Yamazaki, Shuhei et al. (2013) International Worm Meeting "Genetic analysis of dopamine signaling for repulsive odor learning."

Kimura, Kotaro, Yamazaki, Shuhei

[International Worm Meeting, 2013]

Through learning, an animal can optimize its chances for survival and reproduction by modifying its behavior based on prior experiences. Multiple types of learning, and the molecular mechanisms that mediate learning, have been studied in both vertebrates and invertebrates. Nevertheless, How sensory behavior is modulated by learning and how specific molecules are involved in this process are not well understood. Previously, we have shown that dopamine signaling is required for non-associative learning of odor avoidance behavior of worms. Worms exhibit an enhanced avoidance behavior to 2-nonanone after preexposure to the odor, and this enhancement is regulated in RIC neurons by dopamine signaling via the D2-like dopamine receptor DOP-3 (Kimura et al., 2010, J. Neurosci.). Currently, we are working towards identifying new genes that can genetically interact with the dopamine-signaling pathway to regulate and/or enhance 2-nonanone avoidance. We have found some mutant strains that exhibit behavioral defects that are similar to those exhibited by dopamine mutants. We plan to identify these mutations with whole-genome sequencing, and reveal the physiological role of their gene products by using our integrated microscope system (Tanimoto et al., this meeting).

Yamazaki, Shuhei et al. (2015) International Worm Meeting "Identifying neural signaling that cooperates with dopamine signaling in repulsive odor learning."

Kimura, Kotaro, Yamazoe, Akiko, Yamazaki, Shuhei

[International Worm Meeting, 2015]

Dopamine in association with other neural signals plays crucial roles in various brain functions such as locomotory regulation, reward, emotion, learning and memory. However, the mechanism by which multiple neural signals cooperatively regulate brain functions is not well understood because of neural circuit complexity. To address this issue, we are studying repulsive odor learning regulated by dopamine signaling in C. elegans (Kimura et al., 2010, J. Neurosci.). Upon preexposure to 2-nonanone, the animals exhibit enhanced avoidance behavior to this odorant as a type of non-associative learning. This enhancement is regulated by dopamine signaling via the D2-like dopamine receptor, DOP-3, in a pair of RIC interneurons. Currently, we are working towards identifying genes that genetically interact with dopamine signaling for repulsive odor learning. We have identified several mutant strains that exhibit behavioral defects similar to those seen in dopamine mutants. We first plan to identify the site-of-action of identified genes by cell-specific rescue experiments. The physiological role of the gene will then be analyzed by our integrated microscope system to quantify the relationship between odor stimuli, neural responses, and behavior (Tanimoto et al., this meeting). These analyses will help us understand the interaction between the newly identified neural signaling and dopamine signaling in modulation of learning.

Kimura, Kotaro et al. (2017) International Worm Meeting "Pan-behavioromics: comprehensive analysis of worms' behavior using machine learning methods and its application to animals in general."

Yamazaki, Shuhei, Takeuchi, Ichiro, Kimura, Kotaro, Maekawa, Takuya

[International Worm Meeting, 2017]

A brain activity can be measured as a large number of neural activities using simultaneous optical monitoring, which is described as a time series vector data of many neural activities. Behavior, the final output of neural activities, however, is still poorly described, often just with one or a few parameters chosen subjectively. This huge asymmetry in data of neural activity and behavior is caused by the difficulty in analysis of behavior: Which aspects of a behavior such as speed, direction, their duration and changing rate, are related to animal's brain activities like sensory perception, memory, and decision-making? Behavioral analyses using machine learning have been reported to classify animal's behavioral states or behavioral patterns (e.g., Brown et al., PNAS 2013), although they have not yet provided clues to understand the causal relationship between stimuli and behavioral response. To explore new methods for a comprehensive analysis of animals' behavioral response to environmental stimuli in collaboration with data science, we applied 3 classic and cutting-edge machine learning methods-decision tree, deep neural network (DNN), and pattern mining-on worms' repulsive odor learning (Kimura et al., J Neurosci 2010). (1) In decision tree analysis, researchers specify a number of features, and the algorithm chooses the ones that effectively classify two groups. We found that naive worms stop straight migration upon sensing a slight increase in the repulsive odor concentration (dC/dt > 0), although learned worms do not respond to it and continue the migration, suggesting that they ignore the "yellow signal". In addition, multiple mutant strains were classified based on the characteristic patterns of component change. (See Yamazaki et al. this meeting.) (2) Using DNN for time series data analysis, we found that, although the speed of naive worms is relatively constant, that of learned worms changed more vigorously in windows of tens of seconds even though the average speeds were the same. The result may suggest that the learned worms avoid the odor using "accelerator and brake" more effectively. (See Maekawa et al. this meeting.) (3) In discriminative pattern mining, we represented odor concentration change and behavioral responses per second as 30 combinations, and found prominently discriminative behavior patterns between wild-type and mutant worms from an extremely large number of possible patterns by using a new sparse estimation technique. In summary, each machine learning method efficiently provided novel and critical knowledge that cannot be obtained from traditional analyses. Such objective comprehensive study of behavioral response, or "behavioromics," allows us to concentrate on key features of behavior. Currently, we intend to apply the above methods for the behavioral analysis of other animals, such as long-travelling sea birds, disease model mice, and humans.

