Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

David T.J. Broderick; David W. Waite; Robyn L. Marsh; Carlos A. Camargo; Paul Cardenas; Anne B. Chang; William O.C. Cookson; Leah Cuthbertson; Wenkui Dai; Mark L. Everard; Alain Gervaix; J. Kirk Harris; Kohei Hasegawa; Lucas R. Hoffman; Soo Jong Hong; Laurence Josset; Matthew S. Kelly; Bong Soo Kim; Yong Kong; Shuai C. Li; Jonathan M. Mansbach; Asuncion Mejias; George A. O’Toole; Laura Paalanen; Marcos Pérez-Losada; Melinda M. Pettigrew; Maxime Pichon; Octavio Ramilo; Lasse Ruokolainen; Olga Sakwinska; Patrick C. Seed; Christopher J. van der Gast; Brandie D. Wagner; Hana Yi; Edith T. Zemanick; Yuejie Zheng; Naveen Pillarisetti; Michael W. Taylor

doi:10.3389/fmicb.2021.711134

Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

David T.J. Broderick, David W. Waite, Robyn L. Marsh, Carlos A. Camargo, Paul Cardenas, Anne B. Chang, William O.C. Cookson, Leah Cuthbertson, Wenkui Dai, Mark L. Everard, Alain Gervaix, J. Kirk Harris, Kohei Hasegawa, Lucas R. Hoffman, Soo Jong Hong, Laurence Josset, Matthew S. Kelly, Bong Soo Kim, Yong Kong, Shuai C. LiJonathan M. Mansbach, Asuncion Mejias, George A. O’Toole, Laura Paalanen, Marcos Pérez-Losada, Melinda M. Pettigrew, Maxime Pichon, Octavio Ramilo, Lasse Ruokolainen, Olga Sakwinska, Patrick C. Seed, Christopher J. van der Gast, Brandie D. Wagner, Hana Yi, Edith T. Zemanick, Yuejie Zheng, Naveen Pillarisetti, Michael W. Taylor^*

^*Corresponding author for this work

Pediatrics

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

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.

Original language	English (US)
Article number	711134
Journal	Frontiers in Microbiology
Volume	12
DOIs	https://doi.org/10.3389/fmicb.2021.711134
State	Published - Dec 23 2021

Funding

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.

Keywords

individual participant data (IPD) meta-analysis
meta-analysis
microbiota (16S)
paediatrics
respiratory infection
respiratory tract

ASJC Scopus subject areas

Microbiology
Microbiology (medical)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3389/fmicb.2021.711134

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Broderick, D. T. J., Waite, D. W., Marsh, R. L., Camargo, C. A., Cardenas, P., Chang, A. B., Cookson, W. O. C., Cuthbertson, L., Dai, W., Everard, M. L., Gervaix, A., Harris, J. K., Hasegawa, K., Hoffman, L. R., Hong, S. J., Josset, L., Kelly, M. S., Kim, B. S., Kong, Y., ... Taylor, M. W. (2021). Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis. Frontiers in Microbiology, 12, Article 711134. https://doi.org/10.3389/fmicb.2021.711134

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title = "Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis",

abstract = "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.",

keywords = "individual participant data (IPD) meta-analysis, meta-analysis, microbiota (16S), paediatrics, respiratory infection, respiratory tract",

author = "Broderick, {David T.J.} and Waite, {David W.} and Marsh, {Robyn L.} and Camargo, {Carlos A.} and Paul Cardenas and Chang, {Anne B.} and Cookson, {William O.C.} and Leah Cuthbertson and Wenkui Dai and Everard, {Mark L.} and Alain Gervaix and Harris, {J. Kirk} and Kohei Hasegawa and Hoffman, {Lucas R.} and Hong, {Soo Jong} and Laurence Josset and Kelly, {Matthew S.} and Kim, {Bong Soo} and Yong Kong and Li, {Shuai C.} and Mansbach, {Jonathan M.} and Asuncion Mejias and O{\textquoteright}Toole, {George A.} and Laura Paalanen and Marcos P{\'e}rez-Losada and Pettigrew, {Melinda M.} and Maxime Pichon and Octavio Ramilo and Lasse Ruokolainen and Olga Sakwinska and Seed, {Patrick C.} and {van der Gast}, {Christopher J.} and Wagner, {Brandie D.} and Hana Yi and Zemanick, {Edith T.} and Yuejie Zheng and Naveen Pillarisetti and Taylor, {Michael W.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O{\textquoteright}Toole, Paalanen, P{\'e}rez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor.",

year = "2021",

month = dec,

day = "23",

doi = "10.3389/fmicb.2021.711134",

language = "English (US)",

volume = "12",

journal = "Frontiers in Microbiology",

issn = "1664-302X",

publisher = "Frontiers Media SA",

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Broderick, DTJ, Waite, DW, Marsh, RL, Camargo, CA, Cardenas, P, Chang, AB, Cookson, WOC, Cuthbertson, L, Dai, W, Everard, ML, Gervaix, A, Harris, JK, Hasegawa, K, Hoffman, LR, Hong, SJ, Josset, L, Kelly, MS, Kim, BS, Kong, Y, Li, SC, Mansbach, JM, Mejias, A, O’Toole, GA, Paalanen, L, Pérez-Losada, M, Pettigrew, MM, Pichon, M, Ramilo, O, Ruokolainen, L, Sakwinska, O, Seed, PC, van der Gast, CJ, Wagner, BD, Yi, H, Zemanick, ET, Zheng, Y, Pillarisetti, N & Taylor, MW 2021, 'Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis', Frontiers in Microbiology, vol. 12, 711134. https://doi.org/10.3389/fmicb.2021.711134

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 - 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

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UR - http://www.scopus.com/inward/citedby.url?scp=85122363708&partnerID=8YFLogxK

U2 - 10.3389/fmicb.2021.711134

DO - 10.3389/fmicb.2021.711134

M3 - Article

C2 - 35002989

AN - SCOPUS:85122363708

SN - 1664-302X

VL - 12

JO - Frontiers in Microbiology

JF - Frontiers in Microbiology

M1 - 711134

ER -

Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

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UN SDGs

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