Mass spectrometry-based fecal metabolome analysis

Trends in Analytical Chemistry 112 (2019) 161e174

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Trends in Analytical Chemistry journal homepage: www.elsevier.com/locate/trac

Mass spectrometry-based fecal metabolome analysis Jing Xu 1, Qin-Feng Zhang 1, Jie Zheng, Bi-Feng Yuan**, Yu-Qi Feng* Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), Department of Chemistry, Wuhan University, Wuhan 430072, China

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 7 January 2019

The past decade has witnessed remarkable progress in our understanding of the important roles that gut microbial metabolites play in modulating the human health. The dynamic interplay between the host and gut microbiota is critical for maintaining the host homeostasis. The gut microbial metabolites are increasingly being recognized as an important part of human physiology. The symbiosis between human and gut microbiota relies on the communications with metabolites acting as the important mediators. Analysis of gut microbial metabolites is essential to understand the molecular mechanisms of the interaction between the host and gut microbiota. Feces contain a wide array of metabolites that may reflect the results of nutrient ingestion, digestion and absorption by gut microbiota and the gastrointestinal tract. Analysis of fecal metabolites therefore can provide a non-invasive manner to study the outcome of the host-gut microbiota interactions. It is evident that mass spectrometry (MS)-based fecal metabolome analysis is rapidly growing and covers a broad investigation on the functional roles of microbial metabolites with tumorigenesis, gut microflora activity, diet, lifestyle, and intestinal physiology. In the past several years, considerable advances have been made in the development of MS-based analytical methods to decipher the fecal metabolome. In this review, we discuss the current practices for processing fecal samples for metabolomics study. And we summarize the MS-based methods with focus on the liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GCMS), and capillary electrophoresis-mass spectrometry (CE-MS) that are commonly applied to assess the fecal metabolome. We hope this review can benefit the investigation of the functions of gut microbial metabolites and promote the translation of fecal metabolomics into clinical applications. © 2019 Elsevier B.V. All rights reserved.

Keywords: Fecal metabolome Gut microbial metabolites Host-gut microbiota interactions Fecal sample pretreatment Liquid chromatography-mass spectrometry Gas chromatography-mass spectrometry Capillary electrophoresis-mass spectrometry Targeted analysis Untargeted analysis

1. Introduction The gut microbiota is emerging as an important factor involved in human health and diseases [1]. In the past decade, our understanding of the microbiome that inhabit our gut, their functionality, and their roles in human health and diseases has greatly advanced [2]. It has been recognized that the gut microbiota shows a great influence on immune regulation [3], neurological signaling [4], energy biogenesis [5], and provides protection against pathogen overgrowth [6]. The gut microbiota can substantially vary between subjects and is influenced by nutrition, lifestyle, immune status, and medication use [7,8]. Given the reported diverse functions, the

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (B.-F. (Y.-Q. Feng). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.trac.2018.12.027 0165-9936/© 2019 Elsevier B.V. All rights reserved.

Yuan),

[email protected]

gut microbiota is demonstrated to be involved in various diseases, such as tumorigenesis [9], diabetes [10], obesity [11], inflammatory diseases [12], and neurodevelopmental disorders [13]. The dynamic interplay between the host and gut microbiota is critical for maintaining host homeostasis [14]. A detailed understanding of the functional microbiome is an essential prerequisite for rational interventions of the gut microbiota to alter host phenotype [15]. Although some communication mechanisms have been learnt about these interrelationships between the host and gut microbiota, much still remains to be discovered concerning how microbes interact with the host [16]. The extensive diversity of microbes and metabolites in the gut implies that our understanding requires a comprehensive toolset to enable in-depth functional elucidation and new discoveries. The generation and integration of omics studies derived from genomic, transcriptomic, proteomic, and metabolomic analysis allow functional assessment of the gut microbiota [17].

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Gut microbiota-generated metabolites are increasingly being recognized as an essential part of human physiology [18,19]. The symbiosis between mammals and gut microbiota relies on mutual communications with metabolites acting as the important mediators [20]. The change of microbes in gut could therefore result in significant alterations in the extracellular metabolome [21]. In this respect, analysis of gut microbial metabolites is useful to understand the molecular mechanisms of the host-gut microbiota interaction. A variety of sample types including biofluids and tissue biopsies can be analyzed to investigate the microbial metabolites [22]. Particularly, feces contain a wide array of metabolites that may serve a good reflection of nutrient ingestion, digestion and absorption by gut microbiota and the gastrointestinal tract [23,24]. Therefore, analysis of fecal metabolites provides a non-invasive manner to study the outcome of the host-gut microbiota interactions [23]. A growing number of researches reported that considerable information could be gained from fecal metabolome analysis [23]. The fecal metabolome can provide a functional readout of microbial activity and can be used to mediate host-microbiome interactions [25]. However, the simultaneous determination of numerous fecal metabolites is challenging due to the fact that they have diverse structures with varied chemical and physical properties [26,27]. Many fecal metabolome studies have been carried out using mass spectrometry (MS)-based metabolomics analysis, which covered a broad investigation of the relationships between fecal metabolites and tumorigenesis, gut microflora activity, and diet [28,29]. In the past several years, various MS-based analytical strategies have been developed to decipher the fecal metabolome. In this review, we summarize the MS-based methods including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and capillary electrophoresis (CE-MS) that are commonly applied to assess the fecal metabolome. In addition, fecal matrix generally is a semi-solid mixture, which can affect the results of fecal metabolome analysis by different sample pretreatment strategies [30]. So far, there is still no standardized sample pretreatment method for fecal samples. Therefore, we also discuss the current practices for processing fecal samples for metabolomics study. 2. Fecal sample pretreatment The sample pretreatment prior to the analysis of fecal samples is of high importance as it greatly affects the metabolic profile, especially for feces with diverse and complex matrix [22]. Fecal samples are susceptible to environmental effects, such as postcollection metabolite deterioration due to exposure to aerobic conditions and to temperature changes [31]. Here we summarized and discussed the current practices of processing fecal samples for metabolomics study, including sample collection and storage, freeze-thaw cycles, and extraction. Shown in Fig. 1 is the schematic illustration for fecal sample pretreatment. 2.1. Collection and storage Sample storage is a crucial and sensitive step for fecal samples. To minimize the effect of microbial fermentation, fecal samples are generally collected and then stored under
80  C to
20  C [32e35]. It is also worth noting that fecal samples were placed in an anaerobic chamber within 5e10 min of collection in some laboratories [36], or fecal sample collection was carried out in special anaerobic pouch systems [37]. Gratton et al. [38] investigated the effects of storage temperature and duration on the metabolic profiles of crude feces and fecal water. The results showed that intact fecal samples should be kept at 4  C or on ice during transportation,

