Analysis method of intestinal microorganism tryptophan metabolite

Abstract

The invention discloses a method for analyzing tryptophan metabolites of intestinal microorganisms, which comprises the following steps: sample pretreatment, on-machine detection and data processing, and can be used for quantitatively analyzing 11 indole derivatives (IAA, IPA, ILA, IArA, IAM, IPYA, IAld, TRM, IET, ICA and IS) in a serum or excrement sample. According to the method, through comprehensive methodology verification, on the basis of keeping high sensitivity and accuracy, one-time analysis can be completed only in 6 minutes on a machine, and the detection efficiency is high; the TRP microbial metabolite indole metabolite detection kit can be used for detecting TRP microbial metabolite indole metabolite, covering 11 types of indole metabolites in serum and excrement samples, covering IAld, IET, IArA and other key indole derivatives related to intestinal flora, realizing relatively comprehensive detection of TRP microbial metabolite indole metabolites, and providing a new tool for revealing a complex mutual relationship between intestinal microorganisms and host health.

Description

Technical Field

[0001] This invention relates to an analytical method for tryptophan metabolites, and more particularly to an analytical method for tryptophan metabolites from intestinal microorganisms. Background Technology

[0002] A link exists between the gut microbiota and host health, and tryptophan (TRP) metabolism is associated with the development of mental illness. TRP is an essential amino acid, and its metabolites play an indispensable role in gut immunity, nervous system homeostasis, and mood regulation. TRP metabolites, especially indole derivatives (see...), are important components of TRP metabolism. Figure 1 It has been shown to play a key role in the regulation of host-microbe interactions, immune responses, and neuroendocrine function.

[0003] Indole derivatives, such as indole-3-carboxaldehyde, indole-3-acetic acid, and indole-3-propionic acid, play a regulatory role in brain physiological function, and their metabolic disorders are closely related to various neurological and psychiatric diseases. Notably, the levels of these indole derivatives are significantly associated with changes in the composition and abundance of gut microbiota. Studies have shown that gut microbiota dysbiosis in Alzheimer's disease model mice is accompanied by a decrease in indole derivative levels, and exogenous supplementation with IPA can effectively improve neurological function and reduce neuroinflammatory responses. In human studies, serum IPA levels significantly increased after probiotic supplementation in the elderly, and this indicator was positively correlated with brain-derived nutritional factor (BDNF) levels, suggesting that gut microbiota-derived indole derivatives may have neuroprotective effects. Furthermore, indole derivative levels are generally reduced in patients with depression, and their concentration is significantly negatively correlated with depression severity scores.

[0004] Currently, the detection of indole derivatives is mostly limited to the quantitative analysis of a few metabolites. Commonly used detection techniques include enzyme-linked immunosorbent assay (ELISA), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography-mass spectrometry (HPLC-MS), and high performance liquid chromatography coupled with fluorescence detection. Although these methods can effectively detect some indole derivatives, they have obvious limitations.

[0005] First, many methods can only detect single or a few indole derivatives, failing to comprehensively reflect the entire metabolic pathway of TRP microorganisms. This is because the polarity and physicochemical properties of indole derivatives vary considerably, directly affecting their separation, quantification, and stability during detection. For example, Chong et al. used LC-MS to detect only five indole derivatives in serum; Pan et al. analyzed indole derivatives in serum samples using targeted metabolomics, but still failed to detect key metabolites such as indole-3-pyruvate, which has neuroprotective effects; patent application CN 120385766 A discloses a method for detecting indole and its derivatives in serum and feces, but only three indole derivatives can be detected.

[0006] Secondly, some detection methods are complex to operate, time-consuming, and applicable only to a limited range of sample types. For example, Anesi et al. required 14 minutes to complete a single detection of seven indole derivatives; the TRP detection platform established by Antoine et al., containing five indole derivatives, took 16.5 minutes to detect. Clinical or research applications often require the analysis of large numbers of samples, and existing methods are still insufficient to meet efficiency requirements.

