Development of a specialized method for simultaneous quantification of functional intestinal metabolites by GC/MS-based metabolomics

Development of a specialized method for simultaneous quantification of functional intestinal metabolites by GC/MS-based metabolomics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of a specialized method for simultaneous quantification of functional intestinal metabolites by GC/MS-based metabolomics Kazuki Funahashi, Shinji Fukuda, Chol Gyu Lee, Kuniyo Sugitate, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4708066/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Intestinal metabolites produced by gut microbes play a significant role in host health. Due to their different chemical structures, they are often analyzed using multiple analyzers and methods, such as gas chromatography/mass spectrometry (GC/MS) for SCFAs and liquid chromatography/mass spectrometry (LC/MS) for bile acids (BAs), amino acids (AAs), and sugars. In this study, we aimed to develop a specialized method for the simultaneous determination of important intestinal metabolites, specifically addressing the main issue of SCFA volatilization during the dry solidification process. We discovered that these compounds can all be measured in fecal samples by GC/MS after trimethylsilyl (TMS) derivatization despite the expected volatility of SCFAs. Validating the results using SCFA standards suggested that the fecal matrix exerts a stabilizing effect. This method enabled the simultaneous quantification of 65 metabolites. For further validation in a biological context, a mouse study showed that high-MAC and high-fat diets increased SCFAs and BAs in feces, respectively, and showed a negative correlation between Alistipes and sugars, all consistent with previous studies. As a result, we successfully developed a specialized simultaneous quantification method for SCFAs, BAs, AAs, AA derivatives, and sugars in fecal samples using GC/MS-based metabolomics in conjunction with a TMS derivatization pretreatment process. Gut microbiome intestinal metabolites short-chain fatty acids bile acids GC/MS TMS derivatization volatility metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gut microbes metabolize undigested food components and produce a variety of metabolites, including short-chain fatty acids (SCFAs), bile acids (BAs), amino acids (AAs) and sugars. Their contribution to our health and implication in various diseases, such as infections, food allergies, cardiovascular disease, and intestinal inflammation, are well known [ 1 , 2 ]. SCFAs identified in feces, such as formate, acetate, propionate, and butyrate, are interlinked with the host immune system [ 3 ]. Specifically, acetate is suggested to protect host epithelial cells, contributing to the prevention of infectious diseases in vivo [ 4 ]. Propionate and butyrate have been shown to promote the epigenetic differentiation of regulatory T cells [ 5 ]. Besides their association with the immune system, SCFAs also contribute to diabetes prevention [ 6 ] and endurance exercise performance [ 7 ]. They are also linked to improvement of glucose tolerance [ 8 ], underscoring the intricate relationship between SCFAs in the gut and overall host health. Bile acids, amphipathic digestive compounds derived from the host, are secreted into duodenum from the bile duct in response to meal intake. Although 95% of these acids are reabsorbed in the small intestine, a portion remains within the intestinal tract where it is metabolized by gut microbes. The resulting BAs induce germination of spore-forming bacteria [ 9 ], regulate the expression of pathogenic bacterial genes [ 10 ], and ameliorate influenza virus and SARS-CoV-2 infections [ 11 ], and are thus instrumental in maintaining gut homeostasis. Recent studies of the intestinal metabolome, extending beyond SCFAs and BAs, have further illuminated their connection to host health. For instance, levels of proline, an amino acid derived from gut microbes, are elevated in individuals suffering from depression [ 12 ]. Indole-3-acetic acid, a metabolite derived from tryptophan, an essential amino acid, has also been reported to influence chemotherapy efficacy in pancreatic cancer [ 13 ]. Such observations illustrate the systemic influence of gut-derived AAs. Similarly, monosaccharide-utilizing bacteria correlated with insulin resistance exist in the gut, linking the presence or absence of certain sugars directly to host blood glucose levels [ 14 ]. These findings showed that SCFAs and BAs, as well as AAs and sugars, are metabolites that play functional roles in the gut. Although the value of studying the intestinal metabolome is clear, no analytical method that can simultaneously quantify SCFAs, BAs, AAs, and sugars in fecal samples has been reported. Although a UPLC-Q/TOF-MS method specific for the intestinal metabolome can be found in the literature, this method is not able to measure bile acids, which are deeply implicated in intestinal health [ 15 ]. The limitations arise from the distinct properties of each metabolic compound, such as polarity, structure, and molecular weight. Indeed, several studies have successfully quantified SCFAs and BAs in human feces, but separate measurement systems were necessary to achieve this [ 11 , 16 , 17 ]. Two major pretreatment methods exist for gas chromatography/mass spectrometry (GC/MS): metabolite extraction from fecal samples and post-extraction derivatization. For water-soluble and non-volatile AAs and sugars, a known method involves extracting the water-soluble fraction via liquid-liquid separation in a water/methanol/chloroform mixture, followed by centrifugal drying/lyophilization and a two-step derivatization process consisting of oximization and trimethylsilylation (TMS) before quantification by GC/MS [ 18 ]. In contrast, BAs are extracted using methanol as the solvent, followed by centrifugal solidification/lyophilization, a two-step derivatization through esterification and TMS, and subsequent quantification by GC/MS [ 19 – 21 ]. It has been demonstrated that a combination of water/methanol solvent followed by drying and subsequent oximization and TMS derivatization can enable the quantification of metabolites with diverse characteristics, such as BAs, AAs, and sugars, using the same pretreatment [ 22 ]. However, while this approach could detect C5 and C6 SCFAs, C2, C3, and C4 SCFAs (acetic, propionic, and butyric acids) could not be detected. Due to the volatilization of these SCFAs during the pretreatment processes, namely dry solidification, quantifying these vital metabolites in studying the gut environment is challenging [ 23 ]. As an alternative, diethyl ether extraction under acidified conditions using hydrochloric acid and subsequent derivatization with tert-butyldimethylsilyl has been proposed [ 5 , 24 ]. However, Ueyama et al. (2020) [ 25 ] showed that volatile SCFAs, such as acetic acid and butyric acid, can be stably preserved when treated and processed with feces without prior purification, with volatilization significantly reduced even after drying treatment. This study suggests a potential solution to the previously insurmountable issue of SCFA volatilization post-lyophilization, potentially enabling the simultaneous quantification of various metabolites, including SCFAs. Based on these studies, we hypothesized that SCFAs, BAs, AAs, and sugars could undergo the same pretreatment process and be simultaneously quantified by GC/MS after TMS derivatization. We first evaluated the quantification of SCFAs during the dry solidification process prior to TMS derivatization. To ensure accurate quantification, we applied the standard addition method approach. This technique is especially useful when the samples' complexity might interfere with the measurement of specific components. By adding a known quantity of standards to fecal samples, we could create a calibration curve for our analysis. The effectiveness and reliability of this method were verified through tests on mouse feces, to confirm its suitability for analyzing biological samples. The aim of this study is to develop a specialized method for simultaneous quantification of functional intestinal metabolites such as SCFAs, BAs, AAs, and sugars in fecal samples using a comprehensive pretreatment and processing method. Materials & Methods 2.1 Chemicals, reagents, instruments, and quality control fecal samples Standards were purchased from CHEM SERVICE, Kanto Chemical (Tokyo, Japan), TCI (Tokyo, Japan), SIGMA-Aldrich (STL, USA), ICN (CA, USA), Fluka (NC, USA), FUJIFILM Wako (Osaka, Japan), Cayman Chemical (MI, USA), and others. Ribitol used as an internal standard was purchased from Wako. Myristic acid was from the Fiehn GC/MS Metbolomics standards kit (Agilent, 400505). N-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) and methoxyamine were purchased from SIGMA-Aldrich. Quality control (QC) fecal samples were prepared by mixing 10 freeze-dried human fecal samples. 2.2 Evaluation of SCFA volatility with or without fecal co-treatment To verify whether SCFA samples volatilize during the extraction and derivatization procedures, we tested the effects of fecal co-treatment on measurement of SCFAs. Acetic acid, propionic acid, and butyric acid were used for the validation. The analysis compared the relative peak areas of 2,500 µM standard solutions of each SCFA with and without the addition of QC feces, and the relative area of the samples added with QC feces was calculated by subtracting the relative area of the QC feces alone. Standard solutions of each SCFA were analyzed in two forms: as salts and in their free states. The extraction and derivatization procedures are described below. 2.3 Extraction and derivatization of metabolites from fecal samples The experimental procedure is shown in Fig. 1. Fecal samples were freeze-dried for at least 24 hours using a VD-800R freeze-dryer (TAITEC, Japan). They were then subjected to vigorous shaking with 3.0 mm zirconia beads using a Shake Master (Biomedical Science, Japan) to rupture the cell membrane (1,500 rpm., 10 minutes). After disruption, 10 mg of the samples were weighed, and 0.1 mm zirconia/silica beads were added. Then, 600 μL of a water:methanol (1:2) solution containing ribitol as an internal standard was added. The mixture was then shaken again for 10 minutes and centrifuged (20 °C, 4,600 x g, 10 min). Then, 300 μL of supernatant was transformed to Bond Elut C18 (Agilent 12102058) and dispensed into a 2 mL tube. The hydrophilic compound was extracted with 500 μl of water, and the hydrophobic compound with 600 μl of methanol. The mixture was dried using a centrifugal concentrator under reduced pressure (40 °C, 10 hours). After drying, 20 μl of methoxyamine hydrochloride (20 mg/mL, pyridine solution) was added and the mixture was incubated under 30 °C for 90 min. Then, 70 μl of N-Methyl-N-trimethylsilyl trifluoroacetamide (MSTFA) and 10 μl of myristic acid-d27, serving as an internal standard for retention time locking, were added. The TMS derivatization was performed at 37°C for 30 minutes. 