Tissue-Specific Metabolomics Analysis Identifies the Liver as a Major

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Tissue-specific metabolomics analysis identifies the liver as a major organ of metabolic disorders in amyloid precursor protein/presenilin 1 (APP/PS1) mice of Alzheimer's disease Hong Zheng, Aimin Cai, Qi Shu, Yan Niu, Pengtao Xu, Chen Li, Li Lin, and Hongchang Gao J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00847 • Publication Date (Web): 28 Dec 2018 Downloaded from http://pubs.acs.org on December 31, 2018

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Journal of Proteome Research

Tissue-specific metabolomics analysis identifies the liver as a major organ of metabolic disorders in amyloid precursor protein/presenilin 1 (APP/PS1) mice of Alzheimer's disease

Hong Zheng1, Aimin Cai1, Qi Shu1, Yan Niu1, Pengtao Xu1, Chen Li1, Li Lin2,*, and Hongchang Gao1,*

1

Institute of Metabonomics & Medical NMR, School of Pharmaceutical Sciences, Wenzhou

Medical University, Wenzhou 325035, China 2Institute

of Molecular Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical

University, Wenzhou 325035, China

*Correspondence and requests for materials should be addressed to L.L. (Tel.: +86 577 86699715; Email: [email protected]); H.C.G. (Tel.: +86 577 86699715; Email: [email protected]).

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Abstract Alzheimer's disease (AD) is regarded as a metabolic disorder and more attention has been paid to brain metabolism. However, AD may also affect metabolism in the peripheral organs beyond the brain. In this study, therefore, we investigated metabolic changes in the liver, kidney and heart of amyloid precursor protein/presenilin 1 (APP/PS1) mice at 1, 5 and 10 months of age by using 1H NMR-based metabolomics and chemometrics. Metabolomic results reveal that the liver was the earliest affected organ in APP/PS1 mice during amyloid pathology progression, followed by the kidney and heart. Moreover, a hypometabolic state was found in the liver of APP/PS1 mice at 5 months of age, and the disturbed metabolites were mainly involved in energy metabolism, amino acid metabolism, nucleic acid metabolism as well as ketone and fatty acid metabolism. In conclusion, our results suggest that AD is a systemic metabolic dysfunction and hepatic metabolic abnormality may reflect amyloid pathology progression. Keywords: amyloid pathology; liver; organ-specific; peripheral tissue; metabolism.

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1. Introduction Alzheimer's disease (AD) is a major cause of dementia in many elderly people.1 At present, AD is becoming a huge public health problem with the increase in the aging population. In 2010, there were 35.6 million individuals with dementia worldwide, and notably this number will rise to 65.7 million in 2030 and 115.4 million in 2050.2 The economic impact of dementia is considerable. Wimo et al. reported that the total cost of dementia has reached US$604 billion worldwide in 2010.3 Consequently, it is imperative to explore the pathogenesis of AD for developing effective treatments. A series of factors, such as amyloid-beta plaque, tau protein deposition, neurofibrillary tangle, gliosis, neuronal loss, oxidative stress and mitochondrial dysfunction, have been implicated in the onset and development of AD.4 Accordingly, several therapeutic strategies were proposed including antioxidant therapy,5 antiamyloid therapy,6 tau-aggregation inhibitor therapy,7 synaptic therapy,8 and stem cell therapy9. However, the application of these methods in clinical practice is still premature. AD is currently regarded as a metabolic syndrome and even called type 3 diabetes.10-12 Metabolic characteristics have been explored in the transgenic mouse models of AD, but these studies mainly focus on brain and blood metabolism.13-17 Monte & Tong reported that brain metabolic dysfunction may be a significant driver of AD.18 In our previous study, we found that the hypothalamus could be the primary brain region of metabolic abnormalities in amyloid precursor protein/presenilin 1 (APP/PS1) transgenic mouse model of AD.19 But how is metabolism altered in other organs of AD? In fact, González‐Domínguez and his colleagues have paid attention to 3



