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Poudel S, Gupta S, Saigal S. Basics and Art of Immunosuppression in Liver Transplantation. J Clin Exp Hepatol 2024; 14:101345. [PMID: 38450290 PMCID: PMC10912712 DOI: 10.1016/j.jceh.2024.101345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 01/09/2024] [Indexed: 03/08/2024] Open
Abstract
Liver transplantation is one of the most challenging areas in the medical field. Despite that, it has already been established as a standard treatment option, especially in decompensated cirrhosis and selected cases of hepatocellular carcinoma and acute liver failure. Complications due to graft rejection, including mortality and morbidity, have greatly improved over time due to better immunosuppressive agents and management protocols. Currently, immunosuppression in liver transplant patients makes use of the best possible combinations of effective agents to achieve optimal immunosuppression for long-term graft survival. Induction agents are no longer used routinely, and the aim is to provide minimal immunosuppression in the maintenance phase. Currently available immunosuppressive agents are mainly classified as biological and pharmacological agents. Though the protocols may vary among the centers and over time, the basics of effective use usually remain similar. Most protocols use the combination of multiple agents with different mechanisms of action to reduce the dose and minimize the side effects. Along with the improvement in operative and perioperative techniques, this art of immunosuppression has contributed to the recent progress made in the outcomes of liver transplants. In this review, we will discuss the various types of immunosuppressive agents currently in use, the different protocols of immunosuppression used, and the art of optimal use for achieving maximum immunosuppression without increasing toxicity. We will also discuss the practical aspects of various immunosuppression regimens, including drug monitoring, and briefly discuss the concepts of immunosuppression minimization and withdrawal.
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Affiliation(s)
- Shekhar Poudel
- Fellow Transplant Hepatology, Centre for Liver and Biliary Sciences, Max Super Specialty Hospital, Saket, New Delhi, India
| | - Subhash Gupta
- Liver Transplant and Gastrointestinal Surgery, Centre for Liver and Biliary Sciences, Max Super Speciality Hospital, Saket, New Delhi, India
| | - Sanjiv Saigal
- Principal Director and Head, Transplant Hepatology, Centre for Liver and Biliary Sciences, Max Super Specialty Hospital, Saket, New Delhi, India
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2
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Pravallika G, Rajasekaran R. Stage II oesophageal carcinoma: peril in disguise associated with cellular reprogramming and oncogenesis regulated by pseudogenes. BMC Genomics 2024; 25:135. [PMID: 38308202 PMCID: PMC10835973 DOI: 10.1186/s12864-024-10023-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 01/17/2024] [Indexed: 02/04/2024] Open
Abstract
INTRODUCTION Pseudogenes have been implicated for their role in regulating cellular differentiation and organismal development. However, their role in promoting cancer-associated differentiation has not been well-studied. This study explores the tumour landscape of oesophageal carcinoma to identify pseudogenes that may regulate events of differentiation to promote oncogenic transformation. MATERIALS AND METHOD De-regulated differentiation-associated pseudogenes were identified using DeSeq2 followed by 'InteractiVenn' analysis to identify their expression pattern. Gene expression dependent and independent enrichment analyses were performed with GSEA and ShinyGO, respectively, followed by quantification of cellular reprogramming, extent of differentiation and pleiotropy using three unique metrics. Stage-specific gene regulatory networks using Bayesian Network Splitting Average were generated, followed by network topology analysis. MEME, STREME and Tomtom were employed to identify transcription factors and miRNAs that play a regulatory role downstream of pseudogenes to initiate cellular reprogramming and further promote oncogenic transformation. The patient samples were stratified based on the expression pattern of pseudogenes, followed by GSEA, mutation analysis and survival analysis using GSEA, MAF and 'survminer', respectively. RESULTS Pseudogenes display a unique stage-wise expression pattern that characterizes stage II (SII) ESCA with a high rate of cellular reprogramming, degree of differentiation and pleiotropy. Gene regulatory network and associated topology indicate high robustness, thus validating high pleiotropy observed for SII. Pseudogene-regulated expression of SOX2, FEV, PRRX1 and TFAP2A in SII may modulate cellular reprogramming and promote oncogenesis. Additionally, patient stratification-based mutational analysis in SII signifies APOBEC3A (A3A) as a potential hallmark of homeostatic mutational events of reprogrammed cells which in addition to de-regulated APOBEC3G leads to distinct events of hypermutations. Further enrichment analysis for both cohorts revealed the critical role of combinatorial expression of pseudogenes in cellular reprogramming. Finally, survival analysis reveals distinct genes that promote poor prognosis in SII ESCA and patient-stratified cohorts, thus providing valuable prognostic bio-markers along with markers of differentiation and oncogenesis for distinct landscapes of pseudogene expression. CONCLUSION Pseudogenes associated with the events of differentiation potentially aid in the initiation of cellular reprogramming to facilitate oncogenic transformation, especially during SII ESCA. Despite a better overall survival of SII, patient stratification reveals combinatorial de-regulation of pseudogenes as a notable marker for a high degree of cellular differentiation with a unique mutational landscape.
