A primary research on male patients diagnosed as acute brucellosis with untargeted metabolomics technique

A primary research on male patients diagnosed as acute brucellosis with untargeted metabolomics technique | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A primary research on male patients diagnosed as acute brucellosis with untargeted metabolomics technique Qiang Li, Qiong Wang, Jianling Wang, Xin Zhou, Yanmei Zhao, Hongmei Xue, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3008499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Brucellosis is a worldwide zoonotic disease through the consumption of unpasteurized dairy products, inhalation of infected aerosolized particles, and direct or indirect contact with infected animals. A timely and accurate diagnosis is key to the clinical management of brucellosis. Method The study included 8 male brucellosis patients and 8 control subjects. The serum samples were analyzed using Ultra performance liquid chromatography/tandem mass spectrometry ( UPLC- MS/MS ). The structural identification of these different metabolites was performed by comparing the exact mass data, retention time, and corresponding MS/MS fragments with those of mzCloud, mzVault and MassList database. We applied univariate analysis to calculate the statistical significance.The metabolites with VIP > 1 and P-value < 0.05 and fold change(FC) ≥ 2 or FC ≤ 0.5 were considered to be differential metabolites. Results 25 different metabolites were identified. 6 metabolites were down-regulated, and 19 metabolites were up-regulated. Different metabolites identified in positive ionizationmodewereL-Kynurenine, (3,4-Dimethoxyphenyl) acetic acid, D- Sphingosine, D-(+)-Proline, 2-Amino-1,3-octadecanediol, Kahweol, 2- Hydroxycinnamic acid, Kynurenic acid, 5-(tert-butyl)-2- methyl-N-(4-nitrophenyl) – 3-furamide, 2-chloro-6-(4- methoxypheno xy)benzonitrile, and 1,4- dihydroxyheptadec =-16-en-2-yl acetate; Different metabolites identified in negative ionization mode were Lignoceric acid, Pentacosanoic acid, Xanthine, L-Phenylalanine, D-(+)-Tryptophan, Oleoyl-L-α-lysophosphatidic acid, γ- Aminobutyric acid, L- Glutamic acid, Citric acid, 2-(1H-benzimidazol-2-yl)-3 -(1,3- benzodioxol − 5-yl) acrylonitrile, Perfluorooctanoic acid, 4-Hexylresorcinol, Sorbitan monopalmitate, and Deoxycholic acid. Conclusion There were existing the metabolic changes of male patients diagnosised as acute brucellosis, which were involved in tryptophan metabolism, glyoxylate and dicarboxylate metabolism,as well as biosynt hesis and metabolism of amino acids . Brucellosis Metabolomics Metabolic profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Brucellosis is a worldwide zoonotic disease caused by Brucella spp. Human brucellosis is most often caused by B. melitensis and less often by B. abortus or B. suis [1], through the consumption of unpasteurized dairy products or inhalation of infected aerosolized particles, and direct or indirect contact with infected animals [2]. Brucella infection causes lesions in many organs and systems in the human body, such as spleen, liver, testis, bone marrow, reticuloendothelial cells as well as cardiovascular system and osteoarticular system [3].The main clinic manifestions are fever, fatigue, back pain, joint and muscle pain, hyperhidrosis[4]. The diagnosis of brucellosis is challenging because unusual presentations and non-specific symptoms can lead to misdiagnosis and treatment delay [5]. A timely and accurate diagnosis is key to the clinical management of brucellosis. Metabolomics, as an important sub-discipline of systematic biology, is another research field of systematic biology after genomics, proteomics and transcription [6,7]. Therefore, Metabolomics has the potential to identify associations between metabolism and phenotype, and to further support studies on related metabolic pathways and networks in the organism[8,9]. Untargeted metabolomics aims to measure the broadest range of metabolites present in an extracted sample without a priori knowledge of the metabolomics[10]. High-resolution metabolomics methods provides an unbiased comprehensive analysis of metabolites in a biological system，coupled with advanced methods in data extraction and bioinformatics[11]. Metabolites are the final products of cellular regulatory progression, and their levels can be regarded as the ultimate response of biological systems to genetic or ecological alterations[12]. In the mainland of China, brucellosis is a major public health problem due to increasing of human brucellosis prevalence, up to 2019 , there were 44 036 cases with the incidence of 3.2513/100 000 [13]. The incidence rate of brucellosis in Qinghai province has increased from 0.04 /100 000 in 2011 to 1.96 /100 000 in 2018, and the confirmed cases were distributed across 31 counties within the entire province [14]. Brucellosis more commonly affects males rather than females with a ratio of 5:2 - 5:3 in endemic areas[15], meanwhile, most brucellosis patients in China were male due to their interaction with livestock and products [16]. The metabolic adaptation of Brucella to specific stresses such as high temperature, nutrient starvation, or peroxide has been explored in previous studies[16,17]. Brucella regulate its metabolism by decreasing its energy usage and secondary metabolite biosynthesis, while enhancing two-component system and iron acquisition to cope with the intracellular environment[18]. We first went on untargeted metabolomics research among male brucellosis patients at acute period to probe metabolic changes of human brucellosis, which understand its body changes for further prevention and treatment . Materials and Methods Study participants 16 male were included in this study, case group including 8 male brucellosis patients, control group including 8 matching male. These people all were from Qinghai province. Through matching, brucellosis patients and controls were similar to in occupations and age. According to the national diagnostic criteria of brucellosis in China (WS/T207-2019), male patients were diagnosed as acute brucellosis with epidemical investigation, clinical symptoms and positive serum examination results of rose bengal plate agglutination test ( RBPT) and serum agglutination test (SAT, a titer was above or equal 1/100), while controls had no clinical symptoms and negative results of serum RBPT and SAT. Meanwhile, male who were diagnosed as chronic systemic disease and acute inflammatory disease were also excluded. Metabolites Extraction The samples (100 µL) were placed in the EP tubes and resuspended with prechilled 80% methanol and 0.1% formic acid by well vortex. Then the samples were incubated on ice for 5 min and centrifuged at 15,000 g, 4°C for 20 min. Some of supernatant was diluted to final concentration containing 53% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and then were centrifuged at 15000 g, 4°C for 20 min. Finally, the supernatant was injected into the UHPLC-MS/MS system analysis [ 19 , 20 , 21 ]. UHPLC-MS/MS Analysis Ultra performance liquid chromatography/tandem mass spectrometry ( UPLC- MS/MS ) analyses were performed using a Vanquish UHPLC system (ThermoFisher, Germany) coupled with an Orbitrap Q ExactiveTM HF mass spectrometer (Thermo Fisher, Germany) in Novogene Co., Ltd. ( Beijing, China). Samples were injected onto a Hypesil Gold column (100×2.1 mm, 1.9µm) using a 17-min linear gradient at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, Ph 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min;100-2% B, 14.1 min༛2% B, 17 min. Q Exactive TM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 40 arb and aux gas flow rate of 10 arb. Data quality control Due to the characteristics of external factors and changes rapidly of metabolomics after being disturbed, so, it is necessary to establish data quality control ( QC ) for obtaining stable and accurate metabolomics results. QC samples were made of equal volume mixing of experimental samples. The QC samples were detected before, during and after UHPLC-MS/MS injection. Before injection, the QC samples were used to monitor instrument status and balance LC-MS system; during detecting, the QC samples inserted were used to evaluate stability of the whole experiment process and data quality control analysis; after detecting, the QC samples together with detecting samples were scanned partly to obtain Secondary mass spectrometry for metabolites qualitatively . Data processing and metabolite identification The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, ThermoFisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity,100, 000.After that, peak intensities were normalized to the total spectral intensity. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud ( https://www.mzcloud.org/ ), mzVault and MassList database to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS ( CentOS release 6.6),When data were not normally distributed, normal transformations were attempted using of area normalization method. Data Analysis These metabolites were annotated using the KEGG database ( https://www.genome.jp/kegg/pathway.html ), HMDB database ( https://hmdb.ca/ metabolites) and LIPIDMaps database ( http://www.lipidmaps.org/ ). Principal components analysis ( PCA ) and Partial least squares discriminant analysis ( PLS-DA) were performed at metaX (a flexible and comprehensive software for processing metabolomics data).We applied univariate analysis (t-test) to calculate the statistical significance (P- value).The metabolites with VIP > 1 and P -value < 0.05 and fold change(FC) ≥ 2 or FC ≤ 0.5 were considered to be differential metabolites. Volcano plots were used to filter metabolites of interest which based on log2 (Fold Change) and -log10( P -value) of metabolites. For clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were ploted by Pheatmap package in R language. The correlation between differential metabolites were analyzed by cor ( ) in R language ( method = pearson ).Statistically significant of correlation between differential metabolites were calculated by cor.mtest ( ) in in R language. P -value < 0.05 was considered as statistically significant and correlation plots were ploted by corrplot package in R language. The functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathways enrichment of differential metabolites was performed, when ratio were satisfied by x/n > y/N, metabolic pathway were considered as enrichment, when P -value of metabolic pathway < 0.05, metabolic pathway were considered as statistically significant enrichment. Results General conditions of target population Average ages of male patients were (37.75 ± 10.29 ) years old ,while that of controls were (41.00 ± 12.41) years old, there were not statistic significance in age comparsion ( t = 0.570, p = 0.578 ) . With epidemical investigation, 8 male patients had a direct or indirect history of contacting with infected animals. The main clinic manifestation are fatigue, fever, hyperhidrosis, joint pain, back pain, and muscle pain. And, serum samples were detected, which RBPT were all positive and high SAT titers ( 1:100 ,or above 1:100), while controls had no clinical symptoms and negative serum examination results of RBPT and SAT. Table 1 . Table 1 , General conditions of target population Groups patients controls Number 8 8 Gender male male Age (years) 37.75 ± 10.29 41.00 ± 12.41 Symptoms fatigue 37.5% ( 3/8 ) 0 fever 37.5% ( 3/8 ) 0 hyperhridrosis 37.5% ( 3/8 ) 0 joint pains 75.0% ( 6/8 ) 0 back pains 12.5% ( 1/8 ) 0 muscule pains 12.5% ( 1/8 ) 0 Experimention examination RBPT + - SAT ≥ 1:100 - Metabolomics detecting and data analyzing Data quality control Based on the relative quantitative values of metabolites, The Pearson correlation coefficient of three QC samples was calculated. The higher the correlation coefficient of QC samples ( R 2 is closer to 1), the better the stability of the whole detecting process and the data quality. Meanwhile, the smaller the difference of QC samples, the better the stability of the whole method and the data quality, which were reflected in the PCA analysis diagram, that is, the distribution of QC samples might gather together. Figure 1 . Quantitative results of metabolites Samples were detected in positive ion and negative ion models with UHPLC- MS/MS. 947 metabolites were detected. Among of them, 348 spectral features from each sample were detected in negative ion model with the mass-to- charge ratio range from 101.02 to 1151.71, meanwhile, 600 spectral features from each sample were detected in positive ion model with the mass- to-charge range ratio were from 104.07 to 81179.32. The peaks extracted from all samples and QC samples were analyzed by PCA after Univariate scaling ( Univariate normalization) Fig. 2 , 3 Metabolite classification annotation KEGG, HMDB, and LIPIDMaps databases were used for functional and classification annotation of metabolites identified. By using these databases, the metabolites identified were annotated to understand the functional characteristics and classification.7 classifications were annotated in KEGG database. 12 classifications were annotated in HMDB database.7 classifications were annotated in LIPIDMaps database. Figure 4 Different metabolites judgement The PCA method and PLS-DA model were used to observe the total distribution tendency between patients and controls, as well as stability of model among two groups. According to VIP value, FC and P - value, 80 different metabolites were screened. 45 different metabolites were found. Table 2 . Table 2 Detecting results of metabolites with UHPLC-MS/MS Compared Samples Num. of Total Ident. Num. of Total Sig. Num. of Sig.Up Num. of Sig.down patients.vs.control_pos 600 39 25 14 patients.vs.control_neg 347 41 20 21 Total 947 80 45 35 The structural identification of these different metabolites was performed by comparing the exact mass data, retention time, and corresponding MS/MS fragments with those of mzCloud,mzVault and MassList database. Different metabolites identified in positive ionization mode were L-Kynurenine, (3,4- Dimethoxyphenyl) acetic acid, D-Sphingosine, D-(+)-Proline, 2-Amino-1,3- octadecanediol, Kahweol, 2-Hydroxycinnamic acid, Kynureni c acid, 5-(tert-butyl)-2- methyl-N-(4-nitrophenyl) − 3-furamide, 2-chloro-6-(4-methoxyphenoxy)benzonitrile, and 1,4-dihydroxyheptadec − 16-en-2-yl acetate; Different metabolites identified in negative ionization mode were Lignoceric acid, Pentacosanoic acid, Xanthine, L-Phenylalanine, D-(+)- Tryptophan, Oleoyl-L-α-lysophosphatidic acid, γ- Aminobutyric acid, L- Glutamic acid, Citric acid, 2-(1H-benzimidazol-2-yl)-3 -(1,3-benzodioxol-5-yl) acrylonitrile, Perfluorooctanoic acid, 4-Hexylresorcinol, Sorbitan monopalmitate, and Deoxycholic acid. These different metabolites were associated with brucellosis changes at acute period. Table 3 . Table 3 Different metabolites between male brucellosis patients and controls ID Name Formula Molecular Weight RT [min] m/z Pvalue ROC VIP Up Down Com_1996_pos L-Kynurenine C10H12N2O3 208.08475 3.804 209.09203 0.0042 0.890625 2.28 up Com_507_pos D-Sphingosine C18H37NO2 299.28178 13.247 300.28894 0.0187 0.84375 1.97 up Com_6114_pos (3,4-Dimethoxyphenyl)acetic acid C10H12O4 196.07342 11.369 197.08058 0.0196 0.84375 1.99 up Com_498_pos 1,4-dihydroxyheptadec-16-en-2-yl acetate C19H36O4 328.26061 14.695 329.26791 0.0204 0.8125 1.94 up Com_130_pos D-(+)-Proline C5H9NO2 115.06321 1.472 116.0705 0.0228 0.8125 1.97 up Com_2275_pos 2-Amino-1,3-octadecanediol C18H39NO2 301.29748 13.365 302.30463 0.0288 0.828125 1.85 up Com_5716_pos Kahweol C20H26O3 314.18763 13.178 315.19498 0.0296 0.84375 1.86 down Com_6439_pos 5-(tert-butyl)-2-methyl-N-(4-nitrophenyl)-3-furamide C16H18N2O4 302.12623 10.497 303.13348 0.0404 0.796875 1.82 up Com_133_pos 2-Hydroxycinnamic acid C9H8O3 164.04726 2.351 165.05453 0.0413 0.828125 1.75 up Com_8577_pos Kynureni c acid C10H7NO3 189.0425 7.763 190.04974 0.0426 0.828125 1.74 up Com_10729_pos 2-chloro-6-(4-methoxyphenoxy)benzonitrile C14H10ClNO2 259.0392 11.777 260.04651 0.0474 0.859375 1.81 up Com_1077_neg Lignoceric Acid C24H48O2 368.36556 16.245 367.35831 0.0004 0.953125 2.33 down Com_335_neg Pentacosanoic acid C25H50O2 382.38139 16.548 381.37415 0.005 0.859375 2 down Com_400_neg Xanthine C5H4N4O2 152.03325 2.106 151.02597 0.008 0.890625 1.92 up Com_95_neg L-Phenylalanine C9H11N O2 165.07883 4.533 164.07155 0.0122 0.84375 1.87 up Com_71_neg D-(+)-Tryptophan C11H12N2O2 204.08983 5.907 203.08263 0.0163 0.828125 1.9 up Com_931_neg Oleoyl-L-α-lysophosphatidic acid C21H41O7P 436.25918 14.919 435.25195 0.0181 0.84375 1.76 down Com_1097_neg γ-Aminobutyric acid C4H9NO2 103.06328 1.273 102.056 0.0207 0.78125 1.74 up Com_96_neg L-Glutamic acid C5H9NO4 147.053 1.274 146.0457 0.0242 0.8125 1.7 up Com_440_neg Citric acid C6H8O7 192.02699 1.218 191.01967 0.0281 0.8125 1.71 down Com_2657_neg 2-(1H-benzimidazol-2-yl)-3-(1,3-benzodioxol-5-yl)acrylonitrile C17H11N3O2 289.08398 2.259 288.07672 0.0294 0.8125 1.64 up Com_2614_neg Perfluorooctanoic acid C8HF15O2 413.97433 12.385 412.96689 0.0308 0.859375 1.73 up Com_366_neg 4-Hexylresorcinol C12H18O2 194.13055 11.898 193.12325 0.0397 0.78125 1.56 down Com_2268_neg Sorbitan monopalmitate C22H42O6 402.29824 14.886 401.29099 0.0446 0.765625 1.52 up Com_109_neg Deoxycholic acid C24H40O4 392.29303 13.144 391.28568 0.0472 0.8125 1.56 up Different metabolites analyzing Hierarchical cluster analysis was carried out to obtain the differences of metabolic expression patterns between two groups and within the same comparison. Correlation analysis of differential metabolites and richment analysis of KEGG were also went. Figure 4 Discussion Brucellosis poses a threat to public health, meanwhile, brucellosis also produces significant economic losses to the animal industry worldwide because of abortion, infertility and decrease in milk and meat production caused by Brucella [22 ]. At least 500,000 cases of human brucellosis are reported annually worldwide [ 23 ], especially in the Mediterranean region, South and Central America, Africa, Asia, the Arabian