Cargando…

Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers

End stage renal disease (ESRD) engenders detrimental effects in the Immune system, manifested as quantitative alterations of lymphocyte subpopulations, akin, albeit not identical to those observed during the ageing process. We performed dimensionality reduction of an extended lymphocyte phenotype pa...

Descripción completa

Detalles Bibliográficos
Autores principales: Lioulios, Georgios, Fylaktou, Asimina, Xochelli, Aliki, Sampani, Erasmia, Tsouchnikas, Ioannis, Giamalis, Panagiotis, Daikidou, Dimitra-Vasilia, Nikolaidou, Vasiliki, Papagianni, Aikaterini, Theodorou, Ioannis, Stangou, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126282/
https://www.ncbi.nlm.nih.gov/pubmed/35615367
http://dx.doi.org/10.3389/fimmu.2022.841031
_version_ 1784712094966349824
author Lioulios, Georgios
Fylaktou, Asimina
Xochelli, Aliki
Sampani, Erasmia
Tsouchnikas, Ioannis
Giamalis, Panagiotis
Daikidou, Dimitra-Vasilia
Nikolaidou, Vasiliki
Papagianni, Aikaterini
Theodorou, Ioannis
Stangou, Maria
author_facet Lioulios, Georgios
Fylaktou, Asimina
Xochelli, Aliki
Sampani, Erasmia
Tsouchnikas, Ioannis
Giamalis, Panagiotis
Daikidou, Dimitra-Vasilia
Nikolaidou, Vasiliki
Papagianni, Aikaterini
Theodorou, Ioannis
Stangou, Maria
author_sort Lioulios, Georgios
collection PubMed
description End stage renal disease (ESRD) engenders detrimental effects in the Immune system, manifested as quantitative alterations of lymphocyte subpopulations, akin, albeit not identical to those observed during the ageing process. We performed dimensionality reduction of an extended lymphocyte phenotype panel of senescent and exhaustion related markers in ESRD patients and controls with Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). The plane defined by the first two principal components of PCA showed two fuzzy clusters, for patients and controls, respectively, with loadings of non-senescent markers pointing towards the controls’ centroid. Naive lymphocytes were reduced in ESRD patients compared to controls (CD4+CD45RA+CCR7+ 200(150-328) vs. 426(260-585cells/μl respectively, P = 0.001, CD19+IgD+CD27- 54(26-85) vs. 130(83-262)cells/μl respectively, P < 0.001). PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in hemodialysis compared to hemodiafiltration treated patients. Lastly, clustering based on UMAP revealed three distinct patient groups, exhibiting gradual changes for naive, senescent, and exhausted lymphocyte markers. Machine learning algorithms can distinguish ESRD patients from controls, based on their immune-phenotypes and also, unveil distinct immunological groups within patients’ cohort, determined possibly by dialysis prescription.
format Online
Article
Text
id pubmed-9126282
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-91262822022-05-24 Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers Lioulios, Georgios Fylaktou, Asimina Xochelli, Aliki Sampani, Erasmia Tsouchnikas, Ioannis Giamalis, Panagiotis Daikidou, Dimitra-Vasilia Nikolaidou, Vasiliki Papagianni, Aikaterini Theodorou, Ioannis Stangou, Maria Front Immunol Immunology End stage renal disease (ESRD) engenders detrimental effects in the Immune system, manifested as quantitative alterations of lymphocyte subpopulations, akin, albeit not identical to those observed during the ageing process. We performed dimensionality reduction of an extended lymphocyte phenotype panel of senescent and exhaustion related markers in ESRD patients and controls with Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). The plane defined by the first two principal components of PCA showed two fuzzy clusters, for patients and controls, respectively, with loadings of non-senescent markers pointing towards the controls’ centroid. Naive lymphocytes were reduced in ESRD patients compared to controls (CD4+CD45RA+CCR7+ 200(150-328) vs. 426(260-585cells/μl respectively, P = 0.001, CD19+IgD+CD27- 54(26-85) vs. 130(83-262)cells/μl respectively, P < 0.001). PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in hemodialysis compared to hemodiafiltration treated patients. Lastly, clustering based on UMAP revealed three distinct patient groups, exhibiting gradual changes for naive, senescent, and exhausted lymphocyte markers. Machine learning algorithms can distinguish ESRD patients from controls, based on their immune-phenotypes and also, unveil distinct immunological groups within patients’ cohort, determined possibly by dialysis prescription. Frontiers Media S.A. 2022-05-09 /pmc/articles/PMC9126282/ /pubmed/35615367 http://dx.doi.org/10.3389/fimmu.2022.841031 Text en Copyright © 2022 Lioulios, Fylaktou, Xochelli, Sampani, Tsouchnikas, Giamalis, Daikidou, Nikolaidou, Papagianni, Theodorou and Stangou https://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) and the copyright owner(s) 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
Lioulios, Georgios
Fylaktou, Asimina
Xochelli, Aliki
Sampani, Erasmia
Tsouchnikas, Ioannis
Giamalis, Panagiotis
Daikidou, Dimitra-Vasilia
Nikolaidou, Vasiliki
Papagianni, Aikaterini
Theodorou, Ioannis
Stangou, Maria
Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers
title Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers
title_full Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers
title_fullStr Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers
title_full_unstemmed Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers
title_short Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers
title_sort clustering of end stage renal disease patients by dimensionality reduction algorithms according to lymphocyte senescence markers
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126282/
https://www.ncbi.nlm.nih.gov/pubmed/35615367
http://dx.doi.org/10.3389/fimmu.2022.841031
work_keys_str_mv AT liouliosgeorgios clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT fylaktouasimina clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT xochellialiki clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT sampanierasmia clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT tsouchnikasioannis clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT giamalispanagiotis clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT daikidoudimitravasilia clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT nikolaidouvasiliki clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT papagianniaikaterini clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT theodorouioannis clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers
AT stangoumaria clusteringofendstagerenaldiseasepatientsbydimensionalityreductionalgorithmsaccordingtolymphocytesenescencemarkers