Multi-omics Profiling Predicts Allograft Function after Lung Transplantation

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 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 in 78 patients on bronchoalveolar lavage samples from lung donors and recipients. We analyzed 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 analyzed 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 (FEV1) is a major characteristic of lung allograft dysfunction in transplant recipients. By using a machine learning approach, we could accurately predict future changes in FEV1 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.