Microbial Changes Associated with IBD Mouse Model and Microbiota Transplantation Confers Colitis Symptom in Microbiota Deletion Mice

Background Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and Ulcerative colitis (UC), are chronic and relapsing inflammation occurring among the gastrointestinal tract. Available evidence suggests that host microbiome, as well as various components of the mucosal immune system, are implicated in the pathogenesis of IBD though the exact mechanism remains unknown. Advances in DNA sequencing technologies have provided new insights on the function identification of gut microbiota. In this study, we investigated the gut microbiota response to colitis and discussed the underlying mechanisms of this alteration in combination with latest research. The function of altered microbiota was investigated through microbiota transplantation technology.Results Twenty-seven female C57BL/6J mice were fed DSS solution for 5 days and followed by 5 days normal drinking water. D 0 was considered as normal control while d 5 and d 10 were seen as disease progressive phase and recovery phase, respectively. Alpha diversity results showed that the detected microbiota composition differences among 3 phases were not due to the presence and/or absence of rare phylotypes. In IBD mouse model, community richness decreased but diversity did not change significantly. Besides, the strong negative correlation between phyla Firmicutes and Bacteroidetes decreased in IBD mouse model of which maybe one of the dysbiosis signatures. Furthermore, transplanting microbiota of IBD mouse model to antibiotic-induced microbiota depletion mice (Abx-mice) could induce inflammation similar to colitis. The proportion of the Lactobacillus, as well as other less abundant probiotic taxa, significantly increased in recovery phase, revealing the potential of these probiotic in IBD therapy.Conclusions IBD could induce proportional changes of gut microbiota, and these changes are capable of conferring pathogenicity in Abx-mice. This study provides updated comprehensions of the commensal microbiota alteration.Keywords n = number of independent biological replicates. No samples or excluded from the analyses. Differences between two treatment groups were assessed using two-tailed, unpaired Student t test with 95% confidence level. Differences among > 2 groups with only one variable were assessed using one-way ANOVA with Tukey post hoc test. Taxonomic comparisons from 16S rDNA sequencing analysis were analyzed by Kruskal-Wallis H test with fdr post hoc test. Ternary Plot was made using GGTERN software (http://www.ggtern.com/). LEfSe was performed to discover bacteria indicator that distinguished the treatment-specific microbiota features, LDA value was used to estimate the effect size of each feature. A significance level (alpha) of 0.05 and an effect size threshold of 2 was used for all indicators discussed in this study.

