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Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression.

Young Je Lee1, Soo-Jung Kim1, Matthew B Amrofell1

  • 1Department of Energy, Environmental and Chemical Engineering , Washington University in St. Louis , St. Louis , Missouri 63130 , United States.

ACS Synthetic Biology
|December 6, 2018
PubMed
Summary
This summary is machine-generated.

We developed a new model to predict how well synthetic antisense RNAs (asRNAs) can control gene expression. This model accurately predicts asRNA efficiency across different genes and organisms, enabling predictable gene regulation.

Keywords:
RNA regulatorantisense RNAgene repressionmultivariate model

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Area of Science:

  • Molecular Biology
  • Synthetic Biology
  • Bioinformatics

Background:

  • Advances in RNA biology enable gene regulation via synthetic antisense RNAs (asRNAs).
  • Predicting asRNA efficiency is challenging due to complex native regulatory mechanisms.
  • Current methods lack predictability for diverse applications.

Purpose of the Study:

  • To develop a multivariate model for predicting synthetic asRNA-mediated gene repression efficiency.
  • To validate the model's effectiveness across various genetic contexts and organisms.
  • To demonstrate tunable control of gene expression using the predictive model.

Main Methods:

  • Designed and tested 69 asRNAs targeting multiple mRNAs to build a predictive model.
  • Validated the model using change in free energy of complex formation (ΔGCF) and percent mismatch.
  • Tested the model in plasmids and chromosomes, and in Escherichia coli and Bacillus subtilis.

Main Results:

  • The multivariate model accurately predicts asRNA repression efficiency in diverse contexts.
  • The model demonstrated effectiveness in both plasmid and chromosomal gene targets.
  • Successfully designed asRNAs for tunable control of toxin-antitoxin systems, impacting cell growth.
  • Validated across 434 strain-asRNA combinations in two distinct bacterial species.

Conclusions:

  • A robust multivariate model enhances predictability of synthetic asRNA-mediated gene repression.
  • The model facilitates predictable and tunable gene expression control in synthetic biology.
  • This work advances the application of asRNAs for precise genetic manipulation in biotechnology.