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An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome

INTRODUCTION: Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögren’s syndrome. Currently this relies on the histopathologic diagnosis of focal lymphocytic sialadenitis and computing a focus score by counting lym=phocyte foci. However, those lesions represent advanced sta...

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Autores principales: Urbanski, Geoffrey, Chabrun, Floris, Delattre, Estelle, Lacout, Carole, Davidson, Brittany, Blanchet, Odile, Chao de la Barca, Juan Manuel, Simard, Gilles, Lavigne, Christian, Reynier, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375713/
https://www.ncbi.nlm.nih.gov/pubmed/37520535
http://dx.doi.org/10.3389/fimmu.2023.1205616
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author Urbanski, Geoffrey
Chabrun, Floris
Delattre, Estelle
Lacout, Carole
Davidson, Brittany
Blanchet, Odile
Chao de la Barca, Juan Manuel
Simard, Gilles
Lavigne, Christian
Reynier, Pascal
author_facet Urbanski, Geoffrey
Chabrun, Floris
Delattre, Estelle
Lacout, Carole
Davidson, Brittany
Blanchet, Odile
Chao de la Barca, Juan Manuel
Simard, Gilles
Lavigne, Christian
Reynier, Pascal
author_sort Urbanski, Geoffrey
collection PubMed
description INTRODUCTION: Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögren’s syndrome. Currently this relies on the histopathologic diagnosis of focal lymphocytic sialadenitis and computing a focus score by counting lym=phocyte foci. However, those lesions represent advanced stages of primary Sjögren’s syndrome, although earlier recognition of primary Sjögren’s syndrome and its effective treatment could prevent irreversible damage to labial salivary gland. This study aimed at finding early biomarkers of primary Sjögren’s syndrome in labial salivary gland combining metabolomics and machine-learning approaches. METHODS: We used a standardized targeted metabolomic approach involving high performance liquid chromatography coupled with mass spectrometry among newly diagnosed primary Sjögren’s syndrome (n=40) and non- primary Sjögren’s syndrome sicca (n=40) participants in a prospective cohort. A metabolic signature predictive of primary Sjögren’s syndrome status was explored using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the data set into training, validation, and test sets. RESULTS: Among 126 metabolites accurately measured, we identified a discriminant signature composed of six metabolites with robust performances (ROC-AUC = 0.86) for predicting primary Sjögren’s syndrome status. This signature included the well-known immune-metabolite kynurenine and five phospholipids (LysoPC C28:0; PCaa C26:0; PCaaC30:2; PCae C30:1, and PCaeC30:2). It was split into two main components: the first including the phospholipids was related to the intensity of lymphocytic infiltrates in salivary glands, while the second represented by kynurenine was independently associated with the presence of anti-SSA antibodies in participant serum. CONCLUSION: Our results reveal an immuno-lipidomic signature in labial salivary gland that accurately distinguishes early primary Sjögren’s syndrome from other causes of sicca symptoms.
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spelling pubmed-103757132023-07-29 An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome Urbanski, Geoffrey Chabrun, Floris Delattre, Estelle Lacout, Carole Davidson, Brittany Blanchet, Odile Chao de la Barca, Juan Manuel Simard, Gilles Lavigne, Christian Reynier, Pascal Front Immunol Immunology INTRODUCTION: Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögren’s syndrome. Currently this relies on the histopathologic diagnosis of focal lymphocytic sialadenitis and computing a focus score by counting lym=phocyte foci. However, those lesions represent advanced stages of primary Sjögren’s syndrome, although earlier recognition of primary Sjögren’s syndrome and its effective treatment could prevent irreversible damage to labial salivary gland. This study aimed at finding early biomarkers of primary Sjögren’s syndrome in labial salivary gland combining metabolomics and machine-learning approaches. METHODS: We used a standardized targeted metabolomic approach involving high performance liquid chromatography coupled with mass spectrometry among newly diagnosed primary Sjögren’s syndrome (n=40) and non- primary Sjögren’s syndrome sicca (n=40) participants in a prospective cohort. A metabolic signature predictive of primary Sjögren’s syndrome status was explored using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the data set into training, validation, and test sets. RESULTS: Among 126 metabolites accurately measured, we identified a discriminant signature composed of six metabolites with robust performances (ROC-AUC = 0.86) for predicting primary Sjögren’s syndrome status. This signature included the well-known immune-metabolite kynurenine and five phospholipids (LysoPC C28:0; PCaa C26:0; PCaaC30:2; PCae C30:1, and PCaeC30:2). It was split into two main components: the first including the phospholipids was related to the intensity of lymphocytic infiltrates in salivary glands, while the second represented by kynurenine was independently associated with the presence of anti-SSA antibodies in participant serum. CONCLUSION: Our results reveal an immuno-lipidomic signature in labial salivary gland that accurately distinguishes early primary Sjögren’s syndrome from other causes of sicca symptoms. Frontiers Media S.A. 2023-07-14 /pmc/articles/PMC10375713/ /pubmed/37520535 http://dx.doi.org/10.3389/fimmu.2023.1205616 Text en Copyright © 2023 Urbanski, Chabrun, Delattre, Lacout, Davidson, Blanchet, Chao de la Barca, Simard, Lavigne and Reynier 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
Urbanski, Geoffrey
Chabrun, Floris
Delattre, Estelle
Lacout, Carole
Davidson, Brittany
Blanchet, Odile
Chao de la Barca, Juan Manuel
Simard, Gilles
Lavigne, Christian
Reynier, Pascal
An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome
title An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome
title_full An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome
title_fullStr An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome
title_full_unstemmed An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome
title_short An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren’s syndrome
title_sort immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary sjögren’s syndrome
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375713/
https://www.ncbi.nlm.nih.gov/pubmed/37520535
http://dx.doi.org/10.3389/fimmu.2023.1205616
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