Lactobacillus-based probiotic cocktail inhibits colitis-associated cancer by altering intestinal metabolism

Lactobacillus-based probiotic cocktail inhibits colitis-associated cancer by altering intestinal metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lactobacillus-based probiotic cocktail inhibits colitis-associated cancer by altering intestinal metabolism Weiyi Wang, Ying Xu, Yimin Chu, Haiqin Zhang, Lu Zhou, Haijin Zhu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6436788/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dysbiosis of intestinal microbiome is an important colorectal cancer (CRC) pathogenetic mechanism. Lactobacillus-based probiotic cocktail could inhibit colitis-associated cancer (CAC) by alleviating intestinal dysbiosis. The intestinal microbial metabolites have been linked with CRC aetiology. However, it is still poorly understood the link between Lactobacillus-based probiotic cocktail and the alteration of intestinal metabolism and their functional mechanisms during CAC process. For assessing protective effects of the probiotic cocktail, azomethanes/dextran sodium sulfate (AOM/DSS) induced CAC mice were pretreated with the probiotic cocktail. Colon of C57BL/6 mice were used to assess inflammation and tumorigenesis. Comparative analysis was performed for determining how the probiotic altered intestinal metabolism and gene expression. Meanwhile, intestinal microbiota alterations were analyzed. The concluding integrated analysis of intestinal metabolism and gene expression as well as intestinal microbiota was presented. Pretreatment with the probiotic alleviated intestinal inflammation and limited the formation of tumors. Oncogenes were down-regulated and cancer suppressor genes were up-regulated after probiotic pretreatment. Pretreatment with the probiotic induced a rise of Lactobacillus-dominated genera and a reduction of potential pathogenic bacteria Parasutterella, Helicobacter and Muribaculum, and affected expression of intestinal metabolites that involved 37 metabolic pathways. Lactobacillus-associated intestinal metabolite variations involve five metabolic pathways - arginine and proline metabolism, histidine metabolism, pyrimidine metabolism, purine metabolism, and tyrosine metabolism. Pretreatment with Lactobacillus-based probiotic cocktail protected mice from CAC by interfering with intestinal metabolites that affected the cancer suppressor genes and oncogenes' expression. Furthermore, Lactobacillus affected five metabolite pathways, which was important mechanism for probiotic anti-inflammatory and anti-tumorigenesis. Chronic inflammation Colorectal cancer Intestinal metabolites Intestinal microbiota Probiotic Lactobacillus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Colorectal cancer (CRC) is considered one of the most prevalent and fatal malignant tumors worldwide [ 1 ]. A combination of screening, risk factor prevention, and improvements in treatment has resulted in a decline in morbidity and mortality among over 50 years old adults. However, epidemiologic studies showed CRC incidence continues to increase among adults under 50 years of age [ 2 ]. Therefore, further research on the molecular mechanism of CRC pathology would be an essential approach for prevention and treatment of CRC. Dysbiosis of the intestinal microbiome was involved in pathogenesis of CRC [ 3 , 4 ]. Observations in previous studies linked a healthy lifestyle with a lower risk of CRC, and adherence to a healthy lifestyle could further reduce this risk, which was partly as a result of alterations in intestinal microbiome [ 5 – 7 ]. Moreover, the risk factors for CRC, such as obesity, sedentary lifestyles, tobacco, poor diet (e.g., high-fat diet, high level of red and processed meats, low level of fiber, whole grains and calcium) and alcohol, also affected intestinal microbiome [ 8 – 10 ]. Probiotic are becoming increasingly important in the medical field because of their positive impact on human health. As we found in our previous study, pretreatment of probiotic cocktail Bifico capsules with Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis, relieved intestinal inflammation and minimized tumor formation in azoxymethane/dextran sodium sulfate (AOM/DSS)-induced colitis-associated cancer (CAC) mice by modifying the abundance of intestinal flora [ 11 ]. However, the biological mechanisms of intestinal microbiome are complex and have a bilateral effect on human health. After supplementing with lactobacillus, an infant without underlying diseases or immune deficiencies was reported to develop bacteraemia [ 12 ]. There are also several reports linking Lactobacillus and Bifidobacterium to opportunistic infections in immunocompromised patients and in those with allergic sensitization and autoimmune diseases [ 13 , 14 ]. Moreover, Enterococcus faecalis is an important opportunistic pathogen that can cause a wide variety of infections [ 15 ]. Hense, a deeper understanding of the biological mechanisms of intestinal microbiota's ability to inhibit CRC is needed. More and more researches showed that the intestinal microbiota can synthesize a large number of metabolites or bioactive compounds which play protective and detrimental roles in aetiology of CRC [ 16 ]. Many metabolic biomarkers have been reported in CRC [ 17 ]. In the present study, we utilized metabolomics to investigate which intestinal metabolites are affected by probiotic cocktail that ultimately inhibit tumorigenesis in a mouse CRC model. Seek out effective metabolites of probiotic so as to spare vulnerable patients (infants, immunocompromised, etc.) from the risk of opportunistic infections and autoimmune diseases. 2. Materials and methods 2.1 Experimental animals Approximately four-week-old male C57BL/6 mice were procured from Shanghai Jiaotong University School of Medicine's Animal Science Laboratory and were subsequently housed in pathogen-free animal care facilities at Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine. A controlled laboratory environment with 23°C, 50% humidity, and 12/12 hours of light and darkness was used. Ethics approval was granted by the ethics committees of Shanghai Tong Ren Hospital and Shanghai Jiao Tong University School of Medicine. 2.2 Reagents The chemicals Azoxymethane (AOM) and Dextran sodium sulfate (DSS) were procured from Sigma-Aldrich (St. Louis, MO, USA), while the probiotic cocktail Bifico capsules, which respectively contained a minimum of 1.0×10 7 c.f.u. viable lyophilized Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis per capsule (210 mg), were obtained from Shanghai Sinepharm (Shanghai, China). 2.3 Experimental procedure Protocol of the experimental was based on our prior investigation [ 11 ]. Thirty male C57BL/6 mice were randomly assigned to one of three groups: Control (n = 10), Model (AOM/DSS only, n = 10), and Treatment (probiotic cocktail lavage with AOM/DSS, n = 10). On day 1, mice received a single intraperitoneal dose of AOM (10 mg/kg), followed by three cycles of DSS induction. Mice in the Model and Treatment groups were administered 2% DSS (w/v) in their drinking water for seven consecutive days, followed by two weeks of sterile water in each cycle of DSS induction. The mice in the Treatment group underwent lavage with a probiotic cocktail consisting of 4.2 g/kg dissolved in 200 lL saline, containing at least 1.2 × 10 7 c.f.u./d, on a daily basis at 10:00 a.m. for a duration of one month prior to the commencement of the experiment until its conclusion. Subsequently, all mice were euthanized through the cervical dislocation method after a period of nine weeks. 2.4 Collection and preparation of samples Upon completion of the experiment, all animals were subjected to euthanasia and immediate laparotomy. From the distal cecum to the rectum, the colon was excised, followed by removal of adherent adipose tissue. Subsequently, the colon was dissected longitudinally, collected feces and stored in -80°C, flushed with ice-cold saline, and the fecal residue was meticulously removed and photographed. Similarly to feces, the partial colon tissue samples were stored at -80°C. The isolated colon was subjected to histopathology and gene expression studies. Additionally, the fecal samples were analyzed by 16S rRNA gene sequencing and metabolomics. 2.5 Evaluation of histopathology The process of tumorigenesis was investigated through macroscopic examination of colonic tissue obtained via biopsy. The tissue was subjected to overnight fixation with formalin, followed by replacement of the solution with 70% ethanol prior to paraffin embedding. Standard histological evaluation procedures, as outlined in reference [ 18 ], were employed to stain the paraffin sections with H&E. 2.6 RNA Isolation and Library Preparation The isolation of total RNA from colon tissue was carried out in accordance with the manufacturer's protocol utilizing TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Subsequently, libraries were constructed for each sample utilizing the TruSeq Stranded mRNA LT sample preparation kit (Illumina, San Diego, CA, USA) as per the instructions. OE Biotech (Shanghai, China) sequenced and analyzed the transcriptome. 2.7 Analysis of RNA sequencing and differentially expressed genes (DEGs) The sequencing of the libraries was conducted on the Illumina Novaseq 6000 platform, resulting in the production of paired-end reads with a length of 150 bp. Each sample yielded an average of 49.57 raw reads. The raw reads were subjected to quality control using fastp [ 19 ] to eliminate low-quality reads and obtain clean reads, resulting in approximately 48.61 clean reads per sample for subsequent analyses. HISAT2 [ 20 ] was utilized to map the clean reads onto the reference genome for each sample. The FPKM values for each gene [ 21 ] were calculated and the reads for each gene were obtained using HTSeq-count. The present study utilized PCA analysis in R (v 3.2.0) to evaluate the biological reproducibility of the samples. DESeq2 [ 22 ] was employed for differential expression analysis, with Q 2 or foldchange < 0.5 serving as the thresholds for significant differentially expressed genes (DEGs). Hierarchical clustering in R (v 3.2.0) was utilized to examine the gene expression patterns across different groups, while the R package ggradar was used to map the upregulation or downregulation of DEGs expression on the radar. The top 30 genes were selected for this analysis. By using R (v 3.2.0), enrichment analysis was conducted for Gene Ontology (GO) [ 23 ], Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 24 ] pathway, Reactome pathways, and Wiki pathways for the differentially expressed genes (DEGs). Hypergeometric distribution was used to select significantly enriched terms. For the visualization of enriched terms, bar, chord, and bubble plots were generated using R (v 3.2.0). 