1. Neuroscience
Download icon

A bidirectional network for appetite control in larval zebrafish

  1. Caroline Lei Wee  Is a corresponding author
  2. Erin Yue Song
  3. Robert Evan Johnson
  4. Deepak Ailani
  5. Owen Randlett
  6. Ji-Yoon Kim
  7. Maxim Nikitchenko
  8. Armin Bahl
  9. Chao-Tsung Yang
  10. Misha B Ahrens
  11. Koichi Kawakami
  12. Florian Engert
  13. Sam Kunes  Is a corresponding author
  1. Harvard University, United States
  2. National Institute of Genetics, Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies), Japan
  3. Howard Hughes Medical Institute, Janelia Research Campus, United States
Research Article
  • Cited 1
  • Views 767
  • Annotations
Cite this article as: eLife 2019;8:e43775 doi: 10.7554/eLife.43775

Abstract

Medial and lateral hypothalamic loci are known to suppress and enhance appetite, respectively, but the dynamics and functional significance of their interaction have yet to be explored. Here we report that, in larval zebrafish, primarily serotonergic neurons of the ventromedial caudal hypothalamus (cH) become increasingly active during food deprivation, whereas activity in the lateral hypothalamus (LH) is reduced. Exposure to food sensory and consummatory cues reverses the activity patterns of these two nuclei, consistent with their representation of opposing internal hunger states. Baseline activity is restored as food-deprived animals return to satiety via voracious feeding. The antagonistic relationship and functional importance of cH and LH activity patterns were confirmed by targeted stimulation and ablation of cH neurons. Collectively, the data allow us to propose a model in which these hypothalamic nuclei regulate different phases of hunger and satiety and coordinate energy balance via antagonistic control of distinct behavioral outputs.

https://doi.org/10.7554/eLife.43775.001

eLife digest

How soon after a meal do you start feeling hungry again? The answer depends on a complex set of processes within the brain that regulate appetite. A key player in these processes is the hypothalamus, a small structure at the base of the brain. The hypothalamus consists of many different subregions, some of which are responsible for increasing or decreasing hunger.

Wee, Song et al. now show how two of these subregions interact to regulate appetite and feeding, by studying them in hungry zebrafish larvae. The brains of zebrafish have many features in common with the brains of mammals, but they are smaller and transparent, which makes them easier to study. Wee, Song et al. show that as larvae become hungry, an area called the caudal hypothalamus increases its activity. But when the larvae find food and start feeding, activity in this area falls sharply. It then remains low while the hungry larvae eat as much as possible. Eventually the larvae become full and start eating more slowly. As they do so, the activity of the caudal hypothalamus goes back to normal levels.

While this is happening, activity in a different area called the lateral hypothalamus shows the opposite pattern. It has low activity in hungry larvae, which increases when food becomes available and feeding begins. When the larvae finally reduce their rate of feeding, the activity in the lateral hypothalamus drops back down. The authors posit that by inhibiting each other’s activity, the caudal and lateral hypothalamus work together to ensure that animals search for food when necessary, but switch to feeding behavior when food becomes available.

Serotonin – which is produced by the caudal hypothalamus – and drugs that act like it have been proposed to suppress appetite, but they have varied and complex effects on food intake and weight gain. By showing that activity in the caudal hypothalamus changes depending on whether food is present, the current findings may provide insights into this complexity. More generally, they show that mapping the circuits that regulate appetite and feeding in simple organisms could help us understand the same processes in humans.

https://doi.org/10.7554/eLife.43775.002

Introduction

The regulated intake of food based on caloric needs is a fundamental homeostatically controlled process that is essential for health and survival. The hypothalamus is a highly conserved central convergence point for the neural and biochemical pathways that underlie this regulatory mechanism. Early studies demonstrated by way of electrical stimulation or lesions that specific hypothalamic regions play important roles in the regulation of appetite. For example, while stimulation of ventromedial hypothalamic loci in rodents and cats reduced feeding, activation of more lateral hypothalamic loci increased both hunting behavior and food intake (Anand and Brobeck, 1951; Brobeck et al., 1956; Delgado and Anand, 1952; Krasne, 1962). Conversely, lateral hypothalamic lesions were found to reduce feeding to the point of starvation, whereas medial hypothalamic lesions resulted in overeating (Anand and Brobeck, 1951; Hoebel, 1965; Teitelbaum and Epstein, 1962). Thus, the lateral and medial hypothalamic regions came to be regarded as ‘hunger’ and ‘satiety’ centers, respectively.

Recent experiments employing optical and electrophysiological methods have lent support to these early studies. For example, GABAergic neurons in the lateral hypothalamus were observed to be activated during feeding and essential for enhanced food intake during hunger (Jennings et al., 2015; Stuber and Wise, 2016). However, these experiments have examined only subsets of hypothalamic neurons; their activity patterns and function within the context of the entire network remain unknown. This limited view hampers our understanding of the dynamical interactions between the ensemble of brain circuits thought to be important for the initiation, maintenance and termination of food consumption (Sternson and Eiselt, 2017).

Here, we leverage the small and optically accessible larval zebrafish to identify modulatory regions central to the control of appetite and to shed light on their specific roles and dynamical activity patterns in relation to behavior. Using pERK-based brain-wide activity mapping (Randlett et al., 2015), we first identified neuronal populations that display differential neural activity under conditions that would yield hunger and satiety. We show that lateral and medial hypothalamic regions have anti-correlated activity patterns during food deprivation, and voracious or steady state feeding. Next, through a combination of calcium imaging, optogenetics and ablation analysis, we show that serotonergic neurons in the caudal periventricular zone of the medial hypothalamus (cH) are state-dependent regulators of feeding behavior, most likely via their modulation of lateral hypothalamic activity. These results allow us to propose a model where mutually antagonistic brain states regulate energy balance by encoding distinct signals for different facets of appetite control.

Results

Whole brain activity mapping of appetite-regulating regions

Larval zebrafish hunt prey such as paramecia through a sequence of motor actions that has been considered a hardwired reflex response to external prey stimuli (Bianco et al., 2011; Semmelhack et al., 2015; Trivedi and Bollmann, 2013). Only recently has evidence emerged that this behavior is flexibly modulated by satiation state (Filosa et al., 2016; Jordi et al., 2015; Jordi et al., 2018) and that larvae at 7 days post-fertilization (dpf) display enhanced hunting and enhanced food intake after a period of food deprivation. A robust readout of food intake in larval zebrafish was obtained both by the ingestion of fluorescently-labeled paramecia and by behavioral analysis, using protocols adapted for this study (Johnson et al., 2019; Jordi et al., 2015; Jordi et al., 2018; Shimada et al., 2012). A 2 hr period of food deprivation robustly enhances subsequent food intake (Figure 1a). Up to 15 min after the presentation of prey, food-deprived animals display a strong upregulation of hunting and prey intake relative to fish that have continuous access to food (referred to as fed fish; Figure 1a), on the basis of fluorescent food ingestion (left panel, Figure 1a) and hunting bouts (right panel, Figure 1a). We refer to this behavior as ‘voracious feeding’. Finally, as the fish consume food, their rate of food intake declines to that of continuously fed fish (Figure 1a). These behaviors likely represent internal states that are commonly referred to as hunger and satiety, and reflect the animal’s underlying caloric or metabolic needs.

Figure 1 with 5 supplements see all
Whole brain activity mapping reveals anti-correlated hypothalamic regions.

(a) Top: The protocols used to quantify feeding behavior in larval zebrafish. At 7 or 8 dpf, larvae were either food-deprived for 2 hr, or fed with excess paramecia for this duration. After 2 hr (2–4 hr in the case of behavioral imaging), they were subject to a quick wash, followed either by: 1) addition of excess fluorescently-labeled paramecia (left), 2) high-resolution behavioral imaging (right; see Johnson et al., 2019, and Materials and methods). Gut fluorescence is both cumulative and diminished by digestion (Jordi et al., 2015) and so lags the dynamics of hunting behavior. Bottom left: Gut fluorescence measurements of food-deprived (red) or fed (blue) fish as a function of duration of feeding labeled paramecia. Groups of fed or food-deprived larvae were fixed at the indicated time points (fed: n = 7/18/19/17/17 fish, food-deprived: n = 8/23/20/14/15 fish). Food-deprived fish had significantly higher gut fluorescence than fed fish overall (p = 7.5859×10−10, Two-way ANOVA, asterisk indicates corrected p-values<0.05. Bottom right: The probability of performing a hunting-related swim bout across fed and food-deprived fish groups in 3 min time bins over 45 min. Error bars represent 90% confidence intervals. For all bins except those indicated with triangles, the null hypothesis that initial feeding condition has no effect on hunting-bout probability is rejected (p<0.00001, Fisher’s Exact Test comparing binomial probability distributions per bin). Fed: n = 85655 bouts from 73 fish; Food-deprived: n = 75357 bouts from 57 fish. Since the rate of food intake and hunting behavior was highest in the first 15 min (voracious feeding phase, gray boxes), we chose this time point for subsequent MAP-mapping experiments. (b) Brain-wide activity mapping of food-deprived (Dep.) fish exposed to food for 15 min, with subtraction of activity in continuously fed (Fed) fish. Data from nine experiments were combined to generate this difference map based on anti-pERK staining fluorescence. Relative activation from feeding after food deprivation yields activated regions including the telencephalon (Tel), Arborization field 7 (AF7), cerebellum (CB), hindbrain (HB), Vagal ganglion (VG) and lateral lobe of the intermediate hypothalamus (LH). Reduced activity was observed in the caudal hypothalamus (cH) and some areas of the telencephalon. Scale bar = 100 μm. Also see Video 1. (c) ROI-specific pixel intensity analysis of LH and cH regions in nine independent MAP-mapping experiments (20–30 fish per treatment per experiment). The cH or LH ROI intensities of each individual fish was normalized to the mean cH or LH ROI intensity of all fed fish. Food-deprived fish consistently displayed higher LH and lower cH pERK fluorescence after the onset of feeding (p = 0.0019 for both cH and LH, one-tailed Wilcoxon signed-rank test). (d) Z-projection of same MAP-map as described in (b) in planes revealing the hypothalamus (right panel), where lateral regions (e.g. lateral hypothalamus, LH) display strong relative activation and medial regions (e.g. caudal hypothalamus, cH) display reduced activity in when food-deprived animals were fed for 15 min. The map is overlaid onto a stack for the transgenic line Tg(VMAT:GFP) (left panel) to localize the cH region. (e) Six examples of independent component analysis (ICA) maps. Voxels for each recovered independent component (IC) are shown as maximum projections, with intensity proportional to the z-score of the loadings of the ICA signal. These ICs, along with others (22/30) highlight LH and cH regions of opposite loadings, suggesting they may be included in a network that displays anti-correlated activity patterns between the cH and LH. A subset of these ICs (e.g. #14 and #24) only showed partial anti-correlation between the cH and the LH. All ICs are shown in Figure 1—figure supplement 3. Positive (+) loading and Negative (-) loadings (z-score values of IC signals) are reflected in green and magenta, respectively. (f) Confocal micrographs of anti-pERK antibody stained brains from animals that were continuously fed (panel (i), left), food-deprived for 2 hr (panel (i), center) and fed for 5 min after food deprivation (panel (i), right). cH (ii) and LH (iii) insets are shown at higher magnification on the bottom and right side respectively. The lateral hypothalamus is shown with subdivisions lateral lateral hypothalamus (lLH) and medial lateral hypothalamus (mLH). (i) scale bar: 50 μm; (ii) and (iii) scale bar: 20 μm. Fish are mounted ventral side up. (g) Quantification of cH and LH activities by normalized anti-pERK fluorescence intensity averaging. The normalized anti-pERK staining intensity for each region (ROI) was obtained by dividing the anti-pERK fluorescence from each fish (in all experimental groups) by the average anti-pERK fluorescence for the same ROI of continuously fed fish. Quantitative analysis performed on fish in six independent conditions (n = 13/11/9/9/13/12). Normalized anti-pERK fluorescence intensity (cH/mLH/lLH): Fed vs Dep. (p = 0.016/0.12/0.11), Dep. vs Dep. + 5 min food (p = 3.1×10−4/9.9 × 10−5/0.020), Fed vs Dep. + 5 min food (p = 0.0097/8.5 × 10−4/0.11). Asterisks denote p<0.05, one-tailed Wilcoxon rank-sum test. (h) The active cell count metric (bottom panels) was determined as described in Figure 1—figure supplement 4 by a thresholding protocol to isolate and count individual pERK-positive cells within a z-stack. This approach could be reliably performed for areas of sparse active cells (e.g. mLH and lLH) but not where individually labeled pERK-positive neurons are not well separated (such as the cH). Active cell count (mLH/lLH): Fed vs Dep. (p = 0.001/0.0038), Dep. vs Dep. + 5 min food (p = 9.7×10−5/1.3 × 10−5), Fed vs Dep. + 5 min food (p = 0.0038/0.048). Asterisks denote p<0.05, one-tailed Wilcoxon rank-sum test. (i) Schematic of inferred cH and LH activity in relation to feeding behavior. Note that, based on data in Figure 2, the LH active cell count appears to decline more rapidly than the rise in cH activity (based on cH average fluorescence intensity). Data plotted in Figure 1 are provided in Figure 1—source data 1.

https://doi.org/10.7554/eLife.43775.003

As a first step toward understanding the homeostatic control of feeding in this simple vertebrate system, we employed whole-brain neuronal activity mapping via phosphorylated ERK visualization in post-fixed animals (MAP-mapping; Randlett et al., 2015). Whole brain confocal image datasets of phospho-ERK expression were gathered from animals sacrificed after 15 min of voracious feeding that followed a 2 hr period of food deprivation. For comparison, image sets were also gathered from animals that had been fed continuously (fed fish). The image volumes were registered to a standardized brain atlas. A difference map (Figure 1b) reveals significant specific differences in neural activity when comparing voracious feeding with continuous feeding (Figure 1b–d, Video 1, Supplementary files 12). Since both experimental groups experienced the same sensory stimuli (i.e. exposure to paramecia) prior to sacrifice, differences in brain activity should primarily reflect the animals’ internal states, which could include manifestations of an altered sensitivity to food cues, activity related to hunting and prey capture, or the motivational history resulting from food deprivation. Indeed, multiple sensorimotor loci related to hunting showed enhanced activity during feeding that followed the food-deprived condition, consistent with the increased feeding behavior observed in food-deprived animals. These loci included the retinal Arborization Fields (AFs; optic tectum and AF7), pretectum, as well as downstream hindbrain loci, such as reticulospinal and oculomotor neurons, all of which are known to be engaged during prey capture behavior (Bianco and Engert, 2015; Muto et al., 2017; Semmelhack et al., 2015). In addition, enhanced activity was observed in the cerebellum, inferior olive, vagal sensory and motor neurons, area postrema and locus coeruleus, areas that have been implicated in feeding regulation and behavior (Ahima and Antwi, 2008; Ammar et al., 2001; Dockray, 2009; Zhu and Wang, 2008).

Video 1
Z-stack (dorsal to ventral) of brain activity map shown in Figure 1b.
https://doi.org/10.7554/eLife.43775.010

We focused our attention on brain areas likely to be involved in motivational states related to feeding. These included an area of particularly strong differential activity in the lateral region of the intermediate hypothalamus (Lateral Hypothalamus, LH; Figure 1b–d), which has recently been identified as part of the feeding pathway in larval zebrafish (Muto et al., 2017) and whose mammalian analog has been strongly implicated in appetite control (Sternson and Eiselt, 2017). However, the zebrafish LH, unlike its mammalian counterpart, does not harbor melanin-concentrating hormone (MCH)-positive, orexin (hypocretin)-positive neurons, or other major feeding-related peptides (Figure 1—figure supplements 1 and 2). We therefore characterized the expression of multiple appetite-related neuromodulators (AgRP, MSH, CART, NPY, MCH, Orexin) and found that they are instead expressed in nearby areas of the hypothalamus (Figure 1—figure supplement 1). The zebrafish LH region does however contain glutamatergic and GABAergic cell types (Figure 1—figure supplement 2); these non-peptidergic LH cell types have been shown in rodents to be important for the regulation of feeding (Jennings et al., 2015; Stuber and Wise, 2016).

Among areas that showed relatively decreased neural activity upon feeding food-deprived animals, the most significant was the adjacent caudal hypothalamus (cH), which contains monoaminergic neurons -- mainly serotonergic and dopaminergic cells, with a small fraction of histaminergic cells (Chen et al., 2016; Kaslin and Panula, 2001; Lillesaar, 2011). Indeed, in all of nine independent MAP-mapping experiments, activity was reduced in the cH and increased in the LH within 15 min of food presentation (Figure 1c). The evident inverse relationship between LH and cH neural activity is supported by independent component analysis (Randlett et al., 2015), which was applied to feeding-related MAP-mapping data (Figure 1e, Figure 1—figure supplement 3). Multiple components were uncovered in which cH and LH activities were strongly anti-correlated. These results led us to hypothesize that the lateral and caudal hypothalamic regions form a functionally interconnected network with opposing activity patterns.

Cellular dissection of hypothalamus neural activity reveals modulation by satiation state

To probe these neural activity changes at higher resolution, we performed anti-pERK antibody staining on isolated brains and examined the hypothalamus in time course experiments spanning a period of food deprivation and subsequent feeding (Figure 1f–h, Figure 2). We quantified the mean anti-pERK fluorescence within a region-of-interest (ROI; Figure 1g) as well as the number of active cells or cell clusters (Figure 1h; Figure 1—figure supplement 4). These two metrics were employed because the high density of pERK-positive cells in the cH of food-deprived animals made high-throughput quantitation of active cells unreliable, whereas use of this metric in areas of sparse activity (e.g. mLH and lLH) yielded better differential sensitivity than ROI averaging. Using these respective metrics, we observed that mean fluorescence in the cH was significantly increased in food-deprived fish, while the number of active neurons in the medial and lateral lobes of the LH (mLH and lLH, respectively) was relatively low (Figure 1f–h). Within the cH, enhanced pERK activity during food deprivation was most prevalent in serotonergic neurons, but also present in a smaller proportion of dopaminergic neurons (Figure 1—figure supplement 5, Videos 2 and 3).

Figure 2 with 1 supplement see all
cH and LH activities are modulated by food and satiation state.

