Sleep need driven oscillation of glutamate synaptic phenotype

Reviewer #1 (Public review):

Summary:

This manuscript by Vogt et al examines how the synaptic composition of AMPA and NMDA receptors changes over sleep and wake states. The authors perform whole-cell patch clamp recordings to quantify changes in silent synapse number across conditions of spontaneous sleep, sleep deprivation, and recovery sleep after deprivation. They also perform single nucleus RNAseq to identify transcriptional changes related to AMPA/NMDA receptor composition following spontaneous sleep and sleep deprivation. The findings of this study are consistent with a decrease in silent synapse number during wakefulness and an increase during sleep. However, these changes cannot be conclusively linked to sleep/wake states. Measurements were performed in motor cortex, and sleep deprivation was achieved by forced locomotion, raising the possibility that recent patterns of neuronal activity, rather than sleep/wake states, are responsible for the observed results.

Strengths:

This study examines an important question. Glutamatergic synaptic transmission has been a focus of studies in the sleep field, but AMPA receptor function has been the primary target of these studies. Silent synapses, which contain NMDA receptors but lack AMPA receptors, have important functional consequences for the brain. Exploring the role of sleep in regulating silent synapse number is important to understanding the role of sleep in brain function. The electrophysiological approach of measuring the failure rate ratio, supported by AMPA/NMDA ratio measurements, is a rigorous tool to evaluate silent synapse number.

The authors also perform snRNAseq to identify genes differentially expressed in the spontaneous sleep and sleep deprivation groups. This analysis reveals an intriguing pattern of upregulated genes controlled by HDAC4 and Mef2c, along with synaptic shaping component genes and genes associated with autism spectrum disorder, across cell types in the sleep deprivation group. This unbiased approach identifies candidate genes for follow-up studies. The finding that ASD-risk genes are differentially expressed during SD also raises the intriguing possibility that normal sleep function is disrupted in ASD.

Weaknesses:

A major consideration to the interpretation of this study is the use of forced locomotion for sleep deprivation. Measurements are made from motor cortex, and therefore the effects observed could be due to differences in motor activity patterns across groups, rather than lack of sleep per se. Considering that other groups have failed to find a difference in AMPA/NMDA ratio in mice with different spontaneous sleep/wake histories (Bridi et al., Neuron 2020), confirmation of these findings in a different brain region would greatly strengthen the study.

The electrophysiological measurements and statistical analyses raise several questions. Input resistance (cutoffs and actual values) are not provided, making it difficult to assess recording quality. Parametric one-way ANOVAs were used, although the data do not appear to be normally distributed. In addition, for the AMPA/NMDA and FRR measurements (Figures 1E, F), the SD group (rather than the control sleep group) was used as the control group for post-hoc comparisons, but it is unclear why. While the data appear in line with the authors' conclusions, the number of mice (3/group) and cells recorded is low, and adding more would better account for inter-animal variability and increase the robustness of the findings.

The snRNAseq data are intriguing. However, several genes relevant to the AMPA/NMDA ratio are mentioned, but the encoded proteins would be expected to have variable effects on AMPA/NMDA receptor trafficking and function, making the model presented in Figure 4C oversimplified. A more thorough discussion of the candidate genes and pathways that are upregulated during sleep deprivation, the spatiotemporal/posttranslational control of protein expression, and their effects on AMPA/NMDA trafficking vs function is warranted.

Reviewer #2 (Public review):

Summary:

Here Vogt et al., provide new insights into the need for sleep and the molecular and physiological response to sleep loss. The authors expand on their previously published work (Bjorness et al., 2020) and draw from recent advances in the field to propose a neuron-centric molecular model for the accumulation and resolution of sleep need and basis of restorative sleep function. While speculative, the proposed model successfully links important observations in the field and provides a framework to stimulate further research and advances on the molecular basis of sleep function. In my review, I highlight the important advances of this current work, the clear merits of the proposed model, and indicate areas of the model that can serve to stimulate further investigation.

