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. 2023 Jan 9:9:1082125.
doi: 10.3389/fmed.2022.1082125. eCollection 2022.

Using metabolic potential within the airway microbiome as predictors of clinical state in persons with cystic fibrosis

Gabriella Shumyatsky  1 , Aszia Burrell  2 , Hollis Chaney  3   4 , Iman Sami  3   4 , Anastassios C Koumbourlis  3   4 , Robert J Freishtat  2   3   5 , Keith A Crandall  6 , Edith T Zemanick  7 , Andrea Hahn  2   3   8
Affiliations

Using metabolic potential within the airway microbiome as predictors of clinical state in persons with cystic fibrosis

Gabriella Shumyatsky et al. Front Med (Lausanne). .

Abstract

Introduction: Pulmonary exacerbations (PEx) in persons with cystic fibrosis (CF) are primarily related to acute or chronic inflammation associated with bacterial lung infections, which may be caused by several bacteria that activate similar bacterial genes and produce similar by-products. The goal of our study was to perform a stratified functional analysis of bacterial genes at three distinct time points in the treatment of a PEx in order to determine the role that specific airway microbiome community members may play within each clinical state (i.e., PEx, end of antibiotic treatment, and follow-up). Our secondary goal was to compare the change between clinical states with the metabolic activity of specific airway microbiome community members.

Methods: This was a prospective observational study of persons with CF treated with intravenous antibiotics for PEx between 2016 and 2020 at Children's National Hospital. Demographic and clinical information as well as respiratory samples were collected at hospital admission for PEx, end of antibiotic treatment, and follow-up. Metagenomic sequencing was performed; MetaPhlAn3 and HUMANn3 were used to assign sequences to bacterial species and bacterial metabolic genes, respectively.

Results: Twenty-two persons with CF, with a mean age of 14.5 (range 7-23) years, experienced 45 PEx during the study period. Two-hundred twenty-one bacterial species were identified in the respiratory samples from the study cohort. Ten bacterial species had differential gene abundance across changes in the clinical state including Staphylococcus aureus, Streptococcus salivarius, and Veillonella atypica (all padj < 0.01 and log2FoldChange > |2|). These corresponded to a differential abundance of bacterial genes, with S. aureus accounting for 81% of the genes more abundant in PEx and S. salivarius accounting for 83% of the genes more abundant in follow-up, all compared to the end of treatment. Lastly, 8,653 metabolic pathways were identified across samples, with again S. aureus and S. salivarius contributing to the differential abundance of pathways (106 in PEx vs. 66 in follow-up, respectively). V. atypica was associated with a single metabolic pathway (UDP-N-acetyl-D-glucosamine biosynthesis) increased in follow-up compared to PEx.

Discussion: Taken together, these data suggest that the metabolic potential of bacterial species can provide more insight into changes across clinical states than the relative abundance of the bacteria alone.

Keywords: bacterial gene; cystic fibrosis; lung disease; metabolic pathway; microbiome.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flow diagram of respiratory samples by time point. E, pulmonary exacerbation; T, end of antibiotic treatment; F, follow up; BAL, bronchoalveolar lavage; OP, oropharyngeal.
FIGURE 2
FIGURE 2
Relative abundance plot of the top 40 species observed. The 40 species with the highest contribution to the dataset are included here. The bar plot for each sample can approach a maximum relative abundance of 1.00. In cases where the bar is <1.00, it is because the remaining species contributing to that respiratory sample’s community are from the remaining 181 species observed in the dataset. E, pulmonary exacerbation; T, end of treatment; F, follow up.
FIGURE 3
FIGURE 3
Bray-Curtis non-metric multidimensional scaling (NMDS) plot. E, pulmonary exacerbation; T, end of treatment; F, follow up. The ellipses represent the t distribution. The adonis test was significant at p = 0.001, controlling for repeated patient samples using the strata function.
FIGURE 4
FIGURE 4
Stratified differential abundance of bacterial genes between changes in clinical status. Panel (A) Log2 fold change distribution across bacterial species in Follow-up compared to Treatment across GO categories. Panel (B) Log2 fold change distribution across bacterial species in Exacerbation Onset vs. Treatment across GO categories. Panel (C) Stratified differential abundance of bacterial genes in Follow-up compared to Treatment across GO categories. Panel (D) Stratified differential abundance of bacterial genes in Exacerbation-Onset compared to treatment across GO categories. GO, gene ontology; BP, biological processes; MF, molecular functions; CC, cellular components. The genus name is centered on each column shown.
FIGURE 5
FIGURE 5
Differential abundance of Veillonella atypica genes in follow-up versus pulmonary exacerbation. While ten bacterial species were identified to have a differential abundance of bacterial genes across clinical states (i.e., pulmonary exacerbation, end of treatment, and follow-up), only Veillonella atypica had a differential abundance of bacterial genes when comparing follow-up pulmonary exacerbation onset. BP, biological processes; MF, molecular functions; CC, cellular components.
FIGURE 6
FIGURE 6
Uniquely differentially abundant genes at pulmonary exacerbation versus end of treatment. While ten bacterial species were identified to have a differential abundance of bacterial genes across clinical states (i.e., pulmonary exacerbation, end of treatment, and follow-up), the majority of bacterial genes differentially abundant in exacerbation belonged to Staphylococcus aureus. GO, gene ontology; BP, biological processes; MF, molecular functions; CC, cellular components.
FIGURE 7
FIGURE 7
Stratified differential pathway abundance. Panel (A) Metabolic pathways attributed to Streptococcus salivarius were differentially abundant between follow-up and treatment. Panel (B) Metabolic pathways attributed to Staphylococcus aureus were differentially abundant between pulmonary exacerbation and treatment. Panel (C) A single metabolic pathways attributed to Veillonella atypica was differentially abundant between follow-up and pulmonary exacerbation. When comparing the metabolic pathways between clinical states (i.e., pulmonary exacerbation, end of treatment, and follow-up), only one bacterial species was differentially abundant between each comparison. The different metabolic pathways are shown in different colors for each bacterial species. PEx, pulmonary exacerbation.
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