Bacillus cereus alleviates liver lipid accumulation by activating the BAs-FXR axis and enhanced bile acid excretion in Nile tilapia
Miao Li, NanNan Zhou, Le Zhang, Fang Qiao, Zhen-Yu Du, Mei-Ling Zhang*
School of Life Sciences, East China Normal University, Shanghai 200241, China
*Corresponding author: Mei-Ling Zhang, Email: mlzhang@bio.ecnu.edu.cn, Tel/Fax: 86-21-54345354
Key words: Bacillus cereus, gut microbiota, bile acid metabolism, Farnesoid X receptor (FXR)
Abstract
Utilizing the probiotics is a promising way in treating metabolic related diseases caused by high carbohydrate diets. The purpose of this study is to examine the effects and to understand the mechanisms of the probiotic, Bacillus cereus alleviating hepatic lipid accumulation in tilapia fed with high carbohydrate diet. Bacillus cereus supplementation modified intestinal microbiota towards an active bile acid metabolism by increasing the bile acid related bacteria. Changed hepatic bile acid composition induced activation of FXR, led to inhibition of biosynthesis of liver lipid. In vitro studies showed that FXR agonist suppressed the accumulation of lipid in zebrafish liver cells. These results identify the bile acids as important metabolic effectors under the action of probiotics to alleviate lipid accumulation and highlight the casual relationship of bile acid metabolism by the host diet-induced changes of the gut microbiota and lipid metabolism.
Introduction
Carbohydrate is commonly used in fish feeds as the major energy source because its protein-sparing effect and favourable costs. In addition, carbohydrates can promote stability of feed, acting as stabilisers and swelling agents in diets. Furthermore, high carbohydrate attenuates the antibiotic-induced side effects by binding the antibiotic in physiological[1]. However, fish have a limited ability to efficiently use carbohydrate compared to mammals, in which fish consuming higher carbohydrate intake had an attenuated return to baseline glucose levels[5]. A previous study done in our lab revealed that Nile tilapia (Oreochromis niloticus) is a common fish model for nutrition and metabolism studies[3]. It has been found that high-carbohydrate diet induced lipid metabolism disorder, increased whole body lipid content of nile tilapia and other bony fish species, such as Megalobrama amblycephala[2, 3]. Recently, the potential regulatory mechanism to reduce lipid metabolism diseases caused by high carbohydrate diet has attracted high attention.
Over the past years, the probiotics has ignited keen interest of researchers as a possible bridge between host lipid metabolism. We summarize several mechanisms by which probiotics are involved in alleviating lipid metabolism. First, probiotics might stimulate FA absorption by regulating enterocyte LD size and number. In vitro Lactobacillus paracasei inhibited lipid secretion by increases fat storage in enterocytes’ cytosolic lipid LD[4]. Under conditions of maintenance on a high fat diet, Lactobacillus paracasei colonized in gut resulting in higher levels of energy extraction from complex polysaccharides and, consequently, in lower levels of fat absorption[16]. Second, probiotic effector molecules such as bacterial cellular components (anaerobic lysate, polysaccha ride capsules, or outer-membrane pili-like protein) also play a role in cross talk between probiotics and host lipid metabolism. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium supplementation helped improve features of metabolic syndrome in overweight and obese mice[5]. Third, certain microbial metabolites derived from the microorganisms using dietary nutrients as substrates act as a bridge between diet (microbiota) and obesity, and are of great value in the process of mitigating lipid metabolism process. Butyrate-producing microbes through the intracellular butyrate sensor peroxisome proliferator–activated receptor γ (PPAR-γ), which drove the energy metabolism of colonic epithelial cells (colonocytes) toward β-oxidation[6]. Gut commensal Parabacteroides distasonis was demonstrated that it could alleviate obesity and metabolic dysfunctions through production of succinate and secondary bile acids[7]. Bile acid as key metabolite reduced liver lipid accumulation in blunt-snout bream and alleviated hepatic fibrosis induced by a high starch diet in Micropterus salmoides[8, 9]. Fish dominant microbiota is different from humans or mice[10]. To better resolve extant aquaculture application, the application of probiotic bacteria is one of extremely effective ways.