[0007] Furthermore, current research on TRP metabolism largely focuses on the serotonin and kynurenine pathways. For example, patent application CN 118837449 A discloses a method for detecting tryptophan and its metabolites in blood, specifically targeting the kynurenine metabolic component of the tryptophan metabolic pathway. However, research on the microbial metabolic pathway of TRP (see...) is scarce. Figure 1 A more comprehensive analysis.

[0008] In summary, there is currently no systematic and efficient detection method for indole derivatives, tryptophan metabolites from gut microbiota. Summary of the Invention

[0009] Purpose of the invention: The purpose of this invention is to provide a highly efficient and sensitive method for analyzing 11 TRP microbial metabolites in serum and fecal samples.

[0010] Technical solution: The method for analyzing tryptophan metabolites from gut microbiota includes the following steps:

[0011] (1) Sample pretreatment: The sample is a serum sample or a fecal sample. The pretreatment is to extract indole derivatives from the sample and add an internal standard. Serum samples are extracted with EDTA-2Na, and fecal samples are extracted with organic solvents. The indole derivatives are IAA, IPA, ILA, IArA, IAM, IPYA, IAld, TRM, IET, ICA and IS.

[0012] (2) On-machine detection: Liquid chromatography-mass spectrometry was used to analyze the pretreated samples; reverse phase elution was used in chromatography and multiple reaction monitoring was used in mass spectrometry to collect peak areas;

[0013] (3) Data processing: A standard curve is established with the concentration of the analyte standard as the independent variable and the peak area ratio of the analyte standard to the corresponding internal standard as the dependent variable; the peak area of ​​the sample is substituted into the standard curve to obtain the concentration of the analyte in the sample.

[0014] This study established a method for detecting 11 indole derivatives of TRP metabolites in serum and feces. This method is not only sensitive and efficient, but also enables a relatively comprehensive detection of TRP metabolites from gut microbiota. The study found that the addition of EDTA-2Na to serum samples improved the detection and separation of IPYA and ICA. We hypothesize that EDTA prevents interference caused by certain metal ions in serum by chelating them. Fecal samples, as non-invasive and readily available bioassay materials, are rarely used for detecting TRP microbial metabolism.

[0015] In step (1), the internal standard is preferably an isotopic internal standard: indole-3-acetic acid-D5, indole-3-propionic acid-D2, and tryptamine-D4. Since the sample matrix has a certain ion-suppressing effect on the detection of the analyte, the isotopic internal standard method is used for quantification to improve the accuracy and precision of the method.

[0016] In step (2), the ACQUITY UPLC HSS T3 column is preferred. Previous methods for detecting TRP metabolic pathway substances have often used C18-based reversed-phase columns. However, indole and its derivatives are highly polar, and the partially blocked end of the ACQUITY UPLC HSS T3 column allows for better retention of polar compounds and better separation of highly polar substances. Although the elution order and selectivity of the C18 reversed-phase column and the ACQUITY UPLC HSS T3 column are very similar in the detection of TRP metabolites, the HSS T3 column exhibits higher retention values ​​for indole derivatives than the C18 reversed-phase column, resulting in better peak shapes.

[0017] In step (2), the preferred mobile phase is aqueous phase consisting of water + 0.1% formic acid, and the preferred organic phase is acetonitrile + 0.1% formic acid. The preferred gradient elution procedure is as follows: equilibrate with 1% organic phase; then increase the organic phase from 1% to 70% over 1.5 minutes; then increase the organic phase from 70% to 95% over 2 minutes; after rinsing with the high organic phase, reduce the organic phase from 95% back to 1% to complete reequilibration. The initial equilibration time can be 0.5 minutes, the high organic phase rinsing time can be 1 minute, and the reequilibration time can be 1 minute, so the total time for one analysis is 6 minutes.

