Systemic and Local Metabolic Alterations in Sleep-Deprivation

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Article

Systemic and local metabolic alterations in sleep deprivation-induced stress: A multi-platform massspectrometry-based lipidomics and metabolomics approach Sang Jun Yoon, Nguyen Phuoc Long, Kyung-Hee Jung, Hyung Min Kim, Yu Jin Hong, Zhenghuan Fang, Sun Jo Kim, Tae Joon Kim, Nguyen Hoang Anh, Soon-Sun Hong, and Sung Won Kwon J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00234 • Publication Date (Web): 17 Jul 2019 Downloaded from pubs.acs.org on July 20, 2019

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Journal of Proteome Research 1

Systemic

and

local

deprivation-induced

metabolic stress:

spectrometry-based

A

lipidomics

alterations

in

multi-platform and

sleep mass-

metabolomics

approach

Sang Jun Yoon1, Nguyen Phuoc Long1, Kyung-Hee Jung2, Hyung Min Kim1, Yu Jin Hong1, Zhenghuan Fang2, Sun Jo Kim1, Tae Joon Kim1, Nguyen Hoang Anh1, SoonSun Hong2, Sung Won Kwon1*

1College

of Pharmacy, Seoul National University, Seoul 08826, Korea

2Department

of Biomedical Sciences, College of Medicine, Inha University, Incheon

22212, Korea

Corresponding author: Sung Won Kwon (email: [email protected]), Tel.:+82)2880-7844, Fax.: +82)2-886-7844

ACS Paragon Plus Environment

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ABSTRACT

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at the omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic (ROC) curves was AUC = 0.813 and 0.836 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SDinduced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of


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eight metabolic and lipid metabolic pathways. The metabolism of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

Keywords:

Sleep

deprivation,

metabolomics,

lipidomics,

multivariate analysis, pathway analysis, biomarker analysis


biomarkers,


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■ INTRODUCTION

The quality and quantity of sleep is essential for physiological metabolism in many species. In human, insufficient sleep or sleep deprivation (SD) is known to be associated with a wide range of metabolism-related disorders such as diabetes,1 obesity,2 and cardiovascular disease.3 Long-term partial SD is commonly found in the modern society and is included under the general definition of sleep deprivation. This extensional SD pattern has been widely studied in vivo and is clearly associated with reduction of brain sensitivity to neurotoxicity,4 hypermetabolism,5 systemic immune system impairment,6 and emotional disorders.7 Although a diverse spectrum of research has been conducted on chronic SD, few studies on the systemic and local alterations in the lipidome and metabolome of SD individuals have been conducted using high-throughput approaches.

Recently, lipidomics and metabolomics approaches have emerged as a potential method to investigate SD-associated metabolic disturbances in vitro, in vivo, and at clinical levels.8, 9 Notably, the daily rhythms of several endogenous metabolites in plasma have been observed in a recent study.10 Another study identified that oxalic


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acid and diacylglycerol 36:3 are cross-species serum markers (rat and human) of sleep restriction.11 Diurnal rhythms of the human urine metabolome were confirmed in a total sleep deprivation model.12 Furthermore, a study targeting both saliva and plasma metabolomes was conducted and suggested that the endogenous circadian clock has a strong influence on various metabolic pathways.10 Importantly, this effect was found to be independent of the rest-activity cycle or feeding. However, there is limited knowledge on the global alterations in the lipidome and its association with the metabolome of SD individuals. Further, several SD trigger models have been applied in SD research but the influences of the model characteristics on the omics profiles of SD individuals remain elusive. Finally, systemic alterations of the lipidome and metabolome in serum, plasma, or urine could not denote the actual changes in the lipidome and metabolome profile of brain tissues.13

Because metabolic alteration has a harmonious relationship with the homeostasis of circadian rhythms, this study aimed to investigate the systemic and local alterations of the metabolome and lipidome in the serum, hypothalamus enriched region, and hippocampus CA1 enriched region of SD individuals in vivo. To maximize



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the coverage of biomolecules under investigation, liquid chromatography-quadrupoletime of flight (LC-QToF MS) was used for untargeted lipid profiling whereas liquid chromatography-triple quadrupole (LC-QqQ MS) and gas chromatography-mass spectrometry (GC-MS) were used for the analysis of pseudotargeted and untargeted metabolite profiles, respectively. The in vivo model of SD was established based on multiplatform techniques and was validated using the alterations in insulin and corticosterone. The effects of SD models on the lipidome and metabolome were also investigated by applying two SD-induced strategies: 24 h × 4 d and (20 h SD + 4 h resting) × 10 d. Our findings indicated that systemic metabolic alteration mainly associated compounds like phosphatidylcholine (PC), lysophosphatidylcholine (LPC), triacylglycerol (TAG), diacylglycerol (DAG), sphingomyelin (SM) and amino acids, which were used for biomarker analysis to validate potential markers based on different sleep deprivation models in vivo. Further, metabolic pathways associated with amino acids were commonly discovered as SD-induced local metabolic transition through pathway analysis in the hypothalamus. Finally, lipidome profiles are SDplatform dependent and this should be taken into account in future investigations.


