Fukunaga Tsukasa, Iwasaki Wataru
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan.
Faculty of Science and Engineering, Waseda University, Tokyo, 169-0072, Japan.
BMC Bioinformatics. 2017 Jan 19;18(1):46. doi: 10.1186/s12859-016-1408-8.
With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development.
In this study, we applied the Gaussian mixture model-based binning method to time-series postural data for 322 C. elegans strains. We revealed that the occurrence patterns of the postural states and the transition patterns among these states have a relationship as expected, and such a relationship must be taken into account to identify strains with atypical behaviors that are different from those of wild type. Based on this observation, we identified several strains that exhibit atypical transition patterns that cannot be fully explained by their occurrence patterns of postural states. Surprisingly, we found that two simple factors-overall acceleration of postural movement and elimination of inactivity periods-explained the behavioral characteristics of strains with very atypical transition patterns; therefore, computational analysis of animal behavior must be accompanied by evaluation of the effects of these simple factors. Finally, we found that the npr-1 and npr-3 mutants have similar behavioral patterns that were not predictable by sequence homology, proving that our data-driven approach can reveal the functions of genes that have not yet been characterized.
We propose that elimination of inactivity periods and overall acceleration of postural change speed can explain behavioral phenotypes of strains with very atypical postural transition patterns. Our methods and results constitute guidelines for effectively finding strains that show "truly" interesting behaviors and systematically uncovering novel gene functions by bioimage-informatic approaches.
随着基因组测序和编辑技术的迅速发展，对动物行为进行系统和定量分析有望成为推动数据驱动型行为遗传学发展的另一关键因素。秀丽隐杆线虫是该领域的一种模式生物。有几种视频跟踪系统可用于自动记录线虫的行为数据，但用于分析这些数据的计算方法仍在开发中。
在本研究中，我们将基于高斯混合模型的分箱方法应用于322个秀丽隐杆线虫品系的时间序列姿势数据。我们发现姿势状态的出现模式以及这些状态之间的转换模式具有预期的关系，在识别与野生型行为不同的非典型行为品系时必须考虑这种关系。基于这一观察结果，我们鉴定出了几种表现出非典型转换模式的品系，这些模式无法完全由其姿势状态的出现模式来解释。令人惊讶的是，我们发现两个简单因素——姿势运动的整体加速和静止期的消除——解释了具有非常非典型转换模式的品系的行为特征；因此，对动物行为的计算分析必须同时评估这些简单因素的影响。最后，我们发现npr-1和npr-3突变体具有相似的行为模式，而这种模式无法通过序列同源性预测，这证明我们的数据驱动方法可以揭示尚未表征的基因的功能。
我们提出，消除静止期和姿势变化速度的整体加速可以解释具有非常非典型姿势转换模式品系的行为表型。我们的方法和结果构成了有效寻找表现出“真正”有趣行为的品系，并通过生物图像信息学方法系统地揭示新基因功能的指导原则。