(a) Representative images showing that cH, mLH and lLH activities in the presence and absence of food vary with the extent of food deprivation (dataset quantified in b and c). (b) Normalized pERK average fluorescence intensity in cH significantly increases with food deprivation, and is significantly reduced when food is presented to food-deprived fish. Normalized mLH and lLH pERK average fluorescence intensity does not change significantly during food deprivation and strongly increases during voracious feeding (Dep. 2 hr + 15 min food). Asterisks denote p<0.05. Normalized pERK intensity (cH/mLH/lLH): Fed vs Dep. 30 min (p = 0.53/0.47/0.15), Fed vs Dep. 2 hr (p = 0.0022/0.41/0.59), Dep. 30 min + food vs Dep. 2 hr + food (p = 0.041/0.0022/0.0022), Dep. 30 min vs Dep. 30 min + food (p = 0.62/0.73/0.62), Dep. 2 hr vs Dep. 2 hr + food (p = 0.0022/0.0011/0.0022), Fed vs Dep. 2 hr + food (0.047/0.0011/0.0011). Anti-pERK staining fluorescence was averaged over each entire region of interest (i.e. cH, mLH and lLH; see Materials and methods for details). The normalized anti-pERK staining intensity for each region (ROI) was obtained by dividing the anti-pERK fluorescence from each fish (in all experimental groups) by the average anti-pERK fluorescence for the same ROI of continuously fed fish. (c) The number of active mLH and lLH cells declines within 30 min of food deprivation, and is significantly enhanced during feeding, particularly after a longer period of food deprivation. Active cell count (mLH/lLH): Fed vs Dep. 30 min (p = 0.155/5.8 × 10−4), Fed vs Dep. 2 hr (p = 0.047/0.011), Dep. 30 min + food vs Dep. 2 hr + food (p = 0.0022/0.0043), Dep. 30 min vs Dep. 30 min + food (p = 0.07/0.013), Dep. 2 hr vs Dep. 2 hr + food (p = 0.0011/0.0011), Fed vs Dep. 2 hr + food (p = 0.0022/0.07), n = 6/7/5/6/6 fish, one-tailed Wilcoxon rank-sum test. Data plotted in Figure 2 are provided in Figure 2—source data 1.

https://doi.org/10.7554/eLife.43775.011
Video 2
Z-stack (ventral to dorsal) of anti-5-HT (green) and anti-pERK (magenta) staining in food-deprived fish. Scale bar = 20 μm.
https://doi.org/10.7554/eLife.43775.014
Video 3
Z-stack (ventral to dorsal) of TH2:GCaMP5 transgene expression (green) and anti-pERK (magenta) staining in the same food-deprived fish as in Video 2. Scale bar = 20 μm.
https://doi.org/10.7554/eLife.43775.015

During the period of voracious feeding that followed food deprivation, the pERK-reported activity of cH neurons fell dramatically to a level significantly below that observed in continuously fed fish (Figure 1f–h). This characteristically low cH activity level coincided with a large increase in LH activity, measured by either mean anti-pERK fluorescence or by measurement of the number of individually active neurons, that lasted throughout the period of voracious feeding. Thereafter, as feeding continued at a more moderate pace, and the rate of food ingestion declined, LH neuronal activity likewise declined (especially for lLH neurons; Figure 1h). Reciprocally, cH activity slowly increased back towards baseline levels. After 30 min of feeding, neural activity in both the cH and LH had mostly converged to the baseline level observed for continuously fed fish, consistent with the time course of hunting behavior reduction (Figure 1a, right panel). Thus these cH and LH populations displayed anti-correlated activity over time frames that spanned a progression of distinct behaviors associated with food deprivation, voracious feeding and a gradual return to apparent satiety (Figure 1i).

Satiation state influences the responses of cH and LH populations to food

To more closely align the activity patterns of cH and LH neuronal populations with feeding behavior, we examined these areas after a 30 min (i.e. short) or 2–4 hr (i.e. long) period of food deprivation, with or without a subsequent period of feeding (Figure 2, Figure 2—figure supplement 1). Following food removal, cH activity increased, with an especially large anti-pERK average fluorescence intensity increase after 2 hrs of food deprivation (Figure 2a–b). In contrast to the cH, food removal quickly reduced the frequency of active mLH and lLH neurons (Figure 2a,c). Despite the reduction in LH active cell count over food deprivation, there were no obvious changes in mean LH anti-pERK fluorescence over the course of food deprivation (Figure 2b). This is because there are few active LH cells in continuously fed and food-deprived fish, thus their overall contribution to the fluorescence average of the mLH and lLH regions of interest is small.

Notably, the addition of prey (paramecia) rapidly reversed the food deprivation- induced patterns of cH and LH neural activity, with an amplitude of change that was correlated with the length of food deprivation (Figure 2a–c, Figure 2—figure supplement 1d–e). Fish that had been food-deprived for longer periods (2 hr or 4 hr) displayed a greater increase in the number of active LH neurons compared to feeding animals that had been food-deprived for only 30 min (Figure 2a–c; Figure 2—figure supplement 1d–e). Likewise, the reduction in cH activity after food presentation was greater when it followed a longer period of prior deprivation (Figure 2a–b; Figure 2—figure supplement 1d). In general, the presence of highly active neurons in the LH was correlated with higher food consumption (as measured by gut fluorescence, Figure 2—figure supplement 1a–e).

Caudal and lateral hypothalamic responses to food sensory cues are anti-correlated over short timescales

We next set out to characterize acute effects of food sensory cues on both the cH and LH, and also to analyze in more detail the apparent negative activity relationship between these two nuclei. Such analyses require higher temporal resolution than afforded by anti-pERK staining analysis, thus we switched to in vivo calcium imaging of the cH and LH in live animals (Figure 3). To that end, two transgenic Gal4 drivers, Tg(116A:Gal4) and Tg(76A:Gal4), were combined to express GCaMP6s (Tg(UAS:GCaMP6s)) in neuronal subsets of both the cH and LH (Figure 3—figure supplements 12). The 116A:Gal4 transgene drives expression mainly in serotonergic neurons of the cH (88.9 ± 0.8% 5-HT positive) and paraventricular organ (PVO; Figure 3—figure supplement 1), whereas 76A:Gal4 drives expression in a large proportion of LH cells (Figure 3—figure supplement 2; Muto et al., 2017).

Figure 3 with 3 supplements see all
Caudal and lateral hypothalamic responses to prey sensory cues are anti-correlated over short timescales.

(a) Top: Transgenic fish (2 hr food-deprived) with GCaMP6s expressed in cH and LH neurons were paralyzed, tethered in agarose with their eyes and nostrils freed and exposed to live paramecia (prey), as described in Materials and methods. Top image: GCaMP expression in the cH and LH driven by two transgenic lines, Tg(116A:Gal4) and Tg(76A:Gal4) respectively. Bottom image: Downsampled image stack used for analysis in (f). (b) Top: Mean calcium activity (Δf/f) from respective hypothalamic ROIs (shown in (a)) from four individual fish during a baseline food-deprived period (Dep.), exposure to water alone (Water), and a dense water drop of paramecia (Para). Traces from left and right hypothalamic lobes of the same animal are overlain, revealing a high degree of correlated activity on opposite sides of the midline. Paramecia presentation increases activity in the LH and reduces activity in the cH, revealing opposing activity on short timescales. Bottom: Δf/f traces within area marked by gray box (top), displayed at higher magnification. An increase in LH activity and corresponding reduction in cH activity is observable within seconds of paramecia presentation, except for fish D in which maximal responses only occur after a few minutes (beyond the displayed time window). (c) Average Δf/f triggered on lLH calcium spikes (left and right lobes averaged) shows a mean corresponding reduction in cH activity (n = 159 lLH spikes extracted from mean Δf/f traces from 14 fish across the entire duration of the experiment). (d) Raster plots showing mean calcium activity from the hypothalamic lobes (left and right lobes averaged) of 14 fish before and after presentation of water alone and water with paramecia. (e) Quantification of integrated fluorescence (sum Δf/f %), calcium spike frequency (spikes/min) and calcium spike amplitude (Δf/f %) per fish across experimental epochs (300 s food-deprived baseline (D), 300 s after water (W) delivery or 600 s after paramecia delivery (P)). Each colored line represents data from an individual fish (left and right lobes averaged). Water alone was sufficient to significantly reduce cH integrated fluorescence (p = 6.1×10−5) and spike frequency (p = 0.0127) but not spike amplitude (p = 0.9324). Water alone was similarly sufficient to increase lLH integrated fluorescence (p = 0.029) and spike frequency (p = 0.0098) but not spike amplitude (p = 0.13). Conversely, water alone was not sufficient to significantly modulate mLH integrated fluorescence (p = 0.48) or spike frequency (p = 0.20), but was sufficient to increase spike amplitude (p = 0.039). Paramecia delivery significantly increased mLH and lLH integrated fluorescence (mLH, p = 1.2×10−4; lLH, p = 0.045) and spike frequency (mLH, p = 6.1×10−5; lLH, 6.1 × 10−4), while only significantly increasing mLH spike amplitude (mLH, p = 0.045; lLH, p = 0.43), relative to water delivery. In contrast, paramecia delivery significantly reduced cH integrated fluorescence relative to water delivery alone (p = 3.1×10−4), but not spike frequency (p = 0.52) nor spike amplitude (p = 0.85). Asterisks denote p<0.05, one-tailed Wilcoxon signed-rank test. (f) Top: Cross-correlogram of hypothalamic cell-sized voxels (cells and/or neuropil from downsampled image stacks, see (a)) from four fish. The cH and LH voxels were mostly anti-correlated, whereas voxels within each cluster displayed correlated activity. Black arrowheads indicate region of lLH that appears to be most anti-correlated with the cH. Bottom: Correlation coefficients of other hypothalamic voxels relative to a selected voxel with the cH, mLH or lLH. See color key for numerical translation of color maps. (g) Summary of data from 14 fish, showing the probability of the nth most anti-correlated voxel belonging to each of the other regions (cH, mLH or lLH), normalized to chance probability (gray line) of belonging to each region (i.e. the fraction of all voxels occupied by each region). For example, if we consider all the voxels within the cH, there is a four-fold probability relative to chance of their most anti-correlated voxels (Rank = 1) being part of the lLH.

https://doi.org/10.7554/eLife.43775.016

Using these transgenic animals, we examined calcium dynamics in the cH and LH regions in tethered animals during the controlled presentation of prey stimuli (Figure 3a). In these experiments, live paramecia were released in a puff of water in the vicinity of the immobilized fish, which can neither hunt nor ingest prey. Consistent with the results of anti-pERK analysis of post-fixed brains (Figures 1 and 2), activity in the mLH and lLH regions was increased and cH activity quickly reduced, in fact within seconds of paramecia release (Figure 3b,d). Neurons in all three hypothalamic loci also responded to water flow alone, but these responses were significantly less than those elicited by paramecia (Figure 3b,d,e). These prey-induced changes in activity were particularly striking for the mLH region, which displayed both a strongly enhanced calcium spike frequency and spike amplitude upon the introduction of prey. Thus, prey sensory cues, even in the absence of hunting or prey ingestion, strongly and differentially regulate neuronal activity in the caudal and lateral hypothalamus.

The activities of cH and LH neurons also appeared remarkably anti-correlated; both spontaneous and prey-induced fluctuations in one population were accompanied by corresponding opposing activity changes in the other (Figure 3b–c). This observation was supported by cross-correlation analysis between cH, mLH and lLH voxels (Figure 3f), which revealed high correlation within the same hypothalamic region (red color), and anti-correlation between cH and LH regions (blue color). Further, lLH voxels showed more spatial heterogeneity than mLH voxels (Figure 3f), though a small cluster of cells at the most-anterior part of the lLH was most consistently anti-correlated with cH activity (Fish C and D, black arrowheads). When ranked according to their degrees of anti-correlation with voxels from other lobes, the cH and lLH displayed the greatest anti-correlation (Figure 3g). Overall, these results indicate that cH and LH neurons display generally anti-correlated activities over short timescales, in addition to the anti-correlation observed over longer epochs reflecting motivational states imposed by food deprivation and feeding.

In addition to these studies over short timescales, we also analyzed live imaging traces that spanned extended time periods (up to 2 hr) of food deprivation (Figure 3—figure supplement 3a). This long-term imaging resulted in some confounding modulation of baseline fluorescence over these timescales (Figure 3—figure supplement 3a, particularly lLH trace), that do not necessarily reflect changes in neural firing (Berridge, 1998; Verkhratsky, 2005) and may well be related to modified internal states caused by tethering and immobilization. Nonetheless, we observed significantly higher calcium spike frequencies and amplitudes in the cH as compared to LH regions over the course of food deprivation (Figure 3—figure supplement 3a,c–d), activity patterns that were the opposite of those observed for these regions when prey was presented (Figure 3b,e). For example, the calcium spike amplitude and frequency of the cH region were many-fold greater than those observed in the mLH region during food deprivation (Figure 3—figure supplement 3d), whereas after prey presentation, these relative activities were reversed, with the mLH displaying significantly greater spike amplitude and frequency than the cH (Figure 3b,e). Likewise, lLH calcium spike frequency is significantly lower than the cH during food deprivation, but increases significantly after prey presentation (Figure 3—figure supplement 3d, Figure 3e). Thus, the cH is more active over food deprivation, and the LH under conditions where food is present.

Separation of cH and LH neuronal activities associated with prey detection and ingestion

We next sought to characterize the responses of hypothalamic regions to prey ingestion, as opposed to the mere detection of prey. To distinguish between the consequences of sensory and consummatory inputs, we compared neural activities in food-deprived fish exposed to paramecia or artemia. Artemia are live prey commonly fed to adult zebrafish and are actively hunted by fish at all stages, including larvae (Figure 4a, Video 4). Thus, artemia provide sensory inputs that elicit hunting behavior in larval animals. They are however too large to be swallowed and consumed by larvae. Hence, the comparison between these two types of prey dissociates neural activity triggered by prey detection and hunting from that of food ingestion.

Video 4
Video of larval zebrafish hunting artemia larvae.

Prey-capture behavior, such as J-turns and pursuits, but no capture swims, were observed in response to artemia larvae. Recording rate: 30 fps. Playback rate: Real time.

https://doi.org/10.7554/eLife.43775.020

Prey ingestion can only occur in freely behaving animals and thus we needed to return to pERK- based activity mapping in post-fixed animals for our analysis. We found that artemia exposure caused significant increases in both mLH and lLH activity, whereas little change was detected in cH neurons (Figure 4a–c). Exposure to paramecia on the other hand triggered an even larger response in both LH lobes and led, as expected, to a significant reduction in cH activity. In order to quantify the relative changes in the mLH and lLH lobes, we compared the artemia-induced activity change (θA) to the paramecia-induced activity change (θP) for each lobe. The average mLH anti-pERK fluorescence only displayed a marginally greater artemia-induced increase (θAP = 41%) than the lLH region (θAP = 38%; Figure 4c, top panel). However, when the frequency of active neurons was compared, the mLH displayed a much larger response (θAP = 32%) to artemia than the lLH (θAP = 15%) (Figure 4c, bottom panel). Taken together with our calcium imaging results (Figure 3), these observations indicate that while all three hypothalamic regions (cH, mLH and lLH) are modulated by prey sensory cues, they respond more strongly to prey ingestion. Among these regions, the mLH appears to be the most highly tuned to prey detection in the absence of prey ingestion (Figure 4d).

Sensory cues and prey ingestion differentially regulate cH and LH neural activity.

(a) Representative images of activity induced by exposure of 7–8 dpf larval zebrafish to paramecia or artemia larvae, as examined by anti-pERK antibody staining. Hatched artemia are actively hunted but are too large to consume, allowing for the dissociation of sensory cues and hunting behavior from prey consumption. Scale bar = 50 μm. Rightmost two panels (top and bottom): Larval zebrafish hunt live artemia, performing J-turns and pursuits with eyes converged (see Video 4; Bianco et al., 2011). (b) cH activity (normalized pERK fluorescence intensity) is significantly reduced by exposure to paramecia but not by exposure to artemia (p = 0.016 (paramecia), 0.648 (artemia)). Asterisks denote p<0.05. (c) LH activity can be induced by artemia, and more strongly by paramecia. Both normalized pERK intensity (mLH: p = 2.06×10−5 (paramecia vs control), p = 7.09×10−4 (artemia vs control), p = 5.43×10−5 (artemia vs paramecia); lLH: p = 2.06×10−5 (paramecia vs control), p = 0.020 (artemia vs control), p = 0.0019 (artemia vs paramecia)) and active cell count (mLH: p = 2.06×10−5 (paramecia vs control), p = 9.58×10−5 (artemia vs control), p = 1.77×10−4 (artemia vs paramecia); lLH: p = 2.06×10−5 (paramecia vs control), p = 9.75×10−5 (artemia vs control), p = 9.86×10−5 (artemia vs paramecia)) are shown, with n = 9/9/11 fish, one-tailed Wilcoxon rank-sum test. Anti-pERK staining fluorescence was averaged over each entire region of interest (cH, mLH and lLH; see Materials and methods for details). The normalized anti-pERK staining intensity for each region (ROI) was obtained by dividing the anti-pERK fluorescence from each fish (in all experimental groups) by the average anti-pERK fluorescence for the same ROI of food-deprived (i.e. control) fish. We also compared the artemia-induced activity change (θA) to the paramecia-induced activity change (θP) for each lobe (see main text). (d) Differential neural activation of the cH and LH regions in response to prey sensation and hunting as compared to prey ingestion. Data plotted in Figure 4 are provided in Figure 4—source data 1.

https://doi.org/10.7554/eLife.43775.021

Optogenetic cH activation suppresses lLH neural activity

The observed anti-correlated patterns of caudal and lateral hypothalamus neural activity in both our calcium imaging and pERK-based activity data suggest they might interact via mutual inhibition. For example, during food deprivation, rising cH activity (and the absence of food) could restrain LH activity, while a subsequent experience of prey detection and ingestion might trigger LH activity that inhibits cH activity. This reduction in cH activity may, in turn, relieve suppression of LH activity, a neural ‘switch’ that could drive voracious feeding behavior.

As an initial test of this hypothesis, we determined whether optogenetic excitation of cH neurons would be sufficient to inhibit LH neural activity. We used the Tg(y333:Gal4) line (Marquart et al., 2015) to drive expression of a red-shifted channelrhodopsin (Tg(UAS:ReaChR-RFP)) (Dunn et al., 2016; Lin et al., 2013) in cH neurons (see Figure 5—figure supplement 1 regarding choice of Tg(y333:Gal4)). The Tg(y333:Gal4) line drives ReaChR expression in a large fraction of cH serotonergic neurons (57.4 ± 2.1%; Figure 5—figure supplement 1), as well as a smaller fraction of dopaminergic cells (23.9 ± 2.2%; up to 30% overlap observed, Figure 5—figure supplement 2). Tg(HuC:GCaMP6s) was co-expressed to monitor spontaneous LH neuron calcium activity.

These tethered transgenic fish were subjected to targeted laser (633 nm) illumination of the cH region to locally activate the ReaChR channel. We showed that ReaChR activation in the cH was sufficient to induce cH neural activity (Figure 5a,c). In contrast, ReaChR activation significantly reduced spontaneous lLH calcium spike activity within a 90 s period that followed laser illumination (Figure 5b,d), whereas no significant decrease was observed in mLH activity (Figure 5b,d). Illumination of a control preoptic area region, where Tg(y333:Gal4)-driven ReaChR is not expressed, did not affect lLH activity, though we did observe a small increase in mLH activity (Figure 5e). This effect might be visually induced or driven by light-sensitive opsins known to be expressed in the preoptic area (Fernandes et al., 2012). Since no such increase was observed when the cH itself was optogenetically activated, it is plausible that an inhibitory effect of cH stimulation on the mLH is masked by an opposing light response sensitivity. In sum, optogenetic stimulation of cH neural activity is sufficient to inhibit lLH neural activity, consistent with the notion that cH and LH regions interact to modulate the animal’s motivational state in response to food deprivation and feeding.

Figure 5 with 2 supplements see all
Optogenetic cH stimulation reduces lLH activity in tethered fish.

