Affiliations 

  • 1 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
  • 2 Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore
  • 3 School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
  • 4 Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
  • 5 Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
  • 6 Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore
  • 7 Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore
  • 8 Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia
  • 9 Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
  • 10 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore. schotirmall@ntu.edu.sg
Nat Med, 2021 Apr;27(4):688-699.
PMID: 33820995 DOI: 10.1038/s41591-021-01289-7

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.