Pietsch, Constanze; Konrad, Jonathan; Wernicke von Siebenthal, Elena; Pawlak, Paulina (2024). Multiple faces of stress in the zebrafish (Danio rerio) brain. Frontiers in physiology, 15(1373234) Frontiers Research Foundation 10.3389/fphys.2024.1373234
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The changing expressions of certain genes as a consequence of exposure to stressors has not been studied in detail in the fish brain. Therefore, a stress trial with zebrafish was conducted, aiming at identifying relevant gene regulation pathways in different regions of the brain. As acute stressors within this trial, feed rewarding, feed restriction, and air exposure have been used. The gene expression data from the experimental fish brains have been analyzed by means of principal component analyses (PCAs), whereby the individual genes have been compiled according to the regulation pathways in the brain. The results did not indicate a mutual response across the treatment and gender groups. To evaluate whether a similar sample structure belonging to a large sample size would have allowed the classification of the gene expression patterns according to the treatments, the data have been bootstrapped and used for building random forest models. These revealed a high accuracy of the classifications, but different genes in the female and male zebrafish were found to have contributed to the classification algorithms the most. These analyses showed that less than eight genes are, in most cases, sufficient for an accurate classification. Moreover, mainly genes belonging to the stress axis, to the isotocin regulation pathways, or to the serotonergic pathways had the strongest influence on the outcome of the classification models.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE) |
Subjects: |
500 Science 500 Science > 570 Life sciences; biology |
ISSN: |
1664-042X |
Publisher: |
Frontiers Research Foundation |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
08 May 2024 12:02 |
Last Modified: |
09 May 2024 15:25 |
Publisher DOI: |
10.3389/fphys.2024.1373234 |
PubMed ID: |
38711953 |
Uncontrolled Keywords: |
aquaculture classification gene expression patterns machine learning stressors |
BORIS DOI: |
10.48350/196595 |
URI: |
https://boris.unibe.ch/id/eprint/196595 |
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