Cortical miR-709 links glutamatergic signaling to NREM sleep EEG slow waves in an activity-dependent manner

MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression that have been implicated in a plethora of neuronal processes. Nevertheless, their role in regulating brain activity in the context of sleep has so far received little attention. To test their involvement, we deleted mature miRNAs in post-mitotic neurons at two developmental ages, i.e., in early adulthood using conditional Dicer knockout (cKO) mice and in adult mice using an inducible conditional Dicer cKO (icKO) line. In both models, electroencephalographic (EEG) activity was affected and the response to sleep deprivation (SD) altered; while the rapid-eye-movement sleep (REMS) rebound was compromised in both, the increase in EEG delta (1 to 4 Hz) power during non-REMS (NREMS) was smaller in cKO mice and larger in icKO mice compared to controls. We subsequently investigated the effects of SD on the forebrain miRNA transcriptome and found that the expression of 48 miRNAs was affected, and in particular that of the activity-dependent miR-709. In vivo inhibition of miR-709 in the brain increased EEG power during NREMS in the slow-delta (0.75 to 1.75 Hz) range, particularly after periods of prolonged wakefulness. Transcriptome analysis of primary cortical neurons in vitro revealed that miR-709 regulates genes involved in glutamatergic neurotransmission. A subset of these genes was also affected in the cortices of sleep-deprived, miR-709-inhibited mice. Our data implicate miRNAs in the regulation of EEG activity and indicate that miR-709 links neuronal activity during wakefulness to brain synchrony during sleep through the regulation of glutamatergic signaling

Missense mutations in TENM4, a regulator of axon guidance and central myelination, cause essential tremor

Essential tremor (ET) is a common movement disorder with an estimated prevalence of 5% of the population aged over 65 years. In spite of intensive efforts, the genetic architecture of ET remains unknown. We used a combination of whole-exome sequencing and targeted resequencing in three ET families. In vitro and in vivo experiments in oligodendrocyte precursor cells and zebrafish were performed to test our findings. Whole-exome sequencing revealed a missense mutation in TENM4 segregating in an autosomal-dominant fashion in an ET family. Subsequent targeted resequencing of TENM4 led to the discovery of two novel missense mutations. Not only did these two mutations segregate with ET in two additional families, but we also observed significant over transmission of pathogenic TENM4 alleles across the three families. Consistent with a dominant mode of inheritance, in vitro analysis in oligodendrocyte precursor cells showed that mutant proteins mislocalize. Finally, expression of human mRNA harboring any of three patient mutations in zebrafish embryos induced defects in axon guidance, confirming a dominant-negative mode of action for these mutations. Our genetic and functional data, which is corroborated by the existence of a Tenm4 knockout mouse displaying an ET phenotype, implicates TENM4 in ET. Together with previous studies of TENM4 in model organisms, our studies intimate that processes regulating myelination in the central nervous system and axon guidance might be significant contributors to the genetic burden of this disorde

Sleep-wake-driven and circadian contributions to daily rhythms in gene expression and chromatin accessibility in the murine cortex

The timing and duration of sleep results from the interaction between a homeostatic sleep-wake-driven process and a periodic circadian process, and involves changes in gene regulation and expression. Unraveling the contributions of both processes and their interaction to transcriptional and epigenomic regulatory dynamics requires sampling over time under conditions of unperturbed and perturbed sleep. We profiled mRNA expression and chromatin accessibility in the cerebral cortex of mice over a 3-d period, including a 6-h sleep deprivation (SD) on day 2. We used mathematical modeling to integrate time series of mRNA expression data with sleep-wake history, which established that a large proportion of rhythmic genes are governed by the homeostatic process with varying degrees of interaction with the circadian process, sometimes working in opposition. Remarkably, SD caused long-term effects on gene-expression dynamics, outlasting phenotypic recovery, most strikingly illustrated by a damped oscillation of most core clock genes, including Arntl/Bmal1, suggesting that enforced wakefulness directly impacts the molecular clock machinery. Chromatin accessibility proved highly plastic and dynamically affected by SD. Dynamics in distal regions, rather than promoters, correlated with mRNA expression, implying that changes in expression result from constitutively accessible promoters under the influence of enhancers or repressors. Serum response factor (SRF) was predicted as a transcriptional regulator driving immediate response, suggesting that SRF activity mirrors the build-up and release of sleep pressure. Our results demonstrate that a single, short SD has long-term aftereffects at the genomic regulatory level and highlights the importance of the sleep-wake distribution to diurnal rhythmicity and circadian processes

