TY - JOUR
AU - Xiong,, Chunhua
AB - Abstract Objectives The goal of this study was to evaluate the modulatory effect of chitosan oligosaccharide-nisin conjugate (CON-C) on intestinal microbiota of human flora-associated (HFA) mice and also reveal its effect towards the high-fat diet (HFD)-induced obesity. Both Chitosan oligosaccharides and nisin showed great potential in modulating the intestinal microbiota, so it is worth to explore whether the modulation effect of chitosan oligosaccharide could be improved by covalently binding with nisin. Materials and Methods CON-C was prepared by heating the mixed solution of chitosan oligosaccharide and nisin at 80°C and pH 2.0 for 24h. The structure of CON-C were analyzed by Fourier transform-infrared spectroscopy (FT-IR) and X-ray diffraction (XRD). The CON-C’s anti-obesity effect and modulatory effect toward intestinal microbiota were analyzed using human flora-associated (HFA) mice model. Results CON-C could alleviated HFD-induced gut dysbiosis, by significantly decreasing the numbers of Bifidobacterium and Lactobacillus/Enterococcus spp., and increasing the numbers of Bacteroides–Prevotella and Clostridium groups. CON-C could also enriched the most differentially expressed genes through KEGG pathways of biosynthesis of amino acids, two-component system, and ATP binding cassette (ABC) transporters. Conclusions The improved therapeutic effect of CON-C against HFD-induced obesity has been approved, and hence, CON-C has a great potential to be utilized as a functional food ingredient in reducing body weight. chitosan oligosaccharide, nisin, Maillard reaction, modulatory effect, intestinal microbiota Introduction Obesity is a global concerned health problem nowadays, which is strongly associated with metabolic syndromes (Franks and Mccarthy, 2016). It has been revealed that the imbalance between intake and energy expenditure and environmental and genetic factors are the essential causes of obesity (Rains et al., 2011). In human gastrointestinal tract, the vast majority of microbial residents could form a stable microbial ecosystem, which played a critical role for human’s health and were also closely associated with obesity epidemic (Bäckhed et al., 2005; Gill et al., 2006). More recently, the relationship between intestinal flora and obesity has increasingly aroused general concerns. The intestinal flora were found significantly different between lean and obese individuals (Wang et al., 2014). In a study of high-fat diet (HFD)-induced obesity mice, the gut microbiota were disrupted and hence lead to inflammation and associated disorders, because of the increased intestinal permeability (Cani et al., 2008). it was believed that the goal of weight-loss could be achieved by manipulating microbiota(Wang et al., 2014). Nisin is a small cationic peptide composed of 34 amino acid residues, which is produced by Lactococcus lactis subsp. lactis. Lactococcus lactis is a food grade lactic acid bacterium, generally recognized as safe (GRAS), and nisin was also recognized as a food additive in EU (Balciunas et al., 2013). Nisin exhibits a wide spectrum antimicrobial activity against Gram-positive bacteria, including Lactococcus, Streptococcus, Staphylococcus, Micrococcus, and so on (Juneja et al., 2012; Muppalla et al., 2012). Because of the low molecular weight, high heat-tolerant, excellent water-soluble, no immunogenicity, and no adverse effects on human, nisin showed great advantages and broad application prospects (Hu et al., 2017). However, its antibacterial activity is often affected by environmental factors, including pH, temperature, and food matrix (Xiao et al., 2011). Nisin can interact with food components, such as proteins, lipids, and pro-metabolic enzymes, which lead to the loss of its biological activity. To enhance the stability and prolong the efficacy of nisin, different strategies, such as chemical synthesis, Maillard reaction, and complex with saccharides to form nanoparticels, have been developed (Muppalla et al., 2012; Hu et al., 2017; Zhu et al., 2017). Among them, covalent coupling of carbohydrates through the Maillard-type reaction appears to be a promising way (Li et al., 2014). The Maillard reaction, resulting from the initial condensation between amino group of chitosan and carbonyl moiety of reducing sugars, aldehydes, or ketones, is one of the main reactions taking place in food (Li et al., 2014). Because it does not require additional chemical reagents, Maillard