This study identifies candidate biomarkers for acute total sleep deprivation in humans, as well as promising candidates for a biomarker test of neurobehavioral impairment caused by TSD. Moreover, functional enrichment analyses and prediction of molecular networks advanced mechanistic insights into the impact of sleep deprivation. Some of the difficulty identifying biomarkers for sleep deprivation  may be caused by the large inter-individual variability in responses to sleep loss. In particular, the greater ability of some persons to resist performance degradation during sleep loss has been recognized for over a decade . In the present study three out of 11 TSD subjects were identified as fatigue resistant in terms of PVT lapses (Additional file 2: Supplementary Text – Figure S1). By testing for the relationship of gene expression in blood to PVT lapses that encompass some of this variability, additional biomarkers were found that were not identified by assessment of a simple sleep deprivation Treatment effect. Of course the same was true in reverse, as we identified 212 Treatment effect genes in blood (Additional file 3: Table S2) and a mere 28 genes associated with PVT lapses (Additional file 5: Table S4). Detecting a relationship of gene expression with PVT lapses may be more difficult, considering the added complexity, greater outcome specificity , and perhaps narrower suite of associated genes for neurobehavioral traits.
While identifying genes associated with a sleep loss Treatment has value, biomarkers for neurobehavioral impairment such as our list of genes associated with PVT may aid fundamental understanding of the relationship between sleep and cognition. Shifting the focus from sleep deprivation biomarkers, to biomarkers for impairment from sleep deprivation, strengthens characterization of the molecular basis of the phenotype. By directly assaying the molecular changes associated with neurobehavioral performance, and drawing predictions of associated impacts on function, this research enhances understanding of the relation between sleep loss and capacity for sustained attention.
Most genes identified in this study exhibited down-regulation in TSD relative to C during the Experimental phase, a pattern consistent with prior studies in humans such as . As reviewed by Mackiewicz et al. , sleep is associated with macromolecule biosynthesis, and prolonged wakefulness leads to down-regulation of genes associated with multiple metabolic processes. The current study indicates potential effects on translation in the down-regulation of Cytoplasmic Polyadenylation Element Binding Protein 4 (CPEB4, Treatment and PVT effect lists) and Eukaryotic Translation Initiation Factor 4E Family Member 3 (EIF4E3, Treatment list only). The CPEB4 gene is one of four vertebrate cytoplasmic polyadenylation binding proteins that regulates translation via effects on poly(A) elongation [35, 36]. The phosphorylated form of the CPEB protein promotes translation of mRNAs with roles in learning, memory, and synaptic plasticity [36, 37], which may explain its relation to PVT lapses. Grønli and colleagues report that sleep deprivation leads to reduced phosphorylation of Cpeb in the hippocampus and Eif4e in the dentate gyrus of rats . Although the EIF4E gene was not significantly related to PVT lapses in the present study, it was down-regulated in response to the TSD Treatment. The protein EIF4E is a component of the translation initiation complex ; decreased levels of this protein would be detrimental to synthesis of new protein and could contribute to the known effects of sleep loss on macromolecular biosynthesis.
Sleep deprivation biomarkers and immunity
Cytokine and stress-associated networks frequently are associated with sleep deficits [15, 39], and results here further support the association of TSD with the immune system. Ingenuity Pathway Analysis® Causal Networks detected in both Treatment and PVT analyses have BDKR as the master regulator (Additional file 2: Supplementary Text – Figure S6, Network B). Bradykinin receptors are mediators of the inflammatory response , as indicated by inclusion of differentially expressed genes such as the chemokine CXCR1 and transcription factor LITAF in the PVT network. The LITAF gene is a key mediator of the inflammatory cytokine response to lipopolysaccharides . Multiple genes related to the immune system were down-regulated in both Treatment and PVT lists, including LITAF, CXCR1, and CXCR2. The genes IL17RA and IL1B were down-regulated for Treatment only.
In contrast, several reviews suggest that sleep loss results in increased levels of cytokines such as IL1 [39, 42,43,44]. While many reports are based on protein assays, studies reviewed by Krueger  have shown that in brain, IL1 mRNA increases during sleep deprivation. However, results in the present study are based on blood rather than brain samples. Also, much of the IL1 data in the reviews are derived from studies of animals, particularly rodents, and results may differ in humans. Details of the experimental design such as the time of measurement also may influence results. For example, in human blood higher mRNA levels of IL1B are found in day vs. nighttime samples .
