Liver X Receptor Is a Therapeutic Target for Photoaging and Chronological Skin Aging
Bibliographic record
Abstract
Liver X receptors (LXRalpha and -beta) are liposensors that exert their metabolic effects by orchestrating the expression of macrophage genes involved in lipid metabolism and inflammation. LXRs are also expressed in other tissues, including skin, where their natural oxysterol ligands induce keratinocyte differentiation and improve epidermal barrier function. To extend the potential use of LXR ligands to dermatological indications, we explored the possibility of using LXR as a target for skin aging. We demonstrate that LXR signaling is down-regulated in cell-based models of photoaging, i.e. UV-activated keratinocytes and TNFalpha-activated dermal fibroblasts. We show that a synthetic LXR ligand inhibits the expression of cytokines and metalloproteinases in these in vitro models, thus indicating its potential in decreasing cutaneous inflammation associated with the etiology of photoaging. Furthermore, a synthetic LXR ligand induces the expression of differentiation markers, ceramide biosynthesis enzymes, and lipid synthesis and transport genes in keratinocytes. Remarkably, LXRbeta-null mouse skin showed some of the molecular defects that are observed in chronologically aged human skin. Finally, we demonstrate that a synthetic LXR agonist inhibits UV-induced photodamage and skin wrinkle formation in a murine model of photoaging. Therefore, the ability of an LXR ligand to modulate multiple pathways underlying the etiology of skin aging suggests that LXR is a novel target for developing potential therapeutics for photoaging and chronological skin aging indications.
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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) | 0.002 | 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".