1. Neuroscience
Download icon

Circadian regulation of vertebrate cone photoreceptor function

  1. Jingjing Zang
  2. Matthias Gesemann
  3. Jennifer Keim
  4. Marijana Samardzija
  5. Christian Grimm
  6. Stephan CF Neuhauss  Is a corresponding author
  1. University of Zurich, Department of Molecular Life Sciences, Switzerland
  2. Lab for Retinal Cell Biology, Department of Ophthalmology, University Hospital Zurich, University of Zurich, Switzerland
Research Article
  • Cited 0
  • Views 546
  • Annotations
Cite this article as: eLife 2021;10:e68903 doi: 10.7554/eLife.68903

Abstract

Eukaryotes generally display a circadian rhythm as an adaption to the reoccurring day/night cycle. This is particularly true for visual physiology that is directly affected by changing light conditions. Here we investigate the influence of the circadian rhythm on the expression and function of visual transduction cascade regulators in diurnal zebrafish and nocturnal mice. We focused on regulators of shut-off kinetics such as Recoverins, Arrestins, Opsin kinases, and Regulator of G-protein signaling that have direct effects on temporal vision. Transcript as well as protein levels of most analyzed genes show a robust circadian rhythm-dependent regulation, which correlates with changes in photoresponse kinetics. Electroretinography demonstrates that photoresponse recovery in zebrafish is delayed in the evening and accelerated in the morning. Functional rhythmicity persists in continuous darkness, and it is reversed by an inverted light cycle and disrupted by constant light. This is in line with our finding that orthologous gene transcripts from diurnal zebrafish and nocturnal mice are often expressed in an anti-phasic daily rhythm.

Introduction

Circadian rhythms serve as endogenous clocks that molecularly support the daily occurring oscillations of physiology and ensuing behavior (Brown et al., 2019; Cahill, 2002; Frøland Steindal and Whitmore, 2019; Golombek et al., 2014; Idda et al., 2012; Ukai and Ueda, 2010; Vatine et al., 2011). It has long been recognized that the central pacemaker of circadian rhythms resides in dedicated brain regions, either the suprachiasmatic nucleus in mammals or the pineal gland in non-mammalian vertebrates. The rhythm is entrained by external stimuli (eg, light) that directly act on the core circadian transcriptional feedback loop. Multiple studies have shown that autonomous circadian clocks also exist in other brain regions and in peripheral tissues (Frøland Steindal and Whitmore, 2019; Idda et al., 2012; Vatine et al., 2011). This is particularly true for the retina, which generates its own circadian rhythm (Gladys, 2020). In zebrafish, this rhythmicity is reflected in a number of circadian adaptations, such as a higher response threshold in the morning (Li and Dowling, 1998), photoreceptor retinomotor movement in constant darkness (Menger et al., 2005), and cone photoreceptor synaptic ribbon disassembly at night (Emran et al., 2010). Such adaptations are also found in other animals such as mice, where stronger electrical retinal coupling during the night (Jin et al., 2015; Li et al., 2009; Ribelayga et al., 2008), as well as slower dark adaptation of rods during the day, was observed (Xue et al., 2015). The molecular mechanisms underlying these circadian-dependent retinal regulations are still largely unknown.

In the vertebrate retina, there are two different types of photoreceptors, namely rods and cones (Burns and Baylor, 2001; Fu and Yau, 2007). Rods function mainly during dim light conditions, whereas cones are characterized by lower sensitivity but faster response kinetics, being important for daylight and color vision. About 92% of larval and 60% of adult photoreceptors in the zebrafish retina are cones (Allison et al., 2010; Fadool, 2003; Zimmermann et al., 2018). Although rods and cones generally use the same visual transduction cascade components, the individual reactions are typically mediated by photoreceptor type-specific proteins.

Visual transduction commences by an opsin chromophore-mediated absorption of photons, which triggers the activation of a second messenger cascade including the trimeric G-protein transducin. Activated transducin stimulates the effector enzyme phosphodiesterase (PDE), which leads to a reduction in intracellular cyclic guanosine monophosphate (cGMP) levels, subsequently leading to the closure of cyclic nucleotide -gated (CNG) cation channels, resulting in a membrane potential change (Fain et al., 2001; Lamb and Pugh, 2006).

High-temporal resolution requires a tightly regulated termination of visual transduction (Chen et al., 2012; Matthews and Sampath, 2010; Zang and Matthews, 2012). This depends on the highly effective quenching of both the activated visual pigment (R*) and the PDE-transducin complex (PDE*). R* is phosphorylated by a G-protein receptor kinase (GRK) before being completely deactivated by binding to arrestin. While GRK activity itself is controlled by recoverin (RCV) in a Ca2+-dependent manner (Zang and Neuhauss, 2018), the quenching of PDE* depends on the GTPase activity of its γ-subunit that is regulated by activator protein RGS9 (Regulator of G-protein Signaling 9) (Krispel et al., 2006).

We now show that the expression levels of these important regulators of cone visual transduction decay are modulated by the circadian clock. Moreover, these periodic fluctuations are reflected in oscillating protein levels that correlate with the rhythmicity in visual physiology and behavior observed in zebrafish. Interestingly, we have found that the expression of a selection of mouse orthologs of the investigated regulatory genes is also modulated by the circadian clock. However, the periodicity was opposite to that of zebrafish, fitting the nocturnal lifestyle of mice.

Results

Expression levels of key genes involved in shaping visual transduction decay are regulated by the circadian clock

To determine the influence of the circadian clock on visual behavior, we analyzed gene expression levels of key visual transduction regulators over a 24 hr period using quantitative real-time polymerase chain reacion (qRT-PCR). Eyes from larval (5 days post fertilization [dpf]) and adult zebrafish that were kept under a normal light cycle (LD 14:10, light on at 8 o’clock in the morning), as well as eyes from 5 dpf larvae kept in continuous darkness (DD), were collected every 3 hr over a period of 24 hr and subsequently analyzed. Apart from rcv2a, which seems to have no or weak fluctuating transcript levels in larvae (Figure 1G), expression levels of the other recoverins (rcv1a, rcv1b, which is absent from larval retina, and rcv2b), G-protein receptor kinases (grk7a and grk7b), arrestins (arr3a and arr3b), and regulator of G-protein signaling 9 (rgs9a) were clearly oscillating (statistical information in Supplementary file 1). In many cases, transcripts were most abundant at ZT1 or ZT4 (grk7a, grk7b, rcv2b, arr3a, and arr3b), subsequently declined throughout the day, and recovered during the night. For instance, in adult zebrafish eyes, grk7a expression levels decreased by around 98% from the peak to the lowest expression level (Figure 1A). In the case of adult rgs9a, transcripts reached the highest level at ZT22, with the value very close to ZT1. In situ hybridization (ISH) analysis using digoxigenin-labeled RNA probes validated our qRT-PCR results (Figure 1—figure supplement 2 and Figure 1—figure supplement 3).

Figure 1 with 4 supplements see all
Key visual transduction decay gene transcripts that are under circadian control.

mRNA levels of visual transduction decay genes in the eye of adult and larval zebrafish were measured by qRT-PCR over a 24-hour-period. (A-I). Eye tissues from larval fish either raised under a normal light/dark cycle (LD / gray squares) or in continuous darkness (DD / black squares) and from adult LD zebrafish (gray circles) were collected at eight different time points throughout the day. The name of the analyzed gene transcripts is given on top of each graph. The time point of collection is indicated along the x-axis with ZT01 being the time point one hour after the light was turned on. Dark periods are indicated by the moon symbol and highlighted in gray, whereas the periods under regular light conditions are indicated by the sun symbol and shown in white. For better orientation the different conditions are summarized at the bottom of the figure. Data represents the mean ± standard error of the mean (s.e.m). Statistical analysis was performed by “RAIN” as previously described (Thaben and Westermark, 2014). Statistics information and the numbers of independent repeats are provided in Supplementary file 1. Metadata can be downloaded from DRYAD.

Figure 1—source data 1

mRNA levels of visual transduction decay genes in the eye of adult and larval zebrafish were measured by qRT-PCR over a 24 hr period.

https://cdn.elifesciences.org/articles/68903/elife-68903-fig1-data1-v2.xlsx

Interestingly, two genes, namely rcv1a and rcv2a, displayed different expression profiles in larval and adult eyes (Figure 1E&G). While larval rcv1a mRNA transcript levels peaked around ZT19, larval rcv2a transcript expression was weak/non-cyclic. However, this is in contrast to adult retinas where rcv1a and rcv2a transcripts were highest at ZT7 (Figure 1G). An anti-phasic expression profile between larval and adult stages can also be observed for rod arrestins (arras) (Figure 1—figure supplement 4).

In order to establish that the daily expression changes of these transcripts are indeed regulated by the intrinsic circadian clock, we repeated our experiments in larvae kept in complete darkness (DD), eliminating light as an external factor. Under normal LD, as well as DD conditions, we obtained largely comparable results (Figure 1). Exceptions were arr3a and arr3b, showing a 3-hr phase shift, and rcv1a, showing an almost anti-phase relationship (see ‘Discussion’ section).

