Comparative transcriptomic analysis of<i>Clostridium perfringens</i>biofilms and planktonic cells
Bibliographic record
Abstract
Clostridium perfringens is an opportunistic pathogen that can cause food poisoning in humans and various enterotoxaemias in animal species. Recently, C. perfringens was shown to form biofilms, a structured community of bacterial cells enclosed in a self-produced extracellular matrix. However, very little is known on the subject and no information is available on gene expression in C. perfringens biofilms. To gain insights into the differences between free-living C. perfringens cells and those in biofilms, we used RNA sequencing. In total, 25.7% of genes showed differential expression in the two growth modes; about 12.8% of genes were up-regulated and about 12.9% were down-regulated in biofilms. We show that 772 genes were significantly differentially expressed between biofilms and planktonic cells from the supernatant of biofilms. Genes that were down-regulated in biofilm cells, relative to planktonic cells, included those involved in virulence, energy production, amino acid, nucleotide and carbohydrate metabolism, and in translation and ribosomal structure. Genes up-regulated in biofilm cells were mainly involved in amino acid and carbohydrate metabolism, transcription, inorganic ion metabolism and in defence mechanisms. This study provides new insights into the transcriptomic response of C. perfringens during biofilm formation.
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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 | 0.001 | 0.001 |
| 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".