Abstract
sRNAs represent a powerful class of regulators that influences multiple mRNA targets in response to environmental changes. However, very few direct sRNA–sRNA interactions have been deeply studied in any organism. Zymomonas mobilis is a bacterium with unique ethanol-producing metabolic pathways in which multiple small RNAs (sRNAs) have recently been identified, some of which show differential expression in ethanol stress. In this study, we show that two sRNAs (Zms4 and Zms6) are upregulated under ethanol stress and have significant impacts on ethanol tolerance and production in Z. mobilis. We conducted multi-omics analysis (combining transcriptomics and sRNA-immunoprecipitation) to map gene networks under the influence of their regulation. We confirmed that Zms4 and Zms6 bind multiple RNA targets and regulate their expressions, influencing many downstream pathways important to ethanol tolerance and production. In particular, Zms4 and Zms6 interact with each other as well as many other sRNAs, forming a novel sRNA–sRNA direct interaction network. This study thus uncovers a sRNA network that co-orchestrates multiple ethanol related pathways through a diverse set of mRNA targets and a large number of sRNAs. To our knowledge, this study represents one of the largest sRNA–sRNA direct interactions uncovered so far.
Highlights
As global controllers of gene expression, small RNAs represent powerful tools for engineering complex phenotypes (Cho et al, 2015; Leistra et al, 2019)
Considering the natural ethanol production of Z. mobilis and the tolerance effects, we reasoned that the regulatory contribution of Zms4 and Zms6 to the high ethanol tolerance phenotype could be captured by measuring ethanol production
Two small RNAs (sRNAs), Zms4 and Zms6, that are naturally differentially expressed under ethanol stress in Z. mobilis are shown to be key to ethanol tolerance and shown to coordinate a large network of gene regulation that includes sRNA–sRNA interactions
Summary
As global controllers of gene expression, small RNAs (sRNAs) represent powerful tools for engineering complex phenotypes (Cho et al, 2015; Leistra et al, 2019) These (typically) noncoding RNAs are 5–500 nucleotide (nt) transcripts that act as regulators of protein expression, mostly by blocking translation or changing mRNA stability (Storz et al, 2011). Current approaches take advantage of RNA-seq and proteomics to determine sRNA target networks (Lalaouna and Masse, 2015; Melamed et al, 2016), a challenge remains to decouple direct vs indirect interactions Computational tools such as IntaRNA can be helpful in predicting most favorable sRNA–mRNA interactions and binding sites, in vivo conditions and competition of multiple targets for binding sites cannot be accounted for (Busch et al, 2008). The mapping of sRNA interfaces that could be available in vivo for interactions has been useful to determine biologically relevant mRNA targets (Vazquez-Anderson et al, 2017; Mihailovic et al, 2018)
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