Commensal Lactobacillus Suppress Colon Tumorigenesis and Progression by Modulation of Sphingosine 1-Phosphate Signaling in Mice

Fuqiang Xu Institute of Modern Physics Chinese Academy of Sciences Qiaoqiao Li Institute of Modern Physics Chinese Academy of Sciences Shuyang Wang (  wangsy@impcas.ac.cn ) Institute of Modern Physics Chinese Academy of Sciences Miaoyin Dong Institute of Modern Physics Chinese Academy of Sciences Guoqing Xiao Institute of Modern Physics Chinese Academy of Sciences Jin Bai Institute of Modern Physics Chinese Academy of Sciences Junkai Wang Northwest Normal University Xisi Sun Institute of Modern Physics Chinese Academy of Sciences


Background
Colorectal cancer (CRC) remained the second leading cause of cancer death and almost 1.0 million cancer deaths occurred in 2020 1 . The polypoid adenomas in the colon and rectum were gradually developed CRCs through the multistep mechanism, including adenoma-carcinoma process 2 . Additionally, most of patients diagnosed at the middle and advanced stage owing to the longer development of malignancies in the colon and rectum and higher concealment than other cancers, which resulted in shorter treatment window and higher mortality rate 3,4 . Therefore, e ciently safe prevention strategies for colon cancers in early stages and the development of therapeutic target for new therapeutics strategy of colon cancer are priorities for cancer control.
One fth of human malignancies are implicated in gut microbiota 5 . Accumulating evidence showed that gut microbiota was closely related to digestive diseases occurrence and health maintenance, especially colon cancers 6,7 .Therefore, the regulation of gut microbiota has been considered as a promising strategy for preventing and treating colon cancer 8,9 . Previously, phage-based intervention of gut microbiota and fecal microbiota transplant (FMT) that altered the gut microbiota of the patients and remodeled the homeostasis of intestinal microbiota was proved to be positively therapeutic effects 10,11 . However, the safety and operation standardization remained the biggest challenge for patients with colon cancer, which limited the development of cancer therapy based on the modulation of microbiota by phage-based intervention and FMT. Probiotics, as novel functional foods via microbiota-modulation to enhance host health and inhibit tumor carcinogenesis, have been attracted considerable interests. Most importantly, the recognized safety and complete standardization of probiotics were con rmed 12 . Recent studies revealed potentially positive effects of probiotics to colon cancer therapy 13,14 . Similarly, our previous reported results also showed that Lactobacillus casei inhibited signi cantly tumor progression in animal models of colon cancers 15 . However, the relationship between colon cancer and microbes is fairly complex. The mechanism in which microbes and the metabolites contribute to carcinogenesis, and the antitumor mechanisms in uenced by microbiota and their metabolites with intervention of probiotic remain unclear.
Observation of the roles of microbes and the microbiota and its metabolites in colon cancer were of great importance for early prevention and treatment of colon cancers. Fecal microbial diversity is a useful tool for observation of the altered gut microbiome. Correspondingly, the intestinal metabolites, such as shortchain fatty acids (SCFAs), hydrogen sulphide, acetaldehyde, secondary bile acids, are involved in the initiation and/or progression of colorectal cancer 16 . Therefore, comprehensive microbiome and metabolomic analyses might provide an alternative approach to understanding colon cancer occurrence and development through associated alteration in the gut microbiota and their secreted metabolites with the intervention of probiotics.
The aim of this study was to investigate the preventive and therapeutic e ciency of commensal Lactobacillus, including JY300-8 and JMR-01, and how was involved to colon cancer occurrence and development for gut microbiome and its metabolites by the intervention of probiotics. Accordingly, the preventive and therapeutic e ciency (the tumor formation rate, the overall survival and tumor volume of tumor-bearing mice) in subcutaneous tumor models were assessed when oral administration for commensal Lactobacillus. Furthermore, the fecal microbiota composition and its metabolic pro les were performed to obtain evidence of antitumor effects of fecal microorganisms and metabolites. Our work provides an e cient prevention strategy for colon cancers via intervention of probiotic modulation, and also a potential therapeutic target for new therapeutics strategy of colon cancer.

Cells and probiotic preparation
The murine colon cancer cell lines CT-26 was purchased from Shanghai Cell Bank, cultured in 5% CO 2 of carbon dioxide incubator with RPMI1640 containing 10% of fetal bovine serum and 1% of penicillin / streptomycin at 37 °C.
