1
|
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.
Collapse
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
| |
Collapse
|
2
|
Basuli D, Roy S. Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation. J Clin Med Res 2023; 15:391-398. [PMID: 37822851 PMCID: PMC10563819 DOI: 10.14740/jocmr5012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/23/2023] [Indexed: 10/13/2023] Open
Abstract
The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.
Collapse
Affiliation(s)
- Debargha Basuli
- Department of Nephrology and Hypertension, Brody School of Medicine/East Carolina University, Greenville, NC, USA
| | - Sasmit Roy
- Department of Internal Medicine, Centra Lynchburg General Hospital, Lynchburg, VA, USA
| |
Collapse
|
3
|
Zhang X, Wen Z, Wang Q, Ren L, Zhao S. A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma. Front Immunol 2023; 14:1199869. [PMID: 37575253 PMCID: PMC10413143 DOI: 10.3389/fimmu.2023.1199869] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Background Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored. Results Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. BNIP3 and CXCL12 were significantly differentially expressed between both clusters (P<0.05) and were identified as prognosis-related genes. The risk model showed that the risk score and tumor metastasis were independent prognostic factors of patients with OS. A nomogram combining risk score and tumor metastasis effectively predicted the prognosis. In addition, patients in the high-risk group had low immune scores and high tumor purity. The levels of immune cell infiltration, expression of human leukocyte antigen (HLA) genes, immune response gene sets, and immune checkpoints were lower in the high-risk group than those in the low-risk group. The low-risk group was sensitive to the immune checkpoint PD-1 inhibitor, and the high-risk group exhibited lower inhibitory concentration values by 50% for 24 drugs, including AG.014699, AMG.706, and AZD6482. Conclusion The prognostic stratification framework of patients with OS based on ARGs, such as BNIP3 and CXCL12, may lead to more efficient clinical management.
Collapse
Affiliation(s)
- Xiaoyan Zhang
- Department of Spine Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
- Department of Nutrition, College of Public Health of Sun Yat-Sen University, Guangzhou, China
| | - Zhenxing Wen
- Department of Spine Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Orthopaedics and Traumatology, Guangzhou, China
| | - Qi Wang
- Department of Oncology, Nanyang Central Hospital, Nanyang, China
| | - Lijuan Ren
- Molecular Diagnosis and Gene Testing Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shengli Zhao
- Department of Spine Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Orthopaedics and Traumatology, Guangzhou, China
| |
Collapse
|
4
|
Li Z, Lu Z, Hu C, Zhang Y, Chen Y, Zhang J, Guo F, Wang J, Tang Z, Tang F, He Z. A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation. Cell Transplant 2023; 32:9636897231195116. [PMID: 37650419 PMCID: PMC10475226 DOI: 10.1177/09636897231195116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/22/2023] [Accepted: 08/01/2023] [Indexed: 09/01/2023] Open
Abstract
In this study, we aimed to identify transplantation tolerance (TOL)-related gene signature and use it to predict the different types of renal allograft rejection performances in kidney transplantation. Gene expression data were obtained from the Gene Expression Omnibus (GEO) database, differently expressed genes (DEGs) were performed, and the gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted. The machine learning methods were combined to analyze the feature TOL-related genes and verify their predictive performance. Afterward, the gene expression levels and predictive performances of TOL-related genes were conducted in the context of acute rejection (AR), chronic rejection (CR), and graft loss through heatmap plots and the receiver operating characteristic (ROC) curves, and their respective immune infiltration results were also performed. Furthermore, the TOL-related gene signature for graft survival was conducted to discover gene immune cell enrichment. A total of 25 TOL-related DEGs were founded, and the GO and KEGG results indicated that DEGs mainly enriched in B cell-related functions and pathways. 7 TOL-related gene signature was constructed and performed delightedly in TOL groups and different types of allograft rejection. The immune infiltration analysis suggested that gene signature was correlated with different types of immune cells. The Kaplan-Meier (KM) survival analysis demonstrated that BLNK and MZB1 were the prognostic TOL-related genes. Our study proposed a novel gene signature that may influence TOL in kidney transplantation, providing possible guidance for immunosuppressive therapy in kidney transplant patients.
