GENETICS AND PROTEOMICS IN AUTOANTIBODY-DEFINED SUBGROUPS OF PATIENTS WITH SLE
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".