Evidence map›Paper›PMID 39385282›Full record

ArticleMicrobiome2024

Microbiome and metabolome patterns after lung transplantation reflect underlying disease and chronic lung allograft dysfunction.

Christian Martin, Kathleen S Mahan, Talia D Wiggen, Adam J Gilbertsen, Marshall I Hertz, Ryan C Hunter, Robert A Quinn

Abstract read
In one paragraph

Article in Microbiome, 2024. 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. Bridging bench and bedside: translational omics ofEuropean respiratory review : an official journal of the European Respiratory Society · 2026
    Review
  2. Multi-omics insights into the mechanisms and prognosis of IPF.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2026
    Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. 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

7 authors.

Christian MartinDepartment of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.
Kathleen S MahanDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, 55455, USA.
Talia D WiggenDepartment of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA.
Adam J GilbertsenDepartment of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA.
Marshall I HertzDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, 55455, USA.
Ryan C HunterDepartment of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA. rhunter2@buffalo.edu.
Robert A QuinnDepartment of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA. quinnrob@msu.edu.

Funding

Determining How a Dynamic Microbiome Contributes to Cystic Fibrosis Lung DiseaseR01AI145925 · NIAID · MICHIGAN STATE UNIVERSITY · PI QUINN, ROBERT ANDREW · 2019 to 2022
$2.7M
Cystic Fibrosis Foundation HUNTER18AB0NIAID NIH HHS R01 AI145925NIH HHS R01AI145925
6 · The paper itself

Abstract

backgroundProgression of chronic lung disease may lead to the requirement for lung transplant (LTx). Despite improvements in short-term survival after LTx, chronic lung allograft dysfunction (CLAD) remains a critical challenge for long-term survival. This study investigates the molecular and microbial relationships between underlying lung disease and the development of CLAD in bronchoalveolar lavage fluid (BALF) from subjects post-LTx, which is crucial for tailoring treatment strategies specific to allograft dysfunctions.

methodsPaired 16S rRNA gene amplicon sequencing and untargeted LC-MS/MS metabolomics were performed on 856 BALF samples collected over 10 years from LTx recipients (n = 195) with alpha-1-antitrypsin disease (AATD, n = 23), cystic fibrosis (CF, n = 47), chronic obstructive pulmonary disease (COPD, n = 78), or pulmonary fibrosis (PF, n = 47). Data were analyzed using random forest (RF) machine learning and multivariate statistics for associations with underlying disease and CLAD development.

resultsThe BALF microbiome and metabolome after LTx differed significantly according to the underlying disease state (PERMANOVA, p = 0.001), with CF and AATD demonstrating distinct microbiome and metabolome profiles, respectively. Uniqueness in CF was mainly driven by Pseudomonas abundance and its metabolites, whereas AATD had elevated levels of phenylalanine and a lack of shared metabolites with the other underlying diseases. BALF microbiome and metabolome composition were also distinct between those who did or did not develop CLAD during the sample collection period (PERMANOVA, p = 0.001). An increase in the average abundance of Veillonella (AATD, COPD) and Streptococcus (CF, PF) was associated with CLAD development, and decreases in the abundance of phenylalanine-derivative alkaloids (CF, COPD) and glycerophosphorylcholines (CF, COPD, PF) were signatures of the CLAD metabolome. Although the relative abundance of Pseudomonas was not associated with CLAD, the abundance of its virulence metabolites, including siderophores, quorum-sensing quinolones, and phenazines, were elevated in those with CF who developed CLAD. There was a positive correlation between the abundance of these molecules and the abundance of Pseudomonas in the microbiome, but there was no correlation between their abundance and the time in which BALF samples were collected post-LTx.

conclusionsThe BALF microbiome and metabolome after LTx are particularly distinct in those with underlying CF and AATD. These data reflect those who developed CLAD, with increased virulence metabolite production from Pseudomonas, an aspect of CF CLAD cases. These findings shed light on disease-specific microbial and metabolic signatures in LTx recipients, offering valuable insights into the underlying causes of allograft rejection. Video Abstract.

Indexed as

Bronchoalveolar Lavage FluidLung TransplantationMetabolomeMicrobiotaAdultAgedAllograftsBacteriaCystic FibrosisFemaleHumansLungLung DiseasesMaleMetabolomicsMiddle AgedRNA, Ribosomal, 16SBronchioalveolar lavage fluidsChronic lung allograft dysfunctionCystic fibrosisLung diseasesMetabolomeMicrobiomePseudomonas aeruginosa virulence factors

Identifiers

PMID39385282
PMCPMC11462767

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.