Cholesterol accumulation-induced impairment of AKT signaling in LPS-stimulated macrophages play a dispensable role in suppressing HIF-1α-dependent glycolysis
Bibliographic record
Abstract
Abstract The formation of lipid-laden macrophages (Mφs) is a hallmark of atherosclerosis, yet how the accumulation of cholesterol in Mφs underlies the inflammatory process of atherogenesis remains unclear. It is well recognized that the reprogramming of metabolism in Mφs is critical for supporting their inflammatory responses, which may shed light on how the metabolism of Mφ foam cells is linked to inflammation. Indeed, recent research has now revealed Mφs that accumulate excess cholesterol adopt a distinct metabolic adaptation, a metabolic profile that is unexpectedly associated with a deactivated inflammatory response. Mechanistically, our group has previously shown that upon LPS stimulation, excess cholesterol accumulation in Mφs impaired their induction of AKT-dependent early glycolytic reprogramming and HIF-1α-dependent late glycolytic reprogramming. However, it remains unclear if these events are interconnected and synergistically contribute to the suppression of inflammation observed in these Mφs. Here, we demonstrated that cholesterol loading of Mφs impaired LPS-induced early glycolysis by reducing the phosphorylation of hexokinases, yet complete inhibition of AKT only modestly impaired HIF-1α-dependent glycolytic reprogramming. On the other hand, we confirmed that HIF-1α degradation, but not its reduced synthesis, is the primary mechanism that underlies its impaired expression in cholesterol loaded Mφs. Finally, we showed that cholesterol loading of Mφs alone was sufficient to induce oxidative stress, such as the production of 4-HNE, and deplete the levels of reduced KEAP1 proteins. Mφs lacking NRF2 resisted the effects of cholesterol loading on suppressing the expression of glycolytic and pro-inflammatory proteins.
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 | 0.001 |
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".