Metabolomic Study of Hibernating Syrian Hamster Brains: In Search of

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A Metabolomic study of hibernating Syrian hamster brain: in search of neuroprotective agents Carolina Gonzalez-Riano, GONZALO Leon-Espinosa, Mamen Regalado-Reyes, Antonia García, Javier DeFelipe, and Coral Barbas J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00816 • Publication Date (Web): 09 Jan 2019 Downloaded from http://pubs.acs.org on January 10, 2019

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Journal of Proteome Research

A Metabolomic study of hibernating Syrian hamster brain: in search of neuroprotective agents Carolina Gonzalez-Riano1# · Gonzalo León-Espinosa2,3,5# · Mamen Regalado-Reyes2 · Antonia García1 · Javier DeFelipe2,3,4 · Coral Barbas1* 1

CEMBIO (Centre for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad CEU

San Pablo, Campus Monteprincipe, Boadilla del Monte, 28668 Madrid, Spain. 2

Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Campus

Montegancedo, 28223, Pozuelo de Alarcón (Madrid), Spain. 3

Instituto Cajal (CSIC), Avenida Doctor Arce 37, 28002 Madrid, Spain.

4

CIBERNED, Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas,

Calle de Valderrebollo, 5, 28031 Madrid Spain. 5

Facultad de Farmacia, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte,

28668 Madrid, Spain. # Equal

contribution

* Author

to whom correspondence should be addressed

Center for Metabolomics and Bioanalysis (CEMBIO) Faculty of Pharmacy San Pablo CEU University Campus Monteprincipe Boadilla del Monte 28668 Madrid, Spain Telephone number: 00 34 91 3724753 Fax: +34 913724712

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Abstract Syrian hamster undergoes a reversible hyperphosphorylation of protein tau during hibernation, providing a unique natural model that may unveil the physiological mechanisms behind this critical process involved in the development of Alzheimer’s disease and other tauopathies. The hibernation cycle of these animals fluctuates between two stages: 34 days of torpor bouts interspersed with periods of euthermia called arousals that last several hours. In this study, we investigated for the first time the metabolic changes in brain tissue during hibernation. A total of 337 metabolites showed statistically significant differences during hibernation. Based on these metabolites, several pathways were found to be significantly regulated and, therefore, play a key role in the regulation of hibernation processes. The increase in the levels of ceramides containing more than 20 C atoms was found in torpor animals, reflecting a higher activity of CerS2 during hibernation, linked to neurofibrillary tangle generation and structural changes in the Golgi apparatus. Our results open up the debate about the possible significance of some metabolites during hibernation, which may possibly be related to tau phosphorylation and dephosphorylation events. In general, this study may provide insights into novel neuroprotective agents since the alterations described throughout the hibernation process are reversible.

Keywords: Metabolomics, Hibernation, Tauopathies, Neuroprotection, Tau, Chromatography, Mass spectrometry


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Introduction Hibernation is a biological process that allows for a wide range of mammalian species to survive in adverse environments 1. This process is characterized by profound changes, such as a decrease in body and brain temperature, blood flow reduction and an immune and metabolically depressed state 2-4. Small rodents like the Syrian hamster (Mesocricetus auratus) are considered permissive hibernators; in response to low temperatures and constant darkness, these animals may enter hibernation, which consists of periods of reduced body temperature and reduced metabolic rate (called torpor bouts) that last 34 days, interspersed with short arousal periods of activity in which the animals return to normothermia

5, 6.

Prior to undergoing hibernation,

several biological mechanisms regulate and adapt the neural system to withstand low temperatures, as no apparent neuronal injury takes place as a consequence of these drastic physiological changes. During torpor, neurons suffer structural changes, such as morphological changes in the Golgi apparatus; changes in dendritic spines and synaptic connections; and alterations of microglial cells 7-10. Importantly, all of these changes are reversible. Tau protein modification has been proposed as one of the main events involved in these morphological changes 11, 12. This is of critical importance since tau participates in the assembly and stabilization of microtubules by tubulin interaction and is responsible for axonal transport regulation

13.

Phosphorylation of Tau is associated with a reduced affinity for microtubules,

promoting their destabilization. However, if tau gets abnormally hyperphosphorylated, it may accumulate in the somatodendritic regions of the neuron and generate neurofibrillary tangles (NFTs), which are pathological structures —typically found in Alzheimer’s disease (AD) and other neurodegenerative diseases— that contribute to pathogenesis 14. NFTs are mainly composed of a fibrous structure known as paired helical filaments (PHF). Interestingly, Arendt et al. found PHF-like structures of hyperphosphorylated tau in numerous neurons of the brain of hibernating European ground squirrels (Spermophilus citellus)

15.

However, upon arousal, these PHF-like

structures totally disappear as tau phosphorylation is fully reversed. Once the animal ceases to be in torpor state and wakes up, it returns to normothermia in a few hours — a very fast transition that requires complex remodeling of brain structures (reorganization of cells organelles, synaptic hippocampal plasticity, Tau dephosphorylation, recovery to normal microglial morphology, etc.

10, 15-18).

Therefore, unraveling which mechanisms are triggered

during arousal might be useful to study novel therapeutic strategies to protect the human brain against cerebral damage (reviewed in 19).



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Mammalian hibernation is commonly used as a model for a wide range of studies regarding the regulation of brain hypometabolism. Based on the ability to protect the brain from reduced cerebral blood flow, studies in hibernating animals have been used in the search for therapeutic applications in ischemic stroke 20, 21. In addition, as hibernation affects the immune system by reducing the number of circulating leukocytes, previous studies used this model to improve our understanding of immunologic responses during extreme physiological changes

22, 23.

Finally,

hibernation could also represent a useful physiological model to study the formation of PHF-like aggregated tau and dephosphorylation events, which may contribute to a better understanding of the mechanisms involved in the development of the pathology of tauopathies. A major advantage of this model compared with the more commonly used genetically modified mice is that the mechanisms that lead to hyperphosphorylation/dephosphorylation events and other plastic changes in the brain follow non-artificial pathways in the Syrian hamster. To gain insight into which changes take place during hibernation, several proteomic and genomic screenings have demonstrated seasonal expression changes and showed evidence of neural remodeling and plasticity

24-27.

During the hibernation of thirteen-lined ground squirrels

(Ictidomys tridecemlineatus), changes in microtubule regulatory proteins suggested rapid cytoskeletal reorganization in the torpor-arousal transitions

28,

while changes in miRNA

expression suggested post-transcriptional regulation29. Furthermore, metabolomic analyses have identified changes in the levels of a wide variety of compounds in liver and plasma during the hibernation of ground squirrels

25, 30-32.

However, to date, there are no published

metabolomic screenings in brain tissue from any hibernating species. Metabolomics, which can be considered the ‘omics’ science of biochemistry that allows the analysis of small-molecule metabolites integrated into the metabolome, has proven to be a suitable tool for providing new insights into the biochemical changes that take place in the brain under different experimental and pathophysiological conditions, including post-mortem changes

33

and neurodegenerative diseases and mental illnesses 34. Alterations in the global

composition of metabolites in a biological fluid or tissue reflect the effects of gene variation, post-transcriptional regulation, environmental influence, pathway connections or changes in enzyme activities and levels, and thus provide a ‘snapshot’ of the metabolic state of the organism. Identification of low molecular weight molecules of the Syrian hamster brain during the hibernation may reveal a trail of cellular processes involved in neuroprotection. Here, we used mass spectrometry (MS) coupled to separation techniques including gas chromatography (GC), liquid chromatography (LC), and capillary electrophoresis (CE) to obtain a metabolome coverage 4 ACS Paragon Plus Environment

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as extensive as possible, aiming the analysis of the global metabolic changes in the Syrian hamster brain at 3 stages: late torpor, arousal, and euthermic (control). We have identified significant differences in 337 compounds that may lead to new hypotheses concerning the adaptive brain processes during hibernation and the cellular mechanisms triggering arousal. In addition, these results may provide new neuroprotective therapeutic approaches that could help in the prevention or treatment of human diseases such as stroke or Alzheimer’s disease. Methods Animals All experimental procedures were carried out at the animal facility of the San Pablo CEU University of Madrid (SVA-CEU.USP, registration number ES 28022 0000015) and were approved by the institutional Animal Experiment Ethics Committee. A total of 15 male 3-month-old Syrian hamsters (M. auratus) were purchased from Janvier Labs (Le Genest-Saint-Isle, France). The animals had free access to food and water and were kept at 23C with an 8:16-h light/dark cycle for a four to six-week acclimatization period in our animal facility. As previously described 10, in order to obtain arousal and torpor experimental groups, some animals were transferred to a special chamber that allowed the control of the temperature and photoperiod — two essential factors that affect hibernation. We designed this chamber (developed by Tiselius s.l.) with six individual cages to induce hibernation based on previous studies

15.

The chamber makes it

possible to gradually reduce the temperature (via LM35 sensors), control the illumination (adjustable LED RGB that controls intensity and color) and monitor the hamsters by measuring the general locomotor activity with a passive infrared sensor mounted on top of each cage. We recorded all data obtained in a notebook computer to distinguish between the torpor and arousal phases during the hibernation cycle using the software program Fastwinter1.9 (developed by Tiselius s.l.). The time at which animals had shown periods of inactivity of 24 h was considered to be the starting time point for torpor. The status of the animals was confirmed by body temperature measurements (infra-red thermometer). Since torpor bout durations are non-regular at the start of hibernation, we considered animals to be torpid only when they had completed three full bouts of torpor before they were sacrificed. All animals were euthanized by decapitation. Brains were then removed and immediately transferred to a N2(l)-containing recipient to freeze the tissues. Aroused animals were awakened by gentle handling, using a thermal blanket — a process that takes 4550 min for the hamsters to return to at least 35C (measuring several times with an infra-red thermometer pointed at the hamster’s head). 5 ACS Paragon Plus Environment