and extracted ideally within 1 h of collection, or a maximum of 24 h. Fecal water samples should be stored below
20  C, avoiding multiple freeze-thaw cycles. 2.2. Freeze-thaw cycles The processing of fresh fecal samples generally shows better metabolic stability compared to the processing of frozen samples [38]. But the use of frozen samples is more common and practical. Thus, to prevent possible metabolite degradation, fecal samples should be homogenized and aliquoted prior to freezing to minimize freeze-thaw cycles [39]. Recently, Smirnov et al. [40] reviewed the influence of freeze-thaw cycles on the concentration of metabolites such as cholesterol, micronutrients and hormones. It was shown that no obvious change of concentration was observed for previously mentioned metabolites within 3 freeze-thaw-cycles. However, some metabolites were moderately influenced after 6e10 cycles [41]. Freeze drying should be avoided while analysis of volatile compounds [42]. It's recommended that fecal samples should be homogenized and aliquoted prior to be frozen, to minimize unintended freeze-thaw cycles. As with other biofluids or tissue specimens, it is important to minimize handling time and to use uniform sample handling/storage procedures for the analyzed samples. 2.3. Normalization of water content Different from biofluids like blood and cerebrospinal fluid that don't require adjustment of concentration or volume, human feces are made up of 60e85% water [43]. If the water content consistently differs between experimental groups, it may cause a bias and the false discovery of biomarkers. Two approaches are recommended to normalize the water content of fecal samples [44]. The first method is to consider the water content as a kind of variability. A non-specific correction for between-sample variation is conducted, which can only allow relative quantitation for fecal samples. The other method is to remove all water from the samples or precisely measured the water content of each sample. 2.4. Extraction Liquid phase extraction [45,46], solid phase extraction [22,47,48] and QuEChERS extraction methods [49] have been frequently used for sample preparation of fecal samples. Selection of appropriate extraction method dependents on the aim of the analysis and the choice of analytical techniques. Here, we evaluated some crucial steps of extraction in fecal metabolome analysis. 2.4.1. Homogenization The typical fecal sampling method is to scoop a small portion of feces into a big container, which could introduce analytical bias due to the heterogeneity of fecal samples [38] Sonication and mechanical smashing are two common strategies to obtain more homogeneous extracts. Homogenization is able to disaggregate the samples and enable better penetration of the extracting buffer/ solvent throughout the sample, which can result in more efficient extraction [50,51]. 2.4.2. Extraction solvents The polarity and pH value of extraction solvents can impact the extraction efficiency and the stabilities of fecal metabolites. Polar metabolites are generally extracted with pure polar organic solvents (e.g. methanol, propanol and acetonitrile) [52e56] or solvent mixtures (e.g. methanol-water) [57]. As for extraction of less polar compounds, the non-polar solvents such as chloroform or cyclohexane are normally used [58e60]. Polar solvents like

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Fig. 1. Schematic illustration of the fecal sample pretreatment.

methanol is also an alternative solvent for the extraction of nonpolar compounds such as cholate and some lipids [61]. It was found that organic solvents were more informative compared to the aqueous extracts, and can provide better chromatographic profiles with a larger number of peaks, sharper and more symmetrical peak shape [62]. Besides, organic solvents also play a role on precipitating proteins [28]. Isopropanol/acetonitrile/water (3/2/ 2, v/v/v) was appropriate for the extraction of amino acids, poly and monounsaturated fatty acids and ursodeoxycholic acid [63]. Acetonitrile/methanol-chloroform (3/1, v/v), acetonitrilechloroform (3/1, v/v), methanol, water, mixture of methanol and water were also used for the pretreatment of fecal samples [64e66]. On the other hand, neutral propanol provides good selectively for aspartate and guanine. The pH value of extraction solution should also be considered carefully as pH alteration may lead to the degradation of fecal metabolites through hydrolysis [50] and affect extraction efficiency of ionized or ionizable metabolites [67]. Deda et al. [62] studied the pH effect on fecal sample and found that spectral resolution of neutral and basic extracts was generally higher compared to acidic extracts. For example, short chain fatty acids (SCFAs) appeared to be better extracted at basic or neutral pH, while phenolic acids were favorably extracted at acidic pH [68]. It is worth noting that there will not be one ideal pH value to suit all types of metabolites since the charge and extraction efficiency of different metabolites can vary with pH. Therefore, pH adjustment should be considered in light of the target compounds and the experimental aim. 2.4.3. Number of extraction cycles Another critical point of the sample pretreatment is the extraction cycles. Single time extraction may not be effective in extracting as many as metabolites from feces [69]. Multiple step extraction of fecal sample can provide larger numbers of detected metabolites [70]. Girlanda et al. [71] showed that extraction of fecal samples with 600 mL of methanol followed by 600 mL of chloroform provided more identified numbers of the metabolites. As a result, 2541 metabolites in the positive ion mode (m/z 50e850) were detected.

3. LC-MS-based fecal metabolome analysis In metabolomics studies, the metabolome coverage and detection sensitivity can vary largely by different types of instruments or platforms. LC-MS has undergone great expansion within metabolomics because of its capability for resolving various classes of metabolites. The following section provides typical examples and discussion of the strengths and weaknesses of various LC-MS-based methods for fecal metabolome analysis (Fig. 2). And we also provided the detailed summary of the sample preparation, analytical methods and detected metabolites by the LC-MS-based fecal metabolome analysis in Table 1. Chemical derivatization-based LC-MS provides a promising strategy in metabolomics study [72]. Derivatization aims to modify the structures of metabolites with characteristic groups. The advantages of integrating derivatization with LC-MS analysis include: (1) improvement of selectivity and separation through increasing hydrophobicity of highly polar compounds on the reversed phase liquid chromatography, (2) enhancement of ionization efficiency through the introduction of high electrophilic or nucleophilic moieties in the atmospheric pressure chemical ionization (APCI), and permanently charged or easily ionizable moieties in electrospray ionization (ESI), (3) improvement of structural elucidation for metabolites, and (4) facilitation of isomer separation [53,72e85]. Design, synthesis and selection of appropriate derivatization reagents to achieve fast and specific reactions are important for derivatization-based LC-MS analysis. 3.1. Non-targeted fecal metabolome analysis Generally, the aim of non-targeted metabolome study is to delineate a full view of metabolites in biological samples. For determination of metabolites, the most direct way is to compare the detailed MSn information about fragmentation reactions between authentic standards and potential features detected by MS. To acquire the exact mass of unknown metabolites, high-resolution MS analyzers such as time-of-flight (TOF) and Orbitrap are widely

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Fig. 2. Summary of the methods for MS-based fecal metabolome analysis.

used in non-targeted metabolome analysis. But in most cases, only a small fraction of metabolites can be categorically identified due to the limited commercial standards. So the online metabolites databases that provide a comprehensive description of many metabolites, such as Human Metabolome Database (HMDB, http:// www.hmdb.ca), and METLIN (https://metlin.scripps.edu) are useful tools for the identification of metabolites. 3.1.1. LC-IT-TOF MS Xu et al. [86] used LC-IT-TOF MS with both positive and negative ion mode to investigate the metabolism of mogroside V in rat. The mass range of MS was 300e2000 and MS2 and MS3 fragmentation were performed by a data-dependent mode with mass range 50e2000. To identify metabolites of mogroside V, the possible metabolites predicted by general metabolic reactions were screened and confirmed by comparing their corresponding extracted ion chromatograms with those of the blank group. 58 metabolites were identified in rat fecal samples. By comparing with those of reference compounds, 5 of the metabolites were unequivocally identified as siamenoside I (M7), mogroside IIIE (M13), mogroside IIIA1 (M14), mogroside IIA2 (M19), and mogrol (M31). The structures of the other 53 identified metabolites were tentatively identified by interpretation of the MSn data. These results improved the understanding of the in vivo metabolism, distribution, and effective forms about mogroside V. Similarly, Waters et al. [87] conducted LC-IT-TOF MS analysis to identify and compare metabolic profiling of pinometostat, a DOT1L inhibitor, in rat and dog fecal samples. A prescreening full scan (m/z 50e1250) with positive and negative ion mode was performed for comprehensive analysis of metabolites. The chemical structures of the metabolites were deduced based on the MS1 and MS2 spectra as well as the exact mass. With this strategy, the authors identified a total of 5 metabolites of pinometostat and the major metabolic pathways were found out to be the hydroxylation of the t-butyl group and the N-dealkylation of the central nitrogen. Wang et al. [88] elucidated the metabolism of glycyrol in rats after oral administration by LCIT-TOF MS. Glycyrol, together with three metabolites, were detected in fecal samples and glycyrol mainly undertook hydroxylation metabolism, accompanied by hydration and dehydrogenation as minor reactions. 3.1.2. LC-Q-TOF MS LC-Q-TOF MS platform has been employed to analyze metabolic profile in fecal samples from diet-induced obesity mice [89] and atherosclerosis mice [61]. Respondek et al. [89] evaluated the effect of short-chain fructo-oligosaccharides (scFOS) on obesity mice

