https://orcid.org/0000-0001-7528-4245
Figures
Abstract
Habituation is a foundational learning process critical for animals to adapt their behavior to changes in their sensory environment. Although habituation is considered a simple form of learning, the identification of a multitude of molecular pathways including several neurotransmitter systems that regulate this process suggests an unexpected level of complexity. How the vertebrate brain integrates these various pathways to accomplish habituation learning, whether they act independently or intersect with one another, and whether they act via divergent or overlapping neural circuits has remained unclear. To address these questions, we combined pharmacogenetic pathway analysis with unbiased whole-brain activity mapping using the larval zebrafish. Based on our findings, we propose five distinct molecular modules for the regulation of habituation learning and identify a set of molecularly defined brain regions associated with four of the five modules. Moreover, we find that in module 1 the palmitoyltransferase Hip14 cooperates with dopamine and NMDA signaling to drive habituation, while in module 3 the adaptor protein complex subunit Ap2s1 drives habituation by antagonizing dopamine signaling, revealing two distinct and opposing roles for dopaminergic neuromodulation in the regulation of behavioral plasticity. Combined, our results define a core set of distinct modules that we propose act in concert to regulate habituation-associated plasticity, and provide compelling evidence that even seemingly simple learning behaviors in a compact vertebrate brain are regulated by a complex and overlapping set of molecular mechanisms.
Author summary
Habituation is an evolutionarily ancient form of learning in which responses to repeated stimuli decline over time. While seemingly simple, habituation is nevertheless regulated by multiple molecular mechanisms. This surprising complexity led us to ask: do these diverse mechanisms act independently of one another or do they work in the same pathways to drive habituation? To answer this question, we mapped how individual regulators of habituation alter activity throughout the brain and perturbed habituation-regulating molecular mechanisms in combination to see if they act in the same or different pathways. Our experiments grouped eight molecular regulators of habituation into five different pathways with their own brain activity signatures, indicating that multiple independent pathways regulate even simple learning mechanisms.
Citation: Nelson JC, Shoenhard H, Granato M (2023) Integration of cooperative and opposing molecular programs drives learning-associated behavioral plasticity. PLoS Genet 19(3): e1010650. https://doi.org/10.1371/journal.pgen.1010650
Editor: Cecilia Moens, Fred Hutchinson Cancer Research Center, UNITED STATES
Received: May 31, 2022; Accepted: February 2, 2023; Published: March 27, 2023
Copyright: © 2023 Nelson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All original code is available in S1 Code. All relevant data are within the manuscript and its Supporting information files (S1 Data).
Funding: This work was supported by grants to M.G.: NIH National Institute of Neurological Disorders and Stroke, R01NS118921 and J.N.: NIH National Institute of Neurological Disorders and Stroke, K99NS111736 and R00NS111736. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Learning enables animals to modify their responses to stimuli based on prior experience. One of the simplest forms of learning is a non-associative plasticity mechanism termed habituation, which is defined by a gradual decrease in responding to repeated stimuli [1–3]. Habituation represents a foundation for more complex forms of behavioral plasticity and is observed in all animals. Habituation learning is also a pervasive feature of the nervous system, regulating response rates to stimuli spanning sensory modalities and including complex responses such as fear responses and feeding [4–6]. We previously established larval zebrafish as a model to study short term habituation learning [7]. In response to a sudden acoustic stimulus zebrafish perform a stereotyped acoustic startle response (ASR), comprised of a short-latency C-bend (SLC) escape response regulated by well-described hindbrain circuitry [8–10]. Repeated acoustic stimuli modulate sensory thresholds and result in habituation learning characterized by a gradual decline in response frequency [7,11–14]. Although this process appears simple at first glance, previous work revealed that at least long-term habituation learning is regulated by multiple mechanisms that operate on distinct time scales [2,15]. Similarly, numerous molecular-genetic mechanisms also regulate short-term habituation learning [16–25]. Moreover, pharmacological screens have identified multiple neurotransmitter and neuromodulatory systems contributing to habituation [7]. Despite their known relevance for learning, how individual habituation-regulatory pathways relate to one another, whether they act sequentially or regulate habituation learning in parallel, and whether they are distributed over multiple brain areas or function within a common circuit is unclear.
Here, we utilize at set of well-defined habituation mutants in conjunction with pharmacological agents to determine whether individual molecular regulators of habituation function together with or in parallel to habituation-relevant neurotransmitter systems. To complement this pharmacogenetic approach, we then performed unbiased whole-brain imaging to define activity signatures for each independent pharmacological or genetic manipulation, in order to identify candidate brain regions in which habituation-regulatory modules exert their function. We propose five distinct molecular-circuit modules that regulate habituation. Module 1 consists of the palmitoyltransferase Hip14, as well as NMDA and dopamine signaling, while module 2 consists of Hip14 and one of its identified substrates, the voltage-gated Potassium channel subunit Kv1.1. Module 3 consists of the pregnancy-associated plasma protein A (PAPP-AA) and the AP2 adaptor complex subunit AP2S1, which both act to oppose dopamine signaling. Glycine signaling constitutes module 4, and the voltage dependent calcium channel alpha2/delta subunit 3 gene (cacna2d3) defines module 5. Two of these modules reveal a critical role for dopamine signaling in the bi-directional modulation of habituation learning. Specifically, while the palmitoyltransferase Hip14 cooperates with neurotransmitter signaling through NMDA and dopamine receptors to drive habituation (module 1), the AP2 adaptor complex subunit AP2S1 promotes behavioral plasticity by opposing dopamine signaling (module 3). Moreover, we find that three of the proposed habituation-regulatory modules intersect functionally (modules 1, 2 and 3), while modules 4 and 5 appear functionally independent from each other and from the other three interconnected modules, suggesting multiple habituation-regulatory mechanisms that act in parallel.
Importantly, habituation learning and baseline behavioral response thresholding are tightly linked. Human disorders that impact habituation learning often co-influence baseline sensitivity to stimuli [26]. Although our screens for pharmacological and genetic regulators of habituation learning additionally identified regulators that impact solely habituation or solely hypersensitivity, all of the mutants that we examine here impact both processes. Whether these processes are interdependent or regulated in parallel is difficult to disentangle. Therefore, although we refer to these genes as regulators of habituation learning, our findings may additionally be relevant for understanding how animals establish response thresholds in vivo.
Taken together, our findings highlight the strength of an integrative approach combining genetic and pharmacological manipulation of habituation learning with unbiased whole-brain activity mapping and reveal a more complete picture of the molecular and circuit mechanisms that drive vertebrate habituation learning.
Results
A reduced-intensity habituation assay to uncover pharmacogenetic interactions
From an unbiased genetic screen we previously identified a set of five genes required for habituation learning [25,27–29]. To determine whether the identified molecular and circuit mechanisms regulate habituation learning independently of each other, or whether these genetic mechanisms converge at a common bottleneck, we set out to perform pathway analysis by exposing habituation mutants to pharmacological inhibitors of habituation-regulatory neurotransmitter signaling pathways. We reasoned that genetic and pharmacological manipulations that impinge upon components of independent or parallel pathways would enhance habituation deficits while multiple insults to components of a common pathway would fail to produce additive deficits. However, impeding our ability to perform such analyses, we observed that genetic mutations that affect habituation learning, such as presumptive null mutations in the zinc finger DHHC-type palmitoyltransferase gene zdhhc17, encoding the palmitoyltransferase Hip14, result in a near complete loss of habituation at our standard stimulus intensities of 35.1dB (Fig 1A and 1B) [28]. This ceiling effect interferes with the ability to detect additive habituation learning deficits, preventing us from detecting a further reduction in habituation and thus preventing us from interpreting the results of the proposed pharmacogenetic pathway analysis. We therefore wondered whether reducing stimulus intensity might provide a more sensitive assay in which mutant animals retain some capacity for habituation, and application of a pharmacological inhibitor of habituation learning might reveal more severe deficits. Consistent with previous findings that habituation learning is modulated by stimulus intensity [2], we find that although still impaired relative to their siblings, hip14 mutant animals are capable of habituation learning under conditions of reduced stimulus intensity (i.e. 0.4dB-25.6dB, Fig 1C–1G). Moreover, these data reveal that presumptive null mutations in hip14 fail to fully abolish habituation, and that when presented with lower intensity stimuli hip14 mutant animals are capable of habituation learning, albeit at a reduced level relative to their siblings. We conclude that at lower intensity, further habituation impairments in hip14 mutants induced by pharmacological inhibitors of learning might be readily detectable. We therefore selected 19.8dB for our reduced-intensity habituation assay and utilized this stimulus intensity to test five genetic mutants in combination with individual inhibitors of three neurotransmitter systems.
