Selective Detection and Identification of Sugar Nucleotides by CE−Electrospray-MS and Its Application to Bacterial Metabolomics
Bibliographic record
Abstract
A novel method employing CE-ESMS and precursor ion scanning was developed for the selective detection of nucleotide-activated sugars. By using precursor ion scanning for fragment ions specific to the different nucleotide carriers, i.e., ions at m/z 322 for cytidine monophosphate, m/z 323, 385, and 403 for uridine diphosphate, m/z 362, 424, and 442, for guanosine diphosphate, and m/z 346, 408, and 426 for adenosine diphosphate, it was possible to selectively detect sugar nucleotides involved in the biosynthesis of glycoconjugates such as glycoproteins and lipopolysaccharides. Enhancement of sensitivity was achieved using N-(2-hydroxyethyl)piperazine-N'-(2-ethanesulfonic acid) as a sample stacking buffer and provided detection limits between 0.2 and 3.8 pmol.mL(-)(1). The present CE-ESMS method provided linear dynamic ranges over the concentrations 0.2-164 nM (r(2) = 0.952-0.997) for different nucleotide sugar standards. The application of this method is demonstrated for the identification of intracellular pools of sugar nucleotides in wild type and isogenic mutants from the bacterial pathogen Campylobacter jejuni. By using product ion scanning (with and without front-end collision-induced dissociation), it was possible to determine the precise nature of unexpected sugar nucleotides involved in the biosynthesis of pseudaminic acid, a sialic acid-like sugar previously observed on the flagellin of some pathogenic bacteria.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".