Peptidoglycomics reveals compositional changes in peptidoglycan between biofilm- and planktonic-derived Pseudomonas aeruginosa
Bibliographic record
Abstract
Peptidoglycan (PG) is a critical component of the bacterial cell wall and is composed of a repeating -1,4 -linked disaccharide of N-acetylglucosamine and N-acetylmuramic acid appended with a highly conserved stem peptide. In Gram-negative bacteria, PG is assembled in the cytoplasm and exported into the periplasm where it undergoes considerable maturation, modification, or degradation depending on the growth phase or presence of environmental stressors. These modifications serve important functions in diverse processes, including PG turnover, cell elongation/division, and antibiotic resistance. Conventional methods for analyzing PG composition are complex and time-consuming. We present here a streamlined MS-based method that combines differential analysis with statistical 1D annotation approaches to quantitatively compare PGs produced in planktonic-and biofilm-cultured Pseudomonas aeruginosa. We identified a core assembly of PG that is present in high abundance and that does not significantly differ between the two growth states. We also identified an adaptive PG assembly that is present in smaller amounts and fluctuates considerably between growth states in response to physiological changes. Biofilm-derived adaptive PG exhibited significant changes compared with planktonic-derived PG, including amino acid substitutions of the stem peptide and modifications that indicate changes in the activity of amidases, deacetylases, and lytic transglycosylases. The results of this work also provide first evidence of de-N-acetylated muropeptides from P. aeruginosa. The method developed here offers a robust and reproducible workflow for accurately determining PG composition in samples that can be used to assess global PG fluctuations in response to changing growth conditions or external stimuli.
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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 | 0.001 | 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".