Exploring gene expression biomarker candidates for neurobehavioral impairment from total sleep deprivation

Isobicyclogermacrenal ameliorates hippocampal ferroptosis involvement in neurochemical disruptions and neuroinflammation induced by sleep deprivation in rats

BACKGROUND

Sleep deprivation (SLD) is a widespread condition that disrupts physiological functions and may increase mortality risk. Valeriana officinalis, a traditional medicinal herb known for its sedative and hypnotic properties, contains isobicyclogermacrenal (IG), a newly isolated active compound. However, research on the therapeutic potential of IG for treating SLD remains limited.

METHODS

In this study, IG was extracted and characterized from Valeriana officinalis, and an SLD model was established in rats using p-chlorophenylalanine (PCPA). Behavioral tests and pathological studies were conducted to assess the effects of IG on SLD, and transcriptomic and metabolomic analyses were utilized to investigate its underlying mechanisms.

RESULTS

IG administration significantly improved the cognitive performance of SLD rats in behavioral tests and ameliorated histological injuries in the hippocampus and cerebral cortex. IG treatment increased the levels of brain-derived neurotrophic factor (BDNF) and neurotransmitters such as serotonin (5-HT) in SLD rats. Additionally, IG directly targets TFRC, thereby improving iron metabolism in the hippocampus. Comprehensive transcriptomic and metabolomic analyses revealed that the improvements from IG stemmed from the mitigation of abnormalities in iron metabolism, cholesterol metabolism, and glutathione metabolism, leading to reduced oxidative stress, ferroptosis, and neuroinflammation in the hippocampus caused by SLD.

CONCLUSIONS

Collectively, these findings suggest that IG has the potential to ameliorate neurological damage and cognitive impairment caused by SLD, offering a novel strategy for protection against the adverse effects of SLD.

Human serum proteomics reveals a molecular signature after one night of sleep deprivation

Study Objectives

Sleep deprivation is highly prevalent and caused by conditions such as night shift work or illnesses like obstructive sleep apnea. Compromised sleep affects cardiovascular-, immune-, and neuronal systems. Recently, we published human serum proteome changes after a simulated night shift. This pilot proteomic study aimed to further explore changes in human blood serum after 6 hours of sleep deprivation at night.

Methods

Human blood serum samples from eight self-declared healthy females were analyzed using Orbitrap Eclipse mass spectrometry (MS-MS) and high-pressure liquid chromatography. We used a within-participant design, in which the samples were taken after 6 hours of sleep at night and after 6 hours of sleep deprivation the following night. Systems biological databases and bioinformatic software were used to analyze the data and comparative analysis were done with other published sleep-related proteomic datasets.

Results

Out of 494 proteins, 66 were found to be differentially expressed proteins (DEPs) after 6 hours of sleep deprivation. Functional enrichment analysis revealed the associations of these DEPs with several biological functions related to the altered regulation of cellular processes such as platelet degranulation and blood coagulation, as well as associations with different curated gene sets.

Conclusions

This study presents serum proteomic changes after 6 hours of sleep deprivation, supports previous findings showing that short sleep deprivation affects several biological processes, and reveals a molecular signature of proteins related to pathological conditions such as altered coagulation and platelet function, impaired lipid and immune function, and cell proliferation. Data are available via ProteomeXchange with identifier PXD045729. This paper is part of the Genetic and other molecular underpinnings of sleep, sleep disorders, and circadian rhythms including translational approaches Collection.

Whole genome case-control study of central nervous system toxicity due to antimicrobial drugs

A genetic predisposition to central nervous system (CNS) toxicity induced by antimicrobial drugs (antibiotics, antivirals, antifungals, and antiparasitic drugs) has been suspected. Whole genome sequencing of 66 cases and 833 controls was performed to investigate whether antimicrobial drug-induced CNS toxicity was associated with genetic variation. The primary objective was to test whether antimicrobial-induced CNS toxicity was associated with seventeen efflux transporters at the blood-brain barrier. In this study, variants or structural elements in efflux transporters were not significantly associated with CNS toxicity. Secondary objectives were to test whether antimicrobial-induced CNS toxicity was associated with genes over the whole genome, with HLA, or with structural genetic variation. Uncommon variants in and close to three genes were significantly associated with CNS toxicity according to a sequence kernel association test combined with an optimal unified test (SKAT-O). These genes were LCP1 (q = 0.013), RETSAT (q = 0.013) and SFMBT2 (q = 0.035). Two variants were driving the LCP1 association: rs6561297 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51-8.46]) and the regulatory variant rs10492451 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51-8.46]). No common genetic variant, HLA-type or structural variation was associated with CNS toxicity. In conclusion, CNS toxicity due to antimicrobial drugs was associated with uncommon variants in LCP1, RETSAT and SFMBT2.

