Gut microbiota-derived queuine reprograms colon gene expression and alleviates colorectal cancer synergistically with Limosilactobacillus reuteri

Gut microbiota-derived queuine reprograms colon gene expression and alleviates colorectal cancer synergistically with Limosilactobacillus reuteri | 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 Gut microbiota-derived queuine reprograms colon gene expression and alleviates colorectal cancer synergistically with Limosilactobacillus reuteri Anrui Liu, Jia Li, Zihe Xu, Sisi Liu, Jiajun Fan, Kaixin Cao, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7540054/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 Background Colorectal cancer (CRC) is a multifactorial disease of the colorectal epithelium that could be driven by gut microbiota dysregulation, while the molecular mechanisms of microbial metabolitesin regulating CRC remain unclear. Results We aim to investigate the biological functions of gut microbiota-derived queuine in the host and the underlying molecular mechanisms. Transcriptomic analysis of ten tissues from specific pathogen-free and germ-free mice revealed that queuine supplementation reprogrammed host gene expression, especially in the colon. Functionally, we found that queuine inhibited CRC in two cell lines (HCT116 and HT29), xenograft CRC mouse model, and CRC-derived organoids. Interestingly, we found that queuine supplementation in mice affected gut microbiota compositions, in which L. reuteri showed the most pronounced increase upon queuine treatment. Further experiments confirmed the effect of queuine on the activity of L. reuteri in vitro and in vivo . Moreover, L. reuteri enhanced the inhibitory effect from queuine on spontaneous colorectal cancer in mice. Mechanistically, both queuine and L. reuteri can suppress CRC through the regulation of Cdkn2a and Ctnnb1 . Conclusion s Our findings uncover the role and mechanism of gut microbiota-derived queuine in suppressing CRC, and we highlight the therapeutic potential of queuine and L. reuteri. Gut microbiota queuine colorectal cancer L. reuteri Cdkn2a Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Colorectal cancer (CRC) is the third most prevalent cancer and the second leading cause of cancer-related mortality worldwide[ 1 , 2 ]. By 2030, its incidence in developing countries is projected to rise by 60%[ 3 ]. Similar to many other solid tumors, CRC develops over decades through somatic cell evolution[ 4 ]. During this period, mutations in oncogenes and tumor suppressor genes within the patient's colorectal tissues drive the abnormal proliferation of colorectal epithelial cells, ultimately leading to malignancy[ 5 ]. In the long disease trajectory, many factors contribute to the process of carcinogenesis. Recent evidence suggests that human gut microbiota can influence CRC development by modulating inflammation and shaping microbial communities[ 6 ]. Therefore, gut microbiota-associated and microbial-derived molecules are considered as potential therapeutic strategies in the therapy of CRC. Queuine, a gut microbiota-derived micronutrient/nucleobase, is essential for hosts to maintain normal physiological processes[ 7 ]. As an exogenous micronutrient, queuine has been extensively studied for its role in tRNA queuosine (Q) modification, a post-transcriptional process cruical for accurate and efficienct protein translation[ 8 ]. This modification significantly influences biological processes, including cancers, metabolism, and memory[ 9 – 11 ]. While the role of queuine in tRNA modification has been well characterized, its direct biological functions remain poorly understood. Notably, its potential anti-tumor or disease-fighting effects and related mechanisms remain largely unexplored. In this study, we systematically investigated the biological functions of queuine across different models. Our findings demonstrated that queuine supplementation regulated colon gene expression in both SPF mice and GF mice, and queuine alleviated colorectal cancer cell biology by down-regulating Cdkn2a (encoding cyclin-dependent kinase inhibitor 2A). Importantly, queuine treatment reshaped gut microbiota composition, with a marked increase in L. reuteri , a bacterium which possessed the same function as queuine in suppressing CRC. These results highlight the therapeutic potential of combining queuine and L. reuteri for CRC prevention and treatment. Methods Cells and cell culture HCT116 cells were cultured in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA) and 1% Penicillin-Streptomycin (Gibco, USA). HT29 cells were cultured in RPMI 1640 (Gibco, USA) supplemented with 10% fetal bovine serum (Thermofisher, USA) and 1% Penicillin-Streptomycin (Gibco, USA). Cell lines were cultured at 37°C in a constant-temperature incubator with 5% CO 2 . In the case of queuine treatment, cells were treated with 3 μM queuine (Toronto Research Chemicals, Canada). Patient sample collection Clinical samples for this study were obtained from the Department of Gastroenterology, Panyu Central Hospital of Guangzhou Medical University, and were approved by the Ethics Committee with an approval number: PYRC-2024-262-01. Queuine supplementation in SPF and GF mice Male six-week-old C57BL/6J SPF and GF mice were purchased (Gempharmatech, China), and mice were randomly divided into two groups with three mice in each group. All mice had free access to sterilized diet and water, and the corn bedding was regularly changed. Each mouse was fed with water or queuine by gavage for 14 days at a dose of 100 μL/day containing 100 μM queuine. After 14 days of experiment, the mice were dissected, and the corresponding organs were collected and immediately frozen in liquid nitrogen and stored at -80℃ until use. The protocol for an animal experiment in this study was approved by the Institutional Animal Care and Use Committee (IACUC) of Guangzhou Institute of Biomedicine and Health at Chinese Academy of Sciences (protocol code: #A5748-01). Establishment of CDX mouse model HCT116 cells (1×10 7 ) were injected subcutaneously into the neck of BALB/c nude mice (Gempharmatech, China). After that, mice were randomly divided into two groups. Each mouse was fed with water or queuine by gavage for 14 days at a dose of 100 μL/day containing 100 μM queuine. Tumors were gauged for length every 2 days and the formula (1/2 × width 2 × length) was used to calculate the size of tumors[38]. After 15 days, the tumors were dissected from these nude mice and weighed. CRC patient-derived organoids culture About 1.5 × 10 6 single cells/mL were washed using DMEM and embedded in 20 µL Basement Membrane Matrix for Organoid Culture (#HY-K6007, MedChemExpress, China) in a 48-well plate to develop CRC patient-derived organoids. The organoids were cultured in intestinal tumor culture medium (#YX-C-SJH-01, Eacin Bio, China). The culture medium was refreshed every 2 days, after 21 days organoids were photographed under the IX73 (Olympus, Japan). Establishment of spontaneous colorectal cancer mouse models For the azoxymethane plus dextran sulfate sodium (AOM/DSS) model, 6-week-old male C57BL/6 mice were intraperitoneally injected with 12.5 mg/kg AOM (MP Biomedicals, California, USA), followed by 1 week of 2% DSS and 1 week of water. After that, mice were injected with 8 mg/kg AOM, followed by 1 week of 2% DSS and 1 week of water. For treatment experiments, the control and normal groups were given 100 μL of PBS once a week. The Q group was given 100 μL of PBS containing 100 μM queuine once a week, the L. reuteri group was given 100 μL of PBS containing 10 8 CFU of L. reuteri , and the L. reuteri +queuine group was given 100 μL of PBS containing both 100 μM queuine and 10 8 CFU of L. reuteri . Treatments continued until week 16, after which mice were euthanized for subsequent analysis. HE staining Colons harvested from spontaneous colorectal cancer mouse models were fixed with 4% paraformaldehyde (PFA, Sigma Aldrich) at 4°C. Following fixation, tissues were dehydrated and embedded in paraffin before sectioning. The sections were stained with haematoxylin eosin solution for 6 min, followed by 8 s in 1% acid ethanol (1% HCl in 70% ethanol) and rinsing in distilled water. Subsequently, stained with eosin solution for 3 min, dehydrated with graded alcohol, and cleared in xylene. Finally, images were captured using Tissue FAXS Plus ST (TissueGnostics GmbH, Austria). I mmunohistochemistry (IHC) staining Colons from spontaneous colorectal cancer mouse models were fixed with 4% paraformaldehyde (PFA, Sigma Aldrich) at 4°C, dehydrated, and embedded in paraffin before section. For phenotypic analysis, a CTNNB1 monoclonal antibody (#MB62945, Bioworld) and a CDKN2A polyclonal antibody (#EAB13750, EbioCell) were applied. Following staining, the tissues were imaged using Tissue FAXS Plus ST (TissueGnostics GmbH, Austria). The captured images were analyzed using ImageJ and IHC ToolBox to determine the corresponding IHC scores. Construction of eukaryotic RNA-seq libraries Total RNA from tissues was extracted using the Trizol method according to the manufacturer's instructions. The mRNA libraries from different mouse tissues were constructed for Illumina sequencing. A VAHTS ® Universal V6 RNA-seq Library Prep Kit for Illumina (#NR604-02, Vazyme Biotech) and a VAHTS ® RNA Adapters set3 for Illumina (#N809, Vazyme Biotech) provided the necessary reagents for first- and second-strand cDNA synthesis, adaptor ligation, and library amplification. RNA-seq of the prepared libraries was performed at Berry Genomics on the NovaSeq 6000 platform (Illumina, CA, USA) to obtain paired-end reads of 150 bp. Library quality was evaluated using an Agilent Bioanalyzer 4200 TapeStation prior to sequencing. Approximately 6 GB of raw reads were obtained for each library. RNA-seq data analysis Adapters and low-quality reads were first removed by applying Trim_Galore (version 0.6.6, https://github.com/FelixKrueger/TrimGalore) to all RNA-seq raw sequencing data. The resulting reads of at least 35 bp in length were mapped to the reference genome (mm39). PCR duplicates were removed, and the aligned results were then sorted using samtools[39] (version 1.3.1). The featureCounts program in Subread package[40] (version 2.0.1) was used to count the reads mapping to genes. DESeq2[41] (version 1.36.0) was used for the identification of DEGs by setting a p value 1 as the threshold for significance. Functional enrichment analysis was performed using clusterProfiler 59 (version 4.8.3) to determine significantly enriched pathways. Metagenome data analysis A total of 1 μg of DNA per sample was used as input material for DNA sample preparation. Sequencing libraries were generated using the NEBNext ® Ultra™ DNA Library Prep Kit for Illumina (#E7370L, NEB) according to the manufacturer’s instructions, and indexes were added to the attribute sequences of each sample. Briefly, DNA samples were fragmented to 350 bp by sonication, then DNA fragments are blunt-ended, A-tailed, and ligated to full-length adapters for Illumina sequencing and followed by PCR amplification. Finally, PCR products were purified by an Agilent 2100 Bioanalyzer (AMPureXP system) and size distribution of the libraries was analyzed, after which the library concentration was quantified by CFX96 (Bio-Rad, USA). The clustering of the index-coded samples was performed on a cBot Cluster Generation System according to the manufacturer’s instructions. After cluster generation, the librarieswere sequenced by NovaSeq 6000 (Illumina, CA, USA), and 150 bp paired-end reads were generated. Following raw data acquisition, clean data were generated using fastp (https://github.com/OpenGene/fastp). The clean data were assembled by MEGAHIT[42]. Prodigal was used to predict the open reading frame (ORF), and CD-HIT[43] was used to generate non-redundant gene catalogue. Then, clean reads from each sample were compared to a catalog of non-redundant genes using Bowtie2[44]. Culture of L. reuteri and E. coli The L. reuteri strain was isolated from a human breast milk sample, and the E. coli strain was isolated from a healthy human fecal sample. For bacterial recovery, the MRS (for L. reuteri ) or LB (for E. coli ) liquid medium was prepared in advance and sterilized at 121°C for 15 minutes. Bacterial stocks were taken out from -80°C, and thawed at 4°C. The MRS or LB liquid medium was then added to the bacteria solution and cultured at 37°C for 6~12 hours. When the OD600 value of the bacterial solution reached 1, the bacteria were passaged at 1% inoculum ratio for static culture. Library construction and genome sequencing of L. reuteri We identified the L. reuteri strain using genome sequencing. Library construction and sequencing preparation were performed using the Universal Sample Preparation Kit (Geneus Technologies, Chengdu, China), with its included DNA library preparation reagents. Sequencing library preparation and purification were conducted using the kit’s dedicated reagents and purifications beads, respectively. The specific steps were as follows: First, 1 µg of fragmented genomic DNA was taken, and the DNA Library Preparation Reagents in the kit were used to perform DNA repair and ligation of the samples. After Reaction Solution 1 and barcode adapters were added, the samples were treated at 20°C for 30 minutes and then at 65°C for 5 minutes. Then, Reaction Solution 2 was added for the post-ligation treatment, and the samples were incubated at 37°C for 20 minutes. After purification with magnetic beads, the library was obtained. Finally, the sequencing complex preparation reagents in the kit were used to prepare the sequencing complex. The library was incubated with the nanopore complex and sequencing primers at 27°C for 20 minutes. Then, the long fragments incubation buffer was added and incubated at 35°C for another 20 minutes. After purification with magnetic beads, the sequencing complexes ready for sequencing on the instrument were obtained. The steps for sequencing via the sequencing kit in Universal Sequencing Reagent Kit (Geneus Technologies, Chengdu, China) were as follows: First, the G-seq500 sequencer was powered on, and the G-seq500 chip was installed. Second, the chip was calibrated, the temperature was controlled, and the chip was filmed with a sequencing kit. Once the film formed, a certain dilution of the sequencing complex was added to the well, and four modified deoxyribonucleotides were added to the well for sequencing. After sequencing completion, FASTQ files were exported. Following this, the G-seq500 chip and sequencer were cleaned using the provided cleaning kit. Finally, the G-seq500 chip was removed, and the machine was turned off. All of the above experiments were performed at Geneus Technologies Co., Ltd. (Chengdu, China). Genome assembly and gene annotation of L. reuteri Primary contigs were generated from Geneus sequencing reads using the Canu/Flye[45, 46] assembler with default options. The draft assembly was then polished using Q20 reads in Flye’s polishing mode. Sequencing reads were mapped to the assembled genome using Minimap2[47] for SNP and indels. Genome annotation was performed using Prokka[48],while plasmid annotation was performed using PlasmidFinder[49]. ABRicate (https://github.com/tseemann/abricate), a high-throughput screening tool combining several built-in databases, including NCBI, CARD, ARG-ANNOT, ResFinder, MEGARes, PlasmidFinder, EcOH, Ecoli_VF, and VFDB, was used for detection of resistance and virulence genes. All of the above experiments were performed at Geneus Technologies Co., Ltd. (Chengdu, China). Construction of RNA-seq libraries of L. reuteri Total RNA was extracted using the RNAprep pure Cell/Bacteria Kit (#DP430, TIANGEN, China), following to the manufacturer’s protocol. RNA purity and concentration were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA), and RNA integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The samples with qualified purity, quantity, and integrity were used for subsequent library construction. TIANSeq rRNA Depletion Kit (#NR101-T6, TIANGEN, China) was used to remove ribosomal RNA, then the libraries were constructed using the VAHTS Universal V6 RNA-seq Library Prep Kit (#NR604-01, Vazyme Biotech, China) according to the manufacturer’s instructions. The transcriptome sequencing and analysis were conducted by OE Biotech Co., Ltd. (Shanghai, China). RNA-seq data analysis of L. reuteri Transcriptomic data for L. reuteri underwent the same quality control procedures as those used for the mouse RNA-seq analysis. Clean reads were mapped to the L. reuteri genome. PCR duplicates were removed, and the aligned results were sorted using samtools[39] (version 1.3.1). Gene-level read counts were obtained using the featureCounts program from Subread package[40] (version 2.0.1). DESeq2[41] (version 1.36.0) was used for the identification of DEGs by setting a p value 1 as the threshold for significance. Gene set enrichment analysis (GSEA) was performed using clusterProfiler[50] (version 4.8.3) to identify significantly enriched pathways by setting a p value < 0.05. Queuine treatment and bacterial metabolite treatment Queuine hydrochloride (#Q525000) was purchased from Toronto Research Chemicals and dissolved in water to prepare a 1 mM stock solution. For queuine treatment, HCT116 cells and HT29 cells were incubate with 3 μM queuine, and L. reuteri was treated with 0.1 μM queuine. For the treatment with L. reuteri metabolite, the supernatant from a 220 mL L. reuteri culture medium was lyophilized and reconstituted in 15 mL water. Subsequently, 4 μL of this concentrated metabolite solution was added to 1 mL cell culture medium. Sample preparation for metabolomics of L. reuteri Samples stored at -80℃ were thawed in ice-water bath. A 600 μL aliquot of each sample was loaded onto a solid-phase extraction (SPE) column (C18 packed), and 3 mL of methanol eluate was collected. To resolubilize the metabolite, after drying the sample under a stream of nitrogen gas, 300 μL of a protein precipitant methanol-acetonitrile (V: V=4: 1, including mixed internal standard, 4 μg/mL) was added. The mixture was vortexed for 1 minute and subsequently ultrasonicated in an ice-water bath for 10 minutes. After standing at -40℃ for 2 hours, samples were centrifuged at 13,000 rpm for 20 minutes at 4℃. 