Phosphoinositol 3-kinase-driven NET formation involves different isoforms and signaling partners depending on the stimulus
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) serve to immobilize and kill pathogens, but also can contribute to the progression of several inflammatory and auto-immune diseases, as well as cancer. Whence the importance of elucidating the mechanisms underlying NET formation. In this regard, the PI3K signaling pathway has been shown to be crucial; yet little is known about which of its components are involved. Here, we identified the PI3K isoforms and associated signaling partners that are mobilized in response to different classes of physiological NET inducers (inflammatory cytokines, growth factors, chemoattractants). NET generation was assessed by microscopy and signalling molecule activation by immunoblot using phospho-antibodies. Across the various stimuli, PI3Kα and PI3Kγ isoforms clearly contributed to NET induction, while the participation of other isoforms was stimulus-dependent. Some PI3K isoforms were also found to signal through Akt, the canonical downstream effector of PI3K, while others did not. Downstream of PI3K, mTOR and PLCγ2 were used by all stimuli to control NET generation. Conversely, the involvement of other kinases depended on the stimulus - both TNFα and GM-CSF relied on PDK1 and Akt; and both TNFα and fMLP additionally used S6K. We further established that all PI3K isoforms and downstream effectors act belatedly in NET generation, as reported previously for PI3K. Finally, we revisited the PI3K-PDK1-Akt signaling hierarchy in human neutrophils and again found stimulus-dependent differences. Our data uncover unsuspected complexity and redundancy in the signaling machinery controlling NET formation through the all-important PI3K pathway. Conserved signaling molecules represent therapeutic targets for pathologies involving NETs and in this regard, the existence of drugs currently used in the clinic or undergoing clinical trials (which target PI3K isoforms, mTOR or Akt), underscores the translational potential of our findings.
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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 | 0.001 |
| 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".