Author response: An essential role for MEF2C in the cortical response to loss of sleep in mice (2020)

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Neuronal activity and gene expression in response to the loss of sleep can provide a window into the enigma of sleep function. Sleep loss is associated with brain differential gene expression, an increase in pyramidal cell mEPSC frequency and amplitude, and a characteristic rebound and resolution of slow wave sleep-slow wave activity (SWS-SWA). However, the molecular mechanism(s) mediating the sleep-loss response are not well understood. We show that sleep-loss regulates MEF2C phosphorylation, a key mechanism regulating MEF2C transcriptional activity, and that MEF2C function in postnatal excitatory forebrain neurons is required for the biological events in response to sleep loss in C57BL/6J mice. These include altered gene expression, the increase and recovery of synaptic strength, and the rebound and resolution of SWS-SWA, which implicate MEF2C as an essential regulator of sleep function. Introduction Sleep abnormalities are commonly observed in numerous neurological disorders, including autism spectrum disorder, major depressive disorder, bipolar disorder, post-traumatic stress disorder, neurodegenerative disorders and many others, but our understanding of sleep need and its regulation and resolution is poorly understood. Following an extended period of waking, or sleep deprivation (SD), the mammalian cortex shows an altered pattern of EEG activity characterized by rebound slow wave power (delta power in the frequency range of 0.5–4.5 Hz) during the ensuing slow wave sleep (SWS; also referred to as NREM) periods. The amplitude of the rebound directly correlates with the previous waking or SD duration (Borbély, 1982; Franken et al., 2001) implicating rebound SWS, slow wave activity (SWS-SWA) as a biomarker of sleep need. During SWS episodes, the rebound SWS-SWA increase resolves, consistent with resolution of sleep need (Bjorness et al., 2016). The buildup and resolution of sleep need is correlated with overall cortical, excitatory synaptic strength. Based on sleep-related modulation of excitatory synapse biochemistry, morphology and electrophysiological activity, a scaling-up during waking and a scaling-down during sleep have been observed (Bushey et al., 2010; de Vivo et al., 2017; Diering et al., 2017; Liu et al., 2010), although this may not necessarily be reflected by the neuronal firing rates (Hengen et al., 2016; Watson et al., 2016). Changes in synaptic transcript (Noya et al., 2019) and protein (Diering et al., 2017; Noya et al., 2019) expression, required for synaptic down-scaling, are observed during the sleep phase in association with an average decrease in synapse size (de Vivo et al., 2017; Diering et al., 2017). Characterization of the cell-signaling biochemical and molecular mechanisms responsible for these sleep-related changes in protein expression is needed. MEF2 transcription factors are critical regulators of activity-dependent synapse elimination (reviewed in Assali et al., 2019). MEF2C plays an essential role in development to control cortical synaptic connectivity, function and plasticity (Harrington et al., 2016; Li et al., 2008; Rajkovich et al., 2017). MEF2C’s activation results in excitatory synapse down-regulation with potential relevance for sleep, in that prolonged waking activity leads to excitatory synaptic scaling-down during ensuing sleep, as noted above. Neuronal activity associated with waking, that may include neuronal depolarization, firing and arousal-associated neuromodulator activity, can switch MEF2C from a transcriptional repressor to an activator. The switch depends on dephosphorylation of a conserved Serine (S396) (Kang et al., 2006; Lyons et al., 2012; Zhu and Gulick, 2004), and both the repressor and activator functions of MEF2C appear critical for its regulation of synaptic transmission (Harrington et al., 2016). However, the relationship between MEF2C and sleep-regulated transcripts, sleep-regulated excitatory synaptic strength and sleep-homeostatic EEG activity remains unexplored. In this study, we examined neurobiological changes in the mouse frontal cortex in response to sleep loss, involving transcriptomic architecture, regulation of synaptic strength in excitatory cortical neurons and SWS-SWA, correlated with sleep need. As changes in neuronal activity may be