1. Introduction
Sepsis, characterized by life-threatening organ dysfunction or failure, is linked to an aberration in the equilibrium between inflammatory responses and immune system suppression [
1]. The activation of cytokines, complement components, and the coagulation system contributes to the excessive inflammatory response associated with sepsis [
2,
3,
4]. Despite ongoing advancements in the clinical management of sepsis, specific pharmaceutical interventions are lacking. Sepsis-inducing infections are the leading cause of mortality among critically ill surgical patients [
5]. Previous clinical studies have shown that a cytokine storm, involving a significant systemic release of proinflammatory cytokines in animal experiments, leads to systemic inflammation [
6]. Eliminating proinflammatory cytokines reduces organ damage and mortality associated with inflammation [
7].
The cecal ligation and puncture (CLP) model is used extensively because it closely mimics the clinical progression of sepsis in humans [
8]. When the CLP model is applied, intestinal bacteria, fungi, and metabolites migrate into the abdominal cavity, leading to abdominal infections and systemic sepsis [
9].
The gut microbiome plays a pivotal role in maintaining body homeostasis, exerting a significant influence on pathogen defense, food digestion and absorption, and immune system regulation [
10,
11]. It exhibits a robust correlation with the onset of sepsis [
12,
13].
Fucoxanthin (FX), a naturally occurring carotenoid derived from brown algae, is a promising drug candidate for treating various diseases, including sepsis, with minimal toxicity and adverse reactions [
14]. Previous studies have demonstrated that FX significantly reduces mortality in a CLP-induced sepsis mouse model via interferon regulatory factor 3 (IRF3), effectively suppresses pro-inflammatory cytokines and ROS, improves pathological damage, and activates autoimmune cells [
15,
16,
17,
18].
However, the precise mechanisms underlying the regulatory role of FX-targeted IRF3 in modulating the composition of the bacterial flora and its subsequent impact on sepsis development remain unclear. To examine the impact of FX on peritoneal and intestinal microbes in mice, we generated CLP sepsis models in wild-type (WT) and Irf3−/− mice. This study aims to establish a novel theoretical framework for utilizing FX in sepsis treatment.
2. Results
2.1. Effect of FX on Mice with CLP-Induced Sepsis via the Inhibition of IRF3
CLP-induced sepsis was modeled in mice with FX (1.0 mg/kg/day). In WT mice, the group treated with FX (1.0 mg/kg/day) exhibited decreased agglomeration, enhanced activity, and improved appetite than those in the untreated CLP group. Conversely, in
Irf3−/− mice, there were no statistically significant disparities in physical parameters between the CLP + FX and CLP groups (
Figure 1a,b).
In a CLP-induced sepsis mouse model, FX suppressed pro-inflammatory cytokine levels by inhibiting IRF3. The lung tissues of mice in the CLP group demonstrated higher expression levels with respect to pro-inflammatory cytokines IL-1β, IL-6, and TNF-β at both the protein and mRNA levels than those in the Control group. After FX treatment, WT mice subjected to CLP showed significantly lower protein and mRNA expression levels with respect to pro-inflammatory cytokines than those in the CLP-treated mice (
p < 0.0001;
Figure 1c–e). Conversely, no significant inhibitory effect was observed in
Irf3−/− mice (
p > 0.05;
Figure 1f–h). These results support the inhibitory effect of FX on inflammation in mice with CLP-induced sepsis. Additionally, the observed effects were IRF3-dependent.
2.2. FX Can Effectively Reduce Bacterial Counts in the Abdominal Cavity of Mice with CLP Sepsis via IRF3
To investigate the effect of FX on the abundance of microorganisms within the abdominal cavity of CLP mice, the ascites of WT mice was diluted 105 times and that of Irf3−/− mice was diluted 104 times based on the dilution ratio determined during the preliminary experiment. The coated samples were then subjected to microbial growth and colony enumeration.