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■ MATERIALS AND METHODS

Chemicals and Materials

LPC 12:0, PC (10:0/10:0), lysophosphatidylethanolamine (LPE) 14:0, phosphatidylethanolamine (PE) (10:0/10:0), SM (18:1/17:0), TAG (17:0/17:0/17:0), DAG (12:0/12:0), and ceramide (Cer) (18:1/17:0) were purchased from Avanti (Alabaster, AL). HPLC-grade methanol, water, 2-propanol, and acetonitrile were purchased from JT Baker (Philipsburg, NJ). Benzoic acid (2,3,4,5,6-D5), L-alanine (2,3,3,3-D4), succinic acid (2,2,3,3-D4), D-fructose (U-13C6), citric acid (2,2,4,4-D4), HPLC grade tert-Butyl methyl ether (MTBE), methoxyamine hydrochloride, pyridine, trimethylsilylating

(TMS)

reagent

[N,

O-bis-(trimethylsilyl)trifluoroacetamide

(BSTFA)/trimethylchlorosilane (TMCS) (99:1 v/v), LC-MS-grade ammonium formate, ammonium acetate, formic acid, and ammonia were merchandised from SigmaAldrich (St. Louis, MO). The rat insulin ELISA kit was purchased from ALPCO Diagnostics (Windham, NH) and the ParameterTM corticosterone assay kit was purchased from R&D Systems (Minneapolis, MN). The micro BCATM protein assay



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kit was purchased from Thermo Scientific (Illinois, U.S.A.).

Care and use of laboratory animals

Adult male Sprague-Dawley rats (180-250 g) (Orient Bio, Sungnam, Republic of Korea) were used in all experiments. Animals were maintained under 23 ± 2°C and a 12 h light/dark cycle (light on 07:00 to 19:00), with free access to food and water. Before starting the actual sleep deprivation experiments, “socially stable” groups organized with four rats during a one-week adaptation period were randomly classified as control and SD groups. The experimental protocol was approved by the Institutional Animal Care and Use Committee (IACUC) of Seoul National University (SNU-18062122), and the methods were carried out according to the approved IACUC guidelines of Seoul National University.

Sample collection

After the experimental period, animals were sacrificed using ketamine-xylazine


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anesthesia. Blood was immediately harvested from the heart and added to an evacuated tube (BD Vacutainer®, New Jersey, USA), which was inverted 6 times before standing for 30 min to allow blood clotting. The tubes were then centrifuged at 1500 rcf for 10 min and serum was taken from the upper region of the gel. Serum was then aliquoted to several microcentrifuge tubes at 4°C and was stored at -80°C until the experiments. After the whole brain was excised from the skull, the hypothalamus enriched region was separated and the hypothalamus excluded brain was cut perpendicularly on the brain matrix. The hippocampus CA1 enriched region was then separated from each slice. Every microcentrifuge tube including tissue was immediately immersed in liquid nitrogen and was stored at -80°C before extraction.

Animal models for sleep deprivation

Sleep deprivation was induced using the multiple platform technique, which is the most common method to block rapid eye movement (REM) sleep.14-16 In short, every rat was randomly recruited to a group of four, and these were housed in normal cages for seven days to attain stable social status. Each stable social group (n = 4)



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was then studied in a water bath (54 × 39 × 32 cm) containing either 6 round platforms of 15-cm-diameter (large platforms) or 4-cm-diameter (small platforms). Each water bath was filled with 22-24°C water to 1 cm below the actual height of the platforms. Owing to the design of the small platform, muscle hypotonia caused by sleep resulted in the animal falling into the water, forcing the animals to stay awake. The large platform for the control group, was sufficient for the rats to sleep. In this study, the number of rats in each group was eight from two identical water bath which was synchronized by the same animal experiment protocols, and we conducted two protocols of SD. In the acute partial SD model (SD model 1), every rat was kept on the water bath for 96 h continuously.17 In the chronic partial SD model (SD model 2), the experimental period was 10 days and the rat had 4 hours of rest daily from 19:00 to 23:00 (Figure S1).18

Sample preparation for HPLC-Q-ToF MS and HPLC-QqQ MS

The internal standard (IS) mixtures contained the following lipids and metabolites composed of an odd-chain fatty acid (C17:0), small carbon number fatty


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acid (C12:0), and the deuterium form were freshly prepared prior to the metabolite and lipid extractions: LPC 12:0, PC (10:0/10:0), LPE 14:0, PE (10:0/10:0), and SM (18:1/17:0) at 1.66 μg/mL and benzoic acid (2,3,4,5,6-D5) at 10 μg/mL were prepared in methanol (Mix1). TAG (17:0/17:0/17:0) and DAG (12:0/12:0) at 1 μg/mL and Cer (18:1/17:0) at 5 μg/mL were prepared in MTBE (Mix2). L-alanine (2,3,3,3-D4), succinic acid (2,2,3,3-D4) and citric acid (2,2,4,4-D4) at 40 μg/mL, and D-fructose (U-13C6) at 20 μg/mL were prepared in water (Mix3). The extraction procedure was adapted and modified from the MTBE methodology for lipid and metabolite profiling of serum and tissue samples. For serum samples, 300 μL of Mix2 was added to each 50 μL serum sample in 2 mL microcentrifuge (Eppendorf) tubes and this sample mixture was vortexed following 1 mL Mix1 addition and 1 min vortexing. For tissue samples, 300 μL of Mix2 was added to each weighted tissue (2.5-17 mg) sample in Precellys® lysis kit (KT03961-1-003.2; 1.4 mm ceramic beads) followed by homogenization on the Precellys Evolution system (Bertin technologies, USA) at 6000 rpm, 30 s for 2 cycles. Homogenized sample mixtures were vortexed following 1 mL Mix1 addition and 1 min vortexing. Every sample mixture was incubated for 1 hour in a thermo-shaker at 1500



