Method for stable reproduction of intestinal microbiome in an in vitro gut model and use thereof

Abstract

The application discloses a method for stably reproducing an intestinal microbiome in an intestinal simulation model, which comprises the following steps: constructing an intestinal simulation model comprising a stomach reactor, a small intestine reactor and a large intestine reactor; adding a pH buffer to the large intestine reactor to regulate the content of short-chain fatty acids and / or formic acid in the intestinal simulation model, so that there is no significant difference between the intestinal simulation model and an actual intestine; and regulating the relative abundance of Prevotella, Bacteroides, Succinivibrio and Lactobacillus in the large intestine reactor of the intestinal simulation model, so that the intestinal microbiome in the intestinal simulation model is stably reproduced. The method can precisely regulate the main functional populations in the intestinal microbiome by using the abundance balance mechanism among the main functional populations in the intestinal microbiome, so that the structure of the intestinal microbiome is highly reproduced, and the long-term stable and high reproduction of the intestinal microbiome in the in-vitro intestinal model is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biology, in particular to a method for stable and reproducible intestinal microbiota in an intestinal simulation model and application thereof. BACKGROUND

[0002] Intestinal microbiota is known as the "eighth organ" of the human body, which has multiple functions such as digesting food, metabolizing drugs, secreting metabolites, regulating intestinal endocrine, regulating neural signals, and regulating host immune response system, and thus is closely related to the health of the host.

[0003] Existing studies have shown that the imbalance of intestinal microbiota can cause a series of diseases in the host, including gastrointestinal dysfunction and diseases of the nervous, respiratory, metabolic, and liver and kidney systems. The connection between these diseases and intestinal microbiota is usually inferred based on the results of related studies, and the pathogenic mechanism often needs to be explored and confirmed through animal experiments. The commonly used animal models for current animal experiments include laboratory mice, rabbits, pigs, and monkeys, but there are some problems in using these animal models to study intestinal microbiota: (1) laboratory mice and rabbits are easy to operate, but their intestinal microbiota is less similar to that of humans (<20%), so the clinical relevance of the research results obtained using these animals is low; (2) experimental pigs and monkeys have similar intestinal systems to humans, but using these animals to study intestinal microbiota will face challenges in terms of ethics and practical operation. Therefore, an in vitro intestinal model that can achieve high reproducibility of intestinal microbiota and long-term stability is a suitable platform for studying intestinal microbiota and its interactions and metabolism. These features can further be used to study the relationship between intestinal microbiota and diseases to elucidate the influence and role of intestinal microbiota in some diseases, and can also be used to study the interactions between drugs, probiotics, prebiotics, and other substances, which has important significance for the development of new drugs and individualized medication guidance, and has great application potential.

[0004] Current in vitro intestinal models mainly include reactor-based intestinal models and chip-based intestinal models. The former is mainly used for laboratory simulation and research of the dynamic changes of intestinal microbiota, such as TIM2 and SHIME models; the latter is based on the principles of micro-electro-mechanical systems and fluid dynamics, which can achieve precise control of the intestinal microbiota microenvironment and ecological niche, and thus can be used for co-culture of host cells and intestinal microbiota to explore the interaction between cells and microorganisms.

[0005] However, the two types of models are not reliable in replicating the composition, structure, function and interaction of the intestinal microbiome, and the existing intestinal models usually cannot maintain the stability of the intestinal microbiome for a long time, which makes these models unable to simulate and explore the intestinal microbiome in some chronic diseases (such as neurodegenerative diseases).

[0006] Therefore, there is a need for a method capable of achieving long-term stable and highly reproducible intestinal microbiome in an in vitro intestinal model. SUMMARY

[0007] The purpose of the present application is to overcome the above-mentioned deficiencies of the prior art, and to provide a method for stable and reproducible intestinal microbiome in an intestinal simulation model and its application.

[0008] The first purpose of the present application is to provide a method for stable and reproducible intestinal microbiome in an intestinal simulation model.

[0009] The second purpose of the present application is to provide the above-mentioned method for stable and reproducible intestinal microbiome in an intestinal simulation model.

[0010] In order to achieve the above-mentioned purposes, the present application is realized by the following scheme:

[0011] A method for stable and reproducible intestinal microbiome in an intestinal simulation model, comprising the following steps:

[0012] S1. Constructing a reactor-based intestinal simulation model, including a stomach reactor, a small intestine reactor and a large intestine reactor in communication with each other, and feeding the stomach reactor in the intestinal simulation model, and sequentially digesting in the stomach reactor and the small intestine reactor to obtain the digestion product of the small intestine reactor, and inoculating the large intestine content of the actual intestine corresponding to the intestinal simulation model into the large intestine reactor after mixing with the small intestine reactor to perform digestion;

[0013] The feeding is the feeding of the food of the organisms of the actual intestine corresponding to the intestinal simulation model after crushing;

[0014] S2. Based on the short-chain fatty acid content and / or formic acid content of the actual intestine, adding a pH buffer to the large intestine reactor of the intestinal simulation model constructed in step S1 until the short-chain fatty acid content and / or formic acid content of the intestinal simulation model constructed in step S1 is not significantly different from the short-chain fatty acid content and / or formic acid content of the actual intestine;

[0015] The pH buffer is a sodium hydroxide solution and / or a sodium bicarbonate solution;

[0016] S3. Regulating the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 based on the relative abundance of intestinal microorganisms in the actual intestine, until the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 is not significantly different from the relative abundance of intestinal microorganisms in the actual intestine;

[0017] The intestinal microorganisms are Prevotella, Bacteroides, Succinivibrio and Lactobacillus.

[0018] Preferably, the regulation of the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 in step S3 is specifically:

[0019] The relative abundance of Prevotella and Bacteroides is regulated by adjusting the redox potential in the large intestine reactor.

