Lipoprotein(a) induces caspase-1 activation and IL-1 signaling in human macrophages
Bibliographic record
Abstract
Introduction: Lipoprotein(a) (Lp(a)) is an LDL-like particle with an additional apolipoprotein (apo)(a) covalently attached. Elevated levels of circulating Lp(a) are a risk factor for atherosclerosis. A proinflammatory role for Lp(a) has been proposed, but its molecular details are incompletely defined. Methods and results: To explore the effect of Lp(a) on human macrophages we performed RNA sequencing on THP-1 macrophages treated with Lp(a) or recombinant apo(a), which showed that especially Lp(a) induces potent inflammatory responses. Thus, we stimulated THP-1 macrophages with serum containing various Lp(a) levels to investigate their correlations with cytokines highlighted by the RNAseq, showing significant correlations with caspase-1 activity and secretion of IL-1β and IL-18. We further isolated both Lp(a) and LDL particles from three donors and then compared their atheroinflammatory potentials together with recombinant apo(a) in primary and THP-1 derived macrophages. Compared with LDL, Lp(a) induced a robust and dose-dependent caspase-1 activation and release of IL-1β and IL-18 in both macrophage types. Recombinant apo(a) strongly induced caspase-1 activation and IL-1β release in THP-1 macrophages but yielded weak responses in primary macrophages. Structural analysis of these particles revealed that the Lp(a) proteome was enriched in proteins associated with complement activation and coagulation, and its lipidome was relatively deficient in polyunsaturated fatty acids and had a high n-6/n-3 ratio promoting inflammation. Discussion: Our data show that Lp(a) particles induce the expression of inflammatory genes, and Lp(a) and to a lesser extent apo(a) induce caspase-1 activation and IL-1 signaling. Major differences in the molecular profiles between Lp(a) and LDL contribute to Lp(a) being more atheroinflammatory.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".