Lactobacillus reuteri JMR-01 (NCBI Accession No. MT362007) was isolated from the feces of which cured breast cancer mice treated with carbon-ion beams. Lactobacillus casei JY300-8 15 was preserved in the Institute of Modern Physics, Chinese Academy of Sciences (IMP-CAS). JMR-01or JY300-8 was cultured statically at 37 °C for 24 h in MRS broth, and its fermentation solution were centrifuged at 4000 r/min for 10 min to acquire bacterial precipitates, respectively. Then bacterial precipitates of JY300-8 and JMR-01 were resuspended at 1×10 9 CFU/mL of concentration in PBS to obtain living bacteria JY300-8 and JMR-01, respectively. Subsequently, the living bacteria (LB) were prepared by the mixture of JY300-8 and JMR-01 at the ratio of 1:2 (v/v); in addition, LB were autoclaved at 105 °C for 30 min to prepared inactivated bacteria (IB).
Antitumor e cacy of commensal Lactobacillus in subcutaneous colon cancer models Experimental animal and subcutaneous colon cancer models BABL/C mice (Male, 6 weeks) were purchased from Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences. Mice were maintained on sterilized mice chow, water at constant temperature (20 ± 3 °C) and 40-70% of humidity conditions with a 12/12-h light/dark cycle in a speci c pathogen-free facility after Institutional Animal Care and Use Committee approval at Biomedical Center, IMP-CAS. In accordance with the study schedule, the mice were sacri ced by euthanasia at the end of the experiment. All animal experiments complied with the ARRIVE guidelines and were carried out in accordance with the National Institutes of Health guide for the care and use of Laboratory animals (NIH Publications No. 8023, revised 1978). A subcutaneous mouse model for colon cancer was developed using CT-26 cells. CT-26 cells were inoculated in RPMI1640 medium containing 10% FBS at 37 °C. At the exponential growth phase, 5 × 10 6 CT26 cells were suspended in 100 uL cold PBS and subcutaneously injected into mice at right groin. Then tumor formation rate, tumor volume and survival rate were evaluated at alternate days. Tumor formation rate were counted by the ratio of the number of tumors forming mice to all mice. Tumor volume was 1/2× a 2 b, where a and b represented the largest and shortest tumor diameters, respectively.

Experimental design and anti-tumor e cacy of commensal Lactobacillus
Overall, the experimental scheme was composed of two processes, including the prevention and treatment of subcutaneous colon cancer model in mice. Throughout the experiment, the BABL/C mice were randomly divided into 3 groups with 30 animals each group, that was tumor control (CK), LB and IB group. In the prevention stage, the mice in control, LB and IB group, was performed semidiurnal gavage with 100 uL/ head of PBS, 0.1×10 9 CFU/head of LB, 100 uL/ head of IB for 20 days, respectively.
Subsequently, subcutaneous colon cancer models were induced by CT26 cells. Meanwhile, each group of mice were consistently performed prevention treatment as mentioned above, tumor formation and survival rate were evaluated when tumor model established successfully in control group (the mean tumor volume reached approximately 100 mm 3 ). Then in the treatment stage of colon cancer, tumor-bearing mice were continued to oral administration of PBS, LB and IB until the end of experiment. Meanwhile, tumor volume and survival rate were evaluated at alternate days until the end of experiment when the highly signi cant different (p<0.001) in mean volume between control group and treatment group were consistently achieved in three measurements. Meanwhile, the fecal samples were collected to analysis of the abundance of microbiota and its metabolome.
Sample collection and detection based on the animal model For assessment of prevention effect of commensal Lactobacillus on colon cancer induced by CT26 cells in mice, survival and tumor formation rate in various groups were measured when tumor model established successfully in control. Furthermore, to evaluate the therapeutic e cacy of Lactobacillus on colon cancer, the tumor volume and survival were monitored in treatment stage. Finally, the fecal sample in various groups were aseptically collected for comparative analysis of the potentially altered microbiota and its metabolites in colon cancer mice by LB or IB intervention, then each of group was randomly chosen six of fecal samples to measure the microbiota and corresponding metabolites. GGACTACNNGGGTATCTAAT) with attached Illumina adapter overhang sequences. All PCR reactions were carried out in 30 μL reactions with 15 μL of Phusion®High-Fidelity PCR Master Mix (New England Biolabs). Then PCR products was mixed in equidensity ratios and mixture PCR products was puri ed with AxyPrepDNA Gel Extraction Kit (AXYGEN). Subsequently, sequencing libraries were generated using NEB Next®Ultra™DNA Library Prep Kit for Illumina (NEB, USA) following manufacturer's recommendations.
