Bifidobacteria modulating gut microbiota inhibits breast cancer growth via anti-tumor immune response in mice

Bifidobacteria modulating gut microbiota inhibits breast cancer growth via anti-tumor immune response in mice | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bifidobacteria modulating gut microbiota inhibits breast cancer growth via anti-tumor immune response in mice Jiajie Shi, Cuizhi Geng, Zheng Li, Ping Ma, Xi Zhang, Meng Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3079798/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 To explore the effects of gut microbiota intervention on anti-tumor immune response, Balb/c mice were divided into three groups that were administered one of the following: antibiotics (mixture of ampicillin, streptomycin and polymyxin), bifidobacterial or sterile water via oral gavage. Their feces were collected for Meta 16S DNA sequencing. The differences in gut bacteria composition among three groups through β diversity analysis. To determine the effects of microbiota intervention on tumor growth, the mice were inoculated with 4T1 breast cancer cells, and the tumor growth was measured after tumor formation. IFN-γ and IL-2 levels in tumors and serum were determined by ELISA, and HE staining was used to quantify lymphocytes infiltrated into tumors. The tumor growth in the antobiotics was significantly faster than that of the bifidobacterial group (P = 0.013). β diversity analysis showed significant differences in gut microbiota composition among the three groups (weighted P = 1.5015e-10; unweighted P = 5.5914e-05). The IFN-γ and IL-2 levels in tumors and serum were higher after bifidobacterial administration than those after antibiotics and water treatment (all P < 0.001). Bifidobacteria modulating gut microbiota inhibited breast cancer growth which was likely associated with anti-tumor immune responses. Gut microbiota Meta 16S DNA sequencing IFN-γ IL-2 Tumor infiltrating lymphocytes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The abnormal of gut microbiota is closely related to human health and diseases, including immunity, digestion, obesity ( 1 ), diabetes ( 2 , 3 ), heart disease ( 4 , 5 ), acquired immunodeficiency syndrome ( 6 ) and even many types of cancer, including breast cancer ( 7 ). It has been reported that the microbiota might affect human health by inducing chronic inflammation, regulating immune response and metabolic pathways ( 8 ). For example, Lactococcus lactis has been shown to activate protective cellular immunity, leading to the production of cytokines such as interleukin 2 (IL-2) and IFN-γ ( 9 ). IL-2 is a cell growth factor in the immune system that regulates cell activity of the leuksphere in the immune system and promotes the proliferation of helper T cell 0(Th0) and cytotoxic lymphocyte (CTL), whilst IFN-γ is produced by lymphocytes and its antiviral, antitumor and immunomodulatory effects were well documented ( 10 ). Moreover, gut microbiota can also affect the abundance of tumor-infiltrating lymphocytes (TILs) ( 11 ), which is one of the most critical and predictive indicators of solid tumor immunity ( 12 ). In breast cancer, infiltrated cytotoxic CD8 + T cells were positively correlated with survival ( 13 , 14 ) and response to treatment ( 15 ). In mouse models, the alteration of microbiota composition in small intestine and the activation of selected Gram bacteria species to enter secondary lymphoid organs stimulated the production of "pathogenic" T helper 17 (pTh 17) cells and memory Th1 immune response ( 16 ). Moreover, the application of antibiotics that killed Gram bacteria led to chemotherapy resistance ( 17 ). Previous studies showed that bacteria and their products can affect the immune microenvironment of tumors ( 18 ) and some types of bacteria, for example, the bacterium Methylobacterium radiotolerans, were even found to be relatively enriched in breast cancer tissue ( 19 ). In this study, we aimed to investigate the effect of gut microbiota alteration on tumor growth and underlying mechanisms including immune responses. Materials & Methods Animals and treatments This study was compliance with the Animals (Scientific Procedures) Act 1986 in the UK and Directive 2010/63/EU in Europe. The Laboratory Animal Ethical and Welfare Committee of Fourth Hospital Hebei Medical University, Hebei, China, approved this study (Approval No. IACUC-4th Hos Hebmu-201706).Twenty-four female specific pathogen-free (SPF) Balb/c mice were used in this study (license number: 1707109, purchased from the Animal Center