Multiple faces of stress in the zebrafish (Danio rerio) brain

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 Frontiers 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)


School of Agricultural, Forest and Food Sciences HAFL
School of Agricultural, Forest and Food Sciences HAFL > Agriculture
School of Agricultural, Forest and Food Sciences HAFL > Agriculture > Livestock and Horses


Pietsch, Constanze0000-0002-3572-8945;
Konrad, Jonathan;
Wernicke von Siebenthal, Elena0000-0003-2864-9648 and
Pawlak, Paulina0000-0002-2159-0252


Q Science > Q Science (General)
Q Science > QP Physiology
S Agriculture > SH Aquaculture. Fisheries. Angling








Constanze Pietsch

Date Deposited:

18 Apr 2024 08:32

Last Modified:

18 Apr 2024 09:39

Publisher DOI:


Uncontrolled Keywords:

aquaculture, stressors, gene expression patterns, machine learning, classification




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