(a) ReaChR activation of neurons. Top Panels: Targeted 633 nm laser illumination (see Materials and methods) of a defined cH area (imaged area) in Tg(y333:Gal4;UAS:ReaChR-RFP; UAS:GCaMP6s) fish. These animals express a Tg(UAS:GCaMP6s) reporter in the cH under Tg(y333:Gal4) control. The animals were subjected to repetitive 10 s laser illumination, with a periodicity of 120 s. Following the 633 nm laser pulses, there is widespread induction of cH activity, as indicated by GCaMP fluorescence (Δf/f) in most regions of interest plotted to the right of the image panel. Scale bar = 50 μm. Bottom Panel: Mean Δf/f across the entire outlined cH region versus time. Laser illumination pulses are indicated by orange bars. Gray bars indicate pre- and post-stimulation periods for which metrics shown in (c–e) were determined. (b) Inhibition of LH activity by activation of cH neurons in Tg(y333:Gal4;UAS:ReaChR-RFP; HuC:GCaMP6s) fish. The animals were subjected to repetitive 10 s laser illumination, with a periodicity of 180 s. Laser pulses were delivered to the cH (orange lightning symbol) as in a, and calcium imaging was recorded from the indicated LH areas (white outlines). Region of interest traces are shown to the right of the image panel for the indicated areas (cells and neuropil (NP)). There is an apparent reduction of spontaneous lLH GCaMP fluorescence spikes in the post-stimulation period. Scale bar = 50 μm. Bottom: Mean Δf/f across mLH and lLH ROIs over time. (c–e) Comparison of mean, summed and maximum Δf/f metrics for a 90 s window before and after ReaChR stimulation (gray bars in bottom panels in a and b). Each data point represents a single stimulation event, like those shown in a and b. Asterisks denote p<0.05. (c) cH activity increases after illumination of Tg(y333:Gal4; UAS:ReaChR-RFP)-positive cH neurons, n = 29 stimulations across eight fish, p = 0.0002 (max Δf/f) / 0.036 (sum Δf/f) / 9.2×10−5 (mean Δf/f), one-tailed Wilcoxon signed-rank test. (d) lLH activity is inhibited (p = 0.0003 (max Δf/f) / 1.8×10−6 (sum Δf/f) / 0.049 (mean Δf/f)), whereas mLH activity appears unchanged after after illumination of Tg(y333:Gal4; UAS:ReaChR-RFP)-positive cH neurons (p = 0.74 (max Δf/f) / 0.85 (sum Δf/f) / 0.13 (mean Δf/f)), n = 108 stimulations across nine fish, two-tailed Wilcoxon signed-rank test. (e) Illumination of a control preoptic region (outside of the area labeled by Tg(y333:Gal4; ReaChR-RFP) expression) resulted in a small increase in mLH activity (p = 0.0003 (max Δf/f) / 0.039 (sum Δf/f) / 0.039 (mean Δf/f)) and no change lLH activity (p = 0.099 (max Δf/f) / 0.65 (sum Δf/f) / 0.096 (mean Δf/f)), n = 37 stimulations across five fish, two-tailed Wilcoxon signed-rank test. Data plotted in Figure 5 are provided in Figure 5—source data 1.

https://doi.org/10.7554/eLife.43775.023

Functional dissection of the role of cH serotonergic neurons in feeding behavior

The opposing patterns of cH and LH activity suggest they might encode opposing functions in the motivation and control of feeding behavior. Increased cH activity during food deprivation might encode a motivated state that leads to enhanced prey detection, enhanced hunting behavior and increased prey ingestion following food presentation. In contrast, the incremental increase in cH activity during feeding (Figure 1g) might progressively inhibit lLH activity (Figure 5) and thus inhibit prey ingestion (Muto et al., 2017). To test these expectations, we used optogenetic ReaChR activation to increase cH neuron activity during food deprivation or during voracious feeding. We reasoned that since after a short period of food deprivation (≤30 minutes), cH activity is relatively low (Figure 2a,b), optogenetic cH neuron activation in such animals would mimic a longer food deprivation and yield subsequent voracious feeding. In contrast, animals that are already feeding voraciously will have very low cH activity (Figures 1f–g and 2a–b); cH activation in these animals might thus reduce voracious feeding by mimicking the ‘satiated’ state (Figure 1f,g).

Accordingly, animals expressing ReaChR in cH neurons (Tg(y333:Gal4;UAS:ReaChR-RFP)) were exposed to 630 nm illumination and assessed for ingestion of fluorescently labeled paramecia (Figure 6). Such animals exhibited enhanced cH activity following illumination (Figure 6; Figure 6—figure supplement 1). As expected, animals that had been illuminated during a short period of food deprivation subsequently consumed significantly more paramecia than control fish, which were similarly food-deprived and illuminated, but lacked the ReaChR transgene (Figure 6a). In contrast, fish that had been illuminated at the end of a two-hour food deprivation period displayed a high level of prey ingestion irrespective of whether the ReaChR channel was present. Thus, the high level of cH activity produced by two hours long food deprivation could not be augmented by optogenetic activation.

Figure 6 with 2 supplements see all
Role of the cH in behavioral control.

(a) Animals expressing the ReaChR transgene Tg(UAS:ReaChR-RFP) under control of the Tg(y333:Gal4) driver were exposed to 630 nm illumination (orange bar in schematic) for 10 min prior to feeding and assessed for subsequent ingestion of fluorescently labeled paramecia. Tg(y333:Gal4; UAS:ReaChR-RFP) stimulation increased food intake in 30 min food-deprived but not 2 hr food-deprived fish, during subsequent food presentation. Dep. (30 min): n = 27/26 (ReaChR-/ReaChR+), p = 0.005. Dep. (2 hr): n = 25/29 (ReaChR-/ReaChR+), p = 0.36, one-tailed Wilcoxon rank-sum test. Asterisks denote p<0.05. Since ReaChR expression via Tg(116A:Gal4) was negligible, we used another Gal4 (Tg(y333:Gal4)) line that is also specific to the cH when ReaChR is expressed. Fed and food-deprived fish were assayed simultaneously, thus all results were normalized to fed controls. ReaChR- controls do not have visible Tg(y333:Gal4;UAS:ReaChR-RFP) expression, and thus are a mixture of siblings expressing Tg(y333:Gal4) only, Tg(UAS:ReaChR-RFP) or neither of these transgenes, each with ⅓ probability. (b) Left: Optogenetic activation of Tg(y333:Gal4; UAS:ReaChR-RFP) fish (orange bar in schematic) during feeding in fish that were food-deprived for 30 min does not significantly reduce food intake: n = 19/16 (ReaChR-/ReaChR+), p = 0.44 (N.S.); Right: Optogenetic activation of Tg(y333:Gal4; UAS:ReaChR-RFP) fish during feeding in 2 hr food-deprived fish reduces food intake: n = 53/44 (ReaChR-/ReaChR+), p = 0.042. Since 30 min and 2 hr food-deprived fish were assayed in different experiments, gut fluorescence normalized to their respective controls, one-tailed Wilcoxon rank-sum test. (c) Nitroreductase-mediated ablation of the cH in Tg(116A:Gal4;UAS:nfsb-mCherry)-positive or negative fish treated with metronidazole (MTZ) from 5 to 7 dpf significantly enhances food intake in 8 dpf fish. p = 0.0042/0.041/1.4 × 10−5 (fed control vs fed ablated, 2 hr dep. control vs 2 hr dep. ablated, fed vs 2 hr dep.), n = 29 (fed control)/28 (fed ablated)/22 (dep. control)/29 (dep. ablated), two-tailed Wilcoxon rank-sum test. Controls do not have visible Tg(116A:Gal4;UAS:nfsb-mCherry) expression, and thus are a mixture of siblings expressing Tg(116A:Gal4) only, Tg(UAS:nfsb-mCherry) or neither of these transgenes, each with ⅓ probability. (d) Schematic summarizing our results. We propose distinct roles of the cH during hunger, depending on the presence or absence of food. See Appendix 1 – Conceptual Circuit Model for elaboration. Data plotted in Figure 6 are provided in Figure 6—source data 1.

https://doi.org/10.7554/eLife.43775.027

On the other hand, when cH activity was optogenetically excited during voracious feeding (where cH activity would normally be very low), prey ingestion was reduced (Figure 6b). We presume that increased cH activity inhibits lLH activity (Figure 5), which in turn is associated with satiation and lack of feeding (Figure 1f,g). Indeed, inhibition of LH signaling has been shown to reduce prey capture success in comparable studies (Muto et al., 2017).

Finally, we asked what would happen if cH activity was reduced by partial ablation of serotonergic cells. Chemical-genetic ablation was performed via expression of a transgenic bacterial nitroreductase (Tg(UAS:nfsb-mCherry)) (Curado et al., 2008; Davison et al., 2007; Pisharath and Parsons, 2009) that was driven in cH serotonergic neurons by Tg(116A:Gal4) (Figure 3—figure supplement 1). Tg(116A:Gal4; UAS:nfsb-mCherry)-positive animals displayed a loss of nfsb-mCherry-expressing neurons after treatment with the chemical MTZ (Figure 6—figure supplement 2). These animals were compared to MTZ-treated sibling control animals lacking the Tg(UAS:nfsb-mCherry) transgene (Figure 6c). Fish with ablated cH serotonergic neurons displayed greater food ingestion than control animals irrespective of whether the animals had been food-deprived or continuously fed (Figure 6c). Animals that had been continuously fed displayed greater prey ingestion. They thus appear to display a defect in cH-mediated inhibition of feeding (Figure 6b) that could underlie satiety. Animals that had been food-deprived displayed greater than normal (relative to non-ablated control animals) voracious feeding (Figure 6c). Taken together, these results are consistent with the notion that cH activity regulates hunting and prey ingestion, at least partially via inhibition of hunting and prey ingestion behaviors.

Discussion

Decades-old studies on appetite regulation in mammals have suggested that the hypothalamus consists of modular units that functionally interact to suppress or enhance food intake. Here we show that the larval zebrafish hypothalamic network can similarly be divided into medial and lateral units on the basis of neural activity and function. These units show anti-correlated activity patterns extending through various states and distinct behaviors during periods of food deprivation and feeding. We propose these states are analogous to those commonly referred to as hunger and satiety and reflect the animal’s drive to maintain energy homeostasis (Figure 6d). Furthermore, we show that within these broad neural response classes lie subpopulations that encode specific stimuli and perform distinct functions depending on the timing of their activation.

Mutually opposing hypothalamic networks control zebrafish appetite

We show that the medial hypothalamic zone, especially the caudal hypothalamus (cH), is strongly activated by food deprivation and silent during voracious feeding, and that these changes in activity occur on a timescale of seconds to minutes. Here, we focused mainly on the cH serotonergic neurons, although many medially localized neurons show similar activity patterns. In contrast, the lateral hypothalamus (LH), which contains GABAergic and glutamatergic neurons, can be inhibited by the cH (Figure 5) and is weakly active in the absence of food; conversely it is most strongly active during voracious feeding when cH serotonergic neurons are silent. Interestingly, fish that display satiated feeding behavior exhibit intermediate activity levels in the two hypothalamic regions (Figure 1). Thus, "hunger" in the larval zebrafish is encoded by two alternative and distinct states of activity in opposing brain regions, depending on whether food is absent or present, with the restoration of energy homeostasis (i.e. satiety) paralleled by a return to an intermediate state of balanced activity.

While generally anti-correlated, the cH and LH also appear to be differentially modulated both by internal energy states and external factors such as prey. In the absence of food, LH neural activity decreases rapidly (Figure 2), suggesting a requirement of external food cues to drive LH activity, though some modest rate of spontaneous activity is still observed (Figure 5, Figure 3—figure supplement 3). In contrast, the slower timescale of increasing cH activity during food deprivation (Figure 2, Figure 3—figure supplement 3) may reflect a rising caloric deficit. Notably, many of the cH neurons are cerebrospinal fluid-contacting and thus have access to circulatory nutrient and hormone information (Lillesaar, 2011; Pérez et al., 2013).

When prey is presented to a food-deprived animal, a rapid state change occurs as LH neural activity is strongly increased and cH activity rapidly diminishes (Figures 14). Importantly, the silence of cH neurons and strength of LH activity were correlated with the extent of prior food deprivation (Figure 2), suggesting a role for these nuclei in regulating food intake based on energy needs. The quick timescale of these changes in activity suggests that they do not reflect an alleviation of caloric deficit (i.e. a change in hunger state), which would take a significantly longer time to occur. Further, the striking anti-correlation between the cH and LH is consistent with their mutual inhibition, and suggests that the acute reduction in cH activity allows for rapid LH excitation upon the presentation of prey cues. We supported this notion by showing that optogenetic stimulation of a subset of cH neurons could inhibit lLH activity (Figure 5). However, the mechanisms for cH and LH mutual interactions are still unknown. It is possible that the cH may act via nearby inhibitory GABAergic neurons, and/or exert its effects through direct secretion of monoamines into the ventricles or perineuronal space. The fast (seconds) anti-correlation between cH and LH calcium activity (Figure 3), suggests the presence of direct inhibitory connections. The LH, which was previously characterized in Muto et al. (2017), similarly does not appear to send direct projections to the cH, but could potentially interact via intermediary neurons in the medial/periventricular regions of the hypothalamus.

The cH and LH show differential sensitivity to prey sensory and consummatory cues

Ingestive behavior has been proposed to comprise a series of sequential phases: 1) the initiation phase, triggered by energy deficit, in which the animal begins to forage; 2) the procurement phase, triggered by the presence of food sensory cues, in which the animal seeks and pursues food; and 3) the consummatory phase, which usually involves more stereotyped motor programs (Berthoud, 2002; Watts, 2000). An animal’s energy status is sensed internally and may influence the initiation, procurement and consummatory stages of ingestive behavior. Thus, a hungry animal will be more alert to food cues, seek food more persistently and also eat more voraciously.

In mammals, LH neurons are responsive to both external food sensory cues and consummatory cues (Jennings et al., 2015). Here, we show that the LH lobes in zebrafish also respond to both types of food cues. In the ‘sensory’ stage, the mLH and lLH are already activated, which may reflect an enhanced sensitivity to food cues during hunger. In contrast, cH activity transiently falls (as shown by calcium imaging in Figure 3) but remains overall high.

Notably, cH inhibition and LH activation during the sensory stage is not as strong as post-food consumption (Figure 4), which induces massive and opposing changes in the activity of both domains. Since LH and cH activity are modulated within minutes of food consumption, they are unlikely to reflect satiety signals, and rather might play a role in further driving voracious food consumption, at least until the activity of both populations returns to baseline. While it is unclear which consummatory cues modulate LH and cH activity, based on live imaging results from Muto et al. (2017), the greatest enhancement of LH activity was observed almost immediately (milliseconds to seconds) after paramecia consumption. Thus, the cue is likely a fast pregastric signal (taste/tactile/swallowing), rather than postgastric absorption or hormone secretion.

Finally, our data raise the possibility of functional compartmentalization within the LH. Especially in terms of cellular pERK activity, the lLH is more weakly activated by food sensory cues compared to the mLH, suggesting that the lLH, similar to the cH, may be more sensitive to consummatory cues than sensory food cues alone. These results are also consistent with a generally stronger anti-correlation of lLH and cH activity (compared to mLH), as observed in our calcium imaging and optogenetic experiments. Further molecular, cellular, and functional dissection of the individual LH lobes will allow for a better understanding of their behavioral roles.

Functional roles of the cH and LH in and beyond appetite control

Finally, we test the hypothesis that the cH and LH form mutually antagonistic functional units that dominate different phases of hunger and drive appropriate behavioral responses during each phase (Figure 6). In particular, we show that the activation state of the cH is a crucial regulator of satiation state-dependent food intake. Artificial cH activation in satiated fish prior to feeding is sufficient to drive subsequent voracious feeding. Based on observed cH dynamics, we propose that the degree of cH inhibition during voracious feeding is proportional to the degree of cH activation prior to feeding. This could be mediated by the release of serotonin/other neuromodulators over the course of food deprivation, which may be capable of sensitizing the LH even in the absence of food cues. In this way, zebrafish are able to retain a ‘memory’ of their hunger state, which is released once food is presented. This motif might help ensure that the animal eventually returns to a stable equilibrium, that is, satiety.

We furthermore show that the acute effect of cH activation during feeding is suppression of food intake, whereas cH ablation enhances food intake, which is again consistent with mammalian studies of medial hypothalamic areas. At first glance, the observation that the cH acutely suppresses food intake is inconsistent with the idea that it is most active during food deprivation. However, the critical difference here is the presence or absence of food. Once food is presented to a hungry fish, high activity in the cH may simply suppress LH activity, and hence elevate the initial threshold for food intake.

The seemingly paradoxical roles of the cH during hunger may also make sense when considering that, in the absence of food, consummatory behavior would in fact be counterproductive. Thus, during food deprivation, the cH may play complementary roles such as the sensitization of the LH and/or other feeding-related circuits (as discussed above), or drive alternative behavioral programs, like foraging or energy-conserving measures (see Appendix 1 - Conceptual Circuit Model for a more in-depth discussion). Given that cH neurons are also activated by aversive stimuli (Randlett et al., 2015; Wee et al., 2019), they might generally encode a negative valence state, of which being hungry in the absence of food is an example. The silence of these neurons in a hungry fish where food is present may then imply a positive valence state, a notion that is in ready agreement with human subjective experience. Similar features of hunger-related (i.e. AgRP) neurons have also been described in mammals (Betley et al., 2015; Chen et al., 2015; Dietrich et al., 2015; Mandelblat-Cerf et al., 2015).

Although the cH does not have an exact mammalian homolog, its functions have been proposed to be adopted by other modulatory populations, such as the serotonergic raphe nucleus in mammals (Gaspar and Lillesaar, 2012; Lillesaar, 2011). While shown to be a potent appetite suppressant, serotonin is also released during food deprivation, and can enhance food-seeking behavior (Elipot et al., 2013; Kantak et al., 1978; Pollock and Rowland, 1981; Voigt and Fink, 2015). Thus, our results revealing opposing cH activity patterns during hunger could reflect similarly complex roles of serotonin in zebrafish, potentially explaining some of its paradoxical effects on food intake and weight control in mammals (Harvey and Bouwer, 2000). The cH and PVO also express dopaminergic (intermingled with 5-HT) and a much smaller fraction of histaminergic neurons, which appear to be densely interconnected (Chen et al., 2016; Kaslin and Panula, 2001). We note that our data, while confirming a role of serotonergic neurons, does not rule out an involvement of these other neuromodulators in appetite control, particularly dopamine.

Further, we do not rule out the involvement of other circuits in appetite control; in fact, there are likely numerous players involved. For example, the PVO appears to be modulated by food cues and food deprivation, is anti-correlated with LH activity, and labeled by our transgenic lines (albeit more sparsely), suggesting it may complement the role of the cH. Our conclusions are also limited by the available tools and methodologies -- since different transgenic lines were utilized for stimulation and ablation, we cannot be certain that we are manipulating the same population of neurons, though both share mutual overlap with serotonergic cells. Also, due to the lack of complete transgene specificity, there is a possibility that our manipulations may affect non-specific targets such as the olfactory bulb.

The strong LH activation by the presentation of food after food deprivation suggests that this region is involved in the induction of voracious feeding. This notion is supported by Muto et al. (2017) who recently demonstrated that inhibition of the LH impairs prey capture, a behavior that is clearly related to voracious feeding. Furthermore, electrical stimulation of the homologous region (lateral recess nuclei) in adult cichlids and bluegills (Demski, 1973; Demski and Knigge, 1971) can elicit feeding behavior, which is consistent with our hypothesis. Interestingly, while stimulating parts of this region induced food intake, the activation of other parts induced behaviors such as the ‘snapping of gravel’, which are reminiscent of food search or procurement. In mammals, electrical or optogenetic stimulation of LH neurons triggers voracious feeding, again consistent with our findings that the LH is highly activated during the voracious feeding phase in hungry fish (Delgado and Anand, 1952). In particular, GABAergic neurons that do not co-express MCH or Orexin have been shown to be responsive to food cues and are sufficient to stimulate food intake in mammals (Jennings et al., 2015). Whether the GABAergic and glutamatergic neurons of the zebrafish LH co-express other neuromodulators, as has been recently discovered in mammals (Mickelsen et al., 2019) remains to be explored. Overall, these data suggest that the zebrafish LH may play an important role in driving food intake during hunger, despite some differences in peptidergic expression from the mammalian LH. Certainly, since cues such as water flow and optogenetic stimulation light are sufficient to modulate cH and/or LH neurons, these hypothalamic loci may be also involved in other sensorimotor behaviors beyond appetite regulation.

