Alternative splicing networks regulated by signaling in human T cells
Why this work is in the frame
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Bibliographic record
Abstract
The formation and execution of a productive immune response requires the maturation of competent T cells and a robust change in cellular activity upon antigen challenge. Such changes in cellular function depend on regulated alterations to protein expression. Previous research has focused on defining transcriptional changes that regulate protein expression during T-cell maturation and antigen stimulation. Here, we globally analyze another critical process in gene regulation during T-cell stimulation, alternative splicing. Specifically, we use RNA-seq profiling to identify 178 exons in 168 genes that exhibit robust changes in inclusion in response to stimulation of a human T-cell line. Supporting an important role for the global coordination of alternative splicing following T-cell stimulation, these signal-responsive exons are significantly enriched in genes with functional annotations specifically related to immune response. The vast majority of these genes also exhibit differential alternative splicing between naive and activated primary T cells. Comparison of the responsiveness of splicing to various stimuli in the cultured and primary T cells further reveals at least three distinct networks of signal-induced alternative splicing events. Importantly, we find that each regulatory network is specifically associated with distinct sequence features, suggesting that they are controlled by independent regulatory mechanisms. These results thus provide a basis for elucidating mechanisms of signal pathway-specific regulation of alternative splicing during T-cell stimulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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