Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas.

Autor:	Robertson H; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia., Kim HJ; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia.; Kinghorn Cancer Centre and Cancer Research Theme, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia.; St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia., Li J; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.; Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia., Robertson N; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China., Robertson P; Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia., Jimenez-Vera E; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia., Ameen F; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia., Tran A; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia., Trinh K; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia., O'Connell PJ; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.; Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia.; Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia., Yang JYH; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China., Rogers NM; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.; Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia.; Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia., Patrick E; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia. ellis.patrick@sydney.edu.au.; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, New South Wales, Australia. ellis.patrick@sydney.edu.au.; Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia. ellis.patrick@sydney.edu.au.; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia. ellis.patrick@sydney.edu.au.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China. ellis.patrick@sydney.edu.au.; Centre for Cancer Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia. ellis.patrick@sydney.edu.au.
Jazyk:	angličtina
Zdroj:	Nature medicine [Nat Med] 2024 Dec; Vol. 30 (12), pp. 3748-3757. Date of Electronic Publication: 2024 Jun 18.
DOI:	10.1038/s41591-024-03030-6
Abstrakt:	The pathogenesis of allograft (dys)function has been increasingly studied using 'omics'-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction. Competing Interests: Competing interests: P.J.O. is a consultant for VericiDx and Qihan Biotech. H.R., J.Y.H.Y., N.M.R. and E.P. are inventors on two pending patent applications related to the research presented in this manuscript. These applications are currently under review for novelty and, if granted, could potentially influence the interpretation of the research findings. The authors declare that this does not alter their adherence to all the Nature Medicine policies on sharing data and materials, as detailed in the guidelines for authors. All other authors declare no competing interests. (© 2024. The Author(s).)
Databáze:	MEDLINE
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