Modulation of different behavioral components by neuropeptide and dopamine signalings in non-associative odor learning of Caenorhabditis elegans

Neuroscience Research 99 (2015) 22–33

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Modulation of different behavioral components by neuropeptide and dopamine signalings in non-associative odor learning of Caenorhabditis elegans Akiko Yamazoe-Umemoto a , Kosuke Fujita a,1 , Yuichi Iino b , Yuishi Iwasaki c , Koutarou D. Kimura a,∗ a

Department of Biological Sciences, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan Department of Biological Sciences, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan c Department of Intelligent System Engineering, Ibaraki University, Hitachi, Ibaraki 316-8511, Japan b

a r t i c l e

i n f o

Article history: Received 6 March 2015 Received in revised form 15 May 2015 Accepted 18 May 2015 Available online 8 June 2015 Keywords: Non-associative learning Neuromodulator Quantitative behavioral analysis The nematode Caenorhabditis elegans Odor gradient

a b s t r a c t An animal’s behavior is modulated by learning; however, the behavioral component modulated by learning and the mechanisms of this modulation have not been fully understood. We show here that two types of neural signalings are required for the modulation of different behavioral components in non-associative odor learning in the nematode Caenorhabditis elegans. We have previously found that C. elegans avoid the repulsive odor 2-nonanone, and preexposure to the odor for 1 h enhances the avoidance behavior as a type of non-associative learning. Systematic quantitative analyses of behavioral components revealed that the odor preexposure caused increases in average duration of straight migration (“runs”) only when the animals were migrating away from the odor source within a certain range of bearing, which likely corresponds to odor decrement. Further, genetic analyses revealed that the genes for neuropeptide or dopamine signalings are both required for the enhanced odor avoidance. Neuropeptide signaling genes were required for the preexposure-dependent increase in run duration. In contrast, dopamine signaling genes were required not for the increase in run duration but likely for maintenance of run direction. Our results suggests that multiple behavioral components are regulated by different neuromodulators even in non-associative learning in C. elegans. © 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

1. Introduction Animals modify their behavior through the experience of sensory signals as learning. Multiple neuromodulators, such as biogenic amines and neuropeptides, have been reported to be involved in learning. However, differences in the roles of these neuromodulators and the relationships between them have not been sufficiently elucidated – in the well-studied examples such as Drosophila and rodents, multiple neuromodulators are known to be involved in one process of learning, for example memory acquisition and/or retrieval, while a single neuromodulator is also known to be involved in multiple processes of learning (Borbély et al.,

∗ Corresponding author. Tel.: +81 6 6850 6706; fax: +81 6 6850 6706. E-mail addresses: [email protected], [email protected] (K.D. Kimura). 1 Present address: Department of Ophthalmology, Graduate School of Medicine, Tohoku University, Sendai 980-8574, Japan.

2013; Davis, 2005; Johansen et al., 2011; Keene and Waddell, 2007). One strategy to reveal the relationships as well as the individual roles of such neuromodulators in learning could be to decompose an animal’s behavioral responses quantitatively in a reductionist approach – identify behavioral components that are changed in a specific type of learning and uncover the neuromodulators that are required for the specific behavioral change. The nematode Caenorhabditis elegans is an ideal model for such quantitative behavioral component analysis because of the feasibility in monitoring various behavioral responses and learning-dependent behavioral changes on a plane agar surface and sophisticated genetic techniques to identify genes involved in behavioral regulation (Bargmann, 2006; De Bono and Maricq, 2005). Previous quantitative behavioral analyses revealed that the animal’s responses to an environmental stimulus generally consist of two behavioral states: When a change in the stimulus is undesirable, the animal terminates its straight migrations (“runs”) sooner and initiates series of reverses and turns (“pirouettes”) more frequently to change migratory direction randomly. As a

http://dx.doi.org/10.1016/j.neures.2015.05.009 0168-0102/© 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

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result, the animal navigates toward the desirable direction; this mechanism is called a biased random walk or a pirouette strategy (Lockery, 2011; Pierce-Shimomura et al., 1999). As for the learning-dependent behavioral modulation, the changes in turning probability or magnitude of course correction during a run have been reported in associative learning of feeding status (unconditioned stimulus) and temperature or salt (conditioned stimulus) as well as in experience-dependent changes in olfactory preference to pathogenic bacteria (Ha et al., 2010; Kunitomo et al., 2013; Luo et al., 2014a,b). In these learning paradigms, several neuromodulators, such as serotonin and insulin-like peptides, have been identified (Chen et al., 2013; Kodama et al., 2006; Kunitomo et al., 2013; Mohri et al., 2005; Tomioka et al., 2006; Zhang et al., 2005). However, the relationships between these neuromodulators and their effects on a specific behavioral component remain to be clarified. To identify new relationships between behavioral components and neuromodulators in chemosensory behavior, we studied repulsive odor learning in C. elegans. The animals avoid the repulsive odor 2-nonanone (Bargmann et al., 1993; Troemel et al., 1997), and we have previously shown that this odor-avoidance behavior is enhanced after 1 hr preexposure to the odor, that is, the animals migrate farther away from the odor sources within 12 min of behavioral assay (Kimura et al., 2010). This enhanced odor avoidance does not require the presence or absence of food as an unconditioned stimulus, indicating that the repulsive odor learning is a type of non-associative learning. Non-associative learning is a type of learning, in which an animal’s behavioral response is either increased or decreased by the experience(s) of a single type of stimulus, such as sensitization or habituation, respectively (Kandel et al., 2000). In C. elegans, habituation has been reported in various mechanosensory and chemosensory paradigms (Ardiel and Rankin, 2010 for review) while sensitization has been reported once in mechanosensation (Rankin et al., 1990). We have further revealed that the enhanced odor avoidance requires dopamine signaling (Kimura et al., 2010). Dopamine has been known to function as a “presence of food” signal for short periods to regulate various behaviors in C. elegans (Chase and Koelle, 2007). Dopamine has also been reported to be involved in associative learning of chemical cue and starvation (Hukema et al., 2008; Voglis and Tavernarakis, 2008) and habituation to mechanical stimuli, which is modulated by the food presence (Kindt et al., 2007; Sanyal et al., 2004). Although the enhanced odor avoidance requires dopamine signaling, it is likely regulated by a different mechanism from that shown in previous studies because the enhancement does not require the presence or absence of food during conditioning and because the above-mentioned dopamine signaling is not involved in the enhancement of sensory responses in non-associative form. In this study, we show that neuropeptide and dopamine signalings are required for the regulation of different behavioral components in the enhanced odor avoidance of C. elegans. We find that enhanced odor avoidance is correlated with increase in run duration only when the animals are migrating away from the odor source within a certain range of bearing, which likely corresponds to temporal decrement of odor concentration. Furthermore, genetic analysis revealed that neuropeptide signaling is required for preexposure-dependent increase in run duration. In contrast, dopamine signaling is not required for increase in run duration, but appears to be crucial for preventing odor preexposure-dependent deterioration of migratory direction. Thus, our results suggest that even in non-associative odor learning, multiple neuromodulators are required to regulate different behavioral components for the proper execution of learning-dependent behavioral modulation.

