Microbial marker for screening anti-constipation drugs and application thereof

Abstract

The invention discloses a microbial marker for screening an anti-constipation drug. The microbial marker is one of the following components or a composition of the components: gnanokfmuriacaceae, gRuminococcus, gCandidatus Saccharimonas, gunclassiedfOscillospiraceae, gunclassiedfPrevoteraceae, gBacillus, gUCG-005 and gLactobacillus, and the microbial marker is one of the following components or a composition of the components. The invention further discloses a preparation method of the microbial marker for screening the anti-constipation drug. The invention further discloses a preparation method of the microbial marker for screening the anti-constipation drug, and the preparation method of the microbial marker for screening the anti-constipation drug and a preparation method of the microbial marker for screening the anti-constipation drug. Based on the microbial marker disclosed by the invention, a medicament which not only has an anti-constipation effect, but also can establish an intestinal advantage stable microbial flora and really achieves the purpose of treating intestinal health can be screened out. A new clue and a new idea are provided for prevention and treatment of anti-constipation related diseases in the future.

Description

Technical Field

[0001] The invention belongs to the technical field of microorganisms, and specifically discloses a microbial marker for screening anti-constipation drugs. Background Art

[0002] Constipation is a common gastrointestinal disease, characterized by decreased bowel movements (less than 3 bowel movements per week), incomplete bowel movements, dry stools, and anorectal obstruction. Long-term constipation can cause abdominal cramps, aggravate defecation difficulties, and even seriously affect the patient's quality of life. It also increases the risk of other gastrointestinal diseases, including intestinal inflammation, irritable bowel syndrome, and colorectal cancer. The main causes of constipation are colonic sensory and motor disorders and pelvic floor dysfunction.

[0003] The two main medicinal forms of betel nut and burnt betel nut are widely used among the people and are included in the 2020 edition of the Chinese Pharmacopoeia. Arecoline in betel nut and burnt betel nut is considered to be a bioactive ingredient that improves gastrointestinal function. However, studies have shown that the content of arecoline in processed burnt betel nut is significantly reduced, while the effect of promoting gastrointestinal motility is increased. Therefore, arecoline may not be the main factor causing the difference in the efficacy of betel nut and burnt betel nut in treating constipation, and its intrinsic mechanism for constipation needs further study. Clinical studies have shown that changes in intestinal flora are associated with constipation. Intestinal microbiota participates in a variety of physiological activities in the host, including regulating intestinal motility and barrier function. There are large differences in the community structure of intestinal flora between healthy people and patients with constipation. Maintaining a balance between intestinal flora and the host will help maintain the normal function of the intestine. Changing the dynamic balance of intestinal flora will disrupt human metabolism, cause a series of pathophysiological changes, and aggravate constipation symptoms.

[0004] The metabolites of the intestinal microbiota can be sensed by the abundant chemical sensors in the intestine, thus affecting the movement and secretion of the intestine, thereby promoting the physiological function and environmental stability of the intestine. Therefore, changes in intestinal metabolites are also closely related to constipation, and it is of great significance to find a microbial marker that can screen constipation drugs. Summary of the invention

[0005] The purpose of the present invention is to screen out microbial markers for drugs for treating constipation through a large number of experiments.

[0006] Technical solution: The present invention adopts the following technical solution:

[0007] A microbial marker for screening anti-constipation drugs, wherein the microbial marker is one of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae, g_unclassified_f__Prevotellaceae, g_Bacillus, g_UCG-005, g_Lactobacillus, or a combination thereof.

[0008] As a preferred embodiment, the above-mentioned microbial marker for screening anti-constipation drugs consists of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas and g_unclassified_f__Oscillospiraceae.

[0009] As a preferred embodiment, the above-mentioned microbial marker for screening anti-constipation drugs consists of g_unclassified_f__Prevotellaceae, g_Bacillus, g_UCG-005, and g_Lactobacillus.

[0010] A microbial marker for screening anti-constipation drugs. Anti-constipation drugs can increase the abundance of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae and g_unclassified_f__Prevotellaceae, and decrease the abundance of g_Bacillus, g_UCG-005 and g_Lactobacillus.

[0011] A microbial marker for screening anti-constipation drugs, betel nut can treat constipation caused by decreased abundance of g_Bacillus and g_UCG-005 and increased abundance of g_norank_f__Muribaculaceae, g_Candidatus_Saccharimonas and g_Ruminococcus.

