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. 2023 Feb 6;13(3):77. doi: 10.1007/s13205-023-03491-5

The diarrheal mechanism of mice with a high-fat diet in a fatigued state is associated with intestinal mucosa microbiota

Jing Liu 1, Bo Qiao 1, Na Deng 1, Yi Wu 1, Dandan Li 1,✉, Zhoujin Tan 1,✉
PMCID: PMC9902584  PMID: 36761339

Abstract

Growing evidence has demonstrated that fatigue and a high-fat diet trigger diarrhea, and intestinal microbiota disorder interact with diarrhea. However, the association of intestinal mucosal microbiota with fatigue and high-fat diet trigger diarrhea remains unclear. The specific pathogen-free Kunming male mice were randomly divided into the normal group (MCN), standing group (MSD), lard group (MLD), and standing united lard group (MSLD). Mice in the MSD and MSLD groups stood on the multiple-platform apparatus for four h/d for fourteen consecutive days. From the eighth day, mice in the MLD and MSLD groups were intragastric lard, 0.4 mL/each, twice a day for seven days. Subsequently, we analyzed the characteristics and interaction relationship of intestinal mucosal microbiota, interleukin-6 (IL-6), interleukin-17 (IL-17), malondialdehyde (MDA), superoxide dismutase (SOD), and secretory immunoglobulin A (sIgA). Results showed that mice in the MSLD group had an increased number of bowel movements. Compared with the MCN group, the contents of IL-17, and IL-6 were higher (p > 0.05), and the content of sIgA was lower in the MSLD group (p > 0.05). MDA and SOD increased in MLD and MSLD groups. Thermoactinomyces and Staphyloccus were the characteristic bacteria of the MSLD group. And Staphyloccus were positively correlated with IL-6, IL-17, and SOD. In conclusion, the interactions between Thermoactinomyces, Staphyloccus and intestinal inflammation, and immunity might be involved in fatigue and high-fat diet-induced diarrhea.

Keywords: Fatigue, High-fat diet, Intestinal mucosal microbiota, Immunity, Inflammation

Introduction

Diarrhea is one of the world's leading public health problems and the eighth leading cause of death worldwide (GBD 2020; GBD 2018; Ramos et al. 2019; Lee et al. 2021; Greenhouse-Tucknott et al. 2022; Karshikoff et al. 2017Vaes et al. 2022) and is associated with many chronic inflammatory diseases such as inflammatory bowel disease (IBD) (Nocerino et al. 2020), rheumatoid arthritis (Ifesemen et al. 2022), multiple sclerosis (Gilio et al. 2022), and adversely affects the quality of life. Most studies have shown that a high-fat diet alters the abundance and diversity of the intestinal microbiota (Guo et al. 2022; Li et al. 2022a, b). Community structure changes of lactase bacteria in intestinal mucosa reduce the abundance of key lactase bacteria and promote diarrhea (Zhou et al. 2022). The liver index and blood lipid of mice fed lard increased, and intestinal microbial diversity decreased (Xu et al. 2022). After long-term consumption of lard and vegetable oil, the intestinal digestive enzyme activity decreased, and the number of Bifidobacteria and Lactobacillus in the intestine decreased (Qiao et al. 2022a, b). Long-term lard consumption may affect glycolysis metabolism, and lard may synergize with Coriobacteriaceae UCG-002, which is significantly negatively associated with Glycolysis / Gluconeogenesis (Qiao et al. 2022a, b). Therefore, lard has a significant impact on human health and host health.

The intestinal microbiota is a complex ecosystem consisting of approximately 1014 microbes. The normal human intestinal microbiota has metabolic and nutritional functions, antimicrobial protection, intestinal mucosa integrity maintenance, and immune response regulation (Sebastián Domingo et al. 2018; Becattini et al. 2016). The intestinal mucosa barrier is an important immune barrier that prevents harmful substances from entering the body. Intestinal microbiota affects local and systemic immune responses by affecting the maturation of lymphoid tissue in the intestinal mucosa, enabling the immune system to recognize and attack harmful bacteria, thus preventing bacterial invasion and infection (Gopalakrishnan et al. 2018; Zhou et al. 2020). Disorders in the intestinal microbiota are strongly associated with diarrhea. The diversity and composition of intestinal mucosa microbiota in diarrheal mice were significantly altered, with staphylococcus sciuri and Escherichia fergusonii identified as putative key species (Zhang et al. 2020). The abundance of conditionally pathogenic bacteria in mice with antibiotic diarrhea increased significantly and the diversity of the intestinal microbiota reduced. In contrast, changes in the number and composition of intestinal microbiota further led to dysfunctional intestinal microbiota (Shao et al. 2020).

