and the mechanism described here can hence be expected to improve the resolution at which the neuronal segregation of spatiotemporal PA sensory input patterns occurs. The learning process also resulted in that the individual CN increased the segregation of its responses to different non-training stimuli (**Figures 8C,D**). For the illustrated CN, the learning process resulted in a statistically significant decrease in the correlation between the synaptic responses to the five training stimuli at p = 0.027 whereas the p values for the other four CNs were below 0.001 (Wilcoxon signed rank test). The results with nontraining stimuli hence indicated that the acquired learning was generalizable to previously non-encountered experiences, which is difficult to achieve in traditional pattern recognition systems (Spanne and Jorntell, 2015).

#### Evolution of Synaptic Weight Landscapes and Sparseness During the Learning Process

The gradual evolution of the synaptic weight distributions (or their "landscapes") during the learning process was further studied by plotting the weights of all the 80 PA synapses of each CN in an 80-dimensional space, embedded in 2D plots using multi-dimensional scaling (MDS). First, the analysis showed that the end weight landscape, which is the basis for the segregation of responses between neurons and inputs shown in **Figures 7**, **8**, depended on the seed weight landscape of the CN (**Figure 9A**). Secondly, it showed that the evolution of the synaptic weight landscape initially accelerated but later decelerated to hover around a relatively stable end point (**Figure 9A**), suggesting that the learning was self-stabilizing. Self-stabilization is further quantified in **Figure 9B**, which shows the temporal evolution of the sparseness of the synaptic weight distribution. This selfstabilization was lost on removal of the supralinearity in the calcium dynamic responses and in the local synaptic calcium activity (**Figure 10**). In this case, the main compartment calcium was the sum of the local calcium activity in all the synapses, i.e., without intrinsic calcium dynamics in the main compartment and without supralinear local calcium activity subsynaptically. Under such conditions, the learning process became chaotic, without any stable end point and without any development of sparseness of the synaptic weight distribution (cf. **Figure 9A**).

the same stimulus. (A) Triangle matrix for the correlations between synaptic responses evoked by the same training stimulus before (CN1SW-CN5SW) and after learning for all five cuneate neurons (CN1EW-CN5EW). The difference in correlation between the two groups was statistically significant (p = 0.001, Wilcoxon signed rank test) (B) Averaged synaptic responses to the stimulus ('indentation 20 mm') repeated 100 times for three different CNs (CN2, CN3, CN5) before and after training. (C,D) Similar displays as in (A,B) but for a novel stimulus that was not part of the training set ("indentation 10 mm;" Oddo et al., 2017). The difference in correlation before and after learning was statistically significant (p < 0.001, Wilcoxon signed rank test).

As we will describe in greater detail further below, we found that not only the seed weight landscape but also the intrinsic CN properties affected the outcome of the learning process. Of particular interest in relation to the effects of the calcium dynamic response (**Figure 10**) was the coupling between the synaptic

the same CN. Otherwise the display is similar to Figure 7A. The difference in correlation before and after learning was statistically significant (p < 0.001, Wilcoxon signed rank test) (B) Averaged synaptic responses of the CN (CN1) to the full set of training stimuli. (C,D) Similar display as in (A,B) but for non-training stimuli previously reported for shapes (Oddo et al., 2017) and for textures (Rongala et al., 2017). The difference in correlation between the two groups was statistically significant (p = 0.028, Wilcoxon signed rank test).

input and the intermediate dynamics model of the CN, since the intermediate dynamics model governed the time course of the calcium signal and thereby the learning process. This coupling was strongly affected by the Maximum synaptic conductance constant (gmax), which was the ratio between the maximum synaptic conductance and the leak conductance of the neuron. A decrease in this ratio, or coupling, substantially reduced the learning rate whereas an increase instead caused the learning to become rapid, unstable and with dramatic shifts, unlike the more gradual process when the learning system was in balance (**Figure 9C**). Interestingly, even in the setting with high gmax the system found a relatively stable end point in the synaptic weight

landscape but with a different location compared to the basic configuration (**Figures 9C,D**). However, a high learning speed, or learning rate, may not be beneficial if the aim is to allow the result of the learning process to take a variety of experiences into account, which in turn could limit the range of conditions and novel experiences that the learnt network structure would be applicable to. To explore this, we tested the effects of "monotypic" sensory training.

#### Importance of Varied Sensory Experiences

Monotypic sensory training is here referring to a situation where the same sensory experience, or stimulus, is repeated a high number of times in succession. i.e., instead of using the regular learning process protocol with 1,500 presentations of the set of five stimulus presentations mixed in a pseudo-random order, here we ran the learning process with one single type of stimulus for the whole learning process. Similar to diversified sensory training (**Figure 9A**), monotypic sensory training also resulted in gradual changes in the synaptic weight landscape (**Figure 11A**). However, the type of stimulus applied had a decisive influence on the direction of development of the synaptic weight landscape (**Figures 11A–C**), and that a switch from one monotypic stimulus to another could sharply turn the evolution of the synaptic weight landscape in a new direction (**Figures 11B–D**). Also, with monotypic stimulus presentations, stable end points of synaptic weight landscapes were not clearly identified, in contrast to the relatively stable end points obtained when a mixture of stimuli was used in the learning process (**Figure 9A**). Indeed, instead of reaching a stable end point well before 1,500 trials (as in **Figure 9A**), monotypic sensory training instead could result in rapid changes in synaptic weight distribution at this time point (**Figure 11A**), which on inspection was found to be due to that the calcium activity of the CN could not be held within its set point and started to generate chaotic behavior (as in **Figure 10**). Hence, the order of presentation of the sensory experiences influenced the outcome of the learning process (**Figure 11D**), which indicates that the presence of a mixed, diversified set of sensory experiences over time will influence the result of the learning process. In addition, these findings also suggest that a high learning speed as in **Figures 9C,D** would make the CN more sensitive to individual sensory experiences and their order of presentation than in the basic configuration (**Figures 9A,B**).

# The Intrinsic CN Properties Profoundly Influenced Learning Outcome

Whereas the above analysis (**Figures 5**–**8**, Figure S3) indicated that the seed synaptic weight distribution was an important factor influencing learning outcome, i.e., which set of correlated sensors the CNs picked to selectively strengthen, also the intrinsic CN parameters could conceivably act as a seed factor. Morphological differences between cuneate neurons (Fyffe et al., 1986) is an example of a source of variability in intrinsic neuronal behavior, the level of expression of different ionic conductances another. To explore this issue, we tested to separately vary the time constant of the calcium signal in the local synaptic space, the coupling between the synaptic input and the intrinsic dynamics (already shown in **Figures 9C,D**) and the set point of allowed calcium activity in the intermediate dynamics model (which was controlled by adapting the gain of the inhibitory synaptic

input, see **Figure 3B**). All intrinsic CN parameters, for all values tested (**Table 1**) influenced the outcome of the learning in terms of the end weight landscape (**Figure 12A**). Given that these intrinsic parameters influenced the behavior of the intermediate dynamics model and the calcium signal, either on a per stimulus presentation basis or over longer time, this was in itself perhaps not a surprising finding. However, the impact of these intrinsic parameters was profound and almost of comparative magnitude to the impact of the synaptic seed weight landscapes. The only apparent exceptions were the two lowest values of the gmax (inset) and the lowest τ for the calcium activity in the local synaptic space (triangle) (**Figure 12A**). However, the effect of these latter settings was that they reduced learning speed to a minimum and consequently resulted in little change from seed weight (black dot in **Figure 12A**). To estimate the relative stability of the learning, each configuration was run through the learning process five different times where each process was different as a minor noise was always injected in the sensor spike trains. The highest gmax value, with the fastest learning, resulted in the largest variability between learning processes (star in **Figure 12A**). All other parameter values were associated with lower variability in the learning outcome.

We tested the impact of varying the intrinsic CN parameters for all SWs by plotting them on the same MDS plot. The effect of the seed weight on the end weight landscape was larger than those of the intrinsic CN parameters (**Figure 12B**), but it is clear that both categories of seed factors had a major impact on learning outcome. But irrespective of seed weight or intrinsic seed factors, the learning process always resulted in a selection of correlated sensors to become the end weight winner synapses (**Figure 13**). The fact that the learning worked in a similar fashion across a range of settings of intrinsic parameters for defining the intrinsic dynamics of the CN shows that the model architecture has some degree of robustness, i.e., it is independent of having precise constraints on neuron intrinsic behavior and will work similarly across a population of neurons with different intrinsic dynamics. Such differences within a CN population may be important to identify different subsets of correlated sensors in the learning process, and hence creating a diversification in the tuning of across the population of cuneate neurons, similar to the one observed in vivo (Jörntell et al., 2014).

#### DISCUSSION

Using an integrated model of cuneate nucleus neurons, based on the present in vivo whole cell recording data, and a bionic tactile sensor system, this study investigated what form of representations the brain could automatically learn from tactile interactions with the world. At the single neuron level, the CN learning resulted in the identification of sets of functionally correlated sensors (**Figures 5**, **13**). By reducing or eliminating the synaptic weights of other sensors, this process essentially equals a dimensionality reduction of the sensory space at the single neuron level. At the population level, each CN, as defined by its unique seed factors, gradually identified different sets of functionally correlated sensors (**Figures 7**–**9**, **13**). As a consequence, the learning process induced decorrelations in the synaptic responses of the different CNs to the same stimulus as well as for the individual CN to different stimuli (**Figures 7**, **8**).

Such decorrelated synaptic responses is a key requirement for a population of cuneate neurons to be able to segregate the high-dimensional tactile sensory space, where each sensor potentially represents a unique dimension (Spanne and Jörntell, 2013), into specific projections (Jörntell et al., 2014). These projections are defined by the types of sensory experiences made (**Figure 11**) and the physical-mechanical effects involved (see Introduction). Hence, the representation of the physical world that naturally unfolded given the neurophysiological constraints of the cuneate neurons in vivo (**Figure 1**) was a utility-based decomposition of the sensory space. Thereby, the architecture of the cuneate nucleus network (**Figure 3**) would help the brain focusing to focus on the statistically recurring elements of experienced tactile sensory activation patterns. It follows that an early rich variation of tactile sensory experiences (Pape et al., 2012; Loeb and Fishel, 2014), such as achieved by motor babbling in the infant (Forssberg et al., 1995; Blumberg et al., 2013), is important for the brain because it would be needed to form representations with a sufficiently high dimensionality.

FIGURE 11 | The effects of monotypic sensory experiences. (A) The evolution of the synaptic weight distributing using solely stimulus #1 (latex, Figure 4) as the training stimulus for 3,000 repetitions. (B) Similar experiment, but after 1,500 repetitions, stimulus #1 was replaced with stimulus #5 (indentation, 20 mm). (C) Similar experiment as in (B), but now the 1,500 repetitions of stimulus #5 preceded the 1,500 repetitions of stimulus #1 (reverse order). (D) Synaptic weight landscape evolutions of the three experiments shown in an MDS plot.

FIGURE 12 | The intrinsic CN properties worked as additional seed factors. (A) For the seed weight #1, the end weight landscapes for all tested values of the intrinsic CN parameters, with the radii indicating the standard deviations across five repetitions of the learning. (B) The end weight landscape standard deviation for the whole range of intrinsic parameters is plotted for each seed weight. Note that the scale axes values are unrelated between the MDS plots.

23e-9 and gmax of 9e-9, and τ for the local calcium activity of 50%) no learning took place (Figure 12) and the learning effects of those intrinsic parameter settings are hence omitted from this figure. Based on five CNs and 14 conditions, statistical comparisons were made for 70 conditions. The differences in correlation index between the end weight winners and losers was statistically different in each of these 70 cases (p < 0.05, paired t-test), except one (p = 0.11 for CN3 at gmax of 9e-7).

## Robustness of the Learning Process Suggests Little Dependence on Assumptions

In order to address a breadth of issues that related to the representation of haptic input features in the cuneate nucleus neurons (Jörntell et al., 2014) and their intrinsic properties, but which are too complex to be resolved by experimental recordings, we needed to model the cuneate neurons. Whereas, the model system naturally could have featured a higher number of sensors or a larger number of contexts across which the generalization capability was tested, we find no reason to believe that would have altered the principles reported here. An important component of the model was the learning process that we envisage to be one of the earliest developmental steps toward a functional tactile neural system. Although there are several indications that the PA synapses on the cuneate neurons are extensively plastic at least during some phase of development (Bengtsson et al., 2013) and following removal of PA inputs (Kambi et al., 2014; Mowery et al., 2014), the direct synaptic mechanisms underlying the plasticity have not been explored. Therefore, in order to model this learning process, we used an already validated idea of a calcium activity-dependent mechanism (Helias et al., 2008), which to some extent can be regarded a general synaptic plasticity mechanisms of neurons. It is in principle a variant of the BCM learning rule (Bienenstock et al., 1982) for which we obtained specific data from the biological cuneate projection neurons to approximate how their internal calcium signal can be expected to vary over time and with the input. In comparison with a previous study of how calcium-dependent STDP varies across a range of parameter settings (Graupner and Brunel, 2012), our approach had a less steep rising phase in the postsynaptic calcium response, which can be expected to dampen the dependence of our model on exact spike timings and instead make it rely to a larger extent on the temporal variation in the intensity of the synaptic activation (Graupner et al., 2016) and how it correlated with the overall activity of the postsynaptic neuron. Naturally, the actual cuneate learning process is likely to differ from ours in many details but the fundamental properties of our model, i.e., an extreme dimensionality reduction in each neuron and the identification of different correlated sets of sensors by the population of CNs (**Figure 7**), were remarkably robust to manipulation of a wide variety of neuronal parameters that influenced the learning process (**Figures 12**, **13**). It can also be noted that after learning, our CNs had synaptic weight distributions that were similar to adult cuneate neurons (**Figure 5C** and Figure S3) (Bengtsson et al., 2013) as well as response dynamics that qualitatively captured the dynamics recorded in vivo (**Figure 6D**, Figure S1).

Plasticity in inhibitory synaptic inputs could in principle help improving segregation of inputs (Garrido et al., 2016). Our analysis of the biological inhibitory synaptic inputs in the cuneate nucleus did not overtly support a specific role in such function, though. The recorded inhibitory interneurons in the cuneate nucleus were activated from wide receptive fields and in the projection neurons inhibition was found to originate from similar wide receptive fields, with relatively uniform inhibitory synaptic weights (**Figures 1C,D**). This is in contrast to the excitatory synaptic inputs from PAs, which in cuneate projection neurons have been found to have strongly differentiated weights and hence suggestive of an extensive learning history in these synapses (Bengtsson et al., 2013). In our CN model, the plasticity in the inhibitory synapses instead assumed the role of activity balancing, i.e., ensuring that the excitable calcium responses did not go out of bounds (neither upwards, nor downwards), which was an important function to ensure stable, gradual learning (cf. **Figure 10**).

