- Open Access
Removal of unwanted variation reveals novel patterns of gene expression linked to sleep homeostasis in murine cortex
© The Author(s). 2016
- Published: 25 October 2016
Why we sleep is still one of the most perplexing mysteries in biology. Strong evidence indicates that sleep is necessary for normal brain function and that sleep need is a tightly regulated process. Surprisingly, molecular mechanisms that determine sleep need are incompletely described. Moreover, very little is known about transcriptional changes that specifically accompany the accumulation and discharge of sleep need. Several studies have characterized differential gene expression changes following sleep deprivation. Much less is known, however, about changes in gene expression during the compensatory response to sleep deprivation (i.e. recovery sleep).
In this study we present a comprehensive analysis of the effects of sleep deprivation and subsequent recovery sleep on gene expression in the mouse cortex. We used a non-traditional analytical method for normalization of genome-wide gene expression data, Removal of Unwanted Variation (RUV). RUV improves detection of differential gene expression following sleep deprivation. We also show that RUV normalization is crucial to the discovery of differentially expressed genes associated with recovery sleep. Our analysis indicates that the majority of transcripts upregulated by sleep deprivation require 6 h of recovery sleep to return to baseline levels, while the majority of downregulated transcripts return to baseline levels within 1–3 h. We also find that transcripts that change rapidly during recovery (i.e. within 3 h) do so on average with a time constant that is similar to the time constant for the discharge of sleep need.
We demonstrate that proper data normalization is essential to identify changes in gene expression that are specifically linked to sleep deprivation and recovery sleep. Our results provide the first evidence that recovery sleep is comprised of two waves of transcriptional regulation that occur at different times and affect functionally distinct classes of genes.
- Sleep deprivation
Sleep is thought to be controlled by two processes, 1) a homeostatic process that determines sleep need (or pressure), and 2) a circadian process that determines the timing of sleep and wakefulness. A robust index for sleep need is known as delta power, which refers to “delta” (1–4 Hz) oscillations in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Delta power increases with increased sleep pressure, and declines following sleep. Therefore, sleep deprivation increases delta power, which then naturally decreases during recovery sleep. Previous studies have shown that EEG delta power is under genetic control , suggesting that specific genes contribute to sleep homeostasis. Nevertheless, the molecular mechanisms that regulate sleep need remain incompletely described.
Genome-wide technologies have been used to interrogate gene expression changes that follow sleep deprivation in the mouse brain [2, 3], but there is little agreement between studies. There are also no genome-wide studies that characterize transcriptional changes that occur during subsequent recovery sleep. A major challenge when analyzing genome-wide data in the brain in response to behavior is isolating the signal of interest from other factors (batch effects) that simultaneously influence gene expression. We have previously shown that batch effects are widespread in genome-wide studies of gene expression in experimental neuroscience . Several data normalization strategies are available to correct these batch effects, including Removal of Unwanted Variation (RUV). RUV adjusts for batch effects by performing factor analysis on control genes or replicate samples [5–7]. We have also shown that RUV normalization leads to increased power and reproducibility of results . We now employ RUV to generate an integrated cross-laboratory analysis of differential gene expression following sleep deprivation in the mouse brain. We also provide the first comprehensive genome-wide assessment of transcripts from mouse cortex during recovery sleep. Our analysis improves detection of differentially expressed genes following sleep deprivation and shows that recovery sleep reverses the transcriptional changes it causes. This latter process occurs in waves that happen at different times during recovery sleep and affect functionally distinct classes of genes.
