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Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

OBJECTIVE: Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients. METHOD: Plasma was collected from patients with active SLE w...

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Autores principales: Su, Kevin Y. C., Reynolds, John A., Reed, Rachel, Da Silva, Rachael, Kelsall, Janet, Baricevic-Jones, Ivona, Lee, David, Whetton, Anthony D., Geifman, Nophar, McHugh, Neil, Bruce, Ian N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385905/
https://www.ncbi.nlm.nih.gov/pubmed/37516862
http://dx.doi.org/10.1186/s12014-023-09420-1
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author Su, Kevin Y. C.
Reynolds, John A.
Reed, Rachel
Da Silva, Rachael
Kelsall, Janet
Baricevic-Jones, Ivona
Lee, David
Whetton, Anthony D.
Geifman, Nophar
McHugh, Neil
Bruce, Ian N.
author_facet Su, Kevin Y. C.
Reynolds, John A.
Reed, Rachel
Da Silva, Rachael
Kelsall, Janet
Baricevic-Jones, Ivona
Lee, David
Whetton, Anthony D.
Geifman, Nophar
McHugh, Neil
Bruce, Ian N.
author_sort Su, Kevin Y. C.
collection PubMed
description OBJECTIVE: Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients. METHOD: Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile. RESULTS: In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617). CONCLUSIONS: Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-023-09420-1.
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spelling pubmed-103859052023-07-30 Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus Su, Kevin Y. C. Reynolds, John A. Reed, Rachel Da Silva, Rachael Kelsall, Janet Baricevic-Jones, Ivona Lee, David Whetton, Anthony D. Geifman, Nophar McHugh, Neil Bruce, Ian N. Clin Proteomics Research OBJECTIVE: Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients. METHOD: Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile. RESULTS: In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617). CONCLUSIONS: Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-023-09420-1. BioMed Central 2023-07-29 /pmc/articles/PMC10385905/ /pubmed/37516862 http://dx.doi.org/10.1186/s12014-023-09420-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Su, Kevin Y. C.
Reynolds, John A.
Reed, Rachel
Da Silva, Rachael
Kelsall, Janet
Baricevic-Jones, Ivona
Lee, David
Whetton, Anthony D.
Geifman, Nophar
McHugh, Neil
Bruce, Ian N.
Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
title Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
title_full Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
title_fullStr Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
title_full_unstemmed Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
title_short Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
title_sort proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385905/
https://www.ncbi.nlm.nih.gov/pubmed/37516862
http://dx.doi.org/10.1186/s12014-023-09420-1
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