Sleep and Vigilance

, Volume 2, Issue 1, pp 13–21 | Cite as

The End of Snoring? Application of CRISPR/Cas9 Genome Editing for Sleep Disorders

  • Eric Murillo-Rodríguez
  • Nuno Barbosa Rocha
  • André Barciela Veras
  • Henning Budde
  • Sérgio Machado


Science selected it as the 2015 Breakthrough of the Year: Clustered Regularly Interspaced Short Palindromic Repeats, also known as CRISPR. Adaptive immunity in some bacteria and archaea allow to respond and eliminate invading genetic material; CRISPR and CRISPR-associated (Cas) genes are new molecular techniques that allow deleting, replacing or otherwise editing DNA. Using modified bacterial protein and a RNA that allows a guidance to a specific DNA sequence, CRISPR provides a striking control over several genes by not deleting the entire gene but just inactivating it by deleting few bases. CRISPR/Cas9 has been used to generate knockout cells or animals by co-expressing a gRNA specific to the gene to be targeted and the endonuclease Cas9. This novel system has been tested in diverse species, with promising potential uses in humans. Theoretically, CRISPR/Cas9 will be able to remove genetic mutations related to incurable diseases, such as HIV, and certain cancer types. This scenario draws tentative and promising conditions using CRISPR/Cas9 as preventive and therapeutic tool in medical area. As expected, several groups have begun to test the putative gene editing properties of CRISPR/Cas9 on human cells. Since sleep disorders have been linked with specific genes, in this review, we suggest areas that require further investigation and experimental and/or clinical approaches to treat sleep disturbances using CRISPR/Cas9.


Gene Somnolence DNA Insomnia Sleep Therapy 

1 Gene Expression: An Overview

Genes are subunits of DNA, the information database of a cell that is contained inside the cell nucleus and conform to the basic physical and functional units of heredity. These DNA subunits carry the genetic blueprint that is used to make all the proteins the cell needs. Every gene contains a particular set of instructions that code for a specific protein and the production of these functional molecules is the gene expression [1, 2]. There are two types of genes: regulating and constitutive. The first ones indicate the level of expression of the constitutive genes, which are the ones that produce a single-stranded RNA molecule, derived from a portion of the double-stranded DNA templates. By the expression of specific DNA motifs in the control regions of the regulating genes, the DNA segment is “read” by an enzyme called RNA polymerase, which produces the strand of RNA that is complimentary to the DNA. In this strand, some structural changes take place so that in some cases, the RNA molecule itself is a “finished product” that serves some important function within the cell. Often, however, transcription of an RNA molecule is followed by a translation step, which ultimately results in the production of a protein molecule [3, 4, 5].

For transcription to occur, the area around a prospective transcription zone needs to be unwound. This is a complex process requiring the coordinated modification of basic proteins that associate with DNA in the nucleus and help condense it into chromatin (histones), transcription factor binding and other chromatin remodeling activities. This primary RNA transcript is then modified to convert it into mature messenger RNA (mRNA) that can be used in translation. Interestingly, not all regions of an mRNA molecule correspond to particular aminoacids, the molecules that are then linked together in a chain by a ribosome to create a rudimentary protein chain. The mRNA undergoes splicing of its regions to remove the non-coding parts of the transcript being the introns, so that only the coding sections being the exons remain. This part is called processing and right after this step, the final mRNA carries the information needed to code for proteins, which will transform into a functional protein [4, 6, 7].

Importantly, there is extensive natural variation in human gene expression. Every person has two copies of each gene, one inherited from each parent. Most genes are the same in the population, but a small number of genes (less than 1 percent of the total) are slightly different between people. This characteristic is determined by the presence of various alleles, forms of the same gene with small differences in their sequence of DNA bases. These small differences contribute to each person’s unique physical features. As quantitative phenotypes, expression levels of genes are heritable [8]. As a result, every human being has its own genetic information, inherited from its progenitor, which determines their anatomical characteristics, normal physiology and their predisposition to certain pathologic processes.

On the other hand, not all genes are active at all times and also are limited to control most of the physiological functions of an organism on their own. Rather, they must interact with and respond to the organism’s environment. While some genes are constitutive, or display a “turn-on” mode regardless of environmental conditions, regulating genes are needed only occasionally [6]. Moreover, multiple mechanisms have an effect on one or various steps of the gene expression (Fig. 1).
Fig. 1

Basic genetic mechanism. The gene expression is the molecular process by which information contained within the genes are synthesized in a protein with physiological functions

2 Gene Expression in Pathological Conditions

Given the complex mechanism of gene expression, it is not a surprise to know that many of its steps can be altered in several ways conditioning the cell to an aberrant development. In humans, this situation is traduced to illnesses such as cancer, diabetes, Alzheimer’s disease, osteoporosis and many others caused by a dysregulation in the gene expression. One example of this scenario is the abnormal expression in gene profile of all adipogenic markers that are not expressed in diabetic cells after differentiation [9]. An additional most common example of altered gene expression in human health aspects is cancer. In this regard, it was demonstrated that protein Semaphorin-3E (Sema3E) is overexpressed in human pancreatic cancer, and that high Sema3E levels are associated with tumor progression. Critically, Sema3E is a member of an axon guidance gene family, and has been reported as a contributor to tumor progression and metastasis. Moreover, overexpression of Sema3E in pancreatic cancer cells promotes cell proliferation and migration in vitro, and increases tumor incidence. Conversely, experimental animals (knockout) of Sema3E suppressed cancer cell proliferation and reduced tumor incidence and size in vivo [10].

Lastly, osteoporosis is a common disease in the adult population due to the normal decalcification enhanced by multiple causes including a diet deficient in vitamin D or calcium, alcoholism, tabaquism and genetic causes. Recently, the diagnostic potential of circulating miRNAs for postmenopausal osteoporosis has been investigated and some miRNAs were identified as potential biomarkers [11].