Strengths:

Reviewer comment on new data in Vogt et al., 2024
Using classic slice electrophysiology, the authors conclude that wakefulness (sleep deprivation (SD)) drives a potentiation of excitatory glutamate synapses, mediated in large part by "un-silencing" of NMDAR-active synapses to AMPAR-active synapses. Using a modern single nuclear RNAseq approach the authors conclude that SD drives changes in gene expression primarily occurring in glutamatergic neurons. The two experiments combined highlight the accumulation and resolution of sleep need centered on the strength of excitatory synapses onto excitatory neurons. This view is entirely consistent with a large body of extant and emerging literature and provides important direction for future research.

Consistent with prior work, wakefulness/SD drives an LTP-type potentiation of excitatory synaptic strength on principle cortical neurons. It has been proposed that LTP associated with wake, leads to the accumulation of sleep need by increasing neuronal excitability, and by the "saturation" of LTP capacity. This saturation subsequently impairs the capacity for further ongoing learning. This new data provides a satisfying mechanism of this saturation phenomenon by introducing the concept of silent synapses. The new data show that in mice well rested, a substantial number of synapses are "silent", containing an NMDAR component but not AMPARs. Silent synapses provide a type of reservoir for learning in that activity can drive the un-silencing, increasing the number of functional synapses. SD depletes this reservoir of silent synapses to essentially zero, explaining how SD can exhaust learning capacity. Recovery sleep led to restoration of silent synapses, explaining how recovery sleep can renew learning capacity. In their prior work (Bjorness et al., 2020) this group showed that SD drives an increase in mEPSC frequency onto these same cortical neurons, but without a clear change in pre-synaptic release probability, implying a change in the number of functional synapses. This prediction is now born out in this new dataset.

The new snRNAseq dataset indicates the sleep need is primarily seen (at the transcriptional level) in excitatory neurons, consistent with a number of other studies. First, this conclusion is corroborated by an independent, contemporary snRNAseq analysis recently available as a pre-print (Ford et al., 2023 BioRxiv https://doi.org/10.1101/2023.11.28.569011). A recently published analysis on the effects of SD in drosophila imaged synapses in every brain region in a cell-type dependent manner (Weiss et al., PNAS 2024), concluding that SD drives brain wide increases in synaptic strength almost exclusively in excitatory neurons. Further, Kim et al., Nature 2022, heavily cited in this work, show that the newly described SIK3-HDAC4/5 pathway promotes sleep depth via excitatory neurons and not inhibitory neurons.

The new experiments provided in Fig1-3 are expertly conducted and presented. This reviewer has no comments of concern regarding the execution and conclusions of these experiments.

Reviewer comment on model in Vogt et al., 2024
To the view of this reviewer the new model proposed by Vogt et al., is an important contribution. The model is not definitively supported by new data, and in this regard should be viewed as a perspective, providing mechanistic links between recent molecular advances, while still leaving areas that need to be addressed in future work. New snRNAseq analysis indicates SD drives expression of synaptic shaping components (SSCs) consistent with the excitatory synapse as a major target for the restorative basis of sleep function. SD induced gene expression is also enriched for autism spectrum disorder (ASD) risk genes. As pointed out by the authors, sleep problems are commonly reported in ASD, but the emphasis has been on sleep amount. This new analysis highlights the need to understand the impact on sleep's functional output (synapses) to fully understand the role of sleep problems in ASD.

Importantly, SD induced gene expression in excitatory neurons overlap with genes regulated by the transcription factor MEF2C and HDAC4/5 (Fig. 4). In their prior work, the authors show loss of MEF2C in excitatory neurons abolished the SD transcriptional response and the functional recovery of synapses from SD by recovery sleep. Recent advances identified HDAC4/5 as major regulators of sleep depth and duration (in excitatory neurons) downstream of the recently identified sleep promoting kinase SIK3. In Zhou et al., and Kim et al., Nature 2022, both groups propose a model whereby "sleep-need" signals from the synapse activate SIK3, which phosphorylates HDAC4/5, driving cytoplasmic targeting, allowing for the de-repression and transcriptional activation of "sleep genes". Prior work shows that HDAC4/5 are repressors of MEF2C. Therefore, the "sleep genes" derepressed by HDAC4/5 may be the same genes activated in response to SD by MEF2C. The new model thereby extends the signaling of sleep need at synapses (through SIK3-HDAC4/5) to the functional output of synaptic recovery by expression of synaptic/sleep genes by MEF2C. The model thereby links aspects of expression of sleep need with the resolution of sleep need by mediating sleep function: synapse renormalization.

