Koutarou D. Kimura

Enhancement of odor avoidance regulated by dopamine signaling in Caenorhabditis elegans.

The enhancement of sensory responses after prior exposure to a stimulus is a fundamental mechanism of neural function in animals. Its molecular basis, however, has not been studied in as much depth as the reduction of sensory responses, such as adaptation or habituation. We report here that the avoidance behavior of the nematode Caenorhabditis elegans in response to repellent odors (2-nonanone or 1-octanol) is enhanced rather than reduced after preexposure to the odors. This enhancement effect of preexposure was maintained for at least 1 h after the conditioning. The enhancement of 2-nonanone avoidance was not dependent on the presence or absence of food during conditioning, which generally functions as a strong positive or negative unconditioned stimulus in the animals. These results suggest that the enhancement is acquired as a type of nonassociative learning. In addition, genetic and pharmacological analyses revealed that the enhancement of 2-nonanone avoidance requires dopamine signaling via D2-like dopamine receptor DOP-3, which functions in a pair of RIC interneurons to regulate the enhancement. Because dopamine signaling has been tightly linked with food-related information to modulate various behaviors of C. elegans, it may play different role in the regulation of the enhancement of 2-nonanone avoidance. Thus, our data suggest a new genetic and pharmacological paradigm for nonassociative enhancement of neural responses that is regulated by dopamine signaling.

The C. elegans DAF-2 Insulin-Like Receptor is Abundantly Expressed in the Nervous System and Regulated by Nutritional Status

A Caenorhabditis elegans insulin-like signaling pathway regulates development, metabolism, and longevity. We detected abundant DAF-2 insulin-like receptor protein mainly in the nervous system, consistent with the assignment of DAF-2 pathway regulation of longevity to the nervous system. DAF-2 abundance in the nervous system is dependent on food intake, showing environmental modulation of pathway signaling. DAF-2 abundance is not dependent on downstream PI-3 kinase to DAF-16 transcription factor signaling. The modulation of DAF-2 protein level by nutritional status may constitute an important component in the irreversible commitment to dauer arrest.

FLR‐2, the glycoprotein hormone alpha subunit, is involved in the neural control of intestinal functions in Caenorhabditis elegans

The intestine plays an essential role in organism‐wide regulatory networks in both vertebrates and invertebrates. In Caenorhabditis elegans, class 1 flr genes (flr‐1, flr‐3 and flr‐4) act in the intestine and control growth rates and defecation cycle periods, while class 2 flr genes (flr‐2, flr‐5, flr‐6 and flr‐7) are characterized by mutations that suppress the slow growth of class 1 flr mutants. This study revealed that flr‐2 gene controls antibacterial defense and intestinal color, confirming that flr‐2 regulates intestinal functions. flr‐2 encoded the only glycoprotein hormone alpha subunit in C. elegans and was expressed in certain neurons. Furthermore, FLR‐2 bound to another secretory protein GHI‐1, which belongs to a family of lipid‐ and lipopolysaccharide‐binding proteins. A ghi‐1 deletion mutation partially suppressed the short defecation cycle periods of class 1 flr mutants, and this effect was enhanced by flr‐2 mutations. Thus, FLR‐2 acts as a signaling molecule for the neural control of intestinal functions, which is achieved in a functional network involving class 1 and class 2 flr genes as well as ghi‐1. These results are informative to studies of glycoprotein hormone signaling in higher animals.

A simple optogenetic system for behavioral analysis of freely moving small animals.

We present a new and simple optogenetic system for the behavioral analysis of small animals. This system includes a strong LED ring array, a high-resolution CCD camera, and the improved channelrhodopsin ChRGR. We used the system for behavioral analysis with the nematode Caenorhabditis elegans as a model, and we found that it can stimulate ChRGR expressed in the body wall muscles of the animals to modulate the behavior. Our results indicate that this system may be suitable for optogenetic behavioral analysis of freely moving small animals under various conditions to understand the principles underlying brain functions.

Calcium dynamics regulating the timing of decision-making in C. elegans

Brains regulate behavioral responses with distinct timings. Here we investigate the cellular and molecular mechanisms underlying the timing of decision-making during olfactory navigation in Caenorhabditis elegans. We find that, based on subtle changes in odor concentrations, the animals appear to choose the appropriate migratory direction from multiple trials as a form of behavioral decision-making. Through optophysiological, mathematical and genetic analyses of neural activity under virtual odor gradients, we further find that odor concentration information is temporally integrated for a decision by a gradual increase in intracellular calcium concentration ([Ca2+]i), which occurs via L-type voltage-gated calcium channels in a pair of olfactory neurons. In contrast, for a reflex-like behavioral response, [Ca2+]i rapidly increases via multiple types of calcium channels in a pair of nociceptive neurons. Thus, the timing of neuronal responses is determined by cell type-dependent involvement of calcium channels, which may serve as a cellular basis for decision-making. DOI: http://dx.doi.org/10.7554/eLife.21629.001

In actio optophysiological analyses reveal functional diversification of dopaminergic neurons in the nematode C. elegans