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2. Materials and methods 2.1. Strains The techniques used for culturing and handling C. elegans were performed as described previously (Brenner, 1974). The C. elegans wild-type Bristol strain (N2) and mutant strains MT1219 egl-3(n589), VC671 egl-3(ok979), KP2018 egl-21(n476) and LX703 dop-3(vs106) were obtained from the Caenorhabditis Genetics Center (University of Minnesota, USA) and cultivated in 6 cm nematode growth medium (NGM) agar plates with a lawn of Escherichia coli strain OP50. dop-3(tm1356) and cat-2(tm2261) mutants were obtained from National Bioresource Project (Japan) and backcrossed with N2 either two or five times to generate strains KDK1 and KDK11, respectively, before use. 2.2. Analysis of 2-nonanone avoidance The 2-nonanone avoidance assay was carried out as previously described (Kimura et al., 2010). In brief, the young adult hermaphrodite animals were transferred to the center of a 9-cm NGM agar plate directly (“naive”) or after a 1-h incubation on a 6-cm NGM plate without OP-50 in the presence of 0.6 ␮L of 15% 2-nonanone (diluted in EtOH) (“preexposed”) or of EtOH (“mocktreated”) from the lid: during preexposure, odorant was distributed on the surface of six NGM agar plugs (at the points of a pentagon plus the center) on the lid of a plate to minimize gradient of the odor, and the plate was sealed with Parafilm. For avoidance assay, we put 2 ␮L of 30% 2-nonanone in two spots on the surface of the plate, and images of the animals during the avoidance behavior were acquired by a USB camera at 1 Hz for 12 min with a spatial resolution of ∼40 ␮m as described previously (Kawazoe et al., 2013; Kimura et al., 2010). Representative results are shown in Fig. 1a. In general, 2–5 animals were placed and analyzed per plate, and the assays were repeated for 4 days or more. After the image acquisition, X–Y coordinates of the centroids of the animals in each image were measured off-line by Move-tr/2D software (Library Inc., Tokyo, Japan), and were further analyzed by Excel 2010 (Microsoft) or R (The R Project). Because the animals did not initiate avoidance during the first 2 min on average (Kimura et al., 2010), data between 2 and 12 min were used for the analysis. Twenty-five animals for each condition were used in general in this study, and 50 animals for each condition were used to calculate pirouette initiation rate (Fig. 3a, b, S1c and S1d). 2.3. Computer simulation of 2-nonanone distribution in a gaseous phase To estimate 2-nonanone concentration in the assay condition, we theoretically calculated the 2-nonanone distribution in a gaseous phase. The concentrations Ci = Ci (x, y, z, t) of evaporated 2-nonanone (i = 1) and a solvent ethanol (i = 2) are governed by the three-dimensional diffusion equations: ∂Ci /∂t = Di ∇ 2 Ci . The boundary condition for evaporation is −Di ∂Ci /∂z = (Ei /Mi )( i (t)i (t) − Ci /Ci sat ) at the gas–liquid surface of the odor spots. At 25 ◦ C, the diffusion coefficients in air are D1 = 0.0407 cm2 /s and D2 = 0.123 cm2 /s (Poling et al., 2000), the evaporation rates per unit time and unit area at the gas–liquid surface are E1 = 66.0 × 10−8 g/cm2 s and E2 = 1980 × 10−8 g/cm2 s (ASTM D3539-87, 2004; Nylén and Sunderland, 1965), and the saturation concentrations are C1 sat = 34.5 ␮M and C2 sat = 3.19 mM. Mi , i (t), and i (t) are the molecular weight, the mole fraction in the odorant solution, and the activity coefficient (Ramsbotham, 1980), respectively. The boundary condition for gas leakage from a slight opening h of the cover is −Di ∂Ci /∂r = kCi around the upper rim. The leakage coefficient is kh = 1 × 10−5 cm2 /s. The initial radius of

