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ORIGINAL RESEARCH
published: 21 December 2021
doi: 10.3389/fnagi.2021.759983
Frontiers in Aging Neuroscience | www.frontiersin.org 1December 2021 | Volume 13 | Article 759983
Edited by:
Robert Petersen,
Central Michigan University,
United States
Reviewed by:
Mitsuru Shinohara,
National Center for Geriatrics and
Gerontology (NCGG), Japan
S. Abid Hussaini,
Columbia University Irving Medical
Center, United States
*Correspondence:
Nicola D’Ascenzo
ndasc@hust.edu.cn
Fengfei Ding
fengfei_ding@fudan.edu.cn
†These authors have contributed
equally to this work and share first
authorship
Specialty section:
This article was submitted to
Alzheimer’s Disease and Related
Dementias,
a section of the journal
Frontiers in Aging Neuroscience
Received: 17 August 2021
Accepted: 22 November 2021
Published: 21 December 2021
Citation:
Ba L, Huang L, He Z, Deng S, Xie Y,
Zhang M, Jacob C, Antonecchia E,
Liu Y, Xiao W, Xie Q, Huang Z, Yi C,
D’Ascenzo N and Ding F (2021) Does
Chronic Sleep Fragmentation Lead to
Alzheimer’s Disease in Young
Wild-Type Mice?
Front. Aging Neurosci. 13:759983.
doi: 10.3389/fnagi.2021.759983
Does Chronic Sleep Fragmentation
Lead to Alzheimer’s Disease in Young
Wild-Type Mice?
Li Ba 1†, Lifang Huang 1† , Ziyu He 1, Saiyue Deng 1, Yi Xie 1, Min Zhang 1, Cornelius Jacob 2,
Emanuele Antonecchia 2,3 , Yuqing Liu 2, Wenchang Xiao 2, Qingguo Xie 2, 3,4, Zhili Huang 5,
Chenju Yi 6, Nicola D’Ascenzo 2,3
*and Fengfei Ding 5
*
1Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan,
China, 2Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and
Technology, Wuhan, China, 3Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo Neuromed
Istituto di Ricovero e Cura a Carattere Scientifico (I.R.C.C.S.), Pozzilli, Italy, 4Department of Electronic Engineering and
Information Science, University of Science and Technology of China, Hefei, China, 5Department of Pharmacology, Shanghai
Medical College, Fudan University, Shanghai, China, 6Research Centre, The Seventh Affiliated Hospital of Sun Yat-sen
University, Shenzhen, China
Chronic sleep insufficiency is becoming a common issue in the young population
nowadays, mostly due to life habits and work stress. Studies in animal models of
neurological diseases reported that it would accelerate neurodegeneration progression
and exacerbate interstitial metabolic waste accumulation in the brain. In this paper,
we study whether chronic sleep insufficiency leads to neurodegenerative diseases
in young wild-type animals without a genetic pre-disposition. To this aim, we
modeled chronic sleep fragmentation (SF) in young wild-type mice. We detected
pathological hyperphosphorylated-tau (Ser396/Tau5) and gliosis in the SF hippocampus.
18F-labeled fluorodeoxyglucose positron emission tomography scan (18F-FDG-PET)
further revealed a significant increase in brain glucose metabolism, especially in the
hypothalamus, hippocampus and amygdala. Hippocampal RNAseq indicated that
immunological and inflammatory pathways were significantly altered in 1.5-month SF
mice. More interestingly, differential expression gene lists from stress mouse models
showed differential expression patterns between 1.5-month SF and control mice, while
Alzheimer’s disease, normal aging, and APOEε4 mutation mouse models did not exhibit
any significant pattern. In summary, 1.5-month sleep fragmentation could generate AD-
like pathological changes including tauopathy and gliosis, mainly linked to stress, as
the incremented glucose metabolism observed with PET imaging suggested. Further
investigation will show whether SF could eventually lead to chronic neurodegeneration if
the stress condition is prolonged in time.
Keywords: sleep fragmentation, Alzheimer’s disease, stress, F-18-fluorodeoxyglucose-positron emission
tomography (18F-FDG-PET), neuroinflammation, tau, amyloid-β
INTRODUCTION
Sleep is a highly conserved physiological phenomenon among mammals and important for
multiple physiological processes, including cognitive function, immune function, and hormone
release (Irwin, 2015; Krause et al., 2017). During a normal night sleep, non-rapid eye movement
(NREM) and rapid eye movement (REM) sleep alternately occur for 5–6 episodes in humans.
Ba et al. Sleep Fragmentation in Healthy Mice
Both sleep stages are important for learning and memory
consolidation. It has been shown that the risk of developing
Alzheimer’s disease and the prevalence of all-cause dementia
increases with sleep disorders (Shi et al., 2018). In clinical
observation, sleep disturbance is often present years before the
symptomatic stages of neurodegenerative diseases and becomes
more severe along with the disease progression (Guarnieri
et al., 2012; Irwin and Vitiello, 2019). In the APP/PS1 animal
model of Alzheimer’s disease, partial sleep deprivation could
accelerate Aβplaques depositions and cognition impairment
(Wang et al., 2021). All these important pieces of evidence
pointed to the hypothesis that, sleep disturbance might be a
key etiology of neurodegenerative diseases, especially AD. This
hypothesis was mechanism-wise strongly supported by the recent
discovery of a glia-based system, called the Glymphatic System,
which manages the convection flows through brain parenchymal
and drains the neurotoxic substances out of the brain (Iliff
et al., 2012). In young wild-type mice, much higher efficiency
of cerebrospinal fluid (CSF) -interstitial fluid (ISF) exchange
and drainage was found occurring in the sleep phase in vivo
(Iliff et al., 2012; Xie et al., 2013). Several follow-up studies
reported the functional failure of the Glymphatic System was
evident in neurodegenerative disease animal models, including
i.e., Alzheimer’s disease (AD) and Parkinson’s disease (PD)
(Rasmussen et al., 2018; Nedergaard and Goldman, 2020). These
studies proposed that sleep disturbance could dampen interstitial
space substance clearance, resulting in neurodegeneration due
to excessive neurotoxic protein accumulation (Ross and Poirier,
2004). Neuronal metabolic waste, such as soluble Aβand
adenosine, exhibited circadian rhythms and accumulated in brain
interstitial space after acute sleep deprivation (Kang et al., 2009;
Roh et al., 2012; Wu et al., 2016; Peng et al., 2020). Based
on these results, we were wondering if a young and healthy
brain could be turned into a neurodegenerative brain under
continuous sleep disturbance. It is a frightening hypothesis
especially for people who have long-term inevitable night shifts
and sleep insufficiency. It is a serious issue if we consider
that chronic sleep insufficiency is becoming a common issue
in young populations nowadays, mostly due to life habits and
work stress. A study conducted in the population of university
students reported that 40% of students with an average age of
around 20-year-old had smartphone addiction and these students
exhibited significantly poorer subjective sleep quality and more
severe daytime dysfunction than the ones without smartphone
addiction (Lane et al., 2021). In young to middle-aged medical
workers, more than 50% had abnormal daytime sleepiness and
poor sleep quality (Carvalho et al., 2021).