[0012] Burnt betel nut could treat constipation caused by decreased abundance of g_Lactobacillus and increased abundance of g_Bacillus, g_norank_f__Muribaculaceae, g_Ruminococcus, g_unclassified_f__Oscillospiraceae, and g_unclassified_f__Prevotellaceae.

[0013] Beneficial effects of the present invention:

[0014] The present invention screens out microbial markers that can be used to screen drugs for treating constipation through a large number of experiments. Anti-constipation drugs can increase the abundance of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae and g_unclassified_f__Prevotellaceae, and reduce the abundance of g_Bacillus, g_UCG-005 and g_Lactobacillus. Based on the microbial markers of the present invention, it is possible to screen out microorganisms that not only have anti-constipation effects, but also can establish a dominant and stable intestinal microbial flora, thereby truly achieving the therapeutic purpose of intestinal health. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Effects of betel nut and charred betel nut on loperamide-induced constipation in rats. Figure legend: (A) Daily weight gain of rats. (B) Water content of feces. (C) Gastrointestinal transit rate. Data are shown as mean ± standard deviation. Compared with the normal group, #P<0.05, ##P<0.01, ###P<0.001. Compared with the constipation model group, **P<0.01, ***P<0.001.

[0016] Figure 2 H&E staining of rat colon tissue. Figure legend: Original magnification, ×5, ×20 (A). Levels of SP (B), VIP (C), ACH (D), and MTL (E) in rat serum. Data are shown as mean ± SD. Compared with the normal group, #P<0.05, ##P<0.01. Compared with the constipation model group, *P<0.05, **P<0.01, ***P<0.001.

[0017] Figure 3Diversity analysis of intestinal flora in rats. Figure legend: Rarefaction curves include Shannon index (A) and Coverage index (B). Venn diagram among five groups (C). Alpha diversity includes Ace index (D), Chao index (E), Simpson index (F) and Sobs index (G). NMDS (H). Compared with the normal group, #P<0.05, ##P<0.01.

[0018] Figure 4 Areca nut and burnt betel nut change the microbial composition at the phylum level. Figure legend: (A) Microbial community composition at the phylum level. (B) Ratio of Firmicutes to Bacteroidetes. (C) Relative abundance of Bacteroidetes. (D) Relative abundance of Proteobacteria. Data are shown as mean ± SD. Compared with the normal group, ###P<0.001. Compared with the constipation model group, *P<0.05, **P<0.01, ***P<0.001.

[0019] Figure 5 Figure 1: Changes in microbial composition at the genus level by betel nut and charred betel nut. Figure legend: (A) Community heat map at the genus level. (B) Relative abundance of Bacillus, UCG-005, norank_f_Muribaculaceae, Ruminococcus, and Candidatus_Saccharimonas before and after betel nut treatment. (C) Relative abundance of Lactobacillus, Bacillus, norank_f_Muribaculaceae, Ruminococcus, unclassified_f_Oscillospiraceae, and unclassified_f_Prevotellaceae before and after charred betel nut treatment. Data are shown as mean ± SD. Compared with the normal group, #P<0.05, ##P<0.01, ###P<0.001. Compared with the constipation model group, *P<0.05, **P<0.01, ***P<0.001. DETAILED DESCRIPTION

[0020] The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

[0021] Example 1 Materials and Methods

[0022] 1.1 Materials

[0023] Loperamide hydrochloride capsules (batch number: MLJ1221) were purchased from Xi'an Janssen Pharmaceuticals Co., Ltd. (China). Mosapride citrate (batch number: 230102) was purchased from Yabao Pharmaceutical Group Co., Ltd. (China). Acetonitrile (chromatographic grade), methanol (chromatographic grade), and formic acid (chromatographic grade) were purchased from Merck, Germany. Areca nut (batch number: 200301) and burnt betel nut (batch number: 170801) were purchased from Hainan Linshi Shengtai Pharmaceutical Co., Ltd. (Haikou, China). Substance P (SP) ELISA kit (no.: D751030-0096), rat vasoactive intestinal peptide (VIP) ELISA kit (no.: D731185-0096), acetylcholine (ACH) ELISA kit (no.: D751036-0096), and rat motilin (MTL) ELISA kit (no.: D731179-0096) were purchased from Jiancheng Bioengineering Institute (Nanjing, China).