As shown in
Figure 2 for WT mice, minimal colony growth was observed in the plates of the Sham and Sham + FX groups. Conversely, a substantial number of colonies were observed in the plates for the CLP group (
p < 0.0001). Notably, the number of colonies in the plates of the CLP + FX group was significantly lower than that in the CLP group (
p < 0.0001).
For
Irf3−/− mice, no colony growth was detected in the plates of the Sham and Sham + FX groups (
Figure 2). Conversely, a substantial number of colonies were observed in the plates of the CLP group (
p < 0.0001). However, there was no significant difference in the number of colonies in the plates of the CLP + FX and CLP groups (
p > 0.05).
These findings indicate that CLP induces a lasting intraperitoneal microbial infection in mice, while FX demonstrates reduces the quantity of intraperitoneal microbial bacteria in mice with CLP sepsis by activating IRF3, thereby achieving an anti-bacterial effect.
2.3. FX Can Effectively Reduce the Content of Acetic Acid and Propionic Acid in the Peritoneal Lavage of CLP Sepsis Mice via IRF3
According to the data presented in
Figure 2k,l, the absence of acetic acid and propionic acid was observed in both the Sham and Sham + FX groups. In WT mice, the levels of acetic acid and propionic acid in the CLP group were significantly higher than those in the Sham group (
p < 0.0001). Conversely, acetic acid and propionic acid levels were significantly decreased following FX administration (
p < 0.0001). In
Irf3−/− mice, the levels of acetic acid and propionic acid in the CLP group were significantly higher than those in the Sham group (
p < 0.01). However, the levels of acetic and propionic acid did not change significantly after FX treatment (
p > 0.05). FX had a strong inhibitory effect on acetic acid and propionic acid contents in the peritoneal lavage of mice with CLP sepsis, and these effects were mediated by IRF3.
2.4. FX Regulated Intestinal Flora Homeostasis via IRF3
To investigate the effect of FX on the intestinal flora of WT and
Irf3−/− mice, we used third-generation 16S RNA sequencing technology to examine variations in microbial communities among the groups. A Venn diagram shows that the number of operational taxonomic units (OTUs) unique to WT mice in the Sham, Sham + FX, CLP, and CLP + FX groups was 1, 10, 6, and 1, respectively, while the number of OTUs unique to
Irf3−/− mice in the Sham, Sham + FX, CLP, and CLP + FX groups was 4, 2, 5, and 5, respectively (
Figure 3a). Analyses of alpha diversity indices, including Chao1, Shannon, Simpson, and Ace, revealed significant differences between the CLP and CLP + FX groups of WT mice and the
Irf3−/− mice (
Figure 3b–e). Next, we performed a beta diversity analysis based on weighted UniFrac distances, as illustrated in
Figure 3f,g. A PCoA revealed a significant separation of gut microbial communities between the eight groups of WT and
Irf3−/− mice (
p < 0.05), and ANOSIM confirmed these results (
p < 0.05).
To further investigate variations in the microbiota structure, genus-level abundances of microbes were analyzed (
Figure 4). As shown in
Figure 3h–j,
Akkermansia spp. dominated the Sham group of both WT and
Irf3−/− mice. Among the most abundant genera in the Sham + FX group of both WT and
Irf3−/− mice were
Akkermansia, uncultured Muribaculaceae,
Escherichia,
Shigella,
Bacteroides,
Lactobacillus,
Clostridium sensu stricto 1, and Lachnospiraceae NK4A136. In WT mice,
Akkermansia and
Escherichia spp. were most abundant in the CLP group. Conversely, the abundance of
Akkermansia spp. increased significantly after FX treatment in the CLP + FX group (
p < 0.05). In
Irf3−/− mice, CLP + FX mice did not display a notably higher abundance of
Akkermansia spp. than that of
Irf3−/− mice (
p > 0.05).
