Abstract
Objective
This study aimed to explore the relationship among several indices of circadian rhythms and lipid peroxidation of brain tissue in mice.
Methods
After entrainment of 4-week-old mice, one group was disrupted their circadian rhythms for three days and the other group for seven days (n = 10, respectively). After a recovery period, the Y-maze test, the elevated plus maze test, the tail suspension test, and the forced swimming test were conducted. To assess lipid peroxidation in brain tissue, thiobarbituric acid reactive substances were measured in the cortex, hippocampus, and cerebellum.
Results
When circadian rhythms were disrupted and adapted back to their original rhythm, the recovery time of the 7-day disruption group (median 3.35 days) was significiantly faster than one of the 3-day disruption group (median 4.87 days). In the group with a 7-day disruption, mice that had recovered their rhythms early had higher malondialdehyde levels in their hippocampus compared to those with delayed recovery. The entrainment of circadian rhythms was negatively correlated with the malondialdehyde level of brain tissue. The behavioral test results showed no differences depending on the disruption durations or recovery patterns of circadian rhythms.
Conclusion
These results suggest that disruption types, recovery patterns, and the entrainment of circadian rhythms are likely to affect oxidative stress in adolescents or young adult mice. Future study is needed to confirm and specify these results on the effects of circadian rhythms on oxidative stress and age-dependent effects.
Keywords: Circadian rhythm, Lipid peroxidation, Malondialdehyde, Adolescent, Mice.
INTRODUCTION
Circadian rhythm refers to the biological rhythm of the physiological, neuroendocrine, and neurobiological changes in the body that repeats approximately every 24 hours. The rhythm generated by the suprachiasmatic nucleus is transmitted to other parts of the hypothalamus, the brain, and the whole body organs [1]. The circadian rhythm is an essential factor affecting physical and mental health. The disturbance of the circadian rhythm is related to mental and physical diseases [2-4].
Oxidative stress is also an important factor in human health and disease occurrence [5]. In general, an appropriate balance is needed between the production and elimination of oxidative stress. If this balance is broken, biochemical damage due to oxidative stress may occur, and disease may develop or worsen by this damage [6]. It is related to the aging process and is known as a major risk factor for degenerative diseases in the elderly. Oxidative stress is also involved in the growth and development of children [7-10]. Thus, identifying and reducing the factors associated with oxidative stress could help prevent and manage diseases.
Indices of oxidative stress include reactive oxygen species such as OH−, which is a free radical of the hydroxyl group, superoxide (O2−) or hydrogen peroxide (H2O2), and reactive nitrogen species such as nitrogen monoxide (nitric oxide, NO) or peroxynitrite (ONOO−) [11]. These substances are mostly removed from the body by antioxidant enzymes such as superoxide dismutase, catalase, and glutathione peroxidase, but residual free radicals can chemically destroy body tissue. In addition, free radicals damage proteins to produce dityrosine and 3-nitrotyrosine, and damage lipids to produce malondialdehyde (MDA), a lipid peroxide [12-14].
Considering that both oxidative stress and circadian rhythm are importantly involved in maintaining the homeostasis of the body, there is a possibility that oxidative stress and circadian rhythm are related to each other. Efforts are being made to find a connection between the two [15]. According to the changes in circadian rhythms in humans and animals, oxidative stress reactions and the expression of antioxidant enzymes show circadian changes [16-20]. These studies showed that oxidative stress indicators increased mainly during periods of high activity among the circadian rhythms, and the expression of the antioxidant system increased simultaneously to reduce the occurrence of oxidative stress. Studies showing oxidative stress are increased when the circadian sleep-wake rhythm is disturbed by another factor such as sleep deprivation that also supports the relationship between circadian rhythm and oxidative stress [21]. However, there is insufficient data to understand the mechanism or cause- effect relationship between circadian rhythm and oxidative stress and the resulting disease. There have been reports that show disturbances in circadian rhythm affecting people’s cognition, sleep deprivation affecting emotions, and circadian rhythm disturbances increase the risk of depression and bipolar disorder, but there are few studies related to the relevance of various indicators, the method of disturbing the circadian rhythm, or recovery [22-24].
In this study, we aimed to investigate whether the indices of circadian activity-inactivity rhythm were correlated with cognitive and emotional states, and oxidative stress. Studies on the relationship between circadian rhythm and oxidative stress, which have been conducted so far, have mainly been focused on circadian fluctuations of oxidative stress and the antioxidant system. In addition, studies on circadian rhythm abnormalities and oxidative stress mainly consisted of sleep deprivation studies, and studies examining circadian rhythm indicators themselves were difficult to find.
Considering the relationship between oxidative stress and circadian rhythm discovered through previous studies, the degree of oxidative stress may differ depending on the degree of recovery after circadian rhythm disturbance, and the index of circadian rhythm is Oxidative stress may increase when unstable. In addition, based on previous studies on the relationship between circadian rhythm and mental illness, a research hypothesis was established that whether disturbance or recovery of circadian rhythm and stability were related to cognition and emotional states. Based on this hypothesis, we analyzed the relationship between the circadian rhythm disturbance method in mice, the indicators related to recovery after the disturbance of the circadian rhythm, the indicators of behavioral tests, and lipid peroxidation in the frontal lobe, hippocampus, and cerebellum of the brain.
METHODS
Materials
Twenty healthy male ICR (Institute of Cancer Research) mice (weight: 21−27 g) at four weeks of age were used. The temperature and humidity of the breeding room were maintained at 20 ± 1°C and 40−60%, respectively. The animals were placed in breeding cages individually, and they were allowed to consume drinking water and food freely. This study was conducted after the approval of the Animal Experimental Ethics Committee of Pusan National University Hospital (IRB number: PNUH-2017-118).
Methods
Experiment design
Prior to the evaluation of the circadian rhythm-related characteristics, the experimental animals went through an entrainment period of 14−21 days to ensure that their circadian rhythm was sufficiently stabilized, including acclimatization to the new environment (Fig. 1). Thereafter, ten mice were subjected to a 3-day disruption of circadian rhythm, and the other ten mice went through a 7-day disruption protocol. After two weeks of the recovery period, behavioral tests were conducted twice to evaluate cognitive and emotional indicators. Lastly, thiobarbiturate acid reactive substances (TBARS) was measured to assess the degree of lipid peroxidation in brain tissue.
Fig. 1.
Schematic flow of the experimental design on relationship between circadian rhythms and characteristics of behavior and brain lipid peroxidation in mice. A total of 20 mice were divided into two groups consisting 10 mice each, and the circadian rhythm of the mice in each group was disturbed for 3 days and 7 days, respectively, and then there was a recovery period of 2 weeks. Y-maze test (YMT), elevated plus maze test (EPMT), tail suspension test (TST), and forced swim test (FST) were performed as behavioral tests, and finally, thiobarbiturate acid reactive substances of mice brain tissues were measured. MDA, malondialdehyde.
