Identification of a forkhead box protein transcriptional network induced in human neutrophils in response to inflammatory stimuli
Bibliographic record
Abstract
Introduction: Neutrophils represent the largest proportion of circulating leukocytes and, in response to inflammatory stimuli, are rapidly recruited to sites of infection where they neutralize pathogens. Methods and results: We have identified a novel neutrophil transcription network induced in response to inflammatory stimuli. We performed the first RNAseq analysis of human neutrophils exposed to lipopolysaccharide (LPS), followed by a meta-analysis of our dataset and previously published studies of LPS-challenged neutrophils. This revealed a robustly enhanced transcriptional network driven by forkhead box (FOX) transcription factors. The network is enriched in genes encoding proinflammatory cytokines and transcription factors, including MAFF and ATF3, which are implicated in responses to stress, survival and inflammation. Expression of transcription factors FOXP1 and FOXP4 is induced in neutrophils exposed to inflammatory stimuli, and potential FOXP1/FOXP4 binding sites were identified in several genes in the network, all located in chromatin regions consistent with neutrophil enhancer function. Chromatin immunoprecipitation (ChIP) assays in neutrophils confirmed enhanced binding of FOXP4, but not FOXP1, to multiple sites in response to LPS. Binding to numerous motifs and transactivation of network genes were also observed when FOXP proteins were transiently expressed in HEK293 cells. In addition to LPS, the transcriptional network is induced by other inflammatory stimuli, indicating it represents a general neutrophil response to inflammation. Discussion: Collectively, these findings reveal a role for the FOXP4 transcription network as a regulator of responses to inflammatory stimuli in neutrophils.
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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 | 0.001 | 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) | 0.001 | 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".