Environmental enrichment induces epigenomic and genome organization changes relevant for cognition

In early development, the environment triggers mnemonic epigenomic programs resulting in memory and learning experiences to confer cognitive phenotypes into adulthood. To uncover how environmental stimulation impacts the epigenome and genome organization, we used the paradigm of environmental enrichment (EE) in young mice constantly receiving novel stimulation. We profiled epigenome and chromatin architecture in whole cortex and sorted neurons by deep-sequencing techniques. Specifically, we studied chromatin accessibility, gene and protein regulation, and 3D genome conformation, combined with predicted enhancer and chromatin interactions. We identified increased chromatin accessibility, transcription factor binding including CTCF-mediated insulation, differential occupancy of H3K36me3 and H3K79me2, and changes in transcriptional programs required for neuronal development. EE stimuli led to local genome re-organization by inducing increased contacts between chromosomes 7 and 17 (inter-chromosomal). Our findings support the notion that EE-induced learning and memory processes are directly associated with the epigenome and genome organization.We acknowledge support of the Spanish Ministry of Economy and Competitiveness (SAF2011-26216), “Centro de Excelencia Severo Ochoa 2017-2021,” SEV-2016-0571, the CERCA Programme/Generalitat de Catalunya and Jerome Lejeune Foundation, Swiss National Science Foundation Fellowship (PBLAP3_136878) and Co-funded by Marie Curie Actions to CNH. Resources for analyses conducted by SE-G were partially supported by the U.S. National Institutes of Mental Health Funds R01MH104341 and R01MH117790 and by the Social Sciences and Humanities Research Council of Canada (NFRFE-2018-01305). We acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, Agencia Estatal de Investigaci n (PID2019-110755RB-I00/AEI / 10.13039/501100011033), the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 848077, Jerôme Lejeune Foundation, NIH (Grant Number: 1R01EB 028159-01), Marató TV3 (#2016/20-30). RP-R resources were supported by R01GM109215. We thank the support of the University of Tübingen for the Open Access Publication Funds contribution

EEG delta power in NREM sleep after SD is associated with <i>Kif16b</i> and <i>Wrn</i>.