Spore-forming bacteria such as Bacillus are gaining interest in animal energy metabolism related functional additive research, due to their high tolerance and survivability under hostile environment in gastrointestinal tract, making it more suitable candidates compared to other probiotics[11]. Bacillus species are one of the most widely used probiotics in dietary interventions. The anti-obesity and anti-hyperlipidemic effects have been well documented by numerous studies in Bacillus subtilis consumption could reduce inflammation, improve fat metabolism and restore intestinal barrier functions, thus contributing to the treatment of metabolic syndrome in vivo[12]. Bacillus amyloliquefaciens ameliorates high-carbohydrate diet-induced metabolic phenotypes in fish[13]. However, many properties of probiotic bacteria vary as a function between species and strains as shown for Bacillus subtilis, Bacillus amyloliquefaciens and Bacillus cereus. Bacillus cereus has been traditionally used for enhancing the immune status and affecting the gut microbiota of tilapia, as a water or feed additive. In addition, genome analysis pointed to that Bacillus cereus strains carry genes for utilisation of mono- and disaccharides such as glucose and trehalose[14]. However, the effects of the Bacillus cereus on metabolic homeostasis and the mechanistic details remain largely elusive.
In the present study, we aim to elucidate the therapeutic effects and possible mechanisms of Bacillus cereus against lipid accumulation induced by high carbohydrate diet. We found that addition of Bacillus subtilis Su1, a commensal probiotic applied in aquaculture, with feed protected Nile tilapia completely from lipid accumulation caused by high carbohydrate diet. Our study further showed that a cluster intestinal microbiome induced by Su1 associated with increase of intestinal bile acids (BAs), which in turn promoted bile acids synthesis in liver, resulting in the activation of FXR by the modified bile acids in liver. The activation of hepatic FXR promoted the beneficial effect of Su1 in attenuating lipid accumulation. This is also the first systematic elucidation how Bacillus cereus regulate lipid accumulation through bile acids metabolism in fish.
2.Method details
2.1. Ethical statement
All animal studies were instructed according to guidelines of the Guidance of the Care and Use of Laboratory Animals in China. Animal research performed in this manuscript was approved by the Committee on the Ethics of Animal Experiments of East China Normal university (ECNU)（NO.F20220101）.
2.2. Bacteria and culture condition
Bacillus cereus Su1 was from our laboratory stock, and was isolated from tilapia gut using culture medium which starch was used as carbon source. 16S rRNA gene sequencing showed that the strain was affiliated to Bacillus cereus ATCC 14579. The strain was cultivated in Luria-Bertani (LB) medium at 37 °C for 24 h.
2.3. Animal experiment
All healthy juvenile Nile Tilapia (2±0.5g) were purchased from Shanghai Ocean University (Shanghai, China). All fish were fed under controlled environment (12/12 h light-dark cycles with water temperature of 27-28 ℃). Nile tilapia were arbitrarily divided into three groups: control group (CON), high carbohydrate diet group (HC), high carbohydrate diet with daily Su1 for 8 weeks (HCS). Three repetitive cylinders per group. Live Su1 (suspended in physiological saline) was given to Nile tilapia by adding in feed at a dose of 108 CFU/g for eight weeks. At the end of the feeding trial, fish were fasted overnight and then sacrificed. Blood was collected and centrifuged at 3000 g for 10 min for serum isolation. In addition, all tissue samples, including Liver, adipose tissues, intestine and intestinal contents were frozen with liquid nitrogen immediately before storage at -80°C.
2.4. RNA isolation and qPCR
Total RNA of each liver sample was isolated using the Trizol reagent (Takara, Japan) according to the manufacturer’ s protocol and then reversed transcribed to cDNA synthesis by using a FastQuant reverse transcribed Kit with gDNase (Tiangen, China). Quantitative real-time PCR reactions for the indicated genes were conducted using SYBR Green Supermix (Aidlab, China) by S1000™ Thermal Cycler (BioRad). The qPCR primers are listed in supplementary material. Quantitative real time PCR (qRT-PCR) includes 10 of 2XSYBR qPCR mixture (Aidlab Biotech, Beijing, China), 100 ng of cDNA, 300 nM qPCR primers, and nuclease-free water. The qPCR conditions consisted of 95°C for 10 min, forty cycles of 95°C for 5 s and 60°C for 15 s. The melting curves of the amplified products were analyzed at the end of the qPCR. The amplification efficiency was between 95 and 105 %, and the correlation coefficient was above 0·98 for each gene. The fold difference in mRNA expression was calculated by 2−ΔΔCT method.
2.5. 16S rRNA gene analysis.
PCR amplification of the bacterial 16S rRNA gene V3–V4 region was performed using the forward primer (5′-CCTACGGGNGGCWGCAG-3′) and the reverse primer (5′-GACTACHVGGGTATCTAATCC-3′). Principal component analysis (PCA) was performed using the QIIME and R software. A similarity threshold of 97% classifies sequences into distinct OTUs, each of which is typically considered a microbial species. Principal component analysis (PCA) based on unweighted UniFrac distance metric, was used to analyze all OTUs, affording information on microbial community differences among samples. We selected 22 OTUs for heatmap based on relative abundance of >1% and significantly different among three groups by using Kruskal-Wallis in R package. Spearman’s correlation analyses were used to assess the potential association between the bacterial genera and the bile acids composition in zebrafish using Hmisc package in R[15].
