Cross-species systems analysis identifies gene networks differentially altered by sleep loss and depression

Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing

BACKGROUND

There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS

This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS

The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION

Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

Chronic sleep curtailment expediates brain aging by activating the complement and coagulation cascades in mice

Chronic sleep insufficiency is prevalent in modern society and has been associated with age-related neurodegenerative diseases. Loss of sleep accelerates the progression of neurodegeneration in animal models of neurological diseases. Here, we study whether chronic sleep curtailment leads to brain aging in wild-type animals without a genetic predisposition. We used a wild-type mouse model to simulate modern-day conditions of restricted sleep and compared the brain (cortex) proteome of young sleep-restricted animals with different aged control groups. We report the alteration of 149 proteins related to sleep and 1269 related to age with 96 proteins common between them. Through pathway analysis of proteins common to sleep restriction and aging, we discovered that the complement and coagulation cascade pathways were enriched by alterations of complement component 3 (C3), alpha-2-macroglobulin (A2M), fibrinogen alfa and beta chain (FGA and FGB). This is the first study indicating the possible role of the complement and coagulation pathways in brain aging and by chronic sleep restriction (CSR) in mice.

Chronic pain-induced methylation in the prefrontal cortex targets gene networks associated with cognition and Alzheimer's disease

Chronic pain is prevalent among aging adults. Epidemiologic evidence has demonstrated that individuals with chronic pain have accelerated memory decline and increased probability of dementia. Neurophysiologic, molecular, and pharmacologic hypotheses have been proposed to explain the relationship between chronic pain and cognitive decline, but there remains currently limited evidence supporting any of these. Here, we integrate multi-omic data across human cohorts and rodent species and demonstrate that methylation in the prefrontal cortex induced by chronic pain specifically targets transcriptional networks associated with cognitive ability, memory, and Alzheimer's disease in humans. We validate this with multiple independent data sets and identify cortical microglia as a likely mechanism by which chronic pain can increase dementia risk. Our analyses support the molecular hypothesis for the role of chronic pain in cognitive decline and identifies several potential therapeutic targets.

Distinct glutamatergic projections of the posteroventral medial amygdala play different roles in arousal and anxiety

Sleep disturbance usually accompanies anxiety disorders and exacerbates their incidence rates. The precise circuit mechanisms remain poorly understood. Here, we found that glutamatergic neurons in the posteroventral medial amygdala (MePVGlu neurons) are involved in arousal and anxiety-like behaviors. Excitation of MePVGlu neurons not only promoted wakefulness but also increased anxiety-like behaviors. Different projections of MePVGlu neurons played various roles in regulating anxiety-like behaviors and sleep-wakefulness. MePVGlu neurons promoted wakefulness through the MePVGlu/posteromedial cortical amygdaloid area (PMCo) pathway and the MePVGlu/bed nucleus of the stria terminals (BNST) pathway. In contrast, MePVGlu neurons increased anxiety-like behaviors through the MePVGlu/ventromedial hypothalamus (VMH) pathway. Chronic sleep disturbance increased anxiety levels and reduced reparative sleep, accompanied by the enhanced excitability of MePVGlu/PMCo and MePVGlu/VMH circuits but suppressed responses of glutamatergic neurons in the BNST. Inhibition of the MePVGlu neurons could rescue chronic sleep deprivation-induced phenotypes. Our findings provide important circuit mechanisms for chronic sleep disturbance-induced hyperarousal response and obsessive anxiety-like behavior and are expected to provide a promising strategy for treating sleep-related psychiatric disorders and insomnia.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

The Combination of Quantitative Proteomics and Systems Genetics Analysis Reveals that PTN Is Associated with Sleep-Loss-Induced Cognitive Impairment

