Constant light enhances synchrony among circadian clock cells and promotes behavioral rhythms in VPAC2-signaling deficient mice

Both WT andmice confined the majority of intense wheel-running activity to the dark phase of the LD cycle. On transfer to LL, WT mice behaved in a manner consistent with previous descriptions, exhibiting a large phase delay in locomotor activity (~5 h), and suppression of wheel-running compared to LD (~110 revs/h in LL vs. ~400 rev/h in LD;= 0.001;-Test; n = 12;). Initial disruption of rhythmicity, lasting ~2–3 days, was followed for the majority (11 of 12) of individuals by more robust rhythms that persisted throughout the 36 days of LL examined here. Unlike DD behavior, where arrhythmicity in WT mice is uncommon and unexpected, one WT mouse failed to express identifiable circadian rhythms in behavior in LL demonstrating, as has previously been reported, that LL can be disruptive to WT circadian rhythms in locomotor activity. Over the time course of LL examined here, we observed no significant change in the percentage of rhythmic WT mice (= 0.677; Fisher’s Exact Test with Freeman-Halton extension; n = 12;;early vs. mid vs. late LL; though also see) and no significant change in rhythm strength (= 0.25; paired-Test of FFT spectral power in early vs late LL; n = 12; Fig. 1h). Indeed, 50% of WT individuals exhibited increased spectral power in late LL vs early, while 50% exhibited a decrease (). As is common in LL, rhythmic WT mice expressed a mean period that was substantially longer (25.03 ± 0.11 h; see) than is observed for this strain in DD. Vipr2 p t p p t −/− ^{26 27 28} Fig. 1a,b ^{24 29 30} Fig. 1e 2 3 Fig. 4a Fig. 1i Fig. 1a,b ^{7 31}

The majority ofindividuals exhibited overtly disrupted rhythms on release into LL, though unlike WT mice, this disruption persisted substantially longer than the first 2–3 days in LL (). Indeed, while typically ~50% ofmice generate robust rhythms in locomotor behavior in DD (g andand see), only ~30% ofmice (7 of 24) exhibited identifiable circadian rhythms in wheel-running during early LL (). With increasing duration in LL, however, the percentage of rhythmic individuals significantly increased, reaching 83% (20 of 24 individuals) by late LL (;= 0.00032, Fisher’s Exact Test with Freeman-Halton extension; n = 24). Consistent with this, rhythm strength ofmice significantly increased over time in LL (;= 0.040; paired-Test of FFT spectral power in early vs late LL; n = 24), an increase observed in 75% ofindividuals (). As such, the effect of LL onlocomotor rhythms was time-dependent, with initial disruption of rhythmicity in the short term followed by a rhythm enhancing effect after extended exposure. Vipr2 Vipr2 Vipr2 p Vipr2 p t Vipr2 Vipr2 −/− −/− −/− −/− −/− −/− Fig. 1c,d Figs 1 2 ²⁴ Fig. 1f Fig. 1f Fig. 1h Fig. 1i

Rhythmicmice (n = 20) expressed a mean period of 24.29 ± 0.08 h in LL, significantly shorter than WT mice under these conditions (= 0.000009; n = 11 WT and 20). Once rhythmic, the period ofmice remained consistent throughout the duration examined () and was longer than has been observed for this strain in DD(and see). Wheel-running activity (rev/h) ofmice did not significantly reduce on transfer from LD to LL (173 ± 23 vs. 135 ± 20 rev/h, respectively;= 0.35; n = 24;), and this parameter ofwheel activity under LL was not significantly different to that of WT animals under LL (= 0.50; n = 12 WT and 24). Vipr2 p Vipr2 Vipr2 Vipr2 p Vipr2 p Vipr2 −/− −/− −/− −/− −/− −/− Fig. 1c,d ^{24 32} Fig. 2 Fig. 1c,d

Thus, exposure to LL has a disruptive effect on the expression of WT behavioral rhythms but induces a time-related enhancement of rhythmicity inmice after extended durations. Vipr2 −/−

