Immune gene correlation networks differentiate both chronic lung allograft dysfunction and survival

Area of Science:

• Immunology
• Genomics
• Transplantation Medicine

Background:

• Chronic lung allograft dysfunction (CLAD) significantly limits long-term survival for lung transplant recipients (LTRs).
• CLAD is currently a diagnosis of exclusion with limited therapeutic options.
• A molecular diagnostic is crucial for risk stratification and identifying novel therapeutic targets for CLAD.

Purpose of the Study:

• To identify immune pathways and molecular markers associated with CLAD using airway transcriptome analysis.
• To discover potential diagnostic and prognostic biomarkers for CLAD.

Main Methods:

• Weighted gene correlation network analysis (WGCNA) was applied to RNA-seq data from airway brushings of LTRs with CLAD and stable LTRs.
• Gene co-expression networks (modules) were analyzed for their association with CLAD, its severity, and patient survival.
• Downstream protein and eicosanoid levels were measured in bronchoalveolar lavage, and findings were validated in an external cohort.

Main Results:

• Three gene modules positively correlated with CLAD, its severity, allograft dysfunction, and survival, enriched for acute phase response, Type-1 adaptive immunity, and innate immunity pathways.
• A fourth module showed a protective effect, inversely correlated with CLAD-associated modules.
• Module expression successfully differentiated LTRs with CLAD in an external validation cohort and correlated with poorer survival.

Conclusions:

• The airway transcriptome in CLAD is characterized by co-expression networks associated with immune responses.
• These identified network modules serve as potential molecular biomarkers for CLAD prognosis and therapeutic targeting.
• This study provides a foundation for developing molecular diagnostics to improve outcomes for lung transplant recipients.