model. The mass spectrometry features were converted into elemental chemical formula based on their exact mass. Matching elemental chemical formula to molecular structure was performed through the web interface MZedDB allowing simultaneous databases repository requests. The result showed scFOS most prominently affected the fecal metabolome (e.g. bile acids derivatives and hydroxyl monoenoic fatty acids), which may partly explain their effects on the reduction of insulinaemia. Tian et al. [61] analyzed the metabolic profiling of fecal extracts from atherosclerosis (AS) mice model. Through analyzing the variable importance projection value, a total of 28 ions were mainly contributed to the difference between control and AS group. Among these ions, 16 (12 in positive, 4 in negative) were tentatively identified by comparing the accurate MS and MS2 fragments by searching in metabolome databases (METLIN and HMDB) and then confirmed by the commercial standards. This study revealed potential biomarkers in feces from AS mice and supplied a systematic view of the pathological changes in the gastrointestinal metabolites of AS mice. Analysis of metabolites in fecal samples by LC-Q-TOF MS is also frequently carried out to investigate medicine metabolism, such as amiodarone [90], polycyclic musk [91], tanshinol borneol ester [92], traditional Chinese medicines [93e100], harmane [101], icaritin and central-icaritin [102], Lappaconitine [103], agomelatine [104], A3 adenosine receptor agonist [105], phosphodiesterase type 5 inhibitor [106], and phenolic compounds [107]. Deng et al. [93] identified 66 metabolites of poliumoside in rat feces using LC-QTOF MS platform. Full scan mass spectra of the feces samples after oral administration of poliumoside were compared with those of blank samples. The structures of the metabolites were tentatively characterized based on the accurate mass, relevant drug biotransformation knowledge and the fragmentation pattern of the parent compound. And metabolites were identified by comparing the retention time and MS2 mass spectra with those of the authentic standard. The authors found that hydrolysis, hydroxylation, acetylation, sulfation, hydration, reduction, dehydrogenation and dimethylation were the major metabolic pathways of poliumoside. For the in vivo metabolic profiling of Nauclea officinalis after oral administration, Zhu et al. [96] established a LC-Q-TOF MS method to identify metabolites in rat fecal samples. In this study, the MetaboLynx software package was used to evaluate the full-scan data and to obtain accurate mass and elemental compositions and/or designer compounds detected in the samples. To identify the metabolites in vivo, probable structures were speculated in accordance with the metabolism rule of drugs. And the authors found the metabolic pathway including hydroxylation, deglycosyation, dehydrogenation and acetylation.

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Table 1 Summary of the sample preparation, analytical methods and detected metabolites in the LC-MS-based fecal metabolome analysis. Non-targeted fecal metabolome analysis Entry Samples

Models

Sample preparation

Analytical methods Detected metabolites

LC-Q-TOF MS

1

Fecal samples from rats Stress-induced depression

Ice cold water and ice cold methanol extraction

2

Fecal samples from human

Female healthy human

Water and acetonitrile extraction

3

Fecal samples from children Fecal samples from children Fecal samples from human

Enthesitis-related arthritis

4 5

6

Cystic fibrosis

Three Chinese families (two parents and one infant per family) with no known diseases Fecal samples from rats Notoginsenosides

7

Fecal samples from human

Crohn's disease and ulcerative colitis

8

Fecal samples from mice Newborn Meconium samples Fecal samples from human Fecal samples from human Fecal samples from rats

Atherosclerosis

9 10 11 12

Chemical derivatization-LCQ-TOF MS Formic acid water solution and Nano-LC-Q-TOF MS e ethyl acetate extraction Acetonitrile:H2O (1:1, v/v) LC-LTQ-Orbitrap e extraction MS Amine and phenol Water, methanol and Chemical acetonitrile extraction derivatization-LC- metabolites Q-TOF MS

3012/2223

[53]

1500/-

[114]

39154/2

[120]

6200/3845

[73]

n-butanol extraction

LC-Q-TOF MS

35/35

[94]

Ultrapure water and ice cold water/methanol (80/20) extraction Methanol extraction

LC-LTQ-Orbitrap MS

Protopanaxadiol-type and protopanaxatriol-type notoginsenosides e

9553/4515

[119]

LC-Q-TOF MS

e

28/16

[61]

LC-Q-TOF MS

14/14

[56]

LC-Q-TOF MS

Lipid, amino acid, and purine metabolites Phenolic compounds

37/20

[107]

LC-Q-TOF MS

Bile acids

-/9

[111]

LC-Q-TOF MS

Bile acid and phospholipid

-/7

[109]

LC-Q-TOF MS

Bile acids derivatives, hydroxyl monoenoic fatty acids

152/152

[89]

-/821

[25]

Gestational diabetes mellitus Ultrasonic extraction with methanol Phenolic compounds Sterile saline solution extraction Recurrent Clostridium difficile 50% acetonitrile extraction infection Adenine-induced chronic e renal failure Diet-induced obesity Sonicated extraction with phosphate buffer saline, followed a SPE with sulphonic acid-bonded silica packing for desalination Mice colonized with a human 1:1 methanol: 0.1% formic acid microbiota water solution extraction followed by a solid-phase OASIS extraction Cirrhotic Methanol extraction

13

Fecal samples from mice

14

Fecal samples from mice

15

Fecal samples from human

16 17 18

Fecal samples from rats Model drug candidates (AZD6280 and AZ12488024) Fecal samples from rats Polycyclic musk (AHTN) Fecal samples from rats Glycyrol

19

Fecal samples from rats Mogroside V

20

Fecal samples from rats Pinometostat and dogs

21

Fecal samples from rats Poliumoside

22

Fecal samples from rats Catalpol and acteoside

23

Fecal samples from rats Amiodarone

24

Fecal samples from rats Tanshinol borneol ester

25

Fecal samples from rats Nauclea officinalis

26

Fecal samples from rats Ginsenosides

27

Fecal samples from human

Catalpol

Amino acids, fatty acids, bile acids, hypoxanthine and stercobilins Amine and phenol metabolites

Total detected/ Ref. identified metabolites 16/16 [52]

e e Ultrasonic extraction by methanol Ultrasonic extraction by methanol Ultrasonic extraction by acetonitrile, acetonitrile/water (9:1 v/v) and acetonitrile/water (1:1 v/v) Ultrasonic extraction by methanol Extraction with sterile physiological saline Water and acetonitrile extraction followed by SPE Ultrasonic extraction with phosphate buffered saline Ultrasonic extraction with methanol Ultrasonic extraction with methanol