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the gradient) and ±180◦ when they migrated directly to the left (up the gradient). 2.4. Definition of runs and pirouettes A pirouette is a period of one or more turns and straight migrations whose duration is shorter than a threshold value (Pierce-Shimomura et al., 1999). An animal’s behavioral state in one second was classified as a turn if the absolute value of change in migratory vector (|d|) of the animal’s centroid from the previous second was more than 90◦ ; if the animal’s migratory velocity was <0.1 mm/s in the successive seconds to the |d| > 90◦ second, then these seconds were also included in the turn duration. The distribution of durations of straight migration was well fit by the sum of two exponentials, suggesting that the durations of straight migrations were regulated by two types of stochastic biological mechanisms, as in the case of salt-taxis (Pierce-Shimomura et al., 1999). The threshold value tcrit was determined as the duration at which the probability densities of the short and long migrations were equal. tcrit of naive, mock-treated, and preexposed animals was calculated as 11.9, 14.0, and 13.9 s, respectively, and the average value 13.3 s was used for all conditions; thus, straight migrations longer than tcrit were considered runs, and those shorter than tcrit were included in pirouettes. 2.5. Pirouette initiation rate and bearing of runs

Fig. 1. Quantitative analysis of 2-nonanone avoidance behavior. (a) (top) A schematic drawing of the assay plate. Tracks of three naive (left) or preexposed animals (right) during 12 min of the 2-nonanone avoidance assay were overlaid. Note that all animals migrated in the opposite direction to the odor source (i.e., to the right), and preexposed animals migrated farther away from the odor source. (bottom) A magnified view of an animal’s track and definition of bearing. Pirouettes and runs are indicated with black and gray, respectively. Bearing at each step of run (B: arrow heads), bearing of run initiation (small arrows) and run bearing (Brun : long thin arrow) were determined as indicated according to the putative odor gradient (thick arrow). Note that the bearing at each step and of run initiation are not proportional to the actual length. (b) Computer simulation of 2-nonanone gradient in the assay plate after 9 min. The concentration (C) of 2-nonanone was calculated according to evaporation rate and diffusion equation (see Section 2). Contour lines of concentrations are shown at the bottom and directions of the gradient are shown with arrows. Note that at most of the positions in the right half of the plate the directions of the gradient are quite similar.

the odor spots is 0.6 cm. The diffusion equations were numerically solved by the Crank-Nicolson implicit scheme (Press et al., 1992). We consider the results to be reasonable because the amount of 2nonanone at the source was apparently reduced by 20–30% after 12 min, which is consistent with the simulation (27%). Qualitatively, the same shape of 2-nonanone distribution was obtained in simulations with different parameter values. According to this simulation, in most of the area where the animals existed under our experimental conditions, 2-nonanone was distributed with an almost unidirectional gradient (Fig. 1b). On the basis of this result, we defined the bearing, the angular deviation between the animal’s migration and the putative gradient direction, as B = 0◦ when animals migrated directly to the right along the X axis in Fig. 1b (down

Pirouette initiation rate was determined as the relationship between either the migratory bearing of the velocity vector of an animal’s centroid (arrowheads in Fig. 1a) or the temporal changes in the odor concentration (dC/dt), which was calculated in the odor gradient simulation during one second of a run, and the probability of pirouette initiation after 2 s of the step, as described previously (Pierce-Shimomura et al., 1999). For the bearing of run initiation (short arrow in Fig. 1a) or run termination, we calculated migratory bearing either 0–2 s after the end of or 2–4 s before the initiation of a pirouette, respectively. We used 2 s to reduce noise fluctuations and discarded the data during 0–2 s before a pirouette because the animal’s velocity 2 s before a pirouette was on average lower than that during other periods of runs; when the velocity is lower, errors in migratory direction become larger and the data become less reliable. For Figs. 2d and e and 6, % of run initiations or terminations is calculated as (number of run initiation or termination events within bin)/(number of total run initiation or termination events) × 100, respectively. Run bearing, Brun , (long arrow in Fig. 1a) was determined as the bearing of an animal’s migratory vector throughout a run, which was formed by connecting the start and end points of a run. 2.6. Curving rate for weathervane mechanism Curving rate H was defined as the change of migratory direction per unit length of an animal’s migration as previously described (Iino and Yoshida, 2009). This quantity was calculated according to the equation: H = /d (in degrees per millimeter), where is the angle formed by the two regression lines at two points located ∼0.5 mm behind or ahead of the point of interest, and d is distance between the two points. The regression lines were defined as the line that passes through both points, ∼0.3 mm behind or ahead of the two points located ∼0.5 mm behind or ahead of the point of interest. 2.7. Computer simulation of 2-nonanone avoidance behavior A computer simulation of 2-nonanone avoidance behavior was performed according to that of salt taxis (Iino and Yoshida,

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Fig. 2. Preexposure to 2-nonanone affects run duration only when run bearing is better than a threshold in wild-type animals. (a) Average avoidance distance, the average distance of the animals’ positions from the vertical center line of the assay plate in Fig. 1a after 12 min from the beginning of the assay, was enhanced after preexposure to the odor. Twenty-five wild-type animals for each condition were used unless otherwise indicated throughout figures. Green, light red, and dark red bars indicate results of naive, mock-treated, and preexposed animals, respectively; each bar represents the mean ± SEM throughout figures. (b) Average run duration after preexposure was also significantly increased. (c) Average run velocities were not statistically different after preexposure. (d and e) Distributions of run initiations (d) and terminations (e) in each 20◦ bin of absolute values of bearing (|B|) were not significantly changed after preexposure. (f) Course correction during run. Lines represent the relationship between bearing and curving rate averaged for every 30◦ bin. Histogram (lower part) shows the number of data points in each bin. Only the 60–90◦ bin showed statistical differences between naive versus preexposed and between mock versus preexposed animals. (g) Average pirouette duration was decreased after preexposure. (h) Average run durations of preexposed animals were significantly longer only when absolute value of run bearing (|Brun |) was smaller than 40◦ . In all panels, statistical differences are shown when p value from multiple comparisons test was below 0.05, and asterisks indicate p values from post hoc pairwise comparisons. In the line graphs, asterisks are shown only when p values of naive versus preexposed and mock versus preexposed from post hoc analyses were both below 0.05. These two comparisons reflect the critical differences that are related to the preexposure-dependent behavioral change. The asterisks indicate the higher value between the two p values due to the limited space. All the statistical details are shown in Table S1. *p < 0.05, **p < 0.01, and ***p < 0.001 throughout figures.