[0024] Male SD rats (weight 180 g-220 g) were purchased from Changsha Tianqin Biotechnology Co., Ltd. (certificate number: CSXK Xiang 2022-0011). This animal experiment project was carried out in strict accordance with the Guide for the Care and Use of Laboratory Animals and was approved by the Ethics Committee of Hainan Medical University (Approval No. of the Ethics Committee of Hainan Medical University: HYLL-2021-308).

[0025] 1.2 Experimental methods

[0026] 1.2.1 Preparation of Areca Nut and Burnt Areca Nut Water Extracts

[0027] 100g of betel nut was crushed and soaked in water (1 / 10, w / v) for 1 hour each time, and then hot reflux extraction was performed for 1 hour. The extract was concentrated under reduced pressure to 100ml to obtain betel nut extract. The preparation process of the charred betel nut water extract is the same as that of the betel nut water extract.

[0028] 1.2.2 Animal grouping, modeling, treatment and recording of weight gain

[0029] All rats were placed in an SPF room and fed with standard feed and provided with drinking water ad libitum. After one week of adaptive feeding, 30 rats were randomly divided into 5 groups (6 rats in each group): control group, constipation model group, positive drug group, betel nut treatment group and charred betel nut treatment group. -1 ·d -1 ) was administered intragastrically to induce constipation model. The control group was given normal saline. The model group was given loperamide to establish the model, and the positive drug group was given loperamide to establish the model and mosapride (2 mg kg -1 ·d -1 ) gavage treatment, and the betel nut treatment group was given loperamide modeling and betel nut extract (3 ml·kg -1 ·d-1 ) gavage treatment, the charred betel nut treatment group was given loperamide modeling and charred betel nut extract (3 ml·kg -1 ·d -1 ) was given by gavage and the treatment lasted for 14 days.

[0030] 1.2.3 Recording of rat body weight, water content and gastrointestinal tract turnover rate

[0031] The weight changes of rats in each group were recorded daily. On the last day of treatment, the feces of each rat in each group were collected and weighed within 24 hours. Fresh feces were collected and dried in an oven at 60°C for 24 hours to obtain dry feces. The water content of rat feces was calculated according to the following formula:

[0032] Fecal moisture content (%) = [(fresh feces weight - dry feces weight) / fresh feces weight] × 100%

[0033] After the last administration, all rats were fasted but not watered for 12 hours. 12 hours later, 10% activated carbon solution was gavaged and the gastrointestinal propulsion rate was measured. The equation for calculating the gastrointestinal transport rate of activated carbon is as follows:

[0034] Gastrointestinal transit rate (%) = (carbon powder advancement distance / total length of small intestine) × 100%.

[0035] 1.2.4 Histological evaluation of rat colon

[0036] Part of the colon tissue of the rat was taken and immersed in 4% polyoxymethylene. The colon tissue was dehydrated, embedded in paraffin, sliced ​​at 4 μm, stained with hematoxylin and eosin (HE), and imaged under an optical microscope.

[0037] 1.2.5 Detection of MTL, VIP, SP and ACH in serum

[0038] The blood of the rats was collected and centrifuged at 4°C and 3000 rpm for 10 minutes to collect the serum. The levels of MTL, VIP, SP and ACH in the serum of the rats were detected according to the instructions of the ELISA kit manufacturer.

[0039] 1.2.6 16S rRNA gene sequencing

[0040] The cecal contents of rats were collected for intestinal flora analysis. Total microbial genomic DNA was extracted using a stool DNA kit (Omega Bio-tek, Norcross, GA, US). The quality and concentration of DNA were determined by 1.0% agarose gel electrophoresis and NanoDrop2000 spectrophotometer (Thermo Scientific, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified using primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTAChVGGGTWTCTAAT-3') (28) using a T100 thermal cycler PCR thermal cycler (BIO-RAD, USA). The PCR product was recovered using a 2% agarose gel, purified using a DNA gel recovery and purification kit (PCR Clean-Up Kit, Yuhua, China), and purified using Qubit

[0041] The recovered products were detected and quantified using ELISA 4.0 (Thermo Fisher Scientific, USA). The purified PCR products were library constructed using NEXTFLEXRapid DNA-Seq Kit and sequenced using Illumina PE300 platform (Shanghai Meiji Biopharmaceutical Technology Co., Ltd.).

