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Alemtuzumab is a monoclonal antibody targeting CD52, used as induction therapy after lung transplantation (LTx). Its engagement produces a long-lasting immunodepletion; however, the mechanisms driving cell reconstitution are poorly defined. We hypothesized that miRNAs are involved in this process. The expression of a set of miRNAs, cytokines and co-signaling molecules was measured with RT-qPCR and flow cytometry in prospectively collected serum samples of LTx recipients, after alemtuzumab or no induction therapy. Twenty-six LTx recipients who received alemtuzumab and twenty-seven matched LTx recipients without induction therapy were included in the analysis. One year after transplantation four miRNAs were differentially regulated: miR-23b (p = 0.05) miR-146 (p = 0.04), miR-155 (p < 0.001) and miR-486 (p < 0.001). Expression of 3 miRNAs changed within the alemtuzumab group: miR-146 (p < 0.001), miR-155 (p < 0.001) and miR-31 (p < 0.001). Levels of IL-13, IL-4, IFN-γ, BAFF, IL-5, IL-9, IL-17F, IL-17A and IL-22 were different one year after transplantation compared to baseline. In no-induction group, concentration of sCD27, sB7.2 and sPD-L1 increased overtime. Expression of miR-23b, miR-146, miR-486, miR-155 and miR-31 was different in LTx recipients who received alemtuzumab compared to recipients without induction therapy. The observed cytokine pattern suggested proliferation of specific B cell subsets in alemtuzumab group and co-stimulation of T-cells in no-induction group., (© 2022. The Author(s).)

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]