In conclusion, this experiment investigates the characteristics and relationship of intestinal mucosal microbiota, interleukin-6 (IL-6), interleukin-17 (IL-17), secretory immunoglobulin A (sIgA), superoxide dismutase (SOD), and malondialdehyde (MDA), providing an experimental basis for the pathogenesis and treatment strategy of diarrhea, and a scientific basis for guiding the healthy lifestyles.

Materials and methods

Animals and feeding conditions

We used male mice to rule out the effect of sex on the intestinal microbiota (Wu et al. 2022). 20 Kunming mice (male 20 ± 2 g) were gained from the Slack Jingda Experimental Animal Co, Ltd. (SCXK (Xiang) 2019–0004). The mice were housed at the experimental animal center of Hunan University of Chinese Medicine, Changsha, China. The specific pathogen-free (SPF) conditions are at a temperature of 23–25 ℃, a humidity of 50–70%, and a 12 h dark–light cycle, with ad libitum access to food and water. The experiment complied with the standards of the Animal Ethics and Welfare Committee of Hunan University of Chinese Medicine (permission number: LLBH-202206160001).

Feed

Common feed, including water, crude protein, crude fiber, crude fat, crude ash, calcium, phosphorus, lysine, methionine, cysteine, etc., is provided by the Hunan University of Chinese Medicine Laboratory Animal Center, Jiangsu Madison Biomedical Co., Ltd. Jinluo refined lard, whose main nutrients are energy (44%) and fat (167%), manufactured by Linfen Xincheng Jinluo Meat Products Group Co., Ltd (Linyi City, Shandong Province, license number: SC10337130200099, production batch number: GB 10,146), heated in a 37 °C water bath until the lard melts before use.

Animal grouping and intervention

Animals were fed for 3 days for acclimatization. Mice were randomly divided into the normal group (MCN), standing group (MSD), lard group (MLD), and standing united lard group (MSLD). Model interventions based on the references (Yang et al. 2016; Ma et al. 2015; Li et al. 2011; Lu et al. 1999; Gao 2022) and pre-experimental results. The mice in the MSD and MSLD groups stood on the multiple-platform apparatus for four h/day for fourteen consecutive days. The MCN and MSD groups were not given any intervention from the first to the seventh day. From the 8th day of modeling, mice in the MLD and MSLD groups were intragastric lard, 0.4 mL/each, twice a day for seven days. Mice in the MCN and MSD groups were intragastric with an equal volume of sterile water twice a day for seven days. Figure 1 is an experimental design and specific experimental process.

Fig. 1.

Fig. 1

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Experimental design and general conditions of the animals (MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

General characteristics of mice

The mice were observed in terms of their mental state, voluntary activity, hair shape and color, and fecal characteristic. The body weight of the mice was recorded every other day. In addition, the number of bowel movements was recorded from 9:00 to 9:30 a.m. daily.

The organ index

Each mouse was weighed before blood was taken, and the spleen, thymus, and liver were dissected immediately. The spleen, thymus, and liver were weighed and organ indexes (organ weight/body weight) were calculated (Li et al.2022a, b).

Detection of IL-6, IL-17 and sIgA in serum

Enzyme-linked immunosorbent assay (ELISA) detects IL-6, IL-17, and sIgA in serum. The blood sample was left at 4℃ for 1–2 h and the centrifugation for 10 min at 3000 r/min. According to the instructions, the preparation of holes, adding samples, adding labeled antibodies, incubating, washing the plate, developing color, terminating the reaction, and detecting the OD value on the machine. A standard curve was drawn to calculate the concentration of each sample (kit provided by Quanzhou Konodi Biotech, Inc.).

Detection of MDA and SOD in the liver

The liver was immediately dissected and removed for preservation at 4℃. MDA and SOD extracts were added at a ratio of 1:10, and the mouse liver was homogenized in an ice bath. Following the centrifugation for 10 min at 8000 r/min, take the supernatant. The content of MDA and SOD in the liver was determined strictly according to the kit (SOD kit (Beijing Solabo Technology Co., Ltd, BC0175); MDA kit (Beijing Solabo Technology Co., BC0025)).

Collection of intestinal mucosa samples

According to previous methods, an intestinal mucosa sample was collected (Li et al. 2021). In sterile conditions, intestinal tissue from the gastric pylorus to the returning blind was cut lengthways with sterile scissors, and the contents of the intestine were flushed with saline. Scrape the intestinal mucus with a coverslip and add twice the amount of saline to the solution. Then it was centrifuged at 3000 r/min for 10 min and the supernatant was stored in an -80℃ refrigerator.