2.6. Metabolic assay
Liver or intestinal contents samples was respectively weighed 50 mg. 400ul extracting solution (methanol: water=4:1) was added to the sample. The mixture was ground with the frozen tissue grinder for 6min (-10℃, 50Hz) and ultrasound for 30min (5℃,40KHz) then centrifuged at 12000 g at 4 °C for 15 min. The supernatant was transferred into a 1.5 mL tube and extracted by a 150 μL aliquot of methanol then homogenized for 30 s. Vortexed supernatant was filtered through a 0.22 μm membrane and added to detection bottle. BA analysis was performed on the instrument LC-ESI-MS/MS (UHPLC-Qtrap). The mobile phase was A (0.01% formic acid - aqueous solution), mobile phase B (0.01% formic acid - acetonitrile). The gradient elution conditions were 0 min, A 75%, B 25%; 10 min, A 75%, B 25%; 26 min, A 25%, B 75%; 26.1 min, A0%, B100%; 28 min, A 0%, B 100%; 28.1 min, A 75%, B 25%; 32 min, A 75%, B 25%. The flow rate was 0.35 mL/min. Mass spectrometry conditions: electrospray ionization (ESI) source, negative ion ionization mode. The ion source temperature was 500 ℃, the ion source voltage is -4500V, the collision gas was 6psi, the curtain gas is 30psi, and the atomization gas and auxiliary gas were both 50psi. Multiple reaction monitoring (MRM) was used for scanning.
2.7. Western blotting analysis
Western blotting analysis was performed as described in our previous study[16]. Briefly, proteins were subjected to SDS-PAGE gels (8%-12%) and then transferred for electrophoresis. After being blocked with 5% of dried skimmed milk, the membranes were incubated with primary antibodies overnight at 4 °C. The following antibodies were used: anti FXR/NR1H4 (E4B8P) mouse mAb (#72105, cell signaling technology), GAPDH antibody (AF7021, affinity).
2.8. Oil-Red O Staining
Tilapia Liver tissues were fixed in 4% paraformaldehyde dehydrated and embedded in paraffin. Liver sections (3 μm thickness) were then subjected to 0.5% oil Red O and hematoxylin. The red lipid droplets were visualized by microscopy (Nikon, Japan).
2.9. Liver SM and HMGCR activity measurement
Liver SM and HMGCR activity measurement was quantified using a sandwich ELISA Kit (Shanghai Hengyuan Biological Technology Co, Ltd) following the manufacturer’s instructions. Liver samples was weighed 50mg and then was homogenized with 9 volumes of PBS. Briefly, 100 μL of standards, blank or samples were added to wells pre-coated with the target specific capture antibody of SM, which will bind to the target antigen (SM). Then 100 μL of biotin-conjugated detection antibody (Detection Reagent A) was added to each well, which in turn binds to the capture antigen, and the total mixture was incubated for 1 h at 37 °C followed by a wash step with 350 μL wash buffer per well x3.A100 μL aliquot of avidin-horseradish peroxidase (HRP) conjugate (Detection Reagent B) was then added (binds to the biotin) and the plate was incubated for 1 hour at 37 °C and further washed for 5 times. About 90 μL of TMB substrate was added to each well followed by incubation for 30 min at 37 °C to allow the reaction with the HRP enzyme for detection. Subsequently, 50 μL of sulfuric acid stop solution was added to each well to terminate the color development reaction. The optical density (OD) of each well was measured at a wavelength of 450 nm using a SpextraMax i3 Multi-mode Microplate Reader (Molecular Devices, USA). The standard stock solution was diluted with sample diluent to prepare a standard dilution series from 78 to 5000 pg/mL in order to generate a standard curve. A four-parameter logistic curve fit was selected to generate the standard curve and calculate the concentration of each sample.
2.10. Cell culture and treatments
Adult zebrafish liver cell line (ATCCCRL-2643) was cultivated in L15-DMEM-F12 (LDF) medium supplemented with 50% Leibovitz’s medium (L-15, Biological Industries, United States), 15% Fetal bovine serum (F-12, Biological Industries, United States), and with 50 units/mL penicillin and 50 μg/mL streptomycin (Gibco, United States). Cells were grown in cell culture flasks at 28°C. One day prior to experiments, cells were seeded in 6 well plates with a density of 106 cells or 5×105 cells, into 1 mL of medium per well, respectively. Oleic acid (final concentration of 500 μM) and GW4064 (final concentration of 5 μM) was added and incubated for 24 h. Then, cells were harvested and evaluated with qRT-PCR. Cells were also fixed with 4% paraformaldehyde and followed by staining using BODIPY 493/503 (D3922, Invitrogen) for 15 min in a 10 ug/mL concentration at room temperature, as described earlier[17]. The green lipid droplets were visualized by microscopy (Nikon, Japan).