Measurement of sleep-awake rhythm
Regulation of circadian rhythm
The circadian rhythm was measured through the sleep- wake rhythm. The light-dark phase was adjusted at 12-hour intervals using a lighting system (Philips, Burlington, MA, USA) controlled by computer software. During the stabilization period of the circadian rhythm, the lights were turned on at 5:00 AM to maintain a light cycle for 12-hours and turned off at 5:00 PM to maintain a dark cycle for 12-hours. The disturbance of the circadian rhythm consisted of two protocols, and the circadian rhythm was disturbed for 3 and 7 days, respectively. During this period, the lights of the breeding room were turned off at 5:00 AM and turned on at 5:00 PM, contrary to the stabilization period. After this period, during the recovery period of the circadian rhythm, the lighting schedule was switched back to the same as the stabilization period.
Measurement of locomotor activity
The sleep-wake rhythm of the mice was measured by locomotor activity [25]. The locomotor activity was measured using the Mlog system (Biobserve GmbH, Bonn, Germany), an activity measuring device using infrared rays. While the mouse was moving in the cage, the infrared radiation from the body was detected and then converted into a voltage and recorded for every second. The change in activity-inactivity rhythm over time is graphically represented using the summed values of the Mlog values during the 12-hour light cycle (12L) and the 12-hour dark cycle (12D). After disturbing the circadian rhythm, the original light-dark cycle was applied again, and in order to determine whether the circadian rhythm was recovered, the unique characteristics of mice that were active at night and resting during the day were utilized. The recovery time was judged to be 12D larger than 12L [26]. In addition, after evaluating the recovery time of the circadian rhythm in each experimental animal, the recovery pattern was classified into early recovery group and late recovery group based on the median recovery time in each group.
Analysis of circadian rhythm indicators in sleep-wake rhythm
In order to evaluate whether the stability of the circadian rhythm is related to the MDA value, the indicators of the circadian rhythm were analyzed in the sleep-wake rhythm of mice by measuring the amount of locomotor activity of 24 hours immediately before the TBARS mea-surement. The sum of the Mlog measurements during the 12-hour dark cycle was regarded as the total activity during the dark cycle (12D), and the total Mlog measurements during the 12-hour light cycle were considered the total activity during the light cycle [26,27]. Based on these values, the total activity during the day (12D＋12L), the difference between the total activity during the dark cycle and the total activity during the light cycle (12D−12L), and the ratio (12D/12L) were calculated, respectively. Meanwhile, the amount of activity during the 10 hours of active movement was calculated and defined as M10, and the amount of activity during the 5 hours of the least active period was calculated and expressed as L5 [28]. Then, the amplitude, midline estimating statistic of rhythm (MESOR), and acrophase were calculated using cosine analysis [29].
Behavioral characteristic test
The behavioral test was performed twice after the circadian rhythm was restored. The first two tests were the Y-maze test (YMT) to measure the cognitive state and the elevated plus maze test (EPMT) to measure the anxiety state. The second test was a tail suspension test (TST) and the forced swim test (FST) to measure depression. The tests were performed with an interval of 30 minutes between each test, and the tester analyzed the test process taken by video through video reading.
Y-maze test (YMT)
The Y-maze test evaluates the cognitive states of experimental animals by analyzing the behavioral patterns passing through a Y-shaped maze [30]. The acrylic Y-shaped maze had an arm length of 25 cm, a height of 14 cm, and a width of 5 cm in three directions, and each passage was created at equal intervals of 120 degrees. The testing mouse was placed in the center, and after recording a total of 5 minutes by a video camera, analysis was performed. When the mouse’s hind legs exceeded more than half of each area, entry and exit records were recognized. Spontaneous alternation means choosing a new arm rather than recently visited arm after visiting one arm. Therefore, by measuring the number of arm entries and the number of spontaneous alterations, the percentage of spontaneous alteration was calculated using the following formula.
%Alternation = (Number of Alternations / [Total number of arm entries − 2]) × 100
Elevated Plus Maze Test (EPMT)
The elevated plus maze test is a test to evaluate the anxiety state of experimental animals using the properties of mice that are afraid to stay in an open space [31]. A cross-shaped maze is installed 50 cm above the floor, two of the four passages facing each other (30 cm long and 7 cm wide) are open, and the other two passages are surrounded by a wall with a height of 20 cm. The experimental animal is directed to the open arm from the center, and the action for 5 minutes is recorded as a video, and anxiety is assessed through a video reading. The time spent in the open arm, the closed arm, and the central zone is measured, and the time away from the maze during the experiment is measured and recorded.
Tail suspension test (TST)
The tail suspension test is a test to measure the depressed state through the immobility of giving up the behavior of trying to get out of the situation while the tail of an experimental animal is hung [32,33]. The TST was carried out in a black acrylic box (30 × 30 × 50 cm) with a tape attached 1 cm away from the tip of the tail and hung upside down for a total of 10 minutes. The immobility time for the second half of the 6 minutes was considered as an indicator of depression.
Forced swim test (FST)
The forced swim test is a test to measure the depressed state by investigating the immobility of dropping an experimental animal into a tank filled with water and giving up the action of swimming and running away [34]. The FST was conducted in a cylindrical water tank (diameter 10 cm, height 25 cm, water height 19 cm, water temperature 25 ± 1°C) and observed the experimental animals’ behavior through video recordings of 6 minutes. The immobility time was measured in seconds when the experimental animal did not move or showed only minimal movement to keep the nose above the water for the second half of the total 6 minutes. This immobility time was regarded as an indicator of depression.
TBARS measurement in mice brain tissue
Brain tissue extraction and tissue homogenization
After anesthetizing the mice with isoflurane, its ribcage was incised, and PBS containing heparin was perfused through the heart, and the brain was quickly removed as well. The frontal cortex, hippocampus, and cerebellum were separated, and the tissues were resuspended in 50−100 mg/ml of PBS, then 100X butylated hydroxytoluene solution was added to prevent any further oxida-tion. The tissues were instantly frozen in liquid nitrogen and stored at −80°C until TBARS measurements were conducted.
The tissue samples stored at −80°C were taken out and placed on ice at room temperature, and an extraction buffer was placed in a tube containing the tissue. The tissue in each tube was crushed using a disposable microtube homogenizer. After the tissues were completely fragmented and the color of the liquid changed from transparent to turbid, the tubes containing the tissues were placed on ice and vortexed every 5 minutes. After vortexing a total of 4 times, centrifugation was performed at 16,000 g for 20 minutes at 4°C, and the supernatants were transferred to new tubes.
