. To test the effect of different OTU generation methods, we sampled soils across an abrupt moisture transition that divides the meadow community into a Carex acuta dominated plant community with low species richness in the wetter part, which is visually distinct from the mesic-dry part that has a species rich grass-dominated plant community including a high frequency of F. meleagris. We used the moisture and plant community transition as a framework to investigare how detected belowground microeukaryotic community composition was influenced by OTU generation methods. Soil communities in both moisture regimes were dominated by protists, a large fraction of which were taxonomically assigned to Ciliophora (Alveolata) while 30-40% of all reads were assigned to kingdom Fungi. Ecological patterns were consistently recovered irrespective of OTU generation method used. However, different methods strongly affect richness estimates and the taxonomic and phylogenetic resolution of the characterized community with implications for how well members of the microeukaryotic communities can be recognized in the data.