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Journal of Traditional and Complementary Medicine logoLink to Journal of Traditional and Complementary Medicine
. 2024 Nov 20;15(5):559–572. doi: 10.1016/j.jtcme.2024.11.011

Aurantii fructus immaturus (Rutaceae) flavonoid ameliorated constipation by regulating colonic microbiota and miRNA/mRNA network

Yong Wen a,1, Yu Zhan b,c,1, Li-juan Du d,e, Jun Li a, Xu-long Shen f, Bin He a, Tai-yu Chen g, Xue-gui Tang g,⁎
PMCID: PMC12447141  PMID: 40979483

Abstract

Aurantii fructus immaturus flavonoid (AFIF) is the main constituent of Aurantii fructus immaturus (Rutaceae) (AFI), and is effective against constipation. This study explores the mechanism of AFIF against antibiotics-induced constipation (AC) in mice. Forty six-week-old female C57BL/6 mice were randomly divided into 4 groups (n = 10): control, control + AFIF, model, and model + AFIF groups. The AC model was established by antibiotics mixture for 8 days. Mice were gavaged daily with AFIF (0.1 mL/10 g, 3 g/mL) for 2 weeks. Hematoxylin and eosin (H&E) staining and periodic acid-Schiff (PAS) staining were used for histological analysis. The colonic microbiota was analyzed by 16sRNA sequencing. Transcriptome sequencing was used to detect miRNA and mRNA expression profiles. The results showed that AFIF treatment improved constipation in AC mice: increased fecal number, fecal wet weight, fecal water content, and intestinal propulsion rate; decreased average weight of individual feces. AFIF improved the colonic pathological injury and increased acetylcholine (ACH), gastrin (GAS), motilin (MTL), substance P (SP), and vasoactive intestinal peptide (VIP) levels. Moreover, AFIF might improve AC by regulating colonic microbiota and a “miRNA-mRNA” regulatory network related to cell junction and neuroactive function. This study also found the colonic microbiota at the genus level was connected to the expressions and target mRNA expressions (including Ccdc85b, Dlgap2, Elavl4, and Shisa6) of mmu-miR-5100 and mmu-miR-18b-5p. In conclusions, AFIF could improve AC via regulating colonic microbiota and a “miRNA-mRNA” regulatory network, which provide a theoretical basis for expanding its clinical application.

Keywords: Anti-constipation effects, Gut microbiota, miRNA-mRNA regulatory network

Graphical abstract

Image 1

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Abbreviations

Glucagon-like peptide-1 GLP-1
Fecal microbiota transplantation FMT
Aurantii fructus immaturus AFI
Aurantii fructus immaturus flavonoid AFIF
Antibiotics-induced constipation AC
Hematoxylin-eosin H&E
Acetylcholine ACH
Gastrin GAS
Motilin MTL
Substance P SP
Vasoactive intestinal peptide VIP
Horseradish peroxidase HRP
Differential expression DE
Gene Ontology GO
Kyoto Encyclopedia of Genes and Genomes KEGG
MicroRNA miRNA
Cell adhesion molecules CAMs
Short-chain fatty acids SCFAs
5-Hydroxytryptamine 5-HT
Enteric nervous system ENS
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1. Introduction

Constipation is a globally chronic gastrointestinal condition with a high prevalence and high recurrence rate, particularly in children and old people.1, 2, 3, 4, 5 Constipation is mainly characterized by irregular defecation, and difficulty in defecation,6,7 which becomes a common and serious problem affecting life-quality of patients.7, 8, 9 At present, gut sensory-motor function and biomechanical properties are suggested to be strongly implicated in the pathogenesis of constipation.10, 11, 12 However, the pathophysiological mechanisms underlying constipation remain to further be explored.

Many studies have reported that gut microbiota imbalance plays an important role in the pathogenesis of chronic constipation.7,13,14 The gut microbiota is established early in life, remains relatively stable thereafter, and is subject to shaping by environmental and host factors (e.g., age, diet, lifestyle, and medications).15 Interestingly, the gut microbiota dyscregulation can lead to disruption of intestinal epithelial barrier integrity, sensitivity, motility abnormalities, and emotional disorders.16,17 Some studies showed that gut microbiota metabolites are able to stimulate the production of several neuropeptides, including neuropeptide Y, peptide YY, glucagon-like peptide-1 (GLP-1), cholecystokinin, and substance P, to modulate gut motor and sensory functions.18, 19, 20 Therefore, pathogenesis of the gut microbiota could be an important target in constipation treatment.

In recent years, antibiotics use, not only causes drug-resistant bacteria but also increases the incidence of gastrointestinal diseases, interferes with intestinal homeostasis, and disrupts intestinal barrier integrity.21,22 A number of clinical disorders are found to be associated with disturbances in the gut microbiota due to antibiotic intake.23 A recent study showed that antibiotic use is highly related to constipation.24 Some animal models showed that the use of some antibiotic mixture could cause disturbances in the gut microbiota and constipation.25 Therefore, antibiotic-induced constipation should receive widespread attention.

At present, prebiotics, probiotics, postbiotics, fecal microbiota transplantation (FMT), and magnesium-containing cathartics are suggested to use worldwide for treating chronic constipation.26,27 In recent years, many traditional Chinese medicines were reported to have good improvement effects on constipation.7,28, 29, 30, 31 Aurantii fructus immaturus (AFI), also called Zhishi in Chinese, is the dried young fruit of Citrus aurantium L. (Rutaceae) and its cultivated varieties or Citrus sinensis Osbeck (Rutaceae), which is a Chinese herbal medicine commonly used in clinical practice to improve gastrointestinal motility, treat dyspepsia, and relieve constipation.32, 33, 34 Aurantii fructus immaturus flavonoid (AFIF) is the main constituent of AFI. In recent years, research has found that AFIF contains active ingredients such as naringin and neohesperidin, which have the ability to improve gastrointestinal motility disorders and functional indigestion.35,36 However, the mechanism of AFIF in the treatment of constipation induced by antibiotics is unclear.

Thus, this study aimed to investigate the effect and mechanism of AFIF in the treatment of constipation induced by antibiotics through 16S rRNA gene sequencing and transcriptome sequencing. This study revealed a “colonic microbiota-miRNA-mRNA” regulatory network in the process of AFIF treating the antibiotics-induced constipation (AC) in mice, suggesting that AFIF was an effective agent in the treatment of constipation induced by the antibiotics.

2. Materials and methods

2.1. Animals

Forty six-week-old female C57BL/6 mice were purchased from the Animal Center of West China Medical College, Sichuan University (Chengdu, China) in this study in accordance with the ARRIVE guidelines for reporting animal experiments. All mice were housed in cages with free access to food and water at a controlled temperature (23 ± 2 °C), a humidity of 50 %–55 %, and 12 h light-dark cycle.

2.2. Extraction and identification of AFIF

AFIF extracts based on our previous reports.37 Aurantii Fructus Immaturus (AFI) was purchased from Sichuan Traditional Chinese Medicine Slices Co., Ltd (Sichuan, China), which was identified as the dried young fruit of Citrus aurantium L., family Rutaceae. The dried AFI was crushed and extracted with ethanol to water (80:20, v/v) three times at 60 °C for 2 h. The extract was then filtered and lyophilized to obtain AFIF.