TBARS measurement
TBARS, which can screen and monitor lipid peroxidation level, was measured with the OxiSelectTM TBARS Assay Kit (Cell Biolabs Inc., San Diego, CA, USA). To draw the MDA standard curve, the MDA concentration was diluted from 125 mM to 0 mM and prepared in 9 tubes (125, 62.5, 31.25, 15.63, 7.81, 3.91, 1.95, 0.98, 0) [35]. A standard solution and a sample containing MDA (a solution obtained by homogenizing brain tissue) were prepared in duplicates. 100 ml of the standard or sample were placed in separate microcentrifuge tubes, 100 ml of a sodium dodecyl sulfate solution was added, mixed well, and incubated for 5 minutes at room temperature. There-after, 250 ml of TBA reagent was added, the tube was closed, incubated at 95°C for 60 minutes, and then placed in an ice bath for 5 minutes to cool the tube. After centrifuging all samples at 3,000 rpm for 15 minutes, 200 ml of samples were placed in a 96-well microplate. The plate was read at 540 nm excitation and 590 nm emission with a fluorometer (Wallac 1420 Explorer; PerkinElmer Life and Analytical Sciences, Turku, Finland). The values measured twice, averaged, and substituted into an MDA standard curve using standard reagents ranging from 0 mM to 125 mM to determine the MDA values in the tissue samples.
Statistical analysis
In order to determine whether there is a difference in the behavioral characteristics and oxidative stress levels of mice according to the difference in the disruption duration of the circadian rhythm, Mann−Whitney Utest was performed. Also, to evaluate the relationship between the recovery patterns of circadian rhythm and oxidative stress, mice were divided into early recovery and late recovery groups according to the recovery period to the normal circadian rhythm in the 3-day 7-day disruption groups. Mann−Whitney Utest was performed for the behavioral characteristics and the MDA values between two groups. Spearman correlation test was performed to analyze the correlation between the indicators of circadian rhythm and the MDA concentration. For the indicators of circadian rhythm, cosine analysis was performed using the value of sleep-wake activity [29,36]. In cosine analysis, in order to find the cosine curve that best reflects the given data, the circadian rhythm index is calculated by finding a cosine fitting that minimizes the square value of the difference between an arbitrary value on the cosine curve and the data value. The cosine curve found in the cosine analysis that shows a circadian rhythm for 24 hours is evaluated through a zero-amplitude test. The null hypothesis of zero-amplitude refers to a case where the amplitude is zero, and if the null hypothesis is statistically rejected and the amplitude has an amplitude greater than zero, it is expressed as a circadian rhythm for 24 hours. In a case of statistically significant circadian rhythm, the highest and lowest points were calculated from this cosine curve, and the midpoint was calculated through the MESOR. Two-sided verification was performed with a significance level of 0.05, and KoreaPlus Statistics (Embedded on SPSS 25.0 Statistics) for Windows (Datasolution Inc., Seoul, Korea) was used. Cosine analysis and zero-amplitude test were performed using Matlab R2020b (MathWorks, Natick, MA, USA).
RESULTS
Comparison of Behavioral Characteristics and Oxidative Stress between Groups of 3-day Disruption and 7-day Disruption
The behavioral characteristics (cognition and emotion) and the MDA concentration were compared in the 3-day disruption group and the 7-day disruption group (Table 1). There was no significant difference in YMT, EPMT, FST, and TST between the two groups. In addition, there was no statistically significant difference in the amount of MDA measured in the frontal lobe, hippocampus, and cerebellar tissue between the two groups.
Table 1.
Comparison of cognition, emotion, and oxidative stress between group with 3-day disruption and 7-day disruption
| Measurement | 3-day disruption (n = 10) | 7-day disruption (n = 10) | z | pvalue |
|---|---|---|---|---|
| Y-Maze Test | ||||
| Percentage of spontaneous alterations | 66.4 ± 9.4 | 62.4 ± 12.7 | −1.326 | 0.185 |
| Elevated Plus Maze Testa | ||||
| Open arm time (sec)b | 34.6 ± 26.4 | 31.5 ± 29.3 | −0.390 | 0.696 |
| Percentage of open arm time (%) | 5.8 ± 4.4 | 5.2 ± 4.9 | −0.390 | 0.696 |
| Closed arm time (sec) | 418.6 ± 103.9 | 365.2 ± 97.5 | −1.703 | 0.283 |
| Percentage of closed arm time (%) | 69.8 ± 17.3 | 60.9 ± 16.3 | −1.703 | 0.283 |
| Tail Suspension Test | ||||
| Immobility time (sec) | 163.9 ± 70.5 | 206.0 ± 16.5 | −1.362 | 0.173 |
| Forced Swim Test | ||||
| Immobility time (sec) | 190.9 ± 35.0 | 188.8 ± 41.3 | −0.151 | 0.880 |
| Oxidative stressc | ||||
| MDA (mM) - frontal cortex | 0.5 ± 0.5 | 0.5 ± 0.3 | −0.454 | 0.650 |
| MDA (mM) - hippocampus | 0.7 ± 0.3 | 0.5 ± 0.3 | −1.361 | 0.174 |
| MDA (mM) - cerebellum | 0.8 ± 0.3 | 0.7 ± 0.3 | −1.209 | 0.226 |
Values are presented as mean ± standard deviation.
MDA, malondiadehyde.
Statistics were performed by Mann−Whitney Utest.
aSeventeen animals were analyzed in Elevated Plus Maze Test (3-day group, n = 7, 7-day group, n = 10). bOpen arm time was corrected in case of experiencing fall. cMDA level is measured through thiobarbituric acid reactive substances assay.
Patterns of Recovery from 3-day Disruption and 7-day Disruption
Figure 2 shows changing patterns of activities summation during 12-hour dark and light phases according to the disruption protocol. For comparison of recovery patterns between the three-day and seven-day disruption groups, the recovery times were estimated by the time when 12D/12L ratio cross 1 (Fig. 3). The estimated recovery time of the 7-day disruption group was mean 3.53 ± 0.77 days, and the median was 3.35 days. The minimum was 2.51 days, and the maximum was 4.83 days. Meanwhile, the estimated recovery time of the 3-day disruption group was mean 4.90 ± 0.82 days, and the median was 4.87 days. The minimum was 3.77 days, and the maximum was 6.57 days. By Mann−Whitney Utest, the recovery time of the 7-day disruption group was significantly faster than one of the 3-day disruption group (Mann−Whitney U = 9.000, p = 0.001).
Fig. 2.
Changing patterns of activi-ties summation during 12-hour dark and light phases according to the disruption protocol. (A) Three-day disrup-tion. (B) Seven-day disruption. White bar, light cycle; black bar, dark cycle; gray bar, sleep-wake cycle during disruption phase; red line, early recovery group; blue line, delayed recovery group.
Fig. 3.
