Transglutaminases in Monocytes and Macrophages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Macrophages are key players in various inflammatory disorders and pathological conditions via phagocytosis and orchestrating immune responses. They are highly heterogeneous in terms of their phenotypes and functions by adaptation to different organs and tissue environments. Upon damage or infection, monocytes are rapidly recruited to tissues and differentiate into macrophages. Transglutaminases (TGs) are a family of structurally and functionally related enzymes with Ca2+-dependent transamidation and deamidation activity. Numerous studies have shown that TGs, particularly TG2 and Factor XIII-A, are extensively involved in monocyte- and macrophage-mediated physiological and pathological processes. In the present review, we outline the current knowledge of the role of TGs in the adhesion and extravasation of monocytes, the expression of TGs during macrophage differentiation, and the regulation of TG2 expression by various pro- and anti-inflammatory mediators in macrophages. Furthermore, we summarize the role of TGs in macrophage phagocytosis and the understanding of the mechanisms involved. Finally, we review the roles of TGs in tissue-specific macrophages, including monocytes/macrophages in vasculature, alveolar and interstitial macrophages in lung, microglia and infiltrated monocytes/macrophages in central nervous system, and osteoclasts in bone. Based on the studies in this review, we conclude that monocyte- and macrophage-derived TGs are involved in inflammatory processes in these organs. However, more in vivo studies and clinical studies during different stages of these processes are required to determine the accurate roles of TGs, their substrates, and the mechanisms-of-action.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it