observed across sleep-wake states, we investigated the possible role of MEF2C as a key regulator of sleep-related control of gene expression, sleep-related modulation of synaptic function and sleep-need correlated SWS-SWA buildup and resolution. Results Wake-sleep mediated control of the transcriptome To evaluate the effects of sleep loss, we compared three sleep-related conditions for differentially expressed genes (DEGs) from samples of the frontal cortex (FC, the cortical region showing the highest power of SWS-SWA) of male C57BL/6J (WT) mice (Figure 1A) including: (1) control sleep (CS; mice free to sleep, ad libitum; n = 6), (2) sleep deprived for 6 hr (SD; n = 7), and (3) sleep deprived for 4 hr followed by recovery sleep for 2 hr (RS; n = 7). All samples were collected 6 hr after lights on (ZT6) to control for circadian influence and were processed for RNA-seq analysis (see Materials and methods). Figure 1 with 3 supplements see all Download asset Open asset Sleep need induced transcriptomic changes. (A) Schematic of the experimental design illustrating the protocol for frontal cortex brain tissue collection from three sleep conditions at Zeitgeber Time (ZT) = 6 hours: control sleep (CS), sleep deprived (SD) and recovery sleep (RS). Volcano plots showing differentially expressed genes across (B) CS to SD and (C) CS to RS. Significantly differentially expressed genes (DEGs) (adj. p-value<=0.05, absolute log2 fold change >= 0.3), unique to the sleep condition, are indicated as black dots whereas genes shared by SD and RS are indicated as either red (increased expression) or green (decreased expression) dots. (D) Upset plot showing the shared and unique sets of significant DEGs for CS to SD and CS to RS. (E) Dot plot showing enrichment of significant DEGs for cell-types identified using single cell RNA sequencing data (Hrvatin et al., 2018) from mouse cortex, identified Mef2c target genes (Harrington et al., 2016) and rapid primary response genes (rPRGs) (Tyssowski et al., 2018). A Fisher’s exact test was used to calculate the significance of the overlap of the sleep DEGs and the Mef2c-cKOEmx1 DEGs. The size of the dot indicates the odds ratio for enrichment and the color indicates the p-value. Dot plot showing GO analysis for shared significant DEGs with (F) increased and (G) decreased expression across SD and RS compared to CS (for a complete list of significant GO, see Supplementary file 3). The CS samples expressed, on average, ~13,000 genes with counts per million reads (CPM) ≥1.0 in the FC. Comparing the SD samples to the CS samples, we observed 6,248 DEGs with roughly half the number of genes having decreased expression (FDR <= 0.05; |log2 (fold change)|>=0.3; Figure 1B; Supplementary file 1), confirming an earlier report of massive transcriptome changes associated with SD (Diessler et al., 2018). Similarly, the RS cohort showed 3827 DEGs compared to CS, again with approximately half the number of genes showing decreased expression (Figure 1C; Supplementary file 1). In contrast, only 81 genes showed significantly altered expression comparing the SD condition to RS (Supplementary file 1), suggesting that for the majority of SD-sensitive genes, RS is more than simply a direct recovery of altered gene expression back to CS levels. Rather, more than half of the differential expression initiated during SD is continued during RS (Figure 1D), significantly extending the SD-induced change. Because SD may be considered stressful (Mongrain et al., 2010), we compared classic glucocorticoid-mediated response genes to the increased DEGs observed in response to SD (Figure 1—figure supplement 1, Supplementary file 2). No significant increase in glucocorticoid (GC) intracellular response genes was found in the upregulated SD DEGs (p-value=0.513 using Fisher’s exact test), suggesting a more SD-specific response than other GC-mediated stress responses. This bulk transcriptomic analysis includes mRNA from all cell types found in frontal cortical tissue. Many of the genes expressed in these tissues are, nonetheless, cell-type specific, allowing potential correlation between DEGs specific for a sleep condition and specific cell types. Using previously published single-cell RNA-seq from the adult mouse cortex (Hrvatin et al., 2018), we identified cell-type-specific gene expression patterns across sleep conditions (Figure 1E). Interestingly, the downregulated DEGs shared by SD