The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scienti c) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina Miseq platform and 250/300 bp paired-end reads were generated.

Bioinformatic analysis of microbiota in fecal samples
Sequence analysis was performed by UPARSE software package using the UPARSE-OTU and UPARSE-OTUref algorithms. Sequences with ≥ 97% similarity were assigned to the same OTUs. Then OTU table and phylogenetic tree, UniFrac distance matrix were obtained and weighted 18 . The richness, inverse Simpson and Shannon index were estimated with the phyloseq (version 1.30.0) and vegan (version 2.5-6) packages in R. Graphical representation of the relative abundance of bacterial diversity from phylum to species were visualized using Krona chart. Cluster analysis was preceded by principal component analysis (PCA) using the QIIME software package 19 . LDA scores were calculated by LEfSe 20 with the factorial Kruskal-Wallis test (P < 0.05) to con rm the discriminative genera between groups, and the logarithmic LDA threshold score was set at 2.0. Finally, the heatmap was drawn with heatmap package (version 1.10.12).

Untargeted metabolomics of fecal samples using LC-MS/MS
Quantitative analysis of untargeted metabolites was performed using Waters 2D UPLC (waters, USA) tandem Q Exactive high resolution mass spectrometer (Thermo Fisher Scienti c, USA) 21 . Metabolites were extracted according to previously reported methods 22 . In short, 25 mg fecal sample were weighed and extracted by directly adding 800 µL of precooled extraction reagent (methanol: acetonitrile: water (2:2:1, v/v/v)), 10 µL internal standards mix (L-Leucine-d3, L-PHENYLALANINE (13C9, 99%), L-Tryptophan-d5, Progesterone-2,3,4-13C3) were added for quality. After homogenizing for 5min using TissueLyser (JXFSTPRP, China) with adding two small steel balls, samples were performed ultrasound at 4°C for 10 min and incubated at -20°C for 1 h. And then centrifuged for 15 min at 25000 rpm at 4 °C, then the supernatant was performed vacuum drying. Subsequently, the metabolites were resuspended in 600 µL of 10% methanol and sonicated for 10 min at 4 °C, then the supernatants were used for LC-MS analysis after centrifuging for 15 min at 25000 rpm at 4°C. Additionally, 50 μL of the supernatant of each sample and mixed it into a QC quality control sample to evaluate the repeatability and stability of the LC-MS analysis process.
The analysis of metabolomics data LC-MS/MS data were analyzed by Compound Discoverer 3.1 (Thermo Fisher Scienti c, USA) software.
And the metabolome R software package metaX were employed to perform data preprocessing, statistical analysis, metabolite classi cation and functional annotation 23 . Metabolites were identi ed by combining of mzCloud, the Human Metabolome Database (HMDB), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Lipid Maps databases. Subsequently, principal component analysis (PCA) was employed to analyze similarities and differences within groups as well as outliers 24 . Next, the signi cant differential metabolites were screened by variable important for the projection (VIP) calculated using partial least squares discriminant analysis (PLS-DA) 25 , combined with fold change (FC) of a single variable analysis and p value of Student's t test 26 .
Coalition analysis of 16S rDNA amplicon sequencing and untargeted metabolomics Spearman statistical method was employed to analyze the correlation coe cient between the signi cant difference micro ora and signi cant difference metabolites, and then R and Cytoscape software were used to perform the analysis of the matrix heat map, hierarchical clustering, correlation network to investigate the interaction relationship between microbiota and metabolites.

Statistical analysis
Statistical analysis was performed using SPSS 22.0 software (SPSS Inc., Chicago, IL, USA) and plotted using Origin 2019 (OriginLab Corp., Northampton, MA, USA) and GraphPad Prism 8.0.1 software (GraphPad Inc., California, CA, USA). The signi cant differences between the two groups were analyzed using an independent samples T-test using by one-way ANOVA with Tukey-Kramer comparison test. NS, not signi cant, *P < 0.05, **P < 0.01, ***P < 0.001 indicated statistical signi cance level.

