Targeted Proteomic Analysis of Glycolysis in Cancer Cells
Bibliographic record
Abstract
Altered expression of glycolysis proteins is an important yet poorly understood characteristic of cancer. To better understand the glycolytic changes during tumorigenesis, we designed a liquid chromatography multiple reaction monitoring (LC-MRM) assay targeting the "glycolysis proteome" in MCF-7 breast cancer cells, using isotope-coded dimethylation of peptides for relative quantification. In silico, dimethyl labeled tryptic peptides [M + 2H](2+) (of length n) and their y(n-1) fragment ions were determined based on UniprotKB database sequence entries for glycolysis proteins, related branching pathways, and reference proteins. Using predicted transitions ([M + 2H](2+) → y(n-1)), MRM-initiated detection and sequencing (MIDAS) was performed on a dimethyl-labeled, tryptic digest from MCF-7 cells, using two-dimensional liquid chromatography mass spectrometry analysis. Three transitions for each peptide were selected from identified spectra and assessed using 1D-LC-MRM-MS. Collision energy (CE) and dwell times were optimized and matching transitions for "heavy" isotope-coded dimethylated peptides were calculated. Resulting LC-MRM transitions were then used to measure changes in the glycolytic proteome in insulin-like growth factor-1 (IGF-1)-stimulated MCF-7 cells and other breast cell lines. Increases in the expression of glycolysis proteins leading to lactic acid production were observed common to IGF-1-stimulated MCF-7 cells and the invasive MDA-MB-231 cell line. Preliminary analysis of lung tumors with varied states of differentiation demonstrated the clinical applicability of LC-MRM and showed decreased levels of PGK1 in poorly differentiated tumors.
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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 | 0.001 |
| 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".