Data-Driven Kidney Transplant Phenotyping as a Histology-Independent Framework for Biomarker Discovery

Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions

Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenges persist in optimizing donor-recipient matching, postoperative care, and immunosuppressive strategies. This study employs bibliometric analysis to evaluate 890 publications from 1993 to 2023, using tools such as CiteSpace and VOSviewer, to identify global trends, research hotspots, and future opportunities in applying artificial intelligence (AI) to kidney transplantation. Our analysis highlights the United States as the leading contributor to the field, with significant outputs from Mayo Clinic and leading authors like Cheungpasitporn W. Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. The findings underscore a rapid expansion in AI applications since 2017, with emerging trends in personalized medicine, multimodal data fusion, and telehealth. This bibliometric review provides a comprehensive resource for researchers and clinicians, offering insights into the evolution of AI in kidney transplantation and guiding future studies toward transformative applications in transplantation science.

Unveiling systemic responses in kidney transplantation: interplay between the allograft transcriptome and serum proteins

Immunity, as defined by systems biology, encompasses a holistic response throughout the body, characterized by intricate connections with various tissues and compartments. However, this concept has been rarely explored in kidney transplantation. In this proof-of-concept study, we investigated a direct association between the allograft phenotype and serum protein signatures. Time-matched samples of graft biopsies and blood serum were collected in a heterogeneous cohort of kidney-transplanted patients (n = 15) for bulk RNA sequencing and proteomics, respectively. RNA transcripts exhibit distinct and reproducible, coregulated gene networks with specific functional profiles. We measured 159 serum proteins and investigated correlations with gene expression networks. Two opposing axes-one related to metabolism and the other to inflammation-were identified. They may represent a biological continuum between the allograft and the serum and correlate with allograft function, but not with interstitial fibrosis or proteinuria. For signature validation, we used two independent proteomic data sets (n = 21). Our findings establish a biological link between the allograft transcriptome and the blood serum proteome, highlighting systemic immune effects in kidney transplantation and offering a promising framework for developing allograft-linked biomarkers.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Allorecognition and the spectrum of kidney transplant rejection

Detection of mismatched human leukocyte antigens by adaptive immune cells is considered as the main cause of transplant rejection, leading to either T-cell mediated rejection or antibody-mediated rejection. This canonical view guided the successful development of immunosuppressive therapies and shaped the diagnostic Banff classification for kidney transplant rejection that is used in clinics worldwide. However, several observations have recently emerged that question this dichotomization between T-cell mediated rejection and antibody-mediated rejection, related to heterogeneity in the serology, histology, and prognosis of the rejection phenotypes. In parallel, novel insights were obtained concerning the dynamics of donor-specific anti-human leukocyte antigen antibodies, the immunogenicity of donor-recipient non-human leukocyte antigen mismatches, and the autoreactivity against self-antigens. Moreover, the potential of innate allorecognition was uncovered, as exemplified by natural killer cell-mediated microvascular inflammation through missing self, and by the emerging evidence on monocyte-driven allorecognition. In this review, we highlight the gaps in the current classification of rejection, provide an overview of the expanding insights into the mechanisms of allorecognition, and critically appraise how these could improve our understanding and clinical approach to kidney transplant rejection. We argue that consideration of the complex interplay of various allorecognition mechanisms can foster a more integrated view of kidney transplant rejection and can lead to improved risk stratification, targeted therapies, and better outcome after kidney transplantation.