3 Sleep Disorders and Gene Modulation

According to the International Classification of Sleep Disorders, several disturbances of the sleep–wake cycle have been classified in extensive Axis [12] (Table 1). Sleep disorders have a wider spectrum of factors that promote and maintain the pathologies related to sleep. Regarding the molecular basis of sleep disruptions, several studies have provided milestones linking the presumable role of gene expression with the origin of sleep disorders [13, 14, 15, 16, 17]. Significant advances in understanding the neurobiological role of genes in sleep disorders have been obtained in the last decades. Given that the sleep disorders comprise several disturbances, it would be indeed ambitious to review genetics of all the sleep-related disturbances described so far. However, we highlight the most representative data linking genes and sleep disorders, including insomnia, obstructive sleep apnea (OSA), narcolepsy, advanced sleep phase syndrome and restless legs syndrome.
Table 1

Genes associated with sleep disturbances

Sleep disorder

Genes associated with sleep disturbances


1. Circadian genes (CLOCKTimeless) Per2, 3

2. Serotonin transporter polymorphic region (5-HTTLPR)

3. Orexin/hypocretin gene

4. Catecholamine-O-methyltransferase (COMT) gene

5. Dopamine receptor D4 (DRD4) gene

6. Dopamine transporter 1 (DAT1) gene

7. ABCC9 gene (rs11046209) gene

Obstructive sleep apnea

1. Inflammatory factors: IL-6, IL-8, and TNF-α genes

2. 5-Hydroxytryptamine receptor 2A (5-HTR2A) gene


1. Human leukocyte antigen (HLA)-DRB1 × 15:01-DQB1 × 06:02 haplotype

2. Chemokine (C–C motif) receptor 1 (CCR1) gene

Advanced sleep phase syndrome

1. PER 1, 2, and 3

2. PER3(5/5)

3. PER3(4/4)

4. Cryptochrome genes 1 and 2

5. Arnt-like protein-1 (Bmal1/Aryl

6. Hydrocarbon receptor nuclear translocator-like (ARNTL1) gene

Restless legs syndrome

1. IL-ß gene

2. MEIS1 gene

Multiple gene candidates related to sleep disturbances such as insomnia, obstructive sleep apnea, narcolepsy, advances sleep phase syndrome, and restless legs syndrome have been reported either in experimental model or in humans

3.1 Genes and Insomnia

Insomnia comprises the difficulty to initiate or maintain sleep. Multiple studies have described the role of circadian genes (CLOCKTimeless) in subjects with mood disorders showing a putative relationship with insomnia [18, 19, 20, 21]. For instance, a significant association between Per3 and insomnia was described by Brower and coworkers (2012) [22]. Similar findings were found when period 2 gene (Per2) was associated with insomnia [23]. Another gene candidate related to insomnia has been the serotonin transporter polymorphic region (5-HTTLPR), adenosine, GABA and orexin/hypocretin [18]. In addition, several studies have also indicated the engagement of other wake-related neurotransmitter systems such as dopaminergic system genes (i.e., catecholamine-O-methyltransferase (COMT), dopamine receptor D4 (DRD4), and dopamine transporter 1 (DAT1; [18, 24]). Lastly, the genome-wide association studies (GWAS) have identified novel genes that also appear to be linked to insomnia. For instance a significant association of the ABCC9 gene (rs11046209) with sleep duration has been described in insomniac patients [25, 26, 27, 28]. An overview of the genes related to insomnia is shown in Table 1.

3.2 Obstructive Sleep Apnea and Genes

Obstructive sleep apnea (OSA) is a sleep disturbance characterized by repeated cessation or attenuation of breathing (named “apneas” and “hypopneas”, respectively) during sleep. Moreover, OSA is associated with a wide range of morbidities including metabolic, cardiovascular, coronary artery disease, hypertension, arrhythmia, heart failure, and cognitive dysfunction or even sudden cardiac death [29]. The first-line treatment given to OSA patients includes the use of continuous positive airway pressure (CPAP; [29, 30]). The molecular mechanisms underlying OSA remain unclear, and could be mediated, in part, by OSA-induced genes. In this regard, Cade and coworkers (2016) carried out GWAS in 12,558 Hispanic subjects finding two novel loci at genome level with significance for apnea–hypopnea index [31].

On the other side, a different perspective has been developed regarding the study of genes related to OSA. For example, the pathogenesis of this sleep disturbance is the result of a multifactorial process related to a wide variety of mechanisms, including the engagement of inflammatory responses. As expected, inflammatory factors, such as IL-6, IL-8, and TNF-α, have been found highly expressed in subjects with OSA [32, 33]. Therefore, genes related to inflammation may be involved in OSA.

At present, variants of the 5-hydroxytryptamine receptor 2A (5-HTR2A) and interleukin-6 (IL-6) genes may be susceptible markers to develop for OSA. Intriguingly, associations between the 5-HTR2A and IL-6 single nucleotide polymorphisms (SNPs) and OSA have been recently described [34]. Thus, the role of inflammatory response genes in OSA seems to be a critical factor as a pivotal phenomenon that impacts directly the onset of this sleep disorder [35] (Table 1).

3.3 Genetic Basis of Narcolepsy

Narcolepsy is a life-long condition characterized by two major symptoms, excessive daytime sleepiness and cataplexy. It is widely accepted that the human leukocyte antigen (HLA)-DRB1 × 15:01-DQB1 × 06:02 haplotype is strongly associated with narcolepsy [36, 37, 38]. However, non-HLA susceptibility genes have been also related to this sleep disorder. Recently, Toyoda and coworkers (2015) reported that according to GWAS 525,196 single nucleotide polymorphisms (SNPs) were located outside the HLA region [39]. Moreover, it was found that narcolepsy was associated with a SNP in the promoter region of chemokine (C–C motif) receptor 1 (CCR1). Further evidence has indicated that narcolepsy with cataplexy is tightly associated with the HLA class II allele DQB1 × 06:02 [40] (Table 1).

