Dynamic metabolic profile changes in urine from D-galactose induced aging rats based：1H-NMR metabonomics analysis

ZHAO Fan-fan,ZHOU Yu-zhi,CHANG Yan-fen,GAO Li,QIN Xue-mei,DU Guan-hua,4,ZHANG Xiang,5

(1.Modern Research Center for Traditional Chinese Medicine,2.College of Chemistry and Chemical Engineering,Shanxi University,Taiyuan 030006,China;3.Maternity and Child Care Hospital, Shanxi Provincial Children Hospital,Taiyuan 030006,China;4.Institute of Materia Medica, Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100050, China;5.Department of Chemistry in University of Louisville,Louisville 40228,USA)

·ORlGlNAL ARTlCLE·

Dynamic metabolic profile changes in urine from D-galactose induced aging rats based：1H-NMR metabonomics analysis

ZHAO Fan-fan1,2,ZHOU Yu-zhi1,CHANG Yan-fen3,GAO Li1,QIN Xue-mei1,DU Guan-hua1,4,ZHANG Xiang1,5

(1.Modern Research Center for Traditional Chinese Medicine,2.College of Chemistry and Chemical Engineering,Shanxi University,Taiyuan 030006,China;3.Maternity and Child Care Hospital, Shanxi Provincial Children Hospital,Taiyuan 030006,China;4.Institute of Materia Medica, Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100050, China;5.Department of Chemistry in University of Louisville,Louisville 40228,USA)

OBJECTlVETo investigate the dynamic changes in urine metabolic profiles in rats induced by D-galactose(D-Gal),and to study the correlations between the differential metabolites and behavior indicators using the proton nuclear magnetic resonance(1H-NMR)-based metabonomics.METHODSSubcutaneous injection of D-Gal 100 mg·kg-1for 10 weeks was adopted in the model group.The sample of urine was collected at day 0(d0),d14,d28,d42,d56 and d70.NMR metabonomics technique was used for acquisition of data,which was analyzed by multivariate statistical analysis.The ability of learning and memory were measured by Morris water maze test from d70.After the behavioral test,the rats were sacrificed and the hippocampus was observed by hematoxylin-eosin staining.RESULTSPrincipal component analysis(PCA)results revealed that there was considerable difference between the model group and the normal control group at d70.According to the varible importance plot(VIP) calculation and S-plot scores,a total of 12 metabolites were screened and identified as potential biomarkers at d70.The differences of metabolites and Morris water maze test were subjected to correlation analysis,and the results showed that the levels of choline,lactate and dimethylglycine in the model group were significantly increased and negatively correlated with the times of crossing the platform (r=-0.90,-0.50 and-0.52;n=10).Formate was significantly negatively correlated with the time spent in the target area(r=-0.51,n=10),but choline and formate were significantly positively correlated with the escape latency(r=0.72 and 0.53;n=10).However,the levels of creatine and taurine decreased in the model group,which was significantly positively correlated to the times of acrossing platforms(r= 0.89 and 0.71;n=10),while alanine was significantly positively correlated to the time spent in the target area(r=0.74,n=10).Taurine,alanine and creatine were significantly negatively correlated with the escape latency(r=-0.66,-0.50 and-0.85;n=10).The correlations between the differential metabolites and the behavioral indicators were further proved.CONCLUSlONThe metabolic profile changes in urine from D-Gal induced aging model rats are significantly correlated with impairement of ability in learning and memory.1H-NMR metabonomics in urinary metabolic profile changes may be used as an evaluation index in the D-Gal induced aging rats model.

aging;D-galactose;metabonomics;cognitive disorders

DOl:10.3867/j.issn.1000-3002.2017.06.004

Various diseases follow the increment of aging[1-2].It seems to be very urgent to perform research on mechanisms of aging in order to improve the quality of life and prolong life[3].The D-galactose(D-Gal)induced aging rat model was most widely used in aging related research[4-5]. The D-Gal induced rat aging model could produce similar natural aging characteristic,especially with the decline in cognitive functions[6-8].D-gal is converted to aldose and hydrogen peroxide,which can speed up the generation of superoxide anion and oxygen-derived free radicals［9-10］.This will lead to formation of advanced glycation end products (AGEs)that could enhance oxidative stress damage and abnormal phosphorylation and affect learning and memory functions［11-13］.Furthermore,the growing evidence suggests that the metabolism disordered［14］and oxidative stress［15］are associated with the neurodegenerative changes in D-gal induced aging rats［12，16-18］.

