Targeting Microglial Metabolic Rewiring Synergizes with Immune-Checkpoint Blockade Therapy for Glioblastoma
Bibliographic record
Abstract
Glioblastoma (GBM) constitutes the most lethal primary brain tumor for which immunotherapy has provided limited benefit. The unique brain immune landscape is reflected in a complex tumor immune microenvironment (TIME) in GBM. Here, single-cell sequencing of the GBM TIME revealed that microglia were under severe oxidative stress, which induced nuclear receptor subfamily 4 group A member 2 (NR4A2)-dependent transcriptional activity in microglia. Heterozygous Nr4a2 (Nr4a2+/-) or CX3CR1+ myeloid cell-specific Nr4a2 (Nr4a2fl/flCx3cr1Cre) genetic targeting reshaped microglia plasticity in vivo by reducing alternatively activated microglia and enhancing antigen presentation capacity for CD8+ T cells in GBM. In microglia, NR4A2 activated squalene monooxygenase (SQLE) to dysregulate cholesterol homeostasis. Pharmacologic NR4A2 inhibition attenuated the protumorigenic TIME, and targeting the NR4A2 or SQLE enhanced the therapeutic efficacy of immune-checkpoint blockade in vivo. Collectively, oxidative stress promotes tumor growth through NR4A2-SQLE activity in microglia, informing novel immune therapy paradigms in brain cancer. SIGNIFICANCE: Metabolic reprogramming of microglia in GBM informs synergistic vulnerabilities for immune-checkpoint blockade therapy in this immunologically cold brain tumor. This article is highlighted in the In This Issue feature, p. 799.
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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".