Experimental Neurology 261 (2014) 510–517
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Experimental Neurology journal homepage: www.elsevier.com/locate/yexnr
Regular Article
Functional regulation of neuronal nitric oxide synthase expression and activity in the rat retina☆ Lais Takata Walter a, Guilherme Shigueto Vilar Higa a,b, Christian Schmeltzer c, Erica Sousa a, Erika Reime Kinjo a, Sten Rüdiger c, Dânia Emi Hamassaki d, Giselle Cerchiaro e, Alexandre Hiroaki Kihara a,b,⁎ a
Núcleo de Cognição e Sistemas Complexos, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Brazil Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, Brazil Institute of Physics, Humboldt University at Berlin, Germany d Departamento de Biologia Celular e do Desenvolvimento, Instituto de Ciências Biomédicas, Universidade de São Paulo, Brazil e Núcleo de Cognição e Sistemas Complexos, Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Brazil b c
a r t i c l e
i n f o
Article history: Received 26 May 2014 Revised 16 July 2014 Accepted 29 July 2014 Available online 6 August 2014 Keywords: CNS Development Dark-adaptation Visual system Perception Vision Synaptic plasticity Synapse ROS
a b s t r a c t In the nervous system within physiological conditions, nitric oxide (NO) production depends on the activity of nitric oxide synthases (NOSs), and particularly on the expression of the neuronal isoform (nNOS). In the sensory systems, the role of NO is poorly understood. In this study, we identified nNOS-positive cells in the inner nuclear layer (INL) of the rat retina, with distinct characteristics such as somata size, immunolabeling level and location. Employing mathematical cluster analysis, we determined that nNOS amacrine cells are formed by two distinct populations. We next investigated the molecular identity of these cells, which did not show colocalization with calbindin (CB), choline acetyltransferase (ChAT), parvalbumin (PV) or protein kinase C (PKC), and only partial colocalization with calretinin (CR), revealing the accumulation of nNOS in specific amacrine cell populations. To access the functional, circuitry-related roles of these cells, we performed experiments after adaptation to different ambient light conditions. After 24 h of dark-adaptation, we detected a subtle, yet statistically significant decrease in nNOS transcript levels, which returned to steady-state levels after 24 h of normal light–dark cycle, revealing that nNOS expression is governed by ambient light conditions. Employing electron paramagnetic resonance (EPR), we demonstrated that dark-adaptation decreases NO production in the retina. Furthermore, nNOS accumulation changed in the dark-adapted retinas, with a general reduction in the inner plexiform layer. Finally, computational analysis based on clustering techniques revealed that dark-adaptation differently affected both types of nNOS-positive amacrine cells. Taken together, our data disclosed functional regulation of nNOS expression and activity, disclosing new circuitry-related roles of nNOS-positive cells. More importantly, this study indicated unsuspected roles for NO in the sensory systems, particularly related to adaptation to ambient demands. © 2014 Elsevier Inc. All rights reserved.
Introduction As a gaseous, diffusible chemical transmitter, nitric oxide (NO) may interact with intracellular targets to trigger several signal transduction pathways (Benarroch, 2011). Besides classically related to the modulation of neurotransmission and synaptic plasticity (Calabrese et al., 2007), NO seems to play additional roles in the central nervous system (CNS), such ☆ Funding: This work was supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, #2008/55210-1), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Universidade Federal do ABC (UFABC), and DFG (#IRTG1740). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ⁎ Corresponding author at: Núcleo de Cognição e Sistemas Complexos, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, R. São Paulo, s/n — Jardim Antares, 09606-070 São Bernardo do Campo, SP, Brazil. E-mail address: [email protected] (A.H. Kihara).
http://dx.doi.org/10.1016/j.expneurol.2014.07.019 0014-4886/© 2014 Elsevier Inc. All rights reserved.
as in developmental processes (Contestabile and Ciani, 2004) and in the progression of neurodegenerative diseases (Bonnin et al., 2012; Hall et al., 2012; Virarkar et al., 2013). Indeed, NO is essential for CNS homeostasis, as a product from the conversion of L-arginine to L-citrulline by the members of the NO synthase (NOS) family of proteins (Xu et al., 2004). In this regard, previous studies determined the expression of neuronal NOS (nNOS) in the retina of several species. In rats, it seems consensual that nNOS accumulates in amacrine cells, although the molecular identity of these cells remains subject to debate (Cellerino et al., 1999; Haverkamp et al., 2000; Kim et al., 1999). Indeed, NO plays several roles in the retina, including regulation of phototransduction and cone glutamate release (Savchenko et al., 1997). In addition, NO inhibits dopamine release (Bugnon et al., 1994) and decreases the intercellular homologous coupling between horizontal cells (Xin and Bloomfield, 2000) and the heterologous coupling between cone bipolar and amacrine AII cells (Mills and Massey, 1995). Furthermore, regulation of nNOS
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expression and activity in physiological conditions has been investigated. Notably, increasing evidences indicated that NO modulates light responses in the retina (Pang et al., 2010). For example, it was shown that NO donors increased calcium currents in rods, with NOS inhibition favoring rod hyperpolarization (Noll et al., 1994), supporting a modulation of signal transmission to second-order neurons (Kourennyi et al., 2004). In this study, we combined morphological analysis with computational clustering techniques to determine the number and molecular identity of nNOS amacrine cell populations. Moreover, we directly measured NO production in the retina after dark-adaptation using electron paramagnetic resonance. Finally, combining experimental methods and mathematical analysis, we were able to quantify the effects of darkadaptation on nNOS gene expression and protein distribution in distinct retinal layers and specific amacrine cell populations. Taken together, our results contribute for disclosing new roles for NO in the sensory systems. Methods Ethics statement Experiments with animals were conducted in accordance with the guidelines of the NIH and the Brazilian Scientific Society for Laboratory Animals. Experimental protocol was approved by the Ethics Committee in Animal Experimentation of the Institute of Biomedical Sciences/University of São Paulo (ICB/USP). All animals were housed in cages with free access to food and water throughout the study. Animal procedures Experiments were carried out with adult Long Evans rats (Rattus novergicus) kept on a 12 h light/dark cycle (light phase 80–100 lx) with lights on at 06:00 a.m. Rats were euthanized with a lethal dose of ketamine (30 mg/100 g of body weight, i.m., Parke-Davis, Ann Arbor, MI, USA) and xylazine (2 mg/100 g, i.m., West Haven, CT, USA) between 11:00 and 12:00 a.m. In dark-adaptation experiments, there were four groups of animals: Control group, which are adult animals from 12 h light/dark cycle, and those that were kept in the dark for 3 h, 24 h, or 24 h followed by a 12:12 light/dark cycle, all euthanized between 11:00 and 12:00 a.m. Next, retinas were removed for different methodologies.
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were performed as follows: after initial activation at 50 °C for 2 min and 95 °C for 10 min, cycling conditions were 95 °C, 10 s and 60 °C, 1 min. Dissociation curves of PCR products were obtained by heating samples from 60 °C to 95 °C, in order to evaluate primer specificity. PCR statistical analysis Relative quantification of target gene expression was evaluated using the comparative CT method as previously described in detail (Kihara et al., 2008; Medhurst et al., 2000). In the present study, control refers to animals from the 12:12 light/dark cycle. Values were entered into a one-way analysis of variance (ANOVA), followed by pairwise comparisons (Tukey's HSD test), with the significance level set at 5%. Immunohistochemistry Eyes from six rats were dissected out and the retinas were fixed for 30 min in 4% paraformaldehyde (PFA) in phosphate buffer 0.1 M pH 7.3 (PB), and cryoprotected in 30% sucrose solution for at least 24 h at 4 °C. After embedding in O.C.T. compound (Sakura Finetek, Torrance, CA, USA) they were cut transversally (12 μm) on a cryostat. Retinal sections were incubated overnight with primary antibodies in a solution containing 5% normal donkey serum and 0.5% Triton-X 100 in PBS at room temperature. All antibodies and specific concentrations used in this study are listed in Table 1. After several washes, retinal sections were incubated with donkey antiserum against rabbit, mouse or goat IgG tagged to Alexa 488 (1:250–1:500, Invitrogen) diluted in 3% normal donkey serum containing 0.5% Triton X-100 in PBS for 2 h at room temperature. For double-labeling experiments, we used secondary antibodies conjugated to Alexa 546 and Alexa 647 (1:500, Invitrogen). Controls for the experiments consisted of the omission of primary antibodies; no staining was observed in these cases. Counter-staining of retinas was achieved using 4′,6-diamidino-2-phenylindole (DAPI), by incubating sections at room temperature for 10 min. After washing, the tissue was mounted using Vecta Shield (Vector Labs, Burlingame, CA, USA), and analyzed in a Nikon TS100F inverted microscope (Nikon Instruments Inc., Melville, NY, USA). Figures were mounted with Adobe Photoshop CS. Manipulation of the images was restricted to brightness and contrast adjustments of the whole image.
