Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics.

Autor: Leppek K; Department of Genetics, Stanford University, Stanford, California 94305, USA., Byeon GW; Department of Genetics, Stanford University, Stanford, California 94305, USA., Kladwang W; Department of Biochemistry, Stanford University, California 94305, USA., Wayment-Steele HK; Department of Chemistry, Stanford University, Stanford, California 94305, USA., Kerr CH; Department of Genetics, Stanford University, Stanford, California 94305, USA., Xu AF; Department of Genetics, Stanford University, Stanford, California 94305, USA., Kim DS; Department of Biochemistry, Stanford University, California 94305, USA., Topkar VV; Program in Biophysics, Stanford University, Stanford, California 94305, USA., Choe C; Department of Bioengineering, Stanford University, Stanford, California 94305, USA., Rothschild D; Department of Genetics, Stanford University, Stanford, California 94305, USA., Tiu GC; Department of Genetics, Stanford University, Stanford, California 94305, USA., Wellington-Oguri R; Eterna Massive Open Laboratory., Fujii K; Department of Genetics, Stanford University, Stanford, California 94305, USA., Sharma E; Department of Biochemistry, Stanford University, California 94305, USA., Watkins AM; Department of Biochemistry, Stanford University, California 94305, USA., Nicol JJ; Eterna Massive Open Laboratory., Romano J; Eterna Massive Open Laboratory.; Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, New York, 14260, USA., Tunguz B; Department of Biochemistry, Stanford University, California 94305, USA., Participants E; Eterna Massive Open Laboratory., Barna M; Department of Genetics, Stanford University, Stanford, California 94305, USA., Das R; Department of Biochemistry, Stanford University, California 94305, USA.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2021 Mar 30. Date of Electronic Publication: 2021 Mar 30.
DOI: 10.1101/2021.03.29.437587
Abstrakt: Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.
Databáze: MEDLINE