INVESTIGATING PREDICTIVE SERUM SOLUBLE MEDIATORS SPECIFIC TO ANA+ INDIVIDUALS AT RISK OF SYSTEMIC LUPUS ERYTHEMATOSUS WITH HIGH-THROUGHPUT PROTEOMICS
Notice bibliographique
Résumé
O060 / #518 Topic: AS08 - Cytokines and Cell Trafficking ABSTRACT CONCURRENT SESSION 10: INTEGRATING PROTEOMIC & TRANSCRIPTOMICS IN SLE 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Anti-nuclear autoantibodies (ANAs) are detected years before SLE classification. However, most healthy ANA+ individuals will never develop clinical illness. Patients with incomplete lupus (ILE) exhibit some clinical symptoms with most never progressing to SLE. It is unknown what triggers ANA+ individuals to progress to clinical disease. We sought to identify molecular profiles of serum proteome driving transitions to full immune cell dysregulation. Methods Over 5400 proteins were measured in serum of 67 subjects (ANA-, ANA+ healthy; ILE; SLE) with Proximity Extension Assay (Olink Explore HT). Logistic regression with adjustment for age and genetic ancestry and machine learning approaches (random forest, GENIE3) were used to identify proteomic signatures specific for disease progression. Results Gene set enrichment analysis reveals involvement of pathways related to cellular homeostasis, lymphocyte activation, nucleic acid sensing, cytokine production and apoptosis (Figure 1A). Comparison of serum protein levels found the largest number of differences between ANA+ and ILE (745 proteins, p adj ≤ 0.05), associated with upregulation (in ANA+) of ubiquitin related proteins (ITCH, TAX1BP1, TRIM25, UBE2L6, USP8, USP26, UBL4A, UBE2B, UBOX5), MAPK Signaling (MAP2K6, MAP3K5, MAPKAPK2, MAP7D2, MAPKAP1), pathways related to cell adhesion and cellular regulation (KIT, TGFB2, TNFRSF14, CD46, LGALS1, CD40, IL17D, IL33, TNFSF12) and mitochondrial proteins (MAVS). Significant proteins between ANA-/ANA+ healthy controls (197 proteins, p adj ≤ 0.05) were related to decreased levels of IL7, transcription regulation pathways and increased cell adhesion molecules in ANA+. The lowest variability was found between ILE and SLE (140 proteins, p adj ≤ 0.05) with increase of TNF, BANK1, IL33, IL4R (Figure 1B) Overall, random forest predictions indicate involvement of mitochondrial proteins, dysregulation of ubiquitin related pathways, Th2 Signaling and vesicular trafficking, specific to ANA+ (Figure 1C). Mitochondrial and intracellular sensing proteins, determined with above approaches, are associated with innate cytokine IL1B, mostly in early stages of disease progression (Figure 1D). Inference of gene regulatory networks reveals interactions between pattern recognition proteins driven by mitochondrial MAVS, with variations in disease progression. Those interactions, as well as expression of related proteins, appear to be increased in ANA+, which might highlight MAVS as an initial mitochondrial modulator affecting regulation in early stages of disease (Figure 2A,B) Trajectory of pattern recognition proteins indicates increase in ANA+ and reduction in ILE and SLE, suggesting their potential role in regulating immune response before appearance of clinical symptoms. On the contrary, expression of IFN, CXCL10, IL6, IL10, IL13 gradually increase with disease progression, indicating importance of proinflammatory component during SLE development (Figure 2C). Figure 1. Figure 2. Conclusions Proteomic signatures specific to ANA+ are associated with disruption of cellular homeostasis and dysregulation of proteins related to pattern recognition, antiviral response and ubiquitination. These abnormalities may define important events in the trajectory of preclinical autoimmunity development.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».