RNA isolation, cDNA synthesis and Real-Time PCR
Image quantification
Retinas (n = 6) were directly homogenized in 1 ml TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and total RNA was extracted following the manufacturer's suggested protocol and as previously described (Belmonte et al., 2006; Kihara et al., 2005). In brief, following two chloroform extraction steps, RNA was precipitated with isopropanol and the pellet washed twice in 70% ethanol. After air-drying, RNA was resuspended in DEPC-treated water and the concentration of each sample obtained from A260 measurements. Residual DNA was removed using DNase I (Amersham, Piscataway, NJ, USA) following the manufacturer's protocol. Quantitative analysis of gene expression was carried out with a Rotor-Gene 6000 Real-Time Rotary Analyzer (Corbett Robotics Inc., San Francisco, CA, USA) with specific primers for rat nNOS (forward, 5′- CCAATGTTCACAAAAAACGAGTCT -3′; reverse, 5′- TCGGCT GGACTTAGGGCTTT -3′). GAPDH (forward, 5′-TTCAAAAGAGGGCGCACTTC3′; reverse, 5′-GCGCACTCTGGTTTTTATTTCA-3′) expression was determined as internal control. For each 20 μl reverse transcription reaction, 4 μg total RNA was mixed with 1 μl oligodT primer (0.5 μg; Invitrogen) and incubated for 10 min at 65 °C. After cooling on ice the solution was mixed with 4 μl 5 × first strand buffer, 2 μl of 0.1 M DTT, 1 μl of dATP, dTTP, dCTP and dGTP (each 10 mM), and 1 μl SuperScript III reverse transcriptase (200 U; Invitrogen) and incubated for 60 min at 50 °C. Reaction was inactivated by heating at 70 °C for 15 min. All PCR assays
Image analysis (n = 6) was performed with Image-Pro Plus (Media Cybernetics, Bethesda, MD, USA) and NIS Elements (Nikon Instruments Inc.), as previously described (Paschon et al., 2012). After channel separation (RGB) of the color images, we performed bitmap analysis. X–Y axis analysis generated numerical appended data files corresponding to pixel values. The bitmap analysis was used to view the pixel values of the active window (or area of interest, AOI) in numeric format, where values correspond to the brightness of the pixels. In some cases, AOI was defined by the labeling of one channel, and analysis was performed in another channel, as for instance, labeling of nNOS in the green channel, defined by DAPI labeling in the blue channel. Values were exported to
Table 1 Primary antibodies used in this study. Antibody
Manufacturer
Product number
Dilution
Rabbit anti-nNOS Mouse anti-PKC Goat anti-ChAT Mouse anti-parvalbumin Mouse anti-calbindin Goat anti-calretinin
Sigma BD Transduction Millipore Sigma Sigma Millipore
N7280 612698 AB144P P3088 C9848 AB1550
1:800 1:200 1:100 1:100 1:100 1:100
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Excel (Microsoft, Redmond, WA, USA) for the appropriate mathematical analyses. Cluster analysis From retinal images (n = 6) we measured three distinct parameters of individual cells: brightness, width, and distance to the inner border of the inner nuclear layer. These attributes span a three-dimensional vector space in which each cell corresponds to a data point. The data was clustered around two different centers that can be identified by a k-means clustering analysis. In short, the algorithm works as follows: first, the number of clusters k was assumed a priori. Then, the algorithm finds the clusters and their corresponding centers by minimizing the d distortion. The d distortion is the sum of squared distances between the cluster centers and their assigned data points. In order to estimate the number of clusters in our dataset, we employed a previously developed method (Sugar and James, 2003), which applies rate distortion theory. In this method, a distortion curve d(k) for different values of k is generated by the k-means algorithm. The distortion curve is then rescaled by the negative power −p/2, based on the dimensionality p of the dataset. In our three-dimensional dataset, d(k) → d−3/2(k). Electron paramagnetic resonance (EPR) EPR spectra were recorded in a Bruker EMX Plus Electron-Spin Resonance Spectrometer System instrument, operating at X-band frequency, in the Multiuser Facility Center at UFABC. We utilized standard Wilmad quartz tubes in a high sensitive cavity. A digital temperature control system using liquid and gaseous nitrogen was used to control the temperature at 100 K. DPPH (α,α′-diphenyl-β-picrylhydrazyl) was used for calibrating frequency (g = 2.0036) with samples in a frozen water solution at 100 K. The quantification of radical production was measured with double signal integration of standard aqueous solutions of 4-hydroxy-2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPOL), in the same conditions as in the experiments. Typical conditions used in these measurements were: frequency 9.41 GHz, power 20.00 mW, 4.48 × 103 gain, 5 G modulation amplitude, time constant 20.48 ms, sweep time 80.92 ms, 12 scans, resolution 1024 points, temperature (100 ± 1) K. Pools of 8 retinas (n = 3) were isolated from adult rats after 24 h of dark-adaptation or controls maintained in a 12:12 light/dark cycle. Freshly prepared 1 ml solution of 25 mM HEPES buffer pH 7.4 containing 500 μM Fe(SO4)2 and 500 μM of N,N-diethyldithiocarbamate (DETC) were incubated with retinas for 30 min at 37 °C. Retinas were sonicated, 10% glycerol was added, and samples were frozen in a −80 °C freezer. To calibrate and validate the method, aorta from adult rats was incubated at the same conditions as the retinas (Pieper, 2007). Results Two distinct populations of amacrine cells accumulate nNOS In accordance to previous studies, our immunohistochemistry experiments detected nNOS labeling predominantly in the inner nuclear layer (INL), as well as in the ganglion cell layer (GCL), and the inner plexiform layer (IPL) (Blom et al., 2012; Blute et al., 1997; Darius et al., 1995; Eldred and Blute, 2005; Fischer and Stell, 1999; Haverkamp and Eldred, 1998; Kim et al., 1999). Interestingly, nNOS-positive cells in the INL were mainly observed in both outer and inner borders of the layer (Fig. 1A). We were able to discriminate nNOS-positive cells with different labeling levels (Fig. 1B), and distinct morphologies and positions in the INL (Figs. 1C and D). In order to determine the number of nNOS populations, we performed mathematical analysis using three parameters, i) brightness, ii) width (of the soma) and iii) distance (from the cell center to the inner border of INL). These attributes were plotted in a threedimensional vector space in which each cell corresponds to a data point (Fig. 1E). The data points were clustered around two different
centers and this result was applied to the rate distortion theory (Fig. 1F), which corroborated for the evidence that there are two populations of positive nNOS cells in the inner border of INL. Due to the morphological characteristics, cells corresponding to the red point dataset (width = 8.77 ± 0.09; brightness = 26.05 ± 0.84; distance = 5.06 ± 0.07) were collectively named “Round” cells, whereas the blue dataset (width = 11.45 ± 0.16; brightness = 70.15 ± 2.57; distance = 9.15 ± 0.15) corresponded to the “Balloon” cells. Horizontal cells, bipolar cells and specific amacrine cells do not accumulate nNOS In order to determine the presence of nNOS in specific retinal cell types, we performed double-labeling experiments. We did not detect colocalization of calbindin (CB), a marker for horizontal cells in the rodent retina (Oguni et al., 1998; Wassle, 2004), in nNOS-positive cells located in the outer margin of the inner nuclear layer (Figs. 2A–E). Similarly, we failed to detect colocalization of nNOS and PKC, a marker for rod bipolar cells (Greferath et al., 1990). Although in the pixel profile and in high magnification images it was possible to observe some red and green channel superposition, which clearly originated from distinct cells (Figs. 2F–K). Finally, we were not able to detect accumulation of nNOS in ChAT- and parvalbumin (PV)-positive cells, markers for starburst and AII glycinergic amacrine cells (Kunzevitzky et al., 2013; Wassle, 2004), respectively (Figs. 2L–U). Calretinin-positive amacrine cells accumulate nNOS In order to further analyze the molecular identity of nNOS amacrine cells, we employed another antibody raised against calretinin (CR), which in the inner border of the inner nuclear layer is a marker for AII amacrine cells, diffuse amacrine cells and A19 (Kolb et al., 2002; Wassle, 2004). In these experiments, we observed that some CR-positive