150 μL of the supernatant was loaded into LC-MS injection vials and stored at -80°C until LC-MS analysis. Quality control samples were prepared by mixing aliquots of all samples to be a pooled sample. LC-MS/MS analysis The metabolomic data analysis was performed by Shanghai OE Biotech Co., Ltd. (Shanghai, China). An ACQUITY UPLC I-Class plus (Waters Corporation, Milford, USA) was fitted with a Q-Exactive mass spectrometer equipped with a heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA). The metabolic profiling was analyzed in both ESI positive and ESI negative ion modes. An ACQUITY UPLC HSS T3 column (1.8 μm, 2.1 × 100 mm) was employed in both positive and negative modes. The binary gradient elution system consisted of (A) water (containing 0.1% formic acid, v/v) and (B) acetonitrile, and separation was achieved using the following gradient: 0 minute, 5% B; 2 minutes, 5% B; 4 minutes, 30% B; 8 minutes, 50% B; 10 minutes, 80% B; 14 minutes, 100% B; 15 minutes, 100% B; 15.1 minutes, 5% B; and 16 minutes, 5% B. The flow rate was 0.35 mL/min, and the column temperature was set at 45°C. All samples were kept at 10°C during the analysis. The injection volume was 2 μL. The mass range was from m/z 70 to 1050. The resolution was set at 60,000 for full MS scans and 15,000 for HCD MS/MS scans. The collision energies were set at 10 20, and 40 eV. The mass spectrometer was operated as follows: spray voltage, 3,800 V (+) and 3,200 V (−); sheath gas flow rate, 35 arbitrary units; auxiliary gas flow rate, 8 arbitrary units, capillary temperature, 320°C; auxiliary gas heater temperature, 350°C; and S-lens RF level, 50. Data preprocessing and statistical analysis for metabolomics Raw LC-MS data were processed using Progenesis QI (version 3.0, Nonlinear Dynamics, Newcastle, UK) for baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. Key parameters were set as follows: precursor tolerance at 5 ppm, product tolerance at 10 ppm, and product ion threshold at 5%. Compound identification was based on precise mass-to-charge ratio (m/z), secondary fragments, isotopic distribution, and retention time using the Human Metabolome Database (HMDB)[51], Lipidmaps (version 2.3, http://www.lipidmaps.org/), Metlin[52], and the in-house LuMet-Animal3.0 databases. Peaks with missing values (ion intensity = 0) in more than 50% of samples within any group were excluded. The remaining zero values were replaced with half of the minimum value by log2 transformation. Compounds with resulting scores below 36 (out of 80) points were considered unreliable and removed. A combined data matrix was generated from both positive and negative ion mode data for subsequent analysis. The data matrix was imported into R to conduct PCA, to evaluate overall sample distribution and analysis process stability. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) was utilized to distinguish the metabolites that differ between groups. To prevent overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) were used. Significantly differential metabolites were identified using a two-tailed Student’s t-test with a significance threshold of a p value 1.5. Metabolite pathway enrichment analysis was subsequently performed using the KEGG database. Co-culture of human cells and bacteria Bacteria cultures were diluted 1:100 in fresh MRS ( L. reuteri ) or LB ( E. coli ) liquid medium and grown until OD600 reached 1. HCT116 or HT29 cells were seeded in 24-well plates and cultured for 12 hours prior to coculture with the bacteria (MOI, 100). After 6 hours of coculture, cells were washed three times with PBS. Fresh DMEM (HCT116) or RPMI 1640 (HT29) containing 100 μg/ml ampicillin was then added to eliminate extracellular bacteria. Two hours later, infected cells were washed three times with PBS.Harvested cells were used for subsequent protein or RNA extraction. Bacterial adhesion assay The adhesion assay was performed according to previous studies[53, 54]. Briefly, 1×10 5 HCT116 cells were seeded per well in six-well plates and cultured in DMEM supplemented with 10% (v/v) fetal bovine serum. Prior to bacterila addition, cells were gently washed twice with PBS and incubated with 2 ml of antibiotic- and serum-free DMEM per well at 37°C for 30 minutes. A bacterial culture (approximately 1×10 9 CFU/ml in 1 ml DMEM) was then added to each well at a volume of 1 ml. The plates were incubated at 37°C for 2 hours. After incubation, the cells were rinsed twice with PBS, and 1 ml of 0.25% trypsin was added to each well for 15 minutes at room temperature to create a cellular bacterial suspension. This suspension was subsequently diluted and plated on MRS agar. Colonies were counted following incubation for 24~48 hours at 37°C. Microscopic examination was performed as follows: adherent bacteria and cells were not digested, the samples were directly fixed with methanol, stained with Giemsa solution, and sealed with resin. Images were then captured using an IX73 inverted fluorescence microscope (Olympus, Japan). Bacterial biofilm assay The biofilm assay was performed according to the method described by Stepanovic[55]. Briefly, 5 mL of medium was inoculated with the bacteria and incubated overnight at 37°C. After incubation, the OD600 was adjusted to 0.1, and 200 µL of adjusted bacterial suspension was added to the wells of a sterile 96-well plate for both control and experimental groups. The remaining empty wells were filled with medium, and the plates were sealed and incubated at 37°C for either 12 or 24 hours. Following incubation, the plates were inverted onto paper towels to remove excess liquid and non-adherent cells. Each well was then treated with 200 µL of methanol, centrifuged at 2,500 rpm for 1 minute, and incubated at room temperature for 20 minutes. After fixation, methanol was removed by plate inversion onto paper towels. Plates were air-dried at room temperature for 30 minutes. Next, 200 µL of a 0.1% crystal violet solution was added to each well and left at room temperature for 15 minutes. The wells were rinsed three times with PBS to remove excess dye, then air-dried for 15 minutes. Finally, 200 µL of 33% (v/v) glacial acetic acid was added to each well to dissolve the crystal violet bound to adherent bacteria. The absorbance of the resulting solution was measured at 595 nm (OD595), and OD595 values were normalized by the control group. RNAi using siRNA in cells About 3×10 5 cells were seeded per 6 cm dish containing 4 mL of DMEM (for HCT116) or RPMI 1640 (for HT29). After 24 hours, cells were transfected with Lipofectamine 3000 (L3000008, Invitrogen) and 50 nM siRNA. Following 7 hours of incubation, the transfection mixture was removed and replaced with normal medium or treated with reagents. 48 hours later, the cells were harvested for protein or RNA for subsequent experiments. The Cdkn2a siRNA (GGGUUUUCGUGGUUCACAUUU) was designed based on a published study[56]. Construction of Cdkn2a overexpressing stable cell line In mammals, Cdkn2a primarily encodes two open reading frames (ARF), p14ARF and p16INK4A[56]. Previous studies have demonstrated that ARF is unexpectedly highly expressed or stably present in various cancers, where it functions as a pro-oncogenic factor[57, 58]. The Cdkn2a overexpression vector was constructed by inserting the p14ARF transcript fragment of Cdkn2a into the PLVX-puro vector (Tsingke Biotechnology Co., Ltd.). Following construction of the overexpression vector, the pMD.G and psPAX2 were added into the antibiotic-free medium and waited for 5 minutes, then Hieff Trans ® Polyethylenimine Linear (PEI) (40816ES02, YEASEN) was then added to this mixture, combined them thoroughly, and added the solution to well-cultured 293T cells in the antibiotic-free medium. After 6 hours, the medium was replaced with DMEM supplemented with 10% fetal bovine serum and 1% Penicillin-Streptomycin and cells were incubated for 48 hours. Subsequently, the supernatant was filtered through a 0.45 μm filter membrane, and the filtrate was added to the HCT116 cell culture medium. After 48 hours, the cells were screened using the DMEM medium containing 2 μg/ml puromycin to finally obtain the Cdkn2a overexpression stable cell line. Cell viability assay Cells were seeded in 96-well plates at 2×10 3 cell/well in 0.1 mL of complete DMEM medium supplemented with 10% fetal bovine serum and 1% Penicillin-Streptomycin for HCT116, or RPMI 1640 medium similarly supplemented for HT29. After treatment, 10 μL of CCK-8 solution (40203ES76, YEASEN) was added to each well and incubated at 37°C for 1.5 hours, the absorbance used to evaluate cell viability which was detected at 450 nm by FlexStation 3 (Molecular Devices, USA). Cell migration assay Before initiating the experiment, cells were incubated in DMEM for 24 hours. Matrigel matrix (#356234, Corning, USA) was diluted to 1 mg/ml with DMEM, add 200 μL of the diluted matrix to each chamber (#TCS020012, JETBIOFIL) at 37℃ for 2 hours. The matrix was removed, and cells were seeded to each chamber (5×10 4 /well). Following 24 hours, non-migrated cells were scraped and the migrated cells were fixed using 4% paraformaldehyde fix solution (Beyotime, China), stained with 0.5% Crystal Violet Stain Solution (#60506ES60, Yeasen), and photographed under the IX73 inverted fluorescence microscope (Olympus, Japan). C ell scratch assay Cells were seeded into 12-well plates following the procedure as mentioned in cell viability assay Upon reaching 90%~100% confluency, a pipette tip was used to make several scratches in each well, followed by washing with PBS was used to remove scratched cells. After treatment, photographs of the cells were captured every 24 hours using the IX73 inverted fluorescence microscope (Olympus, Japan). Immunofluorescence Cells were seeded into 12 well cell culture plates about 20%~30%, cultured in 1 mL 10% FBS and 1% Penicillin-Streptomycin DMEM medium (HCT116) or RPMI 1640 medium (HT29). After treatment for 48 hours, the cells were washed with tris buffered saline (TBS) and fixed with 4% paraformaldehyde fix solution (#P0099, Beyotime) for 20 minutes, then permeabilized with immunostaining permeabilization buffer with Triton X-100 (#P0096, Beyotime) for 15 minutes. Following this, cells were blocked with blocking buffer (1% BSA in TBS) and incubated overnight with the appropriate primary antibodies. After washing with TBS cell were incubated with IFKine™ Red Donkey Anti-Rabbit IgG (#A24421, Abbkine) and IFKine™ Green Donkey Anti-Mouse IgG (#A24411, Abbkine). After that, the cells were stained with DAPI and observed under LSM800 (Zeiss, Germany). The mean fluorescence intensity (MFI) was measured by ImageJ. Western blotting Cells were lysed using RIPA Lysis Buffer (#P0013B, Beyotime), and protein concentrations were quantified using a BCA Protein Assay Kit (#20201ES76, Yeasen). Equal amounts of protein were separated by 12% SDS–PAGE and transferred onto PVDF membranes (#1620177, Bio-Rad). The membranes were blocked with 5% nonfat milk for 1 hour, the membranes were then incubated with CTNNB1 monoclonal antibody (#MB62945, Bioworld), CDKN2A Rabbit Polyclonal Antibody (#EAB13750, EbioCell), Anti-beta Actin antibody (#Ab6276, Abcam) on 4°C shaker overnight. The membranes were then incubated with a secondary antibody for 1 hour at room temperature. Proteins were ultimately visualized by Super ECL Detection Reagent (#36208ES60, Yeasen) on SmartChemi 910 (Sinsage, China). qRT-PCR Total RNA was extracted with Trizol reagent. RNA concentration was quantified using a NanoDrop spectrophotometer (Thermo Fisher, USA). Reverse transcription was performed with the Hifair ® AdvanceFast One-step RT-gDNA Digestion SuperMix for qPCR (#11151ES60, Yeasen), according to the manufacturer’s instructions. Three biological replicates were used for quantitative reverse transcription PCR analysis using Hieff® qPCR SYBR Green Master Mix (No Rox) (#11201ES08, Yeasen). The relative mRNA level of gene expression was measured with Gapdh as an internal control and analyzed by the 2 -∆∆Ct method. Primer sequences are listed in Table S5. Flow Cytometry HCT 116 and HT29 cells were seeded into 12-well plates at a density of 5×10 5 cell/well in 1 mL of corresponding media as mentioned earlier. Following treatment, cells were trypsinized, harvested by centrifugation at 1,000 g for 5 minutes. Subsequently, cells were gently mixed with pre-cooled 70% ethanol, fixed at 4°C for 2 hours, centrifuged at 1,000 g for 5 minutes. Finally, the cells were stained using the Cell Cycle and Apoptosis Analysis Kit (40301ES50, Yeasen). The images were acquired by CytoFlex (Beckman Coulter, USA), and data were analyzed using FlowJo software. Caspase 3/7 Activity Assay HCT116 and HT29 cells were seeded into 12-well plates at about 50% confluency in 1 mL of their corresponding medium as mentioned earlier. After 24 hours of treatment, GreenNuc™ Caspase-3 Substrate (#C1168S, Beyotime) was added to the medium and stained for 20 minutes, followed by fixation for 10 minutes, and finally stained with Hoechst 33258 staining solution (#C0003, Beyotime) for 5 minutes, and photographs of the cells were captured using the IX73 inverted fluorescence microscope (Olympus, Japan). Statistical Analysis Data are presented as mean ± standard deviation (SD) and representative results are shown. A two-tailed unpaired Student's t-test was employed to analyze the experimental data. The level of significantly different was set at p < 0.05. Results Transcriptomic landscape of SPF mice reveals the role of queuine in regulating gene expression To systematically investigate the biological functions of queuine, we established a queuine-treated specific pathogen-free (SPF) mouse model (see Methods for details). After treatment, we collected tissue samples from multiple organs, including the brain, heart, lung, spleen, thymus, liver, intestine, colon, kidney, and testis for RNA sequencing (RNA-seq). Principal Component Analysis (PCA) of the transcriptomic data revealed a clear separation between the treatment and control groups in most tissues (Fig. S1A). Among all tissues, the colon, intestine, and spleen exhibited the highest number of differentially expressed genes (DEGs), with nearly 4,000 DEGs identified in the colon alone (Fig. 1A; Fig. S1B-D and Table S1). Gene Ontology (GO) enrichment analysis revealed a positive correlation between the number of GO pathways and the number of DEGs (Fig. 1B and Fig. S1E). Notably, colon and intestine exhibited substantial GO pathway overlapping, suggesting functional response to queuine treatment in these two tissue types. The enriched GO terms associated with DEGs indicated roles in cytoskeletal organization, immune regulation, metabolic processes, and responses to environmental stimuli (Fig. 1C). Across the selected tissues, the most significant transcriptional alterations were observed in colon tissue (Fig. 1D-F). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis further revealed that the colon displayed the most pronounced pathway-level alterations (Fig. 1G). The enriched KEGG pathways encompassed diverse biological functions, including cardiovascular processes, immune response and inflammation, neurodegeneration and aging, cancer and apoptosis, and infectious diseases (Fig. S1F, G). Although the intestine exhibited a comparable range of enriched KEGG pathways, the enrichment levels of these pathways were markedly higher in the colon (Fig. 1H, I). Collectively, these results demonstrate that queuine treatment induced the most extensive transcriptomic remodeling in the colon relative to other tissues. The fold change and relative expression levels of the top 20 up- and down-regulated DEGs in the colon are presented in Fig. 1J. Transcriptomic landscape of GF mice validated tissue-specific regulation by queuine As diet and gut microbiota are only sources for mammals to acquire queuine. To dissect the contributions of dietary and gut microbiota-derived queuine and validate its role in gene expression regulation, we established a germ-free (GF) mouse model, supplemented the mice with queuine, and performed RNA sequencing. PCA of the transcriptomic data revealed clear separation between queuine-treated and control groups (Fig. S2A). Overall, the colon exhibited a higher number of DEGs compared to other tissues (Fig. 2A and Table S2). GO enrichment analysis confirmed a greater number of upregulated pathways in the colon and the intestine, compared to other tissues (Fig. S2B-E), with many pathways were enriched in a tissue-specific manner. We performed further analysis of GF mice colon tissue, where we observed the most extensive queuine-induced transcriptomic change in SPF mice. Comparison of DEGs from GF and SPF mice revealed the colon as the site with the highest number of shared DEGs (Fig. 2B), exceeding than in other tissues (Fig. 2C). While the expression levels of upregulated DEGs were higher in the colon of SPF mice than in GF mice (Fig. 2D), the expression levels of downregulated DEGs were lower in GF mice than in SPF mice (Fig. 2E). Colon tissue expressed the highest number of enriched GO pathways (>1,500), compared to liver and intestine (Fig. 2F, G). Additionally, the colon exhibited the most significant transcriptional variations within the shared GO pathways compared to other tissues (Fig. 2H, I). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis further confirmed the colon as the most responsive tissue, showing the highest number of enriched pathways, relative to the intestine and the liver (Fig. 2J, K, Fig. S2F, G). Collectively, findings from both SPF and GF models demonstrated that queuine-induced transcriptional changes were most pronounced in colon tissue. In addition, queuine’s transcriptional regulatory effects were stronger in SPF mice than GF mice. Queuine inhibits the proliferation and migration of CRC cells The above results showed that the colon was the most responsive tissue to queuine in both SPF and GF mice. To assess the effect of queuine on colon, colorectal cancer cell lines (HCT116 and HT29) were treated with or without quinine. We than performed multiple functional assays to evaluate the queuine’s impact on CRC cell. Specifically, CCK-8 assay demonstrated that queuine treatment inhibited cell proliferation in both cell lines (Fig. 3A). Transwell assays using HCT116 cells revealed that queuine treatment significantly reduced invasive cell numbers (Fig. 3B). Similarly, a cell scratch assay revealed that queuine significantly suppressed the migration ability of both cell lines (Fig. 3C and Fig. S3A). Dysregulation of the cell cycle and resistance to apoptosis are key drivers of cancer progression; therefore, we assessed these processes in queuine-treated cells via flow cytometry and caspase-3 activity assays. Queuine treatment induced an increase in G1-phase cells and decrease in G2/M-phase cells indicating cell cycle inhibition (Fig. 3D and Fig. S3B). Concurrently, elevated caspase-3 activity confirmed increase apoptosis rate (Fig. S3C, D). To further investigate the role of queuine in vivo , we utilized a CRC cell line-derived xenograft (CDX) mouse model. Several tumor characteristics (tumor volume growth curves, final tumor weight, final tumor volume, and the ratio of tumor volume to body weight) were compared between queuine-treated and control groups. All metrics consistently demonstrated significantly suppressed tumor growth in queuine-treated group relative to controls (Fig. 3E-G, Fig. S3E-G). At last, we validated the effect of queuine using human-derived colorectal cancer organoids, and the results also demonstrated that queuine treatment significantly inhibited the growth of colorectal cancer (Fig. 3H). Overall, these results confirmed that queuine effectively inhibited CRC in vitro and in vivo . Cdkn2a promotes the proliferation and migration capacity in CRC cells The above results demonstrated that queuine exerted a significant inhibitory effect on CRC. To further explore underlying molecular mechanisms and identify consistently altered differentially expressed genes (DEGs) in both SPF and GF models, we selected the top ten upregulated and downregulated genes from each model (highlighted in bold orange in Fig. 4A). The qPCR validation revealed Cdkn2a as the only gene consistently down-regulated gene across different groups of samples (Fig. S4A), suggesting that queuine-mediated Cdkn2a regulation may be critically linked to CRC. To further validate the effect of queuine on Cdkn2a expression, we treated two CRC cell lines (HCT116 and HT29) with queuine. This treatment significantly downregulated both CDKN2A protein and mRNA levels (Fig. 4B, C). We also generated a Cdkn2a -overexpressing stable cell lines and confirmed queuine significantly suppressed the CDKN2A protein and mRNA levels in these engineered cells (Fig. 4D, E). To assess whether queuine-induced Cdkn2a suppression mediates CRC phenotypes, we conducted CCK-8, transwell, and cell scratch assays assessing cell proliferation, invasion, and migration, respectively. Specifically, CCK-8 assay demonstrated that Cdkn2a overexpression significantly enhanced cell proliferation, while the effect was inhibited by queuine treatment (Fig. 4F). Similarly, transwell assays demonstrated significant inhibition of Cdkn2a- induced invasiveness by queuine treatment (Fig. 4G) and cell scratch assays confirmed significant inhibition of Cdkn2a- induced cell migration (Fig. 4H). To further elucidate the function of Cdkn2a on the HCT116 cell line, we generated Cdkn2a -knockdown cell lines (Fig. 4I, J). CCK-8, transwell, and scratch assays revealed that Cdkn2a knockdown significantly reduced cell proliferation, invasion, and migration (Fig. 4K-M and Fig. S4B). Overall, these findings demonstrate queuine inhibits CRC progression by downregulating Cdkn2a expression. Cdkn2a positively correlate s with Ctnnb1 in CRC cells and clinical samples To identify target molecules affected by Cdkn2a , we analyzed the expression of multiple CRC-related molecules[12, 13] (Fig. S5A). We found that among these, Ctnnb1 expression was consistently downregulated across various CRC cell lines after queuine treatment (Fig. 5A and Fig. S5B). In Cdkn2a -overexpressing cell lines, we observed a positive correlation between Cdkn2a and Ctnnb1 , while queuine treatment suppressed the regulation (Fig. 5B and Fig. S5C). We further confirmed that the expression of Ctnnb1 could be down-regulated by knocking down Cdkn2a using siRNA (Fig. 5C and Fig. S5D). Supporting these findings, Immunofluorescence analyses after Cdkn2a overexpression showed that queuine treatment decreased expression levels of both CDKN2A and CTNNB1 (Fig. 5D). This correlation was also observed in queuine-treated normal CRC HCT116 cells (Fig. 5E). In summary, these results indicated that Cdkn2a positively correlated with Ctnnb1 expression, and queuine inhibited the expression of both Cdkn2a and Ctnnb1 . We validated the relationship between Cdkn2a and Ctnnb1 using clinical samples by analyzing CRC tumor tissues (CA) and paracancerous tissues (PA) from seven CRC patients. Both genes, Cdkn2a and Ctnnb1 , were significantly upregulated in tumor tissues compared to paracancerous tissues (Fig. 5F and Fig. S5E). Additionally, analysis of colon adenocarcinoma (COAD) patient data from the TCGA database using GEPIA2[14] confirmed significantly higher expression of Cdkn2a and Ctnnb1 in cancer patients compared to healthy individuals (Fig. 5G, H). The qPCR data and TCGA data further supported positive correlation between Cdkn2a and Ctnnb1 (Fig. 5I, J). A significant increase in Cdkn2a expression with cancer progression across COAD stage (I-IV) patients (Fig. 5K) was linked to poorer survival outcomes (Fig. 5L), confirming the clinical relevance of Cdkn2a gene regulation. Collectively, these results indicate that Ctnnb1 is an important gene in CRC, and that the expression of Cdkn2a and Ctnnb1 is positively correlated in both CRC cells and clinical samples. Furthermore, queuine regulates the expression of both genes. Queuine enhances the activity of Limosilactobacillus reuteri in vivo and in vitro Cdkn2a downregulation expression was more pronounced in SPF than in GF mice, indicating that gut microbiome or specific microbial populations contribute to this effect. To investigate differences in microbial community composition of SPF mice treated with queuine (Q group) and an untreated group (Con group), we performed metagenomic sequencing of fecal samples and analyzed species abundance in each group. Microbial community compositions differed significantly between both groups (ANOSIM, R-value > 0, Fig. 6A). Moreover, non-metric multidimensional scaling (NMDS) analysis at both genus and species levels showed stress values < 0.1 (Fig. 6B and Fig. S6A). Linear discriminant analysis (LDA) indicated Limosilactobacillus reuteri ( L. reuteri ) as a main distinguishing factor between Q group and Con group (Fig. 6C). Indeed, L. reuteri showed the most pronounced increase in relative abundance upon queuine treatment, which suggests that queuine may be able to promote L. reuteri activity (Fig. 6D, E). To investigate whether queuine effects the activity of L. reuteri , we obtained a pure culture of L. reuteri originally isolated from human break milk. Using Nanopore long-read sequencing, we assembled a 2.2 megabase (Mb) genome harboring 2,224 predicted genes (Fig. S6B). We compared cultures of L. reuteri grown in medium with and without queuine supplementation. Transcriptomic analysis following queuine treatment showed activation of four metabolic pathways, including biosynthesis of nucleotide sugars, amino sugar and nucleotide sugar metabolism, galactose metabolism, and ABC transporters (Fig. 6F, Fig. S6C and Table S3), suggesting that queuine enhances the growth of L. reuteri by promoting key metabolic processes. Maximal optical density reached by the culture (OD600) as well as the total number of viable cells during exponential growth increased significantly in the presence of queuine (Fig. 6G, H). Additionally, queuine treatment enhanced the ability of L. reuteri to adhere to HTC116 cells (Fig. 6I, J). Similarly, we observed a positive effect of queuine on biofilm formation in L. reuteri, as indicated by the formation of larger colonies than controls (Fig. 6K) and exhibited stronger crystal violet intensity (Fig. 6L). The upregulation of parB [15] and dnaB [16] genes (Fig. S6D, E) are associated with bacterial proliferation. Collectively, these results confirm that queuine can enhance the growth, adhesion and biofilm formation of L. reuteri . L. reuteri also inhibits the proliferation and migration of CRC cells Considering that queuine promoted the growth of L. reuteri , and SPF mice have a more pronounced downregulation of Cdkn2a expression than GF mice, we investigated whether L. reuteri itself influences Cdkn2a expression and CRC progression. Specifically, to determine whether L. reuteri could affect Cdkn2a and Ctnnb1 expression similarly to queuine, we applied the same experimental approach used for queuine in CRC cell models. We isolated L. reuteri and Escherichia coli ( E. coli , as control bacterium), and confirmed by 16S PCR (Fig. S6F, G). Both bacteria were co-cultured L. reuteri or E . coli with CRC cell lines (HCT116 and HT29). In the presence of live L. reuteri , expression levels of Cdkn2a and Ctnnb1 were significantly reduced compared to the control experiments (Fig. 7A-D). Similar to the effect of queuine, treating CRC cells with the metabolites of L. reuteri (SN) also suppressed Cdkn2a and Ctnnb1 expression in both HCT116 and HT29 cell lines (Fig. 7E-H), which was further confirmed by immunofluorescence results (Fig. 7I). The observations indicate a similar mechanism of metabolites produced by L. reuteri and queuine in suppressing CRC progression. Indeed, metabolites from L. reuteri significantly inhibited CRC cell proliferation, invasion, and migration (Fig. 7J-L and Fig. S7A). We also validated the effect of L . reuteri using human-derived colorectal cancer organoids, and the results demonstrated that metabolites from L. reuteri significantly inhibited the growth of colorectal cancer (Fig. 7M), confirming that L. reuteri and queuine can suppress CRC progression by down-regulating Cdkn2a and Ctnnb1 expression. To further elucidate the underlying mechanisms, we performed metabolomic profiling of L. reuteri treated with queuine. We identified 623 differentially abundant metabolites, including 459 upregulated and 164 downregulated metabolites (Fig. S7B and Table S4), demonstrating queuine reprograms the metabolome of L. reuteri . Notably, known CRC-inhibiting metabolites including L-glutamine, and L-aspartic acid were significantly elevated in queuine-treated samples[17, 18]. L. reuteri enhances the inhibitory effect from queuine on spontaneous colorectal cancer in mice In the above experiments, we have demonstrated that both queuine and L. reuteri can significantly inhibit colorectal cancer progression in cellular models by downregulating Cdkn2a and Ctnnb1 expression. To confirm these inhibitory effects in vivo , we established an AOM/DSS-induced spontaneous colorectal cancer mouse model, including: a group without AOM/DSS treatment (Normal group), a PBS-treated control group (Con group), a group treated with queuine (Q group), a group treated with L. reuteri ( L. reuteri group), a group co-treated with both queuine and L. reuteri (Q+ L. reuteri group) (Fig. 8A). Results revealed significantly reduced tumor volume and number in both queuine and L. reuteri groups versus control group (Fig. 8B, C). The combination group (Q+ L. reuteri group) exhibited the most pronounced effect than the Q group and the L. reuteri group (Fig. 8B, C). These results suggested that queuine and L. reuteri can function synergistically to inhibit colorectal cancer. We have performed PCR and qPCR to confirm successful colonization of L. reuteri (Fig. S8A). The results showed that the abundance in Q+ L. reuteri group was significantly higher than in L. reuteri group (Fig. 8D). This also confirmed that queuine has a promoting effect on the abundance of L. reuteri in vivo . We collected colorectal tissue samples and performed qPCR analysis and western blot. The results consistently revealed downregulation of Cdkn2a and Ctnnb1 in both groups receiving queuine or L. reuteri gavage. Notably, the combination of L. reuteri and queuine (Q+ L. reuteri group) led to an even more pronounced reduction in expression levels (Fig. 8E and Fig. S8B). Finally, the HE and IHC results showed that the expression levels of CDKN2A and CTNNB1 in the colon across all groups were decreased (Fig. 8F, G). Collectively, our observations suggest that the gut microbiota-derived queuine can inhibit CRC progression. Additionally, we found that queuine can enhance the activity of L. reuteri , which had a similar CRC-suppressing effect as queuine, they can function in synergy to inhibit CRC more effectively in vitro and in vivo (as the proposed working model in Fig. 8H). Discussion Colorectal cancer is one of the leading causes of cancer-related mortality worldwide[ 1 , 2 ]. Ours and the work of others highlights the importance of gut microbes and their metabolites in regulating a variety of host biological functions[ 19 – 23 ]. However, potential mechanisms by which microbial populations influence CRC development and progression remain largely unclear. Known as the “longevity vitamin”, the bacterial metabolite queuine plays a crucial role in human health, influencing development, metabolism, cancer pathogenesis, and the invasion of disease-causing bacteria in vivo [ 7 ]. In this study, we first investigated the effects induced by queuine across multiple organs in SPF and GF mice by RNA sequencing. We demonstrated the role of queuine in suppressing colorectal cancer through downregulation of Cdkn2a and Ctnnb1 . Furthermore, we explored the influence of queuine on the composition of gut microbiome. We found that queuine modulated the gut microbiome composition and enriched the abundance of L. reuteri , a bacterium with CRC-suppressing effects. Cdkn2a is a well-established tumor suppressor gene with critical roles in cell cycle regulation. Cdkn2a is frequently mutated or deleted in various cancers and generates multiple transcriptional isoforms. These isoforms modulate the G1 phase of the cell cycle through interacting with cyclin dependent kinase 4 (CKD4) and p53. Recent studies have highlighted the context-dependent effects of Cdkn2a gene loss, particularly in epithelial-derived esophageal cancer[ 24 ]. Additionally, the rs10811661 polymorphism within Cdkn2a/b gene locus has been proposed as a prognostic biomarker for gastrointestinal malignancies, including CRC and gastric cancer[ 25 ]. In CRC, aberrant Cdkn2a expression has been reported, often co-occurring with WNT pathway activation[ 26 , 27 ]. Furthermore, Cdkn2a has been implicated in tumor progression and resistance to cuproptosis[ 28 ]. Our study confirms that elevated Cdkn2a expression in patient-derived CRC samples. Functional analyses demonstrate that Cdkn2a knockdown suppresses CRC cell proliferation, migration, and adhesiveness, further supporting its role in CRC pathophysiology. Numerous studies have shown that Ctnnb1 (also known as β-catenin) is aberrantly activated in various cancers[ 29 , 30 ], characterized by increased intracellular Ctnnb1 protein levels. This upregulation enhances the expression of oncogenes, including SRY-box transcription factor 4 (SOX4)[ 31 ] and c-Myc[ 29 ], facilitating the viability and invasive capacity of cancer cells and promoting malignant phenotype of CRC[ 29 , 30 ]. Here, we demonstrated that queuine treatment effectively inhibited the expression of Ctnnb1 , consequently suppressing the viability and invasive capacity of CRC cells. We have observed positive correlation patterns between Cdkn2a and Ctnnb1 , but the detailed modulation relationship between them needs further investigation. The diverse microbial community within human gut significantly influences health and represent a promising target for microbial therapeutics, including probiotics[ 32 ]. In this study, we found that L. reuteri metabolites significantly inhibited CRC cell proliferation and migration. Similar to queuine, these metabolites downregulated Cdkn2a and Ctnnb1 expression, co-culture experiments further confirmed this regulatory relationship. L. reuteri is a well-established intestinal probiotic with significant benefits for human health[ 33 , 34 ], such as reshaping the gut microbiota, stimulating killer T cells, and modulating immune responses within the tumor microenvironment[ 35 , 36 ]. L. reuteri influences host physiology both directly and through its metabolites. Collectively, our results suggest the novel biological functions of L. reuteri and queuine, which could be used to treat CRC in the future. Previous work reported that the bacterial metabolite queuine plays a vital role in maintaining tRNA queuosine modification[ 37 ], we only focused on the role and mechanism of queuine in colorectal cancer progression in this study. Whether queuine and L. reuteri- mediated functions are related to tRNA queuosine modification remains to be investigated in the future. Declarations Acknowledgments The authors would like to thank Dr. Jessika Fuessel from Professor A. Murat Eren lab at University of Oldenburg for the reading and comments on our manuscript. The authors also would like to thank Ting Xian at Geneus Technologies for the help of L. reuteri genome sequencing. We thank all members from the Wang lab for technical assistance and insightful discussion. Ethics approval and consent to participate This study involves human participants and was approved by the Department of Gastroenterology, Panyu Central Hospital of Guangzhou Medical University (PYRC-2024-262-01). Participants gave informed consent to participate in the study before taking part. The protocol for an animal experiment in this study was approved by the Institutional Animal Care and Use Committee (IACUC) of Guangzhou Institute of Biomedicine and Health at Chinese Academy of Sciences (protocol code: #A5748-01). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Declaration This work was supported by the National Natural Science Foundation of China (32570070, 32070615), Guangdong Provincial Natural Science Foundation (2022A1515010569), Guangzhou Science and Technology Project (2024A04J6265), and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme for X.W. This work was also partially supported by Science and Technology Planning Project of Guangdong Province in China (2023B1212060050, 2023B1212120009). Data availability The RNA sequencing data of 60 samples from ten tissues of SPF mice has been deposited at the NCBI GEO database under accession number GSE291769 (reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE291769, reviewer token: inyfmyewxhgnlkz). The RNA sequencing data of 60 samples from ten tissues of GF mice has been deposited at the NCBI GEO database under accession number GSE296839 (reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296839, reviewer token: evihucwwxbmtnub). The RNA sequencing data of 6 samples from L. reuteri has been deposited at the NCBI GEO database under accession number GSE296840 (reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296840, reviewer token: gnytseogdtgdhip). The metagenome sequencing data using mouse fecal samples has been submitted to NCBI SRA database under accession number PRJNA1234476 (reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1234476?reviewer=ufaqh0pa9oulhk1v6m5s3dggui). Author contributions X.W. conceived and proposed the project. A.L. performed experiments with the help from Z.X., S.L., and S.X. J.L. performed data analysis with the help from A.L. and J.F. Y.T. and Y.W. provided CRC tumor tissues and paracancerous tissues. 