Urine metabolomics of rats with chronic atrophic gastritis.

BACKGROUND/AIM: To use liquid chromatography-mass spectrometry (LC-MS) to identify endogenous differential metabolites in the urine of rats with chronic atrophic gastritis (CAG).
MATERIALS AND METHODS: Methylnitronitrosoguanidine (MNNG) was used to produce a CAG model in Wistar rats, and HE staining was used to determine the pathological model. LC-MS was used to detect the differential metabolic profiles in rat urine. Diversified analysis was performed by the statistical method.
RESULTS: Compared with the control group, the model group had 68 differential metabolites, 25 that were upregulated and 43 that were downregulated. The main metabolic pathways were D-glutamine and D-glutamic acid metabolism, histidine metabolism and purine metabolism.
CONCLUSION: By searching for differential metabolites and metabolic pathways in the urine of CAG rats, this study provides effective experimental data for the pathogenesis and clinical diagnosis of CAG.

Introduction

Chronic atrophic gastritis (CAG) is a type of atrophy of gastric mucosal epithelial cells and glands where the number of glands is reduced, the mucosal layer thins, and the mucosal muscle layer thickens and may be accompanied by intestinal metaplasia and dysplasia. Digestive system diseases [1] mainly have the clinical manifestations of bloating, fullness of the stomach, belching, pain in the upper abdomen, loss of appetite, weight loss, etc. CAG has a wide variety of factors and is a common and frequently occurring clinical disease with a 2.55%- 7.46% canceration rate [2]. In 1978, the World Health Organization officially defined chronic atrophic gastritis as a precancerous state. The active treatment of CAG in clinical practice is an important node to block its development into gastric cancer.

As an important branch of systems biology, metabolomics technology is unique because it does not require the establishment of a large database of expressed gene sequences [3]. Metabolomics can express the physiological and biochemical state of the body through biological metabolic structure to better analyze pathogenesis. Among its advantages, liquid chromatography-mass spectrometry (LC-MS) technology can be directly used to analyze biological metabolites to obtain final analysis results with the advantage of finding subtle changes in gene and protein expression during biological metabolism. Thus, LC-MS has become the most commonly used analytical technique in metabolomics research [4]. This study explains the molecular mechanism of action and metabolic pathways of chronic atrophic gastritis through pharmacodynamics and LC-MS.

Materials and methods

Animals

Twenty SPF grade Wistar male rats, 6 weeks old, 180 ± 20 g, were provided by Shandong Pengyue Experimental Animal Co., Ltd. [SCXK (Lu) 20140007]. The feeding environment was a temperature of 26°C ± 2°C, humidity 50 ± 10%, and light illumination/dark cycle 12 h. The experiment started after 7 d of adaptive feeding from the time of purchase. During the period, the animals has free access to food and drinking water, and the experiment met animal ethical requirements.

Experimental reagents and instruments

Methylnitronitrosoguanidine (manufactured by Tokyo Chemical Industry Co., Ltd., NH8JH-DR), vetzyme tablets (Lepu Hengjiuyuan Pharmaceutical Co., Ltd., 20170401), ranitidine hydrochloride capsules (Tianjin Pacific Pharmaceutical Co., Ltd., 20170601), and ammonium hydroxide (Shanghai Wokai Biotechnology Co., Ltd., 20170220) were used. Anhydrous ethanol (Tianjin Fuyu Fine Chemical Co., Ltd., 20170808), methanol (Woke), acetonitrile and formic acid (Aladdin), ammonium formate (Sigma), hematoxylin staining solution, eosin staining solution, differentiation solution, blue back solution (Hebei Bohai Biological Engineering Development Co., Ltd.), and xylene (Tianjin Yongda Chemical Reagent Co., Ltd.) were also utilized in this study.

A refrigerated centrifuge (Eppendorf, H1650-W), mixer (Vortex Mixer, QL-866), liquid chromatograph (Thermo, UltiMate 3000) and mass spectrometer Thermo (Q Exactive Focus) were instruments used in this study.

Animal model

Twenty Wistar rats were prepared, and 10 rats were randomly selected as a blank group. Normal diet was fed until the materials were collected. The remaining rats were model rats according to the following method [5]: rats were given 120 μg/mL MNNG from the 1st day of modeling, given 0.1% ammonia water freely for 24 h fed with 0.03% ranitidine feed using the hunger and satiety method (full food for 2 d, fasting for 1 d), an each given 2 ml of 40% ethanol on the fasting day. The above operation lasted for 16 weeks and each rat was weighed twice a week during the modeling process. During the experiment, the weight, coat color and behavior of the rats were observed.

Example ethics statement: This study was carried out in strict accordance with the recommendations in the Guidelines for ethical review of experimental Animal welfare (National Standard: GB/T 35892–2018). The protocol was approved by the Ethics Review Center of Shandong University of Traditional Chinese Medicine (Protocol Number: SDUTCM20190402003). All surgery was performed under sodium pentobarbital anesthesia, and all efforts were made to minimize suffering.

Urine collection and preparation

Before sampling, they fasted for 24 hours, drank normal water, collected urine, followed by anaesthesia with 2% pentobarbital sodium, blood collection of spleen, stomach and liver, and subsequent death. Urine was centrifuged at 2500 rpm at room temperature for 1 hour in the morning, and the supernatant was divided into centrifuge tubes; each tube was > 0.3 ml. The urine samples were melted at 4°C and 100 μL of each sample was placed into a 1.5 mL centrifuge tube, 100 μL of ddH2O was added followed by shaking for 5 min to fully absorb and centrifugation at 10000 g and 4°C for 10 min. Then, a 0.22 μm membrane was used to filter the supernatant to obtain the samples to be tested; 20 μL of the synthetic QC samples were extracted from each sample to be tested, and the remaining samples were tested by LC-MS.