However,there are still many deficiencies in the D-gal induced aging rats model.The modeling time and evaluation indicators are not unified,which led to large differences in different laboratories. There were no specific and unequivocal unified standards to evaluate the reliability of the aging model.Therefore,a more efficient and new method is needed to evaluate the aging rat model.

Metabonomics technology is focused on the process of biological and biochemical changes in endogenous small molecule metabolites[19-21].Metabonomics technique has been used to analyze and dynamic ally monitor the changes in endogenous products in the model group during the establish ment of the aging model so that we can better understand the aging and non-aging zero-point, evaluate the effect of medicine and explore the mechanism on the model.We collected samples of urine in the normal control group and the model group at five time points.

In this study,we performed a comprehensive analysis of urinary metabolites on rats exposed the D-Gal,and investigated the D-Gal induced responses and metabolic changes to enhance the current understanding of D-Gal induced disorders and establish a new model evaluation method.

1 MATERlALS AND METHODS

1.1 Chemicals

Sodium 3-trimethylsilyl[2,2,3,3-d4]propionate (TSP)was purchased from Cambridge Isotope Laboratories,Inc.(MA,USA)and D2O(99.9%in D) was purchased from Landisville(Norell,USA.). Na2HPO4/NaH2PO4was bought from Tianjin Guangfu Fine Chemical Research Institute.The buffer(K2HPO4/NaH2PO4,1.5 mol·L-1,pH 7.4) was prepared in 100%D2O containing NaN3(0.04%).D-Gal was purchased from Amreco Company(USA,98%pure)and dissolved in 0.9%saline at the concentration of 25 mg·kg-1.

1.2 Animal experiments

A total of 20 young adult male Sprague-Dawley (SD)rats(160～200 g)were purchased from the Vital River Laboratory Animal Technology Co.,Ltd (Beijing,China).The rats were housed in a wellventilated animal experimental laboratory with a 12 h light/dark cycle,at a constant temperature of（24～26）℃,and a relative humidity of 40%～60%.Every five rats were randomly housed to a cage with a standard rodent diet and water ad libitum.Experimental protocols used in the present study were approved by the Committee on the Ethics of Animal Experiments of Shanxi University.

After one week of acclimatization,the rats were randomly divided into two groups with 10 rats in each group:the normal control group and the model group.D-Gal was subcutaneously injected to model group at the dose of 100 mg·kg-1for 10 weeks while the normal control received 0.9% saline.Body mass was examined weekly during D-Gal administration to monitor the general health of the two groups of animals.

1.3 Morris water maze(MWM)test

Spatial memory was assessed by MWM, which consisted of 5-day place navigation training and a probe test on the 6thday(d6).Spatial memory abilities were evaluated by escape latency,the times of rats crossing the platform and the time of the target area.Briefly,the MWM test was conducted in acircular water-maze tank(diameter,150 cm and height,60 cm)filled with water(temperature,25± 1℃)that had been opacified by adding ink.During the training procedure of learning,rats were tested during the light phase between 8∶00 am and 8∶00 pm.Each rat was given 4 swimming trials per day(20-min intertribal interval)for 5 consecutive days.In each trial,the rats were gently placed into water in one of the four quadrants,and the starting quadrant varied randomly over the trials. Rats were allowed a maximum of 60 s to find the escape platform,where it remained for 20 s.If the rats failed to find the platform within 60 s,then the rats were guided to the submerged platform and stay on the submerged platform for 20 s.For all MWM trials,the time that it took the rat to reach the submerged platform(escape latency)was recorded to assess spatial learning ability.On d6,another set of tests consisting of a 90 s trial with the submerged platform removed was conducted.Besides escape latency before reaching the platform,the time spent in the target quadrant and the numbers of target crossings over the previous location of the target submerged platform were recorded and collected by the video tracking equipment and processed by a computer equipped with ananalys-management system(Viewer 2 Tracking Software,Ji Liang Instruments,China)[22-23].

