Microarray analyses identify molecular biomarkers of Atlantic salmon macrophage and hematopoietic kidney response to<i>Piscirickettsia salmonis</i>infection
Why this work is in the frame
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Bibliographic record
Abstract
Piscirickettsia salmonis is the intracellular bacterium that causes salmonid rickettsial septicemia, an infectious disease that kills millions of farmed fish each year. The mechanisms used by P. salmonis to survive and replicate within host cells are not known. Piscirickettsiosis causes severe necrosis of hematopoietic kidney. Microarray-based experiments with QPCR validation were used to identify Atlantic salmon macrophage and hematopoietic kidney genes differentially transcribed in response to P. salmonis infection. Infections were confirmed by microscopy and RT-PCR with pathogen-specific primers. In infected salmon macrophages, 71 different transcripts were upregulated and 31 different transcripts were downregulated. In infected hematopoietic kidney, 30 different transcripts were upregulated and 39 different transcripts were downregulated. Ten antioxidant genes, including glutathione S-transferase, glutathione reductase, glutathione peroxidase, and cytochrome b558 alpha- and beta-subunits, were upregulated in infected macrophages but not in infected hematopoietic kidney. Changes in redox status of infected macrophages may allow these cells to tolerate P. salmonis infection, raising the possibility that treatment with antioxidants may reduce hematopoietic tissue damage caused by this rickettsial infection. The downregulation of transcripts involved in adaptive immune responses (e.g., T cell receptor alpha-chain and C-C chemokine receptor 7) in infected hematopoietic kidney but not in infected macrophages may contribute to infection-induced kidney tissue damage. Molecular biomarkers of P. salmonis infection, characterized by immune-relevant functional annotations and high fold differences in expression between infected and noninfected samples, may aid in the development of anti-piscirickettsial vaccines and therapeutics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it