The chemical components of AFIF were identified using ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS). For sample pre-treatment, 30.9 mg of the sample was accurately weighed into a 1.5 mL centrifuge tube. Using a micropipette, accurately transfer 300 μL of water into the same 1.5 mL centrifuge tube. Shake the tube vigorously at 12,000 rpm for 10 min at a temperature of 4 °C using a low-temperature high-speed centrifuge machine (Microfuge 22R Centrifuge, Beckman Coulter, USA). After centrifugation, collect the supernatant for further analysis. Liquid chromatography conditions: The analysis of components was performed using a capillary high-performance liquid chromatograph (Vanquish, Thermo Fisher Scientific, USA). The chromatographic column used was Waters HSS T3 (1.7 μm × 2.1 mm × 100 mm). The column temperature was maintained at 40 °C. The mobile phase consisted of 0.1 % formic acid (98.0 %, mass spectrometry grade, Sigma-Aldrich) as mobile phase A and 100 % methanol (mass spectrometry grade, Fisher Scientific) as mobile phase B. The flow rate was 0.3 mL/min. Each component was analyzed for a total time of 16.0 min. The gradient elution program used was as follows: 0–1.0 min, 2 % B; 1.0–5.5 min, 2 %–100 % B; 5.5–14.0 min, 100 % B; 14.0–14.1 min, 100 %–2 % B; 14.1–16.0 min, 2 % B. Mass spectrometry conditions: The detection of analyzes was carried out using a Q Exactive™ Plus Hybrid Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Fisher Scientific, USA) operating in both positive and negative ion mode with electrospray ionization (ESI). The first-level mass spectrometric parameters were set as follows: resolution of 70,000, AGC (automatic gain control) target of 1e6, maximum ion trap (IT) time of 50 ms, and scan range from 150 to 1500 m/z. The second-level mass spectrometric parameters were set as follows: resolution of 17,500, AGC target of 1e5, maximum IT time of 50 ms, top N of 10, and stepped normalized collision energy (NCE) of 10, 30, and 55. The wiff files obtained from mass spectrometry were preprocessed using the MS-DIAL 4.70 software. This preprocessing involved peak extraction, denoising, deconvolution, peak alignment, and exporting the data as a CSV format three-dimensional data matrix (raw data matrix). The extracted peak information was then compared with three databases, namely MassBank, Respect, and GNPS. The three-dimensional matrix contained information such as sample information, retention time, mass-to-charge ratio, and the intensity of the mass spectrometry response (peak area).

2.3. Experimental design

We established a mouse model of intestinal flora disorder-type constipation according to our previous studies.37 The sample size was justified by GPower software (version 3.1.9.7, Franz Faul, Christian-Albrechts-Universität Kiel, Kiel, Germany). To calculate the sample size, we used one-way ANOVA with 4 experimental groups, with effect size f = 0.75, α err prob = 0.05, and power (1-β err prob) = 0.80. The total sample size was 24 animals, with 6 mice in each group. Excluding the requirement of three mice per group for measurement of intestinal transit rate and to compensate for subsequent data loss due to death or treatment reasons, n = 10 was chosen for each group. The mice were randomly divided into 4 groups (n = 10) as follows: control, control + AFIF, model, and model + AFIF groups. The antibiotics mixture contained clarithromycin, cephalexin, and amoxicillin, mixed in the ratio of 2:2:1.38,39 The concentration of high and low dose antibiotics mixture were 125 mg/mL and 62.5 mg/mL, respectively. The mice in the model group and model + AFIF group received a low dose antibiotics mixture by intragastric administration (0.1 mL/10 g) for 4 days and then received a high dose antibiotics mixture for 4 days. Meanwhile, the mice in the control group and control + AFIF group received saline by intragastric administration for 8 days. Mice in the AFIF-treated groups were gavaged with AFIF (0.1 mL/10 g, content 3 g/mL) daily for 2 weeks. In the model group and model + AFIF group, mice also received the one-dose antibiotics mixture by intragastric administration (0.1 mL/10 g, one concentration a day) for 2 weeks. The control group and control + AFIF group were administered an equal volume of saline. The body weight was recorded. The 24 h feces were collected after the last treatment for further analysis. To alleviate the mice pain, the symptoms of constipation in mice were determined by observing their stool characteristics (fecal number, fecal wet weight, fecal water content, and the intestinal propulsion rate). After feces were collected, six mice in each group were immediately anesthetized using an overdose of isoflurane and then sacrificed by cervical dislocation to harvest serum, colon tissue, and contents for further analysis. These experiments and data analysis were conducted in 2021–2023.

2.4. 24h defecation and fecal water content

At the end of the last treatment, mice were housed individually in metabolic cages to collect feces once an hour for 24 h, and the feces number and weight were recorded. Fecal water content was calculated after drying the feces in a 60 °C desiccator for 12 h according to the equation: (wet weight - dry weight)/wet weight × 100 %.

2.5. Measurement of intestinal transit rate

To evaluate the intestinal motility, the mice (n = 3) in each group were gavaged with a charcoal meal (20 mL/kg, 3 % suspension of activated charcoal in 0.5 % aqueous methylcellulose) after 30 min at the end of the last treatment. After 30 min, the mice were euthanized by cervical dislocation. The intestinal transit rate was calculated as follows: the traveled distance of activated charcoal in the intestine (cm)/full length of the small intestine (cm) × 100 %.

2.6. Histological analysis

Colon tissues were initially fixed in 4 % paraformaldehyde, embedded into paraffin, and sectioned into 5 μm slides. After deparaffinization and rehydration, hematoxylin-eosin (H&E) staining was performed to measure histological morphology. The periodic acid-Schiff (PAS) staining was used to measure the colonic mucus thickness.

2.7. ELISA

The production of acetylcholine (ACH), gastrin (GAS), motilin (MTL), substance P (SP), and vasoactive intestinal peptide (VIP) in serum was measured using mouse ELISA kits following the manufacturer's instruction. In brief, the serum was centrifuged at 5000×g for 10 min, and the supernatant was taken to be measured. Specific antibodies (ACH, GAS, MTL, SP, and VIP) were pre-coated on high-affinity enzyme labeling plates to form solid-phase antibodies, then horseradish peroxidase (HRP) -labeled antibodies were added and incubated at 37 °C for 40 min. After washing, the substrate was added for color development. The depth of color was proportional to the concentration of the sample. The absorbance (OD value) was measured at a wavelength of 450 nm by a microplate reader (Spectramax plus384, Molecular Devices, USA), and the sample concentration was calculated. The acetylcholine assay kit (Cat. No. A105-2-1), gastrin assay kit (Cat. No. H239), motilin assay kit (Cat. No. H182-1-2), substance P assay kit (Cat. No. H218-1-2), and vasoactive intestinal peptide assay kit (Cat. No. H219) were provided by Nanjing Jiancheng Bioengineering Institute (Nanjing, China).

2.8. 16S rDNA sequencing and analysis

The microbial DNA from colon content samples was extracted by the ZR Fecal DNA Extraction Kit (Zymo Research, CA, USA) and the V3-V4 region was amplified using the universal primers (319F: 5′-ACTCCTACGGGAGGCAGCAG-3′; 806R: 3′-ACTCCTACGGGAGGCAGCAG-5′). The samples were sent to the Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China) for pooling and paired-end sequencing on an Illumina MiSeq (Illumina). The Quantitative Insights Into Microbial Ecology (QIIME, v1.8.0) pipeline and the Quantitative Insights into Microbial Ecology (QIIME) software package, version 1.9.1 were used to process the sequencing data and microbial composition analysis. Clustered heat map of the relative abundances was analyzed by R software and heatmap package; Jaccard based Clustering tree analysis was analyzed by R software and stat and ape package. KEGG pathway was analyzed by KEGG Pathway Database (http://www.genome.jp/kegg/pathway.html).

