Abstract

   Rationale: Currently, there are occasional reports of health problems
   caused by sleep deprivation (SD). However, to date, there remains a
   lack of in-depth research regarding the effects of SD on the growth and
   development of oocytes in females. The present work aimed to
   investigate whether SD influences ovarian folliculogenesis in
   adolescent female mice.

   Methods: Using a dedicated device, SD conditions were established in
   3-week old female mice (a critical stage of follicular development) for
   6 weeks and gut microbiota and systemic metabolomics were analyzed.
   Analyses were related to parameters of folliculogenesis and
   reproductive performance of SD females.

   Results: We found that the gut microbiota and systemic metabolomics
   were severely altered in SD females and that these were associated with
   parameters of premature ovarian insufficiency (POI). These included
   increased granulosa cell apoptosis, reduced numbers of primordial
   follicles (PmFs), correlation with decreased AMH, E2, and increased LH
   in blood serum, and a parallel increased number of growing follicles
   and changes in protein expression compatible with PmF activation. SD
   also reduced oocyte maturation and reproductive performance. Notably,
   fecal microbial transplantation from SD females into normal females
   induced POI parameters in the latter while niacinamide (NAM)
   supplementation alleviated such symptoms in SD females.

   Conclusion: Gut microbiota and alterations in systemic metabolomics
   caused by SD induced POI features in juvenile females that could be
   counteracted with NAM supplementation.

   Keywords: Sleep deprivation, Premature ovarian insufficiency, Gut
   microbiota, Niacinamide metabolism, Mouse

Introduction

   Sleep plays a vital role in human physical and mental health. A lack of
   high-quality sleep weakens bodily defenses against disease and medical
   conditions. Numerous studies have highlighted the negative health
   effects of acute and chronic sleep deprivation (SD), including
   cognitive developmental impairment, decline of creative ability, and
   disorders of the gut microbiota, endocrine system, and immune function,
   thereby inducing various physical and mental diseases and even death
   [41]^1^-[42]^9. Meanwhile, reproductive disfunctions are also connected
   with SD. In animal models, the impaired secretion of sex hormones
   caused by SD is reported to decrease testosterone levels, reduced sperm
   motility, and cause apoptosis of Leydig cells in the testis [43]^10. In
   female mice, SD reduces the expression of uterine circadian genes and
   affects early pregnancy outcomes through inhibition of IL-6-dependent
   endometrium decidualization [44]^7. Furthermore, pregnancy and
   perinatal disorders are reported to be associated with insomnia in
   humans [45]^11. Moreover, sleep disturbances are increasingly
   recognized as important factors in women's reproductive health,
   particularly the menstrual cycle, pregnancy, and menopause [46]^12^,
   [47]^13. However, the effects of sleep deprivation on fertility in
   female animals is largely unknown.

   Premature ovarian insufficiency (POI) is an infertility disorder
   characterized by cessation of ovarian function that affects 3.7% of
   women before the age of 40 [48]^14^, [49]^15. POI is a highly
   heterogeneous disease that may be a result of the following: a small
   pool of primordial follicles, impaired follicular development, follicle
   dysfunction, or premature follicle depletion due to accelerated atresia
   [50]^16^, [51]^17. The etiology of POI is diverse, and may be of
   genetic, iatrogenic, autoimmune, infectious environmental, or metabolic
   origin; these can be grouped into two categories: genetic and
   non-genetic causes [52]^18. For genetic aspects of POI, approximately
   110 causative genes have been identified based on either a
   physiological role in ovaries or the phenotype of mutant mouse models.
   These include genes involved in DNA damage repair (Mcm9, Spidr),
   follicle development and maturation (Gdf9, Bmp15, Wt1, Cpeb1, and
   Foxl2), ovulation (Alox12), and hormones and receptors (Amh, Esr2,
   Fshr, and Lhcgr) [53]^14^, [54]^17. Among non-genetic causes for POI,
   the effects of metabolic disorders cannot be ignored. Accordingly,
   mutation of the GALT gene would impair the function of
   galactose-1-phosphate uridylotransferase, and trigger a metabolic
   change in galactose, leading to POI [55]^19. In addition,
   17-hydroxylase deficiency can block synthesis of all 19-carbon
   steroids, causing POI [56]^20. In addition, gut microbiota dysbiosis,
   such as disturbance of core microbiota (Bacteroides and
   Lachnoclostridium) or an increase of opportunistic pathogenic bacteria,
   has been suggested to be connected with POI [57]^21^, [58]^22.
   Therefore, the pathogenesis of POI is relatively complex, and
   understanding its origins will be conducive to the clinical treatment
   of POI patients.

   Recent studies indicate that sleep deprivation and disruption are
   associated with impaired reproductive function and poor reproductive
   outcomes in women [59]^23. Furthermore, hypothalamic-pituitary-adrenal
   axis (HPA) activation and suppression or augmentation of reproductive
   hormones and alteration of gut microbiota, are important ways in which,
   SD can affect female reproduction and fertility [60]^12^, [61]^24.
   Importantly, the gut microbiota creates a bridge between external
   environmental stimuli and host metabolism since it participates in the
   transformation and absorption of nutrients, and directly or indirectly
   impacts on a variety of tissue and organ functions. Studies have shown
   that the intestinal microbiota plays an important role in the
   occurrence and development of reproductive endocrine diseases, and is
   closely related to host health [62]^25^-[63]^27. For example, studies
   have found that gut microbiome imbalance in a metabolic syndrome model
   significantly lowers bile acid levels and causes abnormal metabolism of
   vitamin A, which in turn affects testicular cells through circulation
   and impaired spermatogenesis [64]^28. Recently, it has been suggested
   that the gut microbiota is associated with metabolic disorders in women
   with PCOS, and that its modification alters bile acid metabolism and/or
   increases IL-22 levels which may be of value for the treatment of PCOS
   [65]^29. In addition, dysregulation of the gut flora leading to
   increased beta-glucuronidase activity, has been reported to elevate
   estrogen levels, leading to reproductive diseases and cancer [66]^30.

   In the present study, we established a mouse model of SD in adolescent
   female mice and analyzed the effects of SD on ovarian follicle dynamics
   and reproductive performance from the perspective of gut microbiota and
   systemic metabolism changes induced under this condition. Results
   indicated that SD was capable of changing gut microbiota and systemic
   metabolism, and these reduced female reproductive performance; this can
   be alleviated by dietary supplementation of NAM metabolite.