Nonetheless, findings in the current study do indicate that specific aspects of the immune system were up-regulated. For example, an up-regulated group of Treatment effect genes (Mfuzz Treatment Group 2 – TSD subjects’ data, Fig. 4) contains members associated with B cell signaling. This is consistent with the study by Aho and colleagues  of leukocyte gene expression in humans following partial sleep restriction, in which B cell activation is among the top up-regulated Gene Ontology pathways. Besides cytokines, the immune genes CAMP and DEFA4 are of interest. These molecules were significantly down-regulated in TSD subjects in both Experimental and Recovery phases. Their continuing down-regulation suggest the need for more than one Recovery night of sleep to restore molecular homeostasis. This idea is further supported by the Mfuzz plots for TSD subjects showing potential circadian disruption in the Recovery phase, with gene expression patterns from morning to evening differing from the temporal patterns observed at Baseline (Figs. 4 and 5). Nevertheless, there appear to be some potential differences during Baseline between Mfuzz TSD and C clusters (Additional file 2: Supplementary text – Figs. S4–S5), warranting some caution in over-interpreting circadian trends from Mfuzz. Yet as aforementioned, care is needed in comparing results of Mfuzz for C and TSD subjects due to differences in Transcript Cluster membership among Mfuzz groups (Results, Additional file 2: Supplementary text).
Besides immunity, sleep deprivation typically is associated with evidence of a stress response including induction of heat shock proteins [34, 39, 46, 47]. One of the PVT Causal Networks predicts up-regulation of stress response genes including HSP70 and HSP90 (Network D, Additional file 2: Supplementary Text – Figure S8). Differentially expressed genes in this network included cytokine receptors CXCR1 and CXCR2, as well as the transcription factors HIF1A and LITAF. While HIF1A is known for its role in activating hypoxic response genes, recent work suggests that HIF1A induction from hypoxia caused by obstructive sleep apnea may disrupt circadian rhythms . Overall stress response networks and cytokines may eventually contribute to a larger biomarker panel for diagnosing TSD, but by themselves such genes may be too variable or too pleiotropic to discriminate between sleep loss and other phenotypes such as illness.
Homeostatic and circadian clock genes
Overlap between the circadian and homeostatic sleep processes is increasingly documented [49, 50], and results here further suggest a link with neurobehavioral function. Among the high-scoring transcription factors in the RIF analysis was BHLHE40 (also known as DEC1), which together with Basic Helix-Loop-Helix Family Member 41 (BHLHE41, also known as DEC2) acts as a transcriptional repressor of the Circadian Locomoter Output Cycles Kaput (CLOCK)/Brain and Muscle ARNT-Like 1 (BMAL1) promoter [51, 52]. Mutations of BHLHE41 have been associated with resisting effects of sleep loss in humans . Interestingly 62 of the genes identified by Möller-Levet et al. as rhythmic in a well-rested condition , were identified in our Treatment list, and six were found in the PVT list (Additional file 4: Table S3).
Additionally, expression profiles of three miRNAs were significantly related to PVT lapses: MIR24, MIR27 B, and MIR152 (Additional file 9: Table S8). MicroRNAs are known for their roles in regulating gene expression , and have been associated with sleep and neurodegenerative disease . Due to their relation to PVT lapses in the current study, these three miRNAs are intriguing candidates for regulating the molecular mechanism linking sleep deprivation and sustained attention. In mice Mir27 b regulates the clock gene Bmal1 at the posttranscriptional level . Although not part of the ADCY Causal Network A generated with IPA® (Fig. 6), bioinformatics analyses suggest that Mir27 b interacts with the Adenylate cyclase 6 (Adcy6) gene . It has been proposed that Mir24 plays a role in regulating the period genes in mice , and based on sequence analysis in humans, MIR24 is predicted to interact with Cryptochrome Circadian Clock 2 (CRY2) and Period Circadian Regulator 2 (PER2) . Finally we note that microRNAs themselves can exhibit circadian rhythm in their expression. For example, Mir152 exhibits diurnal oscillations in mice . Plasma samples in humans revealed diurnal oscillations in MIR24 , although evidence is mixed for such rhythmicity in MIR27 B [57, 59].
