Different Signaling Pathways Expressed by Chicken Naïve CD4<sup>+</sup> T Cells, CD4<sup>+</sup> Lymphocytes Activated with Staphylococcal Enterotoxin B, and Those Malignantly Transformed by Marek’s Disease Virus
Bibliographic record
Abstract
Proteomics methods, based on liquid chromatography and tandem mass spectrometry, produce large "shotgun" proteomes that are most appropriately compared not at the level of differentially expressed proteins only but at the more comprehensive level of biological networks and pathways. This is now possible with the emergence of functional annotation databases and tools, databases of canonical pathways and molecular interactions and computational text mining tools. Here, we used shotgun proteomics, and the differential proteomics modeling functionalities available in the Pathwaystudio network modeling program to define the cell physiology of Hodgkin's disease antigen-overexpressing (CD30 (hi)) CD4 (+) T cell lymphomas using the unique Marek's disease (MD) natural animal model. CD30 (hi) lymphoma cells have characteristics of activated T cells but are also fundamentally different from their nontransformed healthy counterparts. We compared the cell physiology of naïve, superantigen-activated and MD-transformed CD4 (+) T cell proteomes. While the superantigen-activated cells had signaling pathways associated with cell activation, inflammation, proliferation and cell death, the MD-transformed cells had growth factor, cytokine, adhesion, and transcription factor signaling responses associated with oncogenicity, cell proliferation, angiogenesis, motility, and metastasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".