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Comprehensive Profiling of Fecal Metabolome of Mice by Integrated Chemical Isotope Labeling-Mass Spectrometry Analysis Bi-Feng Yuan, Quan-Fei Zhu, Ning Guo, Shu-Jian Zheng, Ya-Lan Wang, Jie Wang, Jing Xu, Shi-Jie Liu, Ke He, Ting Hu, Ying-Wei Zheng, Fuqiang Xu, and Yu-Qi Feng Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b05355 • Publication Date (Web): 06 Feb 2018 Downloaded from http://pubs.acs.org on February 7, 2018

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Analytical Chemistry

Comprehensive Profiling of Fecal Metabolome of Mice by Integrated Chemical Isotope Labeling-Mass Spectrometry Analysis Bi-Feng Yuan,1 Quan-Fei Zhu,1 Ning Guo,1 Shu-Jian Zheng,1 Ya-Lan Wang,1 Jie Wang,2,3 Jing Xu,1 ShiJie Liu,1 Ke He,1 Ting Hu,2,3 Ying-Wei Zheng,2,3 Fu-Qiang Xu,2,3,4 Yu-Qi Feng 1,* 1

Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), Department of Chemistry, Wuhan University, Wuhan 430072, P.R. China; 2 State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance (Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences), Wuhan 430071, China; 3 University of the Chinese Academy of Sciences, Beijing, 100049, China; 4 Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China. * Corresponding author: Yu-Qi Feng. Tel: +86-27-68755595; Fax: +86-27-68755595; E-mail: [email protected] ABSTRACT: Gut microbiota plays important roles in the host health. The host and symbiotic gut microbiota coproduce a large numbers number of metabolites during the metabolism of food and xenobiotics. The Aanalysis of fecal metabolites can provide a non-invasive manner to study the outcome of the host-gut microbiota interaction. Herein we reported the comprehensive profiling of fecal metabolome of mice by an integrated chemical isotope labeling combined with liquid chromatography-mass spectrometry (CIL-LC-MS) analysis. The metabolites are categorized into several submetabolomes based on the functional moieties (i.e., carboxyl, carbonyl, amine, and thiol) and then analysis of the individual submetabolome was performed. The combined data from the submetabolome form the metabolome with relatively high coverage. To this end, we synthesized stable isotope labeling reagents to label metabolites with different groups, including carboxyl, carbonyl, amine, and thiol groups. We detected 2302 potential metabolites, among which, 1388 could be positively or putatively identified in feces of mice. We then further confirmed 308 metabolites based on our established library of chemically-labeled standards library and tandem mass spectrometry analysis. With the identified metabolites in feces of mice, we established mice fecal metabolome database, which can be used to readily identify metabolites from feces of mice. Furthermore, we discovered 211 fecal metabolites exhibited significant difference between Alzheimer’s disease (AD) model mice and wild type (WT) mice, which suggests the close correlation between the fecal metabolites and AD pathology and provides new potential biomarkers for the diagnosis of AD. study the functions of gut microbiota related to human diseases under well-controlled experimental conditions. The Rrecent studies have suggested that alterations in gut microbiota as well as the microbial metabolites contribute to behavioral abnormalities in a mouse model of autism spectrum disorders, highlighting the potential correlation between microbial metabolites and neurological diseases.4,14 Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive decline of cognitive functions.15 Accumulating evidence revealed that metabolic perturbation in various pathways may mediate the occurrence of Alzheimer pathology as well asand the onset of cognitive impairment in patients.16 However, the correlation between gut microbiota and AD remains unclear. A better understanding of the contribution that variations in gut microbiota-host cometabolites make to AD will assist in the development of new strategies for AD prevention and therapeutic intervention. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the two most popular analytical platforms to metabolomics study.17 Recent study demonstrated 223 fecal metabolites were considered as potential readouts of rat-gut microbiota co-metabolism by liquid

