The role of interferon-mediated suppression of monocyte immunothrombosis in infection susceptibility in systemic lupus erythematosus
Bibliographic record
Abstract
Patients with systemic lupus erythematosus (SLE) exhibit significant susceptibility to severe bacterial infections, a leading cause of mortality. A key host defence mechanism is immunothrombosis, wherein activated monocytes rapidly upregulate tissue factor (TF) to initiate localized fibrin deposition that traps and contains pathogens. Effective immunothrombosis is therefore critical for preventing microbial dissemination. This process appears deficient in SLE, a disease defined by a systemic prothrombotic state, yet poor infection outcomes. A recently discovered molecular interaction suggests that TF directly binds to the interferon-α receptor (IFNAR1), acting as a rheostat to suppress interferon signalling. We hypothesize that in SLE, this regulatory axis is disrupted. The dominant, sustained interferon-stimulated gene (ISG) signatures in monocytes limit their capacity for TF upregulation in response to bacterial challenge, thereby impairing immunothrombosis and compromising bacterial containment. Supporting this, SLE patients with secondary antiphospholipid syndrome who have lower interferon signatures display markedly elevated TF levels and a different thrombotic profile, demonstrating the inverse relationship in a clinical subset. Furthermore, TF induction in monocytes is glycolysis-dependent, and SLE monocytes are known to have profound metabolic alterations. The chronic interferon state may thus impose a metabolic constraint that further limits the bioenergetic capacity for a robust TF response. Therefore, the confluence of interferon-driven suppression and metabolic dysfunction in SLE monocytes provides a compelling explanation for the failure of immunothrombosis, directly linking a core disease feature to infection susceptibility.
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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 | 0.001 |
| 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".