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PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients

We tested whether multi-parameter immune phenotyping before or after renal ­transplantation can predict the risk of rejection episodes. Blood samples collected before and weekly for 3 months after transplantation were analyzed by multi-parameter flow cytometry to define 52 T cell and 13 innate lymph...

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Autores principales: Pike, Rebecca, Thomas, Niclas, Workman, Sarita, Ambrose, Lyn, Guzman, David, Sivakumaran, Shivajanani, Johnson, Margaret, Thorburn, Douglas, Harber, Mark, Chain, Benny, Stauss, Hans J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827377/
https://www.ncbi.nlm.nih.gov/pubmed/27148254
http://dx.doi.org/10.3389/fimmu.2016.00126
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author Pike, Rebecca
Thomas, Niclas
Workman, Sarita
Ambrose, Lyn
Guzman, David
Sivakumaran, Shivajanani
Johnson, Margaret
Thorburn, Douglas
Harber, Mark
Chain, Benny
Stauss, Hans J.
author_facet Pike, Rebecca
Thomas, Niclas
Workman, Sarita
Ambrose, Lyn
Guzman, David
Sivakumaran, Shivajanani
Johnson, Margaret
Thorburn, Douglas
Harber, Mark
Chain, Benny
Stauss, Hans J.
author_sort Pike, Rebecca
collection PubMed
description We tested whether multi-parameter immune phenotyping before or after renal ­transplantation can predict the risk of rejection episodes. Blood samples collected before and weekly for 3 months after transplantation were analyzed by multi-parameter flow cytometry to define 52 T cell and 13 innate lymphocyte subsets in each sample, producing more than 11,000 data points that defined the immune status of the 28 patients included in this study. Principle component analysis suggested that the patients with histologically confirmed rejection episodes segregated from those without rejection. Protein death 1 (PD-1)-expressing subpopulations of regulatory and conventional T cells had the greatest influence on the principal component segregation. We constructed a statistical tool to predict rejection using a support vector machine algorithm. The algorithm correctly identified 7 out of 9 patients with rejection, and 14 out of 17 patients without rejection. The immune profile before transplantation was most accurate in determining the risk of rejection, while changes of immune parameters after transplantation were less accurate in discriminating rejection from non-rejection. The data indicate that pretransplant immune subset analysis has the potential to identify patients at risk of developing rejection episodes, and suggests that the proportion of PD1-expressing T cell subsets may be a key indicator of rejection risk.
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spelling pubmed-48273772016-05-04 PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients Pike, Rebecca Thomas, Niclas Workman, Sarita Ambrose, Lyn Guzman, David Sivakumaran, Shivajanani Johnson, Margaret Thorburn, Douglas Harber, Mark Chain, Benny Stauss, Hans J. Front Immunol Immunology We tested whether multi-parameter immune phenotyping before or after renal ­transplantation can predict the risk of rejection episodes. Blood samples collected before and weekly for 3 months after transplantation were analyzed by multi-parameter flow cytometry to define 52 T cell and 13 innate lymphocyte subsets in each sample, producing more than 11,000 data points that defined the immune status of the 28 patients included in this study. Principle component analysis suggested that the patients with histologically confirmed rejection episodes segregated from those without rejection. Protein death 1 (PD-1)-expressing subpopulations of regulatory and conventional T cells had the greatest influence on the principal component segregation. We constructed a statistical tool to predict rejection using a support vector machine algorithm. The algorithm correctly identified 7 out of 9 patients with rejection, and 14 out of 17 patients without rejection. The immune profile before transplantation was most accurate in determining the risk of rejection, while changes of immune parameters after transplantation were less accurate in discriminating rejection from non-rejection. The data indicate that pretransplant immune subset analysis has the potential to identify patients at risk of developing rejection episodes, and suggests that the proportion of PD1-expressing T cell subsets may be a key indicator of rejection risk. Frontiers Media S.A. 2016-04-11 /pmc/articles/PMC4827377/ /pubmed/27148254 http://dx.doi.org/10.3389/fimmu.2016.00126 Text en Copyright © 2016 Pike, Thomas, Workman, Ambrose, Guzman, Sivakumaran, Johnson, Thorburn, Harber, Chain and Stauss. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Pike, Rebecca
Thomas, Niclas
Workman, Sarita
Ambrose, Lyn
Guzman, David
Sivakumaran, Shivajanani
Johnson, Margaret
Thorburn, Douglas
Harber, Mark
Chain, Benny
Stauss, Hans J.
PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients
title PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients
title_full PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients
title_fullStr PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients
title_full_unstemmed PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients
title_short PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients
title_sort pd1-expressing t cell subsets modify the rejection risk in renal transplant patients
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827377/
https://www.ncbi.nlm.nih.gov/pubmed/27148254
http://dx.doi.org/10.3389/fimmu.2016.00126
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