The majority of genes upregulated by sleep deprivation were slow responders, while the majority of genes downregulated by sleep deprivation were fast responders (the exception is a small cluster of 16 genes that includes Cirbp and Dbp), supporting the pattern observed in Fig. 3a. Interestingly, a more fine-grained analysis of these results showed that transcripts known to be upregulated by sleep deprivation (such as Arc and Homer1) [2, 3] include both ‘slow’ and ‘fast’ responders. Transcripts that recover by 1–3 h included Arc, Per1, Per2, Egr1, and Egr2, while transcripts that recovered by 6 h included Homer1, BDNF, Fosb, Hspa5, and Npas2 (Fig. 4). Many of these transcripts also followed a normal circadian expression pattern, which are shown independently (Additional file 5). The smaller set of 46 transcripts upregulated by the 6th hour of recovery sleep (pink cluster, Fig. 3b, Additional file 6) included transcripts with less clearly defined functions, including micro RNA and non-coding RNA and transcripts involved in RNA-binding protein sequestration (Neat1) .
We also computed the decay time constant (τd) for the changes in slow and fast responding transcripts to see if they approximated the τd for the discharge of sleep need . As shown in Fig. 4, these varied across functional classes. Interestingly, when we averaged τd across the different clusters of fast-responder genes the value was identical to that reported for the discharge of sleep need in this mouse strain (average τd fast responder genes: 1.3; τd for discharge of sleep need in c57/bl6: 1.3 ).
The molecular determinants of sleep homeostasis are not well known. Furthermore, transcriptional changes that track both the accumulation and discharge of sleep need have not been well characterized. Here, we present a fully integrated meta-analysis of the effects of sleep deprivation on mouse brain gene expression, by combining our data with three other previous studies [2, 3, 8]. We also provide additional evidence for genome-wide changes in cortical gene expression following various lengths of subsequent recovery sleep. We report that discovery of gene expression changes linked to either sleep deprivation or recovery sleep, and not batch effects, requires a non-traditional method of data normalization (RUV). Therefore, our study more accurately reflects true biological variance due to changes in sleep need, and vastly improves both single laboratory and meta-analysis studies of gene expression previously conducted in the absence of RUV .
Our results represent the first genome-wide examination of differentially expressed cortical genes that includes the response to sleep deprivation and subsequent changes across the recovery sleep period. The majority of genes fall into two general classes of transcriptional changes: transcripts that return to baseline values quickly (i.e., within the first 3 h: ‘fast responders’) and transcripts that return to baseline values slowly (by 6 h of recovery: ‘slow responders’). There was also a small subset of transcripts not affected by sleep deprivation, but upregulated in the 6th hour of the recovery period. Genes that respond “fast” to recovery sleep may constitute the molecular signature underlying the discharge of sleep need based on electrophysiological studies. This is because these molecular changes parallel the discharge of sleep need as measured by changes in NREM EEG delta power. In mice, 6 h of sleep deprivation produces a large increase in delta power which then rapidly declines over the next 3 h of recovery sleep [13, 10]. This pattern is strikingly similar to the time course we find for the ‘fast’ responder genes.
The majority of fast-responders are transcripts initially downregulated by sleep deprivation and then upregulated with recovery sleep. Interestingly we find that these specific transcripts do not show time-of-day differences (Additional file 5) suggesting that the biological functions they serve are more closely tied to sleep homeostasis. These include genes involved in synthesis and degradation of proteins. Examples include ubiquitin-specific-proteases (Usps) and elongation initiation factors (Eifs) (Fig. 4). These results are consistent with previous studies [3, 14–16], supporting the idea that one of the immediate effects of recovery sleep is to influence protein synthesis or turnover. A second class of ‘fast’ responder genes downregulated by sleep deprivation is involved in transcriptional repression linked to histone modification. Histone acetylation and deacetylation modify the structure of chromosomes and influence access of transcription factors to DNA. We find that histone deacetylase 9 (Hdac9) and associated co-repressor Sin3A, together with the GO term “transcriptional co-repression” are downregulated by sleep deprivation (Fig. 4, Additional file 7), as previously shown in the hippocampus . This suggests that part of the compensatory response is a reactivation of transcriptional repression. This may be part of the restorative function of sleep; that it re-establishes a basal level of transcription required for normal neural function. While speculative, it is possible that the cognitive deficits associated with sleep deprivation result from an unchecked expression of certain transcripts; a situation reversed during recovery sleep.