Corresponding retinal genes in nocturnal mice display an anti-phasic expression pattern

As zebrafish are diurnal animals having a cone-dominant retina, we wondered if the observed circadian regulation of visual transduction gene transcripts is also seen in the rod-dominant retina of nocturnal mice. We selected mouse Grk1, the only visual grk gene in mice (Chen et al., 1999; Wada et al., 2006), the sole recoverin (Chen et al., 2012) and Rgs9 (Krispel et al., 2006) genes, and the two arrestins Arrb1 and Arrb3, as the counterparts for the above-mentioned zebrafish genes for our analysis.

Expression of all five regulators fluctuated in a 24 hr period (Figure 2), being highest at the beginning of the dark period (ZT13) for the two arrestins (Figure 2A&B), or around midnight (ZT17) for Grk1, Rgs1, and Recvrn (Figure 2C–E). All of them displayed minimal transcript levels early during the day. This oscillation pattern shows a clear anti-phasic relationship with the cyclic fluctuation of the corresponding zebrafish transcripts. Curiously, the amplitude of gene fluctuation in adult zebrafish retina was generally larger than that in the mouse retina (Figures 1 and 2).

Circadian regulation of key visual transduction genes in nocturnal mice is reversed.

Transcript levels of indicated mouse genes (A-E) were measured using qRT-PCR on retinal tissue of 12-week-old wildtype mice. were measured using qRT-PCR on retinal tissue of 12-week-old wildtype mice. The time point of collection is indicated along the x-axis with ZT01 being the time point one hour after the light was turned on. Dark periods are indicated by the moon symbol and highlighted in gray, whereas the periods under regular light conditions are indicated by the sun symbol and shown in white. Data represents the mean ± s.e.m. Statistical analysis was performed by “RAIN” as previously described (Thaben and Westermark, 2014). Statistics information and the numbers of independent repeats are provided in Supplementary file 2. Metadata can be downloaded from DRYAD.

Figure 2—source data 1

mRNA levels of visual transduction decay genes in mouse eyes were measured by qRT-PCR over a 24 hr period.

https://cdn.elifesciences.org/articles/68903/elife-68903-fig2-data1-v2.xlsx

Levels of key visual transduction regulator proteins fluctuate in the zebrafish retina

While mRNA half-life is typically in the range of minutes, protein turnover rates can range from minutes to days, explaining why fluctuation of mRNA levels is not always reflected in time-shifted oscillations at the protein level (Cunningham and Gonzalez-Fernandez, 2000; Stenkamp et al., 2005). However, as regulatory proteins often have turnover rates of only a few hours, we were examining whether RNA oscillations are mirrored by corresponding protein level fluctuations. In order to assess protein levels, we generated paralog-specific antibodies against GRK7a and ARR3a. Quantitative western blot analysis indicated periodic changes in protein levels for both proteins. Peak expression was shifted 6 - 12 hr between RNA and protein level (Figure 3A&B). ARR3a reached its highest and lowest levels at ZT7 and ZT22, respectively, whereas GRK7a maintained relatively high levels throughout the day, having the lowest concentrations around midnight. Hence, mRNA circadian oscillations in the zebrafish retina are largely conserved at the protein level with a time shift.

GRK7a and ARR3a protein levels show daily changes in adult zebrafish eyes.

GRK7a (A) and ARR3a (B) protein levels were quantified using Western blot analysis. β-Actin was used as a loading control. While mRNA transcript levels (gray circles / RNA structure) were lowest in the evening (ZT10 and ZT13, respectively), lowest protein expression levels (green circles / protein structure) were tailing RNA expression levels by around 6 to 12 hours, reaching lowest levels in the middle of the night at around ZT19. The time point of collection is indicated along the x-axis with ZT01 being the time point one hour after the light was turned on. Dark periods are indicated by the moon symbol and highlighted in gray, whereas the periods under regular light conditions are indicated by the sun symbol and shown in white. Data represents the mean ± s.e.m. Statistical analysis was performed by “RAIN” as previously described (Thaben and Westermark, 2014). Statistics information and the numbers of independent repeats are provided in Supplementary file 3. Metadata can be downloaded from DRYAD.

Figure 3—source data 1

Protein levels of Grk7a and Arr3a in the eye of adult zebrafish were measured by infrared western blotting over a 24 hr period.

https://cdn.elifesciences.org/articles/68903/elife-68903-fig3-data1-v2.xlsx

Larval cone response recovery is delayed in the evening

We next asked whether the observed protein and RNA level fluctuations have an impact on functional aspects of visual transduction. Photoresponses at larval zebrafish stages are dominated by cone photoreceptors (Bilotta et al., 2001). In the electroretinogram (ERG), the a-wave directly represents photoreceptor responses. Since in the zebrafish ERG, it is largely masked by the larger b-wave, reflecting the depolarization of ON-bipolar cells, we used the b-wave amplitude as an indirect measure of the cone photoresponse (Figure 4A1). The protein products of the genes analyzed in our study are known to affect photoresponse recovery in zebrafish (Renninger et al., 2011; Rinner et al., 2005; Zang et al., 2015). Therefore, we assessed their function by using the ERG double-flash paradigm. In this experimental setup, the retina receives a conditioning flash, followed by a probing flash of the same light intensity (Figure 4A1). The b-wave amplitude ratio of probing to conditioning response in relation to the interstimulus interval is a normalized read-out for the visual transduction recovery time (Figure 4A2; full example in Figure 4—figure supplement 1). Photoreceptor recovery is complete when the two flashes evoke responses of equal amplitudes. ERG responses are predicted to be contributed by all cone subtypes, given the light source spectrum.

Figure 4 with 2 supplements see all
Larval cone photoresponse recovery is accelerated in the morning.

(A1) Examples of normal light/dark (LD) larval electroretinogram (ERG) b-wave recordings. A conditioning flash (black line) was followed by a probing flash (yellow and red lines), which were separated by 1000 ms. While the yellow triangle and curve mark the probe response in the morning, the red triangle and curve represent the probe response recorded in the evening. Note that the probe response in the evening is clearly diminished. (A2) b-wave recovery as a function of the interstimulus interval (isi). At 500 ms up to 3000 ms isi, b-wave recovery in the morning (yellow bars) is significantly enhanced when compared to corresponding recordings in the evening (red bars). Note that below 500 ms isi, no b-wave recovery can be observed and that at an interval of 5 s complete recovery can also be found in the evening. Data are presented as mean ± sem (n = 18 in the morning; n = 14 in the evening) of three independent experiments. t-tests and nonparametric tests were performed by GraphPad Prism version 8. p = 0.0149 at 300 ms isi; p = 0.0151 at 500 ms isi; p = 0.0405 at 1000 ms isi; p = 0.0069 at 2000 ms isi. *p<0.05; **p<0.01. (B1) Examples of LD larval ERG a-wave recordings under DL-threo-beta-benzyloxyaspartate (DL-TBOA) and L-2-amino-4-phosphonobutyric acid (L-AP4) inhibition. Under b-wave blocking conditions, a conditioning flash (black line) was followed by a probing flash (yellow and red lines), which were separated by 500 ms. The yellow triangle and curve mark the probe response in the morning, whereas the red triangle and curve represent the probe response recorded in the evening. Note that also the a-wave response recovery is significantly reduced in the evening. (B2) a-wave recovery as a function of isi. At 300 ms up to 1500 ms isi, a-wave recovery in the morning (yellow bars) is significantly enhanced when compared to corresponding recordings in the evening (red bars). Data are presented as mean ± sem (n = 11 in the morning; n = 5 in the evening) of three independent experiments. t-tests and nonparametric tests were performed by GraphPad Prism version 8. Plots with individual data points were provided in metadata from DRYAD. p = 0.0029 at 500 ms isi; p = 0.0003 at 1000 ms isi; p = 0.0375 at 1500 ms isi. *p<0.05; **p<0.01; ***p≤0.001. (C1) Examples of ERG b-wave recordings from a larva kept under constant darkness (DD). A conditioning flash (black line) was followed by a probing flash (light and dark blue lines), which were separated by 1000 ms. The light blue triangle and curve mark the probe response in the morning, whereas the dark blue triangle and curve represent the probe response recorded in the evening. (C2) b-wave recovery as a function of the isi is shown for larvae raised in continuous darkness (DD). Even under continuous darkness, visual function remains under circadian control as at 500 ms up to 3000 ms isi, and the b-wave recovery in the morning (light blue bars) is significantly enhanced when compared to corresponding recordings in the evening (dark blue bars). Data are presented as mean ± sem (n = 17 in the morning; n = 12 in the evening) of three independent experiments. t-tests and nonparametric tests were performed by GraphPad Prism version 8. p = 0.0007 at 1000 ms isi; p = 0.0016 at 2000 ms isi; p = 0.0004 at 3000 ms isi; p = 0.0006 at 5000 ms isi. *p<0.05; **p<0.01; ***p≤0.001. Metadata can be downloaded from DRYAD.

