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. 2023 Dec 16;24:317. doi: 10.1186/s12931-023-02630-z

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — The average microbiome and metabolome diversity of subjects in this study. A Average change in the alpha-diversity (Shannon index) of lung microbiome and metabolome of pwCF off/on ETI treatment per day. Square shapes represent individuals who did not take ETI, while circular shapes are associated with those who did take ETI. B Average of the beta-diversity of the lung microbiome and metabolome of pwCF before and after ETI per day. Weighted UniFrac distance was used for microbiome data and Bray–Curtis dissimilarity for metabolome data. P-values obtained from the Wilcoxon test are displayed. Scatterplots of the predicted vs. observed C microbiome and D metabolome association with the time in days since pwCF started on ETI treatment. This data was obtained from RF regression analysis of each subject as well as their Pearson correlation tests