The recovery patterns after the three-day (A) and seven-day (B) disruption were represented by the 12-hour dark/light (DL) ratio during the recovery phase. Bluelines means the changes of DL ratio each mouse. Red line denotes 12-hour dark/light ratio = 1. Recovery times were esti-mated by the change of 12-hour dark/light into more than 1. The esti-mated recovery times were com-pared between the 3-day and 7-day groups (C). The recovery time of the 7-day group was significantly faster than one of the 3-day group (Mann−Whitney U= 9.000, p = 0.001).
Comparison of Behavioral Characteristics and Oxidative Stress in Early and Delayed Recovery Groups
The behavioral characteristics and the MDA values were compared by dividing the early recovery group and the delayed recovery group in the 3-day disruption group and 7-day disruption group (Table 2). Among the 3-day disruption group, the behavioral characteristics and the MDA values between the early and delayed recovery groups were not statistically significant. On the other hand, there was no statistically significant difference in behavioral characteristics among the 7-day disruption group, but the MDA values in the hippocampus were statistically higher in the early recovery group (p = 0.019).
Table 2.
Comparison of cognition, emotion, and oxidative stress according to recovery pattern from 3-day and 7-day circadian disruption
| Measurement | 3-day circadian rhythm disruption | 7-day circadian rhythm disruption | |||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
||||||||
| Early recovery (n = 5) | Delayed recovery (n = 5) | z | pvalue | Early recovery (n = 6) | Delayed recovery (n = 4) | z | pvalue | ||
| Y-Maze Test | |||||||||
| Percentage of spontaneous alterations | 63.5 ± 9.8 | 69.2 ± 9.1 | −1.362 | 0.173 | 59.1 ± 9.9 | 67.3 ± 16.4 | −1.069 | 0.285 | |
| Elevated Plus Maze Testa | |||||||||
| Open arm time (sec)b | 31.3 ± 34.0 | 39.1 ± 17.7 | −0.707 | 0.480 | 31.2 ± 35.0 | 31.8 ± 23.1 | −0.426 | 0.670 | |
| Percentage of open arm time (%) | 5.2 ± 5.7 | 6.5 ± 3.0 | −0.707 | 0.480 | 5.2 ± 5.8 | 5.3 ± 3.9 | −0.426 | 0.670 | |
| Closed arm time (sec) | 379.6 ± 114.0 | 470.5 ± 76.1 | −1.414 | 0.157 | 377.9 ± 76.2 | 346.3 ± 134.3 | −0.640 | 0.522 | |
| Percentage of closed arm time (%) | 63.3 ± 19.0 | 78.4 ± 12.7 | −1.414 | 0.157 | 63.0 ± 12.7 | 57.725 ± 22.4 | −0.640 | 0.522 | |
| Tail Suspension Test | |||||||||
| Immobility time (sec) | 164.0 ± 84.4 | 163.8 ± 63.9 | −0.313 | 0.754 | 202.0 ± 17.6 | 212.0 ± 14.7 | −0.855 | 0.392 | |
| Forced Swim Test | |||||||||
| Immobility time (sec) | 188.2 ± 26.4 | 193.6 ± 45.1 | −0.731 | 0.465 | 172.5 ± 46.1 | 213.3 ± 15.7 | −1.925 | 0.054 | |
| Oxidative stressc | |||||||||
| MDA (mM) - frontal cortex | 0.3 ± 0.3 | 0.7 ± 0.6 | −1.149 | 0.251 | 0.7 ± 0.3 | 0.3 ± 0.0 | −1.706 | 0.088 | |
| MDA (mM) - hippocampus | 0.5 ± 0.3 | 0.8 ± 0.3 | −1.358 | 0.175 | 0.6 ± 0.3 | 0.2 ± 0.1 | −2.345 | 0.019 | |
| MDA (mM) - cerebellum | 0.8 ± 0.4 | 0.9 ± 0.1 | −0.522 | 0.602 | 0.8 ± 0.3 | 0.5 ± 0.1 | −1.279 | 0.201 | |
All values except the numbers of animals experiencing fall were shown as mean ± standard deviation.
MDA, malondiadehyde.
Statistics were performed by Mann−Whitney Utest.
aSeven animals were analyzed in 3-day disruption group (early recovery group, n = 4; delayed recovery group, n = 3). bOpen arm time was corrected in case of experiencing fall. cMDA level is measured through thiobarbituric acid reactive substances assay.
Relationship between the Stability of Circadian Rhythm and MDA Value
We investigated the correlation of MDA values with indicators of circadian rhythms for 24 hours immediately prior to TBARS measurements (Table 3). When examining the correlation between total activity during the 12-hour dark cycle (12D) and the MDA values, statistically significant negative correlations were shown in the frontal cortex (Rho = −0.519, p = 0.019) and cerebellum (Rho = −0.630, p = 0.003). On the other hand, when the correlation between total activity during the 12-hour light cycle (12L) and the MDA values was examined, only the frontal cortex (Rho = −0.629, p = 0.003) showed a statistically significant negative correlation. When examining the correlation between total activity for 24 hours (12D＋12L) and the MDA values, the frontal cortex (Rho = −0.629, p = 0.003) and cerebellum (Rho = −0.611, p = 0.004) showed statistically significant negative correlations. When analyzing the correlation between the MDA value and the difference between the total activity during the 12-hour dark cycle and the 12-hour light cycle (12D−12L), the hippocampus (Rho = −0.544, p = 0.013) and cerebellar tissue (Rho = −0.605, p = 0.005) showed a statistically significant negative correlation. There was no statistically significant correlation between the ratio of the activity amount of the 12-hour dark cycle and the light cycle (12D/12L) and the MDA values. On the other hand, when the correlation between M10 and MDA was examined, similarly to 12D, there was a statistically significant negative correlation with the frontal cortex (Rho = −0.621, p = 0.003) and cerebellum (Rho = −0.505, p = 0.023).
Table 3.
Correlations between MDA level of the frontal, cerebellum, and hippocampus and indices of circadian rhythms
| Index | MDA - frontal (n = 20) | MDA - hippocampus (n = 20) | MDA - cerebellum (n = 20) | |||||
|---|---|---|---|---|---|---|---|---|
|
|
|
|
||||||
| Rho | pvalue | Rho | pvalue | Rho | pvalue | |||
| 12D | −0.519 | 0.019* | −0.424 | 0.062 | −0.630 | 0.003** | ||
| 12L | −0.629 | 0.003** | −0.123 | 0.605 | −0.260 | 0.268 | ||
| 12D＋12L | −0.629 | 0.003** | −0.392 | 0.087 | −0.611 | 0.004** | ||
| 12D−12L | −0.281 | 0.230 | −0.544 | 0.013* | −0.605 | 0.005** | ||
| 12D/12L | 0.254 | 0.280 | −0.296 | 0.205 | −0.262 | 0.265 | ||
| M10 | −0.621 | 0.003** | −0.340 | 0.143 | −0.505 | 0.023* | ||
| L5 | −0.039 | 0.870 | −0.206 | 0.383 | −0.254 | 0.280 | ||
| Amplitude | −0.492 | 0.028* | −0.397 | 0.083 | −0.442 | 0.051 | ||
| MESOR | −0.623 | 0.003** | −0.361 | 0.118 | −0.442 | 0.033* | ||
| Acrophase | 0.161 | 0.498 | 0.072 | 0.762 | 0.165 | 0.486 | ||
Statistics were performed by Spearman correlation.