Figure 4—source data 1

Larval cone photoresponse recovery was measured by ERG in different conditions.

https://cdn.elifesciences.org/articles/68903/elife-68903-fig4-data1-v2.xlsx

Response recovery was significantly delayed in the evening in comparison to the morning (Figure 4A2). However, as the ERG b-wave is only an indirect measure of the photoreceptor response, we also measured the photoreceptor-induced a-wave by blocking the masking ERG b-wave (Figure 4B1). This was achieved by administering a pharmacological cocktail containing the excitatory amino acid transporter inhibitor DL-threo-beta-benzyloxyaspartate (DL-TBOA) and metabotropic glutamate receptor inhibitor L-2-amino-4-phosphonobutyric acid (L-AP4) (Wong et al., 2004). Consistently, the double-flash paradigm demonstrated that the a-wave response recovery in the evening was delayed (Figure 4B2). According to the light spectrum (Figure 4—figure supplement 2), the a-wave was contributed by all cone subtypes.

In order to prove that increased response recovery times measured in the evening are a bonafide circadian event, we repeated the above experiments on larvae that were kept in constant darkness. At corresponding time points, the decrease in response recovery was comparable (Figure 4C1&C2), verifying that the observed changes are regulated by an intrinsic circadian clock.

As photoresponse recovery is affected by the circadian rhythm, we hypothesized that this should also be apparent in temporal aspects of vision. Therefore, we recorded ERG responses generated by the flickering stimuli with different stimulus frequencies (Figure 5, 5 Hz, 8 Hz, 10 Hz, 12 Hz, and 15 Hz). Fast Fourier transform (FFT) algorithm in MATLAB was used to extract the power at stimulus frequency. This power was then normalized against the power at 50 Hz (line noise), which is far from the stimulus frequencies. In line with our hypothesis, we found that the normalized power at each stimulus frequency was significantly weaker in the evening compared with the power in the morning. This clearly indicates that the cone visual temporal resolution is under circadian control. Note here, the flicker ERG was mainly contributed by double-cone responses because of the spectral content of the stimulus light (Figure 4—figure supplement 2).

Zebrafish larvae show an increased temporal resolution in the morning.

Examples show the flicker electroretinogram (ERG) responses to 5 Hz stimulus (A1) and to 12 Hz stimulus (A2). Example fast Fourier transform (FFT) power plots generated by MATLAB for responses (A1) and (A2) are shown in (B1) and (B4). These four example power plot results are highlighted in the corresponding summarized normalized power results in (B1) and (B2). The power of given frequency was normalized against the power at 50 Hz (line noise). The rest of the summarized plots of normalized power are shown in B2, B3, and B5. t-tests and nonparametric tests were performed by GraphPad Prism version 8. p = 0.0016 at 5 Hz (B1); p = 0.0005 at 8 Hz (B2); p = 0.0001 at 10 Hz (B3); p = 0.0001 at 12 Hz (B4); p<0.0001 at 15 Hz (B5). **p<0.01; ***p≤0.001; ****p≤0.0001. Metadata can be downloaded from DRYAD.

Manipulation of gene expression by light is mirrored by functional changes

Next we measured larvae reared in a reversed light cycle (DL) where the night turns into a day. Under this condition, gene expression levels stayed in the fish’s time. ISH for the genes of interest (Figure 6A) reflected this, with a stronger staining intensity in LD fish at 9 o’clock in the morning compared to DL fish at the same time. Consequently, when both groups were recorded at 120 hr post fertilization, a prolonged response recovery time was obtained in the fish maintained in reversed light cycle, reflecting the situation in fish kept in the normal light and recorded in the evening (Figure 6D).

Light cycle alterations are reflected in adaptations of cone photoresponse recovery.

(A and C) In situ hybridization images using arr3a, arr3b, and grk7a as probes. Tissues were collected from either reverse light cycle (DL) (A, left panel), normal light cycle (LD) (A, right panel) or light/light cycle (LL) (C) zebrafish larva (5 days post fertilization [dpf]) at the indicated time points. A reversal in the light cycle from LD to DL is reflected in the reversal of the in situ hybridization signal, with low expression levels observed at 9 o’clock (A). The ratio of gene expression levels between evening (ZT13) and morning (ZT1) for fish raised under a normal LD cycle or under LL is shown in (B). In contrast to the observed circadian regulation under LD conditions, under LL conditions, expression levels remain continuously elevated not displaying any circadian fluctuation (B, C). (D) A reversal of the light cycle is reflected in a corresponding reversal of b-wave recovery. The comparison of b-wave recovery of LD and DL larvae recorded at the same time in the morning clearly indicates that immediately before darkness, b-wave recovery rates are reduced. Data are presented as mean ± sem (n = 16 larvae raised in LD; n = 9 larvae raised in DL) of three independent experiments. t-tests and nonparametric tests were performed by GraphPad Prism version 8. Plots with individual data points were provided in metadata from DRYAD. p = 0.001 at 500 ms interstimulus interval (isi); p = 0.0019 at 1000 ms isi; p = 0.0221 at 2000 ms isi; p = 0.0009 at 3000 ms isi; p = 0.0022 at 5000 ms isi. *p<0.05; **p<0.01; ***p≤0.001. (E) No changes in b-wave recovery between morning and evening can be observed under constant light conditions (LL). Data are presented as mean ± sem (n = 15 in the morning; n = 12 in the evening) of three independent experiments. t-tests and nonparametric tests were performed by GraphPad Prism version 8. p = 0.0107 at 500 ms isi; *p<0.05. Metadata can be downloaded from DRYAD.

Figure 6—source data 1

Larval cone photoresponse recovery was measured by ERG in different conditions.

https://cdn.elifesciences.org/articles/68903/elife-68903-fig6-data1-v2.xlsx

While the intrinsic circadian clock is maintained in the absence of light, continuous light exposure has been shown to disrupt this intrinsic rhythm (Laranjeiro and Whitmore, 2014). We therefore evaluated if the circadian regulation of mRNA expression persists in larvae kept under constant light (LL). Strikingly, the gene expression differences between morning and evening detected under LD conditions were completely lost in LL larvae (Figure 6B&C). This was also reflected on a functional level with no delay of photoresponse recovery in the evening, as measured by ERG.

Taken together, these results demonstrate that changes in the light cycle are reflected in changes of transcript levels of phototransduction regulators that subsequently lead to altered visual performance at different times during the day.

Circadian clock-dependent expressions of key regulator genes tune the single-cone photoresponse kinetics

We applied a computational model of visual transduction to predict how the relative gene expression changes between morning and evening influence the single-cone photoresponse (Invergo et al., 2013; Invergo et al., 2014). The default model was set as morning value (ZT1). We then put the measured gene expression ratio data (arr3a, grk7a, rcv2b and rgs9) between ZT1 and ZT13 into the model for evening simulation. These four genes have been selected due to their pan-cone expression (grk7a, rcv2b and rgs9) and double-cone expression (arr3a), respectively. Running the model with the relative value of arr3b (blue and ultraviolet [UV] cones) produced comparable results to arr3a (data not shown). Detailed parameters are listed in Supplementary file 4. The computed morning and evening values were then compared.

As predicted by our experimental results, the decay of photoresponse to different light intensities in the model was largely prolonged in the evening (Figure 7A–E). The unsaturating response amplitude was slightly elevated in the evening, which may indicate the prolonged lifetime of the visual pigment (Figure 7F).

Figure 7 with 2 supplements see all
Simulations of single-cone photoresponse in the morning (default) and in the evening.

Simulations of single cone photoresponse in the morning (default) (A) and in the evening (B). 500 ms flash stimuli were delivered at time = 0 s. The flash intensities are 1.7, 4.8, 15.2, 39.4, 125, 444, 1406 and 4630 photons µm-2 (Invergo et al., 2014). (C) & (D) depict response curves normalized to the amplitudes at each light intensity. The dotted line represents 25% recovery of the photoresponse. Response duration for 25% recovery (E) and photoresponse amplitude (F) are plotted as a function of logarithmically increasing stimulus intensities.

Figure 7—source data 1

Single-cone photoresponse was predicted by a computational model.

https://cdn.elifesciences.org/articles/68903/elife-68903-fig7-data1-v2.zip

Discussion

Circadian rhythms have been shown to regulate many biological aspects of vision. An early study demonstrated that zebrafish visual sensitivity is lower before light on and higher prior to light off (Li and Dowling, 1998). Later, another study linked the rhythmic expression of long-wavelength cone opsin to the core clock component CLOCK (Li et al., 2008). A particularly striking finding showed that synaptic ribbons of larval zebrafish photoreceptors disassemble at night. This peculiar phenomenon may save energy in fast-growing larvae (Emran et al., 2010). Our study now demonstrates that regulators of photoresponse decay are not only influenced by the circadian clock but in addition have a clear effect on the varying visual performances throughout a 24 hr cycle. Moreover, kinetics of cone visual transduction quenching is under the control of the circadian clock, which allows the fish to see with better temporal resolution in the morning than in the evening.

It is commonly assumed that circadian gene regulation helps the organism to optimally adapt to its preferential lifestyle and/or environment. Therefore, one would expect that the circadian systems of diurnal and nocturnal animals adapt differently. Our study indeed demonstrates that orthologous zebrafish and mouse genes involved in regulating cone visual transduction decay display an anti-phasic circadian expression pattern, supporting the functional relevance of the oscillating gene expression. While the visual temporal resolution of diurnal species is reduced in the evening, the visual system of nocturnal species is tuned to be most effective during these hours. Zebrafish, therefore, is an interesting model to study the physiology of circadian rhythms of diurnal animals, such as humans.

