Serum and Brain Metabolomic Variations Reveal Perturbation of Sleep Deprivation on Rats and Ameliorate Effect of Total Ginsenoside Treatment

Decoding the nexus: branched-chain amino acids and their connection with sleep, circadian rhythms, and cardiometabolic health

The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and, either directly or indirectly, overall body health, encompassing metabolic and cardiovascular well-being. Given the heightened metabolic activity of the brain, there exists a considerable demand for nutrients in comparison to other organs. Among these, the branched-chain amino acids, comprising leucine, isoleucine, and valine, display distinctive significance, from their contribution to protein structure to their involvement in overall metabolism, especially in cerebral processes. Among the first amino acids that are released into circulation post-food intake, branched-chain amino acids assume a pivotal role in the regulation of protein synthesis, modulating insulin secretion and the amino acid sensing pathway of target of rapamycin. Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors, competing for a shared transporter. Beyond their involvement in protein synthesis, these amino acids contribute to the metabolic cycles of γ-aminobutyric acid and glutamate, as well as energy metabolism. Notably, they impact GABAergic neurons and the excitation/inhibition balance. The rhythmicity of branched-chain amino acids in plasma concentrations, observed over a 24-hour cycle and conserved in rodent models, is under circadian clock control. The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood. Disturbed sleep, obesity, diabetes, and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics. The mechanisms driving these effects are currently the focal point of ongoing research efforts, since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies. In this context, the Drosophila model, though underutilized, holds promise in shedding new light on these mechanisms. Initial findings indicate its potential to introduce novel concepts, particularly in elucidating the intricate connections between the circadian clock, sleep/wake, and metabolism. Consequently, the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle. They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health, paving the way for potential therapeutic interventions.

Mechanism of Panax notoginseng saponins in improving cognitive impairment induced by chronic sleep deprivation based on the integrative analysis of serum metabolomics and network pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Panax notoginseng saponin (PNS) has a variety of biological activities, such as improvement of myocardial ischemia, improvement of learning and memory, hypolipidemia, and immunomodulation. However, its protective mechanism on the central nervous system (CNS) is not clear.

AIM OF THE STUDY

The present study initially evaluated the possible mechanism of PNS to improve cognitive dysfunction due to chronic sleep deprivation (CSD).

MATERIALS AND METHODS

In the present study, we used a modified multi-platform aquatic environment sleep deprivation method to induce a cognitively impaired rat model, and explored the mechanism of action of PNS by integrating serum metabolomics and network pharmacology, which was further verified by molecular docking and experiments.

RESULTS

The results showed that PNS significantly shortened the escape latency, increased the target quadrant time and the number of traversing platforms, and attenuated the inflammatory damage in the hippocampal Cornu Ammonis 1 (CA1) region in CSD rats. The non-targeted metabolomics results indicated that 35 biomarkers significantly altered following PNS therapy intervention, with metabolic pathways enriched for the effects of One carbon pool by folate, Riboflavin metabolism, Glycerophospholipid metabolism, Sphingolipid metabolism, Glycerolipid metabolism, Arachidonic acid metabolism, and Tryptophan metabolism. In addition, network pharmacology identified 234 potential targets for PNS intervention in CSD with cognitive impairment. Metabolite-response-enzyme-gene network was constructed by MetaScape and matched with the network pharmacology results to identify a total of five shared targets (LPL, GPAM, HSD11B1, HSD11B2, and SULT2A1) and two metabolic pathways (Sphingolipid metabolism and Steroid hormone biosynthesis). The results of molecular docking revealed that the five active ingredients had good binding ability with the five core targets. qPCR analysis confirmed the ability of PNS to modulate the above five targets.

CONCLUSIONS

The combination of metabolomics and network analysis provides a scientific basis for promoting the clinical application of PNS in cognitive impairment.