of Hebei Medical University). They were 6–8 weeks old and 18–22 g in body weight. All mice were maintained in the breeding cages of the experimental animal room at 20–22°C with 55–60% humidity and a light/dark cycle. Twenty-four of them were randomly divided into three groups to recive treeatmeents as follows via oral gavage (n = 8/group); The antibiotic (A) group-0.2 ml of the mixture of ampicillin (1mg/ml), streptomycin (5 mg/ml) and polymyxin (1mg/ml) in water ( 20 ); The Bifidobacterium (B) group- 0.2 ml of bifidobacterium water (containing 1×10 8 CFU bacteria) (Livzon Group Livzon Pharmaceutical Factory; two capsules were dissolved in 2 ml of purified water to obtain 0.5×10 9 CFU/ml solution; The control (C) group-0.2 ml of purified water. The treatments were carried out once every 2 days for 3 weeks via oral gavage. After 3 weeks treatment, mice were inoculated with 4T1 cells (see below). The treatments were continued for another 3 weeks while they had cancer burden (see below). 4T1 cells inoculation The 4T1 cell line (a gift from Prof Sang,Tumor Research Center, the Fourth Hospital of Hebei Medical University) were cultured in Opti-RPMI 1640 medium containing 10% fetal bovine serum and 100 U/ml penicillin at 37°C, saturated humidity, and 5% CO 2 balanced with air. The cultured cells were passaged with trypsin-EDTA every 2–3 days. Their suspension of 1×10 7 ml − 1 were then prepared and 0.1 ml of cell suspension was inoculated into each mouse (10 6 cells/mouse). After inoculation, their tumor growth and health condition of mice were monitored daily including tumor appearance time, tumor size, tumor formation rate, three weeks later, the mice were euthanized.The length (a) and width (b) of tumor were measured with a vernier caliper every other day from the first to the third week, and the tumor volume was calculated using the formula V = ab 2 /2. The tumor growth curve was then plotted. Sample collection After the first 3 weeks of treatments, the fresh fecal specimens were collected for extraction of fecal flora DNA. At the end of the third week after 4T1 cells inoculation, 500 ul of mouse venous blood samples were harvested under pentobarbitital sodium (40mg/kg i.p.) and serum was separated and kept at -80°C. Subsequently, they were euthanized by cervical spine dislocation; tumors were collected and fixed in 4% formalin solution. The experimental protocol is presented in Fig. 1 . HE staining and TILs quantification The tumor tissue was embedded in paraffin and cut into 5 µm sections which were stained with hematoxylin-eosin (HE). Tumor-infiltrating lymphocytes (TILs) were asssed under a medium-power field (×100) as reported previously ( 21 ) in a blinded manner. The percentages of area infiltrated by lymphocytes within the tumor itself plus adjacent stroma were defined as low ( 50%) TILs. Determination of IFN-γ and IL-2 levels in tumor and serum The IFN-γ and IL-2 in both tumors and serum as reported previously were determined with an enzyme-linked immunosorbent assay kit (Elabscience Biotechnology Co, Ltd, Wuhan, China) (catalogue numbers: IFN-γ, E-EL-M0048c; IL-2, E-EL-M0042c). The OD value was measured with a microplate reader at 450 nm. The levels of IFN-γ and IL-2 were calculated via standard curve. DNA extraction and amplification of Balb/c mouse feces DNA from mouse feces was extracted with the soil DNA extraction kit (ProbeGene, Jiangsu, China) accordingly. The 16S rDNA v3-v4 regionof the ribosomal RNA was amplified by PCR (primers: 341F: CCTACGGGNGGCWGCAG; 806R: GGACTACHVGGGTATCTAAT ( 22 ). The reaction cycle was: 95°C denature for 2 minutes, 98°C 10 s, 62°C 30 s, 68°C 30 s for 27 cycles, 68°C extension for 10 min. The barcode was the unique 8-bp sequence for each sample. The PCR reactions were performed in triplicate with 50 µL reaction volume, containing 5 µL of 10 × KOD buffer, 5 µL of 2.5 mM dNTPs, 1.5 µL of primers (5 µM), 1 µL of KOD polymerase, and 100 ng of template DNA. Statistics and bioinformatics analysis Data were expressed as mean ± standard deviation (SD) or median (IQR).They were then anlaysed with one or two-way ANOVA followed by the post hoc Turkey for comparison or Fisher’s exact test as appropriate with SPSS 23.0 software. A P < 0.05 was considered to be of statistica significance. Bioinformatics analysis: the effective data with ≥ 97% similarity were clustered into operational taxonomies units (OTUs), and the USPASE ( 23 ) channel was used for clustering. In each cluster, the most abundant marker sequence was selected as the representative sequence. Inter-group Venn analysis was performed using R 3.4.1 to identify unique and common OTUs. The biomarker characteristics of each group were screened using Metastats (20090414 version) ( 24 ) and LEfSe software (1.0 version) ( 25 ). β-diversity comparison among groups was computed using Kruskal-Wallis H test in R. The coordinates of principal component analysis (PCA) were then calculated and plotted. Results Tumor growth After 4T1 cells were inoculated for 3 to 7 days, tumor nodules started to appear at the site of inoculation and expanded. 