reaction is considered as a green way of modification, and is widely used in the modification of proteins and polysaccharides (Kato, 2002; Oliveira et al., 2014). Muppalla et al. (2012) significantly increased the antimicrobial of nisin towards Pseudomonas fluorescens, Escherichia coli, Staphylococcus aureus, and Bacillus cereus by glycating it with dextran and glucose. In this study, we select chitosan oligosaccharide (CO) to conjugate with nisin via Maillard reaction. CO is a natural cationic polysaccharide and one of the most abundant and renewable natural resources (Peng et al., 2013). Because of its unique characteristics of nontoxicity, biocompatibility, biodegradability, and broad antimicrobial spectrum, CO is widely used as food functional ingredient (Rinaudo, 2006; Kong et al., 2010). Currently, CO has been applied to deliver drugs, enzymes, and prepare nano-composite particles (Sanyakamdhorn et al., 2013). We then investigated the modulatory effect of chitosan oligosaccharide and nisin conjugates (CON-C) on intestinal microbiota. By transplanting faecal microbiota from adult human into germ-free mice, we established a relatively stable human gut micro-community in recipient mice model, the objective of the present study was to investigate the effects of CON-C on human faecal microbiota, in addition, the abundance of genes enriched in various metabolic pathways altered by CON-C in the humanized mouse gut microbiome were investigated. Materials and Methods Materials Nisin and CO with the deacetylation degree of 90% (5 kDa) were obtained from Golden Shell (Hangzhou, China). Germ-free C57BL/6J mice were obtained from the Experimental Animal Centre of Academy of the Military Medical Sciences (Beijing, China). Research Diets D12450B 10 kcal% Fat and D12492 60 kcal% Fat were purchased from Research Diets, Inc. (New Brunswick, NJ). All other chemicals and reagents were analytical grade. Preparation of CON-C The CON-C were prepared by the Maillard reaction between CO and nisin, according to the methods of Zhu et al. (2008) with some modifications. Mixtures of CO and nisin in 5:1 ratios (w/w) were dissolved in water at a total concentration of 2%. The sample solutions were stirred on a magnetic stirrer at room temperature for 2 h to completely dissolve the mixture. The pH values of the solution was adjusted to 2.00 ± 0.01 using HCL. The solution were heated in a water batch at 80°C for 24 h samples were then taken out of the water bath, cooled in ice, and freeze dried. Characteristic analysis of CON-C Fourier transform-infrared spectroscopy Samples were powdered and analysed as KBr pellets (1:99, w/w) using Nicolet iS10 Fourier transform-infrared spectroscopy (FT-IR) spectrophotometer (Thermo Fisher Scientific). The pellets were placed in the sample holder. Spectral scanning was taken in the wavelength region between 4000 and 400 cm−1 at a resolution of 4 cm−1 with scan speed of 2 mm s−1. X-ray diffraction assay A D8 ADVANCE X-ray diffraction (XRD) (Bruker-AXS Co., Germany) was applied to detect the crystallinity of samples and their patterns based on the wide-angle X-ray diffraction (WAXD) analysis. 2θ was scanned from 10° to 80° at a coating time of 2 s with an angle step width of 0.05°. The antimicrobial of CON-C The antimicrobial properties of CON-C were evaluated according to the reported method with modifications(Song et al., 2002). Escherichia coli and S. aureus were activated and diluted to the appropriate concentration with 20 mM phosphate buffer (pH 6.0). The 4.5 ml of the cell suspension was mixed with 0.5 ml of chitosan oligosaccharide–nisin mixture (CON-M) and CON-C, respectively (The concentration were 0.001%, 0.002%, 0.004%, and 0.005%), and incubated at 37°C for 1 h. Then a 100 µl portion of each treatment was surface-plated onto agar plate and incubated at 37°C for 24 h. Sample-free solution was used as a control. The antimicrobial properties of each treatments were compared according to the bacterial survival rate, which calculated as follows: The bacterial survival rate% =[The number of bacteria in each treatmentThe number of bacteria in the control group]×100% Animals and experimental design The anti-obesity effects of CON-C were evaluated according to the reported method with modifications (Mei et al., 2017). Firstly, six volunteers (three females and three males, 25–30 years old), who did not have any history of gastrointestinal disorders and had not been treated with antibiotics for the previous 6 months, were