[0020] The relative abundance of Prevotella and Lactobacillus is regulated by adjusting the pH value in the large intestine reactor.

[0021] The relative abundance of Prevotella and Succinivibrio is regulated by adding biological attachment to the large intestine reactor and controlling the volume of biological attachment.

[0022] The biological attachment is a membrane module and / or a sponge.

[0023] More preferably, the biological attachment is a microbial attachment for providing microbial attachment.

[0024] More preferably, the biological attachment is a membrane module.

[0025] Further preferably, the membrane module is composed of a 3D printed polypropylene support and a hollow fiber membrane.

[0026] The present application simultaneously regulates the relative abundance of Prevotella, Bacteroides, Lactobacillus and / or Succinivibrio in the large intestine reactor of the intestinal simulation model constructed in step S1 by the above three methods, so that the relative abundance of Prevotella, Bacteroides, Lactobacillus and / or Succinivibrio in the large intestine reactor of the intestinal simulation model constructed in step S1 is not significantly different from the relative abundance of intestinal microorganisms in the actual intestine.

[0027] More preferably, the method for regulating the redox potential of the large intestine reactor is:

[0028] The redox potential of the large intestine reactor is reduced by liquid sealing and / or nitrogen blowing, and the redox potential of the large intestine reactor is increased by aeration.

[0029] Further preferably, the redox potential of the large intestine reactor is ≤ 0 mV.

[0030] When the redox potential is -150-0 mV, the relative abundance of Prevotella is the highest, up to 20%, and the relative abundance of Bacteroides is the highest, up to 15%; when the redox potential is -300--150 mV, the relative abundance of Prevotella decreases with the decrease of the redox potential, and the relative abundance of Bacteroides increases with the decrease of the redox potential; when the redox potential is <-300 mV, the relative abundance of Prevotella is 0, and the relative abundance of Bacteroides is 50%.

[0031] More preferably, the pH value of the large intestine reactor is 5.0-7.0.

[0032] When the pH value of the large intestine reactor is 5.0-7.0, the relative abundance of Prevotella increases and the relative abundance of lactic acid bacteria decreases with the increase of the pH value of the large intestine reactor; when the pH value of the large intestine reactor is 5.0, the relative abundance of Prevotella is 0, and the relative abundance of lactic acid bacteria is 98%; when the pH value of the large intestine reactor is 6.5, the relative abundance of Prevotella is 55%, and the relative abundance of lactic acid bacteria is 40%.

[0033] When the volume of the biological carrier accounts for 0-30% of the total volume of the large intestine reactor, the relative abundance of Prevotella increases and the relative abundance of Succinivibrio decreases with the increase of the volume of the biological carrier; when the volume of the biological carrier accounts for 0% of the total volume of the large intestine reactor (i.e. without adding the biological carrier), the relative abundance of Succinivibrio is 25%, and the relative abundance of Prevotella is 12%; when the volume of the biological carrier accounts for 5% of the total volume of the large intestine reactor, the relative abundance of Succinivibrio is 23%, and the relative abundance of Prevotella is 18%; when the volume of the biological carrier accounts for 20% of the total volume of the large intestine reactor, the relative abundance of Succinivibrio is 10%, and the relative abundance of Prevotella is 35%; when the volume of the biological carrier accounts for 30% of the total volume of the large intestine reactor, the relative abundance of Succinivibrio is 2%, and the relative abundance of Prevotella is 45%.

[0034] Preferably, the organism of the actual intestine corresponding to the intestinal simulation model in step S1 is a monogastric animal.

[0035] More preferably, the monogastric animal is a mouse, a pig and / or a human.

[0036] Preferably, the digestion in step S1 is that the stomach reactor and the small intestine reactor digest through enzymatic reaction, and the large intestine reactor digests through microorganisms.

[0037] More preferably, the stomach reactor utilizes protease for enzymatic reaction, and the small intestine reactor utilizes amylase, lipase, chymotrypsin and / or trypsin for digestion.

[0038] Further preferably, the pH value of the stomach reactor is 2, and the pH value of the small intestine reactor is 7.

[0039] Preferably, the intestinal contents of the actual intestine corresponding to the intestinal simulation model in step S1 and the digestion products of the small intestine reactor are mixed at a volume ratio of 1:1.

[0040] More preferably, the concentration of the large intestine contents of the actual intestine corresponding to the intestinal simulation model is 0.333 g / mL.

[0041] The present application also claims the use of any of the above-mentioned methods for stable reproduction of the intestinal microbiome.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] The present application provides a method for stable reproduction of the intestinal microbiome in an intestinal simulation model, which comprises constructing an intestinal simulation model comprising a stomach reactor, a small intestine reactor and a large intestine reactor, and adjusting the short-chain fatty acid content and / or the formic acid content of the intestinal simulation model to have no significant difference with the actual intestine by adding a pH buffer to the large intestine reactor, and further adjusting the relative abundance of Prevotella, Bacteroides, Succinivibrio and Lactobacillus in the large intestine reactor of the intestinal simulation model, thereby achieving stable reproduction of the intestinal microbiome in the intestinal simulation model. The method realizes precise regulation of the main functional populations in the intestinal microbiome through the abundance balance mechanism between the main functional populations in the intestinal microbiome, achieves high reproduction of the intestinal microbiome structure, and can achieve long-term stable and high reproduction of the intestinal microbiome in the in vitro intestinal model. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 Figure 1 is a graph showing the relative abundance of the main functional populations of intestinal microorganisms in the large intestine reactor of the intestinal simulation model for stable reproduction of the actual pig intestinal microbiome in Example 1;

[0045] Figure 2 Figure 2 is a graph showing the results of the traceability analysis of the intestinal microbiome in the large intestine reactor of the intestinal simulation model for stable reproduction of the actual pig intestinal microbiome in Example 1;