this issue.20,21 They investigated multiple peripheral organs (liver, kidney, spleen and thymus) in APP/PS1 mice using a mass spectrometry-based metabolomic approach and found a series of metabolic disorders involving energy metabolism, membrane lipid homeostasis, oxidative stress and amino acid metabolism.20,21 However, little is known regarding the metabolic changes associated with amyloid pathology (AP) progression and the interaction effect between AP and age on metabolism in the peripheral organs. In the present study, therefore, we investigated metabolic profiles in the liver, heart and kidney of wild-type and APP/PS1 mice at three different ages through a 1H NMR-based metabolomic approach. APP/PS1 mice were chosen in this study, because this model shows cognitive disorders similar to clinical features of AD, for example, cognitive dysfunction was closely associated with amyloid-beta deposition.22 Xiong et al. reported that impaired cognitive ability appeared as early as 6 months of age and exacerbated at 12 months of age in APP/PS1 mice.23 Similarly, amyloid-beta can also be detected in the hippocampus of APP/PS1 mice at approximately 6 months, and its level was significantly increased with aging.24,25 Therefore, three analysis time points were set up during AP progression: very early stage (1 month), early stage (5 months) and development stage (10 months). The aims of this study were to (1) evaluate the interaction effects between AP and age on metabolic alterations and (2) explore organ-specific metabolic responses in APP/PS1 mice during AP progression. This study contributes to an understanding of the association between AD and metabolic disease. 4


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2. Materials and methods 2.1. Animals APP/PS1 double transgenic (APP/PS1, male, age = 4 weeks, body weight = 18.5 ± 2.7, n = 30) and age-matched wild-type (WT, male, age = 4 weeks, body weight = 15.7 ± 2.9, n = 30) mice were purchased from the Mode Animal Research Center of Nanjing University (Nanjing, China). In the present study, all mice possessed the same genetic background (C57BL/6JNju). APP/PS1 mice were maintained by breeding male APPswe/PSEN1dE9 mice with female C57BL/6JNju WT mice. All mice were housed in a specific pathogen-free (SPF) colony with standard conditions (room temperature = 22oC; light-dark cycle = 12h:12h; lights on at 8:00 a.m.) at the Laboratory Animal Center of Wenzhou Medical University (Wenzhou, China), and given free access to standard chow and tap water. This study was performed according to the Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of Wenzhou Medical University. 2.2. Peripheral tissue collection and extraction Ten APP/PS1 and ten WT mice were sacrificed by decapitation at 1M, 5M and 10M, respectively. The liver, kidney and heart were rapidly removed, immediately frozen in liquid nitrogen, and stored at -80oC until use. The tissue sample was extracted using the methanol/chloroform/water (MCW) extraction method.19 In brief, the frozen tissue was weighed in a centrifuge tube, and added with 4 ml/g of cold methanol and 0.85 ml/g of cold distilled water. The mixture was homogenized by a handheld homogenizer, and added with 2 ml/g of cold chloroform and 2 ml/g of cold distilled 5



water. Then, the mixture was vortex-mixed vigorously for 10 s, stood on ice for 15 min, and centrifuged at 10,000 g for 15 min at 4oC. Finally, the supernatant was transferred to a new tube, lyophilized for 24 h, and stored at -80oC until analysis. The lyophilized extract was reconstituted with 0.6 ml of D2O (99.5%) containing 0.05% of sodium trimethlysilyl propionate-d4 (TSP) and transferred to a 5 mm NMR tube for metabolomic analysis. 2.3. NMR-based metabolomic analysis The 1H NMR spectrum was measured using a Bruker AVANCE III 600 MHz NMR spectrometer (Bruker BioSpin, Rheinstetten, Germany). A standard single-pulse sequence with water signal pre-saturation, namely ‘ZGPR’, was performed for acquiring NMR data at 25oC. Moreover, the main parameters were used as follows: scans = 256, acquisition time = 2.65 sec per scan, data points = 64K, spectral width = 12,000 Hz, and relaxation delay = 6 sec. The NMR spectrum was referenced to the TSP peak at 0.0 ppm, and manually phase- and baseline-corrected using Topspin software (v2.1 pl4, Bruker BioSpin, Germany). The NMR metabolic signals were assigned using Chenomx NMR Suite 7.1 (Chenomx, Alberta, Canada) based on the human metabolome database.26 Furthermore, two-dimensional (2D) 1H-1H correlated spectroscopy (COSY) and 13C-1H

heteronuclear single quantum coherence (HSQC) experiments were conducted

to confirm the tentative assignments. In addition, the concentration of each metabolite was calculated according to its peak area by reference to the internal TSP concentration and was expressed as μmol/g fresh weight tissue (FWT). 6