### Advantages of the Representational Format and of the Architecture

The utility-based decomposition of the tactile sensory space learned with our CN architecture is likely to correspond to an early level acquisition of a representation of the experienced, and therefore useful, haptic input features, which were previously found to be represented in cuneate neurons in vivo in adult animals (Jörntell et al., 2014). Being based on physical-mechanical effects (Hayward, 2011; Hayward et al., 2014), correspondences between the haptic input features and sensory information from other modalities such as vision and hearing can be expected to be abundant. Hence, this type of tactile representation would provide a basis for rich cross-modality representations of rules of object interactions that can be learnt by the higher level centers of the brain to conceptualize the external world (O'Regan and Noe, 2001). This model does not contradict the possibility that the activity of individual cuneate neurons or neurons of the somatosensory cortex could correlate with physical parameters such as edge orientation or texture or even adaptation rate, but indicates that the underlying organizational and coding principle is likely to be more intricate than that.

These principles are potentially portable to AI and robotic systems, which faces challenges of how to design sensor systems to support more general functionality in the interactions with the external world (Brooks, 1991; Davis et al., 1993; Service, 2014). AI systems are generally designed to deliver performance on specific tasks, such as the game of Go (Gibney, 2016), where they deliver impressive performance if given very large amounts of training data (Lecun et al., 2015). However, they seem to be lacking the wide versatility or generalization capability of biological systems (Nguyen et al., 2015; Athalye and Sutskever, 2017), which could be related to the known problems of classical pattern recognition systems (Spanne and Jorntell, 2015) and which the current alternative mode of representation of sensory input data may help resolving. These technical systems also in general suffer from a strong dependence on the initial weight configuration (Glorot and Bengio, 2010; He et al., 2015). This dependence can be eliminated by specifically dedicated computational mechanisms calculating appropriate initial weights or by other computationally intensive methods (Williams and Hinton, 1986; Földiak, 1990; Glorot and Bengio, 2010), which, however, may not be available to the brain. Our architecture, featuring a set of neuronal autoregulatory mechanisms and self-stabilizing learning, provide potential solutions also for these challenges within DNN and AI systems.

## CONCLUDING REMARKS

A main advantage of representing tactile experiences by decomposing them into vector components of their constituent physical effects, rather than by pattern recognition applied to pixelated sensory data directly, is the powerful generalization possibilities (Spanne and Jorntell, 2015). With this mode of representation, the brain could in principle learn to identify the extent of the space of the theoretically possible physical effects within a short time using just a few types of disparate skinobject interactions and later subsequently learn to interpolate new points within this space at a gradually higher resolution. A major issue for future studies is to explore if brain processing operates in terms of such vector components also globally in the neocortex.

# AUTHOR CONTRIBUTIONS

UR, AS, and HJ designed the model. FB and HJ performed the patch clamp recording experiments and analyzed the biological data. UR, AM, and CO developed and experimented the bionic finger for electrophysiological and translational bioengineering studies. UR, AS, AM, CO, and HJ designed the analysis of the model data. UR, CO, AM, and HJ wrote the paper.

## ACKNOWLEDGMENTS

This work was supported by the Ministry of Education, Universities and Research of the Italian Republic and the Swedish Research Council, via the Italy-Sweden bilateral research project J52I15000030005 SensBrain (Brain network mechanisms for integration of natural tactile input patterns), by the EU Grant FET 611687 NEBIAS Project (NEurocontrolled BIdirectional Artificial upper limb and hand prosthesiS), by the EU Grant FP7-NMP 228844 NANOBIOTOUCH project (Nanoresolved multi-scan investigations of human tactile sensations and tissue engineered nanobiosensors), and by the national project B81J12002680008 PRIN/HandBot (Biomechatronic hand prostheses endowed with bio-inspired tactile perception, bi-directional neural interfaces and distributed sensori-motor control), SENSOPAC (EU FP6, IST-028056-SENSOPAC), The Hand Embodied (EU FP7 IST-248587-THE) and the Swedish Research Council (project grant no. K2014-63X-147 80-12-3).

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fncel. 2018.00210/full#supplementary-material

#### REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Rongala, Spanne, Mazzoni, Bengtsson, Oddo and Jörntell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Decreased Number and Expression of nNOS-Positive Interneurons in Basolateral Amygdala in Two Mouse Models of Autism

Xiaona Wang<sup>1</sup> , Jisheng Guo<sup>2</sup> , Yinsen Song<sup>1</sup> , Qi Wang<sup>3</sup> , Shunan Hu<sup>1</sup> , Lingshan Gou<sup>4</sup> and Yinbo Gao<sup>1</sup> \*

<sup>1</sup>Henan Provincial Key Laboratory of Children's Genetics and Metabolic Diseases, Children's Hospital Affiliated of Zhengzhou University, Zhengzhou, China, <sup>2</sup>Center for Translational Medicine, The Sixth People's Hospital of Zhengzhou, Zhengzhou, China, <sup>3</sup>Department of Histology and Embryology, Guizhou Medical University, Guiyang, China, <sup>4</sup>Center for Genetic Medicine, Xuzhou Maternity and Child Health Care Hospital, Xuzhou, China

The basolateral amygdala (BLA) controls socio-emotional behaviors and is involved in the etiology of autism. We have recently shown that virtually every neuronal nitric oxide synthase (nNOS) positive cell is a GABAergic inhibitory interneuron in the mouse BLA. Here, stereology was used to quantify the number of nNOS-expressing interneurons in valproic acid (VPA)-exposed C57BL/6J (B6) and BTBR T<sup>+</sup>Itpr3tf/J (BTBR) mice models of autism. Additionally, the protein and mRNA levels of nNOS in the BLA were quantitatively assessed by western blot and qRT-PCR analysis, respectively. Our results showed the decreased number of nNOS interneurons in the BLA of animal models relative to autism. Consistently, nNOS was significantly reduced in the VPA-exposed and BTBR mice at both protein and mRNA levels. Together, these preliminary findings suggest that down-regulation of nNOS may be an attractive target for the pharmacological intervention in autism.

#### Edited by:

Marylka Yoe Uusisaari, Okinawa Institute of Science and Technology, Japan

#### Reviewed by:

Valerie J. Bolivar, Wadsworth Center, United States Crystal C. Watkins, School of Medicine, Johns Hopkins University, United States

\*Correspondence:

Yinbo Gao<EMAIL_ADDRESS>
Received: 29 March 2018 Accepted: 23 July 2018 Published: 13 August 2018

#### Citation:

Wang X, Guo J, Song Y, Wang Q, Hu S, Gou L and Gao Y (2018) Decreased Number and Expression of nNOS-Positive Interneurons in Basolateral Amygdala in Two Mouse Models of Autism. Front. Cell.  Altered function of several brain areas is thought to underlie the social and cognitive phenotype in autism. Identified brain regions include the amygdala, prefrontal and temporal cerebral cortex and hippocampus, striatum, among others (Sosa-Díaz et al., 2014; Hashemi et al., 2017). Accumulating evidence suggests loss of GABAergic cells in the hippocampus and cerebral cortex of autism (Sgadò et al., 2013; Sabanov et al., 2017; Ariza et al., 2018). Noteworthy, it is reported that morphology and neurophysiology change in the amygdala of autism (Markram et al., 2007; Bringas et al., 2013). Recent efforts have shown the remarkable decrease in neuronal numbers in the lateral nucleus amygdala of autistic subjects (Varghese et al., 2017). Studying in animals has confirmed that GABAergic interneurons within the basolateral amygdala (BLA) are strongly implicated in autistic-relevant behaviors (Prager et al., 2016). Collectively, these results demonstrate that BLA dysfunction may contribute to social and emotional disturbance in autism (Todd and Anderson, 2009; Lin et al., 2013).

Emerging evidence suggests that deficits in GABAergic inhibitory interneurons are the pathophysiological mechanism of autism (Dong et al., 2016; Lee and Kim, 2017). In particular, glutamic acid decarboxylase 65 and 67 (GAD65, GAD67) are reduced in the cortex of valproic acid (VPA)-treated animals (Oblak et al., 2011a). Studies have also shown decreased GAD<sup>65</sup> and GAD<sup>67</sup> mRNA levels of the cerebellum in autistic patients (Yip et al., 2007, 2009). Remarkably, it has been established that the subpopulations of GABAergic interneurons in the mouse BLA consist of calcium-binding proteins (parvalbumin (PV), calbindin and calretinin, neuropeptide Y and neuronal nitric oxide synthase (nNOS); Wang et al., 2017). At the behavioral level, mice with reduced PV interneurons expression display the robust autism-relevant behaviors (Filice et al., 2016). The reduced number of PV-containing cells could disrupt the balance of excitation/inhibition and alter gamma wave oscillations in the cerebral cortex of autistic subjects (Hashemi et al., 2017). Likewise, evidence has accumulated the lower density and number of calretinin, calbindin and neuropeptide Y interneurons in autism (Oblak et al., 2011b; Peñagarikano et al., 2011; Adorjan et al., 2017). Notably, we recently found that virtually every nNOS-positive cell is GABAergic interneuron in the BLA (Wang et al., 2017). Despite these observations, it remains presently unknown whether nNOS-expressing interneurons are affected in the BLA of autism.

Environmental exposure and genetic susceptibility are increasingly being recognized as potential risk factors for autism. More specifically, VPA as antiepileptic medication during pregnancy, exhibits similar to the core symptoms of autism, including impaired social interaction, stereotypic/repetitive behaviors and sensory/communication deficits (Roullet et al., 2013; Nicolini and Fahnestock, 2018). Meanwhile, BTBR T <sup>+</sup>Itpr3tf/J (BTBR), a well-studied mouse model of idiopathic autism, displays repetitive self-grooming, deficiencies in social interactions, as well as minimal vocalization in social settings (McFarlane et al., 2008; Provenzano et al., 2016).

Therefore, in the present study, we were prompted to investigate nNOS-expressing interneurons counts in the BLA with stereological techniques in VPA-exposed and BTBR mice. Additionally, nNOS protein and mRNA levels were quantitatively determined by western blot and qRT-PCR, respectively.

#### MATERIALS AND METHODS

#### Animals

All procedures were approved by the Shandong University Animal Care and Use Committee and were carried out in compliance with the National Institutes of Health guide for the care and use of Laboratory animals (Publication No. 85- 23, revised 1985). BTBR mice were obtained from Model Animal Research Center of Nanjing University. C57BL/6J (B6) mice were provided by Shandong University. Mice, 8 weeks old, were housed in plastic cages (30 cm wide × 18 cm long × 14 cm high) in humidity (30%) and temperature (23◦C) maintained under a 12:12 h light/dark cycle (lights on at 07:00). Food and water were available ad libitum. All efforts were made to minimize the animal suffering and the number of animals used.

### Drug Administration and Experimental Groups

According to the studies of Wu et al. (2017), single female and male B6 mice were randomly allocated to mate overnight. Pregnancy was determined by the presence of a vaginal plug on embryonic day 1 (E1). We removed male mice from cages in the same day. Pregnant mice received an intraperitoneal injection of 600 mg/kg VPA sodium salt (Sigma Aldrich, St. Louis, MO, USA) on E13 (Al-Askar et al., 2017). Control mice received injection of equal volumes of saline. Pup mice were weaned on postnatal day 21 (P21) and housed in groups of four per cage. Besides, B6 animals have been routinely used as the control for the BTBR mice in autism-related studies (Cheng et al., 2017; Meyza and Blanchard, 2017). Only male offspring were used in this study.

On postnatal 35 days, four offspring groups were deeply anesthetized with pentobarbital sodium, decapitated and processed for stereology, western blot and real time-PCR analysis.

#### Immunohistochemistry Staining

Immunohistochemistry was conducted as we have previously described (Wang et al., 2017). Adult mice were intracardially perfused with 4% paraformaldehyde and the brain was removed immediately. The BLA was postfixed for overnight, successively placed in 30% sucrose in 0.1 M phosphate-buffered saline (PBS) overnight and cut on a sliding microtome (40 µm). Free floating sections were quenched for 30 min in 3% H2O2/30% methanol in PBS and incubated in a blocking solution (5% normal goat serum and 2.5% bovine serum albumin in PBST (0.25% Triton X-100)) for 30 min at room temperature. Sections were then incubated with the goat anti-rabbit nNOS (1:5000; Sigma) in the blocking serum at 4◦C overnight. After rinsing in PBST (three times for 10 min), sections were reacted for 2 h at room temperature with biotinylated secondary antibody goat anti-rabbit IgG (1:500, Vector Laboratories; Burlingame, CA, USA) and incubated with avidin biotin peroxidase complex (Vector ABC Kit, Vector Laboratories). The reaction was visualized by detection solution (0.25 mg/ml 3,3<sup>0</sup> -diaminobenzidine in combination with 0.03% H2O<sup>2</sup> in PBS). Sections were then transferred onto mounted on glass slides, dehydrated with ascending alcohols, rinsed in xylene and cover slipped.

#### Stereological Quantification

To determine the total number of nNOS-positive interneurons in the BLA in two mouse models of autism, we used the optical dissector principle as previously described (Wang et al., 2017). Briefly, sections were outlined with Stereo Investigator software (MBF Biosciences, Williston, VT, USA). Brain regions of interest (ROIs) were determined based upon stereotactic coordinates provided by the Franklin and Paxinos atlas (Franklin and Paxinos, 2007) at 0.94–2.18 from bregma for the BLA. A 1-in-6 series of sections was analyzed and the counting frame was 40 × 40 µm. The dissector height was 13 µm. Section thickness was measured for every section counted per mouse (17 µm). A nNOS-containing cell body was counted as it came into focus within the dissector box and above the bottom exclusionary plane. Criteria for counting cells required that interneurons displayed nNOS-expressing immunoreactivity and morphological features in line with each cell type. The total number N of nNOS-expressing cells in the ROIs was calculated using the formula:

$$N = \ \Sigma Q \* t/h \* 1/\text{asf} \* 1/\text{ssf}$$

where ΣQ is the total number in the BLA acquired with the optical dissect, t is the mean thickness of section, h is the optical dissector height, as f represents the area sampling fraction, and ssf is the fraction of section sampling. Images of sections were captured with a Nikon TE2000U microscope.