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rpm and 4°C. After incubation, 250 μL of Mix3 was added to the sample mixture and vortexed for 1 min, followed by centrifugation at 16000 rcf for 10 min. Finally, 1 mL of the upper lipid extract layer was separated and 1 mL of methanol was added to the lower hydrophilic layer and vortexed, followed by centrifugation at 16000 rcf for 10 min, and 1300 μL of the metabolite extract solution was obtained from the mixture of the lower layer and methanol. Both lipid extract and metabolite extract solutions were filtered through a hydrophobic syringe filter unit (PTFE/0.2 μm/13 mm) (ADVANTEC®, Japan), followed by evaporation using nitrogen purge at room temperature until completely dried. Before analyzing the samples, lipid extracts were reconstituted in 200 μL of acetonitrile/2-propanol/water mixture (65:30:3 v/v/v) and metabolite extracts were reconstituted with 60 μL of water. Finally, lipid extracts were analyzed using HPLC-Q-ToF MS and metabolite extracts were analyzed by HPLC-QqQ MS. An equal amount of individual samples was mixed as a quality control (QC) sample to monitor reproducibility within instrument analysis (Figure S2).

Sample preparation for GC-MS


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The IS mixtures contained metabolite species composed of the deuterium form and were freshly prepared prior to metabolite extraction: Benzoic acid (2,3,4,5,6-D5) at 10 μg/mL, L-alanine (2,3,3,3-D4) at 40 μg/mL, succinic acid (2,2,3,3-D4) at 40 μg/mL, citric acid (2,2,4,4-D4) at 40 μg/mL, and D-fructose (U-13C6) at 20 μg/mL were prepared in 80% methanol (-80°C). The extraction procedure was adapted and modified for the metabolite profiling of serum and tissue samples.19 For serum samples, 650 μL of methanol containing the IS mixture was added to each 50 μL serum sample in 1.5 mL tubes and was incubated for 30 min in a thermo-shaker at 1500 rpm and 4°C. For tissue samples, 650 μL methanol containing the IS mixture was added to each of the weighed tissue (2.5-17 mg) samples in the Precellys® lysis kit (KT039611-003.2; 1.4 mm ceramic beads) and was homogenized on the Precellys Evolution system (Bertin technologies, USA) at 6000 rpm, 30 s, 2 cycles, and was incubated for

30 min at -80°C. Thereafter, every sample mixture was centrifuged at 16000 rcf for 10

min. The supernatant was filtered using a hydrophobic syringe filter unit (PTFE/0.2 μm/13 mm) and a small amount of each sample was taken and mixed to prepare the QC sample. Every sample was then purged with nitrogen gas until completely dried.



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For derivatization, methoxyamination of the samples was conducted by adding 60 μL of methoxyamine hydrochloride (1.5% w/v) in pyridine to each tube, and incubation in a 30°C oven for 90 min. The 60 μL of BSTFA using 1% TMCS was mixed with the methoxyaminated samples for trimethylsilylation in a 70°C oven for 15 min. Finally, 100 μL of the sample was used for GC-MS data acquisition. The protocol applied in

this study was adopted with slight modifications according to our previous work.20

Untargeted lipidomics analytical conditions

The lipid extracts were separated on an Agilent 1260 HPLC system (Agilent, CA) and an Acquity UPLC CSH C18 column (100 × 2.1 mm; 1.7 μm) coupled to an Acquity UPLC CSH C18 VanGuard precolumn (5 × 2.1 mm; 1.7 μm) (Waters, Milford, MA). Columns were maintained at 65°C. The gradient conditions and mobile phases were adapted to optimize the separation of a broad range of lipids in the HPLC system.21 The mobile phases consisted of (A) 50:50 (v/v) acetonitrile:water with ammonium formate (10 mM) and formic acid (0.1%) and (B) 80:20 (v/v) 2-


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propanol:acetonitrile with ammonium formate (10 mM) and formic acid (0.1%). The gradient elution began with 30% phase (B) and separation was conducted by the following gradient: 60.2% (B) for 2 min, 61.6% (B) for 16 min, 78% (B) for 18 min, 82% (B) for 29 min, 86% (B) for 31 min, 99% (B) for 32 min, 99% (B) for 37 min, 30% (B) for 37.5 min, and 30% (B) for 55 min. The flow rate was 0.15 mL/min. 3 μL of the sample was injected in ESI(+). The auto-sampler maintained the temperature at 4°C.

Separated lipid peaks were detected on an Agilent 6530 QToF MS (Agilent, CA). Ionization was conducted in the ESI-positive mode. The Jetstream ESI source parameters were as follows: MS resolution, 4 GHz; capillary voltage, 3.5 kV; MS1 range, 300-1200 m/z; MS2 range, 50-1200 m/z; collision energy, 20 eV; nozzle voltage, 1 kV; fragmentor voltage, 135 V; gas temperature, 325°C; drying gas, 8 L/min; nebulizer gas, 35 psi; sheath gas temperature, 350°C; sheath gas flow 11 L/min. External calibration was conducted among instrumental analysis for m/z accuracy using a reference solution including purine and HP-0921 (m/z; 121.0509 and 922.0097, respectively). The data were collected by auto MS/MS data-dependent acquisition with the following parameters: MS1 acquisition speed, 5 spectra/s; MS1 mass range, 300-



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1200 m/z; MS2 acquisition speed, 8 spectra/s; MS2 mass range, 50-1200 m/z; isolation width, narrow (1.3 m/z); precursor ions per cycle, 4; precursor ion threshold, 1000 count (abs) or 0.01% (Rel); exclusion criteria, Disable.

