Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning
Abstract 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 measur...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54059-7 |
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| author | Daniel P. Cetnar Ayaan Hossain Grace E. Vezeau Howard M. Salis |
| author_facet | Daniel P. Cetnar Ayaan Hossain Grace E. Vezeau Howard M. Salis |
| author_sort | Daniel P. Cetnar |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-073d528b62f04b6abc99547c1683f528 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-073d528b62f04b6abc99547c1683f5282025-08-20T02:13:35ZengNature PortfolioNature Communications2041-17232024-11-0115111110.1038/s41467-024-54059-7Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learningDaniel P. Cetnar0Ayaan Hossain1Grace E. Vezeau2Howard M. Salis3Department of Chemical Engineering, The Pennsylvania State UniversityGraduate Program in Bioinformatics and Genomics, The Pennsylvania State UniversityDepartment of Biological Engineering, The Pennsylvania State UniversityDepartment of Chemical Engineering, The Pennsylvania State UniversityAbstract 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.https://doi.org/10.1038/s41467-024-54059-7 |
| spellingShingle | Daniel P. Cetnar Ayaan Hossain Grace E. Vezeau Howard M. Salis Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning Nature Communications |
| title | Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning |
| title_full | Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning |
| title_fullStr | Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning |
| title_full_unstemmed | Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning |
| title_short | Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning |
| title_sort | predicting synthetic mrna stability using massively parallel kinetic measurements biophysical modeling and machine learning |
| url | https://doi.org/10.1038/s41467-024-54059-7 |
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