Results
Antitumor e cacy of commensal Lactobacillus in subcutaneous colon cancer models in mice To determine whether commensal Lactobacillus exerted prevention and treatment activity for colon cancer in vivo, we assessed its anti-tumor e cacy using the CT26 cells induced-mouse colon cancer model. The experimental scheme for tumor prevention and treatment by oral administration of commensal Lactobacillus were shown as Fig.1a, and the anti-tumor e cacy of commensal Lactobacillus on CT26 subcutaneous colon cancer in mice were presented as Fig1(b-e). As shown in Fig1b, in the prevention stage of colon cancer induced by CT26 cells, the survival rates of mice were 100% in tumor control, LB and IB group for 20 days oral administration for probiotics, showing that probiotics were safety for animal. Additionally, the tumor formation rates were 13.33% and 16.67% in LB and IB group, while that was reached 96.67% in control after 10 days CT26 cells were subcutaneously injected, which indicated that living and inactivated bacteria treated-mice reduced signi cantly the tumor formation rate compared to the tumor controls (p<0.01).
Moreover, in the treatment stage of colon cancer induced by CT26 cells, tumor growth was much faster in the tumor control group than in those treated with living bacteria and inactivated bacteria treated group (p 0.001) (Fig.1d), and the mean tumor volume was 4920.63 ± 462.4 mm 3 in tumor control group, while they were 1712.45 ± 388.95 mm 3 and 1909.95 ± 292.9 mm 3 in living bacteria and inactivated bacteria treatment groups at the end of the experiment, respectively. In addition, though the tumor new formation rates were gradually increased in LB and IB group, which was 66.67% and 73.33% of tumor formation rates, those were remarkable lower than control group (p<0.05), which was an indirectly proof that oral administration for living commensal Lactobacillus could suppressed the colon cancer progression, followed by inactivated bacteria treated-group. Meanwhile, the survival ratios of bearing mice were 93.33% both in tumor control group and IB group, when compared to 100% of survival rate in LB group at 30 days. Therefore, these results demonstrated that the commensal Lactobacillus reduced signi cantly the occurrence of colon cancer, suppressed su ciently the tumor growth and progression, and enhanced the survival rate of bearing tumor mice.
Commensal Lactobacillus enhanced the antitumor responsiveness by modulating gut microbiota In order to further explore the potential effects of the commensal Lactobacillus on gut microbiota in the CT26 induced-murine model of colon cancer, and assess the interaction of altered microbiota and colon cancer progression, Miseq sequencing analysis of the V3-V4 region of the 16S ribosomal RNA gene of the fecal samples were performed. The structure and abundance of microbiota in three groups were shown in Fig.2. According to the rarefaction curve (Fig.2a), the amount of sequencing data in samples was su cient to perform the analysis of the diversity and abundance of gut microbiota. At the end of the experiment, the diversity of microbial ora (Shannon index and Simpson index) was no signi cant difference among the tumor control (CK), living bacteria, and inactivated bacteria group (Fig.2b). The abundance of microbial ora both in living bacteria and inactivated bacteria were higher than that of tumor control group (p<0.001), and there was not signi cant difference in living bacteria and inactivated bacteria (Fig.2b). Additionally, Principle-coordinates analysis (PCoA) results showed that phylogenetic community structures were notably different between tumor control group and others, however the PCoA plots also suggested that the living bacteria and inactivated bacteria groups were not obviously dispersed at 30 days (Fig. 2c). It demonstrated that commensal Lactobacillus administration signi cantly changed the gut microbiota composition of tumor-bearing mice. As the similarity of results in Fig 2b, Venn analysis also showed that living bacteria treatment affected signi cantly the structure of the intestinal ora (Fig.2d). Distribution and abundance of microbial taxa at the phylum level in three groups at 30 days were examined as shown in Fig. 2e. The results indicated that bacteria in the Bacteroidetes and Firmicutes phyla dominated the mouse gut microbiota. The dominant phyla in the gut microbiota were Bacteroidetes, Firmicutes, Proteobacteria and TM7 (relative abundance > 0.5%). At genus level, the uctuation of relative abundance depended on the various treatment. The dominant gut microbiota were Bacteroides, Oscillospira, Prevotella, Ruminococcus, AF12, Roseburia, Parabacteroides, Coprococcus, Ruminococcus (relative abundance > 0.5%) (Fig. 2f.). Compared to the control, the living bacteria treated group was signi cantly increased in Oscillospira, Prevotella, AF12, Roseburia, Coprococcus, while Bacteroides, Ruminococcus and Parabacteroides increased. These results showed that the gut microbiota with the capacity of producing short chain fatty acid (SCFAs) were enriched in living bacteria treatment group. Similarly, the Prevotella, AF12, Roseburia, Coprococcus and Ruminococcus were increased, while the Bacteroides and Ruminococcus reduced in inactivated bacteria treatment group in comparison to tumor control group. Thus, our data from colon cancer mouse with various treatment demonstrated that both the living bacteria and inactivated bacteria contributed to alleviating dysbiosis induced by tumor by modulating gut microbiota structure with respect to increased alpha and beta diversity and the proportion of potentially bene cial taxa, which enhanced the anti-tumor response.