LC-MS chromatographic mass spectrometry conditions

A Thermo Ultimate 3000 chromatograph and an ACQUITY UPLC® HSS T3 1.8 μm (2.1 × 150 mm) chromatographic column were used with an autosampler temperature of 8°C, a flow rate of 0.25 mL/min, and a column temperature of 40°C. The sample was eluted with an injection volume of 2 μl, and the positive mode mobile phases were 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The gradient elution program was 0 ~ 2 min, 2% B; 2 ~ 10 min, 2% ~ 50% B; 10 ~ 15 min, 50% ~ 98% B; 15 ~ 20 min, 98% B; 20 ~ 22 min, 98% ~ 2% B; 22 ~ 25 min, 2% B. The negative mode mobile phases were 5 mM ammonium formate (A) and acetonitrile (B). The gradient elution program was 0 ~ 2 min, 2% B; 2 ~ 10 min, 2% ~ 50% B; 10 ~ 15 min, 50% ~ 98% B; 15 ~ 20 min, 98% B; 20 ~ 22 min, 98% ~ 2% B; 22 ~ 25 min, 2% B [6]. The Thermo Q Exactive Focus mass spectrometer was operated with the following conditions: electrospray ion (ESI) source, positive and negative ion ionization mode, positive ion spray voltage of 3.50 kV, negative ion spray voltage of -2.50 kV, sheath gas of 30 arb, auxiliary gas of 10 arb, capillary temperature of 325°C, full scan with a resolution of 70,000, scan range of m/z 81–1000, secondary cracking with HCD, collision voltage of 30 eV, and dynamic exclusion to remove unnecessary MS/MS information.

Data processing

The analysis software used for multidimensional statistical analysis is SIMCA-P (V13.0). In addition, the calculation of P value is t-test. The t-test that we use for univariate statistical analysis, in general, t-test is p<0.05 is significant, p<0.01 is very significant, biological statistical methods are basically such a display of differences; This section provides references for significance of biological repeated screening p values. The obtained raw data was converted to mzXML format with ProteoWizard software (v3.0.8789) [7] and the RCMS (v3.3.2) XCMS package was used for peak identification, peak filtering, and peak alignment analysis. The main parameters were bw = 5, ppm = 15, peakwidth = c (10, 20), mzwid = 0.015, mzdiff = 0.01, and method = centWave, which includes the mass to charge ratio (m/z) and information data matrix such as retention time (rt) and intensity. According to the results, the original peak area, which is the relative strength value, is calculated, then the original peak area is standardized, batch normalization of peak area of data. Data analysis is based on standardized data. Multidimensional statistical analysis was performed based on the data after standardized processing.

LC-MS data is carried out on the basis of normalization, eliminating very few data that do not exist or have too low strength. The experiment adopts most data retained after QC, QA and normalization processing. Prior to urine metabolomics analysis, the Proteowizard software (V3.0.8789) was used to convert the obtained original data into mzXML format (XCMS input file format). Using R (v3.3.2) XCMS package is used to identify the peaks identification, peaks filtration, peaks alignment, the main parameters are bw = 5, PPM = 15, peakwidth = c (10, 20), mzwid = 0.015, mzdiff = 0.01, the method = centWave. The data matrix, including mass to charge ratio (M/Z), retention time (RT) and intensity, is obtained. In the positive ion mode 22,540 precursor molecules and the negative ion mode 18,837 precursor molecules were obtained. The data were exported to Excel for subsequent analysis. In order to make comparison of data of different orders, batch normalization of data regarding peak area was conducted.

Results

General situation

Control group: In good condition, sturdy body, strong limbs, neat, supple and shiny fur, mental state is excellent, responsive to external conditions, body weight gradually increases, and the stool is normal. Model group: Poor condition, thin body, weak limbs, messy fur, dryness, and dullness, poor mental state, drowsiness, unresponsive to external conditions, insignificant changes in body mass, slower rise, and less stool that is hard. Body mass changes are shown in Fig 1. In the registration of weight changes at 16 weeks, normality test was performed first, and then one-way ANOVA was used to conduct statistical test of weight between the blank group and the model group, and *P<0.01 was found between the two groups, the difference was statistically significant.

Fig 1

Mass variation diagram of the control group and model group.

Observation of pathological tissues

As shown in Fig 2A, the gastric tissue mucosa lamina propria in the blank group pathological section is rich in gastric glands that are closely arranged with a normal structure, and the gastric gland epithelial cells have a normal morphology. In the model group, the lamina propria were loosely arranged, the lamina propria of the gastric mucosa was severely congested (black arrow), and there were a large number of inflammatory cells (blue arrow) under the mucosa with edema, as shown in Fig 2B.

Fig 2

HE staining pathological sections.

A. HE Staining Pathological Sections of Gastric Mucosa in the Control Group (×200); 2-B: HE Staining Pathological Sections of Gastric Mucosa in the Model Group (×200).

Chromatogram in total ion mode

The components separated by chromatography entered into mass spectrometry (MS) analysis, and data collection was performed by continuous scanning of the mass spectrum. The intensity is on the ordinate, and the time is on the abscissa. The resulting spectrum is the base peak chromatogram (BPC); see Fig 3A and 3B (G: model group, H: control group).

Fig 3

Chromatogram in total ion mode.

3-A: Typical Sample BPC in Positive Ion Mode, 3-B: Typical Sample BPC in Negative Ion Mode.

Urine metabolomics analysis in positive ion mode

After data pretreatment (format conversion peak recognition, filtering alignment and normalization), the data screened out have strong repeatability and good effect for urine metabolomics analysis After the data were preprocessed, the principal component analysis (PCA) method was used to explore CAG urine in positive ion mode. Changes in the fluid metabolism profile yielded a model with three principal components (R2 = 0.548) and a score chart reflecting the degree of dispersion between groups, as shown in Fig 4A. The PCA score graph shows that most samples are within the ellipse of the 95% confidence interval except for individual outliers. The PCA score graph shows that the urine samples of the two groups are significantly separated and are statistically significant. Furthermore, PLS-DA and OPLS-DA analysis methods (Fig 4B and 4C) were used to remove information that was not related to sample classification, and pattern discrimination analysis was performed on the full spectrum of the urine. Permutation is the result of 200 permutation tests, PLD-DA permutation test in positive ion mode is R2 = (0.0,0.91), Q2 = (0.0,0) PLD-DA permutation test in negative ion mode is R2 = (0.0,0.9), Q2 = (0.0,-0.39) The results showed that the two groups of samples could be significantly separated. In order to check whether the repeatability of the model is good and ensure the reliability of the data model, a permutation test was performed on the model (Fig 4D). The above results show that the multivariate data model of urine samples meets the parameter standard, indicating that the model has high stability and good predictive ability.

Fig 4

Urine metabolism profile of CAG model rats in positive ion mode.

