1
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Sun Y, Florio TJ, Gupta S, Young MC, Marshall QF, Garfinkle SE, Papadaki GF, Truong HV, Mycek E, Li P, Farrel A, Church NL, Jabar S, Beasley MD, Kiefel BR, Yarmarkovich M, Mallik L, Maris JM, Sgourakis NG. Structural principles of peptide-centric chimeric antigen receptor recognition guide therapeutic expansion. Sci Immunol 2023; 8:eadj5792. [PMID: 38039376 PMCID: PMC10782944 DOI: 10.1126/sciimmunol.adj5792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/08/2023] [Indexed: 12/03/2023]
Abstract
Peptide-centric chimeric antigen receptors (PC-CARs) recognize oncoprotein epitopes displayed by cell-surface human leukocyte antigens (HLAs) and offer a promising strategy for targeted cancer therapy. We have previously developed a PC-CAR targeting a neuroblastoma-associated PHOX2B peptide, leading to robust tumor cell lysis restricted by two common HLA allotypes. Here, we determine the 2.1-angstrom crystal structure of the PC-CAR-PHOX2B-HLA-A*24:02-β2m complex, which reveals the basis for antigen-specific recognition through interactions with CAR complementarity-determining regions (CDRs). This PC-CAR adopts a diagonal docking mode, where interactions with both conserved and polymorphic HLA framework residues permit recognition of multiple HLA allotypes from the A9 serological cross-reactive group, covering a combined global population frequency of up to 46.7%. Biochemical binding assays, molecular dynamics simulations, and structural and functional analyses demonstrate that high-affinity PC-CAR recognition of cross-reactive pHLAs necessitates the presentation of a specific peptide backbone, where subtle structural adaptations of the peptide are critical for high-affinity complex formation, and CAR T cell killing. Our results provide a molecular blueprint for engineering CARs with optimal recognition of tumor-associated antigens in the context of different HLAs, while minimizing cross-reactivity with self-epitopes.
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Affiliation(s)
- Yi Sun
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler J. Florio
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Sagar Gupta
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael C. Young
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Quinlen F. Marshall
- Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Samuel E. Garfinkle
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Georgia F. Papadaki
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hau V. Truong
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Emily Mycek
- Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Peiyao Li
- Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Alvin Farrel
- Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | | | | | - Mark Yarmarkovich
- Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Perlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY, USA
| | - Leena Mallik
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - John M. Maris
- Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Nikolaos G. Sgourakis
- Center for Computational and Genomic Medicine and Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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2
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Papadaki GF, Woodward CH, Young MC, Winters TJ, Burslem GM, Sgourakis NG. A Chicken Tapasin ortholog can chaperone empty HLA-B∗37:01 molecules independent of other peptide-loading components. J Biol Chem 2023; 299:105136. [PMID: 37543367 PMCID: PMC10534222 DOI: 10.1016/j.jbc.2023.105136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/07/2023] Open
Abstract
Human Tapasin (hTapasin) is the main chaperone of MHC-I molecules, enabling peptide loading and antigen repertoire optimization across HLA allotypes. However, it is restricted to the endoplasmic reticulum (ER) lumen as part of the protein loading complex (PLC), and therefore is highly unstable when expressed in recombinant form. Additional stabilizing co-factors such as ERp57 are required to catalyze peptide exchange in vitro, limiting uses for the generation of pMHC-I molecules of desired antigen specificities. Here, we show that the chicken Tapasin (chTapasin) ortholog can be expressed recombinantly at high yields in a stable form, independent of co-chaperones. chTapasin can bind the human HLA-B∗37:01 with low micromolar-range affinity to form a stable tertiary complex. Biophysical characterization by methyl-based NMR methods reveals that chTapasin recognizes a conserved β2m epitope on HLA-B∗37:01, consistent with previously solved X-ray structures of hTapasin. Finally, we provide evidence that the B∗37:01/chTapasin complex is peptide-receptive and can be dissociated upon binding of high-affinity peptides. Our results highlight the use of chTapasin as a stable scaffold for protein engineering applications aiming to expand the ligand exchange function on human MHC-I and MHC-like molecules.