cells also accumulated nNOS (Figs. 3A–B). Interestingly, distinct nNOSpositive cells were also CR-positive, including those that we previously classified as round (Figs. 3C–E) and balloon (Figs. 3F–H) cells. However, colocalization degree of both round and balloon nNOS-positive cells was only partial, 30.78 ± 2.95% and 38.96 ± 4.49%, respectively (Fig. 3I). Ambient light conditions govern nNOS gene expression in a reversible manner To access the functional regulation of nNOS in the retina, we carried out dark-adaptation experiments. Experiments were performed in 4 groups: control, 3 and 24 h of dark-adaptation, and 24 h of darkadaptation followed by 24 h in the 12:12 light/dark cycle. Significant downregulation (2^−1.02 ± 0.26, −50.85 ± 6.80%, P b 0.05) of nNOS transcripts was observed after 24 h of dark-adaptation (Figs. 4A–B). Interestingly, 24 h after the return to the 12:12 cycle the gene expression levels returned approximately to the steady-state, control levels. Taking together, these results revealed that nNOS gene expression levels depend on the ambient light conditions. GAPDH gene expression levels were used as internal control (Fig. 4C). Dark-adaptation downregulates NO production in the retina Downregulation of nNOS transcripts triggered by dark rearing might indicate that NO plays a role in retinal adaptation mechanisms. Thus, we next aimed to directly measure NO production in the retina after darkadaptation. With this purpose, we employed the spin trap technique to capture the NO free radical generated in the rat retinas. The spin trap Fe(DETC)2 is able to enter in the plasmatic membrane and form the stable radical adduct complex NO–Fe(DETC)2 easily identified by EPR spectra (Mikoyan et al., 1997; Piehl et al., 2007; Shen et al., 1998; Tsuchiya et al., 1996). The g-factor to identify this complex has already been established, g = 2.038, and we used this value to determine the NO concentration in the samples. In tissues, other dinitrosyl iron species
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Fig. 1. Neuronal nitric oxide synthase (nNOS) immunolabeling in transverse sections of the rat retina. (A) In transverse sections of the rat retina counterstained with 4′,6-diamidino-2phenylindole (DAPI, blue), nNOS labeling (green) was mainly observed in the inner plexiform layer (IPL) and inner nuclear layer (INL). (B) In the inner border of the INL, we observed nNOS-positive cells with distinct characteristics, including width and brightness, as shown in the pixel profile corresponding to the horizontal line shown in A. We observed the presence of both weak (white arrows) and strong signals (white arrowhead). (C, D) From high magnification of selected areas 1 and 2, we observed that the center of cells' soma were located at different distances from the inner border of INL. (E) Three-dimensional representation of the cells according to width, brightness and distance (from the inner border of the INL). The dataset was segregated using k-means algorithm into k = 2 clusters (blue/balloon cells and red/round cells). The position of centroids is represented by gray dots. (F) Rescaled distortion curve (gray squares) and jumps in the rescaled distortion curve (black dots). The highest jump occurs at k = 2, which strongly indicates the presence of two different clusters in the dataset. (G) According to the projection of the centroid in the three axes, width = 8.77 ± 0.09; brightness = 26.05; ± 0.84; and distance = 5.06 ± 0.07 for the red dataset and width = 11.45 ± 0.16; brightness = 70.15 ± 2.57; and distance = 9.15 ± 0.15 for the blue dataset, values were used for a computational design of the typical round (dark green) and balloon (light green) cells. Red and black arrows indicated typical width and position of round and balloon cells, respectively. Scale bar = 20 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
(DNIC) may be present in the samples, as we detected in our spectra. In Fig. 5 we can observe spectra from both representative control (Fig. 5A) and 24 h dark-adapted (Fig. 5B) retinas. To calculate the concentration of the radical adduct trapped, we used stable free radical solutions of TEMPOL radical, and the signal area of spectra was integrated and compared with NO–Fe(DETC)2. The amount of spin adduct formed is proportional to the intensity of the EPR signal. Following this quantification, it was possible to determine that NO production is higher in controls when compared to dark-adapted retinas, 7.11 ± 0.99 and 4.41 ± 0.21, P b 0.05, respectively (Fig. 5C).
labeling in sublamina off, mainly. Thus, we quantified both off and on sublaminas and calculated the ratio (Fig. 6F). Values obtained from control and 24 h dark-adaptation groups were not statistically different (0.98 ± 0.03 and 0.98 ± 0.01, respectively). When values for off (61.15 ± 2.23 vs. 58.88 ± 1.00) and on (61.71 ± 3.68 vs. 57.42 ± 1.43) sublaminas from control and 24 h dark-adaptation groups were compared, we found a small decrease of the brightness in both sublaminas, although not statistically significant (Fig. 6G).
Dark-adaptation differentially affects nNOS accumulation in balloon and round cells
Dark-adaptation changes nNOS distribution in specific retinal layers Considering that ambient light conditions regulate nNOS gene expression and NO production, we analyzed the distribution of nNOS in the retina after 24 h of dark-adaptation. When compared to controls (Figs. 6A–B), it is possible to see that dark-adapted retinas apparently show faint, weaker labeling in the IPL (Figs. 6C–D), as revealed by the pixel intensity profiles. In addition, we also performed quantification of pixel intensity to confirm changes in nNOS labeling. We quantified the brightness and calculated the IPL/INL ratio for both groups. Values obtained for the control were significant higher when compared to the dark-adaptation group, (1.37 ± 0.07 and 1.11 ± 0.03, respectively; P b 0.01; Fig. 6E). As quantification was taken as the ratio of the two layers, we were interested in knowing where this decrease occurred. We started evaluating the IPL, which apparently showed a decrease of
Since we determined the existence of two distinct nNOS amacrine cell populations, and that nNOS gene expression is regulated by ambient light conditions, we examined whether dark-adaptation differentially regulates nNOS in these amacrine cells. When comparing nNOS labeling in control (Figs. 7A–D) vs. 24 h DA (Figs. 7E–H) groups, it is possible to see that dark-adaptation triggers a general reduction in the labeling of both populations. To specifically assess how the intensity labeling of the two populations changed in the dark, we performed computational cluster analysis. Once cells were classified as previously described, we observed that in the controls (Fig. 7I), data points were displaced upwards when compared to the dataset of the group kept in the dark (Fig. 7J). Thereafter, we evaluated the brightness parameter through the relation of balloon/round cells (Fig. 7K). Medians from several retinal slices were obtained for each
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Fig. 2. Neuronal nitric oxide synthase (nNOS) and specific retinal markers in transverse sections of the rat retina. In order to evaluate the presence of nNOS in specific cell populations in the inner nuclear layer (INL), we performed double-labeling experiments to simultaneously localize nNOS (green) and specific markers (red) in transverse sections of the retina counterstained with 4′,6-diamidino-2-phenylindole (DAPI, blue). (A) Double-labeling of nNOS and calbindin (CB), a marker for horizontal cells. (B) Typically, pixel analysis revealed absence of overlapped signals. (C–E) In high magnification of selected areas, it was possible to observe that nNOS did not accumulate in CB-positive cells. (F–K) Similar analysis was performed for protein kinase C (PKC) labeling, a marker for rod bipolar cells. We did not detect accumulation of nNOS in PKC-positive cells. (L–U) Finally, double-labeling experiments were carried out using antiparvalbumin (PV) and -choline acetyltransferase (ChAT). In both cases, we were not able to detect colocalization of nNOS and these markers. Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
animal (n = 6) and means and EPM were calculated. Values for the control and 24 h DA groups, 1.48 ± 0.07 and 1.24 ± 0.07, respectively, indicated that round and balloon cells were differentially affected by dark-adaptation (P b 0.05).
