The kinematics of directional control in the fast start of zebrafish larvae.

Larval fish use the 'fast start' escape response to rapidly evade the strike of a predator with a three-dimensional (3D) maneuver. Although this behavior is essential for the survival of fishes, it is not clear how its motion is controlled by the motor system of a larval fish. As a basis for understanding this control, we measured the high-speed kinematics of the body of zebrafish (Danio rerio) larvae when executing the fast start in a variety of directions. We found that the angular excursion in the lateral direction is correlated with the yaw angle in the initial stage of bending (stage 1). In this way, larvae moved in a manner similar to that reported for adult fish. However, larvae also have the ability to control the elevation of a fast start. We found that escapes directed downwards or upwards were achieved by pitching the body throughout the stages of the fast start. Changes in the pitching angle in each stage were significantly correlated with the elevation angle of the trajectory. Therefore, as a larva performs rapid oscillations in yaw that contribute to undulatory motion, the elevation of an escape is generated by more gradual and sustained changes in pitch. These observations are consistent with a model of motor control where elevation is directed through the differential activation of the epaxial and hypaxial musculature. This 3D motion could serve to enhance evasiveness by varying elevation without slowing the escape from a predator.

Building Bridges from Genome to Physiology Using Machine Learning and Drosophila Experimental Evolution.

Drosophila experimental evolution, with its well-defined selection protocols, has long supplied useful genetic material for the analysis of functional physiology. While there is a long tradition of interpreting the effects of large-effect mutants physiologically, identifying and interpreting gene-to-phenotype relationships has been challenging in the genomic era, with many labs not resolving how physiological traits are affected by multiple genes throughout the genome. Drosophila experimental evolution has demonstrated that multiple phenotypes change because of the evolution of many loci across the genome, creating the scientific challenge of sifting out differentiated but noncausal loci for individual characters. The fused lasso additive model method allows us to infer some of the differentiated loci that have relatively greater causal effects on the differentiation of specific phenotypes. The experimental material that we use in the present study comes from 50 populations that have been selected for different life histories and levels of stress resistance. Differentiation of cardiac robustness, starvation resistance, desiccation resistance, lipid content, glycogen content, water content, and body masses was assayed among 40-50 of these experimentally evolved populations. Through the fused lasso additive model, we combined physiological analyses from eight parameters with whole-body pooled-seq genomic data to identify potentially causally linked genomic regions. We have identified approximately 2,176 significantly differentiated 50-kb genomic windows among our 50 populations, with 142 of those identified genomic regions that are highly likely to have a causal effect connecting specific genome sites to specific physiological characters.