INVESTIGATING PREDICTIVE SERUM SOLUBLE MEDIATORS SPECIFIC TO ANA+ INDIVIDUALS AT RISK OF SYSTEMIC LUPUS ERYTHEMATOSUS WITH HIGH-THROUGHPUT PROTEOMICS
Bibliographic record
Abstract
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.
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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 | 0.001 | 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".