(A) Stimulus paradigm used. ISI for baseline phase is 40 seconds, ISI for habituation phase is 3 seconds. (B) hip14 mutants exhibit a complete failure to habituate to 35.1 dB acoustic stimuli. (C-G) Reduction in stimulus intensity as indicated results in a gradual increase in the ability of hip14 mutants to habituate. (H) MK-801 is an NMDA inhibitor that strongly reduces habituation learning in 5-day old zebrafish larvae (n = 58 DMSO-treated, n = 57 MK-801-treated, stimulus intensity = 35.1dB). (I) Strychnine is a glycine receptor antagonist that strongly reduces habituation learning in 5-day old zebrafish larvae (n = 38 DMSO-treated, n = 38 Strychnine-treated, stimulus intensity = 35.1dB). (J) Butaclamol is a dopamine inhibitor that strongly reduces habituation learning in 5-day old zebrafish larvae (n = 16 DMSO-treated, n = 18 Butaclamol-treated, stimulus intensity = 35.1 dB). (K-M) Regions upregulated by the specified drug treatment under “Habituating Stimuli” conditions are indicated in green; regions upregulated in the vehicle (can also be interpreted as downregulated in drug-treated) are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. Molecular targets of pharmacological agents are indicated with diagrams above each column. See S1 Fig for brain activity maps under “No Stimulus” condition and “Non-Habituating Stimuli” condition. Also see S1–S3 Tables for ROIs identified in the experiments presented as well as in an independent replicate of each drug condition.
Pharmacological inhibitors of habituation learning produce distinct patterns of neuronal activity
For the pharmacogenetic pathway analysis we selected the NMDA receptor inhibitor MK-801, the glycine receptor inhibitor Strychnine, and the dopamine receptor inhibitor Butaclamol. As previously reported, in 5-day old larval zebrafish, application of MK-801 results in significant impairments in habituation learning (Fig 1H) [7,30]. Whereas vehicle-exposed animals rapidly learn to ignore repeated acoustic stimuli, i.e. habituate, animals exposed acutely to the NMDA inhibitor continue to respond at a high rate (they fail to habituate). Similar effects are observed when animals are exposed to Strychnine (Fig 1I) or Butaclamol (Fig 1J) [7]. Despite their similar effects on habituation learning, we hypothesized that given their regulation of different neurotransmitter systems, these pharmacological agents might regulate habituation through distinct effects on neuronal activity. In order to broadly assess brain activity signatures associated with each pharmacological treatment, we performed unbiased whole-brain activity mapping using the MAP-mapping technique [31]. This approach uses immunohistochemistry to measure ratios between phosphorylated ERK (extracellular signal-regulated kinase) and total ERK as a readout for neural activity in the minutes preceding fixation. We performed this assay for each of the three pharmacological inhibitors under three different acoustic stimulation conditions: “No Stimuli,” “Non-Habituating Stimuli,” and “Habituating Stimuli.” The resulting activity maps highlight brain areas whose activity is greater in drug-treated relative to vehicle-exposed animals (green = up-regulated in drug-treated) and brain areas whose activity is greater in vehicle-exposed relative to drug-treated animals (magenta = down-regulated in drug-treated). We find that each pharmacological agent produces a distinct activity pattern (Fig 1K–1M and S1A–S1F Fig). Furthermore we find that the distinct patterns of activity induced by each inhibitor of habituation learning are largely maintained across stimulation conditions (Fig 1K–1M and S1A–S1F Fig, S1 Table). In particular, MK-801 suppresses activity within the subpallium, habenula, and hypothalamus (activity maps indicate increased activity in vehicle-treated relative to MK-801-treated: Fig 1K, S1A and S1D Fig), Strychnine produces widespread hyperactivation (Fig 1L, S1B and S1E Fig), and Butaclamol treatment results in hindbrain hyperactivation and forebrain and diencephalic suppression relative to vehicle controls (Fig 1M, S1C and S1F Fig). Our results that differences between inhibitor conditions, but not stimulation conditions were readily detected reflects the design of our experiments, which were optimized to detect differences between drug conditions at the expense of sensitivity to differences in stimulation conditions. Together, these data are consistent two scenarios: (1) that the neurotransmitter systems that regulate habituation impinge upon different sets of circuit loci, which separately regulate learning, and/or (2) that their effects on habituation are mediated through the limited regions that show overlapping activity changes.
Hip14 acts through NMDA and dopamine signaling and mutants show broad hyperactivity of neuronal circuits
Having developed a more sensitive habituation assay, and having established that pharmacological inhibitors of habituation impinge upon activity within distinct brain regions, we set out to perform our pharmacogenetic analysis to examine all possible interactions between three neurotransmitter signaling inhibitors and five habituation mutants. We first tested whether Hip14 and NMDA receptors act together to regulate habituation learning by exposing mutant and sibling animals to either vehicle (DMSO) or the NMDA inhibitor MK-801 and then performing the reduced-intensity habituation assay (Fig 1A and 1D). We found that while MK-801 severely reduced habituation in sibling animals, the same pharmacological manipulation in hip14 mutant animals did not further reduce habituation learning (Fig 2A). This is consistent with a model in which Hip14 and NMDA act in a common pathway to drive habituation learning. When we plotted response frequency versus stimulus number in order to analyze the kinetics of habituation learning (learning curves), we found that compared to MK-801-treated mutants, sibling animals treated with MK-801 exhibited more severe habituation deficits (Fig 2B). This raises the possibility that compensatory, NMDA-independent plasticity mechanisms are upregulated in hip14 mutant animals. When we performed the same protocol in the presence of the glycine-receptor inhibitor Strychnine, we observed further reductions in habituation in both siblings and hip14 mutants, consistent with the idea that the regulation of habituation learning involves independent roles for glycine and Hip14 (Fig 2C and 2D). Finally, we performed our reduced-intensity habituation assay in the context of the dopamine receptor inhibitor Butaclamol. Here we found that in sibling animals dopamine receptor inhibition significantly impaired habituation. In contrast, in hip14 mutant animals the same manipulation did not reduce habituation learning (Fig 2E and 2F). Taken together, our data provide strong evidence that Hip14 acts in a common pathway with dopamine and NMDA receptor signaling yet independently of glycine receptor signaling to drive habituation learning.
(A-B) MK-801 impairs habituation learning in siblings, (p<0.0001 Sidak’s test for multiple comparisons, n = 38 DMSO, n = 38 MK-801), but does not enhance habituation learning deficits observed in hip14 mutant larvae (p = 0.9848, n = 24 DMSO, n = 29 MK-801, p<0.0001 indicates significant interaction between drug treatment and genotype; these and all subsequent statistical analyses use Two-way ANOVA with Sidak’s multiple comparisons test unless otherwise indicated). **indicate sibling+MK801 individuals with % hab values below y-axis limit at -100% and -180%. (C-D) Strychnine significantly enhances habituation learning deficits observed in hip14 mutant larvae, indicating that glycine signaling and hip14 may act within parallel molecular or circuit pathways to regulate habituation learning (p<0.0001, n = 40 DMSO-siblings, n = 42 Strychnine-siblings, n = 32 DMSO-mutants, n = 32 Strychnine-mutants, p = 0.0639 indicates non-significant interaction between drug treatment and genotype). (E-F) Butaclamol impairs habituation learning in siblings (p<0.0001, n = 33 DMSO, n = 26 Butaclamol), but does not enhance habituation learning deficits observed in hip14 mutant larvae (p = 0.3705, n = 34 DMSO, n = 35 Butaclamol, p = 0.0239 indicates significant interaction between drug treatment and genotype), indicating that dopamine receptor signaling and hip14 may act within the same molecular or circuit pathway to regulate habituation. *indicates a mutant+butaclamol individual with % hab value below y-axis limit at -60%. (G-I) Regions upregulated in hip14 mutants are indicated in green; regions upregulated in siblings (can also be interpreted as downregulated in hip14 mutants) are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. Patterns of neuronal activity are similar between “No Stimuli” vs “Non-Habituating Stimuli” vs. “Habituating Stimuli” (restricted diencephalic downregulation of activity; nearly global upregulation of activity across the telencephalon, diencephalon, and rhombencephalon). See also S1–S3 Tables for ROIs up- and down-regulated in each condition, as well as in independent replicates.
In light of the finding that Hip14, dopamine, and NMDA signaling act in a common pathway to regulate habituation, we wondered whether hip14 mutant animals might display similar activity signatures to those obtained from larvae treated with NMDA or dopamine inhibitors. To address this question, we performed whole-brain activity mapping in animals lacking hip14, analyzed the resultant MAP-maps, and compared them with those obtained from NMDA and dopamine inhibitor treated animals. In hip14 mutant brains we observed broad hyperexcitability across the forebrain and hindbrain (Fig 2G–2I). Despite this being a distinct pattern from that observed in NMDA- and dopamine-inhibited animals, we observed commonalities in the activity signatures. In particular, activity in the diencephalon was reduced, and a handful of rhombencephalic areas were upregulated by all three manipulations (Fig 2G–2I, S1 Table). These overlapping activity changes, observed across multiple treatments, represent potential habituation-regulating loci through which these putative regulatory modules may exert their function.
