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Impact of the CFTR-potentiator ivacaftor on airway microbiota in cystic fibrosis patients carrying a G551D mutation.

Bernarde C, Keravec M, Mounier J, Gouriou S, Rault G, Férec C, Barbier G, Héry-Arnaud G - PLoS ONE (2015)

Bottom Line: There was no significant difference in total bacterial load before and after treatment.Comparison of global community composition found no significant changes in microbiota.Two OTUs, however, showed contrasting dynamics: after initiation of ivacaftor, the relative abundance of the anaerobe Porphyromonas 1 increased (p<0.01) and that of Streptococcus 1 (S. mitis group) decreased (p<0.05), possibly in relation to the anti-Gram-positive properties of ivacaftor.

View Article: PubMed Central - PubMed

Affiliation: EA 3882-Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, Université de Brest, Brest, France.

ABSTRACT

Background: Airway microbiota composition has been clearly correlated with many pulmonary diseases, and notably with cystic fibrosis (CF), an autosomal genetic disorder caused by mutation in the CF transmembrane conductance regulator (CFTR). Recently, a new molecule, ivacaftor, has been shown to re-establish the functionality of the G551D-mutated CFTR, allowing significant improvement in lung function.

Objective and methods: The purpose of this study was to follow the evolution of the airway microbiota in CF patients treated with ivacaftor, using quantitative PCR and pyrosequencing of 16S rRNA amplicons, in order to identify quantitative and qualitative changes in bacterial communities. Three G551D children were followed up longitudinally over a mean period of more than one year covering several months before and after initiation of ivacaftor treatment.

Results: 129 operational taxonomy units (OTUs), representing 64 genera, were identified. There was no significant difference in total bacterial load before and after treatment. Comparison of global community composition found no significant changes in microbiota. Two OTUs, however, showed contrasting dynamics: after initiation of ivacaftor, the relative abundance of the anaerobe Porphyromonas 1 increased (p<0.01) and that of Streptococcus 1 (S. mitis group) decreased (p<0.05), possibly in relation to the anti-Gram-positive properties of ivacaftor. The anaerobe Prevotella 2 correlated positively with the pulmonary function test FEV-1 (r=0.73, p<0.05). The study confirmed the presumed positive role of anaerobes in lung function.

Conclusion: Several airway microbiota components, notably anaerobes (obligate or facultative anaerobes), could be valuable biomarkers of lung function improvement under ivacaftor, and could shed light on the pathophysiology of lung disease in CF patients.

No MeSH data available.


Related in: MedlinePlus

Principal component analysis (PCA) of the 20 sputum samples according to different quantitative variables.For each sample, the name contained the group (BT: before ivacaftor treatment; AT: after the beginning of ivacaftor treatment), cytologic class (1 to 5) and presence (AB+) or absence (AB-) of antibiotic treatment. The F1 and F2 axes explained respectively 35% and 22% of the variability. Only 15 samples are represented, because FEV-1 data were lacking for 5 samples (Table 1). The F1 axis was positively correlated with FEV-1, Peptostreptococcus (Pept), Prevotella 1&2 (Pre1&2), Porphyromonas 1 (Por1), Rothia 1&2 (Rot1&2) and Streptococcus 2 (Str2). These variables also correlated positively with each other, suggesting that these taxa may be positively correlated with FEV-1 improvement. In contrast, the F1 axis correlated negatively with qPCR, Haemophilus 1 (Ha1), Neisseria 1&2 (Neis1&2), Staphylococcus aureus (Sta) and Streptococcus 3 (Str3), indicating that these taxa may be more abundant and with higher bacterial density when respiratory capacity is lower. The F2 axis opposed S. aureus and OTUs not belonging to the core microbiota (Oth) to Veillonella (Veil), Neisseria 1&2, Haemophilus 1, Gemella (Ge) and higher diversity indices (Shannon index (Shan), phylogenetic diversity whole tree (PDwt) and observed species (ObsSp)).
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pone.0124124.g003: Principal component analysis (PCA) of the 20 sputum samples according to different quantitative variables.For each sample, the name contained the group (BT: before ivacaftor treatment; AT: after the beginning of ivacaftor treatment), cytologic class (1 to 5) and presence (AB+) or absence (AB-) of antibiotic treatment. The F1 and F2 axes explained respectively 35% and 22% of the variability. Only 15 samples are represented, because FEV-1 data were lacking for 5 samples (Table 1). The F1 axis was positively correlated with FEV-1, Peptostreptococcus (Pept), Prevotella 1&2 (Pre1&2), Porphyromonas 1 (Por1), Rothia 1&2 (Rot1&2) and Streptococcus 2 (Str2). These variables also correlated positively with each other, suggesting that these taxa may be positively correlated with FEV-1 improvement. In contrast, the F1 axis correlated negatively with qPCR, Haemophilus 1 (Ha1), Neisseria 1&2 (Neis1&2), Staphylococcus aureus (Sta) and Streptococcus 3 (Str3), indicating that these taxa may be more abundant and with higher bacterial density when respiratory capacity is lower. The F2 axis opposed S. aureus and OTUs not belonging to the core microbiota (Oth) to Veillonella (Veil), Neisseria 1&2, Haemophilus 1, Gemella (Ge) and higher diversity indices (Shannon index (Shan), phylogenetic diversity whole tree (PDwt) and observed species (ObsSp)).