To test whether LL-induced improvements in behavioral rhythmicity persisted in the subsequent absence of light, following the initial 36 days of LL (LL1) a subgroup of 12mice were transferred to DD for 36 days. The rhythm characteristics of this subset were representative of the wholegroup examined in LL1. On transfer into DD,mice rapidly reverted to behavioral phenotypes well-documented for this genotype under DD conditions; a continuum of phenotypes was observed across individuals, spanning arrhythmicity through to rhythmic with a short period (). Whilst 83% of this cohort had expressed a circadian rhythm in behavior at the end of LL1, only 50% were rhythmic during DD, and the period of these rhythms, where present, was reduced by ~1.6 h from near 24 h to ~22.4 h (24.03 ± 0.04 h for this subset in LL1 (n = 10) to 22.41 ± 0.04 h in DD (n = 6);< 0.001;). The rhythmicity of these mice did not overtly change across the duration DD examined here (g anda). This subgroup ofmice were subsequently returned to LL for a further 36 days (LL2) during which each individual expressed a locomotor phenotype consistent with its behavior during LL1 (83% (10 of 12) rhythmic in LL2, mean period 24.05 ± 0.05 h;).mice reexposed to this second epoch of LL tended to regain the ‘LL-like’ behavioral phenotype more rapidly than during the original LL exposure (e.g.). Vipr2 Vipr2 Vipr2 p Vipr2 Vipr2 −/− −/− −/− −/− −/− - ^{24 32 33} Fig. 2a Fig. 2 Figs 1 2 Fig. 2 Fig. 2a

Given the time-dependent effect of LL onbehavior, we next assessed the correlation of LL-induced behavioral changes with::eGFP expression in the SCN. A separate cohort of behaviorally phenotyped WT andmice were culled at randomly assigned timepoints after increasing durations of LL and, using confocal microscopy, we imaged::eGFP expression in live SCN-containing brain slice cultures from each individual (). Vipr2 per1 Vipr2 per1 −/− −/− Fig. 3

Mice used in this part of the study showed the same trends in behavioral rhythmicity as those in the initial behavioral examinations presented previously. Indeed, ranking allmice used for confocal imaging in order of behavioral rhythmicity at the time of cull revealed a significant positive correlation between time in LL and ranked behavioral rhythmicity (Spearman’svalue 0.522;= 0.038;= 0.387; n = 16;). A linear trend line poorly fitted the data for WT time in LL plotted against behavioral rhythmicity rank (= 0.085;), and these data were not significantly correlated (Spearman’s= −0.442;= 0.15; n = 12). However, visual interpretation of the data revealed that with extended durations of LL (>29 days), rhythmicity consistently decreased in WT mice, an association that did reach statistical significance for this subset of animals (Spearman’s= −0.813;= 0.005; n = 10;). Vipr2 rho p R R rho p rho p −/− 2 2 Fig. 4d Fig. 4a Fig. 4a

To assess the relationship between behavioral rhythmicity and circadian function in the SCN at single cell level, we divided::eGFP expression data for each genotype into 2 groups; individuals behaviorally rhythmic at the time of cull (n = 9 WT; n = 10), and individuals behaviorally arrhythmic at cull (n = 3 WT; n = 6). The time of cull for each individual was assigned prior to the experiment commencing and therefore was not influenced by the degree of behavioral rhythmicity of the animals. SCN slices from behaviorally rhythmic WT mice contained a significantly higher percentage of rhythmic individual cells than slices from behaviorally arrhythmic conspecifics (99.6 ± 0.4% vs. 96.7 ± 1.9%;= 0.016;). Intriguingly, however, we found no significant difference in the percentage of rhythmic cells between slices from behaviorally rhythmic and arrhythmicmice (both ~80% of cells rhythmic;= 0.380;). per1 Vipr2 Vipr2 p Vipr2 p −/− −/− −/− Fig. 3a,c Fig. 3b,c

Crucially, this intergenotype difference in the relationship of behavioral and SCN cellular rhythmicity was not seen for synchrony between cellular rhythms in the SCN; we observed significantly greater intercellular synchrony within slices from behaviorally rhythmic WT andmice than in slices from arrhythmic conspecifics (0.36 ± 0.05 vs. 0.18 ± 0.01;= 0.0039 [synchrony in slices from rhythmic WT mice vs. arrhythmic WT mice, respectively]; and 0.41 ± 0.07 vs. 0.22 ± 0.03;= 0.0284 [slices from rhythmic vs. arrhythmicanimals]; Rayleigh R values;). Further, while 67% of slices from behaviorally rhythmic WT animals were significantly synchronized, and 60% of slices from behaviorally rhythmicmice, no slices from behaviorally arrhythmic mice of either genotype exhibited significant intercellular synchrony (). We found no significant differences in the mean period or amplitude of cells between slices from behaviorally rhythmic and arrhythmic individuals for either WT ormice (all< 0.05) though both the period and amplitude ofoscillations were consistently lower than those of WT oscillations, regardless of behavioral rhythmicity (all< 0.001;). Vipr2 p p Vipr2 Vipr2 Vipr2 p Vipr2 p −/− −/− −/− −/− −/− Fig. 3a,b,d Fig. 3a,b,e Fig. 3f,g