4-A: PCA Scores, 4-B: PLS-DA Scores, 4-C: OPLS-DA Scores, 4-D: Replacement Test of the CAG Model Urine Fit Model in Positive Ion Mode.

Analysis of urine metabolomics in negative ion mode

The PCA method was used to explore changes in the CAG urine metabolic spectrum. After data preprocessing, a model with 3 principal components (R2 = 0.515) and the degree of dispersion between groups were obtained from the score chart. The PCA score graph shows that most samples fall within the ellipse of the 95% confidence interval, with only a few outliers. The PCA score (Fig 5A) graph shows the spatial distribution of the urine samples of the two groups, which can be significantly separated. PLS-DA and OPLS-DA analysis methods were used to further analyze the full spectrum of urine, and the results showed that the two groups of samples could be significantly separated (Fig 5B and 5C). In order to test whether the repeatability of the model is good and to ensure reliability of the data model, the model was replaced and verified (Fig 5D). The intercept of Q2 is negative, indicating that the model is valid. The above results indicate that the multivariate data model of urine samples meets the parameter standard, indicating that the model has high stability and good predictive ability.

Fig 5

Urine metabolism profile of CAG model rats in negative ion mode.

5-A: PCA Scores, 5-B: PLS-DA Scores, 5-C: OPLS-DA Scores, 5-D: Replacement Test of the CAG Model Urine Fit Model in Negative Ion Mode.

Extraction and analysis of differential metabolites

From the PCA, PLS-DA, OPLS-DA analysis model group and blank group, the screening conditions were in accordance with a P-value ≤0.05, VIP ≥1 [6], and molecular weight error <20 ppm). According to the fragmentation information obtained from MS/MS mode, further matching annotations were obtained in the HMDB, METLIN, MassBank, LipidMaps, and mzCloud databases to obtain accurate metabolite information. A total of 68 differential metabolites were screened, of which 25 were upregulated and 43 that were downregulated, compared with metabolites with the same or similar metabolic modes clustered to obtain differential metabolite heat maps and metabolite correlation heat maps (Fig 6). These differential metabolites relied on the Marker-view, KEGG, HMDB, MetaboAnalyst and other databases, which were searched and identified, and the results are shown in Table 1.

Fig 6

Heat map of the differential metabolites.

A: Heat Map of the Differential Metabolites in CAG Rats; 6-B: Correlation Heat Map of Differential Metabolites in CAG Rats.

Table 1

Differential metabolic markers in urine of CAG rats (upregulated ↑, downregulated ↓).