Biomarkers linking habitual short sleep duration with risk of cardiometabolic disease: current progress and future directions

Approximately one in three adults in the United States sleeps less than the recommended 7 h per night. Decades of epidemiological data and data from experimental sleep restriction studies demonstrate short sleep duration is associated with adverse cardiometabolic risk, including risk of type 2 diabetes and cardiovascular disease. However, the precise mechanisms underlying this risk are not fully elucidated and there is a lack of sleep-based interventions designed to mitigate such risk. One strategy to overcome these limitations is to develop biomarkers that link habitual short sleep duration with adverse cardiometabolic risk. Such biomarkers could inform biochemical mechanisms, identify new targets for interventions, support precision medicine by identifying individuals most likely to benefit from sleep-based interventions, and ultimately lead to improved cardiometabolic health in people with habitual short sleep durations. Early progress demonstrates proof-of-principle that omics-based technologies are a viable approach to create biochemical signatures (biomarkers) of short sleep duration, primarily derived from acute studies of experimental sleep restriction. Yet, much work remains. Notably, studies that translate early findings from experimental sleep restriction to free-living adults with habitual short sleep duration have high potential to advance the field. Such studies also create an exciting opportunity for larger randomized controlled trials that simultaneously identify biomarkers of habitual short sleep duration and evaluate the efficacy of sleep-based interventions. Ultimately, early progress in developing molecular biomarkers of short sleep duration combined with the prior decades of progress in the sleep and metabolism fields provide the foundation for exciting progress in the biomarker development space.

The needle in the haystack: Identifying and validating common genes of depression, insomnia, and inflammation

BACKGROUND

Insomnia, inflammation, and depression are often co-occurring conditions. The mechanisms underlying these conditions remain unclear.

MATERIALS AND METHODS

We collected microarray datasets of depression and insomnia from GEO and analyzed them for differentially expressed genes (DEGs). We then overlapped the DEGs with a list of inflammatory response-related genes to identify genes associated with all three conditions. We next performed analyses of enrichment analyses, KEGG mapping, and protein-protein interaction to identify hub genes. Furthermore, we established a depression rat model with inflammation and insomnia to validate the potential genes. At last, a two-sample Mendelian randomization (MR) study was conducted to confirm the association of identified target genes with depression outcomes.

RESULTS

We obtained 32 common DEGs associated with the depression, insomnia and inflammatory, and found that the PI3K-AKT signaling pathway might be involved in the inflammatory response in insomnia and depression. CREB1, CYBB, FYN, and CCR5 were identified as targets for the next validation. In model rats, the CCR5 and PI3K-AKT pathways were significantly up-regulated, while the model group exhibited significantly lower hippocampal p-CREB protein expression. The MR study suggested a potential causal relationship between CREB1 and the risk of depression (OR = 1.11, p = 0.013).

LIMITATIONS

The identified potential genes and pathways require further laboratory and clinical evidence verification.

CONCLUSION

We identified four potential inflammatory related-genes (CREB1, CYBB, FYN, and CCR5). CREB1 may be a potential inflammatory response-related biomarker and drug target for depression and insomnia, as validated by the followed rat model and MR study.

Sleep and memory: The impact of sleep deprivation on transcription, translational control, and protein synthesis in the brain

In countries around the world, sleep deprivation represents a widespread problem affecting school-age children, teenagers, and adults. Acute sleep deprivation and more chronic sleep restriction adversely affect individual health, impairing memory and cognitive performance as well as increasing the risk and progression of numerous diseases. In mammals, the hippocampus and hippocampus-dependent memory are vulnerable to the effects of acute sleep deprivation. Sleep deprivation induces changes in molecular signaling, gene expression and may cause changes in dendritic structure in neurons. Genome wide studies have shown that acute sleep deprivation alters gene transcription, although the pool of genes affected varies between brain regions. More recently, advances in research have drawn attention to differences in gene regulation between the level of the transcriptome compared with the pool of mRNA associated with ribosomes for protein translation following sleep deprivation. Thus, in addition to transcriptional changes, sleep deprivation also affects downstream processes to alter protein translation. In this review, we focus on the multiple levels through which acute sleep deprivation impacts gene regulation, highlighting potential post-transcriptional and translational processes that may be affected by sleep deprivation. Understanding the multiple levels of gene regulation impacted by sleep deprivation is essential for future development of therapeutics that may mitigate the effects of sleep loss.

