Evidence map›Paper›PMID 42284356›Full record

ArticlePLoS biology2026

Evolutionary inference reveals global natural histories and predicted pathways of antimicrobial resistance in Klebsiella pneumoniae.

Olav N L Aga, Sabrina J Moyo, Joel Manyahi, Upendo Kibwana, Iren H Löhr, Nina Langeland, Bjørn Blomberg, Iain G Johnston

Abstract read
In one paragraph

Article in PLoS biology, 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

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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

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

8 authors.

Olav N L AgaDepartment of Clinical Science, University of Bergen, Bergen, Norway.
Sabrina J MoyoDepartment of Medicine, Haukeland University Hospital, Bergen, Norway.
Joel ManyahiMuhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
Upendo KibwanaMuhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
Iren H LöhrDepartment of Clinical Science, University of Bergen, Bergen, Norway.
Nina LangelandDepartment of Clinical Science, University of Bergen, Bergen, Norway.
Bjørn BlombergDepartment of Clinical Science, University of Bergen, Bergen, Norway.
Iain G JohnstonComputational Biology Unit, University of Bergen, Bergen, Norway.ORCID https://orcid.org/0000-0001-8559-3519

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial resistance (AMR) is a substantial and growing global health burden. Understanding, and predicting, its evolution in specific pathogens will help responses across scales from individual patient cases to large-scale policy. Here, we use global data on AMR features, predicted from 47k Klebsiella pneumoniae genomes, with hypercubic transition path sampling to infer the evolutionary pathways by which AMR features in K. pneumoniae (KpAMR) are acquired across 102 countries, territories, and areas. We identify "globally consistent" evolutionary behaviors that hold across countries, and "globally divergent" behaviors including carbapenem and fluoroquinolone resistance that vary across countries. We show how these divergent dynamics covary both with public health superregion and drug use policy, and reveal competing evolutionary pathways within and between countries. Using newly sequenced data across several decades from sub-Saharan Africa, we show that this inferred global roadmap of KpAMR evolution successfully predicts prospective evolutionary dynamics. Together, we hope that the ability to characterize and predict evolutionary dynamics of AMR acquisition, connected to socio-economic and drug policy predictors, will help strengthen our understanding of AMR evolution worldwide.

Indexed as

Drug Resistance, BacterialEvolution, MolecularKlebsiella pneumoniaeAnti-Bacterial AgentsCarbapenemsGenome, BacterialHumansKlebsiella InfectionsAnti-Bacterial AgentsCarbapenems

Identifiers

PMID42284356
PMCPMC13278573

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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.