Evidence map›Paper›PMID 40971902›Full record

ArticleThe Journal of infectious diseases2025

Phylogenetic Context of Antibiotic Resistance Provides Insights into the Dynamics of Resistance Emergence and Spread.

Kyle J Gontjes, Aryan Singh, Sarah E Sansom, James D Boyko, Stephen A Smith, Ebbing Lautenbach, Evan Snitkin

Abstract read
In one paragraph

Article in The Journal of infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Defining thebioRxiv : the preprint server for biology · 2026
    Article
  2. Resolving competing evolutionary histories in joint ancestral state reconstruction.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Frontiers in bioinformatics · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Kyle J GontjesDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA.ORCID 0000-0001-8671-7990
Aryan SinghDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA.ORCID 0000-0002-1850-5598
Sarah E SansomDepartment of Internal Medicine, Division of Infectious Diseases, Rush University Medical Center, Chicago, Illinois, USA.ORCID 0000-0002-1924-8941
James D BoykoDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-0952-169X
Stephen A SmithDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-2035-9531
Ebbing LautenbachCenter for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-4772-2409
Evan SnitkinDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA.ORCID 0000-0001-8409-278X

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Institutional Career Development Core (Chapter 1)KL2TR002387 · NCATS · UNIVERSITY OF CHICAGO · PI ERIC C BEYER, Lisa L Barnes · 2017 to 2026
$7.6M
The Michigan Infectious Disease Genomics (MIDGE) CenterU19AI181767 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LAURING, ADAM, SNITKIN, EVAN · 2024 to 2024
$6.2M
Regional genomic epidemiology to identify drivers of resistance, transmission and infection with carbapenem-resistant Klebsiella pneumoniaeR01AI148259 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SNITKIN, EVAN · 2020 to 2023
$2.2M
Genome-driven identification of patient and bacterial drivers of resistance to novel beta-lactam/beta-lactamase inhibitor combination therapiesF31AI186288 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GONTJES, KYLE JAMES · 2024 to 2025
$84k
National Institute of Allergy and Infectious Diseases 5R01AI148259National Institute of Allergy and Infectious Diseases F31-AI186288National Institute of Allergy and Infectious Diseases U19AI181767National Science Foundation 2217116NCATS NIH HHS KL2 TR002387NHGRI NIH HHS T32 HG000040NHGRI NIH HHS T32-HG000040NIAID NIH HHS F31 AI186288NIAID NIH HHS R01 AI148259NIAID NIH HHS U19 AI181767
6 · The paper itself

Abstract

backgroundTo ameliorate the antibiotic resistance crisis, the drivers of resistance emergence and resistance spread must be better understood.

methodsWhole-genome sequencing and susceptibility testing were performed on clinical carbapenem-resistant Klebsiella pneumoniae isolates collected from August 2014 to July 2015 across 12 long-term acute care hospitals. Ancestral state reconstruction partitioned patients with resistant strains into those that likely acquired resistance via de novo evolution or cross-transmission. Logistic regression was used to evaluate the associations between patient characteristics/exposures and these 2 pathways: resistance due to predicted within-host emergence of resistance and resistance due to predicted cross-transmission. This framework is available in the user-friendly R package, phyloAMR (https://github.com/kylegontjes/phyloAMR).

resultsPhylogenetic analysis of 386 epidemic lineage carbapenem-resistant K. pneumoniae sequence type 258 isolates revealed differences in the relative contribution of de novo evolution and cross-transmission to the burden of resistance to 5 antibiotics. Clade-specific variations in rates of resistance emergence and their frequency and magnitude of spread were detected for each antibiotic. Phylogenetically informed regression modeling identified distinct clinical risk factors associated with each pathway. Exposure to the cognate antibiotic was an independent risk factor for resistance emergence (trimethoprim-sulfamethoxazole, colistin, and novel beta-lactam/beta-lactamase inhibitors) and resistance spread (trimethoprim-sulfamethoxazole, amikacin, and colistin). In addition to antibiotic exposures, comorbidities (eg, stage IV + decubitus ulcers) and indwelling medical devices (eg, gastrostomy tubes) were detected as unique risk factors for resistance spread.

conclusionsPhylogenetic contextualization generated insights and hypotheses into how bacterial genetic background, patient characteristics, and clinical practices influence the emergence and spread of antibiotic resistance.

Indexed as

Anti-Bacterial AgentsDrug Resistance, BacterialKlebsiella InfectionsKlebsiella pneumoniaeAgedCarbapenemsDrug Resistance, Multiple, BacterialFemaleHumansMaleMicrobial Sensitivity TestsMiddle AgedPhylogenyWhole Genome SequencingAnti-Bacterial AgentsCarbapenemsantibiotic resistanceclinical driversKlebsiella pneumoniaephylogeneticswhole-genome sequencing

Identifiers

PMID40971902
PMCPMC12718047

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.