Discussion In physiological conditions, NO production in the nervous system depends on the expression and activity of the nNOS enzyme. Previous
Fig. 3. Colocalization of neuronal nitric oxide synthase (nNOS) and calretinin (CR) in transverse sections of adult rat retina. (A) In order to confirm the presence of nNOS in specific amacrine cell populations, we performed double-labeling experiments to simultaneously localize nNOS (green) and calretinin (CR, red) in transverse sections of rat retinas counterstained with 4′,6diamidino-2-phenylindole (DAPI, blue). (B) Pixel analysis revealed overlap of green and red signals, in typical profiles of both round (white arrow) and balloon (white arrowhead) cells. We also observed red signals that did not overlap green signals, revealing that some CR-positive cells do not accumulate nNOS (red arrows). (C–E) In high magnification of selected areas, it was possible to observe a typical round cell which is also CR-positive. (F–H) Similarly, we were able to detect balloon cells which also accumulate CR. (I) Graph represents the percentage of round (red, 30.78 ± 2.95%) and balloon (blue, 38.96 ± 4.49%) cells that simultaneously accumulate CR. Bars represent standard errors of mean (n = 6). Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. Dark-adaptation regulates neuronal nitric oxide synthase (nNOS) gene expression in the retina. (A) Amplification plots using nNOS primers from retinas obtained from controls (green) and rats submitted to 24 h of dark-adaptation (24 h DA, red). It is possible to observe differences in green vs. red amplification plots. (B) Using quantitative real time PCR, we compared nNOS gene expression levels after 3 and 24 h DA and 24 h of dark-adaptation followed by return to 12:12 light/dark cycle (DA/RC) for 24 h. We detected downregulation of nNOS gene expression in 24 h DA when compared to control group (2^−1.02 ± 0.26, −50.85 ± 6.80%, P b 0.05). (C) Gene expression of GAPDH was used as internal control. Bars represent standard errors of mean (n = 6). *P b 0.05 vs. P60 in Tukey's pairwise comparisons after one-way ANOVA. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
studies indicated the presence of nNOS in the retina of several species. However, characterization of these cells remains controversial (Vielma et al., 2012). In this study, employing mathematical cluster analysis, we were able to positively identify two distinct populations of nNOS amacrine cells. Interestingly, our results revealed that nNOS-positive cells could not be associated with common markers in the retina, since we did not detect significant colocalization with CB and PKC, precluding the presence of this enzyme in horizontal and rod bipolar cells, respectively. In addition, we were able to observe that PV- and ChAT-amacrine cells do not accumulate nNOS. On the other hand, we observed that both balloon and round cells share similar partial colocalization with CR-positive amacrine cells. It is possible that this partial colocalization is related to the fact that CR accumulates in different amacrine cell populations, comprising AII, diffuse amacrine cells and A19 (Kolb et al., 2002; Wassle, 2004). Indeed, the wide variety of amacrine cell types implicates in specific functions (MacNeil and Masland, 1998), including participation in temporal and spatial filtering, and contrast and brightness adaptation (Gustincich et al., 1997; Sandell et al., 1989). In the retina, independent evidences indicated that NO modulates light responses. For example, cone light responses seem to be amplified by exogenous NO and the NO precursor L-arginine, and reduced through NOS inhibition (Levy et al., 2004; Sato et al., 2011). In rods, NO donors increased calcium currents, with NOS inhibition favoring rod hyperpolarization (Noll et al., 1994), supporting a modulation of signal transmission to second-order neurons (Kourennyi et al., 2004). Although attractive, hypotheses on the role of NO in response to ambient light levels lacked direct and more compelling evidences. In this study, employing EPR, we were able to quantify the regulation of NO triggered by darkadaptation. Furthermore, we were able to demonstrate that nNOS gene
expression restores to steady-state levels after returning to the normal light/dark cycle. Intricate molecular mechanisms have been found to participate in retinal adaptation to ambient light levels. Notably, the dopaminergic system has been implicated in this process (Jackson et al., 2014; Witkovsky, 2004). It was proposed that light responses of rod bipolar cells are enhanced by sustained chloride currents via GABA(C) receptor channels. This sensitizing GABAergic input would be controlled by dopamine D1 receptors, with GABA provided by horizontal cells (Herrmann et al., 2011). In addition to chemical synapses, it has been reported that changes in cell coupling provided by connexin (Cx) channels in the electrical synapses provide a mechanism whereby sensory circuits simultaneously adjust and enhance the dynamic range (Kihara et al., 2006a; Kinouchi and Copelli, 2006), a hypothesis not restricted to the visual processing (Christie et al., 2005). Indeed, it has been proposed that fine tuning of the sensory input, including the visual system, should take place in the initial processing layers, as indicated by mathematical/ computational modeling (Publio et al., 2009), psychophysical findings (Kihara et al., 2006b) and cellular/molecular evidences (Kihara et al., 2009). This tuning would involve cell–cell coupling provided by electrical synapses, which are regulated by both dopamine and NO (Bloomfield and Volgyi, 2009). In fact, changes in homologous coupling between AII amacrine cells triggered by light adaptation seem to be regulated by dopamine, while coupling between AII amacrine and cone bipolar cells is mainly affected by NO (Bloomfield and Volgyi, 2004; Xia and Mills, 2004). Finally, our results revealed that nNOS distribution in the retina is controlled by ambient light conditions. Considering that the presence of nNOS is essential for NO production, it was remarkable that a decrease in the NO levels correlates with downregulation of nNOS
Fig. 5. Nitric oxide (NO) production in the rat retina after dark-adaptation. (A) Representative NO spectra obtained with electron paramagnetic resonance (EPR) assay in retinas of control group. Arrow indicates the paramagnetic signal of the NO–Fe2+-(DETC)2 adduct. (B) Representative NO spectra obtained with EPR assay in retinas of rats submitted to 24 h of dark-adaptation (24 h DA). (C) Estimative of NO concentration in retinas of control (green) and 24 h DA (red) groups, 7.11 ± 0.99 and 4.41 ± 0.21, respectively, revealed that dark-adaptation significantly reduced NO production (P b 0.05). Bars represent standard errors of mean (n = 3). *P b 0.05 in T-Test. Supplementary data associated with this article can be found, in the online version, at doi:(Please insert doi here).
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Fig. 6. Distribution of neuronal nitric oxide synthase (nNOS) in specific retinal layers after dark-adaptation. (A) Representative photomicrograph of nNOS labeling (green) in transverse sections of control retinas counterstained with 4′,6-diamidino-2-phenylindole (DAPI, blue). (B) Intensity profile performed within the vertical line shown in A. We typically observed signal from the green channel throughout both inner plexiform layer (IPL) and inner nuclear layer (INL) as well. (C) nNOS labeling (green) in transverse sections of retinas counterstained with DAPI (blue) after 24 h of dark-adaptation (24 h DA). (D) Intensity profile performed within the vertical line shown in C. (E) Integration of pixel analyses permitted quantitation of nNOS distribution in the IPL and INL. Comparison of values obtained from control (green) and 24 h DA (red) retinas, 1.37 ± 0.07 and 1.11 ± 0.03, respectively, revealed significant changes in the distribution of nNOS (P b 0.01). (F) We did not detect significant differences of nNOS labeling ratio in the on and off sublaminas performed in control (green) and 24 h DA (red) retinas. (G) We also specifically analyzed nNOS labeling in off and on sublaminas in control (green) and 24 h DA (red) retinas. We did not detect significant changes in the pixel intensity in both sublaminas. Bars represent standard errors of mean (n = 6). *P b 0.01 in T-Test. Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
expression, especially in the synaptic contacts located in the inner plexiform layer. Moreover, we determined that accumulation of nNOS is differentially regulated in balloon and round cells. Our findings indicated that these neurons may play specific roles in the adaptation of the retina to the ambient light levels, since nNOS accumulation is differentially affected by dark-adaptation. This particular topic should be investigated in future studies.
Author contributions Conceived and designed experiments: LTW, GVH, ES, ERK, GC, AHK. Performed experiments: LTW, GVH, ES, ERK, CS, GC, Contributed reagents, materials, and analysis tools: SR, DEH, GC, AHK. Wrote the paper: LTW, DEH, GC, AHK.
References Competing interests The authors have declared that no competing interests exist.
Acknowledgments The authors would like to thank Izabella Carozzo and Leandro Teodoro for their technical assistance.