Many neuronal groups such as dopamine-releasing (dopaminergic) neurons are functionally divergent, although the details of such divergence are not well understood. Dopamine in the nematode Caenorhabditis elegans modulates various neural functions and is released from four left-right pairs of neurons. The terminal identities of these dopaminergic neurons are regulated by the same genetic program, and previous studies have suggested that they are functionally redundant. In this study, however, we show functional divergence within the dopaminergic neurons of C. elegans. Because dopaminergic neurons of the animals were supposedly activated by mechanical stimulus upon entry into a lawn of their food bacteria, we developed a novel integrated microscope system that can auto-track a freely-moving (in actio) C. elegans to individually monitor and stimulate the neuronal activities of multiple neurons. We found that only head-dorsal pair of dopaminergic neurons (CEPD), but not head-ventral or posterior pairs, were preferentially activated upon food entry. In addition, the optogenetic activation of CEPD neurons alone exhibited effects similar to those observed upon food entry. Thus, our results demonstrated functional divergence in the genetically similar dopaminergic neurons, which may provide a new entry point toward understanding functional diversity of neurons beyond genetic terminal identification.

Ultradian rhythm in the intestine of Caenorhabditis elegans is controlled by the C-terminal region of the FLR-1 ion channel and the hydrophobic domain of the FLR-4 protein kinase

Defecation behavior in Caenorhabditis elegans is driven by an endogenous ultradian clock in the intestine. Its periods are positively regulated by FLR‐1, an ion channel of the epithelial sodium channel/degenerin superfamily, and FLR‐4, a protein kinase with a hydrophobic domain at the carboxyl terminus. FLR‐1 has many putative phosphorylation sites in the C‐terminal intracellular region. This structure implies that the periods may be regulated by the phosphorylation of FLR‐1 by FLR‐4, but it remains to be clarified. Here, we show that a truncated FLR‐1 lacking the C‐terminal intracellular region resulted in longer periods, suggesting that this region is involved in the negative regulation of defecation cycle periods. Contrary to our expectation, FLR‐4 was still necessary for the function of the truncated FLR‐1. Furthermore, FLR‐4 containing a kinase‐dead mutation or lacking the whole kinase domain was sufficient for normal defecation cycle periods. FLR‐4 was necessary for the stable expression of FLR‐1::GFP, and its hydrophobic domain was sufficient also for this function. FLR‐1 and FLR‐4 are often colocalized in the plasma membrane. These data showed an unexpected role of FLR‐4: its hydrophobic domain stabilizes the FLR‐1 ion channel, a key regulator of defecation cycle periods in the intestine.

Experience-dependent modulation of behavioral features in sensory navigation of nematodes and bats revealed by machine learning

Animal behavior is the final and integrated output of the brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient previous knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed to understand brain function. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach to efficiently estimate behavioral states and to extract important behavioral features only from low-content animal trajectory data. As proof of principle experiments, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales, from mm to 1000 km in space and from sub-seconds to days in time. We estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.

Finding Discriminative Animal Behaviors from Sequential Bio-Logging Trajectory Data

Recent advancement of bio-logging devices such as GPS sensor enables researchers in ecology to quantitatively measure animal trajectories. These animal trajectory data are often represented in the form of multi-dimensional time-series. In this paper, we develop a method for extracting interesting animal behaviors from these multi-dimensional time-series. To this end, we represent a multi-dimensional time-series as a discrete symbol sequence, and introduce some techniques developed in the context of sequential pattern mining, which has been actively studied in the literature of knowledge discovery and data mining. In animal behavior studies, it is often desired to conduct comparative studies for finding different animal behaviors in different groups, e.g, different behaviors between male and female animals etc. We use a sequential pattern mining method designed for finding so-called discriminative sequential patterns, i.e., sequential patterns that are useful for discriminating different group of animals. We apply the method to several animal trajectory datasets for demonstrating its effectiveness.

Neural Mechanisms of Animal Navigation.

Animals navigate to specific destinations for survival and reproduction. Notable examples include birds, fishes, and insects that are driven by their inherited motivation and acquired memory to migrate thousands of kilometers. The navigational abilities of these animals depend on their small and imprecise sensory organs and brains. Thus, understanding the mechanisms underlying animal navigation may lead to the development of novel tools and algorithms that can be used for more effective human-computer interactions in self-driving cars, autonomous robots and/or human navigation. How are such navigational abilities implemented in the animal brain? Neurons (i.e., nerve cells) that respond to external signals related to the animal’s direction and/or travel distance have been found in insects, and neurons that encode the animal’s place, direction, or speed have been identified in rats and mice. Although the research findings accumulated to date are not sufficient for a complete understanding of the neural mechanisms underlying navigation in the animal brain, they do provide key insights. In this review, we discuss the importance of neurobiological studies of navigation for engineering and computer science researchers and briefly summarize the current knowledge of the neural bases of navigation in model animals, including insects, rodents, and worms. In addition, we describe how modern engineering and computer technologies, such as virtual reality and machine learning, can help advance navigation research in animals.