SIMILARITY AND DIVERSITY IMMUNOLOGICAL ABERRATIONS IN STABLE BENIGN IMMUNITY AND TOWARD ANA-RELATED AUTOIMMUNE DISEASES IN ANA-POSITIVE AT-RISK POPULATIONS
Notice bibliographique
Résumé
PT009 / #712 Topic: AS20 - Precision Medicine POSTER TOUR 02: RECENT INSIGHTS ON THE PATHOGENESIS OF LUPUS NEPHRITIS 23-05-2025 10:00 AM - 10:40 AM Background/Purpose Previous genetic and transcriptome studies revealed shared immune dysregulation in ANA-at-risk individuals. Upregulation of interferon-stimulated genes signature while mitochondrial oxidative phosphorylation downregulation is linked to disease progression. However, functional protein studies in this population are limited. This study aims to investigate baseline functional protein dysregulation in ANA-at-risk individuals and discriminate an immune aberration between the groups. Methods Stored samples from 103 ANA-at-risk individuals (progressors = 32, nonprogressors = 67) were included in a proteome study using the EXPLORE Inflammation I and II panel, Proximity Extension Assay (PEA) technology (Olink®, Uppsala, Sweden). The module eigengenes (MEs) were constructed from 193 proteins with greater than 0.5 differential expression proteins (DEPs) when compared to healthy control (HC) by using Weighted Gene Co-Expression Network Analysis (WGCNA) from R package version 4.3.1. Exclusively, the modular analysis from IFN-inducible proteins was computed to improve validity and reliable interpretation. The module trait correlation was analyzed using the Pearson correlation. Enrichment analysis was performed to determine the biological significance of identified modules. The false positivity was mitigated using the Benjamini-Hochberg multiple testing method; an adjusted p-value < 0.05 was considered statistically significant. Cytoscape and bioinformatics platforms were employed for data visualization. Results The unique and overlapping significant protein number was demonstrated in the Venn diagram (Figure 1A). Seven co-expressed protein modules were constructed, and 2 modules showed significant correlations with RMD progression, independent of age, gender, or antibody status. The grey module exhibited a positive correlation (r = 0.23, p = 0.02), while the green module showed a negative correlation (r = -0.22, p = 0.02) (Figure 1B). Proteins in the grey module were enriched in innate immunity pathways, particularly IFN and B cell signaling, and were highly elevated in progressors (Figure 2A). In contrast, higher in nonprogressors, the green module proteins were associated with viral processes and cell-cycle regulation (Figure 2B). Figure 2C shows the 4 IFN modules constructed from WGCNA; only the cyan module, containing both IFN-I and IFN-II inducible proteins, overlapped with the proteins in the grey module and had a significant positive correlation with RMD progression (Figure 2D). In nonprogressors, the IFN-I negative regulators YY1, PTPN6, and YTHDF3 were elevated, and an inverse relationship between positive and negative IFN-inducible protein DEPs was demonstrated (Figure 2E). Besides this, the proteins included in the remaining 5 modules had significantly higher DEPs when compared to HC (Figure 1C), implicating biological processes such as cellular stress response, apoptosis, protein phosphorylation, and cytokine signaling (Figure 1D). Figure 1. The significant protein numbers between ANA+ and HC (A), Proteomic modular from weighted gene co-expression network analysis and trait correlation (B), the DEPs between ANA+ and HC from 5 nonsignificant modules (C), and biological function (D). Figure 2. The significant RMDs progression modules (A) and IFN-inducible proteins modules (B-F). Conclusions This comprehensive proteomic study revealed robust immunological and cellular metabolism disequilibrium among ANA at-risk individuals regardless of progression to RMDs. IFN play a crucial role in ANA positivity. However, it might only be a surrogate marker of cellular response to external (eg, viral infection) or internal stressors (eg, DNA damage response). This finding underscores the influence of IFN-I, and a dual IFN and B cell regulation aberration promotes the progression to RMDs. Identifying the biomarkers involved in these pathways might have resulted in higher RMD progression predictive accuracy than conventional ANA.
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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 | 0,002 | 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) | 0,004 | 0,001 |
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 ».