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Br J Cancer 2006, 95:1670-1677. Humbey O, Pimkina J, Zilfou JT, Jarnik M, Dominguez-Brauer C, Burgess DJ, Eischen CM, Murphy ME: The ARF tumor suppressor can promote the progression of some tumors. Cancer Res 2008, 68:9608-9613. Sanchez-Aguilera A, Sanchez-Beato M, Garcia JF, Prieto I, Pollan M, Piris MA: p14(ARF) nuclear overexpression in aggressive B-cell lymphomas is a sensor of malfunction of the common tumor suppressor pathways. Blood 2002, 99:1411-1418. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1SupplementaryFiguresS1S8.docx SupplementaryMaterial2SupplementaryTablesS1S5.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":504952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of queuine-induced changes in SPF mice. (A)\u003c/strong\u003eGrouped dot plot displaying the number of up- and down-regulated DEGs in queuine-treated groups compared to controls across ten tissues. Numbers indicate the corresponding DEGs. \u003cstrong\u003e(B)\u003c/strong\u003eCytoscape network illustrating shared GO pathways among up- and down-regulated DEGs across ten tissues. \u003cstrong\u003e(C)\u003c/strong\u003eDot plot of enriched GO pathways associated with up-regulated DEGs across ten tissues. Pathways enriched in at least seven tissues are shown. \u003cstrong\u003e(D)\u003c/strong\u003e Comparison of intra- and inter-tissue \u003cem\u003ep\u003c/em\u003e-values for GO pathways enriched in up-regulated DEGs. Pathways present in at least two tissues were analyzed, where ‘intra-tissue’ indicates the \u003cem\u003ep\u003c/em\u003e-value within examined species, while ‘inter-tissue’ means corresponding \u003cem\u003ep\u003c/em\u003e-value in other species. \u003cstrong\u003e(E)\u003c/strong\u003eDot plot of enriched GO pathways associated with down-regulated DEGs across ten tissues. Pathways enriched in at least six tissues are shown. \u003cstrong\u003e(F)\u003c/strong\u003e Comparison of intra- and inter-tissue \u003cem\u003ep\u003c/em\u003e-values for GO pathways enriched in down-regulated DEGs. Pathways present in at least two tissues were analyzed, where ‘intra-tissue’ indicates the \u003cem\u003ep\u003c/em\u003e-value within examined species, while ‘inter-tissue’ means corresponding \u003cem\u003ep\u003c/em\u003e-value in other species. \u003cstrong\u003e(G)\u003c/strong\u003eEnriched KEGG pathways identified across these ten tissues. \u003cstrong\u003e(H)\u003c/strong\u003e Comparison of \u003cem\u003ep\u003c/em\u003e-values for KEGG pathways enriched in up-regulated DEGs between colon and intestine. The ‘intra-tissue’ indicates the \u003cem\u003ep\u003c/em\u003e-value within examined species, while ‘inter-tissue’ means corresponding \u003cem\u003ep\u003c/em\u003e-value in other species. \u003cstrong\u003e(I)\u003c/strong\u003eComparison of \u003cem\u003ep\u003c/em\u003e-values for KEGG pathways enriched in down-regulated DEGs between colon and intestine. The ‘intra-tissue’ indicates the \u003cem\u003ep\u003c/em\u003e-value within examined species, while ‘inter-tissue’ means corresponding \u003cem\u003ep\u003c/em\u003e-value in other species. \u003cstrong\u003e(J)\u003c/strong\u003eFold change and relative expression levels of the top 20 up-regulated and down-regulated DEGs in colon. The cross label indicates the gene without expressionin the sample. Significance was assessed using the independent samples t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: **** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003ens\u003c/em\u003e \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/7d83ef9a777efe3df832cfa5.png"},{"id":94640470,"identity":"6166da5b-f7f3-413f-8739-919ae23839a4","added_by":"auto","created_at":"2025-10-29 07:49:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":339312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of queuine-induced variations in GF mice. (A)\u003c/strong\u003e Grouped dot plot displaying the number of up- and down-regulated DEGs in queuine-treated groups compared to controls across different tissues. Numbers indicate the corresponding DEGs. \u003cstrong\u003e(B)\u003c/strong\u003eVenn diagram showing shared and tissue-specific DEGs in three tissues (liver, intestine, and colon) between SPF and GF models. \u003cstrong\u003e(C) \u003c/strong\u003eVenn diagram showingshared and tissue-specific DEGs in other seven tissues between SPF and GF models. \u003cstrong\u003e(D-E)\u003c/strong\u003eFold change comparison of up-regulated \u003cstrong\u003e(D)\u003c/strong\u003e and down-regulated \u003cstrong\u003e(E) \u003c/strong\u003eDEGs between SPF and GF models. ‘GF-shared’ and ‘SPF-shared’ represent fold changes of shared DEGs in each model. \u003cstrong\u003e(F)\u003c/strong\u003e Number of enriched GO pathways across liver, intestine, and colon. \u003cstrong\u003e(G)\u003c/strong\u003e Shared and tissue-specific GO pathways among three tissues. \u003cstrong\u003e(H-I)\u003c/strong\u003e Gene count comparison of intra- and inter-tissue GO pathway in up-regulated DEGs (\u003cstrong\u003eH\u003c/strong\u003e) and down-regulated DEGs (\u003cstrong\u003eI\u003c/strong\u003e). Pathways present in at least two tissues were analyzed, where ‘intra-tissue’ indicates gene count within examined species, while ‘inter-tissue’ means corresponding gene count in other species. \u003cstrong\u003e(J) \u003c/strong\u003eEnriched KEGG pathways identified across liver, intestine, and colon. \u003cstrong\u003e(K)\u003c/strong\u003e Shared and tissue-specific KEGG pathways among three tissues. Significance was assessed using the independent samples t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: **** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/7ea1533ce81439df373a8aa9.png"},{"id":94632286,"identity":"6024bf25-fc9a-4bf4-8c48-b64ba6d80a63","added_by":"auto","created_at":"2025-10-29 06:28:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":416547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQueuine inhibits the proliferation and migration of CRC \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A) \u003c/strong\u003eCCK-8 assay showing OD450 in HCT116 cells at multiple time points across different groups. (\u003cem\u003en\u003c/em\u003e = 5). HCT116 cells were treated with (abbreviated as Q group) or without queuine (abbreviated as Con group). \u003cstrong\u003e(B) \u003c/strong\u003eTranswell assay measuring the number of invasive HCT116 cells in each group (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(C)\u003c/strong\u003e Cell scratch assay of HT29 cells showing the percentage of healed area of cells in each group at different time points (\u003cem\u003en\u003c/em\u003e= 3). \u003cstrong\u003e(D)\u003c/strong\u003eCell cycle assay of HCT116 cells showing cycle distribution of cells across different phases among various groups (\u003cem\u003en\u003c/em\u003e= 3). \u003cstrong\u003e(E)\u003c/strong\u003eViolin plot showing the ratio of tumor volume to body weight (Con group \u003cem\u003en\u003c/em\u003e = 6, Q group \u003cem\u003en\u003c/em\u003e = 7). \u003cstrong\u003e(F)\u003c/strong\u003eViolin plot showing the ratio of tumor weight to body weight (Con group \u003cem\u003en\u003c/em\u003e = 6, Q group \u003cem\u003en\u003c/em\u003e = 7). \u003cstrong\u003e(G)\u003c/strong\u003eThe ratio of solid tumor volume to body weight was measured.\u003cstrong\u003e \u003c/strong\u003eThe measurement was calculated every two days (Con group \u003cem\u003en\u003c/em\u003e = 6, Q group \u003cem\u003en\u003c/em\u003e = 7). \u003cstrong\u003e(H) \u003c/strong\u003eStatistics of organoids after treatment with or without queuine (Con group \u003cem\u003en\u003c/em\u003e= 14, Q group \u003cem\u003en\u003c/em\u003e= 15, Scale bar=100μm). Significance was assessed using the unpaired, two-tailed Student's t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003ens\u003c/em\u003e \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/de780fec3f872eef7e3908ab.png"},{"id":94632284,"identity":"b04af2e7-c44a-4614-844d-3b68d480e9e2","added_by":"auto","created_at":"2025-10-29 06:28:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":421331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQueuine suppresses \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCdkn2a\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexpression in CRC cells. (A) \u003c/strong\u003eFold change and relative expression levels of selected 35 up-regulated and down-regulated DEGs in the colon across SPF and GF models. \u003cstrong\u003e(B) \u003c/strong\u003eWestern blot analysis of CDKN2A in HCT116 and HT29 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(C) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a \u003c/em\u003emRNA expression levels in HCT116 and HT29 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(D) \u003c/strong\u003eWestern blot analysis of CDKN2A after overexpressing \u003cem\u003eCdkn2a \u003c/em\u003ein HCT116 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(E) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a \u003c/em\u003emRNA expression levels after overexpressing \u003cem\u003eCdkn2a \u003c/em\u003ein HCT116 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e= 3). \u003cstrong\u003e(F) \u003c/strong\u003eCCK-8 assay showing OD450 in different groups at different time points (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(G) \u003c/strong\u003eTranswell assay detecting the number of invasive cells in each group (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(H) \u003c/strong\u003eCell scratch assay showing the percentage of healed area of cells in each group at different time points (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(I) \u003c/strong\u003eWestern blot analysis of CDKN2A after si\u003cem\u003eCdkn2a \u003c/em\u003ein HCT116 cells (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(J) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a \u003c/em\u003emRNA expression levels after si\u003cem\u003eCdkn2a \u003c/em\u003ein HCT116 cells (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(K) \u003c/strong\u003eCCK-8 assay showing OD450 in different groups at different time points after adding the CCK-8 reagent (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(L) \u003c/strong\u003eCell scratch assay showing the percentage of healed area of cells in each group at different time points (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(M) \u003c/strong\u003eTranswell assay showing the number of invasive cells in each group (\u003cem\u003en\u003c/em\u003e = 5). Significance was assessed using the unpaired, two-tailed Student's t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/aa3f69220010d8efb3136e56.png"},{"id":94640276,"identity":"e40ee53a-9ea2-4b1b-bc21-fc5ede121777","added_by":"auto","created_at":"2025-10-29 07:49:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":292481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCdkn2a \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epositively correlates with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCtnnb1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexpression in CRC cells and clinical samples. (A) \u003c/strong\u003eWestern blot analysis of CTNNB1 in HCT116 and HT29 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(B) \u003c/strong\u003eWestern blot analysis of CTNNB1 after overexpressing \u003cem\u003eCdkn2a \u003c/em\u003ein HCT116 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(C) \u003c/strong\u003eWestern blot analysis of CTNNB1 after \u003cem\u003eCdkn2a \u003c/em\u003eknockdown in HCT116 cells (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(D) \u003c/strong\u003eImmunofluorescence\u003cstrong\u003e \u003c/strong\u003eof CDKN2A, CTNNB1, DAPI after overexpressing \u003cem\u003eCdkn2a \u003c/em\u003ein HCT116 cells treated with or without queuine (\u003cem\u003en\u003c/em\u003e = 6). \u003cstrong\u003e(E) \u003c/strong\u003eImmunofluorescence\u003cstrong\u003e \u003c/strong\u003eof CDKN2A, CTNNB1, DAPI after\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eCdkn2a \u003c/em\u003eknockdown in HCT116 cells (\u003cem\u003en\u003c/em\u003e = 8). \u003cstrong\u003e(F) \u003c/strong\u003eWestern blot analysis of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e in CRC cancer tissue and paracancerous tissue (CA = 7, PA =7). \u003cstrong\u003e(G) \u003c/strong\u003eAnalysis of \u003cem\u003eCdkn2a\u003c/em\u003e expression levels in COAD patients compared to normal using GEPIA2 (COAD = 275, Normal = 349). \u003cstrong\u003e(H) \u003c/strong\u003eAnalysis of \u003cem\u003eCtnnb1\u003c/em\u003e expression levels in COAD patients compared to normal using GEPIA2 (COAD = 275, Normal = 349). \u003cstrong\u003e(I) \u003c/strong\u003eThe correlation between \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e expression levels in COAD patients. \u003cstrong\u003e(J) \u003c/strong\u003eCorrelation between \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e qPCR results in CRC tumor samples (\u003cem\u003en\u003c/em\u003e = 7). \u003cstrong\u003e(K) \u003c/strong\u003eThe expression levels of \u003cem\u003eCdkn2a\u003c/em\u003e at various cancer stages in COAD patients. \u003cstrong\u003e(L) \u003c/strong\u003eSurvival curves for COAD patients with high and low \u003cem\u003eCdkn2a\u003c/em\u003eexpression. Significance was assessed using the unpaired, two-tailed Student's t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/1ba234583b2fb72ae092c5eb.png"},{"id":94640692,"identity":"2754e169-8015-45f1-b73e-9618afd9b624","added_by":"auto","created_at":"2025-10-29 07:50:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":391636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQueuine promotes \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eL. reuteri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e growth \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A)\u003c/strong\u003e ANOSIM analysis of the gut microbial composition at the species level (\u003cem\u003en\u003c/em\u003e = 6). \u003cstrong\u003e(B) \u003c/strong\u003eNMDS analysis of the gut microbial composition at the species level (\u003cem\u003en\u003c/em\u003e = 6). \u003cstrong\u003e(C)\u003c/strong\u003e LDA values for representative differential species in control and queuine-treated groups. \u003cstrong\u003e(D) \u003c/strong\u003eStacked\u003cstrong\u003e \u003c/strong\u003ehistogram of the top ten species showing changes in relative abundance. \u003cstrong\u003e(E) \u003c/strong\u003eHorizontal histograms of the top five up-regulatedand down-regulated species showing changes in relative abundance. \u003cstrong\u003e(F)\u003c/strong\u003eEnrichment plot showing significantly activated pathways in \u003cem\u003eL. reuteri \u003c/em\u003eafter queuine treatment.\u003cstrong\u003e (G) \u003c/strong\u003eOD600 values for control and queuine-treated groups during logarithmic growth phase (\u003cem\u003en\u003c/em\u003e = 6). \u003cstrong\u003e(H) \u003c/strong\u003eCounts of viable bacteria in logarithmic growth phase for\u003cstrong\u003e \u003c/strong\u003econtrol and queuine-treated groups (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(I) \u003c/strong\u003ePhotographs of \u003cem\u003eL. reuteri\u003c/em\u003e adhering to cells in control and queuine-treated groups. \u003cstrong\u003e(J) \u003c/strong\u003eCounts of \u003cem\u003eL. reuteri\u003c/em\u003e adhering to cells in control and queuine-treated groups (\u003cem\u003en\u003c/em\u003e = 4). \u003cstrong\u003e(K)\u003c/strong\u003eGrowth size of monoclonal colonies on MRS agar plates in\u003cstrong\u003e \u003c/strong\u003econtrol and queuine-treated groups (\u003cem\u003en\u003c/em\u003e = 10). \u003cstrong\u003e(L) \u003c/strong\u003eOD595 values after crystal violet staining of the biofilms in control and queuine-treated groups (\u003cem\u003en\u003c/em\u003e = 9). Significance was assessed using the unpaired, two-tailed Student's t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance level: *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/24430fe70006d9f1f98ccac0.png"},{"id":94632294,"identity":"dee1d29f-3a86-4ecd-a79a-640bd5253b52","added_by":"auto","created_at":"2025-10-29 06:28:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":361897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eL. reuteri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e suppresses the proliferation and migration of CRC cells\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e. \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(A) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1 \u003c/em\u003emRNA levels in HCT116 cells co-cultured with or without \u003cem\u003eL. reuteri\u003c/em\u003e, \u003cem\u003eE. coli\u003c/em\u003e was used as bacterium control (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(B)\u003c/strong\u003e Western blot analysis of CDKN2A and CTNNB1 in HCT116 cells co-cultured with or without \u003cem\u003eL. reuteri\u003c/em\u003e, \u003cem\u003eE. coli\u003c/em\u003e was used as bacterium control (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(C) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1 \u003c/em\u003emRNA levels in HT29cells co-cultured with or without \u003cem\u003eL. reuteri\u003c/em\u003e, \u003cem\u003eE. coli\u003c/em\u003e was used as bacterium control (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(D)\u003c/strong\u003e Western blot analysis of CDKN2A and CTNNB1 in HT29 cells co-cultured with or without\u003cem\u003e L. reuteri\u003c/em\u003e(\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(E) \u003c/strong\u003eWestern blot analysis of CDKN2A and CTNNB1 in HCT116 cells treated with or without\u003cem\u003e L. reuteri \u003c/em\u003eSN (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(F) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1 \u003c/em\u003emRNA levels in HCT116 cells treated with or without \u003cem\u003eL. reuteri \u003c/em\u003eSN (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(G)\u003c/strong\u003e Western blot analysis of CDKN2A and CTNNB1 in HT29 cells treated with or without\u003cem\u003e L. reuteri \u003c/em\u003eSN (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(H) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1 \u003c/em\u003emRNA levels in HT29 cells treated with or without \u003cem\u003eL. reuteri \u003c/em\u003eSN (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003e(I)\u003c/strong\u003e Immunofluorescence\u003cstrong\u003e \u003c/strong\u003eof CDKN2A, CTNNB1, DAPI after\u003cstrong\u003e \u003c/strong\u003etreatment with or without \u003cem\u003eL. reuteri \u003c/em\u003eSN\u003cem\u003e \u003c/em\u003ein HCT116 cells (\u003cem\u003en\u003c/em\u003e = 6). \u003cstrong\u003e(J) \u003c/strong\u003eCCK-8 assay showing OD450 in different groups at different time points (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(K) \u003c/strong\u003eTranswell assay showing the number of invasive cells in each group (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003e(L) \u003c/strong\u003eCell scratch assay showing the percentage of healed area of cells in each group at different time points (\u003cem\u003en\u003c/em\u003e = 7). \u003cstrong\u003e(M)\u003c/strong\u003eStatistics of organoids after treatment with or without \u003cem\u003eL. reuteri \u003c/em\u003eSN (Con group \u003cem\u003en\u003c/em\u003e = 14, Q group \u003cem\u003en\u003c/em\u003e= 16, scale bar=100μm). Significance was assessed using the unpaired, two-tailed Student's t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/aba4984685d5166347d0a576.png"},{"id":94632298,"identity":"f6d4c977-d8c0-4ca8-9dab-07dac463903f","added_by":"auto","created_at":"2025-10-29 06:28:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":617345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQueuine and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eL. reuteri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e’s effect on spontaneous colorectal cancer. (A)\u003c/strong\u003eExperimental design for mouse models. \u003cstrong\u003e(B)\u003c/strong\u003eRepresentative images of the colon from each group of mice. \u003cstrong\u003e(C)\u003c/strong\u003e Tumor number and tumor volume in each group (Con group \u003cem\u003en\u003c/em\u003e= 10, Q group \u003cem\u003en\u003c/em\u003e = 11, \u003cem\u003eL. reuteri\u003c/em\u003e group \u003cem\u003en\u003c/em\u003e = 11, Q+\u003cem\u003eL. reuteri\u003c/em\u003e group \u003cem\u003en\u003c/em\u003e= 11). \u003cstrong\u003e(D) \u003c/strong\u003eqPCR results of DNAs from fecal samples of \u003cem\u003eL. reuteri\u003c/em\u003e group and Q+\u003cem\u003eL. reuteri\u003c/em\u003e group using specific primers (\u003cem\u003en\u003c/em\u003e= 10). \u003cstrong\u003e(E)\u003c/strong\u003e Western blot analysis of CDKN2A and CTNNB1 in each group (\u003cem\u003en\u003c/em\u003e = 6). \u003cstrong\u003e(F)\u003c/strong\u003e H\u0026amp;E staining and immunohistochemical (IHC) staining for CDKN2A and CTNNB1 expression in mice colons. \u003cstrong\u003e(G)\u003c/strong\u003e Statistical results of immunohistochemical staining scores of CDKN2A and CTNNB1 expression in mice colon (\u003cem\u003en\u003c/em\u003e = 3). Significance was assessed using the unpaired, two-tailed Student's t-test. Mean ± \u003cem\u003eSD\u003c/em\u003e; significance levels: *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. \u003cstrong\u003e(H) \u003c/strong\u003eA schematic work model illustrating the role and mechanism of queuine and \u003cem\u003eL. reuteri\u003c/em\u003e in suppressing CRC.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/3201ba6a3cf3f7f1f2c24c07.png"},{"id":95000473,"identity":"5ecce318-9c7f-4b83-80f0-9da93c1a4db2","added_by":"auto","created_at":"2025-11-03 08:58:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5267202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/25d54218-0474-4bfa-9ee4-197b20d7c3e0.pdf"},{"id":94640319,"identity":"d10706a4-8ac7-4fd1-9f5b-4c742b37bceb","added_by":"auto","created_at":"2025-10-29 07:49:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5691286,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1SupplementaryFiguresS1S8.docx","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/eefa4dc0d207ad4c47bd409d.docx"},{"id":94632287,"identity":"1f729801-f4e1-4bc1-a566-68660e3ce964","added_by":"auto","created_at":"2025-10-29 06:28:52","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1153847,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2SupplementaryTablesS1S5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7540054/v1/43376edb13164e329daa92bb.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut microbiota-derived queuine reprograms colon gene expression and alleviates colorectal cancer synergistically with Limosilactobacillus reuteri","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most prevalent cancer and the second leading cause of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2030, its incidence in developing countries is projected to rise by 60%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similar to many other solid tumors, CRC develops over decades through somatic cell evolution[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. During this period, mutations in oncogenes and tumor suppressor genes within the patient's colorectal tissues drive the abnormal proliferation of colorectal epithelial cells, ultimately leading to malignancy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the long disease trajectory, many factors contribute to the process of carcinogenesis. Recent evidence suggests that human gut microbiota can influence CRC development by modulating inflammation and shaping microbial communities[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, gut microbiota-associated and microbial-derived molecules are considered as potential therapeutic strategies in the therapy of CRC.\u003c/p\u003e\u003cp\u003eQueuine, a gut microbiota-derived micronutrient/nucleobase, is essential for hosts to maintain normal physiological processes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As an exogenous micronutrient, queuine has been extensively studied for its role in tRNA queuosine (Q) modification, a post-transcriptional process cruical for accurate and efficienct protein translation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This modification significantly influences biological processes, including cancers, metabolism, and memory[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While the role of queuine in tRNA modification has been well characterized, its direct biological functions remain poorly understood. Notably, its potential anti-tumor or disease-fighting effects and related mechanisms remain largely unexplored.\u003c/p\u003e\u003cp\u003eIn this study, we systematically investigated the biological functions of queuine across different models. Our findings demonstrated that queuine supplementation regulated colon gene expression in both SPF mice and GF mice, and queuine alleviated colorectal cancer cell biology by down-regulating \u003cem\u003eCdkn2a\u003c/em\u003e (encoding cyclin-dependent kinase inhibitor 2A). Importantly, queuine treatment reshaped gut microbiota composition, with a marked increase in \u003cem\u003eL. reuteri\u003c/em\u003e, a bacterium which possessed the same function as queuine in suppressing CRC. These results highlight the therapeutic potential of combining queuine and \u003cem\u003eL. reuteri\u003c/em\u003e for CRC prevention and treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eCells and cell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCT116 cells were cultured in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA) and 1% Penicillin-Streptomycin (Gibco, USA). HT29 cells were cultured in RPMI 1640 (Gibco, USA) supplemented with 10% fetal bovine serum (Thermofisher, USA) and 1% Penicillin-Streptomycin (Gibco, USA). \u0026nbsp; Cell lines were cultured at 37\u0026deg;C in a constant-temperature incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e. In the case of queuine treatment, cells were treated with 3\u0026thinsp;\u0026mu;M queuine (Toronto Research Chemicals, Canada).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePatient sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical samples for this study were obtained from the Department of Gastroenterology, Panyu Central Hospital of Guangzhou Medical University, and were approved by the Ethics Committee with an approval number: PYRC-2024-262-01.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eQueuine supplementation in SPF and GF mice\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale six-week-old C57BL/6J SPF and GF mice were purchased (Gempharmatech, China), and mice were randomly divided into two groups with three mice in each group. All mice had free access to sterilized diet and water, and the corn bedding was regularly changed. Each mouse was fed with water or queuine by gavage for 14 days at a dose of 100\u0026thinsp;\u0026mu;L/day containing 100\u0026thinsp;\u0026mu;M queuine. After 14 days of experiment, the mice were dissected, and the corresponding organs were collected and immediately frozen in liquid nitrogen and stored at -80℃ until use. The protocol for an animal experiment in this study was approved by the Institutional Animal Care and Use Committee (IACUC) of Guangzhou Institute of Biomedicine and Health at Chinese Academy of Sciences (protocol code: #A5748-01).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEstablishment of CDX mouse model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCT116 cells (1\u0026times;10\u003csup\u003e7\u003c/sup\u003e) were injected subcutaneously into the neck of BALB/c nude mice (Gempharmatech, China). After that, mice were randomly divided into two groups. Each mouse was fed with water or queuine by gavage for 14 days at a dose of 100\u0026thinsp;\u0026mu;L/day containing 100\u0026thinsp;\u0026mu;M queuine. Tumors were gauged for length every 2 days and the formula (1/2 \u0026times; width\u003csup\u003e2\u003c/sup\u003e\u0026times; length)\u003csup\u003e\u0026nbsp;\u003c/sup\u003ewas used to calculate the size of tumors[38]. After 15 days, the tumors were dissected from these nude mice and weighed.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCRC patient-derived organoids culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 1.5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e single cells/mL were washed using DMEM and embedded in 20 \u0026micro;L Basement Membrane Matrix for Organoid Culture (#HY-K6007, MedChemExpress, China) in a 48-well plate to develop CRC patient-derived organoids. The organoids were cultured in intestinal tumor culture medium (#YX-C-SJH-01, Eacin Bio, China). The culture medium was refreshed every 2 days, after 21 days organoids were photographed under the IX73 (Olympus, Japan).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEstablishment of spontaneous colorectal cancer mouse models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the azoxymethane plus dextran sulfate sodium (AOM/DSS) model, 6-week-old male C57BL/6 mice were intraperitoneally injected with 12.5 mg/kg AOM (MP Biomedicals, California, USA), followed by 1 week of 2% DSS and 1 week of water. After that, mice were injected with 8 mg/kg AOM, followed by 1 week of 2% DSS and 1 week of water. For treatment experiments, the control and normal groups were given 100 \u0026mu;L of PBS once a week. The Q group was given 100 \u0026mu;L of PBS containing 100 \u0026mu;M queuine once a week, the \u003cem\u003eL. reuteri\u003c/em\u003e group was given 100 \u0026mu;L of PBS containing 10\u003csup\u003e8\u003c/sup\u003e CFU of \u003cem\u003eL. reuteri\u003c/em\u003e, and the \u003cem\u003eL. reuteri\u003c/em\u003e+queuine group was given 100 \u0026mu;L of PBS containing both 100 \u0026mu;M queuine and 10\u003csup\u003e8\u003c/sup\u003e CFU of \u003cem\u003eL. reuteri\u003c/em\u003e. Treatments continued until week 16, after which mice were euthanized for subsequent analysis.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eHE staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColons harvested from spontaneous colorectal cancer mouse models were fixed with 4% paraformaldehyde (PFA, Sigma Aldrich) at 4\u0026deg;C. Following fixation, tissues were dehydrated and embedded in paraffin before sectioning. The sections were stained with haematoxylin eosin solution for 6 min, followed by 8 s in 1% acid ethanol (1% HCl in 70% ethanol) and rinsing in distilled water. Subsequently, stained with eosin solution for 3 min, dehydrated with graded alcohol, and cleared in xylene. Finally, images were captured using Tissue FAXS Plus ST (TissueGnostics GmbH, Austria).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003emmunohistochemistry (IHC) staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColons from spontaneous colorectal cancer mouse models were fixed with 4% paraformaldehyde (PFA, Sigma Aldrich) at 4\u0026deg;C, dehydrated, and embedded in paraffin before section. For phenotypic analysis, a CTNNB1 monoclonal antibody (#MB62945, Bioworld) and a CDKN2A polyclonal antibody (#EAB13750, EbioCell) were applied. Following staining, the tissues were imaged using Tissue FAXS Plus ST (TissueGnostics GmbH, Austria). The captured images were analyzed using ImageJ and IHC ToolBox to determine the corresponding IHC scores.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConstruction of eukaryotic RNA-seq libraries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA from tissues was extracted using the Trizol method according to the manufacturer\u0026apos;s instructions. The mRNA libraries from different mouse tissues were constructed for Illumina sequencing. A VAHTS\u003csup\u003e\u0026reg;\u003c/sup\u003e Universal V6 RNA-seq Library Prep Kit for Illumina (#NR604-02, Vazyme Biotech) and a VAHTS\u003csup\u003e\u0026reg;\u003c/sup\u003e RNA Adapters set3 for Illumina (#N809, Vazyme Biotech) provided the necessary reagents for first- and second-strand cDNA synthesis, adaptor ligation, and library amplification. RNA-seq of the prepared libraries was performed at Berry Genomics on the NovaSeq 6000 platform (Illumina, CA, USA) to obtain paired-end reads of 150 bp. Library quality was evaluated using an Agilent Bioanalyzer 4200 TapeStation prior to sequencing. Approximately 6 GB of raw reads were obtained for each library.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRNA-seq data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdapters and low-quality reads were first removed by applying\u0026nbsp;Trim_Galore (version 0.6.6, https://github.com/FelixKrueger/TrimGalore)\u0026nbsp;to all RNA-seq raw sequencing data. The resulting reads of at least 35 bp in length were mapped to the reference genome (mm39).\u0026nbsp;PCR duplicates were removed, and the aligned results were then sorted using samtools[39]\u0026nbsp;(version 1.3.1). The featureCounts program in Subread package[40]\u0026nbsp;(version 2.0.1) was used to count the reads mapping to genes. DESeq2[41]\u0026nbsp;(version 1.36.0) was used for the identification of DEGs by setting a \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 and |log2FC| \u0026gt; 1 as the threshold for significance. Functional enrichment analysis was performed using clusterProfiler\u003csup\u003e59\u003c/sup\u003e (version 4.8.3) to determine significantly enriched pathways.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMetagenome data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1\u0026nbsp;\u0026mu;g of DNA per sample was used as input material for DNA sample preparation. Sequencing libraries were generated using the NEBNext\u003csup\u003e\u0026reg;\u003c/sup\u003eUltra\u0026trade; DNA Library Prep Kit for Illumina (#E7370L, NEB) according to the manufacturer\u0026rsquo;s instructions, and indexes were added to the attribute sequences of each sample. Briefly, DNA samples were fragmented to 350 bp by sonication, then DNA fragments are blunt-ended, A-tailed, and ligated to full-length adapters for Illumina sequencing and followed by PCR amplification. Finally, PCR products were purified by an Agilent 2100 Bioanalyzer (AMPureXP system) and size distribution of the libraries was analyzed, after which the library concentration was quantified by CFX96 (Bio-Rad, USA). The clustering of the index-coded samples was performed on a cBot Cluster Generation System according to the manufacturer\u0026rsquo;s instructions. After cluster generation, the librarieswere sequenced by NovaSeq 6000 (Illumina, CA, USA), and 150 bp paired-end reads were generated. Following raw data acquisition, clean data were generated using fastp (https://github.com/OpenGene/fastp). The clean data were assembled by MEGAHIT[42]. Prodigal was used to predict the open reading frame (ORF), and CD-HIT[43] was used to generate non-redundant gene catalogue. Then, clean reads from each sample were compared to a catalog of non-redundant genes using Bowtie2[44].\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCulture of \u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;E.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003cem\u003ecoli\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;\u003cem\u003eL. reuteri\u003c/em\u003e strain was isolated from a human breast milk sample, and the\u003cem\u003e\u0026nbsp;E.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ecoli\u003c/em\u003e strain was isolated from a healthy human fecal sample. For bacterial recovery, the MRS (for \u003cem\u003eL. reuteri\u003c/em\u003e) or LB (for \u003cem\u003eE.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ecoli\u003c/em\u003e) liquid medium was prepared in advance and sterilized at 121\u0026deg;C for 15 minutes. \u0026nbsp; Bacterial stocks were taken out from -80\u0026deg;C, and thawed at 4\u0026deg;C. The MRS or LB liquid medium was then added to the bacteria solution and cultured at 37\u0026deg;C for 6~12 hours. When the OD600 value of the bacterial solution reached 1, the bacteria were passaged at 1% inoculum ratio for static culture.