CNS control of arousal maintains waking neuronal activities needed for foraging, danger avoidance, and reproductive behaviors. Reduction of arousal to unconsciousness can gate sleep, however, the function served by this sleep, in contrast to wake function, remains enigmatic. A reasonable teleologically oriented goal is more effective future foraging, danger avoidance, and reproductive behaviors, based on procedural and episodic learning from past waking experiences. Effective learning involves memory of waking experience and underlying long-term plasticity (LTP) (Moser et al., 1998; Morris et al., 2003; Stickgold, 2005). At the cellular level, extensive activity-dependent LTP of cortical glutamate synapses can lead to a well-documented increase in synaptic strength of cortical glutamatergic synapses during waking. It is reflected by the increased frequency and amplitude of glutamatergic miniature synaptic currents (mEPSC) that are restored by subsequent sleep (Liu et al., 2010; Torrado Pacheco et al., 2021), consistent with the synaptic homeostasis hypothesis of sleep (SHY) (Tononi and Cirelli, 2014).

Our recent findings indicate that during waking, not only are individual synapses strengthened (overall glutamate mEPSC amplitude in layer 2–3 pyramidal cells is increased by SD) but, also, the number of electro-physiologically active synapses is increased (increased frequency in correlation with unaltered or decreased probability of release) (Bjorness et al., 2020). The molecular underpinnings of these changes in glutamate synaptic strength and number were examined to determine the changes in the glutamate synaptic phenotype and what the implication(s) of these changes, if any, might be with regard to a propensity for synaptic plasticity (meta-plasticity).

We investigated sleep/wake mediated electrophysiological changes in glutamatergic synapses in the motor cortex, layer 2–3, pyramidal neurons since our previous work demonstrates wake/sleep up-regulation and down-regulation, respectively, in these cells’ glutamate synapses (Bjorness et al., 2020). We observed a wake/sleep oscillation of AMPA/NMDA synaptic response ratio in pyramidal neurons of the motor neocortex coupled with an oscillating fraction of silent synapses (NMDAR-mediated synaptic response but no AMPAR-mediated response) in response to sleep need. We then, extended these physiological findings to include the characterization of sleep-need-related differential expression of glutamatergic synaptic shaping genes that can control the observed oscillating glutamate synapse phenotype.

The major claims of this study are: (1) SD increases the AMPA/NMDA receptor ratio and RS restores it; (2) SD decreases silent synapses compared to CS and RS restores their number after SD; (3) the majority of SD-induced DEGs are found in ExIT cells (glutamate pyramidal neurons projecting within the telencephalon); (4) ExIT SD-induced DEGs are enriched for genes encoding synaptic shaping components and for autism spectrum disorder risk and; (5) these DEGs are also enriched for DEGs induced by Mef2c loss of function restricted to forebrain glutamate neurons (ExIT cells comprise a subset of these) and by over-expression of constitutively nuclear HDAC4 that represses MEF2c transcriptional function. The last claim is consistent with an intracellular signaling model (presented as a hypothesis to be tested, in Figure 4B).

Prolonged waking is associated with increased glutamatergic synaptic strength (Vyazovskiy et al., 2008; Liu et al., 2010; Bjorness et al., 2020) and increased number of functional AMPAR synapses (Bjorness et al., 2020). Both effects are reversed by recovery sleep (Bjorness et al., 2020). The evidence presented here suggests this wake/sleep homeostasis also reflects a waking-induced increase in functional AMPA/NMDA ratio of glutamate synapses of the frontal cortex together with loss of silent glutamatergic synapses. The latter suggests a reduced availability of glutamate synapses for conversion from silent to active, reflecting saturation of LTP. Accordingly, it is reasonable to summarize the implications of sleep-related glutamate synaptic phenotype oscillation as: (1) prolonged waking induces a generalized synaptic strengthening coupled with a negative bias against potentiating plasticity as available slots for AMPARs saturate, and (2) a functional recovery by sleep (Figure 4C).