2.9. Transcriptome resequencing and analysis

Collected colon tissue, after RNA extraction, purification, and library construction, the libraries were sequenced by next-generation Sequencing (NGS) based on the Illumina Sequencing platform. DESeq was used for the analysis of differential expression (DE) gene, and volcanic map and venn map were analyzed by ggplots2 package of R software. We used miRanda package to predict the target genes of DE miRNA sequences with the 3 ‘UTR’ sequence of mRNA of the species as the target sequence.40 GO term was analyzed by Gene Ontology (http://geneontology.org/). KEGG pathway was analyzed by the Kyoto Encyclopedia of Genes and Genomes (http://www.kegg.jp/).

2.10. Statistical analysis

All data were shown as mean ± SD. The GraphPad Prism 7 (GraphPad software, USA) and SPSS 19.0 (IBM SPSS software, USA) were used to analyze the data. One-way analysis of variance (ANOVA) was used to compare the difference among groups. The Pearson correlation test was analyzed by SPSS 19.0. p < 0.05 were considered statistically significant.

3. Results

3.1. Composition of AFIF

The chemical components of AFIF were identified using UHPLC-MS/MS. The positive ion chromatogram and negative ion chromatogram can be found in Fig. S1. The results show that, in positive ion mode, AFIF contains important flavonoids and flavonoid glycosides such as neohesperidin dihydrochalcone, daidzein-8-C-glucoside, naringenin-7-O-beta-D-glucoside, isorhamnetin 3-O-neohesperoside, apigenin-8-C-glucoside, naringin, narirutin, naringenin-7-O-glucoside, hesperidin, rhoifolin, isorhamnetin-3-O-rutinoside, naringin dihydrochalcone, hesperidin methyl chalcone, isorhamnetin-3-O-beta-D-glucoside, kaempferol 3-alpha-L-arabinopyranoside, isosinensetin, tangeritin, 6-demethoxytangeretin, hexamethylquercetagetin, camellikaempferoside B, 3,5,6,7,8,3′,4′-heptamethoxyflavone, liquiritin, 5-demethylnobiletin, gardenin B, and nobiletin (Figure S1 A). In negative ion mode, AFIF contains important flavonoids and flavonoid glycosides such as isorhamnetin 3-galactoside, luteolin 4′-O-glucoside, apigenin-6-C-glucoside-7-O-glucoside, quercetin-3-O-pentosyl(1–2)pentoside, puerarin, vitexin, prunin, hesperetin, hesperetin-7-O-neohesperidoside, isosakuranetin-7-O-neohesperidoside, haploside C, tiliroside, and baicalin (Fig. S1B). Detailed ion information was listed in Table S1.

3.2. AFIF improved the constipation and intestinal motility of the AC mice

As shown in Fig. 1A–D, on day 7 and day 14, the antibiotics significantly decreased the fecal number, fecal wet weight, and fecal water content of normal mice, increased the average weight of individual feces of normal mice, indicating the AC mice were successfully established. With AFIF treatment, the fecal number and fecal wet weight increased, and the average weight of individual feces decreased on day 7 and day 14. Moreover, AFIF had no obvious effects on the fecal water content of the AC mice on day 7. In addition, on day 14, compared to model mice, the fecal water content was increased in the model + AFIF group. Other than that, no other changes were found in the normal mice treated with AFIF.

Fig. 1.

Fig. 1

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AFIF improved constipation and bowel movement of the AC mice. (A) Fecal number; (B) Fecal wet weight (g); (C) Fecal water content (%); (D) Average weight of individual feces (g). (E) The intestinal propulsion rate; (F) The acetylcholine (ACH) level in the serum; (G) The gastrin (GAS) level in the serum. (H) The motilin (MTL) level in the serum; (I) The substance P (SP) level in the serum; (J) The vasoactive intestinal peptide (VIP) level in the serum. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001.

Then the effects of AFIF on intestinal motility were investigated. As shown in Fig. 1E, antibiotics significantly decreased the intestinal propulsion rate of normal mice, and with AFIF treatment, the intestinal propulsion rate was increased. Interestingly, AFIF decreased the intestinal propulsion rate in normal mice. Next, the effects of AFIF on bowel motion were investigated. As shown in Fig. 1F–J, the levels of GAS, ACH, MTL, SP, and VIP were significantly decreased in the AC mice compared to normal mice and increased with AFIF treatment. Moreover, AFIF had no significant effects on the levels of GAS, ACH, MTL, SP, and VIP in the serum of normal mice. These data suggested that AFIF could improve the intestinal motility of the AC mice.

3.3. AFIF improved the colonic pathological injury in the AC mice

As shown in Fig. 2, the H&E results and PAS results showed that the mixed antibiotics caused the necrosis of the mucosal layer, degeneration, and necrosis of the intestinal glands, decreased goblet cell number, and decreased mucus layer thickness in the colon of the normal mice. With AFIF treatment, these changes were inhibited in the AC mice. Moreover, AFIF didn't have obvious effects on the colonic pathological injury in the normal mice.

Fig. 2.

Fig. 2

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The colonic pathological injury was detected by H&E staining and PAS staining. Light green arrow: Necrosis of the mucosal layer of the colon; Dark green arrow: Degeneration and necrosis of the intestinal glands; Red arrow: Goblet cells decreased; Yellow arrow: colonic mucus layer. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001.

3.4. AFIF altered the composition and structure of the dominant genus of the colonic microbiota in the AC mice

16S rRNA results showed that the composition and structure of the top 20 genus among the four groups were significantly different (Fig. 3A). Compared to the control group and model group, the mixed antibiotics reduced the number of OTU/ASV in the control + AFIF group and model + AFIF group, suggesting the diversity and number of the colonic microbiota were significantly decreased (Fig. 3B). The clustered heat map suggested that the major dominant genus in the four groups were significantly different (Fig. 3C). The Jaccard based Clustering tree showed that the model + AFIF group had closer clustering distances to the control group compared to the model group (Fig. 3D). Moreover, there was a different clustering direction in the control group between the control + AFIF group and the model + AFIF group.

Fig. 3.

Fig. 3

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AFIF altered the composition and structure of the dominant genus of the colonic microbiota in the AC mice. (A) Bar plots showing the relative abundances of the dominant genus; (B) Wayne diagram of OTU/ASV; (C) Clustered heat map of the relative abundances of the different genus; (D) Jaccard based Clustering tree, Samples are clustered according to the similarity between samples. The shorter the branch length between samples, the more similar the two samples are. CTL: Control group, ABX: Model group, FAF: Control + AFIF group, ABX_FAF: Model + AFIF group.

3.5. AFIF altered the function of the colonic microbiota in the AC mice

LEfSe analysis showed that the robust differential species in the respective group were different (Fig. 4A). Then the changes in colonic microbiota function were examined. As shown in Fig. 4B–D, it found that the three comparisons didn't have the same changed KEGG pathway. Those data further demonstrated that AFIF altered the function of the colonic microbiota in the AC mice, but instead of restoring the colonic microbiota function, AFIF changes it to the other direction.