Fast responder genes upregulated by SD include immediate early genes previously identified as ‘induced by sleep deprivation’ (e.g. Arc, Egr1, Egr2 and Nr4a1 [2, 3, 14, 15, 17] (Fig. 4 and Additional file 5). The function of immediate early genes is primarily the regulation of transcription, a functional category that is also enriched in this group. We also find that several of the fast-responder genes are traditional ‘circadian’ genes (e.g. Arntl, Dbp, Per1, and Per2 ) and show time of day differences in their expression (Additional file 5). The precise role of these immediate early and clock genes in sleep homeostasis is unclear. Deletion of Arntl (BMAL1) in mice alters baseline sleep architecture, increases NREM EEG delta-power baseline conditions, and attenuates the homeostatic response to sleep deprivation . In addition, Per1 and Per2 brain expression in various inbred mouse strains correlates with changes in NREM EEG delta-power [19–21], suggesting these genes are tied to the sleep homeostat. However, Per mutants have a normal response to sleep deprivation as measured by NREM delta power [22, 23], indicating they play no central role in sleep homeostasis. Similarly, the expression of the immediate early gene Homer1A also tracks sleep need , but Homer1A null mice have normal sleep homeostasis . Therefore it is possible that the regulation of these particular clock and immediate early genes may not be as closely linked to sleep homeostasis, as appears to be the case for other clock genes [18, 20]. Instead, circadian rhythms or neural activity may play more influential roles in their expression. This interpretation is consistent with earlier studies. It has been shown that several immediate early genes, such as Per1 and Arc, are also upregulated following contextual fear conditioning or object location memory for example [15, 25, 26].
The slow responding transcripts represented the majority of all transcripts upregulated by sleep deprivation (Fig. 4). These included genes previously linked to sleep homeostasis including Homer1, Bdnf and Npas2 [27, 28, 19]. There appears to be functional overlap between these slow responding transcripts and fast responding transcripts downregulated by sleep. GO terms or functional clusters that overlap include: cell adhesion, neurogenesis, GTP signalling and splicing (Fig. 5, Additional file 7). Adhesion molecules (such as Neuroligin 1; Fig. 4, Additional file 3) are particularly interesting because they may link early responses to SD (e.g.. clock gene expression) with slower responses. This is because sleep deprivation induced changes in Neuroligin1 are dependent on clock transcription factors . A much smaller subset (16 genes) was downregulated by sleep deprivation. The functions of these genes are not well understood.
Lastly, we identified a small number of genes that were unaffected by sleep deprivation but were upregulated in the 6th hour of recovery sleep (pink cluster, Fig. 3b). The function of many of these genes is also obscure. One example is the long non-coding RNA Neat1. Neat1 is retained in the nucleus where it forms the core structural component of the paraspeckle sub-organelle located within the eukaryotic nucleus . Neat1 has been shown to regulate transcription via protein sequestration within paraspeckles . Paraspeckles are believed to function as a reservoir of mRNA that are released into the cytoplasm under certain conditions (e.g. cellular stress) and/or provide a means of RNA sequestration. The reason for this delayed expression of transcripts is unclear. In mice, 6 h SD not only increases NREM delta power in the first three hours of recovery sleep, but also leads to a delayed ‘rebound’ in REM sleep time that can occur at or after 6 h . Therefore, it is possible that the expression of this small subset of transcripts is driven by REM sleep [1, 30, 31].
While our study represents a significant improvement over previous studies that set out to identify transcripts associated with sleep homeostasis, there are some limitations. For example, our current methods do not allow for the identification of spatial resolution of specific cortical layers, or regional differences between frontal and parietal cortices. Further, the cell-type specificity for the changes in expression identified in our study is not characterized. In addition, it is difficult to differentiate which gene expression changes are responding to sleep pressure from those that are responding to stress hormones . Future studies using improved techniques, such as the use of TRAP technology [33, 34] will be necessary to identify cell-type specific changes in transcripts associated with sleep homeostasis.
