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Urine metabonomics reveals early biomarkers in diabetic cognitive dysfunction Lili Song, Pengwei Zhuang, Mengya Lin, Mingqin Kang, Hongyue Liu, Yuping Zhang, Zhen Yang, Yunlong Chen, and Yanjun Zhang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00168 • Publication Date (Web): 19 Jul 2017 Downloaded from http://pubs.acs.org on July 20, 2017

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Journal of Proteome Research

Urine metabonomics reveals early biomarkers in diabetic cognitive dysfunction Lili Song§1, Pengwei Zhuang§1, Mengya Lin1, Mingqin Kang2, Hongyue Liu1, Yuping Zhang1, Zhen Yang1, Yunlong Chen1 and Yanjun Zhang*,1

1

Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of

Traditional Chinese Medicine, Tianjin, People's Republic of China. 2

Jilin Entry-exit Inspection and Quarantine Bureau, Changchun, People's Republic of

China §

Co-first author

*Author to whom correspondence should be addressed. Yanjun Zhang, E-mail: [email protected]

1

ACS Paragon Plus Environment


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Abstract Recently, increasing attention has been paid to diabetic encephalopathy which is one of frequent diabetic complications and affects nearly 30% diabetics. Since cognitive dysfunction from diabetic encephalopathy might develop irreversible dementia, early diagnosis and detection of this disease is of great significance for its prevention and treatment. This study is to investigate the early specific metabolites biomarkers in the urine prior to the onset of diabetic cognitive dysfunction (DCD) by using metabolomics technology. Ultra high performance liquid-flight time-mass spectrometry (UPLC-Q/TOF-MS) platform was used to analyze the urine samples from the diabetic mice which were associated with mild cognitive impairment (MCI) and non-associated with MCI at the stage of diabetes (prior to the onset of DCD), and then screened and validated the early biomarkers using OPLS-DA model and support vector machine (SVM) method. Following multivariate statistical and integration analysis, we found 7 metabolites could be accepted as early biomarkers of DCD. And the SVM results showed that the prediction accuracy is as high as 91.66%. The identities of four biomarkers were determined by mass spectrometry. The identified biomarkers were largely involved in nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism and sphingolipid metabolism. The present study firstly revealed reliable biomarkers for early diagnosis of DCD. It would provide a new insight and strategy for the early diagnosis and treatment of DCD.

Keywords: diabetic recognition dysfunction; early biomarkers; SVM; urine metabonomics

2


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1 Introduction Although the prevalence of diabetes has increased over last decades by the changes in diet and lifestyle,1 good control of blood sugar, blood lipids, blood pressure, etc. using advanced medical techniques could significantly improve the life quality of diabetic patients and prolonged their survival time. Some novel complications are frequently observed in clinical and has been paid more attention, such as diabetes cognitive dysfunction (DCD). Diabetic encephalopathy is a progressive disease and mainly characterized by cognitive dysfunction accompanied by some specific pathological changes in brain tissue. The finding from clinical studies show diabetic patients with a cognitive function of patients would gradually develop dementia.2,3 Dementia is an irreversible disorder, so early diagnosis and detection of this disease is of great significance for its prevention and treatment. However, there still lack of proper and reliable diagnosis standards in DCD, so it becomes difficult for early discovery of diabetic cognitive impairment. Since diabetes is an endocrine and metabolic disease, specific metabolic changes also exist in the development of diabetic encephalopathy. In recent years, metabolomics techniques have been used frequently to diagnose disease-specific metabolites. Metabonomics techniques covers the nuclear magnetic resonance (NMR), gas chromatography (GC), liquid chromatography (LC) and mass spectrometry (MS), which are reliable and effective in screening biomarkers for neurological diseases, such as Parkinson's disease, schizophrenia, depression.4 These studies show that metabonomics is a scientific approach to investigate metabolic products, which can be applied for screening the biomarkers for early diagnosis of DCD when combined with machine learning methods. Type 2 diabetes mellitus (T2DM) and diabetes cognitive dysfunction (DCD) are the most common age-associated disorders and the prevalence of the diseases is increasing with population aging.5,6 The appropriate intervention in the preclinical long phase of DCD is likely to be effective.7 Epidemiological surveys showed that 25-36% diabetic patients are associated with mild cognitive impairment, and the incidence of dementia in patients with diabetes is 1.5-2.5 times than non-diabetic patients.8,9 This also means that not all of diabetics could develop cognitive dysfunction or dementia. Therefore, early diagnosis of DCD is critical for developing efficient interventions to postpone or prevent AD. 3