Introduction Mammals harbor diverse and metabolically active gut microbiota.1 The dynamic crosstalk between the host and gut microbiota is critical for maintaining host homeostasis.2 Gut microbiota plays a variety of roles in the host immune response,3 neurologic signaling,4 and energy biogenesis.5 Given the diverse functions of the gut microbiota, it is was demonstrated to be involved in a broad range of diseases, including cancer,6 diabetes,7 obesity,8 and neurodevelopmental disorders.9 The host and symbiotic gut microbiota coproduce a large numbers number of metabolites during the metabolism of food and xenobiotics, many of which play important roles in shuttling information between the host and gut microbiota.10 The variable presence and activities of microbes in gut could result in significant metabolic alterations in the host’s body fluids and tissues.11 The Ffeces are easily accessible and analysis of fecal metabolites provides a non-invasive manner to study the outcome of the host-gut microbiota interaction.12 A growing number of studies reported fecal metabolomics of both human and animal models.13 The use of animal models opens the possibility of applying fecal metabolomics to

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chromatography - mass spectrometry (LC-MS) analysis.18 However, compared to the 4229 metabolites identified in serum 19 and 2651 metabolites identified in urine,20 the number of the identified metabolites in feces is relatively low, which restricts the in-depth study of the functions of gut microbiota. Due to the immense chemical diversity and the large dynamic range of metabolite concentrations in feces samples, to achieve the high coverage of fecal metabolome is challenging and there is a great need to develop more powerful analytical platform to increase the coverage of fecal metabolome. Carboxyl and carbonyl compounds account for a large number of endogenous metabolites in living organisms.21 Endogenous ketones and aldehydes are well-dispersed throughout cellular metabolic cycles and alterations of these carbonyls were demonstrated to be linked to many diseases.22 Amine metabolites involve in multiple pathways and play important roles in biosynthesis of proteins, nucleotides and neurotransmitters.23 Biological thiols play essential roles in redox homeostasis and are involved in a variety of biological processes including antioxidant defense network, methionine cycle and protein synthesis.24 Due to these important biological roles and large numbers of these carboxyl, carbonyl, amine, and thiol metabolites in vivo, we mainly focus on the study of these four types of metabolites in fecal sample. Herein we reported comprehensive profiling of fecal metabolome of mice using integrated chemical isotope labeling combined with liquid chromatography-mass spectrometry (CIL-LC-MS) analysis. We synthesized chemical isotope labeling reagents to selectively label metabolites with different groups, including carboxyl, carbonyl, amine, and thiol groups. Using the fecal samples of mice, we detected 2302 paired-peaks, among which, 1388 paired-peaks (60.3% of total) could be positively or putatively identified. We then further confirmed 308 metabolites based on our established library of chemically-labeled standards library and tandem mass spectrometry analysis. In addition, we discovered 211 metabolites exhibited significant difference in feces between AD mice and wild type (WT) mice.

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Collection of feces of mice The APP/PS1 double transgenic mice C57BL/6J (C57) expressing both human mutant APPK670N/M671N 25 and mutant PS1M146L 26 were utilized as the AD mice model. 40 wild type (WT) C57BL/6J mice (Hunan SJA Laboratory Animal Co., Ltd., P.R. China, 20 female and 20 male) and 28 C57BL/6J mice with AD (14 female and 14 male) were raised and kept separately under 23°C in lighting-controlled house (12 h of light and dark) with free access to a standard chow and water. The confirmation of AD mice was based on the genotype. Genotyping of AD mice was performed by polymerase chain reaction (PCR) analysis of toe DNA (Figure S1 in Supporting Information) and the detailed information can be found in the text in Supporting Information. At approximate three months of age (body weight: female, 16 - 17 g; male, 18 20 g), the feces samples from the above 68 mice that were under normal condition (without diarrhoea) were collected. Feces samples collection time is from 7 am to 9 am. The fresh feces samples were kept at room temperature for less than 2 h after collection and then stored at -80°C for further analysis according to previous reported procedure.27 The Ethics Committee of Wuhan Institute of Physics and Mathematics, CAS, approved the experiments and confirmed that all experiments conform to the regulatory standards. Extraction of metabolites from feces of mice Different extraction procedures were evaluated to achieve the efficient extraction of metabolites from feces of mice. Detailed procedures for the evaluation of extraction solvents can be found in the text in Supporting Information and the detected paired-peaks by different extraction method are shown in Figure S2 in Supporting Information. Under the optimized conditions, MeOH and MTBE solvents were selected for the sequential extraction of carboxyl, carbonyl, and amine metabolites, and MeOH was used for the extraction of thiol metabolites. Synthesis of chemical isotope labeling reagents We synthesized four kinds of chemical labeling reagents and their corresponding isotope reagents to selectively label carboxyl, carbonyl, amine, and thiol compounds. 2-Dimethylaminoethylamine (DMED, d4DMED), 2-(2-hydrazinyl-2-oxoethyl) isoquinolin-2-ium bromide (HIQB and d7-HIQB), 4-(N,Ndimethylamino)phenyl isothiocyanate (DMAP, d4DMAP), and ω-bromoacetonylquinolinium bromide (BQB and d7-BQB) were synthesized according to our and others’ previously reported methods.28-31 Chemical labeling The chemical labeling reactions were performed under optimized conditions according to previously described method.30,32-34 The detailed reaction conditions can be found in the text in the Supporting Information. Mass spectrometry analysis The LC-MS analysis was performed on a LTQ Orbitrap Elite mass spectrometer (Thermo Fisher Scientific,

