Transforming Growth Factor Beta Receptor 2 (TGFBR2) Changes Sialylation in the Microsatellite Unstable (MSI) Colorectal Cancer Cell Line HCT116
Bibliographic record
Abstract
Aberrant glycosylation is a common feature of many malignancies including colorectal cancers (CRCs). About 15% of CRC show the microsatellite instability (MSI) phenotype that is associated with a high frequency of biallelic frameshift mutations in the A10 coding mononucleotide microsatellite of the transforming growth factor beta receptor 2 (TGFBR2) gene. If and how impaired TGFBR2 signaling in MSI CRC cells affects cell surface glycan pattern is largely unexplored. Here, we used the TGFBR2-deficient MSI colon carcinoma cell line HCT116 as a model system. Stable clones conferring doxycycline (dox)-inducible expression of a single copy wildtype TGFBR2 transgene were generated by recombinase-mediated cassette exchange (RMCE). In two independent clones, dox-inducible expression of wildtype TGFBR2 protein and reconstitution of its signaling function was shown. Metabolic labeling experiments using the tritiated sialic acid precursor N-acetyl-D-mannosamine (ManNAc) revealed a significant decline (∼30%) of its incorporation into newly synthesized sialoglycoproteins in a TGFBR2-dependent manner. In particular, we detected a significant decrease of sialylated ß1-integrin upon reconstituted TGFBR2 signaling which did not influence ß1-integrin protein turnover. Notably, TGFBR2 reconstitution did not affect the transcript levels of any of the known human sialyltransferases when examined by real-time RT- PCR analysis. These results suggest that reconstituted TGFBR2 signaling in an isogenic MSI cell line model system can modulate sialylation of cell surface proteins like ß1-integrin. Moreover, our model system will be suitable to uncover the underlying molecular mechanisms of altered MSI tumor glycobiology.
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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 | 0.000 | 0.000 |
| 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".