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Fig. 7. Distribution of neuronal nitric oxide synthase (nNOS) in specific amacrine cell subtypes after dark-adaptation. (A) Representative photomicrograph of nNOS labeling (green) in transverse sections of control retinas counterstained with 4′,6-diamidino-2-phenylindole (DAPI, blue). (B) Pixel profile of the green channel within the horizontal line shown in A. It is possible to identify typical intensity profiles of round (white arrows) and balloon (white arrowhead) cells. (C, D) High magnification of selected areas 1 and 2 shown in A, displaying typical balloon and round cells, respectively. (E) nNOS labeling in the rat retina after 24 h of dark-adaptation (24 h DA). (F) Pixel profile of the green channel within the horizontal line shown in C. Once again, it is possible to identify typical intensity profiles of round (white arrows) and balloon (white arrowhead) cells. (G–H) High magnification of selected areas 1 and 2 shown in E, displaying typical round and balloon cells, respectively. (I–J) Representation of 3D cluster analysis performed in the control and 24 h DA groups. Red and blue datasets correspond to round and balloon cells, respectively. (K) Means of brightness ratio from balloon over round cells in control and 24 h DA groups, 1.48 ± 0.07 and 1.24 ± 0.07, respectively, revealed a significant change triggered by dark-adaptation (P b 0.05). Bars represent standard errors of mean (n = 6). *P b 0.05 in T-Test. Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Experimental Neurology journal homepage: www.elsevier.com/locate/yexnr
Regular Article
Functional regulation of neuronal nitric oxide synthase expression and activity in the rat retina☆ Lais Takata Walter a, Guilherme Shigueto Vilar Higa a,b, Christian Schmeltzer c, Erica Sousa a, Erika Reime Kinjo a, Sten Rüdiger c, Dânia Emi Hamassaki d, Giselle Cerchiaro e, Alexandre Hiroaki Kihara a,b,⁎ a
Núcleo de Cognição e Sistemas Complexos, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Brazil Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, Brazil Institute of Physics, Humboldt University at Berlin, Germany d Departamento de Biologia Celular e do Desenvolvimento, Instituto de Ciências Biomédicas, Universidade de São Paulo, Brazil e Núcleo de Cognição e Sistemas Complexos, Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Brazil b c
a r t i c l e
i n f o
Article history: Received 26 May 2014 Revised 16 July 2014 Accepted 29 July 2014 Available online 6 August 2014 Keywords: CNS Development Dark-adaptation Visual system Perception Vision Synaptic plasticity Synapse ROS
a b s t r a c t In the nervous system within physiological conditions, nitric oxide (NO) production depends on the activity of nitric oxide synthases (NOSs), and particularly on the expression of the neuronal isoform (nNOS). In the sensory systems, the role of NO is poorly understood. In this study, we identified nNOS-positive cells in the inner nuclear layer (INL) of the rat retina, with distinct characteristics such as somata size, immunolabeling level and location. Employing mathematical cluster analysis, we determined that nNOS amacrine cells are formed by two distinct populations. We next investigated the molecular identity of these cells, which did not show colocalization with calbindin (CB), choline acetyltransferase (ChAT), parvalbumin (PV) or protein kinase C (PKC), and only partial colocalization with calretinin (CR), revealing the accumulation of nNOS in specific amacrine cell populations. To access the functional, circuitry-related roles of these cells, we performed experiments after adaptation to different ambient light conditions. After 24 h of dark-adaptation, we detected a subtle, yet statistically significant decrease in nNOS transcript levels, which returned to steady-state levels after 24 h of normal light–dark cycle, revealing that nNOS expression is governed by ambient light conditions. Employing electron paramagnetic resonance (EPR), we demonstrated that dark-adaptation decreases NO production in the retina. Furthermore, nNOS accumulation changed in the dark-adapted retinas, with a general reduction in the inner plexiform layer. Finally, computational analysis based on clustering techniques revealed that dark-adaptation differently affected both types of nNOS-positive amacrine cells. Taken together, our data disclosed functional regulation of nNOS expression and activity, disclosing new circuitry-related roles of nNOS-positive cells. More importantly, this study indicated unsuspected roles for NO in the sensory systems, particularly related to adaptation to ambient demands. © 2014 Elsevier Inc. All rights reserved.
Introduction As a gaseous, diffusible chemical transmitter, nitric oxide (NO) may interact with intracellular targets to trigger several signal transduction pathways (Benarroch, 2011). Besides classically related to the modulation of neurotransmission and synaptic plasticity (Calabrese et al., 2007), NO seems to play additional roles in the central nervous system (CNS), such ☆ Funding: This work was supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, #2008/55210-1), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Universidade Federal do ABC (UFABC), and DFG (#IRTG1740). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ⁎ Corresponding author at: Núcleo de Cognição e Sistemas Complexos, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, R. São Paulo, s/n — Jardim Antares, 09606-070 São Bernardo do Campo, SP, Brazil. E-mail address: [email protected] (A.H. Kihara).
http://dx.doi.org/10.1016/j.expneurol.2014.07.019 0014-4886/© 2014 Elsevier Inc. All rights reserved.
as in developmental processes (Contestabile and Ciani, 2004) and in the progression of neurodegenerative diseases (Bonnin et al., 2012; Hall et al., 2012; Virarkar et al., 2013). Indeed, NO is essential for CNS homeostasis, as a product from the conversion of L-arginine to L-citrulline by the members of the NO synthase (NOS) family of proteins (Xu et al., 2004). In this regard, previous studies determined the expression of neuronal NOS (nNOS) in the retina of several species. In rats, it seems consensual that nNOS accumulates in amacrine cells, although the molecular identity of these cells remains subject to debate (Cellerino et al., 1999; Haverkamp et al., 2000; Kim et al., 1999). Indeed, NO plays several roles in the retina, including regulation of phototransduction and cone glutamate release (Savchenko et al., 1997). In addition, NO inhibits dopamine release (Bugnon et al., 1994) and decreases the intercellular homologous coupling between horizontal cells (Xin and Bloomfield, 2000) and the heterologous coupling between cone bipolar and amacrine AII cells (Mills and Massey, 1995). Furthermore, regulation of nNOS
L.T. Walter et al. / Experimental Neurology 261 (2014) 510–517
expression and activity in physiological conditions has been investigated. Notably, increasing evidences indicated that NO modulates light responses in the retina (Pang et al., 2010). For example, it was shown that NO donors increased calcium currents in rods, with NOS inhibition favoring rod hyperpolarization (Noll et al., 1994), supporting a modulation of signal transmission to second-order neurons (Kourennyi et al., 2004). In this study, we combined morphological analysis with computational clustering techniques to determine the number and molecular identity of nNOS amacrine cell populations. Moreover, we directly measured NO production in the retina after dark-adaptation using electron paramagnetic resonance. Finally, combining experimental methods and mathematical analysis, we were able to quantify the effects of darkadaptation on nNOS gene expression and protein distribution in distinct retinal layers and specific amacrine cell populations. Taken together, our results contribute for disclosing new roles for NO in the sensory systems. Methods Ethics statement Experiments with animals were conducted in accordance with the guidelines of the NIH and the Brazilian Scientific Society for Laboratory Animals. Experimental protocol was approved by the Ethics Committee in Animal Experimentation of the Institute of Biomedical Sciences/University of São Paulo (ICB/USP). All animals were housed in cages with free access to food and water throughout the study. Animal procedures Experiments were carried out with adult Long Evans rats (Rattus novergicus) kept on a 12 h light/dark cycle (light phase 80–100 lx) with lights on at 06:00 a.m. Rats were euthanized with a lethal dose of ketamine (30 mg/100 g of body weight, i.m., Parke-Davis, Ann Arbor, MI, USA) and xylazine (2 mg/100 g, i.m., West Haven, CT, USA) between 11:00 and 12:00 a.m. In dark-adaptation experiments, there were four groups of animals: Control group, which are adult animals from 12 h light/dark cycle, and those that were kept in the dark for 3 h, 24 h, or 24 h followed by a 12:12 light/dark cycle, all euthanized between 11:00 and 12:00 a.m. Next, retinas were removed for different methodologies.
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were performed as follows: after initial activation at 50 °C for 2 min and 95 °C for 10 min, cycling conditions were 95 °C, 10 s and 60 °C, 1 min. Dissociation curves of PCR products were obtained by heating samples from 60 °C to 95 °C, in order to evaluate primer specificity. PCR statistical analysis Relative quantification of target gene expression was evaluated using the comparative CT method as previously described in detail (Kihara et al., 2008; Medhurst et al., 2000). In the present study, control refers to animals from the 12:12 light/dark cycle. Values were entered into a one-way analysis of variance (ANOVA), followed by pairwise comparisons (Tukey's HSD test), with the significance level set at 5%. Immunohistochemistry Eyes from six rats were dissected out and the retinas were fixed for 30 min in 4% paraformaldehyde (PFA) in phosphate buffer 0.1 M pH 7.3 (PB), and cryoprotected in 30% sucrose solution for at least 24 h at 4 °C. After embedding in O.C.T. compound (Sakura Finetek, Torrance, CA, USA) they were cut transversally (12 μm) on a cryostat. Retinal sections were incubated overnight with primary antibodies in a solution containing 5% normal donkey serum and 0.5% Triton-X 100 in PBS at room temperature. All antibodies and specific concentrations used in this study are listed in Table 1. After several washes, retinal sections were incubated with donkey antiserum against rabbit, mouse or goat IgG tagged to Alexa 488 (1:250–1:500, Invitrogen) diluted in 3% normal donkey serum containing 0.5% Triton X-100 in PBS for 2 h at room temperature. For double-labeling experiments, we used secondary antibodies conjugated to Alexa 546 and Alexa 647 (1:500, Invitrogen). Controls for the experiments consisted of the omission of primary antibodies; no staining was observed in these cases. Counter-staining of retinas was achieved using 4′,6-diamidino-2-phenylindole (DAPI), by incubating sections at room temperature for 10 min. After washing, the tissue was mounted using Vecta Shield (Vector Labs, Burlingame, CA, USA), and analyzed in a Nikon TS100F inverted microscope (Nikon Instruments Inc., Melville, NY, USA). Figures were mounted with Adobe Photoshop CS. Manipulation of the images was restricted to brightness and contrast adjustments of the whole image.