[0046] Figure 3 Figure 3 is a graph showing the functional comparison results of the intestinal microbiome in the large intestine reactor of the intestinal simulation model for stable reproduction of the actual pig intestinal microbiome in Example 1;

[0047] Figure 4Figure of SCFAs content results and its change over time of the intestinal simulation model for stably reproducing the actual pig intestinal microbiome in Example 1; A is the figure of relative SCFAs content results of the intestinal model for stably reproducing the actual pig intestinal microbiome for 88 days, B is the box plot of SCFAs content of the intestinal model for stably reproducing the actual pig intestinal microbiome for 88 days;

[0048] Figure 5 Figure of the traceability analysis of the intestinal microbiome in the large intestine reactor of the intestinal simulation model for stably reproducing the actual human intestinal microbiome in Example 2;

[0049] Figure 6 Figure of the relative abundance of the main functional populations of intestinal microorganisms in the large intestine reactor of the pig intestinal simulation model in Comparative Example 1;

[0050] Figure 7 Figure of the traceability analysis of the intestinal microbiome in the large intestine reactor of the pig intestinal simulation model in Comparative Example 1;

[0051] Figure 8 Figure of SCFAs content results of the pig intestinal simulation model in Comparative Example 1;

[0052] Figure 9 Figure of the traceability analysis of the intestinal microbiome in the large intestine reactor of the human intestinal simulation model in Comparative Example 2. DETAILED DESCRIPTION

[0053] The present application will be further described below in conjunction with the accompanying drawings and specific examples, which are only used to explain the present application and are not used to limit the scope of the present application. The test methods used in the following examples are conventional methods unless otherwise specified; the materials, reagents, etc. used are commercially available reagents and materials unless otherwise specified.

[0054] Example 1 Reproduction of the intestinal microbiome in the pig intestinal simulation model

[0055] This example demonstrates the reproduction of the intestinal microbiome in the pig intestinal simulation model, in which there is no significant difference in the short-chain fatty acid content (SCFAs) and the intestinal microbiome structure of the ascending colon, transverse colon and descending colon regions of the actual pig intestine; the SCFAs content in the actual pig intestine is 123.68 ± 27.77 mM, of which the acetic acid content is 83.13 ± 9.85 mM, the propionic acid content is 27.12 ± 13.00 mM, the butyric acid content is 6.96 ± 3.56 mM, the valeric acid content is 6.47 ± 1.47 mM, and no formic acid is produced (formic acid content is 0); the relative abundance of the intestinal microbiome in the actual pig intestine is: the relative abundance of Prevotella is 30%, the relative abundance of Lactobacillus is 10%, and Bacteroides and Succinivibrio are almost non-existent.

[0056] I. Experimental Methods

[0057] 1. Construction of an intestinal simulation model stably reproducing the actual pig intestinal microbiome

[0058] S1. Constructing a reactor-based intestinal simulation model comprising a stomach reactor, a small intestine reactor, and a large intestine reactor that are in communication with one another, and feeding the intestinal simulation model for digestion; the feed is a pig feed that is crushed and fed;

[0059] wherein the stomach reactor is adjusted to pH = 2 using HCl, and pepsin is added at a final concentration of 738 U / mL, and the stomach reactor digestion time (stomach residence time) is set to 10 hours; the small intestine reactor is adjusted to pH = 7 using NaOH, and amylase is added at a final concentration of 221 U / mL, lipase is added at a final concentration of 3 U / mL, chymotrypsin is added at a final concentration of 9 U / mL, and trypsin is added at a final concentration of 69 U / mL, and the small intestine reactor digestion time (small intestine residence time) is set to 12 hours; 200 g of pig colon contents (large intestine contents derived from healthy pigs) is diluted to a volume of 600 mL using sterile water (concentration of 0.333 g / mL), and mixed with the feed after small intestine reactor digestion at a volume ratio of 1:1, and then inoculated into the large intestine reactor, and the large intestine reactor digestion time (large intestine residence time) is set to 1.25 days;

[0060] S2. Based on the content of short-chain fatty acids (SCFAs) and formic acid in the actual pig intestine, NaOH is added as a pH buffer to the large intestine reactor of the intestinal simulation model constructed in step S1 until the pH of the large intestine reactor is 5.5-6.0, and the SCFA content and formic acid content of the digestion feed after the addition of NaOH in the large intestine reactor are determined;

[0061] S3. Based on the relative abundance of the intestinal microbiome in the actual pig intestine, the large intestine reactor of the intestinal simulation model constructed in step S1 is not provided with a liquid seal and nitrogen blowing, so that the large intestine reactor is in an anaerobic state, and the oxidation-reduction potential thereof is controlled to be between -150 mV and 0 mV, a membrane module (composed of a 3D printed polypropylene support and a hollow fiber membrane) is added to the large intestine reactor at a volume fraction of 20%, and the pH buffer of step S2 is used to adjust the pH of the large intestine reactor from 5.5-6.0 to 6.1-6.4, thereby obtaining an intestinal simulation model stably reproducing the actual pig intestinal microbiome.

[0062] 2. Testing of the intestinal simulation model

[0063] (1) Intestinal microbiome analysis

[0064] The pig feed was crushed and fed into the intestinal simulation model of step 1 to stably reproduce the actual pig intestinal microflora, and samples in the large intestine reactor were collected every 32 hours during the operation. The TINAamp DNA extraction kit (Tiangen Biotech, Beijing, China) was used to extract the microflora 16S rDNA of the samples in the large intestine reactor as an amplification template according to the instructions. The U515F nucleotide sequence shown in SEQ ID NO: 1 and the U909R nucleotide sequence shown in SEQ ID NO: 2 were used to amplify the V4-V5 high variable region of the microflora 16S rDNA according to the PCR amplification system shown in Table 1, and the amplification product was collected.