chemical compound	m/z	rt	exact mass	chemical formula	Model vs Control_VIP	log2 (FC)	p value
(S)-2-Hydroxyglutarate	147.0287633	82.9724	148.11402	C5H8O5	1.464880663	0.5013	0.047581863↓
®-Noradrenaline	184.0967985	330.939	183.205	C8H11NO3	1.526685758	0.3797	0.037595251↓
10-Hydroxy capric acid	189.1483988	609.2975	188.264	C10H20O3	1.823597606	1.5684	0.008306808↑
2-Aminopteridine-4,7-Diol	178.0362164	219.251	179.1364	C6H5N5O2	2.016289866	1.1144	0.003043425↑
2-Deoxycytidine	228.0976635	149.877	227.2172	C9H13N3O4	1.915004809	1.3307	0.004840515↑
2-Deoxyuridine	227.0667048	283.209	228.202	C9H12N2O5	1.943477192	-0.979	0.004861136↓
2-Hydroxypropanoic acid	89.02311403	93.0333	90.0779	C3H6O3	1.874058772	0.3997	0.00731866↓
2-Methylguanosine	298.1143794	220.051	297.2675	C11H15N5O5	1.751775025	1.367	0.012215874↑
2-Oxoglutaric acid	145.0131155	83.6063	146.0981	C5H6O5	1.495118055	0.9552	0.042475723↓
2-Pyrrolidone-5-Carboxylic acid, Methyl Ester	144.0654693	163.765	143.0582432	C6H9NO3	1.868632508	0.4193	0.006413493↓
3-(3,4-Dihydroxyphenyl)Propanoic acid	181.0496532	227.9005	182.1733	C9H10O4	2.070984986	0.8642	0.002077192↓
3-Hydroxy-3-methylglutaric acid	161.044468	84.9492	162.1406	C6H10O5	1.480741362	0.7542	0.044849483↓
3-Hydroxycapric acid	187.1330551	667.4145	188.264	C10H20O3	1.944065438	0.3836	0.004843594↓
3-Indoleacetic acid	174.0550606	463.637	175.184	C10H9NO2	1.490647745	0.5842	0.043203485↓
3-Methylindole	130.0650034	464.003	131.1745	C9H9N	1.876426797	0.9857	0.007221167↓
3-Methyl-L-histidine	168.0767831	105.457	169.18126	C7H11N3O2	1.872481394	1.4212	0.00738418↑
4-Acetamidobutanoic acid	146.0811334	296.814	145.15648	C6H11NO3	1.641325837	0.526	0.02089112↓
4-Hydroxy nonenal Mercapturic acid	318.1376294	559.432	319.1453439	C14H25NO5S	1.548812817	1.0965	0.034435408↑
5-S-Methyl-5-thioadenosine	298.0965219	450.365	297.3347	C11H15N5O3S	2.284849647	1.8528	0.000234115↑
7-Methylguanosine	296.0994864	384.98	297.1073186	C11H16N5O5	1.568140916	1.549	0.031843615↑
Adenine	134.0460176	301.647	135.1269	C5H5N5	1.792987662	-0.7032	0.011340028↓
Adenosine	268.1038713	289.983	267.24152	C10H13N5O4	1.934286006	2.0713	0.004285334↑
Adenosine 3-monophosphate	346.0554562	184.5985	347.22142	C10H14N5O7P	1.68169368	-1.3227	0.019481197↓
Adipic acid	145.049496	93.721	146.1412	C6H10O4	2.113128751	1.3866	0.001516902↑
AMP	348.0700631	175.3335	347.2212	C10H14N5O7P	1.900260783	-1.5778	0.005302741↓
Arbutin	271.0820178	255.116	272.2512	C12H16O7	1.771574784	2.3461	0.012648447↑
Benzaldehyde	107.0492548	441.291	106.1219	C7H6O	1.762739968	1.0398	0.011540491↑
CMPF	239.0910035	181.3095	240.2524	C12H16O5	2.124552651	1.2416	0.001388456↑
Dacarbazine	183.1015094	706.8775	182.18344	C6H10N6O	1.587732534	0.2827	0.026523445↓
D-Biotin	245.0919668	518.395	244.31172	C10H16N2O3S	1.515098427	1.0857	0.035928770↑
delta-Decalactone	171.1378437	609.401	170.2487	C10H18O2	1.849847045	1.5177	0.007156206↑
D-Glucuronic acid	193.0345141	562.589	194.1394	C6H10O7	2.241280415	1.2756	0.000512579↑
Dihydro-3-coumaric acid	165.0546401	410.747	166.1739	C9H10O3	1.528214666	1.394	0.037370134↑
Dodecanedioic acid	229.1439549	580.91	230.30068	C12H22O4	1.66119894	-0.9546	0.021378107↓
Dopamine	154.0861938	172.7845	153.1784	C8H11NO2	1.825498392	0.753	0.008218865↓
Formononetin	267.066017	806.608	268.2641	C16H12O4	2.025071834	-1.1042	0.002867682↓
Genkwanin	283.0608516	871.0265	284.2635	C16H12O5	2.062070851	-1.1857	0.002214871↓
Gentisic acid	153.0181962	231.027	154.12014	C7H6O4	1.835909917	1.2406	0.009038573↑
Glutaric acid	131.0337596	93.248	132.11462	C5H8O4	1.55606035	0.6234	0.033445496↓
Guanidinoacetic acid	118.0613176	92.6126	117.107	C3H7N3O2	1.705152566	1.0027	0.015441198↑
Guanine	150.0409746	176.319	151.126	C5H5N5O	2.476437968	-1.3203	3.25E-05↓
Guanosine	284.0987672	329.596	283.24092	C10H13N5O5	1.53671651	1.3507	0.032900583↑
Hexanoylcarnitine	260.1854318	582.32	259.1783583	C13H25NO4	1.466692106	0.5425	0.043469353↓
Homovanillic acid	181.0496668	326.991	182.1733	C9H10O4	1.602561332	0.5841	0.027598265↓
Inosine	267.0731584	331.1715	268.2261	C10H12N4O5	1.528975498	2.1558	0.037258488↑
L-Glutamic acid	148.0602887	91.7513	147.1293	C5H9NO4	1.777690879	-1.1167	0.010667276↓
L-Histidine	156.0766725	105.9695	155.15468	C6H9N3O2	1.603838417	0.5723	0.024721392↓
L-Phenylalanyl-L-Proline	263.1388345	500.41	262.3043	C14H18N2O3	2.077282892	0.7931	0.001566615↓
Marmesin acetate	289.1019761	693.8425	288.0997736	C16H16O5	1.720421852	0.9461	0.014319362↓
N(2)-Acetyl-L-Lysine	189.1232505	106.081	188.22432	C8H16N2O3	2.049816081	0.8419	0.001930407↓
N,N-Diethyl-M-Toluamide	192.1381451	1046.76	191.2695	C12H17NO	1.658473782	0.6825	0.019299997↓
N-Acetyl-beta-Alaninate	132.065568	184.861	130.1219	C5H9NO3	1.573650573	0.5139	0.028179966↓
N-Acetylcadaverine	145.1334792	149.971	144.215	C7H16N2O	1.640234776	1.0602	0.020995681↑
N-Acetyl-Glutamic acid	190.0709275	195.285	189.1659	C7H11NO5	1.606367357	0.6886	0.024447196↓
N-Acetyl-L-Histidine	198.0872059	106.068	197.19136	C8H11N3O3	1.573531368	0.4979	0.028194318↓
N-epsilon-Acetyl-L-lysine	187.1078578	100.753	188.22432	C8H16N2O3	2.024611789	0.6354	0.00287668↓
Nicotinic acid	124.0394187	147.588	123.10944	C6H5NO2	2.168087639	-1.2801	0.000737259↓
N-Lactoyl-phenylalanine	238.1071057	597.55	237.100108	C12H15NO4	1.57132921	0.907	0.028460454↓
Nonic acid	187.0966503	487.9895	188.104859	C9H16O4	2.208499912	0.5276	0.000690754↓
O-Toluic acid	137.059717	171.166	136.14792	C8H8O2	1.805758215	0.7836	0.009169037↓
Phenacylamine	136.0745078	3.70776	135.0684139	C8H9NO	1.489849766	-0.4527	0.039727046↓
Pipecolic acid	130.0862919	132.1165	129.157	C6H11NO2	1.781759339	1.5181	0.010438908↑
Quinaldic acid	172.039368	306.26	173.1681	C10H7NO2	1.669168777	0.7797	0.020624237↓
Suberic acid	173.0809113	144.3395	174.19436	C8H14O4	1.612701672	1.3484	0.026434225↑
Syringic acid	197.0447419	256.6915	198.1727	C9H10O5	1.538159222	0.7057	0.035930624↓
Taxifolin	303.0519806	562.747	304.2516	C15H12O7	1.718717369	0.8785	0.016387198↓
Threonate	135.0287318	86.9696	136.10332	C4H8O5	1.547635686	-0.6082	0.034598259↓
TMCA	239.0887447	588.709	238.2366	C12H14O5	1.954028191	1.161	0.003771041↑

CAG model group urine differential metabolite pathway information

This study mapped the differential metabolites to the KEGG database. There are 23 common metabolic pathways involved in the obtained differential metabolites, as shown in Fig 7: D-glutamine and D-glutamine histidine metabolism, histidine metabolism, purine metabolism, nitrogen metabolism, tyrosine metabolism, arginine-proline metabolism, butyric acid metabolism, biotin metabolism, alanine-aspartic acid-glutamic acid metabolism, ascorbic acid-bitter almond metabolism, niacin-nicotinamide metabolism, pentose-glucuronate interconversion, pyrimidine metabolism, lysine degradation, citric acid cycle (TCA cycle), starch-sucrose metabolism, Inositol phosphate metabolism, glutathione metabolism, porphyrin and chlorophyll metabolism, cysteine-methionine metabolism, glycine-serine-threonine metabolism, aminoacyl-tRNA biosynthesis, and tryptophan metabolism. Among them, the metabolic pathways with * P <0.5 and Impact> 0 include D-glutamine and D-glutamic acid metabolism, histidine metabolism, and purine metabolism, as shown in Table 2. In these pathways, D-glutamine and D-glutamic acid metabolism were up-regulated, three metabolites in histamine metabolism were down-regulated, and six metabolites in purine metabolism were down-regulated.