Kv1.1 mutants exhibit a unique activity signature and Kv1.1 acts independently of NMDA, glycine, and dopamine signaling
We have previously shown that Hip14 acts in part through the voltage-gated Potassium channel subunit Kv1.1, which is encoded by the potassium voltage-gated channel, shaker-related subfamily member 1a gene, kcna1a [28]. We performed pharmacogenetic pathway analysis in kcna1a mutants and found that in contrast to hip14 mutants, kcna1a mutants did not show significant habituation deficits in our modified habituation assay. Nonetheless, we hypothesized that if a given neurotransmitter pathway was disrupted in these mutants, it would still likely show an interaction in our assay.
However, we found no evidence that Kv1.1 functions in a pathway with NMDA receptor signaling. Rather, the NMDA receptor inhibitor MK-801 induced significant habituation deficits in both kcna1a siblings and mutants (Fig 3A and 3B). Similarly, glycine receptor inhibition induced significant habituation learning deficits in both kcna1a mutants and siblings (Fig 3C and 3D). Finally, we observed significant enhancement of habituation learning deficits through inhibition of dopamine signaling in both kcna1a siblings and mutants (Fig 3E and 3F). Taken together, these data are consistent with a model in which kcna1a acts independently of NMDA, dopamine, and glycine receptor signaling to regulate habituation learning.
(A-B) MK-801 significantly enhances habituation learning deficits in kcna1a mutants and siblings (p<0.0001, n = 42 DMSO-siblings, n = 46 MK-801-siblings, p = 0.0128, n = 8 DMSO-mutants, n = 20 MK-801 mutants, p = 0.2628 indicates non-significant interaction between drug treatment and genotype). (C-D) Strychnine significantly enhances habituation learning deficits in kcna1a mutants and siblings (p<0.0001, n = 24 DMSO-siblings, n = 31 Strychnine-siblings, p<0.0001, n = 35 DMSO-mutants, n = 26 Strychnine-mutants, p = 0.1063 indicates non-significant interaction between drug treatment and genotype). (E-F) Butaclamol significantly enhances habituation learning deficits in kcna1a mutants and siblings (p<0.0001, n = 30 DMSO-siblings, n = 37 Butaclamol-siblings, p = 0.0018, n = 30 DMSO-mutants, n = 27 Butaclamol-mutants, p = 0.3942 indicates non-significant interaction between drug treatment and genotype). *indicate a sibling+butaclamol and a mutant+butaclamol individual with % hab values below y-axis limit at -100% and -60% respectively. (G-I) Regions upregulated in kcna1a mutants are indicated in green; regions downregulated in kcna1a mutants are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. We observed similar patterns of neuronal activity induced by “no stimuli” vs “non-habituating stimuli” vs. “habituating stimuli”: highly restricted upregulation of activity in the spiral fiber neuron clusters as well as in V2A (including Rom3) neurons. See also S1–S3 Tables for ROIs up- and down-regulated in each condition, as well as in independent replicates.
Next, we performed whole-brain activity mapping to identify where kcna1a might exert its function, and to assess whether its activity signature might overlap with that of other regulators of habituation. We found a remarkably specific and unique pattern of activity induced by the loss of kcna1a. In particular, two populations known to express Kv1.1 [28,32] and involved in the execution of the escape response were found to be hyperactive: spiral fiber neurons and RoM3 excitatory reticulospinal (V2a) neurons (Fig 3G–3I). We previously showed that Kv1.1 requires Hip14 for proper synaptic localization and hence likely acts downstream of Hip14 to regulate habituation learning. Consistent with these findings, we now find that the same populations that are hyperactive in kcna1a mutants are also hyperactive in hip14 mutants in all conditions except for one (Non-Habituating Stimuli Replicate 2 of 3). Moreover, the observation that activity changes are more restricted in kcna1a mutant brains compared to those observed in hip14 mutant brains is consistent with our prior observation of more severe habituation learning deficits in hip14 mutants as compared to kcna1a [28]. These data lend further support to our hypothesis that Hip14 acts through other substrates besides Kv1.1 to regulate habituation. Combining these results with the findings of our pharmacogenetic analysis, we conclude that Kv1.1 may function in a restricted set of hindbrain neurons to carry out NMDA- and dopamine-independent functions downstream from Hip14.
PAPP-AA promotes habituation by limiting endogenous dopamine signaling
The previous genetic screen additionally identified the pregnancy-associated plasma protein A (pappaa) gene as a critical regulator of habituation learning. PAPP-AA has been shown to act through regulation of Insulin Growth Factor Receptor (IGFR) signaling to regulate habituation, yet it is unknown whether PAPP-AA interacts with any of the other identified habituation regulatory pathways. In order to investigate this question, we also performed pharmacogenetic pathway analysis in pappaa mutants. Compared to DMSO-treated pappaa mutants, application of MK-801 or Strychnine to mutant animals resulted in further reduction of habituation learning, providing compelling evidence that PAPP-AA promotes habituation learning independent of NMDA (Fig 4A and 4B), and glycine receptor signaling (Fig 4C and 4D). In contrast, treatment of pappaa mutants with the dopamine receptor antagonist Butaclamol failed to enhance habituation deficits in pappaa mutants when compared to DMSO treated mutants, and in fact trended toward ameliorating habituation deficits in pappaa mutants (p = 0.0731) (Fig 4E and 4F). These data suggest that PAPP-AA may be required to suppress dopamine signaling, and are consistent with a scenario in which dopaminergic inhibition somewhat normalizes behavioral deficits in pappaa mutants.
(A-B) MK-801 significantly enhances habituation learning deficits observed in pappaa mutant larvae, indicating that NMDA signaling and pappaa may act within parallel molecular or circuit pathways to regulate habituation learning (p = 0.0369, n = 39 DMSO-mutants, n = 32 MK-801 mutants; p = 0.0545, n = 16 DMSO-siblings, n = 31 MK-801 siblings; p = 0.7637 indicates non-significant interaction between drug treatment and genotype). (C-D) Strychnine significantly enhances habituation learning deficits observed in pappaa mutant larvae, indicating that glycine signaling and pappaa may act within parallel molecular or circuit pathways to regulate habituation learning (p = 0.0057, n = 16 DMSO-mutants, n = 15 Strychnine-mutants; p = 0.0001, n = 14 DMSO-Siblings, n = 18 Strychnine-siblings; p = 0.4286 indicates non-significant interaction between drug treatment and genotype). (E-F) Butaclamol does not significantly enhance habituation learning deficits observed in pappaa mutant larvae, but rather trends toward significantly restoring habituation learning (p<0.0001, n = 31 DMSO-siblings, n = 35 Butaclamol-siblings; p = 0.0731, n = 28 DMSO mutants, n = 13 Butaclamol mutants; p<0.0001 indicates significant interaction between drug treatment and genotype). *indicates a mutant+DMSO individual with % hab value below y-axis limit at -100% (G-I) Regions upregulated in pappaa mutants are indicated in green; regions downregulated in pappaa mutants are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. Patterns of neuronal activity are similar between “no stimuli” vs “non-habituating stimuli” vs. “habituating stimuli” (increased activity within the telencephalon and hypothalamus; decreased activity within multiple rhombencephalic loci). This pattern is somewhat inverted relative to that observed in Butaclamol-treated animals, consistent with a role for pappaa in regulating dopamine signaling. See also S1–S3 Tables for ROIs up- and down-regulated in each condition, as well as in independent replicates.
Finally, we performed whole-brain activity mapping in pappaa mutant animals. Upon analyzing the resultant MAP-maps, we observed a subtle downregulation of neuronal activity particularly in the hindbrain, as well as upregulation of activity particularly in the olfactory bulb, pallium, subpallium, preoptic area, and hypothalamus (Fig 4G–4I). These activity patterns are inverted when compared to those obtained by treatment of wild type animals with the dopamine antagonist Butaclamol. Specifically, in Butaclamol-treated animals, activity in the hindbrain is increased and activity is decreased within the olfactory bulb, pallium, subpallium, preoptic area, and hypothalamus. These opposing activity signatures in pappaa mutants and dopamine-inhibited animals, together with the finding that Butaclamol restores habituation learning in pappaa mutants, are consistent with a scenario in which PAPP-AA regulates habituation learning by limiting endogenous dopamine signaling.
CACNA2D3 acts independently of other regulators of habituation learning
We recently identified the calcium channel voltage dependent alpha2/delta subunit 3 gene, cacna2d3, encoding an auxiliary subunit of the voltage-gated calcium channel (VGCC) complex, as a genetic regulator of habituation learning [29]. We wondered how this VGCC subunit cooperates with the other regulators of habituation learning and therefore repeated our pharmacogenetic screen in the cacna2d3 mutant background. We found that like pappaa and kcna1a, treatment of cacna2d3 mutants with either the NMDA inhibitor MK801 or the glycinergic signaling inhibitor Strychnine further reduced habituation learning when compared to DMSO treated cacna2d3 mutants (Fig 5A–5D), consistent with a model in which cacna2d3 regulates habituation independently of NMDA and glycinergic signaling. We next examined the interaction between cacna2d3 and dopamine signaling (Fig 5E). Analysis of the learning curves for this experiment revealed an almost flat learning curve for Butaclamol-treated siblings and mutants (Fig 5F), consistent with a strong effect of dopaminergic inhibition on habituation in cacna2d3 mutants, and consistent with a model in which dopamine and cacna2d3 function in parallel to regulate habituation learning. Although this effect failed to reach statistical significance, the impacts of MK801, Strychnine, and Butaclamol on cacna2d3 mutant learning curves suggest that cacna2d3 functions independently from NMDA, glycine, and dopamine signaling.