Peninsula, the Indian subcontinent, Eastern Europe, the Middle East, and China [ 24 ]. At present, human brucellosis has been reported in all 32 mainland provinces of China [ 25 ]. The geographical extent of human brucellosis in China has been expanding with an increasing incidence in each province [ 26 ]. According to the latest data released by the China centers for disease control and prevention ( http://www.chinacdc.cn ), there were a total of 44,036 brucellosis cases occurred in China in 2019[ 8 ]. Brucellosis is caused by Brucella spp. Major virulence factors of Brucella are lipopolysaccharide (LPS), T4SS secretion system and BvrR/BvrS system, which allow interaction with host cell surface, formation of an early, late BCV ( Brucella containing vacuole) and interaction with endoplasmic reticulum (ER) when the bacteria multiply[ 27 ]. Brucella can cause many metabolic changes. The second messenger cyclic di-GMP (c-di-GMP) is an important bacterial regulator that alters metabolism and virulence in response to environmental cues such as phosphate and nutrient availability, oxygen and nitric oxide levels, and light[ 28 ]. During early infection of Brucella , elevating c-di-GMP enhanced type IV secretion system activity, however, during its residence in ER-derived compartments, decreased c-di-GMP levels ramped up nutrient acquisition ( iron in particular)[ 29 ]. This metabolic level has proven an important area of systems biology with the aim to pinpoint putative metabolites related to disease, genetic variation or nutritional interventions[ 30 ]. Through comparative transcriptome analysis, Brucella caused many differential genes expressions, involving the carbohydrate metabolism, nitrogen metabolism, and iron metabolism [ 31 ]. Some amino acid metabolic pathways and transport systems were disrupted in B melitensis, B. abortus , and B suis cellular and animal models of infection [ 32 ]. Bacterial proline utilization (Put) systems are composed of the bifunctional enzyme proline dehydrogenase PutA and its transcriptional activator PutR. PutR acts as the transcriptional activator of putA, with PutA acting as a functional proline dehydrogenase in Brucella to replicate and survive within its host [ 33 ]. In this study, we first probed the metabolic changes of male brucellosis pateints with metabolomics method. With UHPLC-MS/MS and bioinformatics analysis, 25 metabolites were identified. 6 metabolites were down-regulated, while ,19 metabolites were up-regulated. These metabolites were involved in tryptophan metabolism, glyoxylate and dicarboxylate metabolism.,as well as biosynthesis and metabolism of amino acids . Among of these metabolites, D-(+)-Proline and GABA have been found to relate with Brucella . Prolidase, a cytosolic exopeptidase, is related with collagen re-synthesis and cell growth. Serum prolidase levels were increased among brucellosis patients, which indicated that the pathogenesis of brucellosis was in relation to collagen metabolism [ 34 ]. Meanwhile, Proline racemase protein A ( prpA ) gene, a molecular virulence factor which can put the host’s immune system in an anergic state, was presence a very high level in human Brucella isolates [ 35 ]. GABA was transported under nutrient limiting conditions, and GABA transport was regulated by AbcR1 and AbcR2 in B. abortus , which indicated that GABA and the Gts system was important for the pathogenesis of Brucella [ 36 ]. 4Hexylresorcinol (4HR) is a small organic compound, which molecular weight solubiliy in water and octanol-water partition coefficient were 194.27 g/mol, 800 mg/L and 3.9, respectively [37]. 4HR exerted its anti-oxidant effects by reducing expression of reactive oxygen species (ROS) and tumor necrosis factor-α (TNF-α) via HIF-mediated and MMP-mediated pathways[38]. Lysophosphatidic acid (LPA) is an extra-cellular and naturally occurring phospholipid mediator that interacts with G-protein coupled transmembrane receptors (GPCRs) and activates multiple cellular processes such as apoptosis, morphogenesis, differentiation, motility and cell proliferation. Conclusion We first probed the metabolic changes of male patients diagnosised as acute brucellosis with UHPLC-MS/MS technology and bioinformatics analysis. 25 different metabolites were identified. 6 metabolites were down-regulated, and 19 metabolites were up-regulated. These metabolites were involved in tryptophan metabolism, glyoxylate and dicarboxylate metabolism, as well as biosynthesis and metabolism of amino acids . Declarations Ethics approval and consent to participate The study was carried out in compliance with the ethical principles outlined in the world medical association Helsinki’s declaration. This study was approved by the ethics committee of the Qinghai institute for endemic disease prevention and control. All patients and controls signed an informed consent form. Consent for publication Not applicable. Availability of data and materials All data generated or used during the study are available from the corresponding author and first author upon reasonable request. Competing interests The authors have no competing interests to declare Funding NO Authors' contributions Designed the study ZJZ and ZZQ； Performed the analysis and interpretation of the data：YMZ，HMX，JQL，YYC，JC；Analyzed the results and drafted the manuscript：QL， QW，JLW，XZAll authors read and approved the final manuscript. All authors read and approved the manuscript. Acknowledgements NO References Spicic S, Zdelar-Tuk M, Ponsart C, Hendriksen RS, Reil1 L , et al .New Brucella variant isolated from Croatian cattle. BMC Veterinary Research, 2021,17:126. Goonaratna C. （2009）Brucellosis in humans and animals. 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Environmental Health and Preventive Medicine， 25:1 . https://doi.org/10.1186/s12199-019-0839-z GŁOWACKA P, Żakowska D, Naylor K, Niemcwicz M, Bielawska- DRÓZD A. （2018）Brucella – virulence factors, pathogenesis and treatment . Polish J Microbiology，67( 2), 151–161. doi:10.21307/pjm-2018-029. Römling U, Galperin MY, Gomelsky M.（2013）Cyclic di-GMP: the first 25 years of a universal bacterial second messenger. Microbiol Mol Biol Rev 77:1–52. Khan M, Harms JS, Marim FM, Armon L, Hall CL, Liu YP, et al. （2016） The bacterial second messenger cyclic di-GMP regulates Brucella pathogenesis and leads to altered host immune response. Infect Immun 84:3458 –3470. Weckwerth W, Loureiro ME, et al. (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci USA,101(20), 7809–7814. Liu W, Dong H, Li J, Qu QX, Lv YJ, Wang XL, et al. （2015）RNA-seq reveals the critical role of OtpR in regulating Brucella melitensis metabolism and virulence under acidic stress. Sci. Rep. 5, 10864 . Ronneau S, Moussa S, Barbier T, Conde-Álvarez R, Zuniga-Ripa A ,et al. （2016）Brucella, nitrogen and virulence. Crit Rev Microbiol, 2016;42:507–525. Caudill MT , Budnick JA , Sheehanet LM , Lehmanal LR , Purwantini E, Mukhopadhyay B, et al. （2017）Proline utilization system is required for infection by the pathogenic α-proteobacterium Brucella abortus. Microbiology ，163:970–979 DOI 10.1099/mic.0.000490 Budnick JA, Sheehan LM, Benton AH, Pitzer JE, Kang L, Michalak P, et al. (2020) Characterizing the transport and utilization of the neurotransmitter GABA in the bacterial pathogen Brucella abortus. PLoS One 15(8): e0237371. doi:10.1371/journal.pone.0237371. DİZDAR OS ,TURUNÇ ÖZDEMİR A , BAŞPINAR O, KOÇER D, Katircilar Y, Çelik I. (2019) Serum prolidase level in patients with brucellosis and its possible relationship with pathogenesis of the disease: a prospective observational study. Turk J Med Sci49: 1479-1483. Hashemifar I, Masjedian Jazi F, Yadegar A, Amirmozafari N. （2017）Prevalence of proline racemase/ hydroxyproline epimerase gene in human brucella isolates in Iran. Med J Islam RepubIran, 31.57. Nikolaev YA, Tutel’yan AV, Loiko NG, Buck J, Sidorenko SV, Lazareva I, et al. (2020) The use of 4-Hexylresorcinol as antibiotic adjuvant. PLoS ONE 15(9): e0239147. Jo YY , Kim DW, Choi JY , Kim SG . （2019）4-Hexylresorcinol and silk sericin increase the expression of vascular endothelial growth factor via different pathways. Sci Rep. 9(1):3448.doi: 10.1038/s41598-019-40027-5. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3008499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":206965644,"identity":"bfb0fcd9-6cde-44d1-8b9c-fd2c0b15c644","order_by":0,"name":"Qiang Li","email":"","orcid":"","institution":"Qinghai Institute for Endemic Disease Prevention and Control","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""},{"id":206965645,"identity":"045ea608-7293-4587-95f7-5198ea9e33f0","order_by":1,"name":"Qiong Wang","email":"","orcid":"","institution":"Qinghai Health Vocational and Technical College， Xining 810010","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Wang","suffix":""},{"id":206965647,"identity":"a799ec70-30ae-416e-ac0f-28811a6dac36","order_by":2,"name":"Jianling Wang","email":"","orcid":"","institution":"Qinghai Institute for Endemic Disease Prevention and Control","correspondingAuthor":false,"prefix":"","firstName":"Jianling","middleName":"","lastName":"Wang","suffix":""},{"id":206965648,"identity":"ca23ccba-4ec4-41f4-b660-85fe5ecb71ed","order_by":3,"name":"Xin Zhou","email":"","orcid":"","institution":"Qinghai Institute for Endemic Disease Prevention and Control","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhou","suffix":""},{"id":206965650,"identity":"b5eb84ac-a355-4df2-8e25-6161763ac0bd","order_by":4,"name":"Yanmei Zhao","email":"","orcid":"","institution":"Qinghai Institute for Endemic Disease Prevention and 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Endemic Disease Prevention and Control","correspondingAuthor":true,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Zhao","suffix":""},{"id":206965657,"identity":"0ecc5d7d-2688-4603-b319-be3bbf7aaf2d","order_by":10,"name":"Zhizhen Qi","email":"","orcid":"","institution":"Qinghai Institute for Endemic Disease Prevention and Control","correspondingAuthor":false,"prefix":"","firstName":"Zhizhen","middleName":"","lastName":"Qi","suffix":""}],"badges":[],"createdAt":"2023-06-01 07:31:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3008499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3008499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38311504,"identity":"81e9a9d9-1042-4bd0-9ce8-c59ab9bd7f80","added_by":"auto","created_at":"2023-06-09 19:26:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":452536,"visible":true,"origin":"","legend":"\u003cp\u003eA-1 Pearson correlation between pos QC samples;A-2 Pearson correlation between neg QC samples;B-1 Principal component analysis(PCA) score plot in positive ion mode of total samples ;B-2 Principal component analysis(PCA) score plot in negative ion mode of total samples\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3008499/v1/66b6eb2b800de0c05c8798b2.png"},{"id":38311914,"identity":"1356d060-03d6-41a4-a25f-b306b8b8fc96","added_by":"auto","created_at":"2023-06-09 19:34:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249468,"visible":true,"origin":"","legend":"\u003cp\u003eA-1 Partial least squares discrimination analysis( PLS-DA) in positive ion mode between patients and controls samples ;A-2 Partial least squares discrimination analysis( PLS-DA) in negative ion mode between patients and controls samples; B-1 Principal component analysis(PCA) score plot in positive ion modebetween patients and controls samples ;B-2 Principal component analysis(PCA) score plot in negative ion mode between patients and controls samples\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3008499/v1/bce0a21359182ca8927a5b8a.jpg"},{"id":38311507,"identity":"30179a34-6090-4d6e-a596-07a9aac4e99c","added_by":"auto","created_at":"2023-06-09 19:26:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1100581,"visible":true,"origin":"","legend":"\u003cp\u003eA-1 The total ionization graph in positive ion mode of patients;A-2The total ionization graph in negative ion mode of patients;B-1 The total ionization graph in positive ion mode of controls;B-2The total ionization graph in negative ion mode of controls;\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3008499/v1/9fd7a62f106f8ec459c8df7b.png"},{"id":38311505,"identity":"b06357b0-5999-4c14-97dd-dd90e1d33219","added_by":"auto","created_at":"2023-06-09 19:26:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":645103,"visible":true,"origin":"","legend":"\u003cp\u003eA-1 KEGG pathway annotation in positive ion mode;A-2KEGG pathway annotation in negative ion mode;B-1 HMDB in positive ion mode;B-2 HMDB in negative ion mode; C-1 Lidpidmaps \u0026nbsp;annotation in positive ion mode ;C-2 Lidpidmaps annotation in negative ion mode of controls;\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3008499/v1/9f2c631920e1d59dabf13aab.png"},{"id":42343998,"identity":"7e11e665-2fda-46e1-ab63-d2ceedfc2365","added_by":"auto","created_at":"2023-08-30 05:45:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3008499/v1/f659ab03-b9b9-4f29-976e-870b63e2e362.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A primary research on male patients diagnosed as acute brucellosis with untargeted metabolomics technique","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrucellosis is a worldwide zoonotic disease caused by \u003cem\u003eBrucella\u003c/em\u003e spp. Human brucellosis is most often caused by \u003cem\u003eB. melitensis\u003c/em\u003e and less often by \u003cem\u003eB. abortus\u003c/em\u003e or \u003cem\u003eB. suis\u003c/em\u003e[1], through the consumption of unpasteurized dairy products or inhalation of infected aerosolized particles, and direct or indirect contact with infected animals [2]. \u003cem\u003eBrucella\u003c/em\u003e infection causes lesions in many organs and systems in the human body, such as spleen, liver, testis, bone marrow, reticuloendothelial cells as well as cardiovascular system and osteoarticular system [3].The main clinic manifestions are \u0026nbsp; fever, fatigue, back pain, joint and muscle pain, hyperhidrosis[4]. The diagnosis of brucellosis is challenging because unusual presentations and non-specific symptoms can lead to misdiagnosis and treatment delay [5]. A timely and accurate diagnosis is key to the clinical management of brucellosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetabolomics, as an important sub-discipline of systematic biology, is another research field of systematic biology after genomics, proteomics and transcription [6,7]. Therefore, Metabolomics has the potential to identify associations between metabolism and phenotype, and to further support studies on related metabolic pathways and networks in the organism[8,9]. Untargeted metabolomics aims to measure the broadest range of metabolites present in an extracted sample without a priori knowledge of the metabolomics[10]. High-resolution metabolomics methods provides an unbiased comprehensive analysis of metabolites in a biological system，coupled with advanced methods in data extraction and bioinformatics[11]. Metabolites are the final products of cellular regulatory progression, and their levels can be regarded as the ultimate response of biological systems to genetic or ecological alterations[12].\u003c/p\u003e\n\u003cp\u003eIn the mainland of China, brucellosis is a major public health problem due to increasing of human brucellosis prevalence,\u0026nbsp;up to 2019 , there were 44 036 cases with the incidence of 3.2513/100 000\u0026nbsp;[13]. The incidence rate of brucellosis in Qinghai province has increased from 0.04 /100 000 in 2011 to 1.96 /100 000 in 2018, and the confirmed cases were distributed across 31 counties within the entire province [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrucellosis more commonly affects males rather than females with a ratio of 5:2 - 5:3 in endemic areas[15], meanwhile, most brucellosis patients in China were male due to their interaction with livestock and products\u0026nbsp;[16].\u003c/p\u003e\n\u003cp\u003eThe metabolic adaptation of \u003cem\u003eBrucella\u003c/em\u003e to specific stresses such as high temperature, nutrient starvation, or peroxide has been explored in previous studies[16,17]. \u003cem\u003eBrucella\u003c/em\u003e regulate its metabolism by decreasing its energy usage and secondary metabolite biosynthesis, while enhancing two-component system and iron acquisition to cope with the intracellular environment[18]. We first went on untargeted metabolomics research among male brucellosis patients at acute period to probe metabolic changes of human brucellosis, which understand its body changes for further prevention and treatment .\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eStudy participants\u003c/b\u003e \u003c/p\u003e \u003cp\u003e16 male were included in this study, case group including 8 male brucellosis patients, control group including 8 matching male. These people all were from Qinghai province. Through matching, brucellosis patients and controls were similar to in occupations and age.\u003c/p\u003e \u003cp\u003eAccording to the national diagnostic criteria of brucellosis in China (WS/T207-2019), male patients were diagnosed as acute brucellosis with epidemical investigation, clinical symptoms and positive serum examination results of rose bengal plate agglutination test ( RBPT) and serum agglutination test (SAT, a titer was above or equal 1/100), while controls had no clinical symptoms and negative results of serum RBPT and SAT. Meanwhile, male who were diagnosed as chronic systemic disease and acute inflammatory disease were also excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMetabolites Extraction\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe samples (100 \u0026micro;L) were placed in the EP tubes and resuspended with prechilled 80% methanol and 0.1% formic acid by well vortex. Then the samples were incubated on ice for 5 min and centrifuged at 15,000 g, 4\u0026deg;C for 20 min. Some of supernatant was diluted to final concentration containing 53% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and then were centrifuged at 15000 g, 4\u0026deg;C for 20 min. Finally, the supernatant was injected into the UHPLC-MS/MS system analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eUHPLC-MS/MS Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUltra performance liquid chromatography/tandem mass spectrometry ( UPLC- MS/MS ) analyses were performed using a Vanquish UHPLC system (ThermoFisher, Germany) coupled with an Orbitrap Q ExactiveTM HF mass spectrometer (Thermo Fisher, Germany) in Novogene Co., Ltd. ( Beijing, China). Samples were injected onto a Hypesil Gold column (100\u0026times;2.1 mm, 1.9\u0026micro;m) using a 17-min linear gradient at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, Ph 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min;100-2% B, 14.1 min༛2% B, 17 min. Q Exactive TM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320\u0026deg;C, sheath gas flow rate of 40 arb and aux gas flow rate of 10 arb.