DNA extraction, 16S rRNA gene amplicon sequencing and sequence analysis

All samples were processed by Shanghai Personal Biotechnology Co., Ltd (Shanghai, China), and the total microbial genomic DNA from each tube was extracted following the procedure for extracting nucleic acid instructions from the Omega Soil DNA Kit (D5625-01) kit (Omega Bio-Tek, Norcross, GA, USA). The quantity and quality of extracted DNAs were measured using a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively.

PCR amplification of the bacterial 16S rRNA genes V3–V4 region was performed using the forward primer 338F (5'-ACTCCTACGGGAGGCAGCA-3') and the reverse primer 806R (5'-GGACTACHVGGGTWTCTAAT-3'). Thermal cycling consisted of initial denaturation at 98 ℃ for 5 min, followed by 25 cycles consisting of denaturation at 98 ℃ for 30 s, annealing at 53 ℃ for 30 s, and extension at 72 ℃ for 45 s, with a final extension of 5 min at 72 ℃. PCR amplicons were purified with V azyme V AHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons was performed using the Illumina NovaSeq platform with NovaSeq 6000 SP Reagent Kit (500 cycles).

Bioinformatics

To improve the quality of analytical results, the adequacy and quality of sequences be guaranteed. Therefore, the original sequence data were modified (QIIIME2), cut, filtered, de-noised, merged, and chimeric removed (DADA2) to obtain effective sequences before analysis (Callahan et al. 2016). Sequence data analyses were mainly performed using QIIME2 and R packages (v3.2.0). ASV-level alpha diversity indices, such as Chao1 richness estimator, Observed species, Shannon diversity index, and Simpson index. ASV-level ranked abundance curves were generated to compare the richness and evenness of ASVs among samples. Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using Bray–Curtis metrics (Bray et al. 1957) and UniFrac distance metrics (Lozupone et al. 2005, Lozupone et al. 2007) and visualized via principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS). Principal component analysis (PCA) was also conducted based on the genus-level compositional profiles (Ramette. 2007). According to the taxonomy, the community structure was analyzed statistically at different taxonomic levels. For linear discriminant effect size (LefSe (Segata et al. 2011)), the Kruskal–Wallis rank sum test and Wilcoxon rank sum test were performed, followed by linear discriminant analysis (LDA) to assess the effect size of each differential abundance taxa. Based on the above analysis, in-depth statistical and visual analysis of community structure and system development can be carried out.

Statistical analysis

Experimental data were expressed in mean ± standard deviation. All data were statistically analyzed using the SPSS 21.0 statistical software package. Comparison between multiple groups, satisfying normality and homogeneity of variance, using one-way ANOVA and LSD test for comparison between multiple groups. The rank sum test was used for non-conforming normality or variance homogeneity, and Tamhane's T2 (M) test was used for multigroup comparison. p < 0.05 indicates a statistically significant difference.

Results

Effects of fatigue and a high-fat diet on the general characteristics of mice

As visible in Fig. 2a, the body weight of mice in the MLD and MSD groups was significantly higher than the MSLD group at 14 and 12 days (p < 0.05). Compared with the number of bowel movements, we found that the MSD and MSLD groups showed an increasing trend with the increase of modeling time and were higher than that in the MCN group(Fig. 2b). And the mice in the MSLD group were sparse and dull fur, damp bedding, and a dirty perianal area. In summary, we found that the MSLD group had diarrhea and the body weight decreased.

Fig. 2.

Fig. 2

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a Box plot of weight difference for each group of mice (n = 5); b Line graph of the number of fecal for each group of mice within half an hour (*p < 0.05, **p < 0.01, ***p < 0.001), MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

Effects of fatigue and a high-fat diet on immunity and inflammation in mice

The thymus, spleen, and liver are important immune organs, and their indices reflect the host's immune function. As shown in Fig. 3a–c, the spleen index was significantly lower in the MSLD group compared with the MCN and MLD groups (p < 0.05), and the spleen index was significantly lower in the MSD group compared with the MLD group (p < 0.05). The changes in thymic index and liver index were not significant (p > 0.05).

Fig. 3.

Fig. 3

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Box line diagram of organ indices for each group of mice (Organ indices = Organ weight / Mouse weight); (a) Spleen index; (b) Thymic index; (c) Liver index; (d) IL-6 levels in the serum; (e) IL-17 levels in the serum; (f) sIgA levels in the serum; (*p < 0.05, **p < 0.01, ***p < 0.001, MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

IL-6 and IL-17 are inflammatory factors that respond to inflammation within the body (Kany et al. 2019). IL-6 and IL-17 were higher in the MSLD group than in the MCN group (Figs. 3d-e). sIgA is an immunoglobulin that reflects immune levels in the body (Penny et al. 2022). As shown in Fig. 3f, sIgA was reduced in the MSD, MLD, and MSLD groups compared to the MCN group (p > 0.05).