recruited. Their freshly voided faeces were used to colonize the young adult (6-week old) male mice. Mice then were housed in separate cages within a gnotobiotic isolator under 12 h light-dark cycle and with the temperature and humidity controlled to 22–24°C and 50% ± 10%, respectively. After 7 days with high-fat diet (Research Diets D12492 60 kcal% Fat), the mice were then randomly divided into five groups with eight in each group: low fat diet group (LFD, Research Diets D12450B 10 kcal% Fat), high-fat diet group (HFD), high-fat diet with nisin group (HFD-N), high-fat diet with CON-M group (HFD-CON-M), and high-fat diet with CON-C group (HFD-CON-C). Nisin, CON-M, and CON-C were added to the high-fat diet at a final concentration of nisin at 0.1% (w/w). Sample food and water consumption were measured on a per cage basis three times per week and the averages of food and water consumed were calculated weekly. The body mass of each animal was recorded soon after feeding with different diets for the entire period since the start of breeding. Faecal samples were collected from 0 (CON-C0), 2 (CON-C2), 4 (CON-C4), and 8 weeks (CON-C8) after they were divided into each group. All administrations were conducted for eight consecutive weeks. Histopathological evaluation Mouse epididymal white adipose tissue (eWAT) and liver were fixed in 4% neutral buffered formalin and embedded in paraffin. Anti-F4/80 primary antibody (Abcam, British) was used to perform the immunohistochemical staining of paraffin sections (Liu et al., 2017). The representative images of the possible histopathological changes were detected under a high-resolution microscope with photographic facility. Enumeration of bacteria by fluorescent in situ hybridization The bacterial cells used for hybridization were prepared according to Zhang et al. (2013). Faecal slurries of mice were collected at different time points and were mixed with autoclaved phosphate-buffered saline to yield 10% (w/v) suspensions. The samples were inoculated with 150 μl of faecal slurry (10%, w/v) with a manual homogenizer at 37°C in an anaerobic incubator (10% H2, 10% CO2, and 80% N2). The 100 µl culture samples were then added to 300 μl filtered paraformaldehyde solution (4%, w/v), and fixed overnight at 4°C. Hybridization were performed according to the reported methods (Sánchez-Patán et al., 2012). The probes used were Bif164, Lab158, Bac303, and His150. The bacterial cells were counted using an epifluorescence microscope. At least six random fields were counted on each slide, and the bacterial numbers were expressed as log10 cells per milliliter ± standard deviation (SD). Construction of a gut metagenome reference Metagenomic sequencing was conducted using HiSeq4000 and PE150 strategy. DNA from different samples was extracted using the E.Z.N.A. ®Stool DNA Kit (D4015, Omega, Inc.) according to manufacturer’s instructions. The total DNA was eluted in 50 μl of elution buffer and stored at −80°C until measurement in the PCR by LC-Bio Technology Co., Ltd (Hang Zhou, Zhejiang Province, China), and the isolation was confirmed by 1.2% agarose gel electrophoresis. Sequencing libraries were generated using NEB Next Ultra DNA Library Prep Kit for Illumina (NEB) following manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Life Technologies, CA) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina MiSeq platform and 300 bp paired-end reads were generated. All open reading frames (ORFs) predicted from different samples were merged and aligned to each other using BLAT (Bankevich et al., 2012). Gene pairs with greater than 95% identity (no gap allowed) and aligned reads covering over 90% of the shorter reads were grouped together. The longest ORF in each group was used to represent the group, and the other ORFs of the group were regarded as redundant sequences. ORFs with a length less than 100 bp were subsequently filtered out. Finally, a gene catalog containing 255 320 nonredundant genes was constructed. Based on this reference gene set, we carried out taxonomical assignment and functional annotation using the NR (Non-Redundant), KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology), and CAZy (Carbohydrates-Active enzymes) databases of the latest version. Taxonomical assignment and functional classification The nucleotide sequences of predicted genes were translated into protein sequences using the NCBI Genetic Codes 11. Basic local alignment search tool (BLAST) was employed to conduct