and RS were enriched for genes associated with both excitatory and inhibitory neurons, whereas the upregulated DEGs shared by SD and RS were enriched for non-neuronal genes that are down in the Mef2c-cKOEmx1 mice (Harrington et al., 2016 ; Figure 1E). We also examined MEF2C target gene expression in response to SD or RS. We found that the downregulated DEGs shared by SD and RS were enriched for neuronal genes affected by Mef2c loss of function as previously observed in a conditional knockout of pan-neuronal Mef2c, Mef2c-cKOEmx1 (Harrington et al., 2016). Whereas the upregulated DEGs in response to SD and RS were enriched for Mef2c non-neuronal genes (Figure 1E). Thus, the DEGs altered by SD and RS in the cortex significantly overlap those altered by the loss of cortical MEF2C function. We next functionally characterized the sleep-loss induced transcriptional changes shared by SD and RS states by carrying out a gene ontology (GO) analysis. Overall, there are distinct GO categories for the upregulated and downregulated genes (Figure 1F,G; Supplementary file 2). The upregulated categories include mitochondrial components and signaling pathways of oxidative phosphorylation and anaplerosis (Figure 1—figure supplement 2), indicative of energy mobilization, as well as anabolic protein metabolism that includes ribosomal components together with transcriptional, translational and related processes. These results are consistent with and extend those from a previous study using microarrays (Cirelli et al., 2004) that lead to a conclusion, ‘… that sleep, far from being a quiescent state of global inactivity, may actively favor specific cellular functions’. Recently, similar findings were reported in a RNA-seq analysis from C57BL/6J whole cortex and a ‘gentle handling’ method of SD (Hor et al., 2019). Several studies provide converging evidence for a general decrease in cortical excitatory synaptic strength in association with sleep (de Vivo et al., 2017; Liu et al., 2010; Maret et al., 2011) with respect to protein expression in synaptosomes (Diering et al., 2017) and spontaneous miniature excitatory post-synaptic currents and frequency (Liu et al., 2010; Vyazovskiy et al., 2008). Consistent with this, we observed that the downregulated genes observed in SD and RS are enriched for GO categories reflecting synaptic biological processes and cellular components. These genes are also enriched for genes involved in diseases of cognition associated with abnormal synaptic function, including schizophrenia, autism, intellectual disability and neurodegenerative disorders (Figure 1G, Supplementary file 3). Some of the SD upregulated DEGs have a notable role as both transcription factors and/or first responders to neural activity and sensory stimulation. Rapid, primary response genes (rPRG) have been identified in cortical neuronal cultures as responders to both brief or 6 hr exposures to a depolarizing concentration of potassium in the presence of a translational blocker (Tyssowski et al., 2018). We find that DEGs for CS compared to SD and/or RS conditions are significantly enriched for these rPRGs (Figure 1E). Furthermore, upregulated transcription factors were identified in visual cortex that responded to a short exposure to light after being in constant darkness (Hrvatin et al., 2018). We observed that 50% of these were upregulated DEGs for CS to SD (listed in Supplementary file 1). Notably, these upregulated, transcription factors in the visual cortex overlapped with DEGs from the FC, and showed sustained, increased expression even after 6 hr of SD-induced activation. It was recently shown that the influence of circadian rhythms on gene transcript expression in synaptosomes was relatively unaffected by SD, although SD did prevent translation (Noya et al., 2019). Nevertheless, many core clock genes are known to affect sleep homeostasis and the expression of some core clock genes, in particular, Per2, are altered by SD, as previously reviewed (Franken, 2013). To control for circadian-mediated changes in gene expression, we assessed DEGs at the same circadian time, ZT6, changing only the 6 hr, immediate sleep history. In contrast to DEGs in synaptosomes, we observed in FC tissue that SD from ZT = 0 to 6 hr resulted in a number of core clock DEGs (compared to CS; Figure 1—figure supplement 3), including downregulation of Arntl, Clock and Npas2 and downregulation of a set of genes regulating