2.4 GC/MS analysis The GC/MS analysis was performed using an Agilent 5977B GC/MS with the split-splitless inlet, equipped with electron impact (EI) ion source. A DB-5MSUI fused-silica capillary column (30 m, 0.25 mm, 0.5 um, 122-5536UI) was utilized to separate the derivatives. The column thickness generally used in metabolomics is 0.25 μm, but 0.5 μm was employed because the retention time of SCFAs is buried in the peak of the TMS derivatization reagent when 0.25 μm is used. Hydrogen was used as a carrier gas at a constant flow rate of 1.5 mL/min through the column. One microliter of the sample was injected in the split mode at a ratio of 1:50. The solvent delay time was set to 1.8 min. The initial oven temperature was held at 30 °C for 2.76 min, ramped to 100 °C at a rate of 9 °C/min, to 180 °C at a rate of 18 °C/min, to 310 °C at a rate of 2 °C/min, to 325 °C at a rate of 18 °C/min, and finally held at 325 °C for 8.27 min. The temperatures of the injector, transfer line, and EI ion source were set to 250 °C, 300 °C, and 250 °C, respectively. The electron energy was 70 eV, and mass data was collected in a sim/scan mode (m/z 50-700). 2.5 Quantitative validation of compounds To evaluate the quantitative performance of our method, we assessed the linearity, linearity range, recovery rate, lower limit of detection (LLOD), and precision. The compounds used to validate these assessment items are shown in Supplementary Table 1. Verified compounds include SCFAs, BAs, AAs, sugars, as well as vitamins and indole compounds. Calibration curves for each metabolite were created by plotting the peak area ratio of the analyte to the internal standard (ribitol) against the analyte concentrations. The calibration curves were prepared using a QC fecal sample to account for potential of volatilization of SCFAs. We employed 10% QC samples, prepared by diluting QC samples to one-tenth of their original concentrations. This dilution strategy enables a precise examination of SCFA’s behavior at lower concentrations, providing insights into the method’s sensitivity and specificity across the analyte concentration range (Table 1). Linearity was assessed by the coefficient of determination (R2) for the linear regression between concentration and relative peak area of each metabolite. Good linearity was defined as R2 being greater than 0.95. The signal-to-noise (S/N) ratio for determining the limit of detection (LOD) was set to a minimum value of 3. For analytes producing multiple peaks, the peak with the highest intensity was generally selected. To evaluate precision and recovery, a standard mixture of metabolites was spiked into a 100% single feces sample for mimicking the composition of actual fecal samples without any dilution. This approach ensures that our analysis reflects conditions as close to natural fecal samples as possible. For evaluating precision, Ten repeated measurements were conducted once, and five repeated measurements performed twice on separate days. The precision of metabolites was evaluated in terms of intra-day and inter-day variability and expressed as relative standard deviation (RSD) %). The precision was deemed acceptable if less than 15% [26]. The recovery was determined by comparing the concentration of feces spiked with mixed standard solutions before extraction. The recovery rate was calculated as (pre-extraction concentration / post-extraction concentration) × 100 at each spiking level. The acceptable recovery rate ranged from 70 to 130% [26]. Compounds that met all of these criteria were considered to have passed quantitative validation. 2.7 Mouse samples and animal treatment Male C57BL/6J mice, 6 weeks old (n = 4), were obtained from CLEA Japan, Inc. (Tokyo, Japan) and were fed a mixed diet consisting of AIN-93G (EP Trading, Tokyo, Japan), CE-2 (CLEA Japan, Tokyo, Japan) and D12492 (EP Trading, Tokyo, Japan).After acclimation, AIN-93G (Control), D12492 (High Fat), and CE-2 (High MAC) were each fed every week (Fig. 4A). At the end of each week, mouse fecal samples were collected and stored at −80 ◦C for further analysis. Processed and having metabolites extracted similarly to human fecal samples, calibration curves for the concentration and relative area of each metabolite in the mouse study were created using a baseline of 10% mouse QC feces. 2.8 Microbiome analysis from mouse fecal samples The extraction and measurement of fecal microbial DNA were performed as previously described [27]. Briefly, the fecal samples were initially lyophilized and shaken vigorously using a Shake Master. Samples were then suspended in DNA extraction buffer containing 400 μL of a 1% w/v SDS/TE (10 mM Tris-HCl, 1 mM EDTA; pH 8.0) solution, and fecal samples in the buffer were further shaken with 0.1 mm zirconia/silica beads using a Shake Master (1,500 rpm, 5 min). After centrifugation (20 °C, 17,800 x g, 10 min), bacterial DNA was extracted using an automated DNA extraction machine according to the instruction manual (GENE PREP STAR PI-480). After DNA extraction, the V1–V2 variable region of the 16S rRNA gene was amplified using the bacterial universal primers 27F-mod (5′-AGRGT TTGATYMTGGCTCAG-3′) and 338R (5′-TGCTGCCTCCC GTAGGAGT-3′) with Tks Gflex DNA Polymerase (Takara Bio Inc., Japan) [28]. Amplicon DNA was sequenced using MiSeq (Illumina, USA), according to the manufacturer’s protocol. 2.9 Bioinformatics analysis For 16S rRNA gene-based microbiome analysis, QIIME2 (version 2019.10) was used [29]. Primer bases were trimmed using cutadapt (option: –p-discard-untrimmed) [30]. Sequence data were processed using the DADA2 pipeline for quality filtering and denoising (options: –p-trunc-len-f 230 –p-trunc-len-r 130) [31]. Contamination by the human genes was checked by mapping the filtered output sequences, and no contamination was found. The filtered output sequences were assigned to taxa using the “qiime feature-classifier classify-sklearn” command with the default parameters [32]. Silva SSU Ref Nr 99 (version 132) was used as the reference database for taxonomic assignment. Alpha and beta diversities were calculated using “qiime phylogeny align-to-tree-mafft-fasttree” and “qiime diversity core-metrics-phylogenetic” commands with the sampling depth set to the lowest read numbers. 2.10 Statistical analysis All statistical analyses were performed using Python scripts (version 3.7.6). For beta-diversity analysis, microbiome unweighted/weighted UniFrac distance and metabolome spearman correlation distance were used. Distance matrices were visualized via principal coordinate analysis (PCoA) analysis. Each metabolite value was standardized by centering to a mean of 0 and dividing by the standard deviation (z-score) of each metabolite. Z-score was obtained by normalization among all samples. Spearman rank correlation coefficient was used to validate the associations between gut bacteria and metabolites (scipy version 1.5.2). Visualization was performed using Cytoscape 3.10.2 © software based on Spearman correlations between gut bacteria and intestinal metabolites. Results 3.1 Verification of SCFA volatility during the preprocessing step The relative peak areas of pure acetic acid, propionic acid, and butyric acid were significantly diminished in comparison to those co-treated with feces at a free standard concentration of 2,500 µM (Fig. 2 A). Specifically, the relative peak areas of acetic acid, propionic acid, and butyric acid were 11.0, 8.2, and 10.0 times lower, respectively. These results indicate that the addition of feces before pretreatment suppressed volatilization. The relative peak areas of the SCFA salt standards co-treated with feces were significantly higher than those of free SCFA standards, with increases of 1.4 times for acetic acid, 1.6 times for propionic acid, and 1.5 times higher for butyric acid, suggesting that SCFAs in salt form further resisted volatilization. 3.2 Quantitative Validation of SCFAs The R2 of the calibration curve using 10% QC feces sample exceeded 0.990 for all compounds within the test ranges (Fig. 3 , Supplementary Table 2). The linear ranges were 250 − 10,000 µM for acetic acid, 250–5,000 µM for propionic acid and butyric acid, 50 − 1,000 µM for valeric acid, and 25–500 µM for formic acid, isobutyric acid, and isovaleric acid (Supplementary Table 2), determined according to concentration ranges seen in actual fecal samples. The RSDs for the intra-day precision and inter-day precision were 10.0% and 7.1% for formic acid, 3.9% and 3.7% for acetic acid, 0.8% and 1.5% for propionic acid, 3.6% and 3.0% for isobutyric acid, 1.9% and 1.8% for butyric acid, 4.0% and 3.4% for isovaleric acid, and 4.0% and 3.3% for valeric acid (Supplementary Table 2). The recovery rates were 82.3% for formic acid, 98.1% for acetic acid, 96.9% for propionic acid, 110.1% for isobutyric acid, 93.6% for butyric acid, 97.6% for isovaleric acid, and 88.5% for valeric acid (Supplementary Table 2). Overall, these results demonstrate an acceptable level of linearity, inter-day and intra-day precision, and high rates of SCFA recovery. All metrics support the validity of our method for SCFA quantification. 3.3 Quantitative validation of BAs, AAs, sugars and other compounds The same validation methods were similarly applied to other compounds, such as for BAs, AAs, and sugars (Supplementary Table 3). Galactose and talose could not be separated, so quantitative validation was performed for the combined concentration of these two compounds. The R2 of the calibration curve using the 10% QC feces are between 0.959–0.998 for BAs, 0.961–0.996 for AAs, 0.975–0.993 for sugars, and 0.957–0.997 for other compounds within the test range (Supplementary Table 3). The linear ranges were 2.5-1,000 µM for BAs, 25 − 1,000 µM for AAs, 25 − 1,000 µM for sugars, and between 2.5-1,000 µM for other compounds. The RSDs for intra-day precision and inter-day precision were 4.1–11.4% and 4.5–9.3% for BAs, 2.2–11.4% and 2.2–9.1% for AAs, 2.3–12.5% and 3.2–13.4% for sugars, and 1.9–12.4% and 2.3–9.9% for other compounds (Supplementary Table 3). These results suggested that the method exhibited acceptable precision for these metabolites. The recovery rates were 91.2–118.2% for BAs, 77.3–114.2% for AAs, 89.7–122.4% for sugars, and 72.4–114.9% for other compounds (Supplementary Table 3). Quantitative validation was performed for 87 compounds other than SCFAs (Supplementary Table 1), and Supplementary Table 3 shows the compounds that passed quantitative validation, consisting of 10 bile acids, 14 amino acids, 12 sugars, 5 vitamins, and 17 other compounds. 