the taxonomical assignment and functional classification of predicted genes against the corresponding data base with E value ≤1 × 10−3. All genes were searched against IMG (v3.4) with N-BLAST using default parameters except that the E value was set to 1 × 10−5. The taxonomical association of a gene was decided by the lowest common ancestor of all its taxonomical annotation results. Statistical analysis Data were analysed by SPSS and expressed as mean ± standard deviation (SD). Significance was determined at P < 0.05 by ANOVA followed by Duncan’s multiple-comparison tests. Results and Discussion Characterization of CON-C FT-IR spectra was a useful tool to analysis the derivatives formed by Maillard reaction. Figure 1 showed FTIR spectra of CO, nisin, and CON-C. For the nisin, the characteristic absorption bands around 3288 cm−1 and 2960 cm−1 attributed to axial O–H and N–H stretching. The peak at 1652 cm−1 which can be attributed to amide band and the peak at 1449 cm−1 evidenced the presence of COO− symmetric stretching vibrations (Krivorotova et al., 2016). For the CO, the characteristic absorption bands around 3444 and 2991 cm−1 was attributed to –NH, –OH, and –CH– stretching vibration. The characteristic peaks at around 1631, 1526, and 1394 cm−1 were assigned to amide I, amine II, and amide III absorption bands of CO, respectively (Umemura and Kawai, 2007). The FTIR spectra of CON-C exhibited significant differences from that of nisin and CO after Maillard reaction. The absorption peaks of 3385 cm−1 related to stretching vibrations of N–H widened and the peak of amide band was slightly shifted to lower wave number 1625 cm−1, which indicated the formation of the hydrogen bonding between nisin and CO. These results were also consistent with the previous study (Zhu et al., 2015). Figure 1. Open in new tabDownload slide FT-IR spectra of CO, nisin, and CON-C. Figure 1. Open in new tabDownload slide FT-IR spectra of CO, nisin, and CON-C. The packing structures of nisin, CO, and CON-C were determined by XRD technique (Figure 2). The nisin showed a remarkable peak at 31.5°, which corresponded to the characteristic diffraction pattern of sodium chloride (NaCl), a component of Nisaplin (Meira et al., 2015). For CO, the diffraction peaks at 2θ = 12.2° and 2θ = 23.5° were observed, which corresponded to the diffraction peaks of (020) and (200) crystal plane (Luo et al., 2013). After Maillard reaction, the peak of CON-C at 12.2° shift to low degree and the peak at 23.5° disappeared. Besides all the characteristic crystalline peaks were broader and the crystallinity significantly decreased, indicating that Maillard reaction can reduce the crystallinity of the reactants (Li et al., 2013). Figure 2. Open in new tabDownload slide XRD patterns of CO, nisin, and CON-C. Figure 2. Open in new tabDownload slide XRD patterns of CO, nisin, and CON-C. The antimicrobial properties of CON-C As shown in Figure 3, the inhibition of CON-C on E. coli and S. aureus was significantly higher than that of CON-M: the half-lethal doses of CON-C on E. coli and S. aureus were 45% and 80% of the latter. It illustrate that Maillard reaction could efficiently improve the antimicrobial properties of both CO and nisin. Similar to our results, chitosan conjugated with glucose or xylose showed greater antimicrobial properties (Kanatt et al., 2008; Zhu et al., 2013). Figure 3. Open in new tabDownload slide Antimicrobial properties of CON-M and CON-C. Figure 3. Open in new tabDownload slide Antimicrobial properties of CON-M and CON-C. Influence of CON-C on body and organ mass of mice model Compared with the other HFD fed mice, the CON-C treated one showed significantly slower body weight gain rate and fewer liver weight gain (Figure 4b and 4c). Considering there were no significant differences between all FHD fed mice in water intake and food consumptions (Figure 4a and 4b), which indicating that nisin, CON-M, and CON-C did not affect the food intakes behaviour of the mice, the anti-obesity effects of CON-C can hence be confirmed. Figure 4. Open in new tabDownload slide Effect of nisin, CON-M, and CON-C on the water intake (A), food intake (B), liver weight (C), and body weight (D) of high-fat diet-induced mice. Different letters indicate significant differences (P < 0.05) among different groups. Figure 4. Open in new tabDownload slide Effect of nisin, CON-M, and CON-C on the water intake (A), food intake (B), liver weight (C), and body weight (D) of high-fat diet-induced