1.4 Sample collection and preparation for HE staining

24 h after the last injection of D-Gal,rats were anesthetized with 10%urethane.The hippocampus were immediately removed and put into a tube containing 4%paraformal dehyde solution for 24 h at 4℃.After routine tissue processing, the tissues were embedded in paraffin.Then,4 μm thick sections obtained from each paraffin block were stained with HE staining for histopathological evaluation.Finally,histological features of apoptotic cells in the hippocampus were observed under an optical microscope[24-25].

1.5 Sample collection and preparation for NMR measurements

During the experiment,the samples of urine were collected fortnightly at an identical time for all animals.The rats were placed in individual metabolic cages 12 h for urinary collection and removed waters and food at room temperature. The samples of urine were collected on the ice and at d0,d14,d28,d42,d56 and d70 continuously(12 h)in metabolic cages.In 13 201×g, 4℃under the condition of centrifugation for 15 min, the supernatant was obtained and stored at-80℃in a freezer until nuclear magnetic resonance (NMR)analysis.

The samples of urine were thawed at room temperature just prior to NMR analysis.An aliquot (400 μL)of sample was mixed with phosphate buffer(150 μL,0.2 mol·L-1Na2HPO4/NaH2PO4, pH 7.4)and then centrifuged(14 000×g,10 min at 4℃)to remove any precipitates.The 550 μL of supernatant was then pipetted into a 5 mm NMR tube and D2O(80 μL)containing 0.01%sodium 3-trimethylsilyl-(2,2,3,3-2H4)-1-propionate(TSP) was added for the1H-NMR analysis.

1.6 1H-NMR spectral data reduction and pattern recognition

1H-NMR spectra of all the samples were collected at 298 K on a Bruker 600-MHz AVANCEⅢspectrometer,operating at 600.13 MHz for1H.Samples were analyzed using NOE-2D Spectroscopy (NOSEY)NMR spectra with water suppression.1H-NMR spectra were acquired using the following parameters:spectra width 12 345.7 Hz,65 536 complex data points,2.654 s of acquisition time, relaxation delay time 1 s,temperature 298.15 K and 64 scans.The free induction decays(FIDs) were multiplied by an exponential weighting function equivalent to a line boarding of 0.188 Hz prior to Fourier transformation(FT).TSP served as an internal reference standard(δ 0.00).

1.7 NMR data processing

The1H-NMR spectra were processed using MestReNova software(Mestrelab Research,Santiago de Compostella,Spain).All spectra phase and baseline in1H-NMR were manually corrected,and chemical shifts referenced to TSP at δ 0.00 ppm in MestReNova(Mestrelab Research,Spain).The spectra were divided and the signal integral computed in 0.02 ppm intervals across the regionδ 0.00-9.50 ppm.The region of δ 4.60-4.96 ppm was removed to eliminate the influence of water. The remaining spectral segments in each NMR spectrum were normalized to the total sum of the spectral intensities to partially compensate for differences in concentrations between numerous metabolites.

1.8 Multivariate statistical analysis and target profiling analysis

The normalized integral values of the data of d0,d14,d28,d42,d56 and d70 were respectively exported into SIMCA-P 13.0 software(Umetrics, Umeå,Sweden)as variables.

With a metabonomics platform,the patterns of metabolites in urine from rats at five time points after treatment with D-Gal were plotted by the PCA,orthogonal partial least squares-discriminant analysis,(OPLS-DA)and S-plot.First of all, PCA was used to investigate general interrelations between the two groups.Then,OPLS-DA and S-plot were applied to analyse1H-NMR profiling data to identify potential biomarker.The PCA scores plot were used to showed the metabolic profile of the normal control and the model group.OPLS-DA is a supervised method to identify metabolites which could significantly contribute to group differentiations. To prevent over-interpretation,only two components were calculated for OPLS-DA models with yielded R2and Q2values as initial indicators for model quality.The score plot,which highlighted the inherent clustering trends in the urine and provided potential biomarkers,was visualized.The variable importance plot(VIP)value was produced to predict the influence of variables.A VIP value above 1.00 was considered statistically significant. Independent-sample t test was further used to investigate alterations in endogenous metabolites using SPSS 16.0(Chicago,IL,USA).P＜0.05 was considered statistically significant difference.