So far, there is no direct answer to the key scientific
question of whether chronic sleep insufficiency could lead to
neurodegeneration even in the absence of factors involving
genetic pre-disposition and senescence. In our previous study, we
reported chronic sleep fragmentation (SF) interventions induced
AD-like pathology in young wild-type C57BL/6 mice. We found
that 1.5-month SF treatment resulted in cognitive impairment,
intracellular Aβ1−42 accumulation, gliosis, and dysfunction of
the endosomal-autophagosome-lysosomal (EAL) pathway(Xie
et al., 2020a). Takahashi et al. observed in human autopsy
samples that intracellular Aβ1−42 accumulation was only seen
at pre-symptomatic or early stage of AD but was absent at
symptomatic stage (Takahashi et al., 2017). It has not been
tested yet if pathological tau-aggregation, another even more
important hallmark of AD pathogenesis (Wang and Mandelkow,
2016), occurs in chronic SF brain. Meanwhile, it has been
reported glucose metabolic disorder appears before symptomatic
AD in APP/PS1 mice. 18F-labeled fluorodeoxyglucose positron
emission tomography scan (18F-FDG-PET) scan revealed that
glucose utilization increases in multiple brain regions of APP/PS1
Tg mice at 2 and 3.5 months (pre-symptomatic stage) (Li et al.,
2016). So far, no study has tested glucose metabolism in chronic
sleep fragmentation brain in young wild-type mice. Based on our
scientific question and current evidence, we hypothesized that
chronic sleep fragmentation could probably initiate preceding
pathological processes for neurodegeneration even in a young
healthy brain.
In the current study, we further detected the intracellular
deposition of pathological hyperphosphorylated tau, gliosis with
immunohistochemistry, and western blot in 1.5-month chronic
SF mice in comparison with normal young wild-type mice.
We also evaluated the brain glucose metabolism with 18F-FDG-
PET and conducted hippocampal transcriptome mapping with
RNA sequencing to better understand the pathological processes
induced by chronic sleep fragmentation.
MATERIALS AND METHODS
Animals
The study involved 2–3-month-old wild-type male C57BL/6J
mice obtained from the Hubei Research Center for Laboratory
Animals (Hubei, China) which were used for experiments. All
animal procedures were approved by the Institutional Animal
Care and Use Committee of Tongji Hospital, Tongji Medical
College, Huazhong University of Science and Technology.
Chronic Sleep Fragmentation Modeling
Animals were randomly divided into a chronic SF group and a
normal sleep (NS) group with 11–12 mice in each group. Both
populations were kept in a 12-h light-dark cycle (8:00 a.m.−8:00
p.m. light-ON, 8:00 p.m.−8:00 a.m. light-OFF) with free access
to food and water. Following the procedure described previously
(Xie et al., 2020a), chronic SF model cages were secured on an
orbital rotor, vibrating at 110 rpm and with a repetitive cycle
of 10 s-on, 110 s-off, during the light-ON phase (Figure 1A).
This chronic SF procedure was performed continuously for 1.5
months (45 days). And the NS group cages were placed in the
same room as the SF cages, to keep the surrounding environment
and labor effects identical (Xie et al., 2020b).
18F-FDG-PET Scan
Both SF and NS mice were kept fasting for 24 h prior to the
scanning. On the experimentation day, mice were anesthetized by
inhalation of 2% isoflurane. Approximately 7 µCi/g 2-[18F]FDG
diluted in a 0.1 ml solution were injected intraperitoneally.
The average total dose per mouse was ∼200 µCi. Then,
60 min after injection, a 10-min-long PET/CT scan (D180,
Frontiers in Aging Neuroscience | www.frontiersin.org 2December 2021 | Volume 13 | Article 759983
Ba et al. Sleep Fragmentation in Healthy Mice
FIGURE 1 | Chronic SF increases pathologically phosphorylated tau (Ser396) in young wild-type mice hippocampus. (A) The schematic figure of the experimental
design procedure, indicating the timing of the SF model. (B,C) Western blotting and quantitative density of expression of p-tau Ser396, p-tau Thr231, and tau5 in the
cortex (B) and hippocampus (C) show an increase of p-tau Ser396 in SF hippocampus. β-actin was used as loading control. (D) Representative
immunohistochemistry images of phosphorylated tau (p-tau Ser396 and p-tau Thr231) in the cortex and hippocampus of SF and NS group. Scale Bar =20 µm. Local
enlarged images were presented in the boxes. Scale Bar =10 µm. n=8 for NS and n=9 for SF group. **P<0.01.
Frontiers in Aging Neuroscience | www.frontiersin.org 3December 2021 | Volume 13 | Article 759983
Ba et al. Sleep Fragmentation in Healthy Mice
TABLE 1 | 18F-FDG uptake per brain region in mice.