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Since pathophysiological events leading to dementia precede the clinical symptoms, biomarkers for DCD have become an area of great interest to both researchers and clinicians. The metabolite biomarkers become a research hotspot because of its early pathophysiological role in the initiation and development of dementia.10 The biomarker measurements are principally of brain amyloidosis and neurodegeneration.11 However, some difficulties and problems still exist in the practical application, such as trauma, time-consumption and high expenditure. Thus, to validate non-invasive and cost-efficient biomarkers is meaningful for the early diagnosis of DCD. The changes of metabolites in urine are a very convenient approach for diagnosis of DCD. Our study showed that only 50% of diabetic mice were associated with mild cognitive impairment (MCI), and the metabolic profile of urine in diabetes mellitus with MCI and MCI without diabetes mellitus was significantly different in the stage of diabetes, it maybe suggest that specific small molecule metabolites in urine could be the early warning markers of DCD. In this study, we conducted nontargeted metabolomic analysis in urine samples for different time periods of DCD using UPLC−Q-TOF-MS. By using multivariate statistical analysis [principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), orthogonal partial least-squares- discriminant analysis (OPLS-DA) model and support vector machine (SVM) ], diagnostic biomarkers which significant changes in the early stage of DCD were identified. Finally, exclusive biomarkers for the early prediction of DCD were obtained. The aim of this study is to develop a metabolomics method with high sensitivity and strong specificity for the prediction of DCD that would provide a new strategy for the early diagnosis and treatment of DCD.

2 Materials and methodsa 2.1 Reagents and materials High pressure liquid chromatography (HPLC)-grade acetonitrile and formic acid were purchased from Sigma-Aldrich (USA). Ultrapure water was prepared with a Milli-Q water purification system (Millipore, France). Streptozocin(STZ) were purchased from Sigma(USA). Sodium citrate buffer were prepared by ourselves.. Niacinamide, Indoline, 5-Hydroxy-L-tryptophan, Pyroglutamic acid were purchased from Shanghai Macklin Biochemical Co., Ltd (China). Sphinganine and methanol 4


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were purchased from Sigma (St. Louis, MO, USA).

2.2 Study design 60 healthy male C57BL/6J mice (12-weeks-old,19± 1g) were selected. 12 of mice were given a normal diet, and 48 of mice were given a high fat diet (HFD12492). After 3 weeks of HFD feeding, mice were intraperitoneally injected once with STZ (120 mg/kg), the normal group mice were injected once with vehicle citrate buffer (0.1 mol/l sodium citrate buffer, fresh, pH 4.5, 0.1 ml/10g). Three weeks after STZ injection (the 6th week), the mice with high blood glucose value (> 11.1mmol/l) were selected for subsequent experiments. In the 12th week, mice were selected by Morris experiments to confirm if DCD model is established. Weight and blood glucose values were detected every week after STZ injection. Urine samples were collected at 0, 6th and the 12th week respectively, and each mouse was labeled throughout the experiment. At the 12th week, the mice were divided into NC(normal control), DCD(whose escape latency was statistically significant compared with NC), unDCD(whose escape latency was not statistically significant compared with NC) groups by Morris experiments, and the urine samples collected at the 6th mice were grouped and named as NC, DCD-pre(Previous Diabetic cognitive dysfunction), unDCD groups. The flow diagram of animal grouping and sample collection is shown in Fig. 1.

Fig. 1

2.3 Animal treatment 12-week-old male C57BL/6J mice were purchased from Beijing HFK Bioscience Co. Ltd. All animal procedures were performed in accordance with the guidelines and regulations of the Animal Ethics Committee of Tianjin University of Traditional Chinese Medicine (SCXK(京) 2009-0004). Animal housing rooms were maintained at a constant room temperature (25 ℃) in a 12 h light/dark cycle. Water and food were provided ad libitum.

2.4 Urine Sample Preparation 5



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Urine samples were collected from mice for 24 h in individual urine collection cages. The urine samples were collected over ice into 0.1mL of 1% sodium azide solution and then centrifuged for 10 min at 4℃ The supernatant was stored at −80℃ until measurement. The urine samples were thawed at room temperature before the measurement. The supernatant was diluted with 100% methanol in a ratio of 1:1 (v/v). The samples were subsequently mixed by vortex for 30 s and centrifuged at 13,000 rpm for 10 min to remove any particulates. To ensure the stability and repeatability of the UPLC-Q-TOF systems, pooled quality control (QC) samples were prepared from 20 µl of each sample and staggered with the other samples (after every eight samples). 5 µl samples were injected into the UPLC Q-TOF/MS system for analysis.