2 RESULTS

2.1 Effect of D-Gal on learning and memory in rats

The MWM test showed that the D-Gal at the dose of 100 mg·kg-1daily by subcutaneous injection for 10 weeks markedly extended the escape latency(P＜0.01,Fig.1A),reduced the times of crossing platform(Fig.1B)and significantly decreased time spent in the target area(P＜0.01, Fig.1C)compared with the normal control group. These results indicated that the D-Gal induced aging model rats had impaired spatial learning and memory and were statistically different from the normal control group.An aging rat model has been established.

Fig.1Effect of D-Gal on escape latency(A),times of acrossing platforms(B)and time spend in target area (C)on Morris water maze(MWM)test in rats.After the subcutaneous injection of D-Gal 100 mg·kg-1for 10 weeks,the MWM test was performed.x±s,n=10.＊＊P＜0.01,compared with the normal control group.

2.2 Effect of D-Gal on hippocampus histopathology of rats

Rats in the normal control group had full hippocampus neurons,which were arranged tightly and morphologically intact.Pyramidalneurons presented round and large nuclei and clear nucleoli(Fig.2A).Widespread damage was visible in the hippocampus of the model group rats treated with D-gal(Fig.2B).Intercellular space increased,and cells were loosely arranged.Pyramidal neurons either presented a densely stained shrunken appearance with minimal cytoplasm or had disappeared.

Fig.2 Effect of D-gal on hippocampus histopathology of rats at end of the 10thweek(HE×200).See Fig.1 for the treatment.A:normal control group；B:model group.Arrows show the reduction in the number of neurons in the hippocampus, contour,arranged in loose structure,intercellular space widened.

2.3 1H-NMR assignments of major metabolites from rat urine induced by D-Gal

The representative 600 MHz1H-NMR NOESYPR1D spectra of urine from normal control and D-Gal induced group from d0 to d70 are shown in Fig.3.Numbers that represent the metabolites are shown in Tab.1.Assignments of endogenous metabolites were based on literature and confirmed by HMDB.

2.4 Score plot of PCA of urine from D-Gal induced rats

Following1H-NMR data analysis,the score plot of PCA was used to depict the general variation in the urine samples between the two groups that were collected at d14(Fig.4A),d28(Fig.4B),d42 (Fig.4C),d56(Fig.4D)and d70(Fig.4E).The samples of the normal control and the model group at d14 and d28 were not clearly separated, despite a tendency of the samples in the two groups at d42 and d56 to separate.The samples of the two groups at d70 were obviously separated. The analysis showed that urine metabolic profiles might reflect metabonomic perturbations at different times.The PCA results demonstrated that metabolic variations were closely correlated with D-Gal in the model group.The PCA scores plot werecompletely separated at the 10thweek between the normal control and the model group,which is consistent with the time of the models established successfully.

Fig.31H-NMR assignments of major metabolites from rat urine induced by D-Gal.A：chemical shift of 6.2-9.4 ppm；B：chemical shift of 0.8-4.5 ppm.

Tab.11H-NMR assignments of major metabolites from rat urine induced by D-Gal

Fig.4 Principal component analysis（PCA）of score plots of urine from D-Gal induced aging rats at d14(A),d28 (B),d42(C),d56(D)and d70(E).

To find changed metabolites in urine samples collected at d70,the OPLS-DA was applied to filter out variations and missing values unrelated to the classification.The supervised OPLS-DA,which could improve biomarker discovery and separate the samples into two blocks,was applied to obtain better discrimination between the two groups.