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2.4. Statistical analysis In this study, mice were randomly assigned to the experimental procedures, and all samples were randomly analyzed by NMR-based metabolomics. All data were mean-centered and Pareto-scaled prior to multivariate and univariate analyses. ANOVA-simultaneous component analysis (ASCA) was used to evaluate the effects of AP, age and their interaction (AP x age) on the overall metabolome of tissue samples. Two factors, AP (WT and APP/PS1) and age (1M, 5M and 10M), were included. Firstly, the data matrix X was separated into four matrices by ASCA, namely AP (XAP), age (Xage), the interaction effect (XAP×age) and residuals (ε), as described in Eq. (1): Y = 1mT + XAP + Xage + XAP × age + ε

(1)

where 1mT is the overall means. Then, ASCA decomposes each matrix (XAP, Xage and XAP×age) into a score matrix TAP, Tage and TAP×age, a loading matrix PAP, Page and PAP×age, respectively, and a residual matrix ε, as shown in Eq. (2): Y = 1mT + PTAPTAP + PTageTage + PTAP × ageTAP × age + ε

(2)

where 1mT is the overall means, PAP (TAP), Page (Tage) and PAP×age (TAP×age) are the score (loading) matrices of AP, age and their interaction, respectively, and ε is the residual matrix. In the present study, ASCA was performed by using the ASCA toolbox (http://www.bdagroup.nl/Home.php) under MATLAB environment (R2012a, The Mathworks Inc., Natick, MA, USA) and validated via a permutation test. 7



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Furthermore, to assess the interaction effect between AP and age on each metabolite, a linear mixed-model (LMM) ANOVA was performed using SAS software (PROC MIXED procedure, SAS 9.2, SAS Institute Inc., Cary, NC, USA). In this model, AP, age and their interaction were set as fixed effects, and the individual and model intercept as random effects, as given in Eq. (3): M = α1 ∙ AP + β1 ∙ age + γ1 ∙ (AP × age) + δ

(3)

where M is the concentration of metabolite, α1, β1 and γ1are model coefficients, and δ is the random effect. Metabolic data were calculated by a least-square (LS)-means procedure with SAS software and presented as LS-means ± standard error (SE). Pair-wise multiple comparisons were analyzed using Student’s t test with Bonferroni adjustment, and P value < 0.05 was considered as a statistically significant difference.In addition, metabolic pathways were drawn manually using Adobe Photoshop CS6 (Adobe Inc., San Jose CA) in accordance with the KEGG database (www.genome.jp/kegg/).

3. Results 3.1. Liver, kidney and heart metabolic profiles in APP/PS1 mice Typical 1H NMR spectra of liver, kidney and heart extracts in APP/PS1 mice at 5M of age are illustrated in Figures 1A, 1B and 1C, respectively. A series of tissue metabolites were identified from 1H NMR-based metabolic profile, involving amino acid metabolism (Ala, alanine; Glu, glutamate; Gln, glutamine; Val, valine; Iso, isoleucine; Leu, leucine; Asp, asparagine; Lys, lysine; Tau, taurine; Gly, glycine; Tyr, 8


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tyrosine; His, histidine; Phe, phenylalanine; Met, methionine; GSH, glutathione; Nic, nicotinurate), energy metabolism (Lac, lactate; Cre, creatine; Glc, glucose; Fum, fumurate; Suc, succinate), nucleic acid metabolism (IMP, inosine monophosphate; AMP, adenosine monophosphate; Ura, uracil; Uri, uridine; Ino, inosine), membrane metabolism (Cho, choline; PC, phosphocholine; GPC, Glyceryl phosphocholine) as well as ketone and fatty acid metabolism (3-HB, 3-hydroxybutyrate; Ace acetate; For, formate). 3.2. Alterations in metabolic patterns in the liver, kidney and heart of APP/PS1 mice during the progression of amyloid pathology Metabolomic data were analyzed using the ASCA model, which includes each single factor (AP and age) and their interaction (AP×age) effects and facilitates to interpret metabolic variations caused by different factors. Table 1 shows statistical values (P values) using 10,000 permutation tests in the ASCA model based on liver, kidney and heart metabolome data, respectively. The effects of AP, age and their interaction (AP × age) were statistically significant on metabolic changes in the liver and kidney of mice, while only the age effect for heart metabolome (P = 0.0001). The ASCA score and loading plots derived from significant models are shown in Figures 2, 3 and 4. According to Figure 2A, the significant AP effect on liver metabolome (P = 0.02) can be ascribed to a series of metabolites including 3-HB, Ala, Cre, Cho, Glu, Gln, Glc, Lac, Leu, Tau and PC. From kidney metabolome, Ala, Cre, Cho, Gln, Gly, Lac, Myo and Tau were identified as main metabolites for the significant AP effect (P = 0.01; Figure 2B).Figure 3 shows that metabolic patterns were clearly separated 9