2009) with the following modifications. The simulator renewed the position and migratory bearing of the model animals every 1.0 s. The following parameters were based on the migratory statistics of real naive, mock-treated or preexposed wild-type animals and contained no free parameters. In each condition of model animals, we changed one or two specific parameter(s) from that of naive animals to that of either mock-treated or preexposed animals, while other parameters remained the same. Velocity was set to a constant value of 0.13 mm/s in each conditions, which was the average velocity in each condition of real animals. Curving rate was selected from a Gaussian distribution of 0.51 ± 3.47◦ (mean ± SD), −0.09 ± 3.24◦ or 0.10 ± 2.88◦ , which is the approximated distribution of |d| of migratory vector of real naive, mock or preexposed animals during 12 s, respectively. Pirouette was triggered with a rate that dependent on the

value of bearing at the step according to the sigmoidal function 0.000305/(0.00293 + e−0.0677×|bearing| ) + 0.0398 for model naive animals, 0.0000392/(0.000385 + e−0.0928×|bearing| ) + 0.0350 for model mock animals or 0.00000586/(0.0000499 + e−0.100×|bearing| ) + 0.0180 for model preexposed animals based on the fitted line of real data in Fig. 3a. For the bearing of run initiation, we first sampled pairs of bearings of run termination and run initiation before and after a pirouette, respectively, from 244, 234, or 207 pirouettes in naive, mock-treated, or preexposed wild-type animals, respectively. We then sorted the paired data into 30◦ bins according to the bearing of run termination. To determine the bearing of run initiation in the simulation, a data pair was randomly sampled from the bin corresponding to the bearing of run termination. Pirouette duration was set to a constant value of 34, 33, or 26 s, which was the average duration of real naive, mock, or preexposed animals,

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Fig. 3. Preexposure to 2-nonanone affects pirouette initiation rate only when dC/dt < 0 in wild-type animals. (a) The relationships between bearing and pirouette initiation rates were also significantly different when |B| < 40◦ and 60◦ ≤ |B| < 80◦ after preexposure. The boxed right panel shows the magnified view of the bearings between 0◦ and 60◦ . (b and c) The relationships between dC/dt and pirouette initiation rates (b) and average run duration (c) were significantly different mostly when dC/dt < 0 in this odor simulation. The boxed right panel in b shows the magnified view of dC/dt < 0.

respectively. During a pirouette, the model animals did not migrate at all for simplicity. The simulation was repeated 100 times for each condition of model animals. 2.8. Statistical analysis For comparisons of behavioral parameters between naive, mock-treated, and preexposed animals, the Kruskal–Wallis multiple comparison test followed by the post hoc Dunn’s test were used for avoidance distances (Figs. 2a, 4b and 5a), run durations (Figs. 2b, 5b, S1b and S2b), run velocity (Fig. 2c), pirouette durations (Fig. 2g), curving rates (Fig. S1f), run durations in each bearing or dC/dt bin (Figs. 2h, 3c, 5c and d, S1e and S2a), and

curving rates in each bearing bin (Fig. 2f); the chi-square test was used for pirouette initiation rates (Fig. 3a and b and S1d), and the Mardia–Watson–Wheeler test was used for bearing at run initiation (Figs. 2d and 6b) and bearing at run termination (Figs. 2e and 6a). All tests were performed using either Prism ver. 5.0 for Mac OSX (GraphPad Software, San Diego, CA; for Kruskal–Wallis test), Oriana (Kovach Computing Services, Anglesey, UK; for Mardia–Watson–Wheeler test) or Excel 2010 (Microsoft; for chi-square test). For all the tests related to bearing, actual bearing values and not absolute values were used, although absolute values were used in the graph presentations except for Fig. 2f. All the detailed results of statistical tests are listed in Table S1.

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Fig. 4. Change in pirouette initiation rate was sufficient to cause the significant enhancement in model animals. (a) Tracks of real naive animals and naive model animals. Similar to Fig. 1a, the odor sources were to the left and the animals migrated to the right. (b) The effects of changes in each parameter on average avoidance distance of model animals. Changes in pirouette initiation rate (P. i. rate) caused significant increment in avoidance distance in preexposed model animals compared to naive and mock-treated model animals. In the models with pirouette duration change, no statistical difference was found between naive and preexposed model animals. Enhanced distance (%) is calculated as: {(avoidance distance of preexposed model animals/avoidance distance of naive model animals)-1} × 100.

3. Results 3.1. Run duration is regulated in a bearing-dependent manner in enhanced odor avoidance In the previous study (Kimura et al., 2010), we found that preexposure to 2-nonanone causes an increase in avoidance distance as well as in run duration: control (i.e., naive and mock-treated) and preexposed animals migrated on average ∼20 and ∼30 mm away from the center of the plate, respectively, and average run durations of control and preexposed animals were ∼30 and ∼45 s, respectively (Fig. 2a and b; see Table S1 for statistical details). However, it was not clear whether modulations in other behavioral components could also contribute to the increased avoidance distance for the enhanced odor avoidance. To address this issue, we analyzed multiple behavioral components of the odor avoidance of control and preexposed animals. We regarded an animal in 2-nonanone avoidance as a point in 2D space according to the well-studied salttaxis (Pierce-Shimomura et al., 1999), and systematically analyzed the following behavioral parameters: run velocity, the migratory directions at the initiation and termination of runs, the course correction during runs, the durations of runs and pirouettes, and correlations between these parameters. The velocity and migratory