The OPLS-DA score plots of metabolites in urine at d70 was shown in Fig.5A.To identify the metabolites contributing to the D-gal induced metabolic alterations in the model group,we calculated the VIP values,which reflected the importance of chemical shifts with respect to both class segregation and S-plot(Fig.5B).Variations with VIP values exceeding 1.0 for the selection of the chemical shift of the bins relevant to class segregation were first selected for further investigation because these bins were subsequently analyzed by independent-sample t test.Therefore,the significance of the differences in the levels of metabolites was checked and considered to be significant when P＜0.05.

As a result,12 differential metabolites were identified in the rat urine sample of d70.Through comprehensive analysis,we found that formate, methylamine,lactate,glycoprotein,dimethylglycine,and choline were significantly increased in the urine samples from D-Gal treatment for 10 weeks compared with rats of normal control group,while taurine,citrate,hippurate,alanine, creatine and 2-oxoglutarate were significantly decreased(P＜0.01)(Fig.6).

2.5 Differential metabolite related metabolic pathway analysis

In order to study the differentiaI metabolites involved in metabolic pathways,we refer red to KEGG(Kyoto Encyclopedia of Genes and Genomes) and the 12 differential metabolites were introduced into MetPA for analysis.The result was shown in Fig.7.In this study,the impact-value threshold was set to 0.10,three potential targets pathways were filtered out.They were taurine and hypotaurine metabolism,glyoxylate and dicarboxylate metabolism and citrate cycle(TCA cycle).

2.6 Correlation analysis of urinary differential metabolites and performance of MWM

Fig.5 OPLS-DA score plots(A)and S-plots(B)from urine of D-Gal induced aging rats at d70.

In order to measure the correlations between the differential metabolites and the performances of MWM,the Person′s correlation coefficient was introduced.The Person′s correlation coefficient could be used to reflect the degree of correlation betweens two random variables.With the increase in the absolute value of the correlation coefficient,the correlation was stronger. Whent the correlation coefficient was closer to 1 or-1,the correlation was stronger,but when the correlation coefficient was closer to 0,thecorrelation was weaker[26-27].The typical indexes of the MWM test,such as the time spent in the target area,the times of acrossing platforms and the escape latency,were used to evaluate the learning and memory ability of rats.In the model group, the time spend in the target area and the times of acrossing platform were decreased and the escape was latency increased compared with the normal control group.There was significant learning and memory impairment in D-gal induced rats as evaluated by water mirror test.Therefore, in this study,the correlation analysis was used to investigate the relationships between the differential metabolite and the performances of MWM.

The levels of differential metabolites from 10-week model rats and the performances of MWM were correlated using the Pearson′s correlation (Fig.8).In the model group,the level of choline, lactate,dimethylglycine,formate,methylamine and glycoprotein were increased compared with the normal control group,which showed negative correlations with the time spend in the target area and times of acrossing platforms,and positive correlations with the escape latency.However, the level of taurine,citrate,2-oxoglutarate,hippurate, alanine and creatine decreased in the model group, which was positively related to the time spend in target area and the times of acrossing platforms,and negatively correlated with escape latency.All these differential metabolites showed a significant correlation with performance of MWM.The results showed the changes in levels of differential metabolites were consistent with the behavior indicators in the aging rats induced by D-Gal.

Fig.6 Representative metabolites from urine of D-Gal induced aging rats at d70.x±s,n=10.＊P＜0.05，＊＊P＜0.01,compared with the normal control group.

Fig.7Summary diagram of differential metabolite pathwaywith MetPA analysis.MetPA analysis was performed on all the differential metabolites using the Metabo Analyst 3.0(http://www.Metaboanalyst.ca).1：taurine and hypotaurine metabolism;2：glyoxylate and dicarboxylate metabolism;3：citrate cycle(TCA cycle)

Fig.8 Correlation analysis of urinary differential metabolites and performances of MWM test.Pearson′s correlations of relative peak area and performances of MWM.

