Polysialic acid bioengineering of neuronal cells by N-acyl sialic acid precursor treatment
Bibliographic record
Abstract
The inherent promiscuity of the polysialic acid (PSA) biosynthetic pathway has been exploited by the use of exogenous unnatural sialic acid precursor molecules to introduce unnatural modifications into cellular PSA, and has found applications in nervous system development and tumor vaccine studies. The sialic acid precursor molecules N-propionyl- and N-butanoyl-mannosamine (ManPr, ManBu) have been variably reported to affect PSA biosynthesis ranging from complete inhibition to de novo production of modified PSA, thus illustrating the need for further investigation into their effects. In this study, we have used a monoclonal antibody (mAb) 13D9, specific to both N-propionyl-PSA and N-butanoyl-PSA (NPrPSA and NBuPSA), together with flow cytometry, to study precursor-treated tumor cells and NT2 neurons at different stages of their maturation. We report that both ManPr and ManBu sialic acid precursors are metabolized and the resultant unnatural sialic acids are incorporated into de novo surface sialylglycoconjugates in murine and human tumor cells and, for the first time, in human NT2 neurons. Furthermore, neither precursor treatment deleteriously affected endogenous PSA expression; however, with NT2 cells, PSA levels were naturally downregulated as a function of their maturation into polarized neurons independent of sialic acid precursor treatment.
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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 | 0.000 |
| 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".