In conclusion, we have shown here how anatomically-segregated hypothalamic nuclei might interact to control energy homeostasis. We argue that the medial-lateral logic of hypothalamic function that is well established in mammalian systems may be conserved even in non-mammalian vertebrates, though their activity patterns might possibly be more complex than originally believed. Our data suggest diverse roles of neuromodulators such as serotonin in regulating behavioral responses during hunger, which complement mammalian observations. Finally, we propose that investigating large-scale network dynamics can reveal an additional layer of insight into the principles underlying homeostatic behavior, which might be overlooked when studies are restricted to the observation and perturbation of smaller subpopulations.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or referenceIdentifiersAdditional
information
Genetic reagent (Danio rerio)Tg(pGal4FF:116A)Characterized in this manuscriptDr. Koichi Kawakami
(NIG, Japan)
Genetic reagent (Danio rerio)Tg(pGal4FF:76A)PMID: 28425439Dr. Koichi Kawakami
(NIG, Japan)
Genetic reagent (Danio rerio)Tg(y333:Gal4)PMID: 26635538Dr. Harold Burgess (NIH)
Genetic reagent (Danio rerio)Tg(HuC:GCaMP6s)PMID: 28892088Dr. Florian Engert
(Harvard)
Genetic reagent (Danio rerio)Tg(UAS:GCaMP6s)PMID: 28425439Dr. Koichi Kawakami
(NIG, Japan)
Genetic reagent (Danio rerio)Tg(UAS:ReaChR-RFP)Characterized in this manuscriptDr. Misha Ahrens (Janelia Research Campus)
Genetic reagent (Danio rerio)Tg(UAS-E1b:NTR-mCherry)PMID: 17335798Available from ZIRC
Genetic reagent (Danio rerio)Tg(Vglut2a:dsRed)PMID: 19369545
Genetic reagent (Danio rerio)Tg(Gad1b:loxP-dsRed-loxP-GFP)PMID: 23946442
Genetic reagent (Danio rerio)Tg(Gad1b:GFP)PMID: 23946442
Genetic reagent (Danio rerio)Tg(TH2:GCaMP5)PMID: 26774784Dr. Adam Douglass (University of Utah)
Genetic reagent (Danio rerio)Tg(ETvmat2:GFP)PMID:18164283
Genetic reagent (Danio rerio)Tg(HCRT:RFP)PMID: 25725064
Antibodyrabbit monoclonal anti-pERKCell Signaling4370
RRID:AB_2315112
IHC (1:500)
Antibodymouse monoclonal anti-ERKCell Signaling4696
RRID:AB_390780
IHC (1:500)
Antibodyrabbit polyclonal anti-5-HTSigma-AldrichS5545
RRID:AB_477522
IHC (1:500)
Antibodygoat polyclonal anti-5-HTAbCamab66047
RRID:AB_1142794
IHC (1:500), 2% BSA in PBS, 0.3% Triton blocking solution)
Antibodygoat polyclonal anti-MSHEMD MilliporeAB5087
RRID:AB_91683
IHC (1:500), 2% BSA in PBS, 0.3% Triton blocking solution)
Antibodyrabbit polyclonal anti-AGRPPhoenix PharmaceuticalsH-003–53
RRID:AB_2313908
IHC (1:500)
Antibodyrabbit polyclonal anti-MCHPhoenix PharmaceuticalsH-070–47
RRID:AB_10013632
IHC (1:500)
Antibodyrabbit polyclonal anti-CARTPhoenix Pharmaceuticals55–102
RRID:AB_2313614
IHC (1:500)
Antibodyrabbit polyclonal anti-NPYImmunostar22940
RRID:AB_2307354
IHC (1:500)
Antibodymouse monoclonal anti-THImmunostar22941
RRID:AB_1624244
IHC (1:500)
Chemical compound, drugDiD’ solid (lipid dye)Thermo Fisher ScientificD-7757Stock solution (10 mg/ml), working solution (2.5 mg/ml), in ethanol

Fish husbandry and transgenic lines

Larvae and adults were raised in facility water and maintained on a 14:10 hr light:dark cycle at 28°C. All protocols and procedures involving zebrafish were approved by the Harvard University/Faculty of Arts and Sciences Standing Committee on the Use of Animals in Research and Teaching (IACUC). WIK wildtype larvae and mit1fa-/- (nacre) larvae in the AB background, raised at a density of ~40 fish per 10 cm petri dish, were used for behavioral and MAP-mapping experiments.

Transgenic lines Tg(UAS-E1b:NTR-mCherry) (Davison et al., 2007) (referred to as UAS:nfsb-mCherry), Tg(UAS:GCaMP6s) (Muto and Kawakami, 2011; Muto et al., 2017) Tg(HuC:GCaMP6s) (Kim et al., 2017), Tg(Vglut2a:dsRed) (Miyasaka et al., 2009), Tg(Gad1b:loxP-dsRed-loxP-GFP and Tg(Gad1b:GFP) (Satou et al., 2013), Tg(TH2:GCaMP5) (McPherson et al., 2016), Tg(ETvmat2:GFP) (referred to as VMAT:GFP) (Wen et al., 2008), Tg(HCRT:RFP) (Liu et al., 2015) have all been previously described and characterized. Tg(pGal4FF:116A) (referred to as 116A:Gal4) was isolated from a gene trap screen by the Kawakami group (Kawakami et al., 2010), Tg(pGal4FF:76A) was recently published by the same group (Muto et al., 2017). Tg(y333:Gal4) from a different enhancer trap screen was used to drive expression in the cH in cases where 116A:Gal4-driven expression was sparse (Marquart et al., 2015). Tg(UAS:ReaChR-RFP) was generated by Chao-Tsung Yang (Ahrens lab, Janelia Research Campus) using Tol2 transgenesis. The same optogenetic channel was previously validated in zebrafish in Dunn et al. (2016).

MAP-mapping of appetite regions

More details on the MAP-mapping procedure can be found in Randlett et al. (2015). 5–6 dpf, mit1fa-/- (nacre) larvae in the AB background were fed an excess of paramecia once daily. On the day of the experiment (at 7 dpf), the larvae were distributed randomly into two treatment groups: 1) Food-deprived, where larvae were transferred into a clean petri dish of facility water, taking care to rinse out all remaining paramecia or 2) Fed, where after washing and transferring they were fed again with an excess of paramecia. After two hours, larvae in both groups were fed with paramecia. After 15 min, larvae were quickly funneled through a fine-mesh sieve, and the sieve was then immediately dropped into ice-cold 4% paraformaldehyde (PFA) in PBS (PH 7.2–7.4). Fish were then immunostained with procedures as reported below (see Immunostaining methods). The rabbit anti-pERK antibody (Cell Signaling, #4370) and mouse anti-ERK (p44/42 MAPK (Erk1/2) (L34F12) (Cell Signaling, #4696) were used at a 1:500 dilution. Secondary antibodies conjugated with alexa-fluorophores (Life Technologies) were diluted 1:500. For imaging, fish were mounted dorsal-up in 2% (w/v) low melting agarose in PBS (Invitrogen) and imaged at ~0.8/0.8/2 μm voxel size (x/y/z) using an upright confocal microscope (Olympus FV1000), using a 20 × 1.0 NA water dipping objective. All fish to be analyzed in a MAP-Mapping experiment were mounted together on a single imaging dish, and imaged in a single run, alternating between treatment groups.

ICA analysis

ICA analysis was performed exactly as reported in Randlett et al. (2015). The central brain (not including eyes, ganglia, or olfactory epithelia) from each fish was downsampled into 4.7 um3 sized voxels to generate a pERK level vector for each fish. Fish in which any of the voxels was not imaged (due to incomplete coverage) were excluded from the analysis. Fish were normalized for overall brightness by dividing by the 10th percentile intensity value, and voxels normalized by subtracting the mean value across fish. The fish-by-voxel array was then analyzed for spatially independent components using FastICA (http://research.ics.aalto.fi/ica/fastica/, Version 2.5), treating each fish as a signal and each voxel as sample, using the symmetric approach, ‘pow3’ nonlinearity, retaining the first 30 principal components and calculating 30 independent components. Independent component (IC) maps are displayed as the z-score values of the IC signals.

Since ICA analysis requires a substantial sample size, the original analysis reported in Randlett et al. (2015) included 820 fish exposed to various treatments, including fish sampled at different points of the day and night, and fish given various noxious or food stimuli, additional fish stimulated with electric shocks, light flashes, moving gratings, heat, mustard oil, melatonin, clonidine, nicotine, cocaine, ethanol and d-amphetamine.

Here, to focus the analysis on more naturalistic feeding conditions, we restricted the dataset to n = 300 fish that were either food-deprived (2 hr), or presented with food in food-deprived or fed conditions.

Whole-mount immunostaining

24 hr after fixation (4% paraformaldehyde (PFA) in PBS), fish were washed in PBS + 0.25% Triton (PBT), incubated in 150 mM Tris-HCl at pH 9 for 15 min at 70°C (antigen retrieval), washed in PBT, permeabilized in 0.05% Trypsin-EDTA for 45 min on ice, washed in PBT, blocked in blocking solution (10% Goat Serum, 0.3% Triton in Balanced Salt Solution or 2% BSA in PBS, 0.3% Triton) for at least an hour and then incubated in primary and secondary antibodies for up to 3 days at 4°C diluted in blocking solution. In-between primary and secondary antibodies, fish were washed in PBT and blocked for an hour. If necessary, pigmented embryos were bleached for 5 min after fixation with a 5%KOH/3%H2O2 solution.

The protocol was similar for dissected brains, except that the brains were dissected in PBS after 24 hr of fixation, and the permeabilization step in Trypsin-EDTA and occasionally Tris-HCL antigen retrieval were omitted. Dissected brains were mounted ventral up on slides in 70% glycerol prior to imaging. Confocal images of dissected brains were obtained using either a Zeiss LSM 700 or Olympus FV1000.

Quantification of food intake

Paramecia cultures (~1–2 500 ml bottles) were harvested, spun down gently (<3000 rpm) and concentrated, and subsequently incubated with lipid dye (DiD’ solid, D-7757, Thermo Fisher Scientific, dissolved in ethanol) for >2 hr (5 µl of 2.5 mg/ml working solution per 1 ml of concentrated paramecia) on a rotator with mild agitation. They were then spun down gently (<3000 rpm), rinsed and reconstituted in deionized water. An equal amount (100 µl,~500 paramecia) was pipetted into each 10 cm dish of larvae. This method was adapted from Shimada et al. (2012). After the experiment, larvae were fixed and mounted on their sides on glass slides or placed in wells of a 96 well plate. They were then imaged using the AxioZoom V16 (Zeiss) and analyzed using custom Fiji (Schindelin et al., 2012) software. In cases where the identity of larvae needed to be maintained, for example, to correlate food intake with brain activity, larvae were imaged and subsequently stained individually in 96 well plates. This led to more variable staining which affects analysis of mean fluorescence.

Larvae were always distributed randomly into experimental groups.

Quantification of LH and cH activity in dissected brains

Brains within each dataset were usually registered onto a selected reference image from the same dataset using the same CMTK registration software used in MAP-mapping. Further analysis was then performed using custom Fiji and MATLAB software.

Quantification of mean anti-pERK fluorescence

For quantification of cH, mLH and lLH pERK fluorescence intensity, ROIs were manually defined using the reference image, and pERK intensity was quantified over all registered images and averaged across the entire lobe (multiple z-planes) as well as across both lobes. Analysis of cH pERK fluorescence was restricted to the most ventral planes, as more dorsal cH neurons show weaker correlation with feeding states (e.g. Figure 1—figure supplement 5).

Quantification of active cell count

For quantification of mLH and lLH active cell count, automated analysis of cell count was again performed using custom Fiji software, namely: 1) Image processing to reduce background and enhance contrast 2) Adaptive thresholding to isolate strongly-stained cells 3) Applying the ‘Analyze Particles’ function to quantify the number of cells within each manually-defined ROI. Aggregation and visualization of results were performed using custom MATLAB software.

Note that, in experiments in which the data were collected without the tERK channel (e.g. from Figure 2), thus prohibiting image registration, ROIs were drawn manually over each region across all z-planes and averaged to obtain mean fluorescence values. For Figure 2—figure supplement 1, where individual fish were stained, all measurements, including cell count, were made manually. In addition, background fluorescence was measured for each sample and subtracted from measured values.

Semi-automated quantification of ReaChR overlap with anti-pERK staining

This section describes the analysis method for Figure 6—figure supplement 1. The multi-point picker on ImageJ was first used to select all visible ReaChR-positive or ReaChR-negative cells within each z-stack for each fish. A custom Fiji macro was then used to extract mean pERK intensities from all identified cells, and data were further processed using MATLAB. Data were plotted using the notBoxPlot.m Matlab function.

Calcium imaging

For confocal calcium imaging of the cH and LH simultaneously in the presence of food, Tg(76A:Gal4;116A:Gal4; UAS:GCaMP6s) triple transgenic fish were embedded in 1.8% agarose, with their eyes/nostrils released. GCaMP activity from a single z-plane (where the cH and LH neurons could be seen) was imaged using a confocal microscope (Olympus FV1000) at one fps. After a 5 min habituation period and a 5 min baseline period, a dense drop of water, followed by paramecia (5 min later) was pipetted into the dish. Due to paramecia phototaxis, most of the paramecia moved into close vicinity of the fish’s head under the laser, allowing for strong visual/olfactory exposure to paramecia. After image registration (TurboReg Fiji Plugin, Thévenaz et al., 1998), and downsampling (Fiji/MATLAB), manually-segmented ROIs were selected and total fluorescence within the ROI was calculated. Cross-correlation and other analyses were performed using custom MATLAB software.

For long-term 2P imaging of the cH and LH simultaneously in the absence of food (Figure 3—figure supplement 3), Tg(76A:Gal4;116A:Gal4; UAS:GCaMP6s) triple transgenic fish were embedded in 1.8% agarose. GCaMP activity from either multiple slices (3 z-planes spanning a ~ 20 µm volume of the intermediate hypothalamus using an electrically-tunable liquid lens (Edmund Optics, 83–922), 237 ms per z-plane) or a single z-plane where the cH and LH neurons (1.5 fps) could be seen was imaged using custom 2P microscopes. After image registration (Fiji/MATLAB), manually segmented ROIs were selected and total fluorescence within the ROI was calculated. Calcium spike detection and other analyses were performed using custom MATLAB software. Baseline detrending was performed on ‘raw’ Δf/f traces by fitting a quadratic polynomial and subtracting it from the trace. Calculations on calcium spike frequency and amplitude were subsequently performed using baseline-detrended calcium traces.

Optogenetic stimulation and simultaneous calcium imaging

Optogenetic stimulation and calcium imaging was performed on a confocal microscope (Zeiss LSM 880) using a 633 nm laser for ReaChR activation, and a 488 nm laser for calcium imaging. Tg(y333:Gal4;UAS:ReaChR-RFP; HuCGCaMP6s) triple-transgenic fish were used to record LH activity after ReaChR activation. As Tg(HuC:GCaMP6) does not label the cH, in some cases we used fish that also had Tg(UAS:GCaMP6s) co-expressed in the cH, allowing for monitoring of cH activity directly.

The ReaChR activation spectrum is wide and 488 nm laser power at sufficiently high intensities is sufficient to activate ReaChR. Since Tg(y333:Gal4;UASGCaMP6s) is expressed strongly in the cH, weak 488 nm laser power can be used to monitor cH activity after ReaChR activation of cH. On the other hand, Tg(HuC:GCaMP6s) expression in the LH is considerably weaker than Tg(UAS:GCaMP6s) expression driven by Tg(y333:Gal4), and recording LH activity requires high laser power. Thus, during LH recording trials, we could not simultaneously image the cH.

Fed fish were embedded in 1.8–2% agarose, with tails, mouth and eyes freed, 15–20 min before imaging in the absence of food. For baseline recording, spontaneous activities in cH or LH were recorded. ReachR activation was then induced in one side of cH periodically for 10–15 s, and ensuing activity in one or both sides of LH or cH was recorded continuously during intervals (of 120–180 s) between stimuli.

Nitroreductase-mediated ablations

Larvae expressing Tg(116A:Gal4;UAS:nfsb-mCherry), or their non-transgenic siblings were incubated in 2.5 mM Metronidazole (Sigma-Aldrich, M3761) from 4-6 dpf/5–7 dpf. MTZ was subsequently washed out, and food intake was measured at 7 or 8 dpf. For these experiments, the MTZ-treated non-transgenic siblings were used as the control group. Each control or ablated group was food-deprived or fed for 2 hr, and labeled food was added to quantify food intake. In the case of fed fish, unlabeled food was very gently washed out 15 mins before the experiment and the food-deprived fish were also agitated slightly to simulate a short washout.

Optogenetic stimulation with behavior

Optogenetic stimulation was done by placing a square LED panel (630 nm, 0.12 mW/mm2 driven at full current, Soda Vision, Singapore) directly on top of petri dishes containing ReaChR positive or negative fish, for 10 min continuously before or during feeding. We had attempted other methods of stimulating the fish (e.g. pulsed LED stimulation) but found that it was disruptive to behavior.

Artemia hunting video

7 dpf larval fish were food-deprived for 2 hr, acclimatized in 24 well plates for 30 min, and then fed either an excess of hatched artemia or paramecia. Raw videos of hunting behavior were then recorded for 10 min at 30 fps using a high-resolution monochrome camera (Basler acA4924) and custom Python-based acquisition software.

High-resolution behavioral tracking

We developed a system (Johnson et al., 2019) in which a high-speed infrared camera moves on motorized rails to automatically track a zebrafish larvae in a large pool (300 × 300×4 mm). A single fish is recruited to the arena center with motion cues delivered from a projector to initiate each trial. Paramecia are dispersed throughout the middle of the pool. For analysis 60 Hz image frames are centered and aligned. In every frame, the tail was skeletonized and the gaze angle of each eye is calculated. The eyes can each move from around zero degrees (parallel to body-axis) to 40 degrees (converged for hunting). Each bout was then represented as a point in 220-dimensional posture space by accumulating 22 posture measurements (20 tail tangent angles to encode tail shape, and two eye gaze angles) across 10 image frames (~167 ms) from the beginning of each bout. All bouts were then mapped to a 2-D space with t-distributed stochastic neighbor embedding (t-SNE), Four major hunting bout types can be identified from this embedding. Hunts begin with the ‘j-turn’, and fish follow and advance toward prey objects with ‘pursuit’ bouts. Hunts end with an ‘abort’ or a ‘strike’. When the fish is not actively involved in a hunt, it explores the arena with ‘exploratory’ bouts. Fractions of hunting bouts were then compared between fed and food-deprived fish in 3 min time bins over 45 min.

Statistics

All error bars show mean ± SEM over fish. Significance was reported as follows: *p<0.05. Significance was determined using the non-parametric Wilcoxon signed-rank test for paired data and the Wilcoxon rank-sum test for independent samples. One-tailed tests were performed in cases where there was a prior prediction regarding the direction of change. A one-or two-way ANOVA (Tukey-Kramer correction, MATLAB statistical toolbox) was used in cases where multiple comparisons were involved.

Code availability

Analysis code used in this manuscript is available at https://github.com/carolinewee/ROIbasedpERKanalysis (Wee, 2019a; copy archived at https://github.com/elifesciences-publications/ROIbasedpERKanalysis), https://github.com/carolinewee/gutfluorescence (Wee, 2019b; copy archived at https://github.com/elifesciences-publications/gutfluorescence) and https://github.com/carolinewee/CellularpERKanalysis (Wee, 2019c; copy archived at https://github.com/elifesciences-publications/CellularpERKanalysis). 

Appendix 1

Conceptual Circuit Model

The core issue that arose during the review of the manuscript, and the apparent paradox that manifests in the observant reader, is that cH activity correlates both with Hunger and Satiety - depending on the presence or absence of food. This conundrum is also reflected in the reviewers’ comments.

At a more basic level, there was also some concern about usage of words such as hunger and satiety in larval zebrafish, which we believe is largely a semantic issue that is best addressed by providing an operational definition of these terms:

Hunger is an internal state defined by three conditions:

  1. The state of being in a caloric/energy deficit - which is usually the consequence of food deprivation

  2. A state that may promote food-seeking behavior as well as increased food intake

  3. A state that is reflected by - and correlates with - a pattern of modulatory neuronal activity

Voracious feeding is a sub-state of hunger in which food is present and being ingested, but caloric/energy deficit is still high.

Satiety is considered the opposite of hunger and is defined accordingly:

  1. A state of having sufficient levels of calories/energy or levels that rest above a homeostatic baseline

  2. A state that manifests behaviorally in the slower/lower consumption of food relative to the state of hunger, due to indifference to food or its active avoidance

  3. A state that is reflected by an internal modulatory neuronal state that may be antagonistic and opposite to that from hunger

In order to clarify these questions related to our manuscript we give in the following a detailed description of our conceptual model. We describe how this model incorporates the observed activity patterns in all three nuclei (cH, mLH and lLH) and we discuss the role that we propose all three may play in releasing hunting and feeding behavior. We emphasize that this is a working model, and though we have presented partial evidence in support of some aspects, additional studies will be required to conclusively prove and/or refine our hypotheses.