Fig. 4.

Fig. 4

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AFIF altered the function of the colonic microbiota in the AC mice. (A) LEfSe analysis; (B) Different KEGG pathways analysis between CTL group and ABX group; (C) Different KEGG pathways analysis between CTL group and FAF group; Different KEGG pathways analysis between ABX group and ABX_FAF group. CTL: Control group, ABX: Model group, FAF: Control + AFIF group, ABX_FAF: Model + AFIF group.

3.6. AFIF regulated the colonic expressions of mmu-miR-5100 and mmu-miR-18b-5p in the AC mice

Firstly, the miRNA sequencing of the colonic tissue was performed. As shown in Fig. 5A–C, the volcanic maps of DE miRNAs showed the numbers of up- and down-regulated DE miRNAs, including the model group vs. the control group (9 down- and 1 up-regulated), the model + AFIF group vs. the model group (4 down- and 5 up-regulated), and the control + AFIF group vs. the control group (13 down- and 8 up-regulated). The Venn diagram showed the common DE miRNAs were mmu-miR-5100 and mmu-miR-18b-5p (Fig. 5D). As shown in Fig. 5E and F, mmu-miR-18b-5p was decreased in the model group compared to the control group and increased in the model + AFIF group compared to the model group, while mmu-miR-5100 showed the opposite trend, suggesting that AFIF might improve the CA by regulating mmu-miR-5100 and mmu-miR-18b-5p levels in the colon. Therefore, the target mRNA genes of mmu-miR-5100 and mmu-miR-18b-5p were predicted, as shown in Fig. 5G and Table 1, 1274 mRNA genes were predicted to be the target mRNA genes of mmu-miR-18b-5p and 243 mRNA genes were predicted to be the target mRNA genes of mmu-miR-5100.

Fig. 5.

Fig. 5

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The results of miRNA sequencing in the colonic tissue. (A) Volcanic map showed DE miRNAs between CTL group and ABX group; (B) Volcanic map showed DE miRNAs between ABX group and ABX_FAF group; (C) Volcanic map showed DE miRNAs between CTL group and CTL_FAF group. D. Venn diagram showed the common and specific DE miRNAs in the three comparisons. (E) The CPM of mmu-miR-18b-5p in the colon; (F) the CPM of mmu-miR-5100 in the colon; (G) The number of predicted target genes of mmu-miR-18b-5p and mmu-miR-5100. CTL: Control group, ABX: Model group, FAF: Control + AFIF group, ABX_FAF: Model + AFIF group. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001.

Table 1.

The target mRNA genes of mmu-miR-5100 (243 mRNA genes) and mmu-miR-18b-5p (1274 mRNA genes).

MiRNA Target mRNA genes
mmu-miR-5100 Sema4f/Efnb2/Ube2c/Foxm1/Rin2/Kmt2a/Ndufb2/Syce2/Pknox1/Cyp27b1/Sulf2/Fzd3/Nav1/Ankrd52/Tbp/Abca1/Camk1g/Aldoc/Ikzf3/E2f2/Sec14l4/Lrp11/Grm1/Reep3/Lgr5/Ptprb/Gabrp/D10Wsu102e/Kremen1/Havcr2/Alox8/Sel1l/Pcnx/Slc25a48/Golm1/Galnt15/Nisch/Ebpl/Dcp1a/Dnajc15/Mcpt8/Ankrd33b/Rad1/Mtdh/Fbxo32/Nell2/Hoxc5/Zbtb20/Slc4a8/Hes7/Thbs2/Paqr4/Efhb/Dctn4/Fen1/Klc2/Slc29a2/Paox/Chid1/Ncl/Cacna1s/Frmd4a/Sephs1/Rgs5/Rsu1/Elf5/Frmd5/Lrp4/Pdyn/Cyp7a1/Elavl4/Dyrk2/Tfap2c/Slc2a1/Lurap1/Gnat3/Extl1/Zfp593/Lhx5/Wdr54/Mrpl19/Tmem43/Sspn/Fchsd2/Parva/Trim30a/Lyrm1/Sash3/Syp/Gla/Cyp7a1/Cul4a/Adcy7/Crispld2/Mre11a/Aars/Egln1/Cadm1/Mobp/Syde1/Slitrk5/Znrf1/Exoc6b/Ogt/Ccdc15/Tkfc/Rps29/Ttc22/Foxn2/Ids/Ibtk/Dock4/Serbp1/Pcdh8/Commd2/Adamts2/Fam135b/Clic4/Lsm1/Prkci/Cdk19/Susd1/Ptpn3/Prdm16/Tmem120a/Lats1/Rgl3/Lrp1/Rc3h1/Rcc2/Fam117b/Wnk3/Pde3a/Fam169a/Ptpre/Mfsd9/Rapgef5/Cmklr1/Slc35e2/Lgalsl/Tex13b/Fam199x/Kcna4/Gpr153/Tmem145/Olfr323/Pcdhb14/Vwc2l/Ccdc9b/Gm20715/Ifit2/Onecut2/Zfp84/Ermp1/Cxxc5/Vmn1r68/Dlgap2/Fbxo30/Slc38a9/Tob2/C1ql3/Plekhf2/Hmx2/Pla2g4e/Fzd2/Fam78a/Tatdn1/Plcb1/Olfr513/Pcmtd1/Plekhm3/Spryd4/Irf2bp2/Rap2a/Gpr17/Ezr/Atp6v1a/Brsk2/Exoc6/Pgbd1/Rtl6/Tug1/St6galnac2/Znrf2/Kcnc1/Or13a25/Pwwp2b/Caln1/Bloc1s4/Maml3/Hs6st2/Shisal1/Sema3e/Or9s14/Rnf217/Paqr9/Kndc1/Dazap1/Or1p1/Adamtsl3/Gm13043/Pramel20/Or10ag54/Gm13279/Klhl9/Lratd2/Lyrm9/Mob3b/Arfgef2/Mafb/Hmga1b/Sf3b5/Pramel28/Pramel16/AU022252/Zfp456/Nhsl2/3110001I22Rik/Or52d13/Rps6kc1/Vmn2r75/Or2bd2/Gm13279/Tmem121b/Or8g34/Gm13287/Gm13043/Or7e168/Vmn2r53/Gm13043/Cmtm4/Or6c5c/Gm13287/Or1j15/Ifnz/Gm13283/Dchs2/Gm42878/Exosc6/Olfr839-ps1/Gm49355/Vmn1r218/Vmn1r76
mmu-miR-18b-5p 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; Or4x15/Gm44503/Vmn2r79/Vmn2r77/Vmn2r9/Vmn1r185/Vmn2r59/Pramel58/Gm2381/Gm20683/Dpm1/Or6c1/Or8g23/Gm21969/Or1j11/Gm21244/Gm10310/Or52a20/Or12d15/LOC100041806/Or8g26/Pou3f2/Or2y11/Or8g2/Gm13043/H2al1e/Or7e168/Or6c206/H2al1g/Or5p63/Pramel60/Vmn1r40/Gm13057/Or6c205/Pramel38/Pramel59/Or8b1c/H2al1a/H2al1b/Pramel35/Or4f14d/Or4g16/Or8b48/Vmn1r14/Vmn1r218/Vmn1r81/Vmn1r20/Gm49394/Gm49527/Vmn1r88/Vmn1r55
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3.7. AFIF might improve the AC mice via the colonic “miRNA-mRNA” regulatory networks