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direction during pirouettes were not analyzed because the animals did not move much during this period. Run velocities were found not to differ significantly between control and preexposed animals (Fig. 2c) and were therefore not analyzed further. We then investigated the migratory direction during runs. The direction of an animal’s migration was defined in terms of the bearing B with respect to the odor gradient, where B = 0◦ indicates migration directly down the gradient and B = ±180◦ means migration directly up the gradient (see Fig. 1 and Section 2). We analyzed the bearings of run initiation and run termination. Both in run initiation and run termination bearings, the number of runs with smaller absolute value of bearing |B| (i.e., migrating relatively down the gradient) was larger, while the number of runs with larger |B| (i.e., migrating relatively up the gradient) was smaller. No significant differences between control and preexposed animals were found in the distributions of run initiation and termination (Fig. 2d and e). We next investigated the directional change during runs. In salttaxis, animals gradually correct their migratory course during a run by curving toward either higher or lower concentrations of salt in a “weathervane strategy” (Iino and Yoshida, 2009; Kunitomo et al., 2013). In the relationship between bearing and curving rate during runs, we found that the animals showed a slight tendency for relative approach toward, rather than avoidance of, the repulsive odor source. There were no substantial differences in this parameter; preexposed animals differed statistically from both naive and mock-treated animals only in the 60–90◦ bin (Fig. 2f), which should not contribute to the overall preexposure-dependent enhanced odor avoidance. In addition to run duration, statistical differences were also found in pirouette duration (Fig. 2g): the average pirouette duration was shorter in preexposed animals than in control animals. This result suggests that the enhanced odor avoidance was not caused by changes in speed or migratory direction but by changes in the balance between run duration and pirouette duration. To understand how run duration is regulated, we next analyzed correlations between run duration and other parameters. We found a strong correlation between run duration and run bearing, Brun , which was defined as the angular deviation between the simulated odor gradient and the animal’s migratory vector of a run (see long thin arrow in Fig. 1a). In control and preexposed animals, the average run duration was longer when the absolute value of run bearings |Brun | was smaller and shorter when |Brun | was larger (Fig. 2h and S1a). This correlation between longer duration and preferable direction is consistent with the idea that 2-nonanone avoidance is mainly regulated by a biased random walk mechanism. Unexpectedly, however, the average run duration of preexposed animals was significantly longer than that of control animals only when the animals’ run bearings were smaller than a threshold of ∼40◦ (Fig. 2h, hereafter called Bt ). When absolute values of run bearing |Brun | < Bt , the average run duration of preexposed animals was significantly longer than that of control animals. In contrast, when |Brun | ≥ Bt , no significant difference in average run durations was observed between control and preexposed animals. It should be noted that, because there was little odor gradient during the preexposure (Kimura et al., 2010), the bearing-dependent behavioral modulation is likely not caused by association between odor concentration change and migratory bearing during preexposure but by innate properties of the animal’s neural circuit as a hard-wired mechanism. To investigate whether the differences in run duration depend on the 2-nonanone stimulus, we also studied run durations of animals in the absence of 2-nonanone. In that condition, run durations of control animals were ∼1.5 times longer than in the presence of the odor gradient, and preexposed animals exhibited much longer run durations than control animals (Fig. S1b). This result may suggest that run duration is determined by a balance between the

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Fig. 5. Neuropeptide and dopamine signalings are both required for the enhanced odor avoidance but in different behavioral components. (a) Average avoidance distance of wild-type and mutant animals. The significant increase in avoidance distance by preexposure was abolished by all the mutations in neuropeptide and dopamine signalings. (b) Average run durations of wild-type and mutant animals. Increase in run duration was completely suppressed in the neuropeptide mutants. In contrast, mutations in dopamine signaling did not suppress run duration increment in preexposed animals. In dop-3(tm1356), the p value of the multiple comparison test was 0.058 (indicated by §). (c and d) The bearing-dependent increase in run duration by preexposure was completely suppressed in neuropeptide mutants (c) but not in dopamine mutants (d).

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odor gradient-dependent pirouette initiation activity and the odor gradient-independent run continuation activity. However, in the presence of an odor gradient, run duration in preexposed animals was only increased when |B| < Bt but not when |B| ≥ Bt , suggesting that the odor gradient-dependent pirouette initiation activity has a more prominent role than the odor gradient-independent run continuation activity. 3.2. Preexposure decreases pirouette initiation rate dependent on temporal change in 2-nonanone concentration How does the bearing-dependent increase in run duration occur in enhanced odor avoidance? The animals likely do not regulate run duration itself, but rather regulate the probability of pirouette initiation during a run in each moment (Lockery, 2011). Therefore, we analyzed pirouette initiation rate as the relationship between bearing at a step (i.e., 1 s) during a run and the probability of pirouette initiation after the step (Pierce-Shimomura et al., 1999). We found that, like run duration, pirouette initiation rate was strongly correlated with the bearing to the odor gradient (Fig. 3a). In control animals, when the absolute value of the instantaneous bearing |B| was smaller, the pirouette initiation rate was low and relatively constant. In contrast, when |B| was larger, the pirouette initiation rate increased. Moreover, in preexposed animals, the pirouette initiation rate was significantly decreased compared to control animals when |B| < Bt as well as in 60◦ ≤ |B| < 80◦ (Fig. 3a). It should be noted that a small decrease in pirouette initiation rate should have a greater influence on run duration because it acts as an inverse function (Fig. S1c). Then, how do the animals sense migratory bearing? We assumed that the bearing has a strong correlation with temporal changes in odor concentration that each animal senses during the avoidance behavior. Therefore we calculated temporal change in 2-nonanone concentration (dC/dt) in the gas phase based on the diffusion equation and reported evaporation rate of 2-nonanone (ASTM D3539-87, 2004; Nylén and Sunderland, 1965). In the odor simulations, the relationship between pirouette initiation rate and dC/dt showed significant differences between control and preexposed animals mostly when dC/dt was negative (Fig. 3b). The clearer relationships between pirouette initiation rate and dC/dt than the bearing imply that the animals actually respond to dC/dt rather than to the bearing itself; because the odor evaporates from the source continuously during the assay, the relationships between bearing and dC/dt could change to some extent. Similarly, run durations were calculated to be longer in preexposed animals than in control animals when average dC/dt of a run was negative (Fig. 3c). Due to the smaller sample size in dC/dt < −10 nM/s, there was insufficient data to allow reliable statistical comparisons. These results suggest that run duration is dependent on dC/dt, and preexposed animals exhibited longer run duration only when the animals sensed dC/dt < 0 because the decreased pirouette initiation rate caused longer runs toward the proper direction. It should be noted, however, that the threshold value could have changed slightly dependent on the parameter set used for the odor gradient calculation, while overall correlations of run duration and pirouette initiation rate against dC/dt were similar (Fig. S1d and e); This result may suggest that it is important to measure actual odor concentration in the gradient to gain more quantitative insight to the animal’s behavioral response to the odor signal. So far our data suggest that the enhanced odor avoidance is mainly caused by an increase in run duration, which is due to a decrease in pirouette initiation rate, but only when the animals are migrating away from the odor source and sensing odor decrement. Changes in other behavioral components such as velocity or course correction during runs do not seem to be central to the enhancement. To confirm this, we conducted computer modeling of

