Method for stably reproducing intestinal microbiome in intestinal simulation model and application of method

Abstract

The invention discloses a method for stably reproducing intestinal microbiome in an intestinal simulation model, which comprises the following steps of: constructing the intestinal simulation model comprising a stomach reactor, a small intestine reactor and a large intestine reactor; the content of short-chain fatty acid and / or the content of formic acid of the intestinal simulation model are / is regulated to have no significant difference from that of an actual intestinal tract by adding a pH buffering agent into a large intestine reactor, and then the relative abundance of prevotella, bacteroides, vibrio succinic acid and lactic acid bacteria in the large intestine reactor of the intestinal simulation model is regulated; and thus, stable reproduction of the intestinal microbiome in the intestinal simulation model is realized. According to the method, through an abundance balance mechanism existing among main functional populations in the intestinal microbiome, precise regulation and control of the main functional populations in the intestinal microbiome are achieved, so that high reproduction of the structure of the intestinal microbiome is achieved, and long-term stable high reproduction of the intestinal microbiome can be achieved in an in-vitro intestinal model.

Description

Technical Field

[0001] The present invention relates to the field of biological technology, and in particular to a method for stably reproducing an intestinal microbiome in an intestinal simulation model and an application thereof. Background Art

[0002] The intestinal microbiome is known as the "eighth organ" of the human body. It has many functions such as digesting food, metabolizing drugs, secreting metabolites, regulating intestinal endocrine, regulating nerve signals, and regulating the host immune response system. Therefore, it is closely related to the physical health of the host.

[0003] Existing studies have shown that an imbalance in the gut microbiome can cause a series of diseases in the host, including gastrointestinal disorders and neurological, respiratory, metabolic, and liver and kidney diseases. The relationship between these diseases and the gut microbiome is usually inferred based on the results of related studies, and their pathogenic mechanisms often need to be explored and confirmed through animal experiments. Currently, the commonly used animal models in animal experiments are experimental mice, rabbits, pigs, and monkeys. However, there are some problems in using these animal models to study the gut microbiome: (1) Although experimental mice and rabbits are easy to operate, their gut microbiomes are less similar to humans (<20%), so the clinical relevance of the research results obtained using these animals is low; (2) The intestinal systems of experimental pigs and monkeys are similar to those of humans, but using these animals to study the gut microbiome will face ethical and practical challenges. Therefore, an in vitro intestinal model that can achieve high reproducibility of the gut microbiome and long-term stability is a suitable platform for studying the gut microbiome and its interactions and metabolism. These receipts can be further used to study the relationship between the intestinal microbiome and diseases, to explain the impact and role of the intestinal microbiome in some diseases. They can also be used to study the interactions between drugs, probiotics, prebiotics and other substances. They are of great significance for the development of new drugs and individual medication guidance, and have huge application potential.

[0004] At this stage, 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 on the dynamic changes of the intestinal microbiome, such as the TIM2 and SHIME models; the latter is based on the principles of microelectromechanical and fluid dynamics, and can achieve precise control of the intestinal microbiome microenvironment and ecological niche, so that it can be used for the co-cultivation of host cells and intestinal microorganisms to explore the interaction between cells and microorganisms.

[0005] However, both types of models are not reliable in reproducing the composition, structure, function and interactions of the gut microbiome. At the same time, existing gut models are usually unable to maintain the stability of the gut microbiome for a long time, which makes these models unusable for simulating and exploring the status of the gut microbiome in some chronic diseases (such as neurodegenerative diseases).

[0006] Therefore, a method is needed that can achieve long-term stability and high reproducibility of the gut microbiome in in vitro intestinal models. Summary of the invention

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

[0008] The first object of the present invention is to provide a method for stably reproducing the intestinal microbiome in an intestinal simulation model.

[0009] The second object of the present invention is to provide application of the above method in stably reproducing the intestinal microbiome of an intestinal simulation model.

