Our ability to treat cardiac allograft rejection in both acute and chronic states is typically limited by poor existing biomarkers to predict the occurrence of such complications combined with a limited understanding of the complex multi-gene etiology underlying these events. An emerging strategy for identifying better biomolecular predictors for allograft rejection is to utilize next-generation 'omics profiling to identify multi-analyte signatures associated with rejection; in an ideal case this would not be limited to one specific class of biomolecules but could comprise genes, proteins, metabolites allo-antibodies and imaging/pathology.

Here, in a proof-of-principle study, we combine multiple 'omics technologies to monitor the blood and protocol biopsies of 20 heart allograft recipients within the Clinical Trials in Organ Transplantation 3 (CTOT3) study from the point of transplantation over periods of health and rejection. Exome sequencing was performed to comprehensively profile potential loss-of-function alleles that may contribute to rejection coupled with longitudinal RNA sequencing as well as proteome-wide autoantibody profiling. RNA-seq was performed at a depth of approximately 25 million reads per timepoint, and was mapped back to exome data to detect novel transcripts and isoforms. From the longitudinal data we observed reproducible multi-analyte signatures that emerged during periods of rejection, and the genes involved were significantly enriched for known pathways involved in allograft rejection, graft-versus-host disease and antigen presentation (each at p<0.0001), as well as novel targets not previously implicated in the physiology of allograft rejection. We use these data to reconstruct cellular networks associated with rejection that can be tested via follow-up profiling of downstream protein targets and metabolites. These experiments demonstrate a novel approach for bringing large-scale 'omics closer to the clinic as a comprehensive predictive strategy for allograft rejection.

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