3.4 Genes Related to Advanced Sleep Phase Syndrome

Circadian rhythmicity has been described in multiple physiological functions, including the onset or development of pathological issues, such as sleep disturbances [41, 42, 43, 44, 45]. The advance sleep phase syndrome is one example of a circadian rhythm disorder. Subjects with complaint of early evening bedtimes and early morning awakenings belong to the category of advance sleep phase syndrome. In contrast, the delayed sleep phase disorder corresponds with late bedtimes and late awakenings [46, 47]. Circadian rhythm contributes to sleep–wake cycle control by activation of specific genes. For example, approximately 10% of the population are homozygous for the 5-repeat allele (PER3(5/5)) of a variable number tandem repeat polymorphism in the clock gene Per-3. What has been found is that PER3(5/5) is associated with morning preference, whereas homozygosity for the four-repeat allele (PER3(4/4)) is linked with evening preference [48]. The association between sleep timing and the circadian rhythms are crucial for sleep onset as well as for development of sleep disorders, such as sleep phase syndrome.

Approximately 20 clock genes have been characterized, including the key genes such as PER 1, 2, and 3, the cryptochrome genes 1 and 2, and brain and muscle arnt-like protein-1 (Bmal1/Aryl hydrocarbon receptor nuclear translocator-like [ARNTL1], [49, 50]). Among these genes, some have been associated with diurnal preference. For example, a significant association between diurnal preference and a polymorphism in Per-3 and diurnal preference and a polymorphism in aryl hydrocarbon receptor nuclear translocator-like 2 (ARNTL2) has been described [51]. Recent evidence has pointed-out a significant gene-associated loci with morningness. Furthermore, Hu et al. (2016) reported that a GWAS analysis of self-reported morningness showed 15 significantly associated loci, including seven circadian genes [52]. These findings as well as other reports [53, 54, 55, 56, 57] suggest that genes with circadian expression may play a critical role in regulating both the circadian clock and sleep homeostasis as well as circadian-related sleep disturbances such as advanced sleep phase syndrome (Table 1).

3.5 Genes Associated with Restless Legs Syndrome

The restless legs syndrome is a sleep disorder characterized by the urge to move the legs during sleep. The pathophysiology of this sleep disturbance is low iron concentration in the substantia nigra of dopamine neurons that project to the striatum, a critical brain area for modulating movement [58, 59]. Despite the medial advances in the understanding of the genesis of this sleep disorder, the genetics of restless legs syndrome are still unknown. Recent data have identified several genes as candidates for restless legs syndrome development, including IL-ß or BTBD9 [60, 61]. Moreover, since restless legs syndrome has familial aggregation, GWAS association studies have identified single nucleotide polymorphisms linked to this sleep disturbance, including SNP at loci MEIS1 [62, 63, 64]. Although restless legs syndrome etiology may be multifactorial, a significant body of gene-related evidence has been piled up, and these findings, taken together, support the hypothesis that this sleep disturbance may have a genetic component [58, 59, 60, 62, 65, 66, 67, 68] (Table 1).

4 CRISPR–Cas9: A Novel Molecular Approach for Gene Editing

Given the amount of diseases associated with “simple” errors in gene modulation and expression, several gene editing techniques have been developed aimed to treat these pathological conditions. Thus, inducible loss of gene function experiments is necessary to uncover mechanisms underlying development, physiology and disease [69]. One of these gene editing techniques is known as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR). The functions of CRISPR and CRISPR-associated (Cas) genes are essential for adaptive immunity in selected bacteria and archaea enabling the organisms to respond in an efficient manner to eliminate the invading genetic material using short-guide RNAs (sgRNAs) to target and destroy the DNA of exogenous pathogens. This biological phenomenon was observed for the first time in 2007 when a yogurt company identified a bacteria that could remember viruses. CRISPR obtained its name from the observation of remnants of bacterial genomes from previous infections, sandwiched between odd, and DNA sequence repeatedly, with unique sequences in between the duplications. Then, this mechanism was named “clustered regularly interspaced short palindromic repeats,” or CRISPR. Later, between 2012 and 2013, diverse experimental groups experimented with CRISPR in combination with other gene editing tools. Then in 2015, a novel technique for gene editing named CRISPR was published in Science [70, 71].

Briefly, CRISPR uses a guide RNA to send biological “scissors”—usually the CRISPR-associated protein, Cas9—to a precise spot to cut in a genome. Once Cas9 enzymatically cuts, the cell engages healing mechanism in the wounded DNA, one of these mechanisms of repair leads to knockouts, whereas the second leads to knockins [70]. Later, the cell is able to function with altered DNA and continue the normal gene expression. Three types of CRISPR mechanisms have been identified, of which type II is the most studied. In this case, invading DNA from viruses or plasmids is cut into small fragments and incorporated into a CRISPR locus amidst a series of short repeats. Then, loci are transcribed, and transcripts are processed to generate small RNAs, which in turn guide endonucleases to target invading DNA based on sequence complementarity [72].

The advent of site-specific nucleases, particularly CRISPR/Cas9, provides the ability to manipulate genomic sequences, with special interest for develop treatments for human diseases [73, 74]. The biomedical applications of CRISPR are just starting to emerge with a promising active role in the ability to remove genetic mutations associated with diseases, such as cystic fibrosis, HIV, and even certain types of cancers [75, 76, 77, 78, 79, 80]. The authors share interest about the audacious investigations of CRISPR/Cas9-editing genes in insomnia, OSA, narcolepsy, advanced sleep phase syndrome, or restless legs syndrome.