2.5 UPLC-Q/TOF–MS-based data acquisition for untargeted metabolomics profiling. Waters Acquity UPLC I CLASS system (Waters Corp., Milford, MA, USA) was performed to separate the metabolites, which was equipped with a HSS T3 column (2.1 mm×100 mm, 1.7 µm, Waters UK). The column temperature was set to 45°C and the gradient elution program started with 99% solvent A and 1% solvent B (solvent A: 0.1% formic acid in water; solvent B: acetonitrile modified by the addition of 0.1% formic acid). The column was eluted with a linear gradient of 99%→60% A over 0.5 to 10 min, 60%→1% A over 10 to 11 min, 1% A was held for 1 min and then returned to 99% A over 12.0 to 15.0 min at a flow rate of 0.45 ml/min. A Xevo G2-Q-TOF (Waters MS Technologies, Manchester, UK) was performed on mass spectrometry with an electrospray ionisation source. The positive ion mode was performed. Data was collected from m/z 50 to 1200. The capillary and cone voltage was set at 3.0 kV and 45 V, respectively. The desolvation gas was set at 800 L/h at a temperature of 450 °C, cone gas was set at 30 L/h, and the source temperature was set at 120 °C. Leucine enkephalin were used as the lock mass solution in accurate mass measurement. 2.6 UPLC-MS–MS-based data acquisition for quantitative analysis. The analysis was performed on a Waters Acquity H-Class UPLC system connected to a Waters (Milford, MA, USA) Xevo TQ-S triple quadrupole mass spectrometer. Chromatographic separation was achieved on a ACQUITY HSS T3(1.8µm, 6


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2.1×100mm)column at 40℃. The mobile phase consisted of 100% acetonitrile (A) and 0.1% acetic acid water(B), using 95% B at 0 min, 95-5% B at 0-6min, 95% B at 6.1-8 min. The flow rate was 0.4 mL/min, and the sample volume injected was 5 µL. All analytes were quantified without interference, in positive ionization mode using multiple reaction monitoring (MRM). The whole procedure was validated according to the FDA guidelines for bioanalytical methods. The ESI-MS/MS parameters were set as follows: capillary voltage, 3.2 kV in positive mode, 2.5 kV in negtive mode; cone voltage, 45V; source offset, 60V; desolvation temperature, 450℃; desolvation gas flow, 900L•h-1 (N2, 99.9% purity). Detection was obtained by Multiple Reaction Monitoring (MRM) mode, precursor ions and daughter ions，cone voltage (CV), collision energy (CE) were performed as table S1. Data acquisition was carried out by Masslynx 4.1 software and processed by TargetLynx (Waters Corp., Milford, MA, USA).

2.7 Metabolomics statistical analyses. The raw data of the NC, unDCD and DCD-pre groups were collected with MarkerLynx Version 4.1 (Waters Corp., Manchester, USA) based on the UPLC-Q-TOF/MS. The raw data were subsequently processed for peak discovery and peak alignment and filtered to determine potential discriminant variables. The exported data were then processed using multivariate data analysis with SIMCA-P+ 14.0 software (Umetrics AB, Umea, Sweden). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) are widely used in data processing in metabolomics. In this experiment, PCA was used to determine the similarity between the groups to eliminate outlier samples, and OPLS-DA was used to identify significantly changed metabolites in the urine samples between the different groups. A score plot was used to establish a model visualization. Variables with significant differences in their group contribution (Variable importance in projection VIP > 1) and combined with the variable confidence intervals in the VIP plot, coefficient plot were considered biomarkers, and their molecular structures were further identified. The MassFragment™ application manager (Waters MassLynx v4.1) was used to facilitate the MS/MS fragment ion analysis process by way of chemically intelligent

peak-matching

algorithms.

Databases

such

as

HMDB

(http://www.hmdb.ca/) and MassBank (http://www.massbank.jp/) were used for 7



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confirmation. SPSS 19.0 software was used to perform t-tests to determine if the metabolites exhibited statistically significant changes. The SVM model was developed in the Matlab (Matlab R2010a, USA) kernel to map from low-dimensional to high-dimensional spaces to estimate the diagnostic potential of a classifier in clinical applications.

The

correlation

heat

map

was

performed

using

the

“CorrelationCalculator” package for Cytoscape software (version 3.4.0). Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks were generated using the “MetDisease” package for Cytoscape software (version 3.4.0).

