Molecular mechanisms underlying the synergistic induction of CXCL10 by LPS and IFN‐γ in human neutrophils
Bibliographic record
Abstract
The CXCL10 chemokine is a critical chemoattractant for the recruitment of activated Th1 and NK cells into inflammatory sites. CXCL10 is typically produced by myeloid cells in response to IFN-gamma, as well as by neutrophils, though the latter require a costimulation with IFN-gamma and LPS. In this study, we investigated the molecular mechanism(s) whereby IFN-gamma and TLR4 ligation synergize to induce CXCL10 expression in neutrophils. By primary transcript real-time PCR analysis, we demonstrate that the CXCL10 gene is transcriptionally induced by the LPS plus IFN-gamma combination in neutrophils, consistent with previous studies showing that increased CXCL10 gene expression does not reflect enhanced mRNA stability. The IFN-gamma-induced STAT1 activation and the lipopolysaccharide (LPS)-induced NF-kappaB activation were not enhanced if neutrophils were exposed to both stimuli, whereas both transcription factors were activated by IFN-gamma or LPS in monocytes. Finally, pharmacological inhibitors of NF-kappaB demonstrated its role in the induction of CXCL10 expression by LPS plus IFN-gamma in neutrophils, and by LPS or IFN-gamma in monocytes. Together, these results suggest that in neutrophils, the synergy observed between LPS and IFN-gamma toward CXCL10 gene expression likely reflects the cooperative induction of the NF-kappaB and STAT1 transcription factors by LPS and IFN-gamma, respectively.
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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) | 0.002 | 0.001 |
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".