#### Western Blot Analysis

Western blot analysis was carried out as we previously described (Wang et al., 2014). In brief, BLA of homogenates were centrifuged for 15 min at 10,000 g at 4◦C remove any cell debris. Proteins (30 µg per well) were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (100–120 V) and transferred to polyvinylidene difluoride membranes (Millipore, Milford, MA, USA). The membranes were blocked with 5% non-fat milk powder in Tris buffered saline containing 0.1% Tween 20 (TBST) for 60 min at room temperature. Subsequently, the membranes were probed with the primary antibodies overnight at 4◦C: nNOS (1:5000; Sigma) and GAPDH (1:1000; CST, Beijing, China). The membranes were incubated with the secondary antibody horseradish peroxidase conjugated to IgG (1:1000; ZSGB-Bio, Beijing, China) and washed three times with TBST for 10 min each. Protein bands were visualized using the super enhanced chemiluminescence reagent (Pierce, Rockford, IL, USA) and images were captured by luminescent image analysis system (Fujiflm, LAS-4000 mini, Japan). Bands intensities were determined by Image J software program.

#### Quantitative Real-Time PCR

Total RNA (500 ng) extracted from BLA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA concentrations were determined by a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, MA, USA). First-strand cDNA synthesis was performed using Promega's reverse transcriptase kit (Promega, Southampton, UK) following manufacturer's instructions. qRT-PCR was performed to examine the expression of mRNA of the nNOS and GAPDH genes using the universal KAPA SYBR FAST qPCR Universal Master Mix (Kapa Biosystems, Woburn, MA, USA). The sequences of the primers used were as follows: nNOS forward 5<sup>0</sup> -ACC CAA CGT CAT TTC TGT CC-3<sup>0</sup> and reverse 5 0 - AAG GTG GTC TCC AGG TGT GT-3<sup>0</sup> ; GAPDH forward 5 0 -CGGAGTCAACGGATTTGGTCGTAT-3<sup>0</sup> and reverse 5<sup>0</sup> - AGCCTTCTCCATGGTGGTGAAGAC-3<sup>0</sup> . Gene expression quantitation was carried out in a DNA thermal cycler (Bio-Rad, San Diego, CA, USA) as follows: a denaturation step of 95◦C for 15 min, 50 cycles of denaturation at 94◦C for 15 s and annealing/extension/data acquisition ranging from 60◦C to 72◦C for 20 s. The housekeeping gene GAPDH mRNA was used as an internal reference to normalize the mRNA content for each sample. Gene expression data were expressed as a proportion of GAPDH gene, as a reference, using a 1/∆Ct calculation.

#### Statistical Analysis

Statistical analysis was performed with GraphPad Prism 5.01 (GraphPad Software, San Diego, CA, USA), using the Student's t test. The level of statistical significance was set at p < 0.05.

# RESULTS

#### Stereological Quantification

To determine the extent of nNOS-expressing cell loss in the current study, we first counted the total number of nNOS-containing interneurons in the VPA and BTBR mice models of autism. As illustrated in **Figure 1**, the total number of nNOS interneurons in the BLA of saline-treated offspring, estimated by stereologically counting nNOS-positive interneurons, was (5.63 ± 0.92) × 10<sup>3</sup> /mm<sup>3</sup> (N = 8). However, in the VPA-induced autism model, the total number of nNOS-containing cells was (4.23 ± 0.75) × 10<sup>3</sup> /mm<sup>3</sup> , a 15% reduction compared to B6 controls (N = 8, p < 0.01, **Figures 1A,C**).

Similarly, the total number of nNOS-immunoreactive interneurons in the BLA of B6 group was (5.63 ± 1.30) × 10<sup>3</sup> /mm<sup>3</sup> (N = 8). In the BTBR mice, the total number of nNOS-expressing cells was (4.23 ± 0.92) × 10<sup>3</sup> /mm<sup>3</sup> , a 43% reduction as compared to B6 strains (N = 8, p < 0.05, **Figures 1B,D**), indicating that nNOS-containing interneurons in the BLA are vulnerable to VPA-exposed and BTBR mice.

### Alterations in Protein and Gene Levels of nNOS

These nNOS-positive interneurons are suggested to play a crucial role in the regulation of neuronal excitability in the BLA (Ma ´nko et al., 2012). Next, we compared the nNOS protein levels in the VPA mouse model of autism with those in saline-exposed offspring. As shown in **Figure 2**, the results showed that exposure of mice to VPA significantly decreased nNOS protein expression in the BLA compared with the corresponding controls (N = 6, p < 0.01, **Figure 2A**). Likewise, nNOS expression of VPA-treated mice was remarkably decreased as compared to B6 mice (N = 6, p < 0.05, **Figure 2B**). Thus, nNOS protein expression was down-regulated in nNOS-positive GABAergic cells in the BLA of VPA-exposed and BTBR mice.

Furthermore, our results demonstrated that mice treated with VPA markedly exhibited lower nNOS mRNA levels when compared to saline group (N = 6, p < 0.01, **Figure 3A**). As expected, nNOS mRNA expression in the BLA of VPA-treated offspring was dramatically decreased compared with B6 mice (N = 6, p < 0.001, **Figure 3B**). Thus, the nNOS mRNA was dramatically reduced in the nNOS-expressing interneurons in the BLA of VPA-treated and BTBR mice.

#### DISCUSSION

Our study demonstrated that number of nNOS interneurons in BLA was reduced in two well-established mouse models of autism. Furthermore, nNOS expression was remarkably decreased in VPA-exposed and BTBR mice at both protein and mRNA levels.

Converging evidence indicates altered GABAergic signaling is related to autism, culminating in the proposal that increase in excitation/inhibition ratio is critical cause of autism (Han et al., 2012, 2014; Lee and Kim, 2017). Specifically, postmortem neuropathological studies of individuals with autism have shown decreased GABA precursors in certain neuron populations and reduced number, density and expression of GABA receptors in the cortex (Blatt and Fatemi, 2011). Moreover, structural amygdala abnormalities such as reduced neuron number and changes in volume have been observed in individuals with autism (Schumann et al., 2004; Schumann and Amaral, 2006). It has been widely reported that decreasing GABA function decreases sociability in the BLA (Paine et al., 2017). Numerous studies have demonstrated that prenatal exposure to VPA and BTBR mice lead to autism phenotype, which can be used to model the complexity of autism symptoms (Han et al., 2014; Barrett et al., 2017). Accordingly, we first ascertained by stereology that the number of nNOS-expressing cells was altered in the BLA of VPA-exposed and BTBR mice. Of particular noteworthy, our data showed that there was indication of the decrease of nNOS-expressing cells in the BLA of autism, consistent with previous results obtained in the decreased number and density of PV interneurons in the prefrontal cortex in autism (Filice et al., 2016; Ariza et al., 2018). The reduced number of PV-immunoreactive cells could

disrupt the balance of excitation/inhibition and elicit core deficits of autism (Wohr et al., 2015; Hashemi et al., 2017). Of relevance, animal models that exhibit an imbalance in the ratio of pyramidal cells to interneurons in the cortex show core autism-related deficits, including abnormalities in reciprocal social interactions and stereotyped behaviors (Helmeke et al., 2008). It is therefore possible that disruptions in the BLA region through synaptic connectivity formed by reduction of nNOS interneurons may result in disrupted socio-emotional behaviors of autism. Additional researches including direct recordings from nNOS interneurons during the behavioral experiment in autism and observing autism-related phenotypes after modulating the excitation/inhibition balance due to decreased nNOS levels could give us the opportunity to prove the relationship between the two.

While an involvement of nNOS interneurons in autism is rather undoubted, it remains unclear whether the observed reduced nNOS-expressing cells in the BLA of autism are the result of: (I) a truly decreased number of nNOS-positive interneurons due to developmentally immature or perturbed state (e.g., region-improper localization of nNOS-containing cells, enhanced susceptibility, precursor and premature neuronal death) or (II) alternatively from the reduction of nNOS protein (or mRNA) levels or the inability to express sufficient levels of the protein (or mRNA). To resolve this question, one needs to clarify whether the decrease in nNOS-expressing cells in two canonical autism mouse models might in part the result of nNOS down-regulation. Noticeably, the present data showed the decrease in nNOS protein expression for nNOS-positive

cells in the VPA-exposed and BTBR mouse models. The similar magnitude in down-regulation of nNOS mRNA manifests the regulation at the level of transcription, in agreement with the reduced GABA content, GAD<sup>65</sup> and GAD<sup>67</sup> mRNA levels in autism (Chao et al., 2010). Future investigation is necessary to identify possible mechanisms for nNOS interneurons vulnerability and to illustrate the functional consequences for potentially developing autism-like features.

Accumulating evidence indicates that nNOS-containing cells are implicated in inhibitory synaptic transmission (Armstrong et al., 2012; Lange et al., 2012). Particularly, Li et al. (2014) reported that firing of hippocampal nNOS interneurons creates suppression of synaptic inhibition. Previous work has revealed that nNOS-expressing cells evoke a slow inhibitory postsynaptic current (IPSC) caused by volume transmission of GABA and modulate neurons slow inhibition in the BLA (Capogna and Pearce, 2011; Ma ´nko et al., 2012). It should be noted that nNOS-mediated long-term regulation of inhibitory transmission might contribute to fear learning in amygdala (Lange et al., 2012). Following this line of reasoning, thus, identification the slow inhibition properties of nNOS-containing cells within the BLA is crucial for understanding how emotional and cognitive impairments in addition to autistic symptoms are processed. Consequently, the demonstration that nNOS levels are reduced may suggest a shift in the excitation/inhibition balance to a reduced inhibition, considering the proven function of nNOS in synaptic transmission on autism-related defects.

#### REFERENCES

In a complementary fashion, one limitation is that molecular alterations in the VPA model are similar but not identical to those demonstrated in individuals with idiopathic autism (Nicolini and Fahnestock, 2018). On the other hand, the striking feature of BTBR mice is lack of corpus callosum, which is rare among autistic individuals (Meyza et al., 2013). Disc1 mutation, carried by BTBR mice, has been related with autism in some cases. Indeed, a substantial body of evidence indicates that a child is predisposed to autism due to dynamic interplay between environment and genetics (Olexová et al., 2012). Therefore, to further explore the function of nNOS-expressing interneurons in autism, more attention is needed for the development of a combined-model approach, in which the core symptoms and etiological character of autism are represented.

To conclude, the present study has indicated the decrease in the number and expression of nNOS-positive interneurons in autism, which is a fundamental step that will shed light on the origins of altered excitation/inhibition balance in autistic amygdala. As well, testing whether approaches aimed at restoring normal nNOS expression levels and/or nNOS interneuron function might ameliorate autism relevant behavioral phenotypes appears therefore warranted and may represent potential promise for novel therapeutic strategies.

#### AUTHOR CONTRIBUTIONS

XW and YG designed the research and wrote the manuscript. JG, YS, QW, SH and LG conducted experiments and analyzed the data.

to neonatal separation stress. Neuroscience 152, 18–28. doi: 10.1016/j. neuroscience.2007.12.023

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Wang, Guo, Song, Wang, Hu, Gou and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Color Processing in Zebrafish Retina

April Meier <sup>1</sup> , Ralph Nelson<sup>2</sup> and Victoria P. Connaughton<sup>1</sup> \*

<sup>1</sup>Zebrafish Ecotoxicology, Neuropharmacology, and Vision Lab, Department of Biology, and Center for Behavioral Neuroscience, American University, Washington, DC, United States, <sup>2</sup>Neural Circuits Unit, National Institute of Neurological Disorders and Stroke (NINDS), NIH, Bethesda, MD, United States

Zebrafish (Danio rerio) is a model organism for vertebrate developmental processes and, through a variety of mutant and transgenic lines, various diseases and their complications. Some of these diseases relate to proper function of the visual system. In the US, the National Eye Institute indicates >140 million people over the age of 40 have some form of visual impairment. The causes of the impairments range from refractive error to cataract, diabetic retinopathy and glaucoma, plus heritable diseases such as retinitis pigmentosa and color vision deficits. Most impairments directly affect the retina, the nervous tissue at the back of the eye. Zebrafish with long or short-wavelength color blindness, altered retinal anatomy due to hyperglycemia, high intraocular pressure, and reduced pigment epithelium are all used, and directly applicable, to study how these symptoms affect visual function. However, many published reports describe only molecular/anatomical/structural changes or behavioral deficits. Recent work in zebrafish has documented physiological responses of the different cell types to colored (spectral) light stimuli, indicating a complex level of information processing and color vision in this species. The purpose of this review article is to consolidate published morphological and physiological data from different cells to describe how zebrafish retina is capable of complex visual processing. This information is compared to findings in other vertebrates and relevance to disorders affecting color processing is discussed.

#### Edited by:

Philippe Isope, Centre National de la Recherche Scientifique (CNRS), France

#### Reviewed by:

Susan E. Brockerhoff, University of Washington, United States Elvir Becirovic, Ludwig-Maximilians-Universität München, Germany

\*Correspondence:

Victoria P. Connaughton<EMAIL_ADDRESS>
Received: 18 July 2018 Accepted: 10 September 2018 Published: 03 October 2018

#### Citation:

Meier A, Nelson R and Connaughton VP (2018) Color Processing in Zebrafish Retina. Front. Cell. Neurosci. 12:327. doi: 10.3389/fncel.2018.00327 Keywords: Danio rerio, color vision, red, green, blue, ultraviolet

#### ZEBRAFISH RETINAL STRUCTURE AND DEVELOPMENT

Zebrafish retina, like those of other vertebrates, contains five neural types organized into layers. Distal-most are photoreceptors (PRs) with cell bodies in the outer nuclear layer (ONL) and terminals forming synaptic contacts with the dendritic processes of bipolar (BCs) and horizontal (HCs) cells in the outer plexiform layer (OPL). The cell bodies of these second order neurons, as well as those of third order amacrine cells (ACs), reside in the inner nuclear layer (INL). BCs are presynaptic to AC and ganglion cell (GC) dendrites in the inner plexiform layer (IPL), where processes in the distal IPL, or sublamina a, mediate OFF-type responses and processes in sublamina b mediate ON-type responses. GC bodies are in the most proximal layer, the GC layer (GCL).