RNA isolation, cDNA synthesis and Real-Time PCR
Image quantification
Retinas (n = 6) were directly homogenized in 1 ml TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and total RNA was extracted following the manufacturer's suggested protocol and as previously described (Belmonte et al., 2006; Kihara et al., 2005). In brief, following two chloroform extraction steps, RNA was precipitated with isopropanol and the pellet washed twice in 70% ethanol. After air-drying, RNA was resuspended in DEPC-treated water and the concentration of each sample obtained from A260 measurements. Residual DNA was removed using DNase I (Amersham, Piscataway, NJ, USA) following the manufacturer's protocol. Quantitative analysis of gene expression was carried out with a Rotor-Gene 6000 Real-Time Rotary Analyzer (Corbett Robotics Inc., San Francisco, CA, USA) with specific primers for rat nNOS (forward, 5′- CCAATGTTCACAAAAAACGAGTCT -3′; reverse, 5′- TCGGCT GGACTTAGGGCTTT -3′). GAPDH (forward, 5′-TTCAAAAGAGGGCGCACTTC3′; reverse, 5′-GCGCACTCTGGTTTTTATTTCA-3′) expression was determined as internal control. For each 20 μl reverse transcription reaction, 4 μg total RNA was mixed with 1 μl oligodT primer (0.5 μg; Invitrogen) and incubated for 10 min at 65 °C. After cooling on ice the solution was mixed with 4 μl 5 × first strand buffer, 2 μl of 0.1 M DTT, 1 μl of dATP, dTTP, dCTP and dGTP (each 10 mM), and 1 μl SuperScript III reverse transcriptase (200 U; Invitrogen) and incubated for 60 min at 50 °C. Reaction was inactivated by heating at 70 °C for 15 min. All PCR assays
Image analysis (n = 6) was performed with Image-Pro Plus (Media Cybernetics, Bethesda, MD, USA) and NIS Elements (Nikon Instruments Inc.), as previously described (Paschon et al., 2012). After channel separation (RGB) of the color images, we performed bitmap analysis. X–Y axis analysis generated numerical appended data files corresponding to pixel values. The bitmap analysis was used to view the pixel values of the active window (or area of interest, AOI) in numeric format, where values correspond to the brightness of the pixels. In some cases, AOI was defined by the labeling of one channel, and analysis was performed in another channel, as for instance, labeling of nNOS in the green channel, defined by DAPI labeling in the blue channel. Values were exported to
Table 1 Primary antibodies used in this study. Antibody
Manufacturer
Product number
Dilution
Rabbit anti-nNOS Mouse anti-PKC Goat anti-ChAT Mouse anti-parvalbumin Mouse anti-calbindin Goat anti-calretinin
Sigma BD Transduction Millipore Sigma Sigma Millipore
N7280 612698 AB144P P3088 C9848 AB1550
1:800 1:200 1:100 1:100 1:100 1:100
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Excel (Microsoft, Redmond, WA, USA) for the appropriate mathematical analyses. Cluster analysis From retinal images (n = 6) we measured three distinct parameters of individual cells: brightness, width, and distance to the inner border of the inner nuclear layer. These attributes span a three-dimensional vector space in which each cell corresponds to a data point. The data was clustered around two different centers that can be identified by a k-means clustering analysis. In short, the algorithm works as follows: first, the number of clusters k was assumed a priori. Then, the algorithm finds the clusters and their corresponding centers by minimizing the d distortion. The d distortion is the sum of squared distances between the cluster centers and their assigned data points. In order to estimate the number of clusters in our dataset, we employed a previously developed method (Sugar and James, 2003), which applies rate distortion theory. In this method, a distortion curve d(k) for different values of k is generated by the k-means algorithm. The distortion curve is then rescaled by the negative power −p/2, based on the dimensionality p of the dataset. In our three-dimensional dataset, d(k) → d−3/2(k). Electron paramagnetic resonance (EPR) EPR spectra were recorded in a Bruker EMX Plus Electron-Spin Resonance Spectrometer System instrument, operating at X-band frequency, in the Multiuser Facility Center at UFABC. We utilized standard Wilmad quartz tubes in a high sensitive cavity. A digital temperature control system using liquid and gaseous nitrogen was used to control the temperature at 100 K. DPPH (α,α′-diphenyl-β-picrylhydrazyl) was used for calibrating frequency (g = 2.0036) with samples in a frozen water solution at 100 K. The quantification of radical production was measured with double signal integration of standard aqueous solutions of 4-hydroxy-2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPOL), in the same conditions as in the experiments. Typical conditions used in these measurements were: frequency 9.41 GHz, power 20.00 mW, 4.48 × 103 gain, 5 G modulation amplitude, time constant 20.48 ms, sweep time 80.92 ms, 12 scans, resolution 1024 points, temperature (100 ± 1) K. Pools of 8 retinas (n = 3) were isolated from adult rats after 24 h of dark-adaptation or controls maintained in a 12:12 light/dark cycle. Freshly prepared 1 ml solution of 25 mM HEPES buffer pH 7.4 containing 500 μM Fe(SO4)2 and 500 μM of N,N-diethyldithiocarbamate (DETC) were incubated with retinas for 30 min at 37 °C. Retinas were sonicated, 10% glycerol was added, and samples were frozen in a −80 °C freezer. To calibrate and validate the method, aorta from adult rats was incubated at the same conditions as the retinas (Pieper, 2007). Results Two distinct populations of amacrine cells accumulate nNOS In accordance to previous studies, our immunohistochemistry experiments detected nNOS labeling predominantly in the inner nuclear layer (INL), as well as in the ganglion cell layer (GCL), and the inner plexiform layer (IPL) (Blom et al., 2012; Blute et al., 1997; Darius et al., 1995; Eldred and Blute, 2005; Fischer and Stell, 1999; Haverkamp and Eldred, 1998; Kim et al., 1999). Interestingly, nNOS-positive cells in the INL were mainly observed in both outer and inner borders of the layer (Fig. 1A). We were able to discriminate nNOS-positive cells with different labeling levels (Fig. 1B), and distinct morphologies and positions in the INL (Figs. 1C and D). In order to determine the number of nNOS populations, we performed mathematical analysis using three parameters, i) brightness, ii) width (of the soma) and iii) distance (from the cell center to the inner border of INL). These attributes were plotted in a threedimensional vector space in which each cell corresponds to a data point (Fig. 1E). The data points were clustered around two different
centers and this result was applied to the rate distortion theory (Fig. 1F), which corroborated for the evidence that there are two populations of positive nNOS cells in the inner border of INL. Due to the morphological characteristics, cells corresponding to the red point dataset (width = 8.77 ± 0.09; brightness = 26.05 ± 0.84; distance = 5.06 ± 0.07) were collectively named “Round” cells, whereas the blue dataset (width = 11.45 ± 0.16; brightness = 70.15 ± 2.57; distance = 9.15 ± 0.15) corresponded to the “Balloon” cells. Horizontal cells, bipolar cells and specific amacrine cells do not accumulate nNOS In order to determine the presence of nNOS in specific retinal cell types, we performed double-labeling experiments. We did not detect colocalization of calbindin (CB), a marker for horizontal cells in the rodent retina (Oguni et al., 1998; Wassle, 2004), in nNOS-positive cells located in the outer margin of the inner nuclear layer (Figs. 2A–E). Similarly, we failed to detect colocalization of nNOS and PKC, a marker for rod bipolar cells (Greferath et al., 1990). Although in the pixel profile and in high magnification images it was possible to observe some red and green channel superposition, which clearly originated from distinct cells (Figs. 2F–K). Finally, we were not able to detect accumulation of nNOS in ChAT- and parvalbumin (PV)-positive cells, markers for starburst and AII glycinergic amacrine cells (Kunzevitzky et al., 2013; Wassle, 2004), respectively (Figs. 2L–U). Calretinin-positive amacrine cells accumulate nNOS In order to further analyze the molecular identity of nNOS amacrine cells, we employed another antibody raised against calretinin (CR), which in the inner border of the inner nuclear layer is a marker for AII amacrine cells, diffuse amacrine cells and A19 (Kolb et al., 2002; Wassle, 2004). In these experiments, we observed that some CR-positive