2.8 Analysis of untargeted Metabolomics In order to investigate fecal metabolism, Lu-Ming Biotech (Shanghai, China) used both Gas Chromatography-Mass Spectrometry (GC-MS) as well as Liquid Chromatography-Mass Spectrometry (LC-MS). The extraction of metabolites necessitates the consideration of the chemical properties of the multi-target metabolites. Metabolites obtained from a fecal sample weighing 20 mg were preserved at a temperature of -20°C before undergoing analysis via GC-MS and LC-MS. We derivatized samples and analyzed them using Agilent 7890B gas chromatography and Agilent 5977A MSD (Agilent Technologies Inc., CA, USA). Separation of the derivatized metabolites was achieved using a DB-5MS fused-silica capillary column measuring 30m × 0.25mm × 0.25µm. A Nexera UPLC system equipped with a Q Exactive quadrupole-orbitrap mass spectrometer and a heated electrospray ionization source (ESI) was used to analyze the lyophilized specimens for LC-MS analysis. ACQUITY UPLC HSS T3 (1.8µm, 2.1×100mm) columns were employed in both positive and negative modes. Using Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK) software, raw LC-MS data was filtered, identified, integrated, retention time corrected, aligned, and normalized. The primary parameters utilized in this study were tolerances of 5 ppm for precursor, 10 ppm for product, and a 5% threshold for product ion. Identification of compounds was achieved through the use of accurate mass-to-charge ratios (M/z), secondary fragments, and isotopic distributions, with databases such as the Human Metabolome Database (HMDB), lipid maps (V2.3), Metlin, and self-constructed databases being employed. The MS-DIAL software was employed to process the GC-MS data, facilitating peak detection, peak identification, MS2Dec deconvolution, characterization, peak alignment, wave filtering, and missing value interpolation. Analyses of the LUG database were used to characterize metabolites. An investigation of metabolism was carried out by using principle component analysis (PCA) and partial least squares discriminant analysis (O)PLS-DA . By combining the statistical variables impact prediction (VIP) threshold and P value, Differentially Expressed Metabolites (DEMs) were identified if VIP > 1.0 and P > 0.05, respectively. The short-time sequence expression miner (STEM) was utilized to identify significant DEMs (p < 0.05), while the KEGG database was employed to analyze metabolite pathway enrichments. Finally, with Cytoscape, we constructed a gene-pathway-metabolite network. 2.9 Sequencing analysis of 16S rRNA gene The MagPure Soil DNA LQ Kit (Magan) was utilized to extract gross genomic DNA in accordance with the manufacturer's instructions. With the help of universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3'), we analyzed the bacterial diversity within the V3-V4 (or V4-V5) variable region of the 16S rRNA gene. The raw sequencing data was converted to FASTQ format and the paired-end reads were preprocessed using Cutadapt software to detect and remove adapters. Paired-end reads were subjected to a screening process to eliminate low-quality sequences after trimming. The remaining reads were then denoised, merged, and detected using the DADA2 algorithm [ 25 ], with QIIME2 [ 26 ] (2020.11) serving as the default parameter. Chimeric reads were subsequently removed. The software generated representative readings and abundance tables for each amplicon sequence variant (ASV). For each ASV, the QIIME2 package was utilized to select representative reads. Blast analyses were performed using the default parameters of the q2-feature-classifier against the Silva database (version 138) for all representative reads. The software QIIME2 was utilized to perform alpha and beta diversity analyses. We estimated microbial diversity using the Chao1 index [ 27 ] and Shannon index [ 28 ] in our sample. The binary Jaccard distance matrix, generated by the R package, was utilized to estimate beta diversity through binary Jaccard principal coordinate analysis (PCoA). Subsequently, significant differences between groups were analyzed using the R package with an ANOVA statistical test. The taxonomic abundance spectrum was compared using the linear discriminant analysis effect size (LefSe). 2.10 | Statistics Student's t-test was applied in the analysis of gene expression, tumor number, mean tumor size, colon length, and bacterial diversity by using GraphPad Prism 9.0 software. If 2-tailed P values < 0.05, the results were considered significant. 3 Results 3.1 Probiotic pretreatment attenuated AOM/ DSS induced inflammation and tumor development Probiotic cocktails were evaluated for their chemopreventive effect on CAC. After pretreatment with probiotics, the shortened colon length was significantly alleviated, which indirectly indicated a chemopreventive effect of probiotic on colitis (Fig. 1 A). Afterwards, AOM/DSS-induced CAC mice were then analyzed for their neoplastic lesions as shown in Fig. 1 B for macroscopic colon images in different groups. Tumors ranged from 1 to 5 mm in size and mostly occurred in the colorectum. A significant inhibition of tumor multiplicity and size induced by colitis was observed in the Treatment group, with a tumorigenicity rate of 70% in probiotic pretreated mice and an average of 2.9 total tumors per mouse (mean diameter 1.83 mm) compared to 100% in the Model group with 7.6 tumors per mouse (mean diameter 2.47 mm) (Fig. 1 C-F). The histological evaluation revealed multiple adenomas in the Model group as well as invasive adenocarcinomas, whereas mice in the Treatment group had disease features mainly crypt dysplasia and adenomas ( Fig. 1 G ) . 3.2 Transcriptional shifts induced by AOM/DSS and probiotic pretreatment The expression levels of transcriptional genes were compared between the two groups to screen out the DEGs. The results showed that 431 genes’ expression increased and 631 genes’ expression decreased in Treatment-vs.-Model group. In Treatment-vs.-Contrl group, the expression of 758 genes was increased and 446 genes were decreased. There were 610 genes with increased expression and 84 genes with decreased expression in the Model-vs.-Contrl group, resulting in a total of 2124 DEGs screened (Fig. 2 A, B). STEM was also employed to analyze DEGs (Fig. 2 C) and we concluded that differential expression was increased or reduced in the Model group compared to the Contrl group, while it could be correspondingly decreased and elevated in the Treatment group, indicating that differential expression of these genes was influenced by probiotic. Eight models significantly clustered 11,866 of 18,577 DEGs, which included profile 8 (Fig. 2 D), profile 9 (Fig. 2 E), profile 7 (Fig. 2 F), profile 13 (Fig. 2 G), profile 6 (Fig. 2 H), profile 15 (Fig. 2 I), profile 12 (Fig. 2 J) and profile 11 (Fig. 2 K), while only profile 9 (Fig. 2 E) and profile 6 (Fig. 2 H) of 3677 genes met our screening criteria and were screened. In order to further screen DEGs, we selected the combination of STEM trending genes and DEGs as candidate genes for further analysis. Up to 48 genes were screened, of which 10 DEGs decreased in the Model group and rebounded in the Treatment group, and 38 DEGs increased in the Model group and regressed in the Treatment group, which function closely related to the inhibition of tumorigenesis and progression by probiotic (Fig. 2 L). 3.3 Probiotic pretreatment modified the intestinal microbial community composition In general, 42,411 and 61,316 valid tags were yielded by 16S rRNA sequencing. In our result, we got 1512 Amplicon Sequence Variant (ASV), and the number of ASVs ranged from 245 to 396, belonging to 14 phyla, 20 classes, 58 orders, 93 families, and 170 genera. For all samples, species accumulation curves and scarcity curves ( Supplementary Figure. S1 ) reflected detailed sequencing of all samples sufficient to describe their bacterial diversity and richness. At the phylum level, Bacteroidota in the Model group increased and in the Treatment group decreased (Contrl: 49.92% vs. Model: 61.31% vs. Treatment: 50.90%), while Firmicutes in Model group decreased and in Treatment group recovered (Contrl: 43.45% vs. Model: 28.63% vs. Treatment: 37.20%) (Fig. 3 A). Among the top 30 groups at the genus level, the abundance of Muribaculaceae, the most abundant genus after CAC induction by AOM/DSS, increased compared to the Contrl group, while it fell back in the treatment group (Contrl: 44.86% vs. Model: 52.25% vs. Treatment: 42.81%). Clostridia_UCG-014 abundance decreased after induction compared to the Contrl group and rebounded in the Treatment group (Contrl: 13.75% vs. Model: 4.23% vs. Treatment: 5.99%). The major genera of the probiotic cocktail, both Lactobacillus and Bifidobacterium , decreased in abundance after CAC induction and rebounded in the Treatment group (Lactobacillus: Contrl: 3.97% vs. Model: 1.30% vs. Treatment: 2.80%; Bifidobacterium: Contrl: 0.72% vs. Model: 0.29% vs. Treatment: 1.01%). In addition, the abundance of the common causative organism Helicobacter increased after CAC induction by AOM/DSS and decreased in the Treatment group (Contrl: 0.30% vs. Model: 1.57% vs. Treatment: 0.38%) (Fig. 3 B). Despite no significant differences in Chao1 estimates or Shannon index were found between groups (Supplementary Figure. S2), there was a binary PCoA analysis based on jaccard combined with adonis analysis results (P = 0.001), which indicated a significantly different beta diversity among the groups (Fig. 3 C). The ANOVA difference statistics in further multivariate statistical analyses revealed 2 differences phylum: Proteobacteria and Campilobacterota were both elevated in the Model group and fell back in the Treatment group (Fig. 3 D), and 17 differential genera, of which Lactobacillus, Dubosiella, Family_XIII_AD3011_group increased in the Treatment group and decreased in the Model group, while Parasutterella, Helicobacter, Muribaculum, [Eubacterium]_fissicatena_group decreased in the Treatment group and increased in the Model group (Fig. 3 E and Supplementary Table S1 ). A cladogram from LefSe measurements for identification of the specific bacteria associated with the CAC induction and probiotic treatment was used (Fig. 3 F). The result shown that Allobaculum, Muribaculum, Clade_III and UCG_003 (LDA scores (log10) > 2.5) in the Model group, Enterorhabdus, Odoribacter and Ruminococcus_torques_group were the most abundant in the Treatment group (LDA scores (log10) > 2.5), while Anaerostipes, Lactobacillus, Eubacterium_coprostanoligenes_group, UCG_010, Family_XIII_AD3011_group, Eubacterium_siraeum_group, Monoglobus, Gordonibacter, Marvinbryantia, UCG_007 and Eubacterium_brachy_group had LDA scores (log10) > 2.5 in the Control group. Collectively, these findings demonstrated alterations in the composition of the intestinal microbiota linked to the induction of CAC and probiotic treatment. 3.4 AOM/DSS and probiotic pretreatment induce changes in fecal metabolism In the GC-MS and LC-MS datasets, 362 and 9556 putative metabolites were identified, respectively. A total of 16 superclasses were assigned to them, which included 8337 classified metabolites along with 1302 unclassified metabolites (Table 1 ). We used PCA and OPLS-DA to separate and demonstrate the metabolic differences present in each of the two groups. Analysis platform's excellent stability and reproducibility were shown by the permutation test ( Supplymentary Figure S3 ). An excellent predictive power of the model was demonstrated by the model parameters, without any overfitting found ( Supplementary Table 1 ). Table 1 Classification statistics of identified metabolites. Super Class of metabolites GC-MS LC-MS Total Benzenoids 18 761 779 Homogeneous non-metal compounds 1 0 1 Lipids and lipid-like molecules 69 3208 3277 Nucleosides, nucleotides, and analogues 10 155 165 Organic acids and derivatives 78 145 1534 Organic nitrogen compounds 9 147 156 Organic oxygen compounds 59 593 652 Organoheterocyclic compounds 32 1373 1405 Phenylpropanoids and polyketides 15 351 366 others 2 210 212 Unclassified 0 1302 1302 DEMs were screened based on the metabolites detected by GC-MS and LC-MS, comparing levels of metabolite expression in each two groups ( Supplymentary Figure S4 ). A total of 27 and 159 DEMs were derived from GC-MS and LC-MS, respectively. We also performed STEM analysis of metabolites detected in mice feces with GC-MS and LC-MS, and we concluded that metabolites were increased or reduced as compared to the Contrl group in the Model group and able to be correspondingly reduced or reverted in the Treatment group, consistent with trends influenced by probiotic ( Supplymentary Figure S5 ). With the STEM, 67 and 2634 metabolites were extracted from GC-MS and LC-MS, separately. To further refine the target range, we merged STEM trend metabolites with DEMs to select intersections as candidate metabolites for further analysis. In GC-MS, 24 DEMs were obtained, and their expressions decreased in all the Model group and reversed in the Treatment group (Fig. 4 A). The 87 DEMs were also obtained in LC-MS, of which 45 metabolites decreased in the Model group with rebound in the Treatment group and 42 metabolites increased in the Model group with rebound in the Treatment group (Fig. 4 B). 