#### Development of Neurons and Circuits

Eye morphogenesis in zebrafish begins at 12 h postfertilization (hpf; Schmitt and Dowling, 1994). Subsequent structural changes to eye primordia result in well-formed optic cups at 24 hpf (Schmitt and Dowling, 1994), and development of the neural retina progresses in direction from the inner (vitreal) to the outer (scleral) retina. GC and AC form first (∼32 hpf), with a small number of GC axons leaving the eye to form the optic nerve at ∼34–36 hpf (Stuermer, 1988; Schmitt and Dowling, 1994, 1999; Burrill and Easter, 1995). Initial differentiation of PRs occurs in a patch ventral to the optic nerve head at ∼50 hpf (Kljavin, 1987; Raymond et al., 1995; Hu and Easter, 1999; Schmitt and Dowling, 1999; Raymond and Barthel, 2004). Development in the patch continues in advance of other retinal regions until ∼70 hpf, when the entire retina appears homogeneous. BC are the last cell type to form at ∼60 hpf (Schmitt and Dowling, 1999) and between 60–70 hpf all neuronal cell types can be identified and synapses are apparent (Schmitt and Dowling, 1999). The vertical pathway (i.e., PR-to-BC-to-GC) appears functional at 70–74 hpf (Schmitt and Dowling, 1999), corresponding to innervation of the optic tectum (Stuermer, 1988), hatching, and the onset of visually guided behaviors (Easter and Nicola, 1996, 1997). Many of the developmental transcription factors such as cone rod homeobox gene (crx), atonal (ath5) and thyroxin β2 nuclear receptor (trβ2), which are key to retinal development in mammals, also operate in zebrafish retina (Kay et al., 2001; Shen and Raymond, 2004; Jusuf et al., 2011; Suzuki et al., 2013).

## Development of Electrical Signals

Physiological studies show cone inputs dominate electroretinogram (ERG) responses in zebrafish larvae younger than days postfertilization (15 dpf; Bilotta et al., 2001) and recordings from larval GC already reveal complex cone-opponent spectral responses (Connaughton and Nelson, 2015). Rod PR density is low in larvae younger than 10 dpf (Fadool, 2003), explaining the cone-dominated electrical activity of the larval eye. Increased rod outer segment length and synaptic connections occur between 12 dpf and 15 dpf (Branchek and Bremiller, 1984) resulting in an increase in sensitivity (Branchek, 1984). However, the retina does not appear adult-like until after 20 dpf (Branchek and Bremiller, 1984) at which time both rod and cone responses can be detected in the zebrafish ERG (Bilotta et al., 2001). The early development of cones, and later development of rods, is also characteristic of mammalian retinas (Carter-Dawson and Lavail, 1979).

## CONE PHOTORECEPTORS ALLOW DETECTION OF LIGHT RANGING FROM LONG (RED) TO ULTRAVIOLET (UV) WAVELENGTHS

#### Opsins and Cone Morphology

Zebrafish possess four morphological cone types as well as rods. The cones cover an even broader range of the optical spectrum than do human cones. In adults, the cones are classified as short single cones, long single cones, and a double cone pair based on morphology (Engström, 1960), opsin expression, and relative peak absorption wavelengths (λmax). Short single cones have a λmax ∼360 nm; these are the ultraviolet (UV)-sensitive cones (SWS1 opsin). Long single cones are short wavelength sensitive (SWS2 or blue opsin) cones with a λmax ∼415 nm. Double cones include an accessory member, the middle wavelength sensitive (MWS or green) cone, with a λmax ∼480 nm (probably green opsin RH2-2), and a principal member, the long wavelength sensitive (LWS1 or adult red opsin) cone, with a λmax ∼570 nm (Nawrocki, 1985; Robinson et al., 1993; Cameron, 2002; Chinen et al., 2003; Allison et al., 2004; Endeman et al., 2013). Molecular analysis of opsin expression in these different cone types has identified two genes encoding the opsins in red cones, four genes for green opsins, and a single gene each for the blue and UV opsins (Chinen et al., 2003).

Within each PR outer segment, a chromophore (11-cisretinal) binds to an opsin molecule forming the visual pigment complex that absorbs light photons (Hubbard and Kropf, 1958). There are two forms of retinal, derived from either vitamin A1 or A2 (Saari, 2012). Zebrafish use only vitamin A1 based pigments, unless they are experimentally treated with thyroxine, which induces the synthesis of vitamin A2 (Allison et al., 2004; Enright et al., 2015).

#### Cone Mosaics in Larvae and Adults

The four cone types allow zebrafish to respond to wavelengths of light ranging from UV to red. In adults (**Figure 1**), the cones are organized into a row mosaic (Allison et al., 2010) in which double cones alternate with single cones such that the red cone is always adjacent to a blue cone and the green cone is always adjacent to a UV cone (Engström, 1960; Larison and Bremiller, 1990; Raymond et al., 1993; Allison et al., 2010). This row mosaic, first described by Engström (1960), supports a two Red: two Green: one Blue: one UV cone ratio that is constant over the entire retina, except for a small primordial region (Allison et al., 2010). Rod PRs insert into this cone mosaic, with four rods forming a square around each UV cone (Fadool, 2003; Morris and Fadool, 2005). When the retina is viewed in cross-section, it is evident that rods and cones do not form a single row but organize into tiers within the PR layer. The nuclei of short single UV cones are located most proximally. Moving distally one next identifies nuclei of long single blue cones, and then the double cones. Rod nuclei are located proximal to short single cones, while their outer segments are distal to double cones (Branchek and Bremiller, 1984; Robinson et al., 1993).

In contrast to adults, the cone mosaic in larval zebrafish (Larison and Bremiller, 1990) is less well organized and heterotypic, with regularly spaced PRs, but no row pattern (Allison et al., 2010). UV cones form first (Raymond et al., 1995;

FIGURE 1 | Zebrafish cone mosaic. In adult zebrafish, the cone mosaic is highly structured, with red and green double cones (DC) between blue (Long single, LS) and UV (Short single, SS) cones, with the red cone always next to the blue cone and the green cone always next to the UV cone (from Robinson et al., 1993, National Academy of Sciences, U.S.A. Reprinted with permission).

Robinson et al., 1995; Schmitt and Dowling, 1999) and are most abundant in larval retina, followed by blue cones. Red/green double cones form last (Robinson et al., 1995; Schmitt and Dowling, 1999) and are least abundant (Allison et al., 2010). Interestingly, opsin expression displays the opposite pattern with expression of red and blue opsins occurring several hours before expression of UV opsin (Schmitt and Dowling, 1999). Red and blue opsins occur in a tandem genetic pattern under a single promoter (Chinen et al., 2003), as do the four variants of green opsins. In cone types with multiple opsin genes (i.e., red and green cones), in situ hybridization identified sequential expression of these opsin genes during development (Takechi and Kawamura, 2005). It is the shorter-wavelength-peaking LWS2 that is mainly expressed in larvae, while adults express a mixture of LWS1 and LWS2. For green opsins, the shorterwavelength-peaking RH2-1 is earliest expressed, followed by the longer-wavelength-peaking RH2-2, RH2-3 and RH2-4. After the cone mosaic is present, the rod mosaic forms (Fadool, 2003; Morris and Fadool, 2005). Transition from larval to the adult mosaic occurs during the postlarval/juvenile period when the fish are >3 weeks of age (Allison et al., 2010).

#### OUTER RETINA—PROCESSING OF COLOR SIGNALS AT THE FIRST RETINAL SYNAPSE

Processing of color signals from different PRs occurs in outer retina. BCs are morphologically diverse retinal interneurons that receive combinations of inputs from rods and different cone types. Zebrafish HCs similarly contact specific combinations of PR types resulting in mono- and multiphasic spectral response properties that in total reflect inputs from all four cone types. PR inputs to BCs are modified by feedforward and feedback synapses from these spectrally-coded HCs, resulting in significant color processing at this first retinal synapse.

#### Vertebrate HCs

Mammalian retinas have, in general, two morphological HC types. A-cells are axonless, and large in dendritic extent; whereas the dendritic fields of B-cells are smaller and an axon ending in a terminal arbor projects from the cell body (Fisher and Boycott, 1974). Primate and rodent retinas are exceptions to the mammalian pattern. In primates, HI HCs are similar to B-cells, HII HCs are similar to A-cells, but HIII HCs more resemble teleost HCs, with a long ''axon'' without terminal arborization projecting from the cell body (Ahnelt and Kolb, 1994; Dacey et al., 1996, 2000). In rodents, there is only a single, B-type HC morphology (Peichl and González-Soreiano, 1994). In teleosts, all HC types bear an axon lacking terminal arborization. There are four HC morphologies in teleosts: H1–H4. Types H1–H3 are postsynaptic to cone PRs, while H4, the rod horizontal cell (HC), is postsynaptic only to rods (Stell and Lightfoot, 1975; Stell, 1975; Weiler, 1978).

In vertebrates, each HC type receives input from specific numbers and types of PRs resulting in either spectrally monophasic (L-type) or multiphasic (C-type responses; Nelson, 1977; Yang et al., 1983; Siminoff, 1986; Djamgoz et al., 1988;

Negishi et al., 1988; Dacey et al., 1996, 2000; Asi and Perlman, 1998; Twig and Perlman, 2004; Yin et al., 2006). One conspicuous difference in the physiology of mammalian HCs as compared to other vertebrates is the complete lack of C-type responses.

#### Cone Contacts of Horizontal Cell Types

Zebrafish HC contribute to color vision by facilitating coloropponent mechanisms through feedback and feedforward connections to PR and/or BC (Song et al., 2008). As in goldfish (Stell, 1967; Stell and Lightfoot, 1975), zebrafish cone-contacting HCs are morphologically characterized as H1, H2, or H3 types, and rod horizontal cells (RHCs or H4 type) which contact rods (Song et al., 2008; Li et al., 2009). All zebrafish HC are axonbearing, with a snake-like terminal that does not contact PRs running within the OPL (Song et al., 2008).

Zebrafish H1 HC (**Figure 2**) have a plate-shaped soma from which short dendritic processes protrude. H2 HC exhibit a wider profile than H1 HC and have an ellipsoid soma that extends tendril-like dendritic processes. The dendritic terminals of H1 and H2 occur in clusters of 5–6 boutons arranged in a rosette. The rosettes contact both double and single cone rows. When overlapped onto this cone mosaic, patterns consistent with contacts between HCs and either red, green and blue cones (H1 cells) or green, blue and UV cones (H2 cells) appear (Li et al., 2009). Due to H1/2 cell partial clusters and in some cases single boutons, it is probable certain individual cones may make connections with more than one HC. The remaining elements of the cluster would be contacted by another HC. H3 HC have an elongated cell body, with 3–4 long dendritic processes terminating in a rhomboid pattern with boutons arranged in doublets (Connaughton et al., 2004; Song et al., 2008). The dendritic extent of H3 is distinctly wider than either H1 or H2, making it easily recognizable. Examination of the H3 dendritic pattern on a transgenic cone mosaic where UV cone terminals are labeled suggests H3 cells selectively contact long single blue and/or short single UV cones, a pattern appearing to serve short wavelengths (Song et al., 2008; Li et al., 2009).

#### Development of H3 Types

The development of H3 cells was followed in larval zebrafish where the ratio of UV to blue cone synapses increased from about 2:1 at 3 dpf to 5:1 at 10 dpf. This ratio is greater than

the ratio of UV to blue cones, suggesting H3 cells actively seek out contacts with UV cones during development. The trβ2 nuclear receptor is required for red cone development (Ng et al., 2001; Suzuki et al., 2013). When the density of UV cones was increased (and red cones decreased) by suppression of trβ2 nuclear receptor, H3 dendrites grew and accommodated the extra UV cells with synapses. Genetic suppression of blue or UV synaptic transmission suggested that, at least for the UV to H3 synapse, activity is critical for synapse formation (Yoshimatsu et al., 2014).

#### Horizontal Cell Spectral Processing

Intracellular recordings of HC spectral responses identified luminosity (L-type) and chromaticity (C-type) HCs (Connaughton and Nelson, 2010), as in other teleosts (Svaetichin and Macnichol, 1958), including other cyprinidae (Naka and Rushton, 1966a,b; Kaneko, 1970). L-type, or spectrally monophasic responses hyperpolarize to light at all stimulus wavelengths and irradiances (**Figures 3A,B**). Most zebrafish HCs encountered (∼66%) were L-type. Of these L-type responses, the majority were red-preferring L2 cells, with peak sensitivity to wavelengths ∼560 nm; the rest are red/green L1 cells with a mean peak sensitivity ∼490 nm (Connaughton and Nelson, 2010). C-type responses included spectrally biphasic, triphasic, and tetraphasic varieties. Biphasic cells depolarized to long wavelength stimulation (>570 nm) but hyperpolarized to wavelengths ≤530 nm (**Figure 3C**). Spectral responses of triphasic cells, in general, hyperpolarized to long wavelength stimuli (650, 610, and 570 nm), and depolarized to middle and short wavelengths (530, 490, and 450 nm). UV triphasic cells hyperpolarized strongly to 370 and 330 nm stimuli, while blue triphasic cells hyperpolarized weakly to these UV stimuli, but more strongly to a 410 nm stimulus (**Figures 3D,E**). The final HC spectral response identified was a unique tetraphasic response (**Figure 3F**) characterized by hyperpolarization to red-yellow (650–570 nm), depolarization to green and green-blue (530, 490 nm), hyperpolarization to 450, 410 and/or 370 nm, and depolarization to UV (330 nm; Connaughton and Nelson, 2010). Dye fills of recorded cells indicate HCs with L-type and biphasic spectral responses were similar in morphology (Connaughton and Nelson, 2010) and distinct from cells with triphasic and tetraphasic responses. Combining images of microelectrode stains and response properties with the anatomical studies of HC morphologies and PR terminals (Li et al., 2009), suggests: (a) L-type responses occur in H1 HCs; (b) biphasic cells are H2 cells; and (c) triphasic and tetraphasic responses occur in H3-type HC (Li et al., 2009; Connaughton and Nelson, 2010).