2.11. Statistical analyses
All experimental data were showed as the mean ± SEM. Experiments with two groups were analyzed with Student's t tests by GraphPad Prim 6. The p values were corrected for multiple testing by the Benjamini-Hochberg false discovery rate test at the p < 0.05 threshold of statistical significance.
3 Results
3.1 Su1 decreased body weight gain and accumulation of lipid in high-carbohydrate fed tilapia.
Juvenile tilapia was fed with normal chow (CON), high carbohydrate diet (HC), or high carbohydrate diet with Su1 supplementation (HCS) for 8 weeks. Compared to the control diet group, the body weight of the tilapia in HC group was significantly increased since the sixth week (P<0.05, Fig 1A). Administration of Su1 (HCS) significantly decreased the body weight compared with HC (P<0.05, Fig 1A). At the end of the experiment, HC administration was found to induce a pronounced increase in total lipid, somatic indices (hepato-somatic index, mesenteric fat index) in fish, and Su1 significantly attenuated the lipid accumulation of total fish, liver tissues and adipose tissues, compared to HC group (P<0.05, Figure 1B-D).
The results showed that the triglyceride level of serum and liver were decreased by Su1treatment. (P<0.05, Figure 1E-F). Oil red staining was administrated to evaluate the lipid droplets in liver cells and the results showed that HC has promoted lipid droplets accumulation while HCS reverse it as shown in Fig 1G. The increased expressed transcriptional levels of lipid synthesis genes including SREBP1, ACCα, PPARγ were observed in HC group (P<0.05 or 0.01, Fig 1H), while these genes were decreased in HCS (P<0.05 or 0.01, Fig 1H). The expression level of genes related to lipid catabolism were also detected, but there was no statistical difference among groups (P>0.05, Fig 1I). These results suggested that Su1 supplementation alleviated lipid accumulation phenotypes by suppressing lipid synthesis in high-carbohydrate fed tilapia.
3.2 Intestinal microbiota reshaped by administration of Su1 in tilapia.
To detect whether bacterium Su1 ameliorated lipid accumulation through modifying the gut microbiome, we obtained intestinal microflora of three groups by 16S rDNA amplicon sequencing. While the species richness as predicted by the Ace index and Chao index were not different between three groups (P>0.05, Fig 2A-B). For alpha diversity, the flora diversities of HCS group as estimated by the Shannon index was significantly increased in HCS group (P<0.05, Fig 2C), Simpson index was significantly reduced in HCS group (P<0.05, Fig 2D). Beta diversity analyses showed that principal component analysis (PCA) based on the unweighted UniFrac method indicated that the intestinal bacterial composition was different obviously between HC and other two groups, and it was similar between CON and HCS group (Fig 2A). On the phylum level, the abundance of Proteobacteria was increased, whereas the abundance of Actinobacteriota family members was lower in HCS group (P<0.05, Fig 2B). The results of the heatmap construction at the genus level indicated that the abundance of bacteria, which we added in feed, the order of Bacillales, including Bacillaceae were increased in Su1 treatment group. In addition, the increased bacteria species including Gaiellales, Leucobacter, leifsonia, Propionibacteriales, Bacteroidales, Thermonicrobiales, Bacillales, Enterococcaceae, Rhizobiales. Compared with HC group, the Su1 decreased the proportions of Rhodococcus, Aurantimicrobium, Thermomicrobiales. We applied the pearson modeling framework to analyze co-relationships among genus. As shown in Figures 2H, there are a correlation cluster. These positive correlation dominant assemblages cluster Ⅲ contained multiple taxa belonging to OTU1950 Mycobacterium, OTU1918 Microbacteriaceae, OTU1954 Nocardioides, OTU2066 Leucobacter, OTU1905 Bacteroidales, OTU2034 Rhizobiales, OTU2000 Bacillales were associated with bile acid metabolism[18-21]. These results suggested Su1 administration modifies the gut microbiota.