3 DlSCUSSlON

The D-Gal induced rat aging model has been in increasingly applied in aging-associated neurodegenerative diseases,such as Alzheimer disease (AD)[28-29].In the course of the experiment,the rat aging model was constantly monitored and evaluated on the performance of the mirror MWM test[30].The escape latency was extended in model group compared with the normal control group,but times of crossing platform and the time spend in target area were significantly decreased in model group,suggesting that learning and memory had been impaired seriously in the model group of rats[31-32].The aging model was established at the 10thweek.The pathological sections of the hippocampus indicated that the hippocampus neurons were damaged compared with the normal control group.The learning and memory disorders might have been associated with neuron damage[25]. Behavioral results were consisted with the result of HE staining of hippocampus,which proved that the aging model has been successfully established.

On the basis of this D-Gal induced rat aging model,the1H-NMR metabonomics technology was used to investigate the mechanism of aging[33-35].According to the VIP and p(corr),12 differential metabolites were analyzed as the potential diagnosis markers for aging.Aging with the energy metabolism decreased,TCA cycle and pyruvate metabolism are important energy sources for the body.Citrate and α-ketoglutarate,as the important intermediate products in the TCA cycle,significantly decreased in the model group compared with the normal control group in this study,which reflected the aerobic oxidation capacity disorder in D-Gal rats[36].By contrast, lactate,as a metabolic product of anaerobic oxidation,increased significantly in the model group, which indicated that anaerobic metabolism was enhanced.Furthermore,the over accumulate lactate formate could lead to the pH increase and energy depletion,which caused oxidation damage and mitochondrial damage,further promoting cell damage,apoptosis and aging[37].Creatine is an important energy storage material and can permeate the blood-brain barrier[38].The decreased level of creatine in the model group was related to the brain atrophy and the learning and memory impairment.Dimethylglycine as a known feedback inhibitor of betaine-homocysteine methyl transferase (BHMT)can be metabolized to sarcosine or creatine[39].Dimethylglycine increased in the model group compared with the normal control group. This variation of dimethylglycine contents is coincident with creatine.The change in dimethylglycine and creatine contents showed that learning and memory were impaired in the D-Gal induced aging rats.Previous studies showed hippurate content decreased as age increased in SD rats[40].In this study,the hippurate decreased in the model group compared with the normal control group.In addition,abnormal cell apoptosis is considered as a major factor of(accelerated) aging[17],and taurine is an osmotic pressure regulator that decreased in the model group compared with the normal control group,which showed that D-Gal might induce cell apoptosis by osmotic regulation to accelerate the aging process.Glycoprotein,as an inflammatory mediator, that improves immune status of the body and plays an important role in the maintenance of immune balance[38],decreased in this study,which showed that the rats were in excessive inflammation in model group.The result showed that the balance between inflammatory and anti-inflammatory networks was disturbed,which resulted in a concomitant progressive increase in proinflammatory status.Besides,choline is the important components of the cell membrane structure and lipoprotein.It significantly increased in the model group,which indicated that cell membrane wasdamaged by lipid peroxidation and risk of cardiovascular disease was increased in humans. Furthermore,choline can be degraded to methylamine which is an important cell osmotic pressure regulator.The osmotic pressure was too high or too low,which can accelerate cell damage and apoptosis[41-43].However,in this study,the choline and methylamines contents were significantly increased in the model group compared with the normal control group,which might result in an inappropriate osmotic pressure and accelerate the apoptosis of cells in the aging rats.All these potential diagonasis markers were involved in three significant metabolic pathways:taurine and hypotaurine metabolism,glyoxylate and dicarboxylate metabolism and TCA cycle.These signaling pathways are related to energy supply to cells, oxidative stress and apoptosis regulation.All of these accelerated the processes of aging.

In this study,D-Gal induced aging model of rats was successfully reproduced and studied. The results of the metabonomics in this study showed that D-Gal induced aging through disordering the anaerobic glucose metabolism,the intestinal bacteria metabolism and other metabolic pathways.The differential metabolites in urine in urine from the D-Gal induced aging model rats were significantly correlated with the ability of learning and memory impaired.The1H-NMR metabonomics in urinary could be used as an evaluation index in D-Gal induced aging rats model.

