Evidence map›Paper›PMID 42135467›Full record

ReviewNature reviews. Microbiology2026

Concepts of RNA virus evolution for the design of better antiviral countermeasures.

Quang-Dinh Tran, Marco Vignuzzi

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Quang-Dinh TranA*STAR Infectious Diseases Labs, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.ORCID http://orcid.org/0000-0002-9263-0733
Marco VignuzziA*STAR Infectious Diseases Labs, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore. marco_vignuzzi@a-star.edu.sg.ORCID http://orcid.org/0000-0002-4400-771X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This century, global human health has been marked by a seemingly increasing list of outbreaks and epidemics caused by RNA viruses - masters of rapid evolution, host switching and immune escape. With a propensity for mutation, coupled with recombination, reassortment and extensive population interactions, RNA viruses generate remarkable genetic diversity within constrained evolutionary landscapes. Although most mutations are deleterious, a subset fuels adaptation to selective pressures and environments, potentially enabling pathogens to reach new hosts and become epidemic and pandemic threats. Recent advances in molecular virology have clarified how mutation biases, genome organization, epistasis and host factors shape viral diversity, revealing both vulnerabilities and evolutionary constraints. These mechanisms underlying viral evolution are now being leveraged to design evolution-informed countermeasures. These include live-attenuated vaccines with reduced risk of reversion, antivirals that target mutationally constrained regions or drive populations towards extinction, or universal vaccines directed against conserved regions. Looking forward, the integration of high-throughput mutational mapping, structural biology and computational modelling, including artificial intelligence-driven predictive tools, promises to transform our ability to anticipate viral evolutionary trajectories. This Review discusses how embedding evolutionary principles into translational virology may improve preparedness for future outbreaks by shifting the field from reactive to predictive strategies.

Indexed as

Antiviral AgentsEvolution, MolecularRNA VirusesRNA Virus InfectionsAnimalsGenetic VariationGenome, ViralHost-Pathogen InteractionsHumansMutationViral VaccinesAntiviral AgentsViral Vaccines

Identifiers

What OpenQuestion holds

Textmetadata
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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.