Functional and dynamic profiling of transcript isoforms reveals essential roles of alternative splicing in interferon response
Bibliographic record
Abstract
Type I interferon (IFN-I) plays an important role in the innate immune response through inducing IFN-I-stimulated genes (ISGs). However, how alternative splicing (AS) events, especially over time, affect their function remains poorly understood. We generated an annotation (113,843 transcripts) for IFN-I-stimulated human B cells called isoISG using high-accuracy long-read sequencing data from PacBio Sequel II/IIe. Transcript isoform profiling using isoISG revealed that isoform switching occurred in the early response to IFN-I so that ISGs would gain functional domains (e.g., C4B) or higher protein production (e.g., IRF3). Conversely, isoforms lacking functional domains increased during the late phase of IFN-I response, mainly due to intron retention events. This suggests that isoform switching both triggers and terminates IFN-I responses at the translation and protein levels. Furthermore, genetic variants influencing the isoform ratio of ISGs were associated with immunological and infectious diseases. AS has essential roles in regulating innate immune response and associated diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".