Determining of 18 amino acids in plasma of pregnant women with sleep disorders by UHPLC-MS/MS

Many pregnant women experience sleep disorders, and amino acid levels could play a crucial role in affecting maternal sleep. To explore this potential relationship, an accurate and effective UHPLC-MS/MS method has been developed to monitor 18 amino acids in the plasma samples of pregnant women. This method aims to assess how plasma amino acid levels might be linked to sleep disorders during pregnancy. Plasma samples were precipitated with acetonitrile containing 0.2% formic acid. We used 5% seralbumin as the surrogate matrix to establish quantitative curves for amino acid determination in human plasma. The method was validated in both the surrogate matrix and human plasma. The optimized UHPLC-MS/MS method was validated, showing that that the analytes had comparable recovery and negligible matrix effects in both 5% seralbumin and human plasma. The linearity, lower limit of quantification, precision, accuracy, and stability all met the acceptance criteria. The validated method was successfully applied to determination of the plasma levels of 18 amino acids in pregnant women with or without sleep disorders, indicating that alanine, lysine, tryptophan, glutamic acid, and phenylalanine levels had significant changes which may be related to sleep disorders during early pregnancy. An accurate, reliable, and efficient UHPLC-MS/MS method was successfully developed and support to find the specific amino acids as potential biomarkers for sleep disorders in pregnant women.

Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia

BACKGROUND

Sleep disturbances are a common occurrence in patients with schizophrenia, yet the underlying pathogenesis remain poorly understood. Here, we performed a targeted metabolomics-based approach to explore the potential biological mechanisms contributing to sleep disturbances in schizophrenia.

METHODS

Plasma samples from 59 drug-naïve patients with schizophrenia and 36 healthy controls were subjected to liquid chromatography-mass spectrometry (LC-MS) targeted metabolomics analysis, allowing for the quantification and profiling of 271 metabolites. Sleep quality and clinical symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Positive and Negative Symptom Scale (PANSS), respectively. Partial correlation analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to identify metabolites specifically associated with sleep disturbances in drug-naïve schizophrenia.

RESULTS

16 characteristic metabolites were observed significantly associated with sleep disturbances in drug-naïve patients with schizophrenia. Furthermore, the glycerophospholipid metabolism (Impact: 0.138, p<0.001), the butanoate metabolism (Impact: 0.032, p=0.008), and the sphingolipid metabolism (Impact: 0.270, p=0.104) were identified as metabolic pathways associated with sleep disturbances in drug-naïve patients with schizophrenia.

CONCLUSIONS

Our study identified 16 characteristic metabolites (mainly lipids) and 3 metabolic pathways related to sleep disturbances in drug-naïve schizophrenia. The detection of these distinct metabolites provide valuable insights into the underlying biological mechanisms associated with sleep disturbances in schizophrenia.

Serum metabolomics study of narcolepsy type 1 based on ultra-performance liquid chromatography-tandem mass spectrometry

Narcolepsy is a chronic and underrecognized sleep disorder characterized by excessive daytime sleepiness and cataplexy. Furthermore, narcolepsy type 1 (NT1) has serious negative impacts on an individual's health, society, and the economy. Currently, many sleep centers lack the means to measure orexin levels in the cerebrospinal fluid. We aimed to analyze the characteristics of metabolite changes in patients with NT1, measured by ultra-performance liquid chromatography-tandem mass spectrometry. A principal component analysis (PCA), an orthogonal partial least square discriminant analysis (OPLS-DA), t tests, and volcano plots were used to construct a model of abnormal metabolic pathways in narcolepsy. We identified molecular changes in serum specimens from narcolepsy patients and compared them with control groups, including dehydroepiandrosterone, epinephrine, N-methyl-D-aspartic acid, and other metabolites, based on an OPLS-loading plot analysis. Nine metabolites yielded an area under the receiver operating curve > 0.75. Meanwhile, seven abnormal metabolic pathways were correlated with differential metabolites, such as metabolic pathways; neuroactive ligand‒receptor interaction; and glycine, serine, and threonine metabolism. To our knowledge, this is the first study to reveal the characteristic metabolite changes in sera from NT1 patients for the selection of potential blood biomarkers and the elucidation of NT1 pathogenesis.

