Ontology highlight
ABSTRACT:
PROVIDER: PRJNA545795 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9177010_1.fastq.gz | Fastqsanger.gz | |||
| SRR9177010_2.fastq.gz | Fastqsanger.gz | |||
| SRR9177020_1.fastq.gz | Fastqsanger.gz | |||
| SRR9177020_2.fastq.gz | Fastqsanger.gz | |||
| SRR9177001_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 42 |
Similar Datasets
Project description:To understand microbial community functional structures of activated sludge in wastewater treatment plants (WWTPs) and the effects of environmental factors on their structure, 12 activated sludge samples were collected from four WWTPs in Beijing. GeoChip 4.2 was used to determine the microbial functional genes involved in a variety of biogeochemical processes. The results showed that, for each gene category, such as egl, amyA, nir, ppx, dsrA sox and benAB, there were a number of microorganisms shared by all 12 samples, suggestive of the presence of a core microbial community in the activated sludge of four WWTPs. Variance partitioning analyses (VPA) showed that a total of 53% of microbial community variation can be explained by wastewater characteristics (25%) and operational parameters (23%), respectively. This study provided an overall picture of microbial community functional structures of activated sludge in WWTPs and discerned the linkages between microbial communities and environmental variables in WWTPs.
2014-01-15 | GSE54055 | GEO
Project description:To understand microbial community functional structures of activated sludge in wastewater treatment plants (WWTPs) and the effects of environmental factors on their structure, 12 activated sludge samples were collected from four WWTPs in Beijing. GeoChip 4.2 was used to determine the microbial functional genes involved in a variety of biogeochemical processes. The results showed that, for each gene category, such as egl, amyA, nir, ppx, dsrA sox and benAB, there were a number of microorganisms shared by all 12 samples, suggestive of the presence of a core microbial community in the activated sludge of four WWTPs. Variance partitioning analyses (VPA) showed that a total of 53% of microbial community variation can be explained by wastewater characteristics (25%) and operational parameters (23%), respectively. This study provided an overall picture of microbial community functional structures of activated sludge in WWTPs and discerned the linkages between microbial communities and environmental variables in WWTPs. Four full-scale wastewater treatment systems located in Beijing were investigated. Triplicate samples were collected in each site.
2014-01-15 | E-GEOD-54055 | biostudies-arrayexpress
Project description:Wastewater treatment plants use a variety of bioreactor types and configurations to remove organic matter and nutrients. Little is known regarding the effects of different configurations and within-plant immigration on microbial community dynamics. Previously, we found that the structure of ammonia-oxidizing bacterial (AOB) communities in a full-scale dispersed growth activated sludge bioreactor correlated strongly with levels of NO2- entering the reactor from an upstream trickling filter (Wells et al 2009). Here, to further examine this puzzling association, we profile within-plant microbial biogeography (spatial variation) and test the hypothesis that substantial microbial immigration occurs along a transect (raw influent, trickling filter biofilm, trickling filter effluent, and activated sludge) at the same full-scale wastewater treatment plant. AOB amoA gene abundance increased >30-fold between influent and trickling filter effluent concomitant with NO2- production, indicating unexpected growth and activity of AOB within the trickling filter. Nitrosomonas europaea was the dominant AOB phylotype in trickling filter biofilm and effluent, while a distinct ‘Nitrosomonas-like’ lineage dominated in activated sludge. Prior time series indicated that this ‘Nitrosomonas-like’ lineage was dominant when NO2- levels in the trickling filter effluent (i.e., activated sludge influent) were low, while N. europaea became dominant in the activated sludge when NO2- levels were high. This is consistent with the hypothesis that NO2- production may co-occur with biofilm sloughing, releasing N. europaea from the trickling filter into the activated sludge bioreactor. Phylogenetic microarray (PhyloChip) analyses revealed significant spatial variation in taxonomic diversity, including a large excess of methanogens in the trickling filter relative to activated sludge and attenuation of Enterobacteriaceae across the transect, and demonstrated transport of a highly diverse microbial community via the trickling filter effluent to the activated sludge bioreactor. Our results provide compelling evidence that substantial immigration between coupled process units occurs and may exert significant influence over microbial community dynamics within staged bioreactors.
2015-11-03 | GSE52079 | GEO
Project description:The increased urban pressures are often associated with specialization of microbial communities. Microbial communities being a critical player in the geochemical processes, makes it important to identify key environmental parameters that influence the community structure and its function.In this proect we study the influence of land use type and environmental parameters on the structure and function of microbial communities. The present study was conducted in an urban catchment, where the metal and pollutants levels are under allowable limits. The overall goal of this study is to understand the role of engineered physicochemical environment on the structure and function of microbial communities in urban storm-water canals. Microbial community structure was determined using PhyoChio (G3) Water and sediment samples were collected after a rain event from Sungei Ulu Pandan watershed of >25km2, which has two major land use types: Residential and industrial. Samples were analyzed for physicochemical variables and microbial community structure and composition. Microbial community structure was determined using PhyoChio (G3)
2014-12-22 | E-GEOD-64368 | biostudies-arrayexpress