The Disordered Metabolic Profiling in Plasma and Tissues of Mice

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The Disordered Metabolic Profiling in Plasma and Tissues of Mice Infected with Artemisinin-sensitive and -resistant Plasmodium berghei K173 Determined by 1H NMR Spectroscopy Jie Chen, Juanhong Zhang, Xiuli Wu, Jing Chen, Yong Dai, Xueqin Ma, Yongjie Yu, Liming Zhang, and Cheng Liu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00782 • Publication Date (Web): 01 Apr 2019 Downloaded from http://pubs.acs.org on April 2, 2019

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Journal of Proteome Research

Original article The Disordered Metabolic Profiling in Plasma and Tissues of Mice Infected with Artemisinin-sensitive and -resistant Plasmodium berghei K173 Determined by 1H NMR Spectroscopy Jie Chen a, Juanhong Zhang a, Xiuli Wu a, Jing Chen b, c, *, Yong Dai d, Xueqin Ma a, Yongjie Yu a, Liming Zhang a, Cheng Liu a

a School

of Pharmacy, Ningxia Medical University, Yinchuan, China

bInstitute

of Translational Medicine, Medical College, Yangzhou University,

Yangzhou, China c

Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for

Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China dBasic

*

Medical College, Chengdu University of TCM, Chengdu, China

Corresponding author.

E-mail: [email protected] (J. Chen).

Abstract:Background: Artemisinin-resistance has inevitably emerged in several epidemic areas and led to an incremental clinical failure rate for artemisinin based 1

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combination therapy (ACTs) strongly recommended by World Health Organization (WHO). The genetically resilient malaria parasites have evolved anti-malarial drugs evasion mechanisms, meanwhile, the metabolic cross-talk between the malarial parasites and the parasitifer is of significance during the invasion. The intention of this work, therefore, is to propose a feasible method to discover the systematic metabolic phenotypes of mice invaded with artemisinin-sensitive or -resistant Plasmodium berghei K173 when compared with healthy mice. Methods: Collected biological samples including plasma, liver, spleen, and kidney samples of mice after euthanasia at day 7 were subjected to 1 H nuclear magnetic resonance spectroscopy (NMR). Multivariable data analysis (MVDA) means were utilized to estimate the metabolic characteristics of these samples from uninfected and infected mice. Results: In contrast with healthy mice, both sensitive and resistant malarial parasites infected models displayed distinct metabolic profiles. Parasites invasion significantly changed glucolysis, Kreb’s cycle, and amino acids metabolism in plasma and tissues. Decreased N, N-dimethylglycine, and glycine levels in plasma from artemisinin-sensitive P. berghei infected group and increased lactate, lipids, and aspartate in artemisinin-resistant P. berghei infected group were observed, respectively. In the liver, artemisinin-sensitive group up-regulated glutamate level and down-regulated glutamine. Artemisinin-resistant parasites exposure decreased ethanol and allantoin levels. The levels of myo-Inositol and valine in the spleen were increased due to artemisinin-sensitive P. berghei infection together with decreased 2


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trimethylamine N-oxide, phosphocholine, beta-glucose, and acetoacetic acid. In the artemisinin-resistant group, the spleen showed a remarkably increased phosphocholine content along with decreased dimethylglycine and arginine levels. In the kidney, artemisinin-sensitive P. Berghei K173 caused an ascended lysine, glutamate, creatine, and 2-Hydroxybutyrate as well as descended ethanol. Artemisinin-resistant P. berghei led to low glycerophosphorylcholine and high acetate, betaine, and hypoxanthine. Mutual and specific altered metabolites and accordingly metabolic pathways induced by the infection of artemisinin-sensitive or -resistant P. berghei were therefore screened out. Conclusions: This study might be a preliminary study to establish a direct relationship with the host metabolic background and artemisinin-resistance. Key words: metabolomics; artemisinin resistance; P. berghei; sensitive and resistant; 1H

NMR spectrometry

Introduction Malaria morbidity descended by 37% and lethality fell by 60% between 2000 and 2015 due to artemisinin (ART)-based combination therapy (ACTs), the first-class clinical antimalarial drugs.1 Although ART and its related derivatives (ARTs) possess a much better pharmaceutical properties, ARTs resistant malaria occurred in some Southeast Asian countries and partial ART-resistance emerged in Greater Mekong 3



Subregion have threaten worldwide malaria elimination.2-7 Currently, it is reported that dihydroartemisinin-piperaquine failure in Cambodia is arising.8, 9 ART resistance even reoccurred in the regions where the disease has been eradicated.10 Inevitably, the genetically elastic malaria parasites have evolved drugs evasion mechanisms to all available antimalarials inclusive of the ARTs.11-13 The systematic distribution of some parasites, such as Fasciola hepatica, in the whole body may generate a more global impact on the host metabolism.14 In all parasitizations of parasites, a remarkable metabolic interplay between pathogens and host is observable because the parasites tend to transfer the nutrients towards their own growth but the parasitifer struggles to sustain homeostasis, remove waste products and toxins, and repair tissue injury.15 Thus, the pathology of parasitic diseases is closely related to dysregulation of host metabolism. A contrast of metabolic data based on different rodent-parasite models, such as mice models induced by Plasmodium berghei, trypanosomes, and schistosomes, indicates that each parasite species might induce an exclusive metabolic fingerprint in the rodent host, and relevant metabolites connected with the diseases have been authenticated by nuclear magnetic resonance (NMR) analysis.16-21 Furthermore, one of the rodent model malaria parasites with host specificity, scientifically named as Plasmodium berghei, is 150 folds more potentially to occupy reticulocytes and results in infection in the existence of same amounts of mature erythrocytes and reticulocytes, and hence has been commonly considered as a similar model with P. vivax-type parasite 4


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blood-stage development.22,23 Hence, we chose P. berghei as the infectious model in this paper. Metabolomics, aiming to comprehend the global and dynamic metabolic feedbacks of alive organisms on biostimulation or genetic manipulation,24 has been successfully applied to identify the biomarkers relative to the fatal diseases such as diabetes, tuberculosis, cancer, and schistosomiasis.25-28 Metabolic profiling analytical approach has made it possible for researchers to investigate the dynamic changes of metabolites at different physiological levels over time by combining spectroscopic outlines of biospecimen and MVDA.29 It is also regarded as a valid method to quantify the metabolic responses in living creatures to pathophysiological stimuli.30 It, therefore, has been extensively used in host-parasite cross-talk and biomarkers discovery in recent years.31 Mass spectrometry (MS) and 1H NMR are commonly utilized approaches in metabolomics study32 and have respective advantages and disadvantages in metabolic analysis. Hence, reciprocal information could be provided.33 1H NMR measurement, by combining chemometrics means with multivariate statistical analysis, enables us to simultaneously detect hundreds of water-soluble small molecules in a biological matrix. Additionally, it has been proved to be one of the most powerful tools which possess several characteristics such as quantifiability and noninvasive or nondestructive inspection. It is also a nonequilibrium perturbing method to obtain high-quality and comprehensive information from in vivo water-soluble components.34, 35 By contrast with NMR 5



spectroscopy, MS coupled with chromatographic system could detected a great many ionizable metabolites in a biological matrix within a wide concentration range. It enables to globally quantify hundreds of substances and could be employed to authenticate undiscovered metabolites. MS is highly sensitive and selective, especially suitable for targeted analysis.36, 37 Non-ionic compounds, however, might be very difficult to be detected by MS. 1H

NMR-based metabolomics has provided a novel insight into complicated

host-parasite interactions and has been easily used to model parasitic invasions in different in vivo and in vitro systems. In this paper, therefore, untargeted metabonomics based on 1H NMR spectroscopy was chosen to discover the biological responses of mice to the infection of ART-sensitive or -resistant rodent malarial parasite, P. berghei, in a system view by identifying, characterizing, and comparing changed endogenous metabolites within tissues and biofluids, including the plasma, liver, spleen, and kidney samples. ART-resistant P. berghei was screened out by passaging P. berghei K173 under incremental ART dosages until 42 generations. Most importantly, the focus in this work is to screen out the latent markers and metabolic pathways that could be used to clarify the significant difference between wild and mutant malaria parasites. It will draw a comprehensive map of systematic and localized metabolic abnormity and contribute to understand the metabolic response of the mice to ART-sensitive and -resistant malaria parasites.