We would like to point out several additional interesting observations. Although many ohnologs (paralogs generated in a whole-genome duplication event), such as grk7a and grk7b, share a similar circadian phase or oscillatory amplitude, others, such as rcv1a and rcv1b, show an almost anti-phasic relationship. This is remarkable, since these ohnologs have been generated by a teleost-specific whole-genome duplication event (Glasauer and Neuhauss, 2014), implying that initially all ohnologs should have been in synchronicity. Interestingly, these ohnologs also adapted different expression profiles, with rcv1a being expressed in rods and UV cones, while rcv1b being expressed in all cone types in the adult retina (UV, blue, red, and green) (Zang et al., 2015).

While the circadian rhythmicity of most genes persists throughout all developmental stages, some genes do show markedly different expression profiles between larval and adult stages. This may be related to the fact that the larval retina is functionally cone dominant, while the adult retina is a duplex retina with rod and cone contribution. In the case of rcv2 ohnologs, rcv2b displays an in-phase cyclic expression pattern throughout all stages. Conversely, rcv2a did not show an overt cyclic expression pattern at larval stages, but being clearly under circadian control at adult stages (Figure 1). In contrast to the rcv1 ohnologs, both rcv2 genes are expressed in all cone subtypes, and depletion of either one acts to speed up the photoresponse termination (Zang et al., 2015). Other examples of ohnolog-specific cycling have been found for arrs and rgs genes (Figure 1, Figure 1—figure supplement 4). These observations strongly indicate that the transcription of clock-controlled genes (CCGs) is not uniformly regulated.

Interestingly, it has been previously demonstrated that the circadian clock seems to be desynchronized in larvae raised in darkness (Dekens and Whitmore, 2008; Kaneko and Cahill, 2005; Kazimi and Cahill, 1999; Lahiri et al., 2014). The circadian expression of some core clock genes and melatonin rhythms are lost when whole larvae were used as the experimental material in the absence of environmental entrainments. We did not observe this phenomenon in our study of visual transduction genes when only eye tissue was used, consistent with an inheritable maternal clock in the eye. We took care to avoid inadvertent environmental entrainment as described in detail in the ‘Materials and methods’ section. The different experimental results may come from the fact that different experiment materials were used. For example, all the analyzed genes in our study are also expressed in the photoreceptors of the pineal gland, but the transcript fluctuations may not necessarily be synchronized between the eye and pineal gland (eg, rcv1a in Figure 1—figure supplement 1). The use of whole larvae in our qRT-PCR study may have masked the cycling of retinal genes.

Furthermore, in many cases, the DD cycling is in phase with the fluctuations of the transcripts under the LD cycle. Endogenous circadian periods are around, but not exactly, 24 hr, and within 5 days in constant darkness, the peaks may shift relative to the LD cycle. In our experiments, the tissue was collected every 3 hr, and the shift within 5 days may be too small to be visible in the current experimental setting. The observation that the cycle of other genes (arr3a, arr3b, and rcv1a) in DD condition did diverge from LD condition indicates that these genes may be driven by different transcription factors. Furthermore, the DD condition led to the overall upregulation of some (eg, rcv1a, rgs9a) while caused downregulation of other (eg, arr3a, arr3b) genes as calculated from data in Figure 1. This strongly argues against a systematic error.

Among the studied genes in zebrafish, grk7a expression level increased by around 50 times in 1 day (Figure 1A), whereas Grk7a protein level increased by about two times in a 24 hr period (Figure 3A). arr3a transcript increased about 10 times (Figure 1C), while its protein level only grew less than 50% throughout the day (Figure 3A). Therefore, these mRNA expression levels reflect proportionally to protein levels, indicative of a rather fast turnover rate for these proteins.

In the end, we asked whether the observed ERG adaptations between morning and evening directly influence visual behavior. Therefore, we measured the optokinetic response (OKR)(Figure 7—figure supplement 1) and the visual motor response (VMR)(Figure 7—figure supplement 2) . Both behavioral assays showed some changes between the different recording time points, but the direct contribution by visual transduction is hard to assign. Confounding factors, not related to vision, may for instance be circadian regulation of overall activity.

In conclusion, we have shown that key regulators of cone visual transduction at both the mRNA and protein level are under circadian control. Moreover, expression levels of these regulators in diurnal and nocturnal species are anti-phasic, suggesting that circadian changes influencing physiological and behavioral properties of vision are reflected in adaptation to different visual ecologies.

Materials and methods

Zebrafish care

Request a detailed protocol

Zebrafish (Danio rerio) were maintained at a standard 14 hr light:10 hr dark cycle (LD) with light on at 8 am and light off at 10 pm. Water temperatures were kept between 26 and 28°C (Amores et al., 1998). Fish from the WIK wildtype strain were used in our study. Embryos were raised in E3 medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, and 0.33 mM MgSO4) containing either 0.01% methylene blue to suppress fungal growth and/or 0.2 mM 1-phenyl-2-thiourea (PTU; Sigma-Aldrich) to prevent pigment development. Embryos were collected directly after laying. LD condition embryos were then transferred to the incubation room with normal light cycle (14:10). DD-conditioned embryos were placed in a black box before being transferred to the incubation room. Hence, all larvae (LD and DD) grew in the same environment with a stable temperature at 28°C. LL-conditioned fish were raised under constant light. DL condition was light on at 8 pm and light off at 10 am.

Adult zebrafish were sacrificed using ice water following decapitation. All animal experiments were carried out in line with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the Veterinary Authorities of Kanton Zurich, Switzerland (TV4206).

Zebrafish quantitative real-time PCR

Request a detailed protocol

Around thirty 5 dpf larvae or five eyeballs from adult zebrafish were collected per time point (ZT1, 4, 7, 10, 13, 16, 19 and 22) and the tissue stored in RNAlater (Sigma) at 4°C. Dark adapted tissue was collected under dim red light. Only eyeballs were used for RNA extraction using the NucleoSpin RNA kit (Macherey-Nagel). Complementary DNA (cDNA) was produced using 110 ng total RNA as template for reverse transcription with SuperScript III (Invitrogen, Life Technologies; Zug, Switzerland). The samples collected from different time points were masked during RNA extraction and cDNA generation. qRT-PCR (Applied Biosystems Prism SDS 7900HT; Life Technologies) was performed using the MESA Green qPCR Mastermix Plus for SYBR Assay (Eurogentec, Seraing, Belgium) on a liquid handling robot platform (Tecan Genesis). Three technical replicates were conducted. Primers (Sigma-Aldrich) for qRT-PCR were intron-spanning to avoid amplification of non-digested genomic DNA fragments and were designed by online Universal ProbeLibrary Assay Design Center (Roche). Standard housekeeping genes (elongation factor 1, ef1; β-actin 2, actb2 and ribosomal protein L 13, rpl13) were used as reference (Tang et al., 2007). Primer pairs used are listed in Table 1.Expression levels were normalized to 1. Statistical analysis was performed in R 4.1.0 with ‘rain’ package (Thaben and Westermark, 2014).

Table 1
Sequences of primers used for qRT-PCR.
rcv1a S TGAGAACACGCCAGAAAAGC as CATTCAGGGTGTCATGGAGAAC
rcv1b s GCCTTCGCACTCTATGATGTG as CTCGTCGTCAGGAAGGTTTTTC
rcv2a s CTTGGTCCTCTTTGGGAATCAG as AGTGGGCCTTCTCACTCTTC
rcv2b s TGATGTGGACAAGAACGGTTAC as GGGAAGACTTGTCTGCTTGTC
arr3a s GCCATCCCTTCACTTTCAATA as GCTTTTCCTTTGTCGTCTGG
arr3b s ACTCCCCCTTGTTCTGATGTC as TTGCTCCTCACTGGCTGTAG
grk7a s TGAACGTCTTGGCTGCAA as CCCAGGGTGGATCGATTAG
grk7b s ACATTGAGGACCGCCTTG as CCCATGGAGGTGGAATGA
rgs9a s CAACATTATAGGCCACGGATGAC as GATCCCTTCACACCAGTTGATG
ef1 s CTGGAGGCCAGCTCAAACAT as ATCAAGAAGAGTAGTACCGCTAGCATTAC (Lin et al., 2009)
actb2 s CCAGCTGTCTTCCCATCCA as TCACCACGTAGCTGTCTTTCTG (Lin et al., 2009)
rpl13 s TCTGGAGGACTGTAAGAGGTATGC as AGACGCACAATCTTGAGAGCAG (Lin et al., 2009)

Mouse care and gene expression analysis

Request a detailed protocol

Mice were maintained at the Laboratory Animal Services Center (LASC) of the University of Zurich in a 12 hr light:12 hr dark cycle with lights on at 7 am. All animal experiments were performed according to the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and the regulations of Veterinary Authorities of Kanton Zurich, Switzerland.