among 1M, 5M and 10M in the liver (P = 0.0002), kidney (P = 0.0001) and heart (P = 0.0001) metabolome. The corresponding ASCA loading plots demonstrated a series of metabolites that mainly contributed to the significant age effect, such as Ala, Ace, Lac, Leu, Glc, Gln, Tau and Val in the liver metabolome, Ace, Cre, Cho, Gly, GPC, Gln, PC, Lac, Myo and Tau in the kidney metabolome, as well as Ala, Cre, Cho, Suc, Gln and Tau in the heart metabolome. For the liver metabolome there was a significant interaction effect of AP and age, which could be ascribed to changes in Ala, Cho, Glc, Gln, Lac, Leu, PC and Tau as shown in the ASCA loading plots in Figure 4A. In addition, the ASCA results indicated that the AP × age interaction effect affected the kidney metabolome, including Cho, Cre, Lac, Leu, GPC, PC, Myo and Tau (Figure 4B). 3.3. Changes in metabolite levels in the liver, kidney and heart of APP/PS1 mice during the progression of amyloid pathology In order to evaluate the interaction effect of AP and age on specific metabolites in different organs of the mouse, a GLMM model was used and the corresponding results were listed in Table S1 for the liver, Table S2 for the kidney and Table S3 for the heart. Furthermore, metabolites that show a statistically significant interaction effect were highlighted in color in metabolic pathways (Figure 5), and their detailed variations were illustrated in Figures 6 and 7. As can be seen from Figure 5, liver was the primary organ of metabolic abnormalities in APP/PS1 mice during the progression of amyloid pathology. These metabolites were mainly involved in BCAA, energy, amino acid, nucleic, ketogenic and fatty acid metabolism. 10


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In the liver, the concentrations of Tyr (Figure 6A), Met (Figure 6B), Phe (Figure 6C), Lys (Figure 6G), His (Figure 6H), Ala (Figure 6I), Val (Figure 6J), Ileu (Figure 6K), Leu (Figure 6L), Cre (Figure 6O), Cho (Figure 6P), and Ace (Figure 6S) were significantly increased in WT mice from 1M to 5M, but there were no significant differences in APP/PS1 mice. Significant decreases in Gly (Figure 6E), Glu (Figure 6F), GSH (Figure 6M), Fum (Figure 6N), Uri (Figure 6Q), For (Figure 6R), and 3-HB (Figure 6T) were detected in the liver of APP/PS1 mice from 1M to 5M, but not in WT mice. From 1M to 5M, hepatic Tau concentration was significantly reduced in WT mice, while a slight but no statistically significant decrease in APP/PS1 mice (Figure 6D). Additionally, APP/PS1 mice had a decrease in the concentration of Nic in the liver from 1M to 5M, but an increased trend was obtained in WT mice, as shown in Figure 6U. In the kidney, WT mice exhibited significantly increased concentrations of Ace (Figure 7A), Gly (Figure 7B) and Phe (Figure 7D), and significantly decreased Ino concentration (Figure 7E) from 1M to 5M, as compared with APP/PS1 mice. Figure 7C reveals that from 1M to 5M Tau concentration was significantly increased in the kidney of APP/PS1 mice rather than WT mice. However, there were no any metabolites varied significantly from 1M to 5M in the heart of both types of mice. From 5M to 10M, significant reductions in the concentrations of Phe (Figure 6C), Gly (Figure 6E), Lys (Figure 6G), Ala (Figure 6I), Val (Figure 6J), Ileu (Figure 6K), Leu (Figure 6L), Uri (Figure 6Q), and Ace (Figure 6S) were detected in the liver of WT mice, but not in APP/PS1 mice. In the liver, APP/PS1 mice had 11