As regional heterogeneity in period and phase within the SCN have previously been associated with the maintenance of circadian synchrony, we next assessed differences in these parameters between the dorsal and ventral regions of the SCN in behaviorally rhythmic and arrhythmic mice of both genotypes. In WT SCN we found no significant changes in dorsal-ventral period or phase heterogeneity between behaviorally rhythmic and arrhythmic animals (). InSCN, however, the differences in both period and phase between dorsal and ventral regions were significantly smaller (as well as less variable; see smaller SEM) in slices from behaviorally rhythmic mice (), a characteristic consistent with improved cellular synchrony in behaviorally rhythmic animals. ³⁴ Fig. 3h Fig. 3h Vipr2 −/−

Finally, to better describe the genotype-dependent and -independent aspects of the relationships between behavioral rhythmicity, SCN synchrony and time in LL, we calculated Spearman’s Rank Correlation Coefficients for these parameters using behavioral data ranked in order of rhythmicity. For both WT (n = 12) and(n = 16) mice we found a significant correlation between ranked behavioral rhythmicity and intercellular synchrony (= 0.0025 [Spearman= 0.755] for WT and= 0.0215 [0.512]) and that a linear trend line fit the data well (= 0.462 and 0.168, respectively;). Consistent with this, and with the positive correlation between time in LL and behavioral rhythmicity inmice (), we also found a significant positive relationship between time in LL and SCN intercellular synchrony inmice (= 0.007;= 0.6;). In WT mice however, as we observed for the relationship between time in LL and behavioral rhythmicity (see above;), time in LL was not significantly correlated with SCN synchrony (Spearman’s= −0.374) and a linear trend line poorly fitted the data (= 0.010;). Similarly to time in LL vs. behavioral rhythmicity for WT mice however, visual interpretation of the time in LL vs. cellular synchrony plot for WT mice suggested progressively decreasing synchrony later in LL at extended (>29 days) durations of LL; we found a significant correlation between these parameters in WT mice when we assessed this >29 day in LL subset of animals only (Spearman’s= −0.875;= 0.001; n = 10;). Vipr2 p rho p Vipr2 R Vipr2 Vipr2 p rho rho R rho p −/− −/− 2 −/− −/− 2 Fig. 4c,f Fig. 4d Fig. 4e Fig. 4a Fig. 4b Fig. 4b

This study used adult male and female mice that expressed the::eGFP reporter and either expressed thegene (WT) or lacked expression of the VPACreceptor (, see). Prior to experimentation, all mice were group housed under a 12 h light:12 h dark (LD) cycle withaccess to food (Beekay, B&K Universal, Hull, UK) and water. Environmental temperature was maintained at ∼23 °C and humidity at ∼40%. All procedures and experimental protocols were carried out in accordance with the UK Animals (Scientific Procedures) Act 1986 and approved by The University of Manchester Review Ethics Panel. per1 Vipr2 Vipr2 ad libitum 2 −/− ⁷

WT andmice were individually housed in running wheel-equipped cages under a 12:12 LD cycle for 7–14 days (~3 × 10photons/s/cmlights on [~110 μW/cmlights on, from a broad spectrum fluorescent light source]) then released into LL (~3 × 10photons/s/cm). Wheel revolutions were recorded in Chronobiology Kit (Stanford Software Systems, Santa Cruz, California, USA) using 5 min bins for analysis. Animals of each genotype were divided into 2 cohorts for assessment of 1) the effects of LL on wheel-running behavior (n = 12 WT, n = 24) and 2) correlation of the effects of LL on wheel-running behavior with SCN::eGFP expression (n = 12 WT, n = 16). Vipr2 Vipr2 per1 Vipr2 −/− 14 2 2 14 2 −/− −/−

For experiment 1, mice were free-run undisturbed for 36 days of LL (LL1) during which behavioral rhythms were assessed in early (1-12 days), mid (13-24) and late LL (25-36). After LL1, n = 12mice were placed in DD for 36 days before a further 36 day LL epoch (LL2). The remainingmice (n = 12) and all WT mice (n = 12) were removed from the experiment after LL1. Rhythmicity was assessed by 4 experienced observers, blind to genotype and experimental conditions, using a combined evaluation of locomotor actograms, periodograms, average waveforms and the time-frequency spectrogram obtained from sliding window fast Fourier transform (FFT), performed with a 7-day window that slid at 1-hr increments through wheel-running time series data smoothed with the Hodrick-Prescott filter (processed using Mathematica, Wolfram Research). In the majority of cases assessments of rhythmicity were unanimous (>90%), but for the infrequent cases where a consensus decision could not be easily reached, a final decision on rhythmicity was based on objective analysis of average waveforms. We determined that for a behavioral epoch to be considered rhythmic, a window centred on the most active part of the average waveform, consisting of 50% of its duration, should contain at least 75% of total activity. This method was validated on our entire data set and generated results entirely consistent with our prior assessments of behavior where we had initially been able to classify rhythmicity. For behavioral epochs that were clearly arrhythmic and for which no period could be determined from either periodogram analysis or visual inspection of actograms, a nominal value of 24 h was used to generate average waveforms. Where behavioral epochs were considered rhythmic, period was assessed in Chronobiology Kit using eyefit regression lines through onsets of activity and confirmed with spectral power analysis andperiodograms. Vipr2 Vipr2 Chi −/− −/− 2 ³⁴

