bioRxivMicrobiology
The adaptive response to long term nitrogen starvation in Escherichia coli requires the breakdown of allantoin
Abstract
Bacteria initially respond to nutrient starvation by eliciting large-scale transcriptional changes. The accompanying changes in gene expression and metabolism allow the bacterial cells to effectively adapt to the nutrient starved state. How the transcriptome subsequently changes as nutrient starvation ensues is not well understood. We used nitrogen (N) starvation as a model nutrient starvation condition to study the transcriptional changes in Escherichia coli experiencing long-term N starvation. The results reveal that the transcriptome of N starved E. coli undergoes changes that are required to maximise chances of viability and to effectively recover growth when N starvation conditions become alleviated. We further reveal that, over time, N starved E. coli cells rely on the degradation of allantoin for optimal growth recovery when N becomes replenished. This study provides insights into the temporally coordinated adaptive responses that occur in E. coli experiencing sustained N starvation. IMPORTANCEBacteria in their natural environments seldom encounter conditions that support continuous growth. Hence, many bacteria spend the majority of their time in states of little or no growth due to starvation of essential nutrients. To cope with prolonged periods of nutrient starvation, bacteria have evolved several strategies, primarily manifesting themselves through changes in how the information in their genes is accessed. How these coping strategies change over time under nutrient starvation is not well understood and this knowledge is not only important to broaden our understanding of bacterial cell function, but also to potentially find ways to manage harmful bacteria. This study provides insights into how nitrogen starved Escherichia coli bacteria rely on different genes during long term nitrogen starvation.
- organism: E. coli
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
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