Poorvi H C supervised by Dr. Vinod Palakkad Krishnanunni received her Master of Science – Dual Degree in Computational Natural Sciences (CND). Here’s a summary of her research work on Cross-Organ Transcriptomic Analysis for Allograft Rejection Prediction

Solid organ transplantation remains the definitive therapeutic intervention for end-stage organ failure, yet long-term graft survival continues to be limited by allograft rejection, a complex immunemediated process traditionally studied on an organ-specific basis. This thesis presents a network-based systems biology framework to identify a conserved, pan-organ molecular architecture of rejection. To establish a unified framework beyond organ-specific studies, we performed a network-based systems biology analysis of transcriptomic data from 672 liver, kidney, and heart transplant biopsies to identify a conserved, pan-organ molecular framework of rejection. By constructing and comparing organ-specific gene co-expression networks, we identified a consensus, six-module immune cascade that captures the hierarchical nature of the alloimmune response. In addition, we also uncovered a highly conserved 24-gene cell cycle signature consistently upregulated in rejecting allografts, implicating cellular proliferation as a core feature of rejection pathology. From this framework, we derived a 172-gene immune signature and applied machine learning models to assess its predictive performance, achieving accuracy comparable to established benchmarks. We further refined this to a minimal, high-performance 20-gene immune signature (AUC > 0.96). Both the immune and cell cycle signatures demonstrated robust, pan-organ utility when independently validated in a lung transplant cohort (n=243). Collectively, these findings define a pan-organ molecular framework for rejection and highlight cell cycle dysregulation as a conserved hallmark, offering a foundation for standardized, cross-organ diagnostic platforms to improve allograft surveillance and patient outcomes. Shifting from the post-transplant graft to the pre-transplant systemic immune state, peripheral blood mononuclear cell profiling of kidney transplant recipients revealed a baseline immunometabolic state defined by selective cytotoxic immune depletion and dysregulation of eicosanoid and fatty acid metabolism, consolidated into a predictive 15-gene PBMC biomarker panel (AUC = 0.836). Collectively, these findings establish that the immunological fate of a transplanted organ is shaped not only by post-transplant alloimmune programs but also by a metabolic and immune landscape already present in the recipient before surgery. 

June 2026