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData quality control\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDue to the characteristics of external factors and changes rapidly of metabolomics after being disturbed, so, it is necessary to establish data quality control ( QC ) for obtaining stable and accurate metabolomics results. QC samples were made of equal volume mixing of experimental samples.\u003c/p\u003e \u003cp\u003eThe QC samples were detected before, during and after UHPLC-MS/MS injection. Before injection, the QC samples were used to monitor instrument status and balance LC-MS system; during detecting, the QC samples inserted were used to evaluate stability of the whole experiment process and data quality control analysis; after detecting, the QC samples together with detecting samples were scanned partly to obtain Secondary mass spectrometry for metabolites qualitatively .\u003c/p\u003e \u003cp\u003e \u003cb\u003eData processing and metabolite identification\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, ThermoFisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity,100, 000.After that, peak intensities were normalized to the total spectral intensity. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mzcloud.org/\u003c/span\u003e\u003cspan address=\"https://www.mzcloud.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), mzVault and MassList database to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS ( CentOS release 6.6),When data were not normally distributed, normal transformations were attempted using of area normalization method.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThese metabolites were annotated using the KEGG database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), HMDB database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmdb.ca/\u003c/span\u003e\u003cspan address=\"https://hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e metabolites) and LIPIDMaps database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lipidmaps.org/\u003c/span\u003e\u003cspan address=\"http://www.lipidmaps.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Principal components analysis ( PCA ) and Partial least squares discriminant analysis ( PLS-DA) were performed at metaX (a flexible and comprehensive software for processing metabolomics data).We applied univariate analysis (t-test) to calculate the statistical significance (P- value).The metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change(FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 or FC\u0026thinsp;\u0026le;\u0026thinsp;0.5 were considered to be differential metabolites. Volcano plots were used to filter metabolites of interest which based on log2 (Fold Change) and -log10( \u003cem\u003eP\u003c/em\u003e -value) of metabolites.\u003c/p\u003e \u003cp\u003eFor clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were ploted by Pheatmap package in R language. The correlation between differential metabolites were analyzed by cor ( ) in R language ( method\u0026thinsp;=\u0026thinsp;pearson ).Statistically significant of correlation between differential metabolites were calculated by cor.mtest ( ) in in R language. \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant and correlation plots were ploted by corrplot package in R language. The functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathways enrichment of differential metabolites was performed, when ratio were satisfied by x/n\u0026thinsp;\u0026gt;\u0026thinsp;y/N, metabolic pathway were considered as enrichment, when \u003cem\u003eP\u003c/em\u003e-value of metabolic pathway\u0026thinsp;\u0026lt;\u0026thinsp;0.05, metabolic pathway were considered as statistically significant enrichment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eGeneral conditions of target population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAverage ages of male patients were (37.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29 ) years old ,while that of controls were (41.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.41) years old, there were not statistic significance in age comparsion ( t\u0026thinsp;=\u0026thinsp;0.570, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.578 ) .\u003c/p\u003e \u003cp\u003eWith epidemical investigation, 8 male patients had a direct or indirect history of contacting with infected animals. The main clinic manifestation are fatigue, fever, hyperhidrosis, joint pain, back pain, and muscle pain. And, serum samples were detected, which RBPT were all positive and high SAT titers ( 1:100 ,or above 1:100), while controls had no clinical symptoms and negative serum examination results of RBPT and SAT. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, General conditions of target population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtrols\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5% ( 3/8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5% ( 3/8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyperhridrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5% ( 3/8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ejoint pains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.0% ( 6/8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eback pains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5% ( 1/8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emuscule pains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5% ( 1/8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimention examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1:100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMetabolomics detecting and data analyzing\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData quality control\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the relative quantitative values of metabolites, The Pearson correlation coefficient of three QC samples was calculated. The higher the correlation coefficient of QC samples ( R\u003csup\u003e2\u003c/sup\u003e is closer to 1), the better the stability of the whole detecting process and the data quality. Meanwhile, the smaller the difference of QC samples, the better the stability of the whole method and the data quality, which were reflected in the PCA analysis diagram, that is, the distribution of QC samples might gather together. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eQuantitative results of metabolites\u003c/h3\u003e\n\u003cp\u003eSamples were detected in positive ion and negative ion models with UHPLC- MS/MS. 947 metabolites were detected. Among of them, 348 spectral features from each sample were detected in negative ion model with the mass-to- charge ratio range from 101.02 to 1151.71, meanwhile, 600 spectral features from each sample were detected in positive ion model with the mass- to-charge range ratio were from 104.07 to 81179.32. The peaks extracted from all samples and QC samples were analyzed by PCA after Univariate scaling ( Univariate normalization) Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMetabolite classification annotation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKEGG, HMDB, and LIPIDMaps databases were used for functional and classification annotation of metabolites identified. By using these databases, the metabolites identified were annotated to understand the functional characteristics and classification.7 classifications were annotated in KEGG database. 12 classifications were annotated in HMDB database.7 classifications were annotated in LIPIDMaps database. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferent metabolites judgement\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe PCA method and PLS-DA model were used to observe the total distribution tendency between patients and controls, as well as stability of model among two groups. According to VIP value, FC and \u003cem\u003eP\u003c/em\u003e- value, 80 different metabolites were screened. 45 different metabolites were found. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e .\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetecting results of metabolites with UHPLC-MS/MS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompared Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNum. of Total Ident.