Microbial signatures as predictors of transplant outcomes
Lucas-Ruiz F et al, Gut. 2025;74(12):2058-2069
A recent study shows that microbial signatures from donor livers, detectable in organ preservation fluid, are associated with early outcomes after liver transplantation. The findings highlight the intrahepatic microbiome as a previously underrecognized factor influencing early graft function. The results suggest that microbiological profiling may help improve risk assessment and organ allocation in the future.
Background: Liver transplantation (LT) remains hampered by post-transplant complications. While gut microbiota dysbiosis has been linked to transplant outcomes, the role of the intrahepatic graft's native microbiota remains unexplored.
Objective: To characterise the microbial profile detected in organ preservation solution (OPS) and determine whether specific microbial taxa are associated with short-term clinical outcomes, and to develop predictive models for risk stratification.
Design: The authors analysed the OPS microbiota-based metataxonomic signature from 110 LT donors (discovery cohort) and an independent validation cohort (n=29) using 16S rRNA sequencing. Microbial DNA signatures associated with clinical outcomes were identified through MaAsLin2-adjusted models, and relevant gene pathways were uncovered via data mining and enrichment analysis. Machine learning (ML) models were developed to predict outcomes based on microbial features, and host-microbiome interactions were validated through RNA sequencing (RNA-seq of matched liver biopsies).
Results: OPS-derived microbial DNA signature closely resembled liver/bile microbiomes (Proteobacteria-dominated). Specific genera (e.g., Bacillus, Prevotella) were differentially abundant in adverse outcomes (p < 0.05): hyperabundant in non-survivors and hepatic artery thrombosis, hypoabundant in acute rejection (AR). Gene mining linked these taxa to immune/metabolic pathways relevant to LT outcomes. RNA-seq validated upregulation of chemokines (CCL/CXCL families) in liver grafts from non-surviving recipients. ML models accurately predicted global survival (area under the curve (AUC) = 0.95) and AR (AUC = 0.96) based on microbial features, with generalisability confirmed in the validation cohort (AUC = 0.85–0.88).
Conclusion: Donor intrahepatic microbial DNA signature predicts LT outcomes via immune-metabolic modulation. While causality requires further study, these findings position the graft microbiome as a novel biomarker and potential therapeutic target, paving the way for microbiome-informed precision care in transplantation.