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Affiliation(s)
- Georgia F Papadaki
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Claire H Woodward
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael C Young
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Trenton J Winters
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - George M Burslem
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Cancer Biology and Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nikolaos G Sgourakis
- Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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3
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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Papadaki GF, Woodward CH, Young MC, Winters TJ, Burslem GM, Sgourakis NG. A Chicken Tapasin ortholog can chaperone empty HLA molecules independently of other peptide-loading components. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.23.546255. [PMID: 37425753 PMCID: PMC10326978 DOI: 10.1101/2023.06.23.546255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Human Tapasin (hTapasin) is the main chaperone of MHC-I molecules, enabling peptide loading and antigen repertoire optimization across HLA allotypes. However, it is restricted to the endoplasmic reticulum (ER) lumen as part of the protein loading complex (PLC) and therefore is highly unstable when expressed in recombinant form. Additional stabilizing co-factors such as ERp57 are required to catalyze peptide exchange in vitro , limiting uses for the generation of pMHC-I molecules of desired antigen specificities. Here, we show that the chicken Tapasin (chTapasin) ortholog can be expressed recombinantly at high yields in stable form, independently of co-chaperones. chTapasin can bind the human HLA-B * 37:01 with low micromolar-range affinity to form a stable tertiary complex. Biophysical characterization by methyl-based NMR methods reveals that chTapasin recognizes a conserved β 2 m epitope on HLA-B * 37:01, consistent with previously solved X-ray structures of hTapasin. Finally, we provide evidence that the B * 37:01/chTapasin complex is peptide-receptive and can be dissociated upon binding of high-affinity peptides. Our results highlight the use of chTapasin as a stable scaffold for future protein engineering applications aiming to expand the ligand exchange function on human MHC-I and MHC-like molecules.
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5
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Sun Y, Florio TJ, Gupta S, Young MC, Marshall QF, Garfinkle SE, Papadaki GF, Truong HV, Mycek E, Li P, Farrel A, Church NL, Jabar S, Beasley MD, Kiefel BR, Yarmarkovich M, Mallik L, Maris JM, Sgourakis NG. Structural principles of peptide-centric Chimeric Antigen Receptor recognition guide therapeutic expansion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.24.542108. [PMID: 37292750 PMCID: PMC10245919 DOI: 10.1101/2023.05.24.542108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Peptide-Centric Chimeric Antigen Receptors (PC-CARs), which recognize oncoprotein epitopes displayed by human leukocyte antigens (HLAs) on the cell surface, offer a promising strategy for targeted cancer therapy 1 . We have previously developed a PC-CAR targeting a neuroblastoma- associated PHOX2B peptide, leading to robust tumor cell lysis restricted by two common HLA allotypes 2 . Here, we determine the 2.1 Å structure of the PC-CAR:PHOX2B/HLA-A*24:02/β2m complex, which reveals the basis for antigen-specific recognition through interactions with CAR complementarity-determining regions (CDRs). The PC-CAR adopts a diagonal docking mode, where interactions with both conserved and polymorphic HLA framework residues permit recognition of multiple HLA allotypes from the A9 serological cross-reactivity group, covering a combined American population frequency of up to 25.2%. Comprehensive characterization using biochemical binding assays, molecular dynamics simulations, and structural and functional analyses demonstrate that high-affinity PC-CAR recognition of cross-reactive pHLAs necessitates the presentation of a specific peptide backbone, where subtle structural adaptations of the peptide are critical for high-affinity complex formation and CAR-T cell killing. Our results provide a molecular blueprint for engineering CARs with optimal recognition of tumor-associated antigens in the context of different HLAs, while minimizing cross-reactivity with self-epitopes.
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Sun Y, Papadaki GF, Devlin CA, Danon JN, Young MC, Winters TJ, Burslem GM, Procko E, Sgourakis NG. Xeno interactions between MHC-I proteins and molecular chaperones enable ligand exchange on a broad repertoire of HLA allotypes. SCIENCE ADVANCES 2023; 9:eade7151. [PMID: 36827371 PMCID: PMC9956121 DOI: 10.1126/sciadv.ade7151] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/19/2023] [Indexed: 06/01/2023]
Abstract
Immunological chaperones tapasin and TAP binding protein, related (TAPBPR) play key roles in antigenic peptide optimization and quality control of nascent class I major histocompatibility complex (MHC-I) molecules. The polymorphic nature of MHC-I proteins leads to a range of allelic dependencies on chaperones for assembly and cell-surface expression, limiting chaperone-mediated peptide exchange to a restricted set of human leukocyte antigen (HLA) allotypes. Here, we demonstrate and characterize xeno interactions between a chicken TAPBPR ortholog and a complementary repertoire of HLA allotypes, relative to its human counterpart. We find that TAPBPR orthologs recognize empty MHC-I with broader allele specificity and facilitate peptide exchange by maintaining a reservoir of receptive molecules. Deep mutational scanning of human TAPBPR further identifies gain-of-function mutants, resembling the chicken sequence, which can enhance HLA-A*01:01 expression in situ and promote peptide exchange in vitro. These results highlight that polymorphic sites on MHC-I and chaperone surfaces can be engineered to manipulate their interactions, enabling chaperone-mediated peptide exchange on disease-relevant HLA alleles.