circadian entrainment (MGI; GO:0009649). Taken together, these data are consistent with an SD-induced attenuation and destabilization of circadian-mediated gene expression and confirm recent observations using an RNA-seq assessment in time series together with SD (Hor et al., 2019). To further uncover the organizational architecture and prioritize the extensive SD-mediated transcriptome, we used weighted gene co-expression network analysis (WGCNA), with expressed genes from all three conditions (CS, SD, and RS) to generate a network of 59 modules. Many of the modules (53%) were enriched for either up or downregulated DEGs from CS to SD conditions (Figure 2A). Most of those enriched for upregulation from CS to SD (n = 11), were also enriched for upregulation from CS to RS (n = 10), and similarly, most modules enriched for downregulation from CS to SD (n = 18) were also enriched for downregulation from CS to RS (n = 13), showing an organization primarily driven by DEGs, that is consistent between SD and RS conditions. The GO of these shared modules (Supplementary file 4) is also consistent with those seen using only the lists of DEGs. Figure 2 with 1 supplement see all Download asset Open asset Coordinated transcriptomic responses with sleep need indicate a role for MEF2C. (A) Heatmap showing enrichment of sleep-state DEGs across modules of genes identified by weighted gene co-expression network analysis (WGCNA). First three rows show module eigengene correlation to sleep states (CS, SD and RS respectively) ordered by most positive to most negative correlation to CS sleep state. The following four rows highlight the significance of module enrichment for a specific set of DEGs colored by green for genes with decreased expression for CSxSD, CSxRS, SDxRS and MEF2C targets (Harrington et al., 2016) and the next four rows colored by red to highlight the significance of module enrichment for genes with increased expression for CSxSD, CSxRS, SDxRS and MEF2C targets (Harrington et al., 2016). The bottom four rows colored by shades of orangeyellow show enrichment of modules for cell types identified using single-cell RNA sequencing (Hrvatin et al., 2018) from mouse cortex. Each column is a module of genes as indicated by the module ID in last row. (B) Barplot for a selected module (Module ID 04) with eigengene correlation across sleep states showing a strong positive correlation to CS and negative correlation to SD and weak negative correlation to RS. (C) Network plot for selected module (Module ID 04) showing the top 250 connections between genes. Genes that are MEF2C targets (Harrington et al., 2016) are highlighted by red circles. (D) GO plot for genes within the selected module (Module ID 04). The advantage of WGCNA is that it allows for the prioritization of gene modules that may be central drivers of biological networks independent of arbitrary cutoffs of p-values and fold changes for individual genes. WGCNA identifies these modules containing genes with highly correlated expression that can be further assessed for significant expression patterns related to features such as the sleep conditions of CS, SD and RS via determination of module eigengenes. Notably, putative target genes of MEF2C, were enriched in a surprising, 15 of 59 modules generated from the three sleep conditions employed in this study. Using a Monte Carlo model to assess the likelihood of such a result gave a probability of p<0.001 from a sample of 1000 permutations of genes randomly assigned to the experimentally defined number of modules and module size. It is consistent with a widespread influence of MEF2C on the transcriptome as it varies from CS to SD to RS. Furthermore, most of the enriched modules had sleep-condition-related eigengenes indicating that sleep condition was the principal component factor driving their coordinated expression. We identified two modules (M04 and M10) whose module eigengenes were more positively correlated with CS or ad lib sleep or, conversely, more negatively correlated with SD and RS samples (Figure 2B and Figure 2—figure supplement 1), indicating that the primary component driving their pattern of coexpression is CS versus SD and RS conditions. These two modules were of interest due to significant enrichment of several major categories of genes (Figure 2A): (1) DEGs in neuronal Mef2c conditional KO cortex (Mef2c-cKOEmx1; Harrington et al., 2016), (2) excitatory and