3.3 Su1 treatment regulates intestinal bile acid composition.
The intestinal bile acids were examined to determine whether the altered microbiome induced by the administration of Su1 modified the composition of intestinal bile acids. Heatmap visualization indicated that the intestinal bile acids composition is different with HC group. HCS group’ intestinal bile acids composition has difference with HC group, while are similar with CON group (Fig 3A). Compared with the HC group, the concentration of CA, TCA, ACA, CDCA, CDCA-3Gln and LCA-S exhibited elevated levels in HCS group (P<0.05, Fig 4B). Additionally, it was significantly increased in the concentration of GCA, NorCA, TCDCA and TUDCA in the HCS group (P<0.01, Fig 3B). Moreover, as shown in Fig.3C, network correlation analysis was performed to reveal the relationships among intestinal microbial communities and intestinal bile acids. Primary bile acids and their derivatives such as CA, TCDCA, GCDCA, GCA, TCA, CDCA-3-Gln are positively correlated with each other. GDCA and TDCA are negatively correlated with other bile acids. The red line indicated a significantly positive correlation and the blue line presented a significantly negative correlation between each bile acids and intestinal microbial communities. Five genus-level taxa OTU111 (Rhizobiales incertae sedis), OTU1842 (Rhodococcus), OTU2051 (Aurantimicrobium), OTU1837 (Streptococcus), OTU2000 (Anxybacillus) were negatively correlated with GDCA, TDCA, NorCA, CDCA, 3DHCA, whereas 11 genus-level taxa, including Leucobacter, Leifsonia, Nocardioides, Prevotella, Mycobacterium, PeM15, Exiguobacterium, Bacillus, Alsobacter, Thermomicrobiales and Acinetobacter were positively relevant to lipid most bile acids (P<0.05, Fig 3C). These results, combined with the effects of Su1 treatment on intestinal bacteria, provided compelling evidence that Su1 might influence the intestinal bile acid composition.
3.4 Su1 treatment enhanced bile acid excretion.
To further investigate the effects of Su1 on the composition of intestinal bile acids, we measured the ratio of more than 45 different BAs. As shown in Fig 4A, high carbohydrate diet treatment significantly changed the ratios of unconjugated BAs to conjugated BAs. The concentration of CA is increased in HC group, compared to that in the control group. To the contrary, CA concentration is decreased in HCS group. Interestingly, high carbohydrate diet induced decrease of TCA was reversed by administration of Su1. Notably, Su1 suppressed the increase of LCA caused by HC (Fig. 4A). In addition, the concentration of conjugated bile acid and unconjugated bile acid were significantly increased in HCS group (P<0.05, Fig 4B-D). Furthermore, fecal BA excretion was increased in HCS treatment (P<0.05, Fig 4E). On the contrary, the expression of apical sodium-dependent BA transporter (ASBT) which reabsorbed conjugated bile acids from intestine has no significant difference (P<0.05, Fig 4F).
3.5 Su1 treatment promoted BA synthesis.
We observed that total bile acids concentration in serum and liver are significantly increased in HCS group (P<0.05, Fig 5A-B). We identify whether promotion of bile acids synthesis during Su1 treatment is involved in the enhanced concentration of bile acid flux. We observed an increased protein activity of CYP7A1 and CYP27A1, which are the key enzymes for bile acids synthesis in HCS group (P<0.05, Fig 5C-D). However, total cholesterol in liver had no significant difference (P>0.05, Fig 5E). HC caused a significant increase of liver squalene monooxygenase (SM) protein activity, which was accelerated by Su1(P<0.05, Fig 5F). These findings further supported that BA synthesis promotion is partly responsible for the enhanced bile acid flux of Su1 on the bile acids homeostasis.
3.6 Su1 treatment regulates liver bile acid composition.
To assess whether the administration of Su1 changed the BA profiles, liver BA profiles were examined. Heatmap visualization indicated that HCS group promoted many bile acids concentration in liver (Fig 6A). The concentration of TCA, CDCA, ACA, GCA, NorCA, TCDCA, GDCA, THDCA+TUDCA are significantly increased in HCS group (P<0.05, Fig 6B). In addition, CA is increased extremely in HCS group (P<0.01, Fig 6B). HC-induced changes of proportion of TCA and CA were markedly enlarged by administration of Su1 (P<0.01, Fig 6C). Moreover, the types of secondary bile acids in the liver are also enriched in HCS group (P<0.01, Fig 6C). Furthermore, we further conducted the concentration of, conjugated BA and unconjugated BA in the HCS treatment group was significantly higher than HC group (P<0.01, Fig 6D and P<0.05, Fig 6E). The results showed that the composition of hepatic bile acids was changed by administrating of Su1.