Weaknesses:

Areas for further investigation.
In the discussion section Vogt et al., explore the links between excitatory synapse strength, arguably the major target of "sleep function", and NREM slow-wave activity (SWA), the most established marker of sleep need. SIK3-HDAC4/5 have major effects on the "depth" of sleep by regulating NREM-SWA. The effects of MEF2C loss of function on NREM SWA activity are less obvious, but clearly impact the recovery of glutamatergic synapses from SD. The authors point out how adenosine signaling is well established as a mediator of SWA, but the links with adenosine and glutamatergic strength are far from clear. The mechanistic links between SIK3/HDAC4/5, adenosine signaling, and MEF2C, are far from understood. Therefore, the molecular/mechanistic links between a synaptic basis of sleep need and resolution with NREM-SWA activity require further investigation.

Additional work is also needed to understand the mechanistic links between SIK3-HDAC4/5 signaling and MEF2C activity. The authors point out that constitutively nuclear (cn) HDAC4/5 (acting as a repressor) will mimic MEF2C loss of function. This is reasonable, however, there are notable differences in the reported phenotypes of each. Notably, cnHDAC4/5 suppresses NREM amount and NREM SWA but had no effect on the NREM-SWA increase following SD (Zhou et al., Nature 2022). Loss of MEF2C in CaMKII neurons had no effect on NREM amount and suppressed the increase in NREM-SWA following SD (Bjorness et al., 2020). These instances indicate that cnHDAC4/5 and loss of MEF2C do not exactly match suggesting additional factors are relevant in these phenotypes. Likely HDAC4/5 have functionally important interactions with other transcription factors, and likewise for MEF2C, suggesting areas for future analysis.

One emerging theme may be that the SIK3-HDAC4/5 axis are major regulators of the sleep state, perhaps stabilizing the NREM state once the transition from wakefulness occurs. MEF2C is less involved in regulating sleep per se, and more involved in executing sleep function, by promoting restorative synaptic modifications to resolve sleep need.

Finally, advances in the roles of the respective SIK3-HDAC4/5 and MEF2C pathways point towards transcription of "sleep genes", as clearly indicated in the model of Fig.4. Clearly more work is needed to understand how the expression of such genes ultimately lead to resolution of sleep need by functional changes at synapses. What are these sleep genes and how do they mechanistically resolve sleep need? Thus, the current work provides a mechanistic framework to stimulate further advances in understanding the molecular basis for sleep need and the restorative basis of sleep function.

Author response:

The following is the authors’ response to the original reviews.

We greatly appreciate reviewer 2 comments with both insightful and clearly evaluated assessments of this study that include, much appreciated reframing and evaluation of the study’s advances in the sleep field. It is a constructive review and provides considerable added value to this study in better defining the biological significance of the findings, including both advances and limitations.

Reviewer 2 nicely summarized the work as “…highlight(ing) the accumulation and resolution of sleep need centered on the strength of excitatory synapses onto excitatory neurons.”. The reviewer succinctly placed one of the main electrophysiological findings in context of one of the sleep field’s most prevalent views, “that LTP associated with wake, leads to the accumulation of sleep need by increasing neuronal excitability, and by the "saturation" of LTP capacity.” It has been speculated that “This saturation subsequently impairs the capacity for further ongoing learning. This new data provides a satisfying mechanism of this saturation phenomenon (and its restoration by recovery sleep) by introducing the concept of silent synapses.” We want to emphasize that sleep need and its resolution involves more than just homeostasis of excitatory synaptic strength but may also be extended to include homeostasis of excitatory synaptic potential to undergo LTP (a homeostasis of meta-plasticity), with implications for learning and memory.

