Lumacaftor/ivacaftor changes the lung microbiome and metabolome in cystic fibrosis patients

Anne H Neerincx, Katrine Whiteson, Joann L Phan, Paul Brinkman, Mahmoud I Abdel-Aziz, Els J M Weersink, Josje Altenburg, Christof J Majoor, Anke H Maitland-van der Zee, Lieuwe D J Bos, Anne H Neerincx, Katrine Whiteson, Joann L Phan, Paul Brinkman, Mahmoud I Abdel-Aziz, Els J M Weersink, Josje Altenburg, Christof J Majoor, Anke H Maitland-van der Zee, Lieuwe D J Bos

Abstract

Rationale: Targeted cystic fibrosis (CF) therapy with lumacaftor/ivacaftor partly restores chloride channel function and improves epithelial fluid transport in the airways. Consequently, changes may occur in the microbiome, which is adapted to CF lungs.

Objectives: To investigate the effects of lumacaftor/ivacaftor on respiratory microbial composition and microbial metabolic activity by repeatedly sampling the lower respiratory tract.

Methods: This was a single-centre longitudinal observational cohort study in adult CF patients with a homozygous Phe508del mutation. Lung function measurements and microbial cultures of sputum were performed as part of routine care. An oral and nasal wash, and a breath sample, were collected before and every 3 months after starting therapy, for up to 12 months.

Results: Twenty patients were included in this study. Amplicon 16S RNA and metagenomics sequencing revealed that Pseudomonas aeruginosa was most abundant in sputum and seemed to decrease after 6 months of treatment, although this did not reach statistical significance after correction for multiple testing. Two types of untargeted metabolomics analyses in sputum showed a change in metabolic composition between 3 and 9 months that almost returned to baseline levels after 12 months of treatment. The volatile metabolic composition of breath was significantly different after 3 months and remained different from baseline until 12 months follow-up.

Conclusions: After starting CF transmembrane conductance regulator (CFTR) modulating treatment in CF patients with a homozygous Phe508del mutation, a temporary and moderate change in the lung microbiome is observed, which is mainly characterised by a reduction in the relative abundance of Pseudomonas aeruginosa.

Conflict of interest statement

Conflict of interest: A.H. Neerincx has nothing to disclose. Conflict of interest: K. Whiteson has nothing to disclose. Conflict of interest: J.L. Phan has nothing to disclose. Conflict of interest: P. Brinkman has nothing to disclose. Conflict of interest: M.I. Abdel-Aziz reports an Egyptian Government PhD Scholarship outside the submitted work. Conflict of interest: E.J.M. Weersink has nothing to disclose. Conflict of interest: J. Altenburg has nothing to disclose. Conflict of interest: C.J. Majoor has nothing to disclose. Conflict of interest: A.H. Maitland-van der Zee reports an Innovation Grant from Vertex outside the submitted work. Conflict of interest: L.D.J. Bos has nothing to disclose.

Copyright ©ERS 2021.