significantly increased concentrations of Tau (Figure 6D), GSH (Figure 6M) and 3-HB (Figure 6T) from 5M to 10M, while no significant variations were obtained in WT mice. A significant decrease in Fum (Figure 6N) was observed in the liver of both WT and APP/PS1 mice from 5M to 10M. In addition, there were no statistically significant differences in changes of Tyr (Figure 6A), Met (Figure 6B), Glu (Figure 6F), His (Figure 6H), Cre (Figure 6O), Cho (Figure 6P), For (Figure 6R), and Nic (Figure 6U) from 5M to 10M. In the kidney, we found that a significant decrease in Ace was detected in both mice types (Figure 7A), and a significant increase in Ino only in APP/PS1 mice (Figure 7E) from 5M to 10M. Yet, the heart exhibited no any metabolites varied significantly from 5M to 10M in both WT and APP/PS1 mice. Additionally, APP/PS1 mice at 1M of age had a significantly lower hepatic Tau concentration (Figure 6D) and higher Fum (Figure 6N) than age-matched WT mice, whereas most metabolites were not significantly different between these two types of mice in the liver, kidney and heart. Of note, at 5M, most hepatic metabolites were significantly lower in APP/PS1 mice relative to WT mice, excepting Tau, Fum, Cho, For and 3-HB (Figure 6). However, there were no significant metabolic differences between these two types of mice in the kidney and heart, as shown in Figure 7. At 10M, we only found a significantly higher IMP concentration in the heart of APP/PS1 mice than WT mice (Figure 7F). Overall, the liver could be the most affected organ of metabolic abnormalities in APP/PS1 mice at 5M of age (Figure 8).

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4. Discussion The increasing number of AD studies will advance a more comprehensive view of AD. Currently, growing evidence suggests that AD is not only a neurodegenerative disease, but also a widespread systemic disorder.27 This means that AD pathological lesions could also affect peripheral organs beyond the brain. Thus, the investigation of other peripheral tissues may provide a new insight into potential mechanisms of AD. In the present study, we investigated metabolic alterations in the liver, kidney and heart of APP/PS1 mice during amyloid pathology (AP) progression by using 1H NMR metabolomics and chemometrics. ASCA results show that the effect of AP was statistically significant on the overall metabolome in the liver (P=0.02) and kidney (P=0.01), but not in the heart (P=0.18). There were significant age effects in all three tissues. In addition, the liver (P=0.002) and kidney (P=0.03) yielded a significant interaction effect of AP and age, but not the heart (P=0.65). This finding based on the overall metabolome indicates that the liver and kidney were more vulnerable to AP than the heart. Furthermore, we used a GLMM model to assess the interaction effect of AP and age on specific metabolites in different organs of the mouse. Results show that the earliest affected organ during AP progression was the liver, followed by the kidney and heart, which may be attributed to the fact that the liver is a major metabolic organ for maintaining whole-body metabolic homeostasis.28 In addition, a serious of metabolic alterations were identified, involving energy metabolism, amino acid metabolism, nucleic acid metabolism as well as ketone and fatty acid metabolism. Therefore, in this study, we 13



provide new evidence that AD could be a widespread systemic disorder.20,21 Since high energy is needed for maintaining brain normal function, reduction of brain energy metabolism has been a common feature of AD.29,30 In our previous study, we found that disrupted brain energy metabolism could be one of the earliest hallmarks of AD.19 In this study, the level of Fum, as a TCA intermediate, was continuously and significantly decreased in the liver of APP/PS1 mice from 1M to 10M, while for WT mice a significant reduction was only detected from 5M to 10M. Moreover, the interaction effect of AP and age was also found on Cre in the liver, which plays a key role in energy homeostasis.31 We found that Cre concentration was significantly increased in the liver of WT mice from 1M to 5M, but not in APP/PS1 mice. Additionally, at 5M, APP/PS1 mice had a significantly lower Cre concentration in the liver as compared with WT mice. For the other two tissues, only cardiac IMP concentration showed a significant interaction effect of AP and age. Thus, our findings indicate that defects in energy metabolism in AD not only existed in the brain, but also occurred systemically, particularly in the liver. Disturbance in amino acid metabolism has been detected in the cerebrospinal fluid, plasma and urine of AD patients, suggesting the importance of amino acids in the pathogenesis of AD.32-34 In animal studies, amino acid alterations caused by AD were also found in the bio-fluid and brain tissue samples.13,35-37 In addition, González-Domínguez et al. investigated metabolic changes in the peripheral organs (liver, kidney, spleen and thymus) of APP/PS1 mice at 6 months of age and also identified a disrupted amino acid metabolism.20 However, they did not consider the 14


