Resilience and stability of the CF- intestinal and respiratory microbiome during nutritional and exercise intervention

Abstract

Background: Impaired respiratory and intestinal microbiome composition is linked to cystic fibrosis lung disease severity. In people with cystic fibrosis (pwCF), regular exercise is recommended to delay disease progression and preserve a stable lung function. An optimal nutritional status is vital for best clinical outcomes. Our study investigated whether regular and monitored exercise and nutritional support promotes CF microbiome health.

Methods: A personalized nutrition and exercise program promoted nutritional intake and physical fitness in 18 pwCF for 12 months. Throughout the study, patients performed strength and endurance training monitored by a sports scientist via an internet platform. After three months, food supplementation with Lactobacillus rhamnosus LGG was introduced. Nutritional status and physical fitness were assessed before the study started, after three and nine months. Sputum and stool were collected, and microbial composition was analyzed by 16S rRNA gene sequencing.

Results: Sputum and stool microbiome composition remained stable and highly specific to each patient during the study period. Disease-associated pathogens dominated sputum composition. Lung disease severity and recent antibiotic treatment had the highest impact on taxonomic composition in stool and sputum microbiome. Strikingly, the long-term antibiotic treatment burden had only a minor influence.

Conclusion: Despite the exercise and nutritional intervention, respiratory and intestinal microbiomes proved to be resilient. Dominant pathogens drove the composition and functionality of the microbiome. Further studies are required to understand which therapy could destabilize the dominant disease-associated microbial composition of pwCF.

Keywords: Antibiotics; Cystic fibrosis; Gut-lung axis; Intervention; Microbiome; Nutrition; Physical exercise.

Fig. 1

A Composition of sputum microbiome was highly explained by dominant taxa and severity of the disease between patients. Principal Coordinate Analysis (PCoA) based on Bray–Curtis dissimilarity between samples. Points are colored by the dominant taxa detected and the shape indicates the phenotype severity based on ppFEV₁. Crosses indicate the centroid of each cluster and the dashed lines the mean distances between clusters. Ellipses represent the 95% confidence interval of intra-cluster variability of sample distance to the centroid. Distances on PCo1 and PCo2 between groups based on dominant taxa were compared by Mann–Whitney U tests with Benjamini–Hochberg correction for multiple testing (Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1). Cluster of Staphylococcus dominated samples were significantly different to clusters with Pseudomonas and Streptococcus dominated microbiomes on PCo1 and PCo2. B Changes in sputum microbiome composition within patients between visits. A shift of dominance between Staphylococcus to Stenotrophomonas was linked to a high Bray–Curtis dissimilarity between visits for patient 9 (P9). For panel B and E each color represents samples from the same patient. C Association of dominant genera to alpha-diversity measures in sputum microbiomes. D Heatmap of the most abundant genera (> 0.5% mean relative abundance) for each cluster of dominant taxa observed on panel A). Dendrogram indicates Ward clustering of samples. E Relationship between dominant taxa in sputum and lung function. Lung function is represented by ppFEV1 at each visit, points are colored by patient and the size represents the relative abundance in percentage at the sample. Dashed read line indicates the threshold of 70% ppFEV1 to separate moderate (< 70%) from mild (> 70%) severity

Fig. 2

Association of microbial features to clinical and nutritional covariates from sputum microbiome. The plot shows bacterial taxa, at genus levels, altered significantly (post hoc FDR < 0.05) in abundance and showing a study effect, compared to baseline and follow-up visits. Signed effect sizes are shown through marker color. Direction of cuneiform plot indicates if taxa were enriched or depleted within the covariate groups (e.g. pwCF with oral Macrolide treatment displayed lower abundance of Neisseria, higher ppFVC correlated positively with increased Neisseria etc.). Size of cuneiform plots indicate significance. Covariates not shown had no difference in rank-transformed values (Cliff’s delta = 0). PPI = proton pump inhibitors, inh = inhalative, iv = intravenous

Fig. 3

A Impact of antibiotic intake within the last 15 days prior sampling on alpha diversity measures and 16S copy number. Asterisks indicate significance for LMMs with Benjamini–Hochberg correction for multiple testing (FDR: ‘*’ < 0.05). B Relation between antibiotic burden and alpha diversity measures in sputum. Antibiotic burden had a significant impact on sputum evenness