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2-nonanone avoidance. We first modeled naive animals: we implemented the parameters that we had analyzed in the real animals, such as run velocity (Fig. 2c), bearing of run initiation (Fig. 2d), pirouette duration (Fig. 2g), pirouette initiation rate (Fig. 3a), and curving rate (Fig. S1f); run duration and bearing of run termination were subsidiarily determined by run termination, which depends on the correlation between bearing that the model animal experienced and the pirouette initiation rate. The model animals essentially reproduced the avoidance behavior of real naive animals (Fig. 4a). We then changed the pirouette initiation rate from that of naive animals to that of mock-treated or preexposed animals, while the other parameters remained the same: The model preexposed animals showed a significant increase in total avoidance distance and in run duration (Fig. 4b and S2). The changes in pirouette duration partially affected the avoidance distance, and the combination of pirouette initiation rate and pirouette duration had an additive or slightly synergistic effect. However, the magnitude of the increase in avoidance distance caused by pirouette initiation rate change was larger than that caused by pirouette duration change, indicating that the major behavioral component modulated in odor learning is run duration, which is regulated by pirouette initiation rate. Changes in the other parameters did not cause a significant increase in avoidance distance (Fig. 4b). 3.3. Neuropeptide and dopamine signalings are required for modulations of different behavioral components in enhanced odor avoidance To identify the genes involved in preexposure-dependent behavioral modulation in the enhanced odor avoidance, we carried out behavioral component analysis in mutant animals that are defective in neurotransmitter signaling. We found that mutations in two genes required for neuropeptide maturation – 2 alleles of egl-3 proprotein convertase and 1 allele of egl-21 carboxypeptidase (Jacob and Kaplan, 2003; Kass et al., 2001) – abolished the preexposure-dependent increase in avoidance distance as well as in run duration (Fig. 5a–c). It should be noted that the reduction-of-function allele of egl-3(n589) exhibited wild-typelike avoidance distance and bearing-dependent run duration in naive and mock-treated conditions, suggesting that these mutants are able to sense and respond to the odor normally. egl-3(ok979) exhibited more severe defects probably because it is a lossof-function mutation, which has been reported to cause more severe loss of detectable neuropeptides compared to reduction-offunction mutations (Husson et al., 2006). Similarly, egl-21 mutants are known to show similar but more severely impaired phenotypes than egl-3 animals (Jacob and Kaplan, 2003). Nonetheless, the fact that all these neuropeptide signaling mutations abolished the increase in run duration, the major behavioral modulation in the enhanced odor avoidance, strongly suggests that neuropeptides are required for the acquisition and/or retrieval of odor memory. Unexpectedly, we found that dopamine signaling plays a different role from neuropeptide signaling, although both neuromodulators are involved in the enhanced odor avoidance. Dopamine signaling is known to affect multiple aspects of C. elegans behavior via several types of dopamine receptors (Chase and Koelle, 2007), and we previously showed by genetic and pharmacological analyses that dopamine signaling via D2-like receptor DOP-3 in a pair of RIC interneurons is essential for the enhanced avoidance distance (Kimura et al., 2010). In this study, we further quantitatively analyzed two genes in the dopamine signaling pathway, the tyrosine hydroxylase gene cat-2, a gene required for dopamine biosynthesis, and the dop-3 receptor gene. We found that run duration was significantly increased in all of these mutants in a bearing-dependent manner similarly to wild-type animals while avoidance distances were not increased (Fig. 5a,