Experimental Section Chemicals and reagents All of the metabolite standards were purchased from Sigma-Aldrich (St. Louis, MO, USA), J&K Chemical (Beijing, China), Cayman Chemical (Arbor, MI, USA) and Aladdin (Shanghai, China). The detailed information of 476 metabolite standards is listed in Table S1 in Supporting Information. Analytical grade ethyl acetate, methyl tert-butyl ether (MTBE), formic acid (FA), acetic acid, sodium carbonate, sodium bicarbonate, glycine-HCl, 2-chloro-1-methylpyridinium iodide (CMPI), and triethylamine (TEA), HPLC-grade acetonitrile (ACN) and methanol (MeOH), were obtained from Tedia Co. (Fairfield, OH, USA).

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USA) coupled with an UltiMate 3000 UHPLC System (Thermo Fisher Scientific, USA). The detailed LC separation conditions can be found in the text in Supporting Information. The MS analysis was performed under positive ion mode with full scan detection (MS1, m/z 180-650) at the resolution of 60,000. The MSn analysis was performed under positive ion mode. MS2 fragmentation was based on the mass list of pre-selected precursor ions (paired-peaks) under collision induced dissociation (CID). As for the MS3 analysis of carboxyl compounds, the acquisition was depended on the most abundance product ions through neutral lose in MS2 spectra, i.e., product ions of [M+H]+-45.0/49.0 were selected for MS3 analysis. While for carbonyl, amine, and thiol compounds, the acquisition was depended on the pre-selected mass list of MS2 product ions generated from MS1 by losing characteristic groups, i.e., product ions of [M+H]+-129.1/136.1 for carbonyl compounds, [M+H]+178.1/182.1 for amine compounds, [M+H]+-129.1/136.1 for thiol compounds were selected for MS3 analysis. MS2 parameters include isolation width of 2.0 m/z units, normalized collision energy of 35 V, activation Q of 0.25, and activation time of 10 ms. In MS3 analysis, CID with normalized collision energy of 30, 35 and 40 V was used. MS3 scan event parameters include isolation width of 3 m/z units, activation Q of 0.25, and activation time of 10 ms. Determination of metabolites in feces of mice The obtained full scan (MS1) raw data was imported into SieveTM 2.2 software (Thermo Fisher Scientific, USA) for the peaks alignment. The information of detected compounds with accurate m/z, retention time (t), and peak intensity is generated. The paired-peaks with defined mass difference (4.025 Da for light/heavy labeled carboxyl or amine metabolites; 7.044 Da for light/heavy labeled carbonyl or thiol metabolites) within a tolerance window (< 10 ppm) and intensities ratios within 0.66 to 1.33 were extracted from the spectra using an in-house Matlab-based software. The prospective molecular formulas of components were generated based on the accurate m/z using the Xcalibur software (Thermo Fisher Scientific, USA). The selected elements were C, H, N, O and S with a mass tolerance of 10 ppm. Confirmation of detected metabolites was based on the following criteria: (1) by comparing the detected metabolites with standard compounds in the established library of chemicallylabeled standards library in terms of m/z, retention index, and MSn spectra; (2) by interpretation of tandem MS spectra; (3) by searching the online METLIN database (https://metlin.scripps.edu/landing_page.php). The relative quantitation was based on the intensity ratios of given paired-peaks of the metabolites in different pooled samples using IBM SPSS 19.0 software (IBM SPSS Inc, USA). The independent t-test was performed to evaluate the content differences of metabolites in feces between AD and WT mice. The increased or decreased

