Sustained sleep fragmentation induces sleep homeostasis in mice

Abstract

Study objectives: Sleep fragmentation (SF) is an integral feature of sleep apnea and other prevalent sleep disorders. Although the effect of repetitive arousals on cognitive performance is well documented, the effects of long-term SF on electroencephalography (EEG) and molecular markers of sleep homeostasis remain poorly investigated. To address this question, we developed a mouse model of chronic SF and characterized its effect on EEG spectral frequencies and the expression of genes previously linked to sleep homeostasis including clock genes, heat shock proteins, and plasticity-related genes.

Design: N/A.

Setting: Animal sleep research laboratory.

Participants: Sixty-six C57BL6/J adult mice.

Interventions: Instrumental sleep disruption at a rate of 60/h during 14 days.

Measurements and results: Locomotor activity and EEG were recorded during 14 days of SF followed by recovery for 2 days. Despite a dramatic number of arousals and decreased sleep bout duration, SF minimally reduced total quantity of sleep and did not significantly alter its circadian distribution. Spectral analysis during SF revealed a homeostatic drive for slow wave activity (SWA; 1-4 Hz) and other frequencies as well (4-40 Hz). Recordings during recovery revealed slow wave sleep consolidation and a transient rebound in SWA, and paradoxical sleep duration. The expression of selected genes was not induced following chronic SF.

Conclusions: Chronic SF increased sleep pressure confirming that altered quality with preserved quantity triggers core sleep homeostasis mechanisms. However, it did not induce the expression of genes induced by sleep loss, suggesting that these molecular pathways are not sustainably activated in chronic diseases involving SF.

Keywords: BDNF; SWA; heat shock proteins; paradoxical sleep; sleep apneas; sleep fragmentation; slow waves; spindles.

Figure 1

Overview of chronic sleep fragmentation methods. (A) Details of the CaResS device: Cylindrical plexiglas cage (diameter 30 cm) equipped with inner dividing walls (C), rotating circular floor (RD) activated by two electrical motors (M, 1.5 rpm), sawdust (S), free access to water (W) and food (FD) and infrared sensors for locomotor activity recording (IRS). Electroencephalographic/electromyographic (EEG/ EMG) electrodes are connected to swivel on top of the cage. (B) Parallel EEG/EMG traces and example of visual scoring of 4-sec epochs of slow wave sleep (S) and wake (W). Note that the slow wave sleep episode is interrupted by an awakening just after the device activation (ON). Upper trace: maximal length brief awakening (24 sec). Lower trace: minimal length brief awakening (> 4 sec). (C) Summary of the cage rotation schedule for the three groups (m, minutes; s, seconds). (D) Timeline of EEG recordings and tissue collection.

Figure 2

Locomotor activity and plasma corticosterone. (A) Locomotor activity cumulated over 24 h on D1 and D14 (n for quiet control [QC] = 7, motor control [MC] = 10 and F = 8). * MC and fragmented group (F) have significantly higher values than QC (P < 0.001). Significant difference between MC and F (P < 0.01). (B) Corticosterone in trunk plasma of animals sacrificed at the end of the light phase on D1 (n = 7 per group) and D14 (n = 8 per group). (C) Circadian rhythms of locomotor activity determined by cosinus fit of locomotor activity over 14 days for each group. The three figures represent group means during the dark and light phase (gray and white rectangles, respectively) without standard deviation. Note that the saw-tooth ultradian rhythm in MC is artificially caused by the specific pattern of enforced locomotion (40 min/h, dark phase).

Figure 3

Vigilance states distribution during sleep fragmentation. (A) Slow wave sleep (SWS) bout mean duration, bout number, and cumulated SWS time over 24 h (mean ± standard deviation, n for QC = 6, MC = 8, F = 7). *F is different from QC and MC. ^#F is different from QC only. Note effective shortening of sleep bouts on D1 and D14 along with minimal total sleep time restriction. (B) Circadian distribution of time of sleep in minutes cumulated over 2 h on D1 and D14. Upper panel: SWS; lower panel: paradoxical sleep. Black horizontal bars indicate significant difference for the F group compared to both QC and MC groups (Tukey post-analysis of variance, P < 0.05 for both). (C) Consolidated wake (> 10 min without interruption) expressed in percentage of total wake during the 12 h of the active phase on D1, D7, and D14. Note the decreased ability to maintain prolonged wakefulness in the F group. (D,E) Spontaneous sleep latencies measured after any awakening > 12 sec on D1 and D14 showed decrease in the F group. F, fragmented; MC, motor control; QC, quiet control.