mice. Different letters indicate significant differences (P < 0.05) among different groups. Influence of CON-C on liver histopathology of HFD-induced obesity mice model Obesity is characterized by chronic low-degree inflammation. A previous report indicated that adipose tissue macrophage accumulation is directly proportional to measures of adiposity both in mice and humans(Weisberg et al., 2003). As the eWAT and liver staining results (Figure 5) shows, HFD-fed mice showed significantly greater microphage accumulation compared with LFD-fed mice, which is consistent with previous reports(Kanda et al., 2006). While the microphage accumulation in HFD-fed mice was alleviated when supplemented with CON-C. Figure 5. Open in new tabDownload slide Representative F4/80 immunostaining image of eWAT between LFD (i), HFD (ii), and HFD–CON-C (iii) groups, and liver between LFD (I), HFD (II), and HFD–CON-C (III) groups. Figure 5. Open in new tabDownload slide Representative F4/80 immunostaining image of eWAT between LFD (i), HFD (ii), and HFD–CON-C (iii) groups, and liver between LFD (I), HFD (II), and HFD–CON-C (III) groups. Influence of CON-C on bacterial populations In general terms, intestinal bacteria could be divided into species including potentially beneficial or harmful towards the host. Beneficial bacteria mainly include Lactobacillus and Bifidobacterium, which play important roles in nutrition and prevention of disease, while the harmful intestinal bacteria, such as Clostridium, Veillonella, Staphylococcus, occasionally Enterococcus and Escherichia, may produce potentially harmful substances to the host (Hooper and Gordon, 2001). In this study, we selected Bifidobacterium and Lactobacillus/Enterococcus as the representative species of potential benefits, and Bacteroides–Prevotella and Clostridium histolyticum as the representative species of potential harm. In consistent with others’ reports (Cani et al., 2008), the mice fed with LFD diets showed significantly higher amount (P < 0.05) of Bifidobacterium and Lactobacillus/Enterococcus and lower amount of Bacteroides–Prevotella and C. histolyticum (Table 1). Recent study indicated that changes in gut microbiota composition are related to the development of obesity and its associated metabolic disorders (Ridaura et al., 2013), and obesity-induced gut dysbiosis may impairs intestinal integrity (Cani et al., 2009). In our case, the development of obesity in HFD fed group could partially be explained by the change of bacterial populations caused by the HFD. Table 1 Time-dependent changes observed in the numbers (log10 cell ml−1) of Bifidobacterium, Lactobacillus/Enterococcus spp., Bacteroides–Prevotella, and Clostridium histolyticum group in the gut of HFA mice model.* Probe type Group Time (week) 1 2 4 8 Bif 164 LFD 7.83 ± 0.02 a, A 8.01 ± 0.02 c, B 8.16 ± 0.01 d, C 8.21 ± 0.02 c, D HFD 7.82 ± 0.01 a, A 7.95 ± 0.02 a, B 8.07 ± 0.02 a, C 8.10 ± 0.01 a, D HFD-N 7.82 ± 0.02 a, A 7.96 ± 0.02 a, B 8.08 ± 0.02 a, C 8.11 ± 0.02 b, D HFD–CON-M 7.83 ± 0.02 a, A 7.95 ± 0.01 a, B 8.08 ± 0.01 a, C 8.10 ± 0.03 c, D HFD–CON-C 7.83 ± 0.01 a, A 7.99 ± 0.02 b, B 8.11 ± 0.01 b, C 8.16 ± 0.02 b, D Lab 158 LFD 7.71 ± 0.01 a, A 7.82 ± 0.02 b, B 8.02 ± 0.02 c, C 8.11 ± 0.01 c, D HFD 7.70 ± 0.02 a, A 7.74 ± 0.01 a, B 7.88 ± 0.01 a, C 8.01 ± 0.01 a, D HFD-N 7.71 ± 0.01 a, A 7.75 ± 0.02 a, B 7.86 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-M 7.70 ± 0.02 a, A 7.75 ± 0.02 a, B 7.89 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-C 7.71 ± 0.02 a, A 7.76 ± 0.02 a, B 7.95 ± 0.03 b, C 8.07 ± 0.02 b, D Bac 303 LFD 7.20 ± 0.01 a, A 7.21 ± 0.02 a, A 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A HFD 7.20 ± 0.01 a, A 7.23 ± 0.03 b, B 7.28 ± 0.02 b, C 7.30 ± 0.02 b, D HFD-N 7.19 ± 0.02 a, A 7.22 ± 0.03 b, B 7.27 ± 0.02 b, C 7.29 ± 0.03 b, D HFD–CON-M 7.20 ± 0.02 a, A 7.23 ± 0.02 b, B 7.28 ± 0.01 b, C 7.30 ± 0.03 b, D HFD–CON-C 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A 7.21 ± 0.01 a, A 7.21 ± 0.02 a, A His 150 LFD 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A HFD 7.01 ± 0.01 a, A 7.04 ± 0.01 b, B 7.07 ± 0.02 b, C 7.11 ± 0.02 b, D HFD-N 7.02 ± 0.02 a, A 7.03 ± 0.02 b, A 7.06 ± 0.03 b, B 7.10 ± 0.02 b, C HFD–CON-M 7.02 ± 0.02 a, A 7.04 ± 0.02 b, A 7.06 ± 0.02 b, B 7.11 ± 0.02 b, C HFD–CON-C 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.03 ± 0.03 a, A 7.03 ± 0.02 a, A Probe type Group Time (week) 1 2 4 8 Bif 164 LFD 7.83 ± 0.02 a, A 8.01 ± 0.02 c, B 8.16 ± 0.01 d, C 8.21 ± 0.02 c, D HFD 7.82 ± 0.01 a, A 7.95 ± 0.02 a, B 8.07 ± 0.02 a, C 8.10 ± 0.01 a, D HFD-N 7.82 ± 0.02 a, A 7.96 ± 0.02 a, B 8.08 ± 0.02 a, C 8.11 ± 0.02 b, D HFD–CON-M 7.83 ± 0.02 a, A 7.95 ± 0.01 a, B 8.08 ± 0.01 a, C 8.10 ± 0.03 c, D HFD–CON-C 7.83 ± 0.01 a, A 7.99 ± 0.02 b, B 8.11 ± 0.01 b, C 8.16 ± 0.02 b, D Lab 158 LFD 7.71 ± 0.01 a, A 7.82 ± 0.02 b, B 8.02 ± 0.02 c, C 8.11 ± 0.01 c, D HFD 7.70 ± 0.02 a, A 7.74 ± 0.01 a, B 7.88 ± 0.01 a, C 8.01 ± 0.01 a, D HFD-N 7.71 ± 0.01 a, A 7.75 ± 0.02 a, B 7.86 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-M 7.70 ± 0.02 a, A 7.75 ± 0.02 a, B 7.89 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-C 7.71 ± 0.02 a, A 7.76 ± 0.02 a, B 7.95 ± 0.03 b, C 8.07 ± 0.02 b, D Bac 303 LFD 7.20 ± 0.01 a, A 7.21 ± 0.02 a, A 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A HFD 7.20 ± 0.01 a, A 7.23 ± 0.03 b, B 7.28 ± 0.02 b, C 7.30 ± 0.02 b, D HFD-N 7.19 ± 0.02 a, A 7.22 ± 0.03 b, B 7.27 ± 0.02 b, C 7.29 ± 0.03 b, D HFD–CON-M 7.20 ± 0.02 a, A 7.23 ± 0.02 b, B 7.28 ± 0.01 b, C 7.30 ± 0.03 b, D HFD–CON-C 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A 7.21 ± 0.01 a, A 7.21 ± 0.02 a, A His 150 LFD 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A HFD 7.01 ± 0.01 a, A 7.04 ± 0.01 b, B 7.07 ± 0.02 b, C 7.11 ± 0.02 b, D HFD-N 7.02 ± 0.02 a, A 7.03 ± 0.02 b, A 7.06 ± 0.03 b, B 7.10 ± 0.02 b, C HFD–CON-M 7.02 ± 0.02 a, A 7.04 ± 0.02 b, A 7.06 ± 0.02 b, B 7.11 ± 0.02 b, C HFD–CON-C 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.03 ± 0.03 a, A 7.03 ± 0.02 a, A *One-way ANOVA and Tukey tests were used to determine significant differences for total bacterial population. Different lowercases indicate significant differences (P < 0.05) for total bacterial (i.e. within column) among different groups. Different capital letters indicate significant differences (P < 0.05) for total bacterial (i.e. within line) among the different time points. Open in new tab Table 1 Time-dependent changes observed in the numbers (log10 cell ml−1) of Bifidobacterium, Lactobacillus/Enterococcus spp., Bacteroides–Prevotella, and Clostridium histolyticum group in the gut of HFA mice model.* Probe type Group Time (week) 1 2 4 8 Bif 164 LFD 7.83 ± 0.02 a, A 8.01 ± 0.02 c, B 8.16 ± 0.01 d, C 8.21 ± 0.02 c, D HFD 7.82 ± 0.01 a, A 7.95 ± 0.02 a, B 8.07 ± 0.02 a, C 8.10 ± 0.01 a, D HFD-N 7.82 ± 0.02 a, A 7.96 ± 0.02 a, B 8.08 ± 0.02 a, C 8.11 ± 0.02 b, D HFD–CON-M 7.83 ± 0.02 a, A 7.95 ± 0.01 a, B 8.08 ± 0.01 a, C 8.10 ± 0.03 c, D HFD–CON-C 7.83 ± 0.01 a, A 7.99 ± 0.02 b, B 8.11 ± 0.01 b, C 8.16 ± 0.02 b, D Lab 158 LFD 7.71 ± 0.01 a, A 7.82 ± 0.02 b, B 8.02 ± 0.02 c, C 8.11 ± 0.01 c, D HFD 7.70 ± 0.02 a, A 7.74 ± 0.01 a, B 7.88 ± 0.01 a, C 8.01 ± 0.01 a, D HFD-N 7.71 ± 0.01 a, A 7.75 ± 0.02 a, B 7.86 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-M 7.70 ± 0.02 a, A 7.75 ± 0.02 a, B 7.89 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-C 7.71 ± 0.02 a, A 7.76 ± 0.02 a, B 7.95 ± 0.03 b, C 8.07 ± 0.02 b, D Bac 303 LFD 7.20 ± 0.01 a, A 7.21 ± 0.02 a, A 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A HFD 7.20 ± 0.01 a, A 7.23 ± 0.03 b, B 7.28 ± 0.02 b, C 7.30 ± 0.02 b, D HFD-N 7.19 ± 0.02 a, A 7.22 ± 0.03 b, B 7.27 ± 0.02 b, C 7.29 ± 0.03 b, D HFD–CON-M 7.20 ± 0.02 a, A 7.23 ± 0.02 b, B 7.28 ± 0.01 b, C 7.30 ± 0.03 b, D HFD–CON-C 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A 7.21 ± 0.01 a, A 7.21 ± 0.02 a, A His 150 LFD 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A HFD 7.01 ± 0.01 a, A 7.04 ± 0.01 b, B 7.07 ± 0.02 b, C 7.11 ± 0.02 b, D HFD-N 7.02 ± 0.02 a, A 7.03 ± 0.02 b, A 7.06 ± 0.03 b, B 7.10 ± 0.02 b, C HFD–CON-M 7.02 ± 0.02 a, A 7.04 ± 0.02 b, A 7.06 ± 0.02 b, B 7.11 ± 0.02 b, C HFD–CON-C 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.03 ± 0.03 a, A 7.03 ± 0.02 a, A Probe type Group Time (week) 1 2 4 8 Bif 164 LFD 7.83 ± 0.02 a, A 8.01 ± 0.02 c, B 8.16 ± 0.01 d, C 8.21 ± 0.02 c, D HFD 7.82 ± 0.01 a, A 7.95 ± 0.02 a, B 8.07 ± 0.02 a, C 8.10 ± 0.01 a, D HFD-N 7.82 ± 0.02 a, A 7.96 ± 0.02 a, B 8.08 ± 0.02 a, C 8.11 ± 0.02 b, D HFD–CON-M 7.83 ± 0.02 a, A 7.95 ± 0.01 a, B 8.08 ± 0.01 a, C 8.10 ± 0.03 c, D HFD–CON-C 7.83 ± 0.01 a, A 7.99 ± 0.02 b, B 8.11 ± 0.01 b, C 8.16 ± 0.02 b, D Lab 158 LFD 7.71 ± 0.01 a, A 7.82 ± 0.02 b, B 8.02 ± 0.02 c, C 8.11 ± 0.01 c, D HFD 7.70 ± 0.02 a, A 7.74 ± 0.01 a, B 7.88 ± 0.01 a, C 8.01 ± 0.01 a, D HFD-N 7.71 ± 0.01 a, A 7.75 ± 0.02 a, B 7.86 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-M 7.70 ± 0.02 a, A 7.75 ± 0.02 a, B 