Fig 7

Metabolic pathways of CAG rat metabolites mapped to KEGG.

Table 2

Differential metabolic pathways in urine of CAG rats.

	D-Glutamine and D-glutamate metabolism	Histidine metabolism	Purine metabolism
p value	0.013036	0.016826	0.039418
Impact	1	0.24194	0.076410
Pathway links	http://www.kegg.jp/pathway/rno00471+C00025+C00026	http://www.kegg.jp/pathway/rno00340+C00025+C00135+C01152	http://www.kegg.jp/pathway/rno00230+C00020+C00212+C00294+C00242+C00387+C00147

Discussion

In recent years, metabonomics has been widely used in modern Chinese medicine treatment of chronic gastritis. Cui Jiajia Et al. [8] found that 3 plasma biomarkers (arginine, succinate and 3-hydroxybutyrate) and 2 urine biomarkers (α-ketoglutarate and valine) might be markers of CAG in the study on plasma and urine metabolites. Chen jiaolong [9] by using nuclear magnetic metabonomics technology including observation of the stomach meridian the CAG treatment, use of electroacupuncture in the rat stomach meridians beam door and foot three mile, found that serum ghrelin level stomach metabolism of liver kidney brain cortex spectral change, the rat gastric mucosa arrangement and the thickness of the gastric mucosa has different degrees of improvement, ghrelin and substance P expression in serum increased to normal level, serum glucose glycogen content increased, the stomach tissue of glutathione and glutamine levels, hypoxanthine creatinine nicotinamide in brain tissueThe content of malonic acid and dimethyl malonate increased, the content of glucodicarbonate and glycerin in liver tissues increased, and the content of glutamine hypoxanthine nucleoside asparagine asparagine and nicotinamide in kidney tissues increased. Liu Caichun Et al. [10] found that both electroacupuncture and moxibustion could restore various caG-induced metabolic changes, including membrane metabolism, energy metabolism and neurotransmitter function. Liu Yuetao [11] based on 1 H-NMR technical analysis astragalus chienchung soup to the CAG rats serum endogenous metabolites regulation function disorder, through multivariate statistical analysis, to clarify its regulation on chronic atrophic gastritis targets, related indicators associated with efficacy is established, by partial least-squares regression analysis and Met PA screening and treatment effect is most related metabolic pathways, find the root of remembranous milk vetch chienchung soup can obviously inhibit the CAG lesions, can obviously regulate 3—hydroxy butyric acid lactic acid acetate succinate metabolites such as disorder, the CAG treatment is the main metabolic pathways of arginine—proline metabolismGlycerol metabolism and glycine—serine—threonine metabolism pathways. Sun Yina [12] found 59 qualitative and quantitative metabolites, and then PCA OPLS-DA VIP value was used to find 8 potential differential metabolites related to dampis-heat syndrome of spleen and stomach. The metabolites of trimethyl-oxide of taurine gonosaccharide glycerol and glucose were up-regulated, and the metabolites of trimethyl-trimethyl-oxide and trigonelline phosphate creatine were down-regulated. It is concluded from the above studies that metabonomic techniques have been widely used in traditional Chinese medicine. However, 1H-NMR metabolism technology is mostly used in the studies on chronic atrophic gastritis. In this study, LC-MS technology is used to elucidate the small molecule action mechanism and related target pathways of CAG. In this study, the animal model of chronic atrophic gastritis was mainly prepared by MNNG combined with ammonia-free drinking water and hunger and satiety. The process of MNNG alkylating the DNA bases does not depend on enzymatic metabolism and can directly penetrate into the pylorus and stomach to cause canceration [13]. Alcohol can trigger acute ischemic damage to the gastric mucosa, causing damaged genes to fail to recover over time, which may be an important factor for initiating oncogenes [14]. Moreover, alcohol can accelerate the dissolution of MNNG and increase the mutation rate. Ammonia can simulate toxic damage to the stomach after Helicobacter pylori infection and maintain acute inflammation of the gastric mucosa [15, 16]. Ranitidine hydrochloride can inhibit gastric acid secretion, but hunger and satiety are the fusion of spleen and stomach damage. CAG is a complex disease with multiple factors and multiple genes. Compound factor modeling can simulate human disease characteristics to a greater extent and is currently the most widely used and most mature CAG model application.

Through PCA, PLS-DA and OPLS-DA LC-MS diversified analysis, using statistics, bioinformatics, chemometrics and other methods to analyze and compare the differential metabolites, the model group and the blank group of rat urine had significant metabolic differences. A total of 68 different metabolites were screened, and 23 metabolic disturbance pathways were predicted. The metabolic pathways can regulate the growth, differentiation, apoptosis and the immune system of tumor cells [17]. The statistically significant metabolic pathways are D-glutamine and D-glutamic acid metabolism, histidine metabolism, and purine metabolism. Among the metabolic pathways, the significantly different metabolites included L-glutamic acid and 10 different products, including ketoglutaric acid, histidine, 3-methyl-L-histidine, adenosine monophosphate, adenosine, adenine, hypoxanthine, guanosine and guanine.

L-Glutamic acid, which is in the metabolic pathway of D-glutamine and D-glutamic acid, plays an important role in protein metabolism in organisms. Studies have found that L-glutamic acid can inhibit cerebral cortex, hippocampal, gastric cancer cell and neural stem cell proliferation and differentiation and induce apoptosis [18, 19]. Decreased glutamate expression levels will cause digestive system diseases. Based on this performance, L-glutamic acid is a commonly used therapeutic drug for the digestive system, especially gastric cancer and pancreatic cancer. Penicillin can induce the generation of glutamic acid and upregulate cycle-related expression genes and sugar degradation process of glucose to 2-oxoglutaric acid [20].

