Insights into the regulatory mechanisms of <i>Clostridioides difficile</i> biofilm formation
Bibliographic record
Abstract
Abstract Mucosal biofilms play an important role in intestinal health; however, the mucosal bacterial community has been implicated in persistent infections. Clostridioides difficile is an important nosocomial pathogen, with an unacceptable high rate of recurrence following antibiotic treatment. As C. difficile is a known biofilm producer, a property which may contribute to this suboptimal therapeutic response, we have investigated the transcriptional changes and regulatory pathways during the transition from planktonic to biofilm mode of growth. Widespread metabolic reprogramming during biofilm formation was detected, characterised by an increased usage of glycine metabolic pathways to yield key metabolites, which are used for energy production and synthesis of short chain fatty acids. We detected the expression of 107 small non-coding RNAs that appear to, in some part, regulate these pathways; however, 25 of these small RNAs were specifically expressed during biofilm formation, indicating they may play a role in regulating biofilm-specific genes. Similar to Bacillus subtilis , biofilm formation is a multi-regulatory process and SinR negatively regulates biofilm formation independently of other known mechanisms. This comprehensive analysis furthers our understanding of biofilm formation in C. difficile , identifies potential targets for anti-virulence factors, and provides evidence of the link between metabolism and virulence traits.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".