Identification and analysis of differentially expressed genes in immune tissues of Atlantic cod stimulated with formalin-killed, atypical<i>Aeromonas salmonicida</i>
Bibliographic record
Abstract
Physiological changes, elicited in animal immune tissues by exposure to pathogens, may be studied using functional genomics approaches. We created and characterized reciprocal suppression subtractive hybridization (SSH) cDNA libraries to identify differentially expressed genes in spleen and head kidney tissues of Atlantic cod (Gadus morhua) challenged with intraperitoneal injections of formalin-killed, atypical Aeromonas salmonicida. Of 4,154 ESTs from four cDNA libraries, 10 genes with immune-relevant functional annotations were selected for QPCR studies using individual fish templates to assess biological variability. Genes confirmed by QPCR as upregulated by A. salmonicida included interleukin-1 beta, interleukin-8, a small inducible cytokine, interferon regulatory factor 1 (IRF1), ferritin heavy subunit, cathelicidin, and hepcidin. This study is the first large-scale discovery of bacteria-responsive genes in cod and the first to demonstrate upregulation of IRF1 in fish immune tissues as a result of bacterial antigen stimulation. Given the importance of IRF1 in vertebrate immune responses to viral and bacterial pathogens, the full-length cDNA sequence of Atlantic cod IRF1 was obtained and compared with putative orthologous sequences from other organisms. Functional annotations of assembled SSH library ESTs showed that bacterial antigen stimulation caused changes in many biological processes including chemotaxis, regulation of apoptosis, antimicrobial peptide production, and iron homeostasis. Moreover, differences in spleen and head kidney gene expression responses to the bacterial antigens pointed to a potential role for the cod spleen in blood-borne pathogen clearance. Our data show that Atlantic cod immune tissue responses to bacterial antigens are similar to those seen in other fish species and higher vertebrates.
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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".