In the histidine metabolism pathway, 3-methyl-L-histidine, histidine, and L-glutamic acid play the role of substrate, intermediate, and product, respectively, and protein nutrition comes from the content of 3-methyl-L-histidine. Each of these compounds are effective indicators of histidine metabolic status [21]. Studies have confirmed that histidine can inhibit the proliferation and migration of lung cancer cells, thereby exerting an antitumor effect [22]. Histamine formed after the decarboxylation of histidine can relax blood vessels and is associated with inflammation. In gastritis and in the duodenum, the reaction in ulcers is sensitive. Currently, histidine is mostly used for the treatment of reducing gastric acid, relieving gastrointestinal pain and as a blood pressure treatment. L-Glutamic acid is formed after a series of processes, such as phosphoester and propionic acid formation, and its antagonists can reverse the abnormal expression of mGlu R5 and PSD-95 in the striatum of LID rats [23].

Purine metabolism provides cells with the necessary energy and cofactors to promote the growth and proliferation of cells. The most common disease with purine dysfunction is gout, and purine metabolism and its metabolites include adenosine monophosphate, adenosine, adenine, and, at times, the abnormal expression of xanthine, guanosine and guanine will promote the occurrence of gastric cancer [24]. The decomposition of purine nucleotides will promote the dephosphorylation of inosine or guanylic acid and generate inosine or guanosine, which can decompose into xanthine or guanine. The CN-II enzyme is highly expressed in tumor cells [25]. Studies have shown that purine nucleotides are essential for metabolic functions. Hypoxanthine, guanine phosphoribosyl transferase and other related purines can affect hematopoietic stem cell cycle progression, proliferation kinetics and changes in mitochondrial membrane potential [26].

Conclusions

Metabolomics is an important technical means for studying the pathogenesis of diseases. This experiment is the first to use LC-MS metabolomics to study the pathogenesis of CAG from the perspective of urine metabolites. Fromm the method (PCA) and supervised analysis method (PLS-DA and OPLS-DA), differential metabolites of the model group and the control group were screened. These differences were mainly distributed among 23 metabolic pathways, which were glutamine metabolism with L-glutamic acid, 2-ketoglutarate in the D-glutamic acid metabolism pathway, 3-methyl-L-histidine, histidine, L-glutamic acid and purine in the histidine metabolism pathway. Adenosine monophosphate, adenosine, adenine, inosine, guanosine and guanine may be potential biomarkers for the diagnosis of CAG.

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26 Aug 2020

PONE-D-20-19248

Urine metabolomics of rats with chronic atrophic gastritis

PLOS ONE

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Reviewer #1: In the work under review, interesting data have been obtained on the metabolic profile of urine in chronic atrophic gastritis, which can be used for diagnostic purposes. However, I have a number of questions for the authors. For routine diagnosis of gastritis, a gastropanel is used, instrumental diagnostic methods, firstly, they pay attention to the presence of Helicobacter pylori. We would like the authors to clarify in the introduction the need for urinalysis for diagnostic purposes. What is the place of this analysis in clinical practice? Is there a real need for it? Why was the rat model chosen? Since the authors use a non-invasive diagnostic method, they could conduct appropriate research on patients, especially given that this diagnosis is widespread. What is the likelihood that the resulting metabolic profiles will match those for humans? The revealed metabolic changes can be observed with a sufficiently large number of pathologies, since they are not specific. How can these compounds be subsequently used for diagnostic purposes?

Reviewer #2: General comment:

In this work the authors studied, with the aid of LC-MS technique, urinary metabolic alterations behind chronic atrophic gastritis, a common functional gastrointestinal disorder, using rats as animal model. Since CAG is considered as a pre-cancerous state, methylnitronitrosoguanidine, a carcinogen and mutagen biochemical substance, was used to induced CAG in the animals. Following LC-MS analysis, statistical tools were applied to identify the most discriminating metabolites and the related metabolic pathways.

The experimental work was formally well conducted, and the results were sufficiently discussed. However, some issues should be solved before I could recommend publication. Also, English must be carefully revised and improved through the entire manuscript.

Specific comments:

1. PCA: The confidence ellipse in PCA graph could be useful; how have the presence of outliers been investigated?

PLS-DA and OPLS-DA: R2X, R2Y and Q2 relative to each model should be reported, as well as the number of wrong classifications in the training data set (i.e. internal validation). How many permutations have been chosen for models validation? Also, a more extensive cross-validation of the OPLS-DA model should be carried out if possible using CV-ANOVA (p < 0.01, at least) to exclude over fitting.

Independent samples T-test should be used to determine if the different biomarker candidates obtained from the OPLS-DA models are statistically significant between the two groups at the univariate level.

2. The authors should report if body mass change registered for model group was significant or not by applying a statistical test.

3. Have the LC-MS data been normalized and/or scaled? Have all features been retained, or any of them excluded if not present in most part of the samples or if their intensity was too low? Which program have the authors used for data processing?

4. Page 9 line 153: “CAG urine metabolomics analysis is corrected positive ion data”, what did the authors mean?

5. Have xenobiotics been eliminated from the list of possible metabolites? As far as I know, compounds as marmesin acetate, syringic acid o taxifolin are not endogenous. Have the authors performed potential biomarker identification with the analysis of standard compounds or MS/MS experiments? I also suggest to add to Table 1 the following information for each compound: features (tR and m/z), detected adduct, calculated m/z and delta m/z in ppm.

6. The authors could report if the metabolites belonging to the most relevant pathways were up regulated or down regulated in model group.

7. Lines 221-250 at pages 16-18 in my opinion are not useful, as they do not contribute to a critical discussion. The authors should rather discuss their results at the light of the existing literature, reporting differences respect to previous works and enlightening the novelty of their own. Several works, where authors identified potential biomarkers associated with CAG pathology in rat urine sample by NMR and LC‐MS, have not been cited nor discussed. See:

- Cui, Jiajia, et al. "NMR-based metabonomics and correlation analysis reveal potential biomarkers associated with chronic atrophic gastritis." Journal of Pharmaceutical and Biomedical Analysis 132 (2017): 77-86.

- Liu, Cai-chun, et al. "Comparative metabolomics study on therapeutic mechanism of electro-acupuncture and moxibustion on rats with chronic atrophic gastritis (CAG)." Scientific Reports 7.1 (2017): 1-11.

- Liu, YueTao, et al. "Urinary metabolomics research for Huangqi Jianzhong Tang against chronic atrophic gastritis rats based on 1H NMR and UPLC‐Q/TOF MS." Journal of Pharmacy and Pharmacology 72.5 (2020): 748-760.

- Liu, Yuetao, et al. "Material basis research for Huangqi Jianzhong Tang against chronic atrophic gastritis rats through integration of urinary metabonomics and SystemsDock." Journal of ethnopharmacology 223 (2018): 1-9.