Methods

Animal treatment and sample collection

   Three-week-old C57BL/6J female mice were purchased from Jinan Pengyue
   Experimental Animal Breeding Center (Jinan, China). They were housed
   under a standard 12-h light/12-h dark cycle at 23±1°C, 50%-60%
   humidity, and ad libitum food and water consumption for 3 days before
   experimental treatment. All procedures were approved by the Animal
   Ethics Committee of Qingdao Agricultural University (approval No.
   2022-0021).

   The mouse model of sleep deprivation (SD) was established for 20 h per
   day for 6 weeks by using the dedicated device KW-BD, purchased from
   NJKEW Biotechnology Co., Ltd (Nanjing, China). Briefly, a disturbance
   bar at the bottom of the SD device, rotates and slides at a constant
   frequency with a working speed of 1.0 r/min, moving under the mouse's
   body, thereby preventing sleep (for a detailed description of the
   instrument, see [67]http://www.kw689.com/product/yd/info128.html). SD
   time was set from 18:00 to 14:00 h of the next day. Animals were
   weighed every 2 days and at the end of SD treatment blood samples were
   collected from the eye and the mice were sacrificed by neck
   dislocation. Ovaries, small intestine, and small intestinal contents
   (gut microbiota) were rapidly collected in a sterile environment, snap
   frozen in liquid nitrogen, and stored at -80°C for further analysis.

Fecal microbiota transplantation

   Fecal microbiota transplantation (FMT) was performed as previously
   described [68]^31^, [69]^32. Briefly, small intestinal content was
   collected and thoroughly mixed with 20% glycerol at a ratio of 1:1,
   quick frozen in liquid nitrogen, and stored at -80°C. Before use, the
   sample was diluted with saline solution to a working concentration of
   50 mg/ml and filtered through a 70 μm cell sieve. Each recipient mouse
   received 100 μL/d filtrate by oral gavage for 2 weeks followed by
   regular feeding for 3 weeks.

Niacinamide supplementation

   Niacinamide (NAM) powder was stored and sealed at -20°C, to ensure the
   consistency of experimental results. NAM was diluted with water to the
   required concentration (100, 200, 400 mg/ml) before use, so that
   similar volumes were administered to mice daily at a working dose of
   100, 200, or 400 mg/kg body weight. Intragastric NAM was administered
   for 6 weeks under SD conditions.

Histology and immunohistochemistry

   The procedures for ovarian histology and immunohistochemistry (IHC)
   have been previously described in detail [70]^33. Briefly, ovaries were
   fixed overnight in paraformaldehyde (Solarbio, P1110), flushed with
   water, dehydrated with graded alcohols, and transferred into xylene
   before paraffin embedding. Ovaries were serially sectioned to a
   thickness of 5 µm and sections were placed onto slides for drying on a
   heated plate at 42°C. Samples were then left in an incubator at 60°C
   for 2 h and treated with xylene twice for 15 min each at room
   temperature (RT). After rehydration in graded alcohols, the sections
   were washed in PBS for 10 min, placed in citric acid antigen retrieval
   solution at 96°C for 10 min, and after cooling, sequentially treated
   with tris-buffered saline (TBS) and TBST (TBS with Tween-20) for 5 min.
   Samples were then treated with 3% H[2]O[2] to remove endogenous
   peroxidase activity, reacted with BDT (TBS containing 3% BSA and 10%
   goat serum) at RT for 30 min, and incubated in primary antibodies
   overnight at 4°C. The next day, slides were incubated with a
   peroxidase-conjugated secondary antibody (Beyotime, A0208, Shanghai,
   China) at 37°C for 45 min. Finally, the samples were sequentially
   stained using a DAB kit (ZSGB-BIO, ZLI-9017, Beijing, China) and
   hematoxylin (Sangon Biotech, E607317), sealed with neutral resin, and
   observed under an Olympus BX51 microscope.

   Starting from the middle section of the ovary, additional sections were
   selected every 15-20, both forward and backward, to give a total of 9
   sections/ovary that were scored. Follicles were classified as i)
   primordial if they contained an oocyte surrounded by a single layer of
   flattened granulosa cells; ii) primary if they contained an oocyte
   surrounded by a single layer of flattened and cuboidal granulosa cells;
   iii) secondary if they contained an oocyte surrounded by 2 to 3 layers
   of cuboidal granulosa cells and no antral space; and iv) antral if they
   contained an oocyte surrounded by >3 layers of granulosa cells and an
   antral space. To avoid double-counting, only follicles containing
   oocytes with visible nuclei were considered [71]^34.

Small intestine histology and immunofluorescence staining

   Similar to ovarian tissues, small intestine tissues were fixed,
   embedded, and then continuously sectioned at 7 μm thickness. After
   treatment with xylene and alcohol, the samples were stained with
   hematoxylin and eosin (Solarbio, G1100) for histological observation.

   For small intestine tissue immunofluorescence (IF) staining, slides
   carrying sections were sequentially immersed in xylene, alcohol, PBS,
   and sodium citrate buffers before staining. After cooling, the slides
   were reacted with BDT at RT for 30 min; subsequently, they were
   incubated with the primary antibody at 4°C overnight. The next day, the
   slides were incubated with secondary antibodies in a dark room at 37°C
   for 45 min, and then the nuclei were stained with Hoechst 33342 at RT
   for 5 min. Finally, each slide was sealed with an anti-fluorescence
   attenuation quencher. The slides were then viewed under a fluorescence
   microscope.

ELISA

   The levels of NAM, FSH, LH, AMH, and E2 in blood serum samples were
   measured using dedicated enzyme-linked immunosorbent assay (ELISA)
   kits: JM-11773M2, JM-02838M2, JM-11607M2 (Jingmei Biological
   Technology, Jiangsu, China), and JB1334-Mu, JB973-Mu (Jinma
   Biotechnology, Shanghai, China), respectively, according to the
   manufacturer's instructions.

