Alternative splicing in the lung influences COVID-19 severity and respiratory diseases
Bibliographic record
Abstract
Abstract Hospital admission for COVID-19 remains common despite the successful development of vaccines and treatments. Thus, there is an ongoing need to identify targets for new COVID-19 therapies. Alternative splicing is an essential mechanism for generating functional diversity in protein isoforms and influences immune response to infection. However, the causal role of alternative splicing in COVID-19 severity and its potential therapeutic relevance is not fully understood. In this study, we evaluated the causal role of alternative splicing in COVID-19 severity and susceptibility using Mendelian randomization (MR). To do so, we performed two-sample MR to assess whether cis -sQTLs spanning 8,172 gene splicing in 5,295 genes were associated with COVID-19 outcomes in the COVID-19 Host Genetics Initiative, including up to 158,840 COVID-19 cases and 2,782,977 population controls. We identified that alternative splicing in lungs, rather than total RNA expression of OAS1, ATP11A, DPP9 and NPNT , was associated with COVID-19 severity. MUC1 splicing was associated with COVID-19 susceptibility. Further colocalization analyses supported a shared genetic mechanism between COVID-19 severity with idiopathic pulmonary fibrosis at ATP11A and DPP9 loci, and with chronic obstructive lung diseases at NPNT . We lastly showed that ATP11A, DPP9, NPNT , and MUC1 were highly expressed in lung alveolar epithelial cells, both in COVID-19 uninfected and infected samples. Taken together, these findings clarify the importance of alternative splicing of proteins in the lung for COVID-19 and other respiratory diseases, providing isoform-based targets for drug discovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".