Mentions: To go further in the comparison of the community structure of samples, principal coordinate analysis (PCoA) and unweighted pair-group method using average linkages (UPGMA) clustering were performed using Bray-Curtis (Fig 2) and UniFrac (S2 Fig) distance metrics. Both analyses highlighted a clustering of samples from patient GM (Pseudomonas aeruginosa (Pa) status: “never”), whereas the Pa-intermittent patients RM and PM exhibited more similar microbiotas (Figs 2 and S2; see Table 1 for patient characteristics). This may suggest that, whereas it was thought that each CF patient harbors a specific airway microbiota [8], shared microbiological history, such as P. aeruginosa acquisition, can make for common points in the microbiota. Moreover, patient GM was the only one who was not under antibiotherapy at the time of sampling (Table 1), which could also be an explanation. Likewise, principal component analysis (PCA) distinguished GM’s samples, which were all negatively located on the F1 axis (Fig 3).


Impact of the CFTR-potentiator ivacaftor on airway microbiota in cystic fibrosis patients carrying a G551D mutation.

Bernarde C, Keravec M, Mounier J, Gouriou S, Rault G, Férec C, Barbier G, Héry-Arnaud G - PLoS ONE (2015)

Principal component analysis (PCA) of the 20 sputum samples according to different quantitative variables.For each sample, the name contained the group (BT: before ivacaftor treatment; AT: after the beginning of ivacaftor treatment), cytologic class (1 to 5) and presence (AB+) or absence (AB-) of antibiotic treatment. The F1 and F2 axes explained respectively 35% and 22% of the variability. Only 15 samples are represented, because FEV-1 data were lacking for 5 samples (Table 1). The F1 axis was positively correlated with FEV-1, Peptostreptococcus (Pept), Prevotella 1&2 (Pre1&2), Porphyromonas 1 (Por1), Rothia 1&2 (Rot1&2) and Streptococcus 2 (Str2). These variables also correlated positively with each other, suggesting that these taxa may be positively correlated with FEV-1 improvement. In contrast, the F1 axis correlated negatively with qPCR, Haemophilus 1 (Ha1), Neisseria 1&2 (Neis1&2), Staphylococcus aureus (Sta) and Streptococcus 3 (Str3), indicating that these taxa may be more abundant and with higher bacterial density when respiratory capacity is lower. The F2 axis opposed S. aureus and OTUs not belonging to the core microbiota (Oth) to Veillonella (Veil), Neisseria 1&2, Haemophilus 1, Gemella (Ge) and higher diversity indices (Shannon index (Shan), phylogenetic diversity whole tree (PDwt) and observed species (ObsSp)).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4390299&req=5