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b and d), suggesting that, in the absence of dopamine signaling via DOP-3, any change in other behavioral component may nullify the increase in run duration. The longer avoidance distance in cat-2 mutants independent of preexposure may be mediated by other dopamine receptor(s). Further analysis revealed that the bearings of run termination were deteriorated by preexposure in cat-2 and dop-3 mutants but not in wild-type or egl mutants: in the dopamine signaling mutants, the relative number of run terminations was reduced for smaller |B| and increased for larger |B|, and the average run termination bearing was increased significantly (Figs. 2e and 6a and Table S2). The bearings of run initiation did not change significantly by preexposure in wildtype or any mutant animals (Figs. 2d and 6b). These data suggest that, while run durations are increased by preexposure in a bearing-dependent manner in dopamine signaling mutants, the deteriorated migratory bearing may countervail the increase in run duration (Fig. 7a). Taken together, neuropeptide signaling is required for odor learning-dependent proper behavioral modulation, while dopamine signaling is required to properly execute the neuropeptide-dependent behavioral modulation – the functions of these two neuromodulator signalings are necessary to execute the enhanced odor avoidance. 4. Discussion 4.1. Regulatory pathway for the learning-dependent modulation of odor avoidance behavior From quantitative analysis of behavioral components of the enhanced odor avoidance in C. elegans, we found that the preexposure-dependent increase in run duration is key to the enhancement. Because the odor stimulus during preexposure was not associated with directional information, the increase in run duration could have occurred in any direction. We found, however, that an increase in run duration occurred only when the migratory bearing was smaller (i.e., better) than a threshold value. As the computer simulation indicated that the gradient of 2-nonanone is predicted to be almost unidirectional (Fig. 1b), a migratory bearing smaller or larger than the threshold likely corresponds to negative or positive dC/dt of 2-nonanone, respectively (Fig. 7b). Taken together, these results suggest that run duration is regulated by two independent pathways for dC/dt > 0 or dC/dt < 0 and that only the pathway for dC/dt < 0 is modulated by odor preexposure (Fig. 7b). 4.2. Neural basis for the learning-dependent modulation of odor avoidance behavior How are the two pathways for odor avoidance implemented in the animal’s nervous system? A pair of AWB sensory neurons have been shown to be essential for 2-nonanone avoidance (Troemel et al., 1997). If AWB is the only sensory neuron class that is involved in 2-nonanone avoidance, our results may suggest that AWB neurons play two independent roles in the two regulatory pathways. This resembles the roles of AWCON sensory neurons in attraction as well as repulsion (Tsunozaki et al., 2008). Another possibility is that other class of sensory neurons may be involved in 2-nonanone avoidance, similar to the other repulsive odor 1-octanol, in which multiple sensory neurons are required for avoidance behavior at least in certain conditions (Chao et al., 2004). In this case, AWB neurons are likely activated when the animals migrate down the gradient because AWB neurons respond to step-decrements in 2-nonanone concentration in aqueous solution (Ha et al., 2010); when |B| is higher than the threshold, other sensory neurons may be activated. The model of two neuronal pathways nicely explains why the increase in run duration is bearing-dependent, whereas

Fig. 6. Distributions of bearings at run initiation and termination. Those of wildtype animals are in Fig. 2d and e, respectively. (a) The preexposure did not affect the bearings of run termination in neuropeptide mutants but deteriorated those of dopamine mutants. (b) The direction of run initiation was not significantly affected by preexposure both in the neuropeptide mutants and in dopamine mutants.

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Fig. 7. Models for the modulation of odor avoidance with neuropeptide and dopamine signalings. (a) Preexposure-dependent increase in avoidance distance of wild-type animals is mainly caused by an increase in run duration while run bearing is maintained. Neuropeptide signaling is required to increase run duration, and dopamine signaling is necessary to prevent the deterioration of run bearing after preexposure. (b) Information processing for the non-associative odor learning. C. elegans sense the repulsive odor 2-nonanone, and that information is processed to regulate both the run bearing and the pirouette initiation rate, that is, run duration. When the animals are preexposed to the odor, the pathway for pirouette initiation rate with the dC/dt < 0 is modulated by neuropeptide signaling to increase run duration (red solid lines). The preexposure also affects run bearing; however, it is not apparent in wild-type animals because dopamine signaling prevents the deteriorative effect (red dashed lines).

the odor stimulation is not associated with migratory direction during the preexposure (Fig. 7b). It is possible that activity of AWB or its downstream interneuron is increased after the preexposure by non-associative learning, while that of the other pathway is not. Where, then, is the preexposure-dependent change encoded? One possibility is that AWB sensory neurons are not the site of learning. Ha et al. (2010) did not find changes in AWB responsiveness after olfactory learning of pathogenic bacteria, which is also mediated by the AWBs. In addition, preexposure to 1-octanol also causes enhanced 2-nonanone avoidance and vice versa (Kimura et al., 2010). Thus, interneuron(s) downstream of AWBs are good candidates for the learning. Learning-dependent temperature preference, in contrast, is encoded in the responsiveness of the AFD temperature sensory neurons (Kimura et al., 2004). 4.3. Molecular mechanism of the learning-dependent modulation of odor avoidance behavior In our previous study, we showed that dopamine signaling is required for the enhanced odor avoidance. In this study, however, our data indicate that dopamine signaling does not affect the increase in run duration but appears to be required to maintain the direction of runs. In contrast, mutations in two genes that are required for neuropeptide biosynthesis abolished the increase in run duration. It is interesting to note that different neuromodulators regulate different behavioral components, even in simple non-associative learning. Over 250 neuropeptides are reported in C. elegans genome and classified as FMRF amide-like (flp), neuropeptide-like (nlp), and insulin-like (ins) peptides (Komuniecki et al., 2012; Li and Kim, 2008). Some of these neuropeptides are found to be involved in various neural functions, such as regulation of locomotion, dauer formation, egg laying, social behavior, and learning. Among three neuropeptide groups, insulin-like peptides are well-known to be involved in associative learning between feeding status (unconditioned stimulus) and temperature or taste (conditioned stimulus) as well as in experience-dependent changes in olfactory preference (Chen et al., 2013; Kodama et al., 2006; Tomioka et al., 2006). The ins gene products require BLI-4 as proprotein convertase for maturation (Leinwand and Chalasani, 2013), while EGL-3 and EGL21 are required for flp and nlp gene products (Husson et al., 2006, 2007). Vasopressin/oxytocin-related peptide NTC-1 is known to be required for associative learning between starvation and salt