2.5. Linear Discriminant Analysis Effect Size (LEfSe) of the Intestinal Microbiota
The microbiota in the Sham, CLP, and FX treatment groups were examined via an LEfSe analysis (
Figure 5a). Using linear discriminant analysis (LDA) and a score of >4 as the screening condition, 42 species with significant differences in information were detected.
Lactobacillus was the predominant bacterial genus in the Sham group.
Clostridia,
Clostridiales, and
Escherichia were more abundant in the CLP group than in the Sham group.
Bacteroides and
Muribaculum were more abundant in the FX group than in the CLP group.
In total, 42 biomarkers differed significantly from the phylum to the species level among the three groups of samples assessed and were mainly distributed in Bacteroidetes, Firmicutes, Proteobacteria, and Verrucomicrobia (
Figure 5b). At the genus level, the leading bacterial genera in the Sham, CLP, and FX groups were
Lactobacillus and
Akkermansia;
Escherichia and
Lachnospiraceae; and
Bacteroides and Muribaculaceae. These results indicate that FX alters the dominant intestinal microbial taxa and reshapes the structure of the intestinal microbiota in mice with sepsis.
2.6. FX Affects the Function of Intestinal Flora in Mice with CLP Sepsis via IRF3
To investigate the effect of FX on the function of the intestinal flora in mice with CLP-induced sepsis, we used PICRUSt2 to predict functional genes in the CLP and CLP + FX groups and found seven metabolic pathways with significant differences between groups (
Figure 6). The top three significant differences were observed with respect to nucleotide metabolism, environmental adaptation, and carbohydrate metabolism. The three most abundant genes were involved in carbohydrate metabolism, nucleotide metabolism, and the cellular community of prokaryotes. Nucleotide metabolism was observed in the CLP group, while carbohydrate metabolism occurred in the CLP + FX group, which may be the main mechanism by which FX affects the intestinal microflora in CLP sepsis.
2.7. FX Promoted the CLP-Induced Production of SCFAs in the Intestinal Flora of Mice with Sepsis via IRF3
Short-chain fatty acids (SCFAs) are the major metabolites of the intestinal flora and are strongly associated with inflammatory and immune responses in the host. As shown in
Figure 7a,b, in WT mice, the levels of both acetic and propionic acids were remarkably lower in the CLP group than in the Sham group (
p < 0.0001), whereas the levels of both acids were higher in the CLP + FX group than in the CLP group (
p < 0.0001). In
Irf3−/− mice, compared with levels in the Sham group, the levels of both acids were remarkably lower in the CLP and CLP + FX groups (
p < 0·0001). These results revealed that the effect of FX treatment in
Irf3−/− mice differed significantly from that in WT mice.
To further investigate the relationship between the changes in the intestinal flora and pro-inflammatory factors, we searched for significant differences in species composition between WT and
Irf3−/− mice. As shown in
Figure 7c,d, the mRNA and protein expression of pro-inflammatory factors were positively correlated with
Morganella spp. (
p < 0.001) in WT mice and were positively correlated with
Bacteroides spp.,
Helicobacter spp., and
Lysinibacillus spp. in
Irf3−/− mice (
p < 0.05).
Escherichia spp. and
Klebsiella spp. were positively correlated with the protein levels of TNF-β, IL-1β, and IL-6 in WT mice (
p < 0.001) and with
Clostridium sensu stricto 1 and
Bacillus spp. in
Irf3−/− mice (
p < 0.001). The serum levels of IL-1β, IL-6, and TNF-α were negatively correlated with
Parasutterella spp. and
Roseburia spp. in WT mice; and with
Akkermansia spp.,
Ligilactobacillus spp.,
Turicibacter spp., and uncultured Bacteroidales in
Irf3−/− mice (
p < 0.01).