3 dysbiosis, inflammation bowel disease, DSS, microbiota transplantation Background The prevalence of inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), remains high in Europe and continues to rise across the world [1,2]. IBD is characterized by intestinal ulcers which accumulated within whole digestive tract and may occur in other extra-intestinal organs [3]. The specific etiology of IBD is complex and remains unclear, but it was generally agreed that genetics, epigenetics, gut microbiota and host immune system factors were involved. Previous studies reported that altered composition or balance of gut bacteria were important in initiation and progression of IBD. For example, changes in abundance of Firmicutes and Proteobacteria was correlated strongly with IBD [4].
There exist significant interactions between gut microbiota and the host in several ways such as dietary energy extraction, immune system development, vitamin production and drug metabolism [5,6], thus involved in many diseases. IBD is one of the most frequently reported diseases [4,7,8]. Some pathogens, such as Vibrio cholerae and Clostridium perfringens, could secrete enzymes capable of utilizing mucus or disrupt tight junctions [9][10][11]. StcE, a member of E. coli metalloprotease, could assist pathogens to reach gut epithelium, disrupt colonic mucus and tight junctions thus contributing to IBD [12]. On the other hand, dysbiosis can lead to an imbalance in metabolites production that in turn may link to IBD pathogenesis [13]. The decrease of butyrate-producing species, such as Roseburia hominis and Faecalibacterium prausnitzii, play an important role in epithelial barrier integrity [14].
Rodent colitis models are important tools to investigate IBD pathogenesis and have contributed greatly to the identification of IBD pathogenesis process. DSS-induced IBD mouse model is one of the most widely used models due to its rapidity and reproducibility 4 [15]. DSS is toxic to colonic epithelial cells and could destroy the mucosal barrier integrity [16,17], allowing the entry of luminal bacteria into mucosa and the dissemination of proinflammatory intestinal contents into underlying tissue [18,19]. Therefore, DSSinduced colitis might be mediated by gut microbiota dysbiosis and corresponding inflammatory response. Previous study using DSS-induced IBD mouse model, revealed the importance of IL-17F itself and IL-17F-induced Treg cells in protecting against colitis [20].
In other studies, germ-free mice showed a higher mortality rate than wild-type mice when given the same dose of DSS [21], and the depletion of microbiota induced by broad spectrum antibiotics also exacerbates DSS-induced colitis [22], further suggesting the important role of gut microbiota in maintaining gut health. Thus, we used a DSS-induced colitis model to further explore the interactions between gut microbiota and colon inflammation.
However, there is still no conclusion concerning how microbial dysbiosis is linked to the onset and development of IBD. Fecal microbiota transplantation (FMT) provides new option for manipulating gut microbiota, thus becoming an encouraging therapeutic approach for intestinal disease and a powerful tool for investigating mechanisms of microbiological related disease. Combining FMT and antibiotics contributed to the microbiological improvement of intestinal dysbiosis in UC patients [23]. Previous study found the microbiota could alleviate murine chronic intestinal inflammation through modulating immune cell functions in terms of FMT [24].
In our study, we established IBD mouse model by drinking DSS solution and eliminated cage effects by randomly selecting mice from multiple cages during samples collection.
Colonic content samples were collected on d 0, d 5 and d 10 to investigate gut microbiota composition changes; colon tissues was assessed as research indicates that microbial abundance and diversity are directly related to the physiological state of the 5 gastrointestinal tract segment; combination of Abx-mice model and FMT manipulation may elucidate the relationship between altered gut microbiota composition and gut inflammation responses.

Clinical Signs of IBD mouse model
After 5 days of DSS treatment, IBD mouse model showed significant decrease in body weight, and continued to decline for the next 5 days (Fig.1). Disease activity index began to rise significantly on the 3th day and peaked on d 8 (Fig.1). Colon length was decreased compared with the normal control ( Fig.1). We also found an obvious edema, severely damaged mucosal structures and abundant inflammatory cell infiltration from H&E staining of distal colon tissue (Fig.2). Also, the inflammatory cytokine TNF-α in serum significantly increased (Fig.2). It was worth noting that, compared with d 5-model, the colon length of d 10-model significantly increased whereas the level of TNF-α in serum significantly decreased.

Gut microbiota altered in IBD mouse model
In addition to colitis development, the DSS altered the composition of the gut microbiota by 5 days administration. PCoA revealed clear clustering by DSS-treatment time point using both unweighted-unifrac and weighted-unifrac method (Fig.3). Compared with normal control, Sobs and Chao index decreased in IBD mice (Fig.4, S1). Nevertheless, the Simpson index did not change significantly (Fig.4).
We further analyzed changes in the relative abundance of specific phylotypes. The overall abundances of the dominant phyla Firmicutes (37.77% in normal control and 38.16% in d 5-model) and Bacteroidetes (58.36% in normal control and 47.37% in d 5-model) were not significantly affected by DSS treatment but taxa within these two phyla showed dramatic changes (Fig.5). However, after a 5-day recovery period, the phyla Firmicutes (76.92%) 6 significantly increased and Bacteroidetes (13.4%) significantly decreased (Fig.5).
Results indicated that the proportions of phylum Firmicutes showed a negative relationship with phyla Bacteroidetes (R 2 = 0.9883, P 0.0001) in normal control, and this correlation became weaker in IBD mouse model (Fig.6). To further identify the differences of key microbiota among groups, LEfSe analysis was performed (Table.S1 and Fig.S2).
Among phylum Firmicutes, the Bacillales in the normal control were more abundant than that of the IBD mice; d 5-model group increased the Firmicutes-Erysipelotrichia- In addition to the bacteria concentrated in one phase mentioned above, the lower relative abundances taxa Clostridium and Bifidobacterium concentrated in d 5-model but decreased in d 10-model. Furthermore, the abundances of Mucispirillum, Helicobacter and Ruminiclostridium increased in both d 5 and d 10 mouse model (Fig.S3).