Ten 12-week-old wildtype mice (129S6; Taconic, Ejby, Denmark) were used in our experiments. Dark-phase mice were killed under red light and retinas were processed further under normal light conditions. Three mice at each time point (ZT1, 5, 9, 13, 17 and 21) were sacrificed and RNA was extracted (Macherey-Nagel, Oensingen, Switzerland) according to the manufacturer’s instructions. cDNA synthesized using oligo-dT was done as previously described (Storti et al., 2019). The samples collected from different time points were masked during RNA extraction and cDNA generation. qRT-PCR was performed by ABI QuantStudio3 machine (Thermo Fisher Scientific) with the PowerUp Sybr Green master mix (Thermo Fisher Scientific). Two technical replicates were conducted. Primer pairs used are listed in Table 2 for each gene of interest. Beta-actin (Actb) was used as a housekeeping gene to normalize gene expression with the comparative threshold cycle method (DDCt) using the Relative Quantification software (Thermo Fisher Scientific). The highest expression level was normalized to 1. Statistical analysis was performed in R 4.1.0 with ‘rain’ package (Thaben and Westermark, 2014).

Table 2
Mouse primer sequences.
Arrb1 S GCTCTGTGCGGTTACTGATCC as TGTCGGTGTTGTTGGTCACG
Arrb3 s GCTAACCTGCCCTGTTCAGT as GCTAACCTGCCCTGTTCAGT
Grk1 s TGAAGGCGACTGGCAAGATG as AGGTCCGTCTTGGTCTCGAA
Rgs9 s TTCGCTCCCATTCGTGTTGT as ATGTCCTTCACCAGGGCTTC
Recvrn1 s AGTGGGCCTTCTCGCTCTA as ATCATCTGGGAGGAGTTTCACA
Actb s CAACGGCTCCGGCATGTGC as CTCTTGCTCTGGGCCTCG

In situ hybridization

Request a detailed protocol

Primers used to generate in situ probes are listed in Table 3. Probes were digoxigenin‐labeled using the DIG RNA Labeling Mix purchased from Roche.

Table 3
Primer sequences for in situ probe preparation.
rcv1a s GGACCAGAGTACAATTTAAG as GAAGCTCTAATCAGTCATAG (Zang et al., 2015)
rcv1b s CAGACCAGCACCACATAC as TCTTGCACTTTCTGTGGTT (Zang et al., 2015)
rcv2a s CAACATCTTTCTGAGCCC as ATAGCGTCTTCATTCTCC (Zang et al., 2015)
rcv2b s CACTCAGACAGAAGTCAT as GTAGACCATCATCGCTTG (Zang et al., 2015)
grk7a s GCATCTTCTAGTCTGATAGCAC as ACAGCTTCAATCATGTTAGTGA (Rinner et al., 2005)
grk7b s CCCAGAGCGTCATATAGTG as AGTCACAGGAATAAGCTATGAA (Rinner et al., 2005)
rgs9a s TTCCGGAATACAAAATGACAA as GCCTCGTGGGTCATTGAG
rgs9b s GAAGCGAATATGACCATAAGG as ATCAGCCCTTCCTCGTTG
arr3a s ATGGCTGACAAAGTTTACAAG as GCCCTGTGGAATCTGATATG (Renninger et al., 2011)
arr3b s CATGACAAAGGTTTACAAGAAG as TGCTCCTCACTGGCTGTAG (Renninger et al., 2011)
arrSa s CAATGAGTCCAAAAAATGTCG as TAACCGAGAAGTGCTCTTTC (Renninger et al., 2011)
arrSb s ATGAGTCCCAAGCACATCATC as CAGCCAGCTCAAAACACG (Renninger et al., 2011)

For whole-mount ISH, embryos were treated with E3 containing 0.2 mM PTU (Sigma-Aldrich) to avoid pigmentation. 5 dpf larvae were fixed in 4% paraformaldehyde (PFA; Sigma) in phosphate-buffered saline (PBS) overnight at 4°C. Time points with maximal differences were chosen according to qRT-PCR results. Embryos were washed three times in PBS containing 1% Tween (PBST), dehydrated step wise (25, 50, and 70% methyl alcohol (MeOH) in PBST), and stored in 100% MeOH at –20°C. When comparing two groups of samples fixed at different time points, the tails of the group that may produce weaker staining were cut and mixed with the other group during staining.

For slide ISH, eyeballs were removed from adult zebrafish at different time points and fixed overnight at 4°C using 4% PFA. Detailed ISH processes have been previously described (Haug et al., 2015). When comparing two groups of samples fixed at different time points, both samples were placed on the same slide.

Infrared western blotting

Request a detailed protocol

Five to six eyeballs from adult zebrafish were homogenized in ice-cold 150 ml RIPA buffer (150 mM NaCl, 1% Triton-X, 0.5% sodiumdeoxycholate, 50 mM Tris (pH 8), 1 mM ethylenediaminetetraacetic acid [EDTA], 0.1% sodium dodecyl sulfate [SDS]) containing cOmplete Protease Inhibitor Cocktail ([Roche]). After 2 hr of incubation on a 4°C shaker, lysates were centrifuged for 30 min at 4°C. During this procedure, all the samples were masked. Supernatants were stored at –80°C. Nitrocellulose membranes with 0.45 µm pore size were used. Primary antibodies were diluted to the following concentrations: rabbit anti-Arr3a: 1:4000; rabbit anti-Grk7a: 1:3000; mouse anti-β-actin: 1:6000 (Renninger et al., 2011; Rinner et al., 2005). Anti-arr3a and anti-β-actin antibodies or anti-Grk7a and anti-β-actin antibodies were applied simultaneously. Secondary antibodies IRDye 800CW Goat anti-Rabbit IgG and IRDye 680RD Goat anti-Mouse IgG (LI-COR) were diluted in 1:20,000 ratio in blocking buffer (1% bovine serum albumin [BSA] in PBST). Signal was detected by the Odyssey CLx Imaging System (LI-COR) and data were normalized to the internal loading control β-actin by IMAGEJ (Schindelin et al., 2012).

Electroretinography

Request a detailed protocol

ERG was recorded as previously described (Zang et al., 2015). Light intensity (light source: Zeiss XBO 75 W) was measured using a spectrometer (Ocean Optics, USB2000b; software Spectra Suite, Ocean Optics) with a spectral range described previously (Supplemental Material 2A in Zang et al., 2015). Pairs of two light flashes with equal intensity and duration (500 ms) were applied (Rinner et al., 2005). Intervals between two flashes were either 100, 200, 300, 500, 1000, 2000, 3000, or 5000 ms. The interval between two pairs was 20 s. b-wave recovery is defined as the ratio of the second b-wave amplitude to the first one in the same pair.

To measure ERG a-wave, 5 dpf larval eyeballs were treated with 400 µM L-AP4 and 200 µM TBOA in Ringer’s solution (111 mM NaCl, 2.5 mM KCl, 1 mM CaCl2, 1.6 mM MgCl2, 10 μm EDTA as a chelator for heavy metal ions, 10 mM glucose, and 3 mM 4- (2-hydroxyethyl) -1-piperazineethanesulfonic acid [HEPES] buffer, adjusted to pH 7.7–7.8 with NaOH). A HPX-2000 Xenon light source (Ocean Optics) was used and its light spectrum was measured by a spectrometer (Ocean Optics, USB2000b; software Spectra Suite, Ocean Optics; Figure 4—figure supplement 2). Electronic signals were amplified 1000 times by a pre-amplifier (P55 AC Preamplifier; Astro-Med. Inc, Grass Technology), digitized by DAQ Board (SCC-68; National Instruments), and recorded by a self-written Labview program (National Instruments). Intervals between two flashes were 300 ms, 500 ms, 1000 ms, and 1500 ms, respectively. a-wave recovery is defined as the ratio of the second a-wave amplitude to the first one in the same pair.

Flicker-fusion ERGs were measured with a white light emitting diode (LED) light source (Ocean Optics; LSM serie) controlled by LDC-1 controller (Ocean Optics). The spectrum of this light source was was measured by a spectrometer (Ocean Optics, USB2000b; software Spectra Suite, Ocean Optics; Figure 4—figure supplement 2). Except for the light source, flicker ERG was performed in the same setup as a-wave ERG. The flicker frequencies of 5 Hz, 8 Hz, 10 Hz, 12 HZ, and 15 Hz at 50% duty cycle were used. Flicker-fusion ERG data were analyzed by MATLAB (R2020b).

Phototransduction modeling

Request a detailed protocol

The computational model of vertebrate phototransduction was introduced and verified previously (Invergo et al., 2014; Invergo et al., 2013). We simulated the photoresponse to different light intensities of 1.7, 4.8, 15.2, 39.4, 125, 444, 1406, and 4630 photons µm–2 with a flash duration of 500 ms. Default parameters in the model were kept for morning (ZT1) simulation. For evening (ZT13) simulation, the relative gene expression change between ZT1 and ZT13 of larvae LD conditions was applied. Parameters for each gene are listed in Supplementary file 4. The simulation was performed in COPASI (Hoops et al., 2006).

Visual motor response

Request a detailed protocol

The VMR was measured using a Zebrabox (ViewPoint Life Science, Lyon, France). 5 dpf larvae were placed in a 96-well plate, subjected to dark adaptation for 10 min inside the Zebrabox, and the larval movement recorded with light off, on, and off for 5 min each. The distance that a single larva moved was measured every 2 s. Baseline activity was calculated as the average movement 1 min before light on or off.

