Alternative splicing networks regulated by signaling in human T cells
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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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) | 0.000 | 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) | 0.001 | 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".