[0010] In order to achieve the above object, the present invention is implemented by the following scheme:

[0011] A method for stably reproducing an intestinal microbiome in an intestinal simulation model comprises 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 that are interconnected, feeding into the stomach reactor in the intestinal simulation model, digesting in the stomach reactor and the small intestine reactor in sequence, obtaining a digestion product of the small intestine reactor, mixing the large intestine contents of the actual intestinal tract corresponding to the intestinal simulation model with the small intestine reactor, and then inoculating into the large intestine reactor for digestion;

[0013] The feed is the food of the actual intestinal organisms corresponding to the intestinal simulation model, which is crushed and then fed;

[0014] 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 there is no significant difference between the short-chain fatty acid content and / or formic acid content of the intestinal simulation model constructed in step S1 and the short-chain fatty acid content and / or formic acid content of the actual intestine;

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

[0016] S3. Based on the relative abundance of intestinal microorganisms in the actual intestine, the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 is adjusted 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, Vibrio succinici and lactic acid bacteria.

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

[0019] By adjusting the redox potential in the large intestine reactor, the relative abundance of Prevotella and Bacteroidetes was regulated;

[0020] By adjusting the pH value in the large intestine reactor, the relative abundance of Prevotella and lactic acid bacteria was adjusted;

[0021] The relative abundance of Prevotella and Succinivibrio was adjusted by adding biofouling to the large intestine reactor and controlling the volume of the biofouling;

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

[0023] More preferably, the biological attachment body is a microorganism attachment body, which is used to provide attachment for microorganisms.

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

[0025] Further preferably, the membrane assembly consists of a 3D printed polypropylene support and a hollow fiber membrane.

[0026] The present invention simultaneously adjusts the relative abundance of Prevotella, Bacteroides, Lactobacillus and / or Succinivibrio in the large intestine reactor of the intestinal simulation model constructed in step S1 through 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 has no significant difference with the relative abundance of intestinal microorganisms in the actual intestine.

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

[0028] The redox potential of the E. coli reactor is lowered by liquid sealing and / or nitrogen blowing; the redox potential of the E. coli reactor is increased by aeration.

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

[0030] Among them, when the redox potential is -150~0mV, the relative abundance of Prevotella is as high as 20%, while the relative abundance of Bacteroides is as high as 15%; when the redox potential is -300~-150mV, 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 <-300mV, 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] Among them, when the pH value of the large intestine reactor is between 5.0 and 7.0, as the pH of the large intestine reactor increases, the relative abundance of Prevotella increases and the relative abundance of lactic acid bacteria decreases; 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] Among them, when the biological attachment accounts for 0-30% of the total volume of the large intestine reactor, as the volume of the biological attachment increases, the relative abundance of Prevotella increases and the relative abundance of Succinivibrio decreases; when the biological attachment accounts for 0% of the total volume of the large intestine reactor (that is, when no biological attachment is added), the relative abundance of Succinivibrio is 25%, and the relative abundance of Prevotella is 12%; when the biological attachment 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 biological attachment 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 biological attachment 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 actual intestinal organism corresponding to the intestinal simulation model in step S1 is a monogastric animal.

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

[0036] Preferably, the digestion in step S1 is as follows: the stomach reactor and the small intestine reactor perform digestion by enzymatic reaction, and the large intestine reactor performs digestion by microorganisms.

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

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

[0039] Preferably, the large intestine 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 in 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 invention also claims the use of any of the above methods in the stable reproduction of intestinal microbial communities.