5 Application of CRISPR/Cas9-Based Gene Therapy for Managing Sleep Disorders

Indeed, it may be that rather than a causal link, there are shared genetic risk factors for sleep disturbances. In terms of moving the field forward, further studies of gene edition–sleep disorders interaction could improve our understanding of the association between gene editions and sleep disorders. Translational animal models are an attractive approach for studying human-like sleep disorders. Altogether, the animal models have been used to study the most common human sleep disturbances including insomnia, OSA, narcolepsy, advanced sleep phase syndrome, restless legs syndrome or other sleep disturbances [81, 82] (Fig. 2).
Fig. 2

Hypothetical use of CRISPR/Cas9 to edit genes related to sleep disorders. Molecular manipulation of genes linked to sleep disturbances might represent a novel and audacious therapeutical approach. Experimental advances would include testing CRISPR/Cas 9 editing genes in animal models that display human-like sleep disturbances such as insomnia, OSA, narcolepsy, advanced sleep phase syndrome, restless legs syndrome or other sleep disturbances

At last, considering the large degree of phenotypic variance in clinical presentation of sleep disturbances and the risk for sleep-associated morbidities, further studies should also consider detailed assessments for specific gene editing to gain increased insights into the potential genetic pathways involved in sleep disturbances. This scenario will present the possibility prior to a diagnosis of sleep disorders and perhaps even prior to the prodromal phase of sleep disturbances.

6 Conclusions

Sleep abnormalities such as insomnia, OSA, narcolepsy, advanced sleep phase syndrome and restless legs syndrome, among many others seem to have a genetic basis. Currently, CRISPR–Cas9 is an experimental technique that allows genome editing, targeting, and regulation in a wide range of organisms and cell types. The progress and the future potential of the CRISPR–Cas9 towards biomedical area are quite interesting. In this regard, novel uses of CRISPR–Cas9 as therapeutic tool for treating sleep disorders represent a new tempting research horizon. Using animal models of sleep disorders, it might be a convenient strategy for studying the putative benefits of CRISPR–Cas9 in sleep-related gene edition. We expect that in the coming years, further advances in the sleep medicine field would be achieved by gene edition experimental approach.


Compliance with Ethical Standards

Ethical standards

All data reported in this paper are from public repositories.


This work was supported by The University of California Institute for Mexico and the United States (UC MEXUS) and Consejo Nacional de Ciencia y Tecnología (CONACyT(Grant(CN-17-19) and Escuela de Medicina, Universidad Anáhuac Mayab Grant (PresInvEMR2014) given to E.M.-R.

Conflict of interest

Authors declare no conflict of interest.


