Ontology highlight
ABSTRACT:
PROVIDER: PRJNA606421 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Population dynamics of methanogenic genera was investigated in pilot anaerobic digesters. Cattle manure and two-phase olive mill wastes were codigested at a 3:1 ratio in two reactors operated at 37 ï¾°C and 55 ï¾°C. Other two reactors were run with either residue at 37 ï¾°C. Sludge DNA extracted from samples taken from all four reactors on days 4, 14 and 28 of digestion was used for hybridisation with the AnaeroChip, an oligonucleotide microarray targeting those groups of methanogenic archaea that are commonly found under mesophilic and thermophilic conditions (Franke-Whittle et al. 2009, in press, doi:10.1016/j.mimet.2009.09.017).
2010-11-10 | E-MEXP-2453 | biostudies-arrayexpress
Project description:Metaviromes from batch mesophilic anaerobic digestion reactors, raw sequence reads
| PRJNA1134235 | ENA
Project description:Proteinaceous substrates lead to phenyl acid formation during mesophilic AD
| PRJNA564060 | ENA
Project description:Biogas batch reactors, mesophilic Targeted Locus (Loci)
| PRJNA264180 | ENA
Project description:Caldicellulosiruptor bescii is an anaerobic hyper thermophile that can utilize a wide range of substrates. However, inhibitors released from biomass can result in unfavorable growth conditions and limit bioconversion to products. Medium as well as intracellular pH are conditions critical for growth and prone to change in effect of fermentation end or by products such as, CO2, organic acids etc. Growth pH for C. bescii as currently reported is a narrow range of 6.8-7.3. In this study, we examined the physiological and systems level responses of C. bescii to growth at acidic pH. Samples collected from bottles, controlled batch, fed-batch and chemostat systems were subjected to growth, product and integrated omics profiling. It was discovered that in batch reactors, lowering pH from 7.2 to 6.0 at the mid-log phase, led to a significant increase in growth and product yields. Time course transcriptomics data collected from these batch reactors was analyzed to try and get a better understanding of the underlying mechanisms for improved growth.
2020-06-19 | GSE109442 | GEO
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:<p>The impact of ammonia on anaerobic digestion performance and microbial dynamics has been extensively studied, but the concurrent effect of anions brought by ammonium salt should not be neglected. This paper studied this effect using metabolomics and a time-course statistical framework. Metabolomics provides novel perspectives to study microbial processes and facilitates a more profound understanding at the metabolic level. The advanced statistical framework enables deciphering the complexity of large metabolomics data sets. More specifically, a series of lab-scale batch reactors were set up with different ammonia sources added. Samples of 9 time points over the degradation were analyzed with liquid chromatography-mass spectrometry. A filtering procedure was applied to select the promising metabolomic peaks from 1262 peaks, followed by modeling their intensities across time. The metabolomic peaks with similar time profiles were clustered, evidencing the correlation of different biological processes. Differential analysis was performed to seek the differences in metabolite dynamics caused by different anions. Finally, tandem mass spectrometry and metabolite annotation provided further information on the molecular structure and possible metabolic pathways. For example, the consumption of 5-aminovaleric acid, a short-chain fatty acid obtained from l-lysine degradation, was slowed down by phosphates. Overall, by investigating the effect of anions on anaerobic digestion, our study demonstrated the effectiveness of metabolomics in providing detailed information in a set of samples from different experimental conditions. With the statistical framework, the approach enables capturing subtle differences in metabolite dynamics between samples while accounting for the differences caused by time variations.</p>
2024-03-01 | MTBLS7859 | MetaboLights
Project description:Anaerobic digestion laboratory scale reactors
| PRJEB15367 | ENA