Pseudotargeted metabolomics analytical conditions

The metabolite extracts were separated using an Amide XBridge HPLC Column (100 × 4.6 mm; 3.5 μm) coupled to a guard column (Waters, Milford, MA) by the 1260 HPLC system (Agilent, CA). Columns were maintained at 30°C during the analysis. The gradient conditions and mobile phases were modified to achieve good separation of the targeted compounds.22 The mobile phase consisted of (A) 95:5 (v/v) water:acetonitrile with ammonium hydroxide (20 mM) and ammonium acetate (20 mM) and (B) 100% acetonitrile were prepared right before each batch of analysis. The gradient elution began with 85% phase (B) and separation was conducted by the following gradient: 30% (B) for 3 min, 2% (B) for 12 min, 2% (B) for 15 min, 85% (B) for 16 min, and 85% (B) for 23 min. The flow rate was 0.3 mL/min. 5 μL of the sample was injected in the ESI(+)/(-) ion-switching. All samples were kept in an auto-sampler


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at 4°C.

All MRM transitions were adopted form Yuan et al. (Nature Protocols 2012, 7, 872.) and confirmed via publicly available data gathered on the XCMS-MRM platform.22 Thereafter internal optimization was performed with our Agilent LC-MS/MS platform on major compounds. The full MRM list can be found in Yuan et al. (Nature Protocols 2012, 7, 872.). In particular, separated metabolite transitions were detected using an Agilent 6460 QqQ MS (Agilent, CA). Ionization was conducted in the ESIpositive/negative ion switching mode with a polarity switching time of 30 ms. Dwell time was 3 ms for each transition (total duty cycle time of approximately 1.56 s). The Jetstream ESI source parameters were as follows: capillary voltage, 3.5 kV; nozzle voltage, 500 V; gas temperature, 325°C; drying gas, 5 L/min; nebulizer gas, 45 psi; sheath gas temperature, 350°C; sheath gas flow 11 L/min.

Untargeted metabolomics analytical conditions

We followed the established protocol for untargeted metabolomics using GCMS with slight modifications adjusting for the concentrations of analyzed samples.23



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The GC-MS analysis was conducted on a Shimadzu GCMS-QP2010 system (Shimadzu, Kyoto, Japan). A DB-5MS capillary column (30 m × 0.25 mm, 0.25 μm) (Agilent, PA, USA) was used. The GC system was set in the split mode (1:2 split) for serum sample analysis and a splitless mode for tissue samples with the injector kept at 300°C. During analysis, the column flow was maintained at 1 mL/min with helium gas. Programs of the column oven temperature were as follows: primary temperature of 100°C was held for 2 min, and increased to 170°C at 10°C/min, increased to 300°C at 5°C/min and held for 5 min. The temperatures of the interface and the ion source were 300°C and 200°C, respectively. MS detection was performed in the electron impact mode, and the ionization energy was kept as 70 eV. The start and end m/z of mass range scan were 40 and 500, respectively.

Untargeted lipidomics data processing

The raw data were converted to the abf format and imported into MS-DIAL ver. 3.40. Following the official tutorial (http://prime.psc.riken.jp/Metabolomics_Software), a processed peak table that listed the alignment ID, average RT (min), average m/z,


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metabolite name, adduct ion, fill (%), MS/MS included, s/n average, MS1 isotopic spectrum, MS/MS spectrum, and intensity was generated by a series of parameters (data collection, peak detection, MS2 deconvolution, identification, adduct, and alignment). The detail of the parameters is mentioned in Table S1. After the peak annotation, relative standard deviation (RSD, %) in every QC sample was calculated and peaks were excluded when the RSD in QC samples were more than 30%.

Pseudotargeted metabolomics data processing

The raw data acquired as the .d format from metabolite analysis were converted

to

.mzML

format

files

(http://proteowizard.sourceforge.net/download.html).24

using

MSConvert XCMS-MRM

(https://xcmsonline-mrm.scripps.edu), the online open software for spectral processing and transition quantification was utilized with a predefined list of metabolites and the following parameters: peak width, 8; virtual scans per second, 4; limit of detection, 3.



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GC-MS data processing

The GC-MS raw data were converted to the abf format. Next, data collection, peak detection, MS1 deconvolution, and alignment were processed using MS-DIAL ver. 3.40.25 The data acquisition parameters are mentioned in Table S1. The intensities of peaks Identified and relative standard deviation (RSD, %) in every QC sample were calculated. The peaks that had high variations (RSD > 30% in QC samples) were excluded in the data processing. For serum samples, the metabolite peaks were normalized to their nearest internal standard. For tissue samples, the metabolite peaks were normalized to their nearest internal standard and tissue weight.

Lipid and metabolite identification

Lipid annotation was performed using an in-house lipid library established based on LipidBlast and LIPID MAPS. We excluded the lipid species that are unlikely to be endogenous molecules in accordance with the expected human fatty acid moieties. The accurate mass similarity score was at least 900 and MS/MS matching was performed with a reverse product similarity score of at least 700. Moreover, dot


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product peak similarity and isotropic distribution were also considered in the identification.

Using the NIST14 MS database for GC-MS, a list of metabolite candidates was identified by NIST MS Search 2.2. The identity of each peak was selected using the following criteria: match score of NIST MS Search 2.2 > 700, comparison of electron ionization mass spectrometry between the database and samples.

Insulin and corticosterone quantification by ELISA

To validate two in vivo SD models (SD model 1, 2) that were established in the present study, we quantified the concentration of insulin and corticosterone in the serum following the protocols of commercial ELISA kits and compared their levels between the control and SD group.