(Beets et al., 2012). Thus, the egl-3 and egl-21-dependent enhanced odor avoidance may involve a new class of neuropeptides for the regulation of learning in C. elegans. Furthermore, we showed that neuropeptides and dopamine play different roles in the modulation of different behavioral components for enhanced odor avoidance. Mutations in the genes required for neuropeptide signaling abolished the increase in run duration after odor preexposure. The mutants were able to respond to 2-nonanone properly without preexposure, suggesting that neuropeptide signaling is not required for odor sensation or locomotion but for learning-specific processes such as memory acquisition and/or retrieval. In contrast, in dopamine signaling mutants, run duration after preexposure was significantly increased compared to control animals, suggesting that the ability of sensing 2-nonanone and acquisition and retrieval of memory were likely unaffected. However, mutations in dopamine signaling did not show enhanced odor avoidance. One possibility to explain this discrepancy may be improper adaptation to the odor stimulus: Preexposure to an odor generally causes adaptation to the odor, and if adaptation occurs to the sensory pathway for dC/dt > 0 of the repulsive odor, the animal would not properly initiate pirouette even when it moves up the odor gradient. The dopamine could also regulate a much higher order neural function for learning. Taken together, our results demonstrate that, in the enhanced odor avoidance, neuropeptide signaling is required for learning-dependent behavioral modulation, and dopamine signaling is required to properly execute the learning-dependent behavioral modulation. 4.4. Roles of multiple neuromodulators in learning In higher animals, various neuromodulators, including dopamine and neuropeptides, are known to be involved in learning although the exact roles of individual neuromodulators are not fully understood. In Aplysia, where non-associative learning is well-studied, serotonin and neuropeptides (SCP and sensorin) are both required to enhance gill withdrawal reflex (Abrams et al., 1984; Hu et al., 2004; Kandel, 2001). In flies and rodents, dopamine and other biogenic amines as well as neuropeptides are known to be involved in learning (Borbély et al., 2013; Davis, 2005; Johansen et al., 2011; Keene and Waddell, 2007). For instance, in associative learning in Drosophila, dNPF, an ortholog of mammalian neuropeptide Y, and dopamine regulate motivation for retrieval of appetitive memory (Krashes et al., 2009). In the Morris water maze and fear

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conditioning in rodents, neuropeptides such as somatostatin and tachykinin, acetylcholine, noradrenaline, serotonin and dopamine are reported to be involved in memory acquisition; noradrenaline is also involved in memory (re-)consolidation and dopamine in motivation step (Borbély et al., 2013; D’Hooge and De Deyn, 2001; Johansen et al., 2011). In most of these studies, changes in only a few conventional behavioral outputs that are a summation of total behavioral responses such as preference or distance by learning were analyzed. Each of these behavioral outputs should consist of more elementary behavioral components such as speed, migratory direction, and duration of movement. Therefore, one likely possibility is that each behavioral component may be affected by a specific neuromodulator; in combination, these modified components could elicit an overall change in the behavioral output. As we have revealed that different behavioral components in one behavioral output are modulated by neuropeptide and dopamine signaling, the combination of systematic behavioral component analysis and genetic analysis in conventional behavioral paradigms may reveal novel important functions of neuromodulators, which may coordinately function to regulate learning. Acknowledgements We thank Drs. J. Pierce-Shimomura, N. Masuda, J. Okubo, M. Fujiwara, A. Takahashi, Y. Ikegaya, M.S. Kitazawa, K. Fujimoto, I. Takeuchi, and members of Kimura and Fujimoto laboratories for their suggestions and comments, S.J. Yamazaki for technical help, and I. Katsura and G.Y. Zheng for critical reading of this manuscript. Some nematode strains used in this work were provided by the Caenorhabditis Genetics Center, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440), and by National Bioresource Project funded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas “Systems Molecular Ethology” (Grant numbers: 20115002 and 23115711) (Y. Iino, Y. Iwasaki and K.D. K.) and the Osaka University Life Science Young Independent Researcher Support Program through Special Coordination Funds for Promoting Science and Technology from MEXT (K.D.K.). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neures.2015.05. 009 References Abrams, T.W., Castellucci, V.F., Camardo, J.S., Kandel, E.R., Lloyd, P.E., 1984. Two endogenous neuropeptides modulate the gill and siphon withdrawal reflex in Aplysia by presynaptic facilitation involving cAMP-dependent closure of a serotonin-sensitive potassium channel. Proc. Natl. Acad. Sci. U. S. A. 81, 7956–7960. Ardiel, E.L., Rankin, C.H., 2010. An elegant mind: learning and memory in Caenorhabditis elegans. Learn. Mem. 17, 191–201. ASTM D3539-87, 2004. Standard Test Methods for Evaporation Rates of Volatile Liquids by Shell Thin-film Evaporometer. American Society for Testing and Materials. Bargmann, C.I., 2006. Chemosensation in C. elegans. WormBook, pp. 1–29. Bargmann, C.I., Hartwieg, E., Horvitz, H.R., 1993. Odorant-selective genes and neurons mediate olfaction in C. elegans. Cell 74, 515–527. Beets, I., Janssen, T., Meelkop, E., Temmerman, L., Suetens, N., Rademakers, S., Jansen, G., Schoofs, L., 2012. Vasopressin/oxytocin-related signaling regulates gustatory associative learning in C. elegans. Science 338, 543–545. Borbély, É., Scheich, B., Helyes, Z., 2013. Neuropeptides in learning and memory. Neuropeptides 47, 439–450. Brenner, S., 1974. The genetics of Caenorhabditis elegans. Genetics 77, 71–94. Chao, M.Y., Komatsu, H., Fukuto, H.S., Dionne, H.M., Hart, A.C., 2004. Feeding status and serotonin rapidly and reversibly modulate a Caenorhabditis elegans chemosensory circuit. Proc. Natl. Acad. Sci. U. S. A. 101, 15512–15517.

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