Histidine-rich Glycoprotein Binds Fibrin(ogen) with High Affinity and Competes with Thrombin for Binding to the γ′-Chain
Bibliographic record
Abstract
Histidine-rich glycoprotein (HRG) is an abundant protein that binds fibrinogen and other plasma proteins in a Zn(2+)-dependent fashion but whose function is unclear. HRG has antimicrobial activity, and its incorporation into fibrin clots facilitates bacterial entrapment and killing and promotes inflammation. Although these findings suggest that HRG contributes to innate immunity and inflammation, little is known about the HRG-fibrin(ogen) interaction. By immunoassay, HRG-fibrinogen complexes were detected in Zn(2+)-supplemented human plasma, a finding consistent with a high affinity interaction. Surface plasmon resonance determinations support this concept and show that in the presence of Zn(2+), HRG binds the predominant γ(A)/γ(A)-fibrinogen and the γ-chain elongated isoform, γ(A)/γ'-fibrinogen, with K(d) values of 9 nm. Likewise, (125)I-labeled HRG binds γ(A)/γ(A)- or γ(A)/γ'-fibrin clots with similar K(d) values when Zn(2+) is present. There are multiple HRG binding sites on fibrin(ogen) because HRG binds immobilized fibrinogen fragment D or E and γ'-peptide, an analog of the COOH terminus of the γ'-chain that mediates the high affinity interaction of thrombin with γ(A)/γ'-fibrin. Thrombin competes with HRG for γ'-peptide binding and displaces (125)I-HRG from γ(A)/γ'-fibrin clots and vice versa. Taken together, these data suggest that (a) HRG circulates in complex with fibrinogen and that the complex persists upon fibrin formation, and (b) by competing with thrombin for γ(A)/γ'-fibrin binding, HRG may modulate coagulation. Therefore, the HRG-fibrin interaction may provide a novel link between coagulation, innate immunity, and inflammation.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".