Reviewer 2 also identified another advance made by this study, summarized as, “The new snRNAseq dataset indicates the sleep need is primarily seen (at the transcriptional level) in excitatory neurons, consistent with a number of other studies.” References for these studies are nicely provided by the reviewer. Our analysis of this data extends the evidence for transcriptional sleep-need-driven changes, observed by us and others in excitatory neurons to more particularly involve the excitatory neurons in layers 2-5, targeting intra-telencephalic neurons.

Reviewer 2, importantly noted, “New snRNAseq analysis indicates that SD drives the expression of synaptic shaping components (SSCs) consistent with the excitatory synapse as a major target for the restorative basis of sleep function”, and that “SD-induced gene expression is also enriched for autism spectrum disorder (ASD) risk genes”. These comments are well appreciated as they emphasize that beyond identification of the major target cell type of sleep function, the major sleep-target, gene-ontological characteristics are starting to be addressed.

Reviewer 2 commented on the molecular sleep model, making a key observation that “SDinduced gene expression in excitatory neurons overlaps with genes regulated by the transcription factor MEF2C and HDAC4/5 (Figure 4),” and accurately discusses the significance with respect to the proposed model.

We are in complete agreement with the observation that the molecular sleep model presented is not “definitively supported by the new data and in this regard should be viewed as a perspective…”. One of the more glaring gaps in supporting evidence is the absence of understanding of the role of HDAC4/5 (part of the SIK3-HDAC4/5 pathway) in sleep need modulation of excitatory synapses. Resolution of this issue might be approached by assessment of the synaptic effects of constitutively nuclear HDAC4/5. The current study provides a first step in the assessment by showing a correlation between HDAC4/5 and MEF2c target genes and a subset of differentially expressed synaptic shaping component (SSC) genes that modulate excitatory synapse strength and phenotype. However, the functional studies have yet to be completed. Complimentary studies on SD-induced SSC-DEGs (identified in this study) are also needed for follow-up characterization of their sleep need induced functional impact (both strength and meta-plasticity modulation) on the most relevant excitatory synapses (as identified in the current study).

We agree with both reviewers 1 and 2 that, “Additional work is also needed to understand the mechanistic links between SIK3-HDAC4/5 signaling and MEF2C activity”. Reviewer 2 clarifies the key unresolved issue as, “cnHDAC4/5 suppresses NREM amount and NREM SWA but had no effect on the NREM-SWA increase following SD (Zhou et al., Nature 2022). Loss of MEF2C in CaMKII neurons had no effect on NREM amount and suppressed the increase in NREM-SWA following SD (Bjorness et al., 2020)”. One may conclude with reviewer 2, “These instances indicate that cnHDAC4/5 and loss of MEF2C do not exactly match suggesting additional factors are relevant in these phenotypes.”

An understanding of the mechanism(s) responsible for the relationship between sleep need and SWA are critical to the evaluation of sleep need’s correlation with sleep DEGs and synaptic transmission, including “additional factors” as suggested by reviewer 2. SWA might result from a decrease of cortical glutamatergic neurotransmission below some threshold, which might occur in response to prolonged waking (possibly in response to waking activity-induced local increases of adenosine?), rather than being a cause of, or, being intimately involved in resolving sleep need.

An increase of SWA in association with SD can result directly from an acute SD-induced increase in local adenosine concentration. This will elicit an ADORA1-mediated down-regulation of glutamate excitatory neurotransmission in the cortex (Bjorness et al., 2016) and in cholinergic arousal centers (Rainnie et al., 1994; Porkka-Heiskanen et al., 1997; Portas et al., 1997; Li et al., 2023). When MEF2c is derepressed by chronic loss of HDAC4 function, SWA is facilitated (Kim et al., 2022). It is plausible that loss of HDAC4 function contributes to the increased SWA by downscaling glutamate excitatory transmission (independent of sleep need). This is expected to result from derepressed, MEF2c mediated sleep-gene expression.

Similarly, over-expression of constitutively active HDAC4 (cnHD4) can contribute to chronic upscaling of cortical glutamate synaptic strength to depress SWA (again, independent of sleep need). Thus, facilitation or depression of SWA correlates with up or down scaling effects on cortical glutamate neurotransmission, respectively, even in the absence of a direct effects 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. Finally, it is important to acknowledge that direct evidence for this proposed link of SWA to cortical glutamate transmission remains in need of further investigation. Thus, SWA may reflect generalized cortical glutamate synaptic activity whether modulated by sleep function or by other agents.