Brain region Average SUV in NS (g/cc) Average SUV in SF (g/cc) Percentage difference in SF P-value
(SF-NS)/NS,%
Whole brain 1.47 ±0.17 2.12 ±0.44 44% 0.0154*
RSTR 1.77 ±0.25 2.23 ±0.40 26% 0.0627
LSTR 1.84 ±0.26 2.35 ±0.54 28% 0.0890
CTX 1.55 ±0.14 2.05 ±0.22 32% 0.0025*
RHIP 1.45 ±0.18 2.27 ±0.61 57% 0.0381*
LHIP 1.44 ±0.20 2.27 ±0.74 58% 0.0642
THA 1.46 ±0.21 2.25 ±0.77 54% 0.0824
CB 1.49 ±0.20 2.36 ±0.70 58% 0.0502
BFS 1.45 ±0.25 1.79 ±0.36 23% 0.1205
HYP 1.04 ±0.13 1.57 ±0.38 51% 0.0182*
RAMY 1.16 ±0.14 1.63 ±0.20 41% 0.0023*
LAMY 1.23 ±0.18 1.97 ±0.42 60% 0.0063*
BS 1.21 ±0.21 2.01 ±0.59 66% 0.0204*
CG 1.65 ±0.25 2.61 ±0.98 58% 0.0925
SC 1.62 ±0.22 2.47 ±0.83 52% 0.0829
OLF 1.39 ±0.24 1.88 ±0.47 35% 0.0662
RMID 1.37 ±0.24 2.27 ±0.84 66% 0.0742
LMID 1.42 ±0.28 2.48 ±0.97 75% 0.0695
LIC 1.68 ±0.27 2.70±0.95 61% 0.0735
RIC 1.63 ±0.26 2.58 ±1.00 58% 0.1023
SUV, standard uptake value; NS, normal sleep; SF, sleep fragmentation; RSTR, right striatum; LSTR, left striatum; CTX, cortex; RHIP, right hippocampus; LHIP, left hippocampus; THA,
thalamus; CB, cerebellum; BFS, basal forebrain/septum; HYP, hypothalamus; RAMY, right amygdala; LAMY, left amygdala; BS, brain stem; CG, central gray; SC, superior colliculi; OLF,
olfactory bulb; RMID, right midbrain; LMID, left midbrain; LIC, left inferior colliculus; RIC, right inferior colliculus; *P<0.05.
RAYCAN, China) was performed. An OSEM-3D-PSF algorithm
including random and attenuation corrections was adopted for
image reconstruction. The CT scan consisted of 400 angular
projections. CT data were reconstructed using a cone-beam
algorithm for visualizing the skull structure. The voxels of the
reconstructed PET and CT images had a size of 0.5 ×0.5 ×
0.5 mm and 0.156 ×0.156 ×0.088 mm, respectively. The PET
and CT images were co-registered with a mutual information
method. Finally, the brain of the mouse was segmented with
a threshold-based algorithm by using the standardized uptake
value (SUV) of the PET image, followed by a topological
closure. The segmented brain images were co-registered with
a mouse brain atlas (Ma et al., 2005; D’Ascenzo et al., 2020)
by using a mutual information method, and 19 brain regions
were identified, as reported in Table 1. Finally, the SUV of each
mouse was calculated both in the entire brain and in each of the
segmented brain regions separately.
Tissue Preparation
The mice (n=8 for NS and n=9 for SF group) were sacrificed by
decapitation and the brains were extracted. Brains were quickly
divided into left and right hemispheres by sagittal incision.
The left hemispheres were fixed in 4% paraformaldehyde
(PFA) for the preparation of paraffin sections and subsequent
immunohistochemical staining. Cortex and hippocampus tissues
of the right hemispheres were dissected on ice, and immediately
frozen in liquid nitrogen, and stored at −80◦C for protein
extraction and western blotting detection. A separate group pair
of SF and NS mice (n=3 for each group) were prepared for
hippocampus dissection from fresh brain tissue and sent for RNA
extraction and sequencing.
Immunohistochemistry and Silver Staining
Immunohistochemistry was performed on 4 µm coronal
paraffin-embedded sections. The sampled slices were
deparaffinized and rehydrated in xylene and graded ethanol.
Then, slices were placed in citrate antigen retrieval solution (PH
6) at 96◦C for 20 min for heat-induced antigen retrieval. Slices
were incubated with 3% H2O2for 25 min to block endogenous
peroxidase. Non-specific binding sites were blocked by 10%
donkey serum for 1 h at room temperature. The slices were
incubated with the following primary antibodies: mouse anti-
Tau5 (dilution 1:400, ab80579, Abcam, UK), rabbit anti-p-Tau
(Ser396) (dilution 1:200, ab109390, Abcam), rabbit anti-p-Tau
(Thr231) (dilution 1:400, ab151559, Abcam), mouse anti-p-Tau
(Ser202/Thr205) (AT8, dilution 1:200, MN1020, Invitrogen,
USA), rabbit anti-Iba-1 (dilution 1:500, 019–19741, Wako,
Japan), and mouse anti-GFAP (dilution 1:50, 3670, CST)
overnight at 4◦C. In order to detect the specific binding of
primary antibodies, the horseradish peroxidase-conjugated
appropriate secondary antibodies (Servicebio Inc., China)
were applied for 1 h at room temperature. Antibody binding
was visualized by reaction with diaminobenzidine. The slices
with hematoxylin were counterstained and then dehydrated.
Frontiers in Aging Neuroscience | www.frontiersin.org 4December 2021 | Volume 13 | Article 759983
Ba et al. Sleep Fragmentation in Healthy Mice
The micrographs were quantified by a blinded investigator
using the Image-Pro Plus software (MediaCybernetics, USA).
Then, after deparaffinization and rehydration, slices were also
conducted silver staining by using a silver staining kit (Servicebio
Inc., China).