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age effect of AD mice on metabolic changes. In our study, the interaction effect of AP and age was explored on amino acid metabolism in the peripheral organs (liver, kidney and heart). Interestingly, we found that the concentrations of amino acids, including Tyr, Met, Phe, Lys, His, Ala, Val, IIeu, Leu, and Nic, were significantly increased in the liver of WT mice from 1M to 5M, but not for APP/PS1 mice. At 5M, APP/PS1 mice had significantly lower concentrations of Tyr, Met, Phe, Gly, Glu, Lys, His, Ala, Val, IIeu, Leu, and Nic in the liver tissue as compared with WT mice. From 5M to 10M, these amino acids were obviously reduced in the liver of WT mice. In the kidney tissue, increased concentrations of Gly and Phe were found in WT mice from 1M to 5M, but not in APP/PS1 mice. Moreover, the interaction effect of AP and age was obtained on Tau level in both the liver and kidney tissues. Taken together, in this study, we found the amino acid hypometabolic pattern in early life of APP/PS1 mice as compared with WT mice. It is well known that the presence of amino acids is indispensable for the survival of organisms. On the one hand, amino acids can be used to synthesize proteins or other biomolecules for normal growth.38 On the other hand, amino acids can be metabolized to support the TCA cycle for energy supply.38 Therefore, the amino acid hypometabolic state in early life of APP/PS1 mice may indicate the lack of substrate and energy for normal growth. GSH is mainly located in the liver and derived from glutamate, cysteine and glycine by γ-glutamylcysteine ligase and GSH synthetase.39 In agreement with changes in Gly and Glu, therefore, the concentration of GSH was significantly reduced in the liver of APP/PS1 mice from 1M to 5M and reached a lower level than 15



that in WT mice. GSH has been reported to involve in a serious of cellular physiological processes, such as antioxidant defense, cell signaling, gene expression, protein function, as well as cell proliferation and differentiation.39 Thus, a decrease in GSH level in early life of APP/PS1 mice may cause abnormal cellular events. In addition, the concentration of Cho was significantly increased in the liver of WT mice from 1M to 5M, but not in APP/PS1 mice. From 5M to 10M, there was no statistical difference in the Cho change in both types of mice. Since Cho plays a key role in phospholipid synthesis of cell membranes,40 our finding indicate that in early life of APP/PS1 mice phospholipid synthesis may be insufficient to maintain the normal structure of cell membranes. The interaction effect of AP and age was also found on nucleic acid metabolism. For example, liver uridine concentration was continuously reduced in APP/PS1 mice from 1M to 10M, whereas in WT mice its decrease was only detected from 5M to 10M. Czech et al. have detected a reduction of uridine in the cerebrospinal fluid of AD patients.41 In this study, we reveal that this phenomenon may also occur in the AD liver. Moreover, Ino, as an important nucleoside, is derived from adenosine by oxidative deamination. In this study, from 1M to 5M, a significant decrease in Ino level was obtained in the kidney of WT mice, but no significant change in APP/PS1 mice; from 5M to 10M, Ino concentration was significantly increased in the kidney tissue of APP/PS1 mice. Nucleosides can be used for new nucleic acid synthesis or catabolized for energy generation.42 Boison suggested that brain diseases could be treated by regulating nucleoside metabolism.43 Our study herein indicates that 16


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disrupted nucleic acid metabolism could be not only localized in the brain, but also the peripheral organs. In the liver, the concentration of 3-HB was significantly decreased from 1M to 5M and then increased from 5M to 10M in APP/PS1 mice relative to WT mice. 3-HB, as a degradation product of microbial polyhydroxybutyrate (PHB), is one of ketone bodies that produced in the liver and served as an energy source in other tissues.44 3-HB has been reported to possess a neuroprotective effect in AD.45,46 Therefore, for diagnosis and treatment of AD, a reduction of hepatic 3-HB level in early life may need to be paid attention. Additionally, short-chain fatty acids (SCFAs), derived from microbial fermentation, were also disrupted in the peripheral organs of APP/PS1, particularly in the liver. For example, the concentration of For was significantly reduced in the liver of APP/PS1 mice from 1M to 5M as compared with WT mice. Ace concentration was increased from 1M to 5M and then decreased from 5M to 10M in the liver of WT mice, but no significant alteration was observed in APP/PS1 mice from 1M to 10M. These findings may reflect the fact that altered gut microbiota plays an important role in the pathogenesis of AD.47 In conclusion, metabolic disorders were found in the peripheral organs of APP/PS1 mice during the progression of AP using NMR-based metabolomics. The liver could be the target organ of metabolic abnormalities, mainly involving energy metabolism, amino acid metabolism, nucleic acid metabolism as well as ketogenic and fatty acid metabolism. In our previous study, we reported that brain region-specific metabolic alterations, particularly in the hypothalamus, may be 17