Still, other factors that can have a role mediating some of the mis-match between cnHD4/5 DEGs and Mef2c-cKO DEGs, include the broader over-expression of AAV-cnHD4 compared to CamKII- driven Cre KO of Mef2c. The cnHD4 overexpression can increase arousal center activity in the hypothalamus and other arousal areas to interfere with SWA, but not to the exclusion of SD-DEG repression resulting from a repression of MEF2c-mediated sleep gene expression.

The critique by reviewer 1 raises a number of important technical issues with this study. A key, potentially critical issue raised by reviewer 1, is that of our method of experimental sleep deprivation (ESD). The reviewer suggests that “…neuronal activity/induction of plasticity”, peculiar to the ESD methodology employed in this study, “…rather than sleep/wake states are responsible for the observed results…”.

In this study, a slow-moving treadmill (SMTM; 0.1km/hour, as stated in the methods), requiring locomotion to avoid bumping into the backwall of a false bottomed plexiglass cage was used to induce ESD. A mouse, in its home cage, typically moves much faster than 0.1km/hour and the mouse is able to eat and drink freely while in the cage (see file: video 1). Furthermore, our observations using a beam-break cage, indicate that mice spontaneously travel for comparable to longer distances over 6 hours than the treadmill moves (during the ESD of 6 hours). Finally, our EEG recordings of mice on the active treadmill show 100% waking while it is on (Bjorness et al., 2009), whereas prevention of NREM sleep (including transition time) using the “gentle handling” (GH) technique occurs depending on the diligence of the experimenter.

The accommodation (one week prior to ESD) included exposure to the treadmill-on for 30minutes ~ZT=2 & ZT= 14 hours (now spelled out in the “Materials & Methods” section). Thus, the likelihood of motor learning seems vanishingly small.

As with all ESD methods, there must be some associated increase in sensory and motor neuronal activity to drive arousal and prevent transition to sleep. For example, the more widely employed GH method of ESD involves sensory stimulation (tactile and or auditory) of sufficient intensity to induce postural change from that associated with sleep to that associated with wake (often involving some locomotion). Like the SMTM, both sensory and motor systems are likely to be engaged. Unlike the SMTM method, the stimulation used in GH is variably-intermittent from mouse to mouse and from experimenter to experimenter as it is applied only when the experimenter judges the mouse to be falling asleep. . It can even be argued that the varied and unpredictable ways in which these interactions happen cause plastic changes with a higher likelihood than the constant slow motion of a treadmill – the mice know how to walk, after all. In other protocols, novel objects are introduced to the animals – those will certainly trigger plastic processes –something that is avoided using a slow-running treadmill to which the mouse has been accommodated, for sleep deprivation.

The changes induced by SMTM technique are reproducible and induce arousal by somatic stimulation of sufficient intensity to induce natural motor activity as with GH. All ESD methods induce motor activity and it is reasonable to speculate that induced, motor activity is essential for effective ESD for the prolonged durations (>4 hours in mice) that elicit high sleep need. Electrophysiological assessment of SD-evoked increases in mEPSC amplitude and frequency using GH-ESD (Liu et al., 2010) are similar in all respects to our observations of the response to SMTMESD (Bjorness et al., 2020). Further studies might directly address a comparison of SMTM-ESD to GH-ESD as suggested by reviewer 1 but are regrettably outside the scope and resources of our study.

The model presented in Figure 4C is consistent with the experimental findings with respect to the observed electrophysiological changes (including loss of silent synapses and increased AMPA/NMDA ratio after ESD of 6 hours) and altered gene expression that includes enrichment of SSC genes, many of which (7 candidates are listed) can affect both AMPA/NMDA ratio and silent synapses. No claim of mechanism linking the changed expression to altered AMPAR or NMDAR activity can be made at this point, even as to polarity of gene expression, related to electrophysiological outcome. Furthermore, some transcripts may involve receptor trafficking while others more directly affect activated receptor function. To help illustrate the complexity of interpreting gene up-regulation, consider the following hypothetical scenario. If a gene like upregulated Grin3a acts rapidly, it may facilitate reduction of NMDAR function (decreasing plasticity) during ESD, whereas upregulation of a gene like Kif17, if acting in a more delayed manner, might enhance NMDAR surface expression and activity (increasing silent synapses) in response to ESD, during recovery sleep. Relevant references, consistent with these various outcomes are supplied in the manuscript but further investigation is clearly needed, or as reviewer 2 so aptly commented, this work “…provides a framework to stimulate further research and advances on the molecular basis of sleep function”.