Transcription factor analyses highlight further regulators with possible roles in both homeostatic and circadian processes, such as USF1. In mammals, the CLOCK/BMAL1 protein heterodimer binds E-boxes in the promoters of the Period (PER1 and PER2) and Cryptochrome (CRY1 and CRY2) genes leading to their activation, and the protein products of these genes repress the CLOCK/BMAL1 complex and in turn their own expression, until degradation of PER and CRY products releases CLOCK/BMAL1 [61,62,63]. Oscillations in this molecular clock contribute to initiating circadian rhythms. USF1, like the CLOCK/BMAL1 heterodimer, binds E-box regulatory sites but with peak binding at night, antiphase to CLOCK/BMAL1 . It has been proposed that USF1 may help generate circadian rhythms by maintaining an open chromatin state, enhancing the ability of CLOCK/BMAL1 binding to the E-boxes on the next circadian cycle . In the current study, not only was there a strong prediction of a regulatory role for USF1 in the RIF analysis, but also the Biobase F-match tool revealed over-representation of E-box binding sites in the differentially expressed genes.
Other genes with regulatory roles supported by both RIF and F-match analyses were GABPA, TCF4, and ELK3. The protein encoded by the GABPA gene is a transcription factor that may function in human cognition . Previous research on chronic sleep restriction in humans suggested a possible association between GABPA and gene down-regulation , but more work is needed to elucidate the relation of TCF4 and ELK3 to sleep deprivation. It is noted that RIF and F-match test for regulatory effects in distinctly different ways (Additional file 2: Supplementary text) and should be considered complementary, not necessarily confirmatory.
Novel biomarkers and genes specific to neurobehavioral impairment
Altogether 13 genes represented by 15 Transcript Clusters were associated with PVT lapses but not with Treatment (Additional file 2: Supplementary text; Additional file 5: Table S4), including FLOT1. In mice, flotillins are up-regulated with sleep and down-regulated with sleep deprivation , which in the present study would be seen as a Treatment effect. Due to their association with lipid rafts, flotillins may have a role in neurotransmitter signaling [34, 47]. In contrast to the results of Mackiewicz et al. , lack of a Treatment effect in the current study could reflect a difference between mice and humans. Confirming the absence of a Treatment effect for the 13 genes specific to the PVT analysis will require additional studies with more individuals. Nevertheless, a tantalizing hypothesis is that these 13 genes are specifically related to the mechanism by which TSD affects the capacity for sustained attention.
Another down-regulated gene specifically associated with PVT was KCNJ15, an inward rectifying potassium channel proposed to be a key component of the potassium circadian cycle . It has been suggested that cycling of sodium and potassium currents is an evolutionarily conserved mechanism of governing clock neurons in the brain . Recent work points to the role of neuromodulators influencing extracellular ion concentrations in the brain, in turn impacting sleep/wake activity . Further evidence linking PVT lapses and ion channels exists in the IPA® Causal Network A (Fig. 6). The direction of change of the differentially expressed genes within this network was consistent with inhibition of an L-type calcium channel complex and activation of Potassium Calcium-Activated Channel Subfamily N Member 4 (KCCN4). In mice, knockouts of Kccn4 lead to reduced sleep duration .
Beyond suggesting a role of ion signaling in TSD and the resulting neurobehavioral deficits, Network A (Fig. 6) was intriguing due to the implications for cyclic adenosine monophosphate (cAMP) signaling. In general, activation of adenylate cyclase leads to production of cAMP . Here it was predicted that ADCY was down-regulated, which in turn would reduce cAMP levels. Other components of Network A include predicted down-regulation of complexes for protein kinase A (PKA) and phospho-cAMP response element binding protein (CREB), which are implicated in memory storage . A study in mice demonstrated that increasing cAMP in hippocampal neurons can rescue the typical memory consolidation impairment caused by sleep deprivation . Via its impact on cAMP, this network also implicates the immune system.
