Ontology highlight
ABSTRACT:
PROVIDER: PRJNA527092 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8728870_1.fastq.gz | Fastqsanger.gz | |||
| SRR8728870_2.fastq.gz | Fastqsanger.gz | |||
| SRR8728880_1.fastq.gz | Fastqsanger.gz | |||
| SRR8728880_2.fastq.gz | Fastqsanger.gz | |||
| SRR8728890_1.fastq.gz | Fastqsanger.gz |
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Project description:By means of semi-continuous experiment, the washout effect of incoming and outgoing materials and long-term accumulation of endogenous ammonia in actual anaerobic digestion plant were simulated, and the ammonia inhibition mechanism in anaerobic digestion was explored.
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Project description:Microorganisms are exposed to large variations in nutrient availability in nature. To cope with these variations and sustain growth, they maximize the utility of the available nutrients and adapt to nutritional deficiencies. We studied the transcriptional and metabolic dynamics in Saccharomyces cerevisiae in response to a gradual transition from glucose-limited growth to ammonia-limited growth under aerobic or anaerobic conditions. Through exposing yeast to a gradual increase in glucose availability, we discovered new aspects of regulation that ensured a balanced metabolism of glucose and ammonia to sustain growth. This required tight coordination of metabolism with different cellular processes. The coordinated expression of the genes involved in key cellular processes implicated the role of signaling pathways mediated by Tor1, Pka1 and Hog1. This is in contrast to the rapid increase in glucose availability, when Snf1 appeared to be the key regulator. The results presented here provide clear insight into key cellular processes that are affected by nutrient limitation and have direct implications in further studies on genome-scale regulation of metabolism. Gene expression was measured in Saccharomyces cerevisiae as it was exposed to gradualy increasing amounts of glucose under aerobic or anaerobic conditions. Eight samples were collected in each case and gene expression measured.
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Project description:Oxygen minimum zones (OMZs) are expanding due to increased sea surface temperatures, subsequent increased oxygen demand through respiration, reduced oxygen solubility, and thermal stratification driven in part by anthropogenic climate change. Devil's Hole, Bermuda is a model ecosystem to study OMZ microbial biogeochemistry because the formation and subsequent overturn of the suboxic zone occur annually. During thermally driven stratification, suboxic conditions develop, with organic matter and nutrients accumulating at depth. In this study, the bioavailability of the accumulated dissolved organic carbon (DOC) and the microbial community response to reoxygenation of suboxic waters was assessed using a simulated overturn experiment. The surface inoculated prokaryotic community responded to the deep (formerly suboxic) 0.2 μm filtrate with cell densities increasing 2.5-fold over 6 days while removing 5 μmol L<sup>-1</sup> of DOC. After 12 days, the surface community began to shift, and DOC quality became less diagenetically altered along with an increase in SAR202, a Chloroflexi that can degrade recalcitrant dissolved organic matter (DOM). Labile DOC production after 12 days coincided with an increase of <i>Nitrosopumilales,</i> a chemoautotrophic ammonia oxidizing archaea (AOA) that converts ammonia to nitrite based on the ammonia monooxygenase (<i>amoA</i>) gene copy number and nutrient data. In comparison, the inoculation of the deep anaerobic prokaryotic community into surface 0.2 μm filtrate demonstrated a die-off of 25.5% of the initial inoculum community followed by a 1.5-fold increase in cell densities over 6 days. Within 2 days, the prokaryotic community shifted from a <i>Chlorobiales</i> dominated assemblage to a surface-like heterotrophic community devoid of <i>Chlorobiales</i>. The DOM quality changed to less diagenetically altered material and coincided with an increase in the ribulose-1,5-bisphosphate carboxylase/oxygenase form I (<i>cbbL</i>) gene number followed by an influx of labile DOM. Upon reoxygenation, the deep DOM that accumulated under suboxic conditions is bioavailable to surface prokaryotes that utilize the accumulated DOC initially before switching to a community that can both produce labile DOM via chemoautotrophy and degrade the more recalcitrant DOM.
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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>
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