Western blotting

   Ovarian tissues were lysed in appropriate amounts of RIPA (Beyotime,
   P0013C) and sodium dodecyl sulfate (SDS) buffers. The extracted
   proteins were denatured by boiling the samples for 5 min and then
   stored at -20°C. Electrophoresis was conducted using a
   BioRad-PowerPacBasic electrophoresis apparatus (BIO-RAD, Taiwan,
   China). Proteins were transferred from the gel to a poly (vinylidene
   fluoride) membrane (PVDF, Millipore, ISEQ00010) at 200 mA using the
   wet-transfer method. After blocking for 4 h in TBST containing 5% BSA,
   the membranes were incubated with primary antibodies overnight at 4°C.
   The next morning, after washing with TBST, they were incubated with
   horseradish peroxidase-conjugated secondary antibodies for 90 min at RT
   and target protein bands were detected using a BeyoECL Plus kit
   (Beyotime, P0018). Proteins were quantified using ImageJ software. The
   intensities of the protein bands were normalized to GAPDH, and the
   relative amounts of the target proteins compared to that of the
   control. The antibody information used is listed in [72]Table S1.

TUNEL staining

   Paraffin ovary sections were dewaxed with xylene and rehydrated with
   graded alcohols. TUNEL staining was conducted using a TUNEL BrightGreen
   Apoptosis Detection Kit (Vazyme, A113-02, Nanjing, China), following
   the manufacturer's instructions and counterstained with Hoechst 33342
   (Beyotime, C1022) for 5 min. Finally, after the addition of
   anti-fluorescence quencher, the slides were sealed and stored in the
   dark at 4°C or directly observed under a microscope.

In vitro maturation and meiotic spindle staining of the oocytes

   Fully-grown germinal vesicle (GV) oocytes were collected and cultured
   following standard protocols as described in previous papers [73]^33.
   The number of oocytes undergoing germinal vesicle break-down (GVBD) and
   first polar body extrusion (PBE) were counted after 4-6 h and 12-16 h,
   respectively. In order to visualize the meiotic spindle, oocytes were
   fixed in 4% paraformaldehyde for 30 min at RT, and permeabilizated in
   PBS supplemented with 0.5% Triton X-100 for 20 min. After blocking in
   PBS supplemented with 1% BSA for 1 h, the oocytes were incubated with
   α-tubulin antibody (Invitrogen, 322588) for 2 h and finally rinsed in
   washing buffer (PBS plus 0.1% Tween 20 and 0.01% Triton X-100) and
   stained with Hoechst 33342 for 15 min at RT. A laser-scanning confocal
   microscope (Leica TCS SP5 II, Mannheim, Germany) was used to observe
   and capture representative images.

Real-Time Quantitative PCR

   Total RNA was extracted from ovarian tissue according to the
   instructions of a SPARKeasy Improved Tissue/Cell RNA Extraction Kit
   (Sparkjade, AC0202, Shandong, China), followed by reverse transcription
   using SPARKscript II RT Plus (Sparkjade, AG0304, Shandong, China). Real
   Time quantitative PCRs (RT-qPCRs) were conducted using a SYBR Premix Ex
   Taq^TM fluorescent dye and CFX96 Real-Time PCR instruments
   (BioRad-CFX96, USA). The primers used are listed in the [74]Table S2.

Single cell RNA sequencing (scRNA-seq)

   Oocytes were individually lysed, and cDNA synthesis and amplification
   performed following the SMART-seq2 protocol. The cDNA libraries were
   then checked on a Bioanalyzer instrument (Agilent), and the passed
   samples were finally chosen for sequencing. Sequence libraries were
   constructed using Annoroad Gene Technology Corporation (Beijing,
   China), and were sequenced by Illumina NovaSeq 6000 using the 150 bp
   paired-end strategy. Oocyte samples from 3 different ovaries were mixed
   and treated as one replicate, with 2 replicates per group for
   scRNA-seq. The scRNA-seq data were treated with FastQC (v0.11.8) for
   quality control and then aligned with the reference genome sequence
   [75]^35. FPKM (fragment per kilobase of transcript per million mapped
   reads) values were calculated to quantify gene expression abundance.
   Differential expression RNA analysis was performed using DESeq2
   software comparing 2 different groups [76]^36. Differentially expressed
   genes (DEGs) were identified with a parameter of false discovery rate
   (FDR) <0.05 and absolute fold change (FC) ≥2. Pathway enrichment
   analysis was performed using Metascape [77]^37.

RNA sequencing (RNA-seq)

   Total RNA was extracted from ovaries according to the manufacturer's
   instructions. High quality RNA was used for cDNA library construction
   and Illumina sequencing by Annoroad Gene Technology Corporation
   (Beijing, China). Firstly, FastQC (v0.11.8) was used to check the
   quality of sequencing raw data. FASTP (v0.23.1) software was used to
   remove low-quality data, including joint contaminated reads and
   low-quality reads. High-quality data were aligned to a reference genome
   using STAR (v2.7.0f) software, and gene counts were calculated using
   FeatureCounts (v1.6.3) software.

16S rRNA sequencing and analysis

   16S rRNA sequencing analysis was performed with 15 samples in each
   group (30 in total). First, genomic DNA was extracted from the small
   intestine contents. The amplified V3-V4 region sequences were pooled
   and purified using a Qiagen gel extraction kit. Sequencing libraries
   were generated using TruSeq® DNA PCR-Free Sample Preparation Kit
   (Illumina, USA) following manufacturer's instructions. The library was
   sequenced using the Illumina NovaSeq platform (Novogene, Beijing,
   China) and 250 bp paired-end reads were generated. Subsequently, we
   used QIIME2 software (v2020.6) to merge paired ends and perform quality
   control; the DADA2 amplicon algorithm was used to remove sequencing
   noise, delete chimera sequences; and amplicon sequence variants (ASVs)
   were selected to generate signatures of each sample (feature table).
   Sequences with a similarity ≥97% were assigned to the same operational
   taxonomic unit (OTU). Based on bioinformatics, the alpha diversity
   indices of samples in groups were analyzed (including ACE, Chao1,
   Shannon, and Simpson). ANOSIM similarity analysis using the Bray-Curtis
   algorithm was used to determine differences between groups.
   Representative species were determined using an online tool
   ([78]https://huttenhower.sph.harvard.edu/galaxy/) for linear
   discriminant analysis effect size (LEfSe) analysis.

