Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
Graduate Program in Bioinformatics and Genomics, The Pennsylvania State University, University Park, PA, USA.
Nat Commun. 2024 Nov 6;15(1):9601. doi: 10.1038/s41467-024-54059-7.
mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.
mRNA 降解是一个影响所有基因表达水平的核心过程，但由于许多控制降解速率的耦合相互作用，要从其序列预测 mRNA 的稳定性仍然具有挑战性。在这里，我们使用一种设计学习的方法对超过 50000 个细菌 mRNA 进行了大规模平行的动力学衰减测量，以开发和验证一种预测性的 mRNA 稳定性序列到功能模型。mRNA 被设计为系统地改变翻译速率、二级结构、序列组成、G-四联体、i -motif 和 RppH 活性，从而导致 mRNA 的半衰期从大约 20 秒到 20 分钟不等。我们结合生物物理模型和机器学习，开发了具有高精度和通用性的 mRNA 稳定性的稳态和动力学衰减模型，利用转录率模型来识别 mRNA 同工型，利用翻译率模型来计算核糖体保护。总的来说，该模型定量描述了共同控制细菌操纵子中 mRNA 稳定性的关键相互作用，并预测了改变 mRNA 序列如何改变 mRNA 稳定性，这在研究和工程化细菌遗传系统时非常重要。