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Therefore, Syrian hamsters were compared at three stages: control or euthermic (n = 5), torpor (n = 5) and arousal (n = 5). Tissue processing and DAB Immunostaining The left brain hemisphere of each animal was postfixed by immersion in 4% paraformaldehyde in 0.1 M phosphate buffer (PB, pH7.4) for 24h at 4C. Serial coronal sections (50-μm thick) were obtained with a Vibratome (St Louis, MO, USA). Free-floating sections were rinsed 3 times (5 min per rinse) in PB (0.1M). Samples were then incubated with PB 0.25% Triton-X and 3% normal goat serum (Vector Laboratories). The sections were then incubated overnight at 4C with mouse anti-AT8 antibody (Pierce Endogen, 1:2000) and the following day they were rinsed and incubated for 1h in biotinylated goat anti-mouse IgG (1:200; Vector Laboratories). After several washes in PB buffer, the sections were incubated for 1 h at room temperature with avidin–biotin peroxidase complex (Immuno Pure ABC, Pierce, Rockford, IL; diluted 1:125). Peroxidase activity was revealed with 0.01% hydrogen peroxide, using the chromogen 3,3′ diaminobenzidine tetrahydrochloride (Sigma, St Louis. MO; 0.05%). After staining, the sections were dehydrated, cleared with xylene, and coverslipped with DEPEX (VWR, Rannor, Pennsylvania). Reagents All the reagents, solvents and standards used for the sample treatment and subsequent analyses are described in Supporting Information (Material S1). Sample Treatment After removal of the brains (n = 15), the right hemispheres were dissected, immediately frozen on liquid nitrogen and stored at -80°C until processed to avoid any post mortem on-going metabolic processes. Sample preparation for GC-MS, LC-MS, and CE-MS was performed at CEMBIO (Madrid, Spain). For metabolite extraction, the method employed was first described and validated at CEMBIO for a multiplatform analysis of lung tissue 35, and then used for the analysis of hippocampus tissue 36. In more detail, the whole right hemisphere (300 mg approx.) was analyzed to decrease possible biological variability due to the brain region employed. Brain homogenate was prepared by adding cold (-20°C) methanol:water (1:1, v/v), (1:10 tissue:solvent). Tissue disruption was achieved with TissueLyser LT homogenizer (Qiagen, Germany) for metabolite extraction. Subsequently, 100 μL of brain tissue homogenate was vortex-mixed with 320 μL of methanol for 2 min, followed by the addition of 80 μL of MTBE for the extraction of non-polar compounds. Then, vials were rapidly capped and placed on a shaker for 1 h at room temperature. The extracted samples were centrifuged at 4000 g for 20 min at 6 ACS Paragon Plus Environment

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20°C. For GC-MS analysis, 300 μL of supernatant was evaporated to dryness (SpeedVac Concentrator System, Thermo Fisher Scientific, Waltham, MA, USA). Methoxymation was then performed with 20 µL O-methoxyamine hydrochloride (15 mg/mL in pyridine) and vigorously vortex-mixed for 5 min. Vials were then incubated in darkness at room temperature for 16 hours. For silylation, 20 μL of BSTFA:TMCS (99:1) was added, vortex-mixed for 5 min, and capped vials were placed in the oven at 70°C for 1 h. Finally, 100 μL of heptane containing C18:0 methyl ester (10 ppm) as Internal Standard (IS) was added to each vial prior to injection. For LC-MS analysis, 90 μL of supernatant was transferred to an Ultra-High Performance Liquid Chromatography-Mass (UHPLC-MS) chromatography vial with insert and was directly injected into the system. For CE-MS analysis, 200 μL of initial brain homogenate was centrifuged separately at 16000 g for 30 min at 15°C. 150 μL of supernatant was evaporated to dryness using the SpeedVac, and re-suspended in 150 μL of 0.2 mM Methionine Sulfone (IS) in 0.1 M formic acid. Samples were vortex-mixed, sonicated, and then centrifuged at 16000 g for 20 min at 4°C. Finally, 100 μL of supernatant was transferred to a CE-MS vial for the analysis. Quality control samples (QC) were prepared by pooling equal volumes of brain tissue homogenate from each sample and were treated identically to the rest of the samples. All samples were randomized before being treated and analyzed. Metabolomics analysis Non-targeted metabolomics fingerprinting In order to cover a wider spectrum of the cerebral metabolome, brain tissue samples were analyzed by three analytical platforms — gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS) and capillary electrophoresis–mass spectrometry (CE-MS), applying the analytical conditions as described in detail in Supporting Information. The previously prepared QC samples were regularly analyzed throughout the run to provide a measurement not only of the stability and performance of the system, but also of the reproducibility of the sample treatment procedure. Two blank solutions were prepared along with the rest of the samples and were analyzed at the beginning and at the end of each analytical run. The data is available at Metabolomics Workbench platform (access number ST001117) (http://www.metabolomicsworkbench.org/). Data processing Quality assurance of the results was provided by using different software packages to reprocess the data files obtained after GC-MS, LC-MS, and CE-MS analyses.



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For GC-MS data reprocessing, deconvolution and identification of metabolites were accomplished using Agilent MassHunter Unknowns Analysis Tool 7.0. This software assigned a chemical identity by searching against two commercial libraries — Fiehn library version 2008, NIST (National Institute of Standards and Technology, library 2.2 version 2014), and the ‘inhouse’ CEMBIO spectral library for brain tissue. The data obtained were aligned using MassProfiler Professional B.12.1 (Agilent Technologies) and exported into Agilent MassHunter Quantitative Analysis version B.07.00 for the assignment of the target ions and signal integration. Finally, the data matrix obtained was normalized according to the IS abundance prior to statistical analysis. The raw data obtained after analysis with LC-MS and CE-MS were processed with MassHunter Profinder software version B.06.00, applying the Molecular Feature Extraction (MFE) and Recursive Analysis tools included in the software. Data matrices obtained after data reprocessing of each platform were imported into Microsoft Excel (Microsoft Office 2016) for filtration based on the coefficient of variation (CV) of metabolite levels in the QCs, establishing a threshold of 30%. Data treatment steps are described in more detail in Supporting Information. Statistical analysis Univariate (UVDA) and multivariate (MVDA) statistical analyses were performed to investigate differences among the groups. For UVDA, the Shapiro-Wilk test was applied for normality testing and results showed that the data did not follow a normal distribution. Differences between the three hibernation stages were then evaluated for each individual metabolite by performing the non-parametrical Kruskal-Wallis test (p ≤ 0.05) using MATLAB (R2015a, MathWorks). Post-hoc, pairwise analyses were performed using the Mann–Whitney U test to conclude whether the metabolite was significant or not in a comparison (A vs C; T vs C; A vs T). Finally, the false discovery rate at level α = 0.05 was controlled by Benjamini–Hochberg correction test. Compounds reported with a Kruskall-Wallis p value slightly over 0.05 were kept to enhance the biological information about this hibernation model based on their significant p value obtained in at least one of the comparisons performed with Mann–Whitney U test, and taking into account that their levels followed the same trend as the metabolites of their class. For MVDA, both unsupervised (Principal Component Analysis, PCA) and supervised (partial least squares discriminant analysis, PLS-DA; and orthogonal PLS-DA, OPLS-DA) models were obtained with SIMCA-P + 14.0 (Umetrics, Umea, Sweden). The MVDA was performed for each platform to assess the reliability of the analytical procedure, the classification of the samples, and to determine differences between the groups. All the models presented satisfactory quality parameter values according to the explained variance (R2) and the predicted variance (Q2), 8 ACS Paragon Plus Environment

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supplied by the software. The metabolites presenting a variable importance in projection (VIP) ≥ 1 and jackknifing confidence interval not including the zero value were selected as statistically significant from the OPLS-DA models. The OPLS-DA models generated for each technique were validated by cross-validation test, leaving 1/3 of the samples out and building up the models with the remaining samples until all of the excluded samples were predicted by the new model at least once. The percentage of correctly classified samples was calculated each time that the process was performed. Finally, trend visualization within each experimental group was achieved by building a heatmap of the metabolites that were statistically significant, using MetaboAnalyst 3.537, converting the numerical data matrix into the corresponding 2-D color map (Fig. 2A-B) 38. Compound identification for LC-MS and CE-MS analysis Initially, a tentative identification based on the m/z of the compounds showing significant differences were searched against several databases available online such as METLIN (http://metlin.scripps.edu),

lipidsMAPS

(http://

lipidMAPS.org)

and

KEGG

(http://www.genome.jp/kegg/), all of which have been joined into an ‘in-house’ developed search engine, CEU MassMediator (http://ceumass. eps.uspceu.es/mediator) . In order to obtain supplementary information for some identities, HMDB (http://hmdb.ca) was also consulted. Features that were tentatively assigned to metabolites from the databases were based on: mass accuracy (maximum error mass 15 ppm for LC-MS, and 25 ppm for CE-MS), isotopic pattern distribution, possibility of cation and anion formation and adduct formation 39. LC-MS/MS analyses for both positive and negative MS ionization modes were then performed by applying identical chromatographic conditions to those used in the analysis described above. Ions were targeted for CID fragmentation on-the-fly on the basis of the previously acquired accurate mass and retention time. Accurate mass and isotopic distributions for the precursor and product ion were studied for final confirmation of the selected compounds and inspected with Agilent MassHunter Qualitative Analysis Software B.06.00. Then, metabolites were identified using different software and strategies. A manual MS/MS spectra interpretation; a comparison of the MS/MS spectra acquired with available spectral data included in METLIN40, LipidMAPS41, along with an ‘in-house’ built database and a product ion structure elucidation by means of ChemSketch MS Fragmenter (ACD/Laboratories, v.12) were performed for the assignment of the corresponding identity for each selected compound. In the case of CE–MS, the compound identification was possible by injecting available standards, brain samples, and spiked brain tissue samples for confirmation of the most significant metabolites. Finally, for fragmentation pattern identification, samples were analyzed with the same analytical conditions 9 ACS Paragon Plus Environment


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as used in the previous analysis but applying a higher voltage in the MS fragmentor (175 V instead of 125V) 42. Results The metabolic fingerprint of Syrian hamster brain tissue obtained in torpor and arousal stages, as well as for control animals, was achieved by performing a multiplatform non-targeted metabolomics analysis. Tau hyperphosphorylation events take place in neurons from different brain regions of the Syrian hamster. Although the biological purpose of this process is still unknown, the mechanisms involved in the suggested neuroprotection of the brain do not appear to be region specific, but rather involve a general process that affects numerous neurons in several brain regions 43, as shown in figure 1. Thus, we performed the metabolomics analysis in the right hemisphere of the brain, whereas the left hemisphere was used to perform anatomical studies.


