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The pulmonary microbiome after lung transplantation has recently come into the focus of research. Especially, host-microbiome interactions are thought to be an important factor in graft-related immunological processes. In lung transplantation, temporal changes of the pulmonary microbiome have not yet been elucidated. In a total cohort of 80 patients receiving standard double lung transplantation, 50 bronchioalveolar lavage (BAL) samples from donors were collected prior to cold ischemia. After transplantation, 128 BAL samples were collected at various time points of routine follow-up bronchoscopies between 0 and 400 days post-transplant. Bacterial 16S rRNA gene sequencing was conducted to analyze the composition of the pulmonary microbiome in these samples. The lung microbiome showed significant temporal dynamics after lung transplantation. Recipient-donor similarity between matched samples decreased after the first week post transplantation. Underlying transplant indications were significantly associated with microbial profiles even after transplantation. Shannon diversity and Chao1 richness at the species level showed an increasing diversity of the microbiome over time. Our data provides new insights into the dynamics of microbial profiles after transplantation. We observed that recipient-associated factors, rather than the donor microbiome, shape the lung microbiome after transplantation. [ABSTRACT FROM AUTHOR]

Spirometric lung function is one of the central clinical parameters to assess allograft function after lung transplantation (LTX). Reduction in FEV1 can be the result of infections, rejections episodes and most importantly development of CLAD. Currently, there is no means to predict short-term changes in lung function after LTX. Bronchoalveolar samples of patients after standard double LTX were taken during follow-up bronchoscopy at multiple different time points. Lipidomic, metabolomic, flow cytometric and bacterial 16S rRNA gene sequencing (microbiome) data were analyzed. We then used a machine learning approach to predict future changes in FEV1 from these sample data to characterize patient lung function trajectories. We trained support vector machine (SVM) regressors on the collected lipidomic, metabolomic, microbiome and flow cytometry datasets. Changes in FEV1 within 30, 60 or 90 days after lavage sample collection were used as response variables. To train hyper-parameters and evaluate model accuracy, a nested leave-one-out cross validation scheme was used. Model accuracy was benchmarked against a model trained on clinical metadata. At a prediction timeframe of 30 days, lipidomic data were available from 34 samples of 20 patients. Lipidomics showed the highest predictive power for short-term changes 30 and 60 days after sample collection. Prediction accuracy (R2) of intra-alveolar lipid composition for a 30-day projection was 0.24, meaning that BAL lipid profiles could explain more than 20 percent of total variation in relative change in FEV1 for this time span. R² for clinical metadata alone was only 0.1, and metabolomics, microbiome and FACS analysis of BAL showed no predictive accuracy at this time point. Our results suggest that the intra-alveolar lipid composition is a powerful predictor of short-term changes in lung allograft function. This could potentially facilitate pre-emptive therapeutic interventions. [ABSTRACT FROM AUTHOR]

Allograft adaptation after lung transplantation is a multi-factor process. We hypothesized that early adaptation can influence the risk of matrix remodeling and CLAD development by shaping the inflammatory state of the lung environment. In a longitudinal study, we followed a defined patient cohort in year 1 p.t. and detected ordered cell composition changes with repercussions in the microbiome, small molecular composition and lipidome in BAL samples. We delimited clusters of pro-inflammatory factors and identified long-chain lipid species from the lung environment that modulated inflammatory response in vitro. We used FACS to identify cell populations and 16S sequencing, metabolomics, lipidomics to characterize the lung environment. With correlations and hierarchical clustering we extracted pro- and anti-inflammatory clusters around well-characterized cell populations. For detection of inflammation drivers, we quantified feature differential abundances in samples with more or less neutrophilia. Predicted candidates were tested to modify inflammatory responses in vitro by screening IL-6 production of LPS-triggered macrophages. Early after surgery, the allograft experienced neutrophilia, a lack of resident macrophages and a gradual influx of host-derived macrophage-precursors directly resonating with changes in the pulmonary microenvironment. The computational analysis revealed features that clustered either with neutrophils or alveolar macrophages. From these, 30% showed significant abundance differences in high and low neutrophilia conditions. Consistently, certain ceramide species displayed antagonistic behavior to several phosphatidylcholines. We tested their capability of modifying macrophage response and found the ceramide to enhance, and PCs to reduce IL-6 production. Our study suggests that the lung microenvironment plays a key role in allograft adaptation and specific lipid species are capable of modulating cellular inflammatory response in a differentiated manner early after transplantation. Our results provide first fundamental concepts for developing concrete precision therapies based on reprogramming tissue homoeostasis and inflammation management. [ABSTRACT FROM AUTHOR]