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Affiliation(s)
- Govada Pravallika
- Quantitative Biology Lab, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Ramalingam Rajasekaran
- Quantitative Biology Lab, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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3
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Quinino RM, Agena F, Modelli de Andrade LG, Furtado M, Chiavegatto Filho ADP, David-Neto E. A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation. Transplantation 2023; 107:1380-1389. [PMID: 36872507 DOI: 10.1097/tp.0000000000004510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms. METHODS Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. RESULTS Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified. CONCLUSIONS Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.
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Affiliation(s)
- Raquel M Quinino
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Fabiana Agena
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | | | - Mariane Furtado
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil
| | | | - Elias David-Neto
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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4
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Benincasa G, Viglietti M, Coscioni E, Napoli C. "Transplantomics" for predicting allograft rejection: real-life applications and new strategies from Network Medicine. Hum Immunol 2023; 84:89-97. [PMID: 36424231 DOI: 10.1016/j.humimm.2022.11.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
Although decades of the reductionist approach achieved great milestones in optimizing the immunosuppression therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cellular and molecular mechanisms, current biomedical research is increasingly focusing on "transplantomics" which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision medicine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The application of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original "graft rejection interactome" and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation.
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Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy.
| | - Mario Viglietti
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy
| | - Enrico Coscioni
- Division of Cardiac Surgery, AOU San Giovanni di Dio e Ruggi d'Aragona, 84131, Salerno, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", 80138, Naples, Italy; U.O.C. Division of Clinical Immunology, Immunohematology, Transfusion Medicine and Transplant Immunology, Department of Internal Medicine and Specialistics, University of Campania "Luigi Vanvitelli", Naples, Italy
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5
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Shen X, Zeng Y, Yang C, Jiang L, Chen S, Chen F, Cao P. The diagnostic and prognostic value of pseudogene SIGLEC17P in lung adenocarcinoma and a preliminary functional study. Cell Biol Int 2023; 47:86-97. [PMID: 36183365 DOI: 10.1002/cbin.11919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 01/19/2023]
Abstract
Among malignant tumors, lung adenocarcinoma (LUAD) is the leading cause of death worldwide. This study explored the diagnostic, prognostic value, and preliminary functional verification of sialic acid binding Ig like lectin 17, pseudogene (SIGLEC17P) in LUAD. Prognostic lncRNAs for LUAD were identified by The Cancer Genome Atlas and quantitative real-time PCR (qRT-PCR) was used to detect the expression of SIGLEC17P in LUAD and paracarcinoma tissues. Subsequently, lentiviral vectors were used to overexpress SIGLEC17P in A549 and H1299 cells. The effects of SIGLEC17P overexpression on the proliferation, migration, and invasiveness of LUAD cells (A549 and H1299) were evaluated by Cell Counting Kit-8, wound healing, and transwell migration assays, respectively. Bioinformatics analyses were performed to reveal the potential pathways in which SIGLEC17P is involved in LUAD. qRT-PCR results revealed low SIGLEC17P expression in LUAD tissues and a significant association with the N stage, T stage, and tumor node metastasis stage. Furthermore, the receiver operating characteristic curve demonstrated a reliable diagnostic value. The proliferation, migration, and invasion of LUAD cells were inhibited by overexpression of SIGLEC17P. Bioinformatics analyses suggested that SIGLEC17P might exert antioncogenic effects in LUAD through the mir-20-3p/ADH1B or mir-4476-5p/DPYSL axis. In summary, our results revealed that SIGLEC17P acts as a prognostic biomarker, independent prognostic factor, and potential therapeutic target for patients with LUAD.