Methods 6


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Ethics statement The experimental protocol with ethic approval number 2015-013 has been accepted by the University Ethics Committee from Ningxia Medical University, China. All the procedures concerning animals conformed to the Regulations of the Experimental Animal Administration, State Committee of Science and Technology, People’s Republic of China. Chemicals D2O and D2O (containing 0.05% sodium 3-trimethylsilyl-(2, 2, 3, 3-2H4)-1-propionate, TSP) were purchased from Qingdao Tenglong Weibo Technology Co., Ltd. (Shandong, China). All other chemical compounds and reagents were of analytical purity. Passage and infection of P. berghei K173 in mice ART-sensitive P. berghei K173 was donated by National Engineering Technology Research Center for the Development of Beijing Traditional Chinese Medicine Compound Drugs. ART-resistant P. berghei was obtained by a serial passaging of sensitive malaria parasites under incremental ART dosages until 42 generations and donated by Prof. Dai from Basic Medical College, Chengdu University of Traditional Chinese Medicine. The resistant index of ART was determined to be 14.153 in his laboratory, which indicated a moderate resistance.38 Animal handling and treatment

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Male ICR mice (18-22 g) were provided by the Laboratory Animal Center of Ningxia Medical University (SCXK N, 2015-0001). Mice were accommodated in microisolator cages under specific-pathogen free (SPF) conditions, under a controlled temperature (22 ± 2℃), a humidity of 40 ± 5%, 12 h light/dark cycles and were freely fed with water and food pellets for three days prior to the experiment. Animal infection by P. berghei Blood samples containing with P. berghei cryopreserved in liquid nitrogen were completely thawed within 1 min under the temperature of 38ºC and continuous shaking. The blood was diluted with sodium citrate-normal saline to make sure that each 0.2 mL contained 106 parasites infected erythrocytes. The mice in ART-sensitive or -resistant P. berghei groups were injected with 106 wild or mutant malaria parasites infected red blood cells (RBCs) by intraperitoneal. The healthy mice were injected with an equal volume proportion of sodium citrate-normal saline only. Each mouse was monitored per day through counting 400~500 RBCs stained by Wright Giemsa method collected from caudal vein in an optical microscope (Olympus, Tokyo, Japan). Donor mice infected by P. berghei with parasitaemia over 5% were served as the infected model for later experiments. Mice behavioral performances were observed each day throughout the whole experiment process. Mice (n=18) which were served as the infected model were randomly allocated into three groups: ART-sensitive P. berghei infected group (n=6), ART-resistant P.

8


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berghei infected mice (n=6), and vehicle (n=6). The mice were given water and laboratory standard food pellets ad libitum for 7 days. Plasma and tissue samples collection On day 7, blood samples were respectively harvested by heparinized capillary tubes from the mice orbit after being narcotized using diethyl ether, and directly transferred into microtubes from the capillary. About 700 μl blood samples might be gained from each mouse and were centrifuged at 12000 rpm for 10 minutes to gather the supernatant. The plasma was kept at -80ºC before use. The mice were dissected immediately after blood collection and sacrifice. The livers, spleens, and kidneys were taken out, flushed with ice-cold physiological saline, and weighed. The liver (about 200 mg), kidney (about 200 mg), and spleen (about 50 mg) were quick-frozen in liquid nitrogen and remained at -80ºC till sample preparation for NMR spectroscopy. The rest of tissues were prepared for histopathology test. Histopathology The fresh livers, kidneys, and spleens tissues fixed in 10% neutral buffered formalin were embedded in paraffin. Tissue blocks were sliced into serial segments of 5 μm thickness and pathological changes of these tissues among different groups were observed in hematoxylin eosin (HE) staining under an optical microscope (Olympus, Tokyo, Japan). Metabolite extraction 9



The plasma was thawed and centrifuged at 4ºC, 12000 rpm for 5 minutes. The liquid supernatant was moved into a 5 mm NMR tube and 100 μl of D2O (TSP) was added. The weighed tissues were thawed by being gradually transferred from -80ºC to -20ºC and final to 4ºC. Metabolites were extracted by using the organic protein precipitation method. In brief, the tissues were ground under hydraulic nitrogen in a mortar. An aliquot of 1 ml of ice-cold acetonitrile/water (1:1) solution was added. Then, the samples were ground for 10 minutes, vortexed for 2 minutes, and centrifuged at 4ºC, 12000 rpm for 10 minutes. The supernatant was moved into a centrifuge tube and kept at -4ºC before use. Subsequently, the sediment was repeated the above procedure to guarantee a thorough extraction. The supernatant was concentrated to dryness under nitrogen, redissolved by 600 μl phosphate buffer, and centrifuged at 12000 rpm for 5 minutes. An aliquot of 100 μl of D2O (TSP) was added to the supernatant pre-transferred into a 5 mm NMR tube for further NMR analysis. 1H

NMR metabolic analysis 1H

NMR spectra of plasma or tissues were detected on a Bruker Avance 500

MHz Spectrometer (Bruker Biospin, Erlangen, Germany) at 298 Kona. Water-presaturated standard one-dimensional Carr-Purcell-Meiboom-Gill [CPMG, recycle delay-9° (τ-180°-τ)n-acquisition] was applied to record NMR spectrum by using free induction decays (FIDs). 32 K data points were gained by collecting 128 transients in the spectral width of 10 kHz. A relaxation delay was 3 s, and relaxation 10


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time (2 nτ) was 100 ms. An exponential function with a 0.3 Hz line-broadening factor was used to multiply the free induction decays (FIDs) ahead of Fourier transformation. After Fourier transformation, all of the 1H NMR spectra were baseline and phase corrected by Topspin 2.1 (Bruker Biospin, Germany) with referenced chemical shifts to the methyl lactate at 1.33 (dd) ppm. Effects of water resonance could be eliminated by excluding the region (δ4.70-5.20 in plasma spectra and δ4.60-5.10 in tissue spectra) prior to statistical analysis. NMR spectrum in the range of δ0.5-9.0 was divided into the width of 0.01 ppm (plasma) or 0.005 ppm (tissues) by AMIX (version 3.9.14). The integral regions were normalized to total spectra areas. Multivariate statistical analysis was employed to dispose the obtained data in SIMCA package (Version 14, Umetrics, Sweden). Pattern recognition Principal Components Analysis (PCA) is a commonly used unsupervised pattern recognition method. It transforms the variables within a data set into a smaller amount of new latent variables named principal components (PCs).65 PC1 represents the capability to depict the most evident characteristics of a multidata matrix. In addition to PC1, PC2 represents the ability to describe the second obvious features of a multidata matrix. Initially, the NMR data set was processed by PCA method and intrinsic clustering between different treatment groups was visualized. In order to amplify the differences between two groups, supervised extension method such as 11



partial least-squares discriminant analysis (PLS-DA) or orthogonal signal correction partial least-squares discriminant analysis (OPLS-DA), were chosen. OPLS-DA is a derivative algorithm of PLS; they can obtain target classification and target discrimination by presetting Y value. Compared with PLS-DA, OPLS-DA contains orthogonal signal correction and partial least-squares discriminant analysis, which could decompose X matrix information into two types of information, called related to Y and unrelated. By eliminating the unrelated differences, related parts are concentrated in the first predictive component. Therefore, the OPLS analysis of X and Y could be decomposed into predictive components (tP) and orthogonal components (tO). In this paper, OPLS-DA was used to clarify the significance between control vs. ART-sensitive group and control vs. ART-resistant group. Prior to PCA or OPLS-DA analysis, all NMR data had been UV and pareto scaled. Permutation test (200 times) as well as a 7-cross validation were carried out to discover the statistically significant parameters under the selected predictive quality in the established OPLS-DA model. Model validity against overfitting was evaluated by R2X and R2Y, and Q2 was selected to depict the predictive capacity. The closer these three parameters are to 1, the more stable and reliable the model will be. Commonly, permutation tests could be used to detect the overfitting instead of cross-validation. Response permutation tests (RPT) is a random permutation method to evaluate the accuracy of OPLS model, and it is used to explain that the clustering is not accident by the supervised extension. In general, the closer the slope of R2Y and Q2Y to the Y axis is, the more likely the 12


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model will overfit. When RPT test is chosen to use, Q2Y generally requires to be less than 0. Statistical analysis Multi-criteria assessment (MCA) method through screening the different variables was used to improve the accuracy and credibility of the variables. Discrepant metabolites could be screened out based on the variable importance in projection (VIP) and correlation coefficients (p(corr)) values from OPLS-DA model. The p values were obtained from student’s t-test analyzed by SPSS 20.0 (Statistical Product and Service Solutions, IBM., USA). The significantly changed metabolites (SCM) between control vs. ART-sensitive group and control vs. ART-resistant group were therefore identified with VIP > 1, |p (corr)| > 0.58 and p < 0.05. Student’s t-test is a suitable statistical method for statistical analysis of measurement data/quantitative data that conforms to normal distribution, homogeneity of variance, and small sample. Generally, p value represents the result of Student’s t-test. An experiential p value is always computed through detecting the folds of the permuted data and generated a preferable outcome than that obtained by primitive labels. The acquired statistical p values by permutation test were less than 0.05, thus the validity of OPLS-DA model could be confirmed. The changed metabolites between different treatment groups could be evaluated by performing a parametric student’s t-test or non-parametric Mann-Whitney test on signal integrals.

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Structure visualization of covariance p (1) and correlation p (corr) (1) could be realized by the S-plot between X-variables and predictive scores t (1). The greater contribution rate of variables distributed at both ends of the "S" shape. The loading-correlation coefficients plot contains loading weights, p (corr) values, and chemical shifts of NMR spectra, calculated by MATLAB (Math Works, Inc., USA). It visualized the loading weights and chemical shifts colored based on absolute values of correlation p (corr). Here, we also used a multivariate statistical analysis, named Shared and Unique Structure-plot (SUS-plot). The SUS-plot projects scaled loading p (corr) (1) from OPLS-DA analysis between control and ART-sensitive or -resistant group and makes shared metabolic contributions visible along the diagonals. The metabolite resonances were authenticated by the chemical shifts from Human Metabolome Database (HMDB, http://www.hmdb.ca/). Kyoto Encyclopedia of Genes and Genomes (KEGG, www.genome.jp/kegg/) pathway database was useful to analyze the metabolic pathways. Metaboanalyst 3.0 (http://www.metaboanalyst.ca/), a network-based powerful tool to analyze pathways and visualize metabolomics, was used to evaluate the pathways impact and heatmaps. The body weight, organ coefficient, and potential metabolites were estimated by GraphPad Prism V 7.00 software (GraphPad Software Inc., San Diego, CA, USA). Adobe Illustrator CC 2015 (Adobe Systems Inc., USA) was used to draw the schematic diagram of the disordered metabolic pathways.