folds more than 2.0 and the p-values less than 0.05 were considered as statistically significant difference. The statistical results were presented by Volcano plots, where the -log (p value) was plotted against its corresponding log2 (fold change of AD/WT). Results and Discussion Study design High coverage of metabolome profiling is hampered by the great variety of physical and chemical properties of metabolites as well as their large differences in abundance. One strategy to address this challenge is to categorize the metabolites into several subgroups based on the functional moieties (i.e., carboxyl, carbonyl, amine, and thiol etc.) and then perform in-depth analysis of the individual submetabolome. The combined data from the submetabolome would form the metabolome with relatively high coverage. In this study, we mainly focus on the determination of four types of metabolites (carboxyl, carbonyl, amine, and thiol metabolites) in fecal samples. Metabolites containing the functional groups of carboxyl, carbonyl, amine, and thiol dominate the metabolome of mammals. Thus, analysis of metabolites in these submetabolomes can achieve relatively complete metabolomic profile. In this study, aA differential chemical isotope labeling strategy combined with LC-MS analysis was established for the determination and relative quantification of metabolites in feces of AD and WT mice. To this end, we utilized four pairs of labeling reagents, including DMED/d4-DMED, HIQB/d7-HIQB, DMAP/d4-DMAP, and BQB/d7-BQB to selectively and efficiently label carboxyl, carbonyl, amine, and thiol metabolites, respectively (Figure 1).



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pooled sample A is labeled with heavy reagent as internal standards (Figure 2B). The mixture of the light labeled sample and the heavy labeled sample is then analyzed by LC-MS. The intensity ratios of given peak pairs are reflective of the relative contents of the metabolites in different pooled samples.

Figure 2. Overview of the procedure for the determination and relative quantitation of fecal metabolites in mice by CIL-LC-MS method. (A) Procedure for the determination of fecal metabolites in mice by CIL-LC-MS method. (B) Procedure for the relative quantitation of fecal metabolites in mice by CIL-LC-MS method. L, light reagent-labeled peaks; H, heavy reagent-labeled peaks; WT, wild type; IS, internal standards; AD, Alzheimer’s disease.

Figure 1. Chemical reactions between isotope labeling reagents and metabolites carrying different functional groups. (A) Labeling of carboxyl metabolites by DMED/d4-DMED. (B) Labeling of carbonyl metabolites by HIQB/d7-HIQB. (C) Labeling of amine metabolites by DMAP/d4-DMAP. (D) Labeling of thiol metabolites by BQB/d7-BQB.

Fragmentation of chemically-labeled metabolites The chemical labeling reactions by the four labeling reagents were performed according to our previously optimized conditions.30,32,33 Under the optimized reaction conditions, the reaction efficiencies generally are more than 99% (data not shown). We then investigated the mass spectrometry behavior of chemically-labeled compounds . In this respect, we used using four standards compounds (palmitic acid, 4-hydroxybenzaldehyde, DLproline and γ-glutamylcysteine) as the analytes. As shown in Figure 3, we obtained the expected precursor ions at m/z 327.3/331.3, 306.1/313.2, 294.1/298.2 and 434.1/441.2 from DMED/d4-DMED-labeled palmitic acid, HIQB/d7-HIQB-labeled 4-hydroxybenzaldehyde, DMAP/d4-DMAP-labeled DL-proline, and BQB/d7-BQBlabeled γ-glutamylcysteine, respectively. The DMED/d4-DMED-labeled carboxyl compounds can generate characteristic neutral loss of 45.0/49.0 Da under collision induced dissociation (CID) (Figure 3A and 3B). The product ion formed by the neutral loss from the precursor ion generally is the most abundant ion ([M+H]+-45.0/49.0, m/z 282.3 for palmitic acid, Figure 3A and 3B), which therefore was selected for the MS3 analysis. The HIQB/d7-HIQB-labeled carbonyl compounds typically lose the common group of isoquinoline (m/z 130.1/137.1) from HIQB/d7-HIQB, and the resulting product ion ([M+H]+-129.1/136.1, m/z 177.1 for 4hydroxybenzaldehyde, Figure 3C and 3D) was then selected for the MS3 analysis. Similarly, the DMAP/d4DMAP-labeled amine compounds typically lose the 4(N,N-dimethylamino)phenyl isothiocyanate group (m/z