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Figure 1. Photomicrographs showing the patterns of hyperphosphorylated tau immunostaining (AT8 antibody) in coronal sections from the brain of control (euthermic; A), torpor (B) and arousal (C) Syrian hamsters. Small squared zones in (AC) are shown at higher magnification in DF (neocortex) and GI (hippocampus). Note the intense AT8 immunostaining in B that is distributed throughout the tissue, especially in neocortex (E) and hippocampus (H). Arrows in E and F point out the AT8 immunostaining of apical dendrites in layers II/III, whereas arrows in H and I show the AT8 immunostaining in hilar neurons. Scale bar in DI indicates 57 μm and 1824 μm in AC. A total of 122 features were obtained after GC-MS analysis, 242 features were obtained after CE-MS analysis, 972 features were obtained after LC-MS ESI (+) analysis, and finally 478 features were obtained after LC-MS ESI (-) analysis. Metabolites with a coefficient of variation of their abundances higher than 30% in QC samples were excluded from the data sets. After data filtration, the number of features that passed the CV threshold were 114 for GC-MS, 235 for CEMS, 932 for LC-MS ESI (+), and 441 for LC-MS ESI (-). The fold change of each metabolite was also evaluated. The PCA score plots generated for each technique showed the clustering of the QC samples, reflecting the stability of the system and the robustness of the analytical performance. The plots with the explained variance (R2) and the predicted variance (Q2) with their respective quality parameters are represented in figure S1 in the Supporting Information. Thereafter, the supervised PLS-DA models showed a clear separation between the control animals and the two hibernation-stage groups, which showed that intrinsic metabolic changes take place in the brain during hibernation. All the PLS-DA plots presented a difference between R2 and Q2 lower than 0.3, confirming the power of the models (figure S1, found in Supporting Information). Finally, the OPLS-DA models were generated for the evaluation by pairs of the three groups and validated by using the leaving-1/3-out approach. Results of the quality parameters of the models and the percentage of samples correctly classified are described in the legend of the figure S2 (Supporting Information). Univariate statistical analysis was performed simultaneously to assess the significance of each metabolite separately. All the metabolites that were statistically significant (after both univariate and multivariate data analyses) were identified using different means which are described in table S1 (Supporting Information), including retention time, mass error, adducts, CV, fold change, the identification source, as well as UVDA and MVDA results. The influence of the hibernation stage in the regulation of brain metabolism can be inferred from the results represented in the heatmaps generated in MetaboAnalyst 3.5 for compounds within a broad range of polarities to observe the whole metabolic signature that the hibernation process produced and left behind (Fig. 2AB). In addition, the metabolites that were statistically 11 ACS Paragon Plus Environment


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significant after GC-MS and CE-MS analyses were submitted to MetaboAnalyst 3.5 37 to identify the most relevant pathways based on the impact of those previously selected metabolites within the metabolic route. In this regard, both metabolite pathway analysis (MetPA) and a metabolite set enrichment analysis (MSEA) were set up. The results displayed in Figure 3AB suggested that the most significantly regulated pathways —and therefore those that play a key role in the regulation of hibernation processes— are protein biosynthesis; methionine metabolism; urea cycle; betaine metabolism; TCA cycle; glycine, serine, and threonine metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; and ammonia recycling, among others.


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Figure 2. Heatmap of significant metabolites detected in brain tissue by CE-MS and GC-MS (A), and LC-MS analysis (B), modified by their hibernation/non-hibernation states. The color spectrum ranging from red to blue indicates the range of high to low signal intensities,

respectively, for each metabolite. ■ Control group (C), ■ Torpor state group (T), ■ Arousal state group (A). Figure 3. MetPA (A), and MSEA (B), both obtained from MetaboAnalyst 3.5 after submission of the most significantly altered metabolites found by GC-MS and CE-MS analyses. In the MetPA, the size and the color of the circles vary depending on the importance of the pathway and its p value, respectively. Thus, the pathways located in the upper right corner of the graphic are the ones affecting the hibernation processes more. For the MSEA, the degree of positive association with the corresponding phenotype is represented by the color of the bar.

Regarding the LC-MS analysis results, the largest lipid class found to be altered in the arousal group was comprised of glycerophospholipids (GPL) (47%), the bulk of which were glycerophosphoethanolamines (PE) (18%), and glycerophosphocholines (PC) (10%), together with GPL of intermediate to low abundance, classified as glycerophosphoserines (PS) (7%), glycerophosphates (PA) (6%), glycerophosphoinositols (PI) (3%), and glycerophosphoglycerols (PG) (3%). The second largest lipid category found was glycerolipids (GL) (28%), distributed in diacylglycerols (DAG) (25%), and monoacylglycerols (MAG) (3%). In addition, a wide range of sphingolipids (SL) (23%) also accounted for a relatively large proportion of the lipids in this comparison, consisting of ceramides (Cer) (11%), neutral glycosphingolipids (4%), phosphosphingolipids (5%), acidic glycosphingolipids (2%), and sphingoid bases (1%). Comparatively, fatty acyls (2%), including carnitines (1%) and fatty amides (1%), made up a small proportion of the altered lipidome of this comparison (Fig. 4A). When comparing torpor state against control hamsters, GPL was the largest affected lipid group (58%), divided into PE (18%) 14 ACS Paragon Plus Environment

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as the most abundant class, followed by PI (10%), PC (10%), PA (9%), PS (9%) and a low percentage of PG (2%). GL was the second most affected group (28%), containing DAG (24%) and MAG (4%). One of the least affected lipid classes was the fatty acyls with an intermediate to low percentage of Cer (6%), neutral glycosphingolipids (1%) and phosphosphingolipids (1%). Another lesser affected lipid class was the fatty acyls classified as carnitines (4%) and fatty amides (1%) (Fig. 4B). Sphingolipids represented 50% of the altered lipids found after comparing arousal versus torpor groups, including Cer (26%), neutral glycosphingolipids (11%), phosphosphingolipids (8%), and a low proportion of sphingoid bases (5%). Secondly, GLP were also significantly affected (31%), with PC being the most altered class (18%), together with a low proportion of PS (4%), PE (4%), PI (3%), PG (1%), and PA (1%). Finally, only 10% of the altered lipids corresponded to GL, which was made up of DAG (9%) and MAG (1%) (Fig. 4C).

Figure 4. Percentage of each lipid class found to be biologically relevant by LC-MS analysis in each comparison. Discussion To our knowledge, this is the first multiplatform metabolomic study carried out in the brain of a hibernating mammal. Elevated levels of a metabolite can be linked either to an activation of the enzymes involved in its synthesis or to the inhibition of enzymes which use that metabolite as substrate. In order to interpret a biological outcome, it is necessary to analyze the whole 15 ACS Paragon Plus Environment


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metabolic pathway by comparing the variation of all specific metabolites between our experimental groups. During the torpor bouts, the metabolic rate is decreased by 9698% via a general process that affects all tissues. Consistent with previous genetic and metabolomics analyses in plasma 24, 31, 32, 44,

which indicate a metabolic depression, we found higher levels of sugars and citric acid and

lower levels of lactic acid, malic acid and fumaric acid in torpor animals compared to control animals. These changes indicate reduced glycolysis and inhibition of the initial stages of the tricarboxylic acid cycle. The energy necessary to maintain baseline brain activity is obtained by lipolysis during torpor , which is accompanied by a decrease in the activity of most of the metabolic pathways. Amino acid changes during hibernation Based on LC–MS metabolomic analysis, changes in plasma amino acid levels in hibernating ground squirrels have been reported: torpor was associated with a global decrease in brain tissue content of aspartate, glutamate, isoleucine, tyrosine and tryptophan, supposedly resulting from a reduction in protein degradation 45. In accordance with these results, we have found low methionine and asparagine levels in the brain of hibernating Syrian hamsters compared to euthermic. However, the tissue content of alanine, GABA, serine, threonine, and lysine were globally increased during torpor 45, pointing to an intricate metabolic network during hibernation. Our results include the increment of a previously unreported amino acid, proline, that could be related to the stage’s rollback; experimental data support a beneficial effect of proline-rich polypeptides in a number of neurodegenerative diseases, including AD 46. According to a recent review 47, since amino acids are involved in so many cellular metabolic and signaling pathways, the effects of altered amino acid metabolism in AD brain are far-reaching (see table 1 for an overview). Another example is ornithine, which is highly upregulated in torpor and arousal animals; urea and ornithine are products of arginine resulting from the action of arginase — one of the urea cycle enzymes with expression that appears to be disrupted in AD. Increased arginase activity could account for increased urea and ornithine levels in arousal, the latter being a precursor of polyamine synthesis. According to our results, higher concentrations of ornithine were found in both torpor and arousal versus control, without being a source of increased polyamines (which are easily detected by GC-MS). Two amino acid derivatives with high accumulation in the torpor stage dropped to control values in the arousal stage — Methylthreonine and N-Acetyl-tyrosine. The former amino acid derivative is included in the patent US 20140045764 A1 for neuroprotective peptides, and the latter is a more stable form of the amino acid tyrosine. Tyrosine is important for synthesizing 16 ACS Paragon Plus Environment

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catecholamines, but there are only a few studies measuring its levels specifically in AD brain, although several studies have found that oral tyrosine administration improves memory and cognitive function in depression, Parkinson’s disease and AD 48. In general, aromatic amino acid metabolism is especially important for neural functioning 47. There are several studies indicating that deregulation of amino acid metabolism may contribute to AD progression 49, 50. In particular, methionine excess may contribute to memory impairment

51

and methionine restriction

decreases oxidative stress 52. Methionine is indeed reduced in both torpor and arousal (Table 1), probably with a beneficial role. In view of our results, we suggest that a specific balance in amino acid levels may be required during the hibernation of the Syrian hamster.

TABLE 1 Interestingly, we observed a clear reduction in long-chain acylcarnitine levels during torpor compared to arousal and free carnitine levels were also lower in torpid hamsters. This decrease has been previously detected in liver and plasma samples of hibernating ground squirrels 25, 31. L-Carnitine is synthesized from the amino acids lysine and methionine and is a key metabolite for long-chain fatty acid and palmitate transportation from the cytosol into the mitochondria for β-oxidation, but it is also involved in maintaining the mitochondrial Acyl-CoA/CoA ratio, controlling cytotoxicity, and storing energy as acylcarnitines. Both carnitine and acylcarnitines are present in brain tissues, either obtained through endogenous biosynthesis or diet, since they can be transported into the brain 53. Recently, L-carnitine and acylcarnitines have been proposed as neuroprotective agents for several disorders, including hypoxia-ischemia, traumatic brain injury, and AD 54. Therefore, we hypothesize that the remarkable recovery of their levels during arousal might be linked to preventing energetic failure, preventing oxidative damage to key mitochondrial proteins, and maintaining neuronal and glial functions as well as biosynthetic capabilities 55.

Endocannabinoid system during hibernation The endocannabinoid system is a retrograde signaling system composed of lipid mediators that control circadian processes and other important physiological functions. Cannabinoid receptors are differentially expressed depending on the specific tissue in question. Type 1 receptors (CB1) are predominantly expressed in the brain, whereas type 2 receptors (CB2) are present in high



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levels in microglia and astrocytes. The main endogenous ligands are 2-arachidonoylglycerol and anandamide (arachidonoyl ethanolamine). The concentration of anandamide is higher at wakening than immediately before sleep — a relationship that is disrupted by sleep deprivation. Anandamide may act as a neurotransmitter promoting sleep by enhancing the levels of the sleep-inducing molecule Adenosine

56, 57.

Furthermore, CB2 receptor agonists may protect against cerebral ischemia and reperfusion injury by decreasing the inflammatory and immune response and promoting a vasodilatation effect 58. Anandamide levels have been analyzed in plasma from groundhogs, using LC–MS, but no differences were seen between summer active and torpid animals 59. Here, analyzing brain tissue, we found an increase in 2-arachidonoylglycerol and phospho-anandamide during the hibernation of Syrian hamsters. However, the levels of two different anandamides (20:1,n=9 and 20:2,n=6) were lower in arousal when compared to torpor samples, indicating metabolic pathway activation. It has been shown that there is an increase in oleoylethanolamide —the monounsaturated analog of anandamide— in human cerebrospinal fluid after 24 h of sleep deprivation

60.

