Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression

Recent
advances in our understanding of RNA folding and functions
have facilitated the use of regulatory RNAs such as synthetic antisense
RNAs (asRNAs) to modulate gene expression. However, despite the simple
and universal complementarity rule, predictable asRNA-mediated repression
is still challenging due to the intrinsic complexity of native asRNA-mediated
gene regulation. To address this issue, we present a multivariate
model, based on the change in free energy of complex formation (ΔGCF) and percent mismatch of the target binding
region, which can predict synthetic asRNA-mediated repression efficiency
in diverse contexts. First, 69 asRNAs that bind to multiple target
mRNAs were designed and tested to create the predictive model. Second,
we showed that the same model is effective predicting repression of
target genes in both plasmids and chromosomes. Third, using our model,
we designed asRNAs that simultaneously modulated expression of a toxin
and its antitoxin to demonstrate tunable control of cell growth. Fourth,
we tested and validated the same model in two different biotechnologically
important organisms: Escherichia coli Nissle 1917
and Bacillus subtilis 168. Last, multiple parameters,
including target locations, the presence of an Hfq binding site, GC
contents, and gene expression levels, were revisited to define the
conditions under which the multivariate model should be used for accurate
prediction. Together, 434 different strain-asRNA combinations were
tested, validating the predictive model in a variety of contexts,
including multiple target genes and organisms. The result presented
in this study is an important step toward achieving predictable tunability
of asRNA-mediated repression.