Evidence map›Paper›PMID 40738767›Full record

ArticleJournal of cystic fibrosis : official journal of the European Cystic Fibrosis Society2025

Shallow metagenomic shotgun sequencing improves detection of pathogenic species in cystic fibrosis respiratory samples.

Eline Cauwenberghs, Ilke De Boeck, Lize Delanghe, Tim Van Rillaer, Thomas Demuyser, Irina Spacova, Stijn Verhulst, Kim Van Hoorenbeeck, Sarah Lebeer

Abstract read
In one paragraph

Article in Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Eline CauwenberghsUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
Ilke De BoeckUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
Lize DelangheUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
Tim Van RillaerUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
Thomas DemuyserUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium; Antwerp University Hospital, Department of Microbiology, Wilrijkstraat 10, 2650 Edegem, Belgium; AIMS Lab, Center for Neurosciences, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium.
Irina SpacovaUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
Stijn VerhulstUniversity of Antwerp, Laboratory of Experimental Medicine and pediatrics, Universiteitsplein 1, 2610 Wilrijk, Belgium; Antwerp University Hospital, Department of pediatric Pulmonology, Wilrijkstraat 10, 2650 Edegem, Belgium.
Kim Van HoorenbeeckUniversity of Antwerp, Laboratory of Experimental Medicine and pediatrics, Universiteitsplein 1, 2610 Wilrijk, Belgium; Antwerp University Hospital, Department of pediatric Pulmonology, Wilrijkstraat 10, 2650 Edegem, Belgium.
Sarah LebeerUniversity of Antwerp, Department of Bioscience Engineering, Groenenborgerlaan 171, 2020 Antwerp, Belgium. Electronic address: sarah.lebeer@uantwerpen.be.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic infection and inflammation of the lungs contribute significantly to disease progression in persons with cystic fibrosis (pwCF). Treatment regimens are largely based on isolating the putative causative pathogen(s) from respiratory samples using basic culturing methods. While this strategy has shown to be highly valuable in the management of CF, the approach is time-consuming and often misses detection of pathogenic microbes that are more difficult to culture, including Mycobacterium spp.

methodsIn our proof-of-concept study, we evaluated shallow metagenomic shotgun sequencing to detect potential infection-causing pathogens at species level in sputum, oropharyngeal and salivary samples of pwCF (n = 13), and compared it to culture results from the clinic and standard 16S rRNA V4 amplicon sequencing.

resultsShallow shotgun sequencing improved the detection of pathogenic species in respiratory samples compared to culture methods. In particular, shallow shotgun sequencing could detect pathogenic species associated with CF, specifically Staphylococcus aureus, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Achromobacter xylosoxidans, Haemophilus influenzae and Mycobacterium spp. in sputum, oropharyngeal and/or salivary samples. Notably, Mycobacterium spp. was not detected based on 16S rRNA amplicon sequencing. Moreover, our approach was able to distinguish S. aureus from S. epidermidis and H. influenzae from H. parainfluenzae. This is not possible with 16S amplicon sequencing, but highly valuable in a clinical setting.

conclusionsThe improved detection of CF pathogens and other critical microbiome members as well as insights into their relative abundance within the community, could provide more knowledge on patient's disease status leading to more personalized medicine and ultimately benefit patient care.

Indexed as

Cystic FibrosisMetagenomicsRespiratory Tract InfectionsAdultChildFemaleHumansMaleOropharynxProof of Concept StudyRNA, Ribosomal, 16SSalivaShotgun SequencingSputumRNA, Ribosomal, 16SCystic fibrosisRespiratory microbiomeShallow metagenomic shotgun sequencing

Identifiers

PMID40738767
PMCPMC12463681

What OpenQuestion 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 OpenQuestion graph.