3.5 Integration anaiysis among intestinal microbiota, fecal metabolites and colonic tissue transcriptomics to build probiotic anti-inflammatory and anti-tumor network We performed Spearman correlation analysis and generated Cytoscape plots for the DEMs screened by GC-MS & LC-MS analysis with the 48 DEGs screened by transcriptome, respectively. In the correlation analysis between DEMs and DEGs from GC-MS analysis, DEGs Edn3, Sfrp4, Nos3, Creb5 were significantly correlated with DEMs, especially Sfrp4 , which was significantly correlated with multiple DEMs; DEMs Cytosin, Spermine were significantly correlated with multiple DEGs (Fig. 5 A). In the DEMs-DEGs correlation analysis of LC-MS analysis, all DEGs except Pla2g3 were significantly correlated with at least 1 DEM, especially DEGs Sfrp4, Alox8, Nos3, Creb5, Ar, Gm10591, Gli3, Gng8, Calm4, Amotl2, Nr4a1, Nmu, Edn3 significantly associated with multiple DEMs; DEMs N-Linoleoyl Valine, 3-Sulfinoalanine, Xanthylic acid, Cholesterol glucuronide, Nicotinuric acid, Stachyose, D-Lactic acid, 2- Hydroxybutyric acid, Coproporphyrin III, Fructose 6-phosphate, alpha-Ketoisovaleric acid, Neopterin, D-Gal alpha 1->6D-Gal alpha 1->6D-Glucose were significantly correlated with multiple DEGs (Fig. 5 B). The further Cytoscape revealed that the functions of DEMs and DEGs were co-enriched in 37 metabolic pathways, of which DEMs from GC-MS and DEMs from LC-MS were co-enriched with DEGs functions in 24 and 32 metabolic pathways, respectively ( Supplymentary Figure S6 and S7 ). We made a correlation analysis between the fecal DEMs and the genera Lactobacillus and Bifidobacterium, the main components of probiotic, that showed intergroup differences in the 16s analysis. Bifidobacterium was significantly negatively correlated with DEM Stearic Acid from GC-MS and significantly positively correlated with DEM Gentisate aldehyde from LC-MS (Fig. 6 A and 6 B). Lactobacillus was significantly positively correlated with DEMs Uracil, Cytosin, Thymidine, Spermine, Spermidine derived from GC-MS and with DEMs from LC-MS, cis-Aconitic acid, Uracil, 3-Fumarylpyruvate, Guanine, Xanthine, Deoxyinosine, Dodecanoic acid, Hypoxanthine, Orotidylic acid, Deoxyuridine, Cytosine , and also significantly negatively correlated with DEMs from LC-MS, Citric acid, N Acetylhistamine, 10-Formyltetrahydrofolate, N-palmitoyl alanine, Gentisic acid (Fig. 6 A and 6 B). In both genera, Lactobacillus and Bifidobacterium, only Lactobacillus had significant group differences in the 16S ANOVA, so Lactobacillus significantly correlated DEMs was selected to do correlation analysis with DEG to plot Cytoscape. The results showed that among the five GC-MS DEMs significantly positively correlated with Lactobacillus, i.e., reduced in the Model group and rebounded in the Trearment group, Cytosin, Thymidine, Spermine could identify DEGs significantly positive correlated: Edn3, Ccl25 , and significantly negatively correlated DEGs : Gli3, Sfrp4, C4b, Cd33, Gng8, Nfatc2, Creb5, Alox8, Lama5, Amotl2, Nr4a1, Nlgn2, Nos3, Gm10591 (Fig. 6 C). Among the 16 LC-MS DEMs significantly correlated with Lactobacillus, 7 significantly positively correlated DEMs: 3-Fumarylpyruvate, Guanine, Deoxyinosine, Hypoxanthine, Orotidylic acid, Deoxyuridine and Cytosine were negatively correlated with DEGs: Alox8, Gng8, Sfrp4 and positively correlated with DEGs : Ccl25, Edn3, Nmu ; 2 significantly negatively correlated DEM, i.e., increased in the Model group and receded in the Trearment group, N-Acetylhistamine and N-palmitoyl alanine were negatively correlated with DEGs : Edn3, Nmu and positively correlated with DEGs : Sfrp4,Gm10591 (Fig. 6 D). The DEMs, which involve five metabolic pathways ( arginine and proline metabolism, histidine metabolism, pyrimidine metabolism, purine metabolism, and tyrosine metabolism) build a signal network together with the DEGs. 4 Disscussion Intestinal microbiota and microbial metabolites are attractive substances that enhance the preventive and therapeutic effects against cancer [ 29 , 30 ]. In this study, we reproduced a probiotic cocktail inhibitory effect on AOM/DSS-induced CAC. In particular, Lactobacillus, the predominant genus of normal intestinal microbiota, decreased after AOM/DSS induction, while it rebounded directly after treatment with probiotic containing Lactobacillus as a major component. Moreover, Bifico treatment-related intestinal microbiota alterations were associated with changes in 37 intestinal metabolism systems. In which, the intestinal DEMs significantly associated with Lactobacillus involved: arginine and proline metabolism, histidine metabolism, pyrimidine metabolism, purine metabolism, and tyrosine metabolism , which suppress CAC by suppressing the expression of oncogenes and promoting the expression of cancer suppressor genes. Compared with healthy controls, colorectal cancer individuals had an imbalance in intestinal microbiota, known as a dysbiosis [ 31 ]. Specifically, it can be shown as the richness of normal dominant microbiota decreased, higher relative abundance of putatively pro-carcinogenic microbial members and so-called protective genera reduced [ 32 – 34 ]. In our research, LefSe profiling displayed that the Contrl group presented the most abundant enrichment of dominant genera, of which Anaerostipes, one of butyrate-producing bacterial species [ 35 , 36 ], and Lactobacillus, one of the commonly used probiotic [ 37 , 38 ], was the main genus. After AOM/DSS induction, dominant genera had obvious changes and microbial abundance decreased significantly. Muribaculum, as one of the most abundant enrichment of dominant genera in the model group, which appeared in mice with T cell-induced colitis and was associated with the progression of intestinal inflammation [ 39 ]. The ANONA difference analysis showed significant differences in Lactobacillus, Family_XIII_AD3011_group and Muribaculum, the former two decreased in the Model group and rebounded in the Treatment group, while the third increased in the Model group and regressed in the treatment group. In addition, Odoribacter dominated the treatment group, which is an important microbe for maintaining intestinal homeostasis [ 40 , 41 ]. As non-dominant genera, Parasutterella and Helicobacter pylori were increased in the Model group and regressed in the Treatment group. Parasutterella might be related to IBS patients with chronic intestinal inflammation [ 42 ]. Helicobacter pylori was also associated with CRC as an independent risk factor of promoting colorectal carcinogenesis [ 43 , 44 ]. These findings suggest that normally abundant intestinal genera, represented by Lactobacillus, is crucial for the maintenance of intestinal microbiota balance and intestinal homeostasis. Oral probiotic cocktail therapy not only supplements lactobacilli and other normal genera, but also inhibits the growth of oncogenic genera and promotes the growth of protective genera, consequently affecting the progression of CAC. The other two genera of Bifico, although they did not show significant differences in the 16s analysis, were also most likely involved in the mentioned process. For example, supplementation with bifidobacteria both alleviated the DSS-induced reductions in the abundance of intestinal microbiota and brought about the suppression of pathogenic bacteria [ 45 ]. Microbial metabolites have been regarded as necessary to facilitate the therapeutic and prophylactic effects in CRC, and related researches have gradually been initiated [ 46 , 47 ]. In our study, metabolomic analysis and combined multi-omics analysis suggested that 37 metabolic systems were involved in Bifico's alteration of microbiota composition. Alteration of metabolism, distracted glucose utilization leading to an amount of increased lactic acid, is one of the essential features of cancer, and increased lactic acid promotes multiple critical oncogenic processes [ 48 ]. In the DEMs, D-Lactic acid was elevated in the Model group and decreased after the probiotic pretreatment, which demonstrated the inhibitory effect of Bifico on tumor microenvironment (TME) acidification. DEMs, which also appeared elevated after modelling and declined after treatment, were Phosphate , a tumorigenesis promoter [ 49 ], and Adenosine , immunosuppressive factor in TME [ 50 ]. Additionally, there were several DEMs that rebound after treatment, for example: Dodecanoic acid works as an anticancer agent [ 51 ]; G amma-Tocopherol and D elta-Tocotrienol , main vitamin E forms playing an anti-inflammatory and anticancer role [ 52 ]; Niacinamide shows potential role in cancer prevention and treatment [ 53 ]; S permidine provids protection against intestinal inflammation and suppress tumorigenesis through the induction of autophagy in healthy tissues [ 54 , 55 ]; S permine plays as free radical scavengers [ 56 ]; Oleic acid promotes apoptosis and cell differentiation in colorectal cancer [ 57 ]; Stearic Acid has been as a Colon Cancer therapeutic agent in combination with 5-Fluorouracil due to its consideration as a potential PDK1 inhibitor [ 58 ]. Accordingly, the molecular mechanism underlying Bifico’s CAC inhibition effect was interpreted from the metabolites aspect. Meanwhile, among the screened DEGs, for example, DEGs significantly associated with metabolites in the combined analysis, we detected post-treatment expression of oncogenes decreased (Sfrp4 [ 59 ], Nos3 [ 60 ], Creb5 [ 61 ], Ar [ 62 ], GLI3 [ 63 ], Amotl2 [ 64 ], Nr4a1 [ 65 ] ) and cancer suppressor genes Edn3 [ 66 ] up-regulated. It can be speculated that the changes in metabolic substances brought about by these probiotic treatments have the effect of suppressing CAC by affecting the transcriptome. Nevertheless, there were also some DEMs and DEGs expressions that were not favorable for CAC prevention such as: metabolites with anticancer effects palmitic acid and 3-hydroxybutyric acid [ 67 , 68 ] reduced after treatment; oncogenes Ccl25 and Nmu [ 69 , 70 ] expression increased after treatment. It suggested that we need to deeply explore the changes of potentially anticancer metabolites related to Bifico, especially the Lactobacillus related metabolites that play a major role and their mechanism of anti-cancer, to focus on potential candidate metabolites for further research. The