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Affiliation(s)
- Xiuqing Shen
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, China.,Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Yanfen Zeng
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, China
| | - Caihong Yang
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Lili Jiang
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, China
| | - Shaoting Chen
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, China
| | - Falin Chen
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, China.,Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Pengju Cao
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, China.,Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
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Huang E, Mengel M, Clahsen-van Groningen MC, Jackson AM. Diagnostic Potential of Minimally Invasive Biomarkers: A Biopsy-centered Viewpoint From the Banff Minimally Invasive Diagnostics Working Group. Transplantation 2023; 107:45-52. [PMID: 36508645 PMCID: PMC9746335 DOI: 10.1097/tp.0000000000004339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 07/15/2022] [Accepted: 07/26/2022] [Indexed: 12/14/2022]
Abstract
With recent advances and commercial implementation of minimally invasive biomarkers in kidney transplantation, new strategies for the surveillance of allograft health are emerging. Blood and urine-based biomarkers can be used to detect the presence of rejection, but their applicability as diagnostic tests has not been studied. A Banff working group was recently formed to consider the potential of minimally invasive biomarkers for integration into the Banff classification for kidney allograft pathology. We review the existing data on donor-derived cell-free DNA, blood and urine transcriptomics, urinary protein chemokines, and next-generation diagnostics and conclude that the available data do not support their use as stand-alone diagnostic tests at this point. Future studies assessing their ability to distinguish complex phenotypes, differentiate T cell-mediated rejection from antibody-mediated rejection, and function as an adjunct to histology are needed to elevate these minimally invasive biomarkers from surveillance tests to diagnostic tests.
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Affiliation(s)
- Edmund Huang
- Division of Nephrology, Department of Medicine, Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Michael Mengel
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
| | - Marian C. Clahsen-van Groningen
- Department of Pathology and Clinical Bioinformatics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Institute of Experimental and Systems Biology, RWTH Aachen University, Aachen, Germany
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7
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Yeh H. Applications of Transcriptomics in the Research of Antibody-Mediated Rejection in Kidney Transplantation: Progress and Perspectives. Organogenesis 2022; 18:2131357. [PMID: 36259540 PMCID: PMC9586696 DOI: 10.1080/15476278.2022.2131357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
Antibody-mediated rejection (ABMR) is the major cause of chronic allograft dysfunction and loss in kidney transplantation. The immunological mechanisms of ABMR that have been featured in the latest studies indicate a highly complex interplay between various immune and nonimmune cell types. Clinical diagnostic standards have long been criticized for being arbitrary and the lack of accuracy. Transcriptomic approaches, including microarray and RNA sequencing of allograft biopsies, enable the identification of differential gene expression and the continuous improvement of diagnostics. Given that conventional bulk transcriptomic approaches only reflect the average gene expression but not the status at the single-cell level, thereby ignoring the heterogeneity of the transcriptome across individual cells, single-cell RNA sequencing is rising as a powerful tool to provide a high-resolution transcriptome map of immune cells, which allows the elucidation of the pathogenesis and may facilitate the development of novel strategies for clinical treatment of ABMR.