<p>(A) NREM sleep EEG spectra in the first 3 h after SD (ZT6–9) for the 2 BXD lines that displayed the lowest and highest EEG activity in the fast delta frequency band (2.5–4.25 Hz, δ2; top, see panel E) and for the 2 BXD lines that displayed the smallest and largest increase (or gain) in EEG power in the slow delta band (1.0–2.25 Hz, δ1; bottom, see panel E). Spectra were “1/f-corrected” (and therefore not directly comparable to the values in panel E) for better visualization of activity in higher frequency bands (theta [5–9 Hz, θ], sigma [11–16 Hz, σ], beta [18–30 Hz, β], and slow [32–55 Hz, γ1] and fast gamma [55–80 Hz, γ2]). Subsequent analyses were performed without this correction. (B) QTL mapping and prioritization for δ2 power identified a significant association on chromosome 2 and <i>Kif16b</i> in cortex as top-ranked gene (top). For the δ1 increase after SD, we obtained a suggestive QTL on chromosome 8 and a significant prioritization score for the DNA-helicase <i>Wrn</i>. (C) Hiveplot visualization of network connections for the δ1 and δ2 power after SD (top-left panels) and the SD-induced increase in δ1 and δ2 power over baseline (bottom-left panels). Note the marked differences in the networks and QTLs regulating the expression of these 2 delta bands. Right hiveplots highlight <i>Kif16b</i> in the δ2 power–associated network (top), and <i>Wrn</i> in the network associated with the δ1 increase (bottom). Only <i>Kif16b</i> expression in the cortex was linked to the chromosome 2 <i>cis</i>-<i>e</i>QTL and was not associated with any metabolite. <i>Wrn</i> expression was significantly linked to the chromosome 8 <i>cis-e</i>QTL and to the long phosphatidylcholine, PC-ae-C38:5. (D) <i>Kif16b</i> is highly significantly down-regulated in cortex (left), while it remains unchanged in liver after SD (<i>p</i> = 0.15; not shown). Also, <i>Wrn</i> expression was strongly down-regulated by SD in cortex (right) and only marginally so, albeit significantly, in liver (<i>p</i> = 0.02; not shown). (E) Strain distribution patterns. BXD lines carrying a <i>B6-</i>allele on the chromosome 2–associated region showed higher δ2 power after SD (left) and a significantly higher <i>Kif16b</i> expression (<i>p</i> = 1.3e−15; second to left) than <i>D2-</i>allele carriers. <i>D2-</i>allele carriers of the chromosome 8–associated region showed a larger δ1 increase after SD (second to right) as well as a significantly larger decrease in <i>Wrn</i> expression after SD (right) than <i>B6-</i>allele carriers. For color-coding of genotypes, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005750#pbio.2005750.g004" target="_blank">Fig 4</a>. CPM, counts per million; Ctr, control; EEG, electroencephalography; <i>e</i>QTL, expression quantitative trait locus; FDR, false discovery rate; <i>Kif16b</i>, <i>Kinesin family member 16B</i>; NREM, non-REM; PC-ae, phosphatidylcholine acyl-alkyl; QTL, quantitative trait locus; SD, sleep deprivation; <i>Wrn</i>, <i>Werner syndrome RecQ like helicase</i>; ZT, zeitgeber time</p

NREM sleep gain in the first 6 h of the dark period after SD is associated with <i>Acot11</i>.

<p>(A) Time course of hourly values of time spent in NREM sleep in baseline, SD (red area), and recovery for the 2 BXD lines showing the largest (BXD70; green) and lowest (BXD83; blue) NREM sleep gain during ZT12–18 (left). NREM sleep gain during 4 consecutive 6 h intervals during recovery compared to corresponding baseline intervals shows that in the recovery dark period (gray area), BXD83 mice did not accumulate extra NREM sleep, while BXD70 mice gained 88 min (middle). Strain distribution of ZT12–18 NREM sleep gain (right). B6-allele carriers compensated less for NREM sleep lost during SD than D2-allele carriers. For color-coding, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005750#pbio.2005750.g004" target="_blank">Fig 4</a>. (B) Hiveplots for NREM sleep gain in 4 six-hour recovery intervals after the end of SD at ZT6. Compared to the other 3 intervals, NREM sleep gain was strongly associated with a number of metabolites during the second 6 h interval, i.e., ZT12–18. (C) NREM sleep gain during ZT12–18 mapped to a significant QTL on chromosome 4, explaining 45% of the total phenotypic variance (top left). PC-ae-C38:2 mapped suggestively to the same region (top right). Prioritization of liver transcripts for both phenotypes yielded <i>Acot11</i> as top-ranked, significant gene (bottom). (D) Hiveplot for the ZT12–18 NREM sleep gain, highlighting <i>Acot11</i>. <i>Acot11</i> was positively correlated with several phosphatidylcholines and to <i>Ovgp1</i> expression in the cortex. (E) Allelic effect of the chromosome 4–associated region on <i>Acot11</i> expression and PC-ae-C38:2 levels in the BXDs. <i>Acot11</i> expression in liver after SD was under a strong <i>e</i>QTL effect (<i>p</i> = 1.6e−13) with <i>B6-</i>allele carriers showing a higher <i>Acot11</i> expression than <i>D2</i>-allele carriers. <i>B6-</i>allele carriers also showed higher PC-ae-C38:2 levels after SD. (F) Both <i>Acot11</i> and PC-ae-C38:2 levels changed after SD. <i>Acot11</i> in liver and PC-ae-C38:2 in blood were significantly down-regulated. In the cortex, <i>Acot11</i> was, however, significantly up-regulated, and the chromosome 4–associated region did not modulate cortical <i>Acot11</i> expression. (G) Mice carrying 1 or 2 KO alleles for <i>Acot11</i> displayed less extra NREM sleep during recovery. In contrast to the BXD panel, this difference was present in the second (ZT18–24, right) and not during the first (ZT12–18, left panel) 6 h of the recovery dark period. <i>Acot11</i>, <i>acyl-CoA thioesterase 11</i>; CPM, counts per million; Ctr, control; <i>e</i>QTL, expression quantitative trait locus; KO, knockout; NREM, non-REM; PC-ae, phosphatidylcholine acyl-alkyl; QTL, quantitative trait locus; SD, sleep deprivation; ZT, zeitgeber time</p

