Gut microbiome and metabolome profiles in renal allograft rejection from multiomics integration.

UNLABELLED

The gut microbiome and metabolome play crucial roles in renal allograft rejection progression. Integrated multiomics analyses may provide a comprehensive understanding of specific underlying mechanisms, which remain elusive. This study aimed to identify new approaches for clinical renal allograft rejection diagnosis and treatment. Thirty-five patients were divided into three groups: the rejection ( = 16), dysfunction ( = 7), and control ( = 12) groups. Metagenomic sequencing and nontargeted metabolomics were used to analyze stool and plasma samples. Significant microbiota, metabolites, and signaling pathways were identified. LASSO regression was used to construct a diagnostic model, and its diagnostic value was assessed via receiver operating characteristic curves. The microbiota composition and the related genes in the rejection group significantly differed from that in the dysfunction and control groups at the phylum, genus, and species levels ( < 0.001). The core species in the rejection group networks were and , while core species in the dysfunction group networks were and . The balance of specific microbial species was associated with kidney function in rejection patients. Spearman analysis revealed that specific differential species like and were closely linked to the levels of serum 4-pyridoxic acid, 4-acetamidobutanoate, and fecal tryptamine from specific differential pathways. Finally, we constructed four clinical models to distinguish the rejection and dysfunction groups, and the model had excellent diagnostic performance. Altered gut microbiota may contribute to changes in metabolic pathway activity and metabolite abundance in rejection and dysfunction patients, which are strongly correlated with host immunological rejection. The diagnostic model, developed based on the gut microbiota and metabolites, has high clinical value for diagnosing renal rejection.

IMPORTANCE

This study aimed to screen new markers for non-invasive diagnosis by the gut microbiome and metabolome analysis, providing new insights into rejection mechanisms and identifying new approaches for clinical renal allograft rejection diagnosis.