MDA, malondiadehyde; 12D, total activity during dark phase; 12L, total activity during light phase; 12D＋12L, total daily activity during both dark and light phase; 12D−12L, the difference of total activities between 12-hour dark and light phases; 12D/12L, the ratio of total activities between 12-hour dark and light phases; M10, the activity during a most active 10-hour window; L5, the activity during a least active 5-hour window; MESOR, midline estimating statistic of rhythm.
*p < 0.05, **p < 0.01.
When the correlation between the amplitude, MESOR, acrophase, and MDA values calculated through cosine analysis was examined, the amplitude showed a statistically significant negative correlation only in the frontal cortex (Rho = −0.492, p = 0.028). MESOR showed a statistically significant negative correlation with the frontal cortex (Rho = −0.623, p = 0.003) and cerebellum (Rho = −0.442, p = 0.033). On the other hand, no statistically significant correlation was observed between the acrophase and the MDA values.
DISCUSSION
In this study, the relationship between circadian rhythm-related characteristics, behavioral characteristics tests, and oxidative stress was investigated. When MDA of the brain tissues were analyzed for each group by dividing into the early and delayed recovery groups according to the recovery pattern after the circadian rhythm disruption, the mean hippocampal MDA value of the early recovery group was significantly higher than that of the delayed recovery group in the 7-day disruption group. When analyzing the correlation between the degree of circadian rhythm stabilization for 24 hours before the MDA measurement and the average value of MDA in each brain tissue, oxidative stress in the frontal cortex decreased as the total amount of activity increased. In addition, when the circadian rhythm was stabilized, the average value of MDA in the hippocampus and cerebellar tissues was low; that is, the degree of oxidative stress was low. These results suggest that recovery patterns or indicators of circadian rhythm may be related to oxidative stress after disruption of the circadian rhythm.
In this study, the circadian rhythm disturbance period was set at 3 and 7 days, and the recovery pattern after the disturbance and the characteristics and relationships of the sleep-wake rhythm were evaluated in each group. As a result, there was no statistically significant difference in the mean MDA of brain tissues in the early recovery group and the delayed recovery group in the mice that were disturbed for three days, but the mean hippocampal MDA in the early recovery group of the 7-day disruption group was significantly higher than that of the delayed recovery group. In addition, when the mice with disrupted circadian rhythms for three days and seven days were adjusted again to the original circadian rhythm, the time taken to recover the rhythm was an average of 5.5 days in the 3-day disruption group and 4.2 days in the 7-day disruption group, which the period of disturbance seems to have an influence on the sleep-wake cycle. Chen et al. [37] found that when the reversed light/dark (LD), i.e., DL cycle was applied for 1 day, 3 days, and 5 days, respectively, to mice were entrained in a 12h LD cycle for more than 2 weeks, it took 5 days for the phase reversal of behavioral rhythms to occur by adapting to the DL cycle. Therefore, in the 3-day disruption group, when the normalized light-dark schedule was applied on the 4th day, since they had not yet completed the phase reversal, more time would have been required to advance their circadian phase according to the original light-dark schedule. On the other hand, in the 7-day disruption group, since the phase reversal was already completed under the changed light-dark schedule, when the normalized light-dark schedule was applied to them again on the 8th day, the second phase-reverse would be possible within 5 days. It is necessary to confirm whether these differences are reproduced in future studies, suggesting the possibility of interaction between the disturbance duration and the recovery patterns. In other words, when the disturbance period of the circadian rhythm is short, there is no significance in the degree of oxidative stress according to the recovery pattern, but when the disturbance period is long, there may be a group vulnerable to oxidative stress in the process of recovering the circadian rhythm. In particular, given the high average level of MDA in the hippocampus, which is known as one of the most susceptible parts of the brain tissue to oxidative stress, it is possible to consider the possibility that the interaction between the period of circadian rhythm disturbance and recovery pattern could have affected the results in this study [38,39]. Further-more, the finding that oxidative stress may vary depending on the duration of the disruption of the circadian rhythm and the change in the recovery process after the disturbance suggests that further research on the relationship between circadian rhythm and oxidative stress in the brain is necessary.
In order to confirm the hypothesis that the level of lipid peroxidation in the brain tissue would be lower when the circadian rhythm is more stable, the correlation between the circadian rhythm indicators for 24 hours immediately before the TBARS measurement and the MDA value of the brain tissue was analyzed. There were correlations between some indicators of circadian rhythm and oxidative stress in the brain. The indicators related to the frontal cortex and oxidative stress were 12D, 12L, 12D＋12L, M10, amplitude, and MESOR. The correlated index between the hippocampus and oxidative stress was 12D−12L. The indicators that were related between cerebellar tissue and oxidative stress were 12D, 12D＋12L, 12D−12L, M10, and MESOR. Based on these results, the MDA values measured in the frontal cortex showed a positive correlation with the circadian rhythm indicators related to how actively the mice move and generally similar to those observed in the cerebellar tissue (Supplementary Table 1; available online). However, in the case of 12L and amplitude, which were correlated with MDA values in the frontal cortex, there was no correlation with MDA values in cerebellar tissue. 12L, which was correlated with oxidative stress in the frontal cortex, refers to the amount of activity during the light cycle, so it is difficult to say that the circadian rhythm is stable when the value is high. In addition, in the cosine curve expressing the sleep-wake rhythm, the amplitude or MESOR is calculated by the highest and lowest points, and a large amplitude or MESOR value may mean that there was a lot of activity. Indices associated with oxidative stress in the prefrontal cortex were 12D including 12L, 12D＋12L, M10, amplitude, and MESOR. Therefore, it can be estimated that the factor related to the degree of oxidative stress in the prefrontal cortex is the total amount of activity rather than the degree of stabilization of the circadian rhythm. Further-more, given that previous animal studies reported an increase in activity is associated with a decrease in oxidative stress and a decrease in oxidative stress under regular exercise, the degree of oxidative stress in the frontal cortex in mice is affected by activity [40,41].