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

[0043] The present invention provides a method for stably reproducing an intestinal microbiome in an intestinal simulation model, wherein the method constructs an intestinal simulation model including a stomach reactor, a small intestine reactor and a large intestine reactor, and regulates the short-chain fatty acid content and / or formic acid content of the intestinal simulation model by adding a pH buffer to the large intestine reactor so that there is no significant difference between the actual intestine, and then regulates the relative abundance of Prevotella, Bacteroides, Succinivibrio and Lactobacillus in the large intestine reactor of the intestinal simulation model, thereby stably reproducing the intestinal microbiome in the intestinal simulation model. The method realizes precise regulation of the main functional populations of the intestine through the abundance balance mechanism between the main functional populations in the intestinal microbiome, and highly reproduces the structure of the intestinal microbiome, and can achieve long-term stable and highly reproduced intestinal microbiome in an in vitro intestinal model. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A relative abundance diagram of the main functional populations of intestinal microorganisms in the large intestine reactor of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome in Example 1;

[0045] Figure 2 This is a diagram showing the traceability analysis results of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome in Example 1;

[0046] Figure 3 This is a functional comparison result diagram of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome in Example 1;

[0047] Figure 4: The SCFAs content result graph of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome in Example 1 and its change over time graph; A is the SCFAs relative content result graph of the intestinal model that stably reproduces the actual pig intestinal microbiome for 88 days, and B is the box plot of the SCFAs content of the intestinal model that stably reproduces the actual pig intestinal microbiome for 88 days;

[0048] Figure 5 This is a traceability analysis diagram of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduces the actual human intestinal microbiome in Example 2;

[0049] Figure 6 The relative abundance diagram 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 This is a traceability analysis diagram of the intestinal microbiome in the large intestine reactor of the pig intestinal simulation model in Comparative Example 1;

[0051] Figure 8 This is the result diagram of SCFAs content in the pig intestinal simulation model in Comparative Example 1;

[0052] Fig. 9 This is a traceability analysis diagram of the intestinal microbiome in the large intestine reactor of the human intestinal simulation model in Comparative Example 2. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the accompanying drawings and specific examples of the specification. The examples are only used to explain the present invention and are not used to limit the scope of the present invention. The test methods used in the following examples are conventional methods unless otherwise specified; the materials and reagents used are reagents and materials that can be obtained from commercial channels unless otherwise specified.

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

[0055] This example demonstrates the reproduction of the intestinal microbiome in a pig intestinal simulation model, wherein there are no significant differences in the short-chain fatty acid content (SCFAs) and the intestinal microbiome structure in 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±27.77mM, of which the acetic acid content is 83.13±9.85mM, the propionic acid content is 27.12±13.00mM, the butyric acid content is 6.96±3.56mM, the valeric acid content is 6.47±1.47mM, and no formic acid is produced (the 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 lactic acid bacteria is 10%, and Bacteroides and Succinivibrio are almost non-existent.

[0056] 1. Experimental Methods

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

[0058] S1. Constructing a reactor-based intestinal simulation model, including a stomach reactor, a small intestine reactor and a large intestine reactor that are interconnected, and feeding the intestinal simulation model with feed for digestion; the feed is crushed pig feed;

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

[0060] S2. Based on the short-chain fatty acids (SCFAs) content and formic acid content 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 value of the large intestine reactor is 5.5-6.0, and the SCFAs content and formic acid content of the large intestine reactor after digestion of the feed after adding NaOH are measured;

[0061] S3. Based on the relative abundance of the intestinal microbiome in the actual pig intestine, the large intestine reactor in the intestinal simulation model constructed in step S1 is not equipped with liquid seals and nitrogen blowing, so that the large intestine reactor is in an anaerobic state, and its redox potential is controlled between -150mV and 0mV. At the same time, a membrane component (composed of a 3D-printed polypropylene support and a hollow fiber membrane) accounting for 20% of the total volume of the large intestine reactor is added, and the pH buffer of step S2 is used to adjust the pH range of the large intestine reactor from 5.5 to 6.0 to 6.1 to 6.4, so as to obtain an intestinal simulation model that stably reproduces the actual pig intestinal microbiome.

