Machine Learning for the Identification of a Common Signature for Anti–SSA/Ro 60 Antibody Expression Across Autoimmune Diseases

Dantec, Christelle Le; Bettacchioli, Éléonore; Jamin, Christophe; Alarcón‐Riquelme, Marta E.; Pers, Jacques‐Olivier; Beretta, Lorenzo; Vigone, Barbara; Saraux, Alain; Devauchelle‐Pensec, Valérie; Cornec, Divi; Jousse‐Joulin, Sandrine; Lauwerys, Bernard; Ducreux, Julie; Maudoux, Anne‐Lise; Vasconcelos, Carlos; Tavares, Ana Helena; Neves, Esmeralda; Faria, Raquel; Brandão, Mariana; Campar, Ana; Marinho, António; Farinha, Fátima; Almeida, Isabel; Alonso, Ricardo Blanco; Martínez, Alfonso Corrales; Cervera, Ricard; Rodríguez‐Pintó, Ignasi; Espinosa, Gerard; Lories, Rik; Langhe, Ellen De; Hunzelmann, Nicolas; Belz, D.; Witte, Torsten; Baerlecken, Niklas; Stummvoll, Georg; Zauner, Michael; Lehner, Michaela; Collantes, Eduardo; Ortega‐Castro, Rafaela; Aguirre‐Zamorano, Ma Angeles; Escudero‐Contreras, Alejandro; Castro‐Villegas, Ma Carmen; Gómez, Yolanda Jiménez; Ortego, N.; Roldán, María Concepción Fernández; Raya, Enrique; Moleón, Inmaculada Jiménez; Enrique, Rafael; Quintero, Isabel Díaz; Meroni, Pier Luigi; Gerosa, Maria; Schioppo, Tommaso; Artusi, Carolina; Chizzolini, Carlo; Dufour, Aleksandra Maria; Wynar, Donatienne; Kovács, László; Balog, Attila; Deák, Magdolna; Bocskai, Márta; Dulic, Sonja; Kádár, Gabriella; Hiepe, Falk; Gerl, Velia; Thiel, Silvia; Maresca, Manuel Rodríguez; López‐Berrio, Antonio; Aguilar‐Quesada, Rocío; Navarro‐Linares, Héctor; Ioannou, Yiannis; Chamberlain, Chris; Marovac, Jacqueline; Riquelme, Marta Alarcón; Anjos, Tania; Marañón, Concepción; Lann, Lucas Le; Simon, Quentin; Rouvière, Bénédicte; Varela, Nieves; Muchmore, Brian; Alvarez, Montserrat; Cremer, Jonathan; Barbarroja, Nuria; López‐Pedrera, Chary; Khodadadi, Laleh; Cheng, Qingyu; Buttgereit, Anne; Makowska, Zuzanna; Groof, Aurélie De; Trombetta, Elena; Li, Tianlu; Álvarez-Errico, Damiana; Kniesch, Katja; Azevedo, Nancy; Jouve, P

doi:10.1002/art.42243

Cited by 13 publications

(10 citation statements)

References 42 publications

(51 reference statements)

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“…It is mostly elusive how SNPs affect gene expression, as they mostly act in a cell-type or activation-status-specific manner. Technology to study epigenetics, as well as various innovative computational tools that are now emerging will help to interpret and prioritize disease-associated SNPs [ 353 , 356 , 357 ].…”

Section: Concluding Remarks and Future Perspectivementioning

confidence: 99%

Aberrant B Cell Signaling in Autoimmune Diseases

Corneth

Neys

Hendriks

2022

Cells

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Aberrant B cell signaling plays a critical in role in various systemic and organ-specific autoimmune diseases. This is supported by genetic evidence by many functional studies in B cells from patients or specific animal models and by the observed efficacy of small-molecule inhibitors. In this review, we first discuss key signal transduction pathways downstream of the B cell receptor (BCR) that ensure that autoreactive B cells are removed from the repertoire or functionally silenced. We provide an overview of aberrant BCR signaling that is associated with inappropriate B cell repertoire selection and activation or survival of peripheral B cell populations and plasma cells, finally leading to autoantibody formation. Next to BCR signaling, abnormalities in other signal transduction pathways have been implicated in autoimmune disease. These include reduced activity of several phosphates that are downstream of co-inhibitory receptors on B cells and increased levels of BAFF and APRIL, which support survival of B cells and plasma cells. Importantly, pathogenic synergy of the BCR and Toll-like receptors (TLR), which can be activated by endogenous ligands, such as self-nucleic acids, has been shown to enhance autoimmunity. Finally, we will briefly discuss therapeutic strategies for autoimmune disease based on interfering with signal transduction in B cells.