15 days later, the tumors started to grow faster 15 days after implanted. At 21 days after inoculation, all mice were sacrificed and the tumors were dissected. The tumor was light yellow-pink in color, hard in texture, and adhered to the surrounding tissues. The maximum tumor diameter was 1.99 cm in C group and the maximum tumor volume was 2.72 cm 3 of the A group. There were no statistically significant between the A and C group (P = 0.182) and the B and C group (P = 0.405), but the difference between the A and B group was significant (P = 0.013) (Fig. 2 ). Gut microbiota The A, B and C groupS had 397, 573 and 583 types of OTUs ,respectively. The corresponding species and abundance in the OTUs are shown in Fig. 3 A and 3 B. Based on the species abundance in the OTU list, Principal Component Analysis (PCA) was carried out to examine the compositional distance between groups using dimensionality reduction (Fig. 4 ). In the weighted (P = 1.5015e-10) and unweighted (P = 5.5914e-05) β diversity analysis, the gut microbiota were significantly different among the three groups (Fig. 5 A and 5 B). The differences in gut microbiota were analyzed using LEFse analysis, and the specific flora of each group were identified. Interestingly, bifidobacteria was not the dominant species in the B group (Fig. 6 ). IFN-γ and IL-2 in both serum and tumors The IFN-γ levels in the B and C group were both significantly higher than that in the A group (P = 0.01, P = 0.0009, respectively), and the IL-2 level of the B group was significantly higher than both the A group (P = 0.0003) and C group (P = 0.046) (Fig. 7 A and 7 B). The IFN-γ level of the B group was significantly higher than either the A group (P = 0.0011) and C group (P = 0.0099), and the IL-2 level of the B group was significantly higher than that of the A group (P = 0.0010) (Fig. 7 C and 7 D). Abundance of TILs The example staining of tumor infiltrating lymphocytes is shown in Fig. 8 A.There was no significant difference among the three groups in the ratio of TILs (P = 0.883) (Fig. 8 B). Discussion Our study analyzed the relationship between gut microbiota and immune response in breast cancer in a mouse model. We found that the group with the Bifidobacterium treatment had the smallest tumor size and the slowest growth, while the mice treated with antibiotics had the largest tumor size, suggesting that antibiotics and Bifidobacterium had opposite effects on tumor growth. Furthermore, the gut microbiota in the three mice groups were different in both weighted and unweighted β diversity. Our data reported here indicated that gut microbiota alteration can influence body’s anti-tumor immunity as evidence with IFN-γ and IL-2 level changes in both tumors and serum corresponding to the treatment. The regulatory role of microbiota in immune function was relatively established and, indeed, commensal bacteria have been shown to direct differentiation of T cells leading to expansion of specific molecular subsets, thereby influencing systemic inflammatory processes that was involved in T cell differentiation ( 26 , 27 ). Other studies highlighted a role for commensal bacteria in modulating the activation state of innate antigen-presenting cells (APCs), thereby impacting priming of systemic immune responses ( 28 , 29 ). Sivan A et al. replorted that the mice with Bifidobacterium detected in gastrointestinal microbiota showed slower tumor growth, higher CD8 + T cell activity, and better response to PD-L1 inhibitor treatment than the mice without Bifidobacterium ( 19 ). It has been also reported that antibiotics induced gut microbiota dysbiosis and promoted tumor initiation and development ( 30 ). Additionally, antibiotics were found to inhibit mitochondrial function in mice, leading to further damage of the gut epithelial cells ( 31 ). Therefore, antibiotics can alter gut microbiota and subsequently affect immune homeostasis. All these changes ultimately increase the release of microbial products and the activities of inflammatory