Molecular targets and mechanisms involved in the action of Banxia Shumi decoction in insomnia treatment

Insomnia is a common sleep-wake rhythm disorder, which is closely associated with the occurrence of many serious diseases. Recent researches suggest that circadian rhythms play an important role in regulating sleep duration and sleep quality. Banxia Shumi decoction (BSXM) is a well-known Chinese formula used to treat insomnia in China. However, the overall molecular mechanism behind this therapeutic effect has not yet been fully elucidated. This study aimed to identify the molecular targets and mechanisms involved in the action of BSXM during the treatment of insomnia. Using network pharmacology and molecular docking methods, we investigated the molecular targets and underlying mechanisms of action of BSXM in insomnia therapy. We identified 8 active compounds from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the traditional Chinese medicine integrative database that corresponded to 26 target genes involved in insomnia treatment. The compound-differentially expressed genes of the BXSM network indicated that cavidine and gondoic acid could potentially become key components of drugs used for insomnia treatment. Further analysis revealed that GSK3B, MAPK14, IGF1R, CCL5, and BCL2L11 were core targets significantly associated with the circadian clock. Pathway enrichment analysis of Kyoto Encyclopedia of Genes and Genomes revealed that epidermal growth factor receptor tyrosine kinase inhibitor resistance was the most prominently enriched pathway for BSXM in the insomnia treatment. The forkhead box O signaling pathway was also found to be significantly enriched. These targets were validated using the Gene Expression Omnibus dataset. Molecular docking studies were performed to confirm the binding of cavidine and gondoic acid to the identified core targets. To our knowledge, our study confirmed for the first time that the multi-component, multi-target, and multi-pathway characteristics of BXSM may be the potential mechanism for treating insomnia with respect to the circadian clock gene. The results of this study provided theoretical guidance for researchers to further explore its mechanism of action.

Relationship between circadian genes and memory impairment caused by sleep deprivation

Background

Sleep deprivation (SD)-induced cognitive impairment is highly prevalent worldwide and has attracted widespread attention. The temporal and spatial oscillations of circadian genes are severely disturbed after SD, leading to a progressive loss of their physiological rhythms, which in turn affects memory function. However, there is a lack of research on the role of circadian genes and memory function after SD. Therefore, the present study aims to investigate the relationship between circadian genes and memory function and provide potential therapeutic insights into the mechanism of SD-induced memory impairment.

Methods

Gene expression profiles of GSE33302 and GSE9442 from the Gene Expression Omnibus (GEO) were applied to identify differentially expressed genes (DEGs). Subsequently, both datasets were subjected to Gene Set Enrichment Analysis (GSEA) to determine the overall gene changes in the hippocampus and brain after SD. A Gene Oncology (GO) analysis and Protein-Protein Interaction (PPI) analysis were employed to explore the genes related to circadian rhythm, with their relationship and importance determined through a correlation analysis and a receiver operating characteristic curve (ROC), respectively. The water maze experiments detected behavioral changes related to memory function in SD rats. The expression of circadian genes in several critical organs such as the brain, heart, liver, and lungs and their correlation with memory function was investigated using several microarrays. Finally, changes in the hippocampal immune environment after SD were analyzed using the CIBERSORT in R software.

Results

The quality of the two datasets was very good. After SD, changes were seen primarily in genes related to memory impairment and immune function. Genes related to circadian rhythm were highly correlated with engagement in muscle structure development and circadian rhythm. Seven circadian genes showed their potential therapeutic value in SD. Water maze experiments confirmed that SD exacerbates memory impairment-related behaviors, including prolonged escape latencies and reduced numbers of rats crossing the platform. The expression of circadian genes was verified, while some genes were also significant in the heart, liver, and lungs. All seven circadian genes were also associated with memory markers in SD. The contents of four immune cells in the hippocampal immune environment changed after SD. Seven circadian genes were related to multiple immune cells.

Conclusions

In the present study, we found that SD leads to memory impairment accompanied by changes in circadian rhythm-related genes. Seven circadian genes play crucial roles in memory impairment after SD. Naïve B cells and follicular helper T cells are closely related to SD. These findings provide new insights into the treatment of memory impairment caused by SD.

Gene co-expression analysis identifies modules related to insufficient sleep in humans

BACKGROUND

Insufficient sleep and circadian rhythm disruption may cause cancer, obesity, cardiovascular disease, and cognitive impairment. The underlying mechanisms need to be elucidated.