Pooled samples were prepared by taking equal amount of each fecal sample from all 68 mice (pooled sample A), or from 40 WT mice (pooled sample B), or from 28 AD mice (pooled sample C). Three independent pooled samples were prepared for each group and triplicate measurements were performed. As for the determination of metabolites in feces of mice, an equal amount of pooled sample A (150 mg of feces mixture from 68 mice) was labeled by light or heavy labeling reagents (Figure 2A). Then the light and heavy labeled samples were mixed and analyzed by LC-MS. A true metabolite was defined as a pair of peaks with the same retention times, similar peak intensities and defined mass difference endowed by the mass difference of the light and heavy labeling reagents, while all the background peaks are were detected as singlet peaks. The tandem mass spectrometry (MSn) analysis was further performed to obtain the product ions spectra for the structural interpretation of metabolites. Confirmation of detected metabolites from feces of mice was based on (1) the comparison of the metabolites with our established library of chemically-labeled standards (including retention index, m/z, and tandem MS spectra of 476 standard compounds), (2) tandem MS spectra analysis and (3) METLIN database (https://metlin.scripps.edu/landing_page.php) matching. As for the relative quantification of metabolites in feces between AD mice and WT mice, 150 mg of pooled sample B or C is labeled with light reagent and 150 mg of


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179.1/183.1) and the formed product ion ([M+H]+178.1/182.1, m/z 116.1 for DL-proline, Figure 3E and 3F) was selected for the MS3 analysis. BQB/d7-BQB-labeled thiol compounds lose the quinoline group (m/z 130.1/137.1) and the formed product ion ([M+H]+129.1/136.1, m/z 305.1 for γ-glutamylcysteine, Figure 3G and 3H) was selected for the MS3 analysis. The use of the selected paired-peaks after chemical labeling as the targeted precursor ions facilitates the subsequent MS2 analysis. In addition, the characteristic fragmentations of metabolites after chemical labeling make it feasible for the pre-selection of targeted fragment ions for the subsequent MS3 analysis, which is more sensitive than the normally used untargeted data-dependent acquisition (DDA) MS detection mode since this strategy can narrow down the candidate ions for fragmentation. Enhancement of detection sensitivity upon chemical labeling Introduction of an easily ionizable group to analytes could enhance the ionization efficiency in mass spectrometry analysis.35-42 Along this line, here we used four chemical labeling reagents (DMED, HIQB, DMAP, and BQB) that harbor easily charged tertiary or quaternary amine group to label the corresponding metabolites (Figure 1). Four kinds of standard compounds with different functional moieties were used to evaluate the detection sensitivities. The limits of detection (LOD) determined at concentrations where the S/N ratio of 3 were measured to evaluate the detection sensitivities of compounds. The results showed that the detection sensitivities of these standard compounds generally increased by 5 to 1250 folds upon chemical labeling (Table S2 in Supporting Information), suggesting that chemical labeling efficiently enhances the detection sensitivities of metabolites in addition to forming the characteristic product ions for improving the identification of metabolites. The enhanced detection sensitivities by chemical labeling will lead to the discovery of low-abundant metabolites, which eventually increases the coverage of the fecal metabolome. Library of Cchemically-labeled standards library Definitive identification of detected metabolites requires the use of authentic standards as reference. Here we first constructed a library of chemically-labeled standards. This library currently consists of 476 standard compounds, including 184 carboxyl compounds, 147 carbonyl compounds, 118 amine compounds, and 27 thiol compounds (Table S1 in Supporting Information). To construct the library, each standard compound was labeled with corresponding labeling reagents and analyzed using LC-LTQ-Orbitrap mass spectrometer. The obtained raw MS data were converted by Mass Frontier Server Manager 2.0 software (Thermo Fisher Scientific, USA) to generate the retention time and MSn tree information (including accurate m/z, MS1, MS2 and MS3 spectra) for each standard compound. This library of chemically-labeled

standards library is freely available http://bioanalchem.whu.edu.cn/CLmetabolism.html.

at

Figure 3. Fragmentation of chemically-labeled products. (A) DMED-labeled palmitic acid; (B) d4-DMED-labeled palmitic acid; (C) HIQB-labeled 4-hydroxybenzaldehyde; (D) d7-HIQB-labeled 4-hydroxybenzaldegyde; (E) DMAP-labeled DL-proline; (F) d4DMAP-labeled DL-proline; (G) BQB-labeled γ-glutamylcysteine; (H) d7-BQB-labeled γ-glutamylcysteine. Highlighted in red and blue are the theoretical m/z.