Oleoylethanolamide has been suggested as an endogenous neuroprotective signal to neuronal injury by activating the peroxisome proliferator-activated receptor-α (PPAR-α), which leads to anti-oxidative and anti-inflammatory responses

61.

Here we show an increase in N-

Oleoylethanolamide during torpor and, thus, this cannabinoid can be considered as another metabolite that may aid in the neuroprotection of the hamster brain during hibernation. Oleamide (OA) is a fatty acid amide and another member of the endocannabinoid family which may function as a sleep-inducing agent 62. OA is elevated in the central nervous system of sleepdeprived mammals, probably via the alteration of CB1 receptor function 63. Previous reports indicated that the brains of hibernating squirrels contained significantly more OA than euthermic animals 64. Here, we have found no OA changes during torpor or arousal compared to euthermic hamsters, but we did find low levels of OA in aroused animals compared to torpor animals. Thus, it is possible that OA participates in the torpor-arousal transition. Another interesting feature related to cannabinoids is that they play a role in tau hyperphosphorylation, as cannabidiol reduces the phosphorylated active form of glycogen synthase kinase 3β 65, 66. Furthermore, a reduction in the number of neurofibrillary tangles was described in a mouse model of tauopathy, after administration of 9-tetrahydrocannabinol and cannabidiol natural extracts

67.

In fact, targeting the endogenous cannabinoid system has emerged as a


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potential therapeutic approach to treat patients with AD, where significantly lower levels of anandamide have been reported 68, 69. Finally, arachidonic acid, which participates in the biosynthesis of anandamide, is elevated during arousal and torpor when compared to control samples. This polyunsaturated fatty acid is also a precursor of prostaglandins, leukotrienes and related compounds; thus, it could be a possible candidate to participate in the regulation of inflammation during hibernation. To sum up, there could be a link between the well-known beneficial effects of cannabinoids on neuroprotective responses on the one hand and the transitions from control to torpor and from torpor to arousal on the other. We have summarized the potential role of our findings in relation to the endocanabinoid system during hibernation in Fig. 5.

Figure 5. Summary of the possible neuroprotective effects (green) of cannabinoid compounds that are increased (blue) or decreased (red) during the hibernation of the Syrian hamster. Decreased metabolism and Tau phosphorylation

Tau phosphorylation could be a consequence of reduced metabolism. Reversible Tau phosphorylation has been demonstrated in starving mice 70, indicating that reduced levels of glucose may increase tau phosphorylation levels. Furthermore, it has been described that anesthesia-induced hypothermia may lead to tau hyperphosphorylation by inhibition of protein phosphatase 2A (PP2A) activity 71. Previous studies have reported a reduction in the cerebral metabolic rate for glucose in AD patients

72-75,

which contributes to a progressive cognitive

decline, as the metabolic impairment may occur decades before the appearance of AD 19 ACS Paragon Plus Environment


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symptoms 76. We could speculate that this metabolic situation, over a prolonged period, could lead to permanent tau phosphorylation that would ultimately cause the appearance of NFTs. Thus, the identification of specific metabolites produced after a forced awake (arousal) may be useful to determine the mechanisms involved in the rapid Tau dephosphorylation and the fast reorganization of neuronal structures. The data provided in the present study could contribute to develop additional strategies to diminish phosphorylated tau levels, which are involved in the pathogenesis of AD and other neurodegenerative disorders. Epigenetic control during hibernation Some studies have suggested epigenetic control during hibernation 77, 78; DNA methylation and histone modifications play a role in genomic regulation during the hibernation of thirteen-lined ground squirrels

79, 80

through tissue-specific transcriptional regulation in response to adverse

weather conditions. Furthermore, previous metabolomic studies in liver tissue revealed increased levels of betaine (N,N,N-trimethylglycine) in torpor. Betaine, a methyl donor for SAdenosylmethionine, promotes an effective protective response to osmotic stress that may be involved in providing oxidative defense via glutathione after arousal

30.

A previous report

indicated that betaine improves tau hyperphosphorylation in transgenic mice with insulin resistance by reducing oxidative stress 81. In addition, it has been hypothesized that a decrease in PP2A methylation could lead to tau hyperphosphorylation 82, 83. Our results in the brain of the hibernating Syrian hamster also indicate an increase in betaine levels but, besides, we found other changes in metabolites related to epigenetic regulation in the brain of hibernating Syrian hamster (see table 2). Betaine levels are increased in torpor but the level of methionine is decreased, suggesting that betaine-homocysteine methyltransferase is inhibited in the torpor stage. The principal reactions involved in these metabolic pathways are described in figure 6. TABLE 2


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Figure 6. Metabolic pathway interconnections among homocysteine metabolism; sphingolipid metabolism; polyamine metabolism; folic cycle; and glycine, serine, and threonine metabolism. The metabolites in bold were found to be altered in this hibernating model after LC-MS, GCMS, and CE-MS analyses. Brain cryoprotectants The accumulation of polyols is a well-known strategy used by insects to tolerate temperatures below zero

84, 85.

Polyols stabilize protein structure at low temperatures and

reduce the fraction of frozen water, avoiding the formation of ice crystals which would otherwise destroy cells. Glycerol and other polyols such as ethylene glycol, erythritol, mannitol, ribitol, sorbitol, and threitol are also produced to avoid freezing in insects 86. Among vertebrates, amphibian and reptile species have been documented to survive in cold climates by tolerating the formation of ice crystals in body fluids using cryoprotectants such as low molecular weight carbohydrates

87.

In the present study, we found that threitol levels are elevated in torpid

hamsters. Along with high concentrations of sugars, threitol may contribute to cold tolerance in Syrian hamsters. To our knowledge, this is the first study to show selective accumulation of threitol in the brain of hibernating mammals. The body temperature of the Syrian hamster during torpor reaches approximately 4C, but other hibernating mammals —such as Spermophilusparryii— can reach temperatures below zero during torpor 1. In addition, high concentrations of proline were found in overwintering larvae and have been proposed to play a role in the stabilization of protein structure and biological membranes during cellular freeze-dehydration 88. In the present study, proline was elevated in torpor and arousal versus control brain samples (see table 1).



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Cyclohexanehexol stereoisomers as possible brain-specific neuroprotective agents We have found high levels of cyclohexanehexol stereoisomers, allo-inositol and scyllo-inositol in the brain of torpid hamsters when compared to euthermic hamsters. Scyllo-inositol has been reported to stabilize the non-toxic oligomers of Aβ and to inhibit their toxic aggregation 89, 90. In humans, cerebral scyllo-inositol may be produced in response to functional defects in the brain caused by AD and aging

91

and this cyclohexanehexol has been proposed as a possible

therapeutic agent for the treatment of AD 92. It has been reported previously that scyllo-inositol is accumulated in plasma samples from torpid and aroused ground squirrels 32, but our results revealed a new stereoisomer of inositol that may function as a neuroprotector by avoiding toxic protein aggregations. Role of Sphingolipid,Ceramide and Glycerophospholipid pathways during hibernation In mammalian brain, sphingolipids are considered to be the second largest lipid class, representing approximately 510% of its total lipid content. Modification of their structure through metabolic reactions results in a wide variety of sphingolipids that play important roles in the membrane structure and cell functions 93. Neural tissues are highly sensitive to metabolic dysregulation of sphingolipid metabolism, which could lead to their accumulation, thus contributing to the development of neurological disorders 94. Several stimuli —such as oxidative stress or inflammatory cytokines— might lead to the upregulation of sphingolipids, especially of ceramides (Cer), which are the direct product of sphingomyelin (SM) and glycosphingolipid hydrolysis. Cer, core constituents of all complex sphingolipids, can be described as lipid mediators that are involved in several physiological regulatory processes: cellular proliferation, differentiation, and apoptosis. Apart from their essential role in signal transduction, they also act as regulators of synaptic function, contributing to the maintenance of synapses, dendritic spines, and neuronal transmission in association with other sphingolipids and cholesterol in lipid rafts [43, 44, 51]. The differences showed in the present article regarding Cer levels, may derive from de novo synthesis via ceramide synthases or from the activity of sphingomyelinases (Fig. 7). Ceramide synthase (CerS) is a key enzyme in de novo sphingolipid biosynthesis. Six isoforms of this enzyme have been identified, with CerS1 being the one that is mainly present in brain and muscle tissues and is responsible for the generation of C18 ceramides. CerS2 is expressed in the oligodendrocytes and Schwann cells, especially during the myelination process, and principally synthesizes C20-C26 ceramide. In addition, sphingomyelinases (SMase) are the enzymes involved in the generation of Cer through sphingomyelin hydrolysis. 22 ACS Paragon Plus Environment

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When comparing the torpor with the aroused animals, Cer with more than 20 C atoms are specifically elevated, reflecting a higher activity of CerS2. In a previous report, ablation of CerS2 in the liver induces oxidative stress 95, thereby, the metabolic shift that occurs during hibernation may potentiate the synthesis of long-chain ceramides to prevent oxidative damage. Upregulation of the activities of CerS1 and CerS2, NSMase 2, and ASMase enzymes has been linked to AD progression 96, 97, as the generation of Aβ plaques and NFTs 98, 99 and the accumulation of Cer

100-102

have been reported. This up-regulation is more specifically for CerS1, since the Cer

observed contained acyl chains with up to 20 C atoms. When comparing the torpor with the aroused animals, Cer with more than 20 C atoms are elevated during hibernation, reflecting a higher activity of CerS2 during hibernation. Cer are also involved in the control of the Golgi Apparatus (GA) structure, as it has been described that short-chain Cer participate in the maintenance of the GA stability, whereas longchain Cer seem to play a role in destabilizing GA structure

103,

probably by modulating the

activation of specific kinases and phosphatases 104 and/or enhancing the formation of vesicles 105. During hibernation, it has been demonstrated that the GA suffer a transitory disorganization,

causing a reduction in the volume and surface area of the GA elements and a differential modification in expression levels and distribution patterns of Golgi structural proteins 10. Our data complement this observation, as long-chain Cer levels are elevated during torpor, which suggests a possible role of CerS2 and long-chain Cer in the GA structural changes within the neuronal cells of the hibernating hamster. Furthermore, Cer levels are lower in the arousal animals compared with the control group, which might indicate that this accumulation is reversed upon awakening, perhaps through a downregulation of the CerS2 activity that eventually promotes GA reorganization. However, upregulation of CerS1 is maintained since Cer with less than 20 C are elevated. The accumulation of serine and a decrease in the levels of sphingoid bases, phosphosphingolipids, as well as neutral and acidic glycosphingolipids, were observed during torpor. This may reflect an inhibition of the enzymes involved in their synthesis, especially serine-palmitoyltransferase. The inhibition of this enzyme has been demonstrated to reduce Aβ and tau hyperphosphorylation in an AD mouse model 106. Thus, it may be that there is a possible protection mechanism against neurodegeneration in arousal animals. In spite of all these enzymatic blockages of the sphingolipids metabolism, the transformation of sphinganine 1phosphate into O-phosphoethanolamine seems to continue when the animals arouse from hibernation. Low levels of this phosphomonoester have been found in post-mortem AD brains 107.