Western Blotting
The cortex and hippocampus of the mice were homogenized
in RIPA lysis buffer (Beyotime, China) with protease inhibitor
cocktail and phosphatase inhibitor (Roche, Switzerland). Protein
concentration was determined by using a BCA Protein Assay
Kit (Beyotime, China). A total of 30 µg of protein per well was
loaded on SDS–PAGE gels. Separated proteins on the gel were
then transferred onto the PVDF membrane (0.22 µm, Millipore,
USA) after electrophoresis. The PVDF membranes were blocked
by 5% non-fat milk for 1 h at room temperature and then
incubated with primary antibodies on a shaker overnight at
4◦C. The following primary antibodies were used for Western
blotting: mouse anti-Tau5 (dilution 1:1,000, ab80579, Abcam),
rabbit anti-p-Tau (Ser396) (ab109390, Abcam), rabbit anti-
p-Tau (Thr231) (dilution 1:1,000, ab151559, Abcam), mouse
anti-p-Tau (Ser202/Thr205) (AT8, dilution 1:500, MN1020,
Invitrogen), mouse anti-GFAP (dilution 1:1,000, 3670, CST), and
mouse anti-β-actin (1:5,000, A5316, Sigma-Aldrich, USA). The
membranes were incubated with appropriate HRP-conjugated
secondary antibodies (Jackson ImmunoResearch, USA) for 1 h
at room temperature. The bands were visualized by using
enhanced chemiluminescence kits (Advansta, USA) via a Bio-
Rad ChemiDoc XRS+imaging system (USA). The gray values
of the bands were analyzed by using ImageJ software.
RNA Extraction and Sequencing
The total RNA of the hippocampus was extracted from NS and SF
mice using Trizol (Invitrogen). The hippocampus was grounded
into powder using liquid nitrogen, the appropriate volume of
Trizol was added and homogenized for 2 min. Then, the content
was rested for 5 min and centrifuged at 12,000g for 5 min at 4◦C.
The supernatant was moved into a new EP tube, the appropriate
volume of chloroform was added, and the tubes were shaked for
15 s, then the samples were left at room temperature for 2 min.
The tubes were centrifugated for 15 min at 12,000 g, 4◦C. After
centrifugation, the upper aqueous phase containing RNA was
moved into a new EP tube and isopropyl alcohol was added,
the mixture was incubated at room temperature for 10 min. The
contents were centrifuged at 12,000 g for 10 min at 4◦C, then the
supernatant was poured off and the pellet was kept. The pellet was
washed with 75% ethanol and centrifuged again at 12,000 g for
5 min at 4◦C. The supernatant was decanted into tubes and air-
dried at room temperature for 5 min. The quality of these samples
was verified, and mRNA library construction was performed by
using Illumina Novaseq 6,000 by Shanghai Majorbio Bio-pharm
Tech Co. Ltd. (China).
Differential Expression Gene Analysis and
Functional Annotations
Due to the limited sample size (n=3 for each group), every
sample of the SF group was paired with the 3 samples in the NS
group to sort out the differential expression gene (DEG) lists. It
gave rise to 9 pairs of comparisons. DEG analysis was performed
by using the edgeR software (p-adjust <0.05 and | log2Fold
Change | ≥1). The DEGs present in more than 3 comparisons
as the DEGs between NS and chronic SF group were listed. The
functional annotation of DEGs by classification and enrichment
analysis of the Gene Ontology (GO) and Kyoto Encyclopedia of
Genes and Genomes (KEGG) was conducted.
Comparisons of SF-Induced Hippocampal
Transcriptome Characteristics With Other
Disease Models
Through works of literature and GEO database search, the DEGs
lists were extracted from the published data sources of mouse
hippocampal RNAseq, including GSE168137 for Alzheimer’s
disease (5xFAD, congenic C57BL/6J background), GSE 61915 for
normal aging (29 month old, C57BL/6J background), GSE140205
for APOEε4 mutation (humanized APOEε4 targeted replacement
homozygous mice, C57BL/6J background), as well as the acute
stress model (wild-type C57BL/6J background) (Pulga et al.,
2016) and the stress model induced by hypergravity interventions
(wild-type C57BL/6J background) (Pulga et al., 2016). The
DEseq2 Software was used to sort out DEGs (p-adjust <0.05;
| log2FoldChange | ≥1). Five DEGs lists were generated in
correspondence to the 5 models. Based on these DEGs lists,
the expression levels (quantified as Fragments Per Kilobase per
Million, FPKM) were extracted from the current hippocampal
transcriptome data. Cluster analysis was performed on the 5 gene
lists and the FPKM readouts of the data, respectively, and the
relative expression levels of genes were displayed with heatmaps.
Statistical Analysis
All quantities were characterized with mean and standard
deviation calculated in the NS and SF groups. The comparison
between these two classes was performed with a standard two-
tailed t-test if the data qualified for normal distribution and
homogeneity of variance, otherwise, with a Mann Whitney-test.
Data were analyzed using GraphPad Prism 6. Differences were
considered significant if P<0.05.
RESULTS
Chronic SF Increased Pathologically
Phosphorylated Tau (Ser396) in Young
Wild-Type Mice Brain
To investigate another set of important pathological proteins in
AD, total tau, and phosphorylated tau(p-tau) at residues Thr231,
Ser396, and Ser202/Thr205, we quantified tauopathy in the cortex
and hippocampus of SF and NS mice. Western blot identified that
the ratio of p-tau (Ser396)/total Tau5 was significantly higher in
chronic SF hippocampus (SF vs. NS: 1.5 ±0.42 vs. 1 ±0.28, n=
9 for SF and n=8 for NS, Unpaired t-test, P<0.01). Although
the ratio of p-tau (Ser396)/total Tau5 was higher in the SF cortex,
it was not significant. And the ratio of p-tau (Thr231)/total Tau5
was also comparable between chronic SF and control group. Total
Tau5 protein levels did not show a significant difference between
Frontiers in Aging Neuroscience | www.frontiersin.org 5December 2021 | Volume 13 | Article 759983
Ba et al. Sleep Fragmentation in Healthy Mice
FIGURE 2 | Chronic SF induces gliosis in the mouse cortex and hippocampus. (A–C) Activation of astrocyte and microglia in SF cortex. (A) Representative
immunohistochemistry images of GAFP and Iba1 staining in the cortex of NS and SF mice. Scale Bar =20 µm. Astrocytes labeled by GFAP, and microglia labeled by
Iba1 were shown in the boxes. Scale Bar =10 µm. (B) Quantitative analysis of positive staining of GFAP and Iba1 in the cortex of NS and SF mice.