This is the first study where RUV normalization has been used to compare multiple genome-wide data sets following sleep deprivation. We also used this approach to examine transcriptional changes during the recovery sleep period. We show that RUV vastly improves the meta-analysis of data generated in different laboratories and reveal novel changes in transcription during recovery sleep. We find that sleep produces two waves of transcription during recovery sleep. Some changes occur rapidly, others more slowly across six hours of sleep. The fast responding transcripts may represent the molecular components of sleep homeostasis as they change with a time constant that is remarkably similar to the time constant for the discharge of sleep need. Further characterization of these genes may reveal sleep function and the biological basis for sleep need.
C57BL/6 J adult male mice (2 months of age) were obtained from Jackson laboratories and housed individually for a week in an experimental room on a 12 h./12 h. light/dark schedule with lights on at 7:30 am (Zeitgeber time (ZT) 0). Food and water were available ad libitum throughout the experiment. All experiments were approved by the Institution of Animal Care and Use Committee of the University of Pennsylvania and were carried out in accordance with all National Institutes of Health guidelines.
To examine gene expression in the mouse brain cortex after 5–6 h of sleep deprivation and subsequent recovery sleep we generated a dataset of 96 microarrays (see below). Groups of mice were either sleep deprived or sleep deprived and allowed recovery sleep for 1, 2, 3, or 6 h. Matching control (CC) animals were left undisturbed but sacrificed at the same time of day. Sleep deprivation was achieved by brushing the mice with a soft brush to keep them active. We did not surgically implant EEG electrodes to quantitatively measure sleep. This was done, as true for earlier studies [2, 3], to prevent changes in gene expression that might occur as a result of the surgery. However, analyses of animals implanted with EEG electrodes undergoing the same procedures shows that sleep deprivation procedures similar to ours is effective at maintaining wakefulness in a variety of mouse strains (including c57/bl6) [10, 35]. In addition, when given an opportunity to sleep, mice spend most of the subsequent 6 h period in sleep . The experimental protocol was repeated daily, to obtain one animal per time point per experimental day, to gather 6–7 mice per experimental group. We also performed a meta-analysis using data from three previously published studies that tested the effect of 6 h of sleep deprivation in the mouse cortex, hippocampus and whole brain [2, 3, 8].
Cortical dissections were performed by a single experimenter, and tissue was rapidly dissected and immersed in chilled RNAlater (Qiagen), kept overnight at 4 °C, then frozen at −80 °C. RNA extraction was performed using the miRNeasy kit (Qiagen). Biotinylated sense-strand cDNA were prepared from 300 ng total RNA at the UPENN molecular profiling core using the Affymetrix WT Plus Kit. Single stranded cDNA was hybridized to a Mouse Gene 2.1 ST 96-Array Plate using GeneTitan Hybridization, Wash and Stain Kit for WT Array Plates. The array plate was washed and stained in the GeneTitan multi-channel instrument. Gene 1.1 ST Array Plates were scanned using the GeneTitan® Multi-channel Instrument.
Cross-study data integration
Data from GSE9444 , GSE6514  and GSE50423  were obtained using the R/Bioconductor package GEOquery (v. 2.36.0). Data generated in this study are publicly available through GEO (GSE78215). To allow for cross-platform comparison, Affymetrix probeset IDs were mapped to ENSEMBL gene Ids using the R/Bioconductor package biomaRt (v. 2.26.1). Probesets or ENSEMBL genes that showed multiple mappings were excluded from the cross-platform analysis only. Probesets that showed a log expression value > 4 in >50 % of the samples were included.