8. Images resolution is not sufficient and should be improved.

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22 Sep 2020

Additional Editor Comments:Metabonomics research directions include genetic environment causes many factors, such as synthetic drugs' effects on the body of the system response, the research focus of metabonomics is medicine application is very extensive, widely applied in modern research of traditional Chinese medicine diagnosis drug toxicity evaluation of drug research and development, etc., such as researching the mechanism of action of traditional Chinese medicine (TCM) targets of traditional Chinese medicine syndrome meridian medical reports and safety evaluation of traditional Chinese medicine research field at present most of the potential biomarkers from urine, urine is not only collect invasive, but because in the urine urine rich and need a minimum of preparationTherefore, urine has been the most studied in demonstrating metabolic differences in various common and specific diseases, and the technology is now mature and has achieved good research results.

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1.The manuscript was revised in the style of PLOS ONE.

2.Before sampling, they fasted for 24 hours, drank normal water, collected urine, followed by anaesthesia with 2% pentobarbital sodium, blood collection of spleen, stomach and liver, and subsequent death.

3.Urine was collected and then used for further research.

4.Example ethics statement

This study was carried out in strict accordance with the recommendations in the Guidelines for ethical review of experimental Animal welfare(National Standard:GB/T 35892—2018). The protocol was approved by the Ethics Review Center of Shandong University of Traditional Chinese Medicine (Protocol Number:SDUTCM20190402003). All surgery was performed under sodium pentobarbital anesthesia, and all efforts were made to minimize suffering.

5.S3Fig and S6Fig

Fig. 3. Chromatogram in Total Ion Mode. 3-A: Typical Sample BPC in Positive Ion Mode, 3-B: Typical Sample BPC in Negative Ion Mode.

Fig. 6. Heat Map of the Differential Metabolites. A: Heat Map of the Differential Metabolites in CAG Rats; 6-B: Correlation Heat Map of Differential Metabolites in CAG Rats.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author.

The manuscript has been edited by American Journal Experts.

Reviewer #2:Urine test for diagnostic purposes: the necessity of early gastric cancer lacking characteristic symptoms, usually are diagnosed in the late, leading to poor prognosis and the prognosis of early gastric cancer was superior to that of advanced gastric cancer, so early diagnosis and treatment is the key to improving the prognosis of gastric cancer metabonomics as a new method in the field of systems biology, has become an integral part of the new tool in cancer research, and help to increase understanding of cancer pathogenesis and drug action mechanism of endogenous metabolic changes better understanding will promote the diagnosis and treatment of cancer in recent years, based on plasma urineOrganization and metabonomics analysis of gastric juice as well as the relationship between metabolic regulation and cancer research a series of progress has been made as biological specimens of detecting cancer biomarkers, urine is being more and more attention, not only because the urine is invasive collection, but also because in urine urine rich and need a minimum of preparation.

Rats are easier to get, and also easier to detect, because of their larger size and more pronounced symptoms.Compared to mice, rats are also economically viable.

The metabolic spectrum of rats can only provide a certain direction for clinical practice, but cannot provide complete guidance for clinical practice. These compounds can at least prove the success of the chronic atrophic gastritis model in rats, which is of certain significance for the diagnosis of diseases in rats.

Reviewer #2:

Specific comments:

1.PCA incredible figure outside the confidence interval for the abnormal samples, including format conversion in data processing before peak identification filter alignment and normalization of data pretreatment, its external data check plus or minus the total ion chromatograms of display and based on mass spectrometry of metabonomics research, in order to obtain reliable and high quality of metabolomics data, usually need to quality control, quality control (QC) .When testing using QC samples quality control theory, QC samples are the same, but in the process of sample extraction test analysis will be a system error, result in QC samples, there will be differences between the smaller the difference method of stability, the higher the better data quality, reflect on the PCA analysis diagram is the concentrated distribution of QC samples, specification data reliable QC samples gathered, good repeatability, stable system.1. In order to find biomarkers, the potential characteristic peak's relative standard deviation (RSD) in QC samples, that is, the coefficient of variation should not exceed 30%. If it does, the relevant characteristic peak should be deleted Assurance(QA) to remove the features with poor repeatability in QC samples in order to obtain a higher quality data set, which is more conducive to the detection of biomarkers in QC samples,RSD＜30%;The characteristic peak proportion can reach about 70%, indicating good data . Therefore, the data reflected in PCA confidence graph are valid data, while the samples outside the confidence interval are abnormal samples.PLS-DA and OPLS-DA: R2X, R2Y and Q2 relative to each model should be reported, as well as the number of wrong classifications in the training data set (i.e. internal validation).100 permutations have been chosen for models validation. Independent samples T-test should be used to determine in the different biomarker candidates obtained from the OPLS-DA models are statistically significant between the two groups at the univariate level.

2.In the registration of weight changes at 16 weeks, normality test was performed first, and then one-way ANOVA was used to conduct statistical test of weight between the blank group and the model group, and *P＜0.01 was found between the two groups,the difference was statistically significant.

3.LC-MS data is carried out on the basis of normalization, eliminating very few data that do not exist or have too low strength. The experiment adopts most data retained after QC, QA and normalization processing.Prior to urine metabolomics analysis, the Proteowizard software (V3.0.8789) was used to convert the obtained original data into mzXML format (XCMS input file format).Using R (v3.3.2) XCMS package is used to identify the peaks identification,peaks filtration,peaks alignment, the main parameters are bw = 5, PPM = 15, peakwidth = c (10, 20), mzwid = 0.015, mzdiff = 0.01, the method = centWave.The data matrix, including mass to charge ratio (M/Z), retention time (RT) and intensity, is obtained.In the positive ion mode 22,540 precursor molecules and the negative ion mode 18,837 precursor molecules were obtained. The data were exported to Excel for subsequent analysis. In order to make comparison of data of different orders, batch normalization of data regarding peak area was conducted.