Non-targeted metabolomics

   Non-targeted metabolomics were performed in collaboration with Shanghai
   Applied Protein Technology Co., Ltd (Shanghai, China). First, an
   appropriate amount of serum sample was added to pre-cooled
   methanol/acetonitrile/water solution (2:2:1, v/v), and vortexed, and
   mixed; it was then exposed to ultrasound at low temperature for 30 min,
   subsequently the centrifugal supernatant was taken and dried in a
   vacuum. During mass spectrometry analysis, 100 μL of acetonitrile
   aqueous solution (acetonitrile: water =1:1, v/v) was added for
   resolution. After vortex centrifugation, the supernatant was taken for
   sample analysis. Then the samples were separated by Agilent 1290
   Infinity LC ultra-high performance liquid chromatography (UHPLC) HILIC
   column: column temperature was 25°C; flow rate was 0.5 mL/min;
   injection volume was 2 μL. After separation, mass spectrometry was
   performed with a Triple TOF 6600 mass spectrometer (AB SCIEX) using
   positive and negative ion modes of electrospray ionization (ESI),
   respectively. The raw data were converted into files in mzXML format by
   ProteoWizard, and then XCMS software was used for peak alignment,
   retention time correction, and peak area extraction. Structures of
   metabolites in biological samples were identified by matching with
   information of metabolite retention time, molecular mass (molecular
   mass error is less than 10 ppm), second-order fragmentation spectrum,
   and collision energy [79]^38. According to the results of metabolite
   identification, the quality of experimental data was evaluated, and
   then the data were analyzed. Metabolites measured by positive and
   negative ion channels were combined, and the dimension reduction of the
   data was analyzed based on partial least squares discriminant analysis
   (PLS-DA). According to the results of metabolite comparison, all the
   differentiated metabolites were displayed in a volcano map.
   MetaboAnalyst (v5.0) was used for pathway enrichment analysis to screen
   metabolic pathways with significant changes [80]^39.

Statistical analysis

   All data contained at least 3 independent replicates and the results
   are presented as mean ± standard error of the mean (SEM). Significant
   differences were determined by t-test or one-way analysis of variance
   (ANOVA) using GraphPad Prism 8 software (GraphPad Software Inc., San
   Diego, CA); values with *P < 0.05 and **P < 0.01 were considered to
   have significant and highly significant differences, respectively.

Results

Sleep deprivation caused a reduction of body weight and gut dysbiosis

   SD was induced in female mice from 3 to 9 weeks of age, the period of
   adolescence critical for establishment of the ovarian reserve and its
   dynamic control [81]^40^, [82]^41. During the 6-week experimental
   period, we registered a constant body weight increase and decrease in
   control (CTRL) and SD mice, respectively (Figure [83]1A-B).
   Accordingly, at the end of the treatment, the body weight of SD females
   was significantly lower than CTRL (Figure [84]1C).

Figure 1.

   [85]Figure 1
   [86]Open in a new tab

   Body weight and gut microbiota changes caused by sleep deprivation. (A)
   Schematic diagram of the sleep deprivation (SD) experiment. (B) Changes
   of body weight in control (CTRL) and SD females during the experimental
   time (n=12). (C) Body weight of CTRL and SD females at the end of the
   experimental time (n=12). (D) ANOSIM similarity analysis of gut
   microbiota obtained from CTRL and SD females at the end of the
   experimental time. (E) PCA of the data shown in D. (F) α-Diversity
   indexes, including Shannon, Simpson, Chao1, and ACE of the data shown
   in D. (G) Relative abundance of the bacterial phyla in the gut
   microbiota of CTRL and SD females. (H) Relative abundance of bacterial
   genera in the gut microbiota of CTRL and SD females. (I) LDA algorithm
   of CTRL vs SD gut microbiota bacterial genera with an LDA score
   threshold of 2.0. (J) Cladogram of the main taxa of microbiota in CTRL
   and SD females based on LEfSe analysis.

   The impact of SD on the gut microbiota was investigated by 16S rRNA
   sequencing of the small intestine contents obtained from CTRL and SD
   mice at the end of treatment. Analysis of similarities (ANOSIM)
   indicated that differences between CTRL and SD groups were
   significantly greater than differences within groups (Figure [87]1D).
   The PCA scatter plot confirmed significant changes within the gut
   microbiota caused by SD (Figure [88]1E). Moreover, the α-diversity
   analysis, including Shannon, Simpson, Chao1, and ACE indices, confirmed
   distinct differences in the abundance of microbial species between CTRL
   and SD groups (Figure [89]1F). Namely, bacterial species annotation
   data showed that SD altered the abundance of gut microbes both at the
   phylum and genus level (Figure [90]1G-H). Among phyla, Firmicutes and
   Verrucomicrbia decreased while Bacteroides, Proteobacteria, and
   Actinobacteria increased. To identify the specific microbiota related
   to SD, and using the linear discriminant analysis (LDA) effect size
   (LEfSe) method, we screened out 120 biomarkers. The top genera in SD
   were Allobaculum, Desulfovibrio, and Adlercreuzia; whereas in CTRL,
   Lactobacillus, Candidatus Arthromitus, and Lactococcus prevailed
   (Figure [91]1I-J). On this basis, we also employed STAMP software to
   conduct the difference analysis based on the abundance of gut microbes.
   In total, 102 types of differential bacterial genera were found, of
   which 64 genera were up-regulated after SD, including Kaistobacter,
   Streptomyces, Flavisolibacter, Arthrobacter, and Promicromonospora;
   meanwhile, 38 genera were down-regulated, including Pseudoalteromonas,
   vibrio, Alteromonas, Octadecabacter, and Phaeobacter ([92]Figure S1A).
   Moreover, based on analysis of LEfSe and STAMP results, 96 bacteria
   genera were found in both groups ([93]Figure S1B and [94]Table S3).
   Among them, 12 types of the genera were selected and displayed, 6 of
   which were up-regulated and 6 down-regulated by SD ([95]Figure S1C).
   These results indicated that there were substantial differences in the
   gut microbiome between SD and CTRL groups.