Normalization and statistical analysis
We have previously shown that Removal of Unwanted Variation (RUV)  is a normalization method that is able to properly correct for batch effects in experimental neuroscience data obtained through RNA sequencing . Any difference between biological replicates can be attributed to unwanted effects. The RUV method exploits the fact that genes that should not be changing in a biological system (negative controls) or differences between replicates, carry in their observed levels patterns of unwanted variation that can be used to adjust for unwanted effects. In this study, we used log-transformed RMA normalized data as input to RUVSeq, a Bioconductor package originally designed to perform RUV analysis on RNA sequencing data . The advantage of doing so is the ease of integration with future or currently available RNA-seq studies, since RNA-seq is now the standard for the quantification of genome-wide gene expression. We compared the results of differential expression analysis of data normalized using RUV based on replicate samples with data normalized using RMA with Quantile normalization , the most commonly used method for microarray data. RUV using negative control samples was used to remove k factors of unwanted variation before statistical testing was performed. The choice of a proper k was determined as previously described , resulting in k = 5 for the meta-analysis of sleep deprivation (Fig. 1) and k = 6 for the recovery sleep analysis (Fig. 2). Differential expression analysis was performed using R/Bioconductor package limma (v. 3.26.8) comparing the sleep deprivation and recovery sleep samples to time matched circadian controls (SD vs. CC6, RS1 vs. CC7, RS2vs CC8, RS3 vs. CC8, RS6 vs. CC11). Comparisons between controls CC0, CC6 and CC1 were used to determine circadian changes in expression. Multiple testing corrections were performed using the method of Benjamini and Hochberg . A cutoff of false discovery rate (FDR) <0.01 was used to assess significance. To evaluate performance, we assembled sets of independently validated positive control genes that are known to respond to 6 h of sleep deprivation or 2 h of recovery sleep (see Additional file 1 for details). RUV normalization was performed using the R/Bioconductor package RUVSeq (v. 1.0.0). Differential expression analysis was performed using R/Bioconductor package limma (v. 3.26.8).
PCA plots were performed using the R/Bioconductor package EDASeq (v. 2.0.0). The heatmap was prepared using the R package gplots (v. 2.17.0) with modifications to the row dendrogram using the R package dendextend (v. 1.1.8). All other figures were generated using R base graphics and Microsoft Excel.
Functional enrichment analysis
Affymetrix probeset ID’s were mapped to MGI symbol and ensemble gene ID’s for downstream analysis using the R/Bioconductor package mogene21sttranscriptcluster.db (v. 8.4.0). Functional annotation was based on ENSEMBL Gene IDs and performed using the database for annotation, visualization and integrated discovery v 6.7 (DAVID, https://david.ncifcrf.gov). The following functional categories were used: GO Biological Process and Molecular Function, KEGG pathways and Protein Information Resource keywords. Enrichment cutoff relative to background = EASE score <0.05. All genes present in the array were used as background for enrichment. Clustering was used to reduce complexity. Clustering parameters: similarity threshold 0.2, group membership 2.
We thank H. Rodriguez and the Penn Molecular Profiling Facility for the RNA and Microarray work.
About this supplement
This article has been published as part of BMC Genomics Volume 17 Supplement 8: Selected articles from the Sixth International Conference of the Iberoamerican Society for Bioinformatics on Bioinformatics and Computational Biology for Innovative Genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-17-supplement-8.
Research reported in this article was supported by the National Institute of Mental Health of the National Institutes of Health under award number R01MH099544 to M.G.F. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The publication charges for this article were funded by start-up funds provided by Washington State University to L.P.
Availability of data and materials
Data generated in this study is publicly available through GEO (GSE78215).
Study design by AW and MGF. Data collection by AW and NZ. Data analysis by JK, DR and LP, manuscript preparation by JRG with editing by JK, DR, TPS, MGF and LP. All authors approve of the final version of this manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The Institutional Animal Care and Use Committee (IACUC) of the University of Pennsylvania and Washington State University approved all animal work conducted during the research presented in this article.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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