| Literature DB >> 32384684 |
Jose A Caparrós-Martín1,2, Stephanie Flynn3, F Jerry Reen3, David F Woods3, Patricia Agudelo-Romero4,5, Sarath C Ranganathan6,7,8, Stephen M Stick4,9,10, Fergal O'Gara1,2,3,9.
Abstract
Background: Cystic fibrosis (CF) is a hereditary disorder in which persistent unresolved inflammation and recurrent airway infections play major roles in the initiation and progression of the disease. Little is known about triggering factors modulating the transition to chronic microbial infection and inflammation particularly in young children. Cystic fibrosis respiratory disease starts early in life, with the detection of inflammatory markers and infection evident even before respiratory symptoms arise. Thus, identifying factors that dysregulate immune responsiveness at the earliest stages of the disease will provide novel targets for early therapeutic intervention.Entities:
Keywords: bile acids; cystic fibrosis; gut-lung axis
Year: 2020 PMID: 32384684 PMCID: PMC7277992 DOI: 10.3390/diagnostics10050282
Source DB: PubMed Journal: Diagnostics (Basel) ISSN: 2075-4418
Figure 1Cluster analysis of BALF samples based on their corresponding bile acids profiles. (A). Heatmap showing the bile acid profiles for each BALF sample included in this study. Columns represent individual samples and rows the different bile acid species profiled. An agglomerative hierarchical clustering approach was applied for grouping the samples based on Ward’s minimum variance method [28,29]. Colour key represents the concentration of each bile acid profiled in micromolar (µM) scale. The side colour bar indicates whether the sample was included in cluster 1 (red) or in cluster 2 (blue), after internal validation of the optimum number of clusters. (B). Boxplots show the total bile acid concentration for cluster 1 and cluster 2. Asterisks indicate statistical significance for the differences observed between clusters in the context of Mann-Whitney test. **** p-value < 0.0001.
Quantification of inflammatory markers and white cell composition in the BALF samples analysed in this study. Data represents the median value and the interquartile range (1st quartile-3rd quartile) of each variable per cluster. Mann-Whitney test was used for pairwise comparisons between group levels. Family-wise error rate was controlled using Bonferroni-corrected p-values.
| Variable | Cluster 1 | Cluster 2 | |
|---|---|---|---|
|
| 100 (100–100) | 100 (100–100) | 1 |
|
| 1075 (300–2150) | 810 (250–1890) | 1 |
|
| 83.8 (54.44–122.52) | 10 (10–32.58) | 0.006 |
|
| 57.79 (34.73–77.65) | 10 (10–10) | 0.041 |
|
| |||
|
| 0.32 (0.23–0.57) | 0.29 (0.18–0.38) | 1 |
|
| 74.8 (62.4–80.05) | 82.8 (73.1–87.3) | 0.038 |
|
| 83.33 (61.58–93.42) | 86.67 (70.33–94) | 1 |
|
| 16.33 (6.58–38.24) | 12 (4.33–29) | 1 |
|
| 0 (0–0.33) | 0 (0–1.67) | 1 |
|
| 0 (0–0.33) | 0 (0–0.3) | 1 |
Quantification of structural lung disease at the time of the collection of the BALF samples from patients classified in cluster 1 or cluster 2. Lung disease was scored using either volumetric CT scans or the recently reported PRAGMA-CF methodology [40]. Calculated probabilities for rejecting the null hypothesis of samples coming from the same population in the context of the Mann-Whitney test were corrected for multiple comparisons using Bonferroni approach. BW, bronchial wall; Mu, Mucus; %Dis, volume proportion of the lung with airway disease [40]; % Bx, volume proportion of the lung with bronchiectasis [40]; %TA, volume proportion of the lung with trapped air [40], % Atelec, volume proportion of the lung with atelectasis [40].
| Variable | Cluster 1 | Cluster 2 | |
|---|---|---|---|
|
| 1 (0–2) | 1 (0–3.5) | 1 |
|
| 10 (7–10) | 8 (5–10) | 1 |
|
| 1 (0.75–4) | 2 (1–4) | 1 |
|
| 0 (0–0.25) | 0 (0–1) | 1 |
|
| 2.99 (2.39–4.13) | 2.17 (1.1–3.8) | 0.94 |
|
| 0 (0–0.075) | 0 (0–0.38) | 1 |
|
| 2.06 (0.96–6.14) | 1.28 (0.19–5.86) | 1 |
|
| 0.06 (0–1.77) | 0.2 (0–0.97) | 1 |