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

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 suggeste...

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Autores principales: Broderick, David T. J., Waite, David W., Marsh, Robyn L., Camargo, Carlos A., Cardenas, Paul, Chang, Anne B., Cookson, William O. C., Cuthbertson, Leah, Dai, Wenkui, Everard, Mark L., Gervaix, Alain, Harris, J. Kirk, Hasegawa, Kohei, Hoffman, Lucas R., Hong, Soo-Jong, Josset, Laurence, Kelly, Matthew S., Kim, Bong-Soo, Kong, Yong, Li, Shuai C., Mansbach, Jonathan M., Mejias, Asuncion, O’Toole, George A., Paalanen, Laura, Pérez-Losada, Marcos, Pettigrew, Melinda M., Pichon, Maxime, Ramilo, Octavio, Ruokolainen, Lasse, Sakwinska, Olga, Seed, Patrick C., van der Gast, Christopher J., Wagner, Brandie D., Yi, Hana, Zemanick, Edith T., Zheng, Yuejie, Pillarisetti, Naveen, Taylor, Michael W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733647/
https://www.ncbi.nlm.nih.gov/pubmed/35002989
http://dx.doi.org/10.3389/fmicb.2021.711134
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author Broderick, David T. J.
Waite, David W.
Marsh, Robyn L.
Camargo, Carlos A.
Cardenas, Paul
Chang, Anne B.
Cookson, William O. C.
Cuthbertson, Leah
Dai, Wenkui
Everard, Mark L.
Gervaix, Alain
Harris, J. Kirk
Hasegawa, Kohei
Hoffman, Lucas R.
Hong, Soo-Jong
Josset, Laurence
Kelly, Matthew S.
Kim, Bong-Soo
Kong, Yong
Li, Shuai C.
Mansbach, Jonathan M.
Mejias, Asuncion
O’Toole, George A.
Paalanen, Laura
Pérez-Losada, Marcos
Pettigrew, Melinda M.
Pichon, Maxime
Ramilo, Octavio
Ruokolainen, Lasse
Sakwinska, Olga
Seed, Patrick C.
van der Gast, Christopher J.
Wagner, Brandie D.
Yi, Hana
Zemanick, Edith T.
Zheng, Yuejie
Pillarisetti, Naveen
Taylor, Michael W.
author_facet Broderick, David T. J.
Waite, David W.
Marsh, Robyn L.
Camargo, Carlos A.
Cardenas, Paul
Chang, Anne B.
Cookson, William O. C.
Cuthbertson, Leah
Dai, Wenkui
Everard, Mark L.
Gervaix, Alain
Harris, J. Kirk
Hasegawa, Kohei
Hoffman, Lucas R.
Hong, Soo-Jong
Josset, Laurence
Kelly, Matthew S.
Kim, Bong-Soo
Kong, Yong
Li, Shuai C.
Mansbach, Jonathan M.
Mejias, Asuncion
O’Toole, George A.
Paalanen, Laura
Pérez-Losada, Marcos
Pettigrew, Melinda M.
Pichon, Maxime
Ramilo, Octavio
Ruokolainen, Lasse
Sakwinska, Olga
Seed, Patrick C.
van der Gast, Christopher J.
Wagner, Brandie D.
Yi, Hana
Zemanick, Edith T.
Zheng, Yuejie
Pillarisetti, Naveen
Taylor, Michael W.
author_sort Broderick, David T. J.
collection PubMed
description 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.
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spelling pubmed-87336472022-01-07 Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis Broderick, David T. J. Waite, David W. Marsh, Robyn L. Camargo, Carlos A. Cardenas, Paul Chang, Anne B. Cookson, William O. C. Cuthbertson, Leah Dai, Wenkui Everard, Mark L. Gervaix, Alain Harris, J. Kirk Hasegawa, Kohei Hoffman, Lucas R. Hong, Soo-Jong Josset, Laurence Kelly, Matthew S. Kim, Bong-Soo Kong, Yong Li, Shuai C. Mansbach, Jonathan M. Mejias, Asuncion O’Toole, George A. Paalanen, Laura Pérez-Losada, Marcos Pettigrew, Melinda M. Pichon, Maxime Ramilo, Octavio Ruokolainen, Lasse Sakwinska, Olga Seed, Patrick C. van der Gast, Christopher J. Wagner, Brandie D. Yi, Hana Zemanick, Edith T. Zheng, Yuejie Pillarisetti, Naveen Taylor, Michael W. Front Microbiol Microbiology 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. Frontiers Media S.A. 2021-12-23 /pmc/articles/PMC8733647/ /pubmed/35002989 http://dx.doi.org/10.3389/fmicb.2021.711134 Text en 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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Broderick, David T. J.
Waite, David W.
Marsh, Robyn L.
Camargo, Carlos A.
Cardenas, Paul
Chang, Anne B.
Cookson, William O. C.
Cuthbertson, Leah
Dai, Wenkui
Everard, Mark L.
Gervaix, Alain
Harris, J. Kirk
Hasegawa, Kohei
Hoffman, Lucas R.
Hong, Soo-Jong
Josset, Laurence
Kelly, Matthew S.
Kim, Bong-Soo
Kong, Yong
Li, Shuai C.
Mansbach, Jonathan M.
Mejias, Asuncion
O’Toole, George A.
Paalanen, Laura
Pérez-Losada, Marcos
Pettigrew, Melinda M.
Pichon, Maxime
Ramilo, Octavio
Ruokolainen, Lasse
Sakwinska, Olga
Seed, Patrick C.
van der Gast, Christopher J.
Wagner, Brandie D.
Yi, Hana
Zemanick, Edith T.
Zheng, Yuejie
Pillarisetti, Naveen
Taylor, Michael W.
Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
title Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
title_full Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
title_fullStr Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
title_full_unstemmed Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
title_short Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
title_sort bacterial signatures of paediatric respiratory disease: an individual participant data meta-analysis
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733647/
https://www.ncbi.nlm.nih.gov/pubmed/35002989
http://dx.doi.org/10.3389/fmicb.2021.711134
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