7.89 ± 0.02 a, C 8.03 ± 0.02 a, D HFD–CON-C 7.71 ± 0.02 a, A 7.76 ± 0.02 a, B 7.95 ± 0.03 b, C 8.07 ± 0.02 b, D Bac 303 LFD 7.20 ± 0.01 a, A 7.21 ± 0.02 a, A 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A HFD 7.20 ± 0.01 a, A 7.23 ± 0.03 b, B 7.28 ± 0.02 b, C 7.30 ± 0.02 b, D HFD-N 7.19 ± 0.02 a, A 7.22 ± 0.03 b, B 7.27 ± 0.02 b, C 7.29 ± 0.03 b, D HFD–CON-M 7.20 ± 0.02 a, A 7.23 ± 0.02 b, B 7.28 ± 0.01 b, C 7.30 ± 0.03 b, D HFD–CON-C 7.20 ± 0.02 a, A 7.21 ± 0.02 a, A 7.21 ± 0.01 a, A 7.21 ± 0.02 a, A His 150 LFD 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A HFD 7.01 ± 0.01 a, A 7.04 ± 0.01 b, B 7.07 ± 0.02 b, C 7.11 ± 0.02 b, D HFD-N 7.02 ± 0.02 a, A 7.03 ± 0.02 b, A 7.06 ± 0.03 b, B 7.10 ± 0.02 b, C HFD–CON-M 7.02 ± 0.02 a, A 7.04 ± 0.02 b, A 7.06 ± 0.02 b, B 7.11 ± 0.02 b, C HFD–CON-C 7.02 ± 0.02 a, A 7.02 ± 0.02 a, A 7.03 ± 0.03 a, A 7.03 ± 0.02 a, A *One-way ANOVA and Tukey tests were used to determine significant differences for total bacterial population. Different lowercases indicate significant differences (P < 0.05) for total bacterial (i.e. within column) among different groups. Different capital letters indicate significant differences (P < 0.05) for total bacterial (i.e. within line) among the different time points. Open in new tab Among the mice fed with HFD, the one suppled with CON-C had the highest amount of Bifidobacterium and Lactobacillus and lowest amount of Bacteroides–Prevotella and Clostridium histolyticum (Table 1). As our previous results shows, the CON-C had improved antimicrobial properties against E. coli, and it is possible that the CON-C alleviated the development of obesity induced by HFD, by selectively suppress certain bacterial groups. However, the potential mechanisms of CON-C on the prevention of gut dysbiosis are complicated and need further clarified. Influence of CON-C on the humanized mice gut microbiome The imputed relative abundances of KEGG pathways in each respective sample were used to predict changes in metabolic function within the microbiomes of different CON-C intervention time (Figure 6). The KEGG pathway demonstrated lipid metabolism, drug resistance, metabolism of other amino acids, metabolism of terpenoids and polyketides, transcription and biosynthesis of other secondary metabolites accounted for the highest proportion. In the meanwhile, the greatest statistical difference between CON-C0 and CON-C8 were metabolism of terpenoids and polyketides, transcription, and biosynthesis of other secondary metabolites. The CON-C associated faecal microbiome for 8 weeks was enriched for a number of KEGG pathways involved in biosynthesis of amino acids, two-component system, and ATP-binding cassette (ABC) transporters (Figure 7), and ABC transporters included those predicted to be involved in sugar, amino acid, and cofactor import. Figure 6. Open in new tabDownload slide Imputed metagenomic differences between CON-C0 and CON-C8. The relative abundance of metabolic pathways encoded in each imputed sample metagenome was analysed using STAMP. Figure 6. Open in new tabDownload slide Imputed metagenomic differences between CON-C0 and CON-C8. The relative abundance of metabolic pathways encoded in each imputed sample metagenome was analysed using STAMP. Figure 7. Open in new tabDownload slide KEGG analysis of differentially expressed genes between CON-C0 and CON-C8. Figure 7. Open in new tabDownload slide KEGG analysis of differentially expressed genes between CON-C0 and CON-C8. The database of GO provides three types of systematic definitions for describing the function of gene products. The structures of GO functions include molecular function, biological process, and cellar components, and we selected 10 of the largest GO terms in each function. GO analysis of CON-C0 and CON-C8 showed that the most differentially expressed genes were regulation of transcription, DNA-templated, transport, phosphorelay signal transduction system, metabolic process, and translation in biological process; cytosol, cellular components, cytoplasm, plasma membrane, and membrane in cellular components; ATP binding, transcription factor activity, sequence-specific DNA binding, protein binding, catalytic activity, and DNA binding in molecular functions (Figure 8). Figure 8. Open in new tabDownload slide GO analysis of differentially expressed genes between CON-C0 and CON-C8. Figure 8. Open in new tabDownload slide GO analysis of differentially expressed genes between CON-C0 and CON-C8. CAZy is a