[0065] Table 1 PCR amplification system

[0066] Component Content 1 x Ex Taq buffer (Mg 2+ Plus) 10 μL dNTP mixture 20 μM 0.4 mg / mL bovine serum albumin 8 ng Taq DNA polymerase (Takara, Tokyo, Japan) 0.2U U515F (SEQ ID NO: 1, 0.4 μM) 0.2 μL U909R (SEQ ID NO: 2, 0.4 μM) 0.2 μL gDNA (amplification template) 10 ng ddH2O up to 20 μL

[0067] Amplification procedure: 94°C, 3 min; 94°C, 30 s, 52°C, 30 s, 72°C, 45 s, 30 cycles; 72°C, 10 min.

[0068] The amplification product was purified using the E.Z.N.A gel extraction kit (Omega Biotek, Norcross, Georgia, USA) according to the instructions, and then sequenced using the Illumina Novaseq PE250 platform (Illumina, Suzhou, China) to obtain the sequencing results of the amplification product.

[0069] The sequencing results of the amplification product were filtered, de-redundant and spliced using the DADA2 package (v1.6) of R software (v4.1.2), and the ASV feature table and species annotation table of the sequence were obtained, and then the intestinal microflora and the relative abundance of various groups in the large intestine reactor of the intestinal simulation model of step 1 to stably reproduce the actual pig intestinal microflora were obtained. Based on metagenomic analysis, principal coordinate analysis and FEAST method (FEAST package of R software), the intestinal microflora in the large intestine reactor of the intestinal simulation model to stably reproduce the actual pig intestinal microflora was functionally compared and traced.

[0070] (2) Short-chain fatty acid (SCFA) content test

[0071] In the intestinal simulation model to stably reproduce the actual pig intestinal microflora, 1 g of sample in the large intestine reactor was collected every 32 hours during the operation as an experimental sample, 6.25% (w / w) of the sample was added to the experimental sample, and the sample was acidified with metaphosphoric acid, ultrasonicated for 15 minutes, and then placed at -20°C for 12 hours. Then it was thawed and centrifuged at a speed of 16500xg for 10 min, and the supernatant was collected.

[0072] The SCFAs content of the supernatant was determined using an Agilent 7890B gas chromatograph equipped with a hydrogen ion loading flame detector (Agilent, Delaware, USA) and a DB-FFAP chromatographic column (30 m x 0.25 mm x 0.25 μm, Agilent), and the SCFAs content of the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome was measured.

[0073] The GC operating parameters are as follows: splitless mode, injection port temperature 230°C, injection port pressure 24.56 psi, septum purge flow 30 mL / min, column flow 1.0 mL / min (helium), detector temperature 300°C, oven initial temperature 100°C, maintained for 2 minutes, then increased to 240°C at a rate of 10°C / min, and maintained for 2 minutes.

[0074] II. Experimental results

[0075] The relative abundance map of the intestinal microbiome and various groups in the large intestine reactor of the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome is shown in FIG. 2, and the results show that the relative abundance of Prevotella is 34.74 ± 7.19%, the relative abundance of Bacteroides is 6.43 ± 4.86%, the relative abundance of Succinivibrio is 7.06 ± 6.76%, and the relative abundance of Lactobacillus is 16.46 ± 3.83%. Figure 1 The recurrence rate of the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome compared to the actual pig intestine is 90.48 ± 5.95% calculated using the FEAST package, and the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome can cover 95.89% of the functional characteristics of the actual pig intestine based on metagenomic analysis.

[0076] The results of the traceability analysis of the intestinal microbiome in the large intestine reactor of the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome are shown in FIG. 3, and the functional comparison results of the intestinal microbiome in the large intestine reactor of the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome are shown in FIG. 4. Figure 2 Figure 3

[0077] The results show that the intestinal microbiome in the large intestine reactor of the stable and reproducible intestinal simulation model of the actual pig intestinal microbiome is mostly derived from pigs, and it can realize most of the functions of the actual pig intestinal microbiome, indicating that it can well reproduce the microbiome of the actual pig intestine.

[0078] ​​The concentrations of acetic acid, propionic acid, butyric acid and valeric acid in the SCFAs after the addition of NaOH (pH buffer) in step 1 in the large intestine reactor were 134.43 ± 23.49 mM, 75.12 ± 14.35 mM, 47.35 ± 14.58 mM and 26.21 ± 4.55 mM, respectively, and formic acid was not detected; the total amount of SCFAs in the large intestine reactor and the proportions among acetic acid, propionic acid, butyric acid and valeric acid were not significantly different from the SCFAs content and proportions in the actual pig intestine.

[0079] The results of the SCFAs content of the intestinal simulation model stably reproducing the actual pig intestinal microbiome are shown in FIG. A, which is a results graph of the relative SCFAs content of the intestinal model stably reproducing the actual pig intestinal microbiome for 88 days, and FIG. B, which is a box plot of the SCFAs content of the intestinal model stably reproducing the actual pig intestinal microbiome for 88 days, in which each point in the box plot represents the SCFAs content of 1 day. Figure 4

[0080] The results of A show that the intestinal simulation model stably reproducing the actual pig intestinal microbiome can be stably operated for 88 days, during which the total amount of SCFAs and the proportions and microbial composition remain stable; Figure 4 Figure 4 The results of B show that the concentrations of acetic acid, propionic acid, butyric acid and valeric acid were 106.92 ± 20.4 mM, 62.42 ± 14.06 mM, 42.29 ± 13.55 mM and 23.45 ± 5.83 mM, respectively, and the total amount of SCFAs and the proportions among acetic acid, propionic acid, butyric acid and valeric acid were more close to the actual pig intestine.