Results 14


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The analysis of body weight and organ coefficient Body weights were recorded each day during the whole experimental process. The organ coefficients could be obtained through dividing organ weight by body weights and displayed in Fig.1 a-c. The liver organ coefficient indicated an obvious difference of liver between the control and ART-sensitive group, while there was no significance between the control and ART-resistant group. Organ coefficients of spleen and kidney displayed an extremely significant difference between ART-sensitive or -resistant group and the control. The mice body weights in different groups were monitored during the 7-day experiment period, as shown in Fig.1.d-e, which indicated that both ART-sensitive and -resistant P. berghei infection led to a slight decrease of body weight after day 5. Histopathology test Microscopy analysis showed significantly histological changes in the tissues from malarial parasites infected groups. For the liver, histopathological test showed that ART-sensitive or -resistant malarial parasites infection led to mild edema of hepatic cells due to the accumulation of malarial pigmentation in large quantities and infiltration of inflammatory cells in the manifold area (Fig.2.b-c). Histological changes in spleen showed an addition of hematopoiesis in the extra medullary cell, the mild hyperplasia of fibroid in the spleen cells, and an increased malarial pigmentation (Fig.2.e-f). For the kidney, mild edema in kidney tubular epithelium, blurring cell boundary, and increased glomerular volume are observable (Fig.2.h-i). Taken together, 15



it demonstrated that ART-sensitive or -resistant malarial parasites infection damaged the liver, spleen, and kidney, the main target organs attacked by malarial parasites. The analysis of NMR spectrums and pattern recognition Normalized typical 1H NMR spectrums of plasma, liver, kidney, and spleen samples extracted from the control, ART-sensitive, and -resistant groups were depicted in Fig.3. Detailed information of the endogenous metabolites were exhibited in Table 1-4. Analysis of PCA, OPLS-DA, and permutation tests according to 1H NMR spectra of plasma, liver, spleen, and kidney samples were displayed in Fig.4. The parameters R2X, R2Y, and Q2 were listed in Table 5. The PCA score plots showed a separation and clustering of plasma samples from different groups as indicated in Fig.4A.a. No significances exists in plasma metabolic profiles between the control and infected groups. OPLS-DA showed a significant separation in plasma between the control and ART-sensitive group (Fig.4A.b) or ART-resistant group (Fig.4A.c). The R2 and Q2 from the cross-validation evaluated that the OPLS-DA model was good in fit and predictive ability, which confirmed the model validity. The score plots of PCA and OPLS-DA demonstrated a visible difference between malaria parasites infected and healthy mice both in the liver (Fig.4B) and kidney (Fig.4D) samples. OPLS-DA model between the control and ART-sensitive group (R2=0.924, Q2=0.848, liver; R2=0.919, Q2= 0.712, kidney) or ART-resistant group (R2=0.947, Q2=0.917, liver; R2=0.958, Q2=0.802, kidney) showed a high degree of segregation. However, the 16


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PCA score plot in spleen samples (Fig.4C.a) did not give a good separation between the infected and control group. To better understand the metabolic differences, therefore, OPLS-DA analysis was carried out to obtain a significant separation. The parameters (R2=0.753, Q2=0.512, control vs. ART-sensitive group; R2=0.964, Q2=0.916, control vs ART-resistant group) indicated a complete segregation between malarial parasites infected and control group. The successive permutation test was performed as shown in Fig. 4.d-e to testify whether the good predictive ability of the model was caused by data overfitting or not. The obtained parameters of R2 and Q2 clarified that reliable predictive power of the generated model in this work was not attribute to data overfitting. Altered metabolomic profiles and integrated pathway analysis We compared the metabolic differences of plasma and tissues samples between control and ART-sensitive or -resistant group by S-plot (Fig.5.a-b), SUS-plot (Fig.5.c) and loading-correlation coefficients plot (Fig.5.c-d). The S-plot is a simple way to visualize an OPLS-DA classification model, hence it is always used to screen out putative biomarkers from 1H NMR data. The SUS-plot, by contrast with the scaled loading p (corr) of OPLS-DA models, also visualized the mutual metabolic contributions along diagonal lines as well as exclusive ones along orthogonal axes.40 The colors in loading-correlation coefficient distinguished the significant difference of metabolites, namely, red color indicates ascended metabolites relative to the vehicle and blue one represents descended metabolites. The changed metabolites, whose VIP > 1, |p (corr)| > 0.58 and p value > 0.5, were discovered by pathways analysis in MataboAnalyst 3.0. Altered metabolites pathways were analyzed and plotted in Fig.6. 17



The pathway network mapping and Barchat of selected metabolites were displayed in Fig.7. The comparison of representative metabolites in plasma and tissues between different treated groups could be seen in Fig.8. Changed metabolites and pathway in plasma samples between different groups Totally 16 significantly different metabolites in plasma were found out and listed in Table 1. Among them, 10 metabolites were obtained by comparing ART-sensitive group to control and 12 of them were screened out by comparing ART-resistant group to control. The S-plot (Fig.5A.a-b) revealed that ART-sensitive P. berghei led to an increased alanine, valine, isoleucine, and lysine levels together with a decreased glucose, N, N-dimethylglycine, taurine, and glycine in the blood. Mice in ART-resistant group up-regulated lactate, lipid, isoleucine, N-acetyl aspartate, and glutamate levels accompanied with down-regulated glucose and taurine. Mutual metabolic features to ART-sensitive and -resistant group included an increased isolecucine, alanine, valine, glutamate, and lysine levels and declined glucose and taurine, as displayed in the opposite quadrants of the SUS-plot (Fig.5A.c). As shown in the pathway analysis of the potential metabolites (Fig.6.a-b), the altered metabolic pathways in mice infected with ART-sensitive or -resistant malarial parasites contained metabolism of alanine, aspartate, and glutamine, biosynthesis of valine, leucine, and isoleucine, and metabolism of D-glutamate and D-glutamine together with taurine or hypotaurine metabolism. Interestingly, a disordered glycine, serine, and threonine metabolism was only discovered in mice infected with ART-sensitive P. berghei. Altered metabolites and pathway in liver samples between different groups

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A sum of 45 significantly changed metabolites in liver extracts was discovered (Table 2). Thereinto, 40 of them were selected when compared ART-sensitive group with control and 30 from ART-resistant group. Metabolomic analysis based on S-plot (Fig.5B.a-b) indicated that alanine, leucine, lactate, and isoleucine levels were significantly increased and glucose, N, N-dimethylglycine, trimethylamine N-oxide, and glutamate declined in liver samples obtained from mice infected by ART-sensitive P. berghei. After being infected with ART-resistant P. berghei, levels of lactate, lipid, and arginine increased, and trimethylamine N-oxide, glucose, N, N-dimethylglycine, and lysine in mice liver significantly decreased. Analysis based on SUS-plot (Fig.5B.c) certified aggrandized levels of lactate, lipid and isoleucine and attenuated signals of trimethylamine N-oxide, N, N-dimethylglycine, and lysine in mice infected by malarial parasites. According to the outcomes from pathway analysis of the potential metabolites (Fig.6.c-d), the disturbed pathways induced by P. berghei infection include glycine, serine, and threonine metabolism, arginine and proline metabolism, glycerophospholipid metabolism, glyoxylate and dicarboxylate metabolism, alanine, aspartate, and glutamate metabolism along with D-glutamine and D-glutamate metabolism. Disordered pathways such as valine, leucine, and isoleucine metabolism, ketone bodies synthesis and degradation, taurine and hypotaurine metabolism were merely found in ART-sensitive P. berghei infected mice. Changed metabolites and pathways in spleen samples between different groups 19



Totally 42 significantly different metabolites were observed in the spleen extracts (Table 3). Among them, 29 metabolites were screened out by the comparison between ART-sensitive group and control and 29 metabolites by ART- resistant group vs. control. Data from S-plot (Fig.5C. a-b) displayed that glycerophosphorylcholine, choline, and myo-Inositol increased and trimethylamine N-oxide, phosphocholine, glucose, and acetoacetic acid decreased in ART-sensitive malarial parasites infected mice. In mice spleen infected with ART-resistant P. berghei, choline, phosphocholine, and glycerophosphorylcholine levels were up-regulated and N, N-dimethylglycine, arginine, and acetate levels were down-regulated. SUS-plot (Fig.5C. c) data showed that the infection of wild and mutant malarial parasites resulted in an obvious increase in glycerophosphorylcholine, choline, and myo-Inositol contents as well as a decreased uridine or isoleucine levels. As shown in pathway analysis (Fig.6. e-f), the altered metabolic pathway in infected groups contained glycine, serine, and threonine metabolism, alanine, aspartate, and glutamate metabolism, valine, leucine, and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis along with D-glutamine and D-glutamate metabolism when compared with uninfected group. Phenylalanine metabolism and synthesis together with degradation of ketone bodies were perturbed in mice infected with ART-sensitive parasites. Metabolism of arginine and proline was enriched in mice after being infected with ART-resistant strain of parasites. Changed metabolites and pathways in kidney samples between different groups 20