special database dedicated to the analysing of the genomic, structural, and biochemical information of carbohydrate active enzymes, and it covers six major functional categories: glycoside hydrolases (GHs), glycosyltransferase (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), assisted redox (AAs), and carbohydrate-binding modules (CBMs), which can be further divided into functional subclasses. To investigate if the carbohydrate utilization of mice gut microbiota increased as a response to GTP intervention in high-fat diet-induced obesity, we used CAZy to search for the key enzyme involved in CAZy level 1. As the result showed, GHs, CEs, and PLs were dramatically enriched after GTP intervention, while the abundance of GTs, CBMs, and AAs were less affected, which suggested that the increased capacity for carbohydrate utilization characterizes high-fat diet-induced changes in the gut microbiota affected by CON-C. The next-generation sequencing platforms provide the opportunity to explore the taxonomic, protein-coding gene, or expression diversity nowadays, by applying more comprehensive and less biased measurements to the complicated relationship among diet, microbiota, and host, and they have provided a great deal of new information on the diversity and composition of human gut microbiota (Zoetendal and Rajilic-Stojanovic, 2008). With regard to the Maillard reaction products (CNMs), it has been reported that the CNMs destabilize the outer membrane and inhibit the growth of bacterial cells, due to their excellent surfactant properties (Nakamura et al., 1991).The conjugates of chitosan with soy protein, β-lactoglobulin and glucosamine are all reported to enhance bactericidal action (Chung et al., 2005; Miralles et al., 2007). Muppalla et al. (2012) reported nisin did not show activity against E. coli and P. fluorescens, whereas, both nisin–dextran and nisin–glucose conjugates showed antibacterial activity against these gram-negative bacteria. Microbes stressed by exposure to the bioactive peptides up-regulate proteins related to defensive mechanisms, which protect cells while simultaneously down-regulating various metabolic and biosynthetic proteins involved. Direct metagenomic sequencing is currently generating a qualified understanding of the metabolic potential embedded in selected gene pools of the gut microbiota, and identifying gut bacterial genes from intestinal communities (Turnbaugh et al., 2009). Molecular function describes molecular biology activity such as catalytic or binding activity; biological process is a process which consists of molecular functions with multiple steps, while cellar component refers to the gene products located in the organelles such as, ribosomes, proteasomes, and so on. In our present study, GO analysis of differentially expressed genes showed that most genes in cellular components included cytosol, cytoplasm, plasma membrane, membrane, and integral component of membrane, which indicated that CON-C intervention may affect cytosol, cytoplasm, and membrane components to relieve the negative sequences high-fat diet induced. For the imputed relative abundances of KEGG pathways after CON-C intervention, the greatest statistical difference was focused on excretory system, transcription, and substance dependence, while for the KEGG analysis of differentially expressed genes, although their ranks varied in different time, ABC transporters, two-component system, and biosynthesis of amino acids all occupied the top categories, indicating that CON-C treatment has a significant impact on these pathways. Conclusions In the present study, conjugates based on nisin and CO were prepared using the Maillard reaction. The prepared CON-C showed significant protective effect against obesity induced by HFD. By fermentation in vitro with faecal microbiota of HFD-induced obesity mice, it was found that CON-C significantly promote the growth of Bifidobacterium and Lactobacillus-Enterococcus spp., while inhibiting Bacteroides–Prevotella and C. histolyticum groups. 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TI - Effects of chitosan oligosaccharide-nisin conjugates formed by Maillard reaction on the intestinal microbiota of high-fat diet-induced obesity mice model
JF - Food Quality and Safety
DO - 10.1093/fqsafe/fyz016
DA - 2019-11-14
UR - https://www.deepdyve.com/lp/oxford-university-press/effects-of-chitosan-oligosaccharide-nisin-conjugates-formed-by-KbpajAXL6Y
SP - 169
VL - 3
IS - 3
DP - DeepDyve
ER -