3 Results

3.1 The establishment of the animal model for the diabetic cognitive dysfunction C57BL/6J mice were intraperitoneally injected once with STZ (120 mg/kg) to induce type 2 diabetes mellitus(T2DM) after three weeks of high fat and sugar feeding. Blood glucose of T2DM mice has been maintained at a higher level compared to NC (p < 0.05) (Fig. 2a). The establishment of DCD model was confirmed using the test of the Morris water maze at the 12th weeks. All of C57BL/6J mice were divided into NC, DCD and unDCD. Compared to the NC group, the escape latency period of was significantly increased (p < 0.05), the prolonged escape latency showed the mice had the cognitive decline, they are DCD groups; on the contrary, the mice are unDCD groups. A total of 16 mice with cognitive dysfunction were obtained, and its success rate was only approximately 47%. There was no significant difference in escape latency of the mice in the first six times, however the difference of escape latency time among the different group appeared on the 7th test of the Morris water maze (Fig. 2b).

Fig. 2 3.2 Metabolic profiles of different groups in 6th week We have made positive and negative ion mode analysis of urine samples(Fig. S1), and compared the number and separation of chromatographic peaks in positive and 8


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negative ion modes. It was found that the peaks of positive ions were more than those of negative ions and the separation effect is better than that of the negative ions. Finally we only selected positive ion mode for this study. Fig. S2 shows the positive-ion BPI chromatograms of the urine samples obtained from the NC, DCD-pre, DCD and unDCD four groups of mice by UPLC-Q-TOF/MS. The final data table contained 863 variables (chromatographic peaks) in calculating the RSD of each variable in a QC sample. This value needs to be discarded when the RSD﹥0.2. In order to illustrate the differences of the metabolic profiles, the data in UPLC-Q-TOF/MS was further segmented and subjected to pattern recognition analysis. PCA and OPLS-DA can identify differences between different groups. In this experiment, a principal component analysis (PCA) was first performed to show a trend of intragroup separation on the scores plot (Fig. 3A), in the 6th week, the DCD-pre and unDCD mice were clearly separated from normal controls, but the divisions between the DCD-pre and unDCD mice are not so sharply marked (Fig. 3A). The OPLS-DA was subsequently used to identify differential metabolites between different groups The scores of urine samples at the 6th week are shown in Fig. 3B-D. The results indicate that clear separations between DCD-pre (pink dots) and normal control (black dots) groups (R2X = 0.759, R2Y =0.998, Q2 =0.9963, Fig. 3B), unDCD (blue dots) and control (black dots) groups (R2X = 0.608, R2Y = 0.992,Q2 =0.955, Fig. 3C), DCD-pre (pink dots) and unDCD (blue dots) groups (R2X =0.306, R2Y = 0.831,Q2 = 0.241, Fig. 3D).

Fig. 3

3.3 Screening and discovery the early biomarkers of DCD in the 6th week UPLC-Q/TOF-MS platform was used to analyze the 6th week urine from three groups of mice (NC, unDCD, and DCD-pre). Data was pretreated and normalized by MarkerLynx Version 4.1. Through PCA and OPLS-DA of SIMCA-P+14.0, the different groups of DCD distinguishing model were established. After that, VIP values, variable confidence intervals in the VIP plot and coefficient plot and non parameter test were used to select feature ions which can separate early metabolites of DCD in the three-class OPLS-DA models of 6th week samples. The process of screening and discovery the biomarkers is shown in Fig.1. 11 metabolites were 9



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obtained from the comparative analysis and they are treated as early biomarkers of DCD, including C6H6N2O, C8H10N4O3, C5H7NO3, C9H8O3, C9H14N4O3, C8H9N, C8H15NO4, C11H12N2O3, C10H9NO, C6H14N4O3, and C18H39NO2. SVM were built to evaluate the DCD-pre group and unDCD group in 6th week mice using the 11 metabolites. Data from 12 samples were used as a training set, and the data from an additional 5 samples were used as the test set. The prediction accuracy rate of the model established by the potential biomarkers for the prediction of model was 83.33%. As showed in Table S2, some differences in prediction accuracy can be identified when several potential biomarkers are removed. The model prediction accuracy rate decreased when C5H7NO3, C10H9NO, C8H15NO4, C18H39NO2, C8H9N, C11H12N2O3 and C6H6N2O were removed, which indicates that these substances have positive impacts on the model and strong specificity for the prediction of DCD-pre.The SVM model established by the 7 potential biomarkers for the prediction accuracy rate of the DCD-pre group and unDCD group in 6th week mice. The prediction of model was 91.66%. The Best c, Best g and CV accuracy parameters from the cross-verification method are shown in Fig.4. It indicates that the 7 biomarkers have higher sensitivity and specificity between the DCD-pre group and unDCD group in the urine prior to the onset of DCD.