# CONE SYNAPSES WITH BIPOLAR CELLS

#### Vertebrate BCs Are Similar in Glutamatergic Mechanisms and Axonal Stratification Patterns

Zebrafish BCs were initially classified by their glutamate-gated currents, which identified both ON- and OFF-type cells. ON-cells express metabotropic glutamate receptors and a chloride channel forming glutamate transporter on their dendritic arbor (Grant and Dowling, 1995); whereas, OFF-type cells express AMPA/kainate receptors (Connaughton and Nelson, 2000). The ON-BC dendritic ''glutamate transporter channel'' is not seen in mammals as it provides presynaptic inhibition on axon

terminals of mammalian ON-type BCs (Wersinger et al., 2006). Morphologically, the axon terminals of physiologically ON-BCs were found within IPL sublamina b while OFF-BC terminals were in sublamina a (Connaughton and Nelson, 2000). These sublamina divide the IPL roughly in half, with sublamina a represented by the outer half, near ACs, and sublamina b by the inner half, near GCs, consistent with IPL structure and BC morphology in other vertebrates. Subsequent studies using dye-labeling methods (DiI) introduced a 6-stratum scheme for classifying bipolar axon terminal depth within the zebrafish IPL and identified ∼17 morphological types of BCs based on axonal stratification patterns (Connaughton et al., 2004; Li et al., 2012). This number of types was large as compared to mammals (Kolb et al., 1981; Haverkamp et al., 2005; Masland, 2012) and amphibians (Wu et al., 2000), but similar to the 15 types identified in goldfish with Golgi staining (Sherry and Yazulla, 1993). Of the 17 types in zebrafish, ∼7 were OFFcells, ∼6 were ON-cells, and ∼4 were presumed ON-OFF cells with axon terminals in both sublamina a and b (Connaughton et al., 2004). This latter type, with bistratified axon terminals, physiologically expressed either OFF-type or ON-type glutamate receptors (Connaughton and Nelson, 2000).

# Bipolar Cells Differentially Contact Multiple Cone Types

Superposition of DiI-stained BC dendrites onto the zebrafish cone mosaic revealed BC type-specific synaptic connection patterns with PRs, and further expanded the number of BC types to ∼33 (Li et al., 2012). Nine dominant patterns of PR connections accounted for 96% of a total of 18 patterns found (**Figure 4**). These dominant patterns included both cone-only BCs, exclusively postsynaptic to cone PRs, and mixed-rod-cone input BCs (Li et al., 2012). Only one of the cone-selective patterns was restricted to a single cone type (green cones), and one of the mixed rod-cone patterns received input from all PR types. Examining the axon-terminal stratification types among these PR connectivity patterns, six of the nine patterns included monostratified ON types, with terminals in s4–s6 and either monostratified or bistratified OFF types, with all terminals in s1–s3. Four of the patterns included ON-OFF axon terminal stratification types. The variety in presynaptic connections, the dendritic field size of each BC, and the terminal ramification patterns in the IPL (Li et al., 2012) suggest selective and elaborate color circuits involving BCs across the entire retina.

# Bipolar Cell Spectral Properties

Recent calcium-imaging 2-photon microscopy of BC axon terminals in live zebrafish larvae found BCs that were chromatic (wavelength selective), achromatic (responding to all wavelengths), or opponent (opposite responses at two wavelengths; **Figure 5**). To image light-driven calcium activity, a transgene, consisting of the ribeyeA/ctbp2 promoter, which is selectively expressed by retinal BCs, and the synaptophysin-GCaMP6f fusion protein, a synaptically localized calcium sensor (Dreosti et al., 2009; Zimmermann et al., 2018), was inserted into the zebrafish genome.

different spectral inputs from BCs into the IPL identified achromatic and color-opponent regions in this synaptic layer that were most evident outside the "SZ". For the right panels: black dots = achromatic OFF-responses, white dots = achromatic ON-responses, red dots = color-opponent responses, gray dots = "other" (taken from Figure 3, Zimmermann et al., 2018).

A blue-UV chromatic response was found to be regionally selective, with a preponderance of ON-type UV-excited BC terminals throughout the IPL. In retinal topology, these cells densely occupy a temporal-ventral ''strike zone (SZ),'' the region looking forward and upward at prey (Zimmermann et al., 2018). Both ON and OFF achromatic types responded with excitation or inhibition to all wavelengths tested. Opponent responses included red excited, green, blue and UV inhibited, as well as blue and UV excited, but red, green inhibited. These results are broadly consistent with neuroanatomical connectivity of BCs, particularly in respect to the many cone signals represented in BC terminals throughout the depth of the IPL, which attests to the idea that multi-cone contacting BCs may connect to cones in a color-opponent manner.

To date, there have been no studies reporting the spectral responses of zebrafish BCs using either whole-cell patch or sharp electrode techniques. In the closely related Giant Danio retina (Danio aequipinnatus), however, patch recordings of bistratified cone BCs with axonal boutons in both sublamina a and sublamina b (Cab BCs) revealed color-opponent responses, excited by one wavelength, but inhibited by another. The Cab cells subtract signals from cones with different opsin expression, and multiple spectral patterns were observed. There was evidence that different types of BC dendritic glutamate receptors were stimulated by different spectral types of cone (Wong and Dowling, 2005). Further, spectral light stimulation (red, green, or blue), evoked double color-opponent responses. Selectively blocking AMPA/kainate receptors or glutamate transporter associated chloride channels on these cells revealed that the responses to stimulation with short and long wavelengths are mediated by these two-different glutamate-gated mechanisms (Wong and Dowling, 2005).

#### ERG b-Waves Suggest Bipolar-Cell Spectral Properties

The ERG is a light-evoked retinal field potential that sums activities of retinal neurons. ERG b- and d-waves represent, and are dominated by, the massed activity of ON- and OFF-BC types in zebrafish, respectively. The b-wave in adult zebrafish receives contributions from all four cone types and exhibits evidence of red-green color-opponent mechanisms (Hughes et al., 1998). Modeling these inputs, (Cameron, 2002) separated b-wave signals into four different processing channels, one for each cone type. Two color channels (long and middle wavelengths) were involved in color-opponent processing, while the short and UV wavelength channels were not (Cameron, 2002). This suggests BCs detect wavelengthdependent differences in both chromatic and luminance contrast (Cameron, 2002).

ERG b-wave responses in larval zebrafish are different from those in adults. Adult ERG responses include a-wave, b-wave, and d-wave components at all stimulus wavelengths and irradiance levels (Bilotta et al., 2005). The components present in a larval ERG, however, are wavelength dependent. For example, in response to UV light stimulation, the larval ERG includes a large a-wave, delayed b-wave, and small/no d-wave. However, if middle/long wavelength stimuli are used, the ERG has a small/no a-wave followed by large amplitude b- and d-waves (Bilotta et al., 2005). The ERG of larval zebrafish retina is dominated by UV cones, and the physiological differences in a-wave amplitude evoked using UV vs. longer wavelength stimuli can be attributed to differences in cone representation in larval responses (Bilotta et al., 2005). However, differences in larval b-wave (ON-BC) responses may also be due to differences in glutamate receptor expression on these cells. For example, UV and short wavelength stimuli seemed to be mediated by APB-sensitive metabotropic glutamate receptors on ON-BC dendrites, while longer wavelength stimuli were not (Bilotta et al., 2005). Multiple glutamategated mechanisms are present on adult zebrafish ON-BCs (Connaughton and Nelson, 2000; Nelson and Singla, 2009) and it appears that the mechanisms may be somewhat selective for cone type.

# SPECTRAL PROCESSING IN AMACRINE CELLS

#### AC Characteristics

As a group, and across all vertebrates, ACs are the most diverse class of retinal neuron. ACs are categorized by their physiological response to light, their great variety of neurotransmitter expression as determined using immunolabeling, their dendritic morphology, and dendritic lamination patterns in the IPL. The different types of ACs range from 43 in roach (Wagner and Wagner, 1988) to 22 in cat (Kolb et al., 1981) to ∼30 in rabbit (MacNeil et al., 1999; Masland, 2012). Physiological responses of ACs are similarly diverse. Light stimulation evokes sustained or transient, ON, OFF, or ON-OFF responses in both mammalian (Nelson, 1982; Nelson and Kolb, 1985; Stafford and Dacey, 1997; Menger and Wässle, 2000) and non-mammalian (Kaneko, 1970, 1973; Pang et al., 2002; Miller et al., 2006; Zhang and Wu, 2010) retinas.

Structural and functional diversity is also present in zebrafish ACs where ∼28 morphological types have been identified in 5 dpf larval retina (Jusuf and Harris, 2009). Many larval AC types are also present in adults, as determined by dye (Connaughton et al., 2004) and/or immunolabeling (Marc and Cameron, 2001; Yazulla and Studholme, 2001; Arenzana et al., 2006; Yeo et al., 2009; Jang et al., 2011). These cells exhibit narrow, medium, or wide field dendritic arbors (Jusuf and Harris, 2009; Lewis et al., 2015) and are widely distributed over the entire retina (Yeo et al., 2009; Torvund et al., 2017); although it appears the density of parvalbumin s4, and tyrosine hydroxylase (dopaminergic) types is greater in temporal-ventral, or ventral retina than nasal retina (Yeo et al., 2009; Jang et al., 2011). In adult zebrafish, AC light responses are either transient or sustained, with excitation observed at light ON, light OFF, or both light ON and light OFF (Torvund et al., 2017). Most dendritic branching patterns are highly planar and narrowly restricted to single strata within the IPL. Both monostratified

types (one branching plane) and bistratified types (two branching planes) are observed (Connaughton et al., 2004). Diffuse dendritic branching through multiple IPL strata was uncommon.

# ON-OFF Amacrine Cells Select Red Cone Signals

ON-OFF responses in zebrafish are characteristic of all bistratified ACs (**Figures 6A,B**). Most of these have processes in both IPL sublamina a and b, though some ON-OFF cells have processes bistratified only within sublamina a (Torvund et al., 2017). These cells respond to spectral stimuli ranging from 330 nm to 650 nm; however, modeling of their responses indicates virtually exclusive input from red cones (Torvund et al., 2017), both at light ON and light OFF. For calcium imaging, ACs can be marked by the inhibitory-interneuron selective transgene ptf1a:gal4 (Jusuf and Harris, 2009). Marked cells express the synaptic calcium reporter transgene UAS:SyGCAMP3 (Rosa et al., 2016). In the IPL, bands of ON-OFF responding processes and synapses can be found in both sublamina a and sublamina b, a result consistent with the existence of ON-OFF bistratified AC types. This method, however, is not able to directly link both bands to actions from single bistratified ACs. The frequency responses of these ON-OFF signals included different populations peaking at either ∼5 Hz or ∼10 Hz.

### ON-Sustained and OFF Amacrines Sum Red and Green Cone Signals

Zebrafish OFF-type ACs, like ON-OFF types, receive dominant input from red cones, with lesser input from green or blue cones (Torvund et al., 2017). These cells are monostratified in either s1 or s2 of sublamina a. ON-type AC, with monostratified dendrites in s4, or rarely s3, receive mixed luminosity signals from red and green cones, leading to mid-spectral sensitivity peaks (**Figures 6C,D**). The ON ACs were sustained types, not excited at light OFF, and contributions from blue and UV cones are noticeably lacking in these non-opponent responses (Torvund et al., 2017). The dendrites of both ON and OFF ptf1a ACs, marked for synaptic calcium responses, labeled distinct IPL strata, with a major ON band in sublamina b, and a major OFF band in sublamina a. In addition, calcium imaging suggests a lower intensity representation of both these response types throughout retinal depth (Rosa et al., 2016). Crossover GABAergic inhibition from ON-ACs appears to shape the frequency response characteristics of OFF-BC terminals (Rosa et al., 2016), leading to the speculation that sustained ON-ACs contribute ''low pass'' characteristics to OFF-BC boutons.

### C-Type Amacrine Cells Subtract Cone Signals

Unique as compared to mammals, some zebrafish ACs are coloropponent. UV cone inputs contribute significantly to coloropponent (C-type) responses in zebrafish AC. The dendrites of all these C-type ACs ramify deep in sublamina b, indicating they are ON-type cells morphologically, though physiologically the response sign depends on wavelength (**Figures 6E,F**). In adult retina, both spectrally biphasic and triphasic AC responses are present, as also observed in HC spectral responses (Connaughton and Nelson, 2010). Biphasic AC responses include blue-yellow and red-green units. Red-green opponent cells depolarized to red, but hyperpolarized to green, blue and UV stimulation; while blue-yellow opponent cells responded with a sustained depolarization to short wavelength stimulation, but hyperpolarized to long wavelengths (Torvund et al., 2017). Some triphasic ACs depolarized to UV, hyperpolarized to blue, and depolarized to green. Interestingly, inclusion of (marginally significant) red cone inputs to the triphasic response would cause it to be reclassified as a tetrachromatic AC response (Torvund et al., 2017). Calcium imaging studies have found color-opponent BC terminals within the layer of dendritic branching of coloropponent ACs (Zimmermann et al., 2018). The bistratified, color-opponent Cab bipolar of Giant Danio also sends a terminal bouton to this same layer (Wong et al., 2005). Taken together these studies identify a potential synaptic input for C-type ACs. Wide dendritic fields, sometimes seen with dye coupling, suggest a wavelength-dependent modulatory role.

### MORPHOLOGY AND PHYSIOLOGY OF RETINAL GANGLION CELLS

#### GC Physiology in Vertebrates

As the output neuron of the retina, there have been many studies documenting light responses of retinal GCs.  Therefore, the presence of spectrally selective GC responses in zebrafish would not be surprising. A wide range of GC characteristics/response types makes sense as these cells receive, integrate and process information from BC and AC about various parameters of the visual scene.

## Zebrafish GC Morphology, Development and Tectal Projection

Mammals, in general, have a greater number of morphological types of GCs (Kolb et al., 1981; Sanes and Masland, 2015) than zebrafish (Mangrum et al., 2002). The 11 types identified in adult zebrafish (**Figure 7**) are grouped as either wide-field (2), narrow-field (4), multistratified (3), or diffuse (2) based on dendritic extent and patterns of stratification (Mangrum et al., 2002). A main reason for less types in zebrafish is a closer grouping of cell body diameters (5–8 µm) and dendritic field diameters (80–200 µm) as compared to mammals (cat: 10–40 µm cell bodies; 20–900 µm dendritic fields; (Kolb et al., 1981). A wide spread of metrics allows for more type-features to be distinguished. In mouse retina, similar to zebrafish, GC dendritic fields are restricted in range (150–450 µm; Sun et al., 2002). The greatest density of zebrafish GCs occurs in a temporalventral patch similar to the ''SZ'', a visual space used for feeding (Mangrum et al., 2002; Zimmermann et al., 2018).

