The landscape of differential splicing and transcript alternations in severe <scp>COVID</scp>‐19 infection
Bibliographic record
Abstract
Viral infections can modulate the widespread alternations of cellular splicing, favouring viral replication within the host cells by overcoming host immune responses. However, how SARS-CoV-2 induces host cell differential splicing and affects the landscape of transcript alternation in severe COVID-19 infection remains elusive. Understanding the differential splicing and transcript alternations in severe COVID-19 infection may improve our molecular insights into the SARS-CoV-2 pathogenesis. In this study, we analysed the publicly available blood and lung transcriptome data of severe COVID-19 patients, blood transcriptome data of recovered COVID-19 patients at 12-, 16- and 24-week postinfection and healthy controls. We identified a significant transcript isoform switching in the individual blood and lung RNA-seq data of severe COVID-19-infected patients and 25 common genes that alter their transcript isoform in both blood and lung samples. Altered transcripts show significant loss of the open reading frame, functional domains and switch from coding to noncoding transcript, impacting normal cellular functions. Furthermore, we identified the expression of several novel recurrent chimeric transcripts in the blood samples from severe COVID-19 patients. Moreover, the analysis of the isoform switching into blood samples from recovered COVID-19 patients highlights that there is no significant isoform switching in 16- and 24-week postinfection, and the levels of expressed chimeric transcripts are reduced. This finding emphasizes that SARS-CoV-2 severe infection induces widespread splicing in the host cells, which could help the virus alter the host immune responses and facilitate the viral replication within the host and the efficient translation of viral proteins.
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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 | 0.001 | 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".