Profound effects of SD on transcriptome, metabolome, and phenome.

<p>EEG/behavioral phenotypes, metabolites, and transcripts are organized into 3 “columns” (from left to right). Top 3 panels show the SD response (recovery/baseline fold change). Bottom 3 panels depict examples of allelic effects on the SD responses, with color-coding indicating the presence of a C57BL/6J or DBA/2J haplotype under the mapped QTL peaks (B6: gray for BXD and black for parental; D2: light brown for BXD and dark brown for parental). White bars mark the F1s and hatched bars strain in which haplotype could not be unambiguously determined. (A) Phenotypic changes after SD. The top significantly changed phenotype was the increase in NREM sleep EEG delta power (1–4 Hz) after SD (far-left blue data point). The most up-regulated phenotype was time spent in REM sleep during the first 6 h of darkness (ZT12–18) after SD (highest green data point). (B) Metabolite changes after SD. Most amino acids (blue) were down-regulated and most sphingolipids (brown) up-regulated after SD. The acylcarnitines C18:1 and C18:2 (highest red dots) increased the most. Vertical red line: significant threshold (FDR-adjusted <i>p</i>-value = 0.05). (C) DE analysis (SD/Ctr) for cortex (left) and liver (right). Genes were sorted according to their ranked <i>p</i>-value along the x-axis. Significantly affected transcripts in red (FDR-adjusted <i>p</i>-value < 0.05), nonsignificant results in black. Blue dots indicate 78 genes considered core molecular components of the sleep homeostatic response in the cortex [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005750#pbio.2005750.ref034" target="_blank">34</a>]. Note that no low fold change threshold was applied. (D-F) Examples of genetically driven EEG/behavioral, metabolic, and transcriptional responses to SD, respectively. See text for details. <i>Arc</i>, <i>activity-regulated cytoskeletal-associated protein</i>; Ctr, control; DE, differential gene expression; EEG, electroencephalography; <i>Egr2</i>, <i>early growth response 2</i>; <i>Fam107a</i>, <i>family with sequence similarity 107</i>, <i>A</i>; FDR, false discovery rate; LMA, locomotor activity; <i>Mlycd</i>, <i>malonyl-CoA decarboxylase</i>; NREM, non-REM; <i>Plin4</i>, <i>Perilipin 4; Pla2g4e</i>, <i>phospholipase A2</i>, <i>group IVE</i>; QTL, quantitative trait locus; SD, sleep deprivation; <i>Ttll8</i>, <i>tubulin tyrosine ligase-like family 8</i>; ZT, zeitgeber time.</p

Changes in the frequency of theta oscillation during REM sleep after SD are associated with <i>Cyp4a32</i>.