IBD mouse model colonic commensal microbiota contributed to colitis
Compared with vehicle-mice, colon length of FMT-mice decreased significantly ( Fig.8, S4), and some colon samples of FMT-mice showed obvious blood and hygromata after microbiota transplantation (Fig.S4). However, body weight did not show significant difference and no obvious blood were observed in feces as well (data not shown).
Similarly, mucosal epithelial damage and epithelial loss were observed in almost all FMTmice, and we further found obvious inflammatory infiltration among a few of them ( Fig.9). We also analyzed the difference of microbiota composition between different phase of IBD mouse model and normal control. Both weighted and un-weighted unifrac PCoA separated these 3 group well (Fig.3), indicating that the detected community differences were not due to the presence and/or absence of rarer phylotypes. Also, the decrease of community richness was observed at both progressive phase and recovery phase (Fig.4, S1) but community diversity did not change significantly (Fig.4), suggesting the losses of particular taxa while some "new" taxa rise.

Discussion
Human colonic microbiota is dominated by anaerobic members within phyla Bacteroidetes and Firmicutes [27]. The situation is the same for IBD mice, but the overall abundances of level and/or reduce the proinflammatory cytokines, thus altering the immune system [28][29][30]; it is worth noting that Lactobacillus could suppress TNF-α expression [31], which may explain the declined TNF-α in IBD mouse model on d 10 (Fig.2). Second, Lactobacillus directly competed with pathogenic bacteria therefore contributing to gut health [32,33].
Though whether the change of Lactobacillus is the cause or consequence of the disease pathogenesis remains unclear, this understanding of the ecophysiology of the commensal and probiotic Lactobacillus in the dysbiosis disease states provide useful information for the successful treatment and prevention of chronic intestinal inflammation.
The second most abundant abundance bacteria, Muribaculaceae, changed in manner quite differently from Lactobacillus. The highest abundance was observed in normal control (82.7%) and nearly disappeared in IBD mouse. This phenomenon was similar to that observed in the context of feeding trials using high-calorie and/or carbohydrate-enriched diets [34][35][36]. This is probably the consequence of its ability to degrade particular types of polysaccharides: plant glycans, host glycans, and α-glucans [37,38]. Also, the ability of degrading dietary carbohydrates produces succinate, acetate, and propionate [37], which may lead Muribaculaceae to occupy overlapped niches as Bacteroides does. Bacteroides also specialize in the fermentation of polysaccharides [39], and members of Bacteroides are known to produce succinate, acetate, and propionate by polysaccharides fermentation [40][41][42]. It may explain why Bacteroides exhibit opposite abundance trends compared with Muribaculaceae. The abundance of Bacteroides was low in both normal control (12%) and IBD mouse model recovery phase (17.3%) but became concentrated in IBD mouse model progressive phase (70.7%). This phenomenon may attribute to the ability of Bacteroides to sense and adapt to gut environmental changes and stress. Firstly, species within Bacteroides could utilize host cell surface glycoproteins and glycolipids as nutrients source at sites of infection [43][44][45][46][47][48][49][50], therefore have a superb ability to access nutrients; on the other hand, Bacteroides could modulate its surface polysaccharides to an "on" or "off" position [51], thus avoiding the host immune response. These systems used by Bacteroides may explain its high abundance in the gut inflammatory environment.
Previous study also reported Bacteroides species were found in most of anaerobic infections [52]. Enterotoxigenic B. fragilis, a member of Bacteroides, could secret B.
fragilis enterotoxin [53] and induce cyclooxygenase 2 and fluid secretion in intestinal epithelial cells [54], thus may be associated with occurrence of colorectal cancer and IBD [55,56].
Another notable change occurred at Akkermansia. It has been isolated in 2004 [57] and classified as mucin-utilizing bacteria [58]. However, recent studies indicated that Akkermansia may also exert positive regulation on intestinal mucosal thickness and intestinal barrier integrity [59,60] and be praised as "the next generation of probiotics" [61]. With the deepening of research, Akkermansia was thought to act as both "friend and foe", so it would be unwise to advocate the efficacy of Akkermansia until more research 11 and clinical data emerged. A research in China indicated that Akkermansia was positively correlated with the risk of Chinese type 2 diabetes [62]. Later, Ijssennagger et al found antibiotic treatment enhanced intestinal barrier function by eliminating sulphideproducing and mucus-degrading bacteria such as Akkermansia [63]. Besides, Akkermansia also triggers an excessive immune response that disrupts mucus secretion and damage intestinal barrier in IL-10 -/mice [64]. In the present study, our data suggested Blood, colon tissue and colonic contents were collected; body weight, stool consistency and degree of intestinal bleeding were measured daily. The scoring system displayed in  [18].
For the antibiotic-induced microbiota depletion mouse model (Abx-mice), we used the method as described previously with slight modifications [20,[67][68][69][70]. In brief, 6 weeks aged female C57BL/6J mice were delivered with a cocktail containing ampicillin, neomycin, metronidazole and vancomycin (1 g/L, 1 g/L, 1 g/L and 500 mg/L, respectively and all were obtained from Sigma) in drinking water ad libitum for 2 weeks, then had a "rest" for 2 days to prepare for microbiota transplantation.

