Evidence map›Paper›PMID 34230498›Full record

ArticleNature communications2021

High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies.

Jicong Cao, Eva Maria Novoa, Zhizhuo Zhang, William C W Chen, Dianbo Liu, Gigi C G Choi, Alan S L Wong, Claudia Wehrspaun, Manolis Kellis, Timothy K Lu

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed
8.8field-weighted citation impact, top 1% of its field
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

88 citing papers in PubMed, 151 citations in OpenAlex.

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  10. Designing 5' UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity.Molecular therapy : the journal of the American Society of Gene Therapy · 2026
    Article
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28 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 3 countries.

Jicong Cao *Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-5253-3365
Eva Maria Novoa *Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Zhizhuo Zhang *Broad Institute of MIT and Harvard, Cambridge, MA, USA.
William C W ChenSynthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Dianbo LiuSynthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Gigi C G ChoiSynthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Alan S L WongSynthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Claudia WehrspaunSynthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Manolis KellisBroad Institute of MIT and Harvard, Cambridge, MA, USA. manoli@mit.edu.ORCID 0000-0001-7113-9630
Timothy K LuSynthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA. timlu@mit.edu.ORCID 0000-0002-3918-8923
Broad Institute · USMassachusetts Institute of Technology · US

Funding

MULTIDISCIPLINARY RESEARCH TRAINING IN CARDIOLOGYT32HL007208 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Patrick Thomas Ellinor, David E Sosnovik · 1985 to 2026
$16.1M
Regulatory Morif Discovery in the Human Genome Using Comparative GenomicsR01HG004037 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS · 2007 to 2015
$3.9M
Comparative analysis and regulatory architecture of epigenomics datasetsR01GM113708 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS · 2015 to 2017
$852k
NHGRI NIH HHS R01 HG004037NHLBI NIH HHS T32 HL007208NIGMS NIH HHS R01 GM113708
6 · The paper itself

Abstract

Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major technical challenge. Here, we develop a high-throughput strategy to design, screen, and optimize 5' UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identify naturally occurring 5' UTRs with high translation efficiencies and use this information with in silico genetic algorithms to generate synthetic 5' UTRs. A total of ~12,000 5' UTRs are then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identify three synthetic 5' UTRs that outperform commonly used non-viral gene therapy plasmids in expressing protein payloads. In summary, we demonstrate that high-throughput screening of 5' UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.

Indexed as

Genetic EngineeringGenetic Therapy5' Untranslated RegionsAlgorithmsCell LineGene ExpressionHEK293 CellsHigh-Throughput Screening AssaysHumansPlasmidsPromoter Regions, GeneticRecombinases5' Untranslated RegionsRecombinases

Identifiers

PMID34230498
PMCPMC8260622
OpenAlexW3180908419

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.