A High Resolution Method to Monitor Phosphorylation-dependent Activation of IRF3
Bibliographic record
Abstract
The IRF3 transcription factor is critical for the first line of defense against pathogens mainly through interferon β and antiviral gene expression. A detailed analysis of IRF3 activation is essential to understand how pathogens induce or evade the innate antiviral response. Distinct activated forms of IRF3 can be distinguished based on their phosphorylation and monomer vs dimer status. In vivo discrimination between the different activated species of IRF3 can be achieved through the separation of IRF3 phosphorylated forms based on their mobility shifts on SDS-PAGE. Additionally, the levels of IRF3 monomer and dimer can be monitored using non-denaturing electrophoresis. Here, we detail a procedure to reach the highest resolution to gain the most information regarding IRF3 activation status. This is achieved through the combination of a high resolution SDS-PAGE and a native-PAGE coupled to immunoblots using multiple total and phosphospecific antibodies. This experimental strategy constitutes an affordable and sensitive approach to acquire all the necessary information for a complete analysis of the phosphorylation-mediated activation of IRF3.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".