Comparative inhibitory profile and distribution of bacterial PARPs, using Clostridioides difficile CD160 PARP as a model
Bibliographic record
Abstract
Abstract Poly-ADP-ribose polymerases (PARPs) are involved in the regulation of important cellular processes, such as DNA repair, aging and apoptosis, among others. They have been considered as promising therapeutic targets, since human cancer cells carrying BRCA1 and BRCA2 mutations are highly sensitive to human PARP-1 inhibitors. Although extensive work has been carried out with the latter enzyme, little is known on bacterial PARPs, of which only one has been demonstrated to be active. To extend this limited knowledge, we demonstrate that the Gram-positive bacterium Clostridioides difficile CD160 PARP is a highly active enzyme with a high production yield. Its phylogenetic analysis also pointed to a singular domain organization in contrast to other clostridiales, which could be due to the long-term divergence of C. difficile CD160. Surprisingly, its PARP becomes the first enzyme to be characterized from this strain, which has a genotype never before described based on its sequenced genome. Finally, the inhibition study carried out after a high-throughput in silico screening and an in vitro testing with hPARP1 and bacterial PARPs identified a different inhibitory profile, a new highly inhibitory compound never before described for hPARP1, and a specificity of bacterial PARPs for a compound that mimics NAD+ (EB-47).
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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 | 0.000 |
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".