Narasimamurthy and colleagues  proposed a model in which Cryptochrome 1 (CRY1) inhibits adenylate cyclase, reducing levels of cAMP and ultimately of IL6. In this study the IL6 gene was excluded from analyses due to its low expression across multiple samples, but reduction of this cytokine would be consistent with the predictions of inhibition of the immune system. Additional Causal Networks contained purine type 2X7 (PVT) and 2X4 (Treatment) receptors (Additional file 2: Supplementary text – Figure S7, Network P). Binding of ATP to P2X4 receptors is known to promote REM sleep, whereas binding to P2X7 receptors promotes non-REM sleep . As reviewed previously [73, 74], adenosine binding to P2X7 receptors has been implicated in effecting the cumulative deficits in PVT performance due to chronic sleep loss, but these receptors also can act independently of adenosine to promote the release of sleep regulatory substances. Adenosine itself is a sleep regulatory substance as stated in reviews [43, 74], although causal roles for the molecule in sleep homeostasis are debated .
Genes within the SPDY family constitute a new group of candidate biomarkers for the effects of TSD. In differential expression analysis of both Treatment and PVT lapses, Transcript Clusters for SPDY genes were up-regulated (Figs. 4 and 5), and WGCNA grouped several members of the SPDY family in a co-expression module (White) correlated with PVT lapses (Fig. 3, Additional file 6: Table S5). The SPDY members can activate cyclin-dependent kinases independent of cyclin activity, and they function in cell cycle progression, meiotic maturation, and the DNA damage response [75, 76]. To the authors’ knowledge the SPDY gene family has not previously been associated with sleep deprivation, although Cyclin A has been linked to sleep-wake transitions and the sleep homeostat in Drosophila . The best known member of this family, SpdyA (also known as Spy1), was shown to be expressed in the lumbar spinal cord of adult rats and may function in nerve regeneration [78, 79]. Meanwhile, a growing body of research points to effects of sleep deprivation on adult neurogenesis, albeit results seem to vary among studies and may depend on the extent of sleep deprivation . Combining these findings one can hypothesize that sleep deprivation induces the SPDY family, thereby altering cell cycle progression and neurogenesis. However, this gene family has high sequence homology making unambiguous identification of the relevant family members difficult.
The present investigation was aimed at biomarker discovery, and preliminary analysis of biomarker function and associated molecular networks. As with any such project, further work is required for biomarker validation . This should include additional comparisons with published datasets, and collection of new data from more subjects. Collecting nighttime blood samples from control individuals without disturbing their sleep would be helpful for refining the association of biomarkers with neurobehavioral impairment across the circadian cycle. Also, many of the genes associated with neurobehavioral impairment from TSD in this study are connected to several different regulatory pathways, suggesting the potential for pleiotropic roles. For example, Networks A, B, D, and P for PVT all contain six of the 28 differentially expressed PVT genes, Aquaporin 9 (AQP9), Arrestin Domain Containing 3 (ARRDC3), CPEB4, ELOVL Fatty Acid Elongase 5 (ELOVL5), HIF1A, and Lymphocyte Cytosolic Protein 1 (LCP1) (Fig. 6, Additional File 2: Supplementary text – Figs. S6–S8). Moreover, these four networks all contain two ligand-dependent transcription factors, Nuclear Receptor Subfamily 3, Group C, Member 1 (NR3C1), and Peroxisome Proliferator Activated Receptor Alpha (PPARA). While all four networks clearly predict down-regulation of PPARA, for the glucocorticoid receptor NR3C1 evidence of down-regulation is stronger in networks A and P. The PPARA protein is important to coordinating rhythmic gene expression, and it interacts with the period gene PER2 . Ultimately, confirmation of predicted pathway networks and regulatory molecules will require targeted laboratory studies, and a different approach will be needed to test for causation and verify mechanistic insights (e.g., gene knockout or silencing assays).
In this study (Additional file 3: Table S2, Additional file 5: Table S4), as in prior research on sleep and gene expression , fold change values typically were low. Validation of biomarker panels will require additional data collection from more subjects to increase power, and to adequately represent the continuum of sleep deprivation responses. However, identification of similar themes in multiple different analyses increases confidence in the results presented here. For example, the WGCNA co-expression module most strongly correlated with PVT lapses contains several SPDY genes, congruent with identification of SPDY members in the PVT differential expression list. Of course findings here are specific for measurements in blood. Gene expression may vary across fluid and tissue types, although concordance as high as 80% has been reported between the transcriptome of blood and major tissues [82, 83].
In the future, separate analyses of fatigue resistant individuals to determine any gene expression patterns unique to them could advance understanding of the ability to maintain neurobehavioral functioning during sleep deprivation. Because the current study only included three fatigue resistant subjects, this is left to future studies with a larger sample size.