Several issues are raised by reviewer 1 concerning the electrophysiological methodology and statistical assessment. In regard to the former, we closely followed established protocols employed in the frontal neocortex (Myme et al., 2003). We did not include the details for series resistance monitoring. Series resistance values ranged between 8 and 15 MOhm and experiments with changes larger than 25% not used for further analyses. Thank you for bringing this oversight on our part, to our attention. This essential information, that is unfailingly gathered for all our whole cell recordings, is now added to the version of record.

The -90 mV holding potential was chosen according to precedent (Myme et al., 2003). It increases driving force and permits lower stimulus strength for the same response size – reducing the likelihood for polysynaptic responses. Experiments with multiple response peaks at -90 mV were not included in the analysis. The -90 mV holding potential also increases NMDA receptor Mg++ block resulting in a minimally contaminated AMPA response. This information is now added to our submitted version of record.

The statistical assessments shown in Table 1 refer to two sets of data measured from 3X2=6 different cohorts for each sleep condition (CS, SD, RS): 1) AMPA & NMDA EPSCs and 2) AMPA/NMDA FR ratios (FRR; now bolded in row 1, second tab, Table S1). As stated in the results section, “A two-way ANOVA analysis showed a significant interaction between AMPA matched to NMDA EPSC response for each neuron, and sleep condition (F (2, 21) = 7.268, p<0.004; Figure 1 A, C, E). When considered independently, neither the effect of sleep condition nor of EPSC subtype reached significance at p<0.05 (Figure 1 C)”.

As noted by reviewer 1, we inadvertently dropped one of the data points from the RS FR and FR ratio (FRR) statistical analysis (raw data in the third tab of Table S1, statistical data in fourth and fifth tab and illustrated in figure 1 F). Thanks to this appreciated, rigorous review, we can correct the oversight (using raw data unchanged in Table S1, third tab). The Table S1 and figure 1 F are now corrected for the version of record. For better clarity, we now use two tabs, the fourth and fifth tabs, respectively of Table S1, for separate stat analyses of FR and FRR data.

The significance of the AMPA/NMDA FRR across sleep conditions was assessed with the KruskalWallis test, a non-parametric method. The two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli (BKY) was used to control for the FDR across multiple sleep conditions, in the non-parametric Kruskal-Wallis test but it is usually less powerful than tests presuming normal distributions like the one-way ANOVA and Holm-Sidak’s test. We have now added re-analyzed FRR across CS, SD and RS conditions using a normal one-way ANOVA (Table S1, tab5). The results now read, “The difference between sleep conditions and FRR is significant (F (2, 19) = 11.3, Table S1, tab5). Multiple comparisons (Holm-Sidak, Table S1, tab5) indicate the near absence of silent synapses was reversed by either CS or RS (SD/CS; p<0.0011 and SD/RS: p<0.0006; Table S1, tab 5; Figure 1 F).”. These analyses compare well to the non-parametric assessment using the KruskalWallis test (significant at p= 0.0006) with BYK correction for multiple comparison analysis to give for CS-SD, p<= 0.0262 and for RS-SD, p<= 0.0006 (statistics also shown in Table S1, tab5). [Also shown in tab5 is the “standard approach of correcting for family wise error rate”, namely, Dunn’s test. It is more conservative but less powerful than the BYK correction- in general the tradeoff of greater power/ less conservative is better tolerated when many comparisons are made, however, it can be argued that in the present analysis type 2 errors are also potentially misleading and thus not well tolerated.] The modifications of our statistical analyses, inspired by reviewer 1, did not affect the interpretation of the data nor the conclusions.

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