Effects of fatigue and a high-fat diet on oxidative stress in mice

SOD and MDA are key markers for evaluating fatigue and oxidative stress (Dan et al. 2022). As shown in Fig. 4a, SOD was higher than in the MCN group, and the MSD group was lower (p > 0.05). Compared to the MCN group, MDA increased in the MLD group and decreased in the MSD and MSLD groups (p > 0.05) (Fig. 4b).

Fig. 4.

Fig. 4

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Oxidative stress indicators; (a)SOD levels in the liver; (b) MDA levels in the liver (MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

Effects of fatigue and a high-fat diet on intestinal mucosa microbiota in mice

Analysis of population and diversity of intestinal mucosa microbiota by fatigue and a high-fat diet

As shown in Fig. 5a, where the growth rate of ASVs decreased with the increase in sequencing data, suggesting that the sequencing data were adequate for this analysis. The MCN group had a total of 1995 ASVs with 1297 unique ASVs; the MSD group had 1944 ASVs with 1290 unique ASVs; the MLD group had 2019 ASVs with 1146 unique ASVs; the MLD group had 2222 ASVs with 1408 unique ASVs (Fig. 5b). Alpha diversity analysis reflects the abundance and diversity of the microbiota. Compared to the MCN group, Chao1, Shannon, and Simpson’s indices decreased in MSD, MLD, and MSLD groups (p > 0.05).

Fig. 5.

Fig. 5

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a Shannon–Wiener curves of intestinal mucosal bacteria; b Venn diagram: distribution of the number of ASVs of intestinal mucosal bacteria; c Chaol index; d Shannon index; e Simpson index; f Principal component analysis (MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

Principal Coordinate Analysis (PCOA) is used to study similarities or differences in the composition of sample communities, with two samples being closer, representing a more similar composition of the two species. As shown in Fig. 5f, Pco1 was 36.9%, Pco2 was 15.6%, and samples from MCN and MSD groups were similar and dispersed in the one, two, and three quadrants. MSD and MSLD groups were more similar, mainly in the four quadrants. The Dimensional Scale of Measurement (NMDS) reflects the information of the distance matrix between samples. The MCN group was significantly different from MSD and MSLD groups, as shown in Fig. 5g. The MSD group was more concentrated, and the MLD group had the largest spatial distribution. The results showed that the composition and abundance of intestinal microbiota changed.

Effects of fatigue and a high-fat diet on intestinal mucosal microbiota composition in mice

We analyzed the composition of the intestinal mucosal microbiota at the phylum and genera levels. Figure 6a shows the composition and distribution of intestinal mucosa microbiota at the phylum level, indicating that Firmicutes, Bacteroidetes, and Proteobacteria are the dominant phylum. Compared with the MCN group, the MSD, MLD, and MSLD groups showed an increased abundance of Firmicutes and decreased abundance of Bacteroidetes; MSLD groups showed an increased abundance of Proteobacteria and MLD decreased abundance of Proteobacteria (p > 0.05). Figure 6c showed a decrease in Firmicutes / Bacteroidetes ratio (F/B) in MSD and MLD groups compared to MCN groups and an increase in MSLD groups with no significant difference (p > 0.05).

Fig. 6.

Fig. 6

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a Intestinal mucosal microbiota composition in the phylum level in each group; b Intestinal mucosal microbiota composition in the genus level in each group; c Firmicutes / Bacteroidetes ratio in each group; d–i Genus levels of dominant bacteria in each group (*p < 0.05, MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

Figure 6b shows each group's abundance of intestinal mucosa microbiota at the genus level. By counting the top 20 genera of abundance, there were similarities in the composition of the groups but some differences in abundance. Figure 6d–i is genera level differentially bacteria, in which the abundance of Pediococcus in the MCN and MLD groups was significantly higher than that in the MSLD group (p < 0.05). The abundance of Corynebacterium, Gemella, Candidatus Arthromitus in the MSD group was significantly higher than in the MCN and MLD group (p < 0.05). The abundance of Methylobacterium in the MSLD group was significantly higher than in the MCN and MSD groups (p < 0.05), Gemella, and Staphylococcus were significantly higher than in MCN and MLD groups (p < 0.05), and Candidatus Arthromitus was significantly higher than MLD group (p < 0.05).