Sleep loss is associated with cognitive dysfunction. However, the detailed mechanisms remain unclear. In this study, we established a para-chlorophenylalanine (PCPA)-induced insomniac mouse model with impaired cognitive function. Mass-spectrometry-based proteomics showed that the expression of 164 proteins was significantly altered in the hippocampus of the PCPA mice. To identify critical regulators among the potential markers, a transcriptome-wide association screening was performed in the BXD mice panel. Among the candidates, the expression of pleiotrophin (Ptn) was significantly associated with cognitive functions, indicating that Ptn-mediates sleep-loss-induced cognitive impairment. Gene co-expression analysis further revealed the potential mechanism by which Ptn mediates insomnia-induced cognitive impairment via the MAPK signaling pathway; that is, the decreased secretion of Ptn induced by insomnia leads to reduced binding to Ptprz1 on the postsynaptic membrane with the activation of the MAPK pathway via Fos and Nr4a1, further leading to the apoptosis of neurons. In addition, Ptn is genetically trans-regulated in the mouse hippocampus and implicated in neurodegenerative diseases in human genome-wide association studies. Our study provides a novel biomarker for insomnia-induced cognitive impairment and a new strategy for seeking neurological biomarkers by the integration of proteomics and systems genetics.

Systems biology approach discovers comorbidity interaction of Parkinson's disease with psychiatric disorders utilizing brain transcriptome

Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD patients is crucial for both symptomatic therapy and better knowledge of the pathophysiology of PD. In order to address this issue, we created a bioinformatics framework to determine the effects of PD mRNA expression on understanding its relationship with psychiatric symptoms in PD patients. We have discovered a significant overlap between the sets of differentially expressed genes from PD exposed tissue and psychiatric disordered tissues using RNA-seq datasets. We have chosen Bipolar disorder and Schizophrenia as psychiatric disorders in our study. A number of significant correlations between PD and the occurrence of psychiatric diseases were also found by gene set enrichment analysis, investigations of the protein-protein interaction network, gene regulatory network, and protein-chemical agent interaction network. We anticipate that the results of this pathogenetic study will provide crucial information for understanding the intricate relationship between PD and psychiatric diseases.

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.

Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

Transcriptomic Studies of Antidepressant Action in Rodent Models of Depression: A First Meta-Analysis

Antidepressants (ADs) are, for now, the best everyday treatment we have for moderate to severe major depressive episodes (MDEs). ADs are among the most prescribed drugs in the Western Hemisphere; however, the trial-and-error prescription strategy and side-effects leave a lot to be desired. More than 60% of patients suffering from major depression fail to respond to the first AD they are prescribed. For those who respond, full response is only observed after several weeks of treatment. In addition, there are no biomarkers that could help with therapeutic decisions; meanwhile, this is already true in cancer and other fields of medicine. For years, many investigators have been working to decipher the underlying mechanisms of AD response. Here, we provide the first systematic review of animal models. We thoroughly searched all the studies involving rodents, profiling transcriptomic alterations consecutive to AD treatment in naïve animals or in animals subjected to stress-induced models of depression. We have been confronted by an important heterogeneity regarding the drugs and the experimental settings. Thus, we perform a meta-analysis of the AD signature of fluoxetine (FLX) in the hippocampus, the most studied target. Among genes and pathways consistently modulated across species, we identify both old players of AD action and novel transcriptional biomarker candidates that warrant further investigation. We discuss the most prominent transcripts (immediate early genes and activity-dependent synaptic plasticity pathways). We also stress the need for systematic studies of AD action in animal models that span across sex, peripheral and central tissues, and pharmacological classes.

Pinus halepensis Essential Oil Ameliorates Aβ1-42-Induced Brain Injury by Diminishing Anxiety, Oxidative Stress, and Neuroinflammation in Rats

The Pinus L. genus comprises around 250 species, being popular worldwide for their medicinal and aromatic properties. The present study aimed to evaluate the P. halepensis Mill. essential oil (PNO) in an Alzheimer’s disease (AD) environment as an anxiolytic and antidepressant agent. The AD-like symptoms were induced in Wistar male rats by intracerebroventricular administration of amyloid beta1-42 (Aβ1-42), and PNO (1% and 3%) was delivered to Aβ1-42 pre-treated rats via inhalation route for 21 consecutive days, 30 min before behavioral assessments. The obtained results indicate PNO’s potential to relieve anxious–depressive features and to restore redox imbalance in the rats exhibiting AD-like neuropsychiatric impairments. Moreover, PNO presented beneficial effects against neuroinflammation and neuroapoptosis in the Aβ1-42 rat AD model.

