Transglutaminase-2 Interaction with Heparin
Bibliographic record
Abstract
Heparan sulfate proteoglycans are critical binding partners for extracellular tranglutaminase-2 (TG2), a multifunctional protein involved in tissue remodeling events related to organ fibrosis and cancer progression. We previously showed that TG2 has a strong affinity for heparan sulfate (HS)/heparin and reported that the heparan sulfate proteoglycan syndecan-4 acts as a receptor for TG2 via its HS chains in two ways: by increasing TG2-cell surface trafficking/externalization and by mediating RGD-independent cell adhesion to fibronectin-TG2 matrix during wound healing. Here we have investigated the molecular basis of this interaction. Site-directed mutagenesis revealed that either mutation of basic RRWK (262-265) or KQKRK (598-602) clusters, forming accessible heparin binding sequences on the TG2 three-dimensional structure, led to an almost complete reduction of heparin binding, indicating that both clusters contribute to form a single binding surface. Mutation of residues Arg(19) and Arg(28) also led to a significant reduction in heparin binding, suggesting their involvement. Our findings indicate that the heparin binding sites on TG2 mainly comprise two clusters of basic amino acids, which are distant in the linear sequence but brought into spatial proximity in the folded "closed" protein, forming a high affinity heparin binding site. Molecular modeling showed that the identified site can make contact with a single heparin-derived pentasaccharide. The TG2-heparin binding mutants supported only weak RGD-independent cell adhesion compared with wild type TG2 or mutants with retained heparin binding, and both heparin binding clusters were critical for TG2-mediated cell adhesion. These findings significantly advance our knowledge of how HS/heparin influences the adhesive function of TG2.
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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".