The oral pathogen Porphyromonas gingivalis gains tolerance to the antimicrobial peptide DGL13K by synonymous mutations in hagA
Bibliographic record
Abstract
Porphyromonas gingivalis is a keystone pathogen for periodontal disease. The bacteria are black-pigmented and require heme for growth. P. gingivalis exhibit resistance to many antimicrobial peptides, which contributes to their success in the oral cavity. P. gingivalis W50 was resistant to the antimicrobial peptide LGL13K but susceptible to the all-D-amino acid stereoisomer, DGL13K. Upon prolonged exposure to DGL13K, a novel non-pigmented mutant was isolated. Exposure to the L-isomer, LGL13K, did not produce a non-pigmented mutant. The goal of this study was to characterize the genomic and cellular changes that led to the non-pigmented phenotype upon treatment with DGL13K. The non-pigmented mutant showed a low minimum inhibitory concentration and two-fold extended minimum duration for killing by DGL13K, consistent with tolerance to this peptide. The DGL13K-tolerant bacteria exhibited synonymous mutations in the hagA gene. The mutations did not prevent mRNA expression but were predicted to alter mRNA structure. The non-pigmented bacteria were deficient in hemagglutination and hemoglobin binding, suggesting that the HagA protein was not expressed. This was supported by whole cell enzyme-linked immunosorbent assay and gingipain activity assays, which suggested the absence of HagA but not of two closely related gingipains. In vivo virulence was similar for wild type and non-pigmented bacteria in the Galleria mellonella model. The results suggest that, unlike LGL13K, DGL13K can defeat multiple bacterial resistance mechanisms but bacteria can gain tolerance to DGL13K through mutations in the hagA gene.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".