There is evidence that learning of a motor task or experiencing of forced altered motor activity can result in localized increases in NREM (slow wave sleep)-slow wave activity (Huber et al., 2004; Huber et al., 2006) in the motor cortex. Since SWS-SWA is considered a marker for sleep homeostasis, the altered motor activity-induced increase of SWS-SWA was considered evidence for sleep-related function. Our earlier work has clearly shown that the treadmill method of SD increases frontal cortical SWS-SWA rebound, indicating a sleep-homeostatic process (Bjorness et al., 2016; Bjorness et al., 2020). Furthermore, we have also shown that this means of experimental SD causes similar glutamate synaptic changes as those observed using other means of SD like gentle handling (Liu et al., 2010).

Learning of motor tasks that can be robustly and quantitatively monitored, like bird song in juvenile zebra finches (Derégnaucourt et al., 2005; Kollmorgen et al., 2020), may reflect the sleep/wake oscillation of bias for LTP and synaptic strengthening. At the start of the active phase, facilitated motor learning improves song performance to a level that appears to saturate by the end of the day. After a night’s sleep, performance is degraded but shows enhanced learning, facilitated by decreased AMPA/NMDA ratio and increased availability of synapses that can be readily potentiated. Since there are inevitable, day-to-day, unrelated variances in the environment that should not be learned, mitigation of over-fitting a learned task may require an oscillatory, stairstep-like, learning process. In rats, performance during learning of a novel motor task can display these similar characteristics, especially at the start of learning a novel task when exploration of a multi-dimensional learning space is at a premium (Kim et al., 2023), consistent with an underlying sleep/wake oscillation of glutamate synaptic phenotype.

Our unbiased examination of the ontology of ExIT DEGs indicates a response to sleep loss to modify ExIT glutamate synapses involving SSC DEGs. Our electrophysiological observations, now show more particularly, that the gene-expression modifications of ExIT neurons are coupled to SD-responses of, (1) increased AMPA/NMDA ratio; (2) decreased functional silent synapses and; (3) the previously described, increase in synaptic strength and functional number (Figures 1 and 4).

The ontology of the ExIT SD-response transcriptome is also notable for enrichment by autism-related risk genes, many of which overlap with our curated category of SSCs. A similar overlap has been noted for social affiliative behaviors, synaptic adhesion molecules, other synaptic shaping molecules with autism/autism spectrum disorder (ASD) risk genes in the mammalian CNS (Taylor et al., 2020). The association of sleep disruption with autism is generally appreciated (Mazurek et al., 2019) but the focus was on loss of time spent asleep or disrupted sleep, rather than loss of sleep function. The overlapped SSC and ASD DEGs provide a molecular association between sleep’s functional role in the motor cortex and ASD risk that captures ASD’s association with motor deficits observed in patients (Chukoskie et al., 2013) and in mouse autism models (Cording and Bateup, 2023).

The molecular mechanisms responsible for sleep/wake DEGs observed in ExIT neurons include two critical transcription factors, MEF2c (Bjorness et al., 2020) and HD4/5 (Kim et al., 2022; Zhou et al., 2022). All neuronal and non-neuronal frontal cortical transcriptomic changes are abolished by loss of function of Mef2c. The abolishment of the sleep transcriptome remained even after the restriction of the Mef2c knockout to CamKII-expressing glutamatergic neurons of the forebrain (Bjorness et al., 2020). Importantly, MEF2c was necessary for both the SD-transcriptomic response and for the SD-induced increase of glutamatergic synaptic strength and functional synaptic number as well as for ensuring sleep-mediated recovery (Bjorness et al., 2020). Thus, MEF2c has an essential role in facilitating the expression of sleep genes needed for functional recovery from loss of sleep.