  1. 1.
    Chatterjee S, Ahituv N. Gene regulatory elements, major drivers of human disease. Annu Rev Genomics Hum Genet. 2017;18:45–63.CrossRefPubMedGoogle Scholar
  2. 2.
    Petit F, Sears KE, Ahituv N. Limb development: a paradigm of gene regulation. Nat Rev Genet. 2017;18:245–58.CrossRefPubMedGoogle Scholar
  3. 3.
    Yilmaz A, Grotewold E. Components and mechanisms of regulation of gene expression. Met Mol Biol. 2010;674:23–32.CrossRefGoogle Scholar
  4. 4.
    Gehring NH, Wahle E, Fischer U. Deciphering the mRNP code: RNA-bound determinants of post-transcriptional gene regulation. Trends Biochem Sci. 2017;42:369–82.CrossRefPubMedGoogle Scholar
  5. 5.
    Shin JH, Xu L, Wang D. Mechanism of transcription-coupled DNA modification recognition. Cell Biosci. 2017;22(7):9.CrossRefGoogle Scholar
  6. 6.
    Lusk CP, King MC. The nucleus: keeping it together by keeping it apart. Curr Opin Cell Biol. 2017;44:44–50.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Baralle D, Buratti E. RNA splicing in human disease and in the clinic. Clin Sci (Lond). 2017;131:355–68.CrossRefPubMedGoogle Scholar
  8. 8.
    Cheung VG, Spielman RS. Genetics of human gene expression: mapping DNA variants that influence gene expression. Nat Rev Gen. 2009;10:595–604.CrossRefGoogle Scholar
  9. 9.
    Barbagallo I, Li Volti G, Galvano F, Tettamanti G, Pluchinotta FR, Bergante S, Vanella L. Diabetic human adipose tissue-derived mesenchymal stem cells fail to differentiate in functional adipocytes. Exp Biol Med. 2016;242:1079–85.CrossRefGoogle Scholar
  10. 10.
    Yong LK, Lai S, Liang Z, Poteet E, Chen F, van Buren G, Fisher W, Mo Q, Chen C, Yao Q. Overexpression of Semaphorin-3E enhances pancreatic cancer cell growth and associates with poor patient survival. Oncotarget. 2016;7:87431–48.PubMedPubMedCentralGoogle Scholar
  11. 11.
    Chen J, Li K, Pang Q, Yang C, Zhang H, Wu F, Cao H, Liu H, Wan Y, Xia W, Wang J, Dai Z, Li Y. Identification of suitable reference gene and biomarkers of serum miRNAs for osteoporosis. Sci Rep. 2016;6:36347.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    American Academy of Sleep Medicine, International classification of sleep disorders: diagnostic and coding manual, 3rd edn. Darien, IL, USA.: American Academy of Sleep Medicine; 2014.Google Scholar
  13. 13.
    Tononi G, Cirelli C. Modulation of brain gene expression during sleep and wakefulness: a review of recent findings. Neuropsychopharmacology. 2001;25(5 Suppl):S28–35.CrossRefPubMedGoogle Scholar
  14. 14.
    Cirelli C, Faraguna U, Tononi G. Changes in brain gene expression after long-term sleep deprivation. J Neurochem. 2006;98:1632–45.CrossRefPubMedGoogle Scholar
  15. 15.
    Cirelli C, Pfister-Genskow M, McCarthy D, Woodbury R, Tononi G. Proteomic profiling of the rat cerebral cortex in sleep and waking. Arch Ital Biol. 2009;147:59–68.PubMedPubMedCentralGoogle Scholar
  16. 16.
    Crocker A, Sehgal A. Genetic analysis of sleep. Genes Dev. 2010;24:1220–35.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Gehrman PR, Keenan BT, Byrne EM, Pack AI. Genetics of sleep disorders. Psyc Clin North Am. 2015;38:667–81.CrossRefGoogle Scholar
  18. 18.
    Lind MJ, Gehrman PR. Genetic pathways to insomnia. Brain Sci. 2010;6:E64.CrossRefGoogle Scholar
  19. 19.
    Serretti A, Benedetti F, Mandelli L, Lorenzi C, Pirovano A, Colombo C, Smeraldi E. Genetic dissection of psychopathological symptoms: insomnia in mood disorders and CLOCK gene polymorphism. Am J Med Gen. 2010;121B:35–8.CrossRefGoogle Scholar
  20. 20.
    Serretti A, Gaspar-Barba E, Calati R, Cruz-Fuentes CS, Gomez-Sanchez A, Perez-Molina A, De Ronchi D. 3111T/C clock gene polymorphism is not associated with sleep disturbances in untreated depressed patients. Chronobiol Int. 2003;27:265–77.CrossRefGoogle Scholar
  21. 21.
    Utge SJ, Soronen P, Loukola A, Kronholm E, Ollila HM, Pirkola S, Porkka-Heiskanen T, Partonen T, Paunio T. Systematic analysis of circadian genes in a population-based sample reveals association of TIMELESS with depression and sleep disturbance. PLoS One. 2010;5:e9259.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Brower KJ, Wojnar M, Sliwerska E, Armitage R, Burmeister M. PER3 polymorphism and insomnia severity in alcohol dependence. Sleep. 2012;35:571–7.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Li J, Huang C, Lan Y, Wang Y. A cross-sectional study on the relationships among the polymorphism of period2 gene, work stress, and insomnia. Sleep Breath. 2015;19:1399–406.CrossRefPubMedGoogle Scholar
  24. 24.
    Mendlewicz J. Disruption of the circadian timing systems: molecular mechanisms in mood disorders. CNS Drugs. 2009;23(Suppl 2):15–26.CrossRefPubMedGoogle Scholar
  25. 25.
    Ban HJ, Kim SC, Seo J, Kang HB, Choi JK. Genetic and metabolic characterization of insomnia. PLoS ONE. 2011;6:e18455.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Byrne EM, Gehrman PR, Medland SE, Nyholt DR, Heath AC, Madden PA, Hickie IB, Van Duijn CM, Henders AK, Montgomery GW, Martin NG, Wray NR, Chronogen Consortium. A genome-wide association study of sleep habits and insomnia. Am J Med Gen. 2013;162:439–51.CrossRefGoogle Scholar
  27. 27.