[0042] The bioinformatics analysis of the intestinal microbiota was performed using the Major Bio Cloud Platform (https: / / cloud.majorbio.com). ASVs were classified using Qiime2 (2020.2) and the SILVA 16S rRNA database (v138). Qiime2 (version 2020.2) was used for intestinal flora analysis, and all sequences were divided into AVSs according to different similarities, and bioinformatics analysis was performed on AVSs with a similarity of 97%. Based on the AVS sequences, Mothur (version 1.30) software was used to calculate alpha diversity analysis. One-way ANOVA was used to analyze the alpha diversity of the differences between groups. Non-metric multidimensional scaling analysis (NMDS) was used for beta diversity analysis. The non-parametric Kruskal-Wallis (KW) rank sum test was used for LDA effect size (LEfSe) analysis to detect the differences in species richness between different groups and obtain species with significant differences. The Wilcoxon rank sum test was used to evaluate the consistency of the differences in species in the previous step among different groups, and finally linear discriminant analysis (LDA) was used to estimate the effect of these differential species on the differences between groups.

[0043] 1.2.7 Data processing and analysis

[0044] The raw mass spectrometry data were peak identified, extracted, and area normalized using Markview 1.3.1 (AB Sciex) software (29). Multivariate statistical analysis of the preprocessed data was then performed using SIMCA 14.1 (Umetrics, Umea, Sweden), and the accuracy and predictive ability of the model were evaluated using the R2Y and Q2 parameters. Variables were identified as differential metabolites when they met the criteria of VIP>1, P<0.05, FC>1.2, or FC<0.8 (30). Subsequently, the secondary mass spectra of potential metabolites were screened using Peakview 1.2.1 software, and then identified using HMDB (http: / / www)hmdb.ca / ) and KEGG (http: / / www.kegg.ca / ). Finally, the differential metabolites were imported into MetaboAnalyst 5.0 (http: / / www.metaboanalyst.ca / ) for metabolic pathway enrichment analysis (31). The Spearman rank correlation test was used to analyze the relationship between metabolites and intestinal microorganisms.

[0045] 1.2.8 Statistical analysis

[0046] The data were expressed as mean ± standard deviation and processed using GraphPad Prism 5.01 software. Two-tailed test was used for analysis, and p < 0.05 was considered statistically significant.

[0047] 2. Experimental results

[0048] 2.1 Changes in body weight, fecal water content, and gastrointestinal turnover rate of rats

[0049] In order to evaluate the effects of betel nut and burnt betel nut on the weight gain of constipated rats, the rats were weighed and recorded daily ( Figure 1 A). During the whole experimental period, the body weight of rats in each group gradually increased. However, the body weight gain of rats with loperamide-induced constipation was significantly lower than that of normal rats (p<0.001). Among them, the body weight gain of rats in the constipation model group was significantly lower than that of the normal group (p<0.001), the betel nut treatment group (p<0.001) and the charred betel nut treatment group (p<0.001).

[0050] Dry stool is common in constipation. The water content of stool can determine whether the rat constipation model is successful and the severity of constipation. Increased water content in stool indicates increased intestinal motility, which can relieve constipation symptoms. Figure 1As shown in B, on the 14th day of constipation induction, the fecal water content of rats in the constipation model group decreased (17.01±3.99), which was significantly lower than that in the normal group (24.88±0.68, p<0.05). However, after treatment, the fecal water content of rats in the positive drug group (25.75±2.89), betel nut treatment group (25.09±2.19), and burnt betel nut treatment group (24.39±1.35) was significantly higher than that in the constipation model group (p<0.05).

[0051] Gastrointestinal transit rate directly reflects gastrointestinal motility. Figure 1 In C, the intestinal transit rates of the normal model group, constipation model group, positive drug group, betel nut treatment group and charred betel nut treatment group were 69.74±3.09, 49.75±4.12, 67.98±5.06, 60.41±1.61 and 65.85±5.87, respectively. After treatment with betel nut and charred betel nut, the gastrointestinal transit rate was significantly higher than that of the constipation model group (p<0.05). There was no significant difference in gastrointestinal motility between the betel nut treatment group and the charred betel nut treatment group and the positive drug group, indicating that betel nut and charred betel nut can promote gastrointestinal motility.

[0052] 2.2 Histological evaluation of rat colon tissue

[0053] like Figure 2 As shown in A, the colon structure of rats in the normal group was intact and the mucosal glands were arranged regularly. However, in the constipation model group, the colon muscle layer edema was obvious, the crypts were atrophied, and the mucosal goblet cells decreased, indicating abnormal inflammatory infiltration. After treatment with mosapride, betel nut and charred betel nut, the crypt structure of the rat colon was restored, the mucosal goblet cells increased, and the muscle layer edema and abnormal inflammatory infiltration decreased. Histological observations showed that betel nut and charred betel nut can effectively improve the intestinal barrier damage of rats caused by loperamide.

