Defining the structure of the NF-ĸB pathway in human immune cells using quantitative proteomic data
Bibliographic record
Abstract
The NF-ĸB transcription factor is a critical regulator of immune homeostasis and inflammatory responses and is a critical factor in the pathogenesis of inflammatory disease. The pathways to NF-ĸB activation are paradigms for signal-induced ubiquitination and proteasomal degradation, control of transcription factor function by subcellular localisation, and the control of gene transcription and physiological processes by signal transduction mechanisms. Despite the importance of NF-ĸB in disease, the NF-ĸB pathway remains unexploited for the treatment of inflammatory disease. Our understanding of NF-ĸB comes mostly from studies of transgenic mice and cell lines where components of the pathway have been deleted or over expressed. Recent advances in quantitative proteomics offer new opportunities to understand the NF-ĸB pathway using the absolute abundance of individual pathway components. We have analysed available quantitative proteomic datasets to establish the structure of the NF-ĸB pathway in human immune cells under both steady state and activated conditions. This reveals a conserved NF-κB pathway structure across different immune cell lineages and identifies important differences to the current model of the NF-ĸB pathway. These include the findings that the IKK complex in most cells is likely to consist predominantly of IKKβ homodimers, that the relative abundancies of IκB proteins show strong cell type variation, and that the components of the non-canonical NF-ĸB pathway are significantly increased in activated immune cells. These findings challenge aspects of our current view of the NF-κB pathway and identify outstanding questions important for defining the role of key components in regulating inflammation and immunity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".