[0018] In step (2), the preferred mass spectrometer is a heated electrospray ionization source; IAA, IPA, IArA, IAM, IAld, TRM, and IET are preferably in positive ion mode, while ILA, IPYA, ICA, and IS are preferably in negative ion mode. Although most TRP gut microbial metabolites can be analyzed in positive ion mode of ESI, some of these metabolites (such as IPYA, ICA, and IS) have better ionization efficiency in negative ion mode.

[0019] In step (1), the preferred method for pretreatment of serum samples is as follows: serum sample, EDTA-2Na, and internal standard are mixed at a volume ratio of 4:1:1, and the lower layer liquid is collected after ultrafiltration. The ultrafiltration is preferably performed using a Nanosep™ centrifugal filter with a 3K Omega™ membrane.

[0020] Nanosep TM Centrifugal filters are ultrafiltration devices based on membrane separation technology, suitable for processing biological samples with volumes of 50-500 µL. This device operates via high-speed centrifugation, effectively removing large molecular weight proteins and impurities from samples such as serum, plasma, or cell lysates using a molecular weight cutoff mechanism, while retaining small molecule metabolites. It is easy to use, significantly reduces matrix effects, and is commonly used for sample pretreatment before high-sensitivity analyses such as LC-MS / MS. In this study, a 3K Omega membrane was used for serum sample pretreatment, which can retain large molecules with molecular weights above 3000 Da, thereby reducing interference from large molecules such as proteins in serum. Compared with commonly used solid-phase extraction (SPE) or liquid-liquid extraction (LLE) methods, Nanosep... TM Centrifugation devices are not only simple and fast to operate, but also require less serum sample and organic reagents, while improving the efficiency and stability of sample processing.

[0021] In step (1), the preferred pretreatment method for fecal samples is as follows: freeze-dry and grind the fecal sample, take the fecal sample, organic solvent and internal standard in a ratio of 1 mg:8 μL:1 μL, mix, homogenize, centrifuge, take the supernatant, evaporate to dryness and then reconstitute. Among these, it is preferred to use a tissue homogenizer for homogenization.

[0022] In step (3), the preferred linear range of the standard curve is: 1-5000 ng / mL for IAA, IPA, IArA and IET; 2-5000 ng / mL for ILA; 1-500 ng / mL for IAM, IAld and TRM; 20-5000 ng / mL for IPYA; 10-5000 ng / mL for ICA; and 0.5-5000 ng / mL for IS.

[0023] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The method has undergone comprehensive methodological validation. While maintaining high sensitivity and accuracy, it can complete an analysis in just 6 minutes, resulting in high detection efficiency; 2. The method covers 11 indole metabolites in serum and fecal samples, including key indole derivatives related to gut microbiota such as IAld, IET, and IArA. It achieves a relatively comprehensive detection of indole metabolites of TRP microbial metabolites, providing a new tool for revealing the complex relationship between gut microbiota and host health. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the TRP gut microbial metabolic pathway.

[0025] Figure 2 The image shows the LC-MS chromatogram of TRP microbial metabolites (standard solution 20 ng / mL). Detailed Implementation

[0026] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0027] 1. Source of materials.

[0028] (1) Standards: Indole-3-acetamide (IAM), tryptamine (TRM), tryptophol (IET), indole-3-acetic acid (IAA), indole-3-aldehyde (IAld), indole-3-propionic acid (IPA), indole-3-pyruvate acid (IPYA), indole-3-carboxylic acid (ICA), indole-3-sulfate (IS), and indole-3-acrylic acid (IArA) were purchased from Sigma-Aldrich Reagents Ltd. (Missouri, USA); indole-3-lactic acid (IArA) was also purchased from Sigma-Aldrich Reagents Ltd. (Missouri, USA). ILA (acid, ILA) was purchased from Yuanye Reagent Co., Ltd. (Shanghai, China).

[0029] (2) Internal standards: Indole-3-acetic acid-D5 (IAA-D5), indole-3-propionic acid-D2 (IPA-D2), and tryptamine-D4 (TRM-D4) were purchased from Toronto Company (Toronto, Canada). Isotope standards with structures similar to the analytes were selected as internal standards: IAA, ILA, IArA, IAM, IAld, IET, ICA, and IS used IAA-D5 as the internal standard, IPA and IPYA used IPA-D2 as the internal standard, and TRM used TRM-D4 as the internal standard.

