Peatland microbial community response to sulphate
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR498409.fastq.gz | Fastqsanger.gz | |||
| ERR499331.fastq.gz | Fastqsanger.gz | |||
| ERR499413.fastq.gz | Fastqsanger.gz | |||
| ERR499414.fastq.gz | Fastqsanger.gz | |||
| ERR499415.fastq.gz | Fastqsanger.gz |
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Project description:Sulphur is an essential macronutrient for plant growth and development. Reaching a thorough understanding of the molecular basis for changes in plant metabolism depending on the sulphur-nutritional status at the systems level will advance our basic knowledge and help target future crop improvement. Although the transcriptional responses induced by sulphate starvation have been studied in the past, knowledge of the regulation of sulphur metabolism is still fragmentary. This work focuses on the discovery of candidates for regulatory genes such as transcription factors (TFs) using M-bM-^@M-^Xomics technologies. For this purpose a short term sulphate-starvation / re-supply approach was used. ATH1 microarray studies and metabolite determinations yielded 21 TFs which responded more than 2-fold at the transcriptional level to sulphate starvation. Categorization by response behaviors under sulphate-starvation / re-supply and other nutrient starvations such as nitrate and phosphate allowed determination of whether the TF genes are specific for or common between distinct mineral nutrient depletions. Extending this co-behavior analysis to the whole transcriptome data set enabled prediction of putative downstream genes. Additionally, combinations of transcriptome and metabolome data allowed identification of relationships between TFs and downstream responses, namely, expression changes in biosynthetic genes and subsequent metabolic responses. Effect chains on glucosinolate and polyamine biosynthesis are discussed in detail. The knowledge gained from this study provides a blueprint for an integrated analysis of transcriptomics and metabolomics and application for the identification of uncharacterized genes. Arabidopsis seedlings were grown in 30 mL of sterile liquid full nutrition (FN) medium (3 mM sulphate) or 150 M-NM-<M sulphate medium. Transferring pre-grown 7-days old seedlings to a sulphate depleted medium (0 M-NM-<M sulphate) assured immediate and continued sulphate starvation during the next two days of plant cultivation. On day 9 subsets of the sulphate depleted cultures were supplied with sulphate (500 M-NM-<M) and samples taken 30 min and 3 hours after re-supply. Four time points (full nutrition (FN), plants starved for 48 h (-S), plants re-supplied with sulphate for 30 minutes (30M-bM-^@M-^Y S) and plants re-supplied with sulphate for 3 hours (3 h S)) were subjected to the microarray analysis. Two biological repetitions of each sample were analyzed.
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Project description:Sulphur is an essential macronutrient for plant growth and development. Reaching a thorough understanding of the molecular basis for changes in plant metabolism depending on the sulphur-nutritional status at the systems level will advance our basic knowledge and help target future crop improvement. Although the transcriptional responses induced by sulphate starvation have been studied in the past, knowledge of the regulation of sulphur metabolism is still fragmentary. This work focuses on the discovery of candidates for regulatory genes such as transcription factors (TFs) using ‘omics technologies. For this purpose a short term sulphate-starvation / re-supply approach was used. ATH1 microarray studies and metabolite determinations yielded 21 TFs which responded more than 2-fold at the transcriptional level to sulphate starvation. Categorization by response behaviors under sulphate-starvation / re-supply and other nutrient starvations such as nitrate and phosphate allowed determination of whether the TF genes are specific for or common between distinct mineral nutrient depletions. Extending this co-behavior analysis to the whole transcriptome data set enabled prediction of putative downstream genes. Additionally, combinations of transcriptome and metabolome data allowed identification of relationships between TFs and downstream responses, namely, expression changes in biosynthetic genes and subsequent metabolic responses. Effect chains on glucosinolate and polyamine biosynthesis are discussed in detail. The knowledge gained from this study provides a blueprint for an integrated analysis of transcriptomics and metabolomics and application for the identification of uncharacterized genes.
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