bioRxivImmunologyPreprint
A Reproducible Pre-Transplant γδ T-cell Effector Signature Reveals Immune-Metabolic Programs Associated with Kidney Allograft Rejection
Seon Min KimiD, Swastika Sur, Kelvin Koser, Silvia Pineda, Oriol Bestard, Troy Koyama, Thomas Hoffmann, Tara K. Sigdel, Marina Sirota, Joel Babdor, Matthew H. Spitzer, Minnie M. Sarwal
Abstract
Despite optimized immunosuppression, acute kidney transplant rejection affects 10-20% of recipients within the first year, and no reliable pre-transplant molecular test exists to identify patients at high risk, independent of the donor HLA matching. Across three independent cohorts of kidney transplant recipients, profiled by bulk RNA-sequencing with T-cell receptor repertoire analysis, flow cytometry, CyTOF with paired metabolomics, we identified a reproducible depletion of {gamma}{delta} T cells associated with rejection: an elevated {beta}/{gamma}{delta} T cell ratio in two post-transplant, biopsy-matched cohorts, and depletion of specific {gamma}{delta} T effector clusters in a third, longitudinally sampled cohort. In this third cohort, {gamma}{delta} T cell depletion was already present at the pre-transplant baseline in patients who subsequently developed rejection and remained detectable across the transplant course, indicating that it reflects recipient baseline immune biology rather than a consequence of graft injury. Longitudinal metabolomic profiling in this cohort identified time-dependent metabolic changes, including in amino acid metabolism that co-occur with the {gamma}{delta} signature and converge with it upon multi-omics integration. At the pre-transplant baseline, {gamma}{delta} T-cell marker expression alone showed exploratory discriminatory value for rejection risk (AUC = 0.69), improving marginally when integrated with metabolomic features (AUC = 0.71); larger prospective studies will be needed before clinical translation.
- organism: human
- assay: bulk RNA-seq
- assay: flow cytometry
- assay: metabolomics
- assay: CITE-seq
- research focus: differential expression
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
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