cells also accumulated nNOS (Figs. 3A–B). Interestingly, distinct nNOSpositive cells were also CR-positive, including those that we previously classified as round (Figs. 3C–E) and balloon (Figs. 3F–H) cells. However, colocalization degree of both round and balloon nNOS-positive cells was only partial, 30.78 ± 2.95% and 38.96 ± 4.49%, respectively (Fig. 3I). Ambient light conditions govern nNOS gene expression in a reversible manner To access the functional regulation of nNOS in the retina, we carried out dark-adaptation experiments. Experiments were performed in 4 groups: control, 3 and 24 h of dark-adaptation, and 24 h of darkadaptation followed by 24 h in the 12:12 light/dark cycle. Significant downregulation (2^−1.02 ± 0.26, −50.85 ± 6.80%, P b 0.05) of nNOS transcripts was observed after 24 h of dark-adaptation (Figs. 4A–B). Interestingly, 24 h after the return to the 12:12 cycle the gene expression levels returned approximately to the steady-state, control levels. Taking together, these results revealed that nNOS gene expression levels depend on the ambient light conditions. GAPDH gene expression levels were used as internal control (Fig. 4C). Dark-adaptation downregulates NO production in the retina Downregulation of nNOS transcripts triggered by dark rearing might indicate that NO plays a role in retinal adaptation mechanisms. Thus, we next aimed to directly measure NO production in the retina after darkadaptation. With this purpose, we employed the spin trap technique to capture the NO free radical generated in the rat retinas. The spin trap Fe(DETC)2 is able to enter in the plasmatic membrane and form the stable radical adduct complex NO–Fe(DETC)2 easily identified by EPR spectra (Mikoyan et al., 1997; Piehl et al., 2007; Shen et al., 1998; Tsuchiya et al., 1996). The g-factor to identify this complex has already been established, g = 2.038, and we used this value to determine the NO concentration in the samples. In tissues, other dinitrosyl iron species
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Fig. 1. Neuronal nitric oxide synthase (nNOS) immunolabeling in transverse sections of the rat retina. (A) In transverse sections of the rat retina counterstained with 4′,6-diamidino-2phenylindole (DAPI, blue), nNOS labeling (green) was mainly observed in the inner plexiform layer (IPL) and inner nuclear layer (INL). (B) In the inner border of the INL, we observed nNOS-positive cells with distinct characteristics, including width and brightness, as shown in the pixel profile corresponding to the horizontal line shown in A. We observed the presence of both weak (white arrows) and strong signals (white arrowhead). (C, D) From high magnification of selected areas 1 and 2, we observed that the center of cells' soma were located at different distances from the inner border of INL. (E) Three-dimensional representation of the cells according to width, brightness and distance (from the inner border of the INL). The dataset was segregated using k-means algorithm into k = 2 clusters (blue/balloon cells and red/round cells). The position of centroids is represented by gray dots. (F) Rescaled distortion curve (gray squares) and jumps in the rescaled distortion curve (black dots). The highest jump occurs at k = 2, which strongly indicates the presence of two different clusters in the dataset. (G) According to the projection of the centroid in the three axes, width = 8.77 ± 0.09; brightness = 26.05; ± 0.84; and distance = 5.06 ± 0.07 for the red dataset and width = 11.45 ± 0.16; brightness = 70.15 ± 2.57; and distance = 9.15 ± 0.15 for the blue dataset, values were used for a computational design of the typical round (dark green) and balloon (light green) cells. Red and black arrows indicated typical width and position of round and balloon cells, respectively. Scale bar = 20 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
(DNIC) may be present in the samples, as we detected in our spectra. In Fig. 5 we can observe spectra from both representative control (Fig. 5A) and 24 h dark-adapted (Fig. 5B) retinas. To calculate the concentration of the radical adduct trapped, we used stable free radical solutions of TEMPOL radical, and the signal area of spectra was integrated and compared with NO–Fe(DETC)2. The amount of spin adduct formed is proportional to the intensity of the EPR signal. Following this quantification, it was possible to determine that NO production is higher in controls when compared to dark-adapted retinas, 7.11 ± 0.99 and 4.41 ± 0.21, P b 0.05, respectively (Fig. 5C).
labeling in sublamina off, mainly. Thus, we quantified both off and on sublaminas and calculated the ratio (Fig. 6F). Values obtained from control and 24 h dark-adaptation groups were not statistically different (0.98 ± 0.03 and 0.98 ± 0.01, respectively). When values for off (61.15 ± 2.23 vs. 58.88 ± 1.00) and on (61.71 ± 3.68 vs. 57.42 ± 1.43) sublaminas from control and 24 h dark-adaptation groups were compared, we found a small decrease of the brightness in both sublaminas, although not statistically significant (Fig. 6G).
Dark-adaptation differentially affects nNOS accumulation in balloon and round cells
Dark-adaptation changes nNOS distribution in specific retinal layers Considering that ambient light conditions regulate nNOS gene expression and NO production, we analyzed the distribution of nNOS in the retina after 24 h of dark-adaptation. When compared to controls (Figs. 6A–B), it is possible to see that dark-adapted retinas apparently show faint, weaker labeling in the IPL (Figs. 6C–D), as revealed by the pixel intensity profiles. In addition, we also performed quantification of pixel intensity to confirm changes in nNOS labeling. We quantified the brightness and calculated the IPL/INL ratio for both groups. Values obtained for the control were significant higher when compared to the dark-adaptation group, (1.37 ± 0.07 and 1.11 ± 0.03, respectively; P b 0.01; Fig. 6E). As quantification was taken as the ratio of the two layers, we were interested in knowing where this decrease occurred. We started evaluating the IPL, which apparently showed a decrease of
Since we determined the existence of two distinct nNOS amacrine cell populations, and that nNOS gene expression is regulated by ambient light conditions, we examined whether dark-adaptation differentially regulates nNOS in these amacrine cells. When comparing nNOS labeling in control (Figs. 7A–D) vs. 24 h DA (Figs. 7E–H) groups, it is possible to see that dark-adaptation triggers a general reduction in the labeling of both populations. To specifically assess how the intensity labeling of the two populations changed in the dark, we performed computational cluster analysis. Once cells were classified as previously described, we observed that in the controls (Fig. 7I), data points were displaced upwards when compared to the dataset of the group kept in the dark (Fig. 7J). Thereafter, we evaluated the brightness parameter through the relation of balloon/round cells (Fig. 7K). Medians from several retinal slices were obtained for each
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Fig. 2. Neuronal nitric oxide synthase (nNOS) and specific retinal markers in transverse sections of the rat retina. In order to evaluate the presence of nNOS in specific cell populations in the inner nuclear layer (INL), we performed double-labeling experiments to simultaneously localize nNOS (green) and specific markers (red) in transverse sections of the retina counterstained with 4′,6-diamidino-2-phenylindole (DAPI, blue). (A) Double-labeling of nNOS and calbindin (CB), a marker for horizontal cells. (B) Typically, pixel analysis revealed absence of overlapped signals. (C–E) In high magnification of selected areas, it was possible to observe that nNOS did not accumulate in CB-positive cells. (F–K) Similar analysis was performed for protein kinase C (PKC) labeling, a marker for rod bipolar cells. We did not detect accumulation of nNOS in PKC-positive cells. (L–U) Finally, double-labeling experiments were carried out using antiparvalbumin (PV) and -choline acetyltransferase (ChAT). In both cases, we were not able to detect colocalization of nNOS and these markers. Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
animal (n = 6) and means and EPM were calculated. Values for the control and 24 h DA groups, 1.48 ± 0.07 and 1.24 ± 0.07, respectively, indicated that round and balloon cells were differentially affected by dark-adaptation (P b 0.05).