Sleep deprivation impacted on intestinal immunity and the integrity of the
intestinal epithelial barrier

   It is well known that the integrity of the intestinal epithelial
   barrier is essential for maintaining a healthy symbiosis between the
   epithelial cells and the gut bacteria, and that tight junction proteins
   play a key role in maintaining barrier integrity. Thus, we collected
   histological samples of the small intestine and evaluated the
   expression of tight junction proteins in this organ of CTRL and SD
   mice. Histological sections showed that, after SD, the height of
   intestinal villi decreased significantly and, while the crypt depth did
   not change significantly, the ratio of villi height to crypt depth
   decreased significantly. Moreover, the epithelial layer appeared less
   compact, and cells showed hill-defined borders (Figure [96]2A-D). Most
   importantly, after SD, the fluorescence intensity of tight junction
   proteins such as Claudin-1, Occludin, and ZO-1 in tissue sections
   decreased, while their presence, as detected by WB, was reduced (Figure
   [97]2E-G). Interestingly, these changes were associated with increased
   levels of the inflammatory proteins interleukin-6 (IL-6) and tumor
   necrosis factor-alpha (TNF-α) (Figure [98]2F-G), a further indication
   of altered integrity of the epithelial barrier. In addition, an
   accumulation of CD4^+ T cells and CD68^+ macrophages was found in the
   lamina propria of the small intestine in sleep-deprived mice (Figure
   [99]2H-I), suggesting that SD increased immune cell infiltration in the
   lamina propria of the small intestine.

Figure 2.

   [100]Figure 2
   [101]Open in a new tab

   Intestinal barrier disturbance caused by sleep deprivation. (A)
   Representative H&E histological sections of small intestine samples
   collected from CTRL and SD females. (B-D) Comparison of villi height,
   crypt depth, and villi height/crypt depth of small intestine samples of
   CTRL and SD females. (E) Fluorescent immunostaining (IF) for the
   indicated tight junction proteins on small intestinal sections of CTRL
   and SD females. (F-G) Representative WB and relative densitometric
   evaluation of the amount of the indicated tight junction proteins and
   inflammatory cytokines in the small intestine of CTRL and SD females.
   (H-I) IF quantitative statistics of CD4 and CD68 proteins associated
   with intestinal immune cells.

Sleep deprivation caused systemic metabolic changes

   The gut microbiota plays a vital role in its host's metabolism
   [102]^28. Several studies support the notion that metabolic processes
   are correlated with sleep and that SD can cause maladaptive metabolic
   changes [103]^42. In order to verify how our SD model impacted on
   systemic metabolism, we examined the untargeted metabolome profile of
   blood serum samples obtained from CTRL and SD mice. Partial least
   squares discriminant analysis (PLS-DA) of the 1022 identified
   metabolites resulted in distinct metabolite clustering in CTRL and SD
   groups (Figure [104]3A). Using an orthogonal PLS-DA (OPLS-DA) model to
   obtain the variable importance for the projection (VIP) with OPLS-DA
   VIP >1 and P-value < 0.05, 122 differential metabolites were
   identified, 82 of which were up-regulated and 40 down-regulated after
   SD (Figure [105]3B). According to pathway annotation results of the
   Kyoto Encyclopedia of Genes and Genomes (KEGG) database, a total of 18
   metabolic pathways were enriched in the differential metabolites
   between the 2 groups, including the metabolism of galactose, nicotinate
   and nicotinamiade/niacinamide (NAM), starch and sucrose, and fructose
   and mannose. Among them, nicotinate and NAM metabolic pathways had the
   highest enrichment rates (Figure [106]3C). The nicotinate and NAM
   pathways in the CTRL and SD groups are shown in Figure [107]3D. Namely,
   the relative abundance of serum NAM and 1-Methylnicotinamide was higher
   in CTRL than in the SD group (Figure [108]3E-F). To investigate the
   functional relationship between gut microbial dysbiosis and NAM
   metabolism, Spearman's correlation analysis was performed. As shown in
   Figure [109]3G, Spearman's correlation analysis between NAM and genus
   level bacteria showed that a total of 16 bacteria genera were
   significantly correlated with NAM, among which 13 bacteria genera were
   significantly different between CTRL and SD groups ([110]Figure S1B).

Figure 3.

   [111]Figure 3
   [112]Open in a new tab

   Impact of sleep deprivation on the metabolome. (A) PLS-DA discriminant
   analysis of the metabolome in blood serum from CTRL and SD females. (B)
   Volcano map of metabolite changes caused by SD. (C) KEGG metabolic
   pathway enrichment analysis. (D) Heatmap of metabolites in niacinamide
   (NAM) metabolic pathways. (E-F) Comparison of the relative abundance of
   NAM and 1-Methylnicotinamide in the metabolome of CTRL and SD females.
   (G) Spearman correlation analysis between NAM and different bacterial
   genera. The red font represents the bacteria genera with significant
   differences shown between CTRL and SD groups.

Sleep deprivation affected follicle number and dynamics and increased
granulosa cell apoptosis

   At the end of treatment of SD females, the blood concentration of AMH
   and E2 decreased, while that of LH increased compared to CTRL (Figure
   [113]4A). Moreover, in parallel to the decrease in body weight, we
   found a significant reduction in ovary size and index (ovary and body
   weight ratio) in comparison to CTRL (Figure [114]4B-C). The SD effects
   on the ovary were further investigated by comparing the number of
   follicles and the percent of each follicle class in CTRL and SD females
   (Figure [115]4D and [116]Figure S2A).

Figure 4.

   [117]Figure 4
   [118]Open in a new tab

   Effects of sleep deprivation on ovary index and follicle dynamics. (A)
   Blood serum concentrations of AMH, E2, FSH, and LH hormones at the end
   of the experimental time. (B) Representative picture of the CTRL and SD
   ovary at the end of the experimental time, bar =1 mm. (C) Ovary and
   body weight ratio (ovary index) in CTRL and SD females at the end of
   the experimental time (n=12). (D) Representative IHC for the
   oocyte-specific MVH protein on CTRL and SD ovary sections, bar = 200
   μm. (E) Number of different classes of follicles in the 9 sections of
   CTRL and SD ovaries at the end of the experimental time. (F) Percent of
   primordial follicles and growing follicles (all other follicle classes,
   see below) for sections of CTRL and SD ovaries at the end of the
   experimental time. (G-I) Representative WB and relative densitometric
   evaluation of the amount of the indicated proteins from CTRL and SD
   ovaries at the end of the experimental time. PmF: Primordial follicle,
   PF: Primary follicle, SF: Secondary follicle, AF: Antral follicle.