Fig. 4

3.4 Validation and Quantitative Analysis of Specific Markers A highly sensitive and rapid LC–MS/MS method was developed, fully optimized and validated for the simultaneous determination of the specific biomarkers in mice urine. 5 standard substances were obtained, they are niacinamide, pyroglutamic acid, sphinganine, indoline, 5-Hydroxy-L-tryptophan, but we failed to find the standard substances of N-lactoyl-Valine and indoleacetaldehyde. The standards and urine samples were analyzed. The results showed that 4 biomarkers (niacinamide, pyroglutamic acid, sphinganine, 5-Hydroxy-L-tryptophan) were validated using standards. The comparison results of the chromatogram was shown in Fig S3. Unfortunately, we found that C8H9N is not indoline, it needs to do more further study to confirm. Because we failed to obatain the standard substances of N-lactoyl-Valine and indoleacetaldehyde, we only identified these two biomarkers by the accurate mass 10


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and observed MSn fragments and literatures. Finally, In the present study we identified and verified 4 specific biomarkers (niacinamide, pyroglutamic acid, sphinganine, 5-Hydroxy-L-tryptophan) by using standards, and we speculated 2 biomarkers probably were N-lactoyl-Valine and indoleacetaldehyde by analysing the accurate mass and observed MSn fragments and literatures, and C8H9N was unknown. These results were shown in Table 1. Table1

A further quantitative analysis of the four biomarkers (niacinamide, pyroglutamic acid, sphinganine, 5-Hydroxy-L-tryptophan, which were identified by the standards) was using a triple-quadrupole mass spectrometer. The whole procedure was validated according to the FDA guidelines for bioanalytical methods. The quantitative results showed that niacinamide and 5-Hydroxy-L-tryptophan were gradually declined at different stages of cognitive dysfunction, however pyroglutamic acid and sphinganine were increased at the early stage of DCD (6th week), and became to decline in the 12th week (Fig.5a). In the progress of the disease, metabolic levels in DCD mice were abnormal and the metabolic characteristics during different periods were inconsistent. For a more intuitive survey of the mice in the DCD modeling process, a three-class OPLS-DA model was constructed by the different times (0w、6w and 12w ) to observe the cluster patterns between the NC group(0W) and other times. The overall trend showed that the metabolic profile of DCD-pre mice (6th week) and DCD mice (12th week) were clearly separated from NC mice (0W). In addition, and the metabolic profiles of DCD-pre mice (6th week ) and DCD mice ( 12th week ) also have obvious distinction with each other(Fig. 5b).

Fig. 5

3.5 Associations among the metabolic features and connections between metabolites and diseases Metscape provides a bioinformatics framework for the analysis and interpretation of metabolomic data. It provides us an interface to query the database. Linking compounds and reactions to well-known metabolomic data sources helps researchers 11



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to verify and better understand the presented metabolites.12 Heatmap visualization of metabonomic data showed the correlation results between the 7 biomarkers (Fig. 6a). Two major clusters were constructed from the 7 biomarkers.

4

biomarkers

(C8H9N,

5-Hydroxy-L-tryptophan,

niacinamide,

indoleacetaldehyde) in the cluster I and 3 biomarkers (sphinganine, pyroglutamic acid and N-lactoyl-Valine) in the cluster II had a positive correlation respectively with each

other, but not with other biomarkers. MetDisease was used to infer connections between the these biomarkers and diseases through annotated disease genes. As showed in Fig. 6b. The results of MetDisease show that the 7 biomarkers are associated with the nervous system diseases. 5-Hydroxy-L-tryptophan and sphinganine mapped to the nervous system diseases are selected in the network, which means they have big significant correlation with encephalopathy.