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Affiliation(s)
- Yi Sun
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Georgia F. Papadaki
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Christine A. Devlin
- Department of Biochemistry and Cancer Center at Illinois, University of Illinois, Urbana, IL 61820, USA
| | - Julia N. Danon
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Michael C. Young
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Trenton J. Winters
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - George M. Burslem
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
- Department of Cancer Biology and Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erik Procko
- Department of Biochemistry and Cancer Center at Illinois, University of Illinois, Urbana, IL 61820, USA
| | - Nikolaos G. Sgourakis
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
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Seeking Standardized Definitions for HLA-incompatible Kidney Transplants: A Systematic Review. Transplantation 2023; 107:231-253. [PMID: 35915547 DOI: 10.1097/tp.0000000000004262] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND There is no standard definition for "HLA incompatible" transplants. For the first time, we systematically assessed how HLA incompatibility was defined in contemporary peer-reviewed publications and its prognostic implication to transplant outcomes. METHODS We combined 2 independent searches of MEDLINE, EMBASE, and the Cochrane Library from 2015 to 2019. Content-expert reviewers screened for original research on outcomes of HLA-incompatible transplants (defined as allele or molecular mismatch and solid-phase or cell-based assays). We ascertained the completeness of reporting on a predefined set of variables assessing HLA incompatibility, therapies, and outcomes. Given significant heterogeneity, we conducted narrative synthesis and assessed risk of bias in studies examining the association between death-censored graft failure and HLA incompatibility. RESULTS Of 6656 screened articles, 163 evaluated transplant outcomes by HLA incompatibility. Most articles reported on cytotoxic/flow T-cell crossmatches (n = 98). Molecular genotypes were reported for selected loci at the allele-group level. Sixteen articles reported on epitope compatibility. Pretransplant donor-specific HLA antibodies were often considered (n = 143); yet there was heterogeneity in sample handling, assay procedure, and incomplete reporting on donor-specific HLA antibodies assignment. Induction (n = 129) and maintenance immunosuppression (n = 140) were frequently mentioned but less so rejection treatment (n = 72) and desensitization (n = 70). Studies assessing death-censored graft failure risk by HLA incompatibility were vulnerable to bias in the participant, predictor, and analysis domains. CONCLUSIONS Optimization of transplant outcomes and personalized care depends on accurate HLA compatibility assessment. Reporting on a standard set of variables will help assess generalizability of research, allow knowledge synthesis, and facilitate international collaboration in clinical trials.
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Early prediction of renal graft function: Analysis of a multi-center, multi-level data set. Curr Res Transl Med 2022; 70:103334. [DOI: 10.1016/j.retram.2022.103334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/05/2022] [Accepted: 01/14/2022] [Indexed: 11/20/2022]
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9
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Díez-Sanmartín C, Sarasa-Cabezuelo A, Andrés Belmonte A. The impact of artificial intelligence and big data on end-stage kidney disease treatments. EXPERT SYSTEMS WITH APPLICATIONS 2021; 180:115076. [DOI: 10.1016/j.eswa.2021.115076] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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10
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Blazquez-Navarro A, Dang-Heine C, Wehler P, Roch T, Bauer C, Neumann S, Blazquez-Navarro R, Kurchenko A, Wolk K, Sabat R, Westhoff TH, Olek S, Thomusch O, Seitz H, Reinke P, Hugo C, Sawitzki B, Or-Guil M, Babel N. Risk factors for Epstein-Barr virus reactivation after renal transplantation: Results of a large, multi-centre study. Transpl Int 2021; 34:1680-1688. [PMID: 34448272 DOI: 10.1111/tri.13982] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 01/02/2023]
Abstract
Epstein-Barr virus (EBV) reactivation is a very common and potentially lethal complication of renal transplantation. However, its risk factors and effects on transplant outcome are not well known. Here, we have analysed a large, multi-centre cohort (N = 512) in which 18.4% of the patients experienced EBV reactivation during the first post-transplant year. The patients were characterized pre-transplant and two weeks post-transplant by a multi-level biomarker panel. EBV reactivation was episodic for most patients, only 12 patients showed prolonged viraemia for over four months. Pre-transplant EBV shedding and male sex were associated with significantly increased incidence of post-transplant EBV reactivation. Importantly, we also identified a significant association of post-transplant EBV with acute rejection and with decreased haemoglobin levels. No further severe complications associated with EBV, either episodic or chronic, could be detected. Our data suggest that despite relatively frequent EBV reactivation, it had no association with serious complications during the first post-transplantation year. EBV shedding prior to transplantation could be employed as biomarkers for personalized immunosuppressive therapy. In summary, our results support the employed immunosuppressive regimes as relatively safe with regard to EBV. However, long-term studies are paramount to support these conclusions.