[0054] 2.3 Effects of betel nut and burnt betel nut on related gastrointestinal hormone levels

[0055] The results are as follows Figure 2 As shown in BE, the levels of MTL, SP, VIP and ACH in the serum of constipated rats were significantly decreased. After treatment with mosapride, betel nut and charred betel nut, the levels of MTL, SP, VIP and ACH in the serum of rats were significantly increased.

[0056] 2.4 Effects of betel nut and burnt betel nut on intestinal flora diversity

[0057] In order to study the effects of betel nut and burnt betel nut on the intestinal flora of constipated rats, 16S rRNA gene sequencing analysis was used to compare the changes in the intestinal flora of five groups of rats. With the increase in the number of sequencing, the rarefaction curve of the Shannon index gradually stabilized, and the coverage index exceeded 0.9937 ( Figure 3AB), indicating that the sequencing data is sufficient and worthy of further study. The total number of ASVs and the number of different ASVs in each group can be intuitively represented using Venn diagrams, so we used Venn diagrams to analyze the number of ASVs in the samples. There were 117 ASVs in each group, and the betel nut treatment group and the burnt betel nut treatment group had 6 and 10 ASVs, respectively, which was different from the normal group, the constipation model group, and the positive drug group ( Figure 3 C).

[0058] Alpha diversity analysis was performed to determine the species richness and diversity of the gut microbiota. The ACE index and Chao index reflect the species richness of the individual sample community. ACE was used to estimate the index number of ASVs in the community, and the Chao value increased with the increase in the number of ASVs in the sample. The Simpson index reflects the diversity of microorganisms in the sample. The larger the Simpson index value, the lower the community diversity. Sbos represents the actual number of ASVs observed. Figure 3 As shown in DG, the ACE and Chao indices of the constipation model group were significantly lower than those of the normal group (p<0.05). The ACE and Chao indices of the charred betel nut group were significantly higher than those of the constipation model group, indicating that charred betel nut can alleviate the decrease in species richness caused by loperamide-induced constipation in rats. The Simpson index of rats with loperamide-induced constipation was significantly higher than that of normal rats (p<0.05). However, after charred betel nut treatment, the Simpson index showed a significant reversal trend, indicating that charred betel nut increased community diversity. Compared with the normal group, the Sobs index of the constipation model group was significantly decreased (p<0.05). Compared with the constipation model group, the Sobs index of the charred betel nut treatment group was significantly increased. In the Beta diversity analysis, the inter-group differences were obtained by NMDS analysis, and the inter-group differences were reflected in the multidimensional space in the form of points. Figure 3 In H, there was a significant spatial distance between the constipation model group and the normal group, indicating that loperamide changed the intestinal microbial community of rats. At the same time, the samples of rats treated with mosapride, betel nut and charred betel nut were spatially different from those of rats with constipation, indicating that the treatment with mosapride, betel nut and charred betel nut changed the intestinal flora structure of constipated rats.

[0059] 2.5 Effects of betel nut and burnt betel nut on the relative abundance of intestinal flora

[0060] The community structure of the intestinal flora was studied at the phylum level. Figure 4As shown in A, the intestinal microbiota of rats is mainly composed of Firmicutes, Actinobacteriota, Bacteroidota, Patescibacteria, Desulfobacterota and Proteobacteria. Among them, Firmicutes and Bacteroidota are the dominant phyla in healthy rats. Figure 4 (BD) It can be seen that the F / B value of the normal group was 5.29, and the F / B value was significantly increased to 315.46 after loperamide induction. After treatment, the F / B values ​​of the normal group (F / B = 36.35), the betel nut treatment group (F / B = 45.21), and the charred betel nut treatment group (F / B = 44.74) were significantly reduced (p < 0.05). Constipation caused by loperamide led to a decrease in the relative abundance of Bacteroidota, while the normal group, betel nut treatment group, and charred betel nut treatment group significantly reversed this trend. Compared with the normal group, loperamide increased the relative abundance of Proteobacteria, while the positive drug group, betel nut treatment group, and charred betel nut treatment group significantly reduced the relative abundance of Proteobacteria.