[0062] 2. Testing of the intestinal simulation model

[0063] (1) Gut microbiome analysis

[0064] The pig feed was crushed and fed into the intestinal simulation model obtained in step 1 that stably reproduced the actual pig intestinal microbiome. Samples in the large intestine reactor were collected every 32 hours during operation. The microbiome 16S rDNA of the samples in the large intestine reactor was extracted using the TINAamp DNA extraction kit (Tiangen Biochemical Technology, Beijing, China) according to its instructions as an amplification template. The nucleotide sequence U515F shown in SEQ ID NO: 1 and the nucleotide sequence U909R shown in SEQ ID NO: 2 were used to amplify the V4-V5 highly variable region of the microbiome 16S rDNA according to the PCR amplification system shown in Table 1, and the amplified products were collected.

[0065] Table 1 PCR amplification system

[0066] Components content <![CDATA[1×Ex Taq Buffer (Mg 2+ Plus)]]> 10μL dNTP Mix 20μM 0.4mg / mL bovine serum albumin 8ng Taq DNA polymerase (Takara, Tokyo, Japan) 0.2U U515F (SEQ ID NO: 1, 0.4 μM) 0.2μL U909R (SEQ ID NO: 2, 0.4 μM) 0.2μL gDNA (amplification template) 10ng <![CDATA[ddH2O]]> up to 20μL

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

[0068] The amplified product was purified using the EZNA gel extraction kit (Omega Biotek, Norcross, Georgia, USA) according to its instructions, and then sequenced using the Illumina Novaseq PE250 platform (Anshengda Life Sciences, Suzhou, China) to obtain the sequencing results of the amplified product.

[0069] The sequencing results of the amplified products were filtered, de-redundanted and spliced ​​using the DADA2 package (v1.6) of the R software (v4.1.2), and the ASV feature table and species annotation table of the sequence were obtained to obtain the intestinal microbiome and relative abundance of various populations in the large intestine reactor of the intestinal simulation model that stably reproduced the actual pig intestinal microbiome obtained in step 1. Based on metagenomic analysis, principal coordinate analysis and FEAST method (through the FEAST package of R software), functional comparison and traceability analysis of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduced the actual pig intestinal microbiome were performed.

[0070] (2) Short-chain fatty acids (SCFAs) content test

[0071] In an intestinal simulation model that stably reproduces the actual pig intestinal microbiome, 1 g of sample from the large intestine reactor was collected every 32 hours during operation as an experimental sample. 6.25% (w / w) metaphosphoric acid accounting for the sample mass was added to the experimental sample for acidification. After ultrasonication for 15 minutes, it was placed at -20°C for 12 hours, then thawed and centrifuged at 16500×g for 10 minutes, 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 flame detector (Agilent, Delaware, USA) and a DB-FFAP column (30 m × 0.25 mm × 0.25 μm, Agilent), and the SCFAs content of the intestinal simulation model that stably reproduced the actual pig intestinal microbiome was measured.

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

[0074] 2. Experimental Results

[0075] The intestinal microbiome and relative abundance of various groups in the large intestine reactor of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome are shown in the figure. Figure 1 As shown, the results showed that the relative abundance of Prevotella was 34.74±7.19%, the relative abundance of Bacteroidetes was 6.43±4.86%, the relative abundance of Succinic Vibrio was 7.06±6.76%, and the relative abundance of Lactobacillus was 16.46±3.83%. The intestinal simulation model that stably reproduced the actual pig intestinal microbiome calculated by the FEAST package had a reproduction rate of 90.48±5.95% compared with the actual pig intestine. The intestinal simulation model that stably reproduced the actual pig intestinal microbiome based on metagenomic analysis can cover 95.89% of the functional characteristics of the actual pig intestine.

[0076] The traceability analysis results of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome are shown in the figure. Figure 2 As shown in the figure, the functional comparison results of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome are shown in the figure Figure 3 As shown;

[0077] The results showed that the majority of the intestinal microbiota in the large intestine reactor of the intestinal simulation model that stably reproduced the actual pig intestinal microbiota came from pigs, and it was able to realize most of the functions of the actual pig intestinal microbiota, indicating that it can well reproduce the actual pig intestinal microbiota.