Lactobacillus involved in this experiment is Lactobacillus acidophilus, one of the major species of Bifico. Lactobacillus acidophilus has the characteristics of preventing colitis and colon tumors. For example, extracellular polysaccharides (EPSs) synthesized by Lactobacillus acidophilus can stimulate immune response against tumor cells [ 71 ]. Our in-depth multi-omics analysis suggests that Lactobacillus produces anti-inflammatory and tumorigenesis prevention effects by affecting 5 metabolic systems, which play a significant roles in physiological and pathological conditions. Among them, Dodecanoic acid, Spermine and Spermidine , which are elevated after treatment, have anti-cancer properties. Since the metabolite functional database is not fully complete, the related DEMs roles involved need to be explored by further in-depth experimental studies. In summary, the phenotype of CAC inhibition by Bifico was reproduced in our study, which reconfirmed the anti-inflammatory and tumorigenesis prevention effects by supplementation with Lactobacillus-based probiotic, and demonstrated Bifico-related metabolic changes from the metabolite perspective. Among the metabolic changes of Bifico related exhibited overwhelming anticancer effects over cancer-promoting effects, while the anti-inflammatory and tumorigenesis prevention effects of Lactobacillus involved changes in five metabolic systems, which provided a preliminary exploration for further screening with targeted anti-inflammatory and tumorigenesis prevention metabolites. Declarations Conflicts of Interest The authors declare no conflict of interest. Ethics approval and consent to participate All animal experiments were conducted according to the Guide for the Care and Use of Laboratory Animals: Eighth Edition, and were approved by the ethics committees of Shanghai Tong Ren Hospital and Shanghai Jiao Tong University School of Medicine (A2023-022-01). Author Contribution Weiyi Wang designed the experiment, Haiqin Zhang, Lu Zhou, Haijin Zhu, Ji Li, Zixu Zhang, Jinnian Cheng, Fengli Zhou and Daming Yang performed the experiment, Ying Xu and Yimin Chu processed the data, Weiyi Wang wrote the paper, Weisong Xu and Hiaxia Peng revised. All authors approved the final version to be published, and agree to be accountable for all aspects of the work. Acknowledgments The author thanks the financial support of Shanghai Natural Science Foundation (No. 21ZR1458600), the Shanghai Jiaotong University Medical-Engineering Cross Research Fund (No. YG2022ZD031), Scientific research project of Health and Wellness Committee Changning District Shanghai (20214Y007), Shanghai Municipal Health Commission Key Laboratory of Gastrointestinal Tumor Innovation and Translation (No.ZDSYS-2021-01 ), Foundation of Shanghai Tongren Hospital Rising Star (TRKYRC-xx202211), and Shanghai Municipal Health Commission Health Industry Clinical Research Project (No.20234Y0016). References Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2018;68(6):394–424. Epub 2018/09/13. Stoffel EM, Murphy CC. Epidemiology and Mechanisms of the Increasing Incidence of Colon and Rectal Cancers in Young Adults. Gastroenterology. 2020;158(2):341–53. 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01:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6436788/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6436788/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81654090,"identity":"5e0a5c77-9eef-4db5-9a0b-78a1885b3971","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":371351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePretreatment with probiotic cocktail alleviates colitis and tumorigenesis in\u003c/strong\u003e \u003cstrong\u003eCAC mouse model induced by AOM/DSS. A, \u003c/strong\u003eIllustrative pictures depicting the structure of the colon and data on its length.\u003cstrong\u003e B, \u003c/strong\u003eRepresentative macroscopic image impressions. Noteworthy red arrows characterize neoplasms \u0026gt;1 mm in diameter.\u003cstrong\u003e C-F, \u003c/strong\u003eData on the shape, size, and location of tumors.\u003cstrong\u003e G, \u003c/strong\u003eColon samples were examined using representative H\u0026amp;E sections. The results were reported as the average ± standard error of the mean. Statistical significance was indicated by *P \u0026lt; 0.05 when compared to the Model group. The original images were magnified at a scale of ×200.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/381f87a6958baa06aca7ac45.png"},{"id":81654091,"identity":"2504902e-dbd2-400a-a48e-a14b1efdbaf3","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1363482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModifications in gene expression caused by AOM/DSS and prior administration of probiotic. A\u003c/strong\u003e and \u003cstrong\u003eB, \u003c/strong\u003eEffect of probiotic on DEGs in AOM/DSS-induced CAC Mice.\u003cstrong\u003e C-K, \u003c/strong\u003eEvaluation of three groups of gene expression patterns inferred by STEM.\u003cstrong\u003eL, \u003c/strong\u003eScreened heat map of 48 differential genes in AOM/DSS-induced CAC mice induced by probiotic.\u003c/p\u003e","description":"","filename":"OnlineFigure218cm.png","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/a8df135bec8b3bd39b7e39a9.png"},{"id":81654092,"identity":"1ecb6997-ebe9-43fd-b2f2-50082b726349","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":915006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModification of intestinal microbial community composition in AOM/DSS-induced CAC mice through probiotic pretreatment. A\u003c/strong\u003e, Relative abundance of the main bacterial phyla in every group. \u003cstrong\u003eB\u003c/strong\u003e, Relative abundance of dominant bacterial genera in each group. \u003cstrong\u003eC\u003c/strong\u003e, Evaluation of diversity within the microbial community in feces. PCoA graph based on Jaccard index displaying the proportionate presence of OTUs for clustering of bacterial structure. \u003cstrong\u003eD\u003c/strong\u003e, There were notable variations in the relative abundance of the phylum across the Control, Model, and Treatment groups. \u003cstrong\u003eE\u003c/strong\u003e, The Control, Model, and Treatment groups showed significant differences in the relative abundances of the top 10 genera. This was determined through ANOVA, with all p-values being less than 0.05. \u003cstrong\u003eF\u003c/strong\u003e, The Control, Model, and Treatment groups were analyzed using LefSe to determine differences in species composition.\u003c/p\u003e","description":"","filename":"OnlineFigure318cm.png","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/892c0ce7f0c8054c85541c81.png"},{"id":81654651,"identity":"57a8fad5-5c08-42d3-8629-846926c8260b","added_by":"auto","created_at":"2025-04-29 17:46:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":774201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFecal metabolism changes in AOM/DSS and pretreatment with probiotic. \u003c/strong\u003eA. Heatmap of DEMs from GC-MS. B. Heatmap of DEMs from LC-MS.\u003c/p\u003e","description":"","filename":"OnlineFigure418cm.png","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/1f88f57e085e7e6ac3f34bad.png"},{"id":81654458,"identity":"bc782d98-e9f3-477b-8a3e-9fbdb8caa413","added_by":"auto","created_at":"2025-04-29 17:38:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1060509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of correlation between the DEGs and the DEMs (\u003c/strong\u003eA,\u003cstrong\u003e \u003c/strong\u003efrom GC-MS. B, from LC-MS).\u003c/p\u003e","description":"","filename":"OnlineFigure518cm.png","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/74cfaa3808d9e6bba6bb48ee.png"},{"id":81654096,"identity":"a93683cf-1093-4c35-aff1-bea36ffb4ad5","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":762456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe integration of fecal metabolomics and transcriptomics was used to analyze the relationship between major probiotic genera and to establish an anti-inflammatory and anti-tumorigenic network. A\u003c/strong\u003e, \u003cstrong\u003eB\u003c/strong\u003e, Examining the relationship between the fecal DEMs (\u003cstrong\u003eA\u003c/strong\u003e, from GC-MS; \u003cstrong\u003eB\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003efrom LC-MS) and the genera Lactobacillus and Bifidobacterium. \u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e, DEGs-DEMs correlation network analysis of genera Lactobacillus (\u003cstrong\u003eC\u003c/strong\u003e,from GC-MS; \u003cstrong\u003eD\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003efrom LC-MS ).\u003c/p\u003e","description":"","filename":"OnlineFigure618cm.png","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/1e99b7487d71479e7c8fc79c.png"},{"id":97136158,"identity":"c0643931-ff9d-44fa-b05d-e2bd8b6c41c3","added_by":"auto","created_at":"2025-12-01 09:55:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10057599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/d9c1f308-b056-422b-8023-ed54c7db69be.pdf"},{"id":81654457,"identity":"9b2b1edc-d902-417d-8128-c3ee0305adab","added_by":"auto","created_at":"2025-04-29 17:38:40","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1764960,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/519ff5a54d242915b1a60d0d.tif"},{"id":81654095,"identity":"f499519a-58b4-4702-800d-f3a446a1fa31","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":963800,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/242dccf3e260d383b07130af.tif"},{"id":81654105,"identity":"e5561065-8380-4e03-9c3e-a69dc0d1a712","added_by":"auto","created_at":"2025-04-29 17:30:41","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3941592,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/175c804802684f9d21cb7f18.tif"},{"id":81654100,"identity":"15c3e8c7-a078-424f-82b1-16e3221ec368","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":342980,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/b76db7ccc372463a55f301e8.tif"},{"id":81654462,"identity":"340ce81d-4fd5-4ca7-ae1c-350e4dca553b","added_by":"auto","created_at":"2025-04-29 17:38:41","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7180824,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/a5cb09e96448386acd2b8b9d.tif"},{"id":81654113,"identity":"490433f6-86ce-45cd-ae19-ac99c37f9c78","added_by":"auto","created_at":"2025-04-29 17:30:41","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6796624,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/1c85df9e11eee595b923204c.tif"},{"id":81655041,"identity":"7703c5a4-11e2-49ba-9e2d-2df854ba27f0","added_by":"auto","created_at":"2025-04-29 17:54:41","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9633212,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentaryFigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/ff4bf90164ed36b0200421a0.tif"},{"id":81654460,"identity":"90335c7c-3dea-43af-9790-b4f58596028d","added_by":"auto","created_at":"2025-04-29 17:38:41","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15813,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentalFigLegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/a7c62099f00b56f135a39997.docx"},{"id":81654099,"identity":"5cb47d6b-9d06-4767-a8be-9042a83af31f","added_by":"auto","created_at":"2025-04-29 17:30:40","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":17049,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentalTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6436788/v1/7f36a7fd03f1c766c5df55e2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lactobacillus-based probiotic cocktail inhibits colitis-associated cancer by altering intestinal metabolism","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is considered one of the most prevalent and fatal malignant tumors worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A combination of screening, risk factor prevention, and improvements in treatment has resulted in a decline in morbidity and mortality among over 50 years old adults. However, epidemiologic studies showed CRC incidence continues to increase among adults under 50 years of age [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, further research on the molecular mechanism of CRC pathology would be an essential approach for prevention and treatment of CRC.