METHOD

Weighted gene co-expression network analysis (WGCNA) was used to identify co-expressed modules. Connectivity Map tool was used to identify candidate drugs based on top connected genes. R ptestg package was utilized to detected module rhythmicity alteration. A hypergeometric test was used to test the enrichment of insomnia SNP signals in modules. Google Scholar was used to validate the modules and hub genes by literature.

RESULTS

We identified a total of 45 co-expressed modules. These modules were stable and preserved. Eight modules were correlated with sleep restriction duration. Module rhythmicity was disrupted in sleep restriction subjects. Hub genes that involve in insufficient sleep also play important roles in sleep disorders. Insomnia GWAS signals were enriched in six modules. Finally, eight drugs associated with sleep disorders were identified.

CONCLUSION

Systems biology method was used to identify sleep-related modules, hub genes, and candidate drugs. Module rhythmicity was altered in sleep insufficient subjects. Thiamphenicol, lisuride, timolol, and piretanide are novel candidates for sleep disorders.

Genetic Markers of Differential Vulnerability to Sleep Loss in Adults

In this review, we discuss reports of genotype-dependent interindividual differences in phenotypic neurobehavioral responses to total sleep deprivation or sleep restriction. We highlight the importance of using the candidate gene approach to further elucidate differential resilience and vulnerability to sleep deprivation in humans, although we acknowledge that other omics techniques and genome-wide association studies can also offer insights into biomarkers of such vulnerability. Specifically, we discuss polymorphisms in adenosinergic genes (ADA and ADORA2A), core circadian clock genes (BHLHE41/DEC2 and PER3), genes related to cognitive development and functioning (BDNF and COMT), dopaminergic genes (DRD2 and DAT), and immune and clearance genes (AQP4, DQB1*0602, and TNFα) as potential genetic indicators of differential vulnerability to deficits induced by sleep loss. Additionally, we review the efficacy of several countermeasures for the neurobehavioral impairments induced by sleep loss, including banking sleep, recovery sleep, caffeine, and naps. The discovery of reliable, novel genetic markers of differential vulnerability to sleep loss has critical implications for future research involving predictors, countermeasures, and treatments in the field of sleep and circadian science.

Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets

In recent decades, many genome-wide association studies on insomnia have reported numerous genes harboring multiple risk variants. Nevertheless, the molecular functions of these risk variants conveying risk to insomnia are still ill-studied. In the present study, we integrated GWAS summary statistics (N=386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N=329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in six biological pathways including Huntington’s disease (P=5.58 × 10⁻⁵), Alzheimer’s disease (P=5.58 × 10⁻⁵), Parkinson’s disease (P=6.34 × 10⁻⁵), spliceosome (P=1.17 × 10⁻⁴), oxidative phosphorylation (P=1.09 × 10⁻⁴), and wnt signaling pathways (P=2.07 × 10⁻⁴). Further, five of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these five identified genes. Three genes of LDHA (P=0.044), DALRD3 (P=5.0 × 10⁻⁵), and HEBP2 (P=0.032) showed significantly lower expression level in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these five genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, the evidence of the present study strongly suggested that these five identified genes may represent candidate genes and contributed risk to the etiology of insomnia.

Sleep disorders are associated with acetaminophen-induced adverse reactions and liver injury

Risk factors related to the development of acetaminophen (APAP)-induced adverse reactions and liver injury remain uncertain. Sleep disorders have been linked to some health outcomes. This study examined the associations of sleep disorders with APAP-induced adverse reactions or liver injury and the possible mechanisms. From NIS database, adverse reactions, liver injury and sleep disorders were identified. Factors associated with the risk of the total adverse effects or liver injury were examined with logistic regression. From Gene Expression Omnibus database, datasets GSE111828, containing transcriptome data based on RNA-seq analysis from liver samples extracted from mice post APAP administration, and GSE92913, containing transcriptome data based on microarray analysis from liver samples extracted from mice with sleep deprivation, were analyzed. A total of 4372754 patients without and 91314 patients with sleep disorders were eligible for analyses. Both before and after propensity score matching, APAP-induced adverse reactions were higher in patients with sleep disorders than in patients without. In multivariate regression, sleep disorders were associated with higher odds of APAP-induced adverse reactions (adjusted OR [aOR] 2.005, 95 % CI 1.343-2.995) and liver injury (aOR 2.788, 95 % CI 1.310-5.932). Genes that were enriched in bile secretion and retinol metabolism and PPAR signaling pathways were basically down-regulated in livers of mice after APAP administration and livers of mice with sleep deprivation. This study shows that sleep disorders may be novel independent risk factors for APAP-associated adverse reactions and liver injury and provides bioinformation linking sleep disorders to increased risk of APAP-induced liver injury.

