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

Kathrin Leppek, Gun Woo Byeon, Wipapat Kladwang, Hannah K. Wayment-Steele, Craig H. Kerr, Adele F. Xu, Do Soon Kim, Ved V. Topkar, Christian Choe, Daphna Rothschild, Gerald C. Tiu, Roger Wellington-Oguri, Kotaro Fujii, Eesha Sharma, Andrew M. Watkins, John J. Nicol, Jonathan Romano, Bojan Tunguz, Fernando Diaz, Hui Cai, Pengbo Guo, Jiewei Wu, Fanyu Meng, Shuai Shi, Eterna Participants, Philip R. Dormitzer, Alicia Solórzano, Maria Barna () and Rhiju Das ()
Additional contact information
Kathrin Leppek: Stanford University
Gun Woo Byeon: Stanford University
Wipapat Kladwang: Stanford University
Hannah K. Wayment-Steele: Stanford University
Craig H. Kerr: Stanford University
Adele F. Xu: Stanford University
Do Soon Kim: Stanford University
Ved V. Topkar: Stanford University
Christian Choe: Stanford University
Daphna Rothschild: Stanford University
Gerald C. Tiu: Stanford University
Roger Wellington-Oguri: Stanford University
Kotaro Fujii: Stanford University
Eesha Sharma: Stanford University
Andrew M. Watkins: Stanford University
John J. Nicol: Stanford University
Jonathan Romano: Stanford University
Bojan Tunguz: Stanford University
Fernando Diaz: Pfizer Vaccine Research and Development
Hui Cai: Pfizer Vaccine Research and Development
Pengbo Guo: Pfizer Vaccine Research and Development
Jiewei Wu: Pfizer Vaccine Research and Development
Fanyu Meng: Pfizer Vaccine Research and Development
Shuai Shi: Pfizer Vaccine Research and Development
Eterna Participants: Stanford University
Philip R. Dormitzer: Pfizer Vaccine Research and Development
Alicia Solórzano: Pfizer Vaccine Research and Development
Maria Barna: Stanford University
Rhiju Das: Stanford University

Nature Communications, 2022, vol. 13, issue 1, 1-22

Abstract: Abstract 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 an 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 highly structured “superfolder” mRNAs can be designed to improve both stability and expression with further enhancement 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-28776-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28776-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-28776-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().