Effects of fatigue and a high-fat diet on intestinal mucosa characteristic microbiota in mice

As shown in Fig. 7, the LEfSe analysis identified differentially altered characteristic bacterial taxa, with LDA scores greater than 2, no characteristic bacteria identified in MSD and MLD groups, and differences in abundance in MCN and MSLD groups, four of which were identified as key differentiators. The characteristic bacteria Pediocococcus in the MCN group and Thermoactinomyces and Staphyloccus in the MSLD group were significantly enhanced. These results suggest that diarrhea caused by fatigue combined with a high-fat diet is associated with key bacteria, which may be able to identify associated diarrhea diseases.

Fig. 7.

Fig. 7

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a The cladogram generated from the LEfSe analysis indicates the phylogenetic distribution from phylum to microbiota species. b Histogram of LDA to identify bacterial species with different levels (MCN normal group, MSD standing group, MLD lard group, MSLD standing united lard group)

Effects of fatigue and a high-fat diet on intestinal mucosal microbiota function in mice

To determine metabolic and functional changes in mouse intestinal mucosa microbiota induced by a high-fat diet or fatigue, PICRUSt2 analysis based on the KEGG database predicted microbiota-related metabolic pathways. Figure 8a shows the seven main functional types (Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Disorders, Glycan Pathways, Metabolism) composed of 34 functional pathways, of which the Metabolism pathway is the most abundant. Median metabolic function of > 381.686 of the Metabolism tertiary pathway was selected (29 classes), as shown in Fig. 8b, mainly carbohydrate metabolism, amino acid metabolism, glycolysis, glucose metabolism, and fatty acid metabolism.

Fig. 8.

Fig. 8

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Prediction of intestinal mucosal microbiota metabolism based on PICRUSt2. a Predicted dependence of KEGG function, the horizontal coordinates being the abundance of the KEGG function pathway, the longitudinal coordinates being the second level classification of the KEGfunction pathway, and the far right being the first level classification to which the pathway belongs. b The horizontal coordinates are the abundance of the Metabolism pathway, the longitudinal coordinates are the third-order classification of the Metabolism pathway, and the far right is the second-order classification to which the pathway belongs (Median > 381.686); c Comparisons between the groups for each metabolism functional category (Median > 550.3545)

The metabolism function (14 categories) with a median > 550.3545 for Metabolism Level 3 pathway was selected for statistical analysis. Compared to the MCN group, Streptomycin biosynthesis, Thiamine metabolism, and One carbon pool by folate were significantly decreased in the MSD and MSLD groups (p < 0.05) (Fig. 8c); Fatty acid biosynthesis, d-Glutamine and d-glutamate metabolism, d-Alanine metabolism, Biosynthesis of vancomycin group antibiotics were significantly increased in the MSD group (p < 0.05) (Fig. 8c). Compared to the MSD group, MLD group Thiamine metabolism, Pantothenate and CoA biosynthesis, one carbon pool by folate were significantly increased (p < 0.05)., while Pentose phosphate pathway, Fatty acid biosynthesis, d-Glutamine and d-glutamate metabolism, d-Alanine metabolism, Biosynthesis of vancomycin group antibiotics was significantly decreased (p < 0.05) (Fig. 8c).

In conclusion, a high-fat diet can reduce the metabolism of some fatty acids and amino acids and increase the metabolism of thiamine, pantothenic acid, and folic acid. Fatigue increases the metabolism of fatty acids and amino acids and decreases streptomycin, thiamine, and folic acid metabolism. Fatigue combined with a high-fat diet led to an overall decrease in metabolic function in mice.

Correlation analysis of intestinal mucosal microbiota with metabolism, inflammation, immunity, and oxidative stress

To investigate the key role of intestinal microbiota and metabolic function in maintaining the stability of the intestinal microenvironment, we performed Spearman correlation analysis for metabolic pathways and genus-level bacteria (Fig. 9a-9f). Among positive associations, Corynebacterium, and Candidatus Arthromitus were most strongly associated with D-Glutamine, and D-Glutamate Metabolism, Methylobacterium, Gemella, and Staphylococcus were most strongly associated with Selenocompound Metabolism. Pediococcus was most strongly associated with Terpenoid backbone biosynthesis. Taken together, these metabolic functions and pathways may be the main pathways that affect intestinal mucosa microbiota in mice.

Fig. 9.

Fig. 9

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Spearman's correlation analysis network diagram, the solid line indicates a positive correlation, a dotted line indicates a negative correlation, and the thickness of the line indicates the strength of the correlation. a Network diagram of correlation analysis between Pediococcus and metabolic pathways; b Network diagram of correlation analysis between Stapleyococcus and metabolic pathways; c Network diagram of correlation analysis between Candidatus arthromitus and metabolic pathways; d Network diagram of correlation analysis between Corynebacteria. and metabolic pathways; e Network diagram of correlation analysis between Gemella; e Network diagram of correlation analysis between Methylobacterium.