[0081] In summary, the intestinal simulation model stably reproducing the actual pig intestinal microbiome constructed in step one can be used to replace the actual pig intestine for experiments.

[0082] Example 2 Reproduction of intestinal microbiome in a human intestinal simulation model

[0083] This example demonstrates the reproduction of intestinal microbiome in a human intestinal simulation model, in which the relative abundance of Bacteroides in the actual human intestine is 20%, the relative abundance of Prevotella is 10%, and lactic acid bacteria and succinic acid Vibrio are almost non-existent; the total amount of SCFAs in the actual human intestine is 175.23 mM, of which the content of acetic acid is 112.53 mM, the content of propionic acid is 41.98 mM, the content of butyric acid is 18.37 mM, and the content of valeric acid is 2.35 mM.

[0084] I. Experimental methods

[0085] 1. Construction of intestinal simulation model stably reproducing human intestinal microbiome

[0086] ​S1. Construct a reactor-based intestinal simulation model, including a stomach reactor, a small intestine reactor and a large intestine reactor connected to each other, and feed the intestinal simulation model for digestion; the feed is the food of the intestinal sample donor after crushing;

[0087] Wherein the stomach reactor uses HCl to adjust pH = 2, and adds pepsin with a final concentration of 738 U / mL, and sets the stomach digestion time (stomach residence time) to 10 hours; the small intestine reactor uses NaOH to adjust pH = 7, and adds amylase with a final concentration of 221 U / mL, lipase with a final concentration of 3 U / mL, chymotrypsin with a final concentration of 9 U / mL, and trypsin with a final concentration of 69 U / mL, while setting the small intestine digestion time (small intestine residence time) to 12 hours; 200 g of donor feces (large intestine contents) is diluted to a volume of 600 mL (concentration of 0.333 g / mL) using sterile water, and mixed with the feed after small intestine reactor digestion at a volume ratio of 1:1, then inoculated into the large intestine reactor, and the large intestine digestion time (large intestine residence time) is set to 2 days;

[0088] S2. Based on the content of short-chain fatty acids (SCFAs) and formic acid in the actual human intestine, use NaOH as the pH buffer in the large intestine reactor of the intestinal digestion model constructed in step S1;

[0089] S3. Based on the relative abundance of intestinal microbiome in the actual human intestine, set the large intestine reactor of the intestinal simulation model constructed in step S1 to be liquid-tight, and introduce nitrogen gas into it to maintain a strict anaerobic environment inside the large intestine reactor, control the oxidation-reduction potential to be less than -300 mV, at the same time, add a membrane module (composed of a 3D printed polypropylene support and a hollow fiber membrane) accounting for 30% of the total volume of the large intestine reactor, and use the pH buffer of step S2 to adjust the pH range of the large intestine reactor to 6.4-6.8, to obtain an intestinal simulation model that stably reproduces the actual human intestinal microbiome.

[0090] 2. Test of the intestinal simulation model

[0091] According to the test method of the intestinal simulation model shown in step one of example 1, the intestinal simulation model constructed in step 1 that stably reproduces the actual human intestinal microbiome is tested, and the intestinal microbial analysis results and SCFAs content test results of the intestinal simulation model that stably reproduces the actual human intestinal microbiome are obtained.

[0092] II. Experimental results

[0093] The relative abundance of Prevotella in the intestinal simulation model stably reproducing the actual human intestinal microbiome was 11.07%, and the relative abundance of Bacteroides was 32.54%. The reproducibility of the intestinal simulation model stably reproducing the actual human intestinal microbiome compared with the actual human intestine was 96.27±4.92% calculated by FEAST package. The intestinal model stably reproducing the actual human intestinal microbiome could cover 97.83% of the functional characteristics of the actual human intestinal microbiome based on metagenomic analysis.

[0094] The traceability analysis diagram of the intestinal microbiome in the large intestine reactor of the intestinal simulation model stably reproducing the actual human intestinal microbiome is shown in FIG. 1. Figure 5

[0095] The results show that the intestinal microbiome in the large intestine reactor of the intestinal simulation model stably reproducing the actual human intestinal microbiome is mostly derived from humans, indicating that it can well reproduce the actual human intestinal microbiome.

[0096] The total content of SCFAs in the intestinal simulation model stably reproducing the actual human intestinal microbiome was 218.87±16.19 mM, including 116.71±13.46 mM of acetic acid, 51.25±8.80 mM of propionic acid, 35.78±7.95 mM of butyric acid, and 15.12±5.06 mM of valeric acid. There was no significant difference between the content of SCFAs and the ratio of acetic acid, propionic acid, butyric acid, and valeric acid in the actual human intestine.

[0097] And the intestinal simulation model stably reproducing the actual human intestinal microbiome can be stably operated for 180 days, during which the total amount and ratio of SCFAs and the composition of the intestinal microbiome remain stable.

[0098] Example 3 Reproduction of intestinal microbiome in a mouse intestinal simulation model

[0099] This example demonstrates the reproduction of the intestinal microbiome in a mouse intestinal simulation model, in which the relative abundance of lactic acid bacteria in the actual mouse intestine is 23%, the relative abundance of Bacteroides is 12%, the relative abundance of Prevotella is 2%, and the relative abundance of Succinivibrio is almost zero. The total amount of SCFAs in the actual mouse intestine is 1.5×10 -2 mM, the content of acetic acid is 1.2×10 -2 mM, the content of propionic acid is 2.0×10 -3 mM, the content of butyric acid is 1.2×10 -3 mM, and the content of valeric acid is 3.1×10 -4 mM.