   Compared with CTRL, after only 4 weeks of SD treatment, the number of
   follicles in the 9 scored sections of SD ovaries had decreased
   significantly, mostly through a marked reduction in the number of PmF,
   representing the ovarian reserve ([119]Figure S2B). At 6 weeks, this
   trend was even more pronounced (Figure [120]4E), and the percent of
   growing follicles appeared slightly higher in SD (49.66%±1.50%) than
   CTRL (44.92%±1.19%) ovaries (Figure [121]4F), suggesting an increment
   in PmF activation. In line with such a possibility, serum levels of AMH
   (a marker of ovarian reserve) in SD females were significantly lower
   than in CTRL (Figure [122]4A). Moreover, both for mRNA and proteins,
   the expression of AMH as well that of oocyte specific proteins [growth
   differentiation factor 9 (GDF9), bone morphogenetic protein 15 (BMP15),
   and mouse vasa homolog (MVH)] were reduced in SD compared with CTRL
   ovaries (Figure [123]4G-H and [124]Figure S2C). At the same time,
   markers of PmF activation including phosphorylated PI3K, AKT, and mTOR
   proteins were increased whereas the levels of critical inhibitors of
   PmF activation including PTEN and FOXO3 proteins, decreased (Figure
   [125]4I).

   In SD ovaries, besides indication of PmF activation, we found that the
   number of TUNEL-positive cells, mainly among granulosa cells, were
   significantly increase in comparison to CTRL ([126]Figure S3A-B).
   Raised amounts of γ-H2AX protein, a marker of DNA damage, and
   proapoptotic markers such as BAX/BCL2, P53 and cleaved Caspase 9
   paralleled this increase ([127]Figure S3C-D).

Sleep deprivation impaired oocyte mitochondrial function and meiotic
maturation

   To measure the influence of SD on oocyte quality, we compared the
   ability of fully grown germinal vesicle (GV) oocytes cultured in vitro
   from CTRL and SD ovaries to resume meiosis [as germinal vesicle
   break-down (GVBD)], reach metaphase II (extrusion of PB1), and to
   correctly assemble the meiotic spindle. The results reported in Figures
   [128]5A and [129]5B show that a smaller number of SD oocytes underwent
   GVBD and extruded PB1 in comparison to CTRL. Moreover, the percent of
   spindle (Metaphase I) abnormalities and chromosome misalignments
   increased significantly in SD oocytes after 8 h of culture (Figure
   [130]5C-D).

Figure 5.

   [131]Figure 5
   [132]Open in a new tab

   Sleep deprivation affected oocyte maturation, spindle assembly, and
   mitochondrial function. (A) Representative pictures of GV, GVBD, and
   PB1. Scale bar = 100 μm. (B) Percent of GVBD and PB1 in CTRL and SD
   oocytes after in vitro culture for 4-6 h and 12-16 h (n>90). (C)
   Representative pictures of normal and abnormal spindle morphologies and
   chromosome alignment. (D) Percent of aberrant spindles and misaligned
   chromosomes in CTRL and SD oocytes after in vitro culture for 8 h
   (n>90). (E) Volcano map of gene transcript differences in CTRL and SD
   oocytes. (F) Enrichment terms of down-regulated genes. (G) Gene
   expression levels of the indicated genes related to mitochondrial
   function. (H) RT-qPCR of the same genes showed in G. (I) Enrichment
   terms of up-regulated genes.

   In order to identify molecular pathways or altered functions, we next
   performed single-cell transcriptome sequencing (scRNA-seq) analyses of
   CTRL and SD oocytes. Among 1640 differentially expressed genes, we
   found 1006 genes up-regulated and 634 genes down-regulated in SD
   oocytes (Figure [133]5E). Interestingly, down-regulated genes were
   mainly enriched in terms of oxidative phosphorylation, mitochondrial
   gene expression, chromosome segregation, and mitophagy signaling
   pathways (Figure [134]5F). Deleterious effects of SD on the
   mitochondrial function of oocytes were supported by reductions in mRNA
   levels of Ndufa6, Ndufb1, Ndufb11, Cox6c, Mrps33, and Csnk2a1 involved
   in oxidative phosphorylation and respiratory chain complex assembly,
   mitochondrial translation, and mitophagy (Figure [135]5G-H).
   Up-regulated genes were mainly enriched in terms of protein-DNA complex
   organization, DNA metabolic process, and regulation of cell cycle
   process pathways (Figure [136]5I).

   In addition, using RNA-seq analysis of the whole ovaries and c-means
   clustering algorithm, 8 gene modules were generated ([137]Figure S4A).
   Notably, most of the genes in Cluster 1, mainly enriched in
   mitochondrial function, appeared downregulated in SD ovaries
   ([138]Figure S4B), which is consistent with scRNA-seq results of
   oocytes, while several genes in cluster 7 were mainly enriched in
   histone modification, and mRNA processing was up-regulated in SD
   ovaries ([139]Figure S4C). We also identified 133 common differential
   genes both in RNA-seq and scRNA-seq ([140]Figure S4D), which are
   involved in the regulation of growth and development, chromosome
   organization, and ovarian infertility ([141]Figure S4E). In addition,
   the expression pattern of POI-related genes in the ovaries also changed
   significantly after SD ([142]Figure S4F).

Fecal microbiota transplantation in host females recapitulated ovarian damage
induced by sleep deprivation

   Fecal microbiota transplantation (FMT) is used to directly change the
   recipient's gut microbiota [143]^31^, [144]^43. In order to understand
   whether changes of gut microbiota caused by SD could be responsible for
   the ovary disorders described in the previous sections, we transplanted
   the fecal microbiota of SD females to normal mice by oral gavage
   (Figure [145]6A). After 2 weeks of feeding and 3 weeks of colonization,
   we found that in FMT females blood serum concentrations of NAM were
   significantly lower than in CTRL (Figure [146]6D). Furthermore, the
   body and ovary weights of recipient females were lower than those of
   CTRL and comparable to those of SD mice reported in Figure [147]1C and
   Figure [148]4C (Figure [149]6B-C). Notably, the ovary indices of CTRL
   and FMT females were not significantly different (not shown).

Figure 6.