(Continued)
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Ba et al. Sleep Fragmentation in Healthy Mice
FIGURE 2 | (C) Western blotting and quantitative analysis of GFAP in NS and SF cortex. (D–F) Activation of astrocyte and microglia in SF hippocampus. (D)
Representative immunohistochemistry images of GFAP and Iba-1 staining in the hippocampus of NS and SF mice. A representative image of the mouse hippocampus
was shown. Scale Bar =200 µm. GFAP and Iba1 staining in the hippocampus CA1 region were shown in the enlarged images. Scale Bar =20µm. Astrocytes
labeled by GFAP, and microglia labeled by Iba1 were shown in the boxes. Scale Bar =10 µm. (E) Quantitative analysis of positive staining of GFAP and Iba1 in the
hippocampus of NS and SF mice. (F) Western blotting and quantitative analysis of GFAP in NS and SF hippocampus. For immunohistochemistry, n=5; for western
blotting n=4 per group. *P<0.05, **P<0.01.
both groups (Figures 1B,C). Immunohistochemistry staining
was consistent with the results of western blot. Phosphorylated
tau and p-tau (Ser396) exhibited a more condensed deposition
in the cytoplasm of both cortex and hippocampus in the chronic
SF group, while p-tau (Thr231) staining was similar between the
SF group and control group (Figure 1D). However, phosphor-tau
(Ser202/Thr205) was comparable between NS and SF groups. We
further confirmed its expression by western blotting using AT8
antibody and found AT8 was almost absent in cortex samples of
SF mice while it showed strong protein expression in the cortex
of 12-month-old APP/PS1 mice (Supplementary Figure 1).
Therefore, the chronic SF in young wild-type mice could increase
certain pathological p-tau expressions in the hippocampus.
Chronic SF Induced Gliosis in Both Cortex
and Hippocampus
Gliosis was evident in a variety of pathological conditions, which
links tightly with injury repair, neuroinflammation. The positive
staining area of microglial marker (Ionized calcium-binding
adapter molecule 1, Iba-1) Iba-1, were both significantly higher
in chronic SF group vs. control (cortex: 1.84 ±0.56 vs. 1 ±0.22,
n=5, Unpaired t-test, P<0.05; hippocampus: 2.03 ±0.46 vs. 1
±0.09, n=5, Unpaired t-test, P<0.01) (Figures 2A,B,D,E). As
for quantification of the astrocytic marker (glial fibrillary acidic
protein, GFAP) expression, western blot of GFAP protein was
significantly higher in the chronic SF group in both cortex (SF
vs. NS: 1.51 ±0.22 vs. 1 ±0.19, n=4, Unpaired t-test, P<
0.05) (Figure 2C) and hippocampus (SF vs. NS: 1.3 ±0.1 vs. 1
±0.08, n=4, Unpaired t-test, P<0.01) (Figure 2F), while the
positive area of GFAP labeled by immunochemistry staining did
not reach a statistically significant difference (Figures 2A,B,D,E).
These results were consistent with our previous data (Xie et al.,
2020a) and characterized the activation of gliosis in both cortex
and hippocampus in the chronic SF group.
Chronic SF Enhanced Glucose Metabolism
in Multiple Brain Regions
An example of the 18F-FDG PET image of an SF and NS mouse is
shown in Figure 3A. For a quantitative interpretation of the PET
measurement, we summarized the SUV of each brain region, as
well as the total SUV for both groups in Table 1. The chronic SF
group exhibited a total SUV in the entire brain ∼44% higher with
respect to the NS group (Figure 3B) (Unpaired t-test, P<0.05).
More interestingly, the average SUV in all the 19 brain regions
separately was higher in the SF group than in the NS group
(Figure 3C). In particular, the increased glucose metabolism of
the SF group was statistically significant in the cortex (+32%,
Unpaired t-test, P<0.01), right hippocampal region (+57%,
Unpaired t-test, P<0.05), Hypothalamus (+51%, Unpaired t-
test, P<0.05), right amygdala (+41%, Unpaired t-test, P<0.01),
left amygdala (+60%, Unpaired t-test, P<0.01) and brain stem
(+66%, Unpaired t-test, P<0.05).
Hippocampal RNAseq Revealed
SF-Induced Alterations in a Broad Range
of Pathways
Hippocampus is the key region involved in learning and memory.
To explore the molecular alterations induced by chronic SF, we
compared the RNA sequencing in hippocampal tissue between
the two groups (Figure 4A). We sorted out a list of genes as
DEGs following bioinformatics analysis flow and we found 98
DEGs genes (see Supplementary Table 1). We further conducted
GO, KEGG biological pathway classification, and enrichment
analysis. GO terms with enriched gene numbers are shown
in Figure 4B, the highlighted biological processes included
metabolic process, response to stimulus, immune system process,
locomotion, etc. KEGG biological pathway classification is shown
in Figure 4C, where several pathways involving inflammation,
immune system, metabolic functions, aging, cancer, are visible.
Enriched KEGG pathways are listed in Figure 4D. They involve
infectious diseases, autoimmune diseases, allergic diseases,
transcriptional misregulation in cancer, and calcium signaling
pathway, NF-κB signaling pathway. These results found a broad
range of biological processes involved in the pathogenesis
in the hippocampus induced by chronic SF, mostly linking
with immune system dysfunction, inflammation, metabolic
dysregulation, and molecular transcription misregulation.
SF-Induced Transcriptome Alterations
Were Similar With Stress Models But Not
With Neurodegeneration or Aging Models
In order to better understand the pathological processes up-
or down-regulated by chronic SF, we plotted the expression
patterns of the DEGs extracted from hippocampal RNAseq
experiments of several disease models with cluster analysis.
First, we found the DEG lists from the published hippocampal
RNA sequencing data in mouse models of Alzheimer’s disease
model (5xFAD on congenic C57BL/6J background, GSE168137),
normal aging model (29-month-old C57BL/6J mice, GSE
61915), APOEε4 mutation model (humanized APOEε4 targeted
replacement homozygous mice on C57BL/6J background,
GSE140205), as well as two types of stress models (wild-
type C57BL/6J background) (Pulga et al., 2016). Five DEG
lists were generated corresponding to these 5 disease models.