Ikejiri, Yosuke et al. (2021) International Worm Meeting "Learning-dependent gain control by asymmetric modulation of the first- and second-order time-differential of stimulus in sensory neurons"

Ikejiri, Yosuke, Kimura, Kotaro, Yamazaki, Shuhei, Tanimoto, Yuki, Iwatani, Yasushi

[International Worm Meeting, 2021]

Animals respond to environmental stimuli whose intensity varies by approximately 1010-fold, although neural responses can only change by 102-fold, which requires proper adjustment of the relationship between the environmental stimuli and the neural response. One example of this adjustment is neural gain control, defined as the change in the slope of a neural response to a stimulus, instead of a general reduction (adaptation) or enhancement (sensitization) of the response. However, these mechanisms are poorly elucidated. Here, we report that the neural gain control in the ASH nociceptive neuron occurs by asymmetric modulation of the first- and second-order time-differentials of sensory stimulus. Previously, we showed that the worm's avoidance behavior to the repulsive odor 2-nonanone is enhanced by pre-exposure to the odor as a type of non-associative learning (Kimura et al., J Neurosci 2010). We now found that the ASH responses, which are activated by increasing the 2-nonanone concentrations (Tanimoto et al., eLife 2017), are modulated by the odor learning. Quantitative odor stimuli analysis revealed that the naive ASH neurons respond similarly to small and large linear increases in odor concentration, whereas the pre-exposed ASH neurons only respond to large increases. Analysis of the stimulus-response relationships suggested that this learning-dependent change is a neural gain control of response. Interestingly, mathematical analysis revealed that the ASH response is approximated by the sum of the first- and second-order time-differentials of odor concentration, and the second-order time-differential is greatly suppressed by learning. We found that the terms of the first- and second-order time differential are expressed by the variable coefficients. To test the validity of this model, we compared it with the first-order time-differential only model and second-order time-differential only model using the Bayesian information criterion (BIC). As predicted, in naive ASH neurons, the model of the sum of the first- and second-order time-differentials of odor concentration was the best fit, and the first-order time-differential only model was the best fit in the pre-exposed condition. These results may suggest that the ASH response is mediated by the long (corresponding to the first-order term) and transiently (the second-order term) activated voltage-gated calcium channels and that the contribution of these channels are modulated by the odor stimulus.

Tanimoto, Yuki et al. (2013) International Worm Meeting "A virtual reality running machine for worms-a highly integrated microscope system for olfactory behavior."

Nakai, Junichi, Fei, Xianfeng, Kawazoe, Yuya, Miyanishi, Yosuke, Fujita, Kosuke, Hashimoto, Koichi, Tanimoto, Yuki, Yamazaki, Shuhei, Kimura, Kotaro, Gengyo-Ando, Keiko, Busch, Karl Emanuel

[International Worm Meeting, 2013]

A major function of the nervous system is to transform sensory information into an appropriate behavioral response. The neural mechanisms that mediate sensorimotor transformation are commonly studied by quantifying the behavioral and neural responses to a controlled sensory stimulus. Presenting a controlled chemical stimulus to freely behaving animals under a high-power microscope, however, is challenging. Here, we present a novel integrated microscope system that stimulates a freely moving worm with a virtual odor gradient, tracks its behavioral response, and optically monitors or manipulates neural activity in the worm during this olfactory behavior. In this system, an unrestricted worm is maintained in the center of a bright field by an auto-tracking motorized stage that is regulated by a pattern-matching algorithm at 200 Hz [1]. In addition, the worm is stimulated continuously by an odor flowing from a tube, the concentration of which can be temporally controlled. The odor concentration used in this system is based on the concentration used in the traditional plate assay paradigm (Yamazoe et al., CeNeuro 2012), and can be monitored with a semiconductor sensor connected to the end of the tube when necessary. Using this system, we investigated the neural basis of behavioral responses to a repulsive odor 2-nonanone in worms. We monitored and modulated sensory neuron activity in behaving worms by using calcium imaging and optogenetics, respectively, and found that the avoidance behavior to 2-nonanone is achieved by two counteracting sensory pathways that respond to changes in temporal odor concentration as small as ~10 nM/s (Yamazoe et al., this meeting). Our integrated microscope system, therefore, will allow us to achieve a new level of understanding for sensorimotor transformation during chemosensory behaviors. [1] Maru et al., IEEE/SICE Int. Symp. Sys. Integr. Proc., 2011.