Fig. 6

4 DISCUSSION The worldwide prevalence of diabetes has dramatically increased during the past five decades. It has been reported that 382 million people had diabetes in 2013, and it is likely to increase further, which makes diabetes and its complications become important public health issues. Diabetes mellitus is a common metabolic disorder that can result in chronic complications such as cardiovascular disease, nephropathy, retinopathy, and peripheral neuropathy.13 Thus how to control diabetes and related complications become a medical issue which is a great challenge for the health care system. There are currently no consistent criteria for the diagnosis of diabetic cognitive dysfunction,14 therefore conventionally used clinical diagnosis requires the help of some more neuropsychological scale. Since it is still lack of reliable diagnostic criteria, the early diagnosis of cognitive impairment becomes impossible. Thus at explore early detection criteria for DCD becomes the goal of the research at home and abroad. Metabonomics technology combining with the body fluids technology for DCD provides the technical support for screening early diagnostic markers in the biofluid, especially in the urine containing abundant information, no trauma, repeatable 12


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sampling. Therefore urine has been closely watched in terms of metabonomics early diagnosis. There were a lot of researches on the metabonomics study of T2DM, AD and DACD,15-17 however, there has no report on early biomarker of DCD based on urine metabonomic. Therefore, this untargeted urine metabonomics study coupled with multivariate statistical analysis (PCA, OPLS-DA, SVM, etc) is firstly applied to identify early biomarkers and elucidate the molecular mechanisms of DCD. Till now, the exact pathogenesis of DCD is not clear, and it may be related to acid reductase activity, significantly higher sugar, sorbitol, protein enzyme saccharification and irreversible glycosylated end products, overproduced free radicals, and the change of the blood brain barrier transport function, the destruction of two steady state, insulin dysfunction, the changes of neurotransmitter, glucocorticoid and sex hormone, etc.18 (Fig. S4). To gain insight into the metabolic mechanism of DCD and provide accurate diagnostic information of DCD, altered metabolic pathways and the network relationship have been investigated. 7 early biomarkers of DCD were imported into MetPA database for analysis of the related pathways. It generated 4 networks (Fig. 7, Fig S5) including nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism and sphingolipid metabolism (Table S3). The pathway impact value was calculated from a pathway topology analysis, and the threshold was set Raw P＜0.05, and the impact ＞0.1. Unique pathway was filtered out as the potential pathway (nicotinate and nicotinamide metabolism) to DCD. The results suggested that such pathway showed marked perturbations over the early DCD and could contribute to the development of DCD. Network analysis of these metabolic pathways was established by Cytoscape (Fig. 7).

Fig. 7

4.1 Nicotinate and nicotinamide metabolism Nicotinamide is an important substance in the nicotinate and nicotinamide metabolism, and it is also the precursor of dihydrouracil dehydrogenase (NAD +) and nicotinamide - adenine dinucleotide phosphate(NADP). It exerts a protective effect on oxidative stress or inflammatory injury by participating in the energy metabolism of cells, and can effectively prevent cells and cell membranes from free radical damage. 13



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Nicotinamide also affects multiple oxidative stress pathways that regulate cell survival and death in immune system dysfunction, diabetes, and neurodevelopmental disorders.19-21 In animal experiments, niacinamide can enhance cognitive function, reduce trauma-induced edema of the cerebral cortex, and inhibite of axonal degeneration and cerebral ischemia, which can prevent spinal cord injury and reduce Parkinson's disease animal model of disability.22 Interestingly, a significant decrease of niacinamide in DCD-pre mice was observed, and the level of niacinamide in the DCD mice was declined more than that of the normal group (Fig. 5a), which is consistent with the reported in the literature.22 Diabetes, stroke, Alzheimer's disease and aging-related diseases are always associated with pathological apoptosis of functional cells, which are mostly associated with mitochondrial dysfunction in pathology. Poly ADP-ribose polymerase(PARP), a nuclear protein, is a DNA-binding protease that is able to selectively recognizes DNA gaps. The close relationship between nicotinamide and PARP possibly contributes to treatment strategy of vascular and neurodegenerative disease.23

4.2 Sphingolipid metabolism Bioactive lipid molecules and their receptors are closely related to the development of circulatory, immune and nervous systems. Ceramide can be is hydrolyzed to sphingosine during lipid degradation in the cell membrane.24,25 Many of the studies elucidate that sphingosine contributes to diabetes, insulin resistance, Alzheimer’s disease and other diseases,26-28 and the significant decrease of sphinganine is frequently observed in T2DM patients. In our study, sphinganine level was significantly increased in the DCD-pre group (Fig. 5a), which may directly lead to changes in sphingosine-1-phosphate involved in the occurrence of cognitive dysfunction.

4.3 Tryptophan metabolism Previous studies have showed that 5-Hydroxy-L-tryptophan has been used clinically for over 30 years. In addition to depression, the therapeutic administration of 5-Hydroxy-L-tryptophan has been shown to be effective in treating a wide variety of

conditions,

including

fibromyalgia,

insomnia

and

chronic

headaches.

