Combining computational target prioritization and a B cell maturation assay for target evaluation studies in systemic lupus erythematosus
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Systemic lupus erythematosus (SLE) is a systemic autoimmune disease involving production of autoantibodies by B cells. This study aimed at identifying novel drug targets using a computational algorithm to select targets and thereafter validate the top ranked 11 targets by siRNA knockdown in a primary B cell maturation assay. EXPERIMENTAL APPROACH: The top 1 % genes (∼150 genes) from SLE genome-wide association studies were ranked by Priority index (Pi), a computational tool integrating genomic and network information to prioritize disease-relevant genes. These were further filtered by network connectivity, drugability, for ranking highly in autoimmune diseases and for not directly interfering with the B cell stimulation cocktail used. From this, 11 genes were selected for validation by siRNA knockdown: IFNGR1, IL-2, IRF4, IL-12A, IL-12B, VCAM-1, ATF6B, RELA, IKBKG, CHUK and MAPK14. Effects on induced maturation and viability of primary blood B cells were analyzed by flow cytometry, and effects on IgG secretion were investigated by ELISA. RNA-sequencing of B cells treated with siRNA was performed to investigate molecular mechanisms underlying the functional alterations. KEY RESULTS: Experimental results show that several of the targets (IFNGR1, IL-2, IL-12A, MAPK14, IRF4, CHUK, ATF6B, IKBKG, and RELA) are involved in B cell maturation, as knockdown caused reduced IgG production and/or decreased maturation of B cells. The observed variability of effects on IgG secretion and B cell maturation suggests differences in the mechanistic roles of the proteins encoded by these genes. RNA-seq analysis of cells where expression of the targeted genes had been modulated showed effects on the expression level of hundreds of genes involved in cellular processes important for B cell functions. CONCLUSION AND IMPLICATIONS: Combining the target prioritization algorithm with experimental functional validation studies by gene knockdown and whole transcriptomics profiling constitutes a promising approach to identify potential novel drug targets in immune disorders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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) | 0.000 | 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 it