\u003c/p\u003e \u003cp\u003eDysbiosis of the intestinal microbiome was involved in pathogenesis of CRC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Observations in previous studies linked a healthy lifestyle with a lower risk of CRC, and adherence to a healthy lifestyle could further reduce this risk, which was partly as a result of alterations in intestinal microbiome [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the risk factors for CRC, such as obesity, sedentary lifestyles, tobacco, poor diet (e.g., high-fat diet, high level of red and processed meats, low level of fiber, whole grains and calcium) and alcohol, also affected intestinal microbiome [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Probiotic are becoming increasingly important in the medical field because of their positive impact on human health. As we found in our previous study, pretreatment of probiotic cocktail Bifico capsules with Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis, relieved intestinal inflammation and minimized tumor formation in azoxymethane/dextran sodium sulfate (AOM/DSS)-induced colitis-associated cancer (CAC) mice by modifying the abundance of intestinal flora [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the biological mechanisms of intestinal microbiome are complex and have a bilateral effect on human health. After supplementing with lactobacillus, an infant without underlying diseases or immune deficiencies was reported to develop bacteraemia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There are also several reports linking Lactobacillus and Bifidobacterium to opportunistic infections in immunocompromised patients and in those with allergic sensitization and autoimmune diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, Enterococcus faecalis is an important opportunistic pathogen that can cause a wide variety of infections [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hense, a deeper understanding of the biological mechanisms of intestinal microbiota's ability to inhibit CRC is needed.\u003c/p\u003e \u003cp\u003eMore and more researches showed that the intestinal microbiota can synthesize a large number of metabolites or bioactive compounds which play protective and detrimental roles in aetiology of CRC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Many metabolic biomarkers have been reported in CRC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the present study, we utilized metabolomics to investigate which intestinal metabolites are affected by probiotic cocktail that ultimately inhibit tumorigenesis in a mouse CRC model. Seek out effective metabolites of probiotic so as to spare vulnerable patients (infants, immunocompromised, etc.) from the risk of opportunistic infections and autoimmune diseases.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental animals\u003c/h2\u003e \u003cp\u003e Approximately four-week-old male C57BL/6 mice were procured from Shanghai Jiaotong University School of Medicine's Animal Science Laboratory and were subsequently housed in pathogen-free animal care facilities at Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine. A controlled laboratory environment with 23°C, 50% humidity, and 12/12 hours of light and darkness was used. Ethics approval was granted by the ethics committees of Shanghai Tong Ren Hospital and Shanghai Jiao Tong University School of Medicine.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Reagents\u003c/h3\u003e\n\u003cp\u003eThe chemicals Azoxymethane (AOM) and Dextran sodium sulfate (DSS) were procured from Sigma-Aldrich (St. Louis, MO, USA), while the probiotic cocktail Bifico capsules, which respectively contained a minimum of 1.0×10\u003csup\u003e7\u003c/sup\u003e c.f.u. viable lyophilized Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis per capsule (210 mg), were obtained from Shanghai Sinepharm (Shanghai, China).\u003c/p\u003e\n\u003ch3\u003e2.3 Experimental procedure\u003c/h3\u003e\n\u003cp\u003eProtocol of the experimental was based on our prior investigation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thirty male C57BL/6 mice were randomly assigned to one of three groups: Control (n = 10), Model (AOM/DSS only, n = 10), and Treatment (probiotic cocktail lavage with AOM/DSS, n = 10). On day 1, mice received a single intraperitoneal dose of AOM (10 mg/kg), followed by three cycles of DSS induction. Mice in the Model and Treatment groups were administered 2% DSS (w/v) in their drinking water for seven consecutive days, followed by two weeks of sterile water in each cycle of DSS induction. The mice in the Treatment group underwent lavage with a probiotic cocktail consisting of 4.2 g/kg dissolved in 200 lL saline, containing at least 1.2 × 10\u003csup\u003e7\u003c/sup\u003e c.f.u./d, on a daily basis at 10:00 a.m. for a duration of one month prior to the commencement of the experiment until its conclusion. Subsequently, all mice were euthanized through the cervical dislocation method after a period of nine weeks.\u003c/p\u003e\n\u003ch3\u003e2.4 Collection and preparation of samples\u003c/h3\u003e\n\u003cp\u003eUpon completion of the experiment, all animals were subjected to euthanasia and immediate laparotomy. From the distal cecum to the rectum, the colon was excised, followed by removal of adherent adipose tissue. Subsequently, the colon was dissected longitudinally, collected feces and stored in -80°C, flushed with ice-cold saline, and the fecal residue was meticulously removed and photographed. Similarly to feces, the partial colon tissue samples were stored at -80°C. The isolated colon was subjected to histopathology and gene expression studies. Additionally, the fecal samples were analyzed by 16S rRNA gene sequencing and metabolomics.\u003c/p\u003e\n\u003ch3\u003e2.5 Evaluation of histopathology\u003c/h3\u003e\n\u003cp\u003eThe process of tumorigenesis was investigated through macroscopic examination of colonic tissue obtained via biopsy. The tissue was subjected to overnight fixation with formalin, followed by replacement of the solution with 70% ethanol prior to paraffin embedding. Standard histological evaluation procedures, as outlined in reference [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], were employed to stain the paraffin sections with H\u0026amp;E.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 RNA Isolation and Library Preparation\u003c/h2\u003e \u003cp\u003eThe isolation of total RNA from colon tissue was carried out in accordance with the manufacturer's protocol utilizing TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Subsequently, libraries were constructed for each sample utilizing the TruSeq Stranded mRNA LT sample preparation kit (Illumina, San Diego, CA, USA) as per the instructions. OE Biotech (Shanghai, China) sequenced and analyzed the transcriptome.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.7 Analysis of RNA sequencing and differentially expressed genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eThe sequencing of the libraries was conducted on the Illumina Novaseq 6000 platform, resulting in the production of paired-end reads with a length of 150 bp. Each sample yielded an average of 49.57 raw reads. The raw reads were subjected to quality control using fastp [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to eliminate low-quality reads and obtain clean reads, resulting in approximately 48.61 clean reads per sample for subsequent analyses. HISAT2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was utilized to map the clean reads onto the reference genome for each sample. The FPKM values for each gene [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] were calculated and the reads for each gene were obtained using HTSeq-count. The present study utilized PCA analysis in R (v 3.2.0) to evaluate the biological reproducibility of the samples.\u003c/p\u003e \u003cp\u003eDESeq2 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was employed for differential expression analysis, with Q \u0026lt; 0.05 and foldchange \u0026gt; 2 or foldchange \u0026lt; 0.5 serving as the thresholds for significant differentially expressed genes (DEGs). Hierarchical clustering in R (v 3.2.0) was utilized to examine the gene expression patterns across different groups, while the R package ggradar was used to map the upregulation or downregulation of DEGs expression on the radar. The top 30 genes were selected for this analysis.\u003c/p\u003e \u003cp\u003eBy using R (v 3.2.0), enrichment analysis was conducted for Gene Ontology (GO) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Kyoto Encyclopedia of Genes and Genomes (KEGG) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] pathway, Reactome pathways, and Wiki pathways for the differentially expressed genes (DEGs). Hypergeometric distribution was used to select significantly enriched terms. For the visualization of enriched terms, bar, chord, and bubble plots were generated using R (v 3.2.0).\u003c/p\u003e\n\u003ch3\u003e2.8 Analysis of untargeted Metabolomics\u003c/h3\u003e\n\u003cp\u003eIn order to investigate fecal metabolism, Lu-Ming Biotech (Shanghai, China) used both Gas Chromatography-Mass Spectrometry (GC-MS) as well as Liquid Chromatography-Mass Spectrometry (LC-MS). The extraction of metabolites necessitates the consideration of the chemical properties of the multi-target metabolites. Metabolites obtained from a fecal sample weighing 20 mg were preserved at a temperature of -20°C before undergoing analysis via GC-MS and LC-MS.\u003c/p\u003e \u003cp\u003eWe derivatized samples and analyzed them using Agilent 7890B gas chromatography and Agilent 5977A MSD (Agilent Technologies Inc., CA, USA). Separation of the derivatized metabolites was achieved using a DB-5MS fused-silica capillary column measuring 30m × 0.25mm × 0.25µm. A Nexera UPLC system equipped with a Q Exactive quadrupole-orbitrap mass spectrometer and a heated electrospray ionization source (ESI) was used to analyze the lyophilized specimens for LC-MS analysis. ACQUITY UPLC HSS T3 (1.8µm, 2.1×100mm) columns were employed in both positive and negative modes. Using Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK) software, raw LC-MS data was filtered, identified, integrated, retention time corrected, aligned, and normalized. The primary parameters utilized in this study were tolerances of 5 ppm for precursor, 10 ppm for product, and a 5% threshold for product ion. Identification of compounds was achieved through the use of accurate mass-to-charge ratios (M/z), secondary fragments, and isotopic distributions, with databases such as the Human Metabolome Database (HMDB), lipid maps (V2.3), Metlin, and self-constructed databases being employed. The MS-DIAL software was employed to process the GC-MS data, facilitating peak detection, peak identification, MS2Dec deconvolution, characterization, peak alignment, wave filtering, and missing value interpolation. Analyses of the LUG database were used to characterize metabolites. An investigation of metabolism was carried out by using principle component analysis (PCA) and partial least squares discriminant analysis (O)PLS-DA .