Cargando…

B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients

BACKGROUND: Vaccination is considered as most efficient strategy in controlling SARS-CoV-2 pandemic spread. Nevertheless, patients with autoimmune inflammatory rheumatic diseases receiving rituximab (RTX) are at increased risk to fail humoral and cellular responses upon vaccination. The ability to p...

Descripción completa

Detalles Bibliográficos
Autores principales: Stefanski, Ana-Luisa, Rincon-Arevalo, Hector, Schrezenmeier, Eva, Karberg, Kirsten, Szelinski, Franziska, Ritter, Jacob, Chen, Yidan, Jahrsdörfer, Bernd, Ludwig, Carolin, Schrezenmeier, Hubert, Lino, Andreia C., Dörner, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9063458/
https://www.ncbi.nlm.nih.gov/pubmed/35514962
http://dx.doi.org/10.3389/fimmu.2022.822885
_version_ 1784699169637662720
author Stefanski, Ana-Luisa
Rincon-Arevalo, Hector
Schrezenmeier, Eva
Karberg, Kirsten
Szelinski, Franziska
Ritter, Jacob
Chen, Yidan
Jahrsdörfer, Bernd
Ludwig, Carolin
Schrezenmeier, Hubert
Lino, Andreia C.
Dörner, Thomas
author_facet Stefanski, Ana-Luisa
Rincon-Arevalo, Hector
Schrezenmeier, Eva
Karberg, Kirsten
Szelinski, Franziska
Ritter, Jacob
Chen, Yidan
Jahrsdörfer, Bernd
Ludwig, Carolin
Schrezenmeier, Hubert
Lino, Andreia C.
Dörner, Thomas
author_sort Stefanski, Ana-Luisa
collection PubMed
description BACKGROUND: Vaccination is considered as most efficient strategy in controlling SARS-CoV-2 pandemic spread. Nevertheless, patients with autoimmune inflammatory rheumatic diseases receiving rituximab (RTX) are at increased risk to fail humoral and cellular responses upon vaccination. The ability to predict vaccination responses is essential to guide adequate safety and optimal protection in these patients. METHODS: B- and T- cell data before vaccination were evaluated for characteristics predicting vaccine responses in altogether 15 patients with autoimmune inflammatory rheumatic diseases receiving RTX. Eleven patients with rheumatoid arthritis (RA) on other therapies, 11 kidney transplant recipients (KTR) on regular immunosuppression and 15 healthy controls (HC) served as controls. A multidimensional analysis of B cell subsets via UMAP algorithm and a correlation matrix were performed in order to identify predictive markers of response in patients under RTX therapy. RESULTS: Significant differences regarding absolute B cell counts and specific subset distribution pattern between the groups were identified at baseline. In this context, the majority of B cells from vaccination responders of the RTX group (RTX IgG+) were naïve and transitional B cells, whereas vaccination non-responders (RTX IgG-) carried preferentially plasmablasts and double negative (CD27-IgD-) B cells. Moreover, there was a positive correlation between neutralizing antibodies and B cells expressing HLA-DR and CXCR5 as well as an inverse correlation with CD95 expression and CD21low expression by B cells among vaccination responders. SUMMARY: Substantial repopulation of the naïve B cell compartment after RTX therapy appeared to be essential for an adequate vaccination response, which seem to require the additional capability of antigen presentation and germinal center formation. Moreover, expression of exhaustion markers represent negative predictors of vaccination responses.
format Online
Article
Text
id pubmed-9063458
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-90634582022-05-04 B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients Stefanski, Ana-Luisa Rincon-Arevalo, Hector Schrezenmeier, Eva Karberg, Kirsten Szelinski, Franziska Ritter, Jacob Chen, Yidan Jahrsdörfer, Bernd Ludwig, Carolin Schrezenmeier, Hubert Lino, Andreia C. Dörner, Thomas Front Immunol Immunology BACKGROUND: Vaccination is considered as most efficient strategy in controlling SARS-CoV-2 pandemic spread. Nevertheless, patients with autoimmune inflammatory rheumatic diseases receiving rituximab (RTX) are at increased risk to fail humoral and cellular responses upon vaccination. The ability to predict vaccination responses is essential to guide adequate safety and optimal protection in these patients. METHODS: B- and T- cell data before vaccination were evaluated for characteristics predicting vaccine responses in altogether 15 patients with autoimmune inflammatory rheumatic diseases receiving RTX. Eleven patients with rheumatoid arthritis (RA) on other therapies, 11 kidney transplant recipients (KTR) on regular immunosuppression and 15 healthy controls (HC) served as controls. A multidimensional analysis of B cell subsets via UMAP algorithm and a correlation matrix were performed in order to identify predictive markers of response in patients under RTX therapy. RESULTS: Significant differences regarding absolute B cell counts and specific subset distribution pattern between the groups were identified at baseline. In this context, the majority of B cells from vaccination responders of the RTX group (RTX IgG+) were naïve and transitional B cells, whereas vaccination non-responders (RTX IgG-) carried preferentially plasmablasts and double negative (CD27-IgD-) B cells. Moreover, there was a positive correlation between neutralizing antibodies and B cells expressing HLA-DR and CXCR5 as well as an inverse correlation with CD95 expression and CD21low expression by B cells among vaccination responders. SUMMARY: Substantial repopulation of the naïve B cell compartment after RTX therapy appeared to be essential for an adequate vaccination response, which seem to require the additional capability of antigen presentation and germinal center formation. Moreover, expression of exhaustion markers represent negative predictors of vaccination responses. Frontiers Media S.A. 2022-04-19 /pmc/articles/PMC9063458/ /pubmed/35514962 http://dx.doi.org/10.3389/fimmu.2022.822885 Text en Copyright © 2022 Stefanski, Rincon-Arevalo, Schrezenmeier, Karberg, Szelinski, Ritter, Chen, Jahrsdörfer, Ludwig, Schrezenmeier, Lino and Dörner 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
Stefanski, Ana-Luisa
Rincon-Arevalo, Hector
Schrezenmeier, Eva
Karberg, Kirsten
Szelinski, Franziska
Ritter, Jacob
Chen, Yidan
Jahrsdörfer, Bernd
Ludwig, Carolin
Schrezenmeier, Hubert
Lino, Andreia C.
Dörner, Thomas
B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients
title B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients
title_full B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients
title_fullStr B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients
title_full_unstemmed B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients
title_short B Cell Characteristics at Baseline Predict Vaccination Response in RTX Treated Patients
title_sort b cell characteristics at baseline predict vaccination response in rtx treated patients
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9063458/
https://www.ncbi.nlm.nih.gov/pubmed/35514962
http://dx.doi.org/10.3389/fimmu.2022.822885
work_keys_str_mv AT stefanskianaluisa bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT rinconarevalohector bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT schrezenmeiereva bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT karbergkirsten bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT szelinskifranziska bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT ritterjacob bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT chenyidan bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT jahrsdorferbernd bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT ludwigcarolin bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT schrezenmeierhubert bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT linoandreiac bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients
AT dornerthomas bcellcharacteristicsatbaselinepredictvaccinationresponseinrtxtreatedpatients