Atherosclerosis licenses for an exceeding immune response in COVID-19 disease by interferon priming in circulating myeloid cells
Bibliographic record
Abstract
AIMS: Patients with cardiovascular disease (CVD) have an increased risk of developing severe respiratory infections, including COVID-19. However, the underlying molecular mechanisms are not completely understood. It has been previously shown that CVD predisposes to an altered responsiveness to subsequent inflammatory triggers by an imprinted epigenetic memory in innate immune cells. Therefore, we hypothesized that patients with pre-existing atherosclerotic cardiovascular disease (ASCVD) and COVID-19 display a dysregulated inflammatory response compared to patients without ASCVD due to epigenetically altered immune cells leading to increased disease severity. METHODS AND RESULTS: Single-cell RNA sequencing revealed a dysregulated myeloid immune response with hyperinflammatory and immunosuppressive features in patients with ASCVD and moderate COVID-19. Assay for Transposase-Accessible Chromatin sequencing and in vitro experiments with isolated monocytes infected with SARS-CoV-2 showed epigenetic priming of monocytes from patients with ASCVD towards increased expression of inflammatory mediators and type I interferon signalling. In a German nationwide cohort (NAPKON), using multiplex cytokine assays, enzyme-linked immunosorbent assays, and bulk-RNA sequencing, we confirmed that patients with ASCVD display an exaggerated inflammatory response during moderate COVID-19. CONCLUSION: This study demonstrates that patients with ASCVD show a dysregulated myeloid immune response in moderate COVID-19 disease. Mechanistically, epigenetic imprinting sensitizes myeloid cells of patients with ASCVD to an exaggerated type I interferon-associated immune response.
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 | 0.001 |
| 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".