Structure Determination of Lipid A with Multiple Glycosylation Sites by Tandem MS of Lithium-Adducted Negative Ions
Bibliographic record
Abstract
FLAT n is a tandem mass spectrometric technique that can be used to rapidly generate spectral information applicable for structural elucidation of lipids like lipid A from Gram-negative bacterial species from a single bacterial colony. In this study, we extend the scope and capability of FLAT n by tandem MS fragmentation of lithium-adducted molecular lipid A anions and fragments (FLAT n-Li ) that provides additional structural and diagnostic data from FLAT n samples allowing for the discrimination of terminal phosphate modifications in a variety of pathogenic and environmental species. Using FLAT n-Li, we elucidated the lipid A structure from several bacterial species, including novel structures from arctic bacterioplankton of the Duganella and Massilia genera that favor 4-amino-4-deoxy-l-arabinopyranose (Ara4N) modification at the 1-phosphate position and that demonstrate double glycosylation with Ara4N at the 1 and 4′ phosphate positions simultaneously. The structures characterized in this work demonstrate that some environmental psychrophilic species make extensive use of this structural lipid A modification previously characterized as a pathogenic adaptation and the structural basis of resistance to cationic antimicrobial peptides. This observation extends the role of phosphate modification(s) in environmental species adaptation and suggests that Ara4N modification can functionally replace the positive charge of the phosphoethanolamine modification that is more typically found attached to the 1-phosphate position of modified lipid A.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".