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Affiliation(s)
- Hsuan Yeh
- Division of Renal-Electrolyte, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA,CONTACT Hsuan Yeh S976 Scaife Hall 3550 Terrace Street Pittsburgh, PA 15261
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8
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Peloso A, Moeckli B, Delaune V, Oldani G, Andres A, Compagnon P. Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation. Transpl Int 2022; 35:10640. [PMID: 35859667 PMCID: PMC9290190 DOI: 10.3389/ti.2022.10640] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.
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Affiliation(s)
- Andrea Peloso
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- *Correspondence: Andrea Peloso,
| | - Beat Moeckli
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Vaihere Delaune
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Graziano Oldani
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Axel Andres
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Philippe Compagnon
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
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9
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Biological pathways and comparison with biopsy signals and cellular origin of peripheral blood transcriptomic profiles during kidney allograft pathology. Kidney Int 2022; 102:183-195. [PMID: 35526671 PMCID: PMC9231008 DOI: 10.1016/j.kint.2022.03.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 01/04/2023]
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10
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Kong F, Ye S, Zhong Z, Zhou X, Zhou W, Liu Z, Lan J, Xiong Y, Ye Q. Single-Cell Transcriptome Analysis of Chronic Antibody-Mediated Rejection After Renal Transplantation. Front Immunol 2022; 12:767618. [PMID: 35111153 PMCID: PMC8801944 DOI: 10.3389/fimmu.2021.767618] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/27/2021] [Indexed: 12/13/2022] Open
Abstract
Renal transplantation is currently the most effective treatment for end-stage renal disease. However, chronic antibody-mediated rejection (cABMR) remains a serious obstacle for the long-term survival of patients with renal transplantation and a problem to be solved. At present, the role and mechanism underlying immune factors such as T- and B- cell subsets in cABMR after renal transplantation remain unclear. In this study, single-cell RNA sequencing (scRNA-seq) of peripheral blood monocytes (PBMCs) from cABMR and control subjects was performed to define the transcriptomic landscape at single-cell resolution. A comprehensive scRNA-seq analysis was performed. The results indicated that most cell types in the cABMR patients exhibited an intense interferon response and release of proinflammatory cytokines. In addition, we found that the expression of MT-ND6, CXCL8, NFKBIA, NFKBIZ, and other genes were up-regulated in T- and B-cells and these genes were associated with pro-inflammatory response and immune regulation. Western blot and qRT-PCR experiments also confirmed the up-regulated expression of these genes in cABMR. GO and KEGG enrichment analyses indicated that the overexpressed genes in T- and B-cells were mainly enriched in inflammatory pathways, including the TNF, IL-17, and Toll-like receptor signaling pathways. Additionally, MAPK and NF-κB signaling pathways were also involved in the occurrence and development of cABMR. This is consistent with the experimental results of Western blot. Trajectory analysis assembled the T-cell subsets into three differentiation paths with distinctive phenotypic and functional prog rams. CD8 effector T cells and γδ T cells showed three different differentiation trajectories, while CD8_MAI T cells and naive T cells primarily had two differentiation trajectories. Cell-cell interaction analysis revealed strong T/B cells and neutrophils activation in cABMR. Thus, the study offers new insight into pathogenesis and may have implications for the identification of novel therapeutic targets for cABMR.