Figures

FIGURE 1
FIGURE 1
Change in the lung microbiome during lumacaftor/ivacaftor treatment. a) Composition of the lung microbiome measured in sputum by 16S rRNA and metagenomics sequencing. The x-axis indicates the visits, with visit 1 before start of treatment and subsequent visits 3 months apart. The y-axis indicates relative abundance. The upper panel shows results from 16S sequencing and the lower panel from metagenomics analysis. Fill colours correspond to the genus level annotation. b) Cumulative relative abundance of Pseudomonas aeruginosa based on ASVs/metagenomes matched to this particular species. Only patients with a non-zero Pseudomonas aeruginosa count at baseline are included in the graph. The x-axis indicates the visits, with visit 1 before start of treatment and subsequent visits 3 months apart. The y-axis indicates absolute change in relative abundance. The red line indicates the median value per visit with the thinner lines indicating the quantile intervals. The upper panel shows results from 16S sequencing and the lower panel from metagenomics analysis. c) Shannon diversity. The x-axis indicates the visits, with visit 1 before start of treatment and subsequent visits 3 months apart. The y-axis indicates Shannon diversity. The red line indicates the mean value per visit. The upper panel shows results from 16S sequencing and the lower panel from metagenomics analysis.
FIGURE 2
FIGURE 2
Most abundant amplicon sequence variants (ASVs) in sputum, nasal wash and oral wash. The mean relative abundance of the top 20 most prominent ASVs found in a) sputum, b) oral wash and c) nasal wash of the included patients. The bar indicates the mean relative abundance while the error bar gives the standard deviation of the mean. d) Microbial composition between sample materials, with the x-axis indicating principal coordinate 1 (PCo var 1) and the y-axis principal coordinate 2 (PCo var 2) on the Bray–Curtis dissimilarity measure of 16S microbiome data. The sputum samples are distinct from the oral and nasal samples (p<0.0001).
FIGURE 3
FIGURE 3
Change in metabolic composition of sputum and breath during lumacaftor/ivacaftor treatment. The x-axis is the sPLS projected variable 1 (sPLS var 1, containing 9, 9 and 23% of variation) and the y-axis sPLS projected variable 2 (sPLS var 2, containing 12, 6 and 8% of variation). a and b) Change in metabolic composition of sputum after the start of lumacaftor/ivacaftor treatment. There is a significant change from visit 1 (before start of treatment) to visit 3 (6 months after treatment; p=0.0015 for dataset 1 and p=0.004 for dataset 2) with tendency to return to baseline after 12 months (p=0.031 for dataset 1 and p=0.014 for dataset 2). a) Data obtained by GC-TOF-MS (n=69). b) Data obtained by HILIC-TOF-MS (n=79). c) Change in metabolomic composition of exhaled breath (n=68) after the start of lumacaftor/ivacaftor. After start of treatment there is a significant difference in metabolic composition (p<0.0001) that does not return to baseline at the end of the observation period (p=0.0002).
FIGURE 4
FIGURE 4
Tryptophan and its metabolites in sputum and the association with Pseudomonas aeruginosa. a) Tryptophan metabolism in Pseudomonas aeruginosa. There was a strong association between tryptophan and kynurenine and between kynurenine and kynurenic acid concentration in sputum. There was no association between kynurenine and 3-hydroxykynurenine concentration in sputum. This suggests that reaction 2 predominated over reaction 3 in the lungs of CF patients. b) Tryptophan, kynurenine and kynurenic acid are associated with relative abundance of Pseudomonas aeruginosa in sputum, but 3-hydroxykynurenine is not. FOR: formamidase; IDO: indoleamine-2,3-dioxygenase; KAT 1–IV: kynurenine aminotransferase; KYNU: kynureninase; TDO: tryptophan-2,3-dioxygenase.