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Figure 7. Main metabolic reactions involved in sphingolipid synthesis. CerS, ceramide synthase; CERT, ceramide transfer protein; NSMase2, neutral sphingomyelinase 2; ASMase, acidic sphingomyelinase; GlcCer, glucosylceramide; LacCer, lactosylceramide; GSLs, glucosphingolipids.

High serum levels of Cer d18:1–C16:0 are associated with an increased risk of AD 108. We have found that the level of this specific ceramide is elevated during the hibernation of the Syrian hamster. As hamsters do not undergo any neuronal damage due to this process, Cer d18:1– C16:0 could represent another potential neuroprotective metabolite. Phosphatidylserine (PS) is a glycerophospholipid that is of clear relevance to brain cell membranes, as is involved in membrane fluidity and in the transmission of brain cell activity and neural information. PS levels may decrease proportionally with age, affecting memory and cognitive ability. PS supplements significantly enhance brain activity

109

and can improve

memory function and hippocampal inflammation injury in AD patients 110. PS and PE stimulate tau phosphorylation by Ca2+/CaM kinase II and PI were found to be potent inhibitors of tau protein phosphorylation [106, 107]. The variations of these 3 glycerophospholipids during torpor may correlate with an overall increase of Tau phosphorylation. We have found very high levels of phosphatidic acid (methyl-PA(16:0/0:0)) in torpor samples: a 4.39 fold change when compared to control animals (see table S1). PA is a monoacylglycerophosphate, precursor of all membrane glycerophospholipids. Although we detect other differential changes in other glycerophosphates, it is important to highlight that 24 ACS Paragon Plus Environment

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the increase in methyl-PA(16:0/0:0) is the most prominent of any metabolite in our study. PA is produced as a result of the enzymatic activity of phospholipase D. It participates in the regulation of kinases involved in NADPH oxidase activation and it is also the mammalian target of rapamycin (mTOR) kinase. PA-mediated activation of mTORC1 can reduce the toxicity of oxidative species and trigger tau phosphorylation in serine 214, which reduces tau aggregation and paired helical filament (PHF) formation

111, 112.

A study testing different phosphotau

antibodies described that Tau phosphorylation at T212/S214/T217 (AT100) is increased during torpor in Syrian Hamster and Arctic Ground Squirrels 43, 113. Further research is needed to confirm methyl-PA(16:0/0:0) as a candidate to phosphorylate Tau during the hibernation of the Syrian hamster. Glycerophospholipids are the largest class of lipids affected both in torpor (58%) and arousal (47%) when compared to control groups. This group of lipids is the main component of cell membranes, representing approximately 50-60% of the total membrane mass in the brain, together with glycolipids and cholesterol. Glycerophospholipids constitute the main source of fat reserves in mammalian tissue and develop a wide variety of functions, which may depend on the many different combinations of fatty acids attached

114.

Here, we found that most

diglyceride (DG) or diacylglycerols (DAG) accumulate during hibernation where, for example, DG (40:7) and DG (36:5) present a fold change of 3 or more in both torpor or arousal compared to control. In general, DAG acts as a signaling molecule due to its ability to bind to different proteins 115.

For instance, DAG can bind PKC, a protein kinase implicated in neuronal plasticity and cell

polarity, which can inhibit the activity of kinases such as ERK1/2 and GSK-3b, implicated in tau phosphorylation 116. This event could be related to the reversion of tau phosphorylation when the hamster awake, as DG accumulation is more prominent in arousal samples. Whether the elevation of glycerolipids during hibernation is due to fatty acid catabolism or may have a functional significance in tau modification, need further analysis. Conclusions This is the first multiplatform metabolomic study with a holistic approach carried out with the brain of a hibernating mammal. Our findings confirm the crucial role of changes in amino acid metabolism and their derivatives along with reduced glycolysis and attenuation of the tricarboxylic acid cycle. Moreover, the potential role of acylcarnitines, cannabinoids and arachidonic acid during hibernation open up new possibilities for neuroprotective agents and therapies.



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Finally, the Syrian hamster represents an excellent experimental model to study changes in brain metabolomics induced by hibernation that may provide insights into novel neuroprotective agents. The mechanisms involved in these changes may be relevant for better understanding brain alterations that occur in neurodegenerative diseases such as AD. SUPPORTING INFORMATION The following supporting information is available free of charge at ACS website http://pubs.acs.org Material S1. Description of the reagents, analytical conditions for the brain tissue fingerprinting by LC-MS, GC-MS, and CE-MS; and multiplatform data treatment. Figure S1. PCA-X and PLS-DA score plot for the three analytical platforms with their corresponding R2 (explained variance) and Q2 (predicted variance). Figure S2. OPLS-DA models for the three comparisons (A vs C; T vs C; A vs T) obtained after GCMS, LC-MS and CE-MS, with their corresponding quality parameters (R2 and Q2) and the cross validation test results. Table S1. Metabolites found to be statistically significant for any of the comparisons performed at different hibernation stages in hamster brain tissue.


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Abbreviations AD: Alzheimer’s disease; ASMase: Acidic sphingomyelinase; BCAAs: Branched-chain amino acids; BSTFA:TMCS:

N,O-bis(trimethylsilyl)

trifluoroacetamide:

trimethylchlorosilane;

CB1-2:

Cannabinoid receptor type 1 and 2; CE-MS: Capillary electrophoresis-mass spectrometry; Cer: Ceramides; CerS1-2: Ceramide synthase 2; CV: Coefficient of variation; DAG: Diacylglycerols; ER: Endoplasmic reticulum; GA: Golgi apparatus; GABA: 4-Aminobutanoic acid; GC–MS: Gas chromatography-mass spectrometry; GL: Glycerolipids; GPL: Glycerophospholipids; IS: Internal standard;

LC–MS:

Liquid

chromatography-mass

spectrometry;

LC-MS/MS:

Liquid

chromatography-tandem mass spectrometry; MAG: Monoacylglycerols; MetPA: Metabolite pathway analysis; MSEA: Metabolite set enrichment analysis; MVDA: Multivariate data analysis; NFTs: Neurofibrillary tangles; NSMase: Neutral sphingomyelinase; OA: Oleamide; OPLS-DA: Orthogonal partial least squares discriminant analysis; PA: Glycerophosphates; PC: Glycerophosphocholines;

PCA:

Principal

component

analysis;

PE:

Glycerophosphoethanolamines; PG: Glycerophosphoglycerols; PHF: Paired helical filaments; PI: Glycerophosphoinositols; PLS-DA: Partial least squares discriminant analysis; PP2A: Protein phosphatase 2A; PS: Glycerophosphoserines; Q2: Predicted variance; QC: Quality control samples; R2: Explained variance; ROS: Reactive oxygen species; SL: Sphingolipids; SM: Sphingomyelin; SMase: Sphingomyelinase; TCA cycle: Tricarboxylic acid cycle; UVDA: Univariate data analysis; VIP; Variable importance in projection Acknowledgments The authors would like to offer a special thanks to V. Alonso for her technical assistance and to Joanna Godzien for her help in the metabolites identification. CGR would like to thank the Fundación Universitaria San Pablo CEU for her PhD fellowship. Funding sources This work was supported by grants from the following entities: the Spanish Ministerio de Economía y Competitividad (Grants CTQ2014-55279-R to CB and AG and Grant SAF 2015-66603P to JD); and Centro de Investigación en Red sobre Enfermedades Neurodegenerativas (CIBERNED, CB06/05/0066, Spain) to JD.

Ethics approval and consent to participate All experimental procedures were carried out at the animal facility of the San Pablo CEU University of Madrid (SVA-CEU.USP, registration number ES 28022 0000015) and were approved 27 ACS Paragon Plus Environment


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by the institutional Animal Experiment Ethics Committee (PCD 043-1 V2/12). Special efforts were made to minimize the number of animals and to limit discomfort, pain or any other suffering of the experimental animals used in this study. References 1. Ruf, T.; Geiser, F., Daily torpor and hibernation in birds and mammals. Biol Rev Camb Philos Soc 2015, 90 (3), 891-926. 2. Drew, K. L.; Rice, M. E.; Kuhn, T. B.; Smith, M. A., Neuroprotective adaptations in hibernation: therapeutic implications for ischemia-reperfusion, traumatic brain injury and neurodegenerative diseases. Free Radic Biol Med 2001, 31 (5), 563-73. 3. Zhou, F.; Zhu, X.; Castellani, R. J.; Stimmelmayr, R.; Perry, G.; Smith, M. A.; Drew, K. L., Hibernation, a model of neuroprotection. Am J Pathol 2001, 158 (6), 2145-51. 4. Bouma, H. R.; Carey, H. V.; Kroese, F. G., Hibernation: the immune system at rest? J Leukoc Biol 2010, 88 (4), 619-24. 5. Geiser, F., Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu Rev Physiol 2004, 66, 239-74. 6. Geiser, F., Hibernation. Curr Biol 2013, 23 (5), R188-93. 7. Popov, V. I.; Bocharova, L. S., Hibernation-induced structural changes in synaptic contacts between mossy fibres and hippocampal pyramidal neurons. Neuroscience 1992, 48 (1), 53-62. 8. Popov, V. I.; Medvedev, N. I.; Patrushev, I. V.; Ignat'ev, D. A.; Morenkov, E. D.; Stewart, M. G., Reversible reduction in dendritic spines in CA1 of rat and ground squirrel subjected to hypothermia-normothermia in vivo: A three-dimensional electron microscope study. Neuroscience 2007, 149 (3), 549-60. 9. von der Ohe, C. G.; Darian-Smith, C.; Garner, C. C.; Heller, H. C., Ubiquitous and temperature-dependent neural plasticity in hibernators. J Neurosci 2006, 26 (41), 10590-8. 10. Anton-Fernandez, A.; Leon-Espinosa, G.; DeFelipe, J.; Munoz, A., Changes in the Golgi Apparatus of Neocortical and Hippocampal Neurons in the Hibernating Hamster. Front Neuroanat 2015, 9, 157. 11. Bullmann, T.; Seeger, G.; Stieler, J.; Hanics, J.; Reimann, K.; Kretzschmann, T. P.; Hilbrich, I.; Holzer, M.; Alpar, A.; Arendt, T., Tau phosphorylation-associated spine regression does not impair hippocampal-dependent memory in hibernating golden hamsters. Hippocampus 2016, 26 (3), 301-18. 12. Weingarten, M. D.; Lockwood, A. H.; Hwo, S. Y.; Kirschner, M. W., A protein factor essential for microtubule assembly. Proc Natl Acad Sci U S A 1975, 72 (5), 1858-62. 13. Avila, J.; Lucas, J. J.; Perez, M.; Hernandez, F., Role of tau protein in both physiological and pathological conditions. Physiol Rev 2004, 84 (2), 361-84. 14. Avila, J., Tau aggregation into fibrillar polymers: taupathies. FEBS Lett 2000, 476 (1-2), 89-92. 15. Arendt, T.; Stieler, J.; Strijkstra, A. M.; Hut, R. A.; Rudiger, J.; Van der Zee, E. A.; Harkany, T.; Holzer, M.; Hartig, W., Reversible paired helical filament-like phosphorylation of tau is an adaptive process associated with neuronal plasticity in hibernating animals. J Neurosci 2003, 23 (18), 6972-81. 16. Cogut, V.; Bruintjes, J.; Eggen, B.; van der Zee, E.; Henning, R., Brain inflammatory cytokines and microglia morphology changes throughout hibernation phases in Syrian hamster. Brain, behavior, and immunity 2018, 68, 17-22. 17. Arendt, T.; Bullmann, T., Neuronal plasticity in hibernation and the proposed role of the microtubule-associated protein tau as a "master switch" regulating synaptic gain in neuronal networks. Am J Physiol Regul Integr Comp Physiol 2013, 305 (5), R478-89. 28 ACS Paragon Plus Environment