3.4 Comparison of mouse fecal metabolites with dietary variation Having confirmed the validity of our sample processing and quantification method in principle, we next tested the method in a biological model alongside fecal microbiome sequencing results to simulate a plausible application in mouse gut studies. Since previous studies have reported increased SCFAs with high-MAC diets and increased BAs with high-fat diets in mouse studies, we tested whether our results would be consistent with these findings [ 5 , 33 ]. We also measured correlations between measured metabolite concentrations and the gut microbiome to compare them to trends reported in the literature and further validate our metabolite quantification method. In particular, Alistipes and Bacteroides are monosaccharide-utilizing bacteria that have been reported to be negatively correlated with intestinal monosaccharide levels [ 14 ]. We chose to focus on these two genera in our experiment in order to compare their correlations with intestinal metabolites to those found in the literature. In this experiment, we quantified the fecal metabolome in mice prescribed a three-week dietary regimen, with the diet changing every week between a control diet, high-fat diet, and high-MAC diet (Fig. 4 A). A total of 45 of the 65 validated compounds were successfully quantified in mouse feces in at least one of the samples. The composition of intestinal metabolites also changed with diet, as expected (Fig. 4 C). The clustermap showed that increased levels of SCFAs and AAs were characteristic of the high-MAC diet group (Fig. 4 D). Specific bile acids such as cholic acid, β-muricholic acid and ursodeoxycholic acid were found to be elevated in the high-fat diet group (Fig. 4 D). Other bile acids were more prevalent in the control diet group, indicating a diverse response of intestinal metabolites to different dietary regimens. A statistically significant negative correlation (p-value < 0.05) was observed between Bacteroides and branched-chain amino acids (valine, leucine, isoleucine) (Fig. 4 E). Similarly, a significant negative correlation was observed between Alistipes , Bacteroides and monosaccharides (arabinose, glucose, galactose and talose). The gut microbiota was also distinct between each dietary group (Fig. 4 B). These factors make it important to quantify intestinal functional metabolites such as SCFAs, BAs, AAs, and sugars in gut environment studies. The effectiveness and reliability of the pretreatment method were verified through tests on mouse feces. Discussion This study investigated the feasibility of quantifying SCFAs and other metabolites in fecal samples using a comprehensive pretreatment method for GC/MS analysis. A primary challenge identified was the volatilization of SCFAs during the drying and solidification process [ 23 ]. Our study showed that the volatilization rate of SCFAs co-treated with feces was significantly lower than that of SCFAs in isolation, and their recovery rates from fecal mixtures exceeded 80%. These results suggest that SCFAs in feces are less susceptible to volatilization during drying than pure SCFA solutions, presumably due to the formation of fatty acid salts within the fecal matrix. Fecal samples typically contain high levels of potassium and sodium [ 34 ] that can react with free SCFAs to form stable fatty acid salts such as sodium acetate, which have a higher boiling point (approximately 400°C) compared to acetic acid (118°C). Although the exact proportion of SCFAs converted into salts in feces is unknown, the acceptably high recovery rate (Supplementary Table 2), robust calibration curves (Fig. 3 ), and wide coverage of SCFAs in human feces [ 35 ] indicate that a majority of the SCFAs in feces likely exist as salts. The successful development of a simultaneous measurement system for SCFAs, BAs, sugars, and AAs and their derivatives in this study demonstrates the efficacy of the pretreatment method. Biological tests conducted in mice have verified the applicability of this method for gut environment research. Previous studies showed that a high-MAC diet increases SCFAs such as acetic acid, butyric acid, and propionic acid, which are by-products of fiber digestion [ 5 ]. Conversely, a high-fat diet increases primary BAs like cholic acid and β-muricholic acid [ 33 ], supporting their role in fat digestion and absorption [ 33 ]. Interestingly, contrary to findings in previous studies, other bile acids were found to be characteristically elevated in the control diet group. This may be because the cellulose in the control adsorbed bile acids, preventing them from re-entering circulation and to instead be released in feces [ 36 ]. Additionally, negative correlations were observed between the relative abundance of Bacteroides and the concentrations of branched-chain amino acids including valine, leucine, and isoleucine, aligning with the results of a previous study [ 37 ]. For sugars, significant negative correlations were observed between Alistipes and monosaccharides, such as arabinose, glucose, galactose & talose, also consistent with a previous study [ 14 ]. These results indicate that the method is sufficiently applicable to actual fecal samples, allowing for simultaneous quantification of SCFAs, BAs, AAs, and sugars–key compounds in gut environmental studies. There are two limitations of this method. First, this method does not distinguish between galactose and talose, resulting in a combined analysis of these sugars. For more detailed analysis of sugars, using an alternative analytical instrument like LC/MS/MS would be preferable. However, it is worth noting that talose is a rare sugar, so most of the combined galactose and talose value is likely attributable to galactose [ 38 ]. Second, it cannot measure conjugated BAs due to their large molecular weight, which precludes detection by GC/MS. To include these BAs, hydrolysis would need to be added as a pretreatment. However, it is worth noting that fecal bile acids are rarely found to be conjugated and hydrolysis treatment may not be necessary [ 39 ]. Consequently, these limitations may affect the reproducibility of the method compared to prior studies where LC/MS/MS was used to distinguish between galactose and talose and separately measure conjugated BAs. Although LC/MS/MS is more sensitive and has a wider quantitative range than this method, this method is much more rapid due to less processing of samples and also requires less sample material, which addresses a common issue in mouse fecal analytical studies. The GC/MS equipment is also more widely available, allowing this method to be more accessible to researchers as well as being more cost-efficient. Despite certain limitations, this method is a valuable tool for comprehensive studies of gut environments. In conclusion, the study successfully established a specialized method for simultaneous determination of functional intestinal metabolites such as SCFAs, BAs, AAs, and sugars. Notably, despite the volatility of SCFAs, this study showed that SCFAs in feces do not volatilize to the point of loss of quantitation even after drying and solidification. This method can also quantify additional compounds like AAs and sugars, offering a comprehensive tool for gut environment analysis. This method can be used as a first step in intestinal metabolome analysis due to its ability to measure functional metabolites in the gut in a relatively comprehensive manner. Although there are some limitations, we expect gut environmental research to progress in the future using the method. Abbreviations SCFA Short-chain fatty acid BA Bile acid AA Amino acid GC/MS Gas Chromatography / Mass Spectrometry TMS Trimethylsilylation Declarations Acknowledgements Not applicable. Declaration of interest statement S.F. is a founder and CEO and K.F. and C.L. are employees of Metagen, Inc., a company involved in microbiome-based healthcare. K.S. is an employee of Agilent Technologies Japan, Ltd. The other authors declare no competing interests. Ethics approval and consent to participate This clinical trial was approved by the clinical trial ethics review committee of Chiyoda Paramedical Care Clinic (publicly registered at UMIN-CTR, trial number: UMIN000028459). All participants signed a consent form. Consent for publication All participants consented independently when donating samples. All data obtained and generated during the study were kept confidential. Availability of data and materials The microbiome data have been deposited with links to BioProject accession number PRJDB18118 in the DDBJ BioProject database. Data used for analysis and the in-house scripts for performing bioinformatics analysis in this work can be found on GitHub at https://github.com/metagen/article_GC-MSmetabolomics. Funding This work was supported in part by research grants from JSPS KAKENHI (22H03541 to S.F.), AMED-CREST (JP23gm1010009 to S.F.), JST ERATO (JPMJER1902 to S.F.), the Food Science Institute Foundation (to S.F.). Authors' contributions K.F., K.S., C.I. and S.F. conceived and designed the study. K.F., N.K. and N.F. performed all experiments. K.F. performed the bioinformatics analysis. A.H. and S.F. supervised the study. K.F. and C.L. wrote the original draft. K.F., C.L., I.S., A.H. and S.F. reviewed and edited the manuscript. References Mann ER, Lam YK, Uhlig HH. Short-chain fatty acids: linking diet, the microbiome and immunity. Nat Rev Immunol. 2024. https://doi.org/10.1038/s41577-024-01014-8. Vliex LMM, Penders J, Nauta A, Zoetendal EG, Blaak EE. 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Restoration of short chain fatty acid and bile acid metabolism following fecal microbiota transplantation in patients with recurrent Clostridium difficile infection. Anaerobe. 2018;53:64–73. Watanabe K, Yamano M, Masujima Y, Ohue-Kitano R, Kimura I. Curdlan intake changes gut microbial composition, short-chain fatty acid production, and bile acid transformation in mice. Biochem Biophys Rep. 2021;27:101095. Fiehn O, Kopka J, Trethewey RN, Willmitzer L. Identification of Uncommon Plant Metabolites Based on Calculation of Elemental Compositions Using Gas Chromatography and Quadrupole Mass Spectrometry. Anal Chem. 2000;72:3573–80. Andrási N, Helenkár A, Vasanits-Zsigrai A, Záray Gy, Molnár-Perl I. The role of the acquisition methods in the analysis of natural and synthetic steroids and cholic acids by gas chromatography–mass spectrometry. J Chromatogr A. 2011;1218:8264–72. Tsai S-JJ, Zhong Y-S, Weng J-F, Huang H-H, Hsieh P-Y. 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BMC Biol. 2017;15:120. Nishimuta M, Inoue N, Kodama N, Morikuni E, Yoshioka YH, Matsuzaki N, et al. Moisture and Mineral Content of Human Feces-High Fecal Moisture Is Associated with Increased Sodium and Decreased Potassium Content-. J Nutr Sci Vitaminol (Tokyo). 2006;52:121–6. Høverstad T, Fausa O, Bjørneklett A, Bøhmer T. Short-Chain Fatty Acids in the Normal Human Feces. Scand J Gastroenterol. 1984;19:375–81. Shen J, Yang X, Sun X, Gong W, Ma Y, Liu L, et al. Amino-functionalized cellulose: a novel and high-efficiency scavenger for sodium cholate sorption. Cellulose. 2020;27:4019–28. Yoshida N, Yamashita T, Osone T, Hosooka T, Shinohara M, Kitahama S, et al. Bacteroides spp. promotes branched-chain amino acid catabolism in brown fat and inhibits obesity. iScience. 2021;24. Sakoguchi H, Yoshihara A, Izumori K, Sato M. Screening of biologically active monosaccharides: growth inhibitory effects of d -allose, d -talose, and l -idose against the nematode Caenorhabditis elegans . Biosci Biotechnol Biochem. 