Furthermore, in order to determine which bacteria were most likely associated with cancer prevention and treatment e ciency between LB and IB group, we employed the linear discriminant analysis (LDA) and effect size (LEfSe) methods to calculate the LDA scores for the three samples at 30 days. The lists of taxonomic clades ranked according to the effect size, which were differential among groups with statistical and biological signi cance were shown in Fig. 2g. The results indicated that, between the living bacteria and control groups, Coprococcus, AF12, Lactobacillus, Enterobacter and Turicibacter were most discriminative at genus level in living bacteria group. Similarly, Coprococcus was most differential in inactivated bacteria group. However, Butyricimonas, Sphingomonas and Prevotella were most discriminative in tumor control group. Between living bacteria and inactivated bacteria group, most discriminative in living bacteria group included, Lactobacillus and Escherichia.
The analysis of untargeted metabolites associated with anti-tumor e ciency The quantitative metabolomics technology was used to obtain the complete metabolic pro les of gut microbiota in colon cancer mice with various treatments. Overall, As shown in Fig. S1a-b (Additional le 1: Fig. S1a-b), PCA analysis in QC samples of positive and negative ion model showed that the data collection process was reliable. At the same time, both positive and negative ion model, control group and LB group, and control group and IB group were highly distinguished. These results indicated signi cant differences in metabolic pro les with LB and IB treatment when compared with control. It was also indirectly proved that living bacteria or inactivated bacteria had a great in uence on the metabolism of microbiota in colon cancer mouse model. However, the fecal metabolic pro les with LB and IB treatment were not obviously distinguished, which was indicated that there were similar metabolic pro les each other.
Furthermore, the metabolites with signi cant difference between various treatments were obtained (Additional le 2: Table. S1-S3). To further screen the highly signi cant difference metabolites, more stricter screening conditions were employed from signi cant difference metabolites (VIP>3, FC >1 or FC <0.83 as well as p <0.01). Accordingly, cluster analysis of differential metabolites for all samples were shown as Fig.3. Compared to control, the up-regulated signi cant different metabolites in the LB groups Additionally, the results of KEGG enrichment analysis indicated that signi cant different metabolites that were likely associated with the colon cancer progression, were enriched in Bile secretion, Central carbon metabolism in cancer, Phenylalanine metabolism and Alanine, Arginine and proline metabolism as well as Apoptosis process in control_LB groups, respectively (Additional le 3: Fig. S1). In the control_IB groups, the signi cant different metabolites were enriched in Bile secretion, Pyrimidine metabolism, Apoptosis, Arginine and proline metabolism, respectively (Additional le 4: Fig. S1). While the signi cant different metabolites in the LB_IB groups were enriched in Pyrimidine metabolism (Additional le 5: Fig.  S1).