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNum. of Total Sig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNum. of Sig.Up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNum. of Sig.down\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epatients.vs.control_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epatients.vs.control_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe structural identification of these different metabolites was performed by comparing the exact mass data, retention time, and corresponding MS/MS fragments with those of mzCloud,mzVault and MassList database. Different metabolites identified in positive ionization mode were L-Kynurenine, (3,4- Dimethoxyphenyl) acetic acid, D-Sphingosine, D-(+)-Proline, 2-Amino-1,3- octadecanediol, Kahweol, 2-Hydroxycinnamic acid, Kynureni\u003cb\u003ec\u003c/b\u003e acid, 5-(tert-butyl)-2- methyl-N-(4-nitrophenyl) \u0026minus;\u0026thinsp;3-furamide, 2-chloro-6-(4-methoxyphenoxy)benzonitrile, and 1,4-dihydroxyheptadec \u0026minus;\u0026thinsp;16-en-2-yl acetate; Different metabolites identified in negative ionization mode were Lignoceric acid, Pentacosanoic acid, Xanthine, L-Phenylalanine, D-(+)- Tryptophan, Oleoyl-L-α-lysophosphatidic acid, γ- Aminobutyric acid, L- Glutamic acid, Citric acid, 2-(1H-benzimidazol-2-yl)-3 -(1,3-benzodioxol-5-yl) acrylonitrile, Perfluorooctanoic acid, 4-Hexylresorcinol, Sorbitan monopalmitate, and Deoxycholic acid. These different metabolites were associated with brucellosis changes at acute period. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferent metabolites between male brucellosis patients and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMolecular\u003c/p\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT [min]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003em/z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_1996_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Kynurenine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC10H12N2O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208.08475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e209.09203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.890625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_507_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-Sphingosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC18H37NO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299.28178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300.28894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_6114_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3,4-Dimethoxyphenyl)acetic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC10H12O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196.07342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e197.08058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_498_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,4-dihydroxyheptadec-16-en-2-yl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC19H36O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e328.26061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e329.26791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_130_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-(+)-Proline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC5H9NO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115.06321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116.0705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_2275_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-Amino-1,3-octadecanediol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC18H39NO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e301.29748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e302.30463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.828125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_5716_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKahweol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC20H26O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314.18763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e315.19498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_6439_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-(tert-butyl)-2-methyl-N-(4-nitrophenyl)-3-furamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC16H18N2O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302.12623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e303.13348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.796875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_133_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-Hydroxycinnamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC9H8O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164.04726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165.05453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.828125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_8577_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKynureni\u003cb\u003ec\u003c/b\u003e acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC10H7NO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189.0425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e190.04974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.828125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_10729_pos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-chloro-6-(4-methoxyphenoxy)benzonitrile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC14H10ClNO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e259.0392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e260.04651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.859375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_1077_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLignoceric Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC24H48O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e368.36556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e367.35831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.953125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_335_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePentacosanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC25H50O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e382.38139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e381.37415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.859375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_400_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXanthine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC5H4N4O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152.03325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e151.02597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.890625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_95_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Phenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC9H11N O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165.07883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e164.07155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_71_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-(+)-Tryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC11H12N2O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204.08983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e203.08263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.828125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_931_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOleoyl-L-α-lysophosphatidic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC21H41O7P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e436.25918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e435.25195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_1097_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eγ-Aminobutyric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC4H9NO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.06328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_96_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Glutamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC5H9NO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e146.