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Section: Concluding Remarks and Future Perspectivementioning

confidence: 99%

Aberrant B Cell Signaling in Autoimmune Diseases

Corneth

Neys

Hendriks

2022

Cells

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“…[6][7][8] We have recently described that three genes (ATP10A, PARP14, and MX1) are overexpressed, hypomethylated, and mutated in anti-Ro60 + patients and are remarkably associated with the IFN signature regardless of autoimmune disease. 9 Double positivity for anti-Ro52/TRIM21 and anti-Ro60/SSA antibodies is usually observed in approximately two-thirds of patients, and this presentation is often associated with more systemic involvement and severe evolution than in seronegative patients. 3,[10][11][12] The current methods of detection in laboratories may involve combined or individual detection of the two antibodies according to the different local practices of laboratories because the actual classification does not clearly specify the use of one of the two targets.…”

Section: Introductionmentioning

confidence: 99%

“…A positive IFN signature, defined as an overexpression of IFN‐related transcripts, is regularly proposed as a marker of disease activity 6–8 . We have recently described that three genes ( ATP10A , PARP14 , and MX1 ) are overexpressed, hypomethylated, and mutated in anti‐Ro60 + patients and are remarkably associated with the IFN signature regardless of autoimmune disease 9 . Double positivity for anti‐Ro52/TRIM21 and anti‐Ro60/SSA antibodies is usually observed in approximately two‐thirds of patients, and this presentation is often associated with more systemic involvement and severe evolution than in seronegative patients 3,10–12 …”

Section: Introductionmentioning

confidence: 99%

Association of Combined Anti‐Ro52/TRIM21 and Anti‐Ro60/SSA Antibodies With Increased Sjögren Disease Severity Through Interferon Pathway Activation

Bettacchioli,

Saraux,

Tison

et al. 2024

Arthritis & Rheumatology

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ObjectiveThe biological diagnosis of primary Sjögren's disease (SjD) mainly relies on anti‐Ro60/SSA antibodies, while the significance of anti‐Ro52/TRIM21 antibodies currently remains unclear. The aim of this study was to characterize the clinical, serological, biological, transcriptomic and interferon profiles of SjD patients according to their anti‐Ro52/TRIM21 antibody status.MethodsSjD patients from the European PRECISESADS (n=376) and the Brittany DIApSS (n=146) cohorts were divided into four groups: double negative (Ro52‐/Ro60‐), isolated anti‐Ro52/TRIM21 positive (Ro52+), isolated anti‐Ro60/SSA positive (Ro60+), and double positive (Ro52+/Ro60+) patients. Clinical information, ESSDAI, a score representing systemic activity, and biological markers associated with disease severity were evaluated. Transcriptome data obtained from whole blood by RNAseq and type I and type II interferon signatures were analysed for PRECISESADS patients.ResultsIn the DIAPSS cohort, Ro52+/Ro60+ patients showed significantly more parotidomegaly (33.3% vs 0‐11%), along with higher β2‐microglobulin (p=0.0002), total immunoglobulin (p<0.0001), erythrocyte sedimentation rate levels (p=0.002), and rheumatoid factor (RF) positivity (66.2% vs. 20.8%‐25%) compared to other groups. The PRECISESADS cohort corroborated these observations, with increased arthritis (p=0.046), inflammation (p=0.005), hypergammaglobulinemia (p<0.0001), positive RF (p<0.0001), leukopenia (p=0.004), and lymphopenia (p=0.009) in Ro52+/Ro60+ patients. Cumulative ESSDAI results further confirmed these disparities (p=0.002). Transcriptome analysis linked anti‐Ro52/TRIM21 antibody positivity to interferon pathway activation, as an underlying cause for these clinical correlations.ConclusionThese results suggest that the combination of anti‐Ro52/TRIM21 and anti‐Ro60/SSA antibodies is associated with a clinical, biological, and transcriptional profile linked to greater disease severity in SjD, through the potentiation of the interferon pathway activation by anti‐Ro52/TRIM21 antibodies.image

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“…In this study we combine a classical electrochemical method with opportunities of machine learning (ML) technique which has already proved to be effective and promising. ,− Machine learning is a technology that allows a computer to independently acquire information from data. It enables a computer system to respond to an input based on the optimization (through a large amount of data and computing power) of a statistical model and to make predictions with reasonable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms

Aliev,

Belyaev,

Pomytkina

et al. 2023

ACS Appl. Mater. Interfaces

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The present study is dedicated to the problem of electrochemical analysis of multicomponent mixtures, such as milk. A combination of cyclic voltammetry facilities and machine learning techniques made it possible to create a pattern recognition system for the detection of antibiotic residues in skimmed milk. A multielectrode sensor including copper, nickel, and carbon fiber was fabricated for the collection of electrochemical data. Processes occurring at the electrode surface were discussed and simulated with the help of molecular docking and density functional theory modeling. It was assumed that the antibiotic fingerprint reveals a potential drift of electrodes, owing to complexation with metal ions present in milk. The gradient boosting algorithm showed the best efficiency in training the machine learning model. High accuracy was achieved for the recognition of antibiotics in milk. The elaborated method may be incorporated into existing milking systems at dairy farms for monitoring the residue concentrations of antibiotics.

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Machine Learning for the Identification of a Common Signature for Anti–SSA/Ro 60 Antibody Expression Across Autoimmune Diseases

Cited by 13 publications

References 42 publications

Aberrant B Cell Signaling in Autoimmune Diseases

Aberrant B Cell Signaling in Autoimmune Diseases

Association of Combined Anti‐Ro52/TRIM21 and Anti‐Ro60/SSA Antibodies With Increased Sjögren Disease Severity Through Interferon Pathway Activation

Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms

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