The glycoconjugate-degrading enzymes of <i>Clostridium perfringens</i>: Tailored catalysts for breaching the intestinal mucus barrier
Bibliographic record
Abstract
The gastrointestinal (GI) tract of humans and animals is lined with mucus that serves as a barrier between the gut microbiota and the epithelial layer of the intestine. As the proteins present in mucus are typically heavily glycosylated, such as the mucins, several enteric commensal and pathogenic bacterial species are well-adapted to this rich carbon source and their genomes are replete with carbohydrate-active enzymes targeted toward dismantling the glycans and proteins present in mucus. One such species is Clostridium perfringens, a Gram-positive opportunistic pathogen indigenous to the gut of humans and animals. The genome of C. perfringens encodes numerous carbohydrate-active enzymes that are predicted or known to target glycosidic linkages within or on the termini of mucus glycans. Through this enzymatic activity, the degradation of the mucosal layer by C. perfringens has been implicated in a number of GI diseases, the most severe of which is necrotic enteritis. In this review, we describe the wide array of extracellular glycoside hydrolases, and their accessory modules, that is possessed by C. perfringens, and examine the unique multimodularity of these proteins in the context of degrading the glycoconjugates in mucus as a potential component of disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".