[0078] After adding NaOH (pH buffer) in step 1.S2, the concentrations of acetic acid, propionic acid, butyric acid and valeric acid in the SCFAs after digestion of the feed in the large intestine reactor were 134.43±23.49mM, 75.12±14.35mM, 47.35±14.58mM and 26.21±4.55mM, respectively, and no formic acid was detected; the total amount of SCFAs in the large intestine reactor and the ratio of acetic acid, propionic acid, butyric acid and valeric acid therein were not significantly different from the content and ratio of SCFAs in the actual intestine of the pig.

[0079] The SCFAs content results of the intestinal simulation model that stably reproduces the actual pig intestinal microbiome are shown in the figure below. Figure 4 As shown, A is a graph showing the relative SCFAs content of an intestinal model that stably reproduces the actual intestinal microbiome of pigs for 88 days, and B is a box plot of the SCFAs content of an intestinal model that stably reproduces the actual intestinal microbiome of pigs for 88 days, where each point in the box plot represents the SCFAs content for 1 day.

[0080] Figure 4 A Results show that the intestinal simulation model that stably reproduces the actual pig intestinal microbiome can run stably for 88 days, during which the total amount and proportion of SCFAs and the microbial composition remain stable; Figure 4 B Results showed that the concentrations of acetic acid, propionic acid, butyric acid and valeric acid were 106.92±20.4mM, 62.42±14.06mM, 42.29±13.55mM and 23.45±5.83mM, respectively. The total amount of SCFAs and the ratios of acetic acid, propionic acid, butyric acid and valeric acid were closer to the actual pig intestine.

[0081] In summary, the intestinal simulation model constructed in step 1 that stably reproduces the actual pig intestinal microbiome can be used in experiments instead of the actual pig intestine.

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

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

[0084] 1. Experimental Methods

[0085] 1. Construction of an intestinal simulation model that stably reproduces the human intestinal microbiome

[0086] S1. Constructing a reactor-based intestinal simulation model, including a stomach reactor, a small intestine reactor and a large intestine reactor that are interconnected, and feeding food into the intestinal simulation model for digestion; the food of the intestinal sample donor is crushed and then fed;

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

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

[0089] S3. Based on the relative abundance of the intestinal microbiome in the actual human intestine, the large intestine reactor of the intestinal simulation model constructed in step S1 is liquid-sealed and nitrogen is introduced into it to maintain a strictly anaerobic environment inside the large intestine reactor and control the redox potential to be less than -300mV. At the same time, a membrane component (composed of a 3D-printed polypropylene support and a hollow fiber membrane) accounting for 30% of the total volume of the large intestine reactor is added, and the pH buffer of step S2 is used to adjust the pH range of the large intestine reactor to 6.4-6.8, so as to obtain an intestinal simulation model that stably reproduces the actual human intestinal microbiome.

[0090] 2. Testing of the intestinal simulation model

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

[0092] 2. Experimental Results

[0093] In the intestinal simulation model that stably reproduces the actual human intestinal microbiome, the relative abundance of Prevotella is 11.07%, and the relative abundance of Bacteroidetes is 32.54%. The reproduction rate of the intestinal simulation model that stably reproduces the actual human intestinal microbiome calculated by the FEAST package is 96.27±4.92% compared with the actual human intestine. Based on metagenomic analysis, the intestinal model that stably reproduces the actual human intestinal microbiome can cover 97.83% of the functional characteristics of the actual human intestinal microbiome.

[0094] The traceability analysis of the intestinal microbiome in the large intestine reactor of the intestinal simulation model that stably reproduces the actual human intestinal microbiome is shown in the figure Figure 5 shown.

[0095] The results showed that the majority of the intestinal microbiota in the large intestine reactor of the intestinal simulation model that stably reproduced the actual human intestinal microbiota originated from humans, indicating that it can well reproduce the actual human intestinal microbiota.