\u003c/p\u003e \u003cp\u003eBy combining the statistical variables impact prediction (VIP) threshold and P value, Differentially Expressed Metabolites (DEMs) were identified if VIP \u0026gt; 1.0 and P \u0026gt; 0.05, respectively.\u003c/p\u003e \u003cp\u003eThe short-time sequence expression miner (STEM) was utilized to identify significant DEMs (p \u0026lt; 0.05), while the KEGG database was employed to analyze metabolite pathway enrichments. Finally, with Cytoscape, we constructed a gene-pathway-metabolite network.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Sequencing analysis of 16S rRNA gene\u003c/h2\u003e \u003cp\u003e The MagPure Soil DNA LQ Kit (Magan) was utilized to extract gross genomic DNA in accordance with the manufacturer's instructions. With the help of universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3'), we analyzed the bacterial diversity within the V3-V4 (or V4-V5) variable region of the 16S rRNA gene.\u003c/p\u003e \u003cp\u003eThe raw sequencing data was converted to FASTQ format and the paired-end reads were preprocessed using Cutadapt software to detect and remove adapters. Paired-end reads were subjected to a screening process to eliminate low-quality sequences after trimming. The remaining reads were then denoised, merged, and detected using the DADA2 algorithm [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], with QIIME2 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (2020.11) serving as the default parameter. Chimeric reads were subsequently removed. The software generated representative readings and abundance tables for each amplicon sequence variant (ASV). For each ASV, the QIIME2 package was utilized to select representative reads. Blast analyses were performed using the default parameters of the q2-feature-classifier against the Silva database (version 138) for all representative reads.\u003c/p\u003e \u003cp\u003eThe software QIIME2 was utilized to perform alpha and beta diversity analyses. We estimated microbial diversity using the Chao1 index [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Shannon index [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] in our sample. The binary Jaccard distance matrix, generated by the R package, was utilized to estimate beta diversity through binary Jaccard principal coordinate analysis (PCoA). Subsequently, significant differences between groups were analyzed using the R package with an ANOVA statistical test. The taxonomic abundance spectrum was compared using the linear discriminant analysis effect size (LefSe).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 | Statistics\u003c/h2\u003e \u003cp\u003eStudent's t-test was applied in the analysis of gene expression, tumor number, mean tumor size, colon length, and bacterial diversity by using GraphPad Prism 9.0 software. If 2-tailed P values \u0026lt; 0.05, the results were considered significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"3 Results","content":"\u003ch2\u003e3.1 Probiotic pretreatment attenuated AOM/ DSS induced inflammation and tumor development\u003c/h2\u003e\u003cp\u003eProbiotic cocktails were evaluated for their chemopreventive effect on CAC. After pretreatment with probiotics, the shortened colon length was significantly alleviated, which indirectly indicated a chemopreventive effect of probiotic on colitis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Afterwards, AOM/DSS-induced CAC mice were then analyzed for their neoplastic lesions as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB for macroscopic colon images in different groups. Tumors ranged from 1 to 5 mm in size and mostly occurred in the colorectum. A significant inhibition of tumor multiplicity and size induced by colitis was observed in the Treatment group, with a tumorigenicity rate of 70% in probiotic pretreated mice and an average of 2.9 total tumors per mouse (mean diameter 1.83 mm) compared to 100% in the Model group with 7.6 tumors per mouse (mean diameter 2.47 mm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F). The histological evaluation revealed multiple adenomas in the Model group as well as invasive adenocarcinomas, whereas mice in the Treatment group had disease features mainly crypt dysplasia and adenomas \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.2 Transcriptional shifts induced by AOM/DSS and probiotic pretreatment\u003c/h2\u003e\u003cp\u003eThe expression levels of transcriptional genes were compared between the two groups to screen out the DEGs. The results showed that 431 genes’ expression increased and 631 genes’ expression decreased in Treatment-vs.-Model group. In Treatment-vs.-Contrl group, the expression of 758 genes was increased and 446 genes were decreased. There were 610 genes with increased expression and 84 genes with decreased expression in the Model-vs.-Contrl group, resulting in a total of 2124 DEGs screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). STEM was also employed to analyze DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and we concluded that differential expression was increased or reduced in the Model group compared to the Contrl group, while it could be correspondingly decreased and elevated in the Treatment group, indicating that differential expression of these genes was influenced by probiotic. Eight models significantly clustered 11,866 of 18,577 DEGs, which included profile 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), profile 9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), profile 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), profile 13 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG), profile 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), profile 15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI), profile 12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ) and profile 11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK), while only profile 9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and profile 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH) of 3677 genes met our screening criteria and were screened.\u003c/p\u003e\u003cp\u003eIn order to further screen DEGs, we selected the combination of STEM trending genes and DEGs as candidate genes for further analysis. Up to 48 genes were screened, of which 10 DEGs decreased in the Model group and rebounded in the Treatment group, and 38 DEGs increased in the Model group and regressed in the Treatment group, which function closely related to the inhibition of tumorigenesis and progression by probiotic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL).\u003c/p\u003e\u003ch2\u003e3.3 Probiotic pretreatment modified the intestinal microbial community composition\u003c/h2\u003e\u003cp\u003eIn general, 42,411 and 61,316 valid tags were yielded by 16S rRNA sequencing. In our result, we got 1512 Amplicon Sequence Variant (ASV), and the number of ASVs ranged from 245 to 396, belonging to 14 phyla, 20 classes, 58 orders, 93 families, and 170 genera. For all samples, species accumulation curves and scarcity curves (\u003cb\u003eSupplementary Figure. S1\u003c/b\u003e) reflected detailed sequencing of all samples sufficient to describe their bacterial diversity and richness.\u003c/p\u003e\u003cp\u003eAt the phylum level, \u003cb\u003eBacteroidota\u003c/b\u003e in the Model group increased and in the Treatment group decreased (Contrl: 49.92% vs. Model: 61.31% vs. Treatment: 50.90%), while \u003cb\u003eFirmicutes\u003c/b\u003e in Model group decreased and in Treatment group recovered (Contrl: 43.45% vs. Model: 28.63% vs. Treatment: 37.20%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Among the top 30 groups at the genus level, the abundance of Muribaculaceae, the most abundant genus after CAC induction by AOM/DSS, increased compared to the Contrl group, while it fell back in the treatment group (Contrl: 44.86% vs. Model: 52.25% vs. Treatment: 42.81%). Clostridia_UCG-014 abundance decreased after induction compared to the Contrl group and rebounded in the Treatment group (Contrl: 13.75% vs. Model: 4.23% vs. Treatment: 5.99%). The major genera of the probiotic cocktail, both \u003cb\u003eLactobacillus\u003c/b\u003e and \u003cb\u003eBifidobacterium\u003c/b\u003e, decreased in abundance after CAC induction and rebounded in the Treatment group (Lactobacillus: Contrl: 3.97% vs. Model: 1.30% vs. Treatment: 2.80%; Bifidobacterium: Contrl: 0.72% vs. Model: 0.29% vs. Treatment: 1.01%). In addition, the abundance of the common causative organism Helicobacter increased after CAC induction by AOM/DSS and decreased in the Treatment group (Contrl: 0.30% vs. Model: 1.57% vs. Treatment: 0.38%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eDespite no significant differences in Chao1 estimates or Shannon index were found between groups (Supplementary Figure. S2), there was a binary PCoA analysis based on jaccard combined with adonis analysis results (P = 0.001), which indicated a significantly different beta diversity among the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The ANOVA difference statistics in further multivariate statistical analyses revealed 2 differences phylum: Proteobacteria and Campilobacterota were both elevated in the Model group and fell back in the Treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), and 17 differential genera, of which Lactobacillus, Dubosiella, Family_XIII_AD3011_group increased in the Treatment group and decreased in the Model group, while Parasutterella, Helicobacter, Muribaculum, [Eubacterium]_fissicatena_group decreased in the Treatment group and increased in the Model group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eA cladogram from LefSe measurements for identification of the specific bacteria associated with the CAC induction and probiotic treatment was used (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The result shown that \u003cb\u003eAllobaculum, Muribaculum, Clade_III\u003c/b\u003e and \u003cb\u003eUCG_003\u003c/b\u003e (LDA scores (log10) \u0026gt; 2.5) in the Model group, \u003cb\u003eEnterorhabdus, Odoribacter\u003c/b\u003e and \u003cb\u003eRuminococcus_torques_group\u003c/b\u003e were the most abundant in the Treatment group (LDA scores (log10) \u0026gt; 2.5), while \u003cb\u003eAnaerostipes, Lactobacillus, Eubacterium_coprostanoligenes_group, UCG_010, Family_XIII_AD3011_group, Eubacterium_siraeum_group, Monoglobus, Gordonibacter, Marvinbryantia, UCG_007\u003c/b\u003e and \u003cb\u003eEubacterium_brachy_group\u003c/b\u003e had LDA scores (log10) \u0026gt; 2.5 in the Control group. Collectively, these findings demonstrated alterations in the composition of the intestinal microbiota linked to the induction of CAC and probiotic treatment.