associated with the onset and progression of AD.19 Therefore, AD could be a widespread systemic disorder, and the liver and hypothalamus may be the earliest affected organs. Of note, several metabolites in the liver of APP/PS1 mice at 5M of age exhibited a hypometabolic pattern relative to age-matched WT mice, whereas their levels were up again from 5M to 10M. Transient metabolic changes during AP progression were also detected in the brain and blood of APP/PS1 mice17,19, but potential mechanisms still need to be further explored. Although their disturbances were instantaneously but not progressively affected during AP progression, we speculate that these abnormal changes could be highly informative both for the early-diagnosis of AD and the understanding of its pathogenesis. Moreover, we suggest that the metabolomics investigation of other peripheral organs rather than brain may provide a new insight into metabolic mechanisms underlying AD.

Supporting Information

The following supporting information is available

free of charge at ACS website http://pubs.acs.org. Table S1. Metabolic changes in the liver of APP/PS1 (AD) and wild-type (WT) mice at 1 (1M), 5 (5M) and 10 (10M) months of age. Table S2. Metabolic changes in the kidney of APP/PS1 (AD) and wild-type (WT) mice at 1 (1M), 5 (5M) and 10 (10M) months of age. Table S3. Metabolic changes in the heart of APP/PS1 (AD) and wild-type (WT) mice at 1 (1M), 5 (5M) and 10 (10M) months of age.

Abbreviations APP/PS1, amyloid precursor protein/presenilin 1; 1M, 1 month; 5M, 5 months; 10M, 10 months; AD, Alzheimer’s disease; AP, amyloid pathology; WT, wild-type; APP, amyloid precursor protein; PS1, presenilin-1; ASCA, ANOVA-simultaneous component analysis; Ala, alanine; Glu, glutamate; Gln, glutamine; Val, valine; Iso, 18


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isoleucine; Leu, leucine; Asp, asparagine; Lys, lysine; Tau, taurine; Gly, glycine; Tyr, tyrosine; His, histidine; Phe, phenylalanine; Met, methionine; GSH, glutathione; Nic, nicotinurate; Lac, lactate; Cre, creatine; Glc, glucose; Fum, fumurate; Suc, succinate; IMP, inosine monophosphate; AMP, adenosine monophosphate; Ura, uracil; Uri, uridine; Ino, inosine; Cho, choline; PC, phosphocholine; GPC, Glyceryl phosphocholine; 3-HB, 3-hydroxybutyrate; Ace acetate; For, formate.

Acknowledgments This study was supported by the National Natural Science Foundation of China (Nos.: 21605115, 21575105 and 81771386), the Public Welfare Technology Application Research Foundation of Zhejiang Province (No.: 2017C33066) and the Natural Science Foundation of Zhejiang Province (Nos.: LQ18H160027 and LY17H160049).

Author Contributions HCG, LL and HZ contributed to the experimental design. YN and PTX contributed to animal experiments. AMC, QS and CL contributed to the sample collection and NMR metabolomic analysis. HZ and HCG contributed to the data analysis, result interpretation and writing. All authors have read, revised and approved the final manuscript.

Conflict of Interest The authors have no conflict of interest to report.

