Evidence map›Paper›PMID 41096996›Full record

ArticleInternational journal of molecular sciences2025

siRNA Cocktail Targeting Multiple Enterovirus 71 Genes Prevents Escape Mutants and Inhibits Viral Replication.

Yun Ji Ga, Jung-Yong Yeh

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. 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

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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

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

2 authors.

Yun Ji GaDepartment of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, BioComplex, Harmony-ro 265, Yeonsu-gu, Incheon 22014, Republic of Korea.
Jung-Yong YehDepartment of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, BioComplex, Harmony-ro 265, Yeonsu-gu, Incheon 22014, Republic of Korea.ORCID 0000-0002-0939-7783

Funding

Incheon National University 2020
6 · The paper itself

Abstract

RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation in which small interfering RNA (siRNA) is utilized to target and degrade specific RNA sequences. In this study, experiments were conducted to evaluate the efficacy of combination siRNA therapy against enterovirus 71 (EV71) and the potential of this therapy to delay or prevent the emergence of resistance in vitro. siRNAs targeting multiple genes of EV71 were designed, and the effects of a cocktail of siRNAs on viral replication were assessed compared to those of single-siRNA treatment. Cotransfection of multiple siRNAs targeting different protein-coding genes of the EV71 genome effectively suppressed escape mutants resistant to RNAi. Combination therapy with siRNAs targeting multiple viral genes successfully prevented viral escape mutations over five passages. By contrast, serial passaging with a single siRNA led to the rapid emergence of resistance, with mutations identified in the siRNA target sites. The combination of siRNAs specifically targeting different regions demonstrated an additive effect and was more effective than individual siRNAs at inhibiting EV71 replication. This study supports the effectiveness of combination therapy using siRNAs targeting multiple genes of EV71 to inhibit viral replication and prevent the emergence of resistant escape mutants. Overall, the findings identify RNAi targeting multiple viral genes as a potential strategy for therapeutic development against viral diseases and for preventing the emergence of escape mutants resistant to antiviral RNAi.

Indexed as

Enterovirus A, HumanMutationRNA, Small InterferingVirus ReplicationAnimalsCell LineEnterovirus InfectionsHumansRNA InterferenceRNA, Small InterferingantiviralenterovirusRNA interferencesmall interfering RNAvirus

Identifiers

PMID41096996
PMCPMC12524729

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Registered trials

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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.