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中国毒理学会第八次全国毒理学大会暨第七届全国会员代表大会(二轮通知)

中国毒理学会第八次全国毒理学大会（CSOT-VIII）暨第七届全国会员代表大会将于2017年10月15-18日在山东省济南市山东大厦会议中心召开。大会由中国毒理学会主办，山东省医学科学院和山东省毒理学会承办。

一、大会主题：推动毒理创新，促进健康安全。

二、会议交流内容：学术活动包括特邀大会主旨报告、大会报告、分会专题报告和墙报交流。为鼓励科技人员参会并发现毒理学优秀人才，大会将进行优秀论文评选活动（包括优秀壁报展示论文），由中国毒理学会颁发大会优秀论文证书并给予一定奖励。论文截止日期：2017年8月15日。1、主旨报告：

（1）Jun Kanno,Ph.D.IUTOX President Director,Japan Bioassay Research Center/Japan Organization of Occupational Health and Safety,Japan.Percellome Toxicogenomics for the Mechanistic Prediction of Chemical Toxicity（单细胞归一法毒理基因组学技术对化合物毒性的机制预测）

（2）沈建忠，教授中国农业大学动物科学技术学院、中国工程院院士动物性食品中重要化学因子的检测与控制

（3）Gary W.Miller,Ph.D.Professor and Associate Dean for Research,Rollins School of Public Health,Emory University,USA. The Exposome:Advancing the Science of Toxicology（暴露组学：促进毒理科学的发展）

（4）周平坤，研究员军事科学院军事医学研究院放射与辐射医学研究所我国毒理学学科发展现状及前景展望

2、大会报告：

（1）吴永宁，研究员国家食品安全风险评估中心污染物的膳食暴露与食品安全

（2）海春旭，教授第四军医大学军事预防医学院窒息性毒剂光气中毒新机制与急救新策略

（3）付立杰，博士上海益诺思生物技术有限公司（国家上海新药安全评价中心）转基因农作物的健康风险评估与管理

（4）曹佳，教授第三军医大学预防医学院环境优控污染物对男性生殖健康的影响及相关机制

（5）牛侨，教授山西医科大学公共卫生学院铝的神经毒性与阿尔茨海默病

（6）刘征涛，研究员中国环境科学研究院我国水环境基准与生态毒理学研究探讨

（7）孙志伟，教授首都医科大学公共卫生学院我国大气污染亟待解决的若干毒理学问题

（8）岑小波，教授成都华西海圻医药科技有限公司（原国家成都中药安全性评价中心）新型生物治疗技术和产品非临床安全性研究的现状与挑战

（9）汪晖，教授武汉大学基础医学院骨发育毒性及其远期危害的宫内编程机制

（10）夏彦恺，教授南京医科大学生命早期环境暴露的综合评估与转化应用

3、征文专题：

T01.临床毒理、应急与中毒救治；T02.环境、生态毒理；T03.药物毒理与安全评价；T04.食品毒理与风险评估；T05.放射毒理与辐射应急；T06.工业毒理与职业卫生；T07.神经毒理、药物依赖；T08.农药、化妆品与新化学品毒理；T09.纳米与新材料毒理；T10.生物毒素毒理；T11.饲料与兽医毒理；T12.靶器官毒理；T13.生殖与发育毒理；T14.遗传毒理与致癌；T15.系统毒理学与生物标志；T16.毒物代谢与毒代动力学；T17.毒理学替代法与转化毒理；T18.毒性通路与分子毒理；T19.分析毒理与计算毒理；T20.毒性病理学研究；T21.其他。

