Evidence map›Paper›PMID 38523273›Full record

ArticleMicrobiome2024

Ecological patterns and processes of temporal turnover within lung infection microbiota.

Helen Gavillet, Lauren Hatfield, Andrew Jones, Anirban Maitra, Alexander Horsley, Damian Rivett, Christopher van der Gast

Open access · goldAbstract readVideo-Audio Media
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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
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

8 citing papers in PubMed, 6 citations in OpenAlex.

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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 at 5 institutions in 1 country.

Helen GavilletDepartment of Applied Sciences, Northumbria University, Newcastle, UK.
Lauren HatfieldDepartment of Life Sciences, Manchester Metropolitan University, Manchester, UK.
Andrew JonesManchester Adult Cystic Fibrosis Centre, Manchester University NHS Foundation Trust, Manchester, UK.
Anirban MaitraRoyal Manchester Children's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
Alexander HorsleyManchester Adult Cystic Fibrosis Centre, Manchester University NHS Foundation Trust, Manchester, UK.
Damian RivettDepartment of Natural Sciences, Manchester Metropolitan University, Manchester, UK. D.Rivett@mmu.ac.uk.
Christopher van der GastDepartment of Applied Sciences, Northumbria University, Newcastle, UK. chris.vandergast@northumbria.ac.uk.
Manchester Metropolitan University · GBManchester University NHS Foundation Trust · GBNorthumbria Healthcare NHS Foundation Trust · GBNorthumbria University · GBRoyal Manchester Children's Hospital · GB

Funding

Cystic Fibrosis Trust VIA 045National Institute for Health and Care Research CS012-13
6 · The paper itself

Abstract

backgroundChronic infection and consequent airway inflammation are the leading causes of morbidity and early mortality for people living with cystic fibrosis (CF). However, lower airway infections across a range of chronic respiratory diseases, including in CF, do not follow classical 'one microbe, one disease' concepts of infection pathogenesis. Instead, they are comprised of diverse and temporally dynamic lung infection microbiota. Consequently, temporal dynamics need to be considered when attempting to associate lung microbiota with changes in disease status. Set within an island biogeography framework, we aimed to determine the ecological patterns and processes of temporal turnover within the lung microbiota of 30 paediatric and adult CF patients prospectively sampled over a 3-year period. Moreover, we aimed to ascertain the contributions of constituent chronic and intermittent colonizers on turnover within the wider microbiota.

resultsThe lung microbiota within individual patients was partitioned into constituent chronic and intermittent colonizing groups using the Leeds criteria and visualised with persistence-abundance relationships. This revealed bacteria chronically infecting a patient were both persistent and common through time, whereas intermittently infecting taxa were infrequent and rare; respectively representing the resident and transient portions of the wider microbiota. It also indicated that the extent of chronic colonization was far greater than could be appreciated with microbiological culture alone. Using species-time relationships to measure temporal turnover and Vellend's rationalized ecological processes demonstrated turnover in the resident chronic infecting groups was conserved and underpinned principally by the deterministic process of homogenizing dispersal. Conversely, intermittent colonizing groups, representing newly arrived immigrants and transient species, drove turnover in the wider microbiota and were predominately underpinned by the stochastic process of drift. For adult patients, homogenizing dispersal and drift were found to be significantly associated with lung function. Where a greater frequency of homogenizing dispersal was observed with worsening lung function and conversely drift increased with better lung function.

conclusionsOur work provides a novel ecological framework for understanding the temporal dynamics of polymicrobial infection in CF that has translational potential to guide and improve therapeutic targeting of lung microbiota in CF and across a range of chronic airway diseases. Video Abstract.

Indexed as

Cystic FibrosisMicrobiotaPneumoniaAdultBacteriaChildHumansLungChronic infectionCystic fibrosisEcological patterns and processesIsland biogeographyLung ecologyLung microbiomeMicrobiome ecologyRespiratory microbiomeSpecies-time relationshipsTemporal dynamics

Identifiers

PMID38523273
PMCPMC10962200
OpenAlexW4393155192

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.