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Figure caption: Figure 1. Typical 600 MHz 1H NMR spectra of (A) liver, (B) kidney and (C) heart tissues in mice. Metabolite assignment: 1, valine; 2, isoleucine; 3, leucine; 4, 3-hydroxybutyrate; 5, alanine; 6, acetate; 7, lactate; 8, glutamate; 9, glutamine; 10, asparagines; 11, lysine; 12, creatine; 13, choline; 14, phosphocholine; 15, glyceryl phosphocholine; 16, taurine; 17, betaine; 18, glycine; 19, myo-inositol; 20, glucose; 21, uracil; 22, uridine; 23, inosine; 24, fumurate; 25, tyrosine; 26, histidine; 27, phenylalanine; 28, nicotinurate; 29, oxypurinol; 30, succinate; 31, propylene glycol; 32, methionine; 33, glutathione; 34, glycerol; 35, formate; 36, IMP; 37, AMP; 38, unknown.

Figure 2. Effect of amyloid pathology (AP) on liver and kidney metabolism in mice analyzed by ASCA model. ASCA score plot of the effect of AP on (A) liver and (B) kidney metabolism and its corresponding loading plot; Abbreviation: Con, normal control mice; AP, APP/PS1 mice with amyloid pathology; Metabolite assignment: 1, valine; 2, isoleucine; 3, leucine; 4, 3-hydroxybutyrate; 5, alanine; 6, acetate; 7, lactate; 8, glutamate; 9, glutamine; 10, asparagines; 11, lysine; 12, creatine; 13, choline; 14, phosphocholine; 15, glyceryl phosphocholine; 16, taurine; 17, betaine; 18, glycine; 19, myo-inositol; 20, glucose; 21, uracil; 22, uridine; 23, inosine; 24, fumurate; 25, tyrosine; 26, histidine; 27, phenylalanine; 28, nicotinurate; 29, oxypurinol; 30, succinate; 31, propylene glycol; 32, methionine; 33, glutathione; 34, glycerol; 35, formate; 36, IMP; 37, AMP; 38, unknown. 27



Figure 3. Effect of age on liver, kidney and heart metabolism in mice analyzed by ASCA model. ASCA score plot of the effect of age on (A) liver, (B) kidney and (C) heart metabolism and its corresponding loading plot; Abbreviation: Con, normal control mice; AP, APP/PS1 mice with amyloid pathology; 1M, 1 month of age; 5M, 5 months of age; 10M, 10 months of age; Metabolite assignment: 1, valine; 2, isoleucine; 3, leucine; 4, 3-hydroxybutyrate; 5, alanine; 6, acetate; 7, lactate; 8, glutamate; 9, glutamine; 10, asparagines; 11, lysine; 12, creatine; 13, choline; 14, phosphocholine; 15, glyceryl phosphocholine; 16, taurine; 17, betaine; 18, glycine; 19, myo-inositol; 20, glucose; 21, uracil; 22, uridine; 23, inosine; 24, fumurate; 25, tyrosine; 26, histidine; 27, phenylalanine; 28, nicotinurate; 29, oxypurinol; 30, succinate; 31, propylene glycol; 32, methionine; 33, glutathione; 34, glycerol; 35, formate; 36, IMP; 37, AMP; 38, unknown.

Figure 4. Interaction effect of amyloid pathology (AP) and age on liver and kidney metabolism in mice analyzed by ASCA model. ASCA score plot of the interaction effect of AP and age on (A) liver and (B) kidney metabolism and its corresponding loading plot; Abbreviation: Con, normal control mice; AP, APP/PS1 mice with amyloid pathology;1M, 1 month of age; 5M, 5 months of age; 10M, 10 months of age; Metabolite assignment: 1, valine; 2, isoleucine; 3, leucine; 4, 3-hydroxybutyrate; 5, alanine; 6, acetate; 7, lactate; 8, glutamate; 9, glutamine; 10, asparagines; 11, lysine; 12, creatine; 13, choline; 14, phosphocholine; 15, glyceryl phosphocholine; 16, taurine; 17, betaine; 18, glycine; 19, myo-inositol; 20, glucose; 21, uracil; 22, uridine; 28


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23, inosine; 24, fumurate; 25, tyrosine; 26, histidine; 27, phenylalanine; 28, nicotinurate; 29, oxypurinol; 30, succinate; 31, propylene glycol; 32, methionine; 33, glutathione; 34, glycerol; 35, formate; 36, IMP; 37, AMP; 38, unknown.

Figure 5. Metabolic pathway analysis in liver, kidney and heart during the progression of amyloid pathology in APP/PS1 mice. The colored metabolite exhibited a significant interaction effect between amyloid pathology and age based on the linear mixed-model; Significance level: blue, P

Tissue-Specific Metabolomics Analysis Identifies the Liver as a Major

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