GENETICS AND PROTEOMICS IN AUTOANTIBODY-DEFINED SUBGROUPS OF PATIENTS WITH SLE
Notice bibliographique
Résumé
PV106 / #522 Poster Topic: AS12 - Genetics, Epigenetics, Transcriptomics Background/Purpose Using their autoantibody profile, patients with systemic lupus erythematosus (SLE) can be grouped into 4 less heterogeneous subgroups [1] Subgroup 1 was dominated by anti-SSA/SSB, Subgroup 2 by anti-nucleosome/Sm/RNP/dsDNA, Subgroup 3 by aPL, and Subgroup 4 was negative for 13 antibodies tested, but ANA ever positive. Those subgroups differed in cytokine levels, clinical manifestations, and HLA-DRB1 gene associations.[1] Therefore, we hypothesize that the pathogenic mechanisms are different among these subgroups of patients. We aimed to evaluate whether there are differences in known SLE genetic risk factors and protein levels among antibody-defined SLE subgroups. Methods We analyzed 448 patients from our previous study[1] to address differences in genetic risk factors. We compared the SLE polygenic risk scores (PRS) previously described by Reid et al 2020.[2] Using a double-sided Mann-Whitney U test, we tested differences in the distribution of 2 PRS: 1 including 4 Single Nucleotide Polymorphisms (SNPs) in the HLA region or 57 non- HLA SNPs across the subgroups. Furthermore, untargeted liquid chromatography-mass spectrometry (LC-MS) was implemented using plasma samples from 100 SLE patients, 25 representing each subgroup. Differential expression analysis was performed using linear modeling with the limma package,[3] and pairwise comparisons were conducted among the subgroups. P-values for both approaches were corrected using a False Discovery Rate (FDR) multiple-testing correction. Adjusted values (P-adj) lower than 0.05 were considered significant. Results Subgroup 1 displayed significantly higher HLA-P RS compared to subgroup 2 (P-adj = 9.27e-06), subgroup 3 (P-adj =1.65e-10), and subgroup 4 (P-adj =3.64e-04) (Figure 1). Similarly, subgroup 2 had a significantly higher i-PRS than subgroup 3 (P-adj = 7.3e-03). Conversely, for PRS calculated using SNPs outside the HLA region, scores of subgroup 1 were significantly lower than scores from subgroups 2 (P-adj = 3.89e-02) and 3 (P-adj = 3.89e-02), suggesting a higher contribution of SNPs in the HLA region to the overall higher genetic risk of subgroup 1. Differential expression analysis of proteins (n=2625) between the subgroups revealed that an isoform of Complement C4-B (C4B) was significantly overexpressed (P-adj 3.72e0-5) in subgroup 1 compared to subgroup 3, followed by C2, which indicates involvement of the complement system in this subgroup. Likewise, an isoform of Immunoglobulin heavy constant gamma 3 ( IGHG3 ) was significantly overexpressed (P-adj 2.3e-03) in subgroup 2 compared to subgroup 4 (Figure 2). These preliminary results support the concept that unanalyzed or unknown autoantibodies and B cell involvement may be present in patients of subgroup 4. Figure 1. Figure 2. Conclusions Subgroup 1 exhibited higher HLA -PRS than the other subgroups and elevated C4b levels compared to Subgroup 3. This is consistent with the high linkage disequilibrium between C4 gene and HLA risk variants. These preliminary findings support the hypothesis of subgroup-specific pathogenic mechanisms among antibody-defined SLE subgroups of patients. We will further analyze this dataset to incorporate PRS relevant to immune cell phenotypes and calculate PRS targeted to SLE subgroups. We will also validate these findings in independent populations. References: [1.] Diaz-Gallo LM. ACR Open Rheumatol 2022;4(1):27-39. [2.] Reid S. Ann Rheum Dis 2020;79(3):363-9. [3.] Ritchie ME. Nucleic Acids Res 2015;43(7):e47.
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 | 0,000 | 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 ».