Glycoproteomic analysis of two mouse mammary cell lines during transforming growth factor (TGF)-beta induced epithelial to mesenchymal transition
Bibliographic record
Abstract
BACKGROUND: TGF-beta acts as an antiproliferative factor in normal epithelial cells and at early stages of oncogenesis. However, later in tumor development TGF-beta can become tumor promoting through mechanisms including the induction of epithelial-to-mesenchymal transition (EMT), a process that is thought to contribute to tumor progression, invasion and metastasis. To identify EMT-related breast cancer therapeutic targets and biomarkers, we have used two proteomic approaches to find proteins that change in abundance upon the induction of EMT by TGF-beta in two mouse mammary epithelial cell lines, NMuMG and BRI-JM01. RESULTS: Preliminary experiments based on two-dimensional electrophoresis of a hydrophobic cell fraction identified only 5 differentially expressed proteins from BRI-JM01 cells. Since 3 of these proteins were glycoproteins, we next used the lectin, wheat germ agglutinin (WGA), to enrich for glycoproteins, followed by relative quantification of tryptic peptides using a label-free LC-MS based method. Using these approaches, we identified several proteins that are modulated during the EMT process, including cell adhesion molecules (several members of the Integrin family, Fibronectin, Activated leukocyte cell adhesion molecule, and Neural cell adhesion molecule 1) and regulators of cellular signaling (Tumor-associated calcium signal transducer 2, Basigin). CONCLUSION: Interestingly, despite the fact that TGF-beta induces similar EMT phenotypes in NMuMG and BRI-JM01 cells, the proteomic results for the two cell lines showed only minimal overlap. These differences likely result in part from the conservative cut-off values used to define differentially-expressed proteins in these experiments. Alternatively, it is possible that the two cell lines may use different mechanisms to achieve an EMT transition.
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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) | 0.000 | 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 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".