cells, such as tumor-associated macrophages to promote tumor growth and metastasis through activating cellular signalling pathway ( 32 – 34 ). Our work has some limitations. For example, firstly, the specific flora of each group were identified using LEFse analysis, but bifidobacteria was not the dominant species in its treated group. The reason for this is uknown but this might be due to that bifidobacterium medicament rather than more active bifidobacterium lyophilized powder was used in our study. Secondly, because a relative small sample size of our study, some findings, e.g. the TILs among the three groups, was not significantly. Therefore, the role of gut microbiota on tomur microenviroment requires further study. The translational value of our study is unknown. However, interestingly, it was found that the microbiota disruption paired the response of subcutaneous tumors to CpG-oligonucleotide immunotherapy and platinum chemotherapy ( 35 ). Furthermore, in the antibiotics-treated or germ-free mice, tumor-infiltrating myeloid-derived cells responded poorly to the therapies ( 35 ). Nevertheless, our current study and other studies may suggest that the use of wide specrum antibiotics should be avoided during the course of treatments including surgery, immunotherapy and chemotherapy as necessary. Conclusions our study demonstrated that gut microbiota alteration may promote tumor growth, suggesting that the presence or absence of certain gut microbiota might affect the body's anti-tumor immunity. Howeverr, underlying mechanisms of our study are largely unknown and warrant further study. Declarations Acknowledgements No. Funding The present study was supported by grants from Hebei Science and Technology Planning (grant no. 16967788D). 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Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3079798","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":211018928,"identity":"3dc28222-3537-456b-a39e-3940e4542776","order_by":0,"name":"Jiajie Shi","email":"","orcid":"","institution":"the Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Shi","suffix":""},{"id":211018929,"identity":"a4c8fdb6-d664-4caf-83ac-22a88c7c01fc","order_by":1,"name":"Cuizhi Geng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYPACCQYDZiCVUCEhJ0+iljMWxoYNxNpjACIY2yoSGQ4QUCnv3nv4xc82C3tzdvaHHx7Ok0hgbGB++OgGHi2GZ86lWfa2STBbNvMYSyRuk8hjZ2AzNs7Bp2VGjpkxY5sEm8FhHjYGoJZixgYeNmm8Wua/AWvhMTjM/owhcY5EYsMBAlrkJXiMHwO1SBgcZjBjSGwgQosBT44ZY885CQOgw4wlEo5JGBs2E/CLfPsZ4w8/yursDc4ff/jxR02dnDx788PHeG05wMAmgSrEjEc52JYGBuYPBNSMglEwCkbBSAcAs39EXggTGvkAAAAASUVORK5CYII=","orcid":"","institution":"the Fourth Hospital of Hebei Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Cuizhi","middleName":"","lastName":"Geng","suffix":""},{"id":211018930,"identity":"43692e6a-4925-4b18-b579-d3afd1bd0e2d","order_by":2,"name":"Zheng Li","email":"","orcid":"","institution":"the Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Li","suffix":""},{"id":211018931,"identity":"d3ab4f7d-788a-4915-bfa5-cbcd5d881547","order_by":3,"name":"Ping Ma","email":"","orcid":"","institution":"the Second Hospital of Qinhuangdao","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Ma","suffix":""},{"id":211018932,"identity":"0ad5d67e-7c40-41d0-9339-4c4c955130c0","order_by":4,"name":"Xi Zhang","email":"","orcid":"","institution":"the Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Zhang","suffix":""},{"id":211018933,"identity":"3e6ce9d2-16c7-4d45-ade5-4d62693499d9","order_by":5,"name":"Meng Cheng","email":"","orcid":"","institution":"the Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2023-06-18 23:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3079798/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3079798/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39115658,"identity":"2f9a9838-5312-44bb-9065-9d1a4d1e05dc","added_by":"auto","created_at":"2023-06-26 19:37:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244460,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the experimental protocol.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/9ccde709749318ca269a3fe8.png"},{"id":39116625,"identity":"12b06981-839b-4cf5-a4ca-955969f12074","added_by":"auto","created_at":"2023-06-26 19:45:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor growth curve among the three groups after intervention.\u003c/strong\u003e Data are shown as means ± SEM (n = 8); *\u003cem\u003eP\u0026lt;0.05.\u003c/em\u003e A, the antibiotic group; B, the bifidobacterium group; C, the control group.