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Accordingly, 53 discrepant metabolites were screened out from kidney extracts and displayed in Table 4. Thereinto, 43 of them were discovered in contrast with ART-sensitive group and control and 43 were obtained by comparison ART-resistant group with control. S-plot results revealed the ascended and descended metabolites (Fig.5D. a-b) when compared between the different groups. Metabolomic analysis proved that infection with ART-sensitive parasites induced an increased level of leucine, alanine, valine, isoleucine, and lysine as well as decreased choline and trimethylamine N-oxide level. In ART-resistant group, alanine, acetate, valine, and leucine were increased and choline, betaine, and glycerophosphorylcholine declined. Based on SUS-plot (Fig.5D. c), the parasites infected mice had higher alanine, leucine, acetate, and creatine levels, and lower levels of choline, glycine, betaine, and ethanol. As exhibited in Fig.6. g-h, metabolism of glycine, serine, and threonine, metabolism of alanine, aspartate, and glutamine, biosynthesis of valine, leucine, and isoleucine as well as biosynthesis of phenylalanine, tyrosine, and tryptophan involved in the altered pathway induced by two strains of malarial parasites. But anoate metabolism, arginine and proline metabolism, phenylalanine metabolism and synthesis, and degradation of ketone bodies were remarkably changed in kidney samples from the mice infected with sensitive parasites. D-glutamine and D-glutamate metabolism in resistant P. berghei infected mice, was significantly different from the control.

Discussion

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We have studied the significantly different metabolomics profiles of plasma, liver, spleen, and kidney in mice uninfected or infected with ART-sensitive and -resistant P. berghei. Probable disordered metabolic pathways in plasma and tissues of mice is shown in Fig.9. Metabolomic alterations in plasma The homeostasis or its perturbation could be observable through analyzing plasma metabolic profile. The results obtained in this paper suggested an obvious disorder of amino acids and glucose metabolism due to the infection of P. berghei in mice. The metabolomic profiling revealed that alanine, valine, isoleucine, lysine, and glutamate in the plasma of mice were significantly up-regulated, oppositely, beta-glucose and taurine were down-regulated after infection with wild and mutant P. berghei (Table 1). The majority of amino acids can be transformed into organic acids under the transamination effect of alanine aminotransferase (ALT) or aspartate aminotransferase (AST) and afterwards disintegrated into the substrates of TCA cycle. Alanine is one of non-essential amino acids from in vivo transformation of pyruvate and degradation of DNA or carnosine, playing a role of a primary energic source. It also involved in lymphocyte regeneration and immune function. Therefore, up-regulated alanine level in plasma demonstrated that P. berghei might disturb the immunological function and nucleotide metabolism. Two branched-chain amino acids (BCAAs), valine and isoleucine, were also discovered to be increased in the infected groups. BCAAs were of the necessary precursors in protein synthesis and energy 22


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generation.41 Therefore, the enhancement of these compounds suggested arising in the protein synthesis in order to fix the impaired membrane-proteins structure. Lysine produces acetyl-CoA into the Kreb’s cycle under the action of acetoacetyl-CoA, and glutamate produces α-ketoglutaric acid (α-KG) into TCA cycle by degradation. Elevated lysine and glutamate in mice infected with P. berghei hinted that energy metabolism has been disturbed. Decreased taurine level was also found in the plasma collected from malarial parasites infected mice. Taurine, as one of sulphur amino acids, plays a key role in the blood for detoxificaiton of ammonia.42 Its down-regulation might be regarded as a response to a decreased body ability of ammonia elimination. Declined alpha- and beta-glucose level in plasma after infection might be related to an activated glycolysis under an oxygen-free condition because of blood cells rupture induced by malarial parasites proliferation. Increased lactate level in ART-resistant P. berghei invaded mice also proved it to some extent. Glycolysis is an ancient metabolic pathway and occurs in the cytoplasm ultimately to generate two molecules of pyruvate by catalyzing one molecule of glucose. It might be further conformed to lactate or alanine in the blood-starved or mitochondrial dysfunction cells. Moreover, malarial parasites in the erythrocytic stage store few glycogens and therefore glucose is the main energy source for parasites. Parasitism of malarial parasites might alter the red cell membrane and strengthen active transmembrane transport of glucose from the host plasma. Therefore, it should be one of the key 23



reasons which lead to a decreased glucose level in host plasma after being infected by malaria parasites. This is in accordance with a literature which reported that red blood cells invaded by malarial parasites might suppress glucose consumption in parasite-free erythrocytes, specifically during heavy parasite loading time.43 ART-sensitive P. berghei significantly reduced N, N-dimethylglycine (DMG) and glycine concentrations in mice plasma. Glycine and serine metabolism pathways are a branch-out from glycolysis, and they will further be converted into arginine and proline. Glycine could also be conversed to DMG. Conjugation of free bile acids with glycine by amido bonding, called glycocholic acid, can increase the dissolubility of lipids.44 The descended glycine and DMG might hint that ART-sensitive P. berghei K173 perturbed lipid metabolism. The level of lactate, lipid, and aspartate increased in mice plasma infected with ART-resistant P. berghei. Lactate is the end-product generated during the glycolysis. Aspartate produces fumarate into the TCA, which is critical for energetic metabolism. Enhanced lactate and aspartate in the plasma demonstrated that the energy metabolism has been perturbed.45 Lipids are crucial for the biogenesis of cells and parasites membranes because they could assure parasites survival and replication in host cells.46 The up-regulation of lipids induced by ART-resistant P. berghei might prove that mutant strain is potential to produce more lipids to improve its growth and reproduction in the host. Metabolomic alterations in liver 24


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Metabolic profile of liver, being as a critical organ that regulates the metabolism, is helpful to understand the responses of host to external stimulus and the regulations by the parasitifer to sustain homeostasis. We found that liver metabolomic profiles in mice infected with ART-sensitive or -resistant parasites were greatly altered when compared with the healthy mice based on PCA and OPLS-DA score plots (Fig.4B. a-c). Both ART-sensitive or -resistant malarial parasites led to an elevated alanine, lactate, BCAAs including valine, leucine, and isoleucine, lipids, and arginine (Table 2). Oppositely, they collectively descended alpha- and beta-glucose, DMG, trimethylamine N-oxide (TMAO), lysine, and taurine in mice liver (Table 2). Taken together, it demonstrated a disturbed extensive metabolic network including the central carbon metabolism (Kreb’s cycle), and converging/branching pathways such as BCAAs biosynthesis or alanine and aspartate metabolism. Aspartate metabolism is a branch-out from glycolysis and a crucial process to further impact the conversion of taurine, whose function is to repair the damaged hepatic cells. The pathophysiological tests in the study also demonstrated the appearance of inflammatory cells in the portal area. Now that central carbon metabolism provides energy as well as intermediate products required in other metabolic pathways, the reason why the host being infected with ART-sensitive parasites adjusts the liver metabolism to maintain the plasma homeostasis, as one of the host responses to the infection, is understandable. Decreased TMAO and DMG, the metabolites which arise from gut microflora, 47 were 25



discovered in mice liver infected by malarial parasites. The oxidation of trimethylamine (TMA), the decomposed product of choline by intestinal microbiota, will generate TMAO.48 Downregulated TMAO and DMG could be an indicator of interrupted gut microflora.49 Infection of P. berghei, therefore, results in dysbacteriosis. The liver, as a major source of energy, is responsible for keeping the energy homeostasis of the whole body. The imbalanced gut microflora and energy metabolism induced by P. berghei infection might disrupt energy supply. In addition, a statistically significant decrease in glucose and accumulation of lactate were observed. This might reveal a disordered gluconeogenesis or glycolysis in liver. Mice in ART-sensitive group displayed an increase in glutamate level and a decreased glutamine in liver. Glutamate (Glu) generated glutamine (Gln) by glutamine synthetase (GS). Gln supplies nitrogen in purine and pyrimidine synthesis and therefore is a necessary zymolyte for the biosynthesis and energy metabolism or the generation of nucleotide in the period of cell multiplication. In mitochondria, Gln carbon skeleton will become a main energy substance for cell proliferation through being oxidated.50 Gln involved in ammonium detoxification, 40 thus decreased Gln might be indicative of unstable status of ammonium detoxification ability in infected mice liver. ART-resistant P. berghei exposure caused a down-regulated ethanol and allantoin in mice liver. Ethanol could be utilized to generate acetyl-Co A, which 26