[0096] The total content of SCFAs in the intestinal simulation model that stably reproduces the actual human intestinal microbiome is 218.87±16.19mM, of which the acetic acid content is 116.71±13.46mM, the propionic acid content is 51.25±8.80mM, the butyric acid content is 35.78±7.95mM, and the valeric acid content is 15.12±5.06mM; the SCFAs content and the ratio of acetic acid, propionic acid, butyric acid and valeric acid therein are not significantly different from those in the actual human intestine.

[0097] The intestinal simulation model that stably reproduces the actual human intestinal microbiome can operate stably for 180 days, during which the total amount and proportion of SCFAs and the microbial composition remain stable.

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

[0099] This example demonstrates the reproduction of the intestinal microbiome in the mouse intestinal simulation model, in which the relative abundance of lactic acid bacteria in the actual mouse intestinal tract is 23%, the relative abundance of Bacteroidetes is 12%, the relative abundance of Prevotella is 2%, and Succinic Vibrio is almost non-existent; the total amount of SCFAs in the actual mouse intestinal tract is 1.5×10 -2 mM, acetic acid content is 1.2×10 -2 mM, propionic acid content is 2.0×10 -3 mM, butyrate content is 1.2×10 -3 mM, valeric acid content is 3.1×10 -4 mM.

[0100] 1. Experimental Methods

[0101] 1. Construction of an intestinal simulation model that stably reproduces the mouse intestinal microbiome

[0102] S1. Constructing a reactor-based intestinal simulation model, including a stomach reactor, a small intestine reactor and a large intestine reactor that are interconnected, and feeding food into the stomach reactor of the intestinal simulation model for digestion; the feeding is feeding after crushing mouse feed;

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

[0104] S2. Based on the actual short-chain fatty acids (SCFAs) content and formic acid content in the mouse intestine, NaOH is used as a 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, the large intestine reactor of the intestinal simulation model constructed in step S1 is liquid-sealed and nitrogen is introduced into it to maintain a strictly anaerobic environment inside the large intestine reactor and control the redox potential to be less than -300mV. At the same time, a membrane component (composed of a 3D-printed polypropylene support and a hollow fiber membrane) accounting for 15% of the total volume of the large intestine reactor is added, and the pH range of the large intestine reactor is adjusted to 5.5-6.0 using the pH buffer of step S2, so as to obtain an intestinal simulation model that stably reproduces the actual mouse intestinal microbiome.

[0106] 2. Testing of the intestinal simulation model

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

[0108] 2. Experimental Results

[0109] In the intestinal simulation model that stably reproduces the actual mouse intestinal microbiome, the relative abundance of lactic acid bacteria is 21.23%, the relative abundance of Bacteroidetes is 13.48%, the relative abundance of Prevotella is 6.88%, and the relative abundance of Vibrio succinici is basically 0. The intestinal simulation model that stably reproduces the actual mouse intestinal microbiome has a reproduction rate of 95.76±4.72% compared with the actual mouse intestine. The intestinal simulation model that stably reproduces 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 reproduces the actual mouse intestinal microbiome is 1.6×10 -2 mM, acetic acid content is 1.2×10 -2 mM, propionic acid content is 2.7×10 -3 mM, butyrate content is 1.1×10 -3 mM, valeric acid content is 3.8×10 -4 mM; the SCFAs content and the ratio of acetic acid, propionic acid, butyric acid and valeric acid therein were not significantly different from those in the actual mouse intestine.

[0111] The intestinal simulation model that stably reproduces the actual mouse intestinal microbiome was able to operate stably for 64 days, during which the total amount and proportion of SCFAs and the microbial composition remained stable.

[0112] Comparative Example 1: A pig intestinal simulation model

[0113] This comparative example shows a pig intestinal simulation model, in which there are no significant differences in SCFAs and intestinal microbiome structures in 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±27.77mM, of which the acetic acid content is 83.13±9.85mM, the propionic acid content is 27.12±13.00mM, the butyric acid content is 6.96±3.56mM, the valeric acid content is 6.47±1.47mM, and no formic acid is produced (the 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 lactic acid bacteria is 10%, and Bacteroidetes and Succinivibrio are almost non-existent.