Time-lapse imaging experiments in larval zebrafish (Mumm et al., 2006) discovered diverse dendritic growth patterns and laminar targeting mechanisms used by GCs in the IPL, resulting in 15 stratification patterns. GC input to the tectum is somewhat segregated, with inputs relaying similar information (such as stimulus direction) going to similar tectal areas (Johnston and Lagnado, 2012). Though these inputs to the tectum are functional by 66 hpf, it is not until 78 hpf that tectal neurons begin to display mature responses. This suggests that GCs are capable of information processing before structural development in the retina is complete (Niell and Smith, 2005).

#### Ganglion Cell Light Responses

Light responses of larval zebrafish GCs are either transient or sustained, with ON-, OFF- and ON-OFF subtypes (Emran et al., 2007). Most ON-OFF cells are bistratified in sublaminae a and b, while ON- and OFF-cells are monostratified (Zhang et al., 2010). Between 2 dpf and 4 dpf zebrafish GCs undergo depolarizing to hyperpolarizing shift in GABAergic inhibitory ECl (Zhang et al., 2010), a process common to vertebrate early neuronal development (Li et al., 1998). Stable light responses are recorded as early as 4 dpf (Zhang et al., 2010) and color-opponent and non-opponent responses are evident at 5 dpf and 6 dpf, when responses are dominated by spectrally multiphasic types (Connaughton and Nelson, 2015). The most common spectral type is the triphasic GC response with bursts of spikes to both long and short wavelength stimuli, but inhibited firing at middle wavelengths. Other multiphasic responses include biphasic, tetraphasic and pentaphasic units (Connaughton and Nelson, 2015). Like adult ACs, red cone inputs are prominent in larval GC spectral responses. However, most GC responses include significant and commonly dominant, UV excitation, resulting in multiphasic spectral properties. Very few larval zebrafish GCs are spectrally monophasic (**Figure 8**). In adult zebrafish, light-evoked spike discharges are seen in the optic nerve, particularly ON-OFF types (Li and Dowling, 2000; Huang et al., 2005), but the spectral properties are not known.

et al. (2002).

### WAVELENGTH PROCESSING CIRCUITRY IN ZEBRAFISH

The different subtypes of second- and third-order neurons in zebrafish receive inputs from single or, more often, multiple cone PRs, resulting in spectrally monophasic and multiphasic responses. As a result, the signals from each cone type are relayed, through both feedforward and feedback circuitry, throughout the entire retina. One feature of this intermixing of cone signals is both synergistic and antagonistic interactions between different cone signals in distal neurons, such as HC and BC, a feature also noted in AC and GC of the inner retina. The spectral properties of GCs, retinal output neurons, are similar to the color-opponent spectral responses of HCs, suggesting one of the

origins of GC spectral processing lies in distal retina, with the cone connectivity patterns of HC and BC. One expects there is further selective integration of BC and AC physiological types onto the dendrites of GC receptive fields in the IPL, resulting in even greater diversification and refinement of spectral properties. The dominance of red cone signals in spectrally monophasic AC types is reminiscent of the L-type signals from H1 HCs, suggesting the existence of an L-type BC pathway. Despite the evidence for a mixing of cone signals in the image-processing circuits of the inner retina, existing evidence suggests that signals from the 4 retinal cone types in zebrafish are not treated equally.

#### Red Cone Signals

Red cones are only 1/3 of adult cone types. Nonetheless, red cone signals dominate spectral responses in HC and AC. These red (LWS) signals are relayed directly from the principal member of the double cone to H1 HCs(Li et al., 2009) resulting in red-dominated monophasic (L1- or L2-type) physiologies in 2/3 of HCs studied (Connaughton and Nelson, 2010). Even though contacting them, H1 HCs do not receive physiological signals from blue (SWS2) cones, and do not contact UV (SWS1) cones at all. LWS cones are included in seven of nine of BC connectivity patterns or 87% of 321 BCs studied (Li et al., 2012), potentially accounting for the prevalence of red cone signals seen in ACs (Torvund et al., 2017). However, these BC pathways, while contacting red cones, are not red cone selective, as green (MWS) cones overlap, comingling in all but one of these same BC connectivity patterns and 93% of BCs studied (Li et al., 2012).

Non-color-opponent AC responses are either selective for or dominated by red cone signals; these cells are OFF-stratified, ON-stratified, and ON-OFF bistratified cells, altogether accounting for 89% of recorded cells (Torvund et al., 2017). The variety of AC stratification patterns in this group suggest that AC processes receiving LWS-selective cone inputs are present throughout the depth of the IPL (Torvund et al., 2017), consistent with direct input from red cone contacting, and physiologically red cone dominated BCs, also presumably of multiple stratification types. In contrast to adult ACs, LWS inputs to GCs in larval zebrafish retinas contribute mainly to spectrally multiphasic responses. For the latter, red cone inputs are always excitatory (Connaughton and Nelson, 2015), but the responses are dominated by excitation from UV cones. The contrast between dominant AC and GC red cone spectral patterns presents a dilemma, and it is unclear at present how large a role larval development plays, as b-wave spectral sensitivity progresses from UV-dominated in larvae to a more broadly sensitive pattern in juveniles and adults (Saszik et al., 1999). A reasonable conjecture is that both non-opponent and opponent BC pathways exist for red cone signals. This idea is consistent with the finding of a population of red cone opponent ACs in adults with a unique stratification pattern (Torvund et al., 2017).

## Green Cone Signals

Despite a numerical representation in adults identical to red cones, green cone signals are not dominant in zebrafish physiology. In the outer retina, while red cones dominate H1 HC responses, UV cones dominate one physiological subtype of H3, and blue cones dominate the other, there is no HC strictly devoted to excitation from green cones. These signals appear as complementary components in H1 HC, particularly the L2-type response and H2 HC biphasic signals, where they combine with blue cone signals. The most selective and prominent role of green cone signals in the outer retina are as the inhibitory (depolarizing) elements in triphasic and tetraphasic HC responses (Connaughton and Nelson, 2010).

In ACs, green cone inputs to non-color-opponent cells were small and found mainly in sustained-ON AC, with processes monostratified either in the OFF sublamina or in the distal-most layer of the ON sublamina (Torvund et al., 2017). Green cone signals were seldom identified in the numerically dominant ON-OFF types. No ACs that selectively represent green cone signals have been encountered (Torvund et al., 2017). The relative absence of MWS signals in ACs is mysterious, unless green cone-to-BC synapses are commonly either inactive, inhibitory, or opponent. In calcium imaging physiology of BC boutons (Zimmermann et al., 2018) it does appear that the most common form of BC color opponency is red excitatory, green inhibitory (**Figure 5**). These red-ON, green-OFF boutons localize mainly in s2, s3 of IPL sublamina a. Green cones are the only type that selectively innervate BCs, in fact there are three varieties of OFF stratifying BCs that only contact green cones (Li et al., 2012). The green cone signals appear to be largely inhibitory (Connaughton and Nelson, 2015), and speculatively, the inhibition might involve OFF-type, or color-opponent OFFtype, green cone BCs. However, in adults, the spectrally triphasic, green cone inhibited, color-opponent AC did not co-stratify with green cone BC terminals in sublamina a.

#### Blue and UV Cone Signals

In outer retina, blue and UV cone signals are represented in about 1/3 of recorded HC. Blue (SWS2) cones contact H1, H2 and H3 HC (Li et al., 2009), but only contribute signals to biphasic (H2) and blue-preferring triphasic (H3) responses (Connaughton and Nelson, 2010). UV cones directly connect only to H3 cells (Li et al., 2009). However, UV inputs to H3 cells appear variable, as this morphological type can generate three different spectral responses (Connaughton and Nelson, 2010): (1) UV-excited spectrally triphasic responses dominated by UV cone excitation; (2) tetraphasic (UV-inhibitory); and (3) blue-preferring triphasic responses excited by both UV and blue cones. Spectrally multiphasic patterns are presumed evidence of feedback to blue and UV cones from HC types receiving stimulation from LWS cones (Li et al., 2009; Connaughton and Nelson, 2010) to generate HC multiphasic responses.

In adult inner retina, stimulation of blue and UV cones contributes to biphasic and triphasic responses in zebrafish ACs, a pattern similar to outer retina. These include a UV-depolarizing blue-yellow opponent biphasic unit and UV-depolarizing triphasic responses (Torvund et al., 2017). Short wavelength inputs to larval GCs elicit spectrally triphasic responses, similar to triphasic HC responses (Connaughton and Nelson, 2015). Interestingly, adult ACs with C-type responses have processes exclusively in sublamina b, suggesting a functional segregation of short wavelength BC-to-AC synapses in the IPL. Adult connectomic analysis identified 3 BC types directly postsynaptic to UV cones with axon terminals in sublamina b, and 5 BC types postsynaptic to blue cones (Li et al., 2012), suggesting there are a relatively few, possibly selective, circuits that relay short wavelength signals to inner retina, and that short wavelength cone signals are commonly conveyed in a color-opponent pathway.

#### Stratification of IPL Cone Signals

There may be some segregation of cone signals in the zebrafish IPL. BC connectomics provides pathways for red, green, blue and UV cone signals into all IPL strata. This homogeneity is partly enforced by BCs with multistratified axon terminals, as well as the prevalence of BCs contacting multiple cone types (Li et al., 2012). The finding of dominant red cone signals in all non-opponent ACs, regardless of stratification pattern, is in accord with a distribution of L-type, or achromatic, red cone signals throughout the IPL (Torvund et al., 2017). Nonetheless, there are some imbalances. The RRod type BC conducts red cone (and rod) signals only, and projects to IPL sublamina b. The solely green cone contacting BCs project only to s1 and s3 of sublamina a, with most boutons in s3 (Li et al., 2012). Depolarizing sustained ACs stratify in s3-s4. In addition to red cone signals, these cells are strongly excited by green cone stimulation (Torvund et al., 2017). Over 60% of BC types project to sublamina a (Li et al., 2012), a curious imbalance. In calcium imaging studies of BC boutons, Zimmermann et al. (2018) found that the IPL strata containing achromatic OFF-BC boutons may be much narrower than previously supposed, localized to s1 and not all strata of sublamina a. The remaining s2-s3 band contains coloropponent BC boutons of various types. There is also an s5 coloropponent band, perhaps innervating the color-opponent ACs that branch in s5 (Torvund et al., 2017). Torvund et al. (2017) proposed that the multistratified GBUV BC types (Li et al., 2012) might be the color-opponent pathway innervating coloropponent ACs, a theory yet to be tested but consistent with the color-opponent BC axon terminal distribution (Wong and Dowling, 2005; Zimmermann et al., 2018). Stratum s4 and thereabouts is the true achromatic ON-type band. Zimmermann et al. (2018) find that IPL banding varies with retinal region as well, with UV excited boutons being broadly prevalent in the ''SZ''. The indications are that there are multiple functional layers in zebrafish IPL, both for chromatic and achromatic processing.

## COMPARISON WITH MAMMALIAN COLOR CIRCUITRY

In mammals, there are well-defined, specific inner and outer retinal circuits for rod and cone signals. Rod signals are relayed through rod BCs (RBCs) to AII ACs and then to ON-GCs through gap junctions with ON-BC terminals (Kolb and Famiglietti, 1974). Cone signals are relayed through cone BCs to GCs (Kolb, 1970). As true for most fish, zebrafish have mixed input (mb) BCs, a close analog of mammalian RBCs, but receiving inputs from both rods and cones. In mammals, cones synapse on HC dendrites, and rods synapse on HC axon terminals (Nelson et al., 1975; Kolb, 1977; Dacheux and Raviola, 1982). In zebrafish, rods synapse on a separate HC type, while cones selectively innervate 3 distinct HC types and 4 different BC types (Li et al., 2012). Overall, it appears that rod and cone signals in zebrafish, like mammals, are selectively sampled by retinal interneurons. Zebrafish HCs are either spectrally monophasic (red cone signals) or multiphasic (color opponency among cone signals, (Connaughton and Nelson, 2010); while only spectrally monophasic patterns appear in mammalian HCs. Mammalian HC axon terminals are physiologically rod selective, while the mammalian HC cell bodies mix red cone, rod and UV/blue cone signals (Steinberg, 1969; Dacheux and Raviola, 1982; Nelson, 1985; Dacey et al., 1996).

There are BCs selective for UV cones in mammals (Mariani, 1984; Haverkamp et al., 2005). In contrast, there is no anatomically UV cone selective BC in zebrafish (Li et al., 2012). Nonetheless, UV-selective BC physiology is likely present (Saszik et al., 2002; Zimmermann et al., 2018). Zebrafish GC responses include short-wavelength color-opponent responses, as reported in other species. In primate retina, for example, the blue-ON/yellow-OFF response of small bistratified GCs has been particularly well described (Dacey, 1999; Marshak and Mills, 2014). This spectrally multiphasic response is due to feedback circuitry involving HCs in outer retina. In inner retina, it is attributed to separate inputs from ON- and OFF-BCs, which themselves are postsynaptic to different cone types, as well as chromatic AC inputs (Marshak and Mills, 2014). It is likely that the multiphasic responses dominated by UV signals observed in larval zebrafish GCs similarly integrate and reflect inputs from UV-selective outer retinal circuitry involving HCs and direct inputs from spectrally selective presynaptic BCs and/or ACs.

# COLOR PREFERENCE, PERCEPTION AND BEHAVIOR

Zebrafish display color discrimination or preference when given a choice between different sections of a tank that have different background colors (Colwill et al., 2005; Risner et al., 2006; Avdesh et al., 2012; Ahmad and Richardson, 2013; Oliveira et al., 2015; Park et al., 2016). Most studies report a natural preference for blue/short wavelength light in both adult (Colwill et al., 2005; Risner et al., 2006; Bault et al., 2015; Oliveira et al., 2015; Peeters et al., 2016) and larval (Park et al., 2016; Peeters et al., 2016) fish. A few studies, in contrast, report a natural preference for red/long wavelength light (Avdesh et al., 2012; Ahmad and Richardson, 2013), which may be associated with food/foraging and be related to color of prey items (Spence and Smith, 2008).