2016;80:1058–61. Ridlon JM, Kang D-J, Hylemon PB. Bile salt biotransformations by human intestinal bacteria. J Lipid Res. 2006;47:241–59. Table Table 1. Study design for evaluating sample handling protocols in fecal metabolome analysis. Experiment Fecal samples used Objective Volatility 100% QC human feces To verify whether mixing SCFA standards in feces would reduce volatilization Linearity 10% QC human feces To verify the linearity of the calibration curve Recovery 10% QC human feces (calibration curve) 100% single feces (test sample) To verify recovery rates in an actual fecal sample Repeatability 100% single feces To verify stability of repeated measurements on an actual fecal sample Mouse 10% QC mouse feces (calibration curve) 100% single feces (test samples) To verify if the method is sufficient to evaluate differences in the amount of metabolites in feces Additional Declarations The authors declare potential competing interests as follows: S.F. is a founder and CEO and K.F. and C.L. are employees of Metagen, Inc., a company involved in microbiome-based healthcare. K.S. is an employee of Agilent Technologies Japan, Ltd. The other authors declare no competing interests. Supplementary Files SupplementaryTable.docx Supplementary Table 1. List of all compounds tested for quantitative validation. Supplementary Table 2. Analytical performance metrics for SCFA quantitative assays. Supplementary Table 3. Analytical performance metrics for quantitative assays of bile acids, amino acids, sugars, and other metabolites. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4708066","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324928370,"identity":"7734a852-b604-415a-969a-d9fdd14a9f07","order_by":0,"name":"Kazuki Funahashi","email":"","orcid":"","institution":"Metagen Inc.","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Funahashi","suffix":""},{"id":324928371,"identity":"a97cd101-5895-412a-b2bc-0817117fc660","order_by":1,"name":"Shinji Fukuda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDAC5gPMDAxsDHIoggZ4tbAlgLUYAzWTqCWxAVkLXiDfxmNszFNml762vf/gxy8Vdxj42xsYigvwaDE4xmOczHMuOXfbmcPM0jJnnjFInDnAYDwDnxb5HuPDvG3MudtuJDNIS7YdZjCQSGAw5iHgMKCW+nSz+4+Zf0v+I0ILA8hhvG2HE8xuMLNJfmwgQovBMbZiwznnjhtuO5NsZs1w7DCPxJmDDXj9It/GvFniTVm1vNnxg49v/qg5LMff3nzMGF+IoQBmoHuAiLHNmFgdDIw/oFofE61lFIyCUTAKRgIAABBCR9M9wOsTAAAAAElFTkSuQmCC","orcid":"","institution":"Metagen Inc.","correspondingAuthor":true,"prefix":"","firstName":"Shinji","middleName":"","lastName":"Fukuda","suffix":""},{"id":324928372,"identity":"22ec0c72-adaa-4e6d-b010-c4035396ce38","order_by":2,"name":"Chol Gyu Lee","email":"","orcid":"","institution":"Metagen Inc.","correspondingAuthor":false,"prefix":"","firstName":"Chol","middleName":"Gyu","lastName":"Lee","suffix":""},{"id":324928373,"identity":"1f94a770-6909-4a23-8fa9-57dd59c3bb71","order_by":3,"name":"Kuniyo Sugitate","email":"","orcid":"","institution":"Agilent Technologies Japan, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Kuniyo","middleName":"","lastName":"Sugitate","suffix":""},{"id":324928374,"identity":"428b9edc-d702-4005-b1a2-e9dd9b2e9380","order_by":4,"name":"Noriko Kagata","email":"","orcid":"","institution":"Institute for Advanced Biosciences, Keio University","correspondingAuthor":false,"prefix":"","firstName":"Noriko","middleName":"","lastName":"Kagata","suffix":""},{"id":324928375,"identity":"093e5d5e-5da5-451d-975c-aabf56fa5fa3","order_by":5,"name":"Noriko Fukuda","email":"","orcid":"","institution":"Institute for Advanced Biosciences, Keio University","correspondingAuthor":false,"prefix":"","firstName":"Noriko","middleName":"","lastName":"Fukuda","suffix":""},{"id":324928376,"identity":"e7602401-b18b-448f-8e30-48f214665aed","order_by":6,"name":"Isaiah Song","email":"","orcid":"","institution":"Institute for Advanced Biosciences, Keio University","correspondingAuthor":false,"prefix":"","firstName":"Isaiah","middleName":"","lastName":"Song","suffix":""},{"id":324928377,"identity":"20b3958f-55d5-4c9f-91a3-807cf356b458","order_by":7,"name":"Chiharu Ishii","email":"","orcid":"","institution":"Institute for Advanced Biosciences, Keio University","correspondingAuthor":false,"prefix":"","firstName":"Chiharu","middleName":"","lastName":"Ishii","suffix":""},{"id":324928378,"identity":"767e44f3-d71b-46f7-bb2e-3c0e6a330caf","order_by":8,"name":"Akiyoshi Hirayama","email":"","orcid":"","institution":"Institute for Advanced Biosciences, Keio University","correspondingAuthor":false,"prefix":"","firstName":"Akiyoshi","middleName":"","lastName":"Hirayama","suffix":""}],"badges":[],"createdAt":"2024-07-08 22:46:33","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4708066/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4708066/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60054699,"identity":"8de84a8b-3639-415e-86a2-eb4234e50824","added_by":"auto","created_at":"2024-07-11 07:07:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of sample handling protocol for intestinal metabolome analysis in this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/3d63b5d5ca5750ce73f29967.jpg"},{"id":60054700,"identity":"2932038c-4411-4365-b66e-cd69292f0a43","added_by":"auto","created_at":"2024-07-11 07:07:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Volatility of the SCFAs acetic acid, propionic acid, and butyric acid in the pretreatment process. \u003c/strong\u003e(A) Comparison of relative SCFA standard quantities as measured in processed samples, between pure standard (orange) and standard co-treated with fecal sample (blue). All standards were free SCFAs. (B) Comparison of the same SCFAs in free (blue) and salt (green) forms, both co-treated with feces.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/7793513b702805bdb5989c98.jpg"},{"id":60054701,"identity":"cfcd36bb-6c19-4e9a-8856-0220adcad502","added_by":"auto","created_at":"2024-07-11 07:07:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of each SCFA calculated using baseline QC fecal measurement ranges as a reference. \u003c/strong\u003eAnalyzed standards were SCFA salts co-treated with fecal samples during pretreatment.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/a218f611a01cd0b22d5226af.jpg"},{"id":60055268,"identity":"146653df-7396-45a2-86ae-5e3372829b76","added_by":"auto","created_at":"2024-07-11 07:15:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":458019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMouse test to demonstrate the applicability of the proposed method to biological samples.\u003c/strong\u003e \u003cstrong\u003eA \u003c/strong\u003eExperimental overview of mouse conditions.\u003cstrong\u003e \u003c/strong\u003eIndividuals (n=4) were fed a combination of control (AIN-93G), high-fat (D12492), and high-MAC (CE-2) diets for one week each. Fecal samples were collected after each week, and metabolome analysis was performed using the method developed in this study. \u003cstrong\u003eB\u003c/strong\u003e PCoA based on unweighted (left) and weighted (right) UniFrac distances of fecal microbiome compositions and \u003cstrong\u003eC\u003c/strong\u003e Spearman correlation of fecal metabolome data. \u003cstrong\u003eD\u003c/strong\u003eHierarchical clustering analysis heat map of intestinal metabolites (Spearman correlation distance, average-linkage method), represented by z-scores. Z-score was obtained by normalizing all samples of a given metabolite.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/1ab3870b7302a1db715103ac.jpg"},{"id":60054703,"identity":"38a20025-2ce7-4cb3-be9d-514eedf106c1","added_by":"auto","created_at":"2024-07-11 07:07:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1156602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation between gut bacteria and intestinal metabolites in mice. A \u003c/strong\u003eCytoscape network analysis of\u003cstrong\u003e \u003c/strong\u003eSpearman correlation coefficient (SCC) between gut bacteria and intestinal metabolites. Relative abundance ratios of gut bacteria that were greater than 0.001 and P values less than 0.05 were drawn. \u003cstrong\u003eB \u003c/strong\u003eCorrelation analysis between \u003cem\u003eAlistipes/Bacteroides \u003c/em\u003eand intestinal metabolites in mouse feces. Plots are arranged by metabolite type: BCAAs (top row), and sugars (middle, bottom rows).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/f8fecbf98cfa11684a963701.jpg"},{"id":60055289,"identity":"d9a19653-3c38-44a6-804f-4e566bf82ae6","added_by":"auto","created_at":"2024-07-11 07:15:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2759264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/9cbf7cbf-f969-4c48-b500-e8b46b41d1d1.pdf"},{"id":60054698,"identity":"17768918-dc0f-46ab-b55e-0383d046fd31","added_by":"auto","created_at":"2024-07-11 07:07:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41633,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. List of all compounds tested for quantitative validation.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2. Analytical performance metrics for SCFA quantitative assays.\u003c/p\u003e\n\u003cp\u003eSupplementary Table 3. Analytical performance metrics for quantitative assays of bile acids, amino acids, sugars, and other metabolites.\u003c/p\u003e","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4708066/v1/9eb2dfb6fb6a9555187c66f9.docx"}],"financialInterests":"The authors declare potential competing interests as follows: S.F. is a founder and CEO and K.F. and C.L. are employees of Metagen, Inc., a company involved in microbiome-based healthcare. K.S. is an employee of Agilent Technologies Japan, Ltd. The other authors declare no competing interests.","formattedTitle":"\u003cp\u003eDevelopment of a specialized method for simultaneous quantification of functional intestinal metabolites by GC/MS-based metabolomics\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGut microbes metabolize undigested food components and produce a variety of metabolites, including short-chain fatty acids (SCFAs), bile acids (BAs), amino acids (AAs) and sugars. Their contribution to our health and implication in various diseases, such as infections, food allergies, cardiovascular disease, and intestinal inflammation, are well known [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSCFAs identified in feces, such as formate, acetate, propionate, and butyrate, are interlinked with the host immune system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Specifically, acetate is suggested to protect host epithelial cells, contributing to the prevention of infectious diseases \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Propionate and butyrate have been shown to promote the epigenetic differentiation of regulatory T cells [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Besides their association with the immune system, SCFAs also contribute to diabetes prevention [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and endurance exercise performance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They are also linked to improvement of glucose tolerance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], underscoring the intricate relationship between SCFAs in the gut and overall host health.\u003c/p\u003e \u003cp\u003eBile acids, amphipathic digestive compounds derived from the host, are secreted into duodenum from the bile duct in response to meal intake. Although 95% of these acids are reabsorbed in the small intestine, a portion remains within the intestinal tract where it is metabolized by gut microbes. The resulting BAs induce germination of spore-forming bacteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], regulate the expression of pathogenic bacterial genes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and ameliorate influenza virus and SARS-CoV-2 infections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and are thus instrumental in maintaining gut homeostasis.