[0030] (3) Other reagents: Methanol, acetonitrile, and formic acid of HPLC grade were purchased from Merck (Darmstadt, Germany). EDTA-2Na (0.5M, pH 8.0, disodium ethylenediaminetetraacetate) was purchased from Beyotime (Nanjing, China). Laboratory water was prepared using a Milli-Q ultrapure water system (Millipore, Berryford, USA).

[0031] (4) Samples: Human serum and feces were obtained from the outpatient department of Wuxi Center for Disease Control and Prevention.

[0032] 2. The instruments and equipment are shown in Table 1.

[0033] Table 1 Instruments and Equipment

[0034]

[0035] 3. Reagent preparation.

[0036] Accurately weigh appropriate amounts of 11 standards and 3 isotopic internal standards, and prepare 1 mg / ml single-standard stock solutions with methanol according to the properties of the substances. Store at -20°C for later use. Take an appropriate amount of the single-standard stock solution of the standards, dilute it with methanol, and prepare a mixed standard working solution with a concentration of 10 μg / ml. Take an appropriate amount of the single-standard stock solution of the internal standards, dilute it with methanol, and prepare a mixed internal standard working solution (ISs) with a concentration of 200 ng / ml. The mixed standard curve is prepared by diluting the standard working solutions with the initial mobile phase (0.1% formic acid aqueous solution: 0.1% formic acid acetonitrile solution = 99:1, v / v). The linear range of the mixed standard curve is 1-5000 ng / ml (1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000 ng / mL).

[0037] Example 1

[0038] This embodiment provides an analytical method for tryptophan metabolites from intestinal microorganisms, comprising the following three steps.

[0039] (1) Sample pretreatment.

[0040] 1. Serum sample: Accurately pipette 80 μL of serum sample into a 1.5 mL centrifuge tube, add 20 μL of EDTA-2Na and 20 μL of ISs, vortex to mix for 30 s, and transfer to a centrifuge tube with 3K Omega. TM Nanosep of membrane TM In the centrifuge apparatus, centrifuge at 12,000 rpm and 4℃ for 15 min, and transfer the lower layer liquid to a vial with a glass liner. The injection volume is 5 μL.

[0041] 2. Fecal Samples: After removing the fecal sample, freeze-dry and grind it thoroughly. Accurately weigh 50 mg and place it in a 1.5 mL centrifuge tube. Add 400 μL of extraction buffer (methanol:water = 3:1, v / v), 50 μL of bismuth subsoil (ISs), and one stainless steel bead (diameter: 3 mm). Vortex for 30 s, then homogenize using a TissueLyser II homogenizer at 35 Hz for 20 min. Centrifuge at 12000 rpm and 4℃ for 15 min. Transfer the supernatant to another 1.5 mL centrifuge tube and evaporate to dryness using a low-temperature vacuum centrifugation concentration and enrichment system. Then, reconstitute the sample with the initial mobile phase (acetonitrile:water = 1:99, v / v, containing 0.1% formic acid), vortex for 30 s, and centrifuge at 12000 rpm and 4℃ for 15 min. Transfer 40 μL of the supernatant to a vial with a glass-lined tube. The injection volume is 5 μL.

[0042] (2) On-machine testing.

[0043] 1. Chromatographic system conditions.

[0044] Liquid chromatography separation was performed using AB Sciex's ExionLC. TM The AC liquid chromatography system was used, with an ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.8 μm) (Waters Corporation, USA). The column temperature was 35 °C, and the mobile phases used were A (water + 0.1% formic acid) and B (acetonitrile + 0.1% formic acid), with a flow rate of 0.4 mL / min. The gradient elution program for the mobile phase is shown in Table 2.

[0045] Table 2. Chromatographic mobile phase gradient elution program

[0046]

[0047] 2. Mass spectrometry instrument conditions.

[0048] Mass spectrometry detection was performed using SCIEX Triple Quad. TM The 5500 Plus mass spectrometer (AB Sciex, USA) used a heated electrospray ionization (ESI) source and operated in multiple reaction monitoring (MRM) mode. Data acquisition was performed using Analyst® software, and data analysis was conducted using MultiQuant. TM Software. Specific mass spectrometry parameters are shown in Table 3.

[0049] Table 3 Mass Spectrometry Parameters

[0050]

[0051] Note: arb: arbitrary units, which are the instrument's default parameter units.

[0052] To obtain high-intensity and stable precursor and daughter ion signals, this study systematically optimized mass spectrometry parameters. First, based on the mass-to-charge ratio (m / z), the precursor-production pairs for each analyte were determined, and the declustering potential (DP), collision energy (CE), and polarity mode were optimized in MRM (Multiple Reaction Monitoring) mode to ensure optimal sensitivity and selectivity (see Table 4). Regarding chromatographic condition optimization, an ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.8 μm) was used, and parameters such as mobile phase composition, gradient elution program, and flow rate were optimized to ensure good separation of all analytes in the shortest possible time. Finally, the optimized UHPLC-MS / MS method detected 11 analytes within 6 min, with retention times ranging from 2.6 to 3.6 min, good peak shape, good separation, and reliable signal intensity (see Table 4). Figure 2 Furthermore, UHPLC-MS / MS combines retention time and mass-to-charge ratio for qualitative analysis, significantly improving the selectivity and accuracy of target analytes and ensuring efficient and reliable analytical capabilities.

[0053] Table 4 Spectral parameters of the substances to be tested

[0054]

[0055] (3) Data processing.

[0056] A standard curve is established with the concentration of the analyte standard as the independent variable and the ratio of the peak area of ​​the analyte standard to the corresponding internal standard as the dependent variable. The peak area of ​​the sample is then substituted into the standard curve to obtain the concentration of the analyte in the sample.

[0057] Example 2

[0058] This embodiment validates the analytical method of Example 1, including matrix effect, limit of detection and limit of quantitation, standard curve and linear range, accuracy and precision.

[0059] 1. Matrix Effect: The matrix effect refers to the suppression or enhancement of the analyte signal by non-target substances in the sample matrix during detection. It mainly affects the mass spectrometry ionization efficiency, thus impacting the accuracy of the detection results. Since it is difficult to obtain a completely corresponding blank matrix, three internal standards were used as substitutes to evaluate the matrix effect in this experiment. This study selected three concentrations of internal standards: low (LQC), medium (MQC), and high (HQC). The LQC for IAA-D5, IPA-D2, and TRM-D4 was 10 ng / mL, the MQC was 50 ng / mL, and the HQC was 200 ng / mL. These were added to the supernatant of the serum and fecal samples to be extracted to assess the influence of the matrix on the detection of the target analyte. The specific methods are as follows.

[0060] (1) Matrix effect sample preparation: Serum and fecal samples were collected, and the supernatant was obtained after extraction according to the sample pretreatment method described above. Low, medium, and high concentrations of internal standard substances were added to the extracted supernatant, and the mixture was stirred before testing. At the same time, the same concentration of internal standard substance was added to the same volume of methanol standard solution as a control group. The sample was then analyzed.

[0061] (2) Formula for calculating matrix effect (ME):

[0062]

[0063] Among them: A matrix A represents the peak area after adding an internal standard to the matrix extract. standard This represents the peak area of ​​the internal standard in the methanol standard solution.

[0064] 2. Limit of Detection (LOD) and Limit of Quantification (LOQ): The limit of detection (LOD) is the lowest concentration of an analyte that an analytical method can reliably detect. It is typically defined as three times the signal-to-noise ratio (S / N) of the instrument background signal generated by a matrix blank. The limit of quantification (LOQ) is the lowest concentration of an analyte that an analytical method can accurately and repeatedly determine. It is typically defined as ten times the S / N of the instrument background signal generated by a matrix blank. Since the analytes in this study were all endogenous substances, it was difficult to obtain corresponding blank matrices. Therefore, LOD and LOQ were determined by serially diluting standard solutions with methanol. LOD was determined with an S / N of 3, and LOQ was determined with an S / N of 10.

