Network Analysis to Identify Multi-Omic Correlations in the Lower Airways of Children With Cystic Fibrosis

John B. O’Connor, Madison Mottlowitz, Monica E. Kruk, Alan Mickelson, Brandie D. Wagner, Jonathan Kirk Harris, Christine H. Wendt, Theresa A. Laguna

Research output: Contribution to journalArticlepeer-review

Abstract

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus, a group of nontraditional taxa, including Prevotella, and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study.

Original languageEnglish (US)
Article number805170
JournalFrontiers in Cellular and Infection Microbiology
Volume12
DOIs
StatePublished - Mar 10 2022

Bibliographical note

Funding Information:
The authors acknowledge the National Institutes of Health and the Cystic Fibrosis Foundation (CFF) for the funding and support. The authors also acknowledge the generous contributions of dedicated patients and families from pediatric CF centers across the country for their support and participation in this study. We appreciate the contributions of previous members of Dr. Laguna’s lab at the University of Minnesota, including Myra Nunez and Cindy Williams, in collecting and processing samples.

Funding Information:
This work was supported by grants from the National Institutes of Health (NIH R01HL136499) and the Cystic Fibrosis Foundation (CFF LAGUNA17A0). The funders had no role in the design of the study, data collection and analysis, publication decisions, or preparation of the manuscript.

Funding Information:
The authors acknowledge the National Institutes of Health and the Cystic Fibrosis Foundation (CFF) for the funding and support. The authors also acknowledge the generous contributions of dedicated patients and families from pediatric CF centers across the country for their support and participation in this study. We appreciate the contributions of previous members of Dr. Laguna?s lab at the University of Minnesota, including Myra Nunez and Cindy Williams, in collecting and processing samples.

Publisher Copyright:
Copyright © 2022 O’Connor, Mottlowitz, Kruk, Mickelson, Wagner, Harris, Wendt and Laguna.

Keywords

  • bronchoalevolar lavage
  • cystic fibrosis
  • infection
  • inflammation
  • metabolomics
  • microbiota (16S)
  • pediatrics
  • Humans
  • Inflammation/metabolism
  • Bronchoalveolar Lavage Fluid/chemistry
  • Microbiota
  • Lung/microbiology
  • Cystic Fibrosis/microbiology
  • Child

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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