References

    1. Stoltz DA, Meyerholz DK, Welsh MJ. Origins of cystic fibrosis lung disease. N Engl J Med 2015; 372: 351–362. doi:10.1056/NEJMra1300109
    1. Davies JC, Moskowitz SM, Brown C, et al. . VX-659–tezacaftor–ivacaftor in patients with cystic fibrosis and one or two Phe508del alleles. N Engl J Med 2018; 379: 1599–1611. doi:10.1056/NEJMoa1807119
    1. Hoffman LR, Ramsey BW. Cystic fibrosis therapeutics: the road ahead. Chest 2013; 143: 207–213. doi:10.1378/chest.12-1639
    1. Keating D, Marigowda G, Burr L, et al. . VX-445–tezacaftor–ivacaftor in patients with cystic fibrosis and one or two Phe508del alleles. N Engl J Med 2018; 379: 1612–1620. doi:10.1056/NEJMoa1807120
    1. Ramsey BW, Davies J, McElvaney NG, et al. . A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N Engl J Med 2011; 365: 1663–1672. doi:10.1056/NEJMoa1105185
    1. Taylor-Cousar JL, Munck A, McKone EF, et al. . Tezacaftor–ivacaftor in patients with cystic fibrosis homozygous for Phe508del. N Engl J Med 2017; 377: 2013–2023. doi:10.1056/NEJMoa1709846
    1. Wainwright CE, Elborn JS, Ramsey BW, et al. . Lumacaftor–ivacaftor in patients with cystic fibrosis homozygous for Phe508del CFTR. N Engl J Med 2015; 373: 220–231. doi:10.1056/NEJMoa1409547
    1. Bobadilla JL, Macek M Jr, Fine JP, et al. . Cystic fibrosis: a worldwide analysis of CFTR mutations—correlation with incidence data and application to screening. Hum Mutat 2002; 19: 575–606. doi:10.1002/humu.10041
    1. Gräber S, Dopfer C, Naehrlich L, et al. . Effects of Lumacaftor-Ivacaftor Therapy on CFTR Function in Phe508del Homozygous Patients with Cystic Fibrosis. Eur Respiratory Soc 2018; 197: 1433–1442.
    1. Bernarde C, Keravec M, Mounier J, et al. . Impact of the CFTR-potentiator ivacaftor on airway microbiota in cystic fibrosis patients carrying a G551D mutation. PLoS One 2015; 10: e0124124. doi:10.1371/journal.pone.0124124
    1. Harris JK, Wagner BD, Zemanick ET, et al. . Changes in airway microbiome and inflammation with ivacaftor treatment in patients with cystic fibrosis and the G551D mutation. Ann Am Thorac Soc 2020; 17: 212–220. doi:10.1513/AnnalsATS.201907-493OC
    1. Hisert KB, Heltshe SL, Pope C, et al. . Restoring cystic fibrosis transmembrane conductance regulator function reduces airway bacteria and inflammation in people with cystic fibrosis and chronic lung infections. Am J Respir Crit Care Med 2017; 195: 1617–1628. doi:10.1164/rccm.201609-1954OC
    1. Peleg AY, Choo JM, Langan KM, et al. . Antibiotic exposure and interpersonal variance mask the effect of ivacaftor on respiratory microbiota composition. J Cyst Fibros 2018; 17: 50–56. doi:10.1016/j.jcf.2017.08.002
    1. Rogers GB, Taylor SL, Hoffman LR, et al. . The impact of CFTR modulator therapies on CF airway microbiology. J Cyst Fibros 2020; 19: 359–364. doi:10.1016/j.jcf.2019.07.008
    1. Bevivino A, Bacci G, Drevinek P, et al. . Deciphering the ecology of cystic fibrosis bacterial communities: towards systems-level integration. Trends Mol Med 2019; 25: 1110–1122. doi:10.1016/j.molmed.2019.07.008
    1. Quinn RA, Lim YW, Mak TD, et al. . Metabolomics of pulmonary exacerbations reveals the personalized nature of cystic fibrosis disease. PeerJ 2016; 4: e2174. doi:10.7717/peerj.2174
    1. Quinn RA, Lim YW, Maughan H, et al. . Biogeochemical forces shape the composition and physiology of polymicrobial communities in the cystic fibrosis lung. MBio 2014; 5: e00956-13. doi:10.1128/mBio.00956-13
    1. Whiteson KL, Meinardi S, Lim YW, et al. . Breath gas metabolites and bacterial metagenomes from cystic fibrosis airways indicate active pH neutral 2, 3-butanedione fermentation. ISME J 2014; 8: 1247–1258. doi:10.1038/ismej.2013.229
    1. Comeau AM, Douglas GM, Langille MG. Microbiome helper: a custom and streamlined workflow for microbiome research. MSystems 2017; 2: e00127-16. 10.1128/mSystems.00127-16.
    1. Callahan BJ, McMurdie PJ, Rosen MJ, et al. . DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13: 581–583. doi:10.1038/nmeth.3869
    1. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011; 27: 863–864. doi:10.1093/bioinformatics/btr026
    1. Nayfach S, Rodriguez-Mueller B, Garud N, et al. . An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res 2016; 26: 1612–1625. doi:10.1101/gr.201863.115
    1. Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem 2016; 88: 524–545. doi:10.1021/acs.analchem.5b04491
    1. Lê Cao K-A, Boitard S, Besse P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 2011; 12: 253. doi:10.1186/1471-2105-12-253
    1. Rohart F, Gautier B, Singh A, et al. . mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 2017; 13: e1005752. doi:10.1371/journal.pcbi.1005752
    1. Donaldson SH, Laube BL, Corcoran TE, et al. . Effect of ivacaftor on mucociliary clearance and clinical outcomes in cystic fibrosis patients with G551D-CFTR. JCI insight 2018; 3: e122695. doi:10.1172/jci.insight.122695
    1. Barnaby R, Koeppen K, Nymon A, et al. . Lumacaftor (VX-809) restores the ability of CF macrophages to phagocytose and kill Pseudomonas aeruginosa. Am J Physiol Lung Cell Mol Physiol 2018; 314: L432–L438. doi:10.1152/ajplung.00461.2017
    1. Moradali MF, Ghods S, Rehm BH. Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence. Front Cell Infect Microbiol 2017; 7: 39. doi:10.3389/fcimb.2017.00039
    1. Bortolotti P, Hennart B, Thieffry C, et al. . Tryptophan catabolism in Pseudomonas aeruginosa and potential for inter-kingdom relationship. BMC Microbiol 2016; 16: 1–10. doi:10.1186/s12866-016-0756-x
    1. Wirthgen E, Hoeflich A, Rebl A, et al. . Kynurenic acid: the Janus-faced role of an immunomodulatory tryptophan metabolite and its link to pathological conditions. Front Immunol 2018; 8: 1957. doi:10.3389/fimmu.2017.01957
    1. Caverly LJ, Lu J, Carmody LA, et al. . Measures of cystic fibrosis airway microbiota during periods of clinical stability. Ann Am Thorac Soc 2019; 16: 1534–1542. doi:10.1513/AnnalsATS.201903-270OC
    1. Carney SM, Clemente JC, Cox MJ, et al. . Methods in lung microbiome research. Am J Respir Cell Mol Biol 2020; 62: 283–299. doi:10.1165/rcmb.2019-0273TR

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever