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, Ume?,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.

REFERENCES:

［1］Harel I，Benayoun BA，Machado B，Singh PP，Hu CK，Pech MF，et al.A platform for rapid exploration of aging and diseases in a naturally shortlived vertebrate［J］.Cell，2015，160（5）：1013-1026.

［2］Katewa SD，Akagi K，Bose N，Rakshit K，Camarella T，Zheng X，et al.Peripheral circadian clocks mediate dietary restriction-dependent changes in lifespan and fat metabolism in Drosophila［J］. Cell Metab，2016，23（1）：143-15.

［3］Belsky DW，Caspi A，Houts R，Cohen HJ，Corcoran DL，Danese A，et al.Quantification of biological aging in young adults［J］.Proc Natl Acad Sci USA，2015，112（30）：E4104-E4110.

［4］Ntsapi C，Loos B.Caloric restriction and the precision-control of autophagy：A strategy for delaying neurodegenerative disease progression［J］.Exp Gerontol，2016，83：97-111.

［5］Iriondo-DeHond A，Martorell P，Genovés S，Ramón D，Stamatakis K，F(xiàn)resno M，et al.Coffee silverskin extract protects against accelerated aging caused by oxidative agents［J］.Molecules，2016，21（6）：721.

［6］Gong YS，Guo J，Hu K，Gao YQ，Xie BJ，Sun ZD，et al.Ameliorative effect of lotus seed pod proanthocyanidins on cognitive impairment and brain aging induced by D-galactose［J］.Exp Gerontol，2016，74：21-28.

［7］Gao J，He H，Jiang W，Chang X，Zhu L，Luo F，et al.Salidroside ameliorates cognitive impairment in a D-galactose-induced rat model of Alzheimer′s disease［J］.Behav Brain Res，2015，293：27-33.

［8］Zhu L，Chen T，Chang X，Zhou R，Luo F，Liu J，et al.Salidroside ameliorates arthritis-induced brain cognition deficits by regulating Rho/ROCK/NF-κB pathway［J］.Neuropharmacology，2016，103：134-142.

［9］Haider S，Liaquat L，Shahzad S，Sadir S，Madiha S，Batool Z，et al.A high dose of short term exogenous D-galactose administration in young male rats produces symptoms simulating the natural aging process［J］.Life Sci，2015，124：110-119.

［10］Hao L，Huang H，Gao J，Marshall C，Chen Y，Xiao M.The influence of gender，age and treatment time on brain oxidative stress and memory impairment induced by D-galactose in mice［J］. Neurosci Lett，2014，571：45-49.

［11］Ullah F，Ali T，Ullah N，Kim MO.Caffeine prevents D-galactose-induced cognitive deficits，oxidative stress，neuroinflammation and neurodegeneration in the adult rat brain［J］.Neurochem Int，2015，90：114-124.

［12］Xian YF，Su ZR，Chen JN，Lai XP，Mao QQ，Cheng CH，et al.Isorhynchophylline improves learning and memory impairments induced by D-galactose in mice［J］.Neurochem Int，2014，76：42-49.

［13］Budni J，Pacheco R，da Silva S，Garcez ML，Mina F，Bellettini-Santos T，et al.Oral administration of D-galactose induces cognitive impairments and oxidative damage in rats［J］.Behav Brain Res，2016，302：35-43.

［14］Song X，Bao M，Li D，Li YM.Advanced glycationin D-galactose induced mouse aging model［J］. Mech Ageing Dev，1999，108（3）：239-251.

［15］Haider S，Saleem S，Perveen T，Tabassum S，Batool Z，Sadir S，et al.Age-related learning and memory deficits in rats：role of altered brain neurotransmitters，acetylcholinesterase activity and changes in antioxidant defense system［J］. Age（Dordr），2014，36（3）：9653.

［16］Qu Z，Zhang J，Yang H，Huo L，Gao J，Chen H，et al.Protective effect of tetrahydropalmatine against D-galactose induced memory impairment in rat［J］.Physiol Behav，2016，154：114-125.

［17］Ayd?n AF，?oban J，Do gˇan-Ekici I，Betül-Kalaz E，Dogˇru-Abbasogˇlu S，Uysal M.Carnosine and taurine treatments diminished brain oxidative stress and apoptosis in D-galactose aging model［J］.Metab Brain Dis，2016，31（2）：337-345.

［18］Wu DM，Lu J，Zheng YL，Zhou Z，Shan Q，Ma DF. Purple sweet potato color repairs D-galactoseinduced spatial learning and memory impairment by regulating the expression of synaptic proteins［J］.Neurobiol Learn Mem，2008，90（1）：19-27.

［19］Chen X，Kildal PS，Hussain A，Carlsson J.Cold water forced swimming stress induced metabolic alterations in rats［J］.Anal Methods，2014，6（12）：4144-4151.

［20］Wang LL，Zheng LY，Luo R，Zhao XS，Han ZH，Wang YL，et al.A1H-NMR-based metabonomic investigation of time-dependent metabolic trajectories in high salt-induced hypertension rat model［J］.RSC Adv，2015，5（1）：281-290.

［21］Wei T，Zhao L，Jia J，Xia H，Du Y，Lin Q，et al. Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice［J］.Sci Rep，2015，5：11998.

［22］Lei Y，Chen J，Zhang W，F(xiàn)u W，Wu G，Wei H，et al. In vivo investigation on the potential of galangin，kaempferol and myricetin for protection of D-galactose-induced cognitive impairment［J］.Food Chem，2012，135（4）：2702-2707.

［23］Ali T，Badshah H，Kim TH，Kim MO.Melatonin attenuates D-galactose-induced memory impairment，neuroinflammation and neurodegeneration via RAGE/ NF-κB/JNK signaling pathway in aging mouse model［J］.J Pineal Res，2015，58（1）：71-85.

［24］Du Z，Li S，Liu L，Yang Q，Zhang H，Gao C. NADPH oxidase 3-associated oxidative stress and caspase 3-dependent apoptosis in the cochleae of D-galactose-induced aged rats［J］.Mol Med Rep， 2015，12（1）：7883-7890.

［25］Wang PP，Sun HX，Liu CJ，Hu MH，He XQ，Yue S，et al.Racemic oleracein E increases the survival rate and attenuates memory impairment in D-galactose/NaNO2-induced senescent mice［J］. Phytomedicine，2016，23（5）：460-467.

［26］Yan M L，Zhou Y Z，Gao L，Qin XM，Du GH. Gender gap-biased metabolome differences in Drosophila melanogaster in1H-NMR analysis［J］. Chin J Biochem Mol Biol（中國生物化學(xué)與分子生物學(xué)學(xué)報），2016，32（4）：418-425.

［27］Qu Z，Zhang J，Yang H，Huo L，Gao J，Chen H，Gao W.Protective effect of tetrahydropalmatine against D-galactose induced memory impairment in rat［J］.Physiol Behav，2016，154（11）：114-125.

［28］Zhao HF，Li N，Wang Q，Cheng XJ，Li XM，Liu TT. Resveratrol decreases the insoluble Aβ1-42level in hippocampus and protects the integrity of the blood-brain barrier in AD rats［J］.Neuroscience，2015，310：641-649.

［29］Gao J，Zhou R，You X，Luo F，He H，Chang X，et al.Salidroside suppresses inflammation in a D-galactose-induced rat model of Alzheimer′s disease via SIRT1/NF-κB pathway［J］.Metab Brain Dis，2016，31（4）：771-778.

［30］Zhu J，Mu X，Zeng J，Xu C，Liu J，Zhang M，et al. Ginsenoside Rg1 prevents cognitive impairment and hippocampus senescence in a rat model of D-galactose-induced aging［J］.PLoS One，2014，9（6）：e101291.

［31］Li X，Chen Y，Shao S，Tang Q，Chen W，Chen Y，et al.Oxidative stress induces the decline of brain EPO expression in aging rats［J］.Exp Gerontol，2016，83：89-93.

［32］Poli M，Asperti M，Ruzzenenti P，Regoni M，Arosio P.Hepcidin antagonists for potential treatments of disorders with hepcidin excess［J］.Front Pharmacol，2014，5：86.

［33］An Y，Xu W，Li H，Lei H，Zhang L，Hao F，et al. High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats［J］.J Proteome Res，2013，12（8）：3755-3768.

［34］Chiu CY，Yeh KW，Lin G，Chiang MH，Yang SC，Chao WJ，et al.Metabolomics reveals dynamic metabolic changes associated with age in early childhood［J］.PLoS One，2016，11（2）：e0149823.

［35］De Guzman JM，Ku G，F(xiàn)ahey R，Youm YH，Kass I，Ingram DK，et al.Chronic caloric restriction partially protects against age-related alteration in serummetabolome［J］.Age（Dordr），2013，35（4）：1091-1104.

［36］Falegan OS，Vogel HJ，Hittel DS，Koch LG，Britton SL，Hepple RT，et al.High aerobic capacity mitigates changes in the plasma metabolomic profile associated with aging［J］.J Proteome Res，2017，16（2）：798-805.

［37］Paradies G，Petrosillo G，Gadaleta MN，Ruggiero FM. The effect of aging and acetyl-L-carnitine on the pyruvate transport and oxidation in rat heart mitochondria［J］.FEBS Lett，1999，454（3）：207-209.

［38］Zakaria FA.Effect of oxygen derived free radicals and glycine on sodium-potassium adenosine triphosphatase in the basolateral membrane of the kidney in ischemia-reperfusion［J］.Saudi Med J，2002，23（11）：1380-1385.

［39］An L，Lang Q，Shen W，Shi Q，F(xiàn)eng F.Dynamic metabolic profiling of urine biomarkers in rats with alcohol induced liver damage following treatment with Zhi Zi Da Huang decoction［J］.Mol Med Rep，2016，14（3）：2093-2100.

［40］Zhang Y，Yan S，Gao X，Xiong X，Dai W，Liu X，et al.Analysis of urinary metabolic profile in aging rats undergoing caloric restriction［J］.Aging Clin Exp Res，2012，24（1）：79-84.

［41］Daniele S，Da Pozzo E，Iofrida C，Martini C. Human neural stem cell aging is counteracted by α-glycerylphosphorylethanolamine［J］.ACS Chem Neurosci，2016，7（7）：952-963.

［42］Kim JW，Ryu SH，Kim S，Lee HW，Lim MS，Seong SJ，et al.Pattern recognition analysis for hepatotoxicity induced by acetaminophen using plasma and urinary1H NMR-based metabolomics in humans［J］.Anal Chem，2013，85（23）：11326-11334.

［43］Al Rajabi A，Castro GS，da Silva RP，Nelson RC，Thiesen A，Vannucchi H，et al.Choline supplementation protects against liver damage by normalizing cholesterol metabolism in Pemt/Ldlr knockout mice fed a high-fat diet［J］.J Nutr，2014，144（3）：252-257.

（本文編輯：喬虹）

中國毒理學(xué)會第八次全國毒理學(xué)大會暨第七屆全國會員代表大會(二輪通知)

中國毒理學(xué)會第八次全國毒理學(xué)大會（CSOT-VIII）暨第七屆全國會員代表大會將于2017年10月15-18日在山東省濟南市山東大廈會議中心召開。大會由中國毒理學(xué)會主辦，山東省醫(yī)學(xué)科學(xué)院和山東省毒理學(xué)會承辦。

一、大會主題：推動毒理創(chuàng)新，促進健康安全。

二、會議交流內(nèi)容：學(xué)術(shù)活動包括特邀大會主旨報告、大會報告、分會專題報告和墻報交流。為鼓勵科技人員參會并發(fā)現(xiàn)毒理學(xué)優(yōu)秀人才，大會將進行優(yōu)秀論文評選活動（包括優(yōu)秀壁報展示論文），由中國毒理學(xué)會頒發(fā)大會優(yōu)秀論文證書并給予一定獎勵。論文截止日期：2017年8月15日。1、主旨報告：

（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（單細胞歸一法毒理基因組學(xué)技術(shù)對化合物毒性的機制預(yù)測）