Statistical Analysis

All

statistical

analyses

were

implemented


in

MetaboAnalyst

4.0


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(https://www.metaboanalyst.ca) except otherwise stated. For serum samples, the metabolite peaks were normalized to the sum intensity of each sample. For tissue samples, the metabolite peaks were normalized to the sum intensity and quantification results of total protein in tissues by BCA protein assay. Next, log-transformation and Pareto scaling were applied for the processed data from either XCMS-MRM or MSDIAL. In addition, due to the large variance in the SD model, suspected outliers were excluded after a careful investigation using phenotypic expression during the experimentation period, data exploration techniques (Heatmap and PCA), and Random Forest based outlier suggestions before actual statistical analysis. The t-test was conducted followed by the Benjamini-Hochberg procedure to obtain an adjusted p-value (false positive rate, FDR). Biomarker candidates were considered when the FDR values < 0.2 as previously applied.26 An additional multivariate statistical approach using partial least-squares discriminant analysis (PLS-DA) was then conducted and features with variable importance in projection (VIP) score value of 1 or higher were considered as essential features of the model.


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■ RESULTS AND DISCUSSION

Comprehensive multi-platform omics-based strategies in analyzing systematic alterations of the metabolome and lipidome

It has been established that sleep deprivation is involved in several metabolome alterations,10 potential circadian metabolite biomarker candidates,27 SD relevant biomarker candidates in urine,12 and protein alterations.18 Altogether, these discoveries suggest that sleep deprivation alters various processes of the metabolic network. Nevertheless, the systemic and local metabolic shift patterns of the metabolome and lipidome in vivo in acute and chronic partial SD requires a more comprehensive study. To this end, as shown in Figure 1, we designed a two-step approach for the discovery and validation of biologically essential metabolites that are significantly altered in SD and are able to differentiate SD individuals and normal individuals in vivo. Next, after validating the two SD models by measuring insulin and corticosterone, we conducted untargeted lipidomics, pseudotargeted metabolomics, and untargeted metabolomics to comprehensively examine the lipidome and metabolome of SD individuals with acute and chronic partial SD. LC-MS-based



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metabolomics is currently the cutting-edge approach for hypothesis-generating studies.28, 29 However, it suffers from the risks of peak misidentification, interfering compounds, in-source degradation products, and quantitative errors.30 Hence, when investigating

endogenous

metabolic

profiles,

a

large-scale

targeted

(or

pseudotargeted) approach may be more suitable.31, 32 Further, the GC-MS metabolic profiling approach has been acknowledged as it is suitable for apolar and volatile compounds.33 Furthermore, GC-MS based metabolomics is beneficial due to the availability of the highly reliable commercial and public spectrabanks such as NIST14 and the Golm Metabolome Database.

Both insulin and corticosterone were significantly changed (p-value < 0.05) in the SD groups (Figure 2). The alterations of the two hormones were in accordance with a previous study, thus confirming the validity of our SD models.34 The total number of detected biomolecules in pseudotargeted metabolomics, untargeted metabolomics, and untargeted lipidomics of model 1 was 91, 98, and 136, respectively. Similarly, 133, 78, and 78 were detected from pseudotargeted metabolomics, untargeted metabolomics, and untargeted lipidomics of model 2, respectively. These molecules


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were subject to multivariate analysis. The standard model involving a continuous 96 h of SD (SD Model 1) was used for the discovery phase and the model using an interrupted strategy of SD for 10 days (20 h a day plus 4 resting h) was used to confirm the robustness of sleep-associated metabolites (later called SD-induced biomarkers).

Multivariate analysis for indicating potential biomarker candidates of sleep deprivation

Supervised multivariate statistical analysis (PLS-DA) was used as the primary model to discover SD-induced biomarkers. Additionally, metabolites with a significantly different expression by SD were also detected using conventional univariate analysis (Table S2). Importantly, this design allowed us to measure the platform-specific lipidome and metabolome profiles of SD individuals in vivo. For the discovery of metabolite biomarkers, LC-QqQ MS and GC-MS data were used for statistical analysis. In the pseudotargeted experiment, a distinctive separation pattern between the SD group and control was observed in the PCA score plot (Figure S3A). The PLS-DA model was built to demonstrate the metabolome distinction of the SD



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and control group. The PLS-DA 2D score plot is presented (Figure S3B). The optimal Q2 was shown in five components (R2 = 1.00, Q2 = 0.65 and accuracy = 0.93) (Figure S3C). Importance was measured by variable importance in projection (VIP) and 27 metabolites had a VIP score greater than 1 (Figure S3D and Table S2). Similarly, from the untargeted method, a distinguishable separation pattern between the SD group and control was observed in the PCA score plot and PLS-DA score plot (Figure S3E and Figure S3F, respectively). The best Q2 was shown in the model with two components (R2 = 0.95, Q2 = 0.84 and accuracy = 1.00) (Figure S3G). Also, 13 metabolites with a VIP score greater than 1 were observed (Figure S3H and Table S2). In summary, a Venn diagram of overlapped metabolite biomarkers was schematized and the biomarkers included in the red line were later used for ROC analysis (Figure 3A).

A distinct separation pattern between the SD group and control regarding the lipidome was observed in the PCA score plot (Figure S4A). Subsequently, the PLSDA model was built to demonstrate the lipidome distinction of the SD and control group. The PLS-DA 2D score plot is presented (Figure S4B). The best Q2 was shown in the


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model with one component (R2 = 0.71, Q2 = 0.60 and accuracy = 0.94) (Figure S4C). There were 28 lipids that had a VIP score greater than 1 (Figure S4D and Table S2). Collectively, a total of 37 combination metabolite biomarkers (27 from the pseudotargeted method and 13 from an untargeted method with 3 overlapped metabolites) (Figure 3A) and 28 lipid biomarkers were determined as SD-associated molecules (Figure 3B and Table S2). Interestingly, the biomarker candidates included glycerophospholipids, neutral lipids, sphingolipids, amino acids, and other metabolites related to energy metabolism. Some intrinsic metabolic compounds were reported in previous studies.35, 36 However, the association of some candidates with SD has never been reported.