For experiment 2, mice were initially free-run in LL, as in experiment 1, though culled after increasing durations of LL ranging from a minimum of 22 days, to a maximum of 39 days. As the aim of this experiment was to investigate a possible association between time in LL, behavioral rhythmicity and SCN circadian function, mice were randomly assigned a cull timepoint in LL prior to the experiment commencing. This avoided the possibility of unintentional bias associated with selecting cull timepoints after behavioral data had already been collected. Immediately following cull, SCNs were collected for confocal imaging of::eGFP expression. Behavioral rhythms were analyzed for the last 10–14 days before cull and the behavior of all mice ranked in order of rhythmicity for correlation with SCN::eGFP data and duration of time in LL. Ranking was performed blind by 4 experienced observers and correlated strongly (= 0.675;< 0.0001) with the percentage of activity contained during a 50% window of the average waveform using the methods described above. Experimenters were blind to both the identity of mice in behavioral experiments and the parameters of SCN::eGFP data at the time of assessment for ranking of behavior. Behavior was further categorized as either rhythmic or arrhythmic (using the same procedure as in experiment 1) to make within-genotype comparisons of SCN::eGFP data according to behavioral rhythmicity. per1 per1 R p per1 per1 2

Mice were culled by cervical dislocation following isoflurane anesthesia (Baxter Healthcare Ltd., Norfolk, UK), at circadian time (CT) 2–6 (to avoid different cull times influencing circadian parameters of SCN function). Cultures were prepared as described previouslyusing 250 μm thick SCN-containing coronal brain slices and 100 μg/ml and 25 μg/ml penicillin-streptomycin (Gibco Invitrogen Ltd, Paisley, UK) in collection medium and culture medium, respectively. Cultures were stored in darkness at 37 °C for ~24 h before imaging::eGFP fluorescence using a C1 confocal system running on a TE2000 inverted microscope (Nikon, Kingston, UK). Images were captured with a 10 x 0.3NA PlanFluotar objective (Nikon) and the system maintained at 37 °C. A 488 nm laser was used for excitation and emitted fluorescence detected using a 515/30 nm bandpass filter. 8 image ‘Z’ stacks were acquired every hour for ~120 h, using 4x Kalman averaging, 1.5x confocal zoom, an open pinhole and 0.2 Hz frame rate. Each stack covered a total Z depth of 32 μm and images were recorded at 512 × 512 pixels. Stacks were collapsed to an average projection using ImageJ and fluorescence profiles across time assessed for 30 individual cells selected at randomusing a region of interest tool. Raw fluorescence data were corrected for variations in background brightness by subtracting the brightness of a standardized, non-eGFP expressing, extra-SCN region from each data value before corrected data were smoothed using a 3 h running mean. ^{67 68 7 7} per1

Cells were rated as rhythmic or arrhythmic by two experienced observers and amplitude, period and phase (time of peak at 12–36 hours) were assessed for each analyzed cell. The period of cellular rhythms was calculated using peak-peak and trough-trough durations for at least two cycles and phase data for individual cells were used to create Rayleigh plots to quantify the synchrony (phase-clustering) of rhythms between cells within each SCN. Amplitude was calculated as the brightness differential from the peak used for phase analysis (12–36 hours into recording) to the following trough. Regional differences in period and phase within the SCN between behaviorally rhythmic and arrhythmic animals (period and phase heterogeneity) were assessed by classifying the location of each analyzed cell as either dorsal or ventral, based on anatomical characteristics of the SCN, and comparing the mean difference in each parameter between cells located in these two subregions. ⁶⁹

As appropriate, statistically significant differences in continuous data were determined using either-Tests (paired or unpaired) or one/two way ANOVA withpairwise comparisons. Categorical data for the percentage of rhythmic and arrhythmic animals were analyzed using Fisher’s Exact Test with Freeman-Halton extension. Rayleigh Tests were used to determine statistical significance of cellular synchrony and the Rayleigh R statistic used to quantify the degree of synchrony. Statistical significances of correlations for ranked data were assessed using the non-parametric Spearman’s Rank Order Test. All statistical tests were run with α set at< 0.05 required for significance, using Microsoft Excel, Graphpad Prism, the VasserStats online statistical resource () and custom MATLAB scripts provided by Dr. Timothy Brown (University of Manchester). t a priori p http://vassarstats.net/↗