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_440_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC6H8O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192.02699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e191.01967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_2657_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-(1H-benzimidazol-2-yl)-3-(1,3-benzodioxol-5-yl)acrylonitrile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC17H11N3O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e289.08398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e288.07672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_2614_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerfluorooctanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC8HF15O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e413.97433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e412.96689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.859375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_366_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Hexylresorcinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC12H18O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194.13055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e193.12325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_2268_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorbitan monopalmitate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC22H42O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e402.29824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e401.29099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.765625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCom_109_neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeoxycholic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC24H40O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e392.29303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e391.28568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferent metabolites analyzing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHierarchical cluster analysis was carried out to obtain the differences of metabolic expression patterns between two groups and within the same comparison. Correlation analysis of differential metabolites and richment analysis of KEGG were also went. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBrucellosis poses a threat to public health, meanwhile, brucellosis also produces significant economic losses to the animal industry worldwide because of abortion, infertility and decrease in milk and meat production caused by \u003cem\u003eBrucella\u003c/em\u003e [22 ]. At least 500,000 cases of human brucellosis are reported annually worldwide [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e23\u003c/span\u003e], especially in the Mediterranean region, South and Central America, Africa, Asia, the Arabian Peninsula, the Indian subcontinent, Eastern Europe, the Middle East, and China [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. At present, human brucellosis has been reported in all 32 mainland provinces of China [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The geographical extent of human brucellosis in China has been expanding with an increasing incidence in each province [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. According to the latest data released by the China centers for disease control and prevention (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chinacdc.cn\u003c/span\u003e\u003cspan address=\"http://www.chinacdc.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), there were a total of 44,036 brucellosis cases occurred in China in 2019[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBrucellosis is caused by \u003cem\u003eBrucella\u003c/em\u003e spp. Major virulence factors \u003cem\u003eof Brucella\u003c/em\u003e are lipopolysaccharide (LPS), T4SS secretion system and BvrR/BvrS system, which allow interaction with host cell surface, formation of an early, late BCV (\u003cem\u003eBrucella\u003c/em\u003e containing vacuole) and interaction with endoplasmic reticulum (ER) when the bacteria multiply[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eBrucella\u003c/em\u003e can cause many metabolic changes. The second messenger cyclic di-GMP (c-di-GMP) is an important bacterial regulator that alters metabolism and virulence in response to environmental cues such as phosphate and nutrient availability, oxygen and nitric oxide levels, and light[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. During early infection of \u003cem\u003eBrucella\u003c/em\u003e, elevating c-di-GMP enhanced type IV secretion system activity, however, during its residence in ER-derived compartments, decreased c-di-GMP levels ramped up nutrient acquisition ( iron in particular)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This metabolic level has proven an important area of systems biology with the aim to pinpoint putative metabolites related to disease, genetic variation or nutritional interventions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Through comparative transcriptome analysis, \u003cem\u003eBrucella\u003c/em\u003e caused many differential genes expressions, involving the carbohydrate metabolism, nitrogen metabolism, and iron metabolism [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Some amino acid metabolic pathways and transport systems were disrupted in \u003cem\u003eB melitensis, B. abortus\u003c/em\u003e, and \u003cem\u003eB suis\u003c/em\u003e cellular and animal models of infection [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Bacterial proline utilization (Put) systems are composed of the bifunctional enzyme proline dehydrogenase PutA and its transcriptional activator PutR. PutR acts as the transcriptional activator of putA, with PutA acting as a functional proline dehydrogenase in \u003cem\u003eBrucella\u003c/em\u003e to replicate and survive within its host [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we first probed the metabolic changes of male brucellosis pateints with metabolomics method. With UHPLC-MS/MS and bioinformatics analysis, 25 metabolites were identified. 6 metabolites were down-regulated, while ,19 metabolites were up-regulated. These metabolites were involved in tryptophan metabolism, glyoxylate and dicarboxylate metabolism.,as well as biosynthesis and metabolism of amino acids .\u003c/p\u003e \u003cp\u003eAmong of these metabolites, D-(+)-Proline and GABA have been found to relate with \u003cem\u003eBrucella\u003c/em\u003e. Prolidase, a cytosolic exopeptidase, is related with collagen re-synthesis and cell growth. Serum prolidase levels were increased among brucellosis patients, which indicated that the pathogenesis of brucellosis was in relation to collagen metabolism [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Meanwhile, Proline racemase protein A ( prpA ) gene, a molecular virulence factor which can put the host\u0026rsquo;s immune system in an anergic state, was presence a very high level in human \u003cem\u003eBrucella\u003c/em\u003e isolates [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. GABA was transported under nutrient limiting conditions, and GABA transport was regulated by AbcR1 and AbcR2 in \u003cem\u003eB. abortus\u003c/em\u003e, which indicated that GABA and the Gts system was important for the pathogenesis of \u003cem\u003eBrucella\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e4Hexylresorcinol (4HR) is a small organic compound, which molecular weight solubiliy in water and octanol-water partition coefficient were 194.27 g/mol, 800 mg/L and 3.9, respectively [37]. 4HR exerted its anti-oxidant effects by reducing expression of reactive oxygen species (ROS) and tumor necrosis factor-α (TNF-α) via HIF-mediated and MMP-mediated pathways[38].\u003c/p\u003e \u003cp\u003eLysophosphatidic acid (LPA) is an extra-cellular and naturally occurring phospholipid mediator that interacts with G-protein coupled transmembrane receptors (GPCRs) and activates multiple cellular processes such as apoptosis, morphogenesis, differentiation, motility and cell proliferation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe first probed the metabolic changes of male patients diagnosised as acute brucellosis with UHPLC-MS/MS technology and bioinformatics analysis. 25 different metabolites were identified. 6 metabolites were down-regulated, and 19 metabolites were up-regulated. These metabolites were involved in tryptophan metabolism, glyoxylate and dicarboxylate metabolism, as well as biosynthesis and metabolism of amino acids .\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was carried out in compliance with the ethical principles outlined in the world medical association Helsinki\u0026rsquo;s declaration. This study was approved by the ethics committee of the Qinghai institute for endemic disease prevention and control. All patients and controls signed an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or used during the study are available from the corresponding author and first author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesigned the study ZJZ and ZZQ；\u0026nbsp;Performed the analysis and interpretation of the data：YMZ，HMX，JQL，YYC，JC；Analyzed the results and drafted the manuscript：QL，\u0026nbsp;QW，JLW，XZAll authors read and approved the final manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;NO\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSpicic S, Zdelar-Tuk M, Ponsart C, Hendriksen RS, Reil1 L , et al .New \u003cem\u003eBrucella\u003c/em\u003e variant isolated from Croatian cattle. BMC Veterinary Research, 2021,17:126.