（2）沈建忠，教授中國農(nóng)業(yè)大學(xué)動物科學(xué)技術(shù)學(xué)院、中國工程院院士動物性食品中重要化學(xué)因子的檢測與控制

（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（暴露組學(xué)：促進毒理科學(xué)的發(fā)展）

（4）周平坤，研究員軍事科學(xué)院軍事醫(yī)學(xué)研究院放射與輻射醫(yī)學(xué)研究所我國毒理學(xué)學(xué)科發(fā)展現(xiàn)狀及前景展望

2、大會報告：

（1）吳永寧，研究員國家食品安全風(fēng)險評估中心污染物的膳食暴露與食品安全

（2）海春旭，教授第四軍醫(yī)大學(xué)軍事預(yù)防醫(yī)學(xué)院窒息性毒劑光氣中毒新機制與急救新策略

（3）付立杰，博士上海益諾思生物技術(shù)有限公司（國家上海新藥安全評價中心）轉(zhuǎn)基因農(nóng)作物的健康風(fēng)險評估與管理

（4）曹佳，教授第三軍醫(yī)大學(xué)預(yù)防醫(yī)學(xué)院環(huán)境優(yōu)控污染物對男性生殖健康的影響及相關(guān)機制

（5）牛僑，教授山西醫(yī)科大學(xué)公共衛(wèi)生學(xué)院鋁的神經(jīng)毒性與阿爾茨海默病

（6）劉征濤，研究員中國環(huán)境科學(xué)研究院我國水環(huán)境基準與生態(tài)毒理學(xué)研究探討

（7）孫志偉，教授首都醫(yī)科大學(xué)公共衛(wèi)生學(xué)院我國大氣污染亟待解決的若干毒理學(xué)問題

（8）岑小波，教授成都華西海圻醫(yī)藥科技有限公司（原國家成都中藥安全性評價中心）新型生物治療技術(shù)和產(chǎn)品非臨床安全性研究的現(xiàn)狀與挑戰(zhàn)

（9）汪暉，教授武漢大學(xué)基礎(chǔ)醫(yī)學(xué)院骨發(fā)育毒性及其遠期危害的宮內(nèi)編程機制

（10）夏彥愷，教授南京醫(yī)科大學(xué)生命早期環(huán)境暴露的綜合評估與轉(zhuǎn)化應(yīng)用

3、征文專題：

T01.臨床毒理、應(yīng)急與中毒救治；T02.環(huán)境、生態(tài)毒理；T03.藥物毒理與安全評價；T04.食品毒理與風(fēng)險評估；T05.放射毒理與輻射應(yīng)急；T06.工業(yè)毒理與職業(yè)衛(wèi)生；T07.神經(jīng)毒理、藥物依賴；T08.農(nóng)藥、化妝品與新化學(xué)品毒理；T09.納米與新材料毒理；T10.生物毒素毒理；T11.飼料與獸醫(yī)毒理；T12.靶器官毒理；T13.生殖與發(fā)育毒理；T14.遺傳毒理與致癌；T15.系統(tǒng)毒理學(xué)與生物標志；T16.毒物代謝與毒代動力學(xué)；T17.毒理學(xué)替代法與轉(zhuǎn)化毒理；T18.毒性通路與分子毒理；T19.分析毒理與計算毒理；T20.毒性病理學(xué)研究；T21.其他。

4、繼續(xù)教育培訓(xùn)班：

（1）“毒性病理新技術(shù)及其應(yīng)用”，動物致癌實驗。毒性病理專業(yè)委員會承辦；

（2）“水安全”，國際毒理學(xué)聯(lián)合會（IUTOX）與中國毒理學(xué)會合辦，環(huán)境與生態(tài)專業(yè)委員會承辦；

（3）“交叉參考的科學(xué)理論及應(yīng)用”，國際化學(xué)品制造協(xié)會與中國毒理學(xué)會合辦。

5、壁報：

大會優(yōu)秀論文中有一部分從優(yōu)秀壁報交流論文中產(chǎn)生。請參會代表認真準備壁報，并在壁報交流時間到壁報張貼處進行展示。

三、大會聯(lián)系方式

學(xué)術(shù)組聯(lián)系：王會亮（010）66932387，（010）68187038；E-mail:cst@chntox.org

會務(wù)組聯(lián)系：孔祥穎（010）66932387，（010）68187038；E-mail:cst@chntox.org

基于1H-NMR代謝組學(xué)技術(shù)的D-半乳糖致衰老大鼠尿液代謝譜的動態(tài)變化

趙凡凡1，2，周玉枝1，常艷芬3，高麗1，秦雪梅1，杜冠華1，4，張翔1，5
（山西大學(xué)1.中醫(yī)藥現(xiàn)代研究中心，2.化學(xué)化工學(xué)院，山西太原030006；3.山西省兒童醫(yī)院婦幼保健院，山西太原030006；4.中國醫(yī)學(xué)科學(xué)院藥物研究所，北京100050；5.Department of Chemistry，University of Louisville,Louisville 40228，USA）

目的 通過代謝組學(xué)技術(shù)研究D-半乳糖（D-Gal）致衰老大鼠尿液代謝譜的動態(tài)變化，并探究差異代謝物和行為學(xué)指標的相關(guān)性。方法大鼠連續(xù)sc給予D-Gal 100 mg·kg-110周，并分別在第0，14，28，42，56和70天收集每只實驗鼠的尿液，采用代謝組學(xué)技術(shù)對實驗大鼠6次尿液樣本進行核磁共振（NMR）數(shù)據(jù)采集并結(jié)合多元統(tǒng)計進行分析。第70天開始采用Morris水迷宮檢測實驗大鼠的學(xué)習(xí)記憶能力。行為學(xué)實驗結(jié)束后，處死并制備腦切片，HE染色觀察海馬病理改變。結(jié)果 對第0，14，28，42，56和70天模型組和正常對照組大鼠的尿液進行主成分分析發(fā)現(xiàn)，造模2～4周時，兩組的代謝譜無差異；造模6～8周，兩組大鼠逐漸不同；造模10周時，兩組完全不同。采用正交校正的偏最小二乘判別分析尋找兩組之間的差異物，在第10周發(fā)現(xiàn)，乳酸、丙氨酸、α-酮戊二酸和膽堿等12個峰面積具有顯著性差異的潛在生物標志物。將差異代謝物和穿越平臺次數(shù)、潛伏期以及目標象限停留時間進行關(guān)聯(lián)分析。結(jié)果 表明，模型組中含量顯著性升高的差異代謝物膽堿、乳酸和二甲基甘氨酸與穿越平臺次數(shù)具有顯著性負相關(guān)（r=-0.90，-0.50和-0.52；n= 10），甲酸與目標象限停留時間呈顯著性負相關(guān)（r=-0.51，n=10）；膽堿和甲酸與潛伏期呈顯著性正相關(guān)（r= 0.72和0.53；n=10）；而模型組含量顯著性降低的差異代謝物肌酸和?；撬崤c穿越平臺次數(shù)具有顯著性正相關(guān)（r=0.89和0.71；n=10），而丙氨酸與目標象限停留時間呈顯著性正相關(guān)（r=0.74；n=10）；?；撬帷⒈彼岷图∷崤c潛伏期呈顯著性負相關(guān)（r=-0.66，-0.50和-0.85；n=10）；進一步驗證了差異代謝物與行為學(xué)指標的相關(guān)性。結(jié)論D-Gal誘導(dǎo)的衰老大鼠尿液代謝譜變化和其學(xué)習(xí)記憶能力損傷有一定的相關(guān)性，基于1H-NMR代謝組學(xué)的尿液代謝譜變化規(guī)律，可作為D-Gal致衰老大鼠模型成功與否的評價指標之一。

衰老；D-半乳糖；代謝組學(xué)；認知障礙

2017-01-06接受日期：2017-05-23）

山西省應(yīng)用基礎(chǔ)研究項目（201601D021164）；山西省高?？萍紕?chuàng)新項目（2016120）;山西省科技基礎(chǔ)條件平臺建設(shè)項目（2014091022）；山西省科技攻關(guān)項目（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