Discovery and Validation of the Potential Sleep Deprivation Markers

It is important to validate the SD-induced differentially expressed molecules, especially in different SD models, in which all lipidomics and metabolomics are conducted independently, in order to elucidate the robustness of significantly altered metabolites and lipids in both acute and chronic partial SD conditions. This would be



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a proof-of-concept of the biological and technical cross-examination regarding clinical research on sleep quality, which is still an ongoing topic of research. Regarding this matter, the significant differential metabolites and lipids identified in multivariate statistical approach (VIP score ≥ 1) between the SD and control groups of model 1 were subjected to receiver operating characteristic (ROC) analysis to examine whether these metabolome and lipidome profiles were consistent with the chronic SD individuals of model 2. Specifically, the 37 combination metabolite biomarkers derived from the pseudotargeted and untargeted experiments were tested against the hypothesis that they were capable of distinguishing between SD rats and control rats. Similarly, 28 lipids were utilized as a multiplex lipid panel for classification of the SD group and control group. However, some metabolites and lipids were not detected in the second model. As a result, only 25 and 12 metabolites from the pseudotargeted method (by LC-QqQ MS) and untargeted method (GC-MS), respectively, were utilized separately. The results indicate that the SD group and control could be differentiated by 25 metabolites and were supported by the area under the curve (AUC) of ROC curve (Figure 3C) constructed by a PLS-DA with two latent variables (AUC = 0.847,


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95% confidence band (CI) = 0.556 – 1.00). The average accuracy based on 100 crossvalidations was 0.720 (Figure 3D). Likewise, the classification between the two groups appeared to be possible by the AUC of ROC curve (AUC = 0.930, 95% CI = 0.619 – 1.00) (Figure 3E) and the average accuracy was 0.822 (Figure 3F). The top five metabolites of the two models in ROC analyses are shown in Figure S5. Analogously, 15 lipids were collected to validate the untargeted method (by LC-QToF MS). Nonetheless, the SD group and controls could not be differentiated by 15 lipids (AUC = 0.374, 95% confidence band (CI) = 0 – 0.778) (Figure 3G). The average accuracy in this case was only 0.393 (Figure 3H).

Up to this point, the precise criteria for diagnosing SD are not feasible due to the absence of a metabolic determination for the circadian rhythm. Even though previous studies reported that low sleep quality and quantity affect the metabolome and lipidome in plasma and tissues,37-40 the robustness of these biomarkers has remained elusive. In our study, according to the ROC analyses, SD-induced metabolite biomarkers could represent a comparable pattern of alterations between acute and chronic partial SD models in vivo. The metabolite markers provided



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sufficient classification of the SD group from the control group. This provides a new option for biomarkers to diagnose circadian rhythm disruption, even though further investigation with increased in vivo sample size and clinical studies needs to be considered. Further, the lipidome were significantly different between the two SD conditions. Our findings demonstrate that SD-induced lipid biomarkers are changed depending on the design of the SD models. By using a robust design and comprehensive omics analysis, we suggest that alterations of the lipidome may not be suitable to reflect sleep disruption.

Local effects of sleep deprivation on brain tissue

Sleep is vital to a number of brain functions including memory, mood, and cognitive function.41 Synchronically, the brain plays an essential role in regulating the circadian rhythm in a tight harmonization of the endocrine and nervous system. Herein, we particularly focused on the hypothalamus and hippocampus, which are the two well-known sleep-related regions in the brain. Significantly altered metabolites and lipids on these two regions were later subjected to pathway enrichment analysis.


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Our analysis revealed no significant alteration of the metabolome and lipidome in the enriched hippocampus CA1 region. Nevertheless, a considerable number of metabolites and lipids were found to be altered in the hypothalamus enriched region. As shown in Table S3, fatty acids and cholesterol were relatively increased in the SD group whereas amino acids and carbohydrates were found to be decreased. In addition, 37 lipid species were found to be altered. These lipids belong to neutral lipids (TAG and DAG), glycerophospholipids (PC, LPC, PE, LPE, and PS), and ceramide species. We conducted pathway enrichment analysis using significantly altered metabolites in the hypothalamus. With the FDR of 0.1, nine pathways were statistically enriched: ‘Aminoacyl-tRNA biosynthesis’, ‘Arginine and proline metabolism’, ‘Pantothenate and coenzyme A biosynthesis’, ‘Alanine, aspartate, and glutamate metabolism’, ‘Purine metabolism’, ‘Glyoxylate and dicarboxylate metabolism’, ‘Nitrogen metabolism’, ‘Ascorbate and aldarate metabolism’, and ‘Glycolysis or Gluconeogenesis’ (Figure 4 and Table S4).