Altered expression of somatostatin signaling molecules and clock genes in the hippocampus of subjects with substance use disorder

Substance use disorders are a debilitating group of psychiatric disorders with a high degree of comorbidity with major depressive disorder. Sleep and circadian rhythm disturbances are commonly reported in people with substance use disorder and major depression and associated with increased risk of relapse. Hippocampal somatostatin signaling is involved in encoding and consolidation of contextual memories which contribute to relapse in substance use disorder. Somatostatin and clock genes also have been implicated in depression, suggesting that these molecules may represent key converging pathways involved in contextual memory processing in substance use and major depression. We used hippocampal tissue from a cohort of subjects with substance use disorder (n = 20), subjects with major depression (n = 20), subjects with comorbid substance use disorder and major depression (n = 24) and psychiatrically normal control subjects (n = 20) to test the hypothesis that expression of genes involved in somatostatin signaling and clock genes is altered in subjects with substance use disorder. We identified decreased expression of somatostatin in subjects with substance use disorder and in subjects with major depression. We also observed increased somatostatin receptor 2 expression in subjects with substance use disorder with alcohol in the blood at death and decreased expression in subjects with major depression. Expression of the clock genes Arntl, Nr1d1, Per2 and Cry2 was increased in subjects with substance use disorder. Arntl and Nr1d1 expression in comparison was decreased in subjects with major depression. We observed decreased expression of Gsk3β in subjects with substance use disorder. Subjects with comorbid substance use disorder and major depression displayed minimal changes across all outcome measures. Furthermore, we observed a significant increase in history of sleep disturbances in subjects with substance use disorder. Our findings represent the first evidence for altered somatostatin and clock gene expression in the hippocampus of subjects with substance use disorder and subjects with major depression. Altered expression of these molecules may impact memory consolidation and contribute to relapse risk.

Single-cell transcriptomics and cell-specific proteomics reveals molecular signatures of sleep

Every day, we sleep for a third of the day. Sleep is important for cognition, brain waste clearance, metabolism, and immune responses. The molecular mechanisms governing sleep are largely unknown. Here, we used a combination of single-cell RNA sequencing and cell-type-specific proteomics to interrogate the molecular underpinnings of sleep. Different cell types in three important brain regions for sleep (brainstem, cortex, and hypothalamus) exhibited diverse transcriptional responses to sleep need. Sleep restriction modulates astrocyte-neuron crosstalk and sleep need enhances expression of specific sets of transcription factors in different brain regions. In cortex, we also interrogated the proteome of two major cell types: astrocytes and neurons. Sleep deprivation differentially alters the expression of proteins in astrocytes and neurons. Similarly, phosphoproteomics revealed large shifts in cell-type-specific protein phosphorylation. Our results indicate that sleep need regulates transcriptional, translational, and post-translational responses in a cell-specific manner.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

Altered sleep behavior in a genetic mouse model of impaired fear extinction

Sleep disturbances are a common complaint of anxiety patients and constitute a hallmark feature of post-traumatic stress disorder (PTSD). Emerging evidence suggests that poor sleep is not only a secondary symptom of anxiety- and trauma-related disorders but represents a risk factor in their development, for example by interfering with emotional memory processing. Fear extinction is a critical mechanism for the attenuation of fearful and traumatic memories and multiple studies suggest that healthy sleep is crucial for the formation of extinction memories. However, fear extinction is often impaired in anxiety- and trauma-related disorders-an endophenotype that is perfectly modelled in the 129S1/SvImJ inbred mouse strain. To investigate whether these mice exhibit altered sleep at baseline that could predispose them towards maladaptive fear processing, we compared their circadian sleep/wake patterns to those of typically extinction-competent C57BL/6 mice. We found significant differences regarding diurnal distribution of sleep and wakefulness, but also sleep architecture, spectral features and sleep spindle events. With regard to sleep disturbances reported by anxiety- and PTSD patients, our findings strengthen the 129S1/SvImJ mouse models' face validity and highlight it as a platform to investigate novel, sleep-focused diagnostic and therapeutic strategies. Whether the identified alterations causally contribute to its pathological anxiety/PTSD-like phenotype will, however, have to be addressed in future studies.