4.The corrected positive ion data is after data pretreatment (format conversion peak recognition, filtering alignment and normalization), the data screened out have strong repeatability and good effect for urine metabolomics analysis

5.In the analysis of the diversity of urine metabolomics, potential biomarkers were identified through MS/MS experiments. Compounds were added in Table 1, including mass to charge ratio, M/Z Retention time, RT and Exact mass

6.In the * P＜0.5 and Impact＞0;The metabolic pathways of include D-glutamine and D-glutamate metabolizing,histamine and purine metabolizing pathways,In these pathways,D-glutamine and D-glutamic acid metabolism were up-regulated,three metabolites in histamine metabolism were down-regulated,and six metabolites in purine metabolism were down-regulated.

7.In recent years, metabonomics has been widely used in modern Chinese medicine treatment of chronic gastritis.Cui.Jiajia Et al.[8]found that 3 plasma biomarkers (arginine,succinate and 3-hydroxybutyrate) and 2 urine biomarkers (α-ketoglutarate and valine) might be markers of CAG in the study on plasma and urine metabolites.Chen jiaolong [9] by using nuclear magnetic metabonomics technology including observation of the stomach meridian the CAG treatment, use of electroacupuncture in the rat stomach meridians beam door and foot three mile, found that serum ghrelin level stomach metabolism of liver kidney brain cortex spectral change, the rat gastric mucosa arrangement and the thickness of the gastric mucosa has different degrees of improvement, ghrelin and substance P expression in serum increased to normal level, serum glucose glycogen content increased, the stomach tissue of glutathione and glutamine levels, hypoxanthine creatinine nicotinamide in brain tissueThe content of malonic acid and dimethyl malonate increased, the content of glucodicarbonate and glycerin in liver tissues increased, and the content of glutamine hypoxanthine nucleoside asparagine asparagine and nicotinamide in kidney tissues increased.Liu Caichun Et al.[10]found that both electroacupuncture and moxibustion could restore various caG-induced metabolic changes, including membrane metabolism, energy metabolism and neurotransmitter function.Liu Yuetao [11] based on 1 H-NMR technical analysis astragalus chienchung soup to the CAG rats serum endogenous metabolites regulation function disorder, through multivariate statistical analysis, to clarify its regulation on chronic atrophic gastritis targets, related indicators associated with efficacy is established, by partial least-squares regression analysis and Met PA screening and treatment effect is most related metabolic pathways, find the root of remembranous milk vetch chienchung soup can obviously inhibit the CAG lesions, can obviously regulate 3 - hydroxy butyric acid lactic acid acetate succinate metabolites such as disorder, the CAG treatment is the main metabolic pathways of arginine - proline metabolismGlycerol metabolism and glycine - serine - threonine metabolism pathways. Sun Yina [12] found 59 qualitative and quantitative metabolites, and then PCA OPLS-DA VIP value was used to find 8 potential differential metabolites related to dampis-heat syndrome of spleen and stomach. The metabolites of trimethyl-oxide of taurine gonosaccharide glycerol and glucose were up-regulated, and the metabolites of trimethyl-trimethyl-oxide and trigonelline phosphate creatine were down-regulated.It is concluded from the above studies that metabonomic techniques have been widely used in traditional Chinese medicine. However, 1H-NMR metabolism technology is mostly used in the studies on chronic atrophic gastritis. In this study, LC-MS technology is used to elucidate the small molecule action mechanism and related target pathways of CAG.

8.Some of the images resolution has been improved.

Submitted filename: Response to Reviewers.docx

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14 Oct 2020

PONE-D-20-19248R1

Urine metabolomics of rats with chronic atrophic gastritis

PLOS ONE

Dear Dr. Tao Han,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #1: The article is well structured, easy to read and will be interesting to readers. The authors significantly corrected the article in accordance with the comments of the reviewers at the previous stage of the review. I believe that the article in its present form can be recommended for publication.

Reviewer #2: The authors have sufficiently addressed part of the previous comments. English has been improved, as well as the discussion. See the appended file for minor comments. Please answer to each question separately, point-to-point, and add any relevant information to the manuscript.

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Submitted filename: PONE-D-20-19248_reviewer_2.docx

Click here for additional data file.

19 Oct 2020

Response to Reviewers

· Which software have you used for statistical analysis (e.g. SIMCA)?

RE：The analysis software used for multidimensional statistical analysis is SIMCA-P (V13.0).In addition, the calculation of P value is t-test

· Univariate analysis should be conducted with p values adjusted for multiple testing.

RE：The t-test that we use for univariate statistical analysis, in general,t-test is p＜0.05 is significant, p＜0.01 is very significant, biological statistical methods are basically such a display of differences;This section provides references for significance of biological repeated screening p values.

· Please add the information about permutation test in the text.

RE：Permutation is the result of 200 permutation tests，PLD-DA permutation test in positive ion mode is R2=（0.0，0.91），Q2=（0.0，0）PLD-DA permutation test in negative ion mode is R2=（0.0，0.9），Q2=（0.0，-0.39）

· Which signal has been selected for peak normalization? Or do the authors mean that the signals have been normalized to the sum of peak areas?

RE：According to the results, the original peak area, which is the relative strength value, is calculated，then the original peak area is standardized,batch normalization of peak area of data.Data analysis is based on standardized data.

·Have been peak areas centered and/or scaled and/or transformed before multivariate analysis?

RE：According to the results, the original peak area, which is the relative strength value, is calculated，then the original peak area is standardized,batch normalization of peak area of data.Data analysis is based on standardized data.Multidimensional statistical analysis was performed based on the data after standardized processing.

·Have xenobiotics been eliminated from the list of possible metabolites? As far as I know, compounds as marmesin acetate, syringic acid o taxifolin are not endogenous.

RE：Metabolite characterization was based on mass - MZ, RT and comparison of secondary fragments in the database.In the process of mass spectrometry comparison to the database, the exogenous substances may be characterized as the secondary debris with a higher grading value and a consistent retention time of mass and charge ratio, but it may also be other substances, which means that the mass and charge ratio and retention time can correspond to many substances. Therefore, we chose the secondary debris with a high matching degree.Therefore, there will be exogenous substances proposed by the reviewers.

Submitted filename: Response to Reviewers.doc

Click here for additional data file.

27 Oct 2020

Urine metabolomics of rats with chronic atrophic gastritis

PONE-D-20-19248R2

Dear Dr. Tao Han,

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Additional Editor Comments:

Dear Authors, the current version of the manuscript is improved and thus it's now suitable to be published in PloSone Journal.

29 Oct 2020

PONE-D-20-19248R2

Urine metabolomics of rats with chronic atrophic gastritis

Dear Dr. Han:

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