Mechanisms of Incorporation for <scp>D</scp>-Amino Acid Probes That Target Peptidoglycan Biosynthesis
Bibliographic record
Abstract
Abstract Bacteria exhibit a myriad of different morphologies, through the synthesis and modification of their essential peptidoglycan (PG) cell wall. Our discovery of a fluorescent D-amino acid (FDAA)-based PG labeling approach provided a powerful method for observing how these morphological changes occur. Given that PG is unique to bacterial cells and a common target for antibiotics, understanding the precise mechanism(s) for incorporation of (F)DAA-based probes is a crucial determinant in understanding the role of PG synthesis in bacterial cell biology and could provide a valuable tool in the development of new antimicrobials to treat drug-resistant antibacterial infections. Here, we systematically investigate the mechanisms of FDAA probe incorporation into PG using two model organisms Escherichia coli (Gram-negative) and Bacillus subtilis (Gram-positive). Our in vitro and in vivo data unequivocally demonstrate that these bacteria incorporate FDAAs using two extracytoplasmic pathways: through activity of their D,D-transpeptidases, and, if present, by their L,D-transpeptidases and not via cytoplasmic incorporation into a D-Ala-D-Ala dipeptide precursor. Our data also revealed the unprecedented finding that the DAA-drug, D-cycloserine, can be incorporated into peptide stems by each of these transpeptidases, in addition to its known inhibitory activity against D-alanine racemase and D-Ala-D-Ala ligase. These mechanistic findings enabled development of a new, FDAA-based, in vitro labeling approach that reports on subcellular distribution of muropeptides, an especially important attribute to enable the study of bacteria with poorly defined growth modes. An improved understanding of the incorporation mechanisms utilized by DAA-based probes is essential when interpreting results from high resolution experiments and highlights the antimicrobial potential of synthetic DAAs.
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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) | 0.000 | 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 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".