Transcriptomic profiling of severe and critical COVID-19 patients reveals alterations in expression, splicing and polyadenylation
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a multi-systemic illness that became a pandemic in March 2020. Although environmental factors and comorbidities can influence disease progression, there is a lack of prognostic markers to predict the severity of COVID-19 illness. Identifying these markers is crucial for improving patient outcomes and appropriately allocating scarce resources. Here, an RNA-sequencing study was conducted on blood samples from unvaccinated, hospitalized patients divided by disease severity; 367 moderate, 173 severe, and 199 critical. Using a bioinformatics approach, we identified differentially expressed genes (DEGs), alternative splicing (AS) and alternative polyadenylation (APA) events that were severity-dependent. In the severe group, we observed a higher expression of kappa immunoglobulins compared to the moderate group. In the critical cohort, a majority of AS events were mutually exclusive exons and APA genes mostly had longer 3'UTRs. Interestingly, multiple genes associated with cytoskeleton, TUBA4A, NRGN, BSG, and CD300A, were differentially expressed, alternatively spliced and polyadenylated in the critical group. Furthermore, several inflammation-related pathways were observed predominantly in critical vs. moderate. We demonstrate that integrating multiple downstream analyses of transcriptomics, from moderate, severe, and critical patients confers a significant advantage in identifying relevant dysregulated genes and pathways.
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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".