Sterile physiological saline extraction

LC-LTQ-Orbirap MS e

Lysophosphatidylcholines, 9215/16 aromatic amino acids, fatty acids, acylcarnitines, bile acids and bile pigments e 13/13

[115]

AHTN e

6/6 3/3

[91] [88]

LC-IT-TOF MS

e

58/58

[86]

LC-IT-TOF MS

e

16/5

[87]

LC-Q-TOF MS

e

66/66

[93]

LC-Q-TOF MS

e

7/7

[100]

LC-Q-TOF MS

e

22/22

[90]

LC-Q-TOF MS

Phosphatidylglycerol, phosphatidic acids e

55/55

[92]

9/9

[96]

LC-Q-TOF MS

LC-LTQ-Orbitrap MS LC-Q-TOF MS LC-IT-TOF MS

LC-Q-TOF MS LC-Q-TOF MS

LC-Q-TOF MS

35/35 Ginsenoside sulphur derivatives and ginsenoside sulphur derivative metabolites Catalpol aglycone, 4/4 acetylated catalpol,

[108]

[95]

[97]

(continued on next page)

166

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Table 1 (continued ) Non-targeted fecal metabolome analysis

28

Fecal samples from rats Harmane

29

Fecal samples from rats Icaritin and central-icaritin

30

Fecal samples from A3AR selective agonist mice (MRS5980) Fecal samples from rats Lappaconitine

31

32 33 34

Fecal samples from mice Fecal samples from human Fecal samples from rats

Agomelatine Phosphodiesterase type 5 (TPN29) Magnoflorine

35

Fecal samples from human

36

Fecal samples from rats Zhi-Zi-Hou-Po decoction

37 38 39 40 41

Pyronaridine

Ultrasonic extraction with acetonitrile Ultrasonic extraction with methanol Extracted with 50% acetonitrile (5 mM chlorpropamide) Ethyl acetate extraction followed by a preconditioned activated Oasis HLB SPE Methanol extraction Ultrasonic extraction with methanol Ultrasonic extraction by normal saline and methanol (1/3, v/v) e

Ultrasonic extraction by methanol Fecal samples from rats Antibiotic-induced gut Ice cold water and ice cold microbiota dysbiosis methanol extraction Fecal samples from Healthy male and female Ultrapure water and ice cold human volunteers methanol extraction Fecal samples from Fenbendazole and ivermectin e Amur tiger tablets Fecal samples from rats Type 2 diabetes mellitus Methanol extraction Fecal samples from Alzheimer's disease Methanol and methyl tert-butyl mice ether extraction

LC-Q-TOF MS

dimethylated and hydroxylated catalpol aglycone, nitrogencontaining catalpol aglycone e

LC-Q-TOF MS

e

LC-Q-TOF MS

16/16

[101] [102]

e

17/17, 17/17 5/5

[105]

LC-Q-TOF MS

e

44/44

[103]

LC-Q-TOF MS

e

7/7

[180]

LC-Q-TOF MS

e

15/15

[106]

LC-LTQ-Orbitrap MS LC-LTQ-Orbitrap MS and LC-QTRAP MS LC-Q-TOF MS

e

7/7

[116]

e

10/10

[117]

Prototype compounds and metabolites e

83/83

[98]

-/21

[99]

LC-LTQ-Orbitrap MS LC-LTQ-Orbitrap MS LC-Q-TOF MS Chemical derivatization-LCLTQ-Orbitrap MS

e

9672/-

[118]

e

-/15

[121]

-/19 2302/1388

[110] [81]

Succinate, phenylacetylglutamine, hippurate and trimethylamine metabolic pathway Capilliposide A and B

11

[129]

1

[126]

Catechin metabolites

7

[55]

Timosaponin AIII

18

[123]

Vitamin E metabolites

11

[128]

C2eC6 Short-chain fatty acids

10

[127]

Bile acids

14

[125]

Indole, indole-3-acetate, and tryptamine

3

[124]

LC-Q-TOF MS

Carboxyl, carbonyl, amine, and thiol metabolites

Targeted analysis of fecal metabolites 42

Fecal samples from rats 2,4,6trinitrobenzenesulfonicacidinduced Crohn's disease rats

Acetonitrile/water (1:1, v/v) extraction

LC-QqQ MS

43

Fecal samples from rats Intravenous administration of Capilliposide B Fecal samples from rats Oral administration of tea polyphenol or its combination with butter

SPE

LC-QTRAP MS

44

45 46 47

Fecal samples from rats Oral administration of Timosaponin AIII Fecal samples from Oral administration of grodents tocopherol or d-tocopherol Fecal samples from Adults with regular diet human

48

Fecal samples from human

Laparoscopic sleeve gastrectomy

49

Fecal samples from mice

6-week-old C57BL/6 mice

LC-QqQ MS Ultrasonic extraction with methanol, then extracted with ethyl acetate after diluted with water SPE LC-Q-TOF MS and LC-QTRAP MS Extracted by methanol with LC-QqQ MS ascorbate (0.2 mg/mL) 50% aqueous acetonitrile Chemical extraction derivatization-LCQTRAP MS EtOH (>96%)/10 mM phosphate LC-QTRAP MS buffer (pH 7.2) (85:15, v/v) buffer extraction Methanol/chloroform LC-QTRAP MS extraction

In addition to the study of medicine metabolism, LC-Q-TOF MSbased untargeted metabolic analysis of fecal samples has been performed to investigate the molecular mechanisms of cirrhotic disease [108], adenine-induced chronic renal failure [109], maternal gestational diabetes mellitus (GDM) [56], stress-induced depression [52], type 2 diabetes mellitus [110] and recurrent Clostridium difficile infection (CDI) [111]. Peng et al. [56] investigated the metabolome response of newborn meconium with GDM. Based on the mass accuracy, the mass differences were set at 20 mDa for meconium during biomarker identification. Candidate biomarkers were further validated by searching against reported structural information. With this strategy, 14 meconium metabolic

biomarkers were identified for GDM with 9 meconium biomarkers showing a great potential in diagnosing GDM-induced disorders. Huang et al. [108] compared metabolome profiling of fecal sample from cirrhotic patients and healthy individuals. 16 metabolites were identified by comparing their chromatographic retention time and MS2 fragmentation characteristics with the standard compounds. The authors found strong increase of lysophosphatidylcholines, aromatic amino acids, fatty acids and acylcarnitines, while a dramatic decrease of bile acids and bile pigments in cirrhotic patients. This study demonstrated that with severe hepatic injury in patients with liver cirrhosis, malabsorption occurs along with disorders of fatty acid metabolism.