3.7 Modified bile acids activated FXR, resulting in active lipid metabolism
To clarify whether the addition of Su1 activate bile acid receptor FXR, an experiment of expression of protein was performed. In addition, Su1 treatment resulted in activation of FXR (Fig 6A-B). In zebrafish liver cell, we verified the regulation of lipid metabolism under activation of FXR. Our data showed that 5μM of GW4064, as a potent FXR agonist, increased FXR(NR1H4) expression in the cell nucleus, which in turn, up-regulated the expression of SREBP1c and CPT1a in the presence of oleic acid (P<0.05, Fig. 6D). As a result, oleic acid increased the expression of SREBP1c and PPARγ in zebrafish liver cells. Besides, GW4064 slightly increased the expression of SREBP1c (P<0.05, Fig. 6E). As expected, histological examination of zebrafish live cells stained with 4,4-difluoro-4-bora-3a, 4a-diaza-s-indacene (BODYPY) showed GW4064 attenuated lipid droplet accumulated by oleic acid (Fig 6C). These findings further support that FXR activation is partly responsible for the beneficial effects of Su1 on the lipid homeostasis.
4. discussion
Although previous studies have linked intestinal microbiota to the bile acids and host metabolism, these studies establish a correlation relationship between gut bacteria, bile acids and host metabolic status. However, the mechanisms underlying this process are yet to be understood completely. We established a causal relationship pathway involving the intestinal microbiota and BAs to describe how intestinal microbiota improve high carbohydrate diet induced lipid accumulation through regulating bile acid metabolisms in tilapia. This study proceeded with an integrated bile acid profile determination by UHPLC-MS/MS to identify the effect of exogenous probiotics supplementary on the endogenous BA profile and liver health of tilapia and discussed how the probiotics supplementary and BA are transformed in the body, providing a theoretical basis for the application of probiotics supplementary and BAs products in fish and a data basis for revealing the mystery of fish probiotics and BAs.
There are many beneficial effects of probiotics, one among which is the mitigant of the lipid accumulation. Probiotic effector molecules, regulation of the microflora and metabolites mediate the delivery of strain effects. For instance, purified membrane protein from Akkermansia muciniphila, gut microbiota-derived short-chain fatty acids and faecal transplantation could improve metabolism in obese and diabetic mice[5, 22, 23]. However, another metabolite of the intestinal flora-bile acids (BAs) also play a key role in improving metabolic homeostasis. A previous study has shown that the beneficial effects of capsaicin on glucose homeostasis are associated with activation of gut microbiota-BA-GLP-1 signaling resulting from an accumulation of LCA[24]. This is another evidence that bile acid metabolism (activation of the synthesis and transport of bile acids and their receptors) plays a critical role in reversion lipid accumulation of dietary betaine and benefits of blueberry[8]. Although the interaction between BAs and microbiota has received ample scientific attention, these studies to date primarily demonstrated associative relationships rather than causative ones. But in recent years little attention has so far been in the direction of the mechanisms for probiotics to exert their effects through metabolite-bile acids. In this study, we established a causal relationship pathway involving the intestinal microbiota and BAs to describe how a probiotic selected from intestinal microbiota improved high carbohydrate diet induced lipid accumulation through regulating bile acids metabolisms in tilapia.
Our results show that we investigated that the altered gut microbiota influenced bile acid (BA) metabolism, BAs as metabolic regulators could also activate bile acid receptor modulates the metabolism of lipids. Regulation of bile acids by microbes is thought to be key to this mechanism. Su1 modulated gut microbiota of tilapia, the presence of a changed microbiota promoted three distinct phenotypes of bile acids formation within the enterocyte: increased bile acids flux, enhanced bile acid excretion and changed composition. Previous researches have established that bile acid receptor, farnesoid X receptor (FXR, NR1H4), under complex regulatory control of bile salt and functionally linked to lipid metabolism gene. Previous research has revealed that CDCA and CA as potent natural agonists of FXR activated FXR, while other BAs (TCA, GCA, ACA) had no effect on FXR, but could efficiently antagonize CA and CDCA induced FXR activation[25]. These findings were further confirmed in our study, in that increased BA pool resulted in activation of liver FXR, further lead to inhibition of lipid metabolism genes. Recent studies indicate that Pu-erh tea reduced hepatic lipid via activation of hepatic FXR and lipogenesis[26]. In contrast with our in vivo results, an activation of FXR led to inhibition of biosynthesis of lipid. A previous study reveals that the FXR activation reduced hepatic TAGs and lipogenic gene expression through a SHP-SREBP1c pathway in mice, which is agreement with our study[27]. Therefore, the interactive relationship remains to be determined about hepatic lipid metabolism genes, and FXR.
