Multi-omics profiling predicts allograft function after lung transplantation

Abstract

RationaleLung 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.ObjectivesTo better understand these lung adaptation processes after transplantation, and to investigate their association with future changes in allograft function.MethodsWe performed an exploratory cohort study in 78 patients on bronchoalveolar lavage samples from lung donors and recipients. 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 ResultsWe 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 (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.ConclusionBronchoalveolar 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.

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

“…As a novel method to realize personalized risk evaluation, machine learning algorithms can learn the patterns of high-dimensional data of many patients and provide a personalized prediction . Although machine learning is promising in medical practice, few studies have reported its application in LTx . Recently, a study reported a random forests model to predict 1-year survival, but the AUC of this model is 0.62.…”

Bronchoalveolar lavage metabolome dynamics reflect underlying disease and chronic lung allograft dysfunction

“…Bronchoalveolar lavage fluids have been used recently for describing molecular changes in lipids and metabolites after LTX [16]; however, the association of these compounds with allograft dysfunction has not been addressed. One of the strongest signatures in our metabolomic data was the association of the metabolome with the final CLAD stage of each subject, and this was particularly strong for those with CF.…”

“…The copyright holder for this this version posted November 18, 2022. ; https://doi.org/10.1101/2022.11.16.22281980 doi: medRxiv preprint the ability to 'clean up' the metabolome by enabling the identification and visualization of pharmaceuticals and their chemical relatives. Bronchoalveolar lavage fluids have been used recently for describing molecular changes in lipids and metabolites after LTX [16]; however, the association of these compounds with allograft dysfunction has not been addressed. One of the strongest signatures in our metabolomic data was the association of the metabolome with the final CLAD stage of each subject, and this was particularly strong for those with CF.…”

“…Metabolites detected within BALF can be indicative of physiological processes of disease progression, microbial lung burden, and possibly, allograft rejection [12,13]. However, there are few studies of BALF metabolomes describing the composition, microbial virulence factors, amino acids, lipids, and a myriad of pharmaceuticals [14][15][16][17].…”

Biomarkers for Chronic Lung Allograft Dysfunction: Ready for Prime Time?

“…53 A comprehensive study furthermore demonstrated that the microbial signature outperformed BAL cellular composition, metabolome, or lipidome as predictor of future CLAD wherein a specific strong association was found between FEV 1 changes and Capnocytophaga gingivalis and Veillonella dispar . 54 Another study found that CLAD is associated with increased bacterial biomass and a Proteobacteria-enriched airway microbiome and epithelial to mesenchymal transition via N-myc-interactor expression. 55 Other studies, however, failed in pinpointing a certain pathogen to be responsible for the increased risk of CLAD.…”