The activity of either of these factors to alter the sleep transcriptome, is controlled by their phosphorylation state. When sleep need is high, MEF2c is de-phosphorylated (Bjorness et al., 2020) (from pMEF2c to MEF2c) and Salt Induced Kinases (SIKs) phosphorylate HD4 and HD5 (pHD4/5), sequestering them to the cytoplasm (Kim et al., 2022; Zhou et al., 2022). Both the phosphorylation of HD4/5 s (Miska et al., 1999) and de-phosphorylation of MEF2c (Zhu and Gulick, 2004), de-represses MEF2c transcriptional activity. The de-repression of MEF2c and its dephosphorylation are essential for SD-induced differential expression (Bjorness et al., 2020). The target genes of MEF2c activation can be inferred from the cortical, differential transcriptome induced by MEF2c loss of function (Harrington et al., 2016). These same genes overlap with the differentially expressed genes observed in response to overexpression of constitutively nuclear HD4 (HD4^cn). This is predictable as HD4^cn will constitutively bind and repress MEF2c in the nucleus, thus mimicking a MEF2c loss of function. Furthermore, in association with low sleep need, nuclear, and thus transcriptionally active (as a repressor), HD4, is more abundant in a de-phosphorylated state, relative to transcriptionally inactive, phosphorylated cytoplasmic pHD4 (Kim et al., 2022; Zhou et al., 2022). NMDAR activation negatively controls HD4 transcriptional activity and nuclear localization (Sando et al., 2012). Accordingly, as NMDAR activity accumulates during the active period, an expected accumulation of pHD4 in the cytoplasm occurs to derepress MEF2c activity. Thus, HD4 may act to repress MEF2c transcriptional facilitation of sleep genes, during low sleep need and derepress MEF2c as sleep need builds during the active period. We observed DEGs from both MEF2c loss of function and HD4cn also sleep DEGs consistent with their interaction driven by sleep need (Figure 4C).

Both the role of MEF2c to mediate sleep gene expression and nuclear HD4 to repress MEF2c activity, lead to an apparent paradox. When HD4 repression of MEF2c is chronically reduced or lost, sleep need, as indicated by slow wave activity during slow wave sleep (SWS-SWA), is increased (Kim et al., 2022). If MEF2c promotes sleep gene expression, then sleep needs should be reduced not increased by its de-repression. Conversely, when constitutively, nuclear HD4^cn is overexpressed, to chronically repress MEF2c, sleep need, as indicated by SWS-SWA, is unexpectedly, reduced (Zhou et al., 2022). But what is actually being indicated by SWS-SWA?

SWS-SWA has long been employed as a marker for sleep need or intensity, primarily because of its strong correlation with previous time spent awake (Franken et al., 2001; Borbély et al., 2016). Despite this marker’s long history, the mechanisms responsible for its correlation to sleep needs are not well understood.

SWS-SWA correlates with either MEF2c and/or to ADORA1 activation. Either of these activations correlates with a reduction of glutamate synaptic strength (illustrated in Figure 4D). Ado activation of Ado A1 receptors (ADORA1) inhibits AMPAR synaptic activity (Brambilla et al., 2005; Greene et al., 2017). Adk, which encodes adenosine kinase in glial cells is the high-affinity metabolizing enzyme of Ado. Glial knockout of Adk increases extracellular Ado and SWA in both wake and sleep (Bjorness et al., 2016). Rebound SWS-SWA in response to experimental SD increases Ado (Porkka-Heiskanen et al., 1997) and the rebound is blocked by a CamK2a:Cre-driven conditional knockout of Adora1 (Bjorness et al., 2016). Thus Ado tone, whether increased by prolonged waking activity or by glial knockdown of Adk, through its activation of ADORA1, inhibits glutamate synaptic activity, including in arousal centers, to promote SWS-SWA (Greene et al., 2017; Figure 4D).

Sleep, itself, induces a down-regulation of glutamate synaptic strength, synaptic numbers, and AMPA/NMDA ratio. Chronic facilitation of MEF2c-dependent sleep genes due to de-repression of MEF2c by Hdac4/5’s loss of function should induce a chronic down-regulation of glutamatergic synaptic strength and number (Bjorness et al., 2020; Kim et al., 2022; Zhou et al., 2022). The sleep-gene-induced, chronic glutamate synaptic down-regulation can mimic the effect of a tonic increase of extracellular Ado to increase SWA as occurred following glial loss of function of Adk (Bjorness et al., 2016). Furthermore, synaptic glutamate activity in cholinergic arousal centers (Rainnie et al., 1994; Brambilla et al., 2005) can be reduced, decreasing cholinergic-mediated thalamo-cortical activation to additionally increase SWS-SWA.

