Untargeted metabolomics analysis of rat hippocampus subjected to sleep fragmentation
Introduction
Sleep is an essential process, and appropriate amounts of sleep are necessary to achieve normal body function. However, mounting evidence has shown that sleep continuity is also important for health, and decreased sleep continuity has been associated with several pathologic symptoms (e.g., increased inflammatory cytokines, elevated blood pressure, and impaired neurocognitive functions) and risk of chronic disease such as diabetes or cardiovascular disease (Dumaine and Ashley, 2015; Ekstedt et al., 2004; Qian et al., 2016; Ramesh et al., 2012). Sleep fragmentation (SF), defined as brief arousals that occur during sleep, can be induced by external factors such as bright light, high temperature or humidity, and noise during sleep. Moreover, it occurs frequently in patients with sleep apnea, chronic pain (Blagestad et al., 2012), periodic leg movements(Mancebo-Sosa et al., 2016), and asthma (Luyster et al., 2012). SF causes excessive daytime sleepiness (EDS), possibly by inhibiting cholinergic neuronal activity in the basal forebrain and elevating adenosine level (McKenna et al., 2007). EDS increases the risk of motor vehicle accidents (Ward et al., 2013) and decreases daytime functioning (Gooneratne et al., 2003; Stepanski, 2002). In addition, EDS is strongly associated with incident cardiovascular morbidity and mortality (Newman et al., 2000).
The hippocampus is a brain structure especially vulnerable to sleep disturbance in terms of morphological and functional aspects. In a recent experimental study, chronic sleep restriction (SR) resulted in a 10% reduction in hippocampal volume in rats, without an overall decrease in cortical thickness compared with controls (Novati et al., 2011). Human neuroimaging studies that examined the association between short sleep or disturbed sleep and hippocampal volume showed similar findings to those reported in animal studies (Joo et al., 2014; Taki et al., 2012). In animal studies, SF resulted in hippocampus-dependent learning and cognition deficits (Nair et al., 2011; Sportiche et al., 2010; Tartar et al., 2006). However, the appearance of negative impacts of SF varies depending on the duration of SF (Wallace et al., 2015). The possible mechanisms by which SF induces impairments include reduction in hippocampal neurogenesis (Guzman-Marin et al., 2007; Sportiche et al., 2010), increase of reactive oxygen species through upregulation and activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, and loss of N-methyl-D-aspartate (NMDA) receptor-dependent long-term potentiation in hippocampal area CA1 (Tartar et al., 2010, 2006).
Metabolomics is an emerging field of ‘omics’ research for global or system-wide metabolite profiles under a given set of conditions (Goodacre et al., 2004; Wishart, 2008), using analytical chemistry techniques such as nuclear magnetic resonance (NMR), chromatography, and mass spectrometry (MS)(Azad and Shulaev, 2018; Zhang et al., 2012). This field of research promises a better understanding of the pathophysiology of several chronic diseases and development of potential biomarkers (Ivanisevic and Thomas, 2018). Metabolomics has also been used for therapeutic monitoring and development of drugs (Puchades-Carrasco and Pineda-Lucena, 2017; Wei, 2011). Metabolomics is divided into two approaches (Patti et al., 2012), targeted and untargeted. Targeted metabolomics attempts to identify or quantitate defined individual metabolites (Roberts et al., 2012), whereas untargeted metabolomics investigates comprehensive profiles of all measurable metabolites using high-throughput methods (Vinayavekhin and Saghatelian, 2010).