   [150]Figure 6
   [151]Open in a new tab

   Fecal microbiota transplantation from SD females reproduces the effects
   of SD in normal host females. (A) Study design of fecal microbiota
   transplantation (FMT). (B-C) Body and ovary weights of females under
   the indicated experimental conditions. (D) Concentration of niacinamide
   (NAM) in the blood serum obtained from females under the indicated
   experimental conditions. (E) Total number and different follicle class
   number for sections of ovaries from females under the indicated
   experimental conditions. (F) Percent of PmF and growing follicles in
   the ovaries of females under the indicated experimental conditions. (G)
   Representative WB and relative densitometric evaluation of the amount
   of the indicated proteins in the ovaries of females under the indicated
   experimental conditions.

   Moreover, FMT females showed follicle alterations similar to those
   induced by SD including reduced number (mainly PmF), and altered
   dynamics (Figure [152]6E-F), associated with decreased expression of
   AMH, GDF9, BMP15, and MVH proteins (Figure [153]6G).

Supplementation of niacinamide alleviated the effects of sleep deprivation on
follicle dynamics and female reproductive performance

   It is well known that NAM, a nicotinamide adenine dinucleotide (NAD^+)
   precursor, is an essential redox cofactor for cellular reactions in
   mammals and microbes. This is mostly due to the fact that circulating
   NAM enters the gut lumen and is able to support microbial NAD^+
   synthesis [154]^44. For this reason and since we found NAM deficiency
   to be attributable to a specific gut microbiota imbalance, as one of
   the most evident effects of our SD treatment in the female mice, we
   performed experiments to investigate whether NAM supplementation could
   counteract the effects of SD on follicle dynamics described in the
   previous sections (Figure [155]7A). The results showed that although
   the feeding of 100-400 mg/kg NAM for 6 weeks had no significant effect
   on body and ovary weight of mice ([156]Figure S5A), the ovaries of SD
   females fed with 200 mg/kg NAM had follicle numbers and percent of PmFs
   and of the other follicle classes comparable to those of CTRL (Figure
   [157]7B-C and [158]Figure S5B-D). In these ovaries, the levels of AMH
   and the oocyte secreted factors GDF9 and BMP15 were at concentrations
   comparable to those of CTRL (Figure [159]7D). In addition, a higher
   proportion of oocytes from female mice supplemented with NAM reached
   GVBD and expelled PB1 compared to SD (Figure [160]7E-F). In terms of
   reproductive performance, we found a significant reduction in the rate
   of pregnant (marked by the presence of vaginal plug) and a significant
   increase in the rate of false pregnancy or miscarriage in SD female
   mice, which were effectively improved after NAM supplementation,
   although the indexes of litter size and average birth weight showed no
   significant difference (Figure [161]7G-J).

Figure 7.

   [162]Figure 7
   [163]Open in a new tab

   Dietary niacinamide supplementation alleviated the effects of sleep
   deprivation on the ovarian follicle reserve. (A) Study design of
   niacinamide (NAM) supplementation. (B) Total number and different
   follicle class number for sections of ovaries of females under the
   indicated experimental conditions. (C) Percent of PmF and growing
   follicles in the ovaries of females under the indicated experimental
   conditions. (D) Representative WB and relative densitometric evaluation
   of the amounts of the indicated proteins in the ovaries of females
   under the indicated experimental conditions. (E) Representative
   pictures of oocytes at GV, GVBD, and PB1 stages in SD and SD+NAM
   groups. Scale bar = 100 μm. (F) Percentage of oocytes at GVBD and PB1
   stages in SD, and SD+NAM groups after in vitro culture (n>90). (G) The
   statistical effect of vaginal plug rate in CTRL, SD, and SD+NAM groups,
   also including false pregnancies or miscarriage rate (H, number of
   female mice with vaginal plugs in each group >29), pup numbers (I), and
   average birth weight of offspring (J).

Discussion

   SD is a stressor that affects multiple tissues throughout the body. In
   recent years, research has shown that abnormal sleep patterns and
   duration affect systemic metabolism and gut microbiota composition.
   This latter in turn provides substrates for supplying and regulating
   multiple compounds reaching the circulation and influencing the
   function of distal organs. In the present study, using a validated
   device and female mice, we investigated the effects of SD on murine
   ovaries during the adolescence period when follicles, the ovarian
   reserve, and the basic dynamics and control of folliculogenesis are
   established.

   We first confirmed that SD caused gut dysbiosis and systemic metabolic
   changes in the treated females. In line with other studies reporting
   mass death of intestinal epithelial cells, reduction of goblet cells,
   decreased expression of tight junction proteins, and impaired
   intestinal barrier function after SD [164]^45^-[165]^47, we obtained
   histological evidence suggesting the presence of an altered intestinal
   immunity and integrity of the intestinal epithelial barrier. Moreover,
   after performing 16S rRNA sequencing, relevant changes in the
   microbiota were detected. Our data showed that SD caused relevant
   changes in the abundance of gut microbes at the phylum and genus level.
   For example, following SD, bacteria genera including Lactobacillus,
   Lactococcus, Candidatus arthromitus, and Pseudoalteromonas decreased
   whereas Allobaculum, Kaistobacter, Desulfovibrio, and Adlercreutzia
   increased. As an important component of the digestive and female
   reproductive microbiota, Lactobacillus maintains microbiota balance,
   improves digestive function, assists the synthesis of B vitamins and
   vitamin K, inhibits pro-inflammatory mediators, and produces
   antibacterial activity against pathogens [166]^48. Candidatus
   arthromitus is a commensal bacterium necessary for inducing the
   postnatal maturation of homeostatic innate and adaptive immune
   responses in the mouse gut [167]^49. Interestingly, an increase in the
   relative abundance of Allobaculum after SD has also been observed in
   the intestines of mice fed a high-fat diet while an increase in
   Desulfovibrio is indicative of intestinal inflammation and altered
   sensitivity [168]^50. Furthermore, Desulfovibrio is increased in a
   rotenone-induced Parkinson's mouse model and in a
   high-cholesterol-induced non-alcoholic fatty liver mouse model
   [169]^51^, [170]^52.