The composition of bile acids varies among species, resulting in different metabolic characteristics of the host. Previous evidence has revealed that hyocholic acid (HCA) species, including HCA, HDCA, and their glycine- and taurine- conjugated derivatives constituted nearly major component of total BAs in pig plasma while comprised only a small part in the plasma of human and rat[28]. Significant differences have been found between humans and rodents in types of primary bile acids. The primary bile acids produced in rodents are muricholic acids (MCAs), predominantly beta-MCA (β MCA) while humans produced chenodeoxy-cholic acid (CDCA) and cholic acid (CA)[29]. In mice, cold-induced conversion of cholesterol to bile acids is through increasing bile acid synthesis via the alternative pathway[30]. We assume that the bile acid composition of fish may be different from that of mammals because fish tends to perform lower body temperature than mammals. Fish and mammals differ not only in body temperature but also in composition of intestinal microbiota. Proteobacteria is more extensive in fish than mammals[31]. As well, intestinal microbiota has been reported to influence the species of bile acids metabolism. We hypothesize there are numbers of important differences on bile acids composition and metabolism function between fish and mammals. Our results found that CA, as a main component of human primary bile acids, occupied a large proportion in the liver and intestinal of Nile tilapia, which was consistent with the results of previously reported studies about the BA profile of fish (grass carp)[32]. At present, in fish, the profile of BAs generated a lot of results and interesting discoveries, such as TCA was the main BA in the common carp bile, which was consistent with the results of previously reported studies about the BA profile of fish (angelfish (Pterophyllumeimekei))[33]. However, TCA not the first but the second largest bile acids in the liver and intestinal of Nile tilapia. This is a report, for the first time, that bile acids can be conjugated with glycine and taurine in tilapia. In addition, the type of combined bile acid is mainly taurine-bound. These findings differ from ours and are most likely attributable to host-species differences.
It was reported that the activation of the FXR, whether through the reduction of antagonist or the administration of agonist (CA), improved the metabolism in mice[34]. The lipid-lowering effect of Pu-erh tea is achieved by a combination of decreased ileal FXR-FGF15 and increased hepatic FXR-SHP signaling[26]. In our study, we have also shown that FXR is an important contributor to the accumulation of liver lipid and improved lipid metabolism after high carbohydrate diet fed tilapia, which has conservation on tilapia. Moreover, increasing the non-12-OH BA ration resulting in beneficial effects to host metabolic status, we observed that the concentration and proportion of non-12-OH BA were significantly increased in HCS group (P<0.05, Supplementary Fig 2), along with liver lipid reduction[35].
Many researches explore the emerging role of the gut microbiota in modulating lipid metabolism. Our analysis indicated that the abundance of target species increased of bacteria in the order of Bacillales, including Bacillaceae and Bacillus. Accumulating evidence has shown the protective effects of Bacillus probiotics against high-fat diet-induced metabolic disorders in mice. Supplementation of Bacillus sp. DU-106 reduced hypercholesterolemia and ameliorates gut dysbiosis in high-fat diet rats via regulating host metabolism and gut microbiota[36]. High-throughput sequencing of 16S rRNA gene showed that Su1 increased Enterococcus and Lactobacillus abundance. Probiotic Enterococcus faecalis AG5 mitigated high fat diet induced obesity, which is consistent with our research[37]. Moreover, lactobacillus alleviated obesity induced by high-fat diet in mice[38]. Although the beneficial effects are strain dependent, it can highlight that Bacillus, Enterococcus and Lactobacillus have a link with weight loss. Bile acids are metabolized by the gut microbiota and gut microbial ecology is dramatically affected by bile acids[39]. Interactions between bile acids and intestinal microbiota needs to further investigated. Our results indicated that changed microbiota constituted the positive correlation dominant assemblages cluster, which was consistent with bile acid metabolism [12-15]. The promoted effect of bile acids on bacterial growth was observed with individual bacteria species including Microbacteriaceae, Bacillales, Enterococcaceae, Rhizobiales and Bacteroides. Bacillus is positively correlated with increased bile acids, which is consistent with clinical trials: Bacillus. subtilis increased deconjugated plasma bile acids in obese individuals[19]. Bacteroides was demonstrated to be markedly elevated in the changes of gut microbiota, accompanied by reduced tauroursodeoxycholic acid levels in mice, which is keeping with our findings[40]. Moreover, changes in fecal bile acid concentrations were predominantly associated with altered Firmicutes and Firmicutes phylum contributes to bile acid and glucose metabolism in humans[41]. In our results, intestinal bile acids are negatively associated with Proteobacteria (OTU111, 176) and Actinobacteria (OTU1842, 2051) and Bacilli (OTU233, 1837) (Fig 3C). Moreover, the specific contribution of each bacterial species within a complex microbiota to the regulation of host bile acid metabolism need to further study.
