Zekoll Theresa, Waldherr Monika, Tessmar-Raible Kristin
Max Perutz Labs, University of Vienna, Vienna Biocenter, Vienna, Austria.
Research Platform "Rhythms of Life, " University of Vienna, Vienna BioCenter, Vienna, Austria.
Front Physiol. 2021 Oct 22;12:726941. doi: 10.3389/fphys.2021.726941. eCollection 2021.
One of the big challenges in the study of animal behavior is to combine molecular-level questions of functional genetics with meaningful combinations of environmental stimuli. Light and temperature are important external cues, influencing the behaviors of organisms. Thus, understanding the combined effect of light and temperature changes on wild-type vs. genetically modified animals is a first step to understand the role of individual genes in the ability of animals to cope with changing environments. Many behavioral traits can be extrapolated from behavioral tests performed from automated motion tracking combined with machine learning. Acquired datasets, typically complex and large, can be challenging for subsequent quantitative analyses. In this study, we investigate medaka behavior of mutants vs. corresponding wild-types under different light and temperature conditions using automated tracking combined with a convolutional neuronal network and a Hidden Markov model-based approach. The temperatures in this study can occur in summer vs. late spring/early autumn in the natural habitat of medaka fish. Under summer-like temperature, mutants did not exhibit changes in overall locomotion, consistent with previous observations. However, detailed analyses of fish position revealed that the mutants spent more time in central locations of the dish, possibly because of decreased anxiety. Furthermore, a clear difference in location and overall movement was obvious between the mutant and wild-types under colder conditions. These data indicate a role of in behavioral adjustment, at least in part possibly depending on the season.
动物行为研究中的一大挑战是将功能遗传学的分子层面问题与有意义的环境刺激组合相结合。光和温度是重要的外部线索，影响着生物体的行为。因此，了解光和温度变化对野生型与转基因动物的综合影响是理解单个基因在动物应对环境变化能力中所起作用的第一步。许多行为特征可以从结合了机器学习的自动运动跟踪所进行的行为测试中推断出来。获取的数据集通常复杂且庞大，可能给后续的定量分析带来挑战。在本研究中，我们使用自动跟踪结合卷积神经网络和基于隐马尔可夫模型的方法，研究了在不同光照和温度条件下突变体与相应野生型的青鳉行为。本研究中的温度可对应青鳉鱼自然栖息地的夏季与春末/秋初。在类似夏季的温度下，突变体在整体运动方面未表现出变化，这与之前的观察结果一致。然而，对鱼位置的详细分析表明，突变体在培养皿中心位置花费的时间更多，可能是因为焦虑减少。此外，在较冷条件下，突变体与野生型在位置和整体运动方面存在明显差异。这些数据表明了[基因名称]在行为调节中的作用，至少部分可能取决于季节。