The discovery of metabolism-related SD was limited to the investigation of whole brain tissues or biofluids, which are not representative of the actual occurring



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biological processes.42 Moreover, disturbance of the lipidome is an emerging aspect related to dyslipidemia, energy malfunction, membrane structure, and oxidation stress. Yet, local metabolites and lipid alternation in sleep-specific regions such as the hypothalamus, have not been studied well. We focused on discovering and connecting every significant pathway including lipids and metabolites affected by SD. Eventually, we found that significantly altered pathways are tightly associated with amino acid alterations. Besides, these pathways connect with the citrate cycle and lipid metabolism in an indirect manner. Lipid metabolism, carbohydrate metabolism and five representative pathways, which had a high correlation with amino acids, are demonstrated in Figure 5. Twelve amino acids are associated with aminoacyl-tRNA biosynthesis. This metabolism is extensive and indistinct but it is directly connected with other pathways and can mediate the relation between several pathways. ‘Alanine, aspartate and glutamate metabolism’ and ‘Arginine and proline metabolism’ included 6 and 10 SD-induced local metabolite biomarkers, respectively, suggesting that sleeprelated metabolic pathways may be highly involved in the regulation of amino acids. ‘Pantothenate and CoA biosynthesis’ and ‘nitrogen metabolism’ are neighboring


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processes to ‘alanine, aspartate and glutamate metabolism’ with aspartate and glutamate as the connection linkages, respectively. Above all, these pathways connect with lipid metabolism through fatty acid biosynthesis. The other pathways (‘Ascorbate and aldarate metabolism, ‘Glyoxylate and dicarboxylate metabolism’, and ‘Purine metabolism’) can be connected with the previously mentioned pathways by the citrate cycle (Figure S6).

Sleep deprivation can trigger alterations of gene expression that can affect RNA and protein synthesis, neural plasticity, neurotransmission, and metabolism.43, 44 Particularly in lipid metabolism, some studies suggest that SD does not provoke oxidative damage in the brain.45 However, alterations of the lipidome are obviously generated, which may affect the plasticity and malfunction of brain tissues. Interestingly, previous works demonstrated that SD might play an important role in increasing the risk of stroke.46 In the present results, TAGs, which are linked with dyslipidemia, were increased. In addition, a decrease in glycerophospholipids, which are a crucial component of the membrane, can have a negative-synergy effect wherein low sleep quality contributes to the occurrence of stroke.46 Other neutral lipids, DAG,



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are converted to endocannabinoid 2-arachdonoylglycerol by diacylglycerol lipases (DAGL). Recently, inactivation of DAGLs was shown to impair cannabinoid receptordependent synaptic plasticity and to damage neuroinflammatory responses.47 This reveals that the lack of DAG caused by SD may increase the neuroinflammatory process. Lipid alteration can be significantly related to memory and protection of neurons. Administration of PC increases serum choline concentration, which induces up-regulation of brain acetylcholine and finally improve memory.48 Furthermore, the neuron protective effect of PC has been reported.49 Therefore, the reduction of PC caused by SD in the hypothalamus may negatively affect the neighboring regions. This can lead to memory failure or worsen the severity of Alzheimer’s disease. Dietary PCDHA is converted to LPC-DHA, absorbed, and finally re-esterified to PC-DHA. The conversion of LPC-DHA may enhance memory.50 Since LPC was found to be decreased in our experiments, it is likely that SD may cause memory disorders. Alterations of other minor lipids such as LPE, PE, and Cer were also influenced by SD. Their biological impact on sleep quality should thus be investigated in the future.


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Conclusions

In the present study, multiple platforms of lipidomics and metabolomics (untargeted lipidomics, pseudotargeted metabolomics, and untargeted metabolomics) with two SD models were utilized to study the metabolome shift patterns in SD. We discovered and validated SD-induced dysregulated metabolites using a sophisticated multivariate and pathway analysis. We also observed SD-associated metabolic disturbances of the hypothalamus. Altogether, these findings provide deeper insights into the effects of SD at the scale of the metabolic network. Thus, our work confirms the considerable potential of omics approaches to obtain a better understanding of the mechanism and quality of sleep.

Conflict of interest

All authors declared that they have no conflict of interest

Acknowledgment



Page 36 of 48 36

This study was funded by the National Research Foundation (NRF2017R1E1A2A02022658) and Health Fellowship Foundation. This work was supported by BK21 Plus Program in 2019. The funders had no role in devising the study as well as where to publish the results.

SUPPORTING INFORMATION: The following supporting information is available free of charge at the ACS website http://pubs.acs.org Table S1. Data processing parameters of untargeted lipidomics (by LC-QToF MS) and untargeted metabolomics (by GC-MS).

Table S2. SD-induced biomarkers that have a significantly different expression by sleep deprivation.

Table S3. Metabolite biomarker candidates discovered by the t-test and variable importance in the projection of the PLS-DA model.

Table S4. SD-related local pathways in the hypothalamus deprived of sleep. Pathway study based on 80 metabolite biomarkers.

Figure S1. Multiple platform model and experiment schedule.

Figure S2. Sample preparation workflow for HPLC-Q-ToF MS (untargeted lipidomics) and HPLC-QqQ MS (pseudotargeted metabolomics).

Figure S3. Statistical analysis for the discovery of metabolite biomarkers by pseudotargeted and untargeted experiments.


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Figure S4 Statistical analysis for the discovery of metabolite biomarkers by the lipidomics experiment.

Figure S5. The metabolites and lipids that contributed the most to the validation models.

Figure S6. Connection of pathways with the others (Ascorbate and aldarate metabolism, alanine, glyoxylate and dicarboxylate metabolism, and purine metabolism).

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Page 42 of 48 42

Figure 1. Workflow of the study design. A two-step approach was performed for the discovery and validation of biologically essential metabolites that are significantly altered in SD as well as capable of differentiating SD individuals and normal individuals in vivo. A pathway analysis based on local SD-induced biomarkers was also conducted.

Figure 2. In vivo model validation by ELISA. Insulin and corticosterone were significantly different (*; p-value < 0.05, **; p-value < 0.001) between the control and SD in both SD models.