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enters Kreb’s cycle later. It plays an important part in aerobic glycolysis and power supply. Allantoin, a main metabolite in urea cycle, is a metabolic product of arginine and proline. The lower level of ethanol and allantoin revealed that ART-resistant P. berghei disordered energy metabolism and urea cycle in mice. Metabolomic alterations in spleen Spleen is an immune organ acting a critical function in the inherent and adaptive immune responses. During the course of parasites invasion, there is a constantly dynamic metabolic interaction between the host and the parasites that might interfere the biochemical characteristics of both the parasites and the host.51 The parasites intrusion induces a serials of feedbacks by the host which are well-known as “active-phase responses”.52 This phase is featured by metabolic, immunologic, neuroendocrine, and ethological alterations to the host.53 Spleen 1H NMR-based metabolomics and statistical analyses revealed mutual and differential metabolites between ART-sensitive and -resistant P. berghei infected mice which might hint altered immune functions. We found that P. berghei induced significant elevation of glycerophosphorylcholine (GPC) and choline. The synthesis of choline is the production of cholamine in vivo under the action of transmethylation of S-adenosylmethionine (SAM). Choline and acetyl-Co A produced acetylcholine (ACh) catalyzed by choline acetylase and were stored in parasympathetic vesicles. Acetylcholine (ACh) is one of the most important neurotransmitters in the 27



parasympathetic nervous system. Various physiological activities will occur right after ACh is released and broken down into choline as well as ethanoic acid under hydrolysic catalysis of acetylcholinesterase (AChE). GPC is one of the products in in vivo phospholipid metabolism, and is also an important precursor of neurotransmitter acetylcholine biosynthesis. The most important physiological function of GPC in vivo is to cross blood-brain barrier and supply choline for biosynthesis of ACh and phosphatidylcholine. Increased choline and GPC levels in the spleen suggested that P. berghei caused information transmission disorder. It is reported that the parasites need activated phospholipid synthesis for their growing development and propagation in the parasitifer.54 It might be one of the reasons for the substantial increase of choline and GPC.55-56 The level of myo-Inositol (m-I) and valine significantly increased together with decreased TMAO, phosphocholine (PC), beta-glucose, and acetoacetic acid (AA) due to ART-sensitive P. berghei infection. Increased m-I indicates a perturbed metabolism pathway of glycine, serine, and threonine together with a disordered BCAAs biosynthesis. Serine metabolism pathway is a branch-out from glycine metabolism pathway and converses serine to glycerol and m-I. m-I is widely distributed in animals and is a growth factor for animals and microorganisms. It has the function of lowering cholesterol and metabolizing fatty. m-I removes fatty from the liver and helps redistribute fat in the body. The accumulation of m-I in spleen after being infected with ART-sensitive P. berghei proved that malarial parasites caused a disordered 28


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glycine and fatty metabolism. Valine is an essential amino acid in the body. Its functions include promoting the normal growth, repairing tissues damage, regulating blood sugar, and supplying necessary energy for the body. When there is intense exercise or abnormal reaction in the body, valine could provide extra energy to produce glucose to avoid muscle weakness. Increased valine in ART-sensitive group indicated that the body needed a large quantity of energy to repair tissue damage and fight against the parasite invasion. Significantly decreased PC and TMAO suggested an inhibited phosphocholine synthetic pathway and dysbacteriosis. After over-consumption of fatty acids, the content of AA increased. High level of AA might cause ketoacidosis to a large extent. Elevated AA concentration in spleen due to ART-sensitive P. berghei infection, hinted a perturbed metabolism of fatty pathway leading to a ketoacidosis. Increased PC and decreased DMG or arginine levels were observed in the spleen from ART-resistant P. berghei group. PC is produced from choline and ATP under the catalysis of choline kinase. Elevated PC, therefore, certified a disordered phospholipid synthesis. It is reported that malarial parasites infection will cause the accumulation of PC in brain of mice, indicating a changed ACh biosynthetic pathway.54 Accumulated ACh in synapse, termed as cholinergic burst, will induce a cholinergic crisis by disordering the central nervous system. It is caused by AChE suppression which hinders the hydrolysis of ACh. Prolonged cholinergic outburst in human body exhibits some neurological symptoms including tremor and respiratory 29



disfunction, epilepsy, and other neuropathological diseases.56-58 Therefore, altered PC level after malarial parasites infection observed in this paper might be connected to the neurological complications. The lower level of argnine and DMG hinted perturbed Kreb’s cycle or arginine, proline, and glycine metabolism. Arginine and proline are metabolically interchangeable. On account of its multifarious metabolic outlets, arginine is an amino acid with multifunctional characteristics.59 Arginine reduction might be tightly linked to a disordered energy metabolism. Furthermore, lipids related metabolic pathway was also disturbed because of the lower level of DMG. Metabolomic alterations in kidney As an important water metabolic organ, the utmost dominating function for kidney is to reabsorb sodion into the bloodstream from the renal tubular fluid against a concentration gradient. Energy is demanded during the reabsorption, supplying by aerobic or anaerobic respiration occurred in mitochondria. Metabolomics profiles of kidneys after being infected by ART-sensitive or -resistant malarial parasites were greatly changed based on OPLS-DA score plots analysis (Fig.4B.b, c). P. berghei infection contributes to the upregulation of some important amino acids including alanine, valine, leucine, isoleucine, and tyrosine (Tyr) in the mice kidney as well as descended choline, TMAO, and glycine. Amino acid functions as the elementary unit for in vivo protein biosynthesis. Evidences demonstrated that nephric damage has a close relationship with abnormal proteins generation and amino acids reabsorption.41 Increased alanine which is closely connected with pyruvate 30


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metabolism was discovered due to the excessive conversion from lactate and an activated glycolysis pathway during the infection. Ascended levels of BCAAs were found in parasites infected groups. BCAAs are required precursors in proteins production and energic generation; therefore, their up-regulations proved an enabled proteins synthesis in order to recover the corrupted membrane proteins configuration. Tyr together with phenylalanine (Phe), as one of the aromatic amino acids (AAAs), is oxidized into Kreb’s cycle after being converted to fumarate. Consequently, its accumulation in the kidneys after being infected with parasites might be indicative of impaired TCA cycle. Phe will commonly be transferred into Tyr when phenylalanine hydroxylase and biopterin cofactors exist. Incremental Tyr means that abnormal Phe and Tyr biosynthesis possibly disrupted neurotransmitters production since Tyr is a raw substance to generate catecholamine neurotransmitters such as dopamine, epinephrine, and noraderenaline.45,60 Declined glycine might be closely associated with disordered lipid metabolism.44 Choline could be broken down into DMA, TMA, and TMAO by intestinal flora.61 Hence, descended choline and TMAO indicated the dysbacteriosis induced by the invasion of P. berghei. ART-sensitive P. berghei infection altered the content of lysine, glutamate, creatine, 2-Hydroxybutyrate (2-HB), and ethanol in the kidney, representative of being affected energy metabolism, glutamate metabolism, generation and degradation of ketone bodies by ART-sensitive P. berghei. 2-HB constitutes 70% of acetone bodies and is generated in hepatic mitochondria through oxidation of fatty acids. 31



Accumulation of 2-HB suggests mitochondrial dysfunction and inflammatory state occurred in kidney. Creatine is a nitrogen-containing organic acid that supplies energy for muscles and nerve cells and is derived from arginine, glycine, and methionine, which could rapidly improve muscle strength, accelerate fatigue recovery, and enhance explosive power. Creatine always converts to creatinine, a useless product generated when creatine phosphate is slowly degradated spontaneously. Creatine phosphate functions as an energic regulator that reserves the excess energies from ATP.62-63 Elevated creatine might contribute to the perturbation of energy metabolism because the infected mice need a large number of energies to rapidly recover muscles strength and repair damaged cells to a great extent. Moreover, elevated lysine and glutamate together with decreased ethanol, which acts a key role in TCA cycle, also showed a perturbed energy metabolism. Mice exposure to ART-resistant P. berghei resulted in changes of GPC, acetate, betaine, and hypoxanthine (HX). Acetate is closely related with energy metabolism that acetate in liver has a close relationship with hepatic glucose concentration cause acetate could generate acetyl-Co involving in gluconeogenesis and Kreb’s cycle.64 Decreased betaine and elevated GPC in mice kidney after being infected by ART-resistant P. berghei, suggested an inhibited information transmission and activated lipid metabolism. 65-66 Increased betaine indicated the dysbacteriosis of gut microflora.67-68 HX was demonstrated to be transformed by inosine and could be catalyzed into trioxypurine and dioxopurine by xanthine oxidase.69 Reduced HX in 32


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the kidney probably indicated the imbalance of nucleotide catabolism in ART-resistant malarial parasites infected mice.

Conclusions In this 1H NMR-based metabonomics study, we discovered that metabolic phenotypes were significantly different in mice infected with ART-sensitive or -resistant P. berghei when compared with healthy mice. Mutual and specific changed metabolites induced by sensitive and resistant malaria parasites were detected. Perturbed metabolic energy metabolism, lipid metabolism, and biosynthesis of some essential amino acid as well as dysbiosis of gut microbiota in plasma and tissues were therefore found out. As for liver and spleen, the changes of potential biomarkers in ART-sensitive P. berghei infected group might be more significant than those in ART-resistant P. berghei infected group. Interestingly, the latter has specific altered metabolites in plasma and tissues. These obtained findings in this paper should be considered as a preliminary study and might provide a new insight into the metabolic background to the drug resistance to ARTs.

Author Contributions Jing Chen conceived and designed this research. Jie Chen and Juanhong Zhang conducted experiments and wrote the manuscript. Yong Dai provided ART-sensitive or -resistant P. berghei and determined the resistant index. Xiuli Wu, and Cheng Liu

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contributed new reagents or analytical tools. Xueqin Ma, Yongjie Yu, and Liming Zhang dealt with the results.

Conflicts of Interest: All the authors declared no competing financial interests. Acknowledgements The manuscript was funded by the National Natural Science Foundation of China (No. 81560580 and No. 81760377).