\u003c/p\u003e \u003cp\u003eRecent studies of the intestinal metabolome, extending beyond SCFAs and BAs, have further illuminated their connection to host health. For instance, levels of proline, an amino acid derived from gut microbes, are elevated in individuals suffering from depression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Indole-3-acetic acid, a metabolite derived from tryptophan, an essential amino acid, has also been reported to influence chemotherapy efficacy in pancreatic cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Such observations illustrate the systemic influence of gut-derived AAs. Similarly, monosaccharide-utilizing bacteria correlated with insulin resistance exist in the gut, linking the presence or absence of certain sugars directly to host blood glucose levels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings showed that SCFAs and BAs, as well as AAs and sugars, are metabolites that play functional roles in the gut.\u003c/p\u003e \u003cp\u003eAlthough the value of studying the intestinal metabolome is clear, no analytical method that can simultaneously quantify SCFAs, BAs, AAs, and sugars in fecal samples has been reported. Although a UPLC-Q/TOF-MS method specific for the intestinal metabolome can be found in the literature, this method is not able to measure bile acids, which are deeply implicated in intestinal health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The limitations arise from the distinct properties of each metabolic compound, such as polarity, structure, and molecular weight. Indeed, several studies have successfully quantified SCFAs and BAs in human feces, but separate measurement systems were necessary to achieve this [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo major pretreatment methods exist for gas chromatography/mass spectrometry (GC/MS): metabolite extraction from fecal samples and post-extraction derivatization. For water-soluble and non-volatile AAs and sugars, a known method involves extracting the water-soluble fraction via liquid-liquid separation in a water/methanol/chloroform mixture, followed by centrifugal drying/lyophilization and a two-step derivatization process consisting of oximization and trimethylsilylation (TMS) before quantification by GC/MS [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In contrast, BAs are extracted using methanol as the solvent, followed by centrifugal solidification/lyophilization, a two-step derivatization through esterification and TMS, and subsequent quantification by GC/MS [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It has been demonstrated that a combination of water/methanol solvent followed by drying and subsequent oximization and TMS derivatization can enable the quantification of metabolites with diverse characteristics, such as BAs, AAs, and sugars, using the same pretreatment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, while this approach could detect C5 and C6 SCFAs, C2, C3, and C4 SCFAs (acetic, propionic, and butyric acids) could not be detected. Due to the volatilization of these SCFAs during the pretreatment processes, namely dry solidification, quantifying these vital metabolites in studying the gut environment is challenging [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As an alternative, diethyl ether extraction under acidified conditions using hydrochloric acid and subsequent derivatization with tert-butyldimethylsilyl has been proposed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, Ueyama et al. (2020) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] showed that volatile SCFAs, such as acetic acid and butyric acid, can be stably preserved when treated and processed with feces without prior purification, with volatilization significantly reduced even after drying treatment. This study suggests a potential solution to the previously insurmountable issue of SCFA volatilization post-lyophilization, potentially enabling the simultaneous quantification of various metabolites, including SCFAs. Based on these studies, we hypothesized that SCFAs, BAs, AAs, and sugars could undergo the same pretreatment process and be simultaneously quantified by GC/MS after TMS derivatization.\u003c/p\u003e \u003cp\u003eWe first evaluated the quantification of SCFAs during the dry solidification process prior to TMS derivatization. To ensure accurate quantification, we applied the standard addition method approach. This technique is especially useful when the samples' complexity might interfere with the measurement of specific components. By adding a known quantity of standards to fecal samples, we could create a calibration curve for our analysis. The effectiveness and reliability of this method were verified through tests on mouse feces, to confirm its suitability for analyzing biological samples. The aim of this study is to develop a specialized method for simultaneous quantification of functional intestinal metabolites such as SCFAs, BAs, AAs, and sugars in fecal samples using a comprehensive pretreatment and processing method.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e2.1 Chemicals, reagents, instruments, and quality control fecal samples\u003c/p\u003e\n\u003cp\u003eStandards were purchased from CHEM SERVICE, Kanto Chemical (Tokyo, Japan), TCI (Tokyo, Japan), SIGMA-Aldrich (STL, USA), ICN (CA, USA), Fluka (NC, USA), FUJIFILM Wako (Osaka, Japan), Cayman Chemical (MI, USA), and others. Ribitol used as an internal standard was purchased from Wako. Myristic acid was from the Fiehn GC/MS Metbolomics standards kit (Agilent, 400505). N-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) and methoxyamine were purchased from SIGMA-Aldrich. Quality control (QC) fecal samples were prepared by mixing 10 freeze-dried human fecal samples.\u003c/p\u003e\n\u003cp\u003e2.2 Evaluation of SCFA volatility with or without fecal co-treatment\u003c/p\u003e\n\u003cp\u003eTo verify whether SCFA samples volatilize during the extraction and derivatization procedures, we tested the effects of fecal co-treatment on measurement of SCFAs. Acetic acid, propionic acid, and butyric acid were used for the validation. The analysis compared the relative peak areas of 2,500 µM standard solutions of each SCFA with and without the addition of QC feces, and the relative area of the samples added with QC feces was calculated by subtracting the relative area of the QC feces alone. Standard solutions of each SCFA were analyzed in two forms: as salts and in their free states. The extraction and derivatization procedures are described below.\u003c/p\u003e\n\u003cp\u003e2.3 Extraction and derivatization of metabolites from fecal samples\u003c/p\u003e\n\u003cp\u003eThe experimental procedure is shown in Fig. 1. Fecal samples were freeze-dried for at least 24 hours using a VD-800R freeze-dryer (TAITEC, Japan). They were then subjected to vigorous shaking with 3.0 mm zirconia beads using a Shake Master (Biomedical Science, Japan) to rupture the cell membrane (1,500 rpm., 10 minutes). After disruption, 10 mg of the samples were weighed, and 0.1 mm zirconia/silica beads were added. Then, 600 μL of a water:methanol (1:2) solution containing ribitol as an internal standard was added. The mixture was then shaken again for 10 minutes and centrifuged (20 °C, 4,600 x g, 10 min). Then, 300 μL of supernatant was transformed to Bond Elut C18 (Agilent 12102058) and dispensed into a 2 mL tube. The hydrophilic compound was extracted with 500 μl of water, and the hydrophobic compound with 600 μl of methanol. The mixture was dried using a centrifugal concentrator under reduced pressure (40 °C, 10 hours). After drying, 20 μl of methoxyamine hydrochloride (20 mg/mL, pyridine solution) was added and the mixture was incubated under 30 °C for 90 min. Then, 70 μl of N-Methyl-N-trimethylsilyl trifluoroacetamide (MSTFA) and 10 μl of myristic acid-d27, serving as an internal standard for retention time locking, were added. The TMS derivatization was performed at 37°C for 30 minutes.\u003c/p\u003e\n\u003cp\u003e2.4 GC/MS analysis\u003c/p\u003e\n\u003cp\u003eThe GC/MS analysis was performed using an Agilent 5977B GC/MS with the split-splitless inlet, equipped with electron impact (EI) ion source. A DB-5MSUI fused-silica capillary column (30 m, 0.25 mm, 0.5 um, 122-5536UI) was utilized to separate the derivatives. The column thickness generally used in metabolomics is 0.25 μm, but 0.5 μm was employed because the retention time of SCFAs is buried in the peak of the TMS derivatization reagent when 0.25 μm is used. Hydrogen was used as a carrier gas at a constant flow rate of 1.5 mL/min through the column. One microliter of the sample was injected in the split mode at a ratio of 1:50. The solvent delay time was set to 1.8 min. The initial oven temperature was held at 30 °C for 2.76 min, ramped to 100 °C at a rate of 9 °C/min, to 180 °C at a rate of 18 °C/min, to 310 °C at a rate of 2 °C/min, to 325 °C at a rate of 18 °C/min, and finally held at 325 °C for 8.27 min. The temperatures of the injector, transfer line, and EI ion source were set to 250 °C, 300 °C, and 250 °C, respectively. The electron energy was 70 eV, and mass data was collected in a sim/scan mode (m/z 50-700).\u003c/p\u003e\n\u003cp\u003e2.5 Quantitative validation of compounds\u003c/p\u003e\n\u003cp\u003eTo evaluate the quantitative performance of our method, we assessed the linearity, linearity range, recovery rate, lower limit of detection (LLOD), and precision. The compounds used to validate these assessment items are shown in Supplementary Table 1. Verified compounds include SCFAs, BAs, AAs, sugars, as well as vitamins and indole compounds.\u003c/p\u003e\n\u003cp\u003eCalibration curves for each metabolite were created by plotting the peak area ratio of the analyte to the internal standard (ribitol) against the analyte concentrations. The calibration curves were prepared using a QC fecal sample to account for potential of volatilization of SCFAs. We employed 10% QC samples, prepared by diluting QC samples to one-tenth of their original concentrations. This dilution strategy enables a precise examination of SCFA’s behavior at lower concentrations, providing insights into the method’s sensitivity and specificity across the analyte concentration range (Table 1). Linearity was assessed by the coefficient of determination (R2) for the linear regression between concentration and relative peak area of each metabolite. Good linearity was defined as R2 being greater than 0.95. The signal-to-noise (S/N) ratio for determining the limit of detection (LOD) was set to a minimum value of 3. For analytes producing multiple peaks, the peak with the highest intensity was generally selected. \u003c/p\u003e\n\u003cp\u003eTo evaluate precision and recovery, a standard mixture of metabolites was spiked into a 100% single feces sample for mimicking the composition of actual fecal samples without any dilution. This approach ensures that our analysis reflects conditions as close to natural fecal samples as possible. For evaluating precision, Ten repeated measurements were conducted once, and five repeated measurements performed twice on separate days. The precision of metabolites was evaluated in terms of intra-day and inter-day variability and expressed as relative standard deviation (RSD) %). The precision was deemed acceptable if less than 15% [26].