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@article{Broderick:2021:10.3389/fmicb.2021.711134,
author = {Broderick, DTJ and Waite, DW and Marsh, RL and Camargo, CA and Cardenas, P and Chang, AB and Cookson, WOC and Cuthbertson, L and Dai, W and Everard, ML and Gervaix, A and Harris, JK and Hasegawa, K and Hoffman, LR and Hong, S-J and Josset, L and Kelly, MS and Kim, B-S and Kong, Y and Li, SC and Mansbach, JM and Mejias, A and O'Toole, GA and Paalanen, L and Pérez-Losada, M and Pettigrew, MM and Pichon, M and Ramilo, O and Ruokolainen, L and Sakwinska, O and Seed, PC and van, der Gast CJ and Wagner, BD and Yi, H and Zemanick, ET and Zheng, Y and Pillarisetti, N and Taylor, MW},
doi = {10.3389/fmicb.2021.711134},
journal = {Front Microbiol},
title = {Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis.},
url = {http://dx.doi.org/10.3389/fmicb.2021.711134},
volume = {12},
year = {2021}
}

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TY  - JOUR
AB  - Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.
AU  - Broderick,DTJ
AU  - Waite,DW
AU  - Marsh,RL
AU  - Camargo,CA
AU  - Cardenas,P
AU  - Chang,AB
AU  - Cookson,WOC
AU  - Cuthbertson,L
AU  - Dai,W
AU  - Everard,ML
AU  - Gervaix,A
AU  - Harris,JK
AU  - Hasegawa,K
AU  - Hoffman,LR
AU  - Hong,S-J
AU  - Josset,L
AU  - Kelly,MS
AU  - Kim,B-S
AU  - Kong,Y
AU  - Li,SC
AU  - Mansbach,JM
AU  - Mejias,A
AU  - O'Toole,GA
AU  - Paalanen,L
AU  - Pérez-Losada,M
AU  - Pettigrew,MM
AU  - Pichon,M
AU  - Ramilo,O
AU  - Ruokolainen,L
AU  - Sakwinska,O
AU  - Seed,PC
AU  - van,der Gast CJ
AU  - Wagner,BD
AU  - Yi,H
AU  - Zemanick,ET
AU  - Zheng,Y
AU  - Pillarisetti,N
AU  - Taylor,MW
DO  - 10.3389/fmicb.2021.711134
PY  - 2021///
SN  - 1664-302X
TI  - Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis.
T2  - Front Microbiol
UR  - http://dx.doi.org/10.3389/fmicb.2021.711134
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35002989
VL  - 12
ER  -

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