4、继续教育培训班：

（1）“毒性病理新技术及其应用”，动物致癌实验。毒性病理专业委员会承办；

（2）“水安全”，国际毒理学联合会（IUTOX）与中国毒理学会合办，环境与生态专业委员会承办；

（3）“交叉参考的科学理论及应用”，国际化学品制造协会与中国毒理学会合办。

5、壁报：

大会优秀论文中有一部分从优秀壁报交流论文中产生。请参会代表认真准备壁报，并在壁报交流时间到壁报张贴处进行展示。

三、大会联系方式

学术组联系：王会亮（010）66932387，（010）68187038；E-mail:cst@chntox.org

会务组联系：孔祥颖（010）66932387，（010）68187038；E-mail:cst@chntox.org

基于1H-NMR代谢组学技术的D-半乳糖致衰老大鼠尿液代谢谱的动态变化

赵凡凡1，2，周玉枝1，常艳芬3，高丽1，秦雪梅1，杜冠华1，4，张翔1，5
（山西大学1.中医药现代研究中心，2.化学化工学院，山西太原030006；3.山西省儿童医院妇幼保健院，山西太原030006；4.中国医学科学院药物研究所，北京100050；5.Department of Chemistry，University of Louisville,Louisville 40228，USA）

目的 通过代谢组学技术研究D-半乳糖（D-Gal）致衰老大鼠尿液代谢谱的动态变化，并探究差异代谢物和行为学指标的相关性。方法大鼠连续sc给予D-Gal 100 mg·kg-110周，并分别在第0，14，28，42，56和70天收集每只实验鼠的尿液，采用代谢组学技术对实验大鼠6次尿液样本进行核磁共振（NMR）数据采集并结合多元统计进行分析。第70天开始采用Morris水迷宫检测实验大鼠的学习记忆能力。行为学实验结束后，处死并制备脑切片，HE染色观察海马病理改变。结果 对第0，14，28，42，56和70天模型组和正常对照组大鼠的尿液进行主成分分析发现，造模2～4周时，两组的代谢谱无差异；造模6～8周，两组大鼠逐渐不同；造模10周时，两组完全不同。采用正交校正的偏最小二乘判别分析寻找两组之间的差异物，在第10周发现，乳酸、丙氨酸、α-酮戊二酸和胆碱等12个峰面积具有显著性差异的潜在生物标志物。将差异代谢物和穿越平台次数、潜伏期以及目标象限停留时间进行关联分析。结果 表明，模型组中含量显著性升高的差异代谢物胆碱、乳酸和二甲基甘氨酸与穿越平台次数具有显著性负相关（r=-0.90，-0.50和-0.52；n= 10），甲酸与目标象限停留时间呈显著性负相关（r=-0.51，n=10）；胆碱和甲酸与潜伏期呈显著性正相关（r= 0.72和0.53；n=10）；而模型组含量显著性降低的差异代谢物肌酸和牛磺酸与穿越平台次数具有显著性正相关（r=0.89和0.71；n=10），而丙氨酸与目标象限停留时间呈显著性正相关（r=0.74；n=10）；牛磺酸、丙氨酸和肌酸与潜伏期呈显著性负相关（r=-0.66，-0.50和-0.85；n=10）；进一步验证了差异代谢物与行为学指标的相关性。结论D-Gal诱导的衰老大鼠尿液代谢谱变化和其学习记忆能力损伤有一定的相关性，基于1H-NMR代谢组学的尿液代谢谱变化规律，可作为D-Gal致衰老大鼠模型成功与否的评价指标之一。

衰老；D-半乳糖；代谢组学；认知障碍

2017-01-06接受日期：2017-05-23）

山西省应用基础研究项目（201601D021164）；山西省高校科技创新项目（2016120）;山西省科技基础条件平台建设项目（2014091022）；山西省科技攻关项目（20140313008-14）

周玉枝，Tel：（0351）7019178，E-mail：zhouyuzhi@sxu.edu.cn

R969.1

:AArticle lD:1000-3002-(2017)06-0514-13

The project supported by Applied Basic Research Project of Shanxi Province(201601D021164); Innovation Project of Higher Education Institutions In Shanxi Province(2016120);Construction of Science and Technology Basic Condition Platform of Shanxi Province(2014091022);and Program of Science and Technology of Shanxi Province(20140313008-14)

Biography:ZHAO Fan-fan,male,master in pharmacy,focusing on aging and other neurodegenerative disorders.

ZHOU Yu-zhi,Tel:(0351)7019178,E-mail:zhouyuzhi@sxu.edu.cn