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/8ffdcd7fdcffbdb7115cbebf.png"},{"id":39115659,"identity":"2a410650-f28d-4612-b6a6-2fef7c528c06","added_by":"auto","created_at":"2023-06-26 19:37:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":512162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe operational taxonomic units (OTUs) and the species distribution. \u003c/strong\u003e(A) The Venn diagram of OTUs in three groups shows the distribution of common and unique \u0026nbsp;OTUs. The Venn diagram was plotted according to the distribution of OTU sequences in each sample. Each set is filled with different colors and the number of OTUs is labeled inside. (B) Species composition and abundance of each sample at the family level. Only the top 15 species are presented and the remaining species were classified into the ‘others’ category. A, the antibiotic group; B, the sample bifidobacterium group; C, the control group.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/b2714e6f87bc6f3a7295ac6e.png"},{"id":39115661,"identity":"ab64807e-4deb-4f0d-acd2-e6978d939d92","added_by":"auto","created_at":"2023-06-26 19:37:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":277558,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (\u003cstrong\u003ePCA) analysis of the species composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe C group was closer to the B group, while the A group was far away. A, the antibiotic group; B, the bifidobacterium group; C, the control group.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/06f92c3b0372e22d0704c156.png"},{"id":39116626,"identity":"0f751312-5f1b-4bde-b84c-1de6bf2e8215","added_by":"auto","created_at":"2023-06-26 19:45:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eβ diversity.\u003c/strong\u003e (A) Weighted unifrac. (B) Unweighted unifrac. A, the antibiotic group; B, the s bifidobacterium group; C, the control group.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/bebadab42aae0940f5aed78c.png"},{"id":39115664,"identity":"73d588c0-0c28-4f8c-b853-aff264c4ac30","added_by":"auto","created_at":"2023-06-26 19:37:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2188645,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant analysis Effect Size (\u003cstrong\u003eLEFse) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the evolutionary branch diagram, the circle from internal to external radiation represents the classification level from gate to species. Each small circle at different classification levels represents a classification at that level, and the small circle diameter size is proportional to the relative abundance. The species with no significant difference were uniformly colored in yellow. Red represents dominant bacterium in the A group, green represents dominant bacterium in the B group and blue represents dominant bacterium in the C group. A, the antibiotic group; B, the bifidobacterium group; C, the control group.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/8a05edae8f36481ef7026510.png"},{"id":39115665,"identity":"c84ced0f-8842-492b-9ddc-413ca0520db0","added_by":"auto","created_at":"2023-06-26 19:37:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":476665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIFN-γ and IL-2 in both serum and tumor. \u003c/strong\u003e(A) IFN-γ level in serum; (B) IL-2 level in serum; (C) IFN-γ level in tumor; (D) IL-2 level in tumor. Data are box plots (n =8);\u003cstrong\u003e \u003c/strong\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/1291d4cccd6f9039a8fedb6e.png"},{"id":39115663,"identity":"bdf600fa-bc36-4fe9-b684-593e4b4524ce","added_by":"auto","created_at":"2023-06-26 19:37:22","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2316621,"visible":true,"origin":"","legend":"\u003cp\u003eTILs profile in tumors. (A) example micrographs (a, low expression; b, medium expression; c, high expression, HE×100). (B) scoring data plot.