Next, the mRNA sequencing of the colonic tissue was performed. As shown in Fig. 6A–C, the volcanic maps showed the numbers of up- and down-regulated DE mRNAs, including the model group vs. control group (135 down- and 954 up-regulated), and the model + AFIF group vs. model group (159 down- and 105 up-regulated), and the control + AFIF group vs. control group (216 down- and 817 up-regulated). As shown in Fig. 6D, the venn diagram showed the common DE mRNAs number between the comparison (model group vs. control group) and the comparison (model + AFIF group vs. model group) was 119. As shown in Fig. 6E, in the 119 common DE mRNAs, 4 mRNAs (Elavl4, Dlgap2, Paqr9, and Kndc1) were the predicted target genes of mmu-miR-5100 and 4 mRNAs (Snhg11, Shisa6, Lsamp, and Ccdc85b) were the predicted target genes of mmu-miR-18b-5p. As shown in Fig. 6F, compared to the control group, Elavl4, Dlgap2, Paqr9, Kndc1, Snhg11, Shisa6, Lsamp, and Ccdc85b were increased in the model group; compared the model group, Elavl4, Dlgap2, Paqr9, Kndc1, Snhg11, Shisa6, and Lsamp were decreased in the model + AFIF group and Ccdc85b were increased in the model + AFIF. These data suggested “miRNA-mRNA” regulatory networks in the AFIF treatment of the AC mice.

Fig. 6.

Fig. 6

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The results of mRNA sequencing in the colonic tissue. (A) Volcanic map showed DE mRNAs between CTL group and ABX group; (B) Volcanic map showed DE mRNAs between ABX group and ABX_FAF group; (C) Volcanic map showed DE mRNAs between CTL group and CTL_FAF group. (D) Venn diagram showed the common and specific DE mRNAs in the three comparisons. (E) The target genes of mmu-miR-18b-5p and mmu-miR-5100 were selected by comparing venn diagram results of mRNA to the miRNA sequencing results. (F) The CPM and read count levels of the selected mRNA in the colon. CTL: Control group, ABX: Model group, FAF: Control + AFIF group, ABX_FAF: Model + AFIF group. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001.

3.8. A relation between the “miRNA-mRNA” regulatory networks and the colonic microbiota in the AC

Spearman correlation analysis was carried out between the robust differential species and the DE genes. As shown in Fig. 7A, p_Actinobacteria, c_Actinobacteria, o_Oceanospirillales, o_Oceanospirillales, o_Bacillales, f_Halomonadaceae, f_Comamonadaceae, f_Staphylococcaceae, g_Ruminococcus, g_Halomonas genus, and g_Staphylococcus were positively correlated to mmu-miR-5100 expression, and c_Bacilli, o_Lactobacillales, f_Porphyromonadaceae, and g_Parabacteroides were negatively correlated to mmu-miR-5100 expression. Moreover, f_Porphyromonadaceae, and g_Parabacteroides were negatively correlated to mmu-miR-18b-5p expression. The colonic microbiota in phylum, class, order, family, or genus levels were found a positive or a negative correlation with the expression of Elavl4, Dlgap2, Paqr9, Kndc1, Snhg11, Shisa6, Lsamp, and Ccdc85b (Fig. 7B–F). These data suggested that there was a relation between the “miRNA-mRNA” regulatory networks and the colonic microbiota in the AC.

Fig. 7.

Fig. 7

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The relationship between the “miRNA-mRNA” regulatory networks and the colonic microbiota in the AC mice. (A) Spearman correlation analysis was carried out between colonic microbiota and the DE miRNA; (B) Spearman correlation analysis was carried out between colonic microbiota in phylum level and the DE mRNA; (C) Spearman correlation analysis was carried out between colonic microbiota in class level and the DE mRNA. (D) Spearman correlation analysis was carried out between colonic microbiota in order level and the DE mRNA. (E) Spearman correlation analysis was carried out between colonic microbiota in family level and the DE mRNA. (F) Spearman correlation analysis was carried out between colonic microbiota in genus level and the DE mRNA. Red color indicated positive correlations and blue color indicated negative correlations. CTL: Control group, ABX: Model group, FAF: Control + AFIF group, ABX_FAF: Model + AFIF group. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.

3.9. AFIF improved the cell junction and neuroactive function in the colon tissue of the AC mice

GO and KEGG analysis were performed to explore the effects of AFIF on the AC mice. As shown in Fig. 8A–D, the cell junction, synapse, and synaptic membrane were the common enriched GO terms between the comparison (model group vs. control group) and the comparison (model + AFIF group vs. model group), and the neuroactive ligand-receptor interaction and cell adhesion molecules (CAMs) were the common enriched KEGG pathways, suggesting that the improvement of AFIF on the gut peristalsis of AC mice was related to the cell junction and neuroactive function.

Fig. 8.

Fig. 8

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The effects of AFIF on the function in the colon tissue. (A) GO term analysis of DE mRNA in the comparison (model group vs. control group); (B) KEGG pathway analysis of DE mRNA in the comparison (model group vs. control group); (C) GO term analysis of DE mRNA in the comparison (model + AFIF group vs. model group); (D) KEGG pathway analysis of DE mRNA in the comparison (model + AFIF group vs. model group).

Next, the DE mRNA enriched in the selected GO terms and KEGG pathway were analyzed. As shown in Fig. 9A, 37 mRNA were the common DEs enriched in the cell junction GO term, including Ccdc85b, Dlgap2, and Elavl4; 34 mRNA were enriched in the synapse GO term, including Dlgap2 and Elavl4; and 17 mRNA were enriched in the synaptic membrane GO term, including Shisa6. As shown in Fig. 9B and 10 mRNA were enriched in the neuroactive ligand-receptor interaction KEGG pathway and 1 mRNA were enriched in the cell adhesion molecules (CAMs) KEGG pathway but didn't find the target mRNA enriched in the two KEGG pathways. Therefore, the results suggested that the target mRNA of mmu-miR-5100 and mmu-miR-18b-5p might mainly improve the cell function to change the KEGG pathways. Finally, a spearman analysis was performed to reveal a possible “colonic microbiota-miRNA-mRNA” regulatory network in the process of AFIF treating the AC mice. As shown in Fig. 9C, the colonic microbiota in the genus level were connected to the expressions and the target mRNA expressions (including Ccdc85b, Dlgap2, Elavl4, and Shisa6) of mmu-miR-5100 and mmu-miR-18b-5p, and then the changed target mRNA expressions (including Ccdc85b, Dlgap2, Elavl4, and Shisa6) of mmu-miR-5100 and mmu-miR-18b-5p affected the cell junction and synapse function to improve cell adhesion and neuroactive function.

Fig. 9.

Fig. 9

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The “colonic microbiota-miRNA-mRNA” regulatory networks. (A) The common enriched DE mRNA in the Go term; (B) The common enriched DE mRNA in KEGG pathway; (C) A spearman analysis of the “colonic microbiota-miRNA-mRNA” regulatory networks. Red color indicated positive correlations and blue color indicated negative correlations.

