Multi-omics profiling predicts allograft function after lung transplantation.

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.
Competing Interests: Conflict of interest: M.L. Watzenböck has nothing to disclose. Conflict of interest: A.-D. Gorki has nothing to disclose. Conflict of interest: F. Quattrone has nothing to disclose. Conflict of interest: R. Gawish has nothing to disclose. Conflict of interest: S. Schwarz has nothing to disclose. Conflict of interest: C. Lambers has nothing to disclose. Conflict of interest: P. Jaksch has nothing to disclose. Conflict of interest: K. Lakovits has nothing to disclose. Conflict of interest: S. Zahalka has nothing to disclose. Conflict of interest: N. Rahimi has nothing to disclose. Conflict of interest: P. Starkl has nothing to disclose. Conflict of interest: D. Symmank has nothing to disclose. Conflict of interest: T. Artner has nothing to disclose. Conflict of interest: C. Pattaroni has nothing to disclose. Conflict of interest: N. Fortelny has nothing to disclose. Conflict of interest: K. Klavins has nothing to disclose. Conflict of interest: F. Frommlet has nothing to disclose. Conflict of interest: B.J. Marsland has nothing to disclose. Conflict of interest: K. Hoetzenecker has nothing to disclose. Conflict of interest: S. Widder reports grants from Austrian Science Fund (Elise Richter V585-B31), during the conduct of the study. Conflict of interest: S. Knapp reports grants from FWF, during the conduct of the study.
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