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Affiliation(s)
- Arturo Blazquez-Navarro
- Systems Immunology Lab, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Center for Translational Medicine, Universitätsklinikum der Ruhr-Universität Bochum, Medizinische Klinik I, Herne, Germany
| | - Chantip Dang-Heine
- Clinical Study Center (CSC), Berlin Institute of Health, and Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Patrizia Wehler
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Center for Translational Medicine, Universitätsklinikum der Ruhr-Universität Bochum, Medizinische Klinik I, Herne, Germany
| | - Toralf Roch
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Center for Translational Medicine, Universitätsklinikum der Ruhr-Universität Bochum, Medizinische Klinik I, Herne, Germany
| | | | | | | | - Andriy Kurchenko
- Department of Clinical Immunology and Allergology, Bogolomets National Medical University, Kiev, Ukraine
| | - Kerstin Wolk
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Psoriasis Research and Treatment Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Robert Sabat
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Psoriasis Research and Treatment Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Timm H Westhoff
- Center for Translational Medicine, Universitätsklinikum der Ruhr-Universität Bochum, Medizinische Klinik I, Herne, Germany
| | - Sven Olek
- Ivana Türbachova Laboratory for Epigenetics, Epiontis GmbH, Precision for Medicine Group, Berlin, Germany
| | - Oliver Thomusch
- Klinik für Allgemein- und Viszeralchirurgie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Harald Seitz
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics und Bioprocesses, Leipzig, Germany
| | - Petra Reinke
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Center for Advanced Therapies (BeCAT), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Hugo
- Universitätsklinikum Carl Gustav Carus, Medizinische Klinik III - Bereich Nephrologie, Dresden, Germany
| | - Birgit Sawitzki
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michal Or-Guil
- Systems Immunology Lab, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nina Babel
- Berlin Institute of Health Center for Regenerative Therapies (BCRT): Berlin-Brandenburger Centrum für Regenerative Therapien, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Center for Translational Medicine, Universitätsklinikum der Ruhr-Universität Bochum, Medizinische Klinik I, Herne, Germany
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11
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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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Ilca FT, Drexhage LZ, Brewin G, Peacock S, Boyle LH. Distinct Polymorphisms in HLA Class I Molecules Govern Their Susceptibility to Peptide Editing by TAPBPR. Cell Rep 2020; 29:1621-1632.e3. [PMID: 31693900 PMCID: PMC7057265 DOI: 10.1016/j.celrep.2019.09.074] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 07/28/2019] [Accepted: 09/25/2019] [Indexed: 12/20/2022] Open
Abstract
Understanding how peptide selection is controlled on different major histocompatibility complex class I (MHC I) molecules is pivotal for determining how variations in these proteins influence our predisposition to infectious diseases, cancer, and autoinflammatory conditions. Although the intracellular chaperone TAPBPR edits MHC I peptides, it is unclear which allotypes are subjected to TAPBPR-mediated peptide editing. Here, we examine the ability of 97 different human leukocyte antigen (HLA) class I allotypes to interact with TAPBPR. We reveal a striking preference of TAPBPR for HLA-A, particularly for supertypes A2 and A24, over HLA-B and -C molecules. We demonstrate that the increased propensity of these HLA-A molecules to undergo TAPBPR-mediated peptide editing is determined by molecular features of the HLA-A F pocket, specifically residues H114 and Y116. This work reveals that specific polymorphisms in MHC I strongly influence their susceptibility to chaperone-mediated peptide editing, which may play a significant role in disease predisposition.
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Affiliation(s)
- F Tudor Ilca
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP, UK
| | - Linnea Z Drexhage
- Faculty of Biology, University of Freiburg, Schaenzlestrasse 1, 79104 Freiburg, Germany
| | - Gemma Brewin
- Tissue Typing Laboratory, Box 209, Level 6 ATC, Cambridge University Hospitals, NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Sarah Peacock
- Tissue Typing Laboratory, Box 209, Level 6 ATC, Cambridge University Hospitals, NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Louise H Boyle
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP, UK.
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