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/06f08ba4fca776cebaaa79b9.png"},{"id":41578583,"identity":"c7c25469-fdb4-4ffe-bf5b-3581cd80fcf5","added_by":"auto","created_at":"2023-08-15 12:22:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1696902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3079798/v1/388e030c-f0af-4fc9-89a9-ef751a2461f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bifidobacteria modulating gut microbiota inhibits breast cancer growth via anti-tumor immune response in mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe abnormal of gut microbiota is closely related to human health and diseases, including immunity, digestion, obesity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), diabetes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), heart disease (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), acquired immunodeficiency syndrome (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and even many types of cancer, including breast cancer (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It has been reported that the microbiota might affect human health by inducing chronic inflammation, regulating immune response and metabolic pathways (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For example, Lactococcus lactis has been shown to activate protective cellular immunity, leading to the production of cytokines such as interleukin 2 (IL-2) and IFN-γ (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). IL-2 is a cell growth factor in the immune system that regulates cell activity of the leuksphere in the immune system and promotes the proliferation of helper T cell 0(Th0) and cytotoxic lymphocyte (CTL), whilst IFN-γ is produced by lymphocytes and its antiviral, antitumor and immunomodulatory effects were well documented (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Moreover, gut microbiota can also affect the abundance of tumor-infiltrating lymphocytes (TILs) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), which is one of the most critical and predictive indicators of solid tumor immunity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In breast cancer, infiltrated cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells were positively correlated with survival (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and response to treatment (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In mouse models, the alteration of microbiota composition in small intestine and the activation of selected Gram bacteria species to enter secondary lymphoid organs stimulated the production of \"pathogenic\" T helper 17 (pTh 17) cells and memory Th1 immune response (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Moreover, the application of antibiotics that killed Gram bacteria led to chemotherapy resistance (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies showed that bacteria and their products can affect the immune microenvironment of tumors (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and some types of bacteria, for example, the bacterium Methylobacterium radiotolerans, were even found to be relatively enriched in breast cancer tissue (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In this study, we aimed to investigate the effect of gut microbiota alteration on tumor growth and underlying mechanisms including immune responses.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eAnimals and treatments\u003c/h2\u003e\n\u003cp\u003eThis study was compliance with the Animals (Scientific Procedures) Act 1986 in the UK and Directive 2010/63/EU in Europe. The Laboratory Animal Ethical and Welfare Committee of Fourth Hospital Hebei Medical University, Hebei, China, approved this study (Approval No. IACUC-4th Hos Hebmu-201706).Twenty-four female specific pathogen-free (SPF) Balb/c mice were used in this study (license number: 1707109, purchased from the Animal Center of Hebei Medical University). They were 6\u0026ndash;8 weeks old and 18\u0026ndash;22 g in body weight. All mice were maintained in the breeding cages of the experimental animal room at 20\u0026ndash;22\u0026deg;C with 55\u0026ndash;60% humidity and a light/dark cycle. Twenty-four of them were randomly divided into three groups to recive treeatmeents as follows via oral gavage (n\u0026thinsp;=\u0026thinsp;8/group); The antibiotic (A) group-0.2 ml of the mixture of ampicillin (1mg/ml), streptomycin (5 mg/ml) and polymyxin (1mg/ml) in water (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e); The Bifidobacterium (B) group- 0.2 ml of bifidobacterium water (containing 1\u0026times;10\u003csup\u003e8\u003c/sup\u003e CFU bacteria) (Livzon Group Livzon Pharmaceutical Factory; two capsules were dissolved in 2 ml of purified water to obtain 0.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e CFU/ml solution; The control (C) group-0.2 ml of purified water. The treatments were carried out once every 2 days for 3 weeks \u003cem\u003evia\u003c/em\u003e oral gavage. After 3 weeks treatment, mice were inoculated with 4T1 cells (see below). The treatments were continued for another 3 weeks while they had cancer burden (see below).