    Ollila HM, Kettunen J, Pietiläinen O, Aho V, Silander K, Kronholm E, Perola M, Lahti J, Räikkönen K, Widen E, Palotie A, Eriksson JG, Partonen T, Kaprio J, Salomaa V, Raitakari O, Lehtimäki T, Sallinen M, Härmä M, Porkka-Heiskanen T, Paunio T. Genome-wide association study of sleep duration in the Finnish population. J Sleep Res. 2016;23:609–18.CrossRefGoogle Scholar
  28. 28.
    Spada J, Scholz M, Kirsten H, Hensch T, Horn K, Jawinski P, Ulke C, Burkhardt R, Wirkner K, Loeffler M, Hegerl U, Sander C. Genome-wide association analysis of actigraphic sleep phenotypes in the LIFE Adult Study. J Sleep Res. 2016;25:690–701.CrossRefPubMedGoogle Scholar
  29. 29.
    Subramani Y, Singh M, Wong J, Kushida CA, Malhotra A, Chung F. Understanding phenotypes of obstructive sleep apnea: applications in anesthesia, surgery, and perioperative medicine. Anesth Analg. 2017;124:179–91.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Patel N, Donahue C, Shenoy A, Patel A, El-Sherif N. Obstructive sleep apnea and arrhythmia: a systemic review. Int J Cardiol. 2017;228:967–70.CrossRefPubMedGoogle Scholar
  31. 31.
    Cade BE, Chen H, Stilp AM, Gleason KJ, Sofer T, Ancoli-Israel S, Arens R, Bell GI, Below JE, Bjonnes AC, Chun S, Conomos MP, Evans DS, Johnson WC, Frazier-Wood AC, Lane JM, Larkin EK, Loredo JS, Post WS, Ramos AR, Rice K, Rotter JI, Shah NA, Stone KL, Taylor KD, Thornton TA, Tranah GJ, Wang C, Zee PC, Hanis CL, Sunyaev SR, Patel SR, Laurie CC, Zhu X, Saxena R, Lin X, Redline S. Genetic associations with obstructive sleep apnea traits in Hispanic/Latino Americans. Am J Resp Crit Care Med. 2016;194:886–97.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Huang YS, Guilleminault C, Hwang M, Cheng C, Lin CH, Li HY, Lee LA. Inflammatory cytokines in pediatric obstructive sleep apnea. Med. 2016;95:e4944.CrossRefGoogle Scholar
  33. 33.
    Hirotsu C, Albuquerque RG, Nogueira H, Hachul H, Bittencourt L, Tufik S, Andersen ML. The relationship between sleep apnea, metabolic dysfunction and inflammation: the gender influence. Brain Behav Immun. 2017;59:211–8.CrossRefPubMedGoogle Scholar
  34. 34.
    Wu W, Li Z, Tang T, Wu J, Liu F, Gu L. 5-HTR2A and IL-6 polymorphisms and obstructive sleep apnea-hypopnea syndrome. Biomed Rep. 2016;4:203–8.CrossRefPubMedGoogle Scholar
  35. 35.
    de Lima FF, Mazzotti DR, Tufik S, Bittencourt L. The role inflammatory response genes in obstructive sleep apnea syndrome: a review. Sleep Breath. 2016;20:331–8.CrossRefPubMedGoogle Scholar
  36. 36.
    Dong XS, Ma SF, Cao CW, Li J, An P, Zhao L, Liu NY, Yan H, Hu QT, Mignot E, Strohl KP, Gao ZC, Zeng C, Han F. Hypocretin (orexin) neuropeptide precursor gene, HCRT, polymorphisms in early-onset narcolepsy with cataplexy. Sleep Med. 2013;14:482–7.CrossRefPubMedGoogle Scholar
  37. 37.
    Han F, Lin L, Schormair B, Pizza F, Plazzi G, Ollila HM, Nevsimalova S, Jennum P, Knudsen S, Winkelmann J, Coquillard C, Babrzadeh F, Strom TM, Wang C, Mindrinos M, Fernandez Vina M, Mignot E. HLA DQB1*06:02 negative narcolepsy with hypocretin/orexin deficiency. Sleep. 2014;37:1601–8.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Miyagawa T, Toyoda H, Hirataka A, Kanbayashi T, Imanishi A, Sagawa Y, Kotorii N, Kotorii T, Hashizume Y, Ogi K, Hiejima H, Kamei Y, Hida A, Miyamoto M, Imai M, Fujimura Y, Tamura Y, Ikegami A, Wada Y, Moriya S, Furuya H, Kato M, Omata N, Kojima H, Kashiwase K, Saji H, Khor SS, Yamasaki M, Wada Y, Ishigooka J, Kuroda K, Kume K, Chiba S, Yamada N, Okawa M, Hirata K, Uchimura N, Shimizu T, Inoue Y, Honda Y, Mishima K, Honda M, Tokunaga K. New susceptibility variants to narcolepsy identified in HLA class II region. Human Mol Gen. 2015;24:891–8.CrossRefGoogle Scholar
  39. 39.
    Toyoda H, Miyagawa T, Koike A, Kanbayashi T, Imanishi A, Sagawa Y, Kotorii N, Kotorii T, Hashizume Y, Ogi K, Hiejima H, Kamei Y, Hida A, Miyamoto M, Imai M, Fujimura Y, Tamura Y, Ikegami A, Wada Y, Moriya S, Furuya H, Takeuchi M, Kirino Y, Meguro A, Remmers EF, Kawamura Y, Otowa T, Miyashita A, Kashiwase K, Khor SS, Yamasaki M, Kuwano R, Sasaki T, Ishigooka J, Kuroda K, Kume K, Chiba S, Yamada N, Okawa M, Hirata K, Mizuki N, Uchimura N, Shimizu T, Inoue Y, Honda Y, Mishima K, Honda M, Tokunaga K. A polymorphism in CCR1/CCR3 is associated with narcolepsy. Brain Behav Imm. 2015;49:148–55.CrossRefGoogle Scholar
  40. 40.
    Tafti M, Lammers GJ, Dauvilliers Y, Overeem S, Mayer G, Nowak J, Pfister C, Dubois V, Eliaou JF, Eberhard HP, Liblau R, Wierzbicka A, Geisler P, Bassetti CL, Mathis J, Lecendreux M, Khatami R, Heinzer R, Haba-Rubio J, Feketeova E, Baumann CR, Kutalik Z, Tiercy JM. Narcolepsy-associated HLA class I alleles implicate cell-mediated cytotoxicity. Sleep. 2016;39:581–7.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Johansson AS, Owe-Larsson B, Hetta J, Lundkvist GB. Altered circadian clock gene expression in patients with schizophrenia. Schizophrenia Res. 2016;174:17–23.CrossRefGoogle Scholar
  42. 42.
    Luo W, Ma S, Yang Y, Zhang D, Zhang Q, Liu Y, Liu Z. TFEB regulates PER3 expression via glucose-dependent effects on CLOCK/BMAL1. Int J Bioch Cell Biol. 2016;78:31–42.CrossRefGoogle Scholar
  43. 43.
    Panda S. Circadian physiology of metabolism. Science. 2016;354:1008–15.CrossRefPubMedGoogle Scholar
  44. 44.
