| Literature DB >> 31477052 |
Constantin Aschauer1, Kira Jelencsics1, Karin Hu1, Andreas Heinzel1, Julia Vetter2, Thomas Fraunhofer2, Susanne Schaller2, Stephan Winkler2, Lisabeth Pimenov1, Guido A Gualdoni1, Michael Eder1, Alexander Kainz1, Heinz Regele3, Roman Reindl-Schwaighofer1, Rainer Oberbauer4.
Abstract
BACKGROUND: Kidney transplantation is the optimal treatment in end stage renal disease but the allograft survival is still hampered by immune reactions against the allograft. This process is driven by the recognition of allogenic antigens presented to T-cells and their unique T-cell receptor (TCR) via the major histocompatibility complex (MHC), which triggers a complex immune response potentially leading to graft injury. Although the immune system and kidney transplantation have been studied extensively, the subtlety of alloreactive immune responses has impeded sensitive detection at an early stage. Next generation sequencing of the TCR enables us to monitor alloreactive T-cell populations and might thus allow the detection of early rejection events. METHODS/Entities:
Keywords: Alloreactivity; Kidney transplant; Next generation sequencing; Rejection; T cell receptor
Year: 2019 PMID: 31477052 PMCID: PMC6719356 DOI: 10.1186/s12882-019-1541-5
Source DB: PubMed Journal: BMC Nephrol ISSN: 1471-2369 Impact factor: 2.388
Fig. 1Study Flowchart. All eligible patients receiving a kidney transplant from donors evaluated at our center will be included and TCR repertoire of total PBMCs and alloreactive T cells will be analyzed. TCR repertoire in the periphery and in biopsies will be assessed at surveillance biopsies and TCMR. DSA: donor reactive antibodies, TCR: T cell receptor, PBMC: peripheral blood mononuclear cells, KTX: kidney transplant, TCMR: T cell mediated rejection
Fig. 2Detectable effect size versus sample size. Depicted is the relationship between number of samples in the study and minimal detectable difference in population means for standard deviations of 0.05 (red), 0.1 (blue) and 0.15 (pink). SD: Standard deviation
Schematic diagram of the patient’s timeline (adjusted from SPIRIT guidelines for trial protocols)
| Timepoint | Study period | ||||
|---|---|---|---|---|---|
| Enrolment | Biopsy | Close-out | |||
| Transplantation | For cause | M3 | M12 | M24 | |
| Enrolment: | |||||
| Eligibility screen | X | ||||
| Informed consent | X | ||||
| Interventions: | |||||
| PBMC collection | X | X | X | X | |
| Kidney Biopsy | X | X | X | ||
| Assessments: | |||||
| TCR repertoire sequencing of unstimulated PBMCs | X | X | X | X | |
| TCR sequencing of Alloreactive T cells after MLR | X | ||||
| Phenotypic analysis of peripheral T cell populations | X | X | X | X | |
M3 Month 3, M12 Month 12, MLR Mixed lymphocyte reaction, TCR T cell receptor, PBMC Peripheral blood mononuclear cells
Fig. 3Bioinformatic Flowchart. A pipeline for the analysis of T cell repertoires. A: First, barcode analysis is done as well as separating the sequences belonging to the different individuals (B). C: Reads that cannot be assigned to a specific individual are stored in a separate FASTQ file for later investigation regarding the origin of these sequences. D: Adapters and barcodes from sequences are trimmed. During this step the UMIs are determined and stored in a separate file (D1). E: Afterwards the sequences are clustered with respect to their reference genes and UMIs. F/G: Clonotypes are assembled and clonality, diversity and repertoire overlap analysis is performed