Identification of Drug-Targetable Genes for Eczema and Dermatitis Using Integrated Genomic and Proteomic Approaches
Bibliographic record
Abstract
Abstract: Background: Eczema and dermatitis are common inflammatory skin conditions with significant morbidity. Identifying drug-targetable genes can facilitate the development of effective treatments. Methods: This study analyzed data obtained by meta-analysis of 2 genome-wide association studies on eczema/dermatitis (57,311 cases and 896,779 controls, European ancestry). We identified drug-targetable genes from the Drug–Gene Interaction Database and Finan et al’s findings. Cis-expression quantitative trait loci (eQTL) data from human blood and skin tissues were used for Mendelian randomization (MR) analysis. Bayesian colocalization, proteomic MR, and meta-analysis validated the causal relationships. Finally, protein–protein interactions (PPIs) and correlation analysis of potential drug targets and cytokines were performed. Results: We identified 2532 drug-targetable genes; 3378 Single Nucleotide Polymorphism (SNPs) were associated with 1531 genes in blood cis-eQTLs, 664 SNPs with 667 genes in sun-exposed skin eQTLs, and 572 SNPs with 574 genes in nonsun-exposed skin eQTLs. Five genes (SLC22A5, NOTCH4, AGER, HLA-DRB5, and EHMT2) showed causal relationships with eczema/dermatitis across multiple datasets. Single-variable and multi-variable Mendelian randomization (SMR) and multi-SNP SMR analysis identified 8 genes (PIK3R4, DHODH, CXCR2, Interleukin (IL)18, LGALS9, RPS6KB2, SLC22A5, and AGER) across all tissues. Functional Summary Information for Variants in the Online Network (FUSION) analysis confirmed associations for SLC22A5 and AGER. Bayesian colocalization indicated AGER (PPH4: 0.95) as a shared causal variant. Proteomic MR and meta-analysis showed that increased AGER protein levels were associated with a lower risk of eczema or dermatitis (odds ratio: 0.995, 95% confidence interval: 0.997–0.993, P = 0.0002). A PPI network revealed interactions of AGER with NOTCH4 and multiple cytokines, whereas SLC22A5 showed no cytokine interactions. Conclusions: This study identified potential drug-targetable genes, with AGER showing strong potential as a target for reducing eczema/dermatitis risk. These findings provide a basis for developing targeted therapies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".