Molecular changes caused by SF or sleep deprivation (SD) in genes and protein levels of rodent brains have been previously investigated. Reportedly, genes or proteins associated with cellular response to stress, energy metabolism, neuronal transmission, and synaptic plasticity were altered by SF or SD (Cirelli, 2006; Franco-Perez et al., 2012; Guzman-Marin et al., 2006). However, previous studies have several weaknesses regarding use of the appropriate animal model and limited methodologies. First, occurrence of long-term SD in humans is rare. Second, high-throughput gene expression analysis approaches cannot predict a post-translational modification of proteins, complicating direct interpretation of the function of expressed genes. Third, specific types of proteins are difficult to analyze using proteomic technologies (Chandramouli and Qian, 2009). However, alteration of metabolites reflect the actual activity of the cells; thus, the changes in the metabolome are more amplified than those in the transcriptome and the proteome (Urbanczyk-Wochniak et al., 2003). Moreover, the metabolome shows greater diversity and thus is closer to the phenotype of the biological system than the transcriptome and the proteome (Horgan et al., 2009). Therefore, in the present study, the changes of metabolites in the hippocampus, a brain region vulnerable to sleep disturbance, were investigated using untargeted metabolomics. This approach could provide a better understanding of the hippocampus including pathophysiology of hippocampus-dependent learning and cognitive impairments subjected to SF in terms of individual metabolites or specific metabolic pathways.
In this study, 4-day and 15-day SF models were used and the metabolite profiles were compared to the corresponding exercise control groups. We considered both the chronological criteria and the periods in which appropriate phenotypes can be expressed based on previous studies to determine the appropriate duration of the experiment. In terms of duration, acute SF generally refers to a one-night or several-night period in which sleep is interrupted, while chronic SF refers to a relatively long period of interrupted sleep (weeks or months). Indeed, electrophysiological data show that acute SF is different from chronic SF. Another study, with a different duration from our experimental period, have shown distinct effects on non-rapid eye movement (NREM) and rapid eye movement (REM) bout length during recovery periods in 3-day (acute) and 14-day (chronic) sleep-fragmented mice (Wallace et al., 2015). Other studies have also shown that 4-day and 15-day SF caused a reduction in hippocampal neurogenesis (Guzman-Marin et al., 2007) and deficits in hippocampus-dependent cognitive function (Nair et al., 2011), respectively. Thus, we chose 4-day and 15-day to distinguish the acute and chronic effects of SF on biochemical changes in hippocampus.
Section snippets
Animals
Seven-week-old male Wistar rats (Orient Bio, Korea) weighing 210–230 g were used in this study. Animals were maintained in a temperature-controlled room (24 ± 2 °C) with alternating 12-h light and 12-h dark cycles (lights on at 8:00 a.m.) The rats had free access to food and water. After the 1-week acclimatization period, 48 rats were randomly divided into the following four groups: 4-day SF group (n = 12), 4-day exercise control (EC) group (n = 12), 15-day SF group (n = 12), and 15-day EC
Assessment of body weight and the effects of 4 days’ and 15 days’ SF or EC on sleep parameters
Body weight was measured at baseline and on Day 4 (4-day group) or Day 15 (15-day group) in the EC and SF groups. 4-day SF group had a slightly lower body weight than the 4-day EC group on the fourth day of experiment, but the difference was not statistically significant (P = 0.054) (Fig. 1C). There was no significant difference in body weight between 15-day EC and 15-day SF groups at baseline (P = 0.240) and on Day 15 (P = 0.324) (Fig. 1D). Throughout the experimental period of the 4-day
Discussion
In the present untargeted metabolomics study, we found that alanine, aspartate, and glutamate metabolism pathway is the most altered metabolic pathway in the 15-day SF group compared to the 15-day EC group. The 4-day SF group showed a typical pattern of SF in the EEG and clearly differentiated in the metabolites profiles on PCA or PLS-DA compared to the corresponding EC group, but the identified metabolites were not statistically significant. Indeed, only the 4-day SF group showed a significant
Author contributions statement
Conception and design, D.W.Y., H.N.K., C.H.Y., and C.S; data collection, D.W.Y., H.N.K., X.J., S.K.L., and J.K.K; data analysis and interpretation, D.W.Y., C.H.Y., H.N.K., and S.H.P; drafting the manuscript, D.W.Y., H.N.K., X.J., and C.H.Y; revision for important intellectual content, C.H.Y., C.S., and S.H.P.
Declaration of Competing Interest
None of the authors have conflicts of interests to disclose.
Acknowledgements
This work was supported by the Basic Science Research Programs through National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (NRF-2018R1A3B1052328 and 2014M3A9B6069340).
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These two authors equally contributed to this work.