Fructus gardeniae ameliorates anxiety-like behaviors induced by sleep deprivation via regulating hippocampal metabolomics and gut microbiota

Fructus gardeniae (FG) is a traditional Chinese medicine and health food for thousands of years of application throughout Chinese history and is still widely used in clinical Chinese medicine. FG has a beneficial impact on anxiety, depression, insomnia, and psychiatric disorders; however, its mechanism of action requires further investigation. This study aimed to investigate the effects and mechanisms of FG on sleep deprivation (SD)-induced anxiety-like behavior in rats. A model of SD-induced anxiety-like behavior in rats was established by intraperitoneal injection of p-chlorophenylalanine (PCPA). This was accompanied by neuroinflammation and metabolic abnormalities in the hippocampus and disturbance of intestinal microbiota. However reduced SD-induced anxiety-like behavior and decreased levels of pro-inflammatory cytokines including TNF-α and IL-1β were observed in the hippocampus of rats after 7 days of FG intervention. In addition, metabolomic analysis demonstrated that FG was able to modulate levels of phosphatidylserine 18, Phosphatidylinositol 18, sn-glycero-3-phosphocholine, deoxyguanylic acid, xylose, betaine and other metabolites in the hippocampus. The main metabolic pathways of hippocampal metabolites after FG intervention involve carbon metabolism, glycolysis/gluconeogenesis, pentose phosphate, and glycerophospholipid metabolism. 16S rRNA sequencing illustrated that FG ameliorated the dysbiosis of gut microbiota in anxious rats, mainly increased the abundance of Muribaculaceae and Lactobacillus, and decreased the abundance of Lachnospiraceae_NK4A136_group. In addition, the correlation analysis demonstrated that there was a close relationship between hippocampal metabolites and intestinal microbiota. In conclusion, FG improved the anxiety behavior and inhibited of neuroinflammation in sleep-deprived rats, and the mechanism may be related to the FG regulation of hippocampal metabolites and intestinal microflora composition.

Caffeine-Induced Sleep Restriction Alters the Gut Microbiome and Fecal Metabolic Profiles in Mice

Insufficient sleep is becoming increasingly common and contributes to many health issues. To combat sleepiness, caffeine is consumed daily worldwide. Thus, caffeine consumption and sleep restriction often occur in succession. The gut microbiome can be rapidly affected by either one's sleep status or caffeine intake, whereas the synergistic effects of a persistent caffeine-induced sleep restriction remain unclear. In this study, we investigated the impact of a chronic caffeine-induced sleep restriction on the gut microbiome and its metabolic profiles in mice. Our results revealed that the proportion of Firmicutes and Bacteroidetes was not altered, while the abundance of Proteobacteria and Actinobacteria was significantly decreased. In addition, the content of the lipids was abundant and significantly increased. A pathway analysis of the differential metabolites suggested that numerous metabolic pathways were affected, and the glycerophospholipid metabolism was most significantly altered. Combined analysis revealed that the metabolism was significantly affected by variations in the abundance and function of the intestinal microorganisms and was closely relevant to Proteobacteria and Actinobacteria. In conclusion, a long-term caffeine-induced sleep restriction affected the diversity and composition of the intestinal microbiota in mice, and substantially altered the metabolic profiles of the gut microbiome. This may represent a novel mechanism by which an unhealthy lifestyle such as mistimed coffee breaks lead to or exacerbates disease.

Metabolomics in Sleep, Insomnia and Sleep Apnea

Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a large-scale manner offers unique opportunities to provide insights into the pathology of diseases by revealing alterations in metabolic pathways. This review aims to summarize the status of metabolomic analyses-based knowledge on sleep disorders and to present knowledge in understanding the metabolic role of sleep in psychiatric disorders. Overall, findings suggest that sleep-wake disorders lead to pronounced alterations in specific metabolic pathways, which might contribute to the association of sleep disorders with other psychiatric disorders and medical conditions. These alterations are mainly related to changes in the metabolism of branched-chain amino acids, as well as glucose and lipid metabolism. In insomnia, alterations in branched-chain amino acid and glucose metabolism were shown among studies. In obstructive sleep apnea, biomarkers related to lipid metabolism seem to be of special importance. Future studies are needed to examine severity, subtypes and treatment of sleep-wake disorders in the context of metabolite levels.

