Lipid A Structural Determination from a Single Colony
Bibliographic record
Abstract
We describe an innovative use for the recently reported fast lipid analysis technique (FLAT) that allows for the generation of MALDI tandem mass spectrometry data suitable for lipid A structure analysis directly from a single Gram-negative bacterial colony. We refer to this tandem MS version of FLAT as FLAT n . Neither technique requires sophisticated sample preparation beyond the selection of a single bacterial colony, which significantly reduces overall analysis time (∼1 h), as compared to conventional methods. Moreover, the tandem mass spectra generated by FLAT n provides comprehensive information on fragments of lipid A, for example, ester bonded acyl chain dissociations, cross-ring cleavages, and glycosidic bond dissociations, all of which allow the facile determination of novel lipid A structures or confirmation of expected structures. In addition to generating tandem mass spectra directly from single colonies, we also show that FLAT n can be used to analyze lipid A structures taken directly from a complex biological clinical sample without the need for ex vivo growth. From a urine sample from a patient with an E. coli infection, FLAT n identified the organism and demonstrated that this clinical isolate carried the mobile colistin resistance-1 gene ( mcr-1 ) that results in the addition of a phosphoethanolamine moiety and subsequently resistance to the antimicrobial, colistin (polymyxin E). Moreover, FLAT n allowed for the determination of the existence of a structural isomer in E. coli lipid A that had either a 1- or 4′-phosphate group modification by phosphoethanolamine generated by a change of bacterial culture conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".