In WT mice, Lachnoclostridium; Lachnospiraceae NK4A136 group; and Lactobacillus, an uncultured bacterium from Muribaculaceae, Parasutterella, and Roseburia were significantly positively correlated with acetic and propionic acids, whereas Morganella, Proteus, Escherichia, and Klebsiella were significantly negatively correlated. In Irf3−/− mice, Lactobacillus, Parasutterella, and Ligilactobacillus showed significant positive correlations with acetic and propionic acids (p < 0.01), whereas Clostridium sensu stricto 1, unclassified Lachnospiraceae, and unclassified Oscillospiraceae were significantly negatively correlated (p < 0.01). Interestingly, these changes were opposite to the changes in inflammatory factor levels observed in serum and lung tissues.
As shown in
Figure 7e, redundancy analysis and canonical correspondence analysis were used to analyze the correlations between SCFAs and bacterial populations at the genus level. Acetic and propionic acids were positively correlated with the Lachnospiraceae NK4A136 group,
Lactobacillus, and
Akkermansia and negatively correlated with
Morganella,
Alloprevotella,
Proteus,
Klebsiella,
Escherichia, and
Bacteroides. A higher correlation was observed between
Morganella and CLP groups,
Alloprevotella and CLP + FX groups, and Lachnospiraceae NK4A136 and the Sham and Sham + FX groups.
3. Discussion
The composition of intestinal microbes in mice with CLP-induced sepsis was altered by treatment with FX, and the OTU quantity of the intestinal flora in mice with CLP sepsis was altered by IRF3. FX changed the species diversity and species distribution of intestinal microbes in mice with sepsis, and the similarity in intestinal microbial communities among all groups was low. In the gut of mice with CLP sepsis, FX treatment significantly increased the abundance of beneficial bacteria, such as Verrucomicrobiota and Akkermansia. Simultaneously, following FX treatment, the dominant intestinal bacteria in mice with CLP sepsis shifted from Akkermansia, Escherichia, and Morganella to Akkermansia and Escherichia. According to PICRUSt2, FX affects the intestinal flora of mice with CLP-induced sepsis by altering glucose metabolism. Further analyses of the correlations between the intestinal flora, inflammatory factors, and SCFAs can reveal the marker florae that affect the inflammatory response. This analysis indicated that, following FX treatment, the intestinal flora of mice with sepsis exhibited interactions with inflammatory factors and SCFAs. Notably, the levels of acetic acid and propionic acid showed negative correlations with the expression levels of inflammatory factors IL-6, IL-1β, and TNF-α. Conversely, the abundances of Morganella, Proteus, Escherichia, and Klebsiella exhibited positive correlations with the expression levels of IL-6, IL-1β, and TNF-α that were induced by CLP and negative correlations with the production of acetic acid and propionic acid.
The intestinal lumen contains many intestinal microbiota that regulate intestinal immune homeostasis and affect the development and function of host immune cells [
19]. The destruction of the intestinal microbiota integrity may increase susceptibility to sepsis [
20]. The apoptosis of midgut epithelial cells has been observed in patients with sepsis and mouse models. This weakens the gut barrier, which in turn affects inflammation [
21,
22].
In the present study, the alteration of the intestinal microbial composition in mice with CLP sepsis was observed after FX treatment, and this change was linked to the expression of inflammatory factors. Consequently, we further evaluated the relationship between inflammatory factors and the intestinal microbiota. Following FX treatment, the dominant intestinal flora of mice with CLP-induced sepsis changed from Akkermansia, Escherichia, and Morganella to Akkermansia and Escherichia. Based on a Spearman correlation analysis, Morganella was positively correlated with IL-6, IL-1a, and TNFα expression after CLP and negatively correlated with the production of acetic and propionic acids.
The abundance of
Akkermansia, a common anaerobic organism in the human and rodent intestinal microbiome, is negatively correlated with inflammation in inflammatory bowel disease [
23]. FX increased the abundance of
Akkermansia, suggesting that FX has a positive effect on the structure and function of the intestinal flora in mice with sepsis.