Figure 3. Validation of metabolite biomarkers in a pseudo-targeted and untargeted experiment by biomarker analysis. (A) A Venn diagram of overlapped metabolite biomarkers was presented and a total of 38 metabolites (red part) was used for validation. (B) A total of 28 lipids were input to be validated. (C) Area under the curve (AUC) of the ROC curve for the pseudo-targeted method was constructed by a PLSDA with two latent variables (AUC = 0.813, 95% confidence band (CI) = 0.556 – 1.00) and (D) average accuracy based on 100 cross-validations was 0.772. Similarly, (E) AUC of ROC curve for untargeted was (AUC = 0.836, 95% CI = 0.386 – 1.00) and (F) average accuracy based on 100 cross-validations was 0.770. (G) The AUC of ROC curve for untargeted was (AUC = 0.374, 95% CI = 0 – 0.778) and (F) average accuracy based on 100 cross-validations was 0.393.

Figure 4. The results from pathway analysis are presented graphically. In total, 8 pathways had FDR < 0.1.

Figure 5. Connection of pathways showing high relation with amino acid metabolism (Pantothenate and CoA biosynthesis, Aminoacyl-tRNA biosynthesis, Nitrogen metabolism, alanine, aspartate, and glutamate metabolism, and arginine and proline metabolism), carbohydrate metabolism and lipid metabolism.


Page 43 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


For Table of Contents Only


SD model 1 Serum

JournalInstrumental of Proteome Research

analysis

1 2 Hypothalamus GC-MS based 3 Hippocampus CA1 metabolomics 4 5 Serum LC-QqQ MS-based 6 7 metabolomics 8 SD model 2 9 LC-QToF MS-based *Animal models was validated 10 by ELISA of stress factors lipidomics 11 related with SD 12 13 14 15 ROC Analysis Pathway 16 17 Systemic Local 18 Metabolic ACS Paragon Plus Environment Metabolic 19 alteration alteration 20 21

Page 44 of 48 Statistics

Univariate ∙ t-test

Multivariate ∙ PLS-DA

Control vs

Sleep deprivation

Analysis

Page 45 of 48

In s u lin **

Journal of Proteome Research

C o rtic o s te ro n e

**

* 100

C o n c e n t r a tio n ( n g /m L )

1 2 0 .2 0 3 4 0 .1 5 5 0 .1 0 6 7 0 .0 5 8 9 0 .0 0 10 11 12

C o n c e n t r a tio n ( n g /m L )

0 .2 5

80

60

* 40

20

0 C o n tro l

SD

D C o n tro l ACS SParagon Plus Environment

C o n tro l

SD model 1 SD model 2

SD

C o n tro l

SD

SD model 1 SD model 2

D AG 3 6 :4

E

F


G

H

SD-Induced lipid biomarkers

T AG 5 8 :7

T AG 5 8 :6

T AG 5 8 :4

T AG 5 8 :1 2

Journal of Proteome Research

T AG 5 6 :9

T AG 5 6 :7

T AG 5 6 :6

T A G 5 6 : 5 (2 )

T A G 5 6 : 5 (1 )

T AG 5 6 :4

T AG 5 4 :9

T AG 5 4 :7

T AG 5 4 :5

T AG 5 4 :4

T AG 5 4 :3

T AG 5 2 :6

T AG 5 2 :5

T AG 5 2 :4

T AG 5 0 :3

T AG 5 0 :2

T AG 5 0 :1

T AG 4 8 :1

P C 3 6 :5

L P C 2 0 :1

L P C 1 6 :0 e

B

D AG 3 6 :3

D AG 3 4 :2

1 2 3 4 5 6 7 8 9 10 C 11 12 13 14 15 16 17 18 19 20 21 D 22 23 24 25 26 27 28 29 Normalized intensity

A

Page 46 of 48

2

0

-2

-4

Page 47 of 48 1

3

FDR < 0.1

Journal of Proteome Research 1. Arginine and proline metabolism

2

2. Aminoacyl-tRNA biosynthesis

-log(p)

1 3. Alanine, aspartate and glutamate metabolism 4 2 5 4. Purine metabolism 8 3 7 6 9 4 5. Pantothenate and CoA biosynthesis 5 6. Glyoxylate and dicarboxylate metabolism 6 7. Nitrogen metabolism 7 ACS Paragon Plus Environment 8 8. Ascorbate and aldarate metabolism 9 9. Glycolysis or Gluconeogenesis 10 Pathway Impact

Glycolysis or Gluconeogenesis (5)

Dihydroxy acetone phosphate

Glucose -6-phosphate

Pantothenate and CoA biosynthesis (5) Journal of Proteome Research Pantothenate

Page 48 of 48

β-Alanine

1 Aminoacyl-tRNA 2 biosynthesis (12) 3 2,3-diphospho Phosphoenol L-Valine Coenzyme A L-Aspartate glycerate pyruvate 4 5 6 Lactate 2-Oxoglutaramate 7 8 Fumarate 9 Citrate L-Glutamine L-Glutamate 10 cycle 11 L-Aspartate 12 13 Nitrogen metabolism (2) L-Alanine 14 15 16 Alanine, aspartate and glutamate metabolism (6) 17 Fatty acid 18 Ceramide 4-Hydroxybiosynthesis Proline Arginine L-Glutamate 19 proline Glycerol 20 21 TAG DAG 22 Glyoxylate Spermidine Creatine 23 24 LPE PE PS Arginine and proline metabolism (7) 25 ACS Paragon Plus Environment 26 LPC PC 27 28Lipid metabolism (37)

Systemic and Local Metabolic Alterations in Sleep-Deprivation

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