It should be noted that the retention time is a significant parameter for characterization of metabolites. But the retention times could be varied in different LC-MS analysis. To overcome the drifting of retention times from one LC-MS analysis to another, we developed retention index (RI) to calibrate the variations of retention times of metabolites under different experimental conditions. The results showed that good linearity was obtained for RI under different elution gradients with the slope being close to 1.00 (R2 ≥ 0.9998, Figure S3 in Supporting Information), suggesting that RI was suitable for the correction of retention times obtained in different experimental batches or different experimental conditions. The detailed information for generating the RI of each metabolite can be found in Supporting Information. The RI of each detected fecal metabolite is listed in Table S3 in Supporting Information. Determination of metabolites in feces of mice Fecal metabolome profiling can provide valuable insights into the host-gut microbiota interaction. So far,



only a small number of metabolites were identified in feces samples. With the established method, we determined the metabolites in feces of mice. An equal amount of pooled sample A (feces mixture from 68 mice) was labeled with light and heavy labeling reagents, respectively. Then the light and heavy labeled samples were mixed and analyzed by LC-MS (Figure 2A). The total ion chromatograms of full scan by different chemical labeling are shown in Figure 4A-4D. An in-house Matlab-based software was used to align paired-peaks. A metabolite is was detected as paired-peaks with the same retention time, similar peak intensities, and defined mass difference of 4.025 ± 0.001 for DMED-labeled carboxyl metabolites and DMAP-labeled amine metabolites (d4-DMED-labeled metabolites – DMED-labeled metabolites＝4.025 Da; d4DMAP-labeled metabolites – DMAP-labeled metabolites ＝4.025 Da), or 7.044 ± 0.001 for HIQB-labeled carbonyl metabolites and BQB-labeled thiol metabolites (d7-HIQBlabeled metabolites – HIQB-labeled metabolites＝7.044 Da; d7-BQB-labeled metabolites – BQB-labeled metabolites＝7.044 Da). Shown in Figure 4E-4H are representative extracted ion chromatograms at m/z 215.2118/219.2369, m/z 290.1284/297.1723, m/z 344.1424/348.1678, and m/z 462.2036/469.2475, which indicate the potential carboxyl, carboxyl, amine and thiol metabolites, respectively. In addition, the MS spectra of the light and heavy labeled samples (Figure 4I-4L) further confirmed the precursor ions of the paired-peaks from the extracted ion chromatograms.

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Using this integrated CIL-LC-MS analytical method, 2302 potential metabolites (854 carboxyl metabolites, 248 carbonyl metabolites, 1057 amine metabolites and 143 thiol metabolites) were found in feces of mice (Table S3 and Table S4 in Supporting Information). Repeatedly detected compounds in different submetabolomes were removed. The prospective molecular formulas of potential metabolites were obtained by their accurate molecular weights using the Xcalibur software (ThermoFisher Scientific, USA). These compounds were then further confirmed using the established library of chemically-labeled standards. Shown in Figure 5 is the general schematic illustration for the confirmation of metabolites (cholic acid as an example) detected in fecal samples. The LCMS raw data were imported into the library of chemically-labeled standards, and the information of the MSn tree (MS1, MS2, and MS3) can be automatically matched to the MSn trees of standard compounds pre-stored in the library. In this respect, aA total of 193 metabolites (83 carboxyl metabolites, 50 carbonyl metabolites, 46 amine metabolites and 14 thiol metabolites) were confirmed by this library based on the accurate m/z, MSn spectra and retention index (highlighted in red in Table S3 in Supporting Information). The remaining compounds that are not identified in the library were further searched in METLIN database. In this way, a total of 1195 metabolites (after removing the repeatedly detected metabolites in different submetabolomes) (highlighted in blue and green in Table S3 in Supporting Information). We then further interrogated the MSn spectra of these detected metabolites. Among the 1195 potential metabolites, a total of 115 metabolites were confirmed by interpretation of tandem MS spectra (highlighted in blue in Table S3 in Supporting Information). Taken together, out of the 2302 paired-peaks detected, 1388 paired-peaks (60.3% of the total) were positively or putatively identified. In addition, 308 metabolites were further confirmed based on our established library of chemically-labeled standards (193 metabolites, Table S4 in Supporting Information) and tandem mass spectrometry analysis (115 metabolites, Table S4 in Supporting Information). The number of identified metabolites in feces of mice is typically more than , to the best of our knowledge, is the most compared to previous studies (Table S5 in Supporting Information). The other 914 detected paired-peaks that are not identified in the current study could be some new metabolites existing in mice feces. Further investigation is required to decipher these potential new metabolites in feces of mice in the future. Alteration of metabolites in feces of AD mice The diagnosis of AD mainly relies on clinical evaluation and only the late stage of AD can be diagnosed.43 Biomarkers for early detection of the underlying neuropathological changes are still lacking.43 Gut microbiota can interact with the brain to form gut-brain axis system and

