Core signature of rejection-associated cytokines and chemokines in endomyocardial biopsies after heart transplantation - Radomsky, Lena M.-L.; Kühne, Jenny F.; Beushausen, Kerstin; Keil, Jana; Knigina, Ludmilla; Scheibner, Yves; Görler, Adelheid; Ruhparwar, Arjang; Ius, Fabio; Bara, Christoph L.; Falk, Christine S. (Frontiers in Cardiovascular Medicine, 2025)
BackgroundRejection remains a limiting factor for survival after heart transplantation (HTx), and predictive biomarkers are still missing. Therefore, we aimed to define the cytokine/chemokine microenvironment in endomyocardial biopsies (EMB) and plasma after HTx and to identify patterns that reflect ischemia/reperfusion injury as well as allograft rejection. Therefore, we hypothesize distinct cytokine/chemokine patterns in heart biopsies with histopathologically proven rejection compared with the microenvironment in unsuspicious biopsies.MethodsEMB (n = 181; n = 52 patients) and peripheral blood samples (n = 147; n = 52 patients) were obtained between 6 days and 5 years after HTx. 50 immune proteins in EMB tissue lysates and plasma were quantified, and concentrations were compared between EMB with and without histopathologically defined acute rejection (AR), and correlation analyses between tissue and plasma were performed.ResultsRegarding rejection status, distinct cytokine/chemokine patterns were identified with significantly higher concentrations of CCL4, CXCL9, and CXCL10 in EMB with acute rejection (p < 0.001). In addition, we identified individual long-term dynamics of patients after HTx associated with rejection. Elevated chemokine concentrations were also detected in EMB of patients with donor-specific antibodies (DSAs). Moreover, significantly different patterns were observed between heart tissue and plasma without direct correlations.ConclusionA core signature was defined for EMB with histopathologically proven AR, consisting of high concentrations of CXCL9, CXCL10, CCL3, and CCL4. This EMB chemokine signature was clearly distinct from plasma samples, arguing for a local protein microenvironment associated with AR. Further research is also needed with the help of AI to translate the different approaches for the detection and prediction of AR into clinical practice.