pone.0124124.g003: Principal component analysis (PCA) of the 20 sputum samples according to different quantitative variables.For each sample, the name contained the group (BT: before ivacaftor treatment; AT: after the beginning of ivacaftor treatment), cytologic class (1 to 5) and presence (AB+) or absence (AB-) of antibiotic treatment. The F1 and F2 axes explained respectively 35% and 22% of the variability. Only 15 samples are represented, because FEV-1 data were lacking for 5 samples (Table 1). The F1 axis was positively correlated with FEV-1, Peptostreptococcus (Pept), Prevotella 1&2 (Pre1&2), Porphyromonas 1 (Por1), Rothia 1&2 (Rot1&2) and Streptococcus 2 (Str2). These variables also correlated positively with each other, suggesting that these taxa may be positively correlated with FEV-1 improvement. In contrast, the F1 axis correlated negatively with qPCR, Haemophilus 1 (Ha1), Neisseria 1&2 (Neis1&2), Staphylococcus aureus (Sta) and Streptococcus 3 (Str3), indicating that these taxa may be more abundant and with higher bacterial density when respiratory capacity is lower. The F2 axis opposed S. aureus and OTUs not belonging to the core microbiota (Oth) to Veillonella (Veil), Neisseria 1&2, Haemophilus 1, Gemella (Ge) and higher diversity indices (Shannon index (Shan), phylogenetic diversity whole tree (PDwt) and observed species (ObsSp)).
Mentions: To go further in the comparison of the community structure of samples, principal coordinate analysis (PCoA) and unweighted pair-group method using average linkages (UPGMA) clustering were performed using Bray-Curtis (Fig 2) and UniFrac (S2 Fig) distance metrics. Both analyses highlighted a clustering of samples from patient GM (Pseudomonas aeruginosa (Pa) status: “never”), whereas the Pa-intermittent patients RM and PM exhibited more similar microbiotas (Figs 2 and S2; see Table 1 for patient characteristics). This may suggest that, whereas it was thought that each CF patient harbors a specific airway microbiota [8], shared microbiological history, such as P. aeruginosa acquisition, can make for common points in the microbiota. Moreover, patient GM was the only one who was not under antibiotherapy at the time of sampling (Table 1), which could also be an explanation. Likewise, principal component analysis (PCA) distinguished GM’s samples, which were all negatively located on the F1 axis (Fig 3).

Bottom Line: There was no significant difference in total bacterial load before and after treatment.Comparison of global community composition found no significant changes in microbiota.Two OTUs, however, showed contrasting dynamics: after initiation of ivacaftor, the relative abundance of the anaerobe Porphyromonas 1 increased (p<0.01) and that of Streptococcus 1 (S. mitis group) decreased (p<0.05), possibly in relation to the anti-Gram-positive properties of ivacaftor.

View Article: PubMed Central - PubMed

Affiliation: EA 3882-Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, Université de Brest, Brest, France.

ABSTRACT

Background: Airway microbiota composition has been clearly correlated with many pulmonary diseases, and notably with cystic fibrosis (CF), an autosomal genetic disorder caused by mutation in the CF transmembrane conductance regulator (CFTR). Recently, a new molecule, ivacaftor, has been shown to re-establish the functionality of the G551D-mutated CFTR, allowing significant improvement in lung function.

Objective and methods: The purpose of this study was to follow the evolution of the airway microbiota in CF patients treated with ivacaftor, using quantitative PCR and pyrosequencing of 16S rRNA amplicons, in order to identify quantitative and qualitative changes in bacterial communities. Three G551D children were followed up longitudinally over a mean period of more than one year covering several months before and after initiation of ivacaftor treatment.

Results: 129 operational taxonomy units (OTUs), representing 64 genera, were identified. There was no significant difference in total bacterial load before and after treatment. Comparison of global community composition found no significant changes in microbiota. Two OTUs, however, showed contrasting dynamics: after initiation of ivacaftor, the relative abundance of the anaerobe Porphyromonas 1 increased (p<0.01) and that of Streptococcus 1 (S. mitis group) decreased (p<0.05), possibly in relation to the anti-Gram-positive properties of ivacaftor. The anaerobe Prevotella 2 correlated positively with the pulmonary function test FEV-1 (r=0.73, p<0.05). The study confirmed the presumed positive role of anaerobes in lung function.

Conclusion: Several airway microbiota components, notably anaerobes (obligate or facultative anaerobes), could be valuable biomarkers of lung function improvement under ivacaftor, and could shed light on the pathophysiology of lung disease in CF patients.

No MeSH data available.


Related in: MedlinePlus