In behavioral testing, color preference is best explained by a multiple-mechanisms model that includes both non-opponent (UV, B) and opponent (G-B and G-R) systems (Risner et al., 2006). This model is also most accurate in describing spectral sensitivities of ERG b-wave and ON-tectal responses in zebrafish (McDowell et al., 2004), suggesting behavioral responses reflect physiological processing of the visual scene. Interestingly, though color choice experiments indicate zebrafish can behaviorally distinguish color stimuli, the optomotor response (OMR), a standard vision-based behavior used to identify mutant strains, is ''color blind'' (Krauss and Neumeyer, 2003; Orger and Baier, 2005) as it is dominated by LWS (red) cones in adults (Krauss and Neumeyer, 2003) and pooled inputs from red and green cones in larvae (Orger and Baier, 2005). OMRs can be reduced, though, when UV or B cones are selectively ablated (Hagerman et al., 2016), suggesting short wavelength cones do contribute to the OMR, possibly in an inhibitory manner (Krauss and Neumeyer, 2003; Hagerman et al., 2016).

#### ZEBRAFISH AS MODELS OF RETINAL DISEASE

There have been several recent reviews detailing the applicability of zebrafish to the study of ocular/retinal diseases (Gross and Perkins, 2008; Bibliowicz et al., 2011; Link and Collery, 2015). These reviews highlight the experimental strengths of zebrafish: similar retinal/eye anatomy with humans, rapid development and large clutch size, easy manipulation and phenotype observation, and amenability of genome editing techniques to generate mutant and transgenic lines (Bibliowicz et al., 2011; Link and Collery, 2015). Of the >20 diseases for which zebrafish are used, ∼50% target PRs/PR layer (Link and Collery, 2015), and 13 specific genes associated with PR degeneration have been identified (Brockerhoff and Fadool, 2011). Behavioral studies using the optokinetic response (OKR; Brockerhoff et al., 1995), the escape response (Li and Dowling, 1997), or the visual motor response (Emran et al., 2008), coupled to ERG recordings allowed functional assessment of mutant phenotypes. Interestingly, many of the mutations leading to PR degeneration are not associated with transduction, but with protein trafficking (Brockerhoff and Fadool, 2011), consistent with the high metabolic demand and almost-constant functioning of retinal PRs. Here, we focus on those zebrafish lines with mutations in cone PRs. These studies have largely been performed with larval (5–7 dpf) zebrafish because, at this age, the cone-rich retina contains few/no rods (Holzhausen et al., 2009).

#### Mutations Affecting All Cone Types

The OKR is a visually-guided behavior based on saccadic eye movements as a stationary animal tracks a moving grating. This is a reliable test that can be elicited as early as ∼4 dpf in zebrafish (Brockerhoff et al., 1995; Neuhauss, 2003). Brockerhoff et al. (1995, 1997, 1998) were the first to apply this technique to the identification of mutant phenotypes. Most of these behaviorallyidentified zebrafish were later found to have mutations in cone-specific genes.

One of the first mutants identified was no optokinetic response a (noa). As the name suggests, noa larvae do not display an OKR. These mutants have ERG recordings with normal a-waves, but abnormal b-waves (Brockerhoff et al., 1995), consistent with altered glutamate responses in postsynaptic BCs (Connaughton, 2001). Subsequent, molecular analysis identified two alleles of noa: m631 and a13 (Taylor et al., 2004). noaa<sup>13</sup> mutants (originally no optokinetic response b, (Taylor et al., 2004) have a deficient dihydrolipoamide-S-acetyltransferase (dlat) gene, resulting in the absence of the E2 subunit of the enzyme pyruvate dehydrogenase (PDH; Taylor et al., 2004). PDH is a critical enzyme involved in ATP production in mitochondria because it catalyzes the reaction that converts pyruvate to Acetyl Co A (Karp, 1999). Absence of this enzyme results in PDH deficiency, an inherited metabolic disorder (Barnerias et al., 2010; Patel et al., 2012). The dlat (noaa13) mutant is, therefore, a model for human PDH deficiency.

no optokinetic response c (nrc) mutants, in addition to an absent OKR, have a diverse phenotype that includes floating ribbons in cone pedicles (Allwardt et al., 2001) and reduced numbers of unevenly distributed synaptic vesicles (Van Epps et al., 2004). Abnormal ERG recordings (Allwardt et al., 2001) revealed a light adaptation defect in distal retina (Van Epps et al., 2001). ON-GC responses in inner retina are also reduced/absent (Emran et al., 2007). Molecular analysis identified the nrc mutation as a premature stop codon in the synaptojanin 1 (SynJ1) gene (Van Epps et al., 2004) in cone PRs (Holzhausen et al., 2009). Synaptojanin-1 is strongly expressed in brain<sup>1</sup> and the synaptojanin-1 protein is involved in endocytosis of clathrincoated vesicles (Perera et al., 2006) in nerve terminals.

A cone-specific mutation is also observed in zebrafish no optokinetic response f (nof) mutants. nof fish have a nonsense mutation in the gene coding for the α-subunit of cone transducin (TαC; Brockerhoff et al., 2003). Development of the transgenic line TG(3.2TαCP-EGFP) allowed visualization of TαC in all zebrafish cone types, making nof mutants a model for achromatopsia (Kennedy et al., 2007). Interestingly, though nof cones lack transducin, they do respond to bright light stimuli, albeit at a much-reduced level (Brockerhoff et al., 2003).

<sup>1</sup>https://www.ncbi.nlm.nih.gov/gene/8867

A mutation in the cone cGMP-phosphodiesterase α subunit (pde6α <sup>1</sup> or pde6c), also causes cone degeneration (Stearns et al., 2007; Nishiwaki et al., 2008) in zebrafish. cGMP-PDE is an enzyme complex located in PR outer segments that is part of the phototransduction cascade (Rodieck, 1998). In mouse rods, absence/mutations in either the α (Huang et al., 1995), β (Pittler and Baehr, 1991), or γ (Tsang et al., 1996) subunits of cGMP-PDE result in rod degeneration. cGMP-PDE in zebrafish cones is similarly required as mutations lead to cone-specific degeneration.

Mutations/Treatments Affecting Specific Cone Types

The generation of knockout (KO) and mutant lines has also identified genes specific to a given cone type. The transcription factor Tbx2b, for example, is necessary for UV cone formation (Alvarez-Delfin et al., 2009) as a mutation in tbx2b results in the conversion of UV cones to rods, resulting in the lots-of-rods (lor) phenotype (Alvarez-Delfin et al., 2009). Though lor zebrafish have a strong OKR, UV opsin expression in larval retinas is deficient. All other cone types are present and tbx2b mutants have an even and abundant distribution of rods over the entire retina. Similarly, the transcription factor sine oculis homeobox homolog 7 (six7) is required for green cone development (Ogawa et al., 2015). PRs in six7 KO larvae don't express any of the four, green opsin (RH2-1 to RH2-4) genes, resulting in a loss of green cones that persists to adulthood. Interestingly, the six7 KO phenotype also includes reduced blue opsin expression (larval stage), increased number of rods (larvae), and a switch in red opsin expression (adults; Ogawa et al., 2015). Finally, red cone fate is determined by thyroid hormone receptor β2 (trβ2) expression (Suzuki et al., 2013) as knockdown of trβ2 results in a loss of red cones, but an increase in UV cones (Suzuki et al., 2013). Red cone loss is also selective in partial optokinetic response b (pob) mutants (Brockerhoff et al., 1997). pob zebrafish are red color blind due to rapid degeneration of red cones. PR loss in pob is due to a mutation in the pob gene, which codes for a protein required for red cone survival (Taylor et al., 2005).

Allison and colleagues have developed transgenic zebrafish lines in which a single class of PR can be chemically ablated (Fraser et al., 2013; Hagerman et al., 2016). These lines are key in determining how removal of one cone type may alter the existing PR mosaic as well as how the retina will respond. The zebrafish retina continues to grow throughout the life of the fish and is able to regenerate cells in response to damage (reviewed in Brockerhoff and Fadool, 2011). Chemical ablation of a single cone type in adult retina can trigger retinal regeneration (Fraser et al., 2013; Hagerman et al., 2016). For example, selective ablation of UV cones stimulates the formation of new UV cones, at a higher density than predicted (Fraser et al., 2013). This recovery and regeneration is rapid: behavioral responses (OMR) are restored in 72 h

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(Hagerman et al., 2016). Selective loss of blue cones, however, results in an even faster regenerative response, with behavioral responses restored in 24 h (Hagerman et al., 2016). The rapid regeneration capabilities of zebrafish retinal tissue, and the ability of this tissue to respond to the selective loss of a given cone type, is an important step in determining mechanisms that could be used to restore cell loss after retinal degeneration.

Finally, other studies have induced diseases with retinal complications using non-molecular mechanisms. For example, prolonged hyperglycemia, characteristic of diabetes, can be induced by either immersing zebrafish in a glucose solution (Gleeson et al., 2007; Alvarez et al., 2010) or through streptozotocin injection (Olsen et al., 2010; Intine et al., 2013), both of which result in changes to the retina, making zebrafish a model for diabetic retinopathy. In addition to retinal thinning (Gleeson et al., 2007; Olsen et al., 2010) and changes to retinal vasculature (Alvarez et al., 2010), there are also marked changes to cone PRs in hyperglycemic zebrafish retinas with double cones displaying the most severe alterations (Alvarez et al., 2010).

#### CONCLUSION

Zebrafish are capable of rich color processing due to the presence of four cone types that allow detection of light ranging from UV to red. After stimulation of the phototransduction cascade in cones, spectral signals are immediately processed and modified in outer retina through synapses with HC. BC signaling relays spectral information to inner retina, where multiple feedforward inputs converge on AC and GC and feedback inputs from AC further modify spectral signals. These ON-signals are faithfully relayed to the tectum. As a result, zebrafish are able to behaviorally discriminate color and display innate color preferences. This elaborate signaling, coupled to well-known retinal anatomy and targeted genetic manipulation, has resulted in zebrafish that exhibit mutation-induced red blindness (Brockerhoff et al., 1997), UV blindness (Alvarez-Delfin et al., 2009), and green blindness (Ogawa et al., 2015). Zebrafish also serve as models for vision disorders, such as diabetic retinopathy (Gleeson et al., 2007; Alvarez et al., 2010), retinitis pigmentosa (Baye et al., 2011), achromatopsia (Kennedy et al., 2007) and Leber's congenital amaurosis (Baye et al., 2011), making zebrafish a model of choice for study of human retinal disorders.

# AUTHOR CONTRIBUTIONS

AM, RN and VC all contributed to writing the article and selection of figures. Supported by funds from the College of Arts and Science and the American University Library (to VPC).

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in zebrafish. J. Comp. Neurol. 518, 4182–4195. doi: 10.1002/cne. 22447

cone ablation in zebrafish. PLoS One 11:e0166932. doi: 10.1371/journal.pone. 0166932

by zebrafish retinal ganglion cells. Neuron 52, 609–621. doi: 10.1016/j.neuron. 2006.10.004

retinal electrophysiology. PLoS One 9:e113321. doi: 10.1371/journal.pone. 0113321

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Meier, Nelson and Connaughton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Modulation of Hyperpolarization-Activated Inward Current and Thalamic Activity Modes by Different Cyclic Nucleotides

Maia Datunashvili <sup>1</sup> \*, Rahul Chaudhary <sup>1</sup> , Mehrnoush Zobeiri <sup>1</sup> , Annika Lüttjohann<sup>1</sup> , Evanthia Mergia<sup>2</sup> , Arnd Baumann<sup>3</sup> , Sabine Balfanz <sup>3</sup> , Björn Budde<sup>1</sup> , Gilles van Luijtelaar <sup>4</sup> , Hans-Christian Pape<sup>1</sup> , Doris Koesling<sup>2</sup> and Thomas Budde<sup>1</sup> \*

1 Institut für Physiologie I, Westfälische Wilhelms-Universität, Münster, Germany, <sup>2</sup> Institut für Pharmakologie und Toxikologie, Ruhr-Universität Bochum, Bochum, Germany, <sup>3</sup> Institute of Complex Systems, Forschungszentrum Jülich, Jülich, Germany, <sup>4</sup>Donders Centre for Cognition, Radboud University, Nijmegen, Netherlands

#### Edited by:

Lisa Mapelli, University of Pavia, Italy

#### Reviewed by:

Christian Wahl-Schott, Ludwig-Maximilians-Universität München, Germany Andrea Nistri, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Italy

#### \*Correspondence:

Maia Datunashvili<EMAIL_ADDRESS>Thomas Budde<EMAIL_ADDRESS>
Received: 03 August 2018 Accepted: 28 September 2018 Published: 24 October 2018

#### Citation:

Datunashvili M, Chaudhary R, Zobeiri M, Lüttjohann A, Mergia E, Baumann A, Balfanz S, Budde B, van Luijtelaar G, Pape H-C, Koesling D and Budde T (2018) Modulation of Hyperpolarization-Activated Inward Current and Thalamic Activity Modes by Different Cyclic Nucleotides. Front. Cell. Neurosci. 12:369. doi: 10.3389/fncel.2018.00369 The hyperpolarization-activated inward current, Ih, plays a key role in the generation of rhythmic activities in thalamocortical (TC) relay neurons. Cyclic nucleotides, like 3 0 ,50 -cyclic adenosine monophosphate (cAMP), facilitate voltage-dependent activation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels by shifting the activation curve of I<sup>h</sup> to more positive values and thereby terminating the rhythmic burst activity. The role of 3<sup>0</sup> ,50 -cyclic guanosine monophosphate (cGMP) in modulation of I<sup>h</sup> is not well understood. To determine the possible role of the nitric oxide (NO)-sensitive cGMP-forming guanylyl cyclase 2 (NO-GC2) in controlling the thalamic Ih, the voltagedependency and cGMP/cAMP-sensitivity of I<sup>h</sup> was analyzed in TC neurons of the dorsal part of the lateral geniculate nucleus (dLGN) in wild type (WT) and NO-GC2-deficit (NO-GC2<sup>−</sup>/<sup>−</sup>) mice. Whole cell voltage clamp recordings in brain slices revealed a more hyperpolarized half maximal activation (V<sup>1</sup>/<sup>2</sup>) of I<sup>h</sup> in NO-GC2<sup>−</sup>/<sup>−</sup> TC neurons compared to WT. Different concentrations of 8-Br-cAMP/8-Br-cGMP induced dose-dependent positive shifts of V<sup>1</sup>/<sup>2</sup> in both strains. Treatment of WT slices with lyase enzyme (adenylyl and guanylyl cyclases) inhibitors (SQ22536 and ODQ) resulted in further hyperpolarized V<sup>1</sup>/<sup>2</sup>. Under current clamp conditions NO-GC2<sup>−</sup>/<sup>−</sup> neurons exhibited a reduction in the Ih-dependent voltage sag and reduced action potential firing with hyperpolarizing and depolarizing current steps, respectively. Intrathalamic rhythmic bursting activity in brain slices and in a simplified mathematical model of the thalamic network was reduced in the absence of NO-GC2. In freely behaving NO-GC2<sup>−</sup>/<sup>−</sup> mice, delta and theta band activity was enhanced during active wakefulness (AW) as well as rapid eye movement (REM) sleep in cortical local field potential (LFP) in comparison to WT. These findings indicate

**Abbreviations:** AW, active wakefulness; DSWS, deep slow wave sleep; EEG, electroencephalogram; HCN, hyperpolarizationactivated cyclic nucleotide-gated channels; IC, internal capsule; Ih, hyperpolarization activated current; LFP, local field potential; LSWS, light slow wave sleep; LTS, low-threshold Ca2<sup>+</sup> spike; NO, nitric oxide; nonREM, non-rapid eye movement sleep; nRT, reticular thalamus; PIR, passive infrared recording system; REM, rapid eye movement sleep; Rin, input resistance; RMP, resting membrane potential; SSC, somatosensory cortex; SWS, slow wave sleep; TC, thalamocortical; V1/2, half maximal activation; VB, ventrobasal thalamus.

that cGMP facilitates I<sup>h</sup> activation and contributes to a tonic activity in TC neurons. On the network level basal cGMP production supports fast rhythmic activity in the cortex.