Unique transcriptional signatures of sleep loss across independently evolved cavefish populations

Animals respond to sleep loss with compensatory rebound sleep, and this is thought to be critical for the maintenance of physiological homeostasis. Sleep duration varies dramatically across animal species, but it is not known whether evolutionary differences in sleep duration are associated with differences in sleep homeostasis. The Mexican cavefish, Astyanax mexicanus, has emerged as a powerful model for studying the evolution of sleep. While eyed surface populations of A. mexicanus sleep approximately 8 hr each day, multiple blind cavefish populations have converged on sleep patterns that total as little as 2 hr each day, providing the opportunity to examine whether the evolution of sleep loss is accompanied by changes in sleep homeostasis. Here, we examine the behavioral and molecular response to sleep deprivation across four independent populations of A. mexicanus. Our behavioral analysis indicates that surface fish and all three cavefish populations display robust recovery sleep during the day following nighttime sleep deprivation, suggesting sleep homeostasis remains intact in cavefish. We profiled transcriptome-wide changes associated with sleep deprivation in surface fish and cavefish. While the total number of differentially expressed genes was not greater for the surface population, the surface population exhibited the highest number of uniquely differentially expressed genes than any other population. Strikingly, a majority of the differentially expressed genes are unique to individual cave populations, suggesting unique expression responses are exhibited across independently evolved cavefish populations. Together, these findings suggest sleep homeostasis is intact in cavefish despite a dramatic reduction in overall sleep duration.

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Sleep deprivation, vigilant attention, and brain function: a review

Vigilant attention is a major component of a wide range of cognitive performance tasks. Vigilant attention is impaired by sleep deprivation and restored after rest breaks and (more enduringly) after sleep. The temporal dynamics of vigilant attention deficits across hours and days are driven by physiologic, sleep regulatory processes-a sleep homeostatic process and a circadian process. There is also evidence of a slower, allostatic process, which modulates the sleep homeostatic setpoint across days and weeks and is responsible for cumulative deficits in vigilant attention across consecutive days of sleep restriction. There are large inter-individual differences in vulnerability to sleep loss, and these inter-individual differences constitute a pronounced human phenotype. However, this phenotype is multi-dimensional; vulnerability in terms of vigilant attention impairment can be dissociated from vulnerability in terms of other cognitive processes such as attentional control. The vigilance decrement, or time-on-task effect-a decline in performance across the duration of a vigilant attention task-is characterized by progressively increasing response variability, which is exacerbated by sleep loss. This variability, while crucial to understanding the impact of sleep deprivation on performance in safety-critical tasks, is not well explained by top-down regulatory mechanisms, such as the homeostatic and circadian processes. A bottom-up, neuronal pathway-dependent mechanism involving use-dependent, local sleep may be the main driver of response variability. This bottom-up mechanism may also explain the dissociation between cognitive processes with regard to trait vulnerability to sleep loss.

Boolean function network analysis of time course liver transcriptome data to reveal novel circadian transcriptional regulators in mammals

BACKGROUND

Many biological processes in mammals are subject to circadian control at the molecular level. Disruption of circadian rhythms has been demonstrated to be associated with a wide range of diseases, such as diabetes mellitus, mental disorders, and cancer. Although the core circadian genes are well established, there are multiple reports of novel peripheral circadian regulators. The goal of this study was to provide a comprehensive computational analysis to identify novel potential circadian transcriptional regulators.

METHODS

To fulfill the aforementioned goal, we applied a Boolean function network method to analyze the microarray time course mouse and rat liver datasets available in the literature. The inferred direct pairwise relations were further investigated using the functional annotation tool. This approach generated a list of transcription factors (TFs) and cofactors, which were associated with significantly enriched circadian gene ontology (GO) categories.

RESULTS

As a result, we identified 93 transcriptional circadian regulators in mouse and 95 transcriptional circadian regulators in rat. Of these, 19 regulators in mouse and 21 regulators in rat were known, whereas the rest were novel. Furthermore, we validated novel circadian TFs with bioinformatics databases, previous large-scale circadian studies, and related small-scale studies. Moreover, according to predictions inferred from ChIP-Seq experiments reported in the database, 40 of our candidate circadian regulators were confirmed to have circadian genes as direct regulatory targets. In addition, we annotated candidate circadian regulators with disorders that were often associated with disruptions of circadian rhythm in the literature.

CONCLUSION

In summary, our computational analysis, which was followed by an extensive verification by means of a literature review, can contribute to translational study from endocrinology to cancer research and provide insights for future investigation.