5-Hydroxy-L-tryptophan easily crosses the blood-brain barrier and effectively 14


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increases central nervous system (CNS) synthesis of serotonin29-31. Some studies by other researchers show abnormal brain tryptophan metabolism with depression is usually associated with cancer,32-34 and the disrupted tryptophan metabolism could induce cognitive impairment in a mouse model of sepsis-associated encephalopathy.35 Compared to the normal control groups, 5-Hydroxy-L-tryptophan was declined gradually in the development of DCD, which was consistent with that in the urine of Parkinson's patients.36

4.4 Glutathione metabolism Pyroglutamic acid is a cyclized derivative of L-glutamic acid, which is formed nonenzymatically by glutamate, glutamine, and gamma-glutamylated peptides. Also it can be produced by the reaction of gamma-glutamylcyclotransferase on an L-amino acid. Acquired pyroglutamic acid deficiency (penicillins) and glutathione depletion such as malnutrition or sepsis usually lead to moderating to severe encephalopathy in clinic.37 In our study, decreased pyroglutamic acid level was observed in DCD-pre and DCD mice, demonstrating that the imbalance of pyroglutamic acid may be responsible for the occurrence of DCD. 5 CONCLUSION In summary, the metabolic alteration accompanied with the occurrence of DCD was investigated in mice using urine metabonomics based on UPLC-Q/TOF-MS and UPLC-MS-MS. Following multivariate statistical and integration analysis, we found 7 metabolites could be accepted as early biomarkers of DCD. The SVM results showed that the prediction accuracy is as high as 91.66%. The identities of four biomarkers were determined by mass spectrometry. The identified biomarkers were largely involved in nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism and sphingolipid metabolism. These early potential biomarkers may be sensitive to early neurodegeneration of DCD and give a new insight into the pathophysiological changes and molecular mechanisms of DCD.

ASSOCIATED CONTENT *S Supporting Information Figure S1. The positive and negative ion mode analysis of urine samples .a. ESI+; b. ESI-. 15



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Figure S2. Chromatogram of urine obtained from the control mice (NC), the previous stage of the diabetic cognitive dysfunction mice (DCD-pre), diabetes cognitive dysfunction mice (DCD) and diabetes without cognitive dysfunction group (unDCD) respectively. Figure S3. The comparison results of the chromatogram of niacinamide, pyroglutamic acid, sphinganine, 5-Hydroxy-L-tryptophan. Figure S4. The pathogenesis of diabetes cognitive dysfunction. Figure S5. The results of metabolic pathway analysis from MetPA database. Table S1. The ESI-MS/MS parameters of four metabolites Table S2. The results of SVM between DCD-pre and DCD mice at 6th week. Table S3. The results from pathway analysis with MetPA.

Acknowledgments

This work received the support of grants from the National

Natural Science Foundation of China (No. 81403213, 81673707) and Tianjin Natural Science Foundation in China(14JCZDJC37000). It also supported by program for Changjiang Scholars and Innovative Research Team in University (PCSIRT IRT_14R41) and Scientific and Technological Project of the General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China (2012IK162 and 2013IK162).

Duality of interest

The authors declare that there is no duality of interest associated

with this manuscript.

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Table 1 7 specific biomarkers’ ion for prediction of DCD in the positive ion mode by multivariate statistical analysis and integration analysis. Obsd m/z

Calcd m/z

Error (ppm)

Metabolite

Formula

Ms/Ms

pathway

(min)

0.8126

123.0565

123.0558

5.7

Niacinamidea

C6H6N2O

93[M+H-CH4N]+

Nicotinate and nicotinamide metabolism Glutathione metabolism

tR

2.2161 130.0502 130.0504 1.5 Pyroglutamic acida C5H7NO3 86[M+H-CO2]+ 3.1108 120.0811 120.0813 1.7 unknown C8H9N 3.1964 190.1077 190.1079 1.1 N-lactoyl-Valineb C8H15NO4 91[M+H-C2H11O4]+ a 4.1511 221.0923 221.0926 1.4 5-Hydroxy-L-tryptophan C11H12N2O3 39[M+H-C9H12NO3]+ Tryptophan metabolism b 6.6268 160.0758 160.0762 2.5 Indoleacetaldehyde C10H9NO 116[M+H-C2H4O]+ Tryptophan metabolism 11.2574 302.3054 302.3059 1.7 Sphinganinea C18H39NO2 96[M+H-C12H32NO]+ Sphingolipid metabolism a Identified by the standards; bIdentified by the accurate mass and observed MSn fragments which were acquired from UPLC/MS-Q-TOF analysis and literatures.