\u003c/p\u003e\n\u003cp\u003eThe recovery was determined by comparing the concentration of feces spiked with mixed standard solutions before extraction. The recovery rate was calculated as\u003c/p\u003e\n\u003cp\u003e(pre-extraction concentration / post-extraction concentration) × 100\u003c/p\u003e\n\u003cp\u003eat each spiking level. The acceptable recovery rate ranged from 70 to 130% [26].\u003c/p\u003e\n\u003cp\u003eCompounds that met all of these criteria were considered to have passed quantitative validation.\u003c/p\u003e\n\u003cp\u003e2.7 Mouse samples and animal treatment\u003c/p\u003e\n\u003cp\u003eMale C57BL/6J mice, 6 weeks old (n = 4), were obtained from CLEA Japan, Inc. (Tokyo, Japan) and were fed a mixed diet consisting of AIN-93G (EP Trading, Tokyo, Japan), CE-2 (CLEA Japan, Tokyo, Japan) and D12492 (EP Trading, Tokyo, Japan).After acclimation, AIN-93G (Control), D12492 (High Fat), and CE-2 (High MAC) were each fed every week (Fig. 4A). At the end of each week, mouse fecal samples were collected and stored at −80 ◦C for further analysis. \u003c/p\u003e\n\u003cp\u003eProcessed and having metabolites extracted similarly to human fecal samples, calibration curves for the concentration and relative area of each metabolite in the mouse study were created using a baseline of 10% mouse QC feces.\u003c/p\u003e\n\u003cp\u003e2.8 Microbiome analysis from mouse fecal samples\u003c/p\u003e\n\u003cp\u003eThe extraction and measurement of fecal microbial DNA were performed as previously described [27]. Briefly, the fecal samples were initially lyophilized and shaken vigorously using a Shake Master. Samples were then suspended in DNA extraction buffer containing 400 μL of a 1% w/v SDS/TE (10 mM Tris-HCl, 1 mM EDTA; pH 8.0) solution, and fecal samples in the buffer were further shaken with 0.1 mm zirconia/silica beads using a Shake Master (1,500 rpm, 5 min). After centrifugation (20 °C, 17,800 x g, 10 min), bacterial DNA was extracted using an automated DNA extraction machine according to the instruction manual (GENE PREP STAR PI-480). After DNA extraction, the V1–V2 variable region of the 16S rRNA gene was amplified using the bacterial universal primers 27F-mod (5′-AGRGT TTGATYMTGGCTCAG-3′) and 338R (5′-TGCTGCCTCCC GTAGGAGT-3′) with Tks Gflex DNA Polymerase (Takara Bio Inc., Japan) [28]. Amplicon DNA was sequenced using MiSeq (Illumina, USA), according to the manufacturer’s protocol.\u003c/p\u003e\n\u003cp\u003e2.9 Bioinformatics analysis \u003c/p\u003e\n\u003cp\u003eFor 16S rRNA gene-based microbiome analysis, QIIME2 (version 2019.10) was used [29]. Primer bases were trimmed using cutadapt (option: –p-discard-untrimmed) [30]. Sequence data were processed using the DADA2 pipeline for quality filtering and denoising (options: –p-trunc-len-f 230 –p-trunc-len-r 130) [31]. Contamination by the human genes was checked by mapping the filtered output sequences, and no contamination was found. The filtered output sequences were assigned to taxa using the “qiime feature-classifier classify-sklearn” command with the default parameters [32]. Silva SSU Ref Nr 99 (version 132) was used as the reference database for taxonomic assignment. Alpha and beta diversities were calculated using “qiime phylogeny align-to-tree-mafft-fasttree” and “qiime diversity core-metrics-phylogenetic” commands with the sampling depth set to the lowest read numbers.\u003c/p\u003e\n\u003cp\u003e2.10 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using Python scripts (version 3.7.6). For beta-diversity analysis, microbiome unweighted/weighted UniFrac distance and metabolome spearman correlation distance were used. Distance matrices were visualized via principal coordinate analysis (PCoA) analysis. Each metabolite value was standardized by centering to a mean of 0 and dividing by the standard deviation (z-score) of each metabolite. Z-score was obtained by normalization among all samples. Spearman rank correlation coefficient was used to validate the associations between gut bacteria and metabolites (scipy version 1.5.2). Visualization was performed using Cytoscape 3.10.2 © software based on Spearman correlations between gut bacteria and intestinal metabolites.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Verification of SCFA volatility during the preprocessing step\u003c/p\u003e \u003cp\u003eThe relative peak areas of pure acetic acid, propionic acid, and butyric acid were significantly diminished in comparison to those co-treated with feces at a free standard concentration of 2,500 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Specifically, the relative peak areas of acetic acid, propionic acid, and butyric acid were 11.0, 8.2, and 10.0 times lower, respectively. These results indicate that the addition of feces before pretreatment suppressed volatilization. The relative peak areas of the SCFA salt standards co-treated with feces were significantly higher than those of free SCFA standards, with increases of 1.4 times for acetic acid, 1.6 times for propionic acid, and 1.5 times higher for butyric acid, suggesting that SCFAs in salt form further resisted volatilization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.2 Quantitative Validation of SCFAs\u003c/p\u003e \u003cp\u003eThe R2 of the calibration curve using 10% QC feces sample exceeded 0.990 for all compounds within the test ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;2). The linear ranges were 250\u0026thinsp;\u0026minus;\u0026thinsp;10,000 \u0026micro;M for acetic acid, 250\u0026ndash;5,000 \u0026micro;M for propionic acid and butyric acid, 50\u0026thinsp;\u0026minus;\u0026thinsp;1,000 \u0026micro;M for valeric acid, and 25\u0026ndash;500 \u0026micro;M for formic acid, isobutyric acid, and isovaleric acid (Supplementary Table\u0026nbsp;2), determined according to concentration ranges seen in actual fecal samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe RSDs for the intra-day precision and inter-day precision were 10.0% and 7.1% for formic acid, 3.9% and 3.7% for acetic acid, 0.8% and 1.5% for propionic acid, 3.6% and 3.0% for isobutyric acid, 1.9% and 1.8% for butyric acid, 4.0% and 3.4% for isovaleric acid, and 4.0% and 3.3% for valeric acid (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eThe recovery rates were 82.3% for formic acid, 98.1% for acetic acid, 96.9% for propionic acid, 110.1% for isobutyric acid, 93.6% for butyric acid, 97.6% for isovaleric acid, and 88.5% for valeric acid (Supplementary Table\u0026nbsp;2). Overall, these results demonstrate an acceptable level of linearity, inter-day and intra-day precision, and high rates of SCFA recovery. All metrics support the validity of our method for SCFA quantification.\u003c/p\u003e \u003cp\u003e3.3 Quantitative validation of BAs, AAs, sugars and other compounds\u003c/p\u003e \u003cp\u003eThe same validation methods were similarly applied to other compounds, such as for BAs, AAs, and sugars (Supplementary Table\u0026nbsp;3). Galactose and talose could not be separated, so quantitative validation was performed for the combined concentration of these two compounds. The R2 of the calibration curve using the 10% QC feces are between 0.959\u0026ndash;0.998 for BAs, 0.961\u0026ndash;0.996 for AAs, 0.975\u0026ndash;0.993 for sugars, and 0.957\u0026ndash;0.997 for other compounds within the test range (Supplementary Table\u0026nbsp;3). The linear ranges were 2.5-1,000 \u0026micro;M for BAs, 25\u0026thinsp;\u0026minus;\u0026thinsp;1,000 \u0026micro;M for AAs, 25\u0026thinsp;\u0026minus;\u0026thinsp;1,000 \u0026micro;M for sugars, and between 2.5-1,000 \u0026micro;M for other compounds.\u003c/p\u003e \u003cp\u003eThe RSDs for intra-day precision and inter-day precision were 4.1\u0026ndash;11.4% and 4.5\u0026ndash;9.3% for BAs, 2.2\u0026ndash;11.4% and 2.2\u0026ndash;9.1% for AAs, 2.3\u0026ndash;12.5% and 3.2\u0026ndash;13.4% for sugars, and 1.9\u0026ndash;12.4% and 2.3\u0026ndash;9.9% for other compounds (Supplementary Table\u0026nbsp;3). These results suggested that the method exhibited acceptable precision for these metabolites.\u003c/p\u003e \u003cp\u003eThe recovery rates were 91.2\u0026ndash;118.2% for BAs, 77.3\u0026ndash;114.2% for AAs, 89.7\u0026ndash;122.4% for sugars, and 72.4\u0026ndash;114.9% for other compounds (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eQuantitative validation was performed for 87 compounds other than SCFAs (Supplementary Table\u0026nbsp;1), and Supplementary Table\u0026nbsp;3 shows the compounds that passed quantitative validation, consisting of 10 bile acids, 14 amino acids, 12 sugars, 5 vitamins, and 17 other compounds.\u003c/p\u003e \u003cp\u003e3.4 Comparison of mouse fecal metabolites with dietary variation\u003c/p\u003e \u003cp\u003eHaving confirmed the validity of our sample processing and quantification method in principle, we next tested the method in a biological model alongside fecal microbiome sequencing results to simulate a plausible application in mouse gut studies. Since previous studies have reported increased SCFAs with high-MAC diets and increased BAs with high-fat diets in mouse studies, we tested whether our results would be consistent with these findings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We also measured correlations between measured metabolite concentrations and the gut microbiome to compare them to trends reported in the literature and further validate our metabolite quantification method. In particular, \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e are monosaccharide-utilizing bacteria that have been reported to be negatively correlated with intestinal monosaccharide levels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We chose to focus on these two genera in our experiment in order to compare their correlations with intestinal metabolites to those found in the literature.\u003c/p\u003e \u003cp\u003eIn this experiment, we quantified the fecal metabolome in mice prescribed a three-week dietary regimen, with the diet changing every week between a control diet, high-fat diet, and high-MAC diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A total of 45 of the 65 validated compounds were successfully quantified in mouse feces in at least one of the samples. The composition of intestinal metabolites also changed with diet, as expected (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The clustermap showed that increased levels of SCFAs and AAs were characteristic of the high-MAC diet group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Specific bile acids such as cholic acid, β-muricholic acid and ursodeoxycholic acid were found to be elevated in the high-fat diet group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Other bile acids were more prevalent in the control diet group, indicating a diverse response of intestinal metabolites to different dietary regimens. A statistically significant negative correlation (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed between \u003cem\u003eBacteroides\u003c/em\u003e and branched-chain amino acids (valine, leucine, isoleucine) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Similarly, a significant negative correlation was observed between \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e and monosaccharides (arabinose, glucose, galactose and talose). The gut microbiota was also distinct between each dietary group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThese factors make it important to quantify intestinal functional metabolites such as SCFAs, BAs, AAs, and sugars in gut environment studies. The effectiveness and reliability of the pretreatment method were verified through tests on mouse feces.