Keywords: thalamus, dLGN, cyclic nucleotides, NO-GC2, HCN channels, I<sup>h</sup> current, IKIR current, slow oscillations

# INTRODUCTION

Cyclic nucleotides, like cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) bind to the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and stabilize their open state (Zagotta et al., 2003). HCN channels represent the molecular basis of the hyperpolarizationactivated current, termed I<sup>h</sup> (Pape, 1996). HCN isoforms (HCN1-4) reveal different characteristics with respect to voltage dependency, activation kinetics and cyclic nucleotide sensitivity (He et al., 2014). HCN2 and the HCN4 isoforms (Ludwig et al., 2003; Notomi and Shigemoto, 2004) are strongly modulated by cAMP in thalamocortical (TC) relay neurons (Kanyshkova et al., 2009, 2012). A number of brain rhythms are controlled by HCN channels, and epileptogenesis in the TC system is accompanied by changes in HCN expression levels and altered properties of Ih, including cAMP—sensitivity (Budde et al., 2005; Kanyshkova et al., 2012). In thalamic neurons of the rodent brain, I<sup>h</sup> contributes to the resting membrane potential (RMP) and determines cell type-specific firing patterns and postnatal changes in HCN isoform expression profiles are accompanied by the maturation of sleep-related slow oscillations (Meuth et al., 2006; Kanyshkova et al., 2009).

In the dorsal lateral geniculate nucleus (dLGN), neuronal nitric oxide synthase (nNOS) was found in interneurons and cholinergic afferents arising from the ascending brainstem system (Gabbott and Bacon, 1994). NO exerts an important role in behavioral state-dependent gating of visual information and regulating TC oscillations (Pape and Mager, 1992; Yang and Cox, 2008). Thalamic NO concentrations increase during wakefulness and rapid eye movement (REM) sleep and decrease during slow wave sleep (SWS; Burlet and Cespuglio, 1997), pointing to a possible role of NO in regulation of arousal and REM sleep.

NO has been identified as an important modulator of HCN channel activity mediated by the NO-sensitive soluble NO-GC1 and NO-GC2 (Russwurm et al., 2013). Although cyclic nucleotide-dependent modulation of I<sup>h</sup> in the thalamus under physiological and pathophysiological conditions has been assessed before (Pape, 1996; He et al., 2014), it is not known whether I<sup>h</sup> in TC neurons is under the simultaneous control of both cAMP and cGMP. To address this issue, we studied I<sup>h</sup> properties in wild type (WT) and NO-GC2-deficient mice, in the presence of adenylyl and guanylyl cyclase inhibitors as well as by intracellular application of cyclic nucleotides. By combining in vitro voltage and current clamp methods, we examined the characteristics of I<sup>h</sup> current as well as the passive and active properties of NO-GC2−/<sup>−</sup> TC cells. By means of in vitro and in vivo field potential recordings we studied intrathalamic and cortical activities. Based on these results the present study provides a detailed description of the role of cGMP in the regulation of intrathalamic and cortical activities.

# MATERIALS AND METHODS

#### Preparation of Coronal dLGN Slices

All animal work has been approved by local authorities (review board institution: Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen; approval ID: 84-02.04.2015.A574, 84-<IP_ADDRESS>.026). Experiments were performed on NO-GC2-deficient mice (Mergia et al., 2006) ranging in age from postnatal day P16 to P35. These mice lack the α2 subunit of NO-dependent soluble guanylyl cyclase while the α1 and β1 subunits can assemble to enzymatically active NO-GC1. NO-GC2−/<sup>−</sup> mice were produced by breeding heterozygous mice or homozygous mice of the F1 generation. Genotyping of the mice was performed by PCR analysis of DNA extracted from ear biopsies. As the knockout strain was backcrossed over 10 generations onto C57BL/6J background, C57BL/6J mice (postnatal day P16 to P35) were used as WT controls (WT). Mice were anesthetized with isoflurane (3.5 vol%) and sacrificed. After surgically removing their skull cap caudal to bregma, a block of brain tissue containing the thalamus was removed from the cranial vault and submerged in ice-cold aerated (O2) saline containing (in mM): sucrose, 200; PIPES, 20; KCl, 2.5; NaH2PO4, 1.25; MgSO4, 10; CaCl2, 0.5; dextrose, 10; pH 7.35, with NaOH. Thalamic slices (250–300 µm thick) were prepared as coronal sections on a vibratome. Slices were transferred to a holding chamber and kept submerged (at 30◦C for 30 min, thereafter at room temperature) in artificial cerebrospinal fluid (ACSF) containing (in mM): NaCl, 125; KCl, 2.5; NaH2PO4, 1.25; NaHCO3, 24; MgSO4, 2; CaCl2, 2; dextrose, 10; pH adjusted to 7.35 by bubbling with carbogen (95% O<sup>2</sup> and 5% CO<sup>2</sup> gas mixture).

## Voltage Clamp Recordings

Recordings were done on visually identified TC neurons of the dLGN in a solution containing (in mM): NaCl, 120; KCl, 2.5; NaH2PO4, 1.25; HEPES, 30; MgSO4, 2; CaCl2, 2; dextrose, 10; pH 7.35 adjusted with HCl. For some recordings, bicarbonate (NaHCO3) buffered ACSF was used (in mM): NaCl, 125; KCl, 2.5; NaH2PO4, 1.25; NaHCO3, 24; MgSO4, 2; CaCl2, 2; dextrose, 10; pH adjusted to 7.35 by bubbling with carbogen. In order to block inward rectifying K<sup>+</sup> and K2<sup>P</sup> channels, 0.5 mM BaCl<sup>2</sup> was added to the solution. Whole-cell recordings were made from the soma of TC neurons at 30–32◦C. Membrane currents were measured with glass microelectrodes pulled from borosilicate glass capillaries (GC150T-10; Clark Electromedical Instruments, Pangbourne, UK) filled with (in mM): K-gluconate, 95; K3-citrate, 20; NaCl, 10; HEPES, 10; MgCl2, 1; CaCl2, 0.5; BAPTA, 3; Mg-ATP, 3; Na2-GTP, 0.5. The internal solution was set to a pH of 7.25 with KOH and an osmolality of 295 mOsm/kg. A 0.2 µm pore size sterile filter (MP, Hennigsdorf, Germany) was placed between the needle and the syringe to fill the electrodes. Patch electrodes were connected to an EPC-10 amplifier (HEKA Elektronik, Lamprecht, Germany) via a chlorinated silver wire. The resistances of electrodes were in the range of 2.5–3.5 MΩ. Access resistances were between 8 MΩ and 20 MΩ. Series resistance compensation of >50% was routinely applied. Recordings started 2–3 min after obtaining the whole-cell configuration. Voltage clamp experiments were controlled by the software Pulse or PatchMaster (HEKA Elektronik) operating on an IBM-compatible personal computer. All recordings were corrected offline for a liquid junction potential of 10 mV (V<sup>M</sup> = V<sup>P</sup> – 10 mV; with V<sup>M</sup> = membrane potential and V<sup>P</sup> = pipette voltage). Care was exercised to monitor series resistance and recordings were terminated whenever a significant increase (>20%) occurred.

The voltage protocol used to examine I<sup>h</sup> (Kanyshkova et al., 2012) was designed in order to increase the stability of whole cell recordings and account for increasingly fast activation kinetics of the current. Therefore the pulse length was shortened by 500 ms with increasing hyperpolarization (3.5 s pulse length at −130 mV). Steady-state activation of Ih, p(V), was estimated by normalizing the mean tail current amplitudes (I) 50–100 ms after stepping to a constant potential from a variable amplitude step using the following function (equation 1):

$$P(\text{V}) \;= \; \text{(I} - \text{I}\_{\text{min}}) / \; (\text{I}\_{\text{max}} - \text{I}\_{\text{min}}) \tag{1}$$

with Imax being the tail current amplitude for the voltage step from −130 mV to −100 mV and Imin for the voltage step from −40 mV to −100 mV, respectively. I<sup>h</sup> activation was well accounted for by a Boltzmann function of the following form (equation 2):

$$\mathbf{p}\left(\mathbf{V}\right) = 1/\left(1 + \exp\left(\left(\mathbf{V} - \mathbf{V}\_{1/2}\right)/\mathbf{k}\right)\right) \tag{2}$$

where V1/<sup>2</sup> is the voltage of half-maximal activation and k the slope factor.

The current density was calculated by dividing the I<sup>h</sup> amplitude at −130 mV (i.e., subtracting the instantaneous current amplitude from the steady-state current) by the membrane capacitance obtained during whole cell recordings.

The time course of I<sup>h</sup> activation in TC neurons at a temperature of 30–32◦C was best approximated by the following double-exponential equation:

$$I\_{\rm h}(t) := A\_1(1 - \exp^{-t/\tau \text{fast}}) + A\_2(1 - \exp^{-t/\tau \text{slow}}), \quad \text{(3)}$$

Where Ih(t) is the time (ms), A<sup>1</sup> and A<sup>2</sup> are current amplitudes (pA), and τfast and τslow are time constants (ms), respectively. Currents evoked by voltage steps to −130 mV were analyzed.

A series of hyperpolarizing (500 ms) voltage steps in −10 mV increments were injected from the holding potential of −60 to −130 mV in order to evoke inwardly rectifying potassium (IKIR) current. IKIR currents were isolated from I<sup>h</sup> by applying 20 µM ZD7288. IKIR amplitudes were measured manually as the difference of the peak and the steady state current at the beginning and at the end of voltage pulses, respectively.

#### Current Clamp Recordings and Determination of the Intrinsic Electrophysiological Properties

The active and passive membrane properties of TC neurons were determined in current clamp mode. Recordings were performed at RMP in Ba2+-free extracellular solution containing (in mM): NaCl, 125; KCl, 2.5; NaH2PO4, 1.25; NaHCO3, 24; MgSO4, 2; CaCl2, 2; dextrose, 10; pH adjusted to 7.35 by bubbling with carbogen. In order to compare the effects of different buffers and Ba2<sup>+</sup> ions on intrinsic membrane properties of TC neurons, 0.5 mM BaCl<sup>2</sup> was added in HEPES and NaHCO<sup>3</sup> buffered extracellular solutions. Analysis was performed according to established procedures (Leist et al., 2016). Only cells with overshooting APs were included for analysis. The stimulation protocol contained hyperpolarizing and depolarizing current steps (1 s duration, from −230 pA to +370 pA with 40 pA increments; for 8-Br-cGMP experiments, a protocol with steps of 1 s duration, from −120 pA to +260 pA with 20 pA increments, was used). Membrane input resistance (Rin) was deduced from the slope of the current-voltage (I-V) relationship obtained from current injections of −30 and 50 pA. Membrane time constants (τm) were obtained by fitting single or double exponentials (FitMaster, HEKA Elektronik) to negative voltage deflections induced by hyperpolarizing current injections of −30 pA. The Ih-dependent anomalous rectification (or voltage sag) of current injection of −230 pA was calculated as the change between the maximal and steady state voltage deflection (at the end of hyperpolarizing current injection). APs were detected manually by setting an amplitude threshold (Vthresh). FitMaster (HEKA Elektronik) and Clampfit 10.7 (Axon Molecular Devices, Sunnyvale, CA, USA) software was used for the analyses.

#### Immunofluorescence

WT mice were transcardially perfused with 4% (w/v) phosphatebuffered paraformaldehyde (PFA). Brains were removed and post fixed overnight in 4% PFA and later in 30% (w/v) sucrose for 48–72 h. Free-floating coronal sections (40 µm) were cut and slices were collected in phosphate-buffered saline (PBS). Sections were rinsed three times for 10 min in PBS. Slices were then incubated for 2 h in PBS supplemented with 10% (v/v) normal goat serum, Triton-X100 (0.3% (w/v)), and 3% (w/v) bovine serum albumin (BSA). Finally sections were incubated with primary antibodies overnight at 4◦C. Polyclonal rabbit (rb) anti-NO-GCα<sup>2</sup> (1:1,1000; ab42108, Abcam, USA) antibody was utilized to detect a localization of dLGN. Several studies using this antibody revealed detecting of a single protein band of the correct size in quantitative Western blot experiments, thereby pointing to specificity of the reaction (Backer et al., 2008; Thoonen et al., 2015; product data sheet Abcam). In a similar way the monoclonal mouse anti-postsynaptic density protein 95 (PSD95; 1:1,000; 10011435, Cayman, USA) antibody which marks the postsynaptic membrane revealed a single protein band in Wetsern blots (Yao et al., 2004). After incubation with primary antibodies, sections were washed three times for 10 min in PBS and then transferred to the secondary antibody solution (Alexa Fluor 488 goat anti-rabbit-IgG, 1:1,000 and Alexa Fluor 568 goat anti-mouse-IgG, 1:1,000) for 2 h. Finally, sections were washed three times for 10 min in PBS and mounted with a mounting medium (VECTASHIELD, Vector Laboratories Inc., Burlingame, CA, USA) for confocal microscopy (Nikon eC1plus) equipped with a CFI75 LWD × 16/0.8 NA objective (Nikon). Omission of primary or secondary antibodies from the staining procedure resulted in a lack of fluorescent signals.

### Quantification of cGMP in Tissue Samples