To recap, we observe that cH activity is at baseline when the animal is satiated (well-fed). Activity then increases during food deprivation, and drops to very low levels once food is presented. During feeding, and with the resulting return to satiety, the activity rises back to baseline levels (Appendix 1—figure 1).

Appendix 1—figure 1
Summary of cH and LH activity over Hunger and Satiety.

This diagram is the same as in Figure 4d. Differential neural activation of the cH and LH regions in response to prey sensation and hunting as compared to prey ingestion.

https://doi.org/10.7554/eLife.43775.035

The general framework of our conceptual circuit model is based on two core assumptions. These are:

  1. Similar to mammals and adult fish, the LH drives food consumption (Demski and Knigge, 1971; Jennings et al., 2015; Roberts and Savage, 1978). Thus, it makes sense that LH activity in the presence of food/ingestive cues is at medium levels during satiety and higher with increased food deprivation time.

  2. That the cH and the lLH/mLH have mutually inhibitory connectivity, a hypothesis which is backed up by the strong and consistent anti-correlated activity patterns observed in these nuclei. Further, we now provide evidence demonstrating an inhibitory effect of the cH on lLH activity.

We will also be making other assumptions which are explicitly outlined below.

Next, we address the seemingly-complex role of the cH, which is complicated by the fact that it has opposite activity patterns depending on whether food is absent or whether it has been detected/ingested. In our manuscript we put forth a number of non-mutually-exclusive hypotheses for the roles of the cH in each of these stages:

  1. The cH encodes an aversive, negative valence state, which should induce, when activated in the absence of food, a negative association with the current condition and a drive to change this condition and explore alternatives. Activating cH in the presence of food should reduce food intake, as observed with other stressors (De Marco et al., 2014), and optimize the animal’s behavior to remove itself from the negative context. For example in mammals, AgRP neurons, which are activated during hunger, have been shown to encode a similar aversive state (Betley et al., 2015). Indeed, there is some evidence in the zebrafish literature that the cH may encode aversive stimuli (Wee et al., 2019).

  2. The cH drives exploration (food search) as opposed to exploitation (food consumption), which could be reflected by enhanced locomotion, increase in search area, enhanced visual acuity or sensitivity to prey-like objects or other subtle changes that might not manifest significantly in spontaneous swimming behavior in zebrafish larva. When activated in the absence of food it might drive enhanced exploration and higher sensitivity to food-like cues. For example in mammals, AgRP neurons promote food seeking and exploratory behaviors (Dietrich et al., 2015; Krashes et al., 2011). In contrast, activating the cH in the presence of food, while possibly lowering the threshold of initiating food-seeking behavior (i.e. exploration), might ultimately lead to a reduction of food intake and consummation (i.e. exploitation).

  3. The cH induces sensitization/priming of the LH circuit such that it is more responsive to future food and ingestive cues. If accurate, when activated in the absence of food it should drive enhanced feeding after food is presented. This is what we have observed and reported in our manuscript (Figure 6). If the cH is activated in the presence of food, as long as the priming effect is weaker than the acute cH-mediated inhibition of the LH, driving the cH should simply reduce LH activity and thus also reduce consummatory behavior. Otherwise, if the priming effect is strong, consummatory behavior might be increased. Our experiments activating the cH in the presence of food in both fed and food-deprived fish suggest that any effect of priming is weaker than the acute inhibitory effect of the cH on LH activity.

In the current manuscript, we present partial evidence in support of all three hypotheses. We want to make clear that a conclusive verification of the complete conceptual circuit model (see below) is beyond the scope of this manuscript and will require future studies.

A putative circuit diagram (Appendix 1—figure 2)

Appendix 1—figure 2
A putative circuit diagram HEC = Hunger Encoding Circuit, SEC = Satiety Encoding Circuit, which should have anti-correlated activities and report the animal's energy/caloric status.

The cH represents both hunger and satiety state and primes the LH during hunger. It may drive other behaviors such as exploration or aversive behavior, but also suppresses feeding. Other HEC components may also be involved in LH priming/sensitization. We propose mutual inhibition between the cH and LH, though we have only demonstrated unidirectional inhibition (cH on lLH) thus far. The mLH, normally responsive to food cues, may promote hunting, though not necessarily coupled with ingestion, whereas the lLH, which is more responsive to ingestive cues, should enhance further ingestion (i.e. eating). The LH ‘gate’ is a conceptual representation of how its sensitivity to food cues could be modulated by other signals (i.e. reduced by the SEC and enhanced by cH-mediated priming). It does not necessarily represent a physical neuronal population.

https://doi.org/10.7554/eLife.43775.036

We postulate that the cH receives excitatory input from an undefined source (possibly even directly sensing nutrients/hormones from ventricular cerebrospinal fluid) that generally codes for caloric deficit. We will call this the hunger encoding circuit (HEC). In addition we postulate that the cH receives excitatory input from a source analogous to the HEC that generally codes for satiety. We will call this unknown circuit the ‘satiety encoding circuit’ or ‘SEC’. It is possible that different neurons within the cH encode each of these cues, but both need to be represented for cH activity to converge stably at ‘medium’ levels during satiety (where HEC = 0) without drift.

As described before, we propose that the cH also receives inhibitory input from the LH, which is gated by food and ingestive cues, as well as by satiety cues. Thus, in the absence of food, the LH is inactive and the HEC activates the cH, whereas in the presence of food, the cH is initially strongly inhibited by the LH, and then, as the HEC is shut down and the SEC activated, returns to a baseline firing rate.

Finally, on top of this basic circuitry we propose a latent modulatory connection between the cH and LH which primes the sensitivity of the LH during hunger such that it becomes more active once food and ingestive cues are presented. Other yet-to-be-discovered HEC components may also be involved in such priming. Details of the model are outlined below.

Below we discuss in detail the prediction that this model generates for targeted silencing and activation of cH in food-deprived as well as fed fish in both, the presence as well as the absence of food (also see Appendix 1—figure 3). Note that there are eight possible optogenetic experiments to be conducted with the cH as a target, and they are comprised of three pairs of mutually exclusive conditions: activation vs silencing, food-deprived fish vs fed fish, presence of food vs absence of food. Food intake is currently the clearest behavioral readout, and thus also the focus of this manuscript, though we will continue to pursue experiments in support of other predictions (e.g. exploratory behavior, aversive conditioning).

Appendix 1—figure 3
Another schematic summarizing our circuit model, including predictions for cH ablation and activation results.

Here, the proposed mutual inhibition between cH and LH activity is represented by a ‘see-saw’, and relevant inputs/outputs during each phase are represented by ‘forces’. We represent our predictions for what happens when the cH is ablated, as well as when it is activated in the presence of food, for both satiated and hungry fish. We assume that the effects of priming are weaker than the effects of acute mutual inhibition. Color codes are consistent with Figure 2. Note that activation of the cH prior to feeding is not depicted in this diagram: in this scenario we predict that this would cause priming of the LH that increases subsequent feeding, the effect of which may be more obvious in satiated fish where cH activity starts off lower (consistent with our optogenetics results).

https://doi.org/10.7554/eLife.43775.037

An additional 16 experiments are possible if the mLH and lLH are also considered possible targets for specific optogenetic perturbation. However, such experiments are currently difficult to do since specific transgenic lines for these regions are not available.

The serotonergic neurons of the caudal hypothalamus (cH)

We propose that the cH is driven by (i.e. receives excitatory input from) two regulatory centers: the hunger encoding circuit (HEC), and the satiety encoding circuit (SEC), whose existence we postulate, but which we currently cannot back up with supporting experimental data. Thus, the cH can be strongly activated by hunger cues, but also returned to baseline levels by satiety cues after having been strongly suppressed by the LH.

As described above, we postulate that the cH may play multiple roles in regulating feeding behavior. One of these roles may be to ‘prime’ the LH circuit such that it becomes more active once food is presented. This postulate is supported by the observation that the amount of food cue-induced activity in LH increases with food deprivation time and hence with increased integrated cH activity during food deprivation.

Once food is presented, we posit that the LH, driven by food and ingestive cues, shuts down the cH via inhibitory circuitry. It does so more strongly in food-deprived vs fed fish, as it has been primed by prior cH/HEC activation, and also the SEC is not activated (i.e. the putative ‘gate’ is wide open). This is supported by evidence that LH activity is much higher after a longer-period of food deprivation (e.g. Manuscript Figure 2).

In the presence of food, recovering cH activity levels during feeding lead to a progressive reduction in LH levels and thus reduce food intake.

Predictions

Activation of the cH in the presence of food should reduce ingestion and feeding rates. This is consistent with our current optogenetic results in food-deprived fish. In the case of satiated fish (an experiment we did not previously attempt), we predicted that food intake should still be reduced, but counteracted by the effect of priming. We now present results consistent with this hypothesis-- food intake trends lower in continuously fed fish, though not significantly.

Activation of the cH in the absence of food may lead to 1) avoidance and/or aversive conditioning towards the location at which the cH is being stimulated 2) increased exploratory behavior; for example increase of swim frequency, subtle convergence of eyes, or other subtle changes that might not manifest significantly in spontaneous swimming behavior in zebrafish larva.

Activation of the cH in the absence of food may also sensitize the LH circuit to future food cues and enhance food intake. This is consistent with our optogenetic results.

Ablation of the cH should lead to a disinhibition (i.e. a release) of LH activity and thus hunting and ingestion. Though any ‘priming’ effect would now be reduced, we posit that the acute disinhibition of LH activity would drive up food intake regardless of whether it has been previously ‘primed’ by the cH. That is, animals should still eat more in the presence of food. This is consistent with our ablation results.

In the absence of food we expect that fish with an ablated cH would display a reduction in exploratory behavior which might manifest in a decrease in swim frequency, divergence of eyes etc; again, this might be hard to pick up because these expected changes are subtle and might not present yet at larval stages.

The neurons of the lateral part of the lateral hypothalamus (lLH)

We observe that the lLH shows clear anti-correlated activity with the cH. Its activity is at baseline during satiation, activity decreases during food deprivation, switches to high levels when the animal starts feeding, and slowly drops back to baseline level with the return of satiety.

We postulate and now demonstrate using optogenetics that the lLH likely receives inhibitory input from the cH, and that it is also strongly excited by consummatory internal cues, for example food being ingested. An example of such an input is the anterior branch of the esophageal nerve (En2) in Aplysia which is both necessary and sufficient for effective reinforcement of biting behavior (Brembs et al., 2002) and which is known to convey information about the presence of food during ingestive behavior. Based on work from Muto et al. (2017), LH activity (they did not distinguish between the lobes) increases slightly after food detection, but even more strongly immediately after ingestion (Muto et al., 2017). We further propose that activation of lLH only occurs if both inputs are activated together (cH inhibition and food ingestive cues) and that its activity drives ingestive behavior (capture swims, biting and swallowing).

Predictions

Since no specific transgenic lines exist, clean perturbation experiments are not possible; but predictions, of course can be made.

Activation of the lLH should lead to voracious feeding in the presence of food.

Silencing of the nucleus should shut down consummatory behavior.

The neurons of the medial part of the lateral hypothalamus (mLH)

We observe that the mLH basically shows the same activity patterns as the lLH. The only difference is that the switch from depression to activation occurs already with the presentation of food cues, though there is also a further enhancement post-ingestion. This is concluded from: 1) our calcium imaging results with live paramecia (which fish are unable to consume); 2) the fact that exposure to artemia as food cues, which fish can hunt but cannot swallow (they are too big) drives activity in mLH but not in lLH.

We also have evidence (not included in the manuscript) that the mLH is responsive to paramecia odor. In addition, evidence of LH activation by vision was also presented by Muto et al. (2017). We thus postulate that the mLH receives strong excitatory input from the sensory modalities that detect food (vision, olfaction etc), and that it is inhibited by the cH.

We further postulate that the mLH drives the transition from exploratory to hunting behavior, that is it drives the initiation of hunting sequences that start with re-orienting turns (j-turns) and eye-convergence and are followed by pursuits of prey.

Predictions

Similar constraints about the implementation apply, but predictions can be made.

Activation should lead to an increased probability of the initiation of hunts vs exploratory swims.

Silencing should do the reverse, namely induce a relative reduction in the release of such hunting sequences.

https://doi.org/10.7554/eLife.43775.034

References

  1. 1
    Brain regulation of appetite and satiety
    1. RS Ahima
    2. DA Antwi
    (2008)
    Endocrinology and Metabolism Clinics of North America 37:811–823.
    https://doi.org/10.1016/j.ecl.2008.08.005
  2. 2
  3. 3
    Hypothalamic control of food intake in rats and cats
    1. BK Anand
    2. JR Brobeck
    (1951)
    The Yale Journal of Biology and Medicine 24:123–140.
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
    Probing the diversity of serotonin neurons
    1. P Gaspar
    2. C Lillesaar
    (2012)
    Philosophical Transactions of the Royal Society B: Biological Sciences 367:2382–2394.
    https://doi.org/10.1098/rstb.2011.0378
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67
  68. 68
  69. 69
  70. 70

Decision letter

  1. Ronald L Calabrese
    Senior and Reviewing Editor; Emory University, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for sending your article entitled "A bidirectional network for appetite control in zebrafish" for peer review at eLife. Your article has been evaluated by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation is being overseen by Ronald Calabrese as the Senior Editor.

Summary:

Wee and colleagues describe results from a series of studies where they discovered two anticorrelated populations of hypothalamic neurons regulating feeding behaviors in zebrafish. The manuscript focuses on two brain areas, i.e. caudal (CH) and lateral (LH) hypothalamus, that were differentially activated/suppressed under these conditions. They claim to have associated inverse activity patterns of these areas with states of hunger/satiety. Genetic ablation or optogenetic activation of the caudal hypothalamus (CH) suppressed or enhanced food intake. Because a subset of the CH hypothalamic neurons are serotonergic they imply that serotonergic activity anticipates voracious feeding. The authors argue they have identified a neuronal network, which coordinates energy balance by controlling feeding behavior.

They propose a model in which the LH regulates feeding and the CH acts as a homeostatic regulator of LH to control feeding in the context of a hungry vs. satiated state. The experiments are well designed and use cutting-edge methods to generate observations that support the authors' conclusions. The results are interesting and provide an important advance to what is known about circuits that regulate feeding in fish, and that may be relevant to mammals, but require some additional experiments to support the model. There is also a lack of clarity and detail in many parts of the manuscript that were major distractions.

Essential revisions:

Although this paper presents an interesting story, the rationale and approaches for the experiments are at times difficult to follow and require more detail. Furthermore, they postulate a mutually inhibitory CH, LH circuit (subsection “Mutually opposing hypothalamic networks control zebrafish appetite”), but only show correlative data and don't experimentally test this hypothesis.

The authors use broad terms such as "hunger", "food deprivation", "satiety", "food anticipation" which may be inaccurate or even wrong. In their basic experimental design, they constantly feed the larvae from day 5 to day 7, and then compare the response of larvae that were given excess of paramecia to larvae that were food-deprived for only 2 hours and then re-fed. Whether these conditions represent different states of hunger and/or cognitive response to food (anticipation) as the authors claim is not clear, in particular given the presence of yolk nutrients at these stages. This needs clarification and more precise definition of terms being used.

The claim that CH serotonergic population is associated with anticipated voracious feeding is not convincing. In the case of pERK, the authors present one example that a subset of CH neurons co-localize with 5-HT, however they also show that a different subset is dopaminergic. Without using this marker (5-HT) in all ICA samples one cannot be certain that the active/suppressed neurons are indeed serotonergic. The use of GCaMP expressed in the CH (116A:Gal4) to measure activity (Figure 2) is better, however the authors never show colocalization between 116A:Gal4;UAS:GCaMP and 5-HT. This is important, given the expression variability between the different UAS lines shown by the authors. Hence the authors' conclusion that "serotonergic CH activity reports the extent of food deprivation and anticipates the voracity of future hunting and ingestive behaviours" is not sufficiently supported.

In Subsection “Cellular dissection of hypothalamus neural activity reveals a serotonergic population that anticipates voracious feeding” the authors compare and contrast the activities of CH and LH under different feeding paradigms. However, the data in Figure 1f show CH activity presented as "normalized pERK intensity" but mLH and ILH activity presented as "cell count". In the text the authors state that "the number of active CH cells was dramatically reduced" and cite 1f, which does not show cell count data for CH. Is this a mistake? Unless there is a good reason, CH, mLH and ILH activity levels should all be quantified in the same way, with "normalized pERK intensity" being presumably better than the binary on/off quantification of "cell count". Same issue with Figure 3B.

In Figure 2 the authors interpret the differential GCaMP activity of mLH, lLh and CH as hunger-relevant. However, the data indicates that only mLH is exclusively responsive to food while the other two regions respond to other sensory stimuli, such as water flow. The authors allude to this (subsection “Neuronal activities in the caudal and lateral hypothalamus are anti-correlated over short timescales in response to food sensory cues”) but nevertheless insist on interpreting the data as though it corresponds to feeding/ hunger specifically, rather than "general" sensory processing.

Figure 3: I don't understand the purpose of comparing between artemia and paramecia exposure. The authors state that "artemia are a natural prey that larval fish hunt, but which are too large to consume." Firstly, artemia are saltwater animal and therefore not a natural food source for freshwater zebrafish larvae. Second, while artemia are used as a food source for juvenile (three weeks) and adult fish the authors did not show that introduction of artemia evokes feeding/hunting behavior in larval zebrafish as opposed to other non-food related responses. Therefore, the claim that the mLH response to artemia represents the initial sensory response to food cue is not convincing. Related to this figure the authors again use different parameters to measure neuronal activity, i.e. normalized pERK activity for CH and activated neurons cell count for LH.

The authors use genetic ablation using the UAS:nsfb-mCherry under one CH promoter (116A:Gal4) and optogentic activation using UAS:ReaChR driven by a different promoter (y333:Gal4) and therefore they cannot know if the ablated neurons are the same CH neurons as the light-activated cells. This distinction is crucial, as it is the entire rationale behind performing this dual (optogenetic activation versus chemogenetic ablation) experiment.

The assertion that they specifically ablate or activate serotonergic neurons needs further support as only a small subset of 5-HT neurons is targeted by either nsfb or ReaChR as shown in Figure 1—figure supplement 4.

The y333:Gal4 driver is not well characterized. As the optogentic stimulation is not spatially restricted to the CH the authors should show that ReaChR is not expressed in other brain areas.

As the authors show in Figure 1—figure supplement 4, the 116A:Gal4 CH driver is expressed in other hypothalamic regions (e.g. the PVO) and therefore ablation of other cells may affect food intake.

The authors claim that CH activity causes inhibition of LH (subsection “Functional dissection of cH serotonergic neurons in appetite”) in order to interpret their unexpected result that CH activation in the presence of food (as opposed to prior to the introduction of food) results in reduced food intake. To prove this the authors should show that the optogenetic stimulation of CH neurons will result in decreased LH activity using the 76A:GCaMP measurements.

In Figure 2, what is the evidence that the 76A:Gal4 line is expressed in the same ILH and mLH neurons that are labeled by pERK in Figure 1?

Figure 1G. The axes labels are "ILH" for the Y-axis and "mLH" for the X-axis. What does that mean? What are the units on these axes? Is the "Intensity" quantifying gut fluorescence, # of feeding bouts, or something else? What time point after addition of food is shown in these graphs? Is there statistical analysis to support the idea that LH cell count of food intake is increased as a function of food deprivation time? Why wasn't a similar analysis performed for the CH population? In the text (subsection “Cellular dissection of hypothalamus neural activity reveals a serotonergic population that anticipates voracious feeding”) the authors cite 1g as evidence that both "CH and LH activity are modulated by the length of food deprivation" although no data for CH are shown in 1g.

In Figure 1H, why wasn't the same live imaging analysis performed for LH? The authors later (Figure 2) perform calcium imaging for both CH and LH at the same time, so this should be straightforward. In describing 1h, the authors state "To observe the reciprocal relationships between CH and LH populations in real time, we used calcium imaging to measure the response of CH serotonergic neurons to food deprivation." The ending to the opening "To observe the reciprocal relationships between CH and LH populations in real time,…" should be "…we used calcium imaging to simultaneously measure the response of CH serotonergic and LH neurons to food deprivation.".

