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Natural Killer Cells Induce the Formation of Neutrophil Extracellular Traps (NETs) in Venous Thrombosis

2016· article· en· W2980056665 on OpenAlexaff
François Bertin, Sandrine Laurance, Catherine A. Lemarié, Mark Blostein

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsNeutrophil extracellular trapsInnate immune systemThrombosisVenous thrombosisPlateletImmunologyCoagulationExtracellularImmune systemBiologyChemistryInflammationMedicineCell biologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Thrombosis is considered to be a pathological deviation of physiologic hemostasis involving similar mechanisms. Interestingly, recent work demonstrates that innate immune cells promote venous thrombosis. Innate immune cells were shown to collaborate to induce the activation of the coagulation cascade and platelets. In particular, neutrophils contribute to venous thrombosis through the release of neutrophil extracellular traps (NETs). However, the mechanism triggering the formation of NETs during venous thrombosis remain unknown. Of interest, a study showed that IFNγ induced the formation of NETs. Thus, we investigated the role of IFNγ-producing cells in the development of thrombosis. We used mice lacking IFNγ, Tbet (the transcription factor regulating the expression of IFNγ) or wild type mice. Venous thrombosis was induced using the flow restriction model in the inferior vena cava , as has been previously published. In Tbet-/-, IFNγ-/- and WT mice, we show that the absence of Tbet or IFNγ decreases the formation of thrombi after venous thrombosis induction, suggesting that the Tbet+/IFNγ producing cells are required for the early development of venous thrombosis. Comparing the composition of the thrombi from Tbet-/-, IFNγ-/- and WT mice, we show that, in all mice, neutrophils are the main cellular component of thrombi followed by monocytes; however, the number of neutrophil extracellular traps (NETs) formed during thrombosis is significantly lower in Tbet-/- and IFNγ-/- mice. Furthermore, NET formation is also decreased in WT mice specifically depleted of IFNγ and increases in Tbet-/- and IFNγ-/- mice injected with recombinant IFNγ. In vitro, we show that stimulation of WT murine neutrophils with recombinant IFNγ triggers the formation of NETs demonstrating that Tbet and IFNγ are crucial for NET formation by neutrophils. Natural killer (NK) cells are the main producers of IFNγ . Thus, we investigated the role of NK cells in venous thrombosis induced by flow restriction. NK cells were specifically depleted with an antibody during the development of venous thrombosis. The absence of NK cells results in smaller thrombi suggesting that NK cells are required for early thrombus development. Additionally, depletion in NK cells results in decreased in-situ IFNγ production and decreased NET formation. To directly link NK cells to the formation of NETs, WT neutrophils were co-cultured with Tbet-/- and IFNγ-/- NK cells. We show that WT neutrophils release less NETs when cultured with Tbet-/- and IFNγ-/- NK cells as compared to WT NK cells. These data suggest that NK cells trigger the formation of NETs by neutrophils through the production of IFNγ. Hence, we demonstrate that, in a partial flow restriction model of venous thrombosis, Tbet and IFNγ are crucial for thrombus development by promoting the formation of NETs by neutrophils and that NK cells are key effector cells in this process. Disclosures Blostein: boehringer-ingelheim: Research Funding.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.215
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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