Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1092678 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR28476201_1.fastq.gz | Fastqsanger.gz | |||
| SRR28476201_2.fastq.gz | Fastqsanger.gz | |||
| SRR28476202_1.fastq.gz | Fastqsanger.gz | |||
| SRR28476202_2.fastq.gz | Fastqsanger.gz | |||
| SRR28476203_1.fastq.gz | Fastqsanger.gz |
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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:Industrial anaerobic digestion (AD) represents a relevant energy source beyond today’s fossil fuels, wherein organic matter is recycled to methane gas via an intricate and complex microbial food web. Despite its potential, anaerobic reactors often undergo process instability over time, mainly caused by substrate composition perturbations, making the system unreliable for stable energy production. To ensure the reliability of AD technologies, it is crucial to identify microbial- and system responses to better understand the effect of such perturbations and ultimately detect signatures indicative of process failure . Here, we investigate the effect of microalgal organic loading rate (OLR) on the fermentation products profile, microbiome dynamics, and disruption/recovery of major microbial metabolisms. Reactors subjected to low- and high-OLR disturbances were operated and monitored for fermentation products and biogas production over time, while microbial responses were investigated via 16S rRNA gene amplicon data, shotgun metagenomics and metagenome-centric metaproteomics.
2025-02-21 | PXD048315 | Pride
Project description:Two-stage two-phase biogas reactor systems consisting each of one batch downflow hydrolysis reactor (HR, vol. 10 L), one process fluid storage tank (vol. 10 L), and one downstream upflow anaerobic filter reactor (AF, vol. 10 L), were operated at mesophilic (M, 37 °C) and thermophilic (T, 55 °C) temperatures and over a period of > 750 d (Figure 1, Additional file 1). For each reactor system and for each process temperature, two replicates were conducted in parallel, denominated further as biological replicates. Further process details were as previously published. Start-up of all fermenters were performed using liquid fermenter material from a biogas plant converting cattle manure in co-digestion with grass and maize silage and other biomass at varying concentrations and at mesophilic temperatures. Silage of perennial ryegrass (Lolium perenne L.) was digested as sole substrate in batches of varying amounts with retention times of 28 d (storage of bale silage at -20 °C, cutting length 3 cm, volatile substances (VS) 32 % of fresh mass (FM), total Kjeldahl nitrogen 7.6 g kgFM-1, NH4+-N 0.7 g kgFM-1, acetic acid 2.6 g kgFM-1, propionic acid < 0.04 g kgFM-1, lactic acid 2.6 g kgFM-1, ethanol 2.2 g kgFM-1, C/N ratio 19.3, chemical oxygen demand (COD) 357.7 g kgFM-1, analysis of chemical properties according to [6]. No spoilage was observed in the silage. Biogas yields were calculated as liters normalized to 0 °C and 1013 hPa (LN) per kilogram volatile substances (kgVS). For chemical analysis, samples were taken from the effluents of HR and AF. For sequencing of 16S rRNA gene amplicon libraries, microbial metagenomes, and microbial metatranscriptomes, samples were taken from the silage digestate in the HR digested for 2 d. At this time point, high AD rates were detected as indicated by the fast increase of volatile fatty acids (VFA), e.g., acetic acid. Sampling was performed at two different organic loading rates (OLR), i.e., batch-fermentation of 500 g (denominated as “low OLR”, samples MOLR500 and TOLR500) and 1,500 g silage (denominated as “increased OLR”, samples MOLR1500 and TOLR1500).
2019-04-19 | E-MTAB-7533 | biostudies-arrayexpress
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