A sensitive liquid chromatography/mass spectrometry‐based assay for quantitation of amino‐containing moieties in lipid A
Bibliographic record
Abstract
A novel sensitive liquid chromatography/mass spectrometry-based assay was developed for the quantitation of aminosugars, including 2-amino-2-deoxyglucose (glucosamine, GlcN), 2-amino-2-deoxygalactose (galactosamine, GalN), and 4-amino-4-deoxyarabinose (aminoarabinose, AraN), and for ethanolamine (EtN), present in lipid A. This assay enables the identification and quantitation of all amino-containing moieties present in lipopolysaccharide or lipid A from a single sample. The method was applied to the analysis of lipid A (endotoxin) isolated from a variety of biosynthetic and regulatory mutants of Salmonella enterica serovar Typhimurium and Francisella tularensis subspecies novicida. Lipid A is treated with trifluoroacetic acid to liberate and deacetylate individual aminosugars and mass tagged with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate, which reacts with primary and secondary amines. The derivatives are separated using reversed-phase chromatography and analyzed using a single quadrupole mass spectrometer to detect quantities as small as 20 fmol. GalN was detected only in Francisella and AraN only in Salmonella, while GlcN was detected in lipid A samples from both species of bacteria. Additionally, we found an approximately 10-fold increase in the level of AraN in lipid A isolated from Salmonella grown in magnesium-limited versus magnesium-replete conditions. Salmonella with defined mutations in lipid A synthesis and regulatory genes were used to further validate the assay. Salmonella with null mutations in the phoP, pmrE, and prmF genes were unable to add AraN to their lipid A, while Salmonella with constitutively active phoP and pmrA exhibited AraN modification of lipid A even in the normally repressive magnesium-replete growth condition. The described assay produces excellent repeatability and reproducibility for the detection of amino-containing moieties in lipid A from a variety of bacterial sources.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".