Tanimoto, Yuki et al. (2015) International Worm Meeting "Neuronal mechanisms for C. elegans olfactory navigation revealed by a highly integrated microscope system."

Miyanishi, Yosuke, Yamazoe, Akiko, Fei, Xianfeng, Iwasaki, Yuishi, Hashimoto, Koichi, Kawazoe, Yuya, Kimura, Kotaro, Fujita, Kosuke, Nakai, Junichi, Gengyo-Ando, Keiko, Yamazaki, Shuhei, Tanimoto, Yuki

[International Worm Meeting, 2015]

The nervous system of animals transforms dynamically changing sensory information from the environment into appropriate behavioral responses. In particular, olfactory information plays critical roles in adaptive behaviors in the form of long- and short-range chemical cues that encode spatiotemporal information and chemical identity. To elucidate the neuronal mechanisms underlying olfactory behavior, it is desirable to quantify behaviors and neural circuit activities under realistic olfactory stimulus. However, reproducing realistic spatiotemporal patterns in odor concentrations is challenging due to diffusion, turbulent flow, and measurability of odor signals. We have developed an integrated microscope system that produces a virtual odor environment to quantify behaviors and neural circuit activities of the nematode C. elegans. In this system, C. elegans is maintained in the view field of a calcium imaging microscope by an auto-tracking stage using a pattern-matching algorithm. Simultaneously, odor stimulus is controlled with sub-second and sub-muM precision to reproduce realistic temporal patterns. Using this system, we have found that two types of sensory neurons play significant roles to choose a proper migratory direction for navigation in a gradient of the repulsive odor 2-nonanone. Calcium imaging and optogenetic analysis revealed that temporal increments of repulsive odor trigger turns that randomize the migratory direction, while temporal decrements of the odor suppress turning for migration down the gradient. Further mathematical analysis indicated that these sensory neurons are not only antagonizing, but also responding to odor concentration changes at different time scales for the efficient migration. Using this method will lead to comprehensive understanding of cellular mechanisms of decision making in a simple neural circuit.

Yamazoe, Akiko et al. (2013) International Worm Meeting "A neuronal mechanism for navigation along a repulsive odor gradient."

Iwasaki, Yuishi, Hashimoto, Koichi, Kawazoe, Yuya, Fujita, Kosuke, Busch, Karl Emanuel, Iino, Yuichi, Gengyo-Ando, Keiko, Nakai, Junichi, Fei, Xianfeng, Yamazaki, Shuhei, Tanimoto, Yuki, Miyanishi, Yosuke, Yamazoe, Akiko, Kimura, Kotaro

[International Worm Meeting, 2013]

For survival and reproduction, animals navigate toward or away from certain stimuli, which requires the coordinated transformation of sensory information into motor responses. In worms, the pirouette and the weathervane strategies are considered the primary navigation strategies for responding chemosensory stimuli. We found, however, that worms use a novel navigation strategy in odor avoidance behavior: In a gradient of the repulsive odor 2-nonanone, worms efficiently avoid the odor, and ~80% of initiation of long, straight migrations ("runs") were away from the odor source, which cannot be simply explained by the two known major strategies. Direct measurement of local odor concentration suggested that pirouettes are efficiently switched to runs when worms sense negative dC/dt of 2-nonanone. To test whether runs are indeed caused by negative dC/dt, we established an integrated microscope system that tracks a freely moving worm during stimulation with a virtual odor gradient and simultaneously allows for calcium imaging and optogenetic manipulations of neuronal activity (Tanimoto et al., this meeting). Using this system, we found that a realistic temporal decrement in 2-nonanone concentration (~ 10 nM/sec) caused straight migration by suppressing turns. We also found that a pair of AWB sensory neurons were continuously activated during the odor decrement and that optogenetic activation or inactivation of AWB neurons suppressed or increased turning frequency, respectively. In addition, we found that ASH nociceptive neurons increased turning frequency during odor increment. Taken together, our data indicate that the counteracting turn-inducing and turn-suppressing sensory pathways can effectively transform temporal sensory information into spatial movement to select the right path leading away from potential hazards.