Evidence map›Paper›PMID 33821271›Full record

ArticlebioRxiv : the preprint server for biology2021

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 and 11 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Kathrin LeppekDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Gun Woo ByeonDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Wipapat KladwangDepartment of Biochemistry, Stanford University, California 94305, USA.
Hannah K Wayment-SteeleDepartment of Chemistry, Stanford University, Stanford, California 94305, USA.
Craig H KerrDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Adele F XuDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Do Soon KimDepartment of Biochemistry, Stanford University, California 94305, USA.
Ved V TopkarProgram in Biophysics, Stanford University, Stanford, California 94305, USA.
Christian ChoeDepartment of Bioengineering, Stanford University, Stanford, California 94305, USA.
Daphna RothschildDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Gerald C TiuDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Roger Wellington-OguriEterna Massive Open Laboratory.
Kotaro FujiiDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Eesha SharmaDepartment of Biochemistry, Stanford University, California 94305, USA.
Andrew M WatkinsDepartment of Biochemistry, Stanford University, California 94305, USA.
John J NicolEterna Massive Open Laboratory.
Jonathan RomanoEterna Massive Open Laboratory.
Bojan TunguzDepartment of Biochemistry, Stanford University, California 94305, USA.
Eterna ParticipantsEterna Massive Open Laboratory.
Maria BarnaDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Rhiju DasDepartment of Biochemistry, Stanford University, California 94305, USA.

Funding

Next-generation computational/chemical methods for complex RNA structuresR35GM122579 · NIGMS · STANFORD UNIVERSITY · PI Rhiju Das · 2017 to 2026
$7.2M
Specialized Translational Control of Stem Cell Differentiation and Embryonic DevelopmentR01HD086634 · NICHD · STANFORD UNIVERSITY · PI BARNA, MARIA · 2016 to 2025
$5.5M
CELL TYPE-SPECIFIC CONTROL of GENE EXPRESSION by RIBOSOMAL PROTEIN ISOFORMSF30HD100123 · NICHD · STANFORD UNIVERSITY · PI XU, ADELE FRANCIS · 2019 to 2023
$133k
NICHD NIH HHS F30 HD100123NICHD NIH HHS R01 HD086634NIGMS NIH HHS R35 GM122579
6 · The paper itself

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 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.

Identifiers

PMID33821271
PMCPMC8020971

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.