[0100] I. Experimental methods

[0101] 1. Construction of an intestinal simulation model stably reproducing the mouse intestinal microbiome​

[0102] S1. Construct a reactor-based intestinal simulation model, including a stomach reactor, a small intestine reactor and a large intestine reactor connected to each other, and feed the stomach reactor of the intestinal simulation model for digestion; the feed is a mouse feed crushed and fed;

[0103] wherein the stomach reactor is adjusted to pH = 2 with HCl, and pepsin is added at a final concentration of 738 U / mL, and the stomach digestion time (gastric residence time) is set to 1 hour; the small intestine reactor is adjusted to pH = 7 with NaOH, and amylase is added at a final concentration of 221 U / mL, lipase at 3 U / mL, chymotrypsin at 9 U / mL, and trypsin at 69 U / mL, and the small intestine digestion time (small intestine residence time) is set to 1 hour; 5 g of actual mouse intestinal contents is diluted to a volume of 15 mL (concentration of 0.333 g / mL) with sterile water, and mixed with the feed after small intestine digestion at a volume ratio of 1:1, and then inoculated into the large intestine reactor, and the large intestine digestion time (large intestine residence time) is set to 3 hours;

[0104] S2. Based on the content of short-chain fatty acids (SCFAs) and formic acid in the actual mouse intestine, use NaOH as the pH buffer in the large intestine reactor of the intestinal digestion model constructed in step S1;

[0105] S3. Based on the relative abundance of the intestinal microbiome in the actual mouse intestine, set the large intestine reactor of the intestinal simulation model constructed in step S1 to be liquid-tight, and introduce nitrogen gas into it to maintain a strict anaerobic environment inside the large intestine reactor, control the oxidation-reduction potential to be less than -300 mV, at the same time add a membrane module (composed of a 3D printed polypropylene support and a hollow fiber membrane) accounting for 15% of the total volume of the large intestine reactor, and adjust the pH range of the large intestine reactor to 5.5-6.0 using the pH buffer of step S2, to obtain an intestinal simulation model that stably reproduces the actual mouse intestinal microbiome.

[0106] 2. Test of the intestinal simulation model

[0107] According to the test method of the intestinal simulation model shown in step one of example 1, the intestinal simulation model constructed in step 1 that stably reproduces the actual mouse intestinal microbiome is tested, and the intestinal microbial analysis results and SCFAs content test results of the intestinal simulation model that stably reproduces the actual mouse intestinal microbiome are obtained.

[0108] II. Experimental results

[0109] In the intestinal simulation model that stably replicates the actual mouse intestinal microbiome, the relative abundance of lactic acid bacteria is 21.23%, the relative abundance of Bacteroides is 13.48%, the relative abundance of Prevotella is 6.88%, and the relative abundance of Succinivibrio is basically 0. The replication rate of the intestinal simulation model that stably replicates the actual mouse intestinal microbiome compared with the actual mouse intestine is 95.76±4.72%, and the intestinal simulation model that stably replicates the actual mouse intestinal microbiome based on metagenomic analysis can cover 93.22±3.56% of the functional characteristics of the actual mouse intestine.

[0110] The total amount of SCFAs in the intestinal simulation model that stably replicates the actual mouse intestinal microbiome is 1.6×10 -2 mM, the content of acetic acid is 1.2×10 -2 mM, the content of propionic acid is 2.7×10 -3 mM, the content of butyric acid is 1.1×10 -3 mM, and the content of valeric acid is 3.8×10 -4 mM; the content of SCFAs and the ratio between acetic acid, propionic acid, butyric acid and valeric acid in the actual mouse intestine show no significant difference.

[0111] And the intestinal simulation model that stably replicates the actual mouse intestinal microbiome can stably run for 64 days, during which the total amount and ratio of SCFAs and the composition of the microbiome remain stable.

[0112] Comparative Example 1 A pig intestinal simulation model

[0113] This comparative example shows a pig intestinal simulation model, in which there is no significant difference in SCFAs and intestinal microbiome structure in the ascending colon, transverse colon and descending colon regions of the actual pig intestine; the content of SCFAs in the actual pig intestine is 123.68±27.77 mM, of which the content of acetic acid is 83.13±9.85 mM, the content of propionic acid is 27.12±13.00 mM, the content of butyric acid is 6.96±3.56 mM, and the content of valeric acid is 6.47±1.47 mM, and no formic acid is produced (the content of formic acid is 0); the relative abundance of the intestinal microbiome in the actual pig intestine is: the relative abundance of Prevotella is 30%, the relative abundance of lactic acid bacteria is 10%, and Bacteroides and Succinivibrio are almost non-existent.

[0114] I. Experimental Methods

[0115] 1. Construction of a pig intestinal simulation model:

[0116] A reactor-based pig intestinal simulation model was constructed, including a stomach reactor, a small intestine reactor and a large intestine reactor connected to each other, and a feed was fed into the pig intestinal simulation model for digestion; the feed was obtained by crushing pig feed;

[0117] The stomach reactor is adjusted to pH = 2 by using HCl, and pepsin is added at a final concentration of 738 U / mL, and the stomach reactor digestion time (stomach residence time) is set to 10 hours; the small intestine reactor is adjusted to pH = 7 by using NaOH, and amylase, lipase, chymotrypsin and trypsin are added at a final concentration of 221 U / mL, 3 U / mL, 9 U / mL and 69 U / mL respectively, and the small intestine reactor digestion time (small intestine residence time) is set to 12 hours; 200 g of pig colon contents (large intestinal contents from healthy pigs) is diluted to a volume of 600 mL (concentration of 0.333 g / mL) using sterile water, and mixed with the feed after small intestine reactor digestion at a volume ratio of 1:1, and then inoculated into the large intestine reactor, and the large intestine reactor digestion time (large intestine residence time) is set to 1.25 days;

[0118] In the pig intestinal tract simulation model, no membrane module is added, and NaHCO3 is used as a pH buffer to adjust the pH of the large intestine reactor to 5.5-6.0, thereby obtaining a constructed pig intestinal tract simulation model.