Figure 4. Chemical isotope labeled fecal sample of mice analyzed by LC-MS under full scan mode. Total ion chromatograms of DMED/d4-DMED-labeled fecal sample (A), HIQB/d7-HIQBlabeled fecal sample (B), DMAP/d4-DMAP-labeled fecal sample (C), and BQB/d7-BQB-labeled fecal sample (D). Extracted ion chromatograms of caprylic acid at m/z 215.2118 and 219.2369 from DMED and d4-DMED labeled fecal sample (E), benzaldehyde at m/z 290.1284 and 297.1723 from HIQB and d7-HIQB labeled fecal sample (F), L-phenylalanine at m/z 344.1424 and 348.1678 from DMAP and d4-DMAP labeled fecal sample (G), and pantetheine at m/z 462.2036 and 469.2475 from BQB and d7-BQB labeled fecal sample (H). Mass spectra of DMED/d4-DMED-labeled caprylic acid (I), HIQB/d7-HIQB-labeled benzaldehyde (J), DMAP/d4DMAP-labeled L-phenylalanine (K), and BQB/d7-BQB-labeled pantetheine (L).


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play important roles in neurodegenerative diseases.14 In this study, we further investigated the alteration of metabolites in feces of AD mice. We first validated the method and the detail information can be found in Supporting Information. The results showed that the slopes of linear regressions of measured compounds by CIL-LC-MS method were approximate to 1.0 with the coefficients of determination (R2) ranging from 0.9800 to 0.9998 (Table S6 in Supporting Information), indicating the ratios of the chromatographic peak intensity closely matched with the concentration ratios of the differential isotopes labeled analytes. Therefore, the ratios of the chromatographic peak intensity can bewere used for the relative quantification of light/heavy labeled samples. The repeatability of the method was investigated by calculating the RSDs of intensity ratios (light/heavy labeling) for 2302 potential fecal metabolites with triplicate measurements for six times. The histogram of frequency distribution of RSDs for the 2302 potential fecal metabolites showed that more than 8788% compounds have RSDs less than 20% (Figure S4 in Supporting Information), suggesting good repeatability of the developed CIL-LC-MS method.

and 126 metabolites significantly decreased in AD mice compared to WT mice. The detailed information of the fecal metabolites with significant changes between AD and WT mice was listed in Table S7 in Supporting Information. Accumulating data suggest that bile acids had protective and anti-inflammatory effects on the brain.44 Here we observed that the secondary bile acid, muricholic acid, in feces of AD mice was 33% of that in WT mice. Generally, secondary bile acids are produced in intestine from primary bile acids through hydrolytic reaction by gut microbiota.45 The observed decrease of muricholic acid in AD in the current study indicates a close correlation between muricholic acid and AD. Previous studies revealed that the concentrations of butyrylcholinesterase and acetylcholinesterase in plasma and tissue were elevated in AD patients and one of the present treatment strategies of AD is based on the inhibition of acetylcholinesterase and butyrylcholinesterase activities in brain.46 Recently, 4-hydroxyphenylpyruvic acid was demonstrated to be an effective inhibitor of acetylcholinesterase.47 Here we observed that the content of 4-hydroxyphenylpyruvic acid in feces of AD mice is 41% of that in WT mice, suggesting the decreased content of 4-hydroxyphenylpyruvic acid in AD may contribute to the increased activity of acetylcholinesterase and involves in the development of AD. Our understandings of these altered metabolites and their underlying mechanisms remain at preliminary level, and many metabolites that exhibit significant changes in AD mice still require further study.