In conclusion, supplemental Bacillus cereus improved the liver lipid accumulation and enhanced bile acid excretion, the possible mechanism for which was its role in shaping bile acid metabolism. Specifically, Bacillus cereus supplementation modified intestinal microbiota towards an active bile acid metabolism by increasing the bile acid related bacteria, as well as improving the interaction pattern within the community. Changed bile acids resulted in activation of FXR which inhibited lipid biosynthesis genes and led to amelioration of hepatic lipid in Tilapia fed with high-carbohydrate diet. This study can expand our fundamental knowledge concerning the roles of Bacillus cereus in the maintenance of metabolism homeostasis, intestinal microecological balance and bile acid metabolism in animals. In addition, this study discussed how the BAs is transformed in the body, providing a theoretical basis for the application of probiotics products in fish and a data basis for revealing the mystery of fish BAs composition.
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Figure legends
Fig1. Su1 protected Tilapia from high-carbohydrate diet induced lipid accumulation by inhibiting hepatic lipid synthesis. A, Time curves of body weight. B, Total lipid of fish. C, Hepatic somatic index. D, Mesenteric fat index. E, Triglyceride in serum. F, Triglyceride in liver. G, Oil red staining. H and I, Relative mRNA expression in liver. J, Total bile acids in serum. K, Fecal bile acids concentration. N = 12 individuals/group. Data are expressed as the mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001, ****P < 0.0001; N.s., not significant.
Fig2. Su1 altered the gut microbiome and altered microbiome pointed to bile acids metabolism. A-D alpha diversity index. E, Principal component analysis (PCA). F, abundance of gut microbiota on phylum level. G, Relative abundance of gut microbiota on OTU level. H, Pearson correlation analysis. The green dot indicated a positive correlation and the yellow dot presented a negative correlation between each species at the genus level. The size of dots and depths of colors represented the degree of relevance of different species. N = 6 individuals/group. * P < 0.05, ** P < 0.01, *** P < 0.001. Results are expressed as mean ± SEM.
Fig3. Su1 reversed high-carbohydrate induced bile acids changed and increased conjugated bile acids levels. A, Heatmap of intestinal bile acids. B, Concentration of intestinal bile acids. C, Correlation between abundance of intestinal bile acids and microbial genus based on active microbial analysis. The red cube indicated a positive correlation and the blue cube presented a negative correlation between each bile acids. The size of cubes and depth of color represented the degree of relevance of different species, respectively. N = 6 individuals/group. * P < 0.05, ** P < 0.01. Results are expressed as mean ± SEM.
Fig4. Su1 changed composition of intestinal bile acids and enhanced excretion of bile acids. A, Changes in bile acid composition. B, Proportion of conjugated BA and C, unconjugated BA in intestinal contents. D, Total bile acid concentration in intestinal contents. E, ASBT activity in intestine. F, bile acid concentration in fecal excretion. Results are expressed as mean ± SEM. N = 6 individuals/group. * P < 0.05, ** P < 0.01.
Fig5. Su1 promoted hepatic bile acid synthesis. A, Total bile acids in serum. B, Total bile acids in liver. C, Cyp7a1 activity in liver. D, Cyp27a1 activity in liver. E, Total cholesterol in liver. F, SM activity in liver. Results are expressed as mean ± SEM. N = 6 individuals/group. * P < 0.05, ** P < 0.01, *** P < 0.001.
Fig6. Su1 changed hepatic bile acids composition.
A, Heatmap of hepatic bile acids. B, Concentration of hepatic bile acids. C, Proportion of hepatic bile acids. D and E, Concentration of unconjugated BA and conjugated BA. Results are expressed as mean ± SEM. N = 6 individuals/group. * P < 0.05, ** P < 0.01.
Fig7. Modified bile acids activated FXR resulting in active lipid metabolism. A, Western blot of protein expression of FXR in liver of tilapia. B, Intensity analysis of FXR/GAPDH levels. C, Representative images of BODIPY staining of zebrafish liver cells. D and E, Relative mRNA expression in zebrafish liver cell. N = 6 individuals/group. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Results are expressed as mean ± SEM.
Supplemental material
Fig1. Glucose in serum in tilapia. N = 6 individuals/group. * P < 0.05, ** P < 0.01. Results are expressed as mean ± SEM.
Fig2. HMGCR activity in liver. N = 6 individuals/group. **** P < 0.0001. Results are expressed as mean ± SEM.
Fig3. A, 12-OH bile acids in liver, B, Non 12-OH bile acid in liver. C, Proportion of 12-OH and non 12-OH bile acids in liver. N = 6 individuals/group. * P < 0.05, ** P < 0.01. Results are expressed as mean ± SEM.