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(67) Dumas, M.E.; Barton, R.H.; Toye, A.; Cloarec, O.; Blancher, C.; Rothwell, A.; Fearnside, J.; Tatoud, R.; Blanc, V.; Lindon, C. J.; Mitchell, S.C.; Holmes, E.; McCarthy, M.I.; Scott, J.; Gauguier, D.; Nicholson, J.K. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. U. S. A. 2006, 103(33), 12511-6. (68) Wang, Z.N.; Klipfell, E.; Bennett, B.J.; Koeth, R.; Levison, B.S.; Dugar, B.; Feldstein, A.E.; Britt, E.B.; Fu, X.M.; Chung, Y.M.; Wu, Y.P.; Schauer, P.; Smith, J.D.; Allayee, H.; Tang, W.H.W.; DiDonato, J.A; Lusis, A.J.; Hazen, S.L. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011, 472(7341), 57-63. (69) Ohta, Y.; Kongo-Nishimura, M.; Imai, Y.; Kishikawa, T. Contribution of xanthine oxidase-derived oxygen free radicals to the development of carbon tetrachloride-induced acute liver injury in rats. J. Clin. Biochem. Nutr. 2003, 33(3), 83-93.

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Figure 1. The variation of organ coefficient (%) among control, ART-sensitive, and -resistant malarial parasites infected mice (a. Liver; b. Spleen; c. Kidney), and the body weights monitored between uninfected or infected groups during 7 days (d. control versus to sensitive group; e. control versus to resistant group). Statistical significances at *p< 0.05, **p< 0.01, and ***p< 0.001 levels between vehicle and infection groups were obtained by multiple comparison analysis.

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Figure 2. Histopathological characteristics of livers (a, b, c), spleens (d, e, f), and kidneys (g, h, i) sections collected from uninfected (a, d, g), ART-sensitive (b, e, h) and -resistant parasites infected mice (c, f, i). 1. inflammatory cells in the manifold area; 2. accumulation of malarial pigmentation; 3. extramedullary hematopoiesis; 4. mild hyperplasia of fibroid; 5. increased glomerular volume; 6. slight oedema in epithelia of renal tubules; 7. blurred cellar border.

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Figure 3. Representational 500 MHz 1H NMR spectrograms for the control, ART-sensitive, and -resistant P. berghei K173 infected rice (A. plasma; B. livers; C. spleens; D. kidneys).

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Figure 4. Non-supervised and supervised principal component analysis. The plasma and tissues were collected from the uninfected and infected mice on the 7th day (A. plasma; B. livers; C. spleens; D. kidneys). Mice groups are separated in different colors and shapes: control (C, green circle), sensitive group (S, blue square), and resistant group (R, red triangle). PCA analysis was shown in a (C vs. S and R) and OPLS-DA in b (C vs. S) or c (C vs. R). The 48


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statistical validations obtained by 200 permutation test for OPLS-DA analysis were shown in d (C vs. S) and e (C vs. R). The ellipse determines the 95% confidence interval.

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Figure 5. OPLS-DA analysis based on 1H NMR data from plasma and tissues of all groups. The plasma and tissues were collected from the mice on the 7th day (A. plasma; B. livers; C. spleens; D. kidneys). Mice groups are separated in different colors and shapes: control (C), sensitive group (S), and resistant group (R). S-plots: a (control group versus to sensitive group) and b (control group versus to resistant group). Color-coded loading plots: c (control group versus to sensitive group) and d (control group versus to resistant group). Shared and Unique Structures-Plot (SUS-Plot): e (control group versus to sensitive group and control group versus to resistant group).

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Figure 6. Metabolic pathway enrichments analyzed by MetaboAnalyst 3.0 on the basis of the selected metabolites, whose VIP was greater than 1 and t-test p value was lower than 0.05. The figures plotted by pathway impact (x-axis) versus pathway enrichment (y-axis) showed the pathway enrichment degree of the potent metabolites in plasma (a: control versus to sensitive group; b: control versus to resistant group), livers (c: control versus to sensitive group; d: control versus to resistant group), spleens (e: control versus to sensitive group; f: control versus to resistant group) and kidneys (g: control versus to sensitive group; h: control versus to resistant group). Bigger red circles indicated higher pathway enrichment and pathway impact values.

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Figure 7. Pathway network view (a, c) and Barchat view (b, d) analyzed using MetaboAnalyst 3.0 on basis of the selected metabolites with VIP> 1 and t-test p value< 0.05 in plasma (A: a, b: control versus to sensitive group; c, d: control versus to resistant group), livers (B: a, b: control versus to sensitive group; c, d: control versus to resistant group), spleens (C: a, b: control versus to sensitive group; c, d: control versus to resistant group), and kidneys (D: a, b: control versus to sensitive group; c, d: control versus to resistant group). Bigger red circles indicated higher pathway enrichment and pathway impact values.

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Figure 8. The peak height comparison of chosen metabolites in sensitive group (S), resistant group (R), and the control (C), whose VIP> 3 and p< 0.05. Statistical significances between control and sensitive or resistant group were obtained by multiple comparison analysis at *p< 0.05, **p< 0.01, and ***p< 0.001 levels.

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Figure 9. Sketch map of the altered metabolites and metabolic pathways based on 1H NMR non-targeted metabolomics study, indicating the interaction between different changed metabolic pathways. The metabolites in blue color background represent decreased markers and those in yellow color background indicates increased markers. Superscript “P” means plasma; “L” means livers; “S” means spleens; “K” means kidneys.

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Table 1. Identificated metabolites in the plasma by comparing control with sensitive or resistant group C vs. Sb No. Metabolite

Abbreviation Range of Integrated Signal (ppm)a

1

Lipid

Lipid

2

Isoleucine

3

C vs. Rb

VIP

p (corr)

p

VIP

p (corr)

p

0.88 (s), 1.27 (s)

~

~

~

3.364

0.746

0.049

Ile

0.95 (d), 1.02 (d)

1.392

0.678

0.047

2.113

0.739

0.029

Leucine

Leu

0.96 (t), 1.70 (m)

~

~

~

1.316

0.881

0.007

4

Valine

Val

0.97 (d), 1.04 (d)

1.488

0.716

0.025

1.227

0.827

0.019

5

Lactate

Lac

1.33 (d), 4.11 (d)

~

~

~

8.625

0.707

0.033

6

Lysine

Lys

1.46 (m), 1.73 (m), 3.01 (m), 3.76 (m)

1.181

0.751

0.007

1.083

0.854

0.001

7

Alanine

Ala

1.48 (d)

2.094

0.619

0.041

1.249

0.860

0.025

8

N-Acetyl aspartate

NAA

2.00 (s), 2.02 (s), 2.67 (dd), 4.36 (m)

~

~

~

1.452

0.746

0.026

9

Glutamate

Glu

2.05 (s), 2.08 (m), 2.12 (m), 2.14 (dd), 2.35 (m), 3.78 (t)

1.155

0.566

0.013

1.353

0.866

0.024

10

Aspartate

Asp

2.70 (dd), 2.79 (dd), 2.81 (dd), 3.94 (dd)

~

~

~

1.003

0.698

0.028

11

N, N-Dimethylglycine

DMG

2.93 (s), 3.72 (d)

3.024

-0.757

0.022

~

~

~

12

β-Glucose

β-Glu

3.123

-0.780

0.022

1.655

-0.742

0.026

3.25 (dd), 3.41 (t), 3.46 (dd), 3.49 (t), 69



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3.90 (dd), 4.65 (dd) 13

Taurine

Tau

3.40 (d)

2.411

-0.698

0.040

2.867

-0.679

0.033

1.845

-0.721

0.037

14

α-Glucose

α-Glu

3.42 (t), 3.54 (dd), 3.71 (t), 3.73 (m), 3.84 (m), 5.24 (d)

15

Glycine

Gly

3.54 (s)

a

s, singlet; d, doublet; t, triplet; q, quartet; m, multiplet.

b

“ ~ ” means no significant change.

70


1.552

-0.632

0.034

~

~

~

~

~

~

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Table 2. Identificated metabolites in the livers by comparing control with sensitive or resistant group C vs. Sb No. Metabolite


1

Pantothenate

Pan

2

Lipid

3

C vs. Rb

VIP

p (corr)

p

VIP

p (corr)

p

0.84 (s), 0.90 (s)

1.444

0.881

0.006

~

~

~

Lipid

0.88 (s), 1.27 (s)

1.782

0.721

0.038

1.799

0.749

0.004

2-Hydroxybutyrate

2-HB

1.71 (s)

1.506

0.835

0.001

~

~

~

4

Isoleucine

Ile

0.95 (d), 1.02 (d)

2.244

0.880

0.000

1.016

0.637

0.030

5

Leucine

Leu

0.96 (t), 1.70 (m)

2.400

0.762

0.004

~

~

~

6

Valine

Val

0.97 (d), 1.04 (d)

1.894

0.787

0.007

~

~

~

7

Methylisobutyrate

MIB

1.18 (d)

1.023

0.928

0.003

~

~

~

8

3-Hydroxybutyrate

3-HB

1.21 (d), 2.33 (dd), 2.41 (dd), 4.23 (m) 1.277

0.929

0.004

~

~

~

9

Methylmalonate

MM

1.22 (d), 3.13 (q)

1.350

0.900

0.005

~

~

~

10

Lactate

Lac

1.33 (d), 4.11 (d)

2.289

0.737

0.025

2.363

0.775

0.005

11

Lysine

Lys

1.46 (m), 1.73 (m), 3.01 (m), 3.76 (m)

3.308

-0.985

0.000

3.827

-0.955

0.000

12

Alanine

Ala

1.48 (d)