Figure 5. Schematic illustration for the confirmation of fecal metabolites (cholic acid as an example) using the library of chemically-labeled standards. The LC-MS raw data were imported into the library, and the information of the MSn tree (MS1, MS2, and MS3) can be automatically matched to the MSn trees of standard compounds pre-stored in the library. The resulting information (metabolite name, CAS number, and m/z) was exported with a matching score.

Figure 6. Volcano plots showing the contents alteration of metabolites in feces between AD mice and WT mice. (A) Carboxyl metabolites. (B) Carbonyl metabolites. (C) Amine metabolites. (D) Thiol metabolites. The metabolites with significant difference between AD and WT mice are highlighted in red (> 2-fold increase, p < 0.05) and blue (> 2-fold decrease, p < 0.05), respectively. The numbers that represent the metabolites with significant content changes between WT and AD mice were listed in Table S7 in Supporting Information. Metabolites: (A) 53, leukotriene A4; 59, oleanic acid; 174, C8H14O3; 373, C16H30O4; 569, C13H20O2. (B) 28, 4-

Shown in Figure 6 is the relative contents of metabolites between AD mice and WT mice. The metabolites with significant difference between AD and WT mice are highlighted in red (> 2-fold increase, p < 0.05) and blue (> 2-fold decrease, p < 0.05). The results demonstrated that the contents of 85 metabolites significantly increased



hydroxybenzaldehyde; 56, testosterone decanoate; 122, C18H32O; 189, C25H44O4; 196, C28H40O3; 197, C29H44O2; 204, 17-βhydroxyestr-4-en-3-one 17-(undec-10-enoate); 205, C29H46O3. (C) 330, C9H20N2O. (D) 24, 2-ethoxyethyl sulfanylacetate; 30, 2acetamido-2-(mercaptomethyl)succinic acid; 34, thiocyanate; 102, C5H5N8S.

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validation; Retention Index; Table S1 – S8; Figure S1 – S4. This material is available free of charge via the Internet at http://pubs.acs.org. Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes The authors declare no competing financial interest. Acknowledgements The authors thank for the financial support from the National Key R&D Program of China (2017YFC0906800), National Natural Science Foundation of China (21635006, 21475098, 31670373, 21522507, 21672166). The authors thank Ms. Yanqiu Li who identified the phenotypes of mice.

Fecal metabolome database (FMD) With the confirmed metabolites from feces of mice, we established the fecal metabolome database. This database includes 308 fecal metabolites detected in the current study (Table S8 in Supporting Information). The information for each metabolite includes compound name, RI, molecular weight, molecular formula, MSn spectra, CAS number and KEGG number. The established fecal metabolome database can be used for the direct evaluation of the content changes of fecal metabolites of mice with different diseases or stimulations, which makes the identification of metabolites in feces very easy and straightforward. With our established analytical platform, future investigation of the effects of dietary components and medicines on microbial metabolites can be carried out, which will promote the in-depth understanding of the correlation between nutrition and host health. In addition, model mice with various diseases or human fecal samples with diseased individuals can be investigated to study the functions of gut microbiota on diseases development and prevention by virtue of the powerful integrated CIL-LC-MS analytical platform.

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Conclusions In this study, we reported a comprehensive profiling of fecal metabolome of mice using the developed integrated CIL-LC-MS analytical strategy. The integrated CIL-LC-MS approach dramatically enhances the detection sensitivities of metabolites and facilitates the identiﬁcation of metabolitesanalytes by selecting the pairedpeaks with defined mass difference. In addition, the MSn analysis also benefits from the characteristic fragmentations of the chemically-labeled products. Using the integrated CIL-LC-MS method, we detected 2302 pairedpeaks in feces of mice, among which, 1388 paired-peaks were positively or putatively identified. 308 metabolites were further confirmed based on our established library of chemically-labeled standards and tandem MS analysis. Furthermore, we discovered 211 metabolites exhibited significant difference in feces between AD mice and WT mice. The integrated CIL-LC-MS showed to be a promising analytical method to study the functions of gut microbiota and its communications with the host. ASSOCIATED CONTENT Supporting Information Supporting Information Available: Confirmations of AD mice; Evaluation of solvents for extraction of metabolites from feces of mice; Chemical labeling; Method


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