Collapse
Affiliation(s)
- Zhibiao Li
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zechao Lu
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Chuxian Hu
- The Sixth Clinical College of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yixin Zhang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Guangdong Clinical Research Center for Urological Diseases, Guangzhou, Guangdong, China
| | - Yushu Chen
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jiahao Zhang
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Feng Guo
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jinjin Wang
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zhicheng Tang
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Fucai Tang
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zhaohui He
- Department of Urology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| |
Collapse
|
5
|
Halma J, Pierce S, McLennan R, Bradley T, Fischer R. Natural killer cells in liver transplantation: Can we harness the power of the immune checkpoint to promote tolerance? Clin Transl Sci 2021; 15:1091-1103. [PMID: 34866338 PMCID: PMC9099129 DOI: 10.1111/cts.13208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 08/30/2021] [Accepted: 11/14/2021] [Indexed: 11/29/2022] Open
Abstract
The roles that natural killer (NK) cells play in liver disease and transplantation remain ill‐defined. Reports on the matter are often contradictory, and the mechanisms elucidated are complex and dependent on the context of the model tested. Moreover, NK cell attributes, such as receptor protein expression and function differ among species, make study of primate or rodent transplant models challenging. Recent insights into NK function and NK‐mediated therapy in the context of cancer therapy may prove applicable to transplantation. Of specific interest are immune checkpoint molecules and the mechanisms by which they modulate NK cells in the tumor micro‐environment. In this review, we summarize NK cell populations in the peripheral blood and liver, and we explore the data regarding the expression and function of immune checkpoint molecules on NK cells. We also hypothesize about the roles they could play in liver transplantation and discuss how they might be harnessed therapeutically in transplant sciences.
Collapse
Affiliation(s)
- Jennifer Halma
- Pediatric Gastroenterology, Children's Mercy Kansas City, Kansas City, Missouri, USA
| | - Stephen Pierce
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, Missouri, USA
| | - Rebecca McLennan
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, Missouri, USA
| | - Todd Bradley
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, Missouri, USA.,Pediatrics, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - Ryan Fischer
- Pediatric Gastroenterology, Children's Mercy Kansas City, Kansas City, Missouri, USA.,Pediatrics, University of Missouri-Kansas City, Kansas City, Missouri, USA
| |
Collapse
|
6
|
Fu Q, Lee KM, Huai G, Deng K, Agarwal D, Rickert CG, Feeney N, Matheson R, Yang H, LeGuern C, Deng S, Markmann JF. Properties of regulatory B cells regulating B cell targets. Am J Transplant 2021; 21:3847-3857. [PMID: 34327838 PMCID: PMC8639638 DOI: 10.1111/ajt.16772] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/09/2021] [Accepted: 07/21/2021] [Indexed: 01/25/2023]
Abstract
Regulatory B cells (Bregs) have shown promise as anti-rejection therapy applied to organ transplantation. However, less is known about their effect on other B cell populations that are involved in chronic graft rejection. We recently uncovered that naïve B cells, stimulated by TLR ligand agonists, converted into B cells with regulatory properties (Bregs-TLR) that prevented allograft rejection. Here, we examine the granular phenotype and regulatory properties of Breg-TLR cells suppressing B cells. Cocultures of Bregs-TLR with LPS-activated B cells showed a dose-dependent suppression of targeted B cell proliferation. Adoptive transfers of Bregs-TLR induced a decline in antibody responses to antigenically disparate skin grafts. The role of Breg BCR specificity in regulation was assessed using B cell-deficient mice replenished with transgenic BCR (OB1) and TCR (OT-II) lymphocytes of matching antigenic specificity. Results indicated that proliferation of OB1 B cells, mediated through help from CD4+ OT-II cells, was suppressed by OB1 Bregs of similar specificity. Transcriptomic analyses indicated that Bregs-TLR suppression is associated with a block in targeted B cell differentiation controlled by PRDM1 (Blimp1). This work uncovered the regulatory properties of a new brand of Breg cells and provided mechanistic insights into potential applications of Breg therapy in transplantation.
Collapse
Affiliation(s)
- Qiang Fu
- Organ Transplantation Center, Sichuan Provincial People’s Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China,Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Kang Mi Lee
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Guoli Huai
- Organ Transplantation Center, Sichuan Provincial People’s Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China,Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Kevin Deng
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Divyansh Agarwal
- Division of Transplantation, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Charles G. Rickert
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Noel Feeney
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Rudy Matheson
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Hongji Yang
- Organ Transplantation Center, Sichuan Provincial People’s Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Christian LeGuern
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA
| | - Shaoping Deng
- Organ Transplantation Center, Sichuan Provincial People’s Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China,Corresponding author: James F. Markmann , Shaoping Deng
| | - James F. Markmann
- Center for Transplantation Sciences, Massachusetts General Hospital, Boston, MA, USA,Corresponding author: James F. Markmann , Shaoping Deng
| |
Collapse
|