(A-B) MK-801 significantly enhances habituation learning deficits observed in cacna2d3 mutant larvae, indicating that NMDA signaling and cacna2d3 may act within parallel molecular or circuit pathways to regulate habituation learning (p = 0.0010, n = 28 DMSO siblings, n = 27 MK-801 siblings, vs. p<0.0001, n = 31 DMSO mutants, n = 41 MK-801 mutants; p = 0.5130 indicates non-significant interaction between drug treatment and genotype). *indicates a sibling+MK801 individual with a % hab value below y-axis limit at -60% (C-D) Strychnine significantly enhances habituation learning deficits observed in cacna2d3 mutant larvae, indicating that glycine signaling and cacna2d3 may act within parallel molecular or circuit pathways to regulate habituation (p = 0.6012, n = 15 DMSO siblings, n = 21 Strychnine siblings vs. p<0.0001, n = 22 DMSO mutants, n = 19 Strychnine mutants; p<0.0001 indicates significant interaction between drug treatment and genotype owing to minimal reaction of cacna2d3 siblings to Strychnine treatment). (E-F) Butaclamol does not significantly enhance habituation learning deficits observed in cacna2d3 mutant larvae (p = 0.0143, n = 20 DMSO siblings, n = 24 Butaclamol siblings, vs. p = 0.0527, n = 43 DMSO mutants, n = 35 Butaclamol mutants; p = 0.4035 indicates non-significant interaction between drug treatment and genotype). Inspection of the learning curves (F) reveals a dramatic difference in the learning curves of mutants with or without drug. *indicate 2 sibling+Butaclamol and 2 mutant+Butaclamol individuals with % hab value below y-axis limit at -100%, -60%, -256%, and -80% respectively. (G-I) Regions upregulated in cacna2d3 mutants are indicated in green; regions downregulated in cacna2d3 mutants are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. Unlike other mutants, cacna2d3 mutants do not exhibit reproducible changes in neuronal activity relative to their siblings in any stimulation condition. See also S1–S3 Tables for ROIs up- and down-regulated in each condition, as well as in independent replicates.
When we performed whole-brain imaging in cacna2d3 mutants, we observed inconsistent activity changes across all stimulus conditions (Fig 5G–5I). This lack of a defined whole-brain activity signature is unique to cacna2d3 among the three pharmacological and five genetic manipulations that we tested. We interpret these results to reflect that CACNA2D3 may induce only subtle changes in neuronal activity or that it may simultaneously upregulate and downregulate activity within physically commingled neuronal populations.
AP2S1 promotes habituation by limiting endogenous dopamine signaling
We recently identified a splice site mutation in the adaptor related protein complex 2 subunit sigma 1 (ap2s1) gene, positioning the AP-2 adaptor complex as a fifth genetic regulator of habituation learning. We previously demonstrated that besides its role in habituation learning, AP-2 modulates sensorimotor decision-making via the Calcium-Sensing Receptor, CaSR [27,33]. Yet whether AP-2 also modulates NMDA, dopamine, or glycinergic signaling to regulate habituation learning has not been examined. We therefore performed our reduced-intensity habituation assay in ap2s1 mutants and siblings and found that NMDA-receptor inhibition by MK-801 significantly impaired habituation in ap2s1 mutants, indicating that these two regulators of learning function in parallel (Fig 6A and 6B). Although not statistically significant (p = 0.0773), glycinergic inhibition in the context of ap2s1 mutations also revealed a clear and dramatic trend toward enhancement of habituation learning deficits (Fig 6C and 6D). Finally, while Butaclamol-mediated inhibition of dopamine signaling led to significantly impaired habituation in ap2s1 siblings, Butaclamol treatment of ap2s1 mutants significantly restored learning (p = 0.0206; Fig 6E and 6F).
(A-B) MK-801 significantly enhances habituation learning deficits observed in ap2s1 mutant larvae, indicating that NMDA signaling and ap2s1 may act within parallel molecular or circuit pathways to regulate habituation (p<0.0001, n = 54 DMSO Siblings, n = 58 MK-801 siblings; p = 0.0219, n = 16 DMSO mutants, n = 17 MK-801 mutants; p = 0.0875 indicates non-significant interaction between drug treatment and genotype). (C-D) Strychnine trends toward enhancing habituation learning deficits observed in ap2s1 mutant larvae (p = 0.0062, n = 29 DMSO siblings, n = 33 Strychnine siblings; p = 0.0773, n = 7 DMSO mutants, n = 7 Strychnine mutants; p = 0.5651 indicates non-significant interaction between drug treatment and genotype). Inspection of learning curves in (D) shows dramatic differences between vehicle and drug-treated larvae, indicating that glycine signaling and ap2s1 act within parallel molecular or circuit pathways to regulate habituation learning. *indicates a mutant+DMSO individual with a % hab value below y-axis limit at -60% (E-F) While Butaclamol inhibits habituation learning in siblings (p = 0.0003, n = 36 DMSO siblings, n = 42 Butaclamol siblings) it does not significantly enhance habituation learning deficits in ap2s1 mutant larvae. Rather, ap2s1 mutant animals learn significantly more robustly in the presence of the normally habituation-blocking Butaclamol (p = 0.0206, n = 14 DMSO mutants, n = 13 Butaclamol mutants; p<0.0001 indicates significant interaction between drug treatment and genotype). (G-I) Regions upregulated in ap2s1 mutants are indicated in green; regions downregulated in ap2s1 mutants are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. Consistent with the even stronger effect of Butaclamol in driving habituation learning in ap2s1 mutants relative to pappaa mutants, ap2s1 mutant animals exhibit an even more dramatically inverted pattern relative to Butaclamol-treated animals. ap2s1 mutant animals exhibit robust upregulation in the telencephalon (while Butaclamol-treated animals show downregulation here). Similarly, ap2s1 mutants show dramatically downregulated activity within the rhombencephalon, while our Butaclamol results indicate that dopamine inhibition upregulates activity here. See also S1–S3 Tables for ROIs up- and down-regulated in each condition, as well as in independent replicates.
Finally, we performed whole-brain activity mapping in ap2s1 mutants. Given that inhibition of dopamine partially restored habituation in both pappaa and ap2s1 mutants, we predicted that ap2s1 mutants would exhibit a similar activity pattern to that observed in pappaa mutants, and an inverted pattern with respect to dopamine receptor-inhibited animals. Indeed, analysis of whole-brain activity maps in ap2s1 mutants revealed activity patterns similar to those we observed in pappaa mutants (Fig 6G–6I), characterized by a marked downregulation in areas of the hindbrain that were observed to be upregulated in Butaclamol-treated animals, including a small hindbrain cluster of Tyrosine Hydroxylase (th, the enzyme required for dopamine synthesis) positive neurons (S1 Table). Moreover, significant upregulation of activity was observed in the olfactory bulb, subpallium, pallium, and intermediate hypothalamus, all areas that saw significant upregulation in pappaa mutant brains and downregulation in Butaclamol-treated animals (S1 Table). Combined, these results suggest a model in which AP2S1, like PAPP-AA, suppresses dopamine signaling. As in the case of pappaa, loss of ap2s1 results in dysregulated dopaminergic signaling that can be restored through its pharmacological inhibition via Butaclamol.
In summary, comparing pharmacogenetic analyses and brain activity signatures across five different habituation genes and three inhibitors of habituation-regulatory neurotransmitter pathways reveals distinct molecular modules that regulate habituation learning and identifies molecularly defined brain regions associated with each of the modules.
ap2s1 and pappaa regulate activity in brain regions enriched for Tyrosine Hydroxylase expression
To further examine the relationship between pappaa, ap2s1, and dopamine signaling, we overlaid the Z-brain-registered tyrosine hydroxylase (Th) stain [31] with our ap2s1 and pappaa MAP-maps. Tyrosine hydroxylase is required for dopamine synthesis and therefore is expected to label dopamine neurons. Consistent with our hypothesis that ap2s1 and pappaa suppress endogenous dopamine signaling, we found that activity within Th-stained olfactory bulb neurons was significantly upregulated in ap2s1 mutants, while activity within Th-stained preoptic neurons was significantly upregulated in pappaa mutants. To further examine this relationship, we conducted additional independent replicates of our MAP-mapping experiments examining whole-brain activity differences in mutants relative to siblings in response to habituating stimuli. In total, our 6 ap2s1 MAP-maps generated using habituating stimuli consistently identified “Olfactory Bulb Dopaminergic Neuron Area” as the top upregulated region in every replicate. When we overlaid the registered anti-Th stain with our ap2s1 MAP-maps, we found that the upregulated regions indeed consistently included areas that stained positively for tyrosine hydroxylase (Fig 7A–7B”). In contrast, the pappaa replicates showed substantial variability in the pattern of olfactory bulb dopaminergic neuron upregulation (Fig 7C–7D’). Despite this variability, the most consistently upregulated region (upregulated in all 7 replicates) for pappaa was the “Preoptic otpb cluster.” When we overlaid our MAP-maps with the anti-Th stain, we found that this area indeed stains positively for tyrosine hydroxylase (Fig 7E–7F”). Conversely, ap2s1 mutants did not show increased activity within these populations (Fig 7G–7H’).