Optokinetic response

Request a detailed protocol

The OKR was recorded as previously described (Rinner et al., 2005). Briefly, 5 dpf larvae were tested with sinusoidal gratings at different time points (ZT1, 4, 7, 10 and 13). To determine the contrast sensitivity, a spatial frequency of 20 cycles/360° and an angular velocity of 7.5°/s were used with different contrast settings (5, 10, 20, 40, 70, and 100%). To explore the spatial sensitivity, an angular velocity of 7.5°/s and 70% of maximum contrast were applied with a varying spatial frequency (7, 14, 21, 28, 42, and 56 cycles/360°). Figures were prepared by SPSS (version 23.0; Armonk, NY: IBM Corp).

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
gene (Danio rerio)arr3aGenBankBC076177
gene (Danio rerio)arr3bGenBankBC059650
gene (Danio rerio)grk7aGenBankBC163587
gene (Danio rerio)grk7bGenBankAY900005
gene (Danio rerio)rcv1aGenBankKT325590
gene (Danio rerio)rcv1bGenBankKT325591
gene (Danio rerio)rcv2aGenBankKT325592
gene (Danio rerio)rcv2bGenBankKT325593
gene (Danio rerio)rgs9aGenBankCABZ01019467
gene (Danio rerio)actb2GenBankAL929031
gene (Danio rerio)ef1GenBankL47669
gene (Danio rerio)rpl13GenBankAF385081
gene (Mus musculus)Arrb1GenBankAC102630
gene (Mus musculus)Arrb3GenBankAL671299
gene (Mus musculus)Grk1GenBankAC130818
gene (Mus musculus)Rgs9GenBankAK138159
gene (Mus musculus)RecvrnGenBankCK617354
gene (Mus musculus)ActbGenBankAC144818
sequence-based reagentrcv1a sThis paperqRT-PCR primersTGAGAACACGCCAGAAAAGC
sequence-based reagentrcv1a asThis paperqRT-PCR primersCATTCAGGGTGTCATGGAGAAC
sequence-based reagentrcv1b sThis paperqRT-PCR primersGCCTTCGCACTCTATGATGTG
sequence-based reagentrcv1b asThis paperqRT-PCR primersCTCGTCGTCAGGAAGGTTTTTC
sequence-based reagentrcv2a sThis paperqRT-PCR primersCTTGGTCCTCTTTGGGAATCAG
sequence-based reagentrcv2a asThis paperqRT-PCR primersAGTGGGCCTTCTCACTCTTC
sequence-based reagentrcv2b sThis paperqRT-PCR primersTGATGTGGACAAGAACGGTTAC
sequence-based reagentrcv2b asThis paperqRT-PCR primersGGGAAGACTTGTCTGCTTGTC
sequence-based reagentarr3a sThis paperqRT-PCR primersGCCATCCCTTCACTTTCAATA
sequence-based reagentarr3a asThis paperqRT-PCR primersGCTTTTCCTTTGTCGTCTGG
sequence-based reagentarr3b sThis paperqRT-PCR primersACTCCCCCTTGTTCTGATGTC
sequence-based reagentarr3b asThis paperqRT-PCR primersTTGCTCCTCACTGGCTGTAG
sequence-based reagentgrk7a sThis paperqRT-PCR primersTGAACGTCTTGGCTGCAA
sequence-based reagentgrk7a asThis paperqRT-PCR primersCCCAGGGTGGATCGATTAG
sequence-based reagentgrk7b sThis paperqRT-PCR primersACATTGAGGACCGCCTTG
sequence-based reagentrg9a asThis paperqRT-PCR primersCAACATTATAGGCCACGGATGAC
sequence-based reagentrgs9a asThis paperqRT-PCR primersGATCCCTTCACACCAGTTGATG
sequence-based reagentef1 sLin et al., 2009qRT-PCR primersCTGGAGGCCAGCTCAAACAT
sequence-based reagentef1 asLin et al., 2009qRT-PCR primersATCAAGAAGAGTAGTACCGCTAGCATTAC
sequence-based reagentactb2 sLin et al., 2009qRT-PCR primersCCAGCTGTCTTCCCATCCA
sequence-based reagentactb2 asLin et al., 2009qRT-PCR primersTCACCACGTAGCTGTCTTTCTG
sequence-based reagentrpl13 sLin et al., 2009qRT-PCR primersTCTGGAGGACTGTAAGAGGTATGC
sequence-based reagentrpl13 asLin et al., 2009qRT-PCR primersAGACGCACAATCTTGAGAGCAG
sequence-based reagentArr1 sThis paperqRT-PCR primersGCTCTGTGCGGTTACTGATCC
sequence-based reagentArr1 asThis paperqRT-PCR primersTGTCGGTGTTGTTGGTCACG
sequence-based reagentArr3 sThis paperqRT-PCR primersGCTAACCTGCCCTGTTCAGT
sequence-based reagentArr3 asThis paperqRT-PCR primersGCTAACCTGCCCTGTTCAGT
sequence-based reagentGrk1 sThis paperqRT-PCR primersTGAAGGCGACTGGCAAGATG
sequence-based reagentGrk1 asThis paperqRT-PCR primersAGGTCCGTCTTGGTCTCGAA
sequence-based reagentRgs9 sThis paperqRT-PCR primersTTCGCTCCCATTCGTGTTGT
sequence-based reagentRgs9 asThis paperqRT-PCR primersATGTCCTTCACCAGGGCTTC
sequence-based reagentRcv1 sThis paperqRT-PCR primersAGTGGGCCTTCTCGCTCTA
sequence-based reagentRcv1 asThis paperqRT-PCR primersATCATCTGGGAGGAGTTTCACA
sequence-based reagentActb sThis paperqRT-PCR primersCAACGGCTCCGGCATGTGC
sequence-based reagentActb asThis paperqRT-PCR primersCTCTTGCTCTGGGCCTCG
chemical compound, drugDIG RNA Labeling MixRocheSKU11277073910
chemical compound, drug1-Phenyl-2-thiourea (PTU)Sigma-AldrichCAS 103-85-5
chemical compound, drugParaformaldehyde (PFA)Sigma-AldrichCAS 30525-89-4
chemical compound, drugcOmplete, Mini, EDTA-free Protease Inhibitor CocktailRocheSKU11836170001
chemical compound, drugL-AP4Sigma-AldrichSKU A7929-.5MG
chemical compound, drugTBOASigma-Aldrich
antibodyIRDye 680RD Goat (polyclonal) anti-Mouse IgGLI-CORP/N: 926–68070(1:1000)
antibodyIRDye 800CW Goat (polyclonal) anti-Rabbit IgGLI-CORP/N: 926–32211(1:1000)
antibodyAnti-arr3a (Rabbit polyclonal)Renninger et al., 2011WB (1:250)
antibodyAnti-grk7a (Rabbit polyclonal)Rinner et al., 2005WB (1:500)
antibodyAnti-β-Actin (Mouse monoclonal)Sigma-AldrichA1978WB (1:1000)
software, algorithmMATLABMATLAB(https://ch.mathworks.com/)RRID:SCR_001622Version R2020b
software, algorithmRR (https://www.r-project.org/)RRID:SCR_001905Version 4.1.0
software, algorithmCOPASICOPASI (http://copasi.org/)RRID:SCR_014260
software, algorithmPrism - GraphPadGraphPad Prism (https://graphpad.com)RRID:SCR_015807Version 8.0.0
software, algorithmLabviewNational Instruments (https://www.ni.com/)RRID:SCR_014325
software, algorithmImageJImageJ (http://imagej.nih.gov/ij/)RRID:SCR_003070

Data availability

All data generated and analysed during this study are included in the manuscript and supporting files. The dataset has been uploaded to dryad at https://doi.org/10.5061/dryad.0cfxpnw26.

The following data sets were generated
    1. Zang J
    2. Gesemann M
    3. Keim J
    4. Samardzija M
    5. Grimm C
    6. Neuhauss SCF
    (2021) Dryad Digital Repository
    Circadian Regulation of Vertebrate Cone Photoreceptor Function.
    https://doi.org/10.5061/dryad.0cfxpnw26

References

    1. Brown AJ
    2. Pendergast JS
    3. Yamazaki S
    (2019)
    Peripheral Circadian Oscillators
    The Yale Journal of Biology and Medicine 92:327–335.
    1. Cunningham LL
    2. Gonzalez-Fernandez F
    (2000)
    Coordination between production and turnover of interphotoreceptor retinoid-binding protein in zebrafish
    Investigative Ophthalmology & Visual Science 41:3590–3599.
    1. Stenkamp DL
    2. Calderwood JL
    3. Van Niel EE
    4. Daniels LM
    5. Gonzalez-Fernandez F
    (2005)
    The interphotoreceptor retinoid-binding protein (IRBP) of the chicken (Gallus gallus domesticus
    Molecular Vision 11:833–845.

