Plant-Based Diets Induce Transcriptomic Changes in Muscle of Zebrafish and Atlantic Salmon
Bibliographic record
Abstract
With the expansion of the aquaculture industry in the last two decades, there has been a large increase in the use of plant ingredients in aquafeeds, which has created new challenges in fish growth, health and welfare. Fish muscle growth is an important trait that is strongly affected by diet, but our knowledge on the effect of plant protein-based diets on global gene expression in muscle is still scant. The present study evaluated nutrigenomic effects of the inclusion of proteins from pea, soy and wheat into aquafeeds, compared to a control diet with fishmeal as the main protein source using the zebrafish model by RNA-seq; these results were extended to an important aquaculture species by analyzing selected differentially expressed genes identified in the zebrafish model on on-growing Atlantic salmon fed with equivalent plant protein-based diets. Expression of selected Atlantic salmon paralogues of the zebrafish homologues was analyzed using paralogue-specific qPCR assays. Global gene expression changes in muscle of zebrafish fed with plant-based diets were moderate, with the highest changes observed in the soy diet-fed fish, and no change for the pea diet-fed fish compared to the control diet. Among the differentially expressed genes were mylpfb, hsp90aa1.1, col2a1a and odc1, which are important in regulating muscle growth, maintaining muscle structure and function, and muscle tissue homeostasis. Furthermore, those genes and their paralogues were differentially expressed in Atlantic salmon fed with the equivalent percentage of soy or wheat protein containing diets. Some of these genes were similarly regulated in both species while others showed species-specific regulation. The present study expands our understanding on the molecular effects of plant ingredients in fish muscle. Ultimately, the knowledge gained would be of importance for the improved formulation of sustainable plant-based diets for the aquaculture industry.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".