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Robertson H, Kim HJ, Li J, Robertson N, Robertson P, Jimenez-Vera E, Ameen F, Tran A, Trinh K, O'Connell PJ, Yang JYH, Rogers NM, Patrick E. Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas. Nat Med 2024:10.1038/s41591-024-03030-6. [PMID: 38890530 DOI: 10.1038/s41591-024-03030-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 04/29/2024] [Indexed: 06/20/2024]
Abstract
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.
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Affiliation(s)
- Harry Robertson
- 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
| | - Hani Jieun Kim
- 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
| | - Jennifer Li
- 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
| | - Nicholas Robertson
- 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
| | - Paul Robertson
- Department of Renal and Transplantation Medicine, Westmead Hospital, Westmead, New South Wales, Australia
| | - Elvira Jimenez-Vera
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Farhan Ameen
- 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
| | - Andy Tran
- 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
| | - Katie Trinh
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Philip J O'Connell
- 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
| | - Jean Y H Yang
- 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
| | - Natasha M Rogers
- 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
| | - Ellis Patrick
- 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.
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
- Centre for Cancer Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.
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Mizuno H, Murakami N. Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic. CURRENT TRANSPLANTATION REPORTS 2023; 10:173-187. [PMID: 38152593 PMCID: PMC10751044 DOI: 10.1007/s40472-023-00410-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/09/2023] [Indexed: 12/29/2023]
Abstract
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.
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Affiliation(s)
- Hiroki Mizuno
- Transplant Research Center, Division of Renal Medicine, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Ave. EBRC 305, Boston, MA 02115, USA
- Dvision of Nephrology and Rheumatology, Toranomon Hospital, Tokyo, Japan
| | - Naoka Murakami
- Transplant Research Center, Division of Renal Medicine, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Ave. EBRC 305, Boston, MA 02115, USA
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Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation. DISEASE MARKERS 2022; 2022:6575052. [PMID: 36393969 PMCID: PMC9646319 DOI: 10.1155/2022/6575052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
Background Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2%. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology. Methods Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status. Results Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival. Conclusion A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.
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Lim JH, Chung BH, Lee SH, Jung HY, Choi JY, Cho JH, Park SH, Kim YL, Kim CD. Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection. Korean J Intern Med 2022; 37:520-533. [PMID: 35417937 PMCID: PMC9082440 DOI: 10.3904/kjim.2021.518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/11/2022] [Indexed: 11/27/2022] Open
Abstract
Kidney transplantation is the preferred treatment for patients with end-stage kidney disease, because it prolongs survival and improves quality of life. Allograft biopsy is the gold standard for diagnosing allograft rejection. However, it is invasive and reactive, and continuous monitoring is unrealistic. Various biomarkers for diagnosing allograft rejection have been developed over the last two decades based on omics technologies to overcome these limitations. Omics technologies are based on a holistic view of the molecules that constitute an individual. They include genomics, transcriptomics, proteomics, and metabolomics. The omics approach has dramatically accelerated biomarker discovery and enhanced our understanding of multifactorial biological processes in the field of transplantation. However, clinical application of omics-based biomarkers is limited by several issues. First, no large-scale prospective randomized controlled trial has been conducted to compare omics-based biomarkers with traditional biomarkers for rejection. Second, given the variety and complexity of injuries that a kidney allograft may experience, it is likely that no single omics approach will suffice to predict rejection or outcome. Therefore, integrated methods using multiomics technologies are needed. Herein, we introduce omics technologies and review the latest literature on omics biomarkers predictive of allograft rejection in kidney transplant recipients.
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Affiliation(s)
- Jeong-Hoon Lim
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
| | - Byung Ha Chung
- Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul,
Korea
| | - Sang-Ho Lee
- Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul,
Korea
| | - Hee-Yeon Jung
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
| | - Ji-Young Choi
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
| | - Jang-Hee Cho
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
| | - Sun-Hee Park
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
| | - Yong-Lim Kim
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
| | - Chan-Duck Kim
- Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu,
Korea
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Wang Y, Zhang D, Hu X. A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation. Front Mol Biosci 2021; 8:661661. [PMID: 34017855 PMCID: PMC8129004 DOI: 10.3389/fmolb.2021.661661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/21/2021] [Indexed: 12/31/2022] Open
Abstract
Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood. Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation. Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells. Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.
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Affiliation(s)
- Yicun Wang
- Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.,Institute of Urology, Capital Medical University, Beijing, China
| | - Di Zhang
- Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.,Institute of Urology, Capital Medical University, Beijing, China
| | - Xiaopeng Hu
- Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.,Institute of Urology, Capital Medical University, Beijing, China
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