Mice lacking proSAAS display alterations in emotion, consummatory behavior and circadian entrainment

Abstract

ProSAAS is a neuroendocrine protein that is cleaved by neuropeptide-processing enzymes into more than a dozen products including the bigLEN and PEN peptides, which bind and activate the receptors GPR171 and GPR83, respectively. Previous studies have suggested that proSAAS-derived peptides are involved in physiological functions that include body weight regulation, circadian rhythms and anxiety-like behavior. In the present study, we find that proSAAS knockout mice display robust anxiety-like behaviors in the open field, light-dark emergence and elevated zero maze tests. These mutant mice also show a reduction in cued fear and an impairment in fear-potentiated startle, indicating an important role for proSAAS-derived peptides in emotional behaviors. ProSAAS knockout mice exhibit reduced water consumption and urine production relative to wild-type controls. No differences in food consumption and overall energy expenditure were observed between the genotypes. However, the respiratory exchange ratio was elevated in the mutants during the light portion of the light-dark cycle, indicating decreased fat metabolism during this period. While proSAAS knockout mice show normal circadian patterns of activity, even upon long-term exposure to constant darkness, they were unable to shift their circadian clock upon exposure to a light pulse. Taken together, these results show that proSAAS-derived peptides modulate a wide range of behaviors including emotion, metabolism and the regulation of the circadian clock.

Keywords: anxiety; circadian activity; fear; metabolism; mice; proSAAS knockout.

FIGURE 1

Spontaneous motor activity in a novel open field with proSAAS KO mice. All motor activities were monitored over 30 min in 5‐min blocks or as cumulative activities. (A) Horizontal activity as distance traveled for WT and proSAAS KO mice. A RMANOVA found significant effects of time [F _(5,90) = 10.277, p < 0.001] and genotype [F _(1,18) = 5.569, p = 0.030]. Inset, cumulative locomotion over 30 min [t ₍₁₈₎ = 2.360, p = 0.030]. (B) Vertical activity as beam‐breaks. A RMANOVA detected significant effects of genotype [F _(1,18) = 3.523, p = 0.044]. Inset, cumulative rearing over 30 min [t ₍₁₈₎ = 1.804, p = 0.044]. (C) Horizontal activity as distance traveled in the center zone. A RMANOVA detected significant time [F _(5,90) = 7.195, p < 0.001] and genotype effects [F _(1,18) = 5.957, p = 0.025]. Inset, cumulative center distance traveled over 30 min [t ₍₁₈₎ = 2.441, p = 0.025]. (D) Horizontal activity as distance traveled in the perimeter of the open field. A RMANOVA detected a significant time effect [F _(5,90) = 7.968, p < 0.001]. Inset, cumulative perimeter distance traveled over 30 min. N = 10 mice/genotype; *p < 0.05, KO versus WT controls

FIGURE 2

Behavioral responses in the elevated zero maze to diazepam for WT and proSAAS KO mice. (A) Percent time spent in open areas of the maze for mice given the vehicle, or 0.5 or 1 mg/kg diazepam. A two‐way ANOVA identified significant genotype [F _(1,54) = 32.615, p < 0.001] and treatment effects [F _(2,54) = 17.025, p < 0.001]; the genotype by treatment interaction was significant [F _(2,54) = 7.025, p = 0.001]. (B) Open area transitions for mice administered the same regimen. A two‐way ANOVA found the genotype [F _(1,54)=31.770, p < 0.001] and treatment effects [F _(2,54) = 7.484, p = 0.001], and the genotype by treatment interaction to be significant [F _(2,54) = 7.868, p = 0.001]. (C) Stretch‐attend postures into the open areas. A two‐way ANOVA showed the main effects of treatment [F _(2,54) = 4.812, p = 0.012] and the genotype by treatment interaction [F _(2,54) = 9.925, p < 0.001] were significant. (D) Head‐dip behaviors. The two‐way ANOVA observed significant genotype [F _(1,54) = 11.647, p < 0.001] and treatment effects [F _(2,54) = 5.099, p = 0.009], and a significant genotype by treatment interaction [F _(2,54) = 4.879, p = 0.011]. N = 10 mice/genotype/treatment; *p < 0.05, KO versus WT controls; ⁺ p < 0.05, compared with vehicle‐treated mice within genotype; ^# p < 0.05, 0.05 versus 1 mg/kg diazepam within genotype

FIGURE 3

Freezing responses of WT and proSAAS KO mice in fear conditioning. (A) Percent freezing in the 2 min prior to CS presentation, during the CS interval, and following the CS‐UCS pairing. A RAMONA found a significant main effect of time [F _(3,51) = 10.132, p < 0.001] and genotype [F _(1,17) = 12.611, p = 0.002], as well as a significant time by genotype interaction [F _(3,51) = 6.132, p = 0.001]. (B) Percent freezing during context testing shown in 1 min blocks across the 5 min test. An ANOVA failed to detect any significant effects. (C) Percent time spent freezing during cued testing depicted in 1 min blocks across the 5 min test. During the first 2 min no CS or UCS were presented, while in the final 3 min the CS alone was present. A RAMONA noted a significant effect of time [F _(4,68) = 85.041, p < 0.001] and genotype [F _(1,17) = 29.666, p < 0.001]; the time by genotype interaction was also significant [F _(4,68) = 3.048, p = 0.023]. N = 9–10 mice/genotype; *p < 0.05, KO versus WT controls

