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ABSTRACT:
PROVIDER: PRJNA841171 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Anaerobic digestion for simultaneous sewage sludge treatment and syngas biomethanation: process performance and microbial ecology
| PRJDB2219 | ENA
Project description:Anaerobic digestion is a popular and effective microbial process for waste treatment. The performance of anaerobic digestion processes is contingent on the balance of the microbial food web in utilizing various substrates. Recently, co-digestion, i.e., supplementing the primary substrate with an organic-rich co-substrate has been exploited to improve waste treatment efficiency. Yet the potential effects of elevated organic loading on microbial functional gene community remains elusive. In this study, functional gene array (GeoChip 5.0) was used to assess the response of microbial community to the addition of poultry waste in anaerobic digesters treating dairy manure. Consistent with 16S rRNA gene sequences data, GeoChip data showed that microbial community compositions were significantly shifted in favor of copiotrophic populations by co-digestion, as taxa with higher rRNA gene copy number such as Bacilli were enriched. The acetoclastic methanogen Methanosarcina was also enriched, while Methanosaeta was unaltered but more abundant than Methanosarcina throughout the study period. The microbial functional diversity involved in anaerobic digestion were also increased under co-digestion.
2017-01-12 | GSE93419 | GEO
Project description:An innovative bioelectrochemical-anaerobic digestion-coupled system for counteracting ammonia toxicity by in-situ ammonia recovery: process performance and microbial ecology
| PRJDB2641 | ENA
Project description:The anaerobic digestion microbiomes has been puzzling us since the dawn of molecular methods for mixed microbial community analysis. Monitoring of the anaerobic digestion microbiome can either take place via a holistic evaluation of the microbial community through fingerprinting or by targeted monitoring of selected taxa. Here, we compared four different microbial community fingerprinting methods, i.e., amplicon sequencing, metaproteomics, metabolomics and phenotypics, in their ability to reflect the full-scale anaerobic digestion microbiome. The phenotypic fingerprinting reflects a, for anaerobic digestion, novel, single cell-based approach of direct microbial community fingerprinting. Three different digester types, i.e., sludge digesters, digesters treating agro-industrial waste and dry anaerobic digesters reflected different operational parameters. The α-diversity analysis yielded inconsistent results, especially for richness, across the different methods. In contrast, β-diversity analysis resulted in comparable profiles, even when translated into phyla or functions, with clear separation of the three digester types. In-depth analysis of each method's features i.e., operational taxonomic units, metaproteins, metabolites, and phenotypic traits, yielded certain similar features yet, also some clear differences between the different methods, which was related to the complexity of the anaerobic digestion process. In conclusion, phenotypic fingerprinting is a reliable, fast method for holistic monitoring of the anaerobic digestion microbiome, and the complementary identification of key features through other methods could give rise to a direct interpretation of anaerobic digestion process performance.
2021-05-26 | PXD024788 | Pride
Project description:In-Situ Biogas Upgrading with Pulse H2 Additions: Microbial Adaptation and Reactor Performance of Anaerobic Digestion
| PRJEB16929 | ENA
Project description:Simultaneous coke oven gas biomethanation and in-situ biogas upgrading in anaerobic reactor
| PRJDB1843 | ENA
Project description:In order to identify metabolites descriptive of alterations of the working themperature during the process of anaerobic digestion, we performed untargeted metabolomics on samples of sewage sludge collected from two reactors working in parallel but with different temperature settings.
2021-11-26 | ST002004 | MetabolomicsWorkbench