Microbiota transplantation
Colonic content samples were freshly collected from donor mice (IBD mouse model on d 5, n = 9) and homogenized in sterile PBS (50 mg/mL). Homogenates were passed through a 40 µm cell strainer and then centrifuged at 8500 rpm for 5 min. Precipitation was resuspended with the same volume of 10% glycerol/PBS solution and stored at -80℃ until used for microbiota transplantations.
Five Abx-mice were orally intragastric administration with 200 μL suspension above once a day for 5 consecutive days (FMT-mice), while 5 mice were orally intragastric administration with the same volume of 10% glycerol/PBS vehicle as control (Vehiclemice). Then mice were euthanized and colon samples were collected. Body weight, stool consistency and degree of intestinal bleeding were measured daily.

Elisa
Blood (collected on d 0, d 5 and d 10 of IBD mouse model) was centrifuged to obtain serum. The levels of TNF-α was determined using ELISA kits (SLCY Biotech, Beijing, China).
The kit assays were carried out according to the protocol supplied by the manufacturer.
The absorbance was read at 450 nm using a multimode microplate reader (iMark, BIORAD, 14 USA).

Histology
Colon sections were removed, the contents were gently extruded and then washed in saline, fixed in 4% paraformaldehyde and embedded in paraffin. After being cut into 4 μm thickness slices, tissue sections were stained with hematoxylin and eosin and then examined using a light microscope (Nikon Eclipse Ci, Japan). Photomicrographs were captured using a digital camera attached to the microscope (Nikon digital sight DS-FI2, Japan). Histological damage was quantitatively assessed as described by Wirtz S et.al [18]. The sum of two sub-scores results in a combined score ranging from 0 (no changes) to 6 (widespread cellular infiltrates and extensive tissue damage). (Table S3)

Microbiota composition by 16S rRNA sequencing analysis
Colonic contents were collected from IBD mouse model on d rRNA database with 70% confidence threshold [71].

Statistical analysis
Statistical analysis was performed using Prism software (GraphPad 7.0

Ethics approval
All care, maintenance, and experimental protocols were performed in accordance with the Animal Care and Use Committee of China Agricultural University (Beijing, China).

Consent for publication
Not applicable.

Availability of data and materials
All data generated or analyzed during this study are included in this manuscript. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Competing interests
The authors declare that they have no competing interests.   and had significant effects on the differences between the groups; taxa that was not associated with differences between groups, or no significant differences between groups were annotated in yellow.     Data are means ± SD. *p < 0.05, **p < 0.01 compared with the normal control group; #p < 0.05 compared with the d 5-model group and the same below.  The changes of gut microbiota diversity. n = 9, Data are means ± SD and analyzed by one-way ANOVA.