\u003c/li\u003e\n \u003cli\u003eGoonaratna C. （2009）Brucellosis in humans and animals. Ceylon Med J，52(2): 66.\u003c/li\u003e\n \u003cli\u003eKose S, Serin SS, Akkoclu G, Kuzucu L, Ulu Y, Ersan G, et al. （2014）Clinical manifestations, complications, and treatment of brucellosis: evaluation of 72 cases. Turk J Med Sci，44(2):220\u0026ndash;3.\u003c/li\u003e\n \u003cli\u003eJia B, Zhang F , Lu Y, Zhang W, Li J , Zhang Y , et al. (2017) The clinical features of 590 patients with brucellosis in Xinjiang, China with the emphasis on the treatment of complications. 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Infection and drug resistance，12:1565\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eKohler S , Foulongne V, Ouahrani-Bettache S, Bourg G, Teyssier J, Ramuz M, et al. (2002).The analysis of the intramacrophagic virulome of \u003cem\u003eBrucella suis\u003c/em\u003e deciphers the environment encountered by the pathogen inside the macrophage host cell. Proc Natl Acad Sci USA, 99, 15711\u0026ndash;15716.doi: 10.1073/ pnas.232454299.\u003c/li\u003e\n \u003cli\u003eTeixeira-Gomes AP, Cloeckaert A, Zygmunt MS. (2000). Characterization of heat, oxidative, and acid stress responses in \u003cem\u003eBrucella\u003c/em\u003e melitensis. Infect Immun. 68, 2954\u0026ndash;2961. doi: 10.1128/IAI.68.5.2954-2961.\u003c/li\u003e\n \u003cli\u003eWant E J , O\u0026quot;Maille G , Smith C A , et al. （2006）Solvent-Dependent Metabolite Distribution, Clustering, and Protein Extraction for Serum Profiling with Mass Spectrometry. Analytical Chemistry, 78(3):743-752.\u003c/li\u003e\n \u003cli\u003eBarriT ,Dragsted L O . （2013）UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasmaand serum metabolomics: effect of experimental artefacts and anticoagulant[J]. Analytica Chimica Acta, 768(1):118-128.\u003c/li\u003e\n \u003cli\u003eDunn, W.B. et al. (2011)Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature protocols. 6,1060-1083\u003c/li\u003e\n \u003cli\u003eRossetti CA, Arenas-Gamboa AM, Maurizio E. （2017）Caprine brucellosis: a historically neglected disease with significant impact on public health. PloS Negl Trop Dis，11(8):e0005692\u003c/li\u003e\n \u003cli\u003ePappas G, Papadimitriou P, Akritidis N, Christou L, Tsianos EV. The new global map of human brucellosis. Lancet Infect Dis 6, 91\u0026ndash;99.\u003c/li\u003e\n \u003cli\u003eGarofolo G, Di Giannatale E, Platone I, et al. （2017）Origins and global context of Brucella abortus in Italy. BMC Microbiol , 2;17(1):28.\u003c/li\u003e\n \u003cli\u003eLai, S. et al. Changing epidemiology of human Brucellosis, China, 1955\u0026ndash;2014. Emerging Infect. Dis. 23, 184\u0026ndash;194.\u003c/li\u003e\n \u003cli\u003ePeng C, Li JY, Huang DS, Guan P. (2020) Spatial-temporal distribution of human brucellosis in mainland China from 2004 to 2017 and an analysis of social and environmental factors. Environmental Health and Preventive Medicine， 25:1 . https://doi.org/10.1186/s12199-019-0839-z\u003c/li\u003e\n \u003cli\u003eGŁOWACKA P, Żakowska D, Naylor K, Niemcwicz M, Bielawska- DR\u0026Oacute;ZD A. （2018）Brucella \u0026ndash; virulence factors, pathogenesis and treatment . Polish J Microbiology，67( 2), 151\u0026ndash;161. doi:10.21307/pjm-2018-029.\u003c/li\u003e\n \u003cli\u003eR\u0026ouml;mling U, Galperin MY, Gomelsky M.（2013）Cyclic di-GMP: the first 25 years of a universal bacterial second messenger. Microbiol Mol Biol Rev 77:1\u0026ndash;52.\u003c/li\u003e\n \u003cli\u003eKhan M, Harms JS, Marim FM, Armon L, Hall CL, Liu YP, et al. （2016） The bacterial second messenger cyclic di-GMP regulates Brucella pathogenesis and leads to altered host immune response. Infect Immun 84:3458 \u0026ndash;3470.\u003c/li\u003e\n \u003cli\u003eWeckwerth W, Loureiro ME, et al. (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci USA,101(20), 7809\u0026ndash;7814.\u003c/li\u003e\n \u003cli\u003eLiu W, Dong H, Li J, Qu QX, Lv YJ, Wang XL, et al. （2015）RNA-seq reveals the critical role of OtpR in regulating Brucella melitensis metabolism and virulence under acidic stress. Sci. Rep. 5, 10864 .\u003c/li\u003e\n \u003cli\u003eRonneau S, Moussa S, Barbier T, Conde-\u0026Aacute;lvarez R, Zuniga-Ripa A ,et al. （2016）Brucella, nitrogen and virulence. Crit Rev Microbiol, 2016;42:507\u0026ndash;525.\u003c/li\u003e\n \u003cli\u003eCaudill MT , Budnick JA , Sheehanet LM , Lehmanal LR , Purwantini E, Mukhopadhyay B, et al. （2017）Proline utilization system is required for infection by the pathogenic \u0026alpha;-proteobacterium Brucella abortus. Microbiology ，163:970\u0026ndash;979 DOI 10.1099/mic.0.000490\u003c/li\u003e\n \u003cli\u003eBudnick JA, Sheehan LM, Benton AH, Pitzer JE, Kang L, Michalak P, et al. (2020) Characterizing the transport and utilization of the neurotransmitter GABA in the bacterial pathogen Brucella abortus. PLoS One 15(8): e0237371. doi:10.1371/journal.pone.0237371.\u003c/li\u003e\n \u003cli\u003eDİZDAR OS ,TURUN\u0026Ccedil; \u0026Ouml;ZDEMİR A , BAŞPINAR O, KO\u0026Ccedil;ER D, Katircilar Y, \u0026Ccedil;elik I. (2019) Serum prolidase level in patients with brucellosis and its possible relationship with pathogenesis of the disease: a prospective observational study. Turk J Med Sci49: 1479-1483.\u003c/li\u003e\n \u003cli\u003eHashemifar I, Masjedian Jazi F, Yadegar A, Amirmozafari N. （2017）Prevalence of proline racemase/ hydroxyproline epimerase gene in human brucella isolates in Iran. Med J Islam RepubIran, 31.57.\u003c/li\u003e\n \u003cli\u003eNikolaev YA, Tutel\u0026rsquo;yan AV, Loiko NG, Buck J, Sidorenko SV, Lazareva I, et al. (2020) The use of 4-Hexylresorcinol as antibiotic adjuvant. PLoS ONE 15(9): e0239147.\u003c/li\u003e\n \u003cli\u003eJo YY , Kim DW, Choi JY , Kim SG . （2019）4-Hexylresorcinol and silk sericin increase the expression of vascular endothelial growth factor via different pathways. Sci Rep. 9(1):3448.doi: 10.1038/s41598-019-40027-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brucellosis, Metabolomics, Metabolic profiling","lastPublishedDoi":"10.21203/rs.3.rs-3008499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3008499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBrucellosis is a worldwide zoonotic disease through the consumption of unpasteurized dairy products, inhalation of infected aerosolized particles, and direct or indirect contact with infected animals. A timely and accurate diagnosis is key to the clinical management of brucellosis.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe study included 8 male brucellosis patients and 8 control subjects. The serum samples were analyzed using Ultra performance liquid chromatography/tandem mass spectrometry ( UPLC- MS/MS ). The structural identification of these different metabolites was performed by comparing the exact mass data, retention time, and corresponding MS/MS fragments with those of mzCloud, mzVault and MassList database. We applied univariate analysis to calculate the statistical significance.The metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change(FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 or FC\u0026thinsp;\u0026le;\u0026thinsp;0.5 were considered to be differential metabolites.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e25 different metabolites were identified. 6 metabolites were down-regulated, and 19 metabolites were up-regulated. Different metabolites identified in positive ionizationmodewereL-Kynurenine, (3,4-Dimethoxyphenyl) acetic acid, D- Sphingosine, D-(+)-Proline, 2-Amino-1,3-octadecanediol, Kahweol, 2- Hydroxycinnamic acid, Kynurenic acid, 5-(tert-butyl)-2- methyl-N-(4-nitrophenyl) \u0026ndash; 3-furamide, 2-chloro-6-(4- methoxypheno xy)benzonitrile, and 1,4- dihydroxyheptadec =-16-en-2-yl acetate; Different metabolites identified in negative ionization mode were Lignoceric acid, Pentacosanoic acid, Xanthine, L-Phenylalanine, D-(+)-Tryptophan, Oleoyl-L-α-lysophosphatidic acid, γ- Aminobutyric acid, L- Glutamic acid, Citric acid, 2-(1H-benzimidazol-2-yl)-3 -(1,3- benzodioxol \u0026minus;\u0026thinsp;5-yl) acrylonitrile, Perfluorooctanoic acid, 4-Hexylresorcinol, Sorbitan monopalmitate, and Deoxycholic acid.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere were existing the metabolic changes of male patients diagnosised as acute brucellosis, which were involved in tryptophan metabolism, glyoxylate and dicarboxylate metabolism,as well as \u003cem\u003ebiosynt\u003c/em\u003ehesis and metabolism of amino acids .\u003c/p\u003e","manuscriptTitle":"A primary research on male patients diagnosed as acute brucellosis with untargeted metabolomics technique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-09 19:26:46","doi":"10.21203/rs.3.rs-3008499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"345ad60f-d1bf-4eab-a8c2-1d3c02cbc137","owner":[],"postedDate":"June 9th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-30T05:44:28+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-09 19:26:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3008499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3008499","identity":"rs-3008499","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}