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Xu et al. [73] developed a differential 13C2/12C2-dansyl labeling for comprehensive analysis of the amine- and phenol-containing metabolites in human fecal samples. With this method, a total of 6200 peak pairs were detected and 67 were positively identified based on the exact mass and retention time matched to a dansyl standard library; while 581 and 3197 of peaks were putatively identified based on the exact mass matched using MyCompoundID against HMDB and Evidence-based Metabolome Library, respectively. This dansylation chemical isotope labeling strategy was also used to parallel investigate the amine and phenol submetabolome changes among human feces and urine [53]. A total of 3012 unique peak pairs were detected from all the feces samples, and 64 of them could be positively identified using the dansyl standards library. These results reveal that the urine and fecal metabolomes are very different, thereby justifying the consideration of using the above two biospecimens to increase the probability of finding specific biomarkers of diseases. 3.1.3. Nano-LC-Q-TOF MS The main advantage of nano-LC is enhanced sensitivity, as compounds enter the MS in more concentrated bands. This is particularly relevant for determining low abundant metabolites in limited samples [112,113]. To explore the mechanism of arthritis, Stoll et al. [114] performed metabolome profiling of fecal samples from children with enthesitis-related arthritis (ERA) using nanoLC-Q-TOF MS. The m/z, retention times, and ion intensities from the aligned ions for each sample were submitted for statistical analysis using MetaboAnalyst. Out of nearly 1500 negatively charged ions identified, the levels of 154 were significantly decreased in patients, while only one was significantly higher in patients. And differentially present negatively charged ions revealed 21 pathways that were under-abundant in ERA patients. All the results showed that diminished metabolic diversity and alterations in the tryptophan metabolism pathway may be a feature of ERA. 3.1.4. LC-LTQ-orbitrap MS With LC-LTQ-Orbitrap MS platform, several researchers investigated metabolic characters of drugs by using fecal samples, such as AZD6280 and AZ12488024 [115], Magnoflorine [116], and pyronaridine [117]. Guo et al. [115] present a generic strategy for detection of the metabolites of a model drugs (AZ12488024) under data-dependent acquisition mode. Accurate mass-based untargeted data mining tools including background subtraction, mass defect filtering, and a data mining package (MZmine) were used for metabolomic analysis in identification of metabolites. A total of 33 metabolites of AZ12488024 were detected and new metabolic pathways such as thioxidation and thiomethylation reactions occurring on the thiazole ring were proposed. Xue et al. [116] investigated metabolic profiling of magnoflorine which is an important aporphine alkaloid in Coptidis Rhizoma in rat fecal samples after oral or intravenous administration by analysis with LC-LTQ-Orbitrap MS. After comparative analysis of the blank sample and the dosed sample, 7 metabolites were detected and identified according to the metabolic rules of drugs in vivo, the chromatographic retention time and debris characteristics of multistage mass spectrometry of magnoflorine. This study revealed there existed pharmacokinetic interactions between magnoflorine and the rest of ingredients in Coptidis Rhizoma. Using LC-LTQ-Orbitrap MS platform, Marcobal et al. [25] examined the effects of the colonized human gut microbiota on the fecal metabolome of mice. An UHPLC-MS-based untargeted metabolomics approach consisted of either of two types of chromatographic conditions (RPLC or HILIC), and two ionization conditions (ESI in positive mode or negative mode). Unbiased

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metabolite identification was determined using METLIN database using a maximum error of 3 ppm. A total of 821 correlative metabolites were identified and compound identity validations were performed by analyzing pure compounds when available. This metabolism study may reveal potential disease related biomarkers. De Paepe et al. [118] established an untargeted fingerprinting of the polar metabolome fraction for polar metabolic fingerprinting of multiple biological specimens (feces, blood plasma, and urine) from the same individual. A total of 9672 components were retrieved for feces based on their m/z values, C-isotope profile, and retention times relative to those of the internal standards. This study achieved a significantly higher coverage of the metabolome and enclosed significant value to elucidate intricate metabolic pathways. Vanden Bussche et al. [119] established a metabolome fingerprinting workflow by LC-LTQ-Orbitrap MS in polarity switching mode for differentiating metabolic profiles between Crohn's disease and ulcerative colitis in human fecal samples. With this strategy, 9553 ions were detected and 4515 metabolites were putatively identified after cross-referencing to the HMDB. Kaakoush et al. [120] also utilized LC-LTQ-Orbitrap MS under data-dependent acquisition mode to profile fecal metabolome of pancreatic insufficient and sufficient children fecal samples with cystic fibrosis (CF). A total of 25435 and 13719 hits were detected in positive and negative ion modes, respectively. But only 2 metabolites were putatively identified as lipoyl-GMP and glycerol 1,2-didodecanoate 3tetradecanoate through database search. This study identified lipoyl-GMP as a potential novel inflammatory biomarker. Besides, the elevation of glycerol 1,2-didodecanoate 3-tetradecanoate in feces may provide clues to the pathogenesis of intestinal inflammation. He et al. [121] established an integrated approach of LCLTQ-Orbitrap MS to analyze the effects of Fenbendazole and Ivermectin tablets on the fecal metabolic phenotype of the Amur tiger. A total of 15 metabolites were identified according to accurate molecular weight and fragment pattern and by searching the online databases (the Human Metabolome Database, Biofluid Metabolites Database, and MassBank). Meanwhile, distinct changes in the fecal metabolic phenotype of the experimental Amur tigers were also found, including lower levels of acrylic acid, acetoacetate and catechol and higher amounts of 5,6-dihydrouracil, adenine hydrochloride hydrate and galactitol. Our group recently reported a comprehensive profiling of fecal metabolome of mice by an integrated chemical derivatization combined with LC-LTQ-Orbitrap MS analysis (Fig. 3) [81]. We synthesized four different derivatization reagents to label metabolites with different groups, including carboxyl, carbonyl, amine, and thiol groups. With this method, 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 a homemade library. Furthermore, we discovered 211 fecal metabolites exhibited significant difference between Alzheimer's disease (AD) model mice and wild type mice, which suggests the close correlation between the fecal metabolites and AD pathology and provides new potential biomarkers for the diagnosis of AD. The property of retention times of the metabolites is widely used to facilitate the identification of metabolites. However, retention time is not typically standardized because slight changes of LC conditions, such as elution conditions, temperature, columns and instruments, can lead to variability in retention times [122]. Our group recently established the chemical derivatization-based LC retention index (RI) to calibrate the retention time drifts. In this study, 2-dimethylaminoethylamine (DMED) and 4-(N,Ndimethylamino)phenyl isothiocyanate (DMAP) were utilized to selectively label carboxylated compounds and amine compounds,

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respectively. A mixture of 23 DMED-labeled fatty acid standards was used as the calibrants to generate the RIs for DMED- and d4DMED-labeled carboxylated metabolites. Another mixture of DMAP-labeled 14 fatty amine standards was used as the calibrants to generate the RIs for DMAP- and d4-DMAP-labeled amine metabolites. We established the RIs of 854 DMED-labeled carboxylated metabolites and 1057 DMAP-labeled amine metabolites in fecal samples and demonstrated that RIs were highly reproducible under different elution gradients, columns and instrument systems. Using RIs, 267 DMED-labeled carboxylated metabolites and 273 DMAPlabeled amine metabolites in human serum matched well with the fecal metabolome database. The developed RI strategy was demonstrated to be a promising method to facilitate the identification of fecal metabolites in metabolomic analysis.

This study demonstrated that the butter changed the metabolism of catechins in vivo. Recently, Hou et al. [129] developed a targeted metabolome analytical strategy for 11 gut microbiota-host co-metabolites including succinate, phenylacetyl-glutamine, hippurate and trimethylamine metabolic pathways in rat feces. The measured levels for this panel of endogenous metabolites ranged from 1 ng/mL to 173 mg/mL. With this method, the authors were able to simultaneously monitor inflammation-induced alterations of these key microbiota-host co-metabolites in rat feces matrices and to investigate the pathophysiological roles and mechanisms of these metabolites.

