Transglutaminase 2 function in glioblastoma tumor efferocytosis
Bibliographic record
Abstract
Abstract Glioblastoma is an aggressive and incurable type of brain cancer. Regions of tissue necrosis are a distinctive pathological feature of this cancer. These arise through thrombosis of tumor vasculature, driven by tumor-derived pro-coagulation factors. In studies of transglutaminase 2 (TGM2), we observed that TGM2 mRNA expression in glioblastoma was primarily in a subset of tumor-infiltrating myeloid cells with hypoxia gene expression signatures. Analysis of xenograft and human glioblastoma samples by immunohistochemistry showed that macrophages in the vicinity of necrotic regions expressed very high levels of TGM2. These macrophages were engaged in the phagocytosis of apoptotic cells, a process known as efferocytosis. In cell culture, incubation of macrophages with apoptotic cells induced TGM2 expression in macrophages, and TGM2 inhibitors blocked efferocytosis. In patient-derived glioblastoma organoids cultured in 5% O 2 , a basal level of apoptosis was observed, and endogenous macrophages were observed in the process of clearing apoptotic cells. Clearance of apoptotic cells was reduced in organoids treated with a TGM2 inhibitor. Efferocytosis was absent or reduced in organoids grown in 20% O 2 . These data, together with previous work, define a model in which necrotic regions in glioblastoma induce hypoxia-driven apoptosis, which in turn promotes efferocytosis by macrophages. TGM2 is both a marker of efferocytosis and a target for efferocytosis inhibition in this process. Efferocytosis is a potent immunosuppressive mechanism, so this process provides an additional mechanism by which large glioblastoma tumors can evade immune responses.
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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".