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18. Magarinos, A. M.; McEwen, B. S.; Saboureau, M.; Pevet, P., Rapid and reversible changes in intrahippocampal connectivity during the course of hibernation in European hamsters. Proc Natl Acad Sci U S A 2006, 103 (49), 18775-80. 19. Dave, K. R.; Christian, S. L.; Perez-Pinzon, M. A.; Drew, K. L., Neuroprotection: lessons from hibernators. Comp Biochem Physiol B Biochem Mol Biol 2012, 162 (1-3), 1-9. 20. Forreider, B.; Pozivilko, D.; Kawaji, Q.; Geng, X.; Ding, Y., Hibernation-like neuroprotection in stroke by attenuating brain metabolic dysfunction. Progress in neurobiology 2017, 157, 174-187. 21. Frerichs, K. U.; Kennedy, C.; Sokoloff, L.; Hallenbeck, J. M., Local cerebral blood flow during hibernation, a model of natural tolerance to "cerebral ischemia". J Cereb Blood Flow Metab 1994, 14 (2), 193-205. 22. Bouma, H. R.; Kroese, F. G.; Kok, J. W.; Talaei, F.; Boerema, A. S.; Herwig, A.; Draghiciu, O.; van Buiten, A.; Epema, A. H.; van Dam, A.; Strijkstra, A. M.; Henning, R. H., Low body temperature governs the decline of circulating lymphocytes during hibernation through sphingosine-1-phosphate. Proc Natl Acad Sci U S A 2011, 108 (5), 2052-7. 23. Bouma, H. R.; Henning, R. H.; Kroese, F. G.; Carey, H. V., Hibernation is associated with depression of T-cell independent humoral immune responses in the 13-lined ground squirrel. Dev Comp Immunol 2013, 39 (3), 154-60. 24. Schwartz, C.; Hampton, M.; Andrews, M. T., Seasonal and regional differences in gene expression in the brain of a hibernating mammal. PLoS One 2013, 8 (3), e58427. 25. Nelson, C. J.; Otis, J. P.; Martin, S. L.; Carey, H. V., Analysis of the hibernation cycle using LC-MS-based metabolomics in ground squirrel liver. Physiol Genomics 2009, 37 (1), 43-51. 26. Shao, C.; Liu, Y.; Ruan, H.; Li, Y.; Wang, H.; Kohl, F.; Goropashnaya, A. V.; Fedorov, V. B.; Zeng, R.; Barnes, B. M.; Yan, J., Shotgun proteomics analysis of hibernating arctic ground squirrels. Mol Cell Proteomics 2010, 9 (2), 313-26. 27. Yan, J.; Barnes, B. M.; Kohl, F.; Marr, T. G., Modulation of gene expression in hibernating arctic ground squirrels. Physiol Genomics 2008, 32 (2), 170-81. 28. Hindle, A. G.; Martin, S. L., Cytoskeletal regulation dominates temperature-sensitive proteomic changes of hibernation in forebrain of 13-lined ground squirrels. PLoS One 2013, 8 (8), e71627. 29. Luu, B. E.; Biggar, K. K.; Wu, C. W.; Storey, K. B., Torpor-responsive expression of novel microRNA regulating metabolism and other cellular pathways in the thirteen-lined ground squirrel, Ictidomys tridecemlineatus. FEBS letters 2016, 590 (20), 3574-3582. 30. Serkova, N. J.; Rose, J. C.; Epperson, L. E.; Carey, H. V.; Martin, S. L., Quantitative analysis of liver metabolites in three stages of the circannual hibernation cycle in 13-lined ground squirrels by NMR. Physiol Genomics 2007, 31 (1), 15-24. 31. Nelson, C. J.; Otis, J. P.; Carey, H. V., Global analysis of circulating metabolites in hibernating ground squirrels. Comp Biochem Physiol Part D Genomics Proteomics 2010, 5 (4), 265-73. 32. Epperson, L. E.; Karimpour-Fard, A.; Hunter, L. E.; Martin, S. L., Metabolic cycles in a circannual hibernator. Physiol Genomics 2011, 43 (13), 799-807. 33. Gonzalez-Riano, C.; Tapia-Gonzalez, S.; Garcia, A.; Munoz, A.; DeFelipe, J.; Barbas, C., Metabolomics and neuroanatomical evaluation of post-mortem changes in the hippocampus. Brain Struct Funct 2017, 222 (6), 2831-2853. 34. Gonzalez-Riano, C.; Garcia, A.; Barbas, C., Metabolomics studies in brain tissue: A review. Journal of Pharmaceutical and Biomedical Analysis 2016, 130, 141-168. 35. Naz, S.; Garcia, A.; Barbas, C., Multiplatform Analytical Methodology for Metabolic Fingerprinting of Lung Tissue. Analytical Chemistry 2013, 85 (22), 10941-10948. 36. Gonzalez-Riano, C.; Tapia-González, S.; García, A.; Muñoz, A.; DeFelipe, J.; Barbas, C., Metabolomics and neuroanatomical evaluation of post-mortem changes in the hippocampus. Brain Structure and Function 2017, 1-23. 29 ACS Paragon Plus Environment


Page 30 of 43

37. MetaboAnalyst- statistical, f. a. i. a. o. m. d., http://www.metaboanalyst.ca/ Accessed 05 May 2018. 38. Xia, J.; Wishart, D. S., Using metaboanalyst 3.0 for comprehensive metabolomics data analysis. Current Protocols in Bioinformatics 2016, 14.10. 1-14.10. 91. 39. Godzien, J.; Ciborowski, M.; Whiley, L.; Legido-Quigley, C.; Ruperez, F. J.; Barbas, C., In-vial dual extraction liquid chromatography coupled to mass spectrometry applied to streptozotocin-treated diabetic rats. Tips and pitfalls of the method. Journal of Chromatography A 2013, 1304, 52-60. 40. Metlin, S. C. F. M. a. M. S.-. Accessed 05 May 2018. 41. Gateway., L. M.-L. 42. Godzien, J.; Armitage, E. G.; Angulo, S.; Paz Martinez-Alcazar, M.; Alonso-Herranz, V.; Otero, A.; Lopez-Gonzalvez, A.; Barbas, C., In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics. Electrophoresis 2015, 36 (18), 2188-2195. 43. Stieler, J. T.; Bullmann, T.; Kohl, F.; Toien, O.; Bruckner, M. K.; Hartig, W.; Barnes, B. M.; Arendt, T., The physiological link between metabolic rate depression and tau phosphorylation in mammalian hibernation. PLoS One 2011, 6 (1), e14530. 44. Fedotcheva, N. I.; Litvinova, E. G.; Kamzolova, S. V.; Morguno, I. G.; Amerkhanov, Z. G., Mitochondrial metabolites in tissues as indicators of metabolic alterations during hibernation. Cryo Letters 2010, 31 (5), 392-400. 45. Osborne, P. G.; Hashimoto, M., Mammalian cerebral metabolism and amino acid neurotransmission during hibernation. J Neurochem 2008, 106 (4), 1888-99. 46. Gladkevich, A.; Bosker, F.; Korf, J.; Yenkoyan, K.; Vahradyan, H.; Aghajanov, M., Prolinerich polypeptides in Alzheimer's disease and neurodegenerative disorders - Therapeutic potential or a mirage? Progress in Neuro-Psychopharmacology & Biological Psychiatry 2007, 31 (7), 1347-1355. 47. Griffin, J. W. D.; Bradshaw, P. C., Amino Acid Catabolism in Alzheimer's Disease Brain: Friend or Foe? Oxidative Medicine and Cellular Longevity 2017. 48. van de Rest, O.; van der Zwaluw, N. L.; de Groot, L., Literature review on the role of dietary protein and amino acids in cognitive functioning and cognitive decline. Amino Acids 2013, 45 (5), 1035-1045. 49. Larsson, S. C.; Markus, H. S., Branched-chain amino acids and Alzheimer's disease: a Mendelian randomization analysis. Sci Rep 2017, 7 (1), 13604. 50. Gonzalez-Dominguez, R.; Garcia-Barrera, T.; Gomez-Ariza, J. L., Metabolite profiling for the identification of altered metabolic pathways in Alzheimer's disease. J Pharm Biomed Anal 2015, 107, 75-81. 51. Tapia-Rojas, C.; Lindsay, C. B.; Montecinos-Oliva, C.; Arrazola, M. S.; Retamales, R. M.; Bunout, D.; Hirsch, S.; Inestrosa, N. C., Is L-methionine a trigger factor for Alzheimer's-like neurodegeneration?: Changes in Abeta oligomers, tau phosphorylation, synaptic proteins, Wnt signaling and behavioral impairment in wild-type mice. Mol Neurodegener 2015, 10, 62. 52. Sanchez-Roman, I.; Barja, G., Regulation of longevity and oxidative stress by nutritional interventions: role of methionine restriction. Exp Gerontol 2013, 48 (10), 1030-42. 53. WANDERS, R. J., Carnitine biosynthesis in mammals. Biochemical Journal 2002, 361 (3), 417-429. 54. Virmani, A.; Binienda, Z., Role of carnitine esters in brain neuropathology. Molecular aspects of medicine 2004, 25 (5), 533-549. 55. Ferreira, G. C.; McKenna, M. C., l-Carnitine and Acetyl-l-carnitine Roles and Neuroprotection in Developing Brain. Neurochemical Research 2017, 1-15. 56. Murillo-Rodriguez, E.; Poot-Ake, A.; Arias-Carrion, O.; Pacheco-Pantoja, E.; FuenteOrtegon Ade, L.; Arankowsky-Sandoval, G., The emerging role of the endocannabinoid system in the sleep-wake cycle modulation. Cent Nerv Syst Agents Med Chem 2011, 11 (3), 189-96. 30 ACS Paragon Plus Environment