Morganella morganii is a Gram-negative bacterium that causes various infections, including sepsis, leading to high mortality rates [
24]. The results of this study suggested that FX alleviates the CLP-induced dysregulation of the intestinal microflora and inhibits the expression of IL-1β, IL-6, and TNF-β.
SCFAs are produced by fermenting indigestible polysaccharides (e.g., dietary fibers). Approximately 10% of SCFAs are excreted in the stool after they are produced in the gastrointestinal tract, while the remaining SCFAs are absorbed to provide energy to the host epithelium. Via the portal vein and circulation, SCFAs are transported to other organs and play a systemic role [
25,
26,
27]. In humans, acetate, propionate, and butyrate make up more than 95% of intestinal SCFAs [
28,
29,
30]. FX facilitated an increase in acetic and propionic acids, thus attenuating inflammation by regulating SCFA production. FX may have effects against inflammatory and bacterial processes. However, the sequential relationship between the anti-inflammatory and anti-bacterial effects of FX warrants further investigation, especially in the context of intestinal microflora transplants in mice. The relationship between IRF3 and intestinal microbes was not within the scope of this study. Subsequent studies should focus on the impact of FX on molecular signal transduction mechanisms.
4. Materials and Methods
4.1. Chemicals and Reagents
AbMole (Shanghai, China) provided FX with over 98.5% purity, as determined using high-performance liquid chromatography. Other reagents were of domestic analytical purity. For the animal experiments, FX powder was dissolved in DMSO to prepare a working solution with a concentration of 1.0 mg/mL. The FX suspension was then administered intraperitoneally (i.p.) to subjects at a daily dosage of 1.0 mg/kg. The Control group received an equivalent volume of the vehicle.
4.2. Animals
For the animal experiments, pathogen-free C57BL/6 mice (8–10 weeks old) were used with an equal distribution of males and females. Wild-type (WT) mice were obtained from Shanghai SLAC Laboratory Animal Co., Ltd. (Shanghai, China), and Irf3−/− mice were sourced from the RIKEN BioResource Research Center (BRC-No:00858, Tokyo, Japan). The mice had unrestricted access to food and water. Experimental conditions included a temperature range of 23–25 °C, humidity levels of 40–60%, and a 12 h light/dark cycle. Male mice weighing 20–22 g and female mice weighing 18–20 g were randomly assigned to groups. All animal experiments were conducted in accordance with the Guide for the Care and Use of Laboratory Animals. The study protocol was approved by the Institutional Animal Care and Use Committee of the Fujian Normal University (approval no. 201800013).
4.3. Model of CLP-Induced Sepsis
Sepsis was induced using a previously described CLP model [
17]. In the Control group, laparotomies were performed without ligation or puncture.
4.4. Experimental Protocol
Eighty mice (female, n = 40; male, n = 40) were randomly allocated to four groups: Control, Control + FX, CLP-induced sepsis (CLP), and CLP + FX.
To effectively assess the efficacy of sepsis treatment, a mortality rate of 50% was required in mice with CLP-induced sepsis. Mice were administered FX (1.0 mg/kg/day) 2 h after the establishment of the sepsis model. Mice were injected with pentobarbital sodium salt intraperitoneally, and tissue, feces, and ascites samples were collected [
18,
30].
4.5. Quantitative Reverse Transcription PCR (RT-qPCR)
TRIzol reagent (Takara, Tokyo, Japan) was used to isolate total RNA from serum and lung tissues. RT-qPCR was conducted following previously described methods [
16] and using primers listed in
Table 1 for mRNA amplification.
4.6. Enzyme-Linked Immunosorbent Assay (ELISA)
Cytokines IL-1β, IL-6, and TNF-β were quantified using ELISA kits (IL-6: SM6000B; IL-1β: SMLB00C; TNF-β: SMTA00B; R&D Systems, Minneapolis, MN, USA) as per the manufacturer’s protocols.