3.2. Targeted analysis of fecal metabolites

GC-MS is reviewed as the robust platform in metabolomics study due to the high efficiency, reproducibility and sensitivity [130]. GC-MS has also been widely used for fecal metabolome analysis. Volatile metabolites can be directly analyzed by GC-MS, while many more metabolites need to be derivatized to increase the volatility of metabolites before GC-MS analysis. Metabolic profiling by GC-MS has an advantage owing to the available standard compounds libraries for the identification for metabolites. Ion trap (IT), quadrupole (Q) and time-of-flight (TOF) are the most widely used mass analyzer in GC-MS. We summarized the sample preparation, analytical methods and detected metabolites by the GC-MS-based fecal metabolome analysis in Table S1 in Supporting Information. Derivatization is a crucial step in GC/MS analysis. The purpose of derivatization for GC/MS is mainly to increase the volatility of analytes. Chloroformate, methoxyamine and trimethylsilylation are the most commonly used reagents for derivatization of analytes in GC/MS analysis.

The use of targeted analysis generally can increase the confidence of metabolite identification and the precision for the quantifications of measured metabolite. Due to the good selectivity and sensitivity, triple quadrupole (QqQ) mass analyzer with multireactions monitoring (MRM) mode has been frequently used in the targeted fecal metabolome analysis. 3.2.1. LC-QTRAP MS LC-QTRAP MS platform was used to study the metabolism of bioactive substances such as Timosaponin AIII [123], tryptophan metabolites [124], bile acids [125], capilliposide A and B [126] by analyzing the fecal samples. Damms-Machado et al. [125] investigated the mechanism of functional weight loss induced by laparoscopic sleeve gastrectomy (LSG) with regard to bile acids changes. 14 bile acids in fecal samples were quantified by LC-QTRAP MS with MRM mode, and the authors found that LSG resulted in enhanced fecal excretion of bile acids. This study also reported that LSG increased malabsorption due to impairment of bile acid circulation. Han et al. [127] developed an isotope-labeled chemical derivatization method for the quantitation of C2eC6 SCFAs in human feces under MRM mode. 3-nitrophenylhydrazine (3NPH) was employed as the derivatization reagent to convert ten C2eC6 SCFAs to their 3nitrophenylhydrazone derivatives. The stable isotope-labeled 13 C6e3NPH was synthesized as the internal standard to compensate for the matrix effects during MS analysis. Ten SCFAs were well separated on a reversed-phase C18 column after derivatization and quantitation within the range from 0.3 to 45 fmol. This chemical derivatization in conjugation with LC-MS analysis can be applied to the accurate measurements of SCFAs in the human fecal samples. 3.2.2. LC-QqQ MS To understand the potential contribution of metabolites to vitamin E-mediated effects, Jiang et al. [128] employed LC-QqQ MS with MRM mode to analyze the bioavailability of vitamin E metabolites. A total of 11 metabolites of vitamin E were quantified. The authors observed a broad range of linearity between LC/MS response and analyte concentrations (from 10 nM to 5 mM), and the detection limits were 0.1e0.4 pmol on column with a signal-tonoise ratio of >8 under optimized ionization conditions. The results showed that high concentrations of 130 -hydroxychromanols and 130 -carboxychromanols were detected in feces. This quantification method is useful for evaluation of pharmacokinetics of vitamin E metabolite formation and their bioavailability in tissues and excretion. Besides, Zhang et al. [55] carried out targeted quantitative determination of 7 catechin metabolites to investigate the effects of butter on the pharmacokinetics of tea polyphenols using LC-QqQ MS with selected reaction monitoring (SRM) mode.

4. GC-MS-based fecal metabolome analysis

4.1. Non-targeted fecal metabolome analysis For identification of metabolites detected by GC-MS, a tentative chemical structure can be assigned based on its characteristic fragmentation pattern and retention index with subsequent verification using a reference standard. And the most commonly used reference library, National Institute of Standard and Technology (NIST, https://www.nist.gov), provides a comprehensive description of many metabolites. 4.1.1. GC-IT MS Headspace solid-phase microextraction (hSPME) is an effective tool for the preparation of volatile organic metabolites (VOMs). By combining hSPME with GC-IT MS, Ahmed et al. [131] reported the profiling of fecal VOMs in patients with diarrhea-predominant irritable bowel syndrome (IBS). After hSPME using polydimethylsiloxane/carboxane fiber, the sample was immediately transferred to the GC-IT MS for the thermal desorption. As a result, 240 fecal VOMs were identified by comparing the fragment pattern with those in the NIST 2008 library. A drawback of fecal VOMs analysis is that a large proportion of spectral peaks are still unknown, and consequently more effort has to be invested in the compilation of standardized metabolite libraries. 4.1.2. GC-Q MS hSPME coupled with GC-Q MS analysis was used for analysis of fecal samples from obese nonalcoholic fatty liver disease (NAFLD) [132,133] and alcoholics [134]. The metabolites were identified by comparison with the NIST 2008 library followed by manual visual inspection and retention time matching with selected standards. In interpreting the data, only compounds with a 90% or greater

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Fig. 3. Overview of the procedure for the determination and relative quantitation of fecal metabolites in mice by chemical isotope labeling combined with liquid chromatographymass spectrometry (CIL-LC-MS) analysis. (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. Adapted from Bi-Feng Yuan et al. [81] with permission from American Chemical Society.

probability of match to library standards were named. In the former study, univariate analyses were used to discern statistical differences between fecal VOMs composition in obese and control subjects. The latter work proposed a filtered method. Each automated mass spectral deconvolution and identification system outfile that contains a list of identified metabolites and their corresponding peak height values was filtered by custom Perl scripts designed to remove background analytes and eliminate metabolite redundancies. The results showed that a significant increase in fecal ester VOMs was associated with compositional shifts in the microbiome of NAFLD patients. And intestinal microbiota function is altered in alcoholics, which might promote alcohol associated pathologies. However, they didn't consider the effects of some factors such as diet, smoking, and ambient pollution exposure on fecal metabolome. Hough et al. [135] proposed an optimized method using hSPME-GC-Q MS to determine VOMs in feces. The extraction of VOMs should ideally be performed using a divinylbenzene-carboxen-polydimethysiloxane (DVB-CAR-PDMS) SPME fiber. Storage of feces for up to 12 months at
80  C shared a greater percentage of VOMs with a fresh sample than the equivalent stored at
20  C. A larger sample size may be better for evaluating the influence of individual differences including breeds of horse, diet, age and anthelmintic treatment. The factors above may influence the fecal metabolome of horse as well as the optimal extraction method for VOCs. Besides hSPME, direct derivatization of the analytes by methoximation or trimethysilylation prior to analysis was also frequently used. Ng Hublin et al. [136] carried out metabolome analysis of fecal samples from experimentally infected mice by GCQ MS to assess metabolic profile pertaining to the infection. A total of approximately 220 compounds were detected and 101 of them were matched against the in-house library and reference compounds from the NIST. A total of 40 metabolites that contributed most to the variance between the C. parvum infected mice and the control mice were identified, including amino acids, carbohydrates, lipids, and organic acids. The study highlighted the effects of the infection on intestinal permeability and the mechanism of nutrient scavenging by the parasite. To investigate how the gut microbiome