(A) Summed Z-projection of Th signal within the olfactory bulb obtained from the Z-brain Atlas [31]. (A’) Representative example of a summed projection from a single experiment showing regions of increased activity within the olfactory bulb in ap2s1 mutants relative to their siblings during habituation learning. (A”) A merge of A and A’ showing co-registration of Th-immunoreactive areas and regions of increased activity in ap2s1 mutants relative to siblings during habituation. Areas of increased activity in ap2s1 mutants during habituation learning show strong overlap with Th-immunoreactive olfactory bulb regions. (B) A single z-plane through the olfactory bulb Th-stained area shown in (A). (B’) The same z-plane as in B taken from the stack in A’ showing regions of increased activity within the olfactory bulb in ap2s1 mutants relative to their siblings during habituation learning. (B”) Merge of (B) and (B’). The x-y overlap indicated in (A-A”) occurs within the same z-plane. (C-C’) same as (A’-A”) but for pappaa mutants. (D-D’) same as (B’-B) but for pappaa mutants. (E) Summed Z-projection of anti-Th signal within a sub-region of the preoptic area obtained from the Z-brain Atlas [31]. (E’) Representative example of a summed projection from a single experiment showing regions of increased activity within the preoptic area in pappaa mutants relative to their siblings during habituation learning. (E”) A merge of E and E’ showing co-registration of Th-stained areas and regions of increased activity in pappaa mutants relative to siblings during habituation. Areas of increased activity in pappaa mutants during habituation learning show strong overlap with Th-positive preoptic regions. (F) A single z-plane through the preoptic area shown in (E). (F’) The same z-plane as in (F) taken from the stack in E’ showing regions of increased activity within the preoptic area in pappaa mutants relative to their siblings during habituation learning. (F”) Merge of (F) and (F’). The x-y overlap indicated in (E’-E”) occurs within the same z-plane. (G-G’) same as (E’-E”) but for ap2s1 mutants. (H-H’) same as (F’-F”) but for ap2s1 mutants. ap2s1 mutants do not show upregulation within neurons of the preoptic area that stain positive for Th. (I) Quantification of the fold-change in pERK/tERK in ap2s1 mutants relative to their siblings within ROIs encompassing either the olfactory bulb dopaminergic area on each side or ROIs encompassing the preoptic dopamine clusters on each side. Each point represents the region-specific fold-change for a single larva. Larvae are pooled from all replicates. Olfactory bulb, n = 61 mutants, Wilcoxon Signed Rank Test comparing to null hypothesis of median 1 for the fold change in pERK/tERK in mutants as compared to siblings, p<0.0001. Preoptic area, n = 56 mutants, Wilcoxon Signed Rank Test comparing to null hypothesis of 1 for the fold change in pERK/tERK in mutants as compared to siblings, p = 0.9436. (J) As in (I) but for pappaa mutants relative to their siblings. Olfactory bulb n = 89 mutants. Wilcoxon Signed Rank Test comparing to null hypothesis of 1 for the fold change in pERK/tERK in mutants as compared to siblings, p<0.0001. Preoptic area n = 88 mutants, Wilcoxon Signed Rank Test comparing to null hypothesis of 1 for the fold change in pERK/tERK in mutants as compared to siblings, p<0.0001. (K) The same data set as in (I) but showing the aggregated fold-change averages within each of the 6 ap2s1 replicates to demonstrate high degree of replicability across experiments. Each point is the pERK/tERK fold-change averaged across all animals for a single replicate. Olfactory Bulb, n = 6 replicates, Bonferroni-adjusted p = 0.0624 trends toward significance. (L) The same data set as in (J) but showing aggregated fold-change averages within each of the 7 pappaa replicates to demonstrate high degree of replicability across experiments. Each point is the pERK//tERK fold-change averaged across all animals for a single replicate. Olfactory bulb and preoptic area, each n = 7 replicates, Bonferroni-adjusted for both p = 0.0468. See S2 Fig for activity decreases within these same regions.
To further quantify these activity changes, we measured the ratio of phosphorylated ERK to total Erk within ROIs drawn individually around left and right olfactory bulb dopamine neuron clusters as well as left and right preoptic area dopamine clusters. We then calculated a fold change in activity for our mutants relative to their siblings within each ROI. Consistent with our MAP-maps, activity within the olfactory bulb dopaminergic neurons was upregulated in both ap2s1 and pappaa mutants, while activity within the preoptic dopaminergic neurons was upregulated only in pappaa mutant animals (Fig 7I and 7J). To examine the consistency of our findings across replicates, we computed a fold change within each ROI for all of the mutants relative to the sibling average comprising each replicate and plotted these values (Fig 7K and 7L). Taken together, these data support our hypothesis that pappaa and ap2s1 act to suppress endogenous dopamine signaling. Finally, both the olfactory bulb and the preoptic area have previously been shown to increase their activity in response to acoustic stimuli, whereas activity returns toward baseline in response to habituating stimuli [34]. Therefore, we hypothesize that the hyperactivation of these same areas including during habituation in our mutants indicates dysregulation of candidate habituation-relevant brain areas. Our data do not allow us to rule out potential involvement of other candidate regions. Similarly, it is possible that ap2s1 and/or pappaa may additionally exert their effects through the regulation of dopamine receptor signaling rather than through modulating of the activity of dopaminergic populations.
Discussion
We set out to map genetic regulators onto the circuit / neurotransmitter systems that drive habituation learning. We employed two complementary strategies. First, we modified the standard habituation learning assay such that additive habituation learning deficits caused by combining genetic and pharmacological regulators of habituation can readily be detected and quantified. We reasoned that the habituation deficits caused by a given genetic mutation would be enhanced by pharmacological manipulations of independent or parallel habituation-regulatory pathways, but not by manipulation of habituation-regulatory mechanisms in the same pathway module. Importantly, the pharmacological manipulations we employed lack spatial specificity. Therefore, this approach is highly valuable for identifying interactions between pathways, but it does not pinpoint precisely where in the brain those interactions occur, i.e. whether the identified interactions between neurotransmitter signaling pathways and gene products occur within individual cells or within distinct nodes of a circuit regulating habituation learning. Importantly, this approach has been utilized in previous studies in which manipulations that increase dopaminergic signaling render animals hypo-responsive to dopamine receptor agonism [35]. Similarly, manipulations that decrease NMDA receptor localization render animals hypo-responsive to NMDA receptor antagonism [36].
Second, we performed MAP-mapping in the context of each pharmacological or genetic manipulation, resulting in a unique set of brain activity maps, all of which reflect habituation learning deficient patterns of activity. We are struck by the diversity of brain activity patterns associated with deficits in habituation learning. Although overlapping patterns were observed for hip14, MK-801, and Butaclamol, as well as for pappaa and ap2s1, these two putative modules differ from one another, and from the patterns observed for Strychnine and kcna1a, as well as from the observed lack of activity changes in cacna2d3 mutants. Taken together, these results are consistent with at least two potential interpretations. For one, it is possible that although each perturbation broadly impacts brain activity in distinct ways, brain activity maps for regulators of learning overlap within a handful of critical regions that drive habituation. Alternatively, it is possible that habituation learning is tightly regulated and involves the cooperation of multiple parallel genetic-circuit modules. The latter interpretation is consistent with our observation that some genetic regulators of habituation learning show a significant interaction with NMDA and/or dopamine signaling, while others do not. Moreover, our proposal of the existence of parallel short-term habituation-regulatory modules mirrors the previous finding that long-term habituation learning in the larval zebrafish is regulated by multiple parallel processes [15].
Our results suggest the existence of five distinct habituation regulatory modules (Fig 8A). The first module consists of Hip14, as well as NMDA and dopamine signaling (Fig 8B). Our pharmacological screen uncovered significant pharmacogenetic interactions between hip14 and inhibitors of both NMDA and dopamine receptor signaling. Additionally, an unbiased clustering algorithm identified that brain activity patterns produced by the NMDA inhibitor MK-801 and the dopamine receptor antagonist Butaclamol are similar, and the relative strengths of activity changes from these treatments are highly correlated (R-squared = 0.71, p<0.05; S3A and S3B Fig). Although both Strychnine and mutations in hip14 broadly upregulate neuronal activity, the specific regions that they upregulate are only weakly correlated (R-squared = 0.24, p<0.05, S3C Fig). This finding is consistent with the results from our pharmacogenetic approach, which placed these two regulators into pathways independent of each other.
