An Image-Based Genetic Assay Identifies Genes in T1D Susceptibility Loci Controlling Cellular Antiviral Immunity in Mouse
Bibliographic record
Abstract
The pathogenesis of complex diseases, such as type 1 diabetes (T1D), derives from interactions between host genetics and environmental factors. Previous studies have suggested that viral infection plays a significant role in initiation of T1D in genetically predisposed individuals. T1D susceptibility loci may therefore be enriched in previously uncharacterized genes functioning in antiviral defense pathways. To identify genes involved in antiviral immunity, we performed an image-based high-throughput genetic screen using short hairpin RNAs (shRNAs) against 161 genes within T1D susceptibility loci. RAW 264.7 cells transduced with shRNAs were infected with GFP-expressing herpes simplex virus type 1 (HSV-1) and fluorescent microscopy was performed to assess the viral infectivity by fluorescence reporter activity. Of the 14 candidates identified with high confidence, two candidates were selected for further investigation, Il27 and Tagap. Administration of recombinant IL-27 during viral infection was found to act synergistically with interferon gamma (IFN-γ) to activate expression of type I IFNs and proinflammatory cytokines, and to enhance the activities of interferon regulatory factor 3 (IRF3). Consistent with a role in antiviral immunity, Tagap-deficient macrophages demonstrated increased viral replication, reduced expression of proinflammatory chemokines and cytokines, and decreased production of IFN-β. Taken together, our unbiased loss-of-function genetic screen identifies genes that play a role in host antiviral immunity and delineates roles for IL-27 and Tagap in the production of antiviral cytokines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".