[0114] 1. Experimental Methods

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

[0116] A pig intestinal simulation model based on a reactor is constructed, including a stomach reactor, a small intestine reactor and a large intestine reactor which are interconnected, and feeds into the pig intestinal simulation model for digestion; the feed is crushed pig feed and then fed;

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

[0118] No membrane component was added to the pig intestinal simulation model, and NaHCO3 was 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 simulation model.

[0119] 2. Testing of the pig intestinal simulation model

[0120] According to the test method of the intestinal simulation model shown in step 1 of Example 1, the pig intestinal simulation model constructed in step 1 was tested to obtain the intestinal microbial analysis results and SCFAs content test results of the pig intestinal simulation model.

[0121] 2. Experimental Results

[0122] Figure 2 shows the intestinal microbiome and relative abundance of various groups in the large intestine reactor of the pig intestinal simulation model. Figure 6 As shown, the results showed that the relative abundance of Prevotella in the large intestine reactor in the pig intestinal simulation model was 16.86±2.74%, the relative abundance of Bacteroidetes was 13.33±4.61%, the relative abundance of Succinivibrio was 24.66±9.37%, and the relative abundance of Lactobacillus was 24.98±9.27%; the reproduction rate of the pig intestinal simulation model compared with the actual intestine was 12.83±13.58%.

[0123] Figure 1. Traceability analysis of the intestinal microbiome in the large intestine reactor of the pig intestinal simulation model. Figure 7 As shown; the results showed that the intestinal microbiome in the pig intestinal simulation model had a complex origin, and most of the microbiome was microorganisms of unknown origin, not from pigs.

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

[0125] Comparative Example 2 A human intestinal simulation model

[0126] This comparative example shows a human intestinal simulation model, in which the relative abundance of Bacteroidetes in the actual human intestine is 20%, the relative abundance of Prevotella is 10%, and lactic acid bacteria and succinivibrio are almost non-existent; the total amount of SCFAs in the actual human intestine is 175.23mM, of which the acetic acid content is 112.53mM, the propionic acid content is 41.98mM, the butyric acid content is 18.37mM, and the valeric acid content is 2.35mM.

[0127] 1. Experimental Methods

[0128] 1. Construction of human intestinal simulation model

[0129] A reactor-based intestinal simulation model is constructed, including a stomach reactor, a small intestine reactor and a large intestine reactor that are interconnected, and food is fed into the intestinal simulation model for digestion; the food fed is crushed food from an intestinal sample donor;

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

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

[0132] 2. Testing of human intestinal simulation model

[0133] According to the test method of the intestinal simulation model shown in step 1 of Example 1, the human intestinal simulation model constructed in step 1 was tested to obtain the intestinal microbial analysis results and SCFAs content test results of the human intestinal simulation model.

[0134] 2. Experimental Results

[0135] In the human intestinal simulation model, the relative abundance of Prevotella was 18.47%, the relative abundance of Bacteroidetes was 12.28%, the relative abundance of Lactobacillus was 2.76%, and the relative abundance of Vibrio succinici was basically 0. The reproduction rate of the human intestinal simulation model compared with the actual human intestine was 83.32±10.54%.

[0136] The traceability analysis of the intestinal microbiome in the large intestine reactor of the human intestinal simulation model is shown in the figure Fig. 9 As shown, the results showed that some of the intestinal microbiota in the large intestine reactor of the human intestinal simulation model originated from pigs, indicating that the human intestinal simulation model cannot reproduce the actual human intestinal microbiota.

[0137] The total SCFAs content of the human intestinal simulation model was 151.05±18.35mM, of which the acetic acid content was 79.36±12.80mM, the propionic acid content was 29.56±6.45mM, the butyric acid content was 32.04±8.42mM, and the valeric acid content was 10.09±6.70mM; the human intestinal simulation model could not reproduce the actual human intestine well.