[0065] The S / N calculation method is as follows:

[0066] Where S peak For the target peak height, S noiseis the root mean square (RMS) of the baseline noise.

[0067] 3. Standard Curve and Linear Range: Using the same standard stock solution, a series of mixed standard solutions of different concentrations were prepared by dilution with methanol, and the results were determined according to the optimized detection method. With the concentration of the analyte (X-axis) as the independent variable and the peak area ratio of the analyte to the corresponding internal standard (Y-axis) as the dependent variable, a linear regression was performed using the least squares method to establish the standard curve. The linear regression equation and correlation coefficient (R²) were then calculated. 2 Linearity refers to the ability of an instrument to maintain a linear relationship between its response signal and the concentration of the analyte within a certain range. A good linear relationship results in a high correlation coefficient (R²). 2 The value should be ≥0.99 to ensure the accuracy and reproducibility of the method.

[0068] 4. Accuracy and Precision: Accuracy refers to the closeness between the measured result and the true value of the analyte, and is assessed through spiked recovery. Precision refers to the closeness between measured values ​​when repeatedly measuring the same sample, including intra-day precision and inter-day precision. This study used quality control samples (QC) at three concentration levels—low (LQC), medium (MQC), and high (HQC)—to evaluate accuracy and precision. The quality control samples were prepared in serum and fecal samples as follows:

[0069] (1) Preparation of quality control samples for serum samples: Take 80 μL of serum sample into a 1.5 mL centrifuge tube, and add 20 μL of standard solution of known concentration at three levels: LQC, MQC, and HQC, respectively, ensuring thorough mixing. The LQC for IAA, IPA, IPYA, ILA, IAld, IArA, ICA, and IS is 40 ng / mL, the MQC is 500 ng / mL, and the HQC is 2000 ng / mL; the LQC for IAM, TRM, and IET is 10 ng / mL, the MQC is 50 ng / mL, and the HQC is 200 ng / mL. After spiking, process according to the serum sample pretreatment method to finally obtain the sample for analysis.

[0070] (2) Preparation of quality control samples for fecal samples: 50 mg of lyophilized and ground fecal samples were accurately weighed and placed in a 1.5 mL centrifuge tube. 50 μL of standard solutions of known concentrations were added at three levels: LQC, MQC, and HQC. The LQC for IPYA, IAld, TRM, and IET was 40 ng / mL, the MQC was 500 ng / mL, and the HQC was 2000 ng / mL; the LQC for IAA, IPA, ILA, IArA, IAM, ICA, and IS was 10 ng / mL, the MQC was 50 ng / mL, and the HQC was 200 ng / mL. After spiking, the samples were processed according to the fecal sample pretreatment method to obtain the final test samples.

[0071] Detection method: Accuracy assessment (spiking recovery calculation): Spiking recovery (Recovery, %) calculation formula:

[0072]

[0073] Where: C spiked C represents the concentration of the analyte measured in the spiked sample. blank C represents the concentration of the analyte measured in the unspiked sample. added The concentration of the added standard solution is given. Methodological requirements: Recovery should be between 80% and 120%.

[0074] Precision assessment (RSD%): Intra-day precision requires testing 3 control samples of the same concentration on the same day and calculating the RSD%. Inter-day precision requires testing 3 control samples of the same concentration over 3 consecutive days and calculating the RSD%. RSD% calculation formula:

[0075] in: The standard deviation is... This is the average measured value. Methodological requirement: RSD% not exceeding 15%.

[0076] The methodological validation results are as follows.