Decision letter

  1. Kristin Tessmar-Raible
    Reviewing Editor; University of Vienna, Austria
  2. Didier YR Stainier
    Senior Editor; Max Planck Institute for Heart and Lung Research, Germany
  3. Tom Baden
    Reviewer; University of Sussex, United Kingdom
  4. Pamela Menegazzi
    Reviewer; University of Wuerzburg, Germany

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Thank you for submitting your article "Circadian Regulation of Vertebrate Cone Photoreceptor Function" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Didier Stainier as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Tom Baden (Reviewer #1); Pamela Menegazzi (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. While we believe that your manuscript is in principle a very exciting advancement of the understanding of sensory perception, and thus likely well suited for publication in eLife, there were several critical comments and questions raised by the reviewers. Upon discussing the different reviewers' responses, we would ask you to address the following seven points below with particular importance. It is essential to properly address these points in order for us to consider your work for publication:

(1.) The finding that of the experiments examining retinal gene expression in larvae raised under constant darkness is at odds with several previous studies that show that robust circadian clock entrainment occurs only during the first days of zebrafish development and requires LD cycles for it. The finding of a maternal effect is (moderately phrased) "debated". The authors are aware of this, as they discuss this aspect in their own discussion:

line 7-9, page 22, "We did not observe this phenomenon in our study of visual transduction genes in the retina, suggesting the existence of an inheritable maternal clock in the eye (Delaunay et al., 2000).". At present their statement is a serious overinterpretation of their data, as it appears that they did not perform the controls to ensure constant environmental conditions. How was the DD collection really done? How about possible temperature changes the larvae might have been exposed to? Even small temperature changes can serve as entrainment cues.

Furthermore, it is actually rather suspicious that the DD cycling is pretty much in phase with the fluctuations of the transcripts under the LD cycle. Endogenous circadian periods are not exactly 24hrs and within 5days the peaks should have shifted relative to the LD cycle- which is not the case.

This point needs to be very carefully clarified.

(2.) Statistics and data presentation

The information on replicates is insufficient. Clearly define in all cases what "independent measurements" refer to and how many these are exactly in each case. Wherever n is below 10, please provide supplementary plots of the data with individual datapoints shown and refer to this in the respective figure legends.

Please also more carefully fill the transparent reporting form (e.g. information on group allocation (such as if masking was used?) is stated to be in the figure legends: Where is it?) Metadata can be downloaded from DRYAD, please add this as a statement to the Materials and methods section and below each Figure.

Importantly: In several cases these metadata are however NOT really self-explanatory and in part a little odd, e.g. Figure 5B- how do the metadata match with what is actually shown in the figure? (Also be aware that there are sometimes different labels in the Excel file itself versus its name. Or file names that could be interpreted as if the same fish and condition was tested twice, e.g. am: folder fish2: fish2_15_2 and fish2_15).

Statistics: Some stats are using tests that assume a normal distribution (e.g. t-test), but normality is not demonstrated (nor can it be in some cases as the n is too low). Please either verify such analyses with more robust statistical measures or explain why this would not be necessary.

(3.) Please improve the data presentation in Figure 5, which will likely benefit from a less binary analysis strategy. Why not compute FFTs of each response trace, and extract the power at stimulus frequency? This could be normalised if useful, for example against power at another frequency, or total power in the signal. This would allow plotting some consistent measure of response over circadian time for each stimulus frequency (yielding 5 curves). Presumably, there are also repeats in this data (n = 10 fish per condition? How many stimulus repeats?), which then would again allow quantifying variance as well.

(4.) The VMR experiments need to be much more carefully interpreted and discussed.

Firstly, it is not clear at all which of these aspects is due to visual sensation. In fact, zebrafish has 42 opsins, expressed at all kind of places. It has already been shown that eye-independent effects contribute significantly to the movement changes in response to sudden light changes (photokinesis).

Furthermore, the main difference (perhaps unsurprisingly) is that during the day larvae move more (i.e. higher baseline). When the light changes they presumably "twitch" which gives the peak, but since they are already moving, the relative peak is smaller than for fish at night, which move less as a baseline. Presumably, if fish use the same sort of motor programme in both conditions, they would, perhaps independent of baseline activity, move to a similar degree. This could be the simplest explanation of the fact that the two peaks are almost the same height. The subtle difference could then be explained by all kinds of other things ("warmed up" muscles in the light, possible circadian regulation of central circuits etc?). The OKR differences (Figure S7) are also quite subtle.

Taking these points together, it appears to be rather unclear how much of the differences can actually be linked to photoreceptor transduction differences in the eyes. The authors need to take all these points into consideration when interpreting their experiment and tone down their claims. The authors might consider to either remove Figure 7 entirely or to shift it into the Supplemental and instead consider a modelling approach. Numerous past phototransduction modelling studies provided amazingly functional frameworks that can be readily explored by "plugging in" some numbers of relative concentrations to see how this affects the photoreceptors. The authors have a beautiful collection of such numbers, for two ages in zebrafish and one in mice. What is the expected functional consequence for the photoresponse, based on all these cycling regulators? Something along these lines (not for a circadian context) was recently used e.g. for Figure 7 in Yoshimatsu et al., 2020 Neuron.

(5.) The presentation of the bioinformatics analysis for clock-regulated enhancer elements in the retinal gene promoters needs improvement. The data should be expanded significantly to show in detail, for each zebrafish gene, which enhancers have been identified and with which confidence (e.g. divergence from a consensus).

(6.) The graphical plots in Figures 1 and 2 are misleading. At first glance, they give the impression that samples have been collected over a 48 hours time course, which is incorrect. "Double plotting" of circadian data sets is often used in behavioural actograms, since it makes it easier to see trends in rhythmicity over longer time frames. However, in that situation the data is collected over multiple days and so rhythmicity is clearly demonstrated. In the case of gene expression reported here, a more honest way to present the data is, how it was done in Figure 3. One cycle has been sampled, NOT two, and so rhythmicity has not been directly visualised. However, given the amplitude of the changes in expression that have been observed during the 24 hours cycle, the sound statistical basis and the known regulatory input of the clock in the retina, it is acceptable to claim evidence for rhythmicity.

Furthermore, the authors should consider to use a more appropriate analysis to test for oscillations, for instance RAIN (Thaben and Westermark, J Biol Rhythms 2014). Given that RAIN does not make assumption on the waveform on the rhythms, it allows for testing of oscillation even under a light regimes that deviates from the standard LD12:12, as in this case. It should be possible to use the independent measurements as repetitions of the time series.

(7.) In 5 dpf larval zebrafish rods are non-functional. This should be mentioned somewhere, especially since results are to be compared to adults, or to mice, both of which have functional rods.

https://doi.org/10.7554/eLife.68903.sa1

Author response

(1.) The finding that of the experiments examining retinal gene expression in larvae raised under constant darkness is at odds with several previous studies that show that robust circadian clock entrainment occurs only during the first days of zebrafish development and requires LD cycles for it. The finding of a maternal effect is (moderately phrased) "debated". The authors are aware of this, as they discuss this aspect in their own discussion:

line 7-9, page 22, "We did not observe this phenomenon in our study of visual transduction genes in the retina, suggesting the existence of an inheritable maternal clock in the eye (Delaunay et al., 2000).". At present their statement is a serious overinterpretation of their data, as it appears that they did not perform the controls to ensure constant environmental conditions. How was the DD collection really done? How about possible temperature changes the larvae might have been exposed to? Even small temperature changes can serve as entrainment cues.

Furthermore, it is actually rather suspicious that the DD cycling is pretty much in phase with the fluctuations of the transcripts under the LD cycle. Endogenous circadian periods are not exactly 24hrs and within 5days the peaks should have shifted relative to the LD cycle- which is not the case.

This point needs to be very carefully clarified.

This is indeed an important point to clarify. We have detailed the procedure of sample collection and light treatment in the Material and Methods section and have added a paragraph to the discussion. Briefly, we detailed that our experimental conditions showed no evidence of inadvertent entrainment signals. The 5 day survey window is probably not enough to significantly deviate from the external rhythm for some genes. Indeed we found some that did shift, one even reversed the cycle. Hence, a systematic error is unlikely since genes are affected differently. Additionally, overall gene expression levels are also differently affected. Some genes were upregulated, while others showed a downregulation under DD conditions.

One important difference between our study and most published is that we collected eyes, while most studies used whole larvae, which includes other cycling tissues. We argued in the discussion that taking the whole larva may mask cycling of genes that show different expression cycling in different tissues. We show the example of rcv1a in the supplement (Figure 1, supplement Figure 1) where the transcript clearly cycles in the eye but is steady in the pineal.

(2.) Statistics and data presentation

The information on replicates is insufficient. Clearly define in all cases what "independent measurements" refer to and how many these are exactly in each case. Wherever n is below 10, please provide supplementary plots of the data with individual datapoints shown and refer to this in the respective figure legends.

Please also more carefully fill the transparent reporting form (e.g. information on group allocation (such as if masking was used?) is stated to be in the figure legends: Where is it?)

Metadata can be downloaded from DRYAD, please add this as a statement to the Materials and methods section and below each Figure.

Importantly: In several cases these metadata are however NOT really self-explanatory and in part a little odd, e.g. Figure 5B- how do the metadata match with what is actually shown in the figure? (Also be aware that there are sometimes different labels in the Excel file itself versus its name. Or file names that could be interpreted as if the same fish and condition was tested twice, e.g. am: folder fish2: fish2_15_2 and fish2_15).

Statistics: Some stats are using tests that assume a normal distribution (e.g. t-test), but normality is not demonstrated (nor can it be in some cases as the n is too low). Please either verify such analyses with more robust statistical measures or explain why this would not be necessary.

This is again is an important point that we changed. The statistics of all figures have been completely redone according current point (Figures 4 to 6) and according to point 6 (applying to Figure 1 to 3). The statistic information is now provided in supplement files 1 to 3 (Figure 1 to 3) and the rest in the metadata set.