4. Discussion

Recently, constipation may induce by antibiotics triggers general concerns.23,24,41 Our study proved that the antibiotics mixture induced constipation, which was related to the decrease of colonic microbiota number and the impairment of intestinal motility. AFIF could improve the antibiotics-induced constipation by via regulating colonic microbiota and a “miRNA-mRNA” regulatory network in the colonic tissue, the “miRNA-mRNA” regulatory network was related to cell junction and neuroactive function. Moreover, a relation between the “miRNA-mRNA” regulatory networks and the colonic microbiota was found. Therefore, we thought there might be a “colonic microbiota-miRNA-mRNA” regulatory network in the AC development and treatment. In addition, our result suggested that AFIF was a useful treatment for AC via the “colonic microbiota-miRNA-mRNA” regulatory network.

In constipation animal models, loperamide, diphenoxylate, and montmorillonite could reduce weight, moisture content, feces number and intestinal motility in the mice.42, 43, 44, 45 The study found that the antibiotics mixture decreased the fecal number and fecal wet weight of normal mice, increased the fecal water content and the average weight of individual feces, and reduce the intestinal motility, which agrees with a previous study.46 Interestingly, the antibiotics mixture increased the fecal water content, suggesting the mechanism of antibiotics mixture-induced constipation is different from that of loperamide, diphenoxylate, and montmorillonite-induced constipations. Moreover, AFIF increased the fecal number and fecal wet weight, decreased the average weight of individual feces, and increased the intestinal motility in the AC mice, but had no obvious effects on the fecal water content of the AC mice, suggesting that the mechanism of AFIF treating AC mice might improve the intestinal motility.

Many studies showed that gut microbiota is a target for treating constipation.47, 48, 49, 50 Gut microbiota improve gut motility via regulating gut metabolites including short-chain fatty acids (SCFAs), and 5-hydroxytryptamine (5-HT).51 This study found that the mixed antibiotics reduced the number of OTU/ASV, and altered the composition and structure of the colonic microbiota in the AC mice and normal mice, suggesting the mixed antibiotics changed the composition and function of the colonic microbiota in the AC mice. Moreover, AFIF didn't increase the number of OTU/ASV and didn't restore the original microbiota structure and function in the AC mice, but AFIF made the AC mice have a closer clustering distance to normal mice. Therefore, AFIF couldn't restore the diversity, structure, and function of colonic microbiota in the AC mice, but the effect of colonic microbiota on the colonic tissue in the AC mice needs further study.

Constipation may be regarded as a colonic motility disorder, and slow-transit constipation may be caused by the dysfunction of colonic smooth muscle or neural innervation, resulting in neural colonic motor abnormalities.52 In the mammalian digestive tract, the intrinsic, or enteric, nervous system (ENS) contains about 100 million neurons.53 The synaptic pathways in the gut wall respond to sensory input (e.g., of luminal content) and to modulation via vagal and sacral spinal nerves (S2–4), preganglionic parasympathetic fibers (which are generally excitatory), and sympathetic postganglionic nerves (which originate from the fifth thoracic to a second lumbar section of the spinal cord), and are generally inhibitory to muscle layers by allowing activity of intrinsic inhibitory innervation by enteric nerves (e.g., vasoactive intestinal peptidergic [VIPergic], nitrergic [NOergic]). A study showed that colonic motility is improved by the activation of 5-HT2B receptors on interstitial cells of Cajal in diabetic mice.54 Moreover, dopaminergic neurons appear to facilitate nitrergic neurons via D1-like receptors to stabilize asynchronous contractile activity resulting in the generation of colonic peristalsis.55 This study found that the cell junction and synapse function in the colon tissue of the AC mice were changed, and AFIF improved the cell junction and synapse function in the colon tissue of the AC mice, suggesting the cell junction and neuroactive function were destroy in the AC mice and AFIF recovered the cell junction and neuroactive function in the AC mice. Moreover, this study also found a “miRNA-mRNA” regulatory network which enriched in the cell junction GO term and synapse GO term: mmu-miR-5100 and mmu-miR-18b-5p and their target mRNA. Further, the colonic microbiota in the genus level were connected to the expressions and the target mRNA expressions of mmu-miR-5100 and mmu-miR-18b-5p. Therefore, AFIF might recover the cell junction and neuroactive function in the colonic tissue of AC mice via a “colonic microbiota miRNA-mRNA” regulatory network, and this needs further study. In addition, we plan to use differentially expressed miRNAs to further determine the molecular mechanisms by which AFIF ameliorates antibiotic-induced constipation in mice in future studies. Moreover, the Class 1 innovative Citrus aurantium total flavonoid tablets (Aolanti Weikang tablets) were approved to be listed in October 2023 in China, which are clinically used to treat gastrointestinal motility disorders.56 Our study further revealed the molecular mechanism and potential regulatory network of AFIF in treating constipation. This finding contributes to the understanding that AFI and its flavonoid compounds are effective drugs for treating constipation, providing ample theoretical support for promoting the clinical application and target research of AFIF. However, further studies are needed to investigate the active ingredients, safety and toxicity of AFIF in the treatment of constipation in clinical practice.

5. Conclusions

AFIF could improve the constipation induced by the antibiotics mixture via regulating colonic microbiota and a “miRNA-mRNA” regulatory network in the colonic tissue, the “miRNA-mRNA” regulatory network was related to cell junction and neuroactive function. Our result suggested that AFIF was a useful treatment for AC.

Authors' contributions

YW and YZ: Conceptualization, Investigation, Methodology, Project administration, and Writing – original draft. XGT: Conceptualization, Data curation, Funding acquisition, Supervision, and Writing – review & editing. LJD, JL, XLS, BH, and TYC: Formal Analysis, Software, and Visualization. The experiments in animal models were performed by YW, YZ, LJD, and JL. All authors have read and approved the final version of the manuscript.

Disclosure statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Availability of data and material

The raw data in this study can be found in the links: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA945577.

Ethical approval

All animal studies were conducted following the National Institutes of Health Guide for Laboratory Animal Care and approved by the Ethics Committee of North Sichuan Medical College (approval number: 202182), and all efforts were made to minimize animal suffering and to reduce the number of animals used.

Funding

This work was supported by the National Natural Science Foundation of China (No.82004173) and the National Natural Science Foundation of China (No.82074429), Luzhou Science and Technology Plan Project - Key R&D (2022-SYF-99).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We are grateful the support from the North Sichuan Medical College.