4.7. Acquisition of Ascites in Mice
Two hours after CLP modeling, mice were subjected to the intraperitoneal injection of FX solutions at a dosage of 1.0 mg/kg/day or a control solvent lacking any active ingredient. After a 24 h treatment period, the mice were anesthetized, euthanized, and securely positioned on the operating table. Within a biosafety cabinet, 1 mL of sterile saline solution was injected into the abdominal cavities of the mice. Subsequently, the abdomen was gently massaged, and saline was extracted using a syringe, with caution not to puncture the organs or intestines. The collected saline was then transferred to a sterile EP tube for further processing.
4.8. Detection of Bacteria in Mouse Abdominal Cavity
Ascites samples were diluted at a specific ratio; subsequently, 100 µL was inoculated into the agar medium. The culture was incubated at 37 °C for 24 h, during which the growth of colonies was observed and quantified.
4.9. Quantification of Fecal Short-Chain Fatty Acids (SCFAs)
To quantify fecal SCFAs, 50 ± 1 mg of feces was added to 1 mL of deionized water. The mixture was homogenized for 4 min at 40 Hz and centrifuged at 16,000×
g for 30 min at 4 °C. In total, 0.8 mL of the supernatant was mixed with 0.1 mL 50% H
2SO
4 and 0.8 mL of 2-methylvaleric acid (25 mg/L stock in methyl tertbutyl ether) as an internal standard and stored at −20 °C after ultrasonication and centrifugation at 12,000×
g for 10 min. The supernatant was used for gas chromatography–mass spectrometry (GC-MS) analysis (Shimadzu, Japan) using an autosampler with an injection volume of 1.0 µL in accordance with a previously described method [
27].
4.10. DNA Extraction and Barcoded Sequencing of the 16S rRNA Gene
Fecal samples were collected from each mouse at 24 h after FX treatment and sent to Biomarker Technologies Co., Ltd. (Beijing, China) for DNA extraction and 16S rRNA gene sequencing. Library construction, sequencing, and data analysis were performed using the PacBio Sequel II platform (Biomarker Technologies Co., Ltd.).
4.11. Bioinformatic Analysis
Bioinformatics analyses were performed using BMK Cloud (Biomarker Technologies Co., Ltd.). Sequences with ≥97% similarity were clustered into operational taxonomic units (OTUs) using USEARCH (v10 ≥ 0), and OTUs with an abundance of <0.005% were filtered [
28]. Alpha and beta diversities were analyzed at the OTU level using QIIME. Alpha diversity was characterized using Shannon, Acer, Chao1, and Simpson metrics. The differences in microbial composition were further characterized by calculating beta diversity and analyzed based on weighted UniFrac distances. Group differences were compared using Adonis, and the results of the analysis of similarity (ANOSIM) were visualized using principal coordinate analysis (PCoA). For different species, the heat map reflects the similarities and differences in composition between multiple samples based on colors. Using a threshold linear discriminant analysis score of ≥4, the effect size of linear discriminant analyses can identify biomarkers with significant differences between groups and analyze the evolutionary relationships between species. In the plot, dots with different colors represent microbiomes with significant differences in the corresponding groups; ossia indicates significant differences between groups, while light yellow dots indicate a lack of significant influence. Finally, the sequences were compared with data from the Kyoto Encyclopedia of Genes and Genomes, and functional predictions were made using the phylogenetic investigation of communities via the reconstruction of unobserved state 2 (PICRUSt2).
4.12. Statistical Analysis
Data are expressed as the mean ± standard deviation. All results were analyzed using Tukey’s post hoc tests and one-way analysis of variance (ANOVA). Images were processed using Photoshop (Illustrator 2020; Adobe, San Jose, CA, USA) and ImageJ v1.8.0 (National Institutes of Health, Bethesda, MD, USA). GraphPad Prism (v8.0; GraphPad Software, San Diego, CA, USA) was used to perform statistical analyses. p < 0.05 was set as the significance level.