Insights into gene expression responses to infections in teleosts using microarray data: a systematic review
Bibliographic record
Abstract
Abstract The rapid growth of production in aquaculture in the last decades has brought unwanted consequences affecting fish health and increasing the susceptibility to different infections. This systematic review aimed to analyse and summarize the current knowledge of gene expression responses to infectious diseases in teleosts using viruses, bacteria, fungi and parasites as agents through published microarray data. We conducted searches in electronic databases, including PubMed, Web of Science and SCOPUS until 1 May 2019. We identified 862 citations across databases and manual searches. After removing duplicates, we screened 455 unique references using titles and abstracts, of which 262 proved potentially eligible and evaluated using full text. A total of 79 articles proved eligible for this review. From the articles retrieved, we examined 261 different experiments (or ‘studies’) and more than a hundred thousand differentially expressed genes (DEGs). This systematic review represents the first catalogue of genes (and their associated processes) that differentially transcribe in different teleost species (13 species) due to infections generated by a large variety of pathogens (38 types). Although the obtained gene expression results are in considerable measure associated with expected immune response, other genes showed surprising significant transcriptional outcomes that may unravel unknown functions related to fish infections. This type of investigations facilitates the visualization of existing gaps in researches that may inspire future analysis in non‐traditional but relevant host or pathogen species.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".