(A) Module 1 (purple) comprises hip14, NMDA, and dopamine receptors. Module 2 (aqua) comprises hip14 and kcna1a. Module 3 (pink) comprises ap2s1, pappaa, and dopamine receptors. Modules 4 and 5 are comprised of cacna2d3 and glycine receptor signaling acting in parallel to all other modules. (B) Regions commonly upregulated by dopamine inhibition, NMDA inhibition, and mutations in hip14 are indicated in purple. (C) Regions upregulated by mutations in kcna1a and hip14 are indicated in blue. (D) Regions downregulated by dopamine receptor inhibition and upregulated by mutations in ap2s1 or pappaa are indicated in pink. Regions upregulated by dopamine receptor inhibition and downregulated by mutations in pappaa or ap2s1 are indicated in yellow. See also S3 Fig for cluster analysis heat map and correlations between pharmacogenetic treatments. In (B-D), signal intensity is proportional to the sum of the absolute intensity values. Abbreviations in (B): NP2 = Rhombencephalon–Neuropil Region 2, X vagus mn cluster = Rhombencephalon—X Vagus motorneuron cluster, Gad1b S = Rhombencephalon Gad1b Stripe 2, NP3 = Rhombencephalon Neuropil Region 3, NNIV = Rhombencephalon—Noradrendergic neurons of the Interfascicular and Vagal areas, Vmat2 S1 = Spinal Cord—Vmat2 Stripe1, Hcrtr S = Spinal Cord—6.7FDhcrtR-Gal4 Stripe, Gad1b S1 = Spinal Cord—Gad1b Stripe 1, R7 = Rhombomere 7, Hcrtr Clust 5 = Rhombencephalon—6.7FDhcrtR-Gal4 Cluster 5, Area postrema = Rhombencephalon Area Postrema, Gad1b Clust 20 = Rhombencephalon—Gad1b Cluster 20. Abbreviations in (C): SF anterior = Rhombencephalon—Spiral Fiber Neuron Anterior cluster, SF posterior = Rhombencephalon—Spiral Fiber Neuron Posterior cluster, Oxtl Clust MC Axon Cap = Rhombencephalon—Oxtl Cluster 2 Near MC axon cap, RoM2 = Rhombencephalon—RoM2, RoM3 = Rhombencephalon–RoM3, MiM1 = Rhombencephalon—MiM1. Abbreviations in (D): DO = Telencephalon—Olfactory bulb dopaminergic neuron areas, Gad1b-C = Telencephalon—Subpallial Gad1b cluster, Isl1-C = Telencephalon—Isl1 clusters 1 and 2, OB = Telencephalon—Olfactory Bulb, Subpall = Telencephalon–Subpallium, Pallium = Telencephalon–Pallium, Vmat2-S = Rhombencephalon—Vmat2 Stripe2, GlyT2-S = Rhombencephalon—Glyt2 Stripe 2, Isl1b-S = Rhombencephalon Isl1 Stripe1, Gad1b-C = Rhombencephalon—Gad1b Cluster 20, Gad1b-S = Rhombencephalon—Gad1b Stripe 2, TH-C = Rhombencephalon—Small cluster of TH stained neurons
The second module consists of Hip14 and Kv1.1 (Fig 8C). Mutations in both hip14 and kcna1a strongly upregulate activity in the spiral fiber neuron clusters as well as in V2a neurons (S3D Fig). Dysfunction of V2a neurons within the spinal cord was previously proposed as a potential mechanism underlying the kinematic deficits observed in kcna1a mutants [32], and we now hypothesize that hyperactivation of V2a neurons within the hindbrain could contribute to the habituation deficits observed in kcna1a mutants. Spiral fibers also constitute an attractive locus for Kv1.1’s activity in regulating habituation. We previously showed that Hip14 can palmitoylate Kv1.1 and that it regulates its localization to the spiral fiber terminals [28]. Moreover spiral fibers are known to undergo plasticity during habituation learning [37]. Interestingly, these neurons were not reliably identified as showing differential activity in MK-801 or Butaclamol-treated animals (Module 1). One possible explanation for this is that changes within spiral fibers are below the detection threshold of our MAP-mapping analysis. Alternatively, Hip14, NMDA, and dopamine signaling could regulate habituation learning in a pathway that is parallel to the role of Hip14 and Kv1.1 within spiral fibers. This would be consistent with hip14 mutants’ stronger habituation deficit relative to kcna1a mutants, as well as the finding that Hip14 acts in a common pathway with dopaminergic and NMDA signaling while Kv1.1 does not.
The third module includes PAPP-AA and AP2S1 and is defined by its unique relationship to dopamine signaling. While Hip14 seems to promote dopaminergic signaling our data strongly suggest that PAPP-AA and AP2S1 oppose dopamine signaling. We first examined this relationship through overlaying the top upregulated regions with tyrosine hydroxylase stains, reasoning that if pappaa and ap2s1 are required to downregulate endogenous dopamine signaling, then these populations may be upregulated in mutants (Fig 7). Next we used MAP-mapping to identify regions whose activity is oppositely regulated by Butaclamol and ap2s1/pappaa. This revealed that activity is broadly upregulated in the telencephalon in ap2s1 mutants and in pappaa mutants, and downregulated in the telencephalon by Butaclamol treatment. Similarly, activity in the hindbrain is largely upregulated by Butaclamol treatment and downregulated in ap2s1 mutants (Fig 8D, S3E and S3F Fig). We hypothesize that the opposing function of D1 and D2/D3-type dopamine receptors in regulating the startle response may help to explain the surprising bi-directional control of habituation learning by dopamine signaling [38]. While dopamine acts to drive habituation learning (reducing stimulus responsiveness) through D1-type dopamine receptors, it is known that it additionally promotes stimulus responsiveness through D2/D3-type dopamine receptors in mice [38]. We propose that AP2S1 and PAPP-AA are required to limit signaling through D2/D3-type dopamine receptors. In this scenario, mutations in either gene would result in excessive D2/D3 signaling and hyperresponsive larvae that fail to habituate to acoustic stimuli. Under these conditions, applying a dopamine receptor antagonist might normalize D2/D3 signaling and stimulus responsiveness, allowing animals to habituate. In support of this, work in zebrafish has shown that high doses of the D2 receptor antagonist amisulpride can promote habituation learning [39], and that the D2 antagonist haloperidol promotes long-term habituation of the O-bend or visual startle [15].
The whole brain activity patterns observed for AP2S1 resemble the brain activity maps recently published for low-habituating populations obtained through breeding selection, showing increased neuronal activity in the telencephalon and decreased activity in the hindbrain [34]. Moreover, our data suggest that dopaminergic neuromodulation is an important driver of activity in the larval telencephalon and are supported by recent optogenetic experiments performed in Th2-expressing neurons [40]. However, this previous work found that ablating dopaminergic neurons did not alter habituation and that dopamine neuron activity in the caudal hypothalamus was actually elevated in high-habituating populations relative to low [34]. Nonetheless, chemogenetic ablation was restricted to Th1-positive dopamine neurons whereas our pharmacological approach is expected to impact the targets of both Th1 and Th2-expressing neurons. Moreover, the ablation approach is chronic, in contrast to our transient pharmacological inhibition, eliminating dopaminergic neurons while potentially having no immediate effect on neurotransmitter levels [41]. Given these differences, it is perhaps not surprising that these approaches yield somewhat differing results. Indeed, our opposing results underscore the complex roles that dopamine plays in regulating acoustic startle sensitivity and habituation, and further work will be required to understand the role of dopamine in regulating habituation learning.
Finally, our work is consistent with glycine signaling (Module 4) and cacna2d3 (Module 5) functioning in parallel to one another and to the other three modules described. We find no evidence that cacna2d3 acts to regulate dopaminergic, glycinergic, or NMDA signaling, and likewise find no pharmacogenetic interactions between Strychnine and hip14, kcna1a, pappaa, or ap2s1. Although it is possible that we failed to detect a weak interaction between these and the other modules, it will be important to investigate potential interactions between these pathways and other pharmacogenetic regulators of habituation learning. Future studies might examine these cases where our work failed to detect interactions by merging our two approaches. For example, one might examine the interactions between pharmacological and genetic regulators of habituation learning using whole-brain or Calcium imaging analyses. We predict that such studies might uncover a blunted impact of the pharmacological manipulations on neuronal activity in mutants that occupy the same functional module.
One striking and unexpected finding that arose from our data is that each pharmacological manipulation or genetic mutation induced a brain activity pattern that was remarkably consistent across stimulation conditions (S3A Fig). This is consistent with previously published work examining whole-brain activity changes in animals selectively bred for high versus low habituation rates [34]. Moreover, our data are consistent with a model in which our pharmacogenetic perturbations lead to broad impacts on brain activity. Finally, these data are consistent with the reported hypersensitivity of all of the tested mutants to low-intensity stimuli [25,28,29,33], which suggest baseline deficits in the thresholding of acoustic stimuli. Importantly, although habituation deficits may co-occur with changes in response thresholds as they do in these mutants, these deficits may also occur independently [25,42], suggesting that they are mechanistically separable. It is also possible that the similarities between MAP-maps across stimulus conditions indicate that our genetic and pharmacological manipulations impact the animals’ internal state, resulting in baseline brain activity changes that manifest at the behavioral level as habituation deficits. Nonetheless, these findings are highly relevant for human health, where disorders that impact habituation learning often have simultaneous impacts on response thresholds. Taken together our results support a model in which multiple circuit mechanisms regulated by parallel molecular-genetic pathways cooperate to drive habituation learning in vivo.