On the other hand, the difference (12D−12L) between the total activity during the 12-hour dark cycle (12D) and the total activity during the 12-hour light cycle (12L) showed a negative correlation with the MDA concentration in the hippocampal and cerebellar tissues. The ICR mice used in this study are nocturnal like other mice, so when the circadian rhythm is stabilized, they move actively during the dark cycle and sleep during the light cycle [26,42]. Given these characteristics, the 12D−12L level can be a stabilizing indicator of the sleep-wake circadian rhythm. This study suggests that the more stable the circadian rhythm, the lower the oxidative stress in the hippocampus and cerebellar tissues. Varadinova et al. [43] reported that oxidative stress was increased in the hippocampus when the circadian rhythm was disturbed, and Singh et al. [44] reported oxidative stress in the hippocampus, thalamus, and hypothalamus after sleep depriva-tion. In future studies, the causal relationship between the degree of stabilization of the circadian rhythm and oxidative stress needs to be investigated.
In this study, MDA was measured through TBARS analysis as an indicator of lipid peroxidation. Among the substances produced when polyunsaturated fatty acids are peroxidated by chemical reactions or enzymes, the substances that have been widely used in lipid peroxidation studies are MDA, 4-hydroxy-nonenal (HNE) and F2-isoprostane 15(S)- 8-iso-prostaglandin F2a (15(S)-8-iso-PGF2a) [45]. MDA is produced from the lipid structure of cell membranes, and unlike reactive oxygen species, it is easy to measure because it has a relatively long half-life (minutes-several hours). The method of measuring the MDA-TBA adduct through TBARS analysis has been used in several studies because it can be measured relatively easily using a commercial kit, but the disadvantages of the test are that the test sensitivity is not high and other substances showing similar absorbance could be measured together [46,47].
In this study, ICR mice of adolescence or early adulthood were used. Both rodents and humans undergo changes in circadian rhythms during adolescence [48-50]. According to clinical studies, adolescence is a period in which growth and development occur rapidly, and at the same time, there are numerous changes in the circadian rhythm [51]. During this period, the phase of the circadian rhythm is delayed, or an evening chronotype is shown. This may occur due to biological changes or may be due to social influences related to schooling [48, 52,53]. Considering that the circadian rhythm is unstable compared to adulthood in adolescence, the impact of disturbance of the circadian rhythm can affect the emotional and physical state [48,54-56]. Based on these previous studies, this study explores the effects of disruption duration, recovery patterns, and stabilization levels of circadian rhythms on cognitive and emotional indicators and oxidative stress in mice corresponding to adolescence or early adulthood through animal experiments.
This study has several limitations as follows. First, this study is a small-scale experimental study. Therefore, it is necessary to confirm these results in future large-scale experimental studies. Second, since there was no control group that measured the behavioral characteristics and the brain tissue MDA values without disrupting the circadian rhythm, comparison with the case without distur-bance of the circadian rhythm was not possible. Third, the MDA values were measured only through TBARS analysis as an indicator of lipid peroxidation. Fourth, since this study was conducted in male mice, it may bring different results in female mice that may have different hormonal changes. Fifth, since the experimental animals included in this study are adolescent or early adulthood ICR mice, these results cannot be generalized because the results may differ in mice of different weeks of age or various mice types. This result needs to be assessed in other kinds of mice and other species of mice in the future. Sixth, in this study, when the original rhythm was applied after the disruption of the circadian rhythm, the recovery time of the circadian rhythm in mice was determined by the comparison between summed Mlog activities in dark and light phases. We set the recovery time when the sum of dark cycle activity is greater than the sum of light cycle activity. Due to the characteristics of mice that are mainly active in the dark cycle, using this indicator can be intuitively figure out the starting time for the recovery of the circadian rhythm. However, in order to clearly measure the recovery period of the circadian rhythm in these experiments, additional research is needed. Despite these limitations, this study is meaningful as an exploratory study to prove the hypothesis about the relationship between the characteristics of circadian rhythm, cognitive and emotional indicators, and oxidative stress.
This study analyzed cognitive and emotional indicators and oxidative stress according to the recovery pattern and stabilization level after circadian rhythm disturbance in adolescent or young adult mice. Among the 7-day disruption group of circadian rhythm, the early recovery cohort had higher oxidative stress in the hippocampus than the late recovery cohort. Also, as the circadian rhythm stabilized, oxidative stress was lower. These results suggest that there may be differences in oxidative stress in the mouse brain tissue according to the duration of disruption of the circadian rhythm, the recovery pattern, and the degree of stabilization of the circadian rhythm. Considering that oxidative stress is highly related to physical and mental disorders, it is likely that adolescents experiencing changes in circadian rhythm and who are vulnerable to circadian rhythm disturbance will need an intervention to minimize the effects of oxidative stress and stabilize the circadian rhythm. Future studies must evaluate the impact of circadian rhythm disruption, recovery patterns, and stability on oxidative stress, emotions, and cognition and see if there are changes with age.
Supplemental Materials
Footnotes
Funding
None.
Conflicts of Interest
No potential conflict of interest relevant to this article was reported.
Author Contributions
Conceptualization: Chi Eun Oh, Eunsoo Moon. Data acquisition: Chi Eun Oh, Hyun Ju Lim, Jeounghyun Park. Formal analysis: Chi Eun Oh, Eunsoo Moon. Supervision: Ji Kyoung Park, Eunsoo Moon. Writing−original draft: Chi Eun Oh. Writing−review & editing: Chi Eun Oh, Hyun Ju Lim, Jeounghyun Park, Ji Kyoung Park, Eunsoo Moon.