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eLibrary construction and genome sequencing of \u003cem\u003eL. reuteri\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified the\u0026nbsp;\u003cem\u003eL. reuteri\u003c/em\u003e strain\u0026nbsp;using genome sequencing. Library construction and sequencing preparation were performed using the Universal Sample Preparation Kit (Geneus Technologies, Chengdu, China), with its included DNA library preparation reagents. \u0026nbsp;Sequencing library preparation and purification were conducted using the kit\u0026rsquo;s dedicated reagents and purifications beads, respectively. The specific steps were as follows: First, 1 \u0026micro;g of fragmented genomic DNA was taken, and the DNA Library Preparation Reagents in the kit were used to perform DNA repair and ligation of the samples. After Reaction Solution 1 and barcode adapters were added, the samples were treated at 20\u0026deg;C for 30 minutes and then at 65\u0026deg;C for 5 minutes. Then, Reaction Solution 2 was added for the post-ligation treatment, and the samples were incubated at 37\u0026deg;C for 20 minutes. After purification with magnetic beads, the library was obtained. Finally, the sequencing complex preparation reagents in the kit were used to prepare the sequencing complex. The library was incubated with the nanopore complex and sequencing primers at 27\u0026deg;C for 20 minutes. Then, the long fragments incubation buffer was added and incubated at 35\u0026deg;C for another 20 minutes. After purification with magnetic beads, the sequencing complexes ready for sequencing on the instrument were obtained. The steps for sequencing via the sequencing kit in Universal Sequencing Reagent Kit (Geneus Technologies, Chengdu, China) were as follows: First, the G-seq500 sequencer was powered on, and the G-seq500 chip was installed. Second, the chip was calibrated, the temperature was controlled, and the chip was filmed with a sequencing kit. Once the film formed, a certain dilution of the sequencing complex was added to the well, and four modified deoxyribonucleotides were added to the well for sequencing. After sequencing completion, FASTQ files were exported. Following this, the G-seq500 chip and sequencer were cleaned using the provided cleaning kit. Finally, the G-seq500 chip was removed, and the machine was turned off. All of the above experiments were performed at Geneus Technologies Co., Ltd. (Chengdu, China).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eGenome assembly and gene annotation of \u003cem\u003eL. reuteri\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary contigs were generated from Geneus sequencing reads using the Canu/Flye[45, 46] assembler with default options. The draft assembly was then polished using Q20 reads in Flye\u0026rsquo;s polishing mode. Sequencing reads were mapped to the assembled genome using Minimap2[47] for SNP and indels. Genome annotation was performed using Prokka[48],while plasmid annotation was performed using PlasmidFinder[49]. ABRicate (https://github.com/tseemann/abricate), a high-throughput screening tool combining several built-in databases, including NCBI, CARD, ARG-ANNOT, ResFinder, MEGARes, PlasmidFinder, EcOH, Ecoli_VF, and VFDB, was used for detection of resistance and virulence genes. All of the above experiments were performed at Geneus Technologies Co., Ltd.\u0026nbsp;(Chengdu, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of RNA-seq libraries of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eL. reuteri\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted using the RNAprep pure Cell/Bacteria Kit (#DP430, TIANGEN, China), following to the manufacturer\u0026rsquo;s protocol. RNA purity and concentration were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA), and RNA integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The samples with qualified purity, quantity, and integrity were used for subsequent library construction. TIANSeq rRNA Depletion Kit (#NR101-T6,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTIANGEN, China) was used to remove ribosomal RNA, then the libraries were constructed using the VAHTS Universal V6 RNA-seq Library Prep Kit (#NR604-01, Vazyme Biotech, China) according to the manufacturer\u0026rsquo;s instructions. The transcriptome sequencing and analysis were conducted by OE Biotech Co., Ltd. (Shanghai, China).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRNA-seq data analysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eL. reuteri\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptomic data for \u003cem\u003eL. reuteri\u003c/em\u003e underwent the same quality control procedures as those used for the mouse RNA-seq analysis. Clean reads were mapped to the \u003cem\u003eL. reuteri\u003c/em\u003e genome.\u0026nbsp;PCR duplicates were removed, and the aligned results were sorted using samtools[39] (version 1.3.1). Gene-level read counts were obtained using the featureCounts program from Subread package[40] (version 2.0.1). DESeq2[41] (version 1.36.0) was used for the identification of DEGs by setting a \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 and |log2FC| \u0026gt; 1 as the threshold for significance. Gene set enrichment analysis (GSEA) was performed using clusterProfiler[50] (version 4.8.3) to identify significantly enriched pathways by setting a \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eQueuine treatment and bacterial metabolite treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQueuine hydrochloride (#Q525000) was purchased from Toronto Research Chemicals and dissolved in water to prepare a 1 mM stock solution. For queuine treatment, HCT116 cells and HT29 cells were incubate with 3\u0026thinsp;\u0026mu;M queuine, and \u003cem\u003eL. reuteri\u003c/em\u003e was treated with 0.1\u0026thinsp;\u0026mu;M queuine. For the treatment with \u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003emetabolite, the supernatant from a 220 mL \u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003eculture medium was lyophilized and reconstituted in 15 mL water. Subsequently, 4 \u0026mu;L of this concentrated metabolite solution was added to 1 mL cell culture medium.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eSample preparation for metabolomics of \u003cem\u003eL. reuteri\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples stored at -80℃ were thawed in ice-water bath. A 600 \u0026mu;L aliquot of each sample was loaded onto a solid-phase extraction (SPE) column (C18 packed), and 3 mL of methanol eluate was collected. To resolubilize the metabolite, after drying the sample under a stream of nitrogen gas, 300 \u0026mu;L of a protein precipitant methanol-acetonitrile (V: V=4: 1, including mixed internal standard, 4 \u0026mu;g/mL) was added. The mixture was vortexed for 1 minute and subsequently ultrasonicated in an ice-water bath for 10 minutes. After standing at -40℃ for 2 hours, samples were centrifuged at 13,000 rpm for 20 minutes at 4℃. 150 \u0026mu;L of the supernatant was loaded into LC-MS injection vials and stored at -80\u0026deg;C until LC-MS analysis. Quality control samples were prepared by mixing aliquots of all samples to be a pooled sample.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metabolomic data analysis was performed by Shanghai OE Biotech Co., Ltd. (Shanghai, China). An ACQUITY UPLC I-Class plus (Waters Corporation, Milford, USA) was fitted with a Q-Exactive mass spectrometer equipped with a heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA). The metabolic profiling was analyzed in both ESI positive and ESI negative ion modes. An ACQUITY UPLC HSS T3 column (1.8 \u0026mu;m, 2.1 \u0026times; 100 mm) was employed in both positive and negative modes. The binary gradient elution system consisted of (A) water (containing 0.1% formic acid, v/v) and (B) acetonitrile, and separation was achieved using the following gradient: 0 minute, 5% B; 2 minutes, 5% B; 4 minutes, 30% B; 8 minutes, 50% B; 10 minutes, 80% B; 14 minutes, 100% B; 15 minutes, 100% B; 15.1 minutes, 5% B; and 16 minutes, 5% B. The flow rate was 0.35 mL/min, and the column temperature was set at 45\u0026deg;C. All samples were kept at 10\u0026deg;C during the analysis. The injection volume was 2 \u0026mu;L. The mass range was from m/z 70 to 1050. The resolution was set at 60,000 for full MS scans and 15,000 for HCD MS/MS scans. The collision energies were set at 10 20, and 40 eV. The mass spectrometer was operated as follows: spray voltage, 3,800 V (+) and 3,200 V (\u0026minus;); sheath gas flow rate, 35 arbitrary units; auxiliary gas flow rate, 8 arbitrary units, capillary temperature, 320\u0026deg;C; auxiliary gas heater temperature, 350\u0026deg;C; and S-lens RF level, 50.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData preprocessing and statistical analysis for metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw LC-MS data were processed using Progenesis QI (version 3.0, Nonlinear Dynamics, Newcastle, UK) for baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. Key parameters were set as follows: precursor tolerance at 5 ppm, product tolerance at 10 ppm, and product ion threshold at 5%. Compound identification was based on precise mass-to-charge ratio (m/z), secondary fragments, isotopic distribution, and retention time using the Human Metabolome Database (HMDB)[51], Lipidmaps (version 2.3, http://www.lipidmaps.org/), Metlin[52], and the in-house LuMet-Animal3.0 databases. Peaks with missing values (ion intensity = 0) in more than 50% of samples within any group were excluded. The remaining zero values were replaced with half of the minimum value by log2 transformation. Compounds with resulting scores below 36 (out of 80) points were considered unreliable and removed. A combined data matrix was generated from both positive and negative ion mode data for subsequent analysis. The data matrix was imported into R to conduct PCA, to evaluate overall sample distribution and analysis process stability. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) was utilized to distinguish the metabolites that differ between groups. To prevent overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) were used. Significantly differential metabolites were identified using a two-tailed Student\u0026rsquo;s t-test with a significance threshold of a \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 and |FC| \u0026gt; 1.5. Metabolite pathway enrichment analysis was subsequently performed using the KEGG database.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eCo-culture of human cells and bacteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBacteria cultures were diluted 1:100 in fresh MRS (\u003cem\u003eL. reuteri\u003c/em\u003e) or LB (\u003cem\u003eE. coli\u003c/em\u003e) liquid medium and grown until OD600 reached 1. HCT116 or HT29 cells were seeded in 24-well plates and cultured for 12 hours prior to coculture with the bacteria (MOI, 100). After 6 hours of coculture, cells were washed three times with PBS. Fresh DMEM (HCT116) or RPMI 1640 (HT29) containing 100\u0026thinsp;\u0026mu;g/ml ampicillin was then added to eliminate extracellular bacteria. Two hours later, infected cells were washed three times with PBS.Harvested cells were used for subsequent protein or RNA extraction.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eBacterial adhesion assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe adhesion assay was performed according to previous studies[53, 54]. Briefly, 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e HCT116 cells were seeded per well in six-well plates and cultured in DMEM supplemented with 10% (v/v) fetal bovine serum. Prior to bacterila addition, cells were gently washed twice with PBS and incubated with 2 ml of antibiotic- and serum-free DMEM per well at 37\u0026deg;C for 30 minutes. A bacterial culture (approximately 1\u0026times;10\u003csup\u003e9\u003c/sup\u003e CFU/ml in 1 ml DMEM) was then added to each well at a volume of 1 ml. The plates were incubated at 37\u0026deg;C for 2 hours. After incubation, the cells were rinsed twice with PBS, and 1 ml of 0.25% trypsin was added to each well for 15 minutes at room temperature to create a cellular bacterial suspension. This suspension was subsequently diluted and plated on MRS agar. Colonies were counted following incubation for 24~48 hours at 37\u0026deg;C. Microscopic examination was performed as follows: adherent bacteria and cells were not digested, the samples were directly fixed with methanol, stained with Giemsa solution, and sealed with resin. Images were then captured using an IX73 inverted fluorescence microscope (Olympus, Japan).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eBacterial biofilm assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe biofilm assay was performed according to the method described by Stepanovic[55]. Briefly, 5 mL of medium was inoculated with the bacteria and incubated overnight at 37\u0026deg;C. After incubation, the OD600 was adjusted to 0.1, and 200 \u0026micro;L of adjusted bacterial suspension was added to the wells of a sterile 96-well plate for both control and experimental groups. The remaining empty wells were filled with medium, and the plates were sealed and incubated at 37\u0026deg;C for either 12 or 24 hours. Following incubation, the plates were inverted onto paper towels to remove excess liquid and non-adherent cells. Each well was then treated with 200 \u0026micro;L of methanol, centrifuged at 2,500 rpm for 1 minute, and incubated at room temperature for 20 minutes. After fixation, methanol was removed by plate inversion onto paper towels. Plates were air-dried at room temperature for 30 minutes. Next, 200 \u0026micro;L of a 0.1% crystal violet solution was added to each well and left at room temperature for 15 minutes. The wells were rinsed three times with PBS to remove excess dye, then air-dried for 15 minutes. Finally, 200 \u0026micro;L of 33% (v/v) glacial acetic acid was added to each well to dissolve the crystal violet bound to adherent bacteria. The absorbance of the resulting solution was measured at 595 nm (OD595), and OD595 values were normalized by the control group.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRNAi using siRNA in cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 3\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells were seeded per 6 cm dish containing 4 mL of DMEM (for HCT116) or RPMI 1640 (for HT29). After 24 hours, cells were transfected with Lipofectamine 3000 (L3000008, Invitrogen) and 50 nM siRNA. Following 7 hours of incubation, the transfection mixture was removed and replaced with normal medium or treated with reagents. 48 hours later, the cells were harvested for protein or RNA for subsequent experiments. The \u003cem\u003eCdkn2a\u003c/em\u003e siRNA (GGGUUUUCGUGGUUCACAUUU) was designed based on a published study[56].\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConstruction of \u003cem\u003eCdkn2a\u003c/em\u003e overexpressing stable cell line\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn mammals, \u003cem\u003eCdkn2a\u003c/em\u003e primarily encodes two open reading frames (ARF), p14ARF and p16INK4A[56]. Previous studies have demonstrated that ARF is unexpectedly highly expressed or stably present in various cancers, where it functions as a pro-oncogenic factor[57, 58]. The \u003cem\u003eCdkn2a\u003c/em\u003e overexpression vector was constructed by inserting the p14ARF transcript fragment of \u003cem\u003eCdkn2a\u003c/em\u003e into the PLVX-puro vector (Tsingke Biotechnology Co., Ltd.). Following construction of the overexpression vector, the pMD.G and psPAX2 were added into the antibiotic-free medium and waited for 5 minutes, then Hieff Trans\u003csup\u003e\u0026reg;\u003c/sup\u003ePolyethylenimine Linear (PEI) (40816ES02, YEASEN)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethen\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eadded to this mixture, combined them thoroughly, and added the solution to well-cultured 293T cells in the antibiotic-free medium. After 6 hours, the medium was replaced with DMEM supplemented with 10% fetal bovine serum and 1% Penicillin-Streptomycin and cells were incubated for 48 hours. Subsequently, the supernatant was filtered through a 0.45 \u0026mu;m filter membrane, and the filtrate was added to the HCT116 cell culture medium. After 48 hours, the cells were screened using the DMEM medium containing 2 \u0026mu;g/ml puromycin to finally obtain the \u003cem\u003eCdkn2a\u003c/em\u003e overexpression stable cell line.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCell viability assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded in 96-well plates at\u0026nbsp;2\u0026times;10\u003csup\u003e3\u003c/sup\u003ecell/well in 0.1\u0026thinsp;mL of complete DMEM medium supplemented with 10% fetal bovine serum and 1% Penicillin-Streptomycin for HCT116, or RPMI 1640 medium similarly supplemented for HT29. After treatment, 10 \u0026mu;L of CCK-8 solution (40203ES76, YEASEN) was added to each well and incubated at 37\u0026deg;C for 1.5 hours, the absorbance used to evaluate cell viability which was detected at 450 nm by FlexStation 3 (Molecular Devices, USA).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCell migration assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore initiating the experiment, cells were incubated in DMEM for 24 hours. Matrigel matrix (#356234, Corning, USA) was diluted to 1 mg/ml with DMEM, add 200 \u0026mu;L of the diluted matrix to each chamber (#TCS020012, JETBIOFIL) at 37℃ for 2 hours. The matrix was removed, and cells were seeded to each chamber (5\u0026times;10\u003csup\u003e4\u003c/sup\u003e/well). Following 24\u0026thinsp;hours, non-migrated cells were scraped and the migrated cells were fixed using 4% paraformaldehyde fix solution (Beyotime, China), stained with 0.5% Crystal Violet Stain Solution (#60506ES60, Yeasen), and photographed under the\u0026nbsp;IX73 inverted fluorescence microscope (Olympus, Japan).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eell scratch assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded into 12-well plates following the procedure as mentioned in cell viability assay Upon reaching 90%~100% confluency, a pipette tip was used to make several scratches in each well, followed by washing with PBS was used to remove scratched cells. After treatment, photographs of the cells were captured every 24 hours using the IX73 inverted fluorescence microscope (Olympus, Japan).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded into\u0026nbsp;12 well cell culture plates about 20%~30%, cultured in 1\u0026thinsp;mL 10% FBS and 1% Penicillin-Streptomycin DMEM medium (HCT116) or RPMI 1640 medium (HT29). After treatment for 48 hours, the cells were washed with tris buffered saline (TBS) and fixed with 4% paraformaldehyde fix solution (#P0099, Beyotime) for 20 minutes, then permeabilized with immunostaining permeabilization buffer with Triton X-100 (#P0096, Beyotime) for 15 minutes. Following this, cells were blocked with blocking buffer (1% BSA in TBS) and incubated overnight with the appropriate primary antibodies. After washing with TBS cell were incubated with IFKine\u0026trade; Red Donkey Anti-Rabbit IgG (#A24421, Abbkine) and IFKine\u0026trade; Green Donkey Anti-Mouse IgG (#A24411, Abbkine). After that, the cells were stained with DAPI and observed under LSM800 (Zeiss, Germany). The mean fluorescence intensity (MFI) was measured by ImageJ.