To further investigate the relationship between inflammation, immunity, oxidative stress, and intestinal mucosa microbiota, we performed an RDA analysis of 20 genus-level bacteria with L-6, IL-17, sIgA, MDA, and SOD. Figure 10 shows that Candidatus Arthromitus and Lactobacillus were positively correlated with MDA and sIgA, and negatively correlated with IL-6, IL-17, and SOD. Candidatus Arthromitus and Pediococcus were positively correlated with IL-6, sIgA, and MDA and negatively correlated with IL-17 and SOD. Methylobacterium was positively correlated with sIgA, and IL-6 and negatively correlated with MDA, IL-17, and SOD. Staphyloccus and Gemella were positively correlated with IL-6, IL-17, and SOD and negatively correlated with MDA and sIgA. Corynebacterium was positively associated with IL-6, IL-17, SOD, and sIgA and negatively associated with MDA.

Fig. 10.

Fig. 10

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a RDA analysis of the top 20 bacteria in the relative abundance of genus levels with IL-6, IL-17, sIgA, MDA, and SOD. IL-6, IL-17, sIgA, MDA, and SOD are represented by arrows, and a red dot represents each genus; the larger the red dot, the higher the abundance; The longer the arrow is connected to the origin, the greater the correlation. The smaller the angle between the arrow line and the sorting axis, the higher the correlation. The sharp angle is positively correlated, and the sharp angle is negatively correlated

Discussion

Chronic inflammation plays an important role in inflammatory bowel disease (Pei et al. 2018), with diet and exercise being key risk factors for inflammatory bowel disease (Ramos et al. 2019). The health effects of physical activity may be related to the role of intestinal bacteria. Moderate exercise helps prevent and treat disease reducing inflammation and intestinal permeability, improving body composition, and inducing positive changes in the intestinal microbiota (Clauss et al. 2021); whereas excessive exercise reduces microbial diversity and decreases intestinal permeability, damaging the intestinal mucosal barrier and leading to increased levels of inflammation (Clauss et al. 2021; Bonomini-Gnutzmann et al. 2022). Abdominal pain, diarrhea, or hematochezia are common after strenuous exercise, while probiotic therapy can reduce the incidence and severity of gastrointestinal symptoms and improve subjectively assessed health (Smarkusz-Zarzecka et al. 2022). In addition to microbiota changes caused by intense exercise, the incidence of diarrhea was also influenced by diet (Jang et al. 2019).

Dietary interventions partially affect the development of inflammation because of their impact on intestinal microbes. Previous studies have shown that a high-fat, high-protein diet causes diarrhea (Guo et al. 2022; Li et al. 2022a, b), dietary fat affects inflammation in the intestinal and regulates mucosal immunity, and transplanted fats increase the risk of IBD (Ananthakrishnan et al. 2014). Fatty acid intake increased inflammation in calves and altered immunity in breastfed calves (Hill et al. 2011). High saturated fat and n-6 polyunsaturated fat may lead to chronic inflammation (Al-Shaer et al. 2021). To summarize, host immune–microbiota interactions contribute to immune homeostasis and affect disease susceptibility. The effects of exercise and diet on intestinal microbiota composition and metabolic activity can lead to pro-inflammatory or anti-inflammatory effects leading to diarrhea. We also found that a high-fat diet in a fatigued state induces diarrhea.

The immune system is composed of immune organs, immune cells, and immune molecules. It is an important barrier to protect the body from outside pathogenic organisms. When immune organs, cells, and cytokines are dysfunctional, the immune system disrupts and activates an inflammatory response in the gastrointestinal tract, with abnormal expression of cytokines, increased intestinal permeability, electrolyte disturbances, increased intestinal smooth muscle stimulation, and increased gastrointestinal peristalsis leading to diarrhea symptoms in patients (Chen et al. 2022). The immune organ index can be a preliminary indicator of immune organ function. The thymus and spleen are important immune organs, and their immunomodulatory activity is closely associated with changes in the immune organ index (Huang et al. 2021; Ma et al. 2021). Cytokines are small molecule proteins secreted by cells that control cell proliferation and differentiation, regulate angiogenesis and immune and inflammatory responses, and primarily play a role in the differentiation and activation of immune cells (Chen et al. 2022). IL-6 and IL-17 are pro-inflammatory cytokines at the level of immune cytokines, and IgA is the primary defense of intestinal mucosa against pathogen adhesion and colonization (Penny et al. 2022; Kany et al. 2019). In this study, the spleen index and thymus index were lower in MSD and MSLD groups than in the MCN group, while the spleen index and thymus index were higher in the MLD group than in the MCN group. IL-6 and IL-17 were upregulated in the MSLD group but not in MSD and MLD groups, and sIgA was decreased. Excessive exercise leads to increased levels of inflammation (Clauss et al. 2021), which is consistent with our study. The above results indicate that fatigue decreases immune factors and a high-fat diet can elevate immune factors, and fatigue combined with a high-fat diet leads to elevated inflammatory factors and decreased immune factors. Therefore, diarrhea may be associated with the decreased immune function of the intestinal mucosa and mucosal inflammation.