Materials and methods
Ethics statement
All animal protocols were approved by the University of Pennsylvania Institutional Animal Care and Use Committee (IACUC).
Experimental model and subject details
hip14p174, pappaap170, cacna2d3sa16189, and ap2s1p172 mutants were maintained in the TLF background. kcna1ap410 was maintained in the WIK background. Among these, cacna2d3sa16189 is homozygous viable, and crosses were performed between heterozygous carriers and homozygous mutants to obtain clutches of 50% heterozygous, and 50% homozygous mutant offspring. All other crosses were established between heterozygous carriers. Groups labeled as “mutants” are comprised of homozygous mutants, while groups labeled as “siblings” are a mix of WT and heterozygous individuals.
Pharmacogenetic behavior testing was performed in two independent runs on day 5 animals as previously described [7]. Data from these independent runs was pooled for analysis. Stimulation and fixation for MAP-mapping analysis was performed on day 6 to standardize with the reference brain utilized for registration, as previously described [31].
To ensure approximately equal numbers of mutants and siblings (siblings are a mix of heterozygous and WT animals), clutches were enriched for mutant animals prior to behavior testing or MAP-mapping by selecting for animals based on the exaggerated spontaneous movement phenotype (kcna1ap410) or swim bladder defects (pappaap170, hip14p174). To enrich for mutant animals in the absence of such phenotypes, ap2s1 clutches were subjected to live genotyping on Day 3 as previously described [43]. After genotyping, mutant and sibling animals were mixed together in 10cm petri dishes and tested on Day 5 (pharmacogenetic behavior analysis) or stimulated and fixed on Day 6 (MAP-mapping).
For all experiments, behavior was performed and analyzed blind to genotype; genotyping was performed after behavior testing and/or imaging, and mutant animals were compared to siblings from the same clutches. Except in the case of ap2s1 live pre-genotyping, all genotyping was performed by extracting gDNA from each larva individually through incubation in 15ul of 25mM NaOH, 0.2mM EDTA for 15 minutes at 95 degrees C. Following incubation, an equal volume (15ul) of neutralization solution (40mM Tris-HCl, pH 5) was applied to each well. 1-5ul of this mixture was then used directly for PCR or KASP genotyping.
Pharmacogenetic behavior testing
A 200x (100mM) stock of MK-801 (Sigma M107) was prepared by dissolving a new vial of 25mg of MK-801 powder in 750ul of 100% DMSO. The stock solution was then further dissolved in E3 to a final concentration of 500uM (0.5% DMSO final concentration). MK-801 was applied to a 10cm petri dish containing n = 45 5 dpf larvae 30 minutes prior to the first presentation of baseline acoustic stimuli. Control larvae received 0.5% DMSO in E3. The 200x stock of MK-801 was freeze-thawed a maximum of one time and then disposed of.
A 200x (10mM) stock of Strychnine (Sigma S0532) was prepared by dissolving 33.4mg of Strychnine powder in 10mL of 100% DMSO. The stock solution was then further dissolved in E3 to a final concentration of 50uM (0.5% DMSO final concentration). Strychnine was applied to a 10cm petri dish containing n = 45 5 dpf larvae 15 minutes prior to the first presentation of baseline acoustic stimuli. Control larvae received 0.5% DMSO in E3. The 200x stock of Strychnine was frozen at -20. We observed no reduction in the effectiveness of our Strychnine stock solution on WT animals over the course of several months of testing.
A 630x (63mM) stock of Butaclamol (Sigma D033) was prepared by dissolving 25mg of Butaclamol in 1mL of DMSO. The stock solution was further dissolved in E3, and DMSO supplemented to a final concentration of 0.5% DMSO, 100uM Butaclamol. Butaclamol was applied to a 10cm petri dish containing n = 45 5dpf larvae 30 minutes prior to the presentation of baseline acoustic stimuli. Control larvae received 0.5% DMSO in E3. The 630x stock of Butaclamol was freeze-thawed a maximum of one time and then disposed of.
The stimulus level for our behavioral experiments was chosen to allow for both increases and decreases in habituation in our mutant conditions. However we also see a higher degree of variability between experiments using this stimulus level. Therefore, each experiment is internally controlled (mutants and siblings were tested in the same dish) and replicated twice.
All larvae were acclimated to testing room conditions (light, temperature, etc.) for 30 minutes prior to the application of pharmacological agents. Assays for habituation of the acoustic startle response (ASR) were performed on 5 dpf larvae arrayed in a 36-well dish, fabricated by laser-cutting a 6x6 grid of holes into an acrylic sheet, and affixing it to an uncut sheet of the same dimensions using acrylic glue. The dish was mounted on a vibrational exciter (4810; Brüel and Kjaer, Norcross, GA) via an aluminum rod. Acoustic stimuli (2ms duration, 1000Hz waveforms) were delivered during the baseline phase of the assay with an interstimulus interval (ISI) of 40 seconds. During the habituation phase, stimuli were presented with a 3-second ISI.
MAP-mapping
Larvae were acclimated to testing room conditions (light, temperature, etc.) in a 10cm petri dish with n = 45 6 dpf larvae for 30 minutes prior to transfer to the testing arena. Following acclimation, 25 larvae were transferred from the petri dish to a cell strainer with 40um pores (Neta Scientific 431750) nested inside a 6cm petri dish, submerged in E3. The entire cell strainer was then removed and immediately submerged in a 4cm petri dish glued to a circular acrylic base, affixed via a titanium arm to the vibrational exciter (4810; Brüel and Kjaer, Norcross, GA). Larvae were acclimated to the testing arena for 30 minutes with no stimuli. “No Stimuli” runs then proceeded with 17 minutes of additional run time. “Non-Habituating Stimuli” runs proceeded with 10 35.1dB acoustic stimuli with a 90-second ISI, followed by 2 minutes of rest. “Habituating Stimuli” runs proceeded with 180 35.1dB acoustic stimuli with 5-second ISI followed by 2 minutes of rest. Immediately following the completion of the behavior testing protocol, the cell strainer was removed from the testing arena and dropped into a 6-well dish (VWR 10861–554) containing 4% PFA in 1x PBT (1x PBS + 0.25% TritonX100). After 2 minutes, cell strainers were transferred to a second 6-well dish containing cold 4% PFA in 1x PBS, and incubated at 4 degrees overnight. Next, the immunostaining procedure described in [31] was carried out as described with the following modifications: immediately after washing PFA, larvae were bleached for approximately 12 minutes in 1.5% hydrogen peroxide; 0.5% KOH; larvae were then washed twice (quickly) and then once for 5 minutes in PBT; larvae were then incubated in 150mM of Tris-HCl pH 9.0 for 5 minutes at room temperature, followed by 15 minutes at 70 degrees Celcius. Following immunostaining, all larvae for a single experiment were mounted in 1.5% low-melt agarose (Lonza Bioscience 50101) in a 50mm petri dish with a 30mm diameter glass bottom (Mattek P50G-1.5-30-F). Following mounting, a map was drawn noting the orientation and position of each larva, and numbering every larva. Confocal images were acquired using a 20x objective lens on a Zeiss LSM880 confocal microscope using Zen Software. The “tiles” function was used to acquire and stitch together two images of each brain (one centered on the rostral and one on the caudal portion of the head). Confocal images were named according to the larva numbers assigned in our map. Following imaging, each animal was unmounted from the agarose and placed in labeled strip tubes in accordance with their assigned number. We then extracted genomic DNA as described above and performed PCR or KASP genotyping.
Behavior analysis
Behavior videos were background subtracted by computing a max projection of the entire image series using FIJI. Max projections were then subtracted from each image within the series using FIJI. Subtracted image series were tracked using FLOTE software as previously described [7,8]. In the case of Strychnine-treated larvae, the previously described “accordion-like” shape [44] of the SLC response precluded acceptable tracking via FLOTE. Therefore, behavioral responses were manually scored blind to genotype as SLCs or No-Response by isolating the 17ms of video following the delivery of the acoustic pulse, and scoring body bends within this interval as SLCs.
% Habituation was quantified by the following formula: [% Habituation = (1-[response frequency Stimuli 45–54] ÷ [response frequency baseline])*100].
Quantification and statistical analysis
Computation of means, SD, SE, and data set normality were performed using GraphPad Prism. Effects of each drug condition were assessed using Two-Way ANOVA with Sidak’s Multiple Comparisons Test.
For MAP-mapping, image registration and positive and negative significant delta median signals in each brain region across mutant vs. sibling and drug vs. DMSO controls were calculated using the standard MAP-mapping pipeline as described in [31].