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were lysed using RIPA Lysis Buffer (#P0013B, Beyotime), and protein concentrations were quantified using a BCA Protein Assay Kit (#20201ES76, Yeasen). Equal amounts of protein were separated by 12% SDS\u0026ndash;PAGE and transferred onto PVDF membranes (#1620177, Bio-Rad). The membranes were blocked with 5% nonfat milk for 1 hour, the membranes were then incubated with CTNNB1 monoclonal antibody (#MB62945, Bioworld), CDKN2A Rabbit Polyclonal Antibody (#EAB13750, EbioCell), Anti-beta Actin antibody (#Ab6276, Abcam)\u0026nbsp;on 4\u0026deg;C shaker overnight. The membranes were then incubated with a secondary antibody for 1 hour at room temperature. Proteins were ultimately visualized by Super ECL Detection Reagent (#36208ES60, Yeasen)\u0026nbsp;on SmartChemi 910 (Sinsage, China).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eqRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted with Trizol reagent. RNA concentration was quantified using a NanoDrop spectrophotometer (Thermo Fisher, USA). Reverse transcription was performed with the Hifair\u003csup\u003e\u0026reg;\u003c/sup\u003e AdvanceFast One-step RT-gDNA Digestion SuperMix for qPCR (#11151ES60, Yeasen), according to the manufacturer\u0026rsquo;s instructions. Three biological replicates were used for quantitative reverse transcription PCR analysis using Hieff\u0026reg; qPCR SYBR Green Master Mix (No Rox) (#11201ES08, Yeasen). The relative mRNA level of gene expression was measured with \u003cem\u003eGapdh\u003c/em\u003e as an internal control and analyzed by the 2\u003csup\u003e-∆∆Ct\u003c/sup\u003e method. Primer sequences are listed in Table S5.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFlow Cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCT 116 and HT29 cells were seeded into 12-well plates at a density of 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cell/well in 1\u0026thinsp;mL of corresponding media as mentioned earlier. Following treatment, cells were trypsinized, harvested by centrifugation at 1,000 g for 5 minutes. Subsequently, cells were gently mixed with pre-cooled 70% ethanol, fixed at 4\u0026deg;C for 2 hours, centrifuged at 1,000 g for 5 minutes. Finally, the cells were stained using the Cell Cycle and Apoptosis Analysis Kit (40301ES50, Yeasen). The images were acquired by CytoFlex (Beckman Coulter, USA), and data were analyzed using FlowJo software.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCaspase 3/7 Activity Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCT116 and HT29 cells were seeded into 12-well plates at about 50% confluency in 1\u0026thinsp;mL of their corresponding medium as mentioned earlier. After 24\u0026thinsp;hours of treatment, GreenNuc\u0026trade; Caspase-3 Substrate (#C1168S, Beyotime) was added to the medium and stained for 20 minutes, followed by fixation for 10 minutes, and finally stained with Hoechst 33258 staining solution (#C0003, Beyotime) for 5 minutes, and photographs of the cells were captured using the IX73 inverted fluorescence microscope (Olympus, Japan).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation (SD) and representative results are shown. A two-tailed unpaired Student\u0026apos;s t-test was employed to analyze the experimental data. The level of significantly different was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTranscriptomic landscape of SPF mice reveals the role of queuine in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eregulating\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egene expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo systematically investigate the biological functions of queuine, we established a queuine-treated specific pathogen-free (SPF) mouse model (see Methods for details). After treatment, we collected tissue samples from multiple organs, including the brain, heart, lung, spleen, thymus, liver, intestine, colon, kidney, and testis for RNA sequencing (RNA-seq). Principal Component Analysis (PCA) of the transcriptomic data revealed a clear separation between the treatment and control groups in most tissues (Fig. S1A). Among all tissues, the colon, intestine, and spleen exhibited the highest number of differentially expressed genes (DEGs), with nearly 4,000 DEGs identified in the colon alone (Fig. 1A; Fig. S1B-D and Table S1). Gene Ontology (GO) enrichment analysis revealed a positive correlation between the number of GO pathways and the number of DEGs (Fig. 1B and Fig. S1E). Notably, colon and intestine exhibited substantial GO pathway overlapping, suggesting functional response to queuine treatment in these two tissue types. The enriched GO terms associated with DEGs indicated roles in cytoskeletal organization, immune regulation, metabolic processes, and responses to environmental stimuli (Fig. 1C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcross the selected tissues, the most significant transcriptional alterations were observed in colon tissue (Fig. 1D-F). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis further revealed that the colon displayed the most pronounced pathway-level alterations (Fig. 1G). The enriched KEGG pathways encompassed diverse biological functions, including cardiovascular processes, immune response and inflammation, neurodegeneration and aging, cancer and apoptosis, and infectious diseases (Fig. S1F, G). Although the intestine exhibited a comparable range of enriched KEGG pathways, the enrichment levels of these pathways were markedly higher in the colon (Fig. 1H, I). Collectively, these results demonstrate that queuine treatment induced the most extensive transcriptomic remodeling in the colon relative to other tissues. The fold change and relative expression levels of the top 20 up- and down-regulated DEGs in the colon are presented in Fig. 1J.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptomic landscape of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGF\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;mice validated tissue-specific regulation by queuine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs diet and gut microbiota are only sources for mammals to acquire queuine. To dissect the contributions of dietary and gut microbiota-derived queuine and validate its role in gene expression regulation, we established a germ-free (GF) mouse model, supplemented the mice with queuine, and performed RNA sequencing. PCA of the transcriptomic data revealed clear separation between queuine-treated and control groups (Fig. S2A). Overall, the colon exhibited a higher number of DEGs compared to other tissues (Fig. 2A and Table S2). GO enrichment analysis confirmed a greater number of upregulated pathways in the colon and the intestine, compared to other tissues (Fig. S2B-E), with many pathways were enriched in a tissue-specific manner.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe performed further analysis of GF mice colon tissue, where we observed the most extensive queuine-induced transcriptomic change in SPF mice. Comparison of DEGs from GF and SPF mice revealed the colon as the site with the highest number of shared DEGs (Fig. 2B), exceeding than in other tissues (Fig. 2C). While the expression levels of upregulated DEGs were higher in the colon of SPF mice than in GF mice (Fig. 2D), the expression levels of downregulated DEGs were lower in GF mice than in SPF mice (Fig. 2E). Colon tissue expressed the highest number of enriched GO pathways (\u0026gt;1,500), compared to liver and intestine (Fig. 2F, G). Additionally, the colon exhibited the most significant transcriptional variations within the shared GO pathways compared to other tissues (Fig. 2H, I). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis further confirmed the colon as the most responsive tissue, showing the highest number of enriched pathways, relative to the intestine and the liver (Fig. 2J, K, Fig. S2F, G). Collectively, findings from both SPF and GF models demonstrated that queuine-induced transcriptional changes were most pronounced in colon tissue. In addition, queuine\u0026rsquo;s transcriptional regulatory effects were stronger in SPF mice than GF mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQueuine inhibits the proliferation and migration of CRC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above results showed that the colon was the most responsive tissue to queuine in both SPF and GF mice. To assess the effect of queuine on colon, colorectal cancer cell lines (HCT116 and HT29) were treated with or without quinine. We than performed multiple functional assays to evaluate the queuine\u0026rsquo;s impact on CRC cell. Specifically, CCK-8 assay demonstrated that queuine treatment inhibited cell proliferation in both cell lines (Fig. 3A). Transwell assays using HCT116 cells revealed that queuine treatment significantly reduced invasive cell numbers (Fig. 3B). Similarly, a cell scratch assay revealed that queuine significantly suppressed the migration ability of both cell lines (Fig. 3C and Fig. S3A). Dysregulation of the cell cycle and resistance to apoptosis are key drivers of cancer progression; therefore, we assessed these processes in queuine-treated cells via flow cytometry and caspase-3 activity assays. Queuine treatment induced an increase in G1-phase cells and decrease in G2/M-phase cells indicating cell cycle inhibition (Fig. 3D and Fig. S3B). Concurrently, elevated caspase-3 activity confirmed increase apoptosis rate (Fig. S3C, D).\u003c/p\u003e\n\u003cp\u003eTo further investigate the role of queuine \u003cem\u003ein vivo\u003c/em\u003e, we utilized a CRC cell line-derived xenograft (CDX) mouse model. Several tumor characteristics (tumor volume growth curves, final tumor weight, final tumor volume, and the ratio of tumor volume to body weight) were compared between queuine-treated and control groups. All metrics consistently demonstrated significantly suppressed tumor growth in queuine-treated group relative to controls (Fig. 3E-G, Fig. S3E-G). At last, we validated the effect of queuine using human-derived colorectal cancer organoids, and the results also demonstrated that queuine treatment significantly inhibited the growth of colorectal cancer (Fig. 3H). Overall, these results confirmed that queuine effectively inhibited CRC \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCdkn2a\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;promotes the proliferation and migration capacity in CRC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above results demonstrated that queuine exerted a significant inhibitory effect on CRC. To further explore underlying molecular mechanisms and identify consistently altered differentially expressed genes (DEGs) in both SPF and GF models, we selected the top ten upregulated and downregulated genes from each model (highlighted in bold orange in Fig. 4A). The qPCR validation revealed \u003cem\u003eCdkn2a\u003c/em\u003e as the only gene consistently down-regulated gene across different groups of samples (Fig. S4A), suggesting that queuine-mediated \u003cem\u003eCdkn2a\u003c/em\u003e regulation may be critically linked to CRC. To further validate the effect of queuine on \u003cem\u003eCdkn2a\u003c/em\u003e expression, we treated two CRC cell lines (HCT116 and HT29) with queuine. This treatment significantly downregulated both CDKN2A protein and mRNA levels (Fig. 4B, C). We also generated a \u003cem\u003eCdkn2a\u003c/em\u003e-overexpressing stable cell lines and confirmed queuine significantly suppressed the CDKN2A protein and mRNA levels in these engineered cells (Fig. 4D, E).\u003c/p\u003e\n\u003cp\u003eTo assess whether queuine-induced \u003cem\u003eCdkn2a\u003c/em\u003e suppression mediates CRC phenotypes, we conducted CCK-8, transwell, and cell scratch assays assessing cell proliferation, invasion, and migration, respectively. Specifically, CCK-8 assay demonstrated that\u003cem\u003e\u0026nbsp;Cdkn2a\u003c/em\u003e overexpression significantly enhanced cell proliferation, while the effect was inhibited by queuine treatment (Fig. 4F). Similarly, transwell assays demonstrated significant inhibition of \u003cem\u003eCdkn2a-\u003c/em\u003einduced invasiveness by queuine treatment (Fig. 4G) and cell scratch assays confirmed significant inhibition of \u003cem\u003eCdkn2a-\u003c/em\u003einduced cell migration (Fig. 4H). To further elucidate the function of \u003cem\u003eCdkn2a\u003c/em\u003e on the HCT116 cell line, we generated \u003cem\u003eCdkn2a\u003c/em\u003e-knockdown cell lines (Fig. 4I, J). CCK-8, transwell, and scratch assays revealed that \u003cem\u003eCdkn2a\u003c/em\u003e knockdown significantly reduced cell proliferation, invasion, and migration (Fig. 4K-M and Fig. S4B). Overall, these findings demonstrate queuine inhibits CRC progression by downregulating \u003cem\u003eCdkn2a\u0026nbsp;\u003c/em\u003eexpression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCdkn2a\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003epositively correlate\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;with \u003cem\u003eCtnnb1\u003c/em\u003e in CRC cells and clinical samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify target molecules affected by \u003cem\u003eCdkn2a\u003c/em\u003e, we analyzed the expression of multiple CRC-related molecules[12, 13]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(Fig. S5A). We found that among these, \u003cem\u003eCtnnb1\u003c/em\u003e expression was consistently downregulated across various CRC cell lines after queuine treatment (Fig. 5A and Fig. S5B).\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eCdkn2a\u003c/em\u003e-overexpressing cell lines, we observed a positive correlation between \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e, while queuine treatment suppressed the regulation (Fig. 5B and Fig. S5C). We further confirmed that the expression of \u003cem\u003eCtnnb1\u003c/em\u003e could be down-regulated by knocking down \u003cem\u003eCdkn2a\u003c/em\u003e using siRNA (Fig. 5C and Fig. S5D). Supporting these findings, Immunofluorescence analyses after \u003cem\u003eCdkn2a\u003c/em\u003e overexpression showed that queuine treatment decreased expression levels of both CDKN2A and CTNNB1 (Fig. 5D). This correlation was also observed in queuine-treated normal CRC HCT116 cells (Fig. 5E). In summary, these results indicated that \u003cem\u003eCdkn2a\u003c/em\u003e positively correlated with \u003cem\u003eCtnnb1\u003c/em\u003e expression, and queuine inhibited the expression of both \u003cem\u003eCdkn2a\u003c/em\u003e and\u003cem\u003eCtnnb1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eWe validated the relationship between \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e using clinical samples by analyzing CRC tumor tissues (CA) and paracancerous tissues (PA) from seven CRC patients. Both genes, \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e, were significantly upregulated in tumor tissues compared to paracancerous tissues (Fig. 5F and Fig. S5E). Additionally, analysis of colon adenocarcinoma (COAD) patient data from the TCGA database using GEPIA2[14] confirmed significantly higher expression of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e in cancer patients compared to healthy individuals (Fig. 5G, H). The qPCR data and TCGA data further supported positive correlation between \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e (Fig. 5I, J). A significant increase in \u003cem\u003eCdkn2a\u003c/em\u003e expression with cancer progression across COAD stage (I-IV) patients (Fig. 5K) was linked to poorer survival outcomes (Fig. 5L), confirming the clinical relevance of \u003cem\u003eCdkn2a\u003c/em\u003e gene regulation. Collectively, these results indicate that \u003cem\u003eCtnnb1\u003c/em\u003e is an important gene in CRC, and that the expression of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e is positively correlated in both CRC cells and clinical samples. Furthermore, queuine regulates the expression of both genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQueuine enhances\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe activity of\u003cem\u003e\u0026nbsp;Limosilactobacillus reuteri\u003c/em\u003e \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCdkn2a\u003c/em\u003e downregulation expression was more pronounced in SPF than in GF mice, indicating that gut microbiome or specific microbial populations contribute to this effect.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTo investigate differences in microbial community composition of SPF mice treated with queuine (Q group) and an untreated group (Con group), we performed metagenomic sequencing of fecal samples and analyzed species abundance in each group. Microbial community compositions differed significantly between both groups (ANOSIM, R-value \u0026gt; 0, Fig. 6A). Moreover, non-metric multidimensional scaling (NMDS) analysis at both genus and species levels showed stress values \u0026lt; 0.1 (Fig. 6B and Fig. S6A). Linear discriminant analysis (LDA) indicated \u003cem\u003eLimosilactobacillus reuteri\u003c/em\u003e (\u003cem\u003eL. reuteri\u003c/em\u003e) as a main distinguishing factor between Q group and Con group (Fig. 6C). Indeed, \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e showed the most pronounced increase in relative abundance upon queuine treatment, which suggests that queuine may be able to promote \u003cem\u003eL.\u0026nbsp;\u003c/em\u003ereuteri activity (Fig. 6D,\u0026nbsp;E).