Identification of Cold-Temperature-Regulated Genes in <i>Flavobacterium psychrophilum</i>
Bibliographic record
Abstract
Flavobacterium psychrophilum is the etiological agent of bacterial coldwater disease (BCWD) and rainbow trout fry syndrome (RTFS). It causes disease primarily in fresh water-reared salmonids, but other fish species can also be affected. A diverse array of clinical conditions is associated with BCWD, including tail rot (peduncle disease), necrotic myositis, and cephalic osteochondritis. Degradation of connective and muscular tissues by extracellular proteases is common to all of these presentations. There are no effective vaccines to prevent BCWD or RTFS, and antibiotics are often used to prevent and control disease. To identify virulence factors that might permit development of an efficacious vaccine, cDNA suppression subtractive hybridization (SSH) was used to identify cold-regulated genes in a virulent strain of F. psychrophilum. Genes predicted to encode a two-component system sensor histidine kinase (LytS), an ATP-dependent RNA helicase, a multidrug ABC transporter permease/ATPase, an outer membrane protein/protective antigen OMA87, an M43 cytophagalysin zinc-dependent metalloprotease, a hypothetical protein, and four housekeeping genes were upregulated at 8°C versus the level of expression at 20°C. Because no F. psychrophilum gene was known to be suitable as an internal standard in reverse transcription-quantitative real-time PCR (RT-qPCR) experiments, the expression stability of nine commonly used reference genes was evaluated at 8°C and 20°C. Expression of the 16S rRNA was equivalent at both temperatures, and this gene was used in RT-qPCR experiments to verify the SSH findings. With the exception of the ATCC 49513 strain, similar patterns of gene expression were obtained with 11 other representative strains of F. psychrophilum.
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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) | 0.000 | 0.001 |
| 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".