\u003c/p\u003e\u003ch2\u003e3.4 AOM/DSS and probiotic pretreatment induce changes in fecal metabolism\u003c/h2\u003e\u003cp\u003eIn the GC-MS and LC-MS datasets, 362 and 9556 putative metabolites were identified, respectively. A total of 16 superclasses were assigned to them, which included 8337 classified metabolites along with 1302 unclassified metabolites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We used PCA and OPLS-DA to separate and demonstrate the metabolic differences present in each of the two groups. Analysis platform's excellent stability and reproducibility were shown by the permutation test (\u003cb\u003eSupplymentary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). An excellent predictive power of the model was demonstrated by the model parameters, without any overfitting found (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification statistics of identified metabolites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSuper Class of metabolites\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGC-MS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLC-MS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenzenoids\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e761\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHomogeneous non-metal compounds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLipids and lipid-like molecules\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3208\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3277\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNucleosides, nucleotides, and analogues\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrganic acids and derivatives\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1534\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrganic nitrogen compounds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrganic oxygen compounds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e593\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrganoheterocyclic compounds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1373\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1405\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhenylpropanoids and polyketides\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1302\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1302\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eDEMs were screened based on the metabolites detected by GC-MS and LC-MS, comparing levels of metabolite expression in each two groups (\u003cb\u003eSupplymentary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). A total of 27 and 159 DEMs were derived from GC-MS and LC-MS, respectively. We also performed STEM analysis of metabolites detected in mice feces with GC-MS and LC-MS, and we concluded that metabolites were increased or reduced as compared to the Contrl group in the Model group and able to be correspondingly reduced or reverted in the Treatment group, consistent with trends influenced by probiotic (\u003cb\u003eSupplymentary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e). With the STEM, 67 and 2634 metabolites were extracted from GC-MS and LC-MS, separately.\u003c/p\u003e\u003cp\u003eTo further refine the target range, we merged STEM trend metabolites with DEMs to select intersections as candidate metabolites for further analysis. In GC-MS, 24 DEMs were obtained, and their expressions decreased in all the Model group and reversed in the Treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The 87 DEMs were also obtained in LC-MS, of which 45 metabolites decreased in the Model group with rebound in the Treatment group and 42 metabolites increased in the Model group with rebound in the Treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.5 Integration anaiysis among intestinal microbiota, fecal metabolites and colonic tissue transcriptomics to build probiotic anti-inflammatory and anti-tumor network\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe performed Spearman correlation analysis and generated Cytoscape plots for the DEMs screened by GC-MS \u0026amp; LC-MS analysis with the 48 DEGs screened by transcriptome, respectively. In the correlation analysis between DEMs and DEGs from GC-MS analysis, DEGs \u003cb\u003eEdn3, Sfrp4, Nos3, Creb5\u003c/b\u003e were significantly correlated with DEMs, especially \u003cb\u003eSfrp4\u003c/b\u003e, which was significantly correlated with multiple DEMs; DEMs \u003cb\u003eCytosin, Spermine\u003c/b\u003e were significantly correlated with multiple DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In the DEMs-DEGs correlation analysis of LC-MS analysis, all DEGs except Pla2g3 were significantly correlated with at least 1 DEM, especially DEGs \u003cb\u003eSfrp4, Alox8, Nos3, Creb5, Ar, Gm10591, Gli3, Gng8, Calm4, Amotl2, Nr4a1, Nmu, Edn3\u003c/b\u003e significantly associated with multiple DEMs; DEMs \u003cb\u003eN-Linoleoyl Valine, 3-Sulfinoalanine, Xanthylic acid, Cholesterol glucuronide, Nicotinuric acid, Stachyose, D-Lactic acid, 2- Hydroxybutyric acid, Coproporphyrin III, Fructose 6-phosphate, alpha-Ketoisovaleric acid, Neopterin, D-Gal alpha 1-\u0026gt;6D-Gal alpha 1-\u0026gt;6D-Glucose\u003c/b\u003e were significantly correlated with multiple DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The further Cytoscape revealed that the functions of DEMs and DEGs were co-enriched in 37 metabolic pathways, of which DEMs from GC-MS and DEMs from LC-MS were co-enriched with DEGs functions in 24 and 32 metabolic pathways, respectively (\u003cb\u003eSupplymentary Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e and \u003cb\u003eS7\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eWe made a correlation analysis between the fecal DEMs and the genera Lactobacillus and Bifidobacterium, the main components of probiotic, that showed intergroup differences in the 16s analysis. Bifidobacterium was significantly negatively correlated with DEM \u003cb\u003eStearic Acid\u003c/b\u003e from GC-MS and significantly positively correlated with DEM \u003cb\u003eGentisate aldehyde\u003c/b\u003e from LC-MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Lactobacillus was significantly positively correlated with DEMs \u003cb\u003eUracil, Cytosin, Thymidine, Spermine, Spermidine\u003c/b\u003e derived from GC-MS and with DEMs from LC-MS, \u003cb\u003ecis-Aconitic acid, Uracil, 3-Fumarylpyruvate, Guanine, Xanthine, Deoxyinosine, Dodecanoic acid, Hypoxanthine, Orotidylic acid, Deoxyuridine, Cytosine\u003c/b\u003e, and also significantly negatively correlated with DEMs from LC-MS, \u003cb\u003eCitric acid, N Acetylhistamine, 10-Formyltetrahydrofolate, N-palmitoyl alanine, Gentisic acid\u003c/b\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In both genera, Lactobacillus and Bifidobacterium, only Lactobacillus had significant group differences in the 16S ANOVA, so Lactobacillus significantly correlated DEMs was selected to do correlation analysis with DEG to plot Cytoscape. The results showed that among the five GC-MS DEMs significantly positively correlated with Lactobacillus, i.e., reduced in the Model group and rebounded in the Trearment group, \u003cb\u003eCytosin, Thymidine, Spermine\u003c/b\u003e could identify DEGs significantly positive correlated: \u003cb\u003eEdn3, Ccl25\u003c/b\u003e, and significantly negatively correlated DEGs : \u003cb\u003eGli3, Sfrp4, C4b, Cd33, Gng8, Nfatc2, Creb5, Alox8, Lama5, Amotl2, Nr4a1, Nlgn2, Nos3, Gm10591\u003c/b\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Among the 16 LC-MS DEMs significantly correlated with Lactobacillus, 7 significantly positively correlated DEMs: \u003cb\u003e3-Fumarylpyruvate, Guanine, Deoxyinosine, Hypoxanthine, Orotidylic acid, Deoxyuridine and Cytosine\u003c/b\u003e were negatively correlated with DEGs: \u003cb\u003eAlox8, Gng8, Sfrp4\u003c/b\u003e and positively correlated with DEGs : \u003cb\u003eCcl25, Edn3, Nmu\u003c/b\u003e; 2 significantly negatively correlated DEM, i.e., increased in the Model group and receded in the Trearment group, \u003cb\u003eN-Acetylhistamine\u003c/b\u003e and \u003cb\u003eN-palmitoyl alanine\u003c/b\u003e were negatively correlated with DEGs : \u003cb\u003eEdn3, Nmu\u003c/b\u003e and positively correlated with DEGs : \u003cb\u003eSfrp4,Gm10591\u003c/b\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The DEMs, which involve five metabolic pathways (\u003cb\u003earginine and proline metabolism, histidine metabolism, pyrimidine metabolism, purine metabolism, and tyrosine metabolism)\u003c/b\u003e build a signal network together with the DEGs.\u003c/p\u003e"},{"header":"4 Disscussion","content":"\u003cp\u003eIntestinal microbiota and microbial metabolites are attractive substances that enhance the preventive and therapeutic effects against cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, we reproduced a probiotic cocktail inhibitory effect on AOM/DSS-induced CAC. In particular, Lactobacillus, the predominant genus of normal intestinal microbiota, decreased after AOM/DSS induction, while it rebounded directly after treatment with probiotic containing Lactobacillus as a major component. Moreover, Bifico treatment-related intestinal microbiota alterations were associated with changes in 37 intestinal metabolism systems. In which, the intestinal DEMs significantly associated with Lactobacillus involved: \u003cb\u003earginine and proline metabolism, histidine metabolism, pyrimidine metabolism, purine metabolism, and tyrosine metabolism\u003c/b\u003e, which suppress CAC by suppressing the expression of oncogenes and promoting the expression of cancer suppressor genes.\u003c/p\u003e\u003cp\u003eCompared with healthy controls, colorectal cancer individuals had an imbalance in intestinal microbiota, known as a dysbiosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Specifically, it can be shown as the richness of normal dominant microbiota decreased, higher relative abundance of putatively pro-carcinogenic microbial members and so-called protective genera reduced [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e–\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our research, LefSe profiling displayed that the Contrl group presented the most abundant enrichment of dominant genera, of which Anaerostipes, one of butyrate-producing bacterial species [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and Lactobacillus, one of the commonly used probiotic [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], was the main genus. After AOM/DSS induction, dominant genera had obvious changes and microbial abundance decreased significantly. Muribaculum, as one of the most abundant enrichment of dominant genera in the model group, which appeared in mice with T cell-induced colitis and was associated with the progression of intestinal inflammation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The ANONA difference analysis showed significant differences in Lactobacillus, Family_XIII_AD3011_group and Muribaculum, the former two decreased in the Model group and rebounded in the Treatment group, while the third increased in the Model group and regressed in the treatment group. In addition, Odoribacter dominated the treatment group, which is an important microbe for maintaining intestinal homeostasis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As non-dominant genera, Parasutterella and Helicobacter pylori were increased in the Model group and regressed in the Treatment group. Parasutterella might be related to IBS patients with chronic intestinal inflammation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Helicobacter pylori was also associated with CRC