Based on these 5 gene lists, the expression levels (FPKM)
were extracted from our current hippocampal transcriptome
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Ba et al. Sleep Fragmentation in Healthy Mice
FIGURE 3 | Chronic SF enhances the glucose uptake in the brain monitored by 18F-FDG-PET/CT. (A) Coronal, sagittal, and axial view of the brain images of an SF
(top) and NS (bottom) mouse. (B) Statistical analysis of whole-brain SUV in NS and SF group. n=5 per group. *P<0.05. (C) 18F-FDG uptake in NS and SF mice in
different brain regions expressed as SUV. n=5 per group. *P<0.05, **P<0.01. See Table 1 for the nomenclature of the brain regions.
Frontiers in Aging Neuroscience | www.frontiersin.org 8December 2021 | Volume 13 | Article 759983
Ba et al. Sleep Fragmentation in Healthy Mice
FIGURE 4 | Functional annotation of DEGs from the hippocampus in SF mice. (A) The schematic of hippocampus RNA sequencing. (B) The differential gene GO
function classification map of the hippocampus. (C) KEGG classification on the DEGs from the SF hippocampus. (D) Statistics of KEGG pathway enrichment of DEGs
map of SF hippocampus. The X-axis represents the enrichment factor, the Y-axis represents the pathway name, and color represents the Q value; the smaller the
value, the more significant the enrichment result; the size of the point represents the number of DEGs. n=3 per group.
Frontiers in Aging Neuroscience | www.frontiersin.org 9December 2021 | Volume 13 | Article 759983
Ba et al. Sleep Fragmentation in Healthy Mice
FIGURE 5 | Bioinformatic analysis revealed a link between chronic SF and stress. (A) Schematic instruction of cluster analysis of SF and related diseases or
conditions. (B–F) Heat map showing relative gene expression in SF involved in different conditions [(B) acute stress, (C) hypergravity, (D) Alzheimer’s disease, (E)
normal aging, (F) APOEε4] n=3 per group.
data. Cluster analysis was performed based on the 5 gene
lists and the FPKM readouts, respectively (Figure 5A). The
clustering heatmap demonstrates, that the gene lists of both
stress models exhibit clear differential expression patterns
between SF and NS mice (Figures 5B,C). One of the stress
models was induced by intraperitoneal injection of 10 mg
corticosterone, the other stress models were obtained by
placing the mice under a hypergravity condition of 3G for
21 days (Pulga et al., 2016). As visible in the clustering
heatmaps corresponding to the gene lists of Alzheimer’s disease,
normal aging, and APOEε4 mutation model, there are no
significant differential expression patterns between SF and NS
groups (Figures 5D–F). In other words, the major hippocampal
transcriptome alterations induced by those 3 models were
mostly not present in the chronic SF model. This analysis
showed that SF-induced transcriptome alterations were similar
with stress models but not with models of neurodegeneration
or normal aging.
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Ba et al. Sleep Fragmentation in Healthy Mice
DISCUSSION
We have been interested in answering the scientific question:
does chronic sleep fragmentation leads to Alzheimer’s disease
even in mice without genetic pre-disposition and senescence?
Based on our previous study, we found intracellular Aβ1−42
accumulation and protein degradation pathway dysfunction, as
well as obvious cognitive decline in young wild-type mice after
1.5-month sleep fragmentation (Xie et al., 2020a). However,
it was largely unknown what pathological processes were
occurring in the chronic SF brain, and whether they provided
the preceding conditions for developing neurodegeneration
pathogenesis. Herein, as a follow-up study, we further found
1.5-month chronic SF could induce intracellular accumulation
of pathological hyperphosphorylated tau (Ser396) and gliosis
in young wild-type mice brains (Figures 1,2). 18F-FDG PET
scan identified a distinctive feature of chronic SF, namely
the brain glucose utilization was upregulated, indicating an
elevated metabolic activity throughout brain regions (Figure 3).
Hippocampal RNA sequencing further showed that chronic SF
induced alterations in a broad range of pathways, involving
the immune system, inflammation, and metabolic dysregulation,
transcriptional misregulation (Figure 4). In combination with 5
other models available hippocampal RNA seq data, we observed
the stress models resulted in similar alterations of hippocampal
transcriptome characteristics with chronic SF. To our surprise,
the typical transcriptome changes in Alzheimer’s disease, normal
aging, and APOEε4 mutation were almost not present in chronic
SF (Figure 5). Thus, 1.5-month SF in young wild-type mice
induced pathological processes which were similar to stress
models, but not with neurodegenerative disease and senescence
models. However, current data indicated that, even without
genetic background and senescence, the ongoing pathological
processes in sleep fragmented brain provided the preceding basis
for developing neurodegeneration if the stressor continuously
exits. We will discuss it from several perspectives below.
Chronic Sleep Fragmentation Resulted in
Neurotoxic Protein Accumulation
The two main pathological hallmarks of AD are senile plaques
composed of amyloid-β(Aβ) and neurofibrillary tangles (NFTs)
comprised of hyperphosphorylated tau. Clinical evidence found
that cognitively normal individuals with amyloid-βdeposition
showed worse sleep quality compared with those without
amyloid deposition, meanwhile, Aβplaques could occur 10–
15 years before cognitive impairment in AD (Ju et al., 2013).
It matches our previous observation of intracellular Aβ1−42 in
sleep fragmented brain (Xie et al., 2020a). The “intracellular
amyloid hypothesis” suggested that neuronal necrosis occurs in
the ultra-early stage of Alzheimer’s disease due to intracellular
amyloid accumulation. Okazawa et al. explained that the
early accumulation of intracellular amyloid deprives a critical
effector molecule, Yes-associated protein (YAP) in the Hippo
signaling pathway that is essential for cell survival (Tanaka
et al., 2020). The initial neuronal necrosis releases high
mobility group box 1 (HMGB1) into the interstitial space and
induces a cluster of secondary necrosis in the surrounding area
(Okazawa, 2021). Okazawa et al. also reported that inhibition
of HMGB1 by anti-HMGB1 antibody prevents progression
of neurodegeneration (Fujita et al., 2016). These studies
supported the hypothesis that interstitial space Aβdeposition
(senile plaques) doesn’t represent the peak of pathological
cascade since the pathological processes were initiated much
earlier at the stage of intracellular amyloid accumulation. The
significantly increased ratio of pathological hyperphosphorylated
tau (Ser396)/total tau found in sleep fragmented brains could
be partially explained by intracellular protein degradation
dysregulation. We previously reported the aberrant expressions
of critical molecules for endosome-autophagosome-lysosomal
(EAL) pathways (Xie et al., 2020a). Therefore, intracellular AD-
like neurotoxic accumulation could be initiated in young healthy
brains due to chronic sleep fragmentation.