References
- 1.Bollinger T, Schibler U. Circadian rhythms - from genes to physiology and disease. Swiss Med Wkly. 2014;144:w13984. doi: 10.4414/smw.2014.13984. [DOI] [PubMed] [Google Scholar]
- 2.Walker WH, 2nd, Walton JC, DeVries AC, Nelson RJ. Circadian rhythm disruption and mental health. Transl Psychiatry. 2020;10:28. doi: 10.1038/s41398-020-0694-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Farhud D, Aryan Z. Circadian rhythm, lifestyle and health: a narrative review. Iran J Public Health. 2018;47:1068–1076. [PMC free article] [PubMed] [Google Scholar]
- 4.Chung JK, Lee KY, Kim SH, Kim EJ, Jeong SH, Jung HY, et al. Circadian rhythm characteristics in mood disorders: comparison among bipolar I disorder, bipolar II disorder and recurrent major depressive disorder. Clin Psychopharmacol Neurosci. 2012;10:110–116. doi: 10.9758/cpn.2012.10.2.110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Aruoma OI. Free radicals, oxidative stress, and antioxidants in human health and disease. J Am Oil Chem Soc. 1998;75:199–212. doi: 10.1007/s11746-998-0032-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Pisoschi AM, Pop A. The role of antioxidants in the chemistry of oxidative stress: a review. Eur J Med Chem. 2015;97:55–74. doi: 10.1016/j.ejmech.2015.04.040. [DOI] [PubMed] [Google Scholar]
- 7.Albers DS, Beal MF. Mitochondrial dysfunction and oxidative stress in aging and neurodegenerative disease. J Neural Transm Suppl. 2000;59:133–154. doi: 10.1007/978-3-7091-6781-6_16. [DOI] [PubMed] [Google Scholar]
- 8.Granot E, Kohen R. Oxidative stress in childhood--in health and disease states. Clin Nutr. 2004;23:3–11. doi: 10.1016/S0261-5614(03)00097-9. [DOI] [PubMed] [Google Scholar]
- 9.Hovatta I, Juhila J, Donner J. Oxidative stress in anxiety and comorbid disorders. Neurosci Res. 2010;68:261–275. doi: 10.1016/j.neures.2010.08.007. [DOI] [PubMed] [Google Scholar]
- 10.Boveris A, Fraga CG. Oxidative stress in aging and disease. Mol Aspects Med. 2004;25:1–4. doi: 10.1016/j.mam.2004.02.004. [DOI] [PubMed] [Google Scholar]
- 11.Sánchez-Rodríguez MA, Mendoza-Núñez VM. Oxidative stress indexes for diagnosis of health or disease in humans. Oxid Med Cell Longev. 2019;2019:4128152. doi: 10.1155/2019/4128152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Toledo JC, Jr, Augusto O. Connecting the chemical and biological properties of nitric oxide. Chem Res Toxicol. 2012;25:975–989. doi: 10.1021/tx300042g. [DOI] [PubMed] [Google Scholar]
- 13.Esterbauer H, Schaur RJ, Zollner H. Chemistry and biochemistry of 4-hydroxynonenal, malonaldehyde and related aldehydes. Free Radic Biol Med. 1991;11:81–128. doi: 10.1016/0891-5849(91)90192-6. [DOI] [PubMed] [Google Scholar]
- 14.Fındıklı E, Camkurt MA, İzci F, Karaaslan MF, Fındıklı HA, Sümer P, et al. The diagnostic value of malondialdehyde, superoxide dismutase and catalase activity in drug naïve, first episode, non-smoker generalized anxiety disorder patients. Clin Psychopharmacol Neurosci. 2018;16:88–94. doi: 10.9758/cpn.2018.16.1.88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Wilking M, Ndiaye M, Mukhtar H, Ahmad N. Circadian rhythm connections to oxidative stress: implications for human health. Antioxid Redox Signal. 2013;19:192–208. doi: 10.1089/ars.2012.4889. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Fanjul-Moles ML, López-Riquelme GO. Relationship between oxidative stress, circadian rhythms, and AMD. Oxid Med Cell Longev. 2016;2016:7420637. doi: 10.1155/2016/7420637. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kanabrocki EL, Murray D, Hermida RC, Scott GS, Bremner WF, Ryan MD, et al. Circadian variation in oxidative stress markers in healthy and type II diabetic men. Chronobiol Int. 2002;19:423–439. doi: 10.1081/CBI-120002914. [DOI] [PubMed] [Google Scholar]
- 18.Krishnan N, Davis AJ, Giebultowicz JM. Circadian regulation of response to oxidative stress in Drosophila melanogaster. Biochem Biophys Res Commun. 2008;374:299–303. doi: 10.1016/j.bbrc.2008.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lapenna D, De Gioia S, Mezzetti A, Porreca E, Ciofani G, Marzio L, et al. Circadian variations in antioxidant defences and lipid peroxidation in the rat heart. Free Radic Res Commun. 1992;17:187–194. doi: 10.3109/10715769209068165. [DOI] [PubMed] [Google Scholar]
- 20.Tomás-Zapico C, Coto-Montes A, Martínez-Fraga J, Rodríguez- Colunga MJ, Tolivia D. Effects of continuous light exposure on antioxidant enzymes, porphyric enzymes and cellular damage in the Harderian gland of the Syrian hamster. J Pineal Res. 2003;34:60–68. doi: 10.1034/j.1600-079X.2003.02951.x. [DOI] [PubMed] [Google Scholar]
- 21.Villafuerte G, Miguel-Puga A, Rodríguez EM, Machado S, Manjarrez E, Arias-Carrión O. Sleep deprivation and oxidative stress in animal models: a systematic review. Oxid Med Cell Longev. 2015;2015:234952. doi: 10.1155/2015/234952. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wright KP, Lowry CA, Lebourgeois MK. Circadian and wakefulness-sleep modulation of cognition in humans. Front Mol Neurosci. 2012;5:50. doi: 10.3389/fnmol.2012.00050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Killgore WDS, Balkin TJ, Yarnell AM, Capaldi VF., 2nd Sleep deprivation impairs recognition of specific emotions. Neurobiol Sleep Circadian Rhythms. 2017;3:10–16. doi: 10.1016/j.nbscr.2017.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Lyall LM, Wyse CA, Graham N, Ferguson A, Lyall DM, Cullen B, et al. Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank. Lancet Psychiatry. 2018;5:507–514. doi: 10.1016/S2215-0366(18)30139-1. [DOI] [PubMed] [Google Scholar]
- 25.Park J, Jung MS, Moon E, Lim HJ, Oh CE, Lee JH. Prediction of locomotor activity by infrared motion detector on sleep-wake state in mice. Clin Psychopharmacol Neurosci. 2021;19:303–312. doi: 10.9758/cpn.2021.19.2.303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Jung SH, Park JM, Moon E, Chung YI, Lee BD, Lee YM, et al. Delay in the recovery of normal sleep-wake cycle after disruption of the light-dark cycle in mice: a bipolar disorder- prone animal model? Psychiatry Investig. 2014;11:487–491. doi: 10.4306/pi.2014.11.4.487. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Moon E, Choe BM, Park JM, Chung YI, Lee BD, Park JH, et al. Protein kinase C activity and delayed recovery of sleep-wake cycle in mouse model of bipolar disorder. Psychiatry Investig. 2018;15:907–913. doi: 10.30773/pi.2018.05.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Mitchell JA, Quante M, Godbole S, James P, Hipp JA, Marinac CR, et al. Variation in actigraphy-estimated rest-activity patterns by demographic factors. Chronobiol Int. 2017;34:1042–1056. doi: 10.1080/07420528.2017.1337032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Cornelissen G. Cosinor-based rhythmometry. Theor Biol Med Model. 2014;11:16. doi: 10.1186/1742-4682-11-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kraeuter AK, Guest PC, Sarnyai Z. The Y-maze for assessment of spatial working and reference memory in mice. Methods Mol Biol. 2019;1916:105–111. doi: 10.1007/978-1-4939-8994-2_10. [DOI] [PubMed] [Google Scholar]
