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Zinc2+ Promotes Heparin Binding to Fibrin and Subsequent Formation of Ternary Heparin-Thrombin-Fibrin Complexes.

2005· article· en· W2980097874 on OpenAlexaff
Beverly A. Leslie, Alan R. Stafford, James C. Fredenburgh, Jeffrey I. Weitz

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsFibrinHeparinThrombinAntithrombinFibrinogenChemistryTernary complexBiochemistryImmunologyPlateletMedicineEnzyme

Abstract

fetched live from OpenAlex

Abstract Binding of heparin to fibrinogen and fibrin can compromise its anticoagulant function in two ways. First, binding to fibrinogen renders less heparin available to interact with antithrombin. Second, fibrin-bound heparin can sequester thrombin and protect it from inhibition by antithrombin. Protection results from the formation of a ternary heparin-thrombin-fibrin complex mediated by binary heparin-thrombin, heparin-fibrin, and thrombin-fibrin interactions. Thrombin within the ternary complex is protected from inhibition by antithrombin because exosite 2 is occupied by fibrin-bound heparin, which impairs binding of antithrombin-bound heparin. Because heparin binding to fibrin compromises the anticoagulant functions of heparin, it was of interest to further investigate this interaction. Previous studies have not considered the role of Zn2+, a divalent cation that binds both fibrinogen and heparin and promotes their interactions with numerous proteins. The effect of Zn2+ on binding of heparin to fibrin(ogen) and the subsequent effect on formation of the ternary heparin-thrombin-fibrin complex were investigated. Clots prepared with fibrinogen, 125I-heparin, 2 mM CaCl2, and increasing concentrations of Zn2+ were compacted by centrifugation and aliquots of the supernatant were removed to quantify 125I-heparin bound to fibrin. Titration of Zn2+ showed a saturable 3-fold increase in 125I-heparin bound to fibrin, with maximal binding observed at physiological Zn2+ concentration of 12 μM. When 125I-heparin was titrated with fibrinogen and the samples were clotted in the presence of 12 μM Zn2+, 125I-heparin bound to fibrin with 5-fold higher affinity in the presence of Zn2+ than its absence (Kd values of 0.3 and 1.5 μM, respectively). Comparable results were obtained regardless of whether clots were formed with thrombin or batroxobin, demonstrating that the increased affinity of heparin for fibrin was not the result of formation of thrombin-fibrin-heparin complexes. Investigating ternary complex formation, Zn2+ promoted a saturable 60% increase in thrombin binding to fibrin in the presence of heparin and the Zn2+ dose response mirrored that of the heparin-fibrin interaction. In the absence of heparin, Zn2+ had little effect on thrombin binding to fibrin. To further examine the effect of Zn2+ on heparin-fibrinogen interaction, surface plasmon resonance studies were performed. Varying concentrations of fibrinogen were applied to a biotinylated medium molecular weight heparin (6700 kDa) that was bound to a streptavidin sensor chip. Fibrinogen bound immobilized heparin with a Kd of 379 nM in the presence of EDTA. In the presence 2 mM CaCl2 or 12 μM Zn2+, the Kd values were 10 nM and 8 nM, respectively. However, the mass of heparin bound was ten-fold higher in the presence of Zn2+ than it was with CaCl2 or EDTA. These data reveal that Zn2+ promotes the interaction of heparin with fibrinogen and that this effect is maintained when fibrinogen is converted to fibrin. Because the increased heparin that binds to fibrin in the presence of Zn2+ has greater capacity to bind thrombin, this phenomenon may augment the protection of thrombin from inhibition by antithrombin. Therefore, the extent to which fibrin-bound thrombin is protected from inhibition is likely to be underestimated in studies that do not include physiological concentrations of Zn2+. This gives further emphasis for the need to develop heparin-derived anticoagulants that resist formation of the protective ternary complex.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.257
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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