bioRxivMicrobiology
Structure-seq2 probing of RNA structure upon amino acid starvation reveals both known and novel RNA switches in Bacillus subtilis
L. E. Ritchey, D. C. Tack, H. Yakhnin, E. A. Jolley, S. M. Assmann, P. C. Bevilacqua, P. Babitzke
Abstract
RNA structure influences numerous processes in all organisms. In bacteria, these processes include transcription termination and attenuation, small RNA and protein binding, translation initiation, and mRNA stability, and can be regulated via metabolite availability and other stresses. Here we use Structure-seq2 to probe the in vivo RNA structurome of Bacillus subtilis grown in the presence and absence of amino acids. Our results reveal that amino acid starvation results in lower overall dimethyl sulfate (DMS) reactivity of the transcriptome, indicating enhanced protection owing to protein binding or RNA structure. Starvation-induced changes in DMS reactivity correlated inversely with transcript abundance changes. This correlation was particularly pronounced in genes associated with the stringent response and CodY regulons, which are involved in adaptation to nutritional stress, suggesting that RNA structure contributes to transcript abundance change in regulons involved in amino acid metabolism. Structure-seq2 accurately reported on four known amino acid-responsive riboswitches: T-box, SAM, glycine, and lysine riboswitches. Additionally, we discovered a transcription attenuation mechanism that reduces yfmG expression when amino acids are added to the growth medium. We also found that translation of a leader peptide (YfmH) encoded just upstream of yfmG regulates yfmG expression. Our results are consistent with a model in which a slow rate of yfmH translation caused by limitation of the amino acids encoded in YfmH prevents transcription termination in the yfmG leader region by favoring formation of an overlapping antiterminator structure. This novel RNA switch offers a way to simultaneously monitor the levels of multiple amino acids.
- organism: bacteria (other)
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: deep learning
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