Pseudomonas-dominant microbiome elicits sustained IL-1β upregulation in alveolar macrophages from lung transplant recipients

Characteristics of upper and lower respiratory tract microbiota after lung transplantation

BACKGROUND

The composition and characteristics of airway microbiota offer critical insights for clinical decision-making. Current research on chronic lung diseases shows differences in the composition and characteristics of upper and lower respiratory tract microbiota compared with healthy individuals. However, the temporal changes of these microbial communities in lung transplant recipients remain poorly characterized.

METHODS

This is a longitudinal prospective study. Respiratory specimens were collected regularly from lung transplant recipients for testing and analysis. A total of 150 bronchoalveolar lavage fluid (BALF) samples, 150 throat swab samples, 51 sputum samples, and 36 lung tissue samples were collected from the recipients, at 7 days, 14 days, 1 month, 2 months, 3 months, and 6 months post-transplant for 16S rRNA gene sequencing and analysis.

RESULTS

Our study showed that there were significant differences in α-diversity and β-diversity among lung tissue, throat swab, and sputum samples, although α-diversity did not show a significant difference between lung tissue and BALF. Most amplicon sequence variants (ASVs) belonged to the families Enterobacteriaceae, Pseudomonadaceae, and Stenotrophomonas in BALF, while most ASVs belonged to the genera Streptococcus, Pseudomonadaceae, and Stenotrophomonas in sputum samples. Regarding dynamic changes, Corynebacterium and Staphylococcus were more prevalent in the early post-operative period but gradually decreased by 7 days post-operatively, while the common microbiota found in healthy populations based on literature became the most abundant ASVs at 6 months post-operatively in our study participants. Pseudomonadaceae and Stenotrophomonas contributed to the similarity in the composition of upper and lower respiratory microbiota.

CONCLUSIONS

This study demonstrates that lung transplant recipients exhibit unique characteristics in their upper and lower respiratory tract microbiota, which are distinct ecological profiles, and both undergo significant changes within 6 months post-operatively. The similarity between upper and lower respiratory tract microbiota is associated with microbial diversity and taxonomic dominance.

CLINICAL TRIAL

The clinical trial was registered at Chinese Clinical Trial Registry (ChiCTR2200056908) in February 2022.

Predicting Time to First Rejection Episode in Lung Transplant Patients Using a Comprehensive Multi-Indicator Model

Background

Rejection hinders long-term survival in lung transplantation, and no widely accepted biomarkers exist to predict rejection risk. This study aimed to develop and validate a prognostic model using laboratory data to predict the time to first rejection episode in lung transplant recipients.

Methods

Data from 160 lung transplant recipients were retrospectively collected. Univariate Cox analysis assessed the impact of patient characteristics on time to first rejection episode. Kaplan-Meier survival analysis, LASSO regression, and multivariate Cox analysis were used to select prognostic indicators and develop a riskScore model. Model performance was evaluated using Kaplan-Meier analysis, time-dependent ROC curves, and multivariate Cox regression.

Results

Patient characteristics were not significantly associated with the time to the first rejection episode. Six laboratory indicators-Activated Partial Thromboplastin Time, IL-10, estimated intrapulmonary shunt, 50% Hemolytic Complement, IgA, and Complement Component 3-were identified as significant predictors and integrated into the riskScore. The riskScore demonstrated good predictive performance. It outperformed individual indicators, was an independent risk factor for rejection, and was validated in the validation dataset.

Conclusion

The riskScore model effectively predicts time to first rejection episode in lung transplant recipients.

Immune cells in the epithelial immune microenvironment of psoriasis: emerging therapeutic targets

Psoriasis is a chronic autoimmune inflammatory disease characterized by erroneous metabolism of keratinocytes. The development of psoriasis is closely related to abnormal activation and disorders of the immune system. Dysregulated skin protective mechanisms can activate inflammatory pathways within the epithelial immune microenvironment (EIME), leading to the development of autoimmune-related and inflammatory skin diseases. In this review, we initially emphasized the pathogenesis of psoriasis, paying particular attention to the interactions between the abnormal activation of immune cells and the production of cytokines in psoriasis. Subsequently, we delved into the significance of the interactions between EIME and immune cells in the emergence of psoriasis. A thorough understanding of these immune processes is crucial to the development of targeted therapies for psoriasis. Finally, we discussed the potential novel targeted therapies aimed at modulating the EIME in psoriasis. This comprehensive examination sheds light on the intricate underlying immune mechanisms and provides insights into potential therapeutic avenues of immune-mediated inflammatory diseases.