Footnotes

Peer review under responsibility of The Center for Food and Biomolecules, National Taiwan University.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jtcme.2024.11.011.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.xlsx (6.7MB, xlsx)

figs1.

figs1

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References

  • 1.West C.L., Amin J.Y., Farhin S., Stanisz A.M., Mao Y.K., Kunze W.A. Colonic motility and jejunal vagal afferent firing rates are decreased in aged adult male mice and can be restored by an aminosterol. Front Neurosci. 2019;13:955. doi: 10.3389/fnins.2019.00955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Chu J.R., Kang S.Y., Kim S.E., Lee S.J., Lee Y.C., Sung M.K. Prebiotic UG1601 mitigates constipation-related events in association with gut microbiota: a randomized placebo-controlled intervention study. World J Gastroenterol. 2019;25(40):6129–6144. doi: 10.3748/wjg.v25.i40.6129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Yang X.J., Zhang M., Zhu H.M., et al. Epidemiological study: correlation between diet habits and constipation among elderly in Beijing region. World J Gastroenterol. 2016;22(39):8806–8811. doi: 10.3748/wjg.v22.i39.8806. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Tanner S., Chaudhry A., Goraya N., et al. Prevalence and clinical characteristics of dyssynergic defecation and slow transit constipation in patients with chronic constipation. J Clin Med. 2021;10(9):2027. doi: 10.3390/jcm10092027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Iguchi N., Carrasco A., Jr., Xie A.X., Pineda R.H., Malykhina A.P., Wilcox D.T. Functional constipation induces bladder overactivity associated with upregulations of Htr2 and Trpv2 pathways. Sci Rep. 2021;11(1):1149. doi: 10.1038/s41598-020-80794-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Kim J.E., Choi Y.J., Lee S.J., et al. Molecular characterization of constipation disease as novel phenotypes in CRISPR-Cas9-Generated leptin knockout mice with obesity. Int J Mol Sci. 2020;21(24):9464. doi: 10.3390/ijms21249464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Li S., He Y., Zhang H., et al. Formulation of traditional Chinese medicine and its application on intestinal flora of constipated rats. Microb Cell Factories. 2020;19(1):212. doi: 10.1186/s12934-020-01473-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Cao H., Liu X., An Y., et al. Dysbiosis contributes to chronic constipation development via regulation of serotonin transporter in the intestine. Sci Rep. 2017;7(1) doi: 10.1038/s41598-017-10835-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Mori S., Tomita T., Fujimura K., et al. A randomized double-blind placebo-controlled trial on the effect of magnesium oxide in patients with chronic constipation. J Neurogastroenterol Motil. 2019;25(4):563–575. doi: 10.5056/jnm18194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Sha H., Zhao D., Tong X., Gregersen H., Zhao J. Mechanism investigation of the improvement of Chang Run Tong on the colonic remodeling in streptozotocin-induced diabetic rats. J Diabetes Res. 2016;2016 doi: 10.1155/2016/1826281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Kubota M., Ito K., Tomimoto K., et al. Lactobacillus reuteri DSM 17938 and magnesium oxide in children with functional chronic constipation: a double-blind and randomized clinical trial. Nutrients. 2020;12(1):225. doi: 10.3390/nu12010225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Wang Y.Y., Lu R.Y., Shi J., Zhao S., Jiang X., Gu X. CircORC2 is involved in the pathogenesis of slow transit constipation via modulating the signalling of miR-19a and neurotensin/motilin. J Cell Mol Med. 2021;25(8):3754–3764. doi: 10.1111/jcmm.16211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.He Q., Han C., Huang L., et al. Astragaloside IV alleviates mouse slow transit constipation by modulating gut microbiota profile and promoting butyric acid generation. J Cell Mol Med. 2020;24(16):9349–9361. doi: 10.1111/jcmm.15586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tian Y., Zuo L., Guo Q., et al. Potential role of fecal microbiota in patients with constipation. Therap Adv Gastroenterol. 2020;13 doi: 10.1177/1756284820968423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Mamieva Z., Poluektova E., Svistushkin V., et al. Antibiotics, gut microbiota, and irritable bowel syndrome: what are the relations? World J Gastroenterol. 2022;28(12):1204–1219. doi: 10.3748/wjg.v28.i12.1204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Holtmann G.J., Ford A.C., Talley N.J. Pathophysiology of irritable bowel syndrome. Lancet Gastroenterol Hepatol. 2016;1(2):133–146. doi: 10.1016/S2468-1253(16)30023-1. [DOI] [PubMed] [Google Scholar]
  • 17.Ford A.C., Lacy B.E., Talley N.J. Irritable bowel syndrome. N Engl J Med. 2017;376(26):2566–2578. doi: 10.1056/NEJMra1607547. [DOI] [PubMed] [Google Scholar]
  • 18.Worthington J.J., Reimann F., Gribble F.M. Enteroendocrine cells-sensory sentinels of the intestinal environment and orchestrators of mucosal immunity. Mucosal Immunol. 2018;11(1):3–20. doi: 10.1038/mi.2017.73. [DOI] [PubMed] [Google Scholar]
  • 19.Lach G., Schellekens H., Dinan T.G., Cryan J.F. Anxiety, depression, and the microbiome: a role for gut peptides. Neurotherapeutics. 2018;15(1):36–59. doi: 10.1007/s13311-017-0585-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Cani P.D., Knauf C. How gut microbes talk to organs: the role of endocrine and nervous routes. Mol Metabol. 2016;5(9):743–752. doi: 10.1016/j.molmet.2016.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Liu L., Liu Z., Li H., et al. Naturally occurring TPE-CA maintains gut microbiota and bile acids homeostasis via FXR signaling modulation of the liver-gut axis. Front Pharmacol. 2020;11:12. doi: 10.3389/fphar.2020.00012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Petrelli F., Ghidini M., Ghidini A., et al. Use of antibiotics and risk of cancer: a systematic review and meta-analysis of observational studies. Cancers (Basel) 2019;11(8):1174. doi: 10.3390/cancers11081174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Callens M., Watanabe H., Kato Y., Miura J., Decaestecker E. Microbiota inoculum composition affects holobiont assembly and host growth in Daphnia. Microbiome. 2018;6(1):56. doi: 10.1186/s40168-018-0444-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Jones M.P., Shah A., Walker M.M., Koloski N.A., Holtmann G., Talley N.J. Antibiotic use but not gastrointestinal infection frequently precedes first diagnosis of functional gastrointestinal disorders. United European Gastroenterol J. 2021;9(9):1074–1080. doi: 10.1002/ueg2.12164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Inagawa H., Saika T., Nishiyama N., et al. Improvement effect of dewaxed brown rice on constipation in antibiotic-treated mice. In Vivo. 2017;31(4):573–577. doi: 10.21873/invivo.11096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Tatsuki M., Miyazawa R., Tomomasa T., Ishige T., Nakazawa T., Arakawa H. Serum magnesium concentration in children with functional constipation treated with magnesium oxide. World J Gastroenterol. 2011;17(6):779–783. doi: 10.3748/wjg.v17.i6.779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Yeo S., Park H., Seo E., et al. Anti-inflammatory and gut microbiota modulatory effect of Lactobacillus rhamnosus strain LDTM 7511 in a dextran sulfate sodium-induced colitis murine model. Microorganisms. 2020;8(6):845. doi: 10.3390/microorganisms8060845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Dai Q., Yan H., Wu X., Liu Y., Huang F., Dong X. Effectiveness and safety of adjunctive traditional Chinese medicine therapy for constipation after cancer chemotherapy: a protocol for systematic review and meta-analysis. Medicine. 2020;99(34) doi: 10.1097/MD.0000000000021770. e21770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Lin L.W., Fu Y.T., Dunning T., et al. Efficacy of traditional Chinese medicine for the management of constipation: a systematic review. J Alternative Compl Med. 2009;15(12):1335–1346. doi: 10.1089/acm.2008.0373. [DOI] [PubMed] [Google Scholar]