   SD females showed a significant body weight reduction and 18 metabolic
   pathways were enriched in differential metabolites in the blood of CTRL
   and SD animals. Among them, nicotinate and NAM pathways had the highest
   enrichment rates. The higher amounts of NAM present in CTRL in
   comparison to SD, were correlated to decreased numbers of Lactococcus,
   Alteromonas, Pseudoalteromonas, Candidatus arthromitus, and
   Octadecabacter, and an increase in Arthrobacter, Adlercreutzia,
   Flavisolibacter, Phenylobacterium, and Promicromonospora (Figure
   [171]3G). The decreased NAM content after FMT also confirmed the
   association between NAM and gut microbiome changes (Figure [172]6D). In
   this regard, the Firmicutes/Bacteroidetes (F/B) ratio is widely
   believed to play an important role in maintaining normal intestinal
   homeostasis [173]^53^-[174]^55. At the phylum level, higher Bacteroides
   and lower Firmicutes abundances were observed in POI patients [175]^22,
   which coincided with the sequencing data after SD. In addition, studies
   on mice with chronic colitis found that NAM could increase the
   proportion of beneficial bacteria and restore the function of gut
   microbiota [176]^56. Recent results indicate that intestinal bacteria
   can promote host NAD^+ pools through deamidation and de novo synthesis
   pathways [177]^57^, [178]^58. Our investigation, however, did not allow
   us to distinguish between these possibilities.

   During the female reproductive cycle, only a limited number of
   primordial follicles are recruited, therefore disruption of primordial
   follicle activation inevitably leads to abnormal follicle development.
   When primordial follicles are activated too quickly, the size of the
   ovarian reserve is impacted [179]^18. In general, POI is often induced
   by abnormal follicular oocyte development and ovarian dysfunction
   caused by premature depletion of follicle reserve and increased
   follicle atresia. Histological observations showed that SD reduced the
   follicle reserve, likely by increasing both primordial follicle
   activation and follicle atresia. Therefore, we suggest that SD induced
   the POI phenotype in adolescent female mice; these findings are also
   supported by the abnormal genetic changes associated with POI. At the
   same time, decreased ovarian size and changes in the levels of
   circulating sex hormones were observed. Similar to our findings, some
   studies suggest that a lack of sleep contributes to obesity and insulin
   resistance, which in turn promotes PCOS [180]^59^, [181]^60.
   Furthermore, SD may cause harmful alterations to endocrine systems
   (including the ovarian axis) [182]^61. In menstruating women, short
   sleep duration is associated with lower estradiol and luteal phase
   progesterone levels and an increased incidence of anovulation, which
   can also decrease reproductive capacity by impairing the arrival of
   morulas in the uterus [183]^62.

   In our study, the ability of fully grown oocytes to resume and complete
   meiosis I was also observed to be impaired. scRNA-seq analyses
   performed on SD oocytes indicated that mitochondrial disfunction was
   one cause of the reduced quality of such oocytes. This likely impacted
   on the reproductive performance of the SD females and was significantly
   reduced, as shown by the lower number of vaginal plugs and higher
   levels of false pregnancies or miscarriage.

   The central role of microbiota impacting on systemic NAM levels and
   inducing disordered ovarian function, following the SD reported here,
   was clearly supported by FMT. In female hosts, FMT resulted in a marked
   reduction in NAM concentration in the blood, and ovarian
   disorders-including weight reduction, reduced follicle number (mainly
   PmF), and altered folliculogenesis dynamics-associated with the
   decrease in AMH, GDF9, BMP15, and MVH proteins.

   Finally, the ability of NAM supplementation to counteract the effects
   of SD on follicle numbers and dynamics and the decrease of AMH, GDF9,
   and BMP15, points to the reduction of this vitamin as one of the main
   causes of ovarian disfunction resulting from SD. As reported above,
   NAM, as a precursor of NAD^+, is a key component of the NAD^+ salvage
   pathway and an essential redox cofactor for cellular reactions in
   mammals and microbes. Challapa et al., using isotope labeling to track
   NAD metabolism in host tissues and gut microbiota, found that
   circulating host NAM enters the gut lumen and supports microbial NAD
   synthesis; then the microbiota subsequently converts host-derived NAM
   into nicotinic acid, which is used for NAD synthesis in host tissue
   [184]^63. Relevant to the present results, continuous administration of
   NAM has been shown to maintain DNA integrity and prevent apoptosis
   [185]^64. NAM treatment can increase the expression of proteins
   associated with mitochondrial function, including oxidative
   phosphorylation, fatty acid oxidation, and TCA cycling [186]^65.
   Studies in patients with poor ovarian response (POR) have shown that
   this disfunction is related to nicotinate and NAM metabolic pathways
   and appears to impact the ovarian reserve [187]^66. Moreover, NAM is
   very important for oocyte maturation, it is involved in the salvage
   pathway of NAD^+ synthesis in cumulus cells, and is increased
   substantially throughout the course of oocyte maturation [188]^67. We
   speculate that the mitochondrial disfunction observed in oocytes can be
   due, at least in part, to a decrease in NAM availability. Increasing
   NAD^+ levels by supplementing NAD^+ preforms, such as NAM, niacinamide
   ribonucleoside (NR), or niacinamide mononucleotide (NMN), may enhance
   mitochondrial function and promote nuclear and cytoplasmic maturation
   to mitigate fertility damage associated with ovarian aging [189]^68.
   Interestingly, some studies show that the level of NAM in preovulatory
   follicles can be significantly higher than that in small follicles and
   that NAM concentration in the follicular fluid of antral follicles is
   positively correlated with oocyte maturation and fertilization rate.
   These findings support the notion that NAM may favor follicular
   development, oocyte quality, and reproductive performance by increasing
   endogenous NAD^+ levels to improve mitochondrial function [190]^69. In
   line with the present results, NAM supplementation improved oocyte
   quality and offspring development by modulating mitochondrial function
   in an aged Caenorhabditis elegans model [191]^70.

Conclusions

   In conclusion, our results support the notion that, in adolescent
   females, SD can cause changes in the gut microbiota that impact on
   systemic metabolism centered on NAM pathways. In the ovary, this can
   impair oocyte mitochondrial energy pathways resulting in POI and
   reduced oocyte meiotic maturation. Finally, NAM supplementation can be
   used to counteract the deleterious effects of SD on the ovary.

Supplementary Material

   Supplementary figures and tables.
   [192]thnov14p3760s1.pdf^ (7MB, pdf)

Acknowledgments