    Riddle M, Mezias E, Foley D, LeSauter J, Silver R. Differential localization of PER1 and PER2 in the brain master circadian clock. Eur J Neurosci. 2016;45:1357–67.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Videnovic A, Willis GL. Circadian system—a novel diagnostic and therapeutic target in Parkinson’s disease? Mov Dis. 2016;31:260–9.CrossRefGoogle Scholar
  46. 46.
    Potter GD, Skene DJ, Arendt J, Cade JE, Grant PJ, Hardie LJ. Circadian rhythm and sleep disruption: causes, metabolic consequences, and countermeasures. Endocrin Rev. 2016;37:584–608.CrossRefGoogle Scholar
  47. 47.
    van Maanen A, Meijer AM, van der Heijden KB, Oort FJ. The effects of light therapy on sleep problems: a systematic review and meta-analysis. Sleep Med Rev. 2016;29:52–62.CrossRefPubMedGoogle Scholar
  48. 48.
    Dijk DJ, Archer SN. PERIOD3, circadian phenotypes, and sleep homeostasis. Sleep Med Rev. 2010;14:151–60.CrossRefPubMedGoogle Scholar
  49. 49.
    Nordgren A. Genes, body clocks and prevention of sleep problems. Med Health Care Phil. 2016;19:569–79.CrossRefGoogle Scholar
  50. 50.
    Turek FW. Circadian clocks: not your grandfather’s clock. Science. 2016;354:992–3.CrossRefPubMedGoogle Scholar
  51. 51.
    Parsons MJ, Lester KJ, Barclay NL, Archer SN, Nolan PM, Eley TC, Gregory AM. Polymorphisms in the circadian expressed genes PER3 and ARNTL2 are associated with diurnal preference and GNβ3 with sleep measures. Sleep Res. 2014;23:595–604.CrossRefGoogle Scholar
  52. 52.
    Hu Y, Shmygelska A, Tran D, Eriksson N, Tung JY, Hinds DA. GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person. Nat Com. 2016;7:10448.CrossRefGoogle Scholar
  53. 53.
    Amin N, Allebrandt KV, van der Spek A, Müller-Myhsok B, Hek K, Teder-Laving M, Hayward C, Esko T, van Mill JG, Mbarek H, Watson NF, Melville SA, Del Greco FM, Byrne EM, Oole E, Kolcic I, Chen TH, Evans DS, Coresh J, Vogelzangs N, Karjalainen J, Willemsen G, Gharib SA, Zgaga L, Mihailov E, Stone KL, Campbell H, Brouwer RW, Demirkan A, Isaacs A, Dogas Z, Marciante KD, Campbell S, Borovecki F, Luik AI, Li M, Hottenga JJ, Huffman JE, van den Hout MC, Cummings SR, Aulchenko YS, Gehrman PR, Uitterlinden AG, Wichmann HE, Müller-Nurasyid M, Fehrmann RS, Montgomery GW, Hofman A, Kao WH, Oostra BA, Wright AF, Vink JM, Wilson JF, Pramstaller PP, Hicks AA, Polasek O, Punjabi NM, Redline S, Psaty BM, Heath AC, Merrow M, Tranah GJ, Gottlieb DJ, Boomsma DI, Martin NG, Rudan I, Tiemeier H, van Ijcken WF, Penninx BW, Metspalu A, Meitinger T, Franke L, Roenneberg T, van Duijn CM. Genetic variants in RBFOX3 are associated with sleep latency. Eur J Human Gen. 2016;24:1488–95.CrossRefGoogle Scholar
  54. 54.
    Cade BE, Gottlieb DJ, Lauderdale DS, Bennett DA, Buchman AS, Buxbaum SG, De Jager PL, Evans DS, Fülöp T, Gharib SA, Johnson WC, Kim H, Larkin EK, Lee SK, Lim AS, Punjabi NM, Shin C, Stone KL, Tranah GJ, Weng J, Yaffe K, Zee PC, Patel SR, Zhu X, Redline S, Saxena R. Common variants in DRD2 are associated with sleep duration: the CARe consortium. Human Mol Gen. 2016;25:167–79.CrossRefGoogle Scholar
  55. 55.
    Chang AM, Bjonnes AC, Aeschbach D. Circadian gene variants influence sleep and the sleep electroencephalogram in humans. Chronobio Int. 2016;33:561–73.CrossRefGoogle Scholar
  56. 56.
    Dall’Ara I, Ghirotto S, Ingusci S, Bagarolo G, Bertolucci C, Barbujani G. Demographic history and adaptation account for clock gene diversity in humans. Heredity. 2016;117:165–72.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Truong KK, Lam MT, Grandner MA, Sassoon CS, Malhotra A. Timing matters: circadian rhythm in sepsis, obstructive lung disease, obstructive sleep apnea, and cancer. Ann Am Thor Soc. 2016;13:1144–54.CrossRefGoogle Scholar
  58. 58.
    Rizzo G, Li X, Galantucci S, Filippi M, Cho YW. Brain imaging and networks in restless legs syndrome. Sleep Med. 2016;31:39–48.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Scherer JS, Combs SA, Brennan F. Sleep disorders, restless legs syndrome, and uremic pruritus: diagnosis and treatment of common symptoms in dialysis patients. Am J Kidney Dis. 2017;69:117–28.CrossRefPubMedGoogle Scholar
  60. 60.
    Hennessy MD, Zak RS, Gay CL, Pullinger CR, Lee KA, Aouizerat BE. Polymorphisms of interleukin-1 Beta and interleukin-17Alpha genes are associated with restless legs syndrome. Biol Res Nurs. 2014;16:143–51.CrossRefPubMedGoogle Scholar
  61. 61.
    Gan-Or Z, Zhou S, Ambalavanan A, Leblond CS, Xie P, Johnson A, Spiegelman D, Allen RP, Earley CJ, Desautels A, Montplaisir JY, Dion PA, Xiong L, Rouleau GA. Analysis of functional GLO1 variants in the BTBD9 locus and restless legs syndrome. Sleep Med. 2015;16:1151–5.CrossRefPubMedGoogle Scholar
  62. 62.
    Fuh JL, Chung MY, Yao SC, Chen PK, Liao YC, Hsu CL, Wang PJ, Wang YF, Chen SP, Fann CS, Kao LS, Wang SJ. Susceptible genes of restless legs syndrome in migraine. Cephalalgia. 2016;36:1028–37.CrossRefPubMedGoogle Scholar
  63. 63.
    Parish JM. Genetic and immunologic aspects of sleep and sleep disorders. Chest. 2013;143:1489–99.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Szentkirályi A, Völzke H, Hoffmann W, Winkelmann J, Berger K. Lack of association between genetic risk loci for restless legs syndrome and multimorbidity. Sleep. 2016;39:111–5.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Khan FH, Ahlberg CD, Chow CA, Shah DR, Koo BB. Iron, dopamine, genetics, and hormones in the pathophysiology of restless legs syndrome. J Neurol. 2017;264(8):1634–41. CrossRefGoogle Scholar