Disrupted Normal Circadian Rhythms
Induced Neuroendocrinological
Dysregulation and Emotional Stress
It has been proposed that emotional stress, mainly including
anxiety and depression, links with cognitive decline and AD
pathogenesis (Mendez, 2021). Furthermore, according to the
observation, stress disorders, such as PTSD, were associated with
the development of dementia (Bonanni et al., 2018). The aged
primates having a bad early life experience showed more amyloid
plaque deposition in the neocortex and a significant reduction
in synaptophysin, suggesting stress increased the vulnerability
to neurodegeneration (Merrill et al., 2011). We previously
identified both cognitive decline and anxiety-like behaviors in
1.5-month SF mice (Xie et al., 2020a). The hypothalamus-
pituitary-adrenal (HPA) axis responds to stress, inducing the
adrenal cortex release of glucocorticoid hormones, cortisol, in
humans. In patients with AD, high basal cortisol concentration
is associated with smaller hippocampus volume and cognitive
decline (Huang et al., 2009). Similarly, high cortisol levels
in plasma and cerebrospinal fluid (CSF) are found related
to the progression of cognition decline, which is useful in
predicting clinical worsening from MCI to AD (Popp et al.,
2015; Lehallier et al., 2016). Glucocorticoid was reported to
increase the expression of amyloid precursor protein (APP) and
β-site APP-cleaving enzyme1(BACE1) in astrocytes, and decrease
some Aβ-degrading proteases, leading to Aβdeposition (Bai
et al., 2011). Moreover, hippocampal corticosteroid exposure can
promote tau phosphorylation via activating glycogen synthase
kinase 3β(GSK3β) (Dey et al., 2017). In addition, glucocorticoids
impair the endo-lysosomal and autophagic mechanisms of tau
degradation, inducing accumulation of aggregated tau (Vaz-Silva
et al., 2018; Silva et al., 2019).
Sleep has a suppressive effect on the HPA stress system. Sleep
disturbance would maintain or further elevate the activity of
the stress system, which could affect the circadian rhythm of
stress system functional dynamics. Studies have shown that acute
partial sleep loss can alter glucocorticoid regulation, increasing
the cortisol level the next evening (Leproult et al., 1997). In
the current study, we found out that differential expression
gene lists from two stress mice models showed differential
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Ba et al. Sleep Fragmentation in Healthy Mice
expression patterns between 1.5-month SF and control mice,
while Alzheimer’s disease, normal aging, and APOEε4 mutation
models did not exhibit any significant differential expression
patterns (Figure 5). One of the stress models was induced by
intraperitoneal injection of corticosteroid to mimic acute stress,
while the other model was to give continuous hypergravity
stress during certain time blocks for 21 days. The later
model displayed the best clustering pattern (Figure 5), which
also shared the most similarity with our chronic sleep
fragmentation models. In summary, chronic sleep fragmentation
in young wild-type mice induced neuroendocrinology system
dysregulation and hippocampal transcriptome alterations similar
to stress models. It provided supporting evidence for the tight
connections observed clinical-wise between emotional stress and
AD pathogenesis.
Chronic Sleep Fragmentation Induced
Brain Glucose Metabolism Imbalance
In the central nervous system, glucose metabolic rate reflects
neuronal activity, synaptic density, and neuroinflammation. 18F-
FDG, the most commonly used radiotracer analog of brain
glucose, was used to capture the brain glucose metabolic changes
using a pre-clinical PET system. A previous study identified
in APP/PS1 mice that, Tg mice of 2-month-old (equivalent
to pre-clinical) and 3.5-month-old (equivalent to sub-clinical)
exhibited significant glucose utilization increase in multiple
brain regions vs. normal controls. It was explained by a
compensatory state due to neuronal hyperexcitability in pre- or
early AD. In 2- and 3.5-month-old Tg mice, the hyperglycolytic
brain regions were highlighted to be the entorhinal cortex,
hippocampus, and frontal cortex (Li et al., 2016). It was proposed
that hypermetabolism before the onset of clinical dementia
could be an early biomarker for AD diagnosis in clinical
patients (Herholz, 2010). So far as we know, we were the first
to characterize brain glucose metabolism in a chronic sleep
fragmentation model with 18F-FDG-PET in young wild-type
mice. We found out that the SUV values in brain regions of
the cortex, hippocampus, hypothalamus, amygdala, brain stem
were significantly enhanced in the SF group (Figure 3;Table 1).
Hippocampal pathology in AD patients usually happens early
and contributes to cognitive dysfunction (Wang, 2014). Similar
to APP/PS1 mice at the age of 2–3.5 month-old, hippocampus
glucose metabolism in the SF brain was also increased, it matched
with the cognitive decline in behavioral tests reported previously
(Xie et al., 2020a). Meanwhile, the hypothalamus, hippocampus,
amygdala, and brain stem link with the HPA axis (Fries et al.,
2009; Contoreggi, 2015). Hypothalamus is the center regulating
the stress hormone released from the pituitary and control
of the autonomic nervous system via releasing corticotropin-
releasing hormone (CRH) from the paraventricular nucleus
(PVN). Amygdala has abundant CRH receptors, and it provides
excitatory input to PVN and receives excitatory signals
from PVN. Hippocampus also has high concentrations of
corticosteroid receptors and has been implicated in negative
feedback regulation of HPA axis activity. That explains why these
regions were found closely linked with stress-related diseases,
such as anxiety disorder, PTSD, autoimmune conditions, and
metabolic syndrome (Contoreggi, 2015). It is consistent with
the findings of anxiety-like behaviors alterations (Xie et al.,
2020a) and stress-like hippocampal transcriptome changes in
1.5-month SF mice (Figures 4,5). Alternatively, studies showed
that neuronal synaptic strength/connectivity is higher during
wakefulness than during sleep (Vyazovskiy et al., 2008; Bero et al.,
2011). The enhanced glucose metabolism could also possibly be
due to the disrupted circadian rhythms in SF mice, and all the
PET scans were performed during the light-ON phase which is
sleep hours for the NS group. In summary, the enhanced brain
glucose metabolism could be due to hyperexcitability of neurons
and networks, very similarly with pre- and early stages of AD.