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the feasibility of quantifying SCFAs and other metabolites in fecal samples using a comprehensive pretreatment method for GC/MS analysis. A primary challenge identified was the volatilization of SCFAs during the drying and solidification process [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our study showed that the volatilization rate of SCFAs co-treated with feces was significantly lower than that of SCFAs in isolation, and their recovery rates from fecal mixtures exceeded 80%. These results suggest that SCFAs in feces are less susceptible to volatilization during drying than pure SCFA solutions, presumably due to the formation of fatty acid salts within the fecal matrix. Fecal samples typically contain high levels of potassium and sodium [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] that can react with free SCFAs to form stable fatty acid salts such as sodium acetate, which have a higher boiling point (approximately 400\u0026deg;C) compared to acetic acid (118\u0026deg;C). Although the exact proportion of SCFAs converted into salts in feces is unknown, the acceptably high recovery rate (Supplementary Table\u0026nbsp;2), robust calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and wide coverage of SCFAs in human feces [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] indicate that a majority of the SCFAs in feces likely exist as salts. The successful development of a simultaneous measurement system for SCFAs, BAs, sugars, and AAs and their derivatives in this study demonstrates the efficacy of the pretreatment method.\u003c/p\u003e \u003cp\u003eBiological tests conducted in mice have verified the applicability of this method for gut environment research. Previous studies showed that a high-MAC diet increases SCFAs such as acetic acid, butyric acid, and propionic acid, which are by-products of fiber digestion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Conversely, a high-fat diet increases primary BAs like cholic acid and β-muricholic acid [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], supporting their role in fat digestion and absorption [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Interestingly, contrary to findings in previous studies, other bile acids were found to be characteristically elevated in the control diet group. This may be because the cellulose in the control adsorbed bile acids, preventing them from re-entering circulation and to instead be released in feces [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, negative correlations were observed between the relative abundance of \u003cem\u003eBacteroides\u003c/em\u003e and the concentrations of branched-chain amino acids including valine, leucine, and isoleucine, aligning with the results of a previous study [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For sugars, significant negative correlations were observed between \u003cem\u003eAlistipes\u003c/em\u003e and monosaccharides, such as arabinose, glucose, galactose \u0026amp; talose, also consistent with a previous study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These results indicate that the method is sufficiently applicable to actual fecal samples, allowing for simultaneous quantification of SCFAs, BAs, AAs, and sugars\u0026ndash;key compounds in gut environmental studies.\u003c/p\u003e \u003cp\u003eThere are two limitations of this method. First, this method does not distinguish between galactose and talose, resulting in a combined analysis of these sugars. For more detailed analysis of sugars, using an alternative analytical instrument like LC/MS/MS would be preferable. However, it is worth noting that talose is a rare sugar, so most of the combined galactose and talose value is likely attributable to galactose [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Second, it cannot measure conjugated BAs due to their large molecular weight, which precludes detection by GC/MS. To include these BAs, hydrolysis would need to be added as a pretreatment. However, it is worth noting that fecal bile acids are rarely found to be conjugated and hydrolysis treatment may not be necessary [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, these limitations may affect the reproducibility of the method compared to prior studies where LC/MS/MS was used to distinguish between galactose and talose and separately measure conjugated BAs. Although LC/MS/MS is more sensitive and has a wider quantitative range than this method, this method is much more rapid due to less processing of samples and also requires less sample material, which addresses a common issue in mouse fecal analytical studies. The GC/MS equipment is also more widely available, allowing this method to be more accessible to researchers as well as being more cost-efficient. Despite certain limitations, this method is a valuable tool for comprehensive studies of gut environments.\u003c/p\u003e \u003cp\u003eIn conclusion, the study successfully established a specialized method for simultaneous determination of functional intestinal metabolites such as SCFAs, BAs, AAs, and sugars. Notably, despite the volatility of SCFAs, this study showed that SCFAs in feces do not volatilize to the point of loss of quantitation even after drying and solidification. This method can also quantify additional compounds like AAs and sugars, offering a comprehensive tool for gut environment analysis. This method can be used as a first step in intestinal metabolome analysis due to its ability to measure functional metabolites in the gut in a relatively comprehensive manner. Although there are some limitations, we expect gut environmental research to progress in the future using the method.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort-chain fatty acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBile acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGC/MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGas Chromatography / Mass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrimethylsilylation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.F. is a founder and CEO and K.F. and C.L. are employees of Metagen, Inc., a company involved in microbiome-based healthcare. K.S. is an employee of Agilent Technologies Japan, Ltd. The other authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis clinical trial was approved by the clinical trial ethics review committee of Chiyoda Paramedical Care Clinic (publicly registered at UMIN-CTR, trial number: UMIN000028459). All participants signed a consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants consented independently when donating samples. All data obtained and generated during the study were kept confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microbiome data have been deposited with links to BioProject accession number PRJDB18118 in the DDBJ BioProject database.\u003c/p\u003e\n\u003cp\u003eData used for analysis and the in-house scripts for performing bioinformatics analysis in this work can be found on GitHub at https://github.com/metagen/article_GC-MSmetabolomics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by research grants from JSPS KAKENHI (22H03541 to S.F.), AMED-CREST (JP23gm1010009 to S.F.), JST ERATO (JPMJER1902 to S.F.), the Food Science Institute Foundation (to S.F.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.F., K.S., C.I. and S.F. conceived and designed the study. K.F., N.K. and N.F. performed all experiments. K.F. performed the bioinformatics analysis. A.H. and S.F. supervised the study. K.F. and C.L. wrote the original draft. K.F., C.L., I.S., A.H. and S.F. reviewed and edited the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMann ER, Lam YK, Uhlig HH. Short-chain fatty acids: linking diet, the microbiome and immunity. Nat Rev Immunol. 2024. https://doi.org/10.1038/s41577-024-01014-8.\u003c/li\u003e\n\u003cli\u003eVliex LMM, Penders J, Nauta A, Zoetendal EG, Blaak EE. The individual response to antibiotics and diet \u0026mdash; insights into gut microbial resilience and host metabolism. Nat Rev Endocrinol. 2024. https://doi.org/10.1038/s41574-024-00966-0.\u003c/li\u003e\n\u003cli\u003eChen K, Magri G, Grasset EK, Cerutti A. Rethinking mucosal antibody responses: IgM, IgG and IgD join IgA. Nat Rev Immunol. 2020;20:427\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eFukuda S, Toh H, Hase K, Oshima K, Nakanishi Y, Yoshimura K, et al. Bifidobacteria can protect from enteropathogenic infection through production of acetate. Nature. 2011;469:543\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eFurusawa Y, Obata Y, Fukuda S, Endo TA, Nakato G, Takahashi D, et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature. 2013;504:446\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eZhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science. 2018;359:1151\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eMorita H, Kano C, Ishii C, Kagata N, Ishikawa T, Hirayama A, et al. \u003cem\u003eBacteroides\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003euniformis\u003c/em\u003e and its preferred substrate, \u0026alpha;-cyclodextrin, enhance endurance exercise performance in mice and human males. Sci Adv. 2023;9:eadd2120.\u003c/li\u003e\n\u003cli\u003eDe Vadder F, Kovatcheva-Datchary P, Goncalves D, Vinera J, Zitoun C, Duchampt A, et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell. 2014;156:84\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eBrowne HP, Forster SC, Anonye BO, Kumar N, Neville BA, Stares MD, et al. Culturing of \u0026lsquo;unculturable\u0026rsquo; human microbiota reveals novel taxa and extensive sporulation. Nature. 2016;533:543\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eRivera-Cancel G, Orth K. Biochemical basis for activation of virulence genes by bile salts in \u003cem\u003eVibrio\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eparahaemolyticus\u003c/em\u003e. Gut Microbes. 2017;8:366\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eNagai M, Moriyama M, Ishii C, Mori H, Watanabe H, Nakahara T, et al. High body temperature increases gut microbiota-dependent host resistance to influenza A virus and SARS-CoV-2 infection. Nat Commun. 2023;14:3863.\u003c/li\u003e\n\u003cli\u003eMayneris-Perxachs J, Castells-Nobau A, Arnoriaga-Rodr\u0026iacute;guez M, Martin M, Vega-Correa L de la, Zapata C, et al. Microbiota alterations in proline metabolism impact depression. Cell Metab. 2022;34:681-701.e10.\u003c/li\u003e\n\u003cli\u003eTintelnot J, Xu Y, Lesker TR, Sch\u0026ouml;nlein M, Konczalla L, Giannou AD, et al. Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer. Nature. 2023;615:168\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eTakeuchi T, Kubota T, Nakanishi Y, Tsugawa H, Suda W, Kwon AT-J, et al. Gut microbial carbohydrate metabolism contributes to insulin resistance. Nature. 2023;621:389\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eXu H, Wang J, Liu Y, Wang Y, Zhong X, Li C, et al. Development of a simultaneous quantification method for the gut microbiota-derived core nutrient metabolome in mice and its application in studying host-microbiota interaction. Anal Chim Acta. 2023;1251:341039.