  • 30.Liu X., Wang Y., Ye Q., et al. External treatment of traditional Chinese medicine for constipation after thoracolumbar compression fractures: a protocol for systematic review and meta-analysis. Medicine (Baltimore) 2021;100(35) doi: 10.1097/MD.0000000000027110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rao W.W., Yang J.J., Qi H., et al. Efficacy and safety of traditional Chinese herbal medicine for antipsychotic-related constipation: a systematic review and meta-analysis of randomized controlled trials. Front Psychiatr. 2021;12 doi: 10.3389/fpsyt.2021.610171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Li Y.G., Wang X.Y., Chen H.F., Yuan J.B., Meng Y., Yang W.L. Comparison of the chemical constituents of raw Fructus Aurantii and Fructus Aurantii stir-baked with bran, and the biological effects of auraptene. J Ethnopharmacol. 2021;269 doi: 10.1016/j.jep.2020.113721. [DOI] [PubMed] [Google Scholar]
  • 33.Wang S.Y., Liu Y.P., Fan Y.H., Zhang L., Cai L.J., Lv B. Mechanism of aqueous fructus aurantii immaturus extracts in neuroplexus of cathartic colons. World J Gastroenterol. 2015;21(31):9358–9366. doi: 10.3748/wjg.v21.i31.9358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Yan S., Hao M., Yang H., et al. Metabolomics study on the therapeutic effect of the Chinese herb pair Fructus Aurantii Immaturus and Rhizoma Atractylodis Macrocephalae in constipated rats based on UPLC-Q/TOF-MS analysis. Ann Palliat Med. 2020;9(5):2837–2852. doi: 10.21037/apm-20-280. [DOI] [PubMed] [Google Scholar]
  • 35.Wang S., Bao Y.R., Li T.J., et al. Mechanism of Fructus Aurantii flavonoids promoting gastrointestinal motility: from organic and inorganic endogenous substances combination point of view. Phcog Mag. 2017;13(51):372–377. doi: 10.4103/pm.pm_179_16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Chen S.Y., Zhou Q.Y., Chen L., Liao X., Li R., Xie T. The Aurantii Fructus Immaturus flavonoid extract alleviates inflammation and modulate gut microbiota in DSS-induced colitis mice. Front Nutr. 2022;9 doi: 10.3389/fnut.2022.1013899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Wen Y., Zhan Y., Chen T., et al. Total flavonoids of Aurantii Fructus Immaturus regulate miR-5100 to improve constipation by targeting Fzd2 to alleviate calcium balance and autophagy in interstitial cells of Cajal. Mol Neurobiol. 2024;61(8):5882–5900. doi: 10.1007/s12035-024-03958-3. [DOI] [PubMed] [Google Scholar]
  • 38.Hu Q., Sun D.F., Sun L.J., et al. Intervention effect of mussel and oyster complex on constipation in mice with intestinal flora disorder. Mod Food Sci Technol. 2020;36(10):16–26. [Google Scholar]
  • 39.Shen Y., Hu Q., Sun L.J., et al. Banana peel extract improves intestinal motility of constipation model mice by regulating intestinal flora and intestinal neurotransmitter levels. Nat Prod Res Dev. 2023;35:750–760. [Google Scholar]
  • 40.Zhang F., Zhang D., Cheng K., et al. Spontaneous evolution of human skin fibroblasts into wound-healing keratinocyte-like cells. Theranostics. 2019;9(18):5200–5213. doi: 10.7150/thno.31526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Uddin M.N., Alam T., Islam M.A., et al. Evaluation of carbon tetrachloride fraction of Actinodaphne angustifolia Nees (Lauraceae) leaf extract for antioxidant, cytotoxic, thrombolytic and antidiarrheal properties. Biosci Rep. 2020;40(6) doi: 10.1042/BSR20201110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Gan Y., Liang J., Diao W., et al. Lactobacillus plantarum KSFY06 and geniposide counteract montmorillonite-induced constipation in Kunming mice. Food Sci Nutr. 2020;8(9):5128–5137. doi: 10.1002/fsn3.1814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ma H., Xiong H., Zhu X., et al. Polysaccharide from Spirulina platensis ameliorates diphenoxylate-induced constipation symptoms in mice. Int J Biol Macromol. 2019;133:1090–1101. doi: 10.1016/j.ijbiomac.2019.04.209. [DOI] [PubMed] [Google Scholar]
  • 44.Hayeeawaema F., Wichienchot S., Khuituan P. Amelioration of gut dysbiosis and gastrointestinal motility by konjac oligo-glucomannan on loperamide-induced constipation in mice. Nutrition. 2020;73 doi: 10.1016/j.nut.2019.110715. [DOI] [PubMed] [Google Scholar]
  • 45.Liu S., Sui D., Fu W., et al. Laxative effects of Yangyin Tongmi Capsule on a model of diphenoxylate-induced constipation in mice. Evid Based Complement Alternat Med. 2020;2020 doi: 10.1155/2020/1471824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Ge X., Ding C., Zhao W., et al. Antibiotics-induced depletion of mice microbiota induces changes in host serotonin biosynthesis and intestinal motility. J Transl Med. 2017;15(1):13. doi: 10.1186/s12967-016-1105-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Liu J., Gu L., Zhang M., et al. The fecal microbiota transplantation: a remarkable clinical therapy for slow transit constipation in future. Front Cell Infect Microbiol. 2021;11 doi: 10.3389/fcimb.2021.732474. 732474-732474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Parthasarathy G., Chen J., Chen X., et al. Relationship between microbiota of the colonic mucosa vs feces and symptoms, colonic transit, and methane production in female patients with chronic constipation. Gastroenterology. 2016;150(2):367–379.e361. doi: 10.1053/j.gastro.2015.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Khuituan P., Huipao N., Jeanmard N., et al. Sargassum plagiophyllum extract enhances colonic functions and modulates gut microbiota in constipated mice. Nutrients. 2022;14(3):496. doi: 10.3390/nu14030496. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.He Y., Zhu L., Chen J., et al. Efficacy of probiotic compounds in relieving constipation and their colonization in gut microbiota. Molecules. 2022;27(3):666. doi: 10.3390/molecules27030666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Wang J.K., Yao S.K. Roles of gut microbiota and metabolites in pathogenesis of functional constipation. Evid Based Complement Alternat Med. 2021;2021 doi: 10.1155/2021/5560310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Dimidi E., Christodoulides S., Scott S.M., Whelan K. Mechanisms of action of probiotics and the gastrointestinal microbiota on gut motility and constipation. Adv Nutr. 2017;8(3):484–494. doi: 10.3945/an.116.014407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Camilleri M. Gastrointestinal motility disorders in neurologic disease. J Clin Invest. 2021;131(4) doi: 10.1172/JCI143771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Jin B., Ha S.E., Wei L., et al. Colonic motility is improved by the activation of 5-HT(2B) receptors on interstitial cells of Cajal in diabetic mice. Gastroenterology. 2021;161(2):608–622.e607. doi: 10.1053/j.gastro.2021.04.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Nakamori H., Noda K., Mitsui R., Hashitani H. Role of enteric dopaminergic neurons in regulating peristalsis of rat proximal colon. Neuro Gastroenterol Motil. 2021;33(9) doi: 10.1111/nmo.14127. [DOI] [PubMed] [Google Scholar]
  • 56.Deng S.B., Wang Z.W., Liu F.F., Liu D.F., Zhong R.Q., Chen W.B. Research on the element impurities of the innovative Citrus aurantium total flavonoid tablets and its control strategy. Chin Med Pharm. 2024;14(11):46–50. [Chinese] [Google Scholar]

Associated Data

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Supplementary Materials

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Data Availability Statement

The raw data in this study can be found in the links: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA945577.

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