The authors state in subsection “Whole brain activity mapping of appetite-regulating regions” that the mammalian analog of the LH has been implicated in appetite control, but is this a good comparison if the zebrafish "LH" does not express the neuropeptides that are thought to regulate feeding in the mammalian LH (i.e. hypocretin, mch, etc)? i.e. should the zebrafish "LH" be given a different name since it appears to contain different neurons, and thus likely has a different function, than the mammalian "LH"? Do the glutamatergic and GABAergic neurons found in the mouse LH co-express the hypocretin, mch, etc. neuropeptides?

Based on the data presented in Figure 3, CH activity appears to be regulated by food consumption as opposed to the sensory cues generated by presence of food, similar to ILH, while mLH activity appears to be inducible by sensory cues alone (although to a lesser extent than sensory cues and food consumption at the same time). In subsection “The activities of cH and LH neurons are differentially modulated by food sensory cues and ingestion” the authors claim that this in accordance with "the strong anti-correlation of CH with lLH activity (compared to mLH activity, Figure 2F)". However, Figure 2D and 2D suggest the opposite, i.e. that cH strongly anti-correlates with mLH and has a mixed correlation/anti-correlation interaction with ILH. Please explain.

Figure 4A suggests that activation of cH in fed animals (which presumably have medium levels of CH acivity, see Figure 1I) prior to the presentation of food, increases subsequent food consumption by mimicking the high levels of cH activity during hunger. The same manipulation in hungry fish has no effect since presumably their levels of CH activity are already high (again see Figure 1I). Figure 4B is more complicated. The authors drive CH neurons during the feeding of food-deprived animals and see a reduction in feeding. This is counter-intuitive; we would expect that high levels of CH activity would induce hunger and hence increase feeding. To explain this, the authors suggest that this manipulation increases CH activity from the low levels seen during feeding (again see Figure 1I) to the medium levels seen during satiety, but not to the highest levels seen during hunger. This is a reasonable hypothesis (although it could be spelled out more clearly, as this is a key point in the manuscript and is not clearly explained). To test this hypothesis the authors should activate CH neurons during the feeding of fed fish. These animals presumably have medium levels of cH activity (see Figure 1I) and thus optogenetic activation should drive CH activity to high levels (as implied for the experiment in Figure 4A). In this case, they should see increased feeding. It is surprising that the authors have not already performed this experiment since that would make Figure 4B symmetric to 4A

Figure 4A,B: Stimulation of CH neurons during feeding results in reduced gut fluorescence. Could this be due to reasons other than specific suppression of feeding? For example, reduced locomotor activity, reduced ability to see prey, and/or impaired ability to execute specific steps in the prey capture sequence? Analysis of prey capture sequence, as previously described by several zebrafish labs, would strengthen the interpretation of this result. Based on text in the methods, it sounds like the data needed for this analysis may have already been collected.

Figure 4. This experiment is missing a control for the possibility that the ReaChR transgenic animals have altered feeding even in the absence of orange light. It would also be useful to show that stimulation of CH neurons is actually achieved using (for example) GCaMP, pERK or cfos. Please also state the genotype of ReaChR- control animals (i.e. do controls contain only the Gal4 or the UAS transgene, or neither?).

Figure 4C: Data showing efficacy of ablation should be shown (i.e. extent and specificity of cell loss in MTZ treated animals), particularly because 2.5 mM MTZ is insufficient to induce robust ablation for most Gal4 lines. Similar to Figure 4A,B, this experiment is lacking a control for the possibility that the transgene affects behavior in the absence of MTZ.

The brain activity response to the presence of paramecia could be a visual-mediated hunting response that could be mimicked by animated paramecia or other sensory responses, such as olfactory-mediated motor response. Both of which could increase the chances of successful prey capture resulting in increased food intake.

[Editors’ note: the revised article was rejected after discussions between the reviewers, but the authors were invited to resubmit after an appeal against the decision.]

Thank you for submitting your work entitled "A bidirectional network for appetite control in zebrafish" for consideration by eLife. Your article has been reviewed by a Senior Editor, a Reviewing Editor, and two reviewers. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

As you can see, one reviewer raised several remaining substantive concerns. After extensive discussion with both reviewers, we decided that significantly more experiments would be required to address these concerns. Thus, we have decided to reject your submission.

Reviewer #2:

I really struggled to read and understand this revised manuscript. This is particularly disappointing because many of the criticisms of the first version of the manuscript were related to a lack of clarity and details, and if anything, the revision is worse. I commend the authors for adding significant new experiments. However, most of these experiments are poorly described, appear to contain mistakes, and often cannot be evaluated. There is a general lack of rigor and quantification of key measures, including measures that were requested by reviewers, with only general statements about observations, and in some cases improper comparisons.

Essential revisions:

1) In the text, when the authors cite a supplemental figure, most times they do not say which panel they are referring to. This might seem like a trivial issue, but it eventually makes reviewing/reading the paper difficult. This problem is particularly acute for Figure 2—figure supplement 4 and Figure 2—figure supplement 5. These figures are quickly mentioned and not explained at all in the main text or figure legends. Other supplemental panels (e.g. Figure 2—figure supplement 1A and 1C) show data that seem not to be covered in the text. Please, in the main text, reference every panel of every figure, carefully explain the experiments and describe what they show.

2) Figure 1—figure supplement 3: It is inappropriate to generate ICA data using fish from other feeding-related treatments that are not described in this manuscript. While their inclusion may be necessary to achieve statistically significant results, this is a big black box of data that cannot be evaluated by reviewers or readers. This undescribed data should either be added to the manuscript, or the ICA analysis must be removed.

3) Figure 2: The data presented in this figure suggest that 30 minutes of food deprivation is enough to cause a shift in ILH activity (when quantified by active cell count) but not in cH (when that is quantified by normalized pERK intensity). However, when ILH activity is quantified by normalized pERK intensity, 30 minutes of food deprivation is not enough to cause a shift in activity. In their response to the reviews, the authors noted that normalized pERK intensity is less precise than active cell count; perhaps if cH activity was quantifiable through cell count, 30 minutes would be enough to cause a change in that population as well. Regardless, the authors should not compare the timelines of cH and LH activity changes when using different metrics to quantify the activity of each group (as they do in subsection “Satiation state influences the sensitivity of cH and LH populations to food”, subsection “Mutually opposing hypothalamic networks control zebrafish appetite” and Figure 2D).

4) Subsection “Satiation state influences the sensitivity of cH and LH populations to food”: The text here is not justified by the data presented. The idea that the cH response to absence of food, which the authors claim happens after the LH response, is somehow required for LH responsiveness, does not make sense.

5) Figure 2—figure supplement 1A: What is the feeding condition for these graphs? What message are they intended to convey, particularly because the correlations are relatively weak (especially for cH).

6) Figure 2—figure supplement 2A is problematic. The top and right images are at different scales, and the two pERK images looking very different. There seem to be much fewer TH2 positive cells than in other images the authors provide (e.g. panel e of same figure). A full z-stack of the cH area should be provided.

7) Figure 2—figure supplement 3D: "Consistent with our pERK results, the initial calcium-mediated mean fluorescence and firing frequency of a subset of cH neurons scaled with the length of food deprivation prior to imaging (Figure 2—figure supplement 3D)". There are two problems with this statement. First, this data only shows absolute fluorescence (or is it mean fluorescence?), not firing frequency. Second, what is this "subset of cH neurons"? How many cells are quantified? Where are they? Is this a small minority of the cells, or a general feature of most cH neurons? The relevant cells must be indicated.

8) Figure 2—figure supplement 4 and Figure2—figure supplement 5: There is almost no description or explanation of the data shown in these figures, making them completely incomprehensible to this reviewer. As best as I can understand this data, there appear to be several conflicts between what is shown in different panels, with the pERK data, and what is briefly stated in the text. Maybe it's obvious for aficionados, but likely not for most readers. There are also several apparent problems. First, Figure 2—figure supplement 4 panels A and B show a blue line in the bottom line graphs – what does this correspond to? What do the different numbers indicate – # of neurons? The authors imply opposing activity patterns for lLH and cH based on Figure 2—figure supplement 4A and B (although they are not anti-correlated, just shifted relative to each other), and this relationship is not apparent in the data shown in Figure 2—figure supplement 5. These figures simply cannot currently be evaluated.

9) Related to the last point, subsection “Satiation state influences the sensitivity of cH and LH populations to food”: "While some mLH and lLH voxels showed a predicted reduction in baseline fluorescence and firing rate, many others displayed a significant enhancement of baseline activity." It seems (Figure 2—figure supplement 4B) that most of the LH neurons (especially in the ILH) show increased activity during food deprivation using live imaging, which is the opposite of the pERK results shown in Figures1 and Figure 2 and the outline shown in Figure 2D. Unless I am misunderstanding Figure 2—figure supplement 4 (which is quite possible), this is a major problem that is glossed over. Should the authors focus on the GCaMP results as opposed to the pERK "Given the indirect nature of activity mapping in post-fixed animals"?

10) Figure 4: Text describing this figure states: "exposure to this food cue in the absence of ingestion induced a small increase in lLH neural activity and a larger increase in mLH activity (Figure 4A,B). The artemia-induced hypothalamic activity was, however, less than that observed with consumable prey (Figure 4A/B)." This statement accurately describes the data. However, the next sentence: "These observations suggest that the mLH responds primarily to sensory cues and/or induced hunting behavior whereas the induction of lLH activity largely depends on consumption" is not an appropriate interpretation of the data. I would conclude that both lLH and mLH respond to both paramecia and artemia, that both populations are less responsive to artemia, and that ILH is less responsive than mLH to both stimuli, rather than that one population responds primarily to sensory cues while the other population responds to consumption. One could argue that the increase in cell counts in lLH in response to artemia is very small, however a statistically significant difference is indicated. The "active cell count" metric also seems to be flawed because the mLH area is much larger than the lLH area, and thus these values must be normalized in order to make any meaningful comparisons between the cell populations. It looks to me like normalization would likely eliminate any difference in active cell count between lLH and mLH. It is unclear if the "normalized pERK intensity" metric is similarly flawed, i.e. does this quantify the total fluorescence in the region of interest (which would be affected by the size of the region), or is this value normalized according to the area of measurement (it's unclear what "normalized" refers to here – normalized to tERK, to total area, to the control value)?

11) Figure 5E: How do the authors account for the increase in mLH GCaMP fluorescence in response to stimulation of a control area that is not labeled by ReaChR?

12) Figure 5—figure supplement 1: As the authors note, the y333:Gal4 line is much less specific for 5-HT neurons than the 116A:Gal4 line. It is essential to determine whether the ~40% of y333:Gal4 expressing cells that are 5-HT negative are TH2 positive, as was done for the 116A:Gal4 line, since this would provide a significant dopaminergic input to this experiment.

13) Figure 6—figure supplement 1: This is another example of anecdotal evidence that should be quantified.

14) Figure 6—figure supplement 2: There is no quantification of cH cell ablation. Instead, just a single exemplar image is shown. It is therefore impossible to draw any conclusions about whether or not, or to what degree, the ablation was successful. Thus, the authors' claim in the rebuttal that "We can absolutely confirm that this protocol (which we have also utilized for other transgenic lines) is sufficient to ablate most cH neurons (see Figure 6—figure supplement 2)" is in no way substantiated by the data provided.

15) The authors propose that "optogenetic stimulation of cH activity inhibits lLH activity and thereby causes the feeding rate to decrease." (subsection “Functional dissection of cH serotonergic neurons in feeding behaviour”). They provide evidence that cH activation inhibits feeding (although that needs to be clarified; see comment below), and that cH activation also inhibits ILH activity. Is there any causal evidence that reduced ILH activity reduces feeding? If not, this statement should be deleted.

16) Despite the caveats mentioned by the authors, the use of different Gal4 lines with different expression patterns (which remain lightly characterized despite reviewer requests for more details) for genetic ablation and optogenetic activation precludes the authors from drawing firm conclusions from these experiments. It doesn't really make sense that a Gal4 line would be strong enough to drive cell ablation (especially since the authors use an unusually low concentration of mtz) but not optogenetic stimulation. Just because a Gal4 line doesn't produce the hoped for phenotype (for which the data is not shown) does not mean that one can simply substitute another Gal4 line that does produce the hoped for result.

Reviewer #3:

The authors have addressed the majority of my comments to the extent that I now support publication in eLife.

https://doi.org/10.7554/eLife.43775.041

Author response

Essential revisions:

Although this paper presents an interesting story, the rationale and approaches for the experiments are at times difficult to follow and require more detail.

We have revised the text with an emphasis on clarity and more detail when necessary. We have also enclosed a supplementary document (Conceptual_Circuit_Model.pdf) that explains our overall model.

Furthermore, they postulate a mutually inhibitory CH, LH circuit (subsection “Mutually opposing hypothalamic networks control zebrafish appetite”), but only show correlative data and don't experimentally test this hypothesis.

It is true that we had previously not performed targeted perturbation to test specifically for the mutually inhibitory circuit. We thus attempted to address this question using targeted optogenetic activation of the cH and simultaneous calcium imaging, and present the results in new Figure 5. Specifically, we show that optogenetic activation of the cH using Tg(y333:Gal4;UAS:ReaChR-RFP) causes sustained inhibition of the lLH, but not the mLH, unlike stimulation of a nearby control region.

The authors use broad terms such as "hunger", "food deprivation", "satiety", "food anticipation" which may be inaccurate or even wrong. In their basic experimental design, they constantly feed the larvae from day 5 to day 7, and then compare the response of larvae that were given excess of paramecia to larvae that were food-deprived for only 2 hours and then re-fed. Whether these conditions represent different states of hunger and/or cognitive response to food (anticipation) as the authors claim is not clear, in particular given the presence of yolk nutrients at these stages. This needs clarification and more precise definition of terms being used.

We have defined in the text a formal definition of hunger and satiety, and have tried to mostly restrict its usage to the introduction and discussion. We also would like to note that we have previously published work (Jordi et al., 2015, 2018) related to appetite regulation in zebrafish that utilizes a similar experimental design. Further, we are assaying behavior at 7-8 dpf, a stage at which yolk nutrients are largely depleted (Gut et al., 2013).

Here we also define the terms:

Hunger is an internal state defined by three conditions:

1) The state of being in a nutrient/energy deficit – which is usually the consequence of food deprivation or starvation.

2) A state that may promote food-seeking behavior as well as increased food intake.

3) A state that is reflected by – and correlates with – a pattern of modulatory neuronal activity.

Voracious feeding is a sub-state of hunger in which food is present and being ingested, but nutrient/energy deficit is still high.

Satiety is considered the opposite of hunger and is defined accordingly:

1) A state of having sufficient levels of nutrient/energy or levels that rest above a homeostatic baseline.

2) A state that manifests behaviorally in the slower/lower consumption of food relative to the state of hunger, due to indifference to food or its active avoidance.

3) A state that is reflected by an internal modulatory neuronal state that may be antagonistic and opposite to that from hunger.

Of note, none of these definitions require that the fish experiences feelings of hunger/or a “desire” to eat as humans experience it, and we agree with reviewers that the resultant changes in food intake/behavioral output could be due to modulation of sensitivity towards food cues and/or the probability of prey capture success, without invoking additional mechanisms.

Food anticipation: We apologize for the confusion. Our use of the word “anticipation” was simply to describe a neuronal state that precedes and predicts future behavior. Thus, since both natural (i.e. during food deprivation) and artificial (i.e. optogenetic) cH activation occurs before subsequent voracious feeding, we described it as “anticipation”.

We do realize that “anticipation” could be easily interpreted to mean a cognitive expectation of future food and/or imply preparatory behavior, neither of which we claim the zebrafish to be doing. Thus, we have removed it and replaced it with clearer terminology (e.g. “priming” or “sensitization”), as we have done in our discussion of putative roles of the cH in appetite control (please also refer to the supplementary document: Conceptual_Circuit_Model.pdf for more details).

The claim that CH serotonergic population is associated with anticipated voracious feeding is not convincing. In the case of pERK, the authors present one example that a subset of CH neurons co-localize with 5-HT, however they also show that a different subset is dopaminergic. Without using this marker (5-HT) in all ICA samples one cannot be certain that the active/suppressed neurons are indeed serotonergic.

We have now made it clear that we are referring to the cH (not the serotonergic cH) in low-resolution pERK experiments (MAP-mapping) and ICA analysis. We also would like to emphasize that these coarse pERK experiments serve primarily to narrow down regions of interest and focus our attention on subsequent, more detailed, circuit dissection with a combination of calcium imaging, optogenetic activation and chemical ablation.

The use of GCaMP expressed in the CH (116A:Gal4) to measure activity (Figure 2) is better, however the authors never show colocalization between 116A:Gal4;UAS:GCaMP and 5-HT. This is important, given the expression variability between the different UAS lines shown by the authors. Hence the authors' conclusion that "serotonergic CH activity reports the extent of food deprivation and anticipates the voracity of future hunting and ingestive behaviours" is not sufficiently supported.

We have now also demonstrated that the majority of neurons labeled by our Gal4 lines are serotonergic. Specifically, we have quantified the overlap of Tg(116A:Gal4) neurons with 5-HT staining to be 88.9 ± 0.8%, and the Tg(y333:Gal4;UAS:ReaChR-RFP) line to be 57.4 ± 2.1%. As such, we are confident that at least a subset of serotonergic cH neurons are modulated by food deprivation and food cues (i.e. Figure 2—figure supplement 3, Figure 2—figure supplement 4 and Figure 2—figure supplement 5, Figure 3). At the same time, we have now made it explicit in the text that we do not rule out the role of additional neuromodulators, particularly dopamine.

In Subsection “Cellular dissection of hypothalamus neural activity reveals a serotonergic population that anticipates voracious feeding” the authors compare and contrast the activities of CH and LH under different feeding paradigms. However, the data in Figure 1f show CH activity presented as "normalized pERK intensity" but mLH and ILH activity presented as "cell count". In the text the authors state that "the number of active CH cells was dramatically reduced" and cite 1f, which does not show cell count data for CH. Is this a mistake? Unless there is a good reason, CH, mLH and ILH activity levels should all be quantified in the same way, with "normalized pERK intensity" being presumably better than the binary on/off quantification of "cell count". Same issue with Figure 3B.

In the LH (unlike the cH), activated cells are scattered, leaving clear units (i.e. concentrated regions of higher fluorescence) that can be identified by automated thresholding (see new Figure 1—figure supplement 4). Thus, for the LH we used cell count rather than mean fluorescence, as it provides higher-resolution information about activity as compared to averaging the fluorescence over the entire region, and is less susceptible to staining variability. However, we agree with the referees that different metrics can be confusing, and that increased activity also happens in the neuropil. Thus, we have also analyzed our data in terms of absolute fluorescence and present both metrics for readers. Note that fluorescence tends to have larger variability than cell count. We have also changed the text to distinguish between when referring to cell counts vs overall fluorescence.

In Figure 2 the authors interpret the differential GCaMP activity of mLH, lLh and CH as hunger-relevant. However, the data indicates that only mLH is exclusively responsive to food while the other two regions respond to other sensory stimuli, such as water flow. The authors allude to this (subsection “Neuronal activities in the caudal and lateral hypothalamus are anti-correlated over short

timescales in response to food sensory cues”) but nevertheless insist on interpreting the data as though it corresponds to feeding/ hunger specifically, rather than "general" sensory processing.

We certainly do not mean to claim, neither do we believe, that feeding/appetite control is the only role for the cH and the lLH (or even the mLH). We have now explicitly stated this in the discussion to be clearer about our interpretations.

Figure 3: I don't understand the purpose of comparing between artemia and paramecia exposure. The authors state that "artemia are a natural prey that larval fish hunt, but which are too large to consume." Firstly, artemia are saltwater animal and therefore not a natural food source for freshwater zebrafish larvae. Second, while artemia are used as a food source for juvenile (three weeks) and adult fish the authors did not show that introduction of artemia evokes feeding/hunting behavior in larval zebrafish as opposed to other non-food related responses. Therefore, the claim that the mLH response to artemia represents the initial sensory response to food cue is not convincing. Related to this figure the authors again use different parameters to measure neuronal activity, i.e. normalized pERK activity for CH and activated neurons cell count for LH.