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Figure Legends Figure 1. The flow diagram of animal grouping and sample collection and the process of screening and discovery the biomarkers. Figure 2. Clinical Chemistry and behavior Characteristics of T2DM, and DACD and healthy control. a: The change of blood glucose in 0～12 weeks; b: The average latency of the mice in the Morris water maze. Figure 3. PCA model and OPLS-DA models with corresponding values of R2X, R2Y, and Q2 in the 6th week mice. (A) PCA score plot of controls mice (black circle),DCD-pre mice (pink square), and unDCD mice (blue square); (B) OPLS-DA score plot of controls mice (black circle) vs DCD-pre mice (pink square); (C) OPLS-DA score plot of controls mice (black circle) vs unDCD mice (blue square); (D) OPLS-DA score plot of DCD-pre mice (pink square) vs unDCD mice (blue square); The S-plot(E, F, G) of urine samples from the three groups mice respectively. Figure 4. The parameter selection result graph of SVM model of 7 biomarkers for distinguishing DCD-pre and unDCD in 6th week. (the parameters are described below: Best c = 21.1121, Best g =0.082469, CV accuracy = 91.6667%). Figure 5. The metabolic profiling of DCD mice at different stages. a: Peak area of 4 biomarkers in different stages of DCD mice(pyroglutamic acid, sphinganine, 5-Hydroxy-L-tryptophan and niacinamide); b: The score scatter 3D plot of three groups: the 0th week (NC,black square)mice, the 6th week(DCD-pre, red square) and 12th week(DCD,blue square). Figure 6. The correlation between biomarkers and disease. a:Heatmaps visualization for the correlation between 7 metabolites. The heatmaps were constructed based on the potential intervention targets. Rows: samples; columns: metabolites. Color key indicates metabolite expression value, green: lowest; red: highest. b:The network was constructed using the 7 metabolites as nodes and partial correlation coefficients as edges. MetDisease plug-in was used to annotate the metabolites with MeSH disease terms using PubChem IDs. Figure 7. Summary of Pathway Analysis and network analysis of nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism and sphingolipid metabolism.

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Figure 1. The flow diagram of animal grouping and sample collection and the process of screening and discovery the biomarkers. 427x223mm (96 x 96 DPI)



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Figure 2. Clinical Chemistry and behavior Characteristics of T2DM, and DACD and healthy control. a: The change of blood glucose in 0～12 weeks; b: The average latency of the mice in the Morris water maze. 142x169mm (96 x 96 DPI)


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Figure 3. PCA model and OPLS-DA models with corresponding values of R2X, R2Y, and Q2 in the 6th week mice. (A) PCA score plot of controls mice (black circle),DCD-pre mice (pink square), and unDCD mice (blue square); (B) OPLS-DA score plot of controls mice (black circle) vs DCD-pre mice (pink square); (C) OPLS-DA score plot of controls mice (black circle) vs unDCD mice (blue square); (D) OPLS-DA score plot of DCD-pre mice (pink square) vs unDCD mice (blue square); The S-plot(E, F, G) of urine samples from the three groups mice respectively. 493x549mm (96 x 96 DPI)



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Figure 4. The parameter selection result graph of SVM model of 7 biomarkers for distinguishing DCD-pre and unDCD in 6th week. (the parameters are described below: Best c = 21.1121, Best g =0.082469, CV accuracy = 91.6667%). 514x198mm (96 x 96 DPI)


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Figure 5. The metabolic profiling of DCD mice at different stages. a: Peak area of 4 biomarkers in different stages of DCD mice(pyroglutamic acid, sphinganine, 5-Hydroxy-L-tryptophan and niacinamide); b: The score scatter 3D plot of three groups: the 0th week (NC,black square)mice, the 6th week(DCD-pre, red square) and 12th week(DCD,blue square). 446x214mm (96 x 96 DPI)



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Figure 6. The correlation between biomarkers and disease. a:Heatmaps visualization for the correlation between 7 metabolites. The heatmaps were constructed based on the potential intervention targets. Rows: samples; columns: metabolites. Color key indicates metabolite expression value, green: lowest; red: highest. b:The network was constructed using the 7 metabolites as nodes and partial correlation coefficients as edges. MetDisease plug-in was used to annotate the metabolites with MeSH disease terms using PubChem IDs. 429x222mm (96 x 96 DPI)


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Figure 7. Summary of Pathway Analysis and network analysis of nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism and sphingolipid metabolism. 937x636mm (96 x 96 DPI)



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