Coalition analysis of microbiome and metabolomics
In order to obtain a global understanding of interaction with microbiota and its metabolites in colon cancer progression via different treatments, a coalition analysis of the 16S microbial diversity and metabolomics were performed. As spearman correlation analysis on signi cant difference microbiota and metabolites in control_LB groups, sphingosine that involved in Apoptosis was signi cantly positively correlated with Veillonella, and negatively correlated with Coprococcus and Prevotella. And cholic acid and chenodeoxycholate was negatively correlated with Dehalobacterium, respectively. Moreover, deoxycholic acid was positively correlated with Porphyromonas. And succinate was positively correlated with Coprococcus and Dehalobacterium. (Fig4.a, b). Similarly, the coalition analysis of signi cant difference microbiota and metabolites in the control_IB groups, showing that uracil, thymine and cytosine, the key metabolites involved in Pyrimidine metabolism signaling pathway, had the same expression pattern, which were positively correlated with Prevotella, negatively correlated with Bacteroides and Flexispira, respectively. Cholic acid, chenodeoxycholate and deoxycholic acid showed similar expression patterns, and were positively correlated with Prevotella, Flexispira, Bacteroides in addition to Porphyromonas with deoxycholic acid. Additionally, sphingosine was positively correlated with Flexispira, and negatively correlated with Prevotella (Fig 4.c, d).

Discussion
Recently, the relationship among an altered gut microbiota and its metabolites with colon cancer provides a potential strategy for prevention and treatment of colon cancer via the modulation of gut microbiota using probiotics 27,28 . Probiotics are the most commonly consumed food supplements owing to recognized safety and complete standardization 12 , thus probiotics are consider as most potential safe agents for cancer prevention in the form of food supplements, especially colon cancer. Accumulating evidence showed that probiotics, such as Lactobacillus supplementation, could prevent the animal that were susceptible to colon cancer against carcinoma effects in vivo as shown in Table 1. According to Table 1, many probiotics might exert protective effects against colon cancer, but did not act on preexisting colon cancer. However, our results demonstrated that L.casei JY300-8 and L.reuteri JMR-01 pretreated-group not only decreased signi cantly the tumor formation rate but also suppressed remarkably the tumor growth and enhanced the survival compared to those of in tumor control. Similarly, our previous studies also veri ed that L. casei JY300-8 inhibited signi cantly tumor progression and stronger inhibitory effect on tumor (83.48%) in comparison to the therapy drug (DFUR) for colon cancer (65.65%) in animal models of colon cancers 15 . Additionally, although the highest protective effects were reached via probiotic VSL#3 -treated animals (there were no animals developed carcinoma), the merely 29% of animal developed carcinoma in control group 29 . Accordingly, oral administration of JY300-8 and JMR-01 presented more potential protective effects against the occurrence of colon cancer induced by colon cancer cells, which were considered as more promising probiotics in colon cancer prevention and anti-cancer therapies. In this study Probiotics could regulate gut microbiota structure and composition, thus modulating occurrence and development of colon cancer 35,36 . The present studies suggested that the structure and abundance of gut microbiota were correlated with the development of colon cancer via administration for living or inactivated probiotics. And the major microbial alterations correlated with colon cancer progression were shown in Fig.5. The Flexispira, Bacterodes, Porphyromonas, Clostridium and Escherichia enriched in tumor control, especially Clostridium and Escherichia were extensively existed in all tumor mice, were possibly played the important role in the development and progression of colon cancer. Similarly, recent research suggested that Fusobacterium nucleatum, Escherichia coli, Bacteroides fragilis were promoted the neoplastic processes in epithelial cells 37 . We speculated that some species might be associated with colon cancer, including Flexispira and Porphyromonas, which possibly secreted the harmful metabolites, such as cholic acid, chenodeoxycholate, deoxycholic acid. Additionally, the Coprococcus, Veillonella, Lactobacillus, Bi dobacterium and Dehalobacterium in oral administration of living JY300-8 and JMR-01 were likely reduced the occurrence and development of colon cancer. As previously evidence suggested that Coprococcus, produced bene cial metabolites, such as short chain fatty acids including acetate, propionate, butyrate, exerted the antitumor e ciency 38 . Meanwhile, Veillonella could fermented lactate produced by Lactobacillus to yield the acetate, propionate 39 . However, the prevotella that associated with gut in ammation 40 , was enriched in administration for inactivated JY300-8 and JMR-01. Fortunately, an overgrowth of Prevotella was correlated with a reduction of Lactobacillus 41 , thus oral administration living JY300-8 and JMR-01 in IB group would be mitigated the negatively effects of Prevotella. Overall, the altered microbiota was associated with the colon cancer. The Flexispira, Bacterodes, Porphyromonas, Clostridium and Escherichia might play an important role in progression of colon cancer. Contrarily, the Coprococcus, Veillonella, Lactobacillus, Bi dobacterium and Dehalobacterium were possibly exerted the protective effects on colon cancer susceptible mice.
