CD133 Protein N-Glycosylation Processing Contributes to Cell Surface Recognition of the Primitive Cell Marker AC133 Epitope
Bibliographic record
Abstract
The AC133 epitope expressed on the CD133 glycoprotein has been widely used as a cell surface marker of numerous stem cell and cancer stem cell types. It has been recently proposed that posttranslational modification and regulation of CD133 may govern cell surface AC133 recognition. Therefore, we performed a large scale pooled RNA interference (RNAi) screen to identify genes involved in cell surface AC133 expression. Gene hits could be validated at a rate of 70.5% in a secondary assay using an orthogonal RNAi system, demonstrating that our primary RNAi screen served as a powerful genetic screening approach. Within the list of hits from the primary screen, genes involved in N-glycan biosynthesis were significantly enriched as determined by Ingenuity Canonical Pathway analyses. Indeed, inhibiting biosynthesis of the N-glycan precursor using the small molecule tunicamycin or inhibiting its transfer to CD133 by generating N-glycan-deficient CD133 mutants resulted in undetectable cell surface AC133. Among the screen hits involved in N-glycosylation were genes involved in complex N-glycan processing, including the poorly characterized MGAT4C, which we demonstrate to be a positive regulator of cell surface AC133 expression. Our study identifies a set of genes involved in CD133 N-glycosylation as a direct contributing factor to cell surface AC133 recognition and provides biochemical evidence for the function and structure of CD133 N-glycans.
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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) | 0.000 | 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 | 0.000 |
| 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 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".