Inhibition of <i>Clostridioides difficile</i> -Specific DNA Adenine Methyltransferase CamA by Analogs of <i>S</i> -Adenosyl- <scp>l</scp> -methionine
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Epigenetically targeted therapies, especially those inhibiting S -adenosyl- l -methionine (SAM)-dependent methylations of DNA, mRNA, and histones, have advanced rapidly in cancer treatment. However, these therapies remain underexplored for antibiotic development, despite the growing threat of antimicrobial resistance. Here, we screened a focused library of SAM analogs against the DNA adenine methyltransferase CamA specific to the enteric pathogen Clostridioides difficile . At the same time, we examined six other adenine methyltransferases, including two bacterial DNA methyltransferases and four human RNA methyltransferases having distinct RNA substrates. Compound 113 selectively inhibited CamA (IC 50 = 0.15 μM). In addition, compound 67 inhibited Caulobacter crescentus CcrM (IC 50 = 1.8 μM), which has orthologs present in pathogens such as Brucella, while compounds 77 and 37 inhibited the human RNA methyltransferase complexes MettL3-MettL14 and MettL5-Trm112, respectively, at 7–8 μM concentrations. These results provide chemical probes for exploring the role of CamA in sporulation and colonization with potential as antivirulence agents against C. difficile infection. Our study also introduces the first chemical probes for inhibiting bacterial CcrM and human MettL5, each of which plays key roles in their respective hosts.
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 | 0.000 |
| 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".