Trikafta therapy alters the CF lung mucus metabolome reshaping microbiome niche space
Sosinski, L.; Martin-Hernandez, C.; Neugebauer, K. A.; Ghuneim, L.-A. J.; Guzior, D. V.; Castillo-Bahena, A.; Mielke, J.; Thomas, R.; McClelland, M.; Conrad, D.; Quinn, R. A.
Show abstract
BackgroundNovel small molecule therapies for cystic fibrosis (CF) are showing promising efficacy and becoming more widely available since recent FDA approval. The newest of these is a triple therapy of Elexacaftor-Tezacaftor-Ivacaftor (ETI, Trikafta(R)). Little is known about how these drugs will affect polymicrobial lung infections, which are the leading cause of morbidity and mortality among people with CF (pwCF). Methodswe analyzed the sputum microbiome and metabolome from pwCF (n=24) before and after ETI therapy using 16S rRNA gene amplicon sequencing and untargeted metabolomics. ResultsThe lung microbiome diversity, particularly its evenness, was increased (p = 0.044) and the microbiome profiles were different between individuals before and after therapy (PERMANOVA F=1.92, p=0.044). Despite these changes, the microbiomes were more similar within an individual than across the sampled population. There were no specific microbial taxa that were different in abundance before and after therapy, but collectively, the log-ratio of anaerobes to classic CF pathogens significantly decreased. The sputum metabolome also showed changes due to ETI. Beta-diversity increased after therapy (PERMANOVA F=4.22, p=0.022) and was characterized by greater variation across subjects while on treatment. This significant difference in the metabolome was driven by a decrease in peptides, amino acids, and metabolites from the kynurenine pathway. Metabolism of the three small molecules that make up ETI was extensive, including previously uncharacterized structural modifications. ConclusionsThis study shows that ETI therapy affects both the microbiome and metabolome of airway mucus. This effect was stronger on sputum biochemistry, which may reflect changing niche spaces for microbial residency in lung mucus as the drugs effects take hold, which then leads to changing microbiology. FundingThis project was funded by a National Institute of Allergy and Infectious Disease Grant R01AI145925
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Statistical evaluation of metaproteomics and 16s rRNA amplicon sequencing techniques for the study of the gut microbiota establishment of infants with cystic fibrosis 95%
- Low diversity and instability of the sinus microbiota over time in adults with cystic fibrosis 94%
- Mild and severe SARS-CoV-2 infection induces respiratory and intestinal microbiome changes in the K18-hACE2 transgenic mouse model 93%
Similar papers in this journal
Similar papers in this journal
- Responsiveness to pulmonary rehabilitation in COPD is associated with changes in microbiota 94%
- Longitudinal Dynamics and Site-Specific Recovery of the Human Respiratory Microbiome Following Smoking Cessation 91%
- Metagenomic identification of severe pneumonia pathogens with rapid Nanopore sequencing in mechanically-ventilated patients. 91%
Similar papers in this journal
- Optimisation of DNA extraction from nasal lining fluid to assess the nasal microbiome using third-generation sequencing 93%
- Furin Inhibition Protects Against Acute Lung Injury in a Mouse Model of Pseudomonas Aeruginosa Infection 91%
- Targeting ATP12A, a non-gastric proton pump alpha subunit, for idiopathic pulmonary fibrosis treatment 91%
Similar papers in this journal
- Upper respiratory microbial communities of healthy populations are shaped by niche and age 93%
- Remodelling of cystic fibrosis respiratory microbiota in response to extended Elexacaftor/Tezacaftor/Ivacaftor therapy 93%
- Application of an ecology-based analytic approach to discriminate signal and noise in low-biomass microbiome studies: whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.