For ROI-specific computation of pERK/tERK, we used the ROIbasedpERKanalysis script described in [45]. Using this script, we first specified two sets of ROIs used across all of our samples, one encompassing the Th-immunoreactive signal within the preoptic otpb clusters on the left and right side respectively, and a second set encompassing the Th-immunoreactive signal within the olfactory bulb on the left and right sides. The script computed pERK/tERK values across all of our samples within each ROI. Sibling averages were computed within each ROI and within each replicate. Fold change in each mutant was then computed for each ROI as a ratio of mutant pERK/tERK over the sibling average pERK/tERK. Data were then aggregated across all replicates. Finally, Wilcoxon signed-rank tests were used to compare mutant fold changes to a null hypothesis of a median 1 (no change relative to the siblings).
For cluster analysis, positive and negative significant delta median signals were imported into R [46,47]. Signal for each experimental replicate was normalized according to the highest absolute value in that condition, such that the highest magnitude signal for each condition was either -1 or 1. Distances were calculated using the Canberra method, which disregards data when both conditions have a value of 0; this prevented overestimation of similarity between conditions in which many brain regions had zero signal. The factoextra package [48] was used to visualize distances and clusters.
For pairwise plots, normalized negative signal in a given brain region was subtracted from normalized positive signal to obtain a single signal value for that region. We then averaged signal values for each condition (either drug or mutant allele) over all replicate data sets and behavioral stimulation paradigms. After confirming via cluster analysis that these average values captured the general patterns of similarity observed among individual replicates, we then plotted pairwise comparisons between conditions. Module maps (Fig 8B–8D) were created using a modified version of the ZBrainAnalysisOfMAPMaps function [31]. Colors were adjusted in Adobe Illustrator.
Supporting information
S1 Fig. Pharmacological inhibitors of habituation learning induce distinct patterns of brain activity changes.
(A-C) Regions upregulated by the specified drug treatment under “No Stimulus” conditions are indicated in green; regions downregulated are indicated in magenta. (D-F) Regions upregulated by the specified drug treatment under “Non-Habituating Stimuli” conditions are indicated in green; regions downregulated are indicated in magenta. In all images, the left panel is a summed z-projection of the whole-brain activity changes. The middle panel is a summed x-projection of the whole brain activity changes. The right panel is a z-projection of the analyzed MAP-map. Molecular targets of pharmacological agents are indicated with diagrams above each column. Note that the patterns of neuronal activity induced by a given pharmacological agent are relatively consistent across stimulation condition (i.e. “no stimuli”, vs. “non-habituation stimuli”, vs “habituating stimuli” in Fig 1K–1M). Moreover, although all pharmacological agents reduce habituation learning, patterns of neuronal activity are highly dissimilar between individual pharmacological treatments.
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S2 Fig. MAP-map analysis of activity decreases within olfactory bulb and preoptic dopaminergic regions.
(A-D’) Regions with reduced activity in mutants relative to siblings are indicated in magenta. These signals are merged with the Z-brain-registered Th stain in green showing olfactory bulb dopaminergic neurons in figures A-B’ and preoptic dopaminergic neurons C-D’. Fig 7 shows increased activity in olfactory bulb dopaminergic neurons in ap2s1 and pappaa mutants and preoptic dopaminergic neurons in pappaa mutants. Here we show that these same regions are largely devoid of pixels showing reduced activity in mutants relative to siblings according to our MAP-maps. No areas of reduced activity are identified, except medially, outside the Th-stained area of the olfactory bulb in A-A’. These data are consistent with ap2s1 and pappaa upregulating rather than downregulating dopamine neuron activity within these brain areas.
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S3 Fig. Cluster analysis identifies regions of interest.
(A) Heat map indicating replicability across stimulus conditions for each mutant and drug condition. Column and row labels indicate genotype or drug treatment and stimulus condition (i.e. No Stimuli = NoStim, Non-Habituating Stimuli = Taps, Hab = Habituating Stimuli). Note an intermingled cluster containing Butaclamol and MK-801. (B-F) Plots of pairwise comparisons between drugs and or genotypes. Color legend in (B) applies to all. R-square values are indicated when p<0.05. (B) Plot indicating positive correlation between MK-801 and Butaclamol signal changes. (C) Plot indicating a weak positive correlation between hip14 and Strychnine signal changes. (D) Plot showing correlated changes in the rhombencephalon between hip14 and kcna1a. (E) Plot showing 3 populations in Butaclamol vs. ap2s1 changes. Largely telencephalic regions, upregulated in ap2s1 mutants and downregulated by Butaclamol; largely rhombencephalic regions, upregulated by Butaclamol and downregulated in ap2s1 mutants; and a large number of regions downregulated by both manipulations. (F) Plot showing signal changes in pappaa as compared to Butaclamol. Multiple telencephalic as well as diencephalic regions to a lesser degree, are anti-correlated (up-regulated in pappaa mutants but downregulated in Butaclamol). Brain region abbreviations in (B): s1181t = Telencephalon—S1181t Cluster, Gang LLN SO2 = Ganglia—Lateral Line Neuromast SO2, Gang LLN D1 = Ganglia—Lateral Line Neuromast D1, Noradren Interfasc Vagal = Rhombencephalon—Noradrendergic neurons of the Interfascicular and Vagal areas. Brain region abbreviations in (C): Lat Retic Nuc = Rhombencephalon—Lateral Reticular Nucleus. Brain region abbreviations in (D): Spiral Fib Post and Ant = Rhombencephalon—Spiral Fiber Neuron Posterior and Anterior clusters, Mauth Axon Cap = Rhombencephalon—Mauthner Cell Axon Cap. Brain region abbreviations in (E): DO = Telencephalon—Olfactory bulb dopaminergic neuron areas, OB = Telencephalon—Olfactory Bulb, Subpall Gad1b = Telencephalon—Subpallial Gad1b cluster, Subpall = Telencephalon–Subpallium, Oxtl Clust 1 = Rhombencephalon—Oxtl Cluster 1 Sparse, Noradren Interfasc Vagal = Rhombencephalon—Noradrendergic neurons of the Interfascicular and Vagal areas, TH-C = Rhombencephalon—Small cluster of TH stained neurons. Brain region abbreviations in (F): Ant Comm = Telencephalon—Anterior Commisure, Cerebell Olig2 Enriched = Rhombencephalon—Olig2 enriched areas in cerebellum, Gang LLN SO1 and SO2 = Ganglia—Lateral Line Neuromast SO1 and SO2.
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S4 Fig. Analysis of kinematic parameters of acoustic startle response reveal minimal variation in drug-treated and mutant animals.
Kinematic parameters for MK801, Butaclamol, hip14, and pappaa are reported (for ap2s1, cacna2d2, and kcna1a see references 33,29,32 respectively. Bonferroni-adjusted p-values for unpaired t-tests are reported for normally distributed data, and Bonferroni-adjusted p-values for Mann-Whitney tests are reported for data that are not normally distributed. NS = Not significant. (A) Butaclamol significantly increases turn duration during acoustic startle performance n = 17 Butaclamol-treated, n = 16 DMSO-treated, Bonferroni-adjusted p-value for Mann-Whitney test p = 0.044. (B) MK801 significantly reduces turn latency and significantly increases maximum angular velocity during acoustic startle performance. Latency: n = 20 MK801-treated, n = 21 DMSO-treated, Bonferroni-adjusted p-value for Mann-Whitney test p = 0.0004. Maximum angular velocity: n = 20 MK801-treated, n = 21 DMSO-treated, Bonferroni-adjusted p-value for Mann-Whitney test p = 0.0468. (C) Mutations in hip14 significantly reduce turn latency during acoustic startle performance. n = 34 hip14 mutants, n = 34 hip14 siblings, Bonferroni-adjusted p-value for Mann-Whitney test p = 0.0008. (D) Mutations in pappaa significantly reduce turn latency, turn angle, and maximum angular velocity during acoustic startle performance. Latency: n = 28 pappaa mutants, n = 31 pappaa siblings, Bonferroni-adjusted p-value for Mann-Whitney test p = 0.0008. Turn Angle and maximum angular velocity: n = 28 pappaa mutants, n = 31 pappaa siblings, unpaired t-test p<0.0001.
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S1 Video. Example video showing 4x looped acoustic startle responses of Strychnine-exposed animals.
Annotated pink R indicates animals scored as responders. The video is looped 4x to enable better visualization of the subtle movements performed by responders.
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S1 Table. Raw values for signal change in each ROI within each mutant and drug condition across 2–3 replicates.
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S2 Table. Normalized values for increased activity changes in each ROI within each mutant and drug condition across 2–3 replicates.
These values were used for cluster analysis.
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S3 Table. Normalized values for decreased activity changes in each ROI within each mutant and drug condition across 2–3 replicates.
These values were used for cluster analysis.
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S1 Code. R code used to perform cluster analysis.
https://doi.org/10.1371/journal.pgen.1010650.s010
(R)
Acknowledgments
The authors would like to thank the Granato lab members, as well as Dr. Owen Randlett and Dr. Roshan Jain for technical advice and feedback on our findings. The authors would also like to acknowledge Drs. Roshan Jain, Marc Wolman and Nik Santistevan for sharing ap2s1, pappaa, and cacna2d3 mutant zebrafish respectively. The authors would also like to acknowledge the Cell and Developmental Biology Microscopy Core.
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