Page 31 of 43 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


57. Murillo-Rodriguez, E.; Blanco-Centurion, C.; Sanchez, C.; Piomelli, D.; Shiromani, P. J., Anandamide enhances extracellular levels of adenosine and induces sleep: an in vivo microdialysis study. Sleep 2003, 26 (8), 943-7. 58. Pacher, P.; Hasko, G., Endocannabinoids and cannabinoid receptors in ischaemiareperfusion injury and preconditioning. Br J Pharmacol 2008, 153 (2), 252-62. 59. Vaughn, L. K.; Denning, G.; Stuhr, K. L.; de Wit, H.; Hill, M. N.; Hillard, C. J., Endocannabinoid signalling: has it got rhythm? Br J Pharmacol 2010, 160 (3), 530-43. 60. Koethe, D.; Schreiber, D.; Giuffrida, A.; Mauss, C.; Faulhaber, J.; Heydenreich, B.; Hellmich, M.; Graf, R.; Klosterkotter, J.; Piomelli, D.; Leweke, F. M., Sleep deprivation increases oleoylethanolamide in human cerebrospinal fluid. J Neural Transm (Vienna) 2009, 116 (3), 3015. 61. Moran, E. P.; Ma, J. X., Therapeutic Effects of PPAR alpha on Neuronal Death and Microvascular Impairment. PPAR Res 2015, 2015, 595426. 62. Cravatt, B. F.; Prospero-Garcia, O.; Siuzdak, G.; Gilula, N. B.; Henriksen, S. J.; Boger, D. L.; Lerner, R. A., Chemical characterization of a family of brain lipids that induce sleep. Science 1995, 268 (5216), 1506-9. 63. Mendelson, W. B.; Basile, A. S., The hypnotic actions of the fatty acid amide, oleamide. Neuropsychopharmacology 2001, 25 (5 Suppl), S36-9. 64. Stewart, J. M.; Boudreau, N. M.; Blakely, J. A.; Storey, K. B., A comparison of oleamide in the brains of hibernating and non-hibernating Richardson’s ground squirrel (Spermophilus richardsonii) and its inability tobind to brain fatty acid binding protein. Journal of Thermal Biology 2002, 27, 309–315. 65. Esposito, G.; De Filippis, D.; Carnuccio, R.; Izzo, A. A.; Iuvone, T., The marijuana component cannabidiol inhibits beta-amyloid-induced tau protein hyperphosphorylation through Wnt/beta-catenin pathway rescue in PC12 cells. J Mol Med (Berl) 2006, 84 (3), 253-8. 66. Esposito, G.; De Filippis, D.; Steardo, L.; Scuderi, C.; Savani, C.; Cuomo, V.; Iuvone, T., CB1 receptor selective activation inhibits beta-amyloid-induced iNOS protein expression in C6 cells and subsequently blunts tau protein hyperphosphorylation in co-cultured neurons. Neurosci Lett 2006, 404 (3), 342-6. 67. Casarejos, M. J.; Perucho, J.; Gomez, A.; Munoz, M. P.; Fernandez-Estevez, M.; Sagredo, O.; Fernandez Ruiz, J.; Guzman, M.; de Yebenes, J. G.; Mena, M. A., Natural cannabinoids improve dopamine neurotransmission and tau and amyloid pathology in a mouse model of tauopathy. J Alzheimers Dis 2013, 35 (3), 525-39. 68. Jung, K. M.; Astarita, G.; Yasar, S.; Vasilevko, V.; Cribbs, D. H.; Head, E.; Cotman, C. W.; Piomelli, D., An amyloid beta42-dependent deficit in anandamide mobilization is associated with cognitive dysfunction in Alzheimer's disease. Neurobiol Aging 2012, 33 (8), 1522-32. 69. Aso, E.; Ferrer, I., CB2 Cannabinoid Receptor As Potential Target against Alzheimer's Disease. Front Neurosci 2016, 10, 243. 70. Yanagisawa, M.; Planel, E.; Ishiguro, K.; Fujita, S. C., Starvation induces tau hyperphosphorylation in mouse brain: implications for Alzheimer's disease. FEBS Lett 1999, 461 (3), 329-33. 71. Planel, E.; Richter, K. E.; Nolan, C. E.; Finley, J. E.; Liu, L.; Wen, Y.; Krishnamurthy, P.; Herman, M.; Wang, L.; Schachter, J. B.; Nelson, R. B.; Lau, L. F.; Duff, K. E., Anesthesia leads to tau hyperphosphorylation through inhibition of phosphatase activity by hypothermia. J Neurosci 2007, 27 (12), 3090-7. 72. Hoffman, J. M.; Welsh-Bohmer, K. A.; Hanson, M.; Crain, B.; Hulette, C.; Earl, N.; Coleman, R. E., FDG PET imaging in patients with pathologically verified dementia. J Nucl Med 2000, 41 (11), 1920-8. 73. Jagust, W. J.; Seab, J. P.; Huesman, R. H.; Valk, P. E.; Mathis, C. A.; Reed, B. R.; Coxson, P. G.; Budinger, T. F., Diminished glucose transport in Alzheimer's disease: dynamic PET studies. J Cereb Blood Flow Metab 1991, 11 (2), 323-30. 31 ACS Paragon Plus Environment


Page 32 of 43

74. Mielke, R.; Herholz, K.; Grond, M.; Kessler, J.; Heiss, W. D., Differences of regional cerebral glucose metabolism between presenile and senile dementia of Alzheimer type. Neurobiol Aging 1992, 13 (1), 93-8. 75. Cunnane, S.; Nugent, S.; Roy, M.; Courchesne-Loyer, A.; Croteau, E.; Tremblay, S.; Castellano, A.; Pifferi, F.; Bocti, C.; Paquet, N.; Begdouri, H.; Bentourkia, M.; Turcotte, E.; Allard, M.; Barberger-Gateau, P.; Fulop, T.; Rapoport, S. I., Brain fuel metabolism, aging, and Alzheimer's disease. Nutrition 2011, 27 (1), 3-20. 76. de Leon, M. J.; Convit, A.; Wolf, O. T.; Tarshish, C. Y.; DeSanti, S.; Rusinek, H.; Tsui, W.; Kandil, E.; Scherer, A. J.; Roche, A.; Imossi, A.; Thorn, E.; Bobinski, M.; Caraos, C.; Lesbre, P.; Schlyer, D.; Poirier, J.; Reisberg, B.; Fowler, J., Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proc Natl Acad Sci U S A 2001, 98 (19), 10966-71. 77. Morin, P., Jr.; Storey, K. B., Evidence for a reduced transcriptional state during hibernation in ground squirrels. Cryobiology 2006, 53 (3), 310-8. 78. Biggar, Y.; Storey, K. B., Global DNA modifications suppress transcription in brown adipose tissue during hibernation. Cryobiology 2014, 69 (2), 333-8. 79. Alvarado, S.; Mak, T.; Liu, S.; Storey, K. B.; Szyf, M., Dynamic changes in global and gene-specific DNA methylation during hibernation in adult thirteen-lined ground squirrels, Ictidomys tridecemlineatus. J Exp Biol 2015, 218 (Pt 11), 1787-95. 80. Tessier, S. N.; Luu, B. E.; Smith, J. C.; Storey, K. B., The role of global histone posttranslational modifications during mammalian hibernation. Cryobiology 2017, 75, 28-36. 81. Jung, G. Y.; Won, S. B.; Kim, J.; Jeon, S.; Han, A.; Kwon, Y. H., Betaine Alleviates Hypertriglycemia and Tau Hyperphosphorylation in db/db Mice. Toxicol Res 2013, 29 (1), 7-14. 82. Vafai, S. B.; Stock, J. B., Protein phosphatase 2A methylation: a link between elevated plasma homocysteine and Alzheimer's Disease. FEBS Lett 2002, 518 (1-3), 1-4. 83. Sontag, E.; Nunbhakdi-Craig, V.; Sontag, J. M.; Diaz-Arrastia, R.; Ogris, E.; Dayal, S.; Lentz, S. R.; Arning, E.; Bottiglieri, T., Protein phosphatase 2A methyltransferase links homocysteine metabolism with tau and amyloid precursor protein regulation. J Neurosci 2007, 27 (11), 2751-9. 84. Miller, L. K.; Smith, J. S., Production of threitol and sorbitol by an adult insect: association with freezing tolerance. Nature 1975, 258 (5535), 519-20. 85. Walters, K. R., Jr.; Pan, Q.; Serianni, A. S.; Duman, J. G., Cryoprotectant biosynthesis and the selective accumulation of threitol in the freeze-tolerant Alaskan beetle, Upis ceramboides. J Biol Chem 2009, 284 (25), 16822-31. 86. Miller, L. K., Freezing tolerance in relation to cooling rate in an adult insect. Cryobiology 1978, 15 (3), 345-9. 87. Storey, K. B., Biochemistry of natural freeze tolerance in animals: molecular adaptations and applications to cryopreservation. Biochem Cell Biol 1990, 68 (4), 687-98. 88. Rudolph, A. S.; Crowe, J. H., Membrane stabilization during freezing: the role of two natural cryoprotectants, trehalose and proline. Cryobiology 1985, 22 (4), 367-77. 89. Li, G.; Rauscher, S.; Baud, S.; Pomes, R., Binding of inositol stereoisomers to model amyloidogenic peptides. J Phys Chem B 2012, 116 (3), 1111-9. 90. McLaurin, J.; Golomb, R.; Jurewicz, A.; Antel, J. P.; Fraser, P. E., Inositol stereoisomers stabilize an oligomeric aggregate of Alzheimer amyloid beta peptide and inhibit abeta -induced toxicity. J Biol Chem 2000, 275 (24), 18495-502. 91. Griffith, H. R.; den Hollander, J. A.; Stewart, C. C.; Evanochko, W. T.; Buchthal, S. D.; Harrell, L. E.; Zamrini, E. Y.; Brockington, J. C.; Marson, D. C., Elevated brain scyllo-inositol concentrations in patients with Alzheimer's disease. NMR Biomed 2007, 20 (8), 709-16. 92. Ma, K.; Thomason, L. A.; McLaurin, J., scyllo-Inositol, preclinical, and clinical data for Alzheimer's disease. Adv Pharmacol 2012, 64, 177-212. 93. Stiban, J.; Tidhar, R.; Futerman, A. H., Ceramide synthases: roles in cell physiology and signaling. Sphingolipids as Signaling and Regulatory Molecules 2010, 60-71. 32 ACS Paragon Plus Environment

