Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19
Bibliographic record
Abstract
Abstract Background COVID-19 patients experience dynamic changes in immune and cellular function over time with potential clinical implications. However, there is insufficient research investigating, on a gene expression level, the mechanisms that become activated or suppressed over time as patients deteriorate or recover, which can inform use of repurposed and novel drugs as therapies. Objective To investigate longitudinal changes in gene expression profiles throughout the COVID-19 disease timeline. Methods Three-hundred whole blood samples from 128 adult patients were collected during hospitalization from COVID-19, with up to five samples per patient. Transcriptome sequencing (RNA-Seq), differential gene expression analysis and pathway enrichment was performed. Drug-gene set enrichment analysis was used to identify FDA-approved medications that could inhibit critical genes and proteins at each disease phase. Prognostic gene-expression signatures were generated using machine learning to distinguish 3 disease stages. Results Samples were longitudinally grouped by clinical criteria and gene expression into six disease phases: Mild, Moderate, Severe, Critical, Recovery, and Discharge. Distinct mechanisms with differing trajectories during COVID-19 hospitalization were apparent. Antiviral responses peaked early in COVID-19, while heme metabolism pathways became active much later during disease. Adaptive immune dysfunction, inflammation, and metabolic derangements were most pronounced during phases with higher disease severity, while hemostatic abnormalities were elevated early and persisted throughout the disease course. Drug-gene set enrichment analysis predicted repurposed medications for potential use, including platelet inhibitors in early disease, antidiabetic medications for patients with increased disease severity, and dasatinib throughout the disease course. Disease phases could be categorized using specific gene signatures for prognosis and treatment selection. Disease phases were also highly correlated to previously developed sepsis endotypes, indicating that severity and disease timing were significant contributors to heterogeneity observed in sepsis and COVID-19. Conclusions Higher temporal resolution of longitudinal mechanisms in COVID-19 revealed multiple immune and cellular changes that were activated at different phases of COVID-19. Understanding how a patient’s gene expression profile changes over time can permit more accurate risk stratification of patients and provide time-dependent personalized treatments with repurposed medications. This creates an opportunity for timely intervention before patients transition to a more severe phase, potentially accelerating patients to recovery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".