The dynamic balance between the body's oxidative and antioxidant states protects the organism's health (Jiang et al. 2020). When the concentration of reactive oxygen species (ROS) in cells is higher than physiological values, it leads to oxidative stress, oxidative damage and inflammation in the intestinal, and ultimately diarrhea (Piechota-Polanczyk et al. 2014). Therefore, the antioxidant capacity of animals can be used as an indicator of health status, and oxidative stress can be used as a diagnostic biomarker to reflect mortality and morbidity in diarrhea (Cheng et al. 2021). SOD and MDA are commonly used as markers of physiological stress and oxidative damage in cells (Dan et al. 2022). MDA is a byproduct of lipid peroxidation that occurs in inflammatory tissues, and changes in its content can reflect oxidative damage of membrane lipids (Answer et al. 2020). Fatigue occurs when the body is exposed to oxidative stress and ROS, and increasing SOD levels helps improve the body's ability to fight fatigue (Huang et al. 2022a, b). The study found that high-intensity exercise leads to increased oxidative stress damage and inflammation in rats (Kim MJ et al. 2022), which is consistent with our study. We found that SOD in MLD and MSLD groups increased, and the MSD group decreased. MDA was elevated in the MLD group and decreased in the MSD and MSLD groups. In conclusion, standing for 4 h causes fatigue in mice, while the a high-fat diet has an anti-fatigue effect but may lead to oxidative damage through intestinal inflammation.

Firmicutes, Bacteroidetes, Proteobacteria, and Actinomycetes were the dominant phyla in humans and mice (Becattini et al. 2016). The study found that a high-fat diet reduced microbial diversity in mice, increased F/B (Zhao et al. 2021; Xie et al. 2022), decreased the abundance of Firmicutes, and increased the abundance of Bacteroidetes, Actinobacteria, and Proteobacteria (Lin et al. 2022). Among them, an increase in the number or F/B ratio of deformed bacteria resulted in structural imbalance or instability of intestinal microbiota (Shin et al. 2015; Riva et al. 2017; Pammi et al. 2017). These studies are consistent with our findings. We found differences in intestinal microbiota composition between fatigue and a high-fat diet, with an increase in the number of ASVs following a high-fat diet consumption. Fatigue and a high-fat diet decreased the abundance and diversity of the mouse microbiota, while fatigue increased Proteobacteria abundance, leading to colony structure disorders.

Pedioccus pentosaceus LI05, a promising CDI probiotic, prevents host inflammation by maintaining intestinal epithelial integrity and regulating host immunity, intestinal microbiota, and Metabolism (Bian et al. 2020; Xu et al. 2018). Gemella is involved in human and animal diseases and causes infectious endocarditis. Triggering Ig response is an indicator of the immune response during IBD (Kariyanna et al. 2021; Rengarajan et al. 2020). Dysregulation of the microbiota deep in the ileum of Crohn's disease patients, particularly enrichment of Staphylococcus aureus (Pedamallu et al. 2016). Staphyloccus aureus is the main bacteria responsible for antibiotic-related diarrhea in hospitalized patients (Motamedi et al. 2021). Corynebacterium was involved in human and animal diseases, causing various infections (Zasada et al. 2018). Methylobacteria in patients with irritable bowel syndrome and ulcerative colitis (Tikunov et al. 2021; Matsumoto et al. 2021). Pediococcus was significantly enriched as characteristic bacteria in the MCN group and significantly decreased in the MSLD group. Moreover, Pediococcus was positively correlated with IL-6, sIgA, and MDA, and negatively correlated with IL-17 and SOD. Therefore, the decrease in immune factors may be related to the decreased abundance of Pediococcus. Staphylococcus was significantly enriched as the characteristic bacteria of the MSLD group. Staphylococcus and Gemella were significantly elevated in the MSLD group. Moreover, Staphylococcus and Gemella were positively correlated with IL-6, IL-17, and SOD and negatively correlated with MDA and sIgA. Therefore, elevated inflammatory factors may be associated with the elevated abundance of Staphylococcus and Gemell. The abundance of Corynebacterium, Gemella, Candidatus Arthromitus, and Methylobacterium increased in MSD and MSLD groups; the abundance of Pediococcus decreased; the composition of the MLD group was similar to MCN and no significant differences were observed. In summary, fatigue combined with a high-fat diet causes microbiota disorders, increases harmful bacteria such as Corynebacterium, Gemella, and Methylobacterium, decreases beneficial bacteria such as Pediococcus, increases inflammatory factors and decreases immune factors, thus causing diarrhea.