This explains the cognitive and emotional behavioral outcomes
of SF mice from the perspective of energy imbalance and matches
with stress-like hippocampal transcriptome changes.
Chronic Sleep Fragmentation Induced
Pathological Neuroinflammation in the
Brain
Hippocampal RNAseq in 1.5-month SF and NS mice revealed
that inflammatory and immunological pathways were activated
by SF (Figure 4). Enrichment of the KEGG pathway showed
that the signaling pathways involved in the DEGs were also
mainly in infection (virus, malaria, and bacterial infection),
immune-related pathways (IBD, rheumatoid arthritis, viral
myocarditis, autoimmune thyroid disease, SLE, asthma, and
NF-κB pathway) (Figure 4). Sleep deficiency can clearly induce
systemic inflammation which links with the development of
cardiovascular disease, autoimmune and neurodegenerative
diseases (Besedovsky et al., 2019; Irwin and Vitiello, 2019). Inside
the central nervous system, microglia function as macrophages
in peripheral tissue, monitoring the pathogens and maintaining
homeostasis in the brain. They are sensitive to pathological
protein aggregates. Microgliosis has been widely observed in
the pre- or early stages of neurodegenerative diseases. Their
important contribution to neurodegeneration pathogenesis has
been intensively studied (Thawkar and Kaur, 2019). We found
that SF induced significant microgliosis both in the cortex and
hippocampus (Figure 2). Interstitial fluid (ISF) Aβand tau
levels exhibit diurnal fluctuation, negatively linked with sleep
time (Kang et al., 2009; Holth et al., 2019). Sleep deprivation
increased overnight CSF Aβ1−42 levels in healthy adults (Ooms
et al., 2014; Lucey et al., 2018). Worse sleep quality and
slow-wave sleep (SWS) disruption are significantly associated
with higher CSF tau levels (Ju et al., 2017; Holth et al., 2019).
Therefore, the interstitial space microenvironment in SF could
be quite similar to pre- or early stages of neurodegenerative
diseases. The possible explanation could be that microglia
were activated by SF-induced excessive interstitial Aβ, p-tau,
dysregulated glucocorticoids levels (Fonken et al., 2016), and
stress-induced by disrupted circadian rhythms (Fonken et al.,
2015, 2016). We are wondering if these activated microglia
could initiate neurodegenerative pathogenesis in the SF brain.
Besides secretion of inflammatory cytokines which could
directly harm the neuronal cells, microglia was reported
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Ba et al. Sleep Fragmentation in Healthy Mice
to induce synapse elimination via C1q and C3 (Schafer
et al., 2012), while C3 is upregulated after acute and chronic
sleep deprivation (Bellesi et al., 2017). Meanwhile, activated
microglia exhibiting amoeboid phenotype, termed as “primed”
microglia, showed impaired phagocytic function and therefore
dampened neurotoxins clearance (Norden et al., 2015). Thus, SF-
activated microglia could probably undergo similar pathological
processes as in neurodegeneration, since the stressors were quite
similar in the pre- and early stages of neurodegenerative
diseases, such as elevated neuroendocrine hormones,
excessive metabolic waste, as well as cell debris generated
from neuronal necrosis.
CONCLUSION
The current study reported 1.5-month SF in young wild-
type mice could generate AD-like pathological changes.
Based on the hippocampal transcriptome analysis and
literature evidence, we proposed that 1.5-month SF induced
pathological processes which were quite similar with pre-
and early stages of neurodegenerative diseases, however
not yet reaching the symptomatic stage. The mechanisms
shared in common were probably neuroinflammation,
neuroendocrinological dysregulation, energy metabolism
imbalance. Avoiding long-term sleep disturbance or dealing with
chronic insomnia-induced stress could be a preventative strategy
for neurodegenerative diseases.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found below: https://www.ncbi.nlm.
nih.gov/sra/PRJNA757396.
ETHICS STATEMENT
The animal study was reviewed and approved by the Institutional
Animal Care and Use Committee of Tongji Hospital,
Tongji Medical College, Huazhong University of Science
and Technology.
AUTHOR CONTRIBUTIONS
ND’A and FD designed the research and contributed to revisions
and the final draft of the manuscript. LB and LH conducted
the experiment, data analysis, and drafted the manuscript. YX
contributed to brain sample preparations for RNA sequencing.
ZHe participated in the bioinformatic analysis. SD contributed
to setting up the chronic sleep fragmentation system. MZ, ZHu,
and CY contributed to revisions. EA, WX, YL, and QX were
responsible for the PET scans. All authors agree to be accountable
for the content of the work.
FUNDING
This work was supported by the National Natural Science
Foundation of China (81801318) and Shanghai Scientific
Society (No. 20ZR1403500; General project), in part by the
MAECI Great Relevance 2019 contributions Italy-China (Grant
No. PGR00846), in part by the National R&D Program for
Major Research Instruments of Natural Science Foundation of
China (6027808), in part by the National Key Research and
Development of China (2019YFC0118900).
ACKNOWLEDGMENTS
We sincerely acknowledge the contributions of Dr. Chun Liu in
assisting the bioinformatic analysis and plots preparations, and
we acknowledge Xia Hu in assisting us with the communication
with the PET center of the Union hospital, HUST.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnagi.
2021.759983/full#supplementary-material
Supplementary Figure 1 | Phosphor-tau (Ser202/Thr205) is comparable
between NS and SF. (A) Immunohistochemistry of phosphor-tau (Ser202/Thr205)
with AT8 antibody in cortex and hippocampus in slices of NS and SF groups.
Scale bar =20 µm. (B) Western blot of AT8 in SF cortex, using cortex of
12-month-old APP/PS1 mice as the positive control. (C) Silver staining of brain
slices collected from cortex and hippocampus of mice in NS and SF groups as
well as APP/PS1 mice. Scale bar =20 µm.
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