[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 lactic acid bacteria in the actual mouse intestine is 23%, the relative abundance of Bacteroidetes is 12%, the relative abundance of Prevotella is 2%, and Succinic Vibrio is almost non-existent; the total amount of SCFAs in the actual mouse intestine is 1.5×10 -2 mM, acetic acid content is 1.2×10 -2 mM, propionic acid content is 2.0×10 -3 mM, butyrate content is 1.2×10 -3 mM, valeric acid content is 3.1×10 -4 mM.

[0140] 1. Experimental Methods

[0141] 1. Construction of mouse intestinal simulation model

[0142] A reactor-based intestinal simulation model is constructed, including a stomach reactor, a small intestine reactor and a large intestine reactor that are interconnected, and feed is fed into the intestinal simulation model for digestion; the feed is crushed mouse feed;

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

[0144] No membrane component was added to the rat intestinal simulation model, and NaOH was used as a pH buffer to adjust the pH of the large intestine reactor to 6.1-6.4, thereby obtaining a constructed rat intestinal simulation model.

[0145] 2. Testing of the intestinal simulation model

[0146] According to the test method of the intestinal simulation model shown in step 1 of Example 1, the mouse intestinal simulation model constructed in step 1 was tested to obtain the intestinal microbial analysis results and SCFAs content test results of the mouse intestinal simulation model.

[0147] 2. Experimental Results

[0148] In the mouse intestinal simulation model, the relative abundance of lactic acid bacteria was 5.63%, the relative abundance of Bacteroidetes was 8.34%, the relative abundance of Prevotella was 15.98%, and the relative abundance of Vibrio succinici was basically 0; the reproduction rate of the mouse intestinal simulation model compared with the actual mouse intestine was 79.66±8.25%.

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

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above descriptions and ideas. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

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, including a stomach reactor, a small intestine reactor and a large intestine reactor that are interconnected, feeding into the stomach reactor in the intestinal simulation model, digesting in the stomach reactor and the small intestine reactor in sequence, obtaining a digestion product of the small intestine reactor, mixing the large intestine contents of the actual intestinal tract corresponding to the intestinal simulation model with the digestion product of the small intestine reactor, and then inoculating into the large intestine reactor for digestion; The feed is the food of the actual intestinal organisms corresponding to the intestinal simulation model, which is crushed and then 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 there is no significant difference between the short-chain fatty acid content and / or formic acid content of the intestinal simulation model constructed in step S1 and the short-chain fatty acid content and / or formic acid content 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, the relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 is adjusted 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, Vibrio succinici and lactic acid bacteria.

2. The method according to claim 1, characterized in that The relative abundance of intestinal microorganisms in the large intestine reactor of the intestinal simulation model constructed in step S1 is specifically regulated as follows in step S3: By adjusting the redox potential in the large intestine reactor, the relative abundance of Prevotella and Bacteroidetes was regulated; By adjusting the pH value in the large intestine reactor, the relative abundance of Prevotella and lactic acid bacteria was adjusted; The relative abundance of Prevotella and Succinivibrio was adjusted 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.

3. The method according to claim 2, characterized in that The method for adjusting the redox potential of the large intestine reactor is: The redox potential of the colon reactor is lowered by liquid sealing and / or nitrogen blowing; the redox potential of the colon reactor is increased by aeration.

4. The method according to claim 3, characterized in that The redox potential of the large intestine reactor is ≤0mV.

5. The method according to claim 2, characterized in that: The pH value of the large intestine reactor is 5.0-7.

0.

6. The method according to claim 1, characterized in that The actual intestinal organisms corresponding to the intestinal simulation model in step S1 are monogastric animals.

7. The method according to claim 6, characterized in that The monogastric animal is a mouse, a pig and / or a human.

8. 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 are digested by enzymatic reactions, and the large intestine reactor is digested by microorganisms.

9. The method according to claim 8, 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.

10. Use of the method according to any one of claims 1 to 9 in stably reproducing the intestinal microbiome of an intestinal simulation model.

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