Conversely, repression of MEF2c by de-phosphorylated HD4/5 (or by expression of phospho-dead HD4cn) reduces SWS-SWA (Zhou et al., 2022). In this case, glutamate synaptic activity can be chronically increased by the direct effect of loss of sleep function to down-scale glutamate synaptic strength (Bjorness et al., 2020). This can mimic the effect of a loss of Adora1 function that reduces SWS-SWA (Bjorness et al., 2009). It also predicts HD4cn’s effect to alter sleep gene expression in response to SD, consistent with the enrichment of SD genes by both HD4cn and Mef2c-cKO DEGs, that we observed (Figure 4A).

Generally, facilitation or depression of SWA correlates with up or down-scaling effects on cortical glutamate neurotransmission, respectively, even in the absence of a direct effect on sleep need (Figure 4D). Many reagents that reduce the excitability of glutamate pyramidal cells by various mechanisms, including anesthetics like isoflurane, barbiturates or benzodiazepines in addition to those activating ADORA1, increase SWA. Those agents that increase excitability of cortical glutamate pyramidal neurons, correlate with decreased SWA. Accordingly, SWA can be a marker for glutamate synaptic strength and number, modulated under normal conditions, by sleep function.

In summary, at the end of a long episode of waking, when sleep needs is high, glutamate synapses of ExIT cells in the frontal cortex are electro-physiologically strengthened, increased in functional number, and show increased AMPA/NMDA receptor ratio, together with decreased availability of silent synapses. The silent synapse conversion by LTP to active synapses is thus limited, creating a bias for increased strength at the expense of potentiating plasticity. In ExIT neuronal nuclei, the transcription factor, MEF2c is de-repressed by de-phosphorylation and sequestration of its co-repressors, the classII HDACs, pHDAC4/5, to the cytoplasm. This facilitates the transcription of sleep genes, including those encoding SSCs controlling glutamatergic synaptic phenotype and ASD risk genes. Recovery sleep recovers the functional phenotype. These observations suggest a daily oscillation from lowered glutamatergic synaptic strength and increased bias for potentiating plasticity at the start of the active phase when sleep need is low, to increased synaptic strength and saturated plasticity at the end of the active phase, when sleep need is high. In conclusion, we have provided electrophysiological evidence for a wake/sleep oscillation of glutamate synapse phenotype that can mediate a glutamatergic strength/meta-plasticity oscillating bias. We also show that this wake/sleep oscillation is likely to occur in glutamate synapses of ExIT pyramidal neurons and be mediated by a select set of synaptic shaping component genes, a significant number of which are also autism risk genes, whose expression is controlled by MEF2c and HD4 transcription factors. Finally, this study implicates a framework within which optimal cortical-dependent motor training can occur in a recursive incremental manner, facilitated by wake/sleep glutamate synapse phenotypical oscillation.

Raw and processed data are available at National Center for Biotechnology Information GEO under the accession number GSE256140 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE256140. All analysis scripts are available at https://github.com/konopkalab/sleep_need_seq (copy archived at Konopka Lab, 2024).

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Author details

1. Kaspar E Vogt

   International Institute of Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan

   Contribution

   Funding acquisition, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

2. Ashwinikumar Kulkarni

   Department of Neuroscience, Peter O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Formal analysis, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0951-2427

3. Richa Pandi

   Department of Psychiatry, Peter O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Mantre Dehnad

   Department of Psychiatry, Peter O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

5. Genevieve Konopka

   Department of Neuroscience, Peter O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Writing – review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-3363-7302

6. Robert W Greene

   1. International Institute of Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
   2. Department of Neuroscience, Peter O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States
   3. Department of Psychiatry, Peter O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   robertw.greene@utsouthwestern.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1355-9797

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