TY - JOUR
T1 - Bacterial Signatures of Paediatric Respiratory Disease
T2 - An Individual Participant Data Meta-Analysis
AU - Broderick, David T.J.
AU - Waite, David W.
AU - Marsh, Robyn L.
AU - Camargo, Carlos A.
AU - Cardenas, Paul
AU - Chang, Anne B.
AU - Cookson, William O.C.
AU - Cuthbertson, Leah
AU - Dai, Wenkui
AU - Everard, Mark L.
AU - Gervaix, Alain
AU - Harris, J. Kirk
AU - Hasegawa, Kohei
AU - Hoffman, Lucas R.
AU - Hong, Soo Jong
AU - Josset, Laurence
AU - Kelly, Matthew S.
AU - Kim, Bong Soo
AU - Kong, Yong
AU - Li, Shuai C.
AU - Mansbach, Jonathan M.
AU - Mejias, Asuncion
AU - O’Toole, George A.
AU - Paalanen, Laura
AU - Pérez-Losada, Marcos
AU - Pettigrew, Melinda M.
AU - Pichon, Maxime
AU - Ramilo, Octavio
AU - Ruokolainen, Lasse
AU - Sakwinska, Olga
AU - Seed, Patrick C.
AU - van der Gast, Christopher J.
AU - Wagner, Brandie D.
AU - Yi, Hana
AU - Zemanick, Edith T.
AU - Zheng, Yuejie
AU - Pillarisetti, Naveen
AU - Taylor, Michael W.
N1 - Funding Information:
Williamson, K. M., Wagner, B. D., Robertson, C. E., Johnson, E. J., Zemanick, E. T., and Harris, J. K. (2017). Impact of enzymatic digestion on bacterial community composition in CF airway samples. PeerJ 5:e3362. doi: 10.7717/peerj. 3362 Wylie, K. M. (2017). The virome of the human respiratory tract. Clin. Chest Med. 38, 11–19. doi: 10.1016/j.ccm.2016.11.001 Yi, H., Yong, D., Lee, K., Cho, Y., and Chun, J. (2014). Profiling bacterial community in upper respiratory tracts. BMC Infect. Dis. 14:583. doi: 10.1186/ s12879-014-0583-3 Zemanick, E. T., Wagner, B. D., Robertson, C. E., Ahrens, R., Chmiel, J. F., Clancy, J. P., et al. (2017). Airway microbiota across age and disease spectrum in cystic fibrosis. Eur. Respir. J. 50:1700832. doi: 10.1183/13993003.00832-2017 Zemanick, E. T., Wagner, B. D., Robertson, C. E., Stevens, M. J., Szefler, S. J., Accurso, F. J., et al. (2015). Assessment of airway microbiota and inflammation in cystic fibrosis using multiple sampling methods. Ann. Am. Thorac. Soc. 12, 221–229. doi: 10.1513/AnnalsATS.201407-310OC Zhang, W. Z. (2020). The origins of COPD: sometimes the journey matters more than the destination. Am. J. Respir. Crit. Care Med. 202, 159–161. doi: 10.1164/ rccm.202004-0959ed Conflict of Interest: MPi reports personal fees from Mérieux Université, grants from Abacus Diagnostica, outside the submitted work. AM reports grants and personal fees from Janssen, personal fees from Merck, personal fees from Sanofi-Pasteur, personal fees from Roche, outside the submitted work. EZ reports grants and personal fees from Cystic Fibrosis Foundation, outside the submitted work. OS is an employee of Nestlé Research – Societé des Produits Nestlé S.A.
Publisher Copyright:
Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor.
PY - 2021/12/23
Y1 - 2021/12/23
N2 - 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.
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.
KW - individual participant data (IPD) meta-analysis
KW - meta-analysis
KW - microbiota (16S)
KW - paediatrics
KW - respiratory infection
KW - respiratory tract
UR - http://www.scopus.com/inward/record.url?scp=85122363708&partnerID=8YFLogxK
U2 - 10.3389/fmicb.2021.711134
DO - 10.3389/fmicb.2021.711134
M3 - Article
AN - SCOPUS:85122363708
SN - 1664-302X
VL - 12
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
M1 - 711134
ER -