We have removed the word “natural” from the sentence. Since juvenile and adult fish will readily hunt and eat artemia, we believe that the fact that they are not a natural prey is not too big of an obstacle for our interpretation. We now also provide video recordings showing hunting (J-turns and pursuit bouts) of artemia by larval zebrafish, despite being too large to consume.

The appeal of using artemia is that larval fish readily pursue and hunt them, but that they cannot swallow them because they are too big. As such this allows a clear demonstration that all hunting related sensory experience (including the re-afferent experience of the hunts itself) is insufficient to drive high lLH and mLH activity and to suppress cH. Apparently, it is only the act of ingestion/swallowing that induces this switch. We have more clearly described this rationale in the text.

We have also analyzed the pERK data using the same parameters (i.e. mean fluorescence) for both the cH and LH.

The authors use genetic ablation using the UAS:nsfb-mCherry under one CH promoter (116A:Gal4) and optogentic activation using UAS:ReaChR driven by a different promoter (y333:Gal4) and therefore they cannot know if the ablated neurons are the same CH neurons as the light-activated cells. This distinction is crucial, as it is the entire rationale behind performing this dual (optogenetic activation versus chemogenetic ablation) experiment.

We share the reviewer’s concern over use of the two transgenic lines. Multiple unsuccessful attempts were made to robustly express UAS-channelrhodopsin variants with the Tg(116A:Gal4) line. Ultimately, we used the Tg(y333:Gal4) transgenic line for optogenetics, which we have now quantified to show significant (57.4 ± 2.1%) overlap with 5-HT in the cH, and we also verify that this line has relatively specific expression in the cH and PVO expression (Figure 5—figure supplement 1).

As such we can conclude that the ablated neurons likely share significant overlap with the light activated ones. However, we also explicitly discuss this caveat in the text.

The assertion that they specifically ablate or activate serotonergic neurons needs further support as only a small subset of 5-HT neurons is targeted by either nsfb or ReaChR as shown in Figure 1—figure supplement 4.

We have toned down on our claims that we are specifically ablating/activating serotonergic neurons, however, we note that the Tg(116A:Gal4) line is ~90% serotonergic and has minimal overlap with dopaminergic neurons (Figure 2—figure supplement 2).

The y333:Gal4 driver is not well characterized. As the optogentic stimulation is not spatially restricted to the CH the authors should show that ReaChR is not expressed in other brain areas.

We have shown in Figure 5—figure supplement 1 using whole-mount imaging that Tg(y333:ReaChR-RFP) expression is quite specific to the cH. However, there appears to also be labeling of some olfactory bulb neurons as well as some scattered neuron labeling in other parts of the brain. We have noted these possible caveats in the text and Discussion section.

As the authors show in Figure 1—figure supplement 4, the 116A:Gal4 CH driver is expressed in other hypothalamic regions (e.g. the PVO) and therefore ablation of other cells may affect food intake.

The reviewers are right that the Tg(116A:Gal4) line labels neurons in both the cH and PVO. However, given that Tg(UAS:nfsb-mCherry) expression is particularly weak relative to Tg(UAS:GFP) expression in the PVO (we estimate 6-8 cells in the aPVO, 2-4 cells in pPVO, and 30-40 cells in the cH), it is unlikely that there would be substantial PVO ablation (see Figure 6—figure supplement 2).

For Tg(y333:Gal4;UAS:ReaChR-RFP) optogenetic experiments (Figure 6), stimulation of the PVO during free-swimming behavior is unavoidable, which we have discussed in the text. However, in our optogenetics + calcium imaging experiments (Figure 5) we have specifically targeted the cH, and shown that its activation is sufficient to suppress LH activity.

It is certainly possible though that, if the right circuit connectivity exists, the PVO could also be indirectly activated. Overall, we do not rule out a role of the serotonergic PVO neurons in feeding, as many medially-situated neurons besides the cH are activated during food deprivation. Again, we have raised this possibility in the text.

The authors claim that CH activity causes inhibition of LH (subsection “Functional dissection of cH serotonergic neurons in appetite”) in order to interpret their unexpected result that CH activation in the presence of food (as opposed to prior to the introduction of food) results in reduced food intake. To prove this the authors should show that the optogenetic stimulation of CH neurons will result in decreased LH activity using the 76A:GCaMP measurements.

The reduction of food ingestion after cH activation is not unexpected on the basis of our understanding of cH and LH activity patterns (see Conceptual_Circuit_Model.pdf for more details). We have now confirmed using optogenetics that cH activation indeed reduces lLH activity (Figure 5).

In Figure 2, what is the evidence that the 76A:Gal4 line is expressed in the same ILH and mLH neurons that are labeled by pERK in Figure 1?

We have now used pERK staining to measure activity after providing food stimuli to food-deprived Tg(76A:Gal4;UAS-GCaMP6s) transgenic animals. As far as we can tell, the Tg(76A:Gal4) line appears to comprehensively label all LH neurons, and all pERK-positive cells induced by food appear to be double-labeled by Tg(76A:Gal4;UAS:GCaMP6s) (Figure 2—figure supplement 3).

Figure 1G. The axes labels are "ILH" for the Y-axis and "mLH" for the X-axis. What does that mean? What are the units on these axes? Is the "Intensity" quantifying gut fluorescence, # of feeding bouts, or something else? What time point after addition of food is shown in these graphs? Is there statistical analysis to support the idea that LH cell count of food intake is increased as a function of food deprivation time? Why wasn't a similar analysis performed for the CH population? In the text (subsection “Cellular dissection of hypothalamus neural activity reveals a serotonergic population that anticipates voracious feeding”) the authors cite Figure 1G as evidence that both "CH and LH activity are modulated by the length of food deprivation" although no data for CH are shown in Figure 1G.

We apologize for the lack of clarity. In the main figure (new Figure 2), we now show a more comprehensive plot (comprising an independent dataset) showing statistically significant changes in both cH and LH activity as a function of food deprivation time.

We have furthermore added mean fluorescence measurements of the cH and LH lobes, and statistical quantification to this original dataset (originally Figure 1G), which we have now moved to new Figure 2—figure supplement 1. To clarify: the axes refer to the number of active (i.e. pERK-positive cells) cells; we have noted this accordingly and also measured average fluorescence. Intensity refers to gut fluorescence intensity, that is, an approximation of food intake. The time point shown is 15 minutes after food addition -- all this information is now described in the legends.

In Figure 1H, why wasn't the same live imaging analysis performed for LH? The authors later (Figure 2) perform calcium imaging for both CH and LH at the same time, so this should be straightforward.

The cH experiments were initially performed before we were aware of the existence of the LH line. Currently, as the reviewer suggests, we have now performed simultaneous monitoring of the cH and LH over the course of food deprivation using the 116A and 76A transgenic lines, presented in the new Figure 2—Figure Supplement 4 & Figure 2—figure supplement 5.

Briefly, we confirmed that a subset of cH neurons (and also average cH activity) increases over the course of food deprivation, consistent with our pERK results. However, whereas from our pERK data we find that the number of active cells in the mLH and lLH (but not mean pERK fluorescence), is reduced after food deprivation (Figure 2), our calcium imaging results reveal large subsets of LH voxels that increase in baseline fluorescence over the course of food deprivation.

The changes in LH activity may reflect responses to head-fixation. Alternatively, we hypothesize that over the course of food deprivation, the LH is being sensitized by the cH and/or other hunger-related cues, which could explain the subsequent enhanced response to food and food cues. Notably, unlike “active cell count”, the mean LH fluorescence is not clearly reduced over the course of food deprivation, leaving open the possibility of subthreshold increases in cellular or neuropil activity. We have discussed both possibilities in the text. Finally, lLH calcium spikes still were on average accompanied by a reduction in cH fluorescence, suggesting that these loci still maintain an anti-correlated relationship over food deprivation.

We have now moved Figure 1H to Figure 2—figure supplement 3.

In describing Figure 1H, the authors state "To observe the reciprocal relationships between CH and LH populations in real time, we used calcium imaging to measure the response of CH serotonergic neurons to food deprivation." The ending to the opening "To observe the reciprocal relationships between CH and LH populations in real time,…" should be "…we used calcium imaging to simultaneously measure the response of CH serotonergic and LH neurons to food deprivation.".

We thank the reviewer for their suggestion and have made appropriate modifications.

The authors state in subsection “Whole brain activity mapping of appetite-regulating regions” that the mammalian analog of the LH has been implicated in appetite control, but is this a good comparison if the zebrafish "LH" does not express the neuropeptides that are thought to regulate feeding in the mammalian LH (i.e. hypocretin, mch, etc)? i.e. should the zebrafish "LH" be given a different name since it appears to contain different neurons, and thus likely has a different function, than the mammalian "LH"? Do the glutamatergic and GABAergic neurons found in the mouse LH co-express the hypocretin, mch, etc. neuropeptides?

We and others propose the fish LH to be homologous to the mammalian LH due to broad anatomical and functional similarities determined by stimulation, ablation and more lately, also imaging experiments (Demski, 1973; Muto et al., 2017; Roberts and Savage, 1978). We do agree that its known neuromodulatory phenotype evidently does not overlap with that of mammals, however, since other neuronal types in the mammalian LH (e.g. GABAergic neurons that are neither MCH nor Orexin-positive) have been shown to be involved in both food responses and feeding behavior (Jennings et al., 2015), we believe that there may be some unique and conserved feeding-related functions ascribed to the LH that are independent of MCH and Orexin. Interestingly, a recent study (Mikelsen et al., 2019) has identified additional neuromodulators/peptides co-expressed with LH GABAergic and glutamatergic neurons in mice, and that cluster into further subpopulations, which may form the basis of future investigations. Overall, we are inclined to stick with the same terminology, but have tried to be clear in our text about the differences between the fish and mammalian LH.

Based on the data presented in Figure 3, CH activity appears to be regulated by food consumption as opposed to the sensory cues generated by presence of food, similar to ILH, while mLH activity appears to be inducible by sensory cues alone (although to a lesser extent than sensory cues and food consumption at the same time). In subsection “The activities of cH and LH neurons are differentially modulated by food sensory cues and ingestion” the authors claim that this in accordance with "the strong anti-correlation of CH with lLH activity (compared to mLH activity, Figure 2F)". However, Figure 2D and 2D suggest the opposite, i.e. that cH strongly anti-correlates with mLH and has a mixed correlation/anti-correlation interaction with ILH. Please explain.

We note that the results may appear confusing. It can be explained by the fact that while there are fewer anti-correlated voxels between the cH and the lLH, the magnitude of anti-correlation (i.e. r-value) for these voxels tend to be stronger relative to those in the mLH. In contrast, the mLH has many, but less strongly anti-correlated voxels with the cH. We have revised the text to include this explanation.

Figure 4A suggests that activation of cH in fed animals (which presumably have medium levels of CH acivity, see Figure 1I) prior to the presentation of food, increases subsequent food consumption by mimicking the high levels of cH activity during hunger. The same manipulation in hungry fish has no effect since presumably their levels of CH activity are already high (again see Figure 1I). Figure 4B is more complicated. The authors drive CH neurons during the feeding of food-deprived animals and see a reduction in feeding. This is counter-intuitive; we would expect that high levels of CH activity would induce hunger and hence increase feeding. To explain this, the authors suggest that this manipulation increases CH activity from the low levels seen during feeding (again see Figure 1I) to the medium levels seen during satiety, but not to the highest levels seen during hunger. This is a reasonable hypothesis (although it could be spelled out more clearly, as this is a key point in the manuscript and is not clearly explained). To test this hypothesis the authors should activate CH neurons during the feeding of fed fish. These animals presumably have medium levels of cH activity (see Figure 1I) and thus optogenetic activation should drive CH activity to high levels (as implied for the experiment in Figure 4A). In this case, they should see increased feeding. It is surprising that the authors have not already performed this experiment since that would make Figure 4B symmetric to 4A.

Reduced food intake as a result of cH activation was not an unexpected result to us (see Conceptual_Circuit_Model.pdf for detailed explanation). In the presence of food cH activity rises with increasing satiety, which is anti-correlated with LH activity, so we expected that as such increasing cH activity would be associated with lower LH activity and reduced food intake.

Thus, we predicted that activation of the cH during feeding in fed fish would still likely reduce food intake, though the degree of cH-induced “priming” of LH circuitry may also affect the results. As suggested by the reviewer, we have now performed this experiment. While we have a relatively low sample size due to current difficulties in breeding our transgenic fish, the results trend towards a suppression in feeding, and do not support the idea that cH stimulation in fed fish can increase food intake. This, along with optogenetic imaging experiments confirms our model that the acute effect of cH stimulation is a reduction of food intake likely via suppression of the lLH, whereas stimulation of the cH in the absence of food “primes” or “sensitizes” LH circuitry to enhance subsequent feeding.

Figure 4A,B: Stimulation of CH neurons during feeding results in reduced gut fluorescence. Could this be due to reasons other than specific suppression of feeding? For example, reduced locomotor activity, reduced ability to see prey, and/or impaired ability to execute specific steps in the prey capture sequence? Analysis of prey capture sequence, as previously described by several zebrafish labs, would strengthen the interpretation of this result. Based on text in the methods, it sounds like the data needed for this analysis may have already been collected.

This is a very good idea; however, we unfortunately have not performed experiments in which we monitor behavior while the cH is being activated during feeding, as this is technically difficult to do at high throughput as compared to measuring gut fluorescence. In these particular experiments, the LED is placed directly above the dish, precluding video analysis, though we hope to utilize a more sophisticated setup in future experiments.

Figure 4. This experiment is missing a control for the possibility that the ReaChR transgenic animals have altered feeding even in the absence of orange light. It would also be useful to show that stimulation of CH neurons is actually achieved using (for example) GCaMP, pERK or cfos. Please also state the genotype of ReaChR- control animals (i.e. do controls contain only the Gal4 or the UAS transgene, or neither?).

We have now included in Figure 6—figure supplement 1 some evidence using pERK staining that our optogenetic manipulation for free-swimming behavior is sufficient to activate ReaChR-positive cells, particularly the ones with the strongest expression. We also have shown using calcium imaging that ReaChR activation does lead to expected increases in GCaMP fluorescence, though this is using a different setup (Figure 5).

Controls for the ReaChR experiments do not have visible Tg(y333:Gal4;UAS:ReaChR-RFP) expression, and thus are a mixture of siblings expressing Tg(y333:Gal4) only, Tg(UAS:ReaChR-RFP) or neither of these transgenes, each with one third probability. We have now stated this explicitly, also for Tg(116A:Gal4), in the text.

Given that depending on the timing of light stimulation (before or during food), Tg(y333:Gal4;UAS:ReaChR-RFP) fish either increase or decrease their feeding, it is unlikely that the transgene itself affects behavior in a systematic way. We have been having issues generating sufficient ReaChRpositive embryos to do these and other requested experiments, and therefore did not manage to perform this particular control in time. However, we have performed transgene-only controls for the ablation experiment (see below).

Figure 4C: Data showing efficacy of ablation should be shown (i.e. extent and specificity of cell loss in MTZ treated animals), particularly because 2.5 mM MTZ is insufficient to induce robust ablation for most Gal4 lines. Similar to Figure 4A,B, this experiment is lacking a control for the possibility that the transgene affects behavior in the absence of MTZ.

We can absolutely confirm that this protocol (which we have also utilized for other transgenic lines) is sufficient to ablate most cH neurons (see Figure 6—figure supplement 2). We also have data showing that the transgene alone does not affect behavior in the absence of MTZ. (Figure 6—figure supplement 2).

The brain activity response to the presence of paramecia could be a visual-mediated hunting response that could be mimicked by animated paramecia or other sensory responses, such as olfactory-mediated motor response. Both of which could increase the chances of successful prey capture resulting in increased food intake.

We agree that the increased “appetite” we observe could be due to an increased sensitivity to food cues that increases prey capture probability, rather than a “motivation to eat” or the actual sensation of hunger as we humans experience it. However, this concern is touching on more philosophical issues on “what it is like to be a fish” and we believe that such an explanation for the enhancement of feeding will still fall within our operational definitions of “hunger” and “satiety”. We have made this now more explicit in the text.

[Editors’ note: the author responses to the re-review follow.]

Overall, we believe the issues raised by Reviewer 2 are in some cases helpful but in others unwarranted. The criticisms mainly concern presentation and clarity and would be easily resolved by simple revisions; they are not reasonable grounds for rejection, and we respond to the various concerns below.

Reviewer #2:

I really struggled to read and understand this revised manuscript. This is particularly disappointing because many of the criticisms of the first version of the manuscript were related to a lack of clarity and details, and if anything, the revision is worse. I commend the authors for adding significant new experiments. However, most of these experiments are poorly described, appear to contain mistakes, and often cannot be evaluated. There is a general lack of rigor and quantification of key measures, including measures that were requested by reviewers, with only general statements about observations, and in some cases improper comparisons.

Essential revisions:

1) In the text, when the authors cite a supplemental figure, most times they do not say which panel they are referring to. This might seem like a trivial issue, but it eventually makes reviewing/reading the paper difficult. This problem is particularly acute for Figure 2—figure supplement 4 and Figure 2—figure supplement 5. These figures are quickly mentioned and not explained at all in the main text or figure legends. Other supplemental panels (e.g. Figure 2—figure supplement 1A and 1C) show data that seem not to be covered in the text. Please, in the main text, reference every panel of every figure, carefully explain the experiments and describe what they show.

We have now made clear citations to supplemental figure panels when supplemental figures are cited and made an effort throughout the main text to properly represent all of the supplemental work. However, with the editors' agreement, we do not reference nor explain every panel of every supplemental figure in the main text, as this would not be the norm. We have added to the text references to all main figure panels and made a concerted effort to increase the clarity of descriptions in our figure legends. With respect to Figure 2—figure supplements 4 and Figure 2—figure supplement 5, these figures were extensively simplified (reduced to one figure: Figure 3—figure supplement 3) and their interpretation is the subject of a paragraph in subsection “Caudal and lateral hypothalamic responses to food sensory cues are anti-correlated over short timescales” and extensive detailed elaboration in the figure legend.

Generally, we now make an explicit effort to present a cohesive narrative where every Figure and Supplemental Figure is motivated and integrated in a matter that we hope makes a lot more sense.

2) Figure 1—figure supplement 3: It is inappropriate to generate ICA data using fish from other feeding-related treatments that are not described in this manuscript. While their inclusion may be necessary to achieve statistically significant results, this is a big black box of data that cannot be evaluated by reviewers or readers. This undescribed data should either be added to the manuscript, or the ICA analysis must be removed.

We agree that the source of all image data in the Independent Component Analysis (ICA; Figure 1—figure supplement 3) should have been fully described, and that fish undergoing additional manipulations that might alter results should not be included. We now have used more stringent criteria and reduced the dataset to n = 300 fish that were either food-deprived (2 hours), or presented with food in food-deprived or fed conditions, strictly according to the experiment’s conditions. The anti-correlation between the activity of cH and LH neurons is in fact stronger with this more restricted dataset than previously. Moreover, a clear and detailed description of the analysis and data has been added to the legend of Figure 1—figure supplement 3 and also to the Materials and methods section.

3) Figure 2: The data presented in this figure suggest that 30 minutes of food deprivation is enough to cause a shift in ILH activity (when quantified by active cell count) but not in cH (when that is quantified by normalized pERK intensity). However, when ILH activity is quantified by normalized pERK intensity, 30 minutes of food deprivation is not enough to cause a shift in activity. In their response to the reviews, the authors noted that normalized pERK intensity is less precise than active cell count; perhaps if cH activity was quantifiable through cell count, 30 minutes would be enough to cause a change in that population as well. Regardless, the authors should not compare the timelines of cH and LH activity changes when using different metrics to quantify the activity of each group (as they do in –subsection “Satiation state influences the sensitivity of cH and LH populations to food”, subsection “Mutually opposing hypothalamic networks control zebrafish appetite” and Figure 2D).