- 31.Sarkar D Himanshu, author; Dharmila, author; Nutan, author. A review of behavioral tests to evaluate different types of anxiety and anti-anxiety effects. Clin Psychopharmacol Neurosci. 2020;18:341–351. doi: 10.9758/cpn.2020.18.3.341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Steru L, Chermat R, Thierry B, Simon P. The tail suspension test: a new method for screening antidepressants in mice. Psychopharmacology (Berl) 1985;85:367–370. doi: 10.1007/BF00428203. [DOI] [PubMed] [Google Scholar]
- 33.Cryan JF, Mombereau C, Vassout A. The tail suspension test as a model for assessing antidepressant activity: review of pharmacological and genetic studies in mice. Neurosci Biobehav Rev. 2005;29:571–625. doi: 10.1016/j.neubiorev.2005.03.009. [DOI] [PubMed] [Google Scholar]
- 34.Can A, Dao DT, Arad M, Terrillion CE, Piantadosi SC, Gould TD. The mouse forced swim test. J Vis Exp. 2012;(59):e3638. doi: 10.3791/3638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Cell Biolabs, Inc, author. OxiSelectTM TBARS Assay Kit (MDA Quantitation) [Internet] Cell Biolabs, Inc.; San Diego: 2021. [cited at 2021 Mar 2]. https://www.cellbiolabs.com/sites/default/files/STA-330-tbars-assay-kit.pdf. [Google Scholar]
- 36.cheart, author. Cosinor analysis [Internet] The MathWorks, Inc.; Natick: 2008. [cited at 2021 Mar 2]. https://www.mathworks.com/matlabcentral/fileexchange/20329-cosinor- analysis. [Google Scholar]
- 37.Chen R, Seo DO, Bell E, von Gall C, Lee C. Strong resetting of the mammalian clock by constant light followed by constant darkness. J Neurosci. 2008;28:11839–11847. doi: 10.1523/JNEUROSCI.2191-08.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Wang X, Michaelis EK. Selective neuronal vulnerability to oxidative stress in the brain. Front Aging Neurosci. 2010;2:12. doi: 10.3389/fnagi.2010.00012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Salim S. Oxidative stress and the central nervous system. J Pharmacol Exp Ther. 2017;360:201–205. doi: 10.1124/jpet.116.237503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Fletcher KL, Whitley BN, Treidel LA, Thompson D, Williams A, Noguera JC, et al. Voluntary locomotor activity mitigates oxidative damage associated with isolation stress in the prairie vole (Microtus ochrogaster) Biol Lett. 2015;11:20150178. doi: 10.1098/rsbl.2015.0178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Goto S, Naito H, Kaneko T, Chung HY, Radák Z. Hormetic effects of regular exercise in aging: correlation with oxidative stress. Appl Physiol Nutr Metab. 2007;32:948–953. doi: 10.1139/H07-092. [DOI] [PubMed] [Google Scholar]
- 42.Campbell SS, Tobler I. Animal sleep: a review of sleep duration across phylogeny. Neurosci Biobehav Rev. 1984;8:269–300. doi: 10.1016/0149-7634(84)90054-X. [DOI] [PubMed] [Google Scholar]
- 43.Varadinova MG, Valcheva-Traykova ML, Boyadjieva NI. Effect of circadian rhythm disruption and alcohol on the oxidative stress level in rat brain. Am J Ther. 2016;23:e1801–e1805. doi: 10.1097/MJT.0000000000000363. [DOI] [PubMed] [Google Scholar]
- 44.Singh R, Kiloung J, Singh S, Sharma D. Effect of paradoxical sleep deprivation on oxidative stress parameters in brain regions of adult and old rats. Biogerontology. 2008;9:153–162. doi: 10.1007/s10522-008-9124-z. [DOI] [PubMed] [Google Scholar]
- 45.Tsikas D. Assessment of lipid peroxidation by measuring malondialdehyde (MDA) and relatives in biological samples: analytical and biological challenges. Anal Biochem. 2017;524:13–30. doi: 10.1016/j.ab.2016.10.021. [DOI] [PubMed] [Google Scholar]
- 46.Wasowicz W, Nève J, Peretz A. Optimized steps in fluorometric determination of thiobarbituric acid-reactive substances in serum: importance of extraction pH and influence of sample preservation and storage. Clin Chem. 1993;39:2522–2526. doi: 10.1093/clinchem/39.12.2522. [DOI] [PubMed] [Google Scholar]
- 47.Grotto D, Maria LS, Valentini J, Paniz C, Schmitt G, Garcia SC, et al. Importance of the lipid peroxidation biomarkers and methodological aspects FOR malondialdehyde quantification. Quí. m Nova. 2009;32:169–174. doi: 10.1590/S0100-40422009000100032. [DOI] [Google Scholar]
- 48.Logan RW, Hasler BP, Forbes EE, Franzen PL, Torregrossa MM, Huang YH, et al. Impact of sleep and circadian rhythms on addiction vulnerability in adolescents. Biol Psychiatry. 2018;83:987–996. doi: 10.1016/j.biopsych.2017.11.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Randler C, Faßl C, Kalb N. From Lark to Owl: developmental changes in morningness-eveningness from new-borns to early adulthood. Sci Rep. 2017;7:45874. doi: 10.1038/srep45874. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Hagenauer MH, Perryman JI, Lee TM, Carskadon MA. Adolescent changes in the homeostatic and circadian regulation of sleep. Dev Neurosci. 2009;31:276–284. doi: 10.1159/000216538. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Crowley SJ, Acebo C, Carskadon MA. Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Med. 2007;8:602–612. doi: 10.1016/j.sleep.2006.12.002. [DOI] [PubMed] [Google Scholar]
- 52.Carskadon MA, Acebo C, Jenni OG. Regulation of adolescent sleep: implications for behavior. Ann N Y Acad Sci. 2004;1021:276–291. doi: 10.1196/annals.1308.032. [DOI] [PubMed] [Google Scholar]
- 53.Carskadon MA, Vieira C, Acebo C. Association between puberty and delayed phase preference. Sleep. 1993;16:258–262. doi: 10.1093/sleep/16.3.258. [DOI] [PubMed] [Google Scholar]
- 54.Touitou Y, Touitou D, Reinberg A. Disruption of adolescents' circadian clock: the vicious circle of media use, exposure to light at night, sleep loss and risk behaviors. J Physiol Paris. 2016;110(4 Pt B):467–479. doi: 10.1016/j.jphysparis.2017.05.001. [DOI] [PubMed] [Google Scholar]
- 55.Ruby CL, Palmer KN, Zhang J, Risinger MO, Butkowski MA, Swartzwelder HS. Differential sensitivity to ethanol-induced circadian rhythm disruption in adolescent and adult mice. Alcohol Clin Exp Res. 2017;41:187–196. doi: 10.1111/acer.13275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Dueck A, Berger C, Wunsch K, Thome J, Cohrs S, Reis O, et al. The role of sleep problems and circadian clock genes in attention-deficit hyperactivity disorder and mood disorders during childhood and adolescence: an update. J Neural Transm (Vienna) 2017;124(Suppl 1):127–138. doi: 10.1007/s00702-015-1455-8. [DOI] [PubMed] [Google Scholar]
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