Comparative metabolome analysis of serum changes in sheep under overgrazing or light grazing conditions

Background

Overgrazing is a primary contributor to severe reduction in forage quality and production in Inner Mongolia, leading to extensive ecosystem degradation, sheep health impairment and growth performance reduction. Further studies to identify serum biomarkers that reflect changes in sheep health and nutritional status following overgrazing would be beneficial. We hereby hypothesize that reduced sheep growth performance under overgrazing conditions would be associated with metabolic and immune response alterations. This study used an untargeted metabolomics analysis by conducting ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) of sheep serum under overgrazing and light grazing conditions to identify metabolic disruptions in response to overgrazing.

Results

The sheep body weight gains as well as serum biochemical variables associated with immune responses and nutritional metabolism (immunoglobulin G, albumin, glucose, and nonesterified fatty acids) were significantly decreased with overgrazing compared with light grazing condition. In contrast, other serum parameters such as alanine and aspartate aminotransferase, alkaline phosphatase, total bilirubin, blood urea nitrogen, and interleukin-8 were markedly higher in the overgrazing group. Principal component analysis discriminated the metabolomes of the light grazing from the overgrazing group. Multivariate and univariate analyses revealed changes in the serum concentrations of 15 metabolites (9 metabolites exhibited a marked increase, whereas 6 metabolites showed a significant decrease) in the overgrazing group. Major changes of fatty acid oxidation, bile acid biosynthesis, and purine and protein metabolism were observed.

Conclusions

These findings offer metabolic evidence for putative biomarkers for overgrazing-induced changes in serum metabolism. Target-identification of these particular metabolites may potentially increase our knowledge of the molecular mechanisms of altered immune responses, nutritional metabolism, and reduced sheep growth performance under overgrazing conditions.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

Chronic paradoxical sleep deprivation-induced depression-like behavior, energy metabolism and microbial changes in rats

AIMS

Given the lasting impact of chronic paradoxical sleep deprivation (PSD) on behavior and organism metabolic alternations, along with the role of the microbiome in neurobehavioral development and metabolism, we sought to examine the relationship between the microbiota and chronic PSD-induced behavioral and metabolic changes.

MATERIALS AND METHODS

Psychological status of 7-day PSD (7d-PSD) male rats was tested by behavioral method, serum inflammatory cytokines and hypothalamic-pituitary-adrenal (HPA) axis-related hormones. In addition, GC-MS based urine metabolomics and 16S rRNA gene sequencing approaches were applied to estimate the influences of chronic PSD on host metabolism and gut-microbiota. Furtherly, microbial functional prediction and Spearman's correlation analysis were implemented to manifest the relations between the differential urinary metabolites and gut microbiota.

KEY FINDINGS

7d-PSD rats displayed depression-like behavior, metabolic and microbial changes. By integrating differential gut bacteria with indicators of depression and differential metabolites, we found that the alterations of Akkermansia, Oscillospira, Ruminococcus, Parabacteroides, Aggregatibacter and Phascolarctobacterium were closely related to abnormalities of depression symptoms and inflammatory cytokines. These bacteria also had close connections with host energy metabolism concerning arginine and proline metabolism, glycine, serine and threonine metabolism, and glyoxylate and dicarboxylate metabolism, pyruvate metabolism, which overlapped with the results of 16S rRNA gene function annotation.

SIGNIFICANCE

These data suggest that a specific situation of circadian disturbance, chronic PSD-induced alterations in gut microbiota and related host changes in metabolism may be the pathogenesis of depression.