We would like to add that this has really improved the manuscript and forced us to finally get more familiar with R.

(3.) Please improve the data presentation in Figure 5, which will likely benefit from a less binary analysis strategy. Why not compute FFTs of each response trace, and extract the power at stimulus frequency? This could be normalised if useful, for example against power at another frequency, or total power in the signal. This would allow plotting some consistent measure of response over circadian time for each stimulus frequency (yielding 5 curves). Presumably, there are also repeats in this data (n = 10 fish per condition? How many stimulus repeats?), which then would again allow quantifying variance as well.

This part has been completely redone according to the valuable suggestion. Although the reviewers were nice enough not to ask for additional experiments, we modified the light source to make it more controllable and performed new flicker ERG experiments with it.

(4.) The VMR experiments need to be much more carefully interpreted and discussed.

Firstly, it is not clear at all which of these aspects is due to visual sensation. In fact, zebrafish has 42 opsins, expressed at all kind of places. It has already been shown that eye-independent effects contribute significantly to the movement changes in response to sudden light changes (photokinesis).

Furthermore, the main difference (perhaps unsurprisingly) is that during the day larvae move more (i.e. higher baseline). When the light changes they presumably "twitch" which gives the peak, but since they are already moving, the relative peak is smaller than for fish at night, which move less as a baseline. Presumably, if fish use the same sort of motor programme in both conditions, they would, perhaps independent of baseline activity, move to a similar degree. This could be the simplest explanation of the fact that the two peaks are almost the same height. The subtle difference could then be explained by all kinds of other things ("warmed up" muscles in the light, possible circadian regulation of central circuits etc?). The OKR differences (Figure S7) are also quite subtle.

Taking these points together, it appears to be rather unclear how much of the differences can actually be linked to photoreceptor transduction differences in the eyes. The authors need to take all these points into consideration when interpreting their experiment and tone down their claims. The authors might consider to either remove Figure 7 entirely or to shift it into the Supplemental and instead consider a modelling approach. Numerous past phototransduction modelling studies provided amazingly functional frameworks that can be readily explored by "plugging in" some numbers of relative concentrations to see how this affects the photoreceptors. The authors have a beautiful collection of such numbers, for two ages in zebrafish and one in mice. What is the expected functional consequence for the photoresponse, based on all these cycling regulators? Something along these lines (not for a circadian context) was recently used e.g. for Figure 7 in Yoshimatsu et al., 2020 Neuron.

This point is very well taken. We got slightly carried away by our enthusiasm. We gladly follow the suggestion and placed the original Figure 7 into the supplement. We ran the model as suggested and are happy to report that the recovery of phototransduction is indeed correlated to the cyclic gene expression as expected.

(5.) The presentation of the bioinformatics analysis for clock-regulated enhancer elements in the retinal gene promoters needs improvement. The data should be expanded significantly to show in detail, for each zebrafish gene, which enhancers have been identified and with which confidence (e.g. divergence from a consensus).

The detailed enhancer element analysis proved to be very extensive, mainly since only few gene regulatory genes in other vertebrate organisms are well annotated. We feel that the analysis is outside of the scope of this article and decided to remove the data.

(6.) The graphical plots in Figures 1 and 2 are misleading. At first glance, they give the impression that samples have been collected over a 48 hours time course, which is incorrect. "Double plotting" of circadian data sets is often used in behavioural actograms, since it makes it easier to see trends in rhythmicity over longer time frames. However, in that situation the data is collected over multiple days and so rhythmicity is clearly demonstrated. In the case of gene expression reported here, a more honest way to present the data is, how it was done in Figure 3. One cycle has been sampled, NOT two, and so rhythmicity has not been directly visualised. However, given the amplitude of the changes in expression that have been observed during the 24 hours cycle, the sound statistical basis and the known regulatory input of the clock in the retina, it is acceptable to claim evidence for rhythmicity.

Furthermore, the authors should consider to use a more appropriate analysis to test for oscillations, for instance RAIN (Thaben and Westermark, J Biol Rhythms 2014). Given that RAIN does not make assumption on the waveform on the rhythms, it allows for testing of oscillation even under a light regimes that deviates from the standard LD12:12, as in this case. It should be possible to use the independent measurements as repetitions of the time series.

We performed the statistics and adjusted the plots accordingly (see also response to point 2).

(7.) In 5 dpf larval zebrafish rods are non-functional. This should be mentioned somewhere, especially since results are to be compared to adults, or to mice, both of which have functional rods.

This is indeed an important point to make. We have now referenced the functional cone only zebrafish retina and specified in each experiment which cone subtype contributes. Additionally we provide the emission spectrum of the light sources (Figure 4-figure supplement 2).

In some we were rightfully challenged (particularly on the statistics) by the reviewers and their insightful suggestions, including the model, improved the manuscript a lot.

https://doi.org/10.7554/eLife.68903.sa2

Article and author information

Author details

  1. Jingjing Zang

    University of Zurich, Department of Molecular Life Sciences, Zurich, Switzerland
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2186-6001
  2. Matthias Gesemann

    University of Zurich, Department of Molecular Life Sciences, Zurich, Switzerland
    Contribution
    Methodology, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7635-1235
  3. Jennifer Keim

    University of Zurich, Department of Molecular Life Sciences, Zurich, Switzerland
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Marijana Samardzija

    Lab for Retinal Cell Biology, Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
    Contribution
    Investigation, Validation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Christian Grimm

    Lab for Retinal Cell Biology, Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
    Contribution
    Project administration, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9318-4352
  6. Stephan CF Neuhauss

    University of Zurich, Department of Molecular Life Sciences, Zurich, Switzerland
    Contribution
    Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review and editing
    For correspondence
    stephan.neuhauss@mls.uzh.ch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9615-480X

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_200376)

  • Marijana Samardzija

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

Acknowledgements

We would like to thank Kara Kristiansen and Martin Walther for expert animal maintenance.

Ethics

All animal experiments were carried out in the line with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the Veterinary Authorities of Kanton Zurich, Switzerland (TV4206).

Senior Editor

  1. Didier YR Stainier, Max Planck Institute for Heart and Lung Research, Germany

Reviewing Editor

  1. Kristin Tessmar-Raible, University of Vienna, Austria

Reviewers

  1. Tom Baden, University of Sussex, United Kingdom
  2. Pamela Menegazzi, University of Wuerzburg, Germany

Publication history

  1. Received: March 30, 2021
  2. Preprint posted: May 7, 2021 (view preprint)
  3. Accepted: September 20, 2021
  4. Accepted Manuscript published: September 22, 2021 (version 1)
  5. Version of Record published: October 6, 2021 (version 2)

Copyright

© 2021, Zang et al.

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

Metrics

  • 546
    Page views
  • 141
    Downloads
  • 0
    Citations

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

Download links

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

Downloads (link to download the article as PDF)

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

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

Further reading

    1. Neuroscience
    Roni O Maimon-Mor et al.
    Research Article Updated

    The study of artificial arms provides a unique opportunity to address long-standing questions on sensorimotor plasticity and development. Learning to use an artificial arm arguably depends on fundamental building blocks of body representation and would therefore be impacted by early life experience. We tested artificial arm motor-control in two adult populations with upper-limb deficiencies: a congenital group—individuals who were born with a partial arm, and an acquired group—who lost their arm following amputation in adulthood. Brain plasticity research teaches us that the earlier we train to acquire new skills (or use a new technology) the better we benefit from this practice as adults. Instead, we found that although the congenital group started using an artificial arm as toddlers, they produced increased error noise and directional errors when reaching to visual targets, relative to the acquired group who performed similarly to controls. However, the earlier an individual with a congenital limb difference was fitted with an artificial arm, the better their motor control was. Since we found no group differences when reaching without visual feedback, we suggest that the ability to perform efficient visual-based corrective movements is highly dependent on either biological or artificial arm experience at a very young age. Subsequently, opportunities for sensorimotor plasticity become more limited.

    1. Cell Biology
    2. Neuroscience
    Shahzad S Khan et al.
    Research Advance

    Activating LRRK2 mutations cause Parkinson's disease, and pathogenic LRRK2 kinase interferes with ciliogenesis. Previously, we showed that cholinergic interneurons of the dorsal striatum lose their cilia in R1441C LRRK2 mutant mice (Dhekne et al., 2018). Here, we show that cilia loss is seen as early as 10 weeks of age in these mice and also in two other mouse strains carrying the most common human G2019S LRRK2 mutation. Loss of the PPM1H phosphatase that is specific for LRRK2-phosphorylated Rab GTPases yields the same cilia loss phenotype seen in mice expressing pathogenic LRRK2 kinase, strongly supporting a connection between Rab GTPase phosphorylation and cilia loss. Moreover, astrocytes throughout the striatum show a ciliation defect in all LRRK2 and PPM1H mutant models examined. Hedgehog signaling requires cilia, and loss of cilia in LRRK2 mutant rodents correlates with dysregulation of Hedgehog signaling as monitored by in situ hybridization of Gli1 and Gdnf transcripts. Dopaminergic neurons of the substantia nigra secrete a Hedgehog signal that is sensed in the striatum to trigger neuroprotection; our data support a model in which LRRK2 and PPM1H mutant mice show altered responses to critical Hedgehog signals in the nigrostriatal pathway.