<p>(A) Spectral profiles of the REM sleep EEG for 2 strains displaying an opposite shift in the frequency of theta oscillations after SD relative to baseline. This shift was quantified by the decrease and increase in TPF for BXD61 and BXD101, respectively (see panel F). (B) Hiveplot for the SD-induced shift in TPF. (C) One significant QTL for the TPF shift was detected on chromosome 4 and 1 suggestive QTL on chromosome 8. Prioritization yielded <i>Cyp4a32</i> as the top-ranked significant gene, based on the significant <i>cis-e</i>QTL modifying its expression in liver and a predicted damaging variation (V314E). (D) Effects of SD and genotype on liver <i>Cyp4a32</i> expression. Carrying a <i>B6-</i>allele at the <i>Cyp4a32 cis-e</i>QTL–associated marker greatly decreased its expression. (E) Hiveplot for the SD-induced shift in TPF, highlighting <i>Cyp4a32</i>’s links to the amino acid Valine and the chromosome 4 <i>e</i>QTL marker. (F) Strain distribution patterns for TPF differences and liver <i>Cyp4a32</i> expression after SD. <i>B6-</i>allele carriers at the chromosome 4–associated region had lower <i>Cyp4a32</i> liver expression and a decrease in TPF after SD, while <i>D2-</i>carriers increase TPF and have higher <i>Cyp4a32</i> expression. CPM, counts per million; Ctr, control; <i>Cyp4a32</i>, <i>Cytochrome P450</i>, <i>family 4</i>, <i>subfamily a</i>, <i>polypeptide 32</i>; DE, differential expression; EEG, electroencephalography; <i>e</i>QTL, expression quantitative trait locus; lod, logarithm of odds ratio; QTL, quantitative trait locus; SD, sleep deprivation; TPF, theta-peak frequency</p

Study design.

<p>Thirty-three BXD lines plus the 2 parental strains and their reciprocal F1 progeny were phenotyped. Mice were submitted to either one of 2 experiments. In Experiment 1 (left), EEG/EMG signals and LMA were recorded under standard 12:12 h light–dark conditions (white and black bars under top-left panel) for 2 baseline days (B1, B2), a 6 h SD (red bar) from ZT0–6 (ZT0 = light onset), followed by 2 recovery days (R1, R2). The deep sleep-wake phenome consists of 341 sleep-wake state-, LMA-, and EEG-related phenotypes quantified in each mouse, among which time spent in NREM sleep (gray area spans mean maximum and minimum NREM sleep time among BXD lines, respectively, for consecutive 90 min intervals). Mice in Experiment 2 (right) were used to collect cortex, liver, and blood samples at ZT6. Half of the mice were challenged with an SD as in Experiment 1, the other half were left undisturbed and served as controls (labeled Ctr). Cortex and liver samples were used to quantify gene expression by RNA-seq, blood samples for a targeted analysis of 124 metabolites by LC/MS, or with FIA/MS. For <i>ph</i>QTLs, <i>m</i>QTLs, and <i>e</i>QTLs, a high-density genotype dataset (Genome; approximately 11,000 SNPs) was created, merging identified RNA-seq variants with a publicly available database (<a href="http://www.genenetwork.org/" target="_blank">www.genenetwork.org</a>). The entirety of the multilevel dataset was integrated in a systems genetics analysis to chart molecular pathways underlying the many facets of sleep and the EEG, using newly developed computational tools to interactively visualize the results and pathways, and to prioritize candidate genes. EEG/EMG, electroencephalography/electromyogram; <i>e</i>QTL, expression quantitative trait locus; FIA/MS, flow injection analysis/mass spectrometry; LC/MS, liquid chromatography/mass spectrometry; LMA, locomotor activity; <i>m</i>QTL, metabolic quantitative trait locus; NREM, non-REM; <i>ph</i>QTL, phenotypic quantitative trait locus; RNA-seq, RNA sequencing; SD, sleep deprivation; ZT, zeitgeber time.</p