NREM delta power and AD-relevant tauopathy are associated with shared cortical gene networks

Reduced NREM sleep in humans is associated with AD neuropathology. Recent work has demonstrated a reduction in NREM sleep in preclinical AD, pointing to its potential utility as an early marker of dementia. We test the hypothesis that reduced NREM delta power and increased tauopathy are associated with shared underlying cortical molecular networks in preclinical AD. We integrate multi-omics data from two extensive public resources, a human Alzheimer's disease cohort from the Mount Sinai Brain Bank (N = 125) reflecting AD progression and a (C57BL/6J × 129S1/SvImJ) F2 mouse population in which NREM delta power was measured (N = 98). Two cortical gene networks, including a CLOCK-dependent circadian network, are associated with NREM delta power and AD tauopathy progression. These networks were validated in independent mouse and human cohorts. Identifying gene networks related to preclinical AD elucidate possible mechanisms associated with the early disease phase and potential targets to alter the disease course.

Transcriptomic analyses of humans and mice provide insights into depression

Accumulating studies have been conducted to identify risk genes and relevant biological mechanisms underlying major depressive disorder (MDD). In particular, transcriptomic analyses in brain regions engaged in cognitive and emotional processes, e.g., the dorsolateral prefrontal cortex (DLPFC), have provided essential insights. Based on three independent DLPFC RNA-seq datasets of 79 MDD patients and 75 healthy controls, we performed differential expression analyses using two alternative approaches for cross-validation. We also conducted transcriptomic analyses in mice undergoing chronic variable stress (CVS) and chronic social defeat stress (CSDS). We identified 12 differentially expressed genes (DEGs) through both analytical methods in MDD patients, the majority of which were also dysregulated in stressed mice. Notably, the mRNA level of the immediate early gene FOS ( Fos proto-oncogene) was significantly decreased in both MDD patients and CVS-exposed mice, and CSDS-susceptible mice exhibited a greater reduction in Fos expression compared to resilient mice. These findings suggest the potential key roles of this gene in the pathogenesis of MDD related to stress exposure. Altered transcriptomes in the DLPFC of MDD patients might be, at least partially, the result of stress exposure, supporting that stress is a primary risk factor for MDD.

Convergent Functional Genomics approach to prioritize molecular targets of risk in early life stress-related psychiatric disorders

There is an overwhelming evidence proving that mental disorders are not the product of a single risk factor - i.e. genetic variants or environmental factors, including exposure to maternal perinatal mental health problems or childhood adverse events - rather the product of a trajectory of cumulative and multifactorial insults occurring during development, such as exposures during the foetal life to adverse mental condition in the mother, or exposures to adverse traumatic events during childhood or adolescence. In this review, we aim to highlight the potential utility of a Convergent Functional Genomics (CFG) approach to clarify the complex brain-relevant molecular mechanisms and alterations induced by early life stress (ELS). We describe different studies based on CFG in psychiatry and neuroscience, and we show how this 'hypothesis-free' tool can prioritize a stringent number of genes modulated by ELS, that can be tested as potential candidates for Gene x Environment (GxE) interaction studies. We discuss the results obtained by using a CFG approach identifying FoxO1 as a gene where genetic variability can mediate the effect of an adverse environment on the development of depression. Moreover, we also demonstrate that FoxO1 has a functional relevance in stress-induced reduction of neurogenesis, and can be a potential target for the prevention or treatment of stress-related psychiatric disorders. Overall, we suggest that CFG approach could include trans-species and tissues data integration and we also propose the application of CFG to examine in depth and to prioritize top candidate genes that are affected by ELS across lifespan and generations.

Recent advances in understanding the genetics of sleep

Sleep is a ubiquitous and complex behavior in both its manifestation and regulation. Despite its essential role in maintaining optimal performance, health, and well-being, the genetic mechanisms underlying sleep remain poorly understood. Here, we review the forward genetic approaches undertaken in the last four years to elucidate the genes and gene pathways affecting sleep and its regulation. Despite an increasing number of studies and mining large databases, a coherent picture on “sleep” genes has yet to emerge. We highlight the results achieved by using unbiased genetic screens mainly in humans, mice, and fruit flies with an emphasis on normal sleep and make reference to lessons learned from the circadian field.

Psychiatric manifestations of neurologic diseases: Etiology, phenomenology, and treatment

Understanding the etiology and meaning of behavioral symptomatology in the context of neurologic disease, and choosing the most effective intervention is a vexing task. This introduction summarizes the history of our understanding of the relationship between behavioral symptoms and primary neurologic conditions, and considers the ways in which both psychiatric and neurologic disorders occurring simultaneously may inform both knowledge of etiology and treatment decisions.