3.450

0.901

0.000

1.441

0.593

0.049

13

Arginine

Arg

1.75 (m), 1.91 (m)

1.355

0.825

0.003

1.221

0.923

0.000

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14

Acetate

Ace

1.88 (s), 1.90 (s)

1.382

0.828

0.007

~

~

~

15

N-Acetyl aspartate

NAA

2.00 (s), 2.02 (s), 2.67 (dd), 4.36 (m)

1.410

0.817

0.008

~

~

~

16

N-Acetyl-glycoprotein

NAG

2.04 (s)

1.305

0.800

0.004

~

~

~

17

Glutamate

Glu

2.05 (s), 2.08 (m), 2.12 (m), 2.14 (dd), 1.584 2.35 (m), 3.78 (t)

0.742

0.006

3.771

-0.947

0.000

18

Methionine

Met

2.11 (s), 2.16 (s), 2.65 (t)

1.612

0.824

0.005

~

~

~

19

Acetoacetic acid

AA

2.28 (s), 3.43 (s)

1.818

-0.840

0.007

~

~

~

20

Glutamine

Glu

2.45 (m)

3.408

-0.984

0.000

~

~

~

21

Citrate

Ci

2.67 (d), 2.69 (dd)

1.209

0.936

0.000

~

~

~

22

Aspartate

Asp

2.70 (dd), 2.79 (dd), 2.81 (dd), 3.94 (dd)

1.600

-0.840

0.003

2.093

-0.848

0.002

23

Asparagine

Asn

2.88 (dd), 2.95 (dd), 3.92 (s), 3.99 (dd) 1.969

-0.835

0.001

1.543

-0.751

0.010

24

Sarcosine

Sar

3.60 (s)

2.620

-0.956

0.000

2.995

-0.900

0.000

25

Trimethylamine

TMA

2.88 (s), 2.89 (s)

~

~

~

1.208

0.910

0.000

26


DMG

2.93 (s), 3.72 (d)

4.109

-0.961

0.000

3.907

-0.865

0.000

27

Creatine

Cr

3.03 (s), 3.93 (s)

2.057

-0.955

0.000

2.380

-0.902

0.000

28

Ethanolamine

EA

3.15 (t), 3.86 (t)

2.914

-0.987

0.000

2.940

-0.919

0.000

72


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29

Choline

Cho

3.21 (s), 3.51 (s)

2.271

-0.919

0.000

2.455

-0.829

0.001

30

Phosphocholine

PC

3.22 (s)

~

~

~

2.936

-0.798

0.002

31

Glycerophosphorylcholine GPC

3.23 (s), 3.68 (m)

2.180

-0.664

0.036

2.833

-0.878

0.001

32

β-Glucose

β-Glu

3.25 (dd), 3.41 (t), 3.46 (dd), 3.49 (t), 3.90 (dd), 4.65 (dd)

3.991

-0.947

0.000

4.008

-0.831

0.002

33

Trimethylamine N-oxide

TMAO

3.27 (s)

3.632

-0.850

0.000

4.525

-0.836

0.000

34

Threonine

Thr

3.58 (dd)

2.391

-0.955

0.000

2.987

-0.916

0.000

35

myo-Inositol

m-I

3.56 (dd), 3.62 (t), 3.66 (dd), 4.07 (t)

2.352

-0.919

0.000

2.573

-0.872

0.001

36

Proline

Pro

3.30 (m)

1.608

-0.949

0.000

1.868

-0.905

0.000

37

Taurine

Tau

3.40 (d)

2.498

-0.829

0.009

3.740

-0.882

0.000

38

α-Glucose

α-Glu

3.42 (t), 3.54 (dd), 3.71 (t), 3.73 (m), 3.84 (m), 5.24 (d)

4.184

-0.993

0.000

3.635

-0.890

0.000

39

Glycine

Gly

3.54 (s)

2.218

-0.895

0.000

2.067

-0.880

0.001

40

Ethanol

Eth

3.65 (d)

2.696

-0.943

0.000

3.166

-0.894

0.000

41

Phenylacetylglycine

PAG1

3.68 (s), 7.42 (s)

1.815

-0.944

0.000

~

~

~

42

Guanidoacetate

GA

3.80 (s)

1.347

-0.855

0.002

1.581

-0.847

0.001

43

Betaine

Bet

3.91 (s)

~

~

~

2.950

-0.839

0.001

73



Page 74 of 85

44

Allantoin

All

5.41 (s)

~

~

~

3.128

-0.797

0.004

45

Surcose

Sur

5.42 (s)

~

~

~

2.128

-0.761

0.004

a


b


74


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Table 3. Identificated metabolites in the spleens by comparing control with sensitive or resistant group C vs. Sb No. Metabolite


1

Pantothenate

Pan

2

Lipid

3

C vs. Rb

VIP

p (corr)

p

VIP

p (corr)

p

0.84 (s), 0.90 (s)

1.146

-0.671

0.042

1.007

-0.733

0.024

Lipid

0.88 (s), 1.27 (s)

~

~

~

1.116

-0.795

0.008

Isoleucine

Ile

0.95 (d), 1.02 (d)

1.080

-0.629

0.038

1.250

-0.713

0.009

4

Valine

Val

0.97 (d), 1.04 (d)

3.427

0.847

0.042

1.058

0.795

0.009

5

Lactate

Lac

1.33 (d), 4.11 (d)

~

~

~

1.731

0.911

0.000

6

Alanine

Ala

1.48 (d)

~

~

~

1.103

0.767

0.005

7

Arginine

Arg

1.75 (m), 1.91 (m)

~

~

~

5.235

-0.977

0.000

8

Acetate

Ace

1.88 (s), 1.90 (s)

~

~

~

1.989

-0.854

0.004

9

N-Acetyl aspartate

NAA

2.00 (s), 2.02 (s), 2.67 (dd), 4.36 (m)

1.274

-0.686

0.023

~

~

~

10

Glutamate

Glu

2.05 (s), 2.08 (m), 2.12 (m), 2.14 (dd), 2.35 (m), 3.78 (t)

1.054

-0.581

0.024

1.206

0.637

0.046

11

Methionine

Met

2.11 (s), 2.16 (s), 2.65 (t)

1.070

-0.787

0.000

1.222

0.737

0.014

12

Acetoacetic acid

AA

2.28 (s), 3.43 (s)

3.848

-0.576

0.029

~

~

~

75



Page 76 of 85

13

Succinate

Suc

2.41 (s)

~

~

~

1.268

0.822

0.000

14

Glutamine

Glu

2.45 (m)

~

~

~

1.344

0.892

0.001

15

α-ketogultaric acid oxime α-KG

2.48 (s)

~

~

~

1.203

0.799

0.004

16

Citrate

Ci

2.67 (d), 2.69 (dd)

1.284

-0.794

0.001

1.209

0.628

0.009

3.012

0.948

1.400

0.814

0.001

2.650

0.963

0.001

~

~

~

17

Aspartate

Asp

2.70 (dd), 2.79 (dd), 2.81 (dd), 3.94 (dd)

18

Asparagine

Asn

2.88 (dd), 2.95 (dd), 3.92 (s), 3.99 (dd)

19

Dimethylamine

DMA

2.73 (s)

~

~

~

3.316

-0.828

0.003

20

Sarcosine

Sar

3.60 (s)

~

~

~

1.171

0.764

0.005

21


DMG

2.93 (s), 3.72 (d)

~

~

~

6.683

-0.937

0.000

22

Creatine

Cr

3.03 (s), 3.93 (s)

2.798

0.949

0.005

1.375

0.849

0.003

23

Ethanolamine

EA

3.15 (t), 3.86 (t)

2.255

0.900

0.028

~

~

~

24

Choline

Cho

3.21 (s), 3.51 (s)

5.350

0.722

0.039

3.639

0.659

0.039

25

Phosphocholine

PC

3.22 (s)

4.908

-0.727

0.003

3.203

0.609

0.046

26

Glycerophosphorylcholin e GPC

9.273

0.975

2.114

0.714

0.046

27

β-Glucose

4.362

-0.823

~

~

~

β-Glu

3.23 (s), 3.68 (m) 3.25 (dd), 3.41 (t), 3.46 (dd), 3.49 (t), 76


0.008

0.001 0.007

Page 77 of 85 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


3.90 (dd), 4.65 (dd) 28

Trimethylamine N-oxide TMAO

3.27 (s)

5.093

-0.640

0.027

~

~

~

29

myo-Inositol

m-I

3.56 (dd), 3.62 (t), 3.66 (dd), 4.07 (t)

3.756

0.970

0.003

1.747

0.738

0.021

30

Proline

Pro

3.30 (m)

~

~

~

1.133

0.706

0.019

2.340

0.828

0.008

1.906

-0.683

0.024

31

α-Glucose

α-Glu

3.42 (t), 3.54 (dd), 3.71 (t), 3.73 (m), 3.84 (m), 5.24 (d)

32

Glycine

Gly

3.54 (s)

1.821

-0.668

0.015

~

~

~

33

Guanidoacetate

GA

3.80 (s)

~

~

~

1.081

0.881

0.000

34

Phenylalanine

Phe

4.00 (m), 7.33 (m), 7.38 (m), 7.43 (m)

1.750

0.917

0.015

1.677

0.901

0.000

35

Uridine

Ud

4.36 (t), 5.91 (d)

