Assessment of membrane labelling mechanisms with exogenous fatty acids and detergents in bacteria
Bibliographic record
Abstract
Labelling of bacterial membranes using exogenous fatty acids has proven to be a valuable tool to investigate molecular interactions by in-cell solid-state nuclear magnetic resonance (ssNMR) spectroscopy, notably with antimicrobial peptides. However, the mechanism by which this labelling takes place in non-mutated bacteria has not yet been investigated. In this work, we propose a rapid method to assess the fate of the fatty acids during the labelling of bacteria, involving two different methylation schemes and gas chromatography coupled to mass spectrometry. We applied this approach to Gram(+) and Gram(-) bacteria grown with deuterated palmitic acid under different conditions. We assessed the extent of labelling, then the resulting membrane rigidity by 2H ssNMR. Our results reveal that the labelling mechanism depends on the detergent used to micellize the fatty acids. This labelling can be either active or passive, whether the fatty acids are metabolized and used in the phospholipids biosynthesis, or remain unmodified in the membrane. We discuss the best labelling protocol for studying peptide-membrane interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".