[0077] (a) Matrix effect

[0078] The matrix effect results of the substitutes are shown in Table 5. The matrix effect in serum ranged from 59.7% to 84.2%, with an RSD of 1.3% to 8.4%; the matrix effect in feces ranged from 52.8% to 89.5%, with an RSD of 1.5% to 6.1%. These results indicate that the sample matrix has a certain ion-inhibiting effect on the detection of analytes. Therefore, this invention employs the isotope internal standard method for quantification. By introducing substitutes, the errors caused by the matrix effect can be effectively compensated, thereby improving the accuracy and precision of the method. This method ensures reliable analytical results even in complex matrices, improving the stability and repeatability of the experimental method.

[0079] Table 5. Matrix effect of internal standard substances in serum and feces (n=3)

[0080]

[0081] Note: The low concentration of IAA-D5, IPA-D2 and TRM-D4 is 10 ng / mL, the medium concentration is 50 ng / mL and the high concentration is 200 ng / mL.

[0082] (ii) LOD and LOQ

[0083] The LOD and LOQ results of the 11 analytes in this study are shown in Table 6. The LOD and LOQ of IAA were 0.2 ng / mL and 1 ng / mL, respectively; the LOD and LOQ of IPA and IArA were 0.5 ng / mL and 1 ng / mL, respectively; the LOD and LOQ of ILA were 1 ng / mL and 2 ng / mL, respectively; the LOD and LOQ of IAM were 0.1 ng / mL and 0.5 ng / mL, respectively; the LOD and LOQ of IPYA were 10 ng / mL and 20 ng / mL, respectively; the LOD and LOQ of IAld and IS were 0.05 ng / mL and 0.1 ng / mL, respectively; the LOD and LOQ of TRM were 0.05 ng / mL and 0.2 ng / mL, respectively; the LOD and LOQ of IET were 0.2 ng / mL and 0.5 ng / mL, respectively; and the LOD and LOQ of ICA were 5 ng / mL and 10 ng / mL, respectively. The results showed that this method has high detection sensitivity for 11 TRP gut microbial metabolites, which can meet the quantitative requirements for trace TRP microbial metabolites in biological samples.

[0084] (III) Standard Curve and Linear Range

[0085] The standard curve equations and linear correlation coefficients (R²) of the 11 analytes in this study 2The linear ranges are shown in Table 6. The linear correlation coefficients for all targets were greater than 0.99, indicating good linearity of the method. The linear ranges for each analyte are as follows: IAA, IPA, IArA, and IET: 1–5000 ng / mL; ILA: 2–5000 ng / mL; IAM, IAld, and TRM: 1–500 ng / mL; IPYA: 20–5000 ng / mL; ICA: 10–5000 ng / mL; IS: 0.5–5000 ng / mL. The results show that the method established in this study has good linearity over a wide concentration range and can meet the quantitative analysis needs of TRP microbial metabolites in different samples.

[0086] Table 6. LOD, LOQ, and linear range of the analyte

[0087]

[0088] (iv) Accuracy and precision

[0089] The results of intra-day and intra-day precision detection, as well as the spiked recovery rates, are shown in Tables 7 (serum) and 8 (feces). The RSD of intra-day precision in serum ranged from 0.6% to 13.4%, and the RSD of inter-day precision ranged from 2.3% to 13.8%. The RSD of intra-day precision in feces ranged from 1.2% to 15.7%, and the inter-day precision ranged from 3.1% to 16.9%. The spiked recovery rates for serum samples ranged from 82.3% to 119.8%, and for feces samples from 80.8% to 112.4%. The results indicate that the method has good accuracy and precision, meeting the requirements for the analysis of serum and feces samples.

[0090] Table 7. Intra-day and inter-day precision and accuracy in serum.

[0091]

[0092] Note: The low concentration of IAA, IPA, ITYA, ILA, IAld, IArA, ICA, and IS is 40 ng / mL, the medium concentration is 500 ng / mL, and the high concentration is 2000 ng / mL; the low concentration of IAM, TRM, and IET is 10 ng / mL, the medium concentration is 50 ng / mL, and the high concentration is 200 ng / mL.