[0119] 2. Test of the pig intestinal tract simulation model

[0120] According to the test method of the intestinal tract simulation model shown in step one of example 1, the constructed pig intestinal tract simulation model in step 1 is tested, and intestinal tract microbial analysis results and SCFAs content test results of the pig intestinal tract simulation model are obtained.

[0121] II. Experimental results

[0122] The intestinal tract microbial community and the relative abundance of various groups in the large intestine reactor of the pig intestinal tract simulation model are shown in Figure 6 The results show that the relative abundance of Prevotella in the large intestine reactor of the pig intestinal tract simulation model is 16.86 ± 2.74%, the relative abundance of Bacteroides is 13.33 ± 4.61%, the relative abundance of Succinivibrio is 24.66 ± 9.37%, and the relative abundance of Lactobacillus is 24.98 ± 9.27%; the recurrence rate of the pig intestinal tract simulation model compared with the actual intestinal tract is 12.83 ± 13.58%.

[0123] The traceability analysis chart of the intestinal tract microbial community in the large intestine reactor of the pig intestinal tract simulation model is shown in Figure 7 The results show that the intestinal tract microbial community in the pig intestinal tract simulation model is complex, and most of the microbial communities are unknown sources of microorganisms, not from pigs.

[0124] The SCFAs content result chart of the pig intestinal tract simulation model is shown in Figure 8The content of acetic acid, propionic acid and butyric acid were 494.42±106.29 mM, 153.45±55.90 mM and 100.45±44.25 mM, respectively, and the content of valeric acid was 0, and 417.18±186.14 mM of formic acid was also detected; the total amount of SCFAs in the pig intestinal tract simulation model and the ratio among formic acid, acetic acid, propionic acid, butyric acid and valeric acid were significantly different from the actual pig intestinal tract (p<0.001), and the pig intestinal tract simulation model could not achieve excellent reproduction compared with the actual pig intestinal tract.

[0125] Comparative Example 2 A human intestinal tract simulation model

[0126] The present comparative example shows a human intestinal tract simulation model, in which the relative abundance of Bacteroides in the actual human intestinal tract is 20%, the relative abundance of Prevotella is 10%, and Lactobacillus and Succinivibrio are almost absent; the total amount of SCFAs in the actual human intestinal tract is 175.23 mM, in which the content of acetic acid is 112.53 mM, the content of propionic acid is 41.98 mM, the content of butyric acid is 18.37 mM, and the content of valeric acid is 2.35 mM.

[0127] I. Experimental Methods

[0128] 1. Construction of a human intestinal tract simulation model

[0129] A reactor-based intestinal tract simulation model was constructed, including a stomach reactor, a small intestine reactor and a large intestine reactor connected to each other, and a digestion was carried out by feeding the intestinal tract simulation model; the feed was obtained by crushing the food of the intestinal sample donor;

[0130] The stomach reactor was adjusted to pH=2 with HCl, and pepsin was added at a final concentration of 738 U / mL, and the stomach digestion time (gastric residence time) was set to 10 hours; the small intestine reactor was adjusted to pH=7 with NaOH, and amylase, lipase, pepsin and trypsin were added at a final concentration of 221 U / mL, 3 U / mL, 9 U / mL and 69 U / mL, respectively, and the small intestine digestion time (small intestine residence time) was set to 12 hours; 200 g of donor feces (large intestine contents) was diluted with sterile water to a volume of 600 mL (concentration of 0.333 g / mL), and mixed with the feed after small intestine digestion at a volume ratio of 1:1, and then inoculated into the large intestine reactor, and the large intestine digestion time (large intestine residence time) was set to 2 days.

[0131] No membrane component was added to the human intestinal tract simulation model, and NaOH was used as a pH buffer to adjust the pH of the large intestine reactor to 6.4-6.8, to obtain the constructed human intestinal tract simulation model.

[0132] 2. Testing of the human intestinal tract simulation model

[0133] The human intestinal simulation model constructed in step 1 was tested according to the test method of the intestinal simulation model shown in step 1 of embodiment 1, and the intestinal microbial analysis results and SCFA content test results of the human intestinal simulation model were obtained.

[0134] II. Experimental results

[0135] In the human intestinal simulation model, the relative abundance of Prevotella was 18.47%, the relative abundance of Bacteroides was 12.28%, the relative abundance of Lactobacillus was 2.76%, and the relative abundance of Succinivibrio was basically 0. The reproducibility of the human intestinal simulation model compared to the actual human intestine was 83.32±10.54%.

[0136] The traceability analysis chart of the intestinal microbiome in the large intestine reactor of the human intestinal simulation model is shown in Figure 9 The results show that there are some microorganisms in the intestinal microbiome in the large intestine reactor of the human intestinal simulation model, which are derived from pigs, indicating that the human intestinal simulation model cannot reproduce the actual human intestinal microbiome.

[0137] The total SCFA content of the human intestinal simulation model was 151.05±18.35 mM, of which the acetic acid content was 79.36±12.80 mM, the propionic acid content was 29.56±6.45 mM, the butyric acid content was 32.04±8.42 mM, and the valeric acid content was 10.09±6.70 mM. The human intestinal simulation model cannot be reproduced excellently compared to the actual human intestine.

[0138] Comparative Example 3 A mouse intestinal simulation model

[0139] This comparative example shows a mouse intestinal simulation model, in which the relative abundance of Lactobacillus in the actual mouse intestine is 23%, the relative abundance of Bacteroides is 12%, the relative abundance of Prevotella is 2%, and Succinivibrio almost does not exist; the total amount of SCFAs in the actual mouse intestine is 1.5×10 -2 mM, the acetic acid content is 1.2×10 -2 mM, the propionic acid content is 2.0×10 -3 mM, the butyric acid content is 1.2×10 -3 mM, and the valeric acid content is 3.1×10 -4 mM.