1.728

-0.940

0.003

1.060

-0.858

0.003

36

Uridinediphosphate Glucose

UDPG

5.98 (s)

1.180

-0.814

0.008

~

~

~

37

Uracil

Ura

5.82 (d)

1.386

-0.815

0.011

~

~

~

38

Cytidine

Cyd

5.89 (s)

1.226

-0.808

0.012

~

~

~

39

2-Deoxyuridine

DU

7.88 (d)

1.362

-0.768

0.002

~

~

~

40

Tyrosine

Tyr

6.88 (d), 6.90 (d), 7.18 (d), 7.20 (d)

1.695

0.871

0.035

~

~

~

41

Phenylacetyglutamine

PAG2

7.42 (d)

1.617

0.930

0.012

1.008

0.927

0.000

77



42

Hypoxanthine

HX

8.21 (s)

a


b


Page 78 of 85

1.330

78


-0.635

0.048

~

~

~

Page 79 of 85 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


Table 4. Identificated metabolites in the kidneys by comparing control with sensitive or resistant group C vs. Sb No. Metabolite


1

Pantothenate

Pan

2

Lipid

3

C vs. Rb

VIP

p (corr)

p

VIP

p (corr)

p

0.84 (s), 0.90 (s)

1.638

-0.886

0.000

~

~

~

Lipid

0.88 (s), 1.27 (s)

1.249

-0.771

0.004

~

~

~

2-Hydroxybutyrate

2-HB

1.71 (s)

3.209

0.858

0.002

2.623

0.801

0.002

4

Isoleucine

Ile

0.95 (d), 1.02 (d)

4.158

0.860

0.002

3.445

0.794

0.004

5

Leucine

Leu

0.96 (t), 1.70 (m)

5.183

0.793

0.005

3.543

0.760

0.007

6

Valine

Val

0.97 (d), 1.04 (d)

4.300

0.910

0.001

3.566

0.785

0.004

7

3-Hydroxybutyrate

3-HB

1.21 (d), 2.33 (dd), 2.41 (dd), 4.23 (m)

~

~

~

1.566

0.852

0.001

8

Methylmalonate

MM

1.22 (d), 3.13 (q)

1.349

0.825

0.004

1.903

0.914

0.000

9

Lactate

Lac

1.33 (d), 4.11 (d)

~

~

~

2.666

-0.836

0.004

10

Lysine

Lys

1.46 (m), 1.73 (m), 3.01 (m), 3.76 (m)

3.525

0.856

0.002

2.291

0.825

0.002

11

Alanine

Ala

1.48 (d)

4.863

0.905

0.001

3.922

0.729

0.009

12

Arginine

Arg

1.75 (m), 1.91 (m)

2.501

0.852

0.001

2.199

0.871

0.001

79



13

Acetate

Ace

1.88 (s), 1.90 (s)

14

N-Acetyl aspartate

NAA

15

N-Acetyl-glycoprotein

16

Page 80 of 85

~

~

~

3.589

0.938

0.000

2.00 (s), 2.02 (s), 2.67 (dd), 4.36 (m)

1.995

0.918

0.001

1.997

0.788

0.003

NAG

2.04 (s)

1.089

0.708

0.023

~

~

~

Glutamate

Glu

2.05 (s), 2.08 (m), 2.12 (m), 2.14 (dd), 2.35 (m), 3.78 (t)

3.419

0.843

0.001

1.852

0.814

0.003

17

Methionine

Met

2.11 (s), 2.16 (s), 2.65 (t)

1.953

0.835

0.002

1.674

0.748

0.009

18

Acetoacetic acid

AA

2.28 (s), 3.43 (s)

1.120

0.726

0.011

~

~

~

19

Pyruvate

Py

2.39 (s)

~

~

~

1.092

0.813

0.003

20

Succinate

Suc

2.41 (s)

1.344

-0.784

0.013

~

~

~

21

Glutamine

Glu

2.45 (m)

~

~

~

1.085

-0.591

0.043

22

Citrate

Ci

2.67 (d), 2.69 (dd)

1.045

0.634

0.020

~

~

~

23

Glutathione

CSH

2.98 (m)

~

~

~

1.041

0.801

0.009

1.994

0.838

1.306

0.678

0.011

24

Aspartate

Asp

2.70 (dd), 2.79 (dd), 2.81 (dd), 3.94 (dd)

25

Asparagine

Asn

2.88 (dd), 2.95 (dd), 3.92 (s), 3.99 (dd) 1.939

0.813

0.006

1.483

0.835

0.001

26

Sarcosine

Sar

3.60 (s)

1.669

0.693

0.044

2.303

0.885

0.001

27

Trimethylamine

TMA

2.88 (s), 2.89 (s)

1.357

0.769

0.016

~

~

~

80


0.002

Page 81 of 85 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


28


DMG

2.93 (s), 3.72 (d)

2.128

-0.835

0.001

1.893

-0.719

0.019

29

γ-Aminobutyric acid

GABA

3.01 (s)

2.717

0.945

0.000

~

~

~

30

Creatine

Cr

3.03 (s), 3.93 (s)

3.273

0.921

0.000

2.766

0.824

0.002

31

Choline

Cho

3.21 (s), 3.51 (s)

7.964

-0.896

0.001

8.914

-0.833

0.001

32

Phosphocholine

PC

3.22 (s)

1.365

0.647

0.038

1.698

0.726

0.027

33

Glycerophosphorylcholine GPC

3.23 (s), 3.68 (m)

1.652

-0.931

0.000

3.948

0.744

0.002

34

β-Glucose

β-Glu

3.25 (dd), 3.41 (t), 3.46 (dd), 3.49 (t), 3.90 (dd), 4.65 (dd)

1.957

-0.811

0.002

2.685

0.759

0.006

35

Trimethylamine N-oxide

TMAO

3.27 (s)

6.614

-0.597

0.039

8.404

-0.695

0.022

36

Threonine

Thr

3.58 (dd)

2.334

-0.767

0.028

2.719

-0.763

0.006

37

myo-Inositol

m-I

3.56 (dd), 3.62 (t), 3.66 (dd), 4.07 (t)

2.589

-0.823

0.004

2.794

-0.810

0.005

38

Proline

Pro

3.30 (m)

1.366

-0.807

0.007

~

~

~

39

Methanol

Mol

3.35 (s)

1.854

0.854

0.003

~

~

~

40

α-Glucose

α-Glu

3.42 (t), 3.54 (dd), 3.71 (t), 3.73 (m), 3.84 (m), 5.24 (d)

2.037

-0.861

0.001

1.797

-0.845

0.003

41

Glycine

Gly

3.54 (s)

3.248

-0.914

0.000

3.468

-0.787

0.017

42

Ethanol

Eth

3.65 (d)

3.119

-0.760

0.011

2.733

-0.639

0.043

81



Page 82 of 85

43

Guanidoacetate

GA

3.80 (s)

2.575

-0.924

0.001

2.188

-0.695

0.024

44

Betaine

Bet

3.91 (s)

1.875

-0.750

0.020

4.030

-0.730

0.015

45

Phenylalanine

Phe

4.00 (m), 7.33 (m), 7.38 (m), 7.43 (m)

1.988

0.922

0.001

1.831

0.836

0.002

46

Uridine

Ud

4.36 (t), 5.91 (d)

~

~

~

1.314

-0.824

0.002

47

Inosine

Ino

4.26 (dd)

1.122

0.756

0.005

1.168

-0.767

0.002

48

2-Deoxyuridine

DU

7.88 (d)

~

~

~

1.878

0.704

0.011

49

Tyrosine

Tyr

6.88 (d), 6.90 (d), 7.18 (d), 7.20 (d)

2.170

0.923

0.001

2.190

0.871

0.000

50

1-Methylhistidine

1-MH

7.08 (s)

~

~

~

1.213

-0.654

0.024

51

Phenylacetyglutamine

PAG2

7.42 (d)

1.863

0.912

0.001

1.083

0.738

0.034

52

Hippurate

Hip

7.64 (d)

2.914

-0.943

0.000

1.295

0.815

0.007

53

Hypoxanthine

HX

8.21 (s)

~

~

~

3.303

-0.934

0.000

a


b

“ ~ ” means no significant change

82


Page 83 of 85 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


Table 5. Multivariate analysis of PCA and OPLS-DA models of different groups. No.

Plasma

Liver

Spleen

Kidney

Model

Type

R2X (cum)

R2Y (cum)

Q2 (cum)

C vs. S vs. R

PCA

0.955

-

0.863

C vs. S

OPLS-DA

0.801

0.895

0.469

C vs. R

OPLS-DA

0.764

0.748

0.664

C vs. S vs. R

PCA

0.964

-

0.853

C vs. S

OPLS-DA

0.824

0.924

0.848

C vs. R

OPLS-DA

0.767

0.947

0.917

C vs. S vs. R

PCA

0.915

-

0.648

C vs. S

OPLS-DA

0.748

0.753

0.512

C vs. R

OPLS-DA

0.81

0.964

0.916

C vs. S vs. R

PCA

0.745

-

0.514

C vs. S

OPLS-DA

0.657

0.919

0.712

C vs. R

OPLS-DA

0.486

0.958

0.802

83



Graphical Abstract: for TOC only

84


Page 84 of 85

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523x203mm (300 x 300 DPI)


The Disordered Metabolic Profiling in Plasma and Tissues of Mice

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