Systems biology evaluation of immune responses induced by human host defence peptide LL-37 in mononuclear cells
Bibliographic record
Abstract
The immune system is very complex, it involves the integrated regulation and expression of hundreds of proteins. To understand in greater detail how the human host defence immunomodulatory peptide LL-37 interacts with innate immunity, a systems approach was pursued. Polychromatic flow cytometry was employed to demonstrate that within human peripheral blood mononuclear cells, CD14+ monocytes, myeloid and plasmocytoid dendritic cells and T- and B-lymphocytes, all responded to LL-37, with the differential production of intracellular cytokines. Microarray analyses with CD14+ monocytes indicated the differential expression of 475 genes in response to stimulation with LL-37. To understand this complex response, bioinformatic interrogation, using InnateDB, of the gene ontology, signalling pathways and transcription factor binding sites was undertaken. Activation of the IkappaBalpha/NFkappaB, mitogen-activated protein kinases p38, ERK1/2 and JNK, and PI3K signalling pathways in response to LL-37 was demonstrated by pathway and ontology over-representation analyses, and confirmed experimentally by inhibitor studies. Computational analysis of the predicted transcription factor binding sites upstream of the genes that were regulated by LL-37 predicted the involvement of several transcription factors including NFkappaB and five novel factors, AP-1, AP-2, SP-1, E2F1, and EGR, which were experimentally confirmed to respond to LL-37 by performing transcription factor array studies on nuclear extracts from LL-37 treated mononuclear cells. These data are discussed as reflecting the integration of several responsive signalling pathways through the involvement of transcription factor complexes in gene expression activated by LL-37 in human mononuclear cells.
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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 | 0.001 | 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".