of wild primates respond to the host's external environment, Gomez et al. [66] investigated fecal SCFA profiles in 40 wild western lowland gorillas. GC-Q MS analysis of both polar and nonpolar metabolites provided a total of 2500 mass spectral features, among which, 260 compounds were positively identified by comparing with NIST2008, W8N08 and a custom-built library of 520 unique metabolites. To allow comparison between samples, all data were normalized to the internal standard. Metabolite concentrations are reported as ‘analyte concentration relative to hentriacontanoic acid per gram dry weight’. It was found that geographical range may be an important modulator of the gut microbiomes and metabolomes of these gorilla groups. Heat-stabilized rice bran's (SRB) effects on gut metabolism and the resulting implications for health were also investigated [137]. Lyophilized fecal metabolites were extracted using MeOH/H2O (8/2, v/v) and detected using GC-Q MS. Molecular features defined by retention time and m/z was generated using XCMS software. The results showed that 28 fecal metabolites presented significant increases in the SRB group compared to that in the control group. These data supported the feasibility of dietary SRB intervention in adults and indicated that SRB consumption can affect gut microbial metabolism. A simple step of solvent extraction is also common in the nontargeted fecal metabolome analysis. To investigate the effect of the artificial sweetener on gut microbiome, Bian et al. [138] extracted feces with methanol/chloroform/water solution (2:2:1) followed by methoxymation and detection with GC-Q MS. The fecal metabolic profiling suggested that Ace-K consumption can significantly change the gut metabolic profiles in sugar metabolism, which can influence the crosstalk between the host and gut microbiome. In a similar way, Chi et al. [139] found the artificial sweetener neotame consumption also changed the fecal metabolic profiles. The concentrations of multiple fatty acids, lipids as well as cholesterol in the feces of neotame-treated mice were consistently higher than controls. Malic acid and glyceric acid, however, were largely decreased. In another study, Chi et al. [140] applied GC-Q MS to investigate how the metabolic profiles of gut bacteria are affected by nicotine. In nicotine-exposed female mice, phenylalanine, tyrosine, glutamic acid, GABA, serine, and glycine significantly

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increased. For male mice, phenylalanine, tyrosine, glutamic acid, and GABA increased under nicotine exposure, whereas glycine, serine, and aspartic acid significantly decreased. These perturbations influence the chemical signaling of gut-brain interactions and may further mediate the effects of nicotine on the nervous system. The current study is a four-week neotame exposure, while human exposure is frequently long term at lower concentrations. Future studies are necessary to explore effects of long-term exposure in human. 4.1.3. GC-TOF MS GC coupled with TOF MS is increasingly used for metabolic profiling because of fast acquisition rates, particularly useful for an accurate deconvolution of overlapping peaks obtained from complex mixtures [141]. Analysis of metabolites in fecal samples by GC-TOF MS is widely used to investigate metabolism profiling of bioactive compounds, such as MDG-1 [142], wheat bran extract [143] and oligofructose [144]. Zhu et al. [142] investigated the mechanism of MDG-1 (a water-soluble-b-D-fruetan polysaccharide) in a spontaneous diabetic model by GC-TOF MS. The mass spectra of m/z 20e600 were acquired with electron impact ionization (70 eV) at the full scan mode. Twelve metabolites were identified by importing the resolved mass spectra to NIST2008. Metabolites with similarity >700 were considered reliable (similarity 999 means a perfect match between the compound in the sample and the compound in the NIST library) and authentic reference standards were used to further validate the identified metabolites including both retention time and mass spectrum. The authors demonstrated that monosugars and butanedioic acid were the fermentation products of MDG-1 by intestinal microbes. MDG-1 actions against diabetes might be accomplished through the absorbable monosugars and butanedioic acid via suppressing intestinal glucose absorption, enhancing liver glycogenesis, inhibiting glycogenolysis and promoting GLP-1 secretion. GC-TOF MS platform has also been employed in the study of various disease-related fecal metabolome, including Crohn's disease (CD), ulcerative colitis (UC), pouchitis [145], colorectal cancer (CRC) [144,146], diet effect [147], IBS [148], pediatric ulcerative colitis [149] and kidney-yin deficiency [150]. De Preter et al. [145] identified 273 VOMs in patients with CD, UC or pouchitis. They found the levels of medium-chain fatty acids (MCFAs) were significantly decreased in these patients. Analytes in the samples were identified by comparing the mass spectra of tested samples with the NIST library. Compounds showing mass spectra with matching factors 90% in the NIST library were positively identified. Differential peak identities were further confirmed for retention time and mass spectra from the in-house standards library. These identified MCFAs may serve as important metabolic biomarkers of inflammatory bowel disease-related changes. A challenge for future research is to examine the metabolic fate of MCFAs and to correlate the changes in bacterial metabolism with health. Using the same strategy, Phua et al. [146] investigated the intricate changes in the molecular environment of the gut lumen. To detect retention time shifts and facilitate RI calculation, the standard alkane series mixture (C10eC40) was injected at regular intervals. Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed some fecal metabolites (e.g., fructose, linoleic acid, and nicotinic acid) could provide important insights into the tumorigenesis of CRC. 4.1.4. GC  GC-TOF MS Compared with conventional GC-MS, GC  GC-TOF MS can provide better separation of metabolites [151]. This platform has been used to identify metabolites affected by probiotics in mice fed

with alcohol [65]. In this study, metabolites were derivatized by methoxyamine hydrochloride and N-(tert-butyldimethylsilyl)-Nmethyltrifluor-oacetamide (MTBSTFA) mixed with 1% tertbutyldimethyl-chlorosilane (TBDMSCI). 212 metabolites were identified from the liver and fecal samples after RI matching. The metabolome analysis indicated that the levels of the fatty acids increased in mouse liver and decreased in mouse feces when the mice were chronically exposed to alcohol. Using the GC  GC-TOF MS platform, Wei et al. [64] investigated the effect of copper on fecal metabolites of rats by exposing different dietary levels of copper with and without high fructose intake for 4 weeks. A total of 38 metabolites were detected with significant abundance changes due to different doses of copper, fructose, or their synergetic interaction. And they found that C15:0 and C17:0 long chain fatty acids produced by bacteria were increased by either high copper level or high fructose intake. This study revealed that distinct fecal metabolome profiles were associated with high fructose and copper level dietary. 4.2. Targeted analysis of fecal metabolites 4.2.1. GC-IT MS GC-IT MS platform has also been employed in the targeted analysis of sterols in fecal samples [152,153]. Cuevas-Tena et al. [152] developed a method for neutral fecal sterols determination in subjects receiving a normal diet with or without a plant sterolsenriched beverage using GC-IT MS. Identification of sterols was carried out by comparing the relative retention time and mass spectra with those of the sterol standards and the NIST library. The method allows the quantification of 17 sterols including cholesterol, plant sterols and their metabolites. Calibration curves were used for the quantification of fecal sterols and their metabolites. As a result, feces from individuals who ingest a normal diet have a total sterol content of approximately 40 mg/g, with a predominance of cholesterol and its metabolites. However, feces from diets that have incorporated PS-enriched foods have greater sterol and metabolite contents in feces. 4.2.2. GC-Q MS GC-Q MS was normally used for the targeted analysis of metabolites in fecal samples, including SCFAs [154e160], branched chain fatty acids (BCAAs) [161], thymol [162] and the metabolites in the metabolic pathway of CDPS-99 [163]. To investigate microbial influence in CRC development, Weir et al. [154] utilized GC-Q MS platform to analyze 8 kinds of SCFAs. The results revealed higher concentrations of poly- and mono-unsaturated fatty acids, ursodeoxycholic acid and bile acid in fecal samples from healthy adults. Guard et al. [48] carried out targeted analysis of 3 SCFAs and 3 BCAAs in dog feces. The results showed that the concentration of fecal propionic acid was significantly decreased in acute diarrhea dogs. The study demonstrated that the fecal dysbiosis present in acute diarrhea was associated with altered systemic metabolic states. 4.2.3. GC-TOF MS To quantitatively measure a complete panel of microbial metabolites in biological samples, Zhao et al. [164] reported an automated high-throughput quantitative method by GC-TOF MS to simultaneously identify 118 microbial metabolites in human feces samples within 15 min per sample. These metabolites encompass different chemical classes including fatty acids, amino acids, carboxylic acids, hydroxylic acids, and phenolic acids, benzoyl and phenyl derivatives as well as indoles that are involved in a number of important metabolic pathways. And a reference library was developed consisting of 145 methyl and ethyl chloroformate