FIGURE 4

Fear‐potentiated startle in WT and proSAAS KO mice. (A) Startle responses to the 100, 105 and 110 dB white noise stimuli during pre‐conditioning. A RMANOVA found only the main effects of dB on startle intensity [F _(2,34) = 38.246, p < 0.001] to be significant. (B) FPS to the CS preceding the 100, 105, and 110 dB white noise stimuli examined 48 h after CS‐UCS pairings. For post‐conditioning, a RMANOVA detected significant effects of dB on startle intensity [F _(2,34) = 30.033, p < 0.001] and genotype [F _(1,17) = 5.586, p = 0.030], as well as a significant effect of dB on the startle intensity by genotype interaction [F _(2,34) = 19.364, p < 0.001]. N = 9–10 mice/genotype; *p < 0.05, KO versus WT controls; ^† p < 0.05, compared with the 100 dB response; ^‡ p < 0.05, compared with the 105 dB response

FIGURE 5

Cumulative motor activity and feeding behavior, heat production, and indirect calorimetry during the light and dark cycles for WT and proSAAS KO mice. (A–B) Cumulative motor activities of WT and proSAAS KO mice over 3 days of testing. A RMANOVA detected significant main effects of diurnal rhythm [F _(1,18) = 30.765, p < 0.001] and activity periods [F _(1,18) = 345.728, p < 0.001], and a significant diurnal rhythm by activity period interaction [F _(1,18) = 49.920, p < 0.001]. (C–D) Cumulative feeding behaviors of WT and proSAAS KO animals. A RMANOVA for feeding behavior found a significant main effect of activity period [F _(1,18) = 49.138, p < 0.001] and a significant diurnal rhythm by time interaction [F _(1,18) = 6.494, p < 0.020]. (E–F) Cumulative energy expenditure by WT and proSAAS KO mice. The RMANOVA revealed significant main effects of diurnal rhythm [F _(1,18) = 46.397, p < 0.001] and activity periods [F _(1,18) = 287.736, p < 0.001]. (G–H) Cumulative calorimetry in WT and proSAAS KO animals. A RMANOVA for the respiratory exchange ratio noted significant effects of diurnal rhythm [F _(1,18) = 12.519, p < 0.001] and activity period [F _(1,18) = 109.829, p < 0.001), with the diurnal rhythm by activity period [F _(1,18) = 10.691, p < 0.001] and the diurnal rhythm by activity period by genotype interactions being significant [F _(1,18) = 4.040, p < 0.051]. N = 10 (5 males/genotype, 5 females/genotype) mice/genotype/cycle; *p < 0.05, versus the WT controls; ^§ p < 0.05, active versus the inactive period within genotype; ^¤ p < 0.05, light versus dark cycle within genotype and activity phase

FIGURE 6

Wheel running circadian activity for WT and proSAAS KO mice. (A) Representative Actigram showing circadian wheel running activity for a WT mouse across 101 days of testing. Mice were introduced to a 12:12 h LD cycle for 15 days, followed by DD for a 75 day free‐run period during which a 6 h light pulse was given at days 70 (LP1) and 80 (LP2); mice were re‐entrained to a 12:12 h LD cycle for the final 10 days of testing. Note the shift in onset of circadian activity during DD phase and the subsequent shifts in activity to each light pulse. (B) Representative Actigram showing wheel running activity of a proSAAS KO mouse across 101 days of testing as described for the WT animal. (C–D) Mean running wheel activity counts (rpm) for WT and proSAAS KO mice during entrainment (black circle), the DD free run period (FRP) before light pulse 1 (open triangle), and during re‐entrainment (filled square), showing the onset and offset of circadian activity. A RMANOVA reported a significant effect of time [F _(23,1173) = 61.234, p < 0.001] and a significant time by test‐period interaction [F _(46,1173) = 12.736, p < 0.001]. (E–F) Mean running wheel activity counts (rpm) for WT mice for the 3 phases of the 75‐day DD FRP [before light pulse 1 is given (clear triangle), following the light pulse 1 on day 70 (filled triangle), and following light pulse 2 on day 80 (black triangle)]. Note that the light pulses increased wheel activity counts at 11–13 h, with very low activity prior to 10 h. (F) Mean running wheel activity counts (rpm) for all proSAAS KO mice for the 3 phases of the 75‐day DD FRP as described for WT animals. Note that the light pulses do not prevent the leftward shift in increased running wheel activity before 10 h, as seen with the WT controls. A RMANOVA found the effects of time [F _(23,1173) = 2.685, p = 0.027] and the time and genotype [F _(23,1173) = 6.849, p < 0.001], time by test‐period [F _(23,1173) = 2.533, p = 0.008], and time by test‐period by genotype interactions [F _(23,1173) = 1.655, p = 0.040] to be significant. n = 9–10 mice/genotype/condition