PROJECT 1

New York University School Of Medicine, New York NY

Investigators

Abstract

Project Abstract: U19 Project 1 Dynamic Surveillance and Clinical Support for Control of Xenotransplant-Related Pathogens The future success of pig-to-human xenotransplantation is dependent on the provision of pathogen-free organs and robust microbial surveillance post-transplant when human recipients are heavily immunosuppressed. Decades of pioneering xenotransplant studies in NHP models have yielded a ‘high priority’ list of candidate zoonotic pathogens that have mostly been removed from pig herds to ensure the xenografts are free from infection and can thus function post-transplantation without impeding the health of the human recipient. Advances in metagenomic- and other sequencing–based approaches now facilitate unbiased approaches to capture the true diversity of viral, bacterial, fungal, and parasitic organisms that could be impacting transplant outcomes. NYU has been actively involved in pig-to-human xenotransplants; 5 heart or kidney studies have been completed in brain-dead (decedent) models, and recently a pig kidney xenotransplant into a living recipient. NYU has also recently secured industry funding to perform additional pig-to-human decedent xenotransplants comprising 25 heart, kidney, or liver xenografts. Each of the 6 completed studies has relied on screening for the high priority pathogens in the xenograft recipient using targeted real-time PCR assays for 9 porcine viruses. We intend to dramatically expand microbial screening approaches through the development and implementation of a dynamic wet-lab pipeline that is agnostic to microbial genomic makeup. By implementing metagenomic-based approaches, complemented with targeted and sensitive PCR-based approaches, we will characterize the true microbial diversity and use that to inform and advance our microbial screening approaches. To achieve these goals, we will expand our current infectious disease bioinformatic pipeline to identify strain-level human and pig pathogens and use cloud computing and machine learning to improve and accelerate analyses. As a third aim, we will expand return of results (RoR) and clinical decision support (CDS) systems to aid in the management of transplant-related infections in the recipient. Formal reports will include strain-level identifications and treatment options informed by pharmacogenetic interactions to avoid adverse events.