Discussion In physiological conditions, NO production in the nervous system depends on the expression and activity of the nNOS enzyme. Previous
Fig. 3. Colocalization of neuronal nitric oxide synthase (nNOS) and calretinin (CR) in transverse sections of adult rat retina. (A) In order to confirm the presence of nNOS in specific amacrine cell populations, we performed double-labeling experiments to simultaneously localize nNOS (green) and calretinin (CR, red) in transverse sections of rat retinas counterstained with 4′,6diamidino-2-phenylindole (DAPI, blue). (B) Pixel analysis revealed overlap of green and red signals, in typical profiles of both round (white arrow) and balloon (white arrowhead) cells. We also observed red signals that did not overlap green signals, revealing that some CR-positive cells do not accumulate nNOS (red arrows). (C–E) In high magnification of selected areas, it was possible to observe a typical round cell which is also CR-positive. (F–H) Similarly, we were able to detect balloon cells which also accumulate CR. (I) Graph represents the percentage of round (red, 30.78 ± 2.95%) and balloon (blue, 38.96 ± 4.49%) cells that simultaneously accumulate CR. Bars represent standard errors of mean (n = 6). Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. Dark-adaptation regulates neuronal nitric oxide synthase (nNOS) gene expression in the retina. (A) Amplification plots using nNOS primers from retinas obtained from controls (green) and rats submitted to 24 h of dark-adaptation (24 h DA, red). It is possible to observe differences in green vs. red amplification plots. (B) Using quantitative real time PCR, we compared nNOS gene expression levels after 3 and 24 h DA and 24 h of dark-adaptation followed by return to 12:12 light/dark cycle (DA/RC) for 24 h. We detected downregulation of nNOS gene expression in 24 h DA when compared to control group (2^−1.02 ± 0.26, −50.85 ± 6.80%, P b 0.05). (C) Gene expression of GAPDH was used as internal control. Bars represent standard errors of mean (n = 6). *P b 0.05 vs. P60 in Tukey's pairwise comparisons after one-way ANOVA. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
studies indicated the presence of nNOS in the retina of several species. However, characterization of these cells remains controversial (Vielma et al., 2012). In this study, employing mathematical cluster analysis, we were able to positively identify two distinct populations of nNOS amacrine cells. Interestingly, our results revealed that nNOS-positive cells could not be associated with common markers in the retina, since we did not detect significant colocalization with CB and PKC, precluding the presence of this enzyme in horizontal and rod bipolar cells, respectively. In addition, we were able to observe that PV- and ChAT-amacrine cells do not accumulate nNOS. On the other hand, we observed that both balloon and round cells share similar partial colocalization with CR-positive amacrine cells. It is possible that this partial colocalization is related to the fact that CR accumulates in different amacrine cell populations, comprising AII, diffuse amacrine cells and A19 (Kolb et al., 2002; Wassle, 2004). Indeed, the wide variety of amacrine cell types implicates in specific functions (MacNeil and Masland, 1998), including participation in temporal and spatial filtering, and contrast and brightness adaptation (Gustincich et al., 1997; Sandell et al., 1989). In the retina, independent evidences indicated that NO modulates light responses. For example, cone light responses seem to be amplified by exogenous NO and the NO precursor L-arginine, and reduced through NOS inhibition (Levy et al., 2004; Sato et al., 2011). In rods, NO donors increased calcium currents, with NOS inhibition favoring rod hyperpolarization (Noll et al., 1994), supporting a modulation of signal transmission to second-order neurons (Kourennyi et al., 2004). Although attractive, hypotheses on the role of NO in response to ambient light levels lacked direct and more compelling evidences. In this study, employing EPR, we were able to quantify the regulation of NO triggered by darkadaptation. Furthermore, we were able to demonstrate that nNOS gene
expression restores to steady-state levels after returning to the normal light/dark cycle. Intricate molecular mechanisms have been found to participate in retinal adaptation to ambient light levels. Notably, the dopaminergic system has been implicated in this process (Jackson et al., 2014; Witkovsky, 2004). It was proposed that light responses of rod bipolar cells are enhanced by sustained chloride currents via GABA(C) receptor channels. This sensitizing GABAergic input would be controlled by dopamine D1 receptors, with GABA provided by horizontal cells (Herrmann et al., 2011). In addition to chemical synapses, it has been reported that changes in cell coupling provided by connexin (Cx) channels in the electrical synapses provide a mechanism whereby sensory circuits simultaneously adjust and enhance the dynamic range (Kihara et al., 2006a; Kinouchi and Copelli, 2006), a hypothesis not restricted to the visual processing (Christie et al., 2005). Indeed, it has been proposed that fine tuning of the sensory input, including the visual system, should take place in the initial processing layers, as indicated by mathematical/ computational modeling (Publio et al., 2009), psychophysical findings (Kihara et al., 2006b) and cellular/molecular evidences (Kihara et al., 2009). This tuning would involve cell–cell coupling provided by electrical synapses, which are regulated by both dopamine and NO (Bloomfield and Volgyi, 2009). In fact, changes in homologous coupling between AII amacrine cells triggered by light adaptation seem to be regulated by dopamine, while coupling between AII amacrine and cone bipolar cells is mainly affected by NO (Bloomfield and Volgyi, 2004; Xia and Mills, 2004). Finally, our results revealed that nNOS distribution in the retina is controlled by ambient light conditions. Considering that the presence of nNOS is essential for NO production, it was remarkable that a decrease in the NO levels correlates with downregulation of nNOS
Fig. 5. Nitric oxide (NO) production in the rat retina after dark-adaptation. (A) Representative NO spectra obtained with electron paramagnetic resonance (EPR) assay in retinas of control group. Arrow indicates the paramagnetic signal of the NO–Fe2+-(DETC)2 adduct. (B) Representative NO spectra obtained with EPR assay in retinas of rats submitted to 24 h of dark-adaptation (24 h DA). (C) Estimative of NO concentration in retinas of control (green) and 24 h DA (red) groups, 7.11 ± 0.99 and 4.41 ± 0.21, respectively, revealed that dark-adaptation significantly reduced NO production (P b 0.05). Bars represent standard errors of mean (n = 3). *P b 0.05 in T-Test. Supplementary data associated with this article can be found, in the online version, at doi:(Please insert doi here).
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Fig. 6. Distribution of neuronal nitric oxide synthase (nNOS) in specific retinal layers after dark-adaptation. (A) Representative photomicrograph of nNOS labeling (green) in transverse sections of control retinas counterstained with 4′,6-diamidino-2-phenylindole (DAPI, blue). (B) Intensity profile performed within the vertical line shown in A. We typically observed signal from the green channel throughout both inner plexiform layer (IPL) and inner nuclear layer (INL) as well. (C) nNOS labeling (green) in transverse sections of retinas counterstained with DAPI (blue) after 24 h of dark-adaptation (24 h DA). (D) Intensity profile performed within the vertical line shown in C. (E) Integration of pixel analyses permitted quantitation of nNOS distribution in the IPL and INL. Comparison of values obtained from control (green) and 24 h DA (red) retinas, 1.37 ± 0.07 and 1.11 ± 0.03, respectively, revealed significant changes in the distribution of nNOS (P b 0.01). (F) We did not detect significant differences of nNOS labeling ratio in the on and off sublaminas performed in control (green) and 24 h DA (red) retinas. (G) We also specifically analyzed nNOS labeling in off and on sublaminas in control (green) and 24 h DA (red) retinas. We did not detect significant changes in the pixel intensity in both sublaminas. Bars represent standard errors of mean (n = 6). *P b 0.01 in T-Test. Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
expression, especially in the synaptic contacts located in the inner plexiform layer. Moreover, we determined that accumulation of nNOS is differentially regulated in balloon and round cells. Our findings indicated that these neurons may play specific roles in the adaptation of the retina to the ambient light levels, since nNOS accumulation is differentially affected by dark-adaptation. This particular topic should be investigated in future studies.
Author contributions Conceived and designed experiments: LTW, GVH, ES, ERK, GC, AHK. Performed experiments: LTW, GVH, ES, ERK, CS, GC, Contributed reagents, materials, and analysis tools: SR, DEH, GC, AHK. Wrote the paper: LTW, DEH, GC, AHK.
References Competing interests The authors have declared that no competing interests exist.
Acknowledgments The authors would like to thank Izabella Carozzo and Leandro Teodoro for their technical assistance.
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Fig. 7. Distribution of neuronal nitric oxide synthase (nNOS) in specific amacrine cell subtypes after dark-adaptation. (A) Representative photomicrograph of nNOS labeling (green) in transverse sections of control retinas counterstained with 4′,6-diamidino-2-phenylindole (DAPI, blue). (B) Pixel profile of the green channel within the horizontal line shown in A. It is possible to identify typical intensity profiles of round (white arrows) and balloon (white arrowhead) cells. (C, D) High magnification of selected areas 1 and 2 shown in A, displaying typical balloon and round cells, respectively. (E) nNOS labeling in the rat retina after 24 h of dark-adaptation (24 h DA). (F) Pixel profile of the green channel within the horizontal line shown in C. Once again, it is possible to identify typical intensity profiles of round (white arrows) and balloon (white arrowhead) cells. (G–H) High magnification of selected areas 1 and 2 shown in E, displaying typical round and balloon cells, respectively. (I–J) Representation of 3D cluster analysis performed in the control and 24 h DA groups. Red and blue datasets correspond to round and balloon cells, respectively. (K) Means of brightness ratio from balloon over round cells in control and 24 h DA groups, 1.48 ± 0.07 and 1.24 ± 0.07, respectively, revealed a significant change triggered by dark-adaptation (P b 0.05). Bars represent standard errors of mean (n = 6). *P b 0.05 in T-Test. Scale bar = 25 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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