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Affiliation(s)
- Fanhua Kong
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Shaojun Ye
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Zibiao Zhong
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Xin Zhou
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Wei Zhou
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Zhongzhong Liu
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Jianan Lan
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Yan Xiong
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China
| | - Qifa Ye
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Wuhan, China.,National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-Based Medical Materials, Wuhan, China.,The 3rd Xiangya Hospital of Central South University, Research Center of National Health Ministry on Transplantation Medicine Engineering and Technology, Changsha, China
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Abstract
The current standard of serum creatinine and biopsy to monitor allograft health has many limitations. The most significant drawback of the current standard is the lack of sensitivity and specificity to allograft injuries, which are diagnosed only after significant damage to the allograft. Thus, it is of critical need to identify a biomarker that is sensitive and specific to the early detection of allograft injuries. Urine, as the direct renal ultrafiltrate that can be obtained noninvasively, directly reflects intrarenal processes in the allograft at greater accuracy than analysis of peripheral blood. We review transcriptomic, metabolomic, genomic, and proteomic discovery-based approaches to identifying urinary biomarkers for the noninvasive detection of allograft injuries, as well as the use of urine cell-free DNA in the QSant urine assay as a sensitive surrogate for the renal allograft biopsy for rejection diagnosis.
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12
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Shi T, Roskin K, Baker BM, Woodle ES, Hildeman D. Advanced Genomics-Based Approaches for Defining Allograft Rejection With Single Cell Resolution. Front Immunol 2021; 12:750754. [PMID: 34721421 PMCID: PMC8551864 DOI: 10.3389/fimmu.2021.750754] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 09/13/2021] [Indexed: 12/20/2022] Open
Abstract
Solid organ transplant recipients require long-term immunosuppression for prevention of rejection. Calcineurin inhibitor (CNI)-based immunosuppressive regimens have remained the primary means for immunosuppression for four decades now, yet little is known about their effects on graft resident and infiltrating immune cell populations. Similarly, the understanding of rejection biology under specific types of immunosuppression remains to be defined. Furthermore, development of innovative, rationally designed targeted therapeutics for mitigating or preventing rejection requires a fundamental understanding of the immunobiology that underlies the rejection process. The established use of microarray technologies in transplantation has provided great insight into gene transcripts associated with allograft rejection but does not characterize rejection on a single cell level. Therefore, the development of novel genomics tools, such as single cell sequencing techniques, combined with powerful bioinformatics approaches, has enabled characterization of immune processes at the single cell level. This can provide profound insights into the rejection process, including identification of resident and infiltrating cell transcriptomes, cell-cell interactions, and T cell receptor α/β repertoires. In this review, we discuss genomic analysis techniques, including microarray, bulk RNAseq (bulkSeq), single-cell RNAseq (scRNAseq), and spatial transcriptomic (ST) techniques, including considerations of their benefits and limitations. Further, other techniques, such as chromatin analysis via assay for transposase-accessible chromatin sequencing (ATACseq), bioinformatic regulatory network analyses, and protein-based approaches are also examined. Application of these tools will play a crucial role in redefining transplant rejection with single cell resolution and likely aid in the development of future immunomodulatory therapies in solid organ transplantation.
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Affiliation(s)
- Tiffany Shi
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Medical Scientist Training Program, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Krishna Roskin
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Brian M Baker
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - E Steve Woodle
- Division of Transplantation, Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - David Hildeman
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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Integrative Analysis of Prognostic Biomarkers for Acute Rejection in Kidney Transplant Recipients. Transplantation 2021; 105:1225-1237. [PMID: 33148975 DOI: 10.1097/tp.0000000000003516] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Noninvasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients. METHODS Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included. RESULTS All sets of gene signatures had good positive and negative predictive values (79%-96%) for acute rejection within their own cohorts, but the predictability reduced to <50% when tested in other independent datasets. By integrating signature sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4, and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69% and 90%). CONCLUSIONS A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, the integration of gene signature sets with high specificity scores may improve the prediction performance of these markers.
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Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11:277-289. [PMID: 34316452 PMCID: PMC8290997 DOI: 10.5500/wjt.v11.i7.277] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/17/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.
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Affiliation(s)
- Nurhan Seyahi
- Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
| | - Seyda Gul Ozcan
- Department of Internal Medicine, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
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