\u003c/li\u003e\n\u003cli\u003eSeekatz AM, Theriot CM, Rao K, Chang Y-M, Freeman AE, Kao JY, et al. Restoration of short chain fatty acid and bile acid metabolism following fecal microbiota transplantation in patients with recurrent Clostridium difficile infection. Anaerobe. 2018;53:64\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eWatanabe K, Yamano M, Masujima Y, Ohue-Kitano R, Kimura I. Curdlan intake changes gut microbial composition, short-chain fatty acid production, and bile acid transformation in mice. Biochem Biophys Rep. 2021;27:101095.\u003c/li\u003e\n\u003cli\u003eFiehn O, Kopka J, Trethewey RN, Willmitzer L. Identification of Uncommon Plant Metabolites Based on Calculation of Elemental Compositions Using Gas Chromatography and Quadrupole Mass Spectrometry. Anal Chem. 2000;72:3573\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eAndr\u0026aacute;si N, Helenk\u0026aacute;r A, Vasanits-Zsigrai A, Z\u0026aacute;ray Gy, Moln\u0026aacute;r-Perl I. The role of the acquisition methods in the analysis of natural and synthetic steroids and cholic acids by gas chromatography\u0026ndash;mass spectrometry. J Chromatogr A. 2011;1218:8264\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eTsai S-JJ, Zhong Y-S, Weng J-F, Huang H-H, Hsieh P-Y. Determination of bile acids in pig liver, pig kidney and bovine liver by gas chromatography-chemical ionization tandem mass spectrometry with total ion chromatograms and extraction ion chromatograms. J Chromatogr A. 2011;1218:524\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eAndr\u0026aacute;si N, Helenk\u0026aacute;r A, Z\u0026aacute;ray Gy, Vasanits A, Moln\u0026aacute;r-Perl I. Derivatization and fragmentation pattern analysis of natural and synthetic steroids, as their trimethylsilyl (oxime) ether derivatives by gas chromatography mass spectrometry: Analysis of dissolved steroids in wastewater samples. J Chromatogr A. 2011;1218:1878\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eGao X, Pujos-Guillot E, S\u0026eacute;b\u0026eacute;dio J-L. Development of a Quantitative Metabolomic Approach to Study Clinical Human Fecal Water Metabolome Based on Trimethylsilylation Derivatization and GC/MS Analysis. Anal Chem. 2010;82:6447\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eZhang S, Wang H, Zhu M-J. A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples. Talanta. 2019;196:249\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eJing Y, Li A, Liu Z, Yang P, Wei J, Chen X, et al. Absorption of Codonopsis pilosula Saponins by Coexisting Polysaccharides Alleviates Gut Microbial Dysbiosis with Dextran Sulfate Sodium-Induced Colitis in Model Mice. BioMed Res Int. 2018;2018:1781036.\u003c/li\u003e\n\u003cli\u003eUeyama J, Oda M, Hirayama M, Sugitate K, Sakui N, Hamada R, et al. Freeze-drying enables homogeneous and stable sample preparation for determination of fecal short-chain fatty acids. Anal Biochem. 2020;589:113508.\u003c/li\u003e\n\u003cli\u003eAlseekh S, Aharoni A, Brotman Y, Contrepois K, D\u0026rsquo;Auria J, Ewald J, et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods. 2021;18:747\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eMatsuoka H, Tochio T, Watanabe A, Funasaka K, Hirooka Y, Hartanto T, et al. The Effects of Enteral Nutrition on the Intestinal Environment in Patients in a Persistent Vegetative State. Foods. 2022;11:549.\u003c/li\u003e\n\u003cli\u003eKim S-W, Suda W, Kim S, Oshima K, Fukuda S, Ohno H, et al. Robustness of Gut Microbiota of Healthy Adults in Response to Probiotic Intervention Revealed by High-Throughput Pyrosequencing. DNA Res. 2013;20:241\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011; 17(1):10.\u003c/li\u003e\n\u003cli\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eZheng X, Huang F, Zhao A, Lei S, Zhang Y, Xie G, et al. Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice. BMC Biol. 2017;15:120.\u003c/li\u003e\n\u003cli\u003eNishimuta M, Inoue N, Kodama N, Morikuni E, Yoshioka YH, Matsuzaki N, et al. Moisture and Mineral Content of Human Feces-High Fecal Moisture Is Associated with Increased Sodium and Decreased Potassium Content-. J Nutr Sci Vitaminol (Tokyo). 2006;52:121\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eH\u0026oslash;verstad T, Fausa O, Bj\u0026oslash;rneklett A, B\u0026oslash;hmer T. Short-Chain Fatty Acids in the Normal Human Feces. Scand J Gastroenterol. 1984;19:375\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eShen J, Yang X, Sun X, Gong W, Ma Y, Liu L, et al. Amino-functionalized cellulose: a novel and high-efficiency scavenger for sodium cholate sorption. Cellulose. 2020;27:4019\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eYoshida N, Yamashita T, Osone T, Hosooka T, Shinohara M, Kitahama S, et al. Bacteroides spp. promotes branched-chain amino acid catabolism in brown fat and inhibits obesity. iScience. 2021;24.\u003c/li\u003e\n\u003cli\u003eSakoguchi H, Yoshihara A, Izumori K, Sato M. Screening of biologically active monosaccharides: growth inhibitory effects of d -allose, d -talose, and l -idose against the nematode \u003cem\u003eCaenorhabditis\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eelegans\u003c/em\u003e. Biosci Biotechnol Biochem. 2016;80:1058\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eRidlon JM, Kang D-J, Hylemon PB. Bile salt biotransformations by human intestinal bacteria. J Lipid Res. 2006;47:241\u0026ndash;59.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Study design for evaluating sample handling protocols in fecal metabolome analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.609046849757675%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.802907915993536%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFecal samples used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.58804523424879%\"\u003e\n \u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.609046849757675%\"\u003e\n \u003cp\u003eVolatility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.802907915993536%\"\u003e\n \u003cp\u003e100% QC human feces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.58804523424879%\"\u003e\n \u003cp\u003eTo verify whether mixing SCFA standards in feces would reduce volatilization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.609046849757675%\"\u003e\n \u003cp\u003eLinearity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.802907915993536%\"\u003e\n \u003cp\u003e10% QC human feces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.58804523424879%\"\u003e\n \u003cp\u003eTo verify the linearity of the calibration curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.609046849757675%\"\u003e\n \u003cp\u003eRecovery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.802907915993536%\"\u003e\n \u003cp\u003e10% QC human feces (calibration curve)\u003c/p\u003e\n \u003cp\u003e100% single feces (test sample)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.58804523424879%\"\u003e\n \u003cp\u003eTo verify recovery rates in \u0026nbsp;an actual fecal sample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.609046849757675%\"\u003e\n \u003cp\u003eRepeatability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.802907915993536%\"\u003e\n \u003cp\u003e100% single feces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.58804523424879%\"\u003e\n \u003cp\u003eTo verify stability of repeated measurements on an actual fecal sample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.609046849757675%\"\u003e\n \u003cp\u003eMouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.802907915993536%\"\u003e\n \u003cp\u003e10% QC mouse feces (calibration curve)\u003c/p\u003e\n \u003cp\u003e100% single feces (test samples)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.58804523424879%\"\u003e\n \u003cp\u003eTo verify if the method is sufficient to evaluate differences in the amount of metabolites in feces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"11e8d1a4-474c-442c-b44a-a87e63fdc386","identifier":"10.13039/501100001691","name":"Japan Society for the Promotion of Science","awardNumber":"22H03541","order_by":0},{"identity":"576863c1-3c6a-4200-8839-91fe197f74d6","identifier":"10.13039/100009619","name":"Japan Agency for Medical Research and Development","awardNumber":"JP23gm1010009","order_by":1},{"identity":"e49c83bc-24aa-487c-85a3-2d8ae70b2fae","identifier":"10.13039/501100002241","name":"Japan Science and Technology Agency","awardNumber":"JPMJER1902","order_by":2},{"identity":"b0e5a2ea-9344-470c-b7c4-41c05eec10a5","identifier":"10.13039/100016974","name":"Food Science Institute Foundation","awardNumber":"Nothing","order_by":3}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Metagen Inc.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiome, intestinal metabolites, short-chain fatty acids, bile acids, GC/MS, TMS derivatization, volatility, metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-4708066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4708066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntestinal metabolites produced by gut microbes play a significant role in host health. Due to their different chemical structures, they are often analyzed using multiple analyzers and methods, such as gas chromatography/mass spectrometry (GC/MS) for SCFAs and liquid chromatography/mass spectrometry (LC/MS) for bile acids (BAs), amino acids (AAs), and sugars. In this study, we aimed to develop a specialized method for the simultaneous determination of important intestinal metabolites, specifically addressing the main issue of SCFA volatilization during the dry solidification process. We discovered that these compounds can all be measured in fecal samples by GC/MS after trimethylsilyl (TMS) derivatization despite the expected volatility of SCFAs. Validating the results using SCFA standards suggested that the fecal matrix exerts a stabilizing effect. This method enabled the simultaneous quantification of 65 metabolites. For further validation in a biological context, a mouse study showed that high-MAC and high-fat diets increased SCFAs and BAs in feces, respectively, and showed a negative correlation between \u003cem\u003eAlistipes\u003c/em\u003e and sugars, all consistent with previous studies. As a result, we successfully developed a specialized simultaneous quantification method for SCFAs, BAs, AAs, AA derivatives, and sugars in fecal samples using GC/MS-based metabolomics in conjunction with a TMS derivatization pretreatment process.\u003c/p\u003e","manuscriptTitle":"Development of a specialized method for simultaneous quantification of functional intestinal metabolites by GC/MS-based metabolomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-11 07:07:47","doi":"10.21203/rs.3.rs-4708066/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"843a6ea3-6895-4d10-940f-e890fb1c56df","owner":[],"postedDate":"July 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-11T07:07:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-11 07:07:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4708066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4708066","identity":"rs-4708066","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}