[0140] I. Experimental methods

[0141] 1. Construction of a mouse intestinal simulation model

[0142] A reactor-based intestinal simulation model was constructed, including a stomach reactor, a small intestine reactor and a large intestine reactor connected to each other, and a feed was fed into the intestinal simulation model for digestion; the feed was a mouse feed crushed and fed;

[0143] The stomach reactor is adjusted to pH=2 with HC1 and pepsin is added to a final concentration of 738 U / mL, and the stomach reactor digestion time (stomach residence time) is set to 1 hour; the small intestine reactor is adjusted to pH=7 with NaOH and amylase, lipase, chymotrypsin and trypsin are added to a final concentration of 221 U / mL, 3 U / mL, 9 U / mL and 69 U / mL respectively, and the small intestine reactor digestion time (small intestine residence time) is set to 1 hour; 5 g of actual mouse intestinal contents is diluted to a volume of 15 mL (concentration of 0.333 g / mL) with sterile water, mixed with the small intestine reactor digestion feed at a volume ratio of 1:1, and then inoculated into the large intestine reactor, and the large intestine reactor digestion time (large intestine residence time) is set to 3 hours;

[0144] In the mouse intestinal simulation model, no membrane module is added, NaOH is used as a pH buffer, and the pH of the large intestine reactor is adjusted to 6.1-6.4 to obtain the constructed mouse intestinal simulation model.

[0145] 2. Test of the intestinal simulation model

[0146] According to the test method of the intestinal simulation model shown in step one of example 1, the constructed mouse intestinal simulation model in step 1 is tested, and the intestinal microbial analysis results and SCFAs content test results of the mouse intestinal simulation model are obtained.

[0147] II. Experimental results

[0148] In the mouse intestinal simulation model, the relative abundance of lactic acid bacteria is 5.63%, the relative abundance of Bacteroides is 8.34%, the relative abundance of Prevotella is 15.98%, and the relative abundance of Succinivibrio is basically 0; the reproduction rate of the mouse intestinal simulation model compared to the actual mouse intestine is 79.66±8.25%.

[0149] The total amount of SCFAs in the mouse intestinal simulation model is 1.8 x 10 -2 mM, the content of acetic acid is 1.3 x 10 -2 mM, the content of propionic acid is 3.0 x 10 -3 mM, the content of butyric acid is 3.2 x 10 -3 mM, and the content of valeric acid is 5.2 x 10 -4 mM; the mouse intestinal simulation model cannot achieve excellent reproduction compared to the actual mouse intestine.

[0150] It should be pointed out finally that the above embodiments are only used to explain the technical solutions of the present application but not to limit the protection scope of the present application, and any modification, equivalent replacement and improvement etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for stably reproducing the intestinal microbiome in an intestinal simulation model, characterized in that: The following steps are involved: S1. Constructing a reactor-based intestinal simulation model, comprising interconnected stomach reactor, small intestine reactor, and large intestine reactor. Feeding the stomach reactor in the intestinal simulation model, digestion is sequentially performed in the stomach reactor and small intestine reactor to obtain digestion products in the small intestine reactor. The large intestine contents of the actual intestinal tract corresponding to the intestinal simulation model are mixed with the digestion products of the small intestine reactor and then inoculated into the large intestine reactor for digestion; The feed is the food of the actual intestinal organisms corresponding to the intestinal simulation model that is crushed and fed; S2. Based on the short-chain fatty acid content and / or formic acid content of the actual intestine, a pH buffer is added to the large intestine reactor of the intestinal simulation model constructed in step S1 until the short-chain fatty acid content and / or formic acid content of the intestinal simulation model constructed in step S1 is not significantly different from that of the actual intestine; The pH buffer is sodium hydroxide solution and / or sodium bicarbonate solution; S3. Based on the relative abundance of intestinal microorganisms in the actual intestine, regulating the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 until the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 is not significantly different from the relative abundance of intestinal microorganisms in the actual intestine; The intestinal microorganisms are Prevotella, Bacteroides, Succinivibrio and Lactobacillus; The relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in the control step S1 is specifically: By adjusting the redox potential in the large intestine reactor, the relative abundance of Prevotella and Bacteroides was regulated; The method for adjusting the redox potential of the large intestine reactor is: reducing the redox potential of the large intestine reactor by liquid sealing and / or nitrogen blowing; Increase the redox potential of the large intestine reactor through aeration; By adjusting the pH value in the large intestine reactor, the relative abundance of Prevotella and lactic acid bacteria was regulated; The relative abundance of Prevotella and Succinic Vibrio was regulated by adding biofouling to the large intestine reactor and controlling the volume of the biofouling. The biological attachment is a membrane component and / or a sponge.

2. The method according to claim 1, characterized in that The redox potential of the large intestine reactor is ≤0mV.

3. The method according to claim 1, characterized in that The pH value of the large intestine reactor is 5.0-7.

0.

4. The method according to claim 1, wherein The actual intestinal organism corresponding to the intestinal simulation model in step S1 is a monogastric animal.

5. The method according to claim 4, characterized in that The monogastric animal is a mouse, a pig and / or a human.

6. The method according to claim 1, characterized in that The digestion in step S1 is as follows: the stomach reactor and the small intestine reactor perform digestion by enzymatic reactions, and the large intestine reactor performs digestion by microorganisms.

7. The method according to claim 6, characterized in that The gastric reactor utilizes pepsin for enzymatic reactions, and the small intestinal reactor utilizes amylase, lipase, chymotrypsin, and / or trypsin for digestion.

8. Use of the method according to any one of claims 1 to 7 in stably reproducing the intestinal microbiome of an intestinal simulation model.

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