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derivatized compounds with their mass spectral and RI information for metabolite identification, which greatly increased the accuracy of compound identification in biological samples. 4.3. LC-MS in combination with GC-MS for fecal metabolome analysis To increase the coverage of the detected metabolites [165], LCMS in combination with GC-MS analysis was used for the fecal metabolome analysis on various fields, including microbial infection [166], colonization resistance [167], colorectal cancer [57,168,169], and undernourished [170]. Houlden et al. [166] coupled GC-TOF MS with LC-Orbitrap MS to evaluate the changes of fecal metabolome caused by chronic trichuris muris infection in C57BL/6 mice. Metabolome analysis of fecal samples of infected mice at day 41 showed a significant increase in the levels of essential amino acids and a reduction in digestion of diet-derived carbohydrates to uninfected controls. Jump et al. [167] combined LC-IT MS and GC-Q MS to analyze fecal samples. LC-IT MS analysis was performed under positive ions with acidic extracts and negative ions with basic extracts. Finally, 484 compounds were identified. Recovery of colonization resistance after antibiotic treatment coincided with restoration of several fecal bacterial metabolites. These metabolites could provide useful clues for intact or disrupted colonization resistance during and after antibiotic treatment. Goedert et al. [168] detected 1043 small molecules with 773 characterized in fecal samples of colorectal cancer patients by utilizing LC-MS and GC-MS. Individual molecules and their relative levels were identified from the mass spectral peaks, retention times and m/z compared with a chemical reference library generated from 2500 standards. Statistical analysis indicated that only 45% of metabolites with a true relative risk 5.0 would be found in specimens from 500 cases and 500 controls. Brown et al. [57] identified 93 significant fecal metabolites from colorectal cancer survivors consuming rice bran at 4 weeks. The method combined LC-MS with GC-MS analysis and 39 fecal metabolites were significantly different between rice bran diet and control groups. The metabolic levels of glycation end products, steroids and bile acids were increased in the rice bran diet group. Preidis et al. [170] identified 423 metabolites in feces from undernourished neonatal mice by LC-MS and GC-MS. 117 fecal metabolites were altering during 3 weeks after ending feed deprivation. Sinha et al. [171] identified 530 metabolites to study the microbe-metabolite relationships in the gut and potentially colorectal cancer risk reduction. The identification was realized by comparing the mass spectra to a chemical reference library based on mass spectral peaks, retention times, and m/z in feces samples. The results showed that metabolites mediated a direct colorectal cancer association with Fusobacterium and Porphyromonas, but not an inverse association with Clostridia and Lachnospiraceae. This study identified complex microbe-metabolite networks that may provide important insights on neoplasia and targets for intervention. 5. CE-MS-based fecal metabolome analysis CE is particularly useful for the profiling of highly polar metabolites without the need for derivatization and/or extensive sample preparation [172]. In contrast to utilize the difference in interaction between the analytes and the stationary phase, compounds are separated based on their charge-to-size ratio in CE [173,174]. To investigate the effect of prenatal administration of low-dose antibiotics on newborns’ health in later life, Yoshimoto et al. [175]

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performed a metabolome analysis of fecal contents from 13 weeks old mice using a CE-TOF MS platform. Each metabolite was identified and quantified based on the peak information including m/z, migration time and peak area. A total of 160 metabolites were measured in the luminal contents. And the authors found that no metabolite was significantly different between the two tested groups. Uebanso et al. [176] examined the effect of lower daily doses of dietary xylitol intake on gut microbiota and lipid metabolism in mice. Luminal metabolites were determined by CE-TOF MS. A total of 94 metabolites were identified from a metabolite list provided by HMT. These results suggest that the changes in luminal content metabolites in xylitol supplement groups had a little effect on overall metabolism. Wakita et al. [177] determined metabolites in fecal samples from antibiotic-treated mice using a CE-TOF MS approach. Fresh fecal samples were diluted nine-fold using Dulbecco's phosphatebuffered saline and extracted three times by intense mixing for 1 min and resting for 5 min on ice. The upper aqueous portion was collected and filtered through a 5 kDa cut-off filter. The measurement of extracted metabolites was performed in both positive and negative modes, and the alignment of detected peaks was performed according to the m/z value and normalized migration time. Annotation were produced from the measurement of standard compounds and aligned with the datasets according to m/z value and normalized migration time. A total of 205 metabolites were identified from the fecal sample. Similarly, Matsumoto et al. [178] examined the effects of the probiotic Bifidobacterium animalis subsp lactis LKM512 on adult-type atopic dermatitis using CE-TOF MS. A total of 204 metabolites (121 cationic and 83 anionic) were identified. Besides, the authors also investigated metabolites in fecal samples from arginine intake human [179]. A total of 221 metabolites were detected, including 125 cationic and 96 anionic substances. Taken together, CE-MS has the potential to provide complementary information to GC-MS and LC-MS based analysis, thereby increasing the coverage of metabolome. 6. Conclusions and perspectives The past decade has witnessed remarkable progress in our understanding of the important roles that gut microbial metabolites play in modulating the health of their hosts. It is evident that fecal metabolome analysis is rapidly growing and the technological advances will assist in establishing it as an important approach to analyze diseases, diet, lifestyle, intestinal physiology, and the complex interactions between the host and gut microbiota. In this review, we assessed the sample pretreatment methods for fecal samples and summarized the MS-based techniques utilized in fecal metabolome analysis. MS-based studies have identified a large number of fecal metabolites that differ in different conditions. Much work remains to fully characterize the physiological effects of microbial metabolites that are important in human health. Although MS-based metabolomic analysis allow the detection of thousands of metabolite features from fecal samples, current challenges still include the identification of unknown metabolites as well as the difficulty in linking specific metabolites to their microbial provenance. Advances in de novo metabolic network reconstruction and modelling will allow some of these challenges to be addressed in the future. The increasing functional understanding of the microbiome begins to be translated into practice. And gut microbiota exhibit significant potential to revolutionize therapeutic approaches to human diseases. Standardized fecal metabolomics methodology that includes high-quality quantitation and identification of metabolites will certainly promote the translation of fecal metabolomics into clinical applications.

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Conflict of interest The authors declare no competing financial interest. Acknowledgements

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The work is supported by the National Key R&D Program of China (2017YFC0906800), and the National Natural Science Foundation of China (21635006, 21475098, 21522507, 21721005).

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Appendix A. Supplementary data

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Supplementary data to this article can be found online at https://doi.org/10.1016/j.trac.2018.12.027.

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