Page 33 of 43 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


94. Morales, A.; Lee, H.; Goñi, F. M.; Kolesnick, R.; Fernandez-Checa, J. C., Sphingolipids and cell death. Apoptosis 2007, 12 (5), 923-939. 95. Zigdon, H.; Kogot-Levin, A.; Park, J. W.; Goldschmidt, R.; Kelly, S.; Merrill, A. H., Jr.; Scherz, A.; Pewzner-Jung, Y.; Saada, A.; Futerman, A. H., Ablation of ceramide synthase 2 causes chronic oxidative stress due to disruption of the mitochondrial respiratory chain. J Biol Chem 2013, 288 (7), 4947-56. 96. Han, X.; M Holtzman, D.; W McKeel, D.; Kelley, J.; Morris, J. C., Substantial sulfatide deficiency and ceramide elevation in very early Alzheimer's disease: potential role in disease pathogenesis. Journal of neurochemistry 2002, 82 (4), 809-818. 97. Cutler, R. G.; Kelly, J.; Storie, K.; Pedersen, W. A.; Tammara, A.; Hatanpaa, K.; Troncoso, J. C.; Mattson, M. P., Involvement of oxidative stress-induced abnormalities in ceramide and cholesterol metabolism in brain aging and Alzheimer's disease. Proceedings of the National Academy of Sciences 2004, 101 (7), 2070-2075. 98. Patil, S.; Melrose, J.; Chan, C., Involvement of astroglial ceramide in palmitic acid-induced Alzheimer-like changes in primary neurons. European Journal of Neuroscience 2007, 26 (8), 2131-2141. 99. Couttas, T. A.; Kain, N.; Suchowerska, A. K.; Quek, L. E.; Turner, N.; Fath, T.; Garner, B.; Don, A. S., Loss of ceramide synthase 2 activity, necessary for myelin biosynthesis, precedes tau pathology in the cortical pathogenesis of Alzheimer's disease. Neurobiol Aging 2016, 43, 89100. 100. Filippov, V.; Song, M. A.; Zhang, K.; Vinters, H. V.; Tung, S.; Kirsch, W. M.; Yang, J.; Duerksen-Hughes, P. J., Increased ceramide in brains with Alzheimer's and other neurodegenerative diseases. Journal of Alzheimer's Disease 2012, 29 (3), 537-547. 101. He, X.; Huang, Y.; Li, B.; Gong, C.-X.; Schuchman, E. H., Deregulation of sphingolipid metabolism in Alzheimer's disease. Neurobiology of aging 2010, 31 (3), 398-408. 102. Novgorodov, S. A.; Gudz, T. I., Ceramide and mitochondria in ischemia/reperfusion. J Cardiovasc Pharmacol 2009, 53 (3), 198-208. 103. Fukunaga, T.; Nagahama, M.; Hatsuzawa, K.; Tani, K.; Yamamoto, A.; Tagaya, M., Implication of sphingolipid metabolism in the stability of the Golgi apparatus. J Cell Sci 2000, 113 ( Pt 18), 3299-307. 104. Westwick, J. K.; Bielawska, A. E.; Dbaibo, G.; Hannun, Y. A.; Brenner, D. A., Ceramide activates the stress-activated protein kinases. J Biol Chem 1995, 270 (39), 22689-92. 105. Zha, X.; Pierini, L. M.; Leopold, P. L.; Skiba, P. J.; Tabas, I.; Maxfield, F. R., Sphingomyelinase treatment induces ATP-independent endocytosis. J Cell Biol 1998, 140 (1), 3947. 106. Geekiyanage, H.; Upadhye, A.; Chan, C., Inhibition of serine palmitoyltransferase reduces Abeta and tau hyperphosphorylation in a murine model: a safe therapeutic strategy for Alzheimer's disease. Neurobiol Aging 2013, 34 (8), 2037-51. 107. Ellison, D. W.; Beal, M. F.; Martin, J. B., Phosphoethanolamine and ethanolamine are decreased in Alzheimer's disease and Huntington's disease. Brain Res 1987, 417 (2), 389-92. 108. Mielke, M. M.; Bandaru, V. V.; Haughey, N. J.; Xia, J.; Fried, L. P.; Yasar, S.; Albert, M.; Varma, V.; Harris, G.; Schneider, E. B.; Rabins, P. V.; Bandeen-Roche, K.; Lyketsos, C. G.; Carlson, M. C., Serum ceramides increase the risk of Alzheimer disease: the Women's Health and Aging Study II. Neurology 2012, 79 (7), 633-41. 109. Hirayama, S.; Terasawa, K.; Rabeler, R.; Hirayama, T.; Inoue, T.; Tatsumi, Y.; Purpura, M.; Jager, R., The effect of phosphatidylserine administration on memory and symptoms of attention-deficit hyperactivity disorder: a randomised, double-blind, placebo-controlled clinical trial. J Hum Nutr Diet 2014, 27 Suppl 2, 284-91. 110. Zhang, Y. Y.; Yang, L. Q.; Guo, L. M., Effect of phosphatidylserine on memory in patients and rats with Alzheimer's disease. Genet Mol Res 2015, 14 (3), 9325-33. 111. Schneider, A.; Biernat, J.; von Bergen, M.; Mandelkow, E.; Mandelkow, E. M., Phosphorylation that detaches tau protein from microtubules (Ser262, Ser214) also protects it 33 ACS Paragon Plus Environment


Page 34 of 43

against aggregation into Alzheimer paired helical filaments. Biochemistry 1999, 38 (12), 354958. 112. Taga, M.; Mouton-Liger, F.; Paquet, C.; Hugon, J., Modulation of oxidative stress and tau phosphorylation by the mTOR activator phosphatidic acid in SH-SY5Y cells. FEBS Lett 2011, 585 (12), 1801-6. 113. Arendt, T.; Stieler, J.; Holzer, M., Brain hypometabolism triggers PHF-like phosphorylation of tau, a major hallmark of Alzheimer's disease pathology. J Neural Transm (Vienna) 2015, 122 (4), 531-9. 114. Farooqui, A. A.; Horrocks, L. A.; Farooqui, T., Glycerophospholipids in brain: their metabolism, incorporation into membranes, functions, and involvement in neurological disorders. Chem Phys Lipids 2000, 106 (1), 1-29. 115. Almena, M.; Merida, I., Shaping up the membrane: diacylglycerol coordinates spatial orientation of signaling. Trends Biochem Sci 2011, 36 (11), 593-603. 116. Alkon, D. L.; Sun, M. K.; Nelson, T. J., PKC signaling deficits: a mechanistic hypothesis for the origins of Alzheimer's disease. Trends Pharmacol Sci 2007, 28 (2), 51-60.


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Table 1. Summary of amino acids, dipeptides and analogues found in brain samples from torpid and aroused hamsters compared to euthermic (control) animals. Analyte

Torpor

Arousal

Lysine

↑ 1.39

↑ 1.40

Proline

↑ 1.86

↑ 1.84

Alanine

↑ 1.69

↑ 1.54

Serine

↑ 1.31

↑ 1.27

Threonine

↑1.38

↑ 1.34

Glutamine

nssd

↑ 1.34

Ornithine

↑ 2.37

↑ 1.66

Methylthreonine

↑ 1.92

nssd

4-Aminobutanoic acid (GABA)

nssd

↑ 1.23

N-Acetyl-tyrosine

↑ 2.93

nssd

Methionine

↓ 0.64

↓ 0.77

Asparagine

↓ 0.80

nssd

γ-Glutamylglycine

↓ 0.73

↓ 0.79

N,N-dimethyl-Valine

↓ 0.74

↓ 0.71

We indicate the increase (blue) or the decrease (red) along with the fold change (see table S1 for further information). nssd: non-statistically significant differences.

Table 2: Summary of epigenetic related metabolites with statistical differences when brain samples of torpor or arousal hamsters were compared to euthermic hamsters. Metabolite

Torpor

Arousal

Methionine Sulfoxide

↓ 0.67

↓ 0.72

Methionine

↓ 0.64

↓ 0.77

THF

nssd

↑ 1.42

5-Methyl-THF

nssd

↑ 1.39

5-Methyldeoxicytidine

↑ 2.93

nssd

Cytidine

↑ 2.27

↑ 1.72

Choline

↑ 1.32

nssd

Betaine

↑ 2.66

↑ 2.57

Cystathionine*

↑ 1.94

↑ 1.86

Fold change is shown. * this compound is easily studied by CE-MS. nssd: non-statistically significant differences. 35 ACS Paragon Plus Environment


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Figure 1. Photomicrographs showing the patterns of hyperphosphorylated tau immunostaining (AT8 antibody) in coronal sections from the brain of control (euthermic; A), torpor (B) and arousal (C) Syrian hamsters. Small squared zones in (A-C) are shown at higher magnification in D-F (neocortex) and G-I (hippocampus). Note the intense AT8 immunostaining in B that is distributed throughout the tissue, especially in neocortex (E) and hippocampus (H). Arrows in E and F point out the AT8 immunostaining of apical dendrites in layers II/III, whereas arrows in H and I show the AT8 immunostaining in hilar neurons. Scale bar in D-I indicates 57 μm and 1824 μm in A-C. 190x177mm (300 x 300 DPI)

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Heatmap of significant metabolites detected in brain tissue by CE-MS and GC-MS (A), and LC-MS analysis (B), modified by their hibernation/non-hibernation states. The color spectrum ranging from red to blue indicates the range of high to low signal intensities, respectively, for each metabolite. ■ Control group (C), ■ Torpor state group (T), ■ Arousal state group (A). 195x284mm (300 x 300 DPI)


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Figure 3. MetPA (A), and MSEA (B), both obtained from MetaboAnalyst 3.5 after submission of the most significantly altered metabolites found by GC-MS and CE-MS analyses. In the MetPA, the size and the color of the circles vary depending on the importance of the pathway and its p value, respectively. Thus, the pathways located in the upper right corner of the graphic are the ones affecting the hibernation processes more. For the MSEA, the degree of positive association with the corresponding phenotype is represented by the color of the bar. 464x190mm (300 x 300 DPI)



Figure 4. Percentage of each lipid class found to be biologically relevant by LC-MS analysis in each comparison. 279x190mm (300 x 300 DPI)


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Figure 5. Summary of the possible neuroprotective effects (green) of cannabinoid compounds that are increased (blue) or decreased (red) during the hibernation of the Syrian hamster. 322x144mm (300 x 300 DPI)



Figure 6. Metabolic pathway interconnections among homocysteine metabolism; sphingolipid metabolism; polyamine metabolism; folic cycle; and glycine, serine, and threonine metabolism. The metabolites in bold were found to be altered in this hibernating model after LC-MS, GC-MS, and CE-MS analyses. 292x137mm (300 x 300 DPI)


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Figure 7. Main metabolic reactions involved in sphingolipid synthesis. CerS, ceramide synthase; CERT, ceramide transfer protein; NSMase2, neutral sphingomyelinase 2; ASMase, acidic sphingomyelinase; GlcCer, glucosylceramide; LacCer, lactosylceramide; GSLs, glucosphingolipids. 338x190mm (300 x 300 DPI)


Metabolomic Study of Hibernating Syrian Hamster Brains: In Search of

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