TY - JOUR
T1 - Integrative microbiomics in bronchiectasis exacerbations
AU - Mac Aogáin, Micheál
AU - Narayana, Jayanth Kumar
AU - Tiew, Pei Yee
AU - Ali, Nur A’tikah Binte Mohamed
AU - Yong, Valerie Fei Lee
AU - Jaggi, Tavleen Kaur
AU - Lim, Albert Yick Hou
AU - Keir, Holly R.
AU - Dicker, Alison J.
AU - Thng, Kai Xian
AU - Tsang, Akina
AU - Ivan, Fransiskus Xaverius
AU - Poh, Mau Ern
AU - Oriano, Martina
AU - Aliberti, Stefano
AU - Blasi, Francesco
AU - Low, Teck Boon
AU - Ong, Thun How
AU - Oliver, Brian
AU - Giam, Yan Hui
AU - Tee, Augustine
AU - Koh, Mariko Siyue
AU - Abisheganaden, John Arputhan
AU - Tsaneva-Atanasova, Krasimira
AU - Chalmers, James D.
AU - Chotirmall, Sanjay H.
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/4
Y1 - 2021/4
N2 - Bronchiectasis, a progressive chronic airway disease, is characterized by microbial colonization and infection. We present an approach to the multi-biome that integrates bacterial, viral and fungal communities in bronchiectasis through weighted similarity network fusion (https://integrative-microbiomics.ntu.edu.sg). Patients at greatest risk of exacerbation have less complex microbial co-occurrence networks, reduced diversity and a higher degree of antagonistic interactions in their airway microbiome. Furthermore, longitudinal interactome dynamics reveals microbial antagonism during exacerbation, which resolves following treatment in an otherwise stable multi-biome. Assessment of the Pseudomonas interactome shows that interaction networks, rather than abundance alone, are associated with exacerbation risk, and that incorporation of microbial interaction data improves clinical prediction models. Shotgun metagenomic sequencing of an independent cohort validated the multi-biome interactions detected in targeted analysis and confirmed the association with exacerbation. Integrative microbiomics captures microbial interactions to determine exacerbation risk, which cannot be appreciated by the study of a single microbial group. Antibiotic strategies probably target the interaction networks rather than individual microbes, providing a fresh approach to the understanding of respiratory infection.
AB - Bronchiectasis, a progressive chronic airway disease, is characterized by microbial colonization and infection. We present an approach to the multi-biome that integrates bacterial, viral and fungal communities in bronchiectasis through weighted similarity network fusion (https://integrative-microbiomics.ntu.edu.sg). Patients at greatest risk of exacerbation have less complex microbial co-occurrence networks, reduced diversity and a higher degree of antagonistic interactions in their airway microbiome. Furthermore, longitudinal interactome dynamics reveals microbial antagonism during exacerbation, which resolves following treatment in an otherwise stable multi-biome. Assessment of the Pseudomonas interactome shows that interaction networks, rather than abundance alone, are associated with exacerbation risk, and that incorporation of microbial interaction data improves clinical prediction models. Shotgun metagenomic sequencing of an independent cohort validated the multi-biome interactions detected in targeted analysis and confirmed the association with exacerbation. Integrative microbiomics captures microbial interactions to determine exacerbation risk, which cannot be appreciated by the study of a single microbial group. Antibiotic strategies probably target the interaction networks rather than individual microbes, providing a fresh approach to the understanding of respiratory infection.
UR - https://www.scopus.com/pages/publications/85103627344
U2 - 10.1038/s41591-021-01289-7
DO - 10.1038/s41591-021-01289-7
M3 - Article
C2 - 33820995
AN - SCOPUS:85103627344
SN - 1078-8956
VL - 27
SP - 688
EP - 699
JO - Nature Medicine
JF - Nature Medicine
IS - 4
ER -