Figure 4

Slow wave sleep (SWS) and paradoxical sleep (PS) rebound during recovery. (A,B) Upper (SWS) and lower panel (PS) show sleep time in minutes cumulated over 2 h intervals during recovery day R1 and R2 (cage off at t = 17:00 on R1, n = 6 per group, values for quiet control [QC] reported from D14). (C,D) Slow wave activity (SWA) normalized by last 2 h of sleep of R2 calculated for equal sleep intervals (4 in dark phase, 8 in light phase). (E–H) SWS and PS total and bout time cumulated over 12 h. Note significant quantitative SWS and PS rebound during the dark phase around 23:00 and significant qualitative rebound including SWA and bout consolidation throughout R1 with complete resolution on R2. Horizontal black bars and asterisks indicate significance (P < 0.05, Tukey post-analysis of variance versus QC and motor control).

Figure 5

Spectral analysis of sleep electroencephalography during sleep fragmentation (SF) and recovery. (A,B) Frequency spectrum of slow wave sleep (SWS) during dark (upper panel) and light phase (lower panel) of the SF protocol. Power is expressed in percent of baseline for each individual and averaged (n for quiet control [QC] = 6, motor control [MC] = 8, fragmented [F] = 7). Horizontal gray bars indicate significant difference (P < 0.05 on Tukey post-significant one-way analysis of variance). Note the consistent increase in power in theta to gamma bands (4–40 Hz). (C) Note normalization of theta-gamma effect during the dark phase on R1 but significant increase in slow wave activity (SWA; insert). (D–I) Dynamics of the power changes at transitions from wake (W) to SWS (t = 0) and from paradoxical sleep (PS) to SWS (t = 0) during light phase of D1 (n for QC = 6, MC = 8, F = 7), D14 (n = 6 per group) and R1 (n = 6 per group). Power is expressed in percent of SWS values during normalizing day. 1 Hz FFT values (displayed in A–C) were summed up in classic frequency bands, α and β were grouped. Horizontal gray bars indicate significant difference between F compared to both MC and QC (Tukey possignificant one-way analysis of variance, P < 0.05 for both). Note the overshoot of theta to gamma frequencies in the F group following SWS onset during SF (D,E) and its disappearance on R1 (F). Note also the increase in SWA buildup rate on D14 (E). Note that α and β power transitory increase before PS onset is slightly delayed in F group (G,H).

Figure 6

Sleep waves and spindles architecture. Wave triggered average of slow waves (SW) and spindles during the light phase slow wave sleep (SWS). Representative raw electroencephalographic data with corresponding band-pass filtered traces and typical shape and amplitude (A) of SW (1–15 Hz) and spindles (10–15 Hz) is shown in panel A. (B,E) Wave-triggered average of down deflections showing a significant decrease in minima reached on D1 and D14 (P < 0.05, paired t test, inserts). (C,F) Histograms of SW amplitude in relative values for early (E, first third of light phase SWS) and late (L, third third) occurrence (P < 0.001, two-way analysis of variance [ANOVA]). (D,G) Increase in slope 1 (P < 0.005 on D1 and D14) and slope 2 (P < 0.05 on D1) for SW of equal amplitude (percentile 0.4 to 0.6). (H,I) Wave-triggered average where the maximum of spindle is centered on t = 0. Spindle envelope underlines the difference in amplitude for each peak (black squares, P < 0.05, paired t test, inserts). (J,K) Histograms showing increase in the amplitude of spindles on D1 and D14 (P < 0.001, two-way ANOVA for all) with maintenance of circadian effects when considering L versus E (P < 0.005, two-way ANOVA for all).

Figure 7

(A,B) Cortical messenger RNA (mRNA) levels of BiP. Transcript (mRNA) levels are normalized by β-actin (mean ± standard deviation). *P < 0.05 on Tukey test post-one–way analysis of variance for each structure. Complete experiments are reported in Table S3. Note consistent induction of BiP expression after 24 hours of sleep fragmentation (D1, n = 7) compared to 14 days (D14, n = 8). F, fragmented; MC, motor control; QC, quiet control.

Figure 8

Cortical levels of BDNF messenger RNA (mRNA) and peptide. (A,C) Transcript (mRNA) levels normalized by β-actin (mean ± standard deviation). *With horizontal bar: P < 0.05 on Tukey test post-one–way analysis of variance in the cortex only significant compared to MC. (B,D) Peptide levels. Note the stability of BDNF on D1 and D14. F, fragmented; MC, motor control; QC, quiet control.