[0061] Heat map analysis of the gut microbiota of the five groups of rats was performed at the genus level ( Figure 5 A). Loperamide induced significant changes in the relative abundance of gut microbiota, which were reversed after betel nut and charred betel nut treatment. Betel nut treatment reversed the relative abundance imbalance of g_Bacillus, g_UCG-005, g_norank_f__Muribaculaceae, g_Candidatus_Saccharimonas, and g_Ruminococcus caused by constipation ( Figure 5 B). On the other hand, charred betel nut regulated the constipation-induced changes in the relative abundance of g_Lactobacillus, g_Bacillus, g_norank_f__Muribaculaceae, g_Ruminococcus, g_unclassified_f__Oscillospiraceae, and g_unclassified_f__Prevotellaceae ( Figure 5 C). These findings suggest that loperamide-induced constipation disrupts the abundance of intestinal flora at the genus level, and betel nut and charred betel nut can effectively improve the imbalance of intestinal flora by regulating the microbiota, thereby achieving the effect of treating constipation.

[0062] Loperamide is widely considered to be one of the drugs most commonly used to study constipation models. Loperamide inhibits the pressure-induced activity of the longitudinal and circular muscles of the colon, reduces propulsive activity and increases non-propulsive activity, thereby extending the time of intestinal transit. Normal intestinal use of loperamide can lead to intestinal motility disorders, thereby causing constipation. The present invention uses loperamide as a model for screening anti-constipation.

[0063] There is a close relationship between intestinal microbiota and constipation. Alpha diversity analysis showed that betel nut could alleviate the decrease in species abundance induced by loperamide and increase community diversity. In addition, changes in the structure of intestinal microbial communities in constipated rats were observed in the NMDS plot of beta diversity analysis, indicating that betel nut and charred betel nut changed the structure of intestinal microbial communities in constipated rats to varying degrees. At the genus level, the abundance of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae, and g_unclassified_f__Prevotellaceae in the intestinal flora of constipated rats decreased, while the abundance of g_Bacillus, g_UCG-005, and g_Lactobacillus increased, which were restored to varying degrees in the betel nut treatment group and the charred betel nut treatment group, respectively. The structure of the intestinal microbiota of rats was destroyed by loperamide induction, but this destruction was alleviated by betel nut and charred betel nut. Therefore, these microorganisms can be used as markers for subsequent screening of anti-constipation drugs.

[0064] The above-mentioned specific implementation methods are used to explain the present invention and are only preferred embodiments of the present invention, rather than limiting the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A microbial marker for screening anti-constipation drugs, characterized in that: The microbial marker is one of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae, g_unclassified_f__Prevotellaceae, g_Bacillus, g_UCG-005, g_Lactobacillus, or a combination thereof.

2. A microbial marker for screening anti-constipation drugs according to claim 1, characterized in that: The microbial markers consisted of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, and g_unclassified_f__Oscillospiraceae.

3. A microbial marker for screening anti-constipation drugs according to claim 1, characterized in that: Microbial markers consisted of g_unclassified_f__Prevotellaceae, g_Bacillus, g_UCG-005, and g_Lactobacillus.

4. A microbial marker for screening anti-constipation drugs, characterized in that: Anti-constipation drugs can increase the abundance of g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae and g_unclassified_f__Prevotellaceae, and decrease the abundance of g_Bacillus, g_UCG-005 and g_Lactobacillus.

5. A microbial marker for screening anti-constipation drugs according to claim 4, characterized in that: Areca nut can treat constipation caused by decreased abundance of g_Bacillus and g_UCG-005 and increased abundance of g_norank_f__Muribaculaceae, g_Candidatus_Saccharimonas and g_Ruminococcus.

6. A microbial marker for screening anti-constipation drugs according to claim 4, characterized in that: Burnt betel nut could treat constipation caused by decreased abundance of g_Lactobacillus and increased abundance of g_Bacillus, g_norank_f__Muribaculaceae, g_Ruminococcus, g_unclassified_f__Oscillospiraceae, and g_unclassified_f__Prevotellaceae.

7. Application of the microorganisms g_norank_f_Muribaculaceae, g_Ruminococcus, g_Candidatus_Saccharimonas, g_unclassified_f__Oscillospiraceae, g_unclassified_f__Prevotellaceae, g_Bacillus, g_UCG-005, and g_Lactobacillus as microbial markers for screening anti-constipation drugs.

Images