Evidence map›Paper›PMID 41212071›Full record

ArticleeLife2025

Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies.

Kai S Shimagaki, Rebecca M Lynch, John P Barton

Abstract read
In one paragraph

Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Kai S ShimagakiDepartment of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States.ORCID https://orcid.org/0000-0001-7580-4781
Rebecca M LynchDepartment of Microbiology, Immunology and Tropical Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC, United States.ORCID https://orcid.org/0000-0002-7188-8073
John P BartonDepartment of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States.ORCID https://orcid.org/0000-0003-1467-421X

Funding

Methods for quantifying selection in evolving populationsR35GM138233 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BARTON, JOHN P · 2020 to 2024
$1.9M
NIGMS NIH HHS R35 GM138233NIH HHS R35GM138233
6 · The paper itself

Abstract

HIV-1 evolves within individual hosts to escape adaptive immune responses while maintaining its capacity for replication. Coevolution between HIV-1 and the immune system generates extraordinary viral genetic diversity. In some individuals, this process also results in the development of broadly neutralizing antibodies (bnAbs) that can neutralize many viral variants, a key focus of HIV-1 vaccine design. However, a general understanding of the forces that shape virus-immune coevolution within and across hosts remains incomplete. Here, we performed a quantitative study of HIV-1 evolution in humans and rhesus macaques, including individuals who developed bnAbs. We observed strong selection early in infection for mutations affecting HIV-1 envelope glycosylation and escape from autologous strain-specific antibodies, followed by weaker selection for bnAb resistance. The inferred fitness effects of HIV-1 mutations in humans and macaques were remarkably similar. Moreover, we observed a striking pattern of rapid HIV-1 fitness gains that precedes the development of bnAbs. Our work highlights strong parallels between infection in rhesus macaques and humans, and it reveals a quantitative evolutionary signature of bnAb development.

Indexed as

Antibodies, NeutralizingBroadly Neutralizing AntibodiesGenetic FitnessHIV-1HIV AntibodiesHIV InfectionsAnimalsEvolution, MolecularHumansMacaca mulattaMutationAntibodies, NeutralizingBroadly Neutralizing AntibodiesHIV Antibodiesevolutionevolutionary biologyHIVpopulation geneticsstatistical inferenceviruses

Identifiers

PMID41212071
PMCPMC12600013

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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.