[0093] Table 8. Intra-day and inter-day precision and accuracy in fecal samples.

[0094] Note: The low concentration of ITYA, IAld, TRM, and IET is 40 ng / mL, the medium concentration is 500 ng / mL, and the high concentration is 2000 ng / mL; the low concentration of IAA, IPA, ILA, IArA, IAM, ICA, and IS is 10 ng / mL, the medium concentration is 50 ng / mL, and the high concentration is 200 ng / mL.

[0095] In summary, this method maintains high sensitivity and accuracy, completes an analysis in just 6 minutes, significantly improving detection efficiency, and covers 11 indole metabolites in serum and fecal samples, providing a powerful tool for a deeper understanding of the interactions between gut microbiota, gut, and host.

Claims

1. A method for analyzing tryptophan metabolites from intestinal microorganisms, characterized in that, Includes the following steps: (1) Sample pretreatment: The sample is a serum sample or a fecal sample. The pretreatment is to extract indole derivatives from the sample and add an internal standard. Serum samples are extracted with EDTA-2Na, and fecal samples are extracted with organic solvents. The indole derivatives are IAA, IPA, ILA, IArA, IAM, IPYA, IAld, TRM, IET, ICA and IS. (2) On-machine detection: Liquid chromatography-mass spectrometry was used to analyze the pretreated samples; reverse phase elution was used in chromatography and multiple reaction monitoring was used in mass spectrometry to collect peak areas; (3) Data processing: A standard curve is established with the concentration of the analyte standard as the independent variable and the peak area ratio of the analyte standard to the corresponding internal standard as the dependent variable; the peak area of ​​the sample is substituted into the standard curve to obtain the concentration of the analyte in the sample.

2. The analytical method according to claim 1, characterized in that, In step (1), the internal standard is an isotopic internal standard: indole-3-acetic acid-D5, indole-3-propionic acid-D2 and tryptamine-D4.

3. The analytical method according to claim 1, characterized in that, In step (2), an ACQUITY UPLC HSST3 column is used.

4. The analytical method according to claim 3, characterized in that, In step (2), the mobile phase uses water + 0.1% formic acid for the aqueous phase and acetonitrile + 0.1% formic acid for the organic phase; the gradient elution procedure is as follows: equilibrate with 1% organic phase; then increase the organic phase from 1% to 70% in 1.5 minutes; then increase the organic phase from 70% to 95% in 2 minutes; after rinsing with high organic phase, reduce the organic phase from 95% back to 1% to complete the reequilibration.

5. The analytical method according to claim 1, characterized in that, In step (2), the mass spectrometer uses a heated electrospray ionization source; IAA, IPA, IArA, IAM, IAld, TRM and IET use positive ion mode, while ILA, IPYA, ICA and IS use negative ion mode.

6. The analytical method according to claim 1, characterized in that, In step (1), the pretreatment method for serum samples is as follows: take serum samples, EDTA-2Na and internal standard in a volume ratio of 4:1:1, mix them well, and take the lower layer liquid after ultrafiltration.

7. The analytical method according to claim 6, characterized in that, The ultrafiltration uses a Nanosep™ centrifugal filter with a 3K Omega™ membrane.

8. The analytical method according to claim 1, characterized in that, In step (1), the pretreatment method for fecal samples is as follows: freeze-dry and grind the fecal sample, take the fecal sample, organic solvent and internal standard in the ratio of 1 mg:8 μL:1 μL, mix, homogenize and centrifuge, take the upper liquid, evaporate and reconstitute.

9. The analytical method according to claim 8, characterized in that, Homogenization was performed using a tissue homogenizer.

10. The analytical method according to claim 1, characterized in that, In step (3), the linear ranges of the standard curves are as follows: IAA, IPA, IArA and IET are 1–5000 ng / mL; ILA is 2–5000 ng / mL; IAM, IAld and TRM are 1–500 ng / mL; IPYA is 20–5000 ng / mL; ICA is 10–5000 ng / mL; and IS is 0.5–5000 ng / mL.

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