We further predicted the macrogenome function of the microbiota and found significant changes in metabolic pathways in mice following fatigue and a high-fat diet intervention. The abundance of all metabolic pathways decreased in the MSLD group, while Thiamine metabolism, One carbon pool by folate, decreased significantly in the MSD group and increased significantly in the MLD group. Fatty acid biosynthesis, D-Glutamine and D-Glutamate Metabolism, D-Alanine Metabolism, and biosynthesis of vancomycin group antibiotics increased significantly and the MLD group decreased significantly. Thiamine is a B vitamin found in many foods and is a key cofactor in acetyl-coenzyme production during aerobic metabolism. When thiamine is deficient, pyruvate is preferentially metabolized to lactic acid, which accumulates to inhibit fatty acid transport and Metabolism, and mitochondrial ATP production is reduced (Schostak et al. 2022). Compensation disorders caused by thiamine deficiency can lead to neurological disorders, accelerate aging, and promote inflammation (Gibson et al. 2022). Pantocrine kinase 2 regulates mitochondrial function by controlling acetyl-coenzyme Metabolism and thus interacts with PINK1 to regulate neurodegeneration (Huang et al. 2022a, b). Most short-chain fatty acids play an active role in regulating related diseases by regulating inflammation, the immune system, and associated G-protein-coupled receptors to reduce blood pressure, prevent atherosclerosis, and improve cardiac function after cardiac arrest (Hu et al. 2022). Folate deficiency may increase cancer risk, and one-carbon metabolism directly controls processes that determine DNA synthesis and integrity. Thus Folate-dependent one-caibon metabolism is associated with tumor growth (Gigic B et al. 2022). In summary, fatigue will make thiamine, and folic acid metabolism decrease, amino acid, and fatty acid metabolism increase. In contrast, fatigue combined with a high-fat diet significantly reduces the body's metabolic levels. Therefore, fatigue may cause disease by promoting inflammation and accelerating the metabolic functions of sugars, fats, and proteins. A high-fat diet may prevent diseases by reducing the metabolic function of sugar, fat, and protein. Fatigue combined with a high-fat diet may cause a slowed metabolism, leading to diarrhea and thus other diseases.

Conclusion

Fatigue combined with a high-fat diet decreases sIgA, elevates IL-6 and IL-17, leads to oxidative stress, slows carbohydrate metabolism, amino acid metabolism, glycolysis, glucose metabolism, and fatty acid metabolism, reduces the abundance and diversity of mouse microbiota, increases harmful bacteria such as Corynebacteria, Gemella, Candidatus Arthromitus, Methylobacterium, and reduces beneficial bacteria such as Pediococecus that causes diarrhea. The interactions between Thermoactinomyces, Staphyloccus and intestinal inflammation, and immunity might be involved in fatigue and high-fat diet-induced diarrhea. The interaction of inflammation, immunity, oxidation, and microbiota may be important factors in the development of diarrhea and maybe the direction of diarrhea treatment.

Data availability statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The data presented in the study are deposited in the NCBI repository, accession number PRJNA884885.

Acknowledgements

Thanks to the editors and reviewers of this paper for their constructive comments on the manuscript.

Author contributions

LJ performed the experiments, analyzed the data and wrote the original manuscript. QB performed the experiments and analyzed the data. WY analyzed the data. DN and LDD revised the manuscript. TZJ reviewed the manuscript and funded the acquisition. All authors contributed to the article and approved the submitted version.

Funding

This research was financially supported by the National Natural Science Foundation of China (Grant No. 81874460).

Declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Institutional animal care and use committee statement

The Animal Ethics and Welfare Committee of the Hunan University of Chinese Medicine approved this study (permission number: LLBH-202206160001). All authors knew and approved of this animal experiment.

Contributor Information

Dandan Li, Email: 48797696@qq.com.

Zhoujin Tan, Email: tanzhjin@sohu.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. The data presented in the study are deposited in the NCBI repository, accession number PRJNA884885.

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