as an independent risk factor of promoting colorectal carcinogenesis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These findings suggest that normally abundant intestinal genera, represented by Lactobacillus, is crucial for the maintenance of intestinal microbiota balance and intestinal homeostasis. Oral probiotic cocktail therapy not only supplements lactobacilli and other normal genera, but also inhibits the growth of oncogenic genera and promotes the growth of protective genera, consequently affecting the progression of CAC. The other two genera of Bifico, although they did not show significant differences in the 16s analysis, were also most likely involved in the mentioned process. For example, supplementation with bifidobacteria both alleviated the DSS-induced reductions in the abundance of intestinal microbiota and brought about the suppression of pathogenic bacteria [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMicrobial metabolites have been regarded as necessary to facilitate the therapeutic and prophylactic effects in CRC, and related researches have gradually been initiated [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In our study, metabolomic analysis and combined multi-omics analysis suggested that 37 metabolic systems were involved in Bifico's alteration of microbiota composition. Alteration of metabolism, distracted glucose utilization leading to an amount of increased lactic acid, is one of the essential features of cancer, and increased lactic acid promotes multiple critical oncogenic processes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In the DEMs, \u003cb\u003eD-Lactic acid\u003c/b\u003e was elevated in the Model group and decreased after the probiotic pretreatment, which demonstrated the inhibitory effect of Bifico on tumor microenvironment (TME) acidification. DEMs, which also appeared elevated after modelling and declined after treatment, were \u003cb\u003ePhosphate\u003c/b\u003e, a tumorigenesis promoter [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and \u003cb\u003eAdenosine\u003c/b\u003e, immunosuppressive factor in TME [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Additionally, there were several DEMs that rebound after treatment, for example: \u003cb\u003eDodecanoic acid\u003c/b\u003e works as an anticancer agent [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]; G\u003cb\u003eamma-Tocopherol\u003c/b\u003e and D\u003cb\u003eelta-Tocotrienol\u003c/b\u003e, main vitamin E forms playing an anti-inflammatory and anticancer role [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]; \u003cb\u003eNiacinamide\u003c/b\u003e shows potential role in cancer prevention and treatment [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]; S\u003cb\u003epermidine\u003c/b\u003e provids protection against intestinal inflammation and suppress tumorigenesis through the induction of autophagy in healthy tissues [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]; S\u003cb\u003epermine\u003c/b\u003e plays as free radical scavengers [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]; \u003cb\u003eOleic acid\u003c/b\u003e promotes apoptosis and cell differentiation in colorectal cancer [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]; \u003cb\u003eStearic Acid\u003c/b\u003e has been as a Colon Cancer therapeutic agent in combination with 5-Fluorouracil due to its consideration as a potential PDK1 inhibitor [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Accordingly, the molecular mechanism underlying Bifico’s CAC inhibition effect was interpreted from the metabolites aspect. Meanwhile, among the screened DEGs, for example, DEGs significantly associated with metabolites in the combined analysis, we detected post-treatment expression of oncogenes decreased (Sfrp4 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], Nos3 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], Creb5 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], Ar [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], GLI3 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], Amotl2 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], Nr4a1 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] ) and cancer suppressor genes Edn3 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] up-regulated. It can be speculated that the changes in metabolic substances brought about by these probiotic treatments have the effect of suppressing CAC by affecting the transcriptome.\u003c/p\u003e\u003cp\u003eNevertheless, there were also some DEMs and DEGs expressions that were not favorable for CAC prevention such as: metabolites with anticancer effects \u003cb\u003epalmitic acid\u003c/b\u003e and \u003cb\u003e3-hydroxybutyric acid\u003c/b\u003e [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] reduced after treatment; oncogenes \u003cb\u003eCcl25\u003c/b\u003e and \u003cb\u003eNmu\u003c/b\u003e [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] expression increased after treatment. It suggested that we need to deeply explore the changes of potentially anticancer metabolites related to Bifico, especially the Lactobacillus related metabolites that play a major role and their mechanism of anti-cancer, to focus on potential candidate metabolites for further research. The Lactobacillus involved in this experiment is Lactobacillus acidophilus, one of the major species of Bifico. Lactobacillus acidophilus has the characteristics of preventing colitis and colon tumors. For example, extracellular polysaccharides (EPSs) synthesized by Lactobacillus acidophilus can stimulate immune response against tumor cells [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Our in-depth multi-omics analysis suggests that Lactobacillus produces anti-inflammatory and tumorigenesis prevention effects by affecting 5 metabolic systems, which play a significant roles in physiological and pathological conditions. Among them, \u003cb\u003eDodecanoic acid, Spermine and Spermidine\u003c/b\u003e, which are elevated after treatment, have anti-cancer properties. Since the metabolite functional database is not fully complete, the related DEMs roles involved need to be explored by further in-depth experimental studies.\u003c/p\u003e\u003cp\u003eIn summary, the phenotype of CAC inhibition by Bifico was reproduced in our study, which reconfirmed the anti-inflammatory and tumorigenesis prevention effects by supplementation with Lactobacillus-based probiotic, and demonstrated Bifico-related metabolic changes from the metabolite perspective. Among the metabolic changes of Bifico related exhibited overwhelming anticancer effects over cancer-promoting effects, while the anti-inflammatory and tumorigenesis prevention effects of Lactobacillus involved changes in five metabolic systems, which provided a preliminary exploration for further screening with targeted anti-inflammatory and tumorigenesis prevention metabolites.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e All animal experiments were conducted according to the Guide for the Care and Use of Laboratory Animals: Eighth Edition, and were approved by the ethics committees of Shanghai Tong Ren Hospital and Shanghai Jiao Tong University School of Medicine (A2023-022-01).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWeiyi Wang designed the experiment, Haiqin Zhang, Lu Zhou, Haijin Zhu, Ji Li, Zixu Zhang, Jinnian Cheng, Fengli Zhou and Daming Yang performed the experiment, Ying Xu and Yimin Chu processed the data, Weiyi Wang wrote the paper, Weisong Xu and Hiaxia Peng revised. All authors approved the final version to be published, and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe author thanks the financial support of Shanghai Natural Science Foundation (No. 21ZR1458600), the Shanghai Jiaotong University Medical-Engineering Cross Research Fund (No. YG2022ZD031), Scientific research project of Health and Wellness Committee Changning District Shanghai (20214Y007), Shanghai Municipal Health Commission Key Laboratory of Gastrointestinal Tumor Innovation and Translation (No.ZDSYS-2021-01 ), Foundation of Shanghai Tongren Hospital Rising Star (TRKYRC-xx202211), and Shanghai Municipal Health Commission Health Industry Clinical Research Project (No.20234Y0016).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, et al. 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Epub 2022/10/05.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiguer. and legends.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic inflammation, Colorectal cancer, Intestinal metabolites, Intestinal microbiota, Probiotic, Lactobacillus","lastPublishedDoi":"10.21203/rs.3.rs-6436788/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6436788/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDysbiosis of intestinal microbiome is an important colorectal cancer (CRC) pathogenetic mechanism. Lactobacillus-based probiotic cocktail could inhibit colitis-associated cancer (CAC) by alleviating intestinal dysbiosis. The intestinal microbial metabolites have been linked with CRC aetiology. However, it is still poorly understood the link between Lactobacillus-based probiotic cocktail and the alteration of intestinal metabolism and their functional mechanisms during CAC process. For assessing protective effects of the probiotic cocktail, azomethanes/dextran sodium sulfate (AOM/DSS) induced CAC mice were pretreated with the probiotic cocktail. Colon of C57BL/6 mice were used to assess inflammation and tumorigenesis. Comparative analysis was performed for determining how the probiotic altered intestinal metabolism and gene expression. Meanwhile, intestinal microbiota alterations were analyzed. The concluding integrated analysis of intestinal metabolism and gene expression as well as intestinal microbiota was presented. Pretreatment with the probiotic alleviated intestinal inflammation and limited the formation of tumors. Oncogenes were down-regulated and cancer suppressor genes were up-regulated after probiotic pretreatment. Pretreatment with the probiotic induced a rise of Lactobacillus-dominated genera and a reduction of potential pathogenic bacteria Parasutterella, Helicobacter and Muribaculum, and affected expression of intestinal metabolites that involved 37 metabolic pathways. Lactobacillus-associated intestinal metabolite variations involve five metabolic pathways - arginine and proline metabolism, histidine metabolism, pyrimidine metabolism, purine metabolism, and tyrosine metabolism. Pretreatment with Lactobacillus-based probiotic cocktail protected mice from CAC by interfering with intestinal metabolites that affected the cancer suppressor genes and oncogenes' expression. Furthermore, Lactobacillus affected five metabolite pathways, which was important mechanism for probiotic anti-inflammatory and anti-tumorigenesis.\u003c/p\u003e","manuscriptTitle":"Lactobacillus-based probiotic cocktail inhibits colitis-associated cancer by altering intestinal metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 17:30:35","doi":"10.21203/rs.3.rs-6436788/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cd6dc84-82b1-449a-926c-a3146884cbc5","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-28T03:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 17:30:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6436788","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6436788","identity":"rs-6436788","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}