  66. 66.
    García-Martín E, Jiménez-Jiménez FJ, Alonso-Navarro H, Martínez C, Zurdo M, Turpín-Fenoll L, Millán-Pascual J, Adeva-Bartolomé T, Cubo E, Navacerrada F, Rojo-Sebastián A, Rubio L, Ortega-Cubero S, Pastor P, Calleja M, Plaza-Nieto JF, Pilo-de-la-Fuente B, Arroyo-Solera M, García-Albea E, Agúndez JA. Heme oxygenase-1 and 2 common genetic variants and risk for restless legs syndrome. Medicine. 2015;94:e1448.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Winkelman JW, Blackwell T, Stone K, Ancoli-Israel S, Tranah GJ, Redline S, Osteoporotic Fractures in Men (MrOS) Study Research Group. Genetic associations of periodic limb movements of sleep in the elderly for the MrOS sleep study. Sleep Med. 2015;16:1360–665.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Jiménez-Jiménez FJ, García-Martí E, Alonso-Navarro H, Martínez C, Zurdo M, Turpín-Fenoll L, Millán-Pascual J, Adeva-Bartolomé T, Cubo E, Navacerrada F, Rojo-Sebastián A, Rubio L, Ortega-Cubero S, Pastor P, Calleja M, Plaza-Nieto JF, Pilo-de-la-Fuente B, Arroyo-Solera M, García-Albea E, Agúndez JA. Thr105Ile (rs11558538) polymorphism in the histamine-1-methyl-transferase (HNMT) gene and risk for restless legs syndrome. J Neural Transm. 2016;124:285–91.CrossRefPubMedGoogle Scholar
  69. 69.
    Bertero A, Pawlowski M, Ortmann D, Snijders K, Yiangou L, Cardoso de Brito M, Brown S, Bernard WG, Cooper JD, Giacomelli E, Gambardella L, Hannan NR, Iyer D, Sampaziotis F, Serrano F, Zonneveld MC, Sinha S, Kotter M, Vallier L. Optimized inducible shRNA and CRISPR/Cas9 platforms for in vitro studies of human development using hPSCs. Dev. 2016;143:4405–18.CrossRefGoogle Scholar
  70. 70.
    Cohen J. The birth of CRISPR Inc. Science. 2017;355:680–4.CrossRefPubMedGoogle Scholar
  71. 71.
    Cohen J. CRISPR patent ruling leaves license holders scrambling. Science. 2017;355:786.CrossRefPubMedGoogle Scholar
  72. 72.
    Jinek M, Chylinski K, Fonfara I, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA—guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337:816–21.CrossRefPubMedGoogle Scholar
  73. 73.
    Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR-Cas9. Nat Rev Gen. 2015;16:299–311.CrossRefGoogle Scholar
  74. 74.
    Lin J, Zhou Y, Liu J, Chen J, Chen W, Zhao S, Wu Z, Wu N. Progress and application of CRISPR/Cas technology in biological and biomedical investigation. J Cell Biochem. 2017;118(10):3061–71. CrossRefPubMedGoogle Scholar
  75. 75.
    Barrangou R, Doudna JA. Applications of CRISPR technologies in research and beyond. Nat Biotech. 2016;34:933–41.CrossRefGoogle Scholar
  76. 76.
    Doerflinger M, Forsyth W, Ebert G, Pellegrini M, Herold MJ. CRISPR/Cas9-The ultimate weapon to battle infectious diseases? Cell Microbiol. 2017;19:e12693.Google Scholar
  77. 77.
    Koo T, Kim JS. Therapeutic applications of CRISPR RNA-guided genome editing. Brief Func Gen. 2016;16:38–45.CrossRefGoogle Scholar
  78. 78.
    Oude Blenke E, Evers MJ, Mastrobattist E, van der Oost J. CRISPR-Cas9 gene editing: delivery aspects and therapeutic potential. J Control Rel. 2016;244:139–48.CrossRefGoogle Scholar
  79. 79.
    Singh V, Braddick D, Dhar PK. Exploring the potential of genome editing CRISPR-Cas9 technology. Gene. 2017;599:1–18.CrossRefPubMedGoogle Scholar
  80. 80.
    Strong A, Musunuru K. Genome editing in cardiovascular diseases. Nat Rev Cardiol. 2017;14:11–20.CrossRefPubMedGoogle Scholar
  81. 81.
    Toth LA, Bhargava P. Animal models of sleep disorders. Comp Med. 2013;63:91–104.PubMedPubMedCentralGoogle Scholar
  82. 82.
    Gillombardo CB, Darrah R, Dick TE. C57BL/6 J mouse apolipoprotein A2 gene is deterministic for apnea. Resp Physiol Neurobiol. 2017;235:88–94.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Eric Murillo-Rodríguez
    • 1
    • 2
  • Nuno Barbosa Rocha
    • 3
    • 4
  • André Barciela Veras
    • 5
    • 6
    • 7
  • Henning Budde
    • 8
    • 9
    • 10
    • 11
  • Sérgio Machado
    • 7
    • 12
    • 13
  1. 1.Laboratorio de Neurociencias Moleculares e Integrativas, Escuela de Medicina, División Ciencias de la SaludUniversidad Anáhuac MayabMéridaMexico
  2. 2.Intercontinental Neuroscience Research GroupMéridaMexico
  3. 3.Health SchoolPolytechnic Institute of PortoPortoPortugal
  4. 4.Intercontinental Neuroscience Research GroupPortoPortugal
  5. 5.Institute of PsychiatryFederal University of Rio de JaneiroRio de JaneiroBrazil
  6. 6.Dom Bosco Catholic UniversityCampo GrandeBrazil
  7. 7.Intercontinental Neuroscience Research GroupRio de JaneiroBrazil
  8. 8.Faculty of Human SciencesMedical School HamburgHamburgGermany
  9. 9.Sports Science Department, School of Science and Engineering, Physical Activity, Physical Education, Health and Sport Research Centre (PAPESH)Reykjavik UniversityReykjavikIceland
  10. 10.Lithuanian Sports UniversityKaunasLithuania
  11. 11.Intercontinental Neuroscience Research GroupHamburghGermany
  12. 12.Laboratory of Panic and RespirationInstitute of Psychiatry of Federal University of Rio de JaneiroRio de JaneiroBrazil
  13. 13.Physical Activity Neuroscience Laboratory, Physical Activity Sciences Postgraduate Program of Salgado de Oliveira UniversitySalgado de Oliveira UniversityNiteróiBrazil

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