Microbial metabolites in the gut have a particularly important role in progression of colon cancer 16 .
Recent evidence suggested that the bile acid, especially deoxycholic acid, has been implicated in carcinogenesis of intestine owing to the generation of ROS and reactive nitrogen species (RNS), both of which cause DNA damage 42,43 . In the present study, bile acids including cholic acid, deoxycholic acid, Moreover, the reported research demonstrated that higher levels of sphingosine was used as selective pressure to increase SK1 expression and thereby converting sphingosine to S1P to promote tumor cell survival 45 . Therefore, strategies that S1P pathway therapeutics for cancer have been adopted to limit the effects of S1P signaling in cancer, including the reduction of released S1P 46 , inhibition of SK1 and/or SK2 47 and targeting of speci c S1P receptors 48 . Importantly, in our study, the concentration of sphingosine in the intestine of colon cancer mice was signi cantly regulated by oral administration living or inactivated bacteria (JY300-8 and JMR-01), and the concentration reduction of S1P that transformed from sphingosine in intestine or other organs, thus exerting antitumor e ciency, including reduction of transformation the normal cells to cancerous cells, neovascularization, promotion the apoptosis of tumor cell. Additionally, the sphingosine was a strongly positive correlation with Veillonella and Flexispira, and negatively correlated with Coprococcus and Prevotella. Notably, the expression level of sphingosine might be regulated in colon cancer via intervention of excellent probiotics, which provided a potential therapeutic target for new therapeutics strategy via altering S1P signaling and function in cancer.
Additionally, other microbial metabolites in oral administration of living bacteria(JY300-8 and JMR-01), such as succinic acid, which was fermented to produce propionate via the succinate pathway in the context of microbiome 38 , 9-OxoODE 49 , exerted anticarcinogenic activity. As the similar report that conjugated linoleic acids, such as 9-OxoODE, were produced from linoleic acid by the fermentation of Lactobacillus and Bi dobacterium in the intestinal lumen, thus exerting its bene cial effects 50 . Moreover, pyrimidine metabolism, including thymine, cytosine and uracil, is a critical pathway for DNA replication, RNA synthesis. Hence, increased pyrimidine metabolism could guarantee uncontrolled growth of tumors 51 . In the present study, we demonstrated that the concentrations of thymine, uracil and cytosine implicated in pyrimidine metabolism pathway were depleted when administrated the inactivated bacteria JY300-8 and JMR-01 for colon cancer. Consistent with our observation, changes in pyrimidine metabolism regulated cancer cell proliferation 52 . Furthermore, the thymine, cytosine and uracil had similar expression patterns, all of these were positively correlated with Flexispira, Bacteroides, Porphyromonas and negatively correlated with Prevotella. Accordingly, this link between pyrimidine metabolism and tumorigenesis might provide novel targets for anticancer therapy via modulation of microbiota and its bioactive metabolites using excellent probiotics.
Interestingly, based on the shifts of the microbiota and their excreted bioactive metabolites in colon cancer with various treatments, we found that these were complementary in the process of colon cancer

Conclusions
In conclusion, our data demonstrated that commensal Lactobacillus suppressed colon tumorigenesis and progression through the modulation of gut microbial homeostasis and metabolites. For tumorbearing mice with oral administration of living JY300-8 and JMR-01, the therapeutic e ciency to colon cancer mice were performed by down-regulation of secondary bile acids and S1P signaling, and the production of anticarcinogenic compounds. Similarly, the results revealed that reduction of secondary bile acids, S1P signaling in cancer and pyrimidine metabolism pathway could exert antitumor function when orally administrated inactivated JY300-8 and JMR-01 for tumor-bearing mice. Therefore, modulation of gut microbiota and metabolites by intervention of probiotics could be a potential therapeutic strategy in the prevention and treatment of colon cancer.     to the absolute value of the correlation coe cient. Node size was positively correlated with its degree, namely, the larger the degree was associated with the larger the node size.) Figure 5 Graphical representation of major microbial and metabolomic alterations correlated with colon cancer progression using various treatment.