Aminoarabinose is essential for lipopolysaccharide export and intrinsic antimicrobial peptide resistance in <i>Burkholderia cenocepacia</i> <sup>†</sup>
Bibliographic record
Abstract
One common mechanism of resistance against antimicrobial peptides in Gram-negative bacteria is the addition of 4-amino-4-deoxy-L-arabinose (L-Ara4N) to the lipopolysaccharide (LPS) molecule. Burkholderia cenocepacia exhibits extraordinary intrinsic resistance to antimicrobial peptides and other antibiotics. We have previously discovered that unlike other bacteria, B. cenocepacia requires L-Ara4N for viability. Here, we describe the isolation of B. cenocepacia suppressor mutants that remain viable despite the deletion of genes required for L-Ara4N synthesis and transfer to the LPS. The absence of L-Ara4N is the only structural difference in the LPS of the mutants compared with that of the parental strain. The mutants also become highly sensitive to polymyxin B and melittin, two different classes of antimicrobial peptides. The suppressor phenotype resulted from a single amino acid replacement (aspartic acid to histidine) at position 31 of LptG, a protein component of the multi-protein pathway responsible for the export of the LPS molecule from the inner to the outer membrane. We propose that L-Ara4N modification of LPS provides a molecular signature required for LPS export and proper assembly at the outer membrane of B. cenocepacia, and is the most critical determinant for the intrinsic resistance of this bacterium to antimicrobial peptides.
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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) | 0.000 | 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) | 0.001 | 0.001 |
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".