inhibitory neuronal genes, (3) genes downregulated in SD and RS, and (4) genes involved in synaptic function, epilepsy and cognitive pathologies including autism (Figure 2D). Both of these modules have hub genes encoding receptors, ionic channels or cell adhesion, and identified as autism and epilepsy risk factors (for complete GO of M04 and M10 and other modules, see Supplementary file 4). These characteristics are generally consistent with modulation by the MEF2C transcription factor and suggest the modulation by MEF2 is closely associated with sleep conditions. Also, we observe significant enrichment for Mef2c downstream genes (Harrington et al., 2016) in 15 out of 59 modules, which is significantly greater than randomly assigning the genes to simulated 59 modules of the same size (Monte Carlo p-value: 0.001, N: 1000 iterations). The role of MEF2C in the transcriptomic response to sleep loss An association of MEF2C target genes was observed by comparing the CS to SD DEGs; we found enrichment for DEGs in Mef2c-cKOEmx1 compared to WT cortex involved in regulating synaptic strength (Figure 1E,G, Supplementary file 3). Additionally, modules of correlated gene expression across sleep conditions showed a similar enrichment of putative MEF2C targets, pattern of expression and GO as the DEGs across sleep conditions (Figure 2A, Supplementary file 5). To better understand the role of MEF2C across sleep conditions, we examined a conditional knockout mouse using a Cre-loxP system. We crossed a Mef2cf/f mouse line (Arnold et al., 2007; Zang et al., 2013) with a transgenic Camk2a-Cre line and examined these Mef2c-cKOCamk2a-Cre mice (see Materials and methods for details) for their response to sleep loss. Mef2cf/f mice (see Materials and methods for comparisons to WT DEGs in response to SD) had 767 DEGs within 12,403 total expressed genes from CS (n=4) to SD (n=3); whereas, strikingly, the Mef2c-cKOCamk2a-Cre mice had only 36 DEGs within 12,620 total expressed genes in CS (n=3) to SD (n=3; Figure 3A, B, C). Both the Mef2cf/f and the Mef2c-cKOCamk2a-Cre genotypes expressed similar numbers of FC genes as the WT, using a CPM ≥1.0, but the responses of Mef2c-cKOCamk2a-Cre to sleep conditions were attenuated to ~7% of the Mef2cf/f responses, indicating the essential role of Mef2c in the transcriptome response to sleep loss. Figure 3 Download asset Open asset MEF2C, its de-phosphorylation and essential role in sleep-loss-mediated differential gene expression. (A) Volcano plot showing the DEGs identified for Mef2cf/f and another, (B) for Mef2c-cKOCamk2a-Cre between CS to SD sleep condition. DEGs (adj. P-val <= 0.05; absolute log2(change) => 0.3) are indicated by black dots. (C) A Venn diagram showing overlap between DEGs for Mef2cf/f and Mef2c-cKOCamk2a-Cre between CS and SD sleep states. (D) Immunoprecipitation of MEF2C to detect MEF2C, Phospho-S396 MEF2C Phospho-S396 and total MEF2C for each sleep condition. Phospho-MEF2 from of total MEF2C was and to total MEF2C for each sample (n = 4 MEF2C from total cell was and to Data reported as significance was by with comparisons test = test comparing CS to SD (E) showing role of MEF2C, increased to total MEF2C by loss of sleep and to synapse = response = the observed attenuation of Mef2cf/f mouse DEGs from CS to SD induced by the conditional loss of Mef2c is it is of that the number of CS to SD DEGs comparing WT to Mef2cf/f also shows some We further investigated the between WT and Mef2cf/f DEGs by and of the WT samples that the numbers of observations between the two was (n = 4) and SD (n = We observed increased in gene expression and such increased can lead to decreased number of observed DEGs (Supplementary file However, the decrease due to increased not appear of to for the between WT and Mef2cf/f mice. A potential factor result from an of the on MEF2C expression. However, an assessment of the specific expression showed only in expression between WT, Mef2cf/f and Mef2c-cKOCamk2a-Cre samples with only the specific of the in Mef2c-cKOCamk2a-Cre mice (Figure 4). Figure 4 Download asset Open asset analysis showing in expression between WT, Mef2cf/f and Mef2c-cKOCamk2a-Cre samples, also indicating specific of in potential of is from in gene expression due to The Mef2cf/f line employed in this study been employed in several other studies of its role in synaptic function et al., 2008; Harrington et al., 2016; Rajkovich et al., but this not out