Arsenic causes distinct gene expression changes in macrophages polarized<i>in vitro</i>with either interferon-γ or interleukin-4
Bibliographic record
Abstract
Arsenic exposure is correlated with atherosclerosis in epidemiological studies and in animal models. We have previously shown that arsenic exposure enhanced the atherosclerotic plaque size, increased the plaque lipid content, and decreased the plaque smooth muscle cell and collagen contents in the apolipoprotein E knockout (apoE-/-) mice. However, the percentage of plaque-resident macrophages, the primary drivers of atherosclerosis remained unchanged. Therefore, we hypothesized that although arsenic does not change the quantity of macrophages, it alters the macrophage transcriptome towards a proatherogenic state. To test this hypothesis, we used bone marrow-derived macrophages, polarized them to either interferon-γ (IFN-ɣ) stimulated, proinflammatory or interleukin-4 (IL-4) stimulated, alternatively activated macrophages in the presence or absence of 0.67 µM (50 ppb) arsenic and performed RNA sequencing. Arsenic exposure altered the gene expression of the macrophages in a subtype-specific manner. Most differentially expressed genes (88%) were altered specifically in either IFN-ɣ- or IL-4-stimulated macrophages, whereas in the remaining 12% of genes that changed in both cell types, did so in opposite directions. In IL-4-stimulated macrophages, arsenic significantly downregulated the genes involved in cholesterol biosynthesis and the chemokines CCL17/CCL22, whereas in IFN-ɣ-stimulated macrophages, the genes associated with the liver X receptor (LXR) pathway were downregulated by arsenic. Using a bone marrow transplant experiment, we validated that the deletion of LXRα from the hematopoietic compartment rescued arsenic-enhanced atherosclerosis in the apoE-/- mouse model. Together, these data suggest that arsenic modulates subtype-specific transcriptomic changes in macrophages and further emphasize the need to define macrophage heterogeneity in atherosclerotic plaques in order to evaluate the proatherogenic role of arsenic.
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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".