\u003c/p\u003e\n\u003cp\u003eTo investigate whether queuine effects the activity of \u003cem\u003eL. reuteri\u003c/em\u003e, we obtained a pure culture of \u003cem\u003eL. reuteri\u003c/em\u003e originally isolated from human break milk. Using Nanopore long-read sequencing, we assembled a 2.2 megabase (Mb) genome harboring 2,224 predicted genes (Fig. S6B). We compared cultures of \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e grown in medium with and without queuine supplementation. Transcriptomic analysis following queuine treatment showed activation of four metabolic pathways, including biosynthesis of nucleotide sugars, amino sugar and nucleotide sugar metabolism, galactose metabolism, and ABC transporters (Fig. 6F, Fig. S6C and Table S3), suggesting that queuine enhances the growth of \u003cem\u003eL. reuteri\u003c/em\u003e by promoting key metabolic processes. Maximal optical density reached by the culture (OD600) as well as the total number of viable cells during exponential growth increased significantly in the presence of queuine (Fig. 6G, H). Additionally, queuine treatment enhanced the ability of \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e to adhere to HTC116 cells (Fig. 6I, J). Similarly, we observed a positive effect of queuine on biofilm formation in \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri,\u003c/em\u003e as indicated by the formation of larger colonies than controls (Fig. 6K) and exhibited stronger crystal violet intensity (Fig. 6L). The upregulation of \u003cem\u003eparB\u003c/em\u003e[15] and \u003cem\u003ednaB\u003c/em\u003e[16]\u003csup\u003e\u0026nbsp;\u003c/sup\u003egenes\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(Fig. S6D, E) are associated with bacterial proliferation. Collectively, these results confirm that queuine can enhance the growth, adhesion and biofilm formation of \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ealso\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einhibits the proliferation and migration of CRC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering that queuine promoted the growth of \u003cem\u003eL. reuteri\u003c/em\u003e, and SPF mice have a more pronounced downregulation of \u003cem\u003eCdkn2a\u003c/em\u003e expression than GF mice, we investigated whether \u003cem\u003eL. reuteri\u003c/em\u003e itself influences \u003cem\u003eCdkn2a\u003c/em\u003e expression and CRC progression. Specifically, to determine whether \u003cem\u003eL. reuteri\u003c/em\u003e could affect \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e expression similarly to queuine, we applied the same experimental approach used for queuine in CRC cell models. We isolated\u003cem\u003e\u0026nbsp;L. reuteri\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eas control bacterium), and confirmed by 16S PCR (Fig. S6F, G). Both bacteria were co-cultured \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e or \u003cem\u003eE\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;coli\u003c/em\u003e with CRC cell lines (HCT116 and HT29). In the presence of live \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e, expression levels of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e were significantly reduced compared to the control experiments (Fig. 7A-D). Similar to the effect of queuine, treating CRC cells with the metabolites of \u003cem\u003eL. reuteri\u003c/em\u003e (SN) also suppressed \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e expression in both HCT116 and HT29 cell lines (Fig. 7E-H), which was further confirmed by immunofluorescence results (Fig. 7I). The observations indicate a similar mechanism of metabolites produced by \u003cem\u003eL.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003ereuteri\u003c/em\u003e and queuine in suppressing CRC progression. Indeed, metabolites from \u003cem\u003eL. reuteri\u003c/em\u003e significantly inhibited CRC cell proliferation, invasion, and migration (Fig. 7J-L and Fig. S7A). We also validated the effect of \u003cem\u003eL\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e\u0026nbsp;reuteri\u003c/em\u003e using human-derived colorectal cancer organoids, and the results demonstrated that metabolites from\u003cem\u003e\u0026nbsp;L. reuteri\u003c/em\u003e significantly inhibited the growth of colorectal cancer (Fig. 7M), confirming that \u003cem\u003eL. reuteri\u003c/em\u003e and queuine can suppress CRC progression by down-regulating \u003cem\u003eCdkn2a\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCtnnb1\u0026nbsp;\u003c/em\u003eexpression. To further elucidate the underlying mechanisms, we performed metabolomic profiling of \u003cem\u003eL. reuteri\u003c/em\u003e treated with queuine. We identified 623 differentially abundant metabolites, including 459 upregulated and 164 downregulated metabolites (Fig. S7B and Table S4), demonstrating queuine reprograms the metabolome of \u003cem\u003eL. reuteri\u003c/em\u003e. Notably, known CRC-inhibiting metabolites including L-glutamine, and L-aspartic acid were significantly elevated in queuine-treated samples[17, 18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eL. reuteri\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;enhances\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;inhibitory effect\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efrom queuine\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eon spontaneous colorectal cancer\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the above experiments, we have demonstrated that both queuine and \u003cem\u003eL. reuteri\u003c/em\u003e can significantly inhibit colorectal cancer progression in cellular models by downregulating \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e expression. To confirm these inhibitory effects \u003cem\u003ein vivo\u003c/em\u003e, we established an AOM/DSS-induced spontaneous colorectal cancer mouse model, including: a group without AOM/DSS treatment (Normal group), a PBS-treated control group (Con group), a group treated with queuine (Q group), a group treated with \u003cem\u003eL. reuteri\u003c/em\u003e (\u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003egroup), a group co-treated with both queuine and \u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003e(Q+\u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003egroup) (Fig. 8A). Results revealed significantly reduced tumor volume and number in both queuine and \u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003egroups versus control group (Fig. 8B, C). The combination group (Q+\u003cem\u003eL. reuteri\u003c/em\u003e group) exhibited the most pronounced effect than the Q group and the \u003cem\u003eL. reuteri\u003c/em\u003e group (Fig. 8B, C). These results suggested that queuine and \u003cem\u003eL. reuteri\u003c/em\u003e can function synergistically to inhibit colorectal cancer. We have performed PCR and qPCR to confirm successful colonization of \u003cem\u003eL. reuteri\u003c/em\u003e (Fig. S8A). The results showed that the abundance in Q+\u003cem\u003eL. reuteri\u003c/em\u003e group was significantly higher than in \u003cem\u003eL. reuteri\u003c/em\u003e group (Fig. 8D). This also confirmed that queuine has a promoting effect on the abundance of \u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003ein \u003cem\u003evivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eWe collected colorectal tissue samples and performed qPCR analysis and western blot. The results consistently revealed downregulation of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e in both groups receiving queuine or \u003cem\u003eL. reuteri\u003c/em\u003e gavage. Notably, the combination of \u003cem\u003eL. reuteri\u003c/em\u003e and queuine (Q+\u003cem\u003eL. reuteri\u0026nbsp;\u003c/em\u003egroup) led to an even more pronounced reduction in expression levels (Fig. 8E and Fig. S8B). Finally, the HE and IHC results showed that the expression levels of CDKN2A and CTNNB1 in the colon across all groups were decreased (Fig. 8F, G). Collectively, our observations suggest that the gut microbiota-derived queuine can inhibit CRC progression. Additionally, we found that queuine can enhance the activity of \u003cem\u003eL. reuteri\u003c/em\u003e, which had a similar CRC-suppressing effect as queuine, they can function in synergy to inhibit CRC more effectively\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e (as the proposed working model in Fig. 8H).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eColorectal cancer is one of the leading causes of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ours and the work of others highlights the importance of gut microbes and their metabolites in regulating a variety of host biological functions[\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, potential mechanisms by which microbial populations influence CRC development and progression remain largely unclear. Known as the \u0026ldquo;longevity vitamin\u0026rdquo;, the bacterial metabolite queuine plays a crucial role in human health, influencing development, metabolism, cancer pathogenesis, and the invasion of disease-causing bacteria \u003cem\u003ein vivo\u003c/em\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this study, we first investigated the effects induced by queuine across multiple organs in SPF and GF mice by RNA sequencing. We demonstrated the role of queuine in suppressing colorectal cancer through downregulation of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e. Furthermore, we explored the influence of queuine on the composition of gut microbiome. We found that queuine modulated the gut microbiome composition and enriched the abundance of \u003cem\u003eL. reuteri\u003c/em\u003e, a bacterium with CRC-suppressing effects.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCdkn2a\u003c/em\u003e is a well-established tumor suppressor gene with critical roles in cell cycle regulation. \u003cem\u003eCdkn2a\u003c/em\u003e is frequently mutated or deleted in various cancers and generates multiple transcriptional isoforms. These isoforms modulate the G1 phase of the cell cycle through interacting with cyclin dependent kinase 4 (CKD4) and p53. Recent studies have highlighted the context-dependent effects of \u003cem\u003eCdkn2a\u003c/em\u003e gene loss, particularly in epithelial-derived esophageal cancer[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, the rs10811661 polymorphism within \u003cem\u003eCdkn2a/b\u003c/em\u003e gene locus has been proposed as a prognostic biomarker for gastrointestinal malignancies, including CRC and gastric cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In CRC, aberrant \u003cem\u003eCdkn2a\u003c/em\u003e expression has been reported, often co-occurring with WNT pathway activation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, \u003cem\u003eCdkn2a\u003c/em\u003e has been implicated in tumor progression and resistance to cuproptosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study confirms that elevated \u003cem\u003eCdkn2a\u003c/em\u003e expression in patient-derived CRC samples. Functional analyses demonstrate that \u003cem\u003eCdkn2a\u003c/em\u003e knockdown suppresses CRC cell proliferation, migration, and adhesiveness, further supporting its role in CRC pathophysiology.\u003c/p\u003e\u003cp\u003eNumerous studies have shown that \u003cem\u003eCtnnb1\u003c/em\u003e (also known as β-catenin) is aberrantly activated in various cancers[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], characterized by increased intracellular \u003cem\u003eCtnnb1\u003c/em\u003e protein levels. This upregulation enhances the expression of oncogenes, including SRY-box transcription factor 4 (SOX4)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and c-Myc[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], facilitating the viability and invasive capacity of cancer cells and promoting malignant phenotype of CRC[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Here, we demonstrated that queuine treatment effectively inhibited the expression of \u003cem\u003eCtnnb1\u003c/em\u003e, consequently suppressing the viability and invasive capacity of CRC cells. We have observed positive correlation patterns between \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e, but the detailed modulation relationship between them needs further investigation.\u003c/p\u003e\u003cp\u003eThe diverse microbial community within human gut significantly influences health and represent a promising target for microbial therapeutics, including probiotics[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, we found that \u003cem\u003eL. reuteri\u003c/em\u003e metabolites significantly inhibited CRC cell proliferation and migration. Similar to queuine, these metabolites downregulated \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003eexpression, co-culture experiments further confirmed this regulatory relationship. \u003cem\u003eL. reuteri\u003c/em\u003e is a well-established intestinal probiotic with significant benefits for human health[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], such as reshaping the gut microbiota, stimulating killer T cells, and modulating immune responses within the tumor microenvironment[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. \u003cem\u003eL. reuteri\u003c/em\u003e influences host physiology both directly and through its metabolites. Collectively, our results suggest the novel biological functions of \u003cem\u003eL. reuteri\u003c/em\u003e and queuine, which could be used to treat CRC in the future. Previous work reported that the bacterial metabolite queuine plays a vital role in maintaining tRNA queuosine modification[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], we only focused on the role and mechanism of queuine in colorectal cancer progression in this study. Whether queuine and \u003cem\u003eL. reuteri-\u003c/em\u003emediated functions are related to tRNA queuosine modification remains to be investigated in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Dr. Jessika Fuessel from Professor A. Murat Eren lab at University of Oldenburg for the reading and comments on our manuscript. The authors also would like to thank Ting Xian at Geneus Technologies for the help of \u003cem\u003eL. reuteri\u003c/em\u003e genome sequencing. We thank all members from the Wang lab for technical assistance and insightful discussion.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involves human participants and was approved by the Department of Gastroenterology, Panyu Central Hospital of Guangzhou Medical University (PYRC-2024-262-01). Participants gave informed consent to participate in the study before taking part. The protocol for an animal experiment in this study was approved by the Institutional Animal Care and Use Committee (IACUC) of Guangzhou Institute of Biomedicine and Health at Chinese Academy of Sciences (protocol code: #A5748-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32570070, 32070615), Guangdong Provincial Natural Science Foundation (2022A1515010569), Guangzhou Science and Technology Project (2024A04J6265), and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme for X.W. This work was also partially supported by Science and Technology Planning Project of Guangdong Province in China (2023B1212060050, 2023B1212120009).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe RNA sequencing data of 60 samples from ten tissues of SPF mice has been deposited at the NCBI GEO database under accession number GSE291769 (reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE291769, reviewer token: inyfmyewxhgnlkz). The RNA sequencing data of 60 samples from ten tissues of GF mice has been deposited at the NCBI GEO database under accession number GSE296839 (reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296839, reviewer token: evihucwwxbmtnub). The RNA sequencing data of 6 samples from \u003cem\u003eL. reuteri\u003c/em\u003e has been deposited at the NCBI GEO database under accession number GSE296840 (reviewer link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296840, reviewer token: gnytseogdtgdhip). The metagenome sequencing data using mouse fecal samples has been submitted to NCBI SRA database under accession number PRJNA1234476 (reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1234476?reviewer=ufaqh0pa9oulhk1v6m5s3dggui).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.W. conceived and proposed the project. A.L. performed experiments with the help from Z.X., S.L., and S.X. J.L. performed data analysis with the help from A.L. and J.F. Y.T. and Y.W. provided CRC tumor tissues and paracancerous tissues. L.D. helped with germ free animal experiments. J.W. and R.W. provided \u003cem\u003eL. reuteri\u003c/em\u003e strain. A.L., J.L. X.W. wrote the manuscript with the help from K.C., D.L., Y.T., S.W. and T.K.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBiller LH, Schrag D: Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review. \u003cem\u003eJAMA \u003c/em\u003e2021, 325:669-685.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Wagle NS, Cercek A, Smith RA, Jemal A: Colorectal cancer statistics, 2023. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2023, 73:233-254.\u003c/li\u003e\n\u003cli\u003eArnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global patterns and trends in colorectal cancer incidence and mortality. \u003cem\u003eGut \u003c/em\u003e2017, 66:683-691.\u003c/li\u003e\n\u003cli\u003eGerstung M, Jolly C, Leshchiner I, Dentro SC, 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99:1411-1418.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Gut microbiota, queuine, colorectal cancer, L. reuteri, Cdkn2a","lastPublishedDoi":"10.21203/rs.3.rs-7540054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7540054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColorectal cancer (CRC) is a multifactorial disease of the colorectal epithelium that could be driven by gut microbiota dysregulation, while the molecular mechanisms of microbial metabolitesin regulating CRC remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe aim to investigate the biological functions of gut microbiota-derived queuine in the host and the underlying molecular mechanisms. Transcriptomic analysis of ten tissues from specific pathogen-free and germ-free mice revealed that queuine supplementation reprogrammed host gene expression, especially in the colon. Functionally, we found that queuine inhibited CRC in two cell lines (HCT116 and HT29), xenograft CRC mouse model, and CRC-derived organoids. Interestingly, we found that queuine supplementation in mice affected gut microbiota compositions, in which \u003cem\u003eL. reuteri\u003c/em\u003e showed the most pronounced increase upon queuine treatment. Further experiments confirmed the effect of queuine on the activity of \u003cem\u003eL. reuteri\u003c/em\u003e \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Moreover, \u003cem\u003eL. reuteri\u003c/em\u003e enhanced the inhibitory effect from queuine on spontaneous colorectal cancer in mice. Mechanistically, both queuine and \u003cem\u003eL. reuteri\u003c/em\u003ecan suppress CRC through the regulation of \u003cem\u003eCdkn2a\u003c/em\u003e and \u003cem\u003eCtnnb1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003es\u003c/p\u003e\n\u003cp\u003eOur findings uncover the role and mechanism of gut microbiota-derived queuine in suppressing CRC, and we highlight the therapeutic potential of queuine and \u003cem\u003eL. reuteri.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Gut microbiota-derived queuine reprograms colon gene expression and alleviates colorectal cancer synergistically with Limosilactobacillus reuteri","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 06:28:47","doi":"10.21203/rs.3.rs-7540054/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":"5d8d8a4d-f091-4412-8304-01ba91ea2047","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-31T22:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 06:28:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7540054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7540054","identity":"rs-7540054","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}