in transcriptional expression due to the Mef2cf/f mice were to the same of the WT mice for there be in gene expression due to and/or Interestingly, an of the DEGs comparing CS to SD from WT and Mef2cf/f mice used in this study (Supplementary file shows a gene ontology by genes synaptic function (Supplementary file indicating that regulation of synaptic genes is between the two Furthermore, the synaptic and EEG of these genotypes across sleep conditions are as shown (Figure supplement 1 and Figure 5). Figure with 1 supplement see all Download asset Open asset of Mef2c sleep need and its resolution. (A) power to average power the light phase as shown in a Mef2cf/f (B) The is in an from a Mef2c-cKOCamk2a-Cre (C) data n = n = n = power the light phase for WT Mef2cf/f and (D) The average waking power in hr is similar for Mef2c-cKOCamk2a-Cre and Mef2cf/f and is used as a factor to assess SWS (E) SWS-SWA power time from an average of SWS from the time of from waking to SWS, set to = The time constant of = was by of the of power for the Mef2cf/f but the was for Mef2c-cKOCamk2a-Cre (see Materials and methods for = a to single a an and an of of in to The SWS-SWA power for Mef2c-cKOCamk2a-Cre mice is greater than Mef2cf/f as by the and the The of the is as indicated by the (F) The average SWS-SWA power of each following of 4 hr SD, was significantly increased compared to SWS-SWA power conditions. Mef2c-cKOCamk2a-Cre mice to show this rebound response to sleep loss. (G) The time of rebound across the 4 hr SD indicates that the of rebound in Mef2c-cKOCamk2a-Cre is during the MEF2 transcriptional activity is increased activity-dependent dephosphorylation of et al., of a of is to cortical excitatory (Harrington et al., 2016), of excitatory synaptic strength previously reported in response to recovery sleep (Liu et al., This to our that sleep loss decrease MEF2C phosphorylation at Using a phosphorylation et al., we examined the MEF2C phosphorylation in FC three sleep conditions CS, SD and We found that compared to CS, SD a significant of MEF2C (Figure consistent with an increase in MEF2C activity during SD, but total MEF2C protein (Figure In contrast, RS did not significantly MEF2C (Figure suggesting that SD signaling events to transcription and the and/or elimination of excitatory As noted (Figure CS to SD and RS downregulated DEGs were enriched for genes that were downregulated in an earlier study comparing WT to Mef2c-cKOEmx1 in control conditions (Harrington et al., 2016). Similarly, the upregulated genes from CS to SD and RS are enriched for WT to Mef2c-cKOCamk2a-Cre genes. In other the DEGs of Mef2c-cKOCamk2a-Cre (compared to in control conditions are similar to the DEGs of CS to SD in WT mice. However, in the Mef2c-cKOCamk2a-Cre used in our study, there is response to SD (Figure the DEGs of the WT sleep loss response are differentially expressed in CS conditions in Mef2c-cKOCamk2a-Cre a to sleep loss. The role of Mef2c in the synaptic response to sleep loss Based on our transcriptome SD and RS induced many DEGs synaptic strength and synaptic cellular components that required the MEF2C transcription factor 1, 2 and 3). MEF2C can forebrain excitatory synapse strength of pyramidal neurons by synaptic (Harrington et al., 2016) and frequency et al., of cortical synaptic strength is associated with sleep (de Vivo et al., 2017; Diering et al., 2017). observations indicate that loss of sleep is associated with increased miniature excitatory post-synaptic frequency and amplitude and that recovery sleep these changes in cortical neurons from mouse FC (Liu et al., However, the of changes in probability of changes in excitatory synapse number and the role of Mef2c have been previously To directly changes in synaptic strength, the same conditions as employed for the transcription CS, SD, RS) were to Mef2cf/f male mice. of FC were from these mice at ZT = 6 hr, for whole cell from pyramidal Because sleep-loss is associated with an increased concentration of the forebrain et al., et al., which mEPSC frequency and amplitude et al., et al., all were in the presence of 1 and 2 1 As we observed an increase of mEPSC frequency and amplitude after SD and the recovery to in RS condition in the WT and Mef2cf/f mice (Figure Figure supplement Supplementary file Figure 6 with 1 supplement see all Download asset Open asset Mef2c in forebrain excitatory neurons of synaptic excitatory to pyramidal neurons in