Multi-omics profiling predicts allograft function after lung transplantation
Martin L. Watzenboeck, Anna-Dorothea Gorki, Federica Quattrone, Riem Gawish, Stefan Schwarz, Christopher Lambers, Péter Jaksch, Karin Lakovits, Sophie Zahalka, Nina Rahimi, Philipp Starkl, Dörte Symmank, Tyler Artner, Céline Pattaroni, Nikolaus Fortelny, Kristaps Klavins, Florian Frommlet, Benjamin J. Marsland, Konrad Höetzenecker, Stefanie Widder, Sylvia Knapp
Abstract
Rationale Lung transplantation is the ultimate treatment option for patients with end-stage respiratory diseases but bears the highest mortality rate among all solid organ transplantations due to chronic lung allograft dysfunction (CLAD). The mechanisms leading to CLAD remain elusive due to an insufficient understanding of the complex post-transplant adaptation processes. Objectives To better understand these lung adaptation processes after transplantation and to investigate their association with future changes in allograft function. Methods We performed an exploratory cohort study of bronchoalveolar lavage samples from 78 lung recipients and donors. We analysed the alveolar microbiome using 16S rRNA sequencing, the cellular composition using flow cytometry, as well as metabolome and lipidome profiling. Measurements and main results We established distinct temporal dynamics for each of the analysed data sets. Comparing matched donor and recipient samples, we revealed that recipient-specific as well as environmental factors, rather than the donor microbiome, shape the long-term lung microbiome. We further discovered that the abundance of certain bacterial strains correlated with underlying lung diseases even after transplantation. A decline in forced expiratory volume during the first second (FEV 1 ) is a major characteristic of lung allograft dysfunction in transplant recipients. By using a machine learning approach, we could accurately predict future changes in FEV 1 from our multi-omics data, whereby microbial profiles showed a particularly high predictive power. Conclusion Bronchoalveolar microbiome, cellular composition, metabolome and lipidome show specific temporal dynamics after lung transplantation. The lung microbiome can predict future changes in lung function with high precision.