In the original manuscript, we employed different metrics to quantify phospho-ERK staining intensity in the caudal (cH), medial lateral (mLH) and lateral (lLH) hypothalamus because these areas have very different distribution patterns of phospho-ERK positive cells. Labeled cells in the cH are highly clustered, and yet they are well-dispersed in the mLH and lLH. Single cell analysis, using a thresholding algorithm (Figure 1—figure supplement 4) is possible in the mLH and lLH, but cannot be automated for the cH. Such imperfections in quantification tools are unavoidable. We therefore implemented in the first revision the reviewer’s suggestion to also use the same metrics for comparisons within these areas (Figure 1G and Figure 2B). As the reviewer notes, and it is not surprising, ‘active cell count’ detects more subtle changes in LH activity than the average ROI fluorescence metric in some situations. For example, with respect to the reviewer’s specific point concerning changes in cH and lLH activity; since cH activity changes occur broadly in a dense pERK-positive population, they are readily detected as differences in average ROI fluorescence. However, since active (above threshold) neurons are sparse in the mLH and lLH of continuously fed animals, the change in average ROI fluorescence due their absence after 30 minutes of food deprivation is insignificant. The difference is, however, clearly significant in a count of active cells.

4) Subsection “Satiation state influences the sensitivity of cH and LH populations to food”: The text here is not justified by the data presented. The idea that the cH response to absence of food, which the authors claim happens after the LH response, is somehow required for LH responsiveness, does not make sense.

We agree with the reviewer and have removed this statement.

5) Figure 2—figure supplement 1A: What is the feeding condition for these graphs? What message are they intended to convey, particularly because the correlations are relatively weak (especially for cH).

Figure 2—figure supplement 1a includes data from all food deprivation times (30 minutes, 2 hours, 4 hours) that were described in Figure 2—figure supplement 1B. This is now clarified in the legend. The plot indicates that there is a weak correlation between reduced cH pERK labeling and increased food ingestion,whereas there is a more significant correlation between food ingestion and mLH/lLH activity (middle and right panels, Figure 2—figure supplement 1A). The implied message is that, since in the presence of food cH pERK activity correlates positively with satiation, food intake (and gut fluorescence) should show a negative correlation: the lower the cH activity, the more the fish eats, the higher the gut fluorescence, but this relationship may not be perfect due to biological and experimental variability.

6) Figure 2—figure supplement 2A is problematic. The top and right images are at different scales, and the two pERK images looking very different. There seem to be much fewer TH2 positive cells than in other images the authors provide (e.g. panel e of same figure). A full z-stack of the cH area should be provided.

As referred to by the reviewer, the old Figure 2—figure supplement 2a displayed single-plane images of overlap between 5-HT and pERK antibody staining and between TH2:GCaMP5 and pERK antibody staining. As was indicated by scale bars, these images were at different magnifications.

We have replaced these single examples with a more comprehensive analysis in which both pERK and 5-HT staining were performed in a TH2:GCaMP5 background (n = 4 fish). Hence, the pERK expression overlap with dopaminergic and serotonergic populations was obtained in the same images. The new data is presented in Figure 1—figure supplement 5, including a full z-series of pERK with 5-HT staining (Video 2) or with TH2:GCaMP5 (Video 3), as requested. The results support our claim that the majority of pERK-positive neurons in a food-deprived fish are serotonergic, whereas dopaminergic neurons account for a minor fraction.

7) Figure 2—figure supplement 3D: "Consistent with our pERK results, the initial calcium-mediated mean fluorescence and firing frequency of a subset of cH neurons scaled with the length of food deprivation prior to imaging (Figure 2—figure supplement 3D)". There are two problems with this statement. First, this data only shows absolute fluorescence (or is it mean fluorescence?), not firing frequency. Second, what is this "subset of cH neurons"? How many cells are quantified? Where are they? Is this a small minority of the cells, or a general feature of most cH neurons? The relevant cells must be indicated.

An error in the text created this confusion; it should have referred to Figure 2—figure supplement 4 and Figure 2—figure supplement 5 instead of Figure 2—figure supplement 3D. We also realize that the term ‘spike frequency’ should not have been used without clarification, as GCaMP fluorescence is merely correlated with voltage spikes. The ‘spikes’ depicted are spikes in GCaMP fluorescence intensity – we now refer to them as calcium/Ca2+ fluorescence spikes in the text. We address the issue of analysing absolute fluorescence in live imaging experiments in much more detail below.

8) Figure 2—figure supplement 4 and Figure 2—figure supplement 5: There is almost no description or explanation of the data shown in these figures, making them completely incomprehensible to this reviewer. As best as I can understand this data, there appear to be several conflicts between what is shown in different panels, with the pERK data, and what is briefly stated in the text. Maybe it's obvious for aficionados, but likely not for most readers. There are also several apparent problems. First, Figure 2—figure supplement 4 panels A and B show a blue line in the bottom line graphs – what does this correspond to? What do the different numbers indicate – # of neurons? The authors imply opposing activity patterns for lLH and cH based on Figure 2—figure supplement 4A and B (although they are not anti-correlated, just shifted relative to each other), and this relationship is not apparent in the data shown in Figure2—figure supplement 5. These figures simply cannot currently be evaluated.

We apologize for the lack of clarity in the text and the presentation of data in Figure 2—figure supplement 4 and Figure 2—figure supplement 5. We agree with the reviewer and have now revised these figures to simplify the presentation of the data and have clarified their analysis and interpretation in the text (see below our comments in the response to point 10).

To simplify the two supplemental figures they are now condensed to Figure 3—figure supplement 3. We also improved the description of all panels in the figure legends. The blue line referred to was mis-colored; it should be green (indicating mLH df/f). We regret the confusion.

Regarding whether the average calcium traces are anti-correlated rather than shifted relative to each other, as well as the consistency of this relationship, we agree that it is difficult to conclude this from the calcium-triggered averages presented. This is now indicated in the legend. However, we believe that much more convincing data with respect to the negative correlation between cH and lLH/mLH is presented in Figure 3F. We have modified the text in the Results section to be aligned with this line of reasoning.

9) Related to the last point, subsection “Satiation state influences the sensitivity of cH and LH populations to food”: "While some mLH and lLH voxels showed a predicted reduction in baseline fluorescence and firing rate, many others displayed a significant enhancement of baseline activity." It seems (Figure 2—figure supplement 4B) that most of the LH neurons (especially in the ILH) show increased activity during food deprivation using live imaging, which is the opposite of the pERK results shown in Figure 1 and Figure 2 and the outline shown in Figure 2D. Unless I am misunderstanding Figure 2—figure supplement 4 (which is quite possible), this is a major problem that is glossed over. Should the authors focus on the GCaMP results as opposed to the pERK "Given the indirect nature of activity mapping in post-fixed animals"?

We agree and have now extensively revised and simplified these two supplemental figures (Figure 2—figure supplement 4 and Figure 2 —figure supplement 5) and streamlined their explanation in the text. We removed unnecessary analysis from the figures, and aggregated data across fish, allowing them to be combined into one figure, which now appears as Figure 3—figure supplement 3.

As the reviewer noted, there is a confounding long-term increase in the baseline calcium fluorescence of the lLH region observed over the 2-hour time course of food deprivation. Baseline fluctuations in calcium reporter fluorescence can be a significant confounding factor in long-term live calcium imaging and might arise from the animal’s immobilization or the effects of irradiation with intense IR laser light. Measurements of calcium spike frequency and spike amplitude can however be made irrespective of such baseline changes, which is facilitated by baseline-subtraction (detrending). In the revised Figure 3—figure supplement 3, we have included both the raw data (Figure 3—figure supplement 3Ai) as well as baseline-detrended data for clarity (Figure 3—figure supplement 3Aii). Instead of focusing on the long-term baseline changes, we only employ live calcium imaging to look at the acute and quick changes in calcium reporter fluorescence, such as spike frequency and amplitude. Here, irrespective of the baseline change in lLH fluorescence, there is a clear difference in the activity of the cH, mLH and lLH regions in food-deprived animals as compared to live-imaged animals presented with prey (Figure 3), which we quantify and describe in the text. In these respects, our pERK and calcium imaging data are not in conflict.

With regard to the reliability of pERK-based activity measurements, these measurements are always made with reference to control samples within the same dataset, which allows for normalization to baseline activity. In addition, pERK-based activity is measured under entirely non-invasive, non-tethered and completely natural conditions in which a fixed specimen always reports activity that occurs within an approximately 15 minute window prior to sacrifice (Randlett et al., 2015).

10) Figure 4: Text describing this figure states: "exposure to this food cue in the absence of ingestion induced a small increase in lLH neural activity and a larger increase in mLH activity (Figure 4A,B). The artemia-induced hypothalamic activity was, however, less than that observed with consumable prey (Figure 4A/B)." This statement accurately describes the data. However, the next sentence: "These observations suggest that the mLH responds primarily to sensory cues and/or induced hunting behavior whereas the induction of lLH activity largely depends on consumption" is not an appropriate interpretation of the data. I would conclude that both lLH and mLH respond to both paramecia and artemia, that both populations are less responsive to artemia, and that ILH is less responsive than mLH to both stimuli, rather than that one population responds primarily to sensory cues while the other population responds to consumption. One could argue that the increase in cell counts in lLH in response to artemia is very small, however a statistically significant difference is indicated. The "active cell count" metric also seems to be flawed because the mLH area is much larger than the lLH area, and thus these values must be normalized in order to make any meaningful comparisons between the cell populations. It looks to me like normalization would likely eliminate any difference in active cell count between lLH and mLH. It is unclear if the "normalized pERK intensity" metric is similarly flawed, i.e. does this quantify the total fluorescence in the region of interest (which would be affected by the size of the region), or is this value normalized according to the area of measurement (it's unclear what "normalized" refers to here – normalized to tERK, to total area, to the control value)?

The reviewer considers that mLH and lLH activities are equivalently induced by exposure to Artemia, indicating that both areas are equivalently though modestly active in response to food sensory cues in the absence of ingestion. We have performed additional quantification to address the reviewers’ concerns, particularly whether the differences in mLH and lLH size confound our interpretation.

First, we note that since pERK fluorescence intensity is already averaged over the ROI, that ROI size has already been accounted for.

In the case of active cell count, we performed two additional analyses:

1) Quantified artemia-induced activity relative to paramecia-induced activity. Thus, the activity of each lobe is “normalized” to its maximal activity (that is, the activity induced by paramecia). Using this method, we show that indeed the lLH is still more weakly activated by food sensory cues than the mLH. This quantification is described in the text and in Figure 4.

2) Normalized “active cell count” to ROI size, as the reviewer has requested. The results are presented below. Notably, the effect of such normalization does not change the results in any significant way.

Taken together, we are now more confident in our interpretation based on the active cell count metric that the lLH is likely more responsive to consummatory cues. However, noting that the changes in overall fluorescence between the mLH and lLH are much more similar than for active cell count, we have modified our interpretation in the text to take the reviewer’s comments into account.

Finally, regarding the query about ‘normalization’, it was to the control value, hence all controls have a mean of 1. This is also the case for other pERK-related figures. We have clarified this point in the legend.

11) Figure 5E: How do the authors account for the increase in mLH GCaMP fluorescence in response to stimulation of a control area that is not labeled by ReaChR?

The reviewer is understandably puzzled by the fact 633 nm illumination of a control region (preoptic area, PO) triggers an increase in GCaMP fluorescence in the mLH (Figure 5E). We find that such illumination of the PO triggers mLH activity even in the absence of ReaChR expression. This is likely due to the fact that the preoptic area is a multisensory region that responds to visual input (Wee et al., 2019) due to the expression of many non-visual light-sensitive opsins in this region (Fernandes et al., 2012). Hence, we are not surprised that light alone may affect the activity of hypothalamic regions, in addition to the specific ReaChR induced modulation of lLH activity. All this is now explicitly discussed in the text.

12) Figure 5—figure supplement 1: As the authors note, the y333:Gal4 line is much less specific for 5-HT neurons than the 116A:Gal4 line. It is essential to determine whether the ~40% of y333:Gal4 expressing cells that are 5-HT negative are TH2 positive, as was done for the 116A:Gal4 line, since this would provide a significant dopaminergic input to this experiment.

We have now performed the requested quantification and show that 23.9 ± 2.2% (up to 30%) of y333:Gal4;UAS:ReaChR-RFP cells are dopaminergic. This data is displayed and quantified in a new Figure 5—figure spplement 2. Given that 5-HT labels ~60% of y333-labeled cells, and that we have observed cells in the cH that lack both 5-HT and DA labeling (Figure 1—figure supplement 5), the remaining cells are likely accounted for by incomplete 5-HT and DA neuron labeling, or could comprise histaminergic neurons which form a minor subset of the cH (Chen et al., 2016).

13) Figure 6—figure supplement 1: This is another example of anecdotal evidence that should be quantified.

The reviewer requests quantification of pERK staining shown in the image data of Figure 6—figure supplement 1. We now present higher quality images (n = 3 fish) and have quantified the data as requested. Our results confirm that our optogenetic behavioral setup is highly effective in activating ReaChR-positive cells.

14) Figure 6—figure supplement 2: There is no quantification of cH cell ablation. Instead, just a single exemplar image is shown. It is therefore impossible to draw any conclusions about whether or not, or to what degree, the ablation was successful. Thus, the authors' claim in the rebuttal that "We can absolutely confirm that this protocol (which we have also utilized for other transgenic lines) is sufficient to ablate most cH neurons (see Figure 6—figure supplement 2)" is in no way substantiated by the data provided.

We agree that it is best to quantify the ablation and have done so. Cells that express the UAS-nfsb:mCherry construct under control of 116A-Gal4 are visible as mCherry-positive cells. When such fish were incubated with the drug MTZ, which causes ablation, 6.1+/- 0.66 cH cells per fish were mCherry-positive (n = 54 fish). When MTZ was omitted, 31 +/- 1.5 cells were mCherry-positive (n = 3 fish). The reduction resulting from ablation was thus ~80%. The remaining mCherry-positive cells were generally dim and misshapen/deformed, indicating damage that might impair function. This information is now included in the Materials and methods section.

15) The authors propose that "optogenetic stimulation of cH activity inhibits lLH activity and thereby causes the feeding rate to decrease." (subsection “Functional dissection of cH serotonergic neurons in feeding behaviour”). They provide evidence that cH activation inhibits feeding (although that needs to be clarified; see comment below), and that cH activation also inhibits ILH activity. Is there any causal evidence that reduced ILH activity reduces feeding? If not, this statement should be deleted.

The reviewer queries whether there is evidence that lLH activity is required for feeding behavior. This was in fact examined by Muto et al., 2017 who ablated LH neurons and observed reduced feeding behavior (Figure 1F, Muto et al., 2017). This was previously mentioned in the Discussion section, but we have now reiterated it right below the referenced statement.

16) Despite the caveats mentioned by the authors, the use of different Gal4 lines with different expression patterns (which remain lightly characterized despite reviewer requests for more details) for genetic ablation and optogenetic activation precludes the authors from drawing firm conclusions from these experiments. It doesn't really make sense that a Gal4 line would be strong enough to drive cell ablation (especially since the authors use an unusually low concentration of mtz) but not optogenetic stimulation. Just because a Gal4 line doesn't produce the hoped for phenotype (for which the data is not shown) does not mean that one can simply substitute another Gal4 line that does produce the hoped for result.

The reviewer offers the opinion that the Gal4 lines used in this study were “lightly characterized”. Importantly, contrary to the reviewer’s stated concern, the reason the 116A:Gal4 line could not be used in optogenetic experiments is that it failed to drive expression of the UAS:ReaChR-RFP transgene as well as many other existing UAS-driven variants of channelrhodopsin (UAS:ChR2-YFP, UAS:ChRWR-GFP, UAS:ChR2:mCherry). This was noted by virtue of failing to observe the fluorescence tag in 116A:Gal4; UAS-Channelrhodpsin animals, even after post-hoc staining for signal amplification was performed. As a result, optogenetic experiments could not be attempted with the 116A:Gal4 line. We also found that UAS:nfsb-mCherry expression is relatively weak compared to UAS:GFP expression driven by 116A:Gal4. Overall, the reviewer’s assertion that we substituted another Gal4 line because we did not observe the “hoped for phenotype” is unwarranted. Our decisions regarding the choice of Gal4 lines were based entirely on tests of transgene expression.

https://doi.org/10.7554/eLife.43775.042

Article and author information

Author details

  1. Caroline Lei Wee

    1. Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    2. Program in Neuroscience, Harvard University, Boston, United States
    Present address
    Institute of Molecular and Cell Biology, A*STAR, Singapore, Singapore
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Contributed equally with
    Erin Yue Song
    For correspondence
    carolinewee@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4599-550X
  2. Erin Yue Song

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Conceptualization, Data curation, Supervision, Validation, Investigation, Methodology, Writing—review and editing
    Contributed equally with
    Caroline Lei Wee
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7911-6193
  3. Robert Evan Johnson

    1. Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    2. Program in Neuroscience, Harvard University, Boston, United States
    Contribution
    Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  4. Deepak Ailani

    Laboratory of Molecular and Developmental Biology, National Institute of Genetics, Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies), Mishima, Japan
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Owen Randlett

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Software, Formal analysis, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Ji-Yoon Kim

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  7. Maxim Nikitchenko

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Resources, Software
    Competing interests
    No competing interests declared
  8. Armin Bahl

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Resources, Software
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7591-5860
  9. Chao-Tsung Yang

    Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, United States
    Contribution
    Resources
    Competing interests
    No competing interests declared
  10. Misha B Ahrens

    Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, United States
    Contribution
    Resources, Software, Supervision, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3457-4462
  11. Koichi Kawakami

    Laboratory of Molecular and Developmental Biology, National Institute of Genetics, Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies), Mishima, Japan
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9993-1435
  12. Florian Engert

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  13. Sam Kunes

    Department of Molecular and Cell Biology, Harvard University, Cambridge, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review and editing
    For correspondence
    kunes@fas.harvard.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2973-8063

Funding

National Institutes of Health (Brain Initiative grant U19NS104653)

  • Florian Engert
  • Sam Kunes

National Institutes of Health (Brain Initiative grant R24 NS086601)

  • Florian Engert

National Institutes of Health (Brain Initiative grant R43OD024879)

  • Florian Engert

Simons Foundation (542973)

  • Florian Engert

Simons Foundation (325207)

  • Florian Engert

Simons Foundation (325171)

  • Misha B Ahrens

Simons Foundation (542943SPI)

  • Misha B Ahrens

Agency for Science, Technology and Research (National Science Scholarship (PhD))

  • Caroline Lei Wee

Human Frontier Science Program (LT000626/2016)

  • Armin Bahl

AMED (National BioResource Project)

  • Koichi Kawakami

AMED (Fundamental Technologies Upgrading Program)

  • Koichi Kawakami

JSPS (JP18H04988)

  • Koichi Kawakami

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Harold Burgess for kindly providing the y333:Gal4 transgenic line, and Adam Douglass who provided us with the TH2:GCaMP5 transgenic line. We further thank Thomas Panier who assisted Robert Johnson in construction of the rig used for high resolution behavioral imaging. Support from Steve Turney and the CBS imaging facility, and the Harvard Center for Biological Imaging were essential for the successful completion of many experiments. Finally, we would like to thank Jessica Miller, Steve Zimmerman, Karen Hurley and Brittany Hughes at Harvard for providing invaluable fish care.

Ethics

Animal experimentation: All protocols and procedures involving zebrafish were approved by the Harvard University/Faculty of Arts & Sciences Standing Committee on the Use of Animals in Research and Teaching (IACUC). Protocol #12-02-2.

Senior and Reviewing Editor

  1. Ronald L Calabrese, Emory University, United States

Publication history

  1. Received: November 20, 2018
  2. Accepted: September 9, 2019
  3. Version of Record published: October 18, 2019 (version 1)

Copyright

© 2019, Wee et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 767
    Page views
  • 94
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)