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e4T1 cells inoculation\u003c/h2\u003e\n\u003cp\u003eThe 4T1 cell line (a gift from Prof Sang,Tumor Research Center, the Fourth Hospital of Hebei Medical University) were cultured in Opti-RPMI 1640 medium containing 10% fetal bovine serum and 100 U/ml penicillin at 37\u0026deg;C, saturated humidity, and 5% CO\u003csub\u003e2\u003c/sub\u003e balanced with air. The cultured cells were passaged with trypsin-EDTA every 2\u0026ndash;3 days. Their suspension of 1\u0026times;10\u003csup\u003e7\u003c/sup\u003e ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were then prepared and 0.1 ml of cell suspension was inoculated into each mouse (10\u003csup\u003e6\u003c/sup\u003e cells/mouse). After inoculation, their tumor growth and health condition of mice were monitored daily including tumor appearance time, tumor size, tumor formation rate, three weeks later, the mice were euthanized.The length (a) and width (b) of tumor were measured with a vernier caliper every other day from the first to the third week, and the tumor volume was calculated using the formula V\u0026thinsp;=\u0026thinsp;ab\u003csup\u003e2\u003c/sup\u003e /2. The tumor growth curve was then plotted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eSample collection\u003c/h2\u003e\n\u003cp\u003eAfter the first 3 weeks of treatments, the fresh fecal specimens were collected for extraction of fecal flora DNA. At the end of the third week after 4T1 cells inoculation, 500 ul of mouse venous blood samples were harvested under pentobarbitital sodium (40mg/kg i.p.) and serum was separated and kept at -80\u0026deg;C. Subsequently, they were euthanized by cervical spine dislocation; tumors were collected and fixed in 4% formalin solution. The experimental protocol is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eHE staining and TILs quantification\u003c/h2\u003e\n\u003cp\u003eThe tumor tissue was embedded in paraffin and cut into 5 \u0026micro;m sections which were stained with hematoxylin-eosin (HE). Tumor-infiltrating lymphocytes (TILs) were asssed under a medium-power field (\u0026times;100) as reported previously (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) in a blinded manner. The percentages of area infiltrated by lymphocytes within the tumor itself plus adjacent stroma were defined as low (\u0026lt;\u0026thinsp;10%), intermediate (10\u0026ndash;50%) or high (\u0026gt;\u0026thinsp;50%) TILs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eDetermination of IFN-\u0026gamma; and IL-2 levels in tumor and serum\u003c/h2\u003e\n\u003cp\u003eThe IFN-\u0026gamma; and IL-2 in both tumors and serum as reported previously were determined with an enzyme-linked immunosorbent assay kit (Elabscience Biotechnology Co, Ltd, Wuhan, China) (catalogue numbers: IFN-\u0026gamma;, E-EL-M0048c; IL-2, E-EL-M0042c). The OD value was measured with a microplate reader at 450 nm. The levels of IFN-\u0026gamma; and IL-2 were calculated via standard curve.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eDNA extraction and amplification of Balb/c mouse feces\u003c/h2\u003e\n\u003cp\u003eDNA from mouse feces was extracted with the soil DNA extraction kit (ProbeGene, Jiangsu, China) accordingly. The 16S rDNA v3-v4 regionof the ribosomal RNA was amplified by PCR (primers: 341F: CCTACGGGNGGCWGCAG; 806R: GGACTACHVGGGTATCTAAT (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). The reaction cycle was: 95\u0026deg;C denature for 2 minutes, 98\u0026deg;C 10 s, 62\u0026deg;C 30 s, 68\u0026deg;C 30 s for 27 cycles, 68\u0026deg;C extension for 10 min. The barcode was the unique 8-bp sequence for each sample. The PCR reactions were performed in triplicate with 50 \u0026micro;L reaction volume, containing 5 \u0026micro;L of 10 \u0026times; KOD buffer, 5 \u0026micro;L of 2.5 mM dNTPs, 1.5 \u0026micro;L of primers (5 \u0026micro;M), 1 \u0026micro;L of KOD polymerase, and 100 ng of template DNA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistics and bioinformatics analysis\u003c/h2\u003e\n\u003cp\u003eData were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (IQR).They were then anlaysed with one or two-way ANOVA followed by the \u003cem\u003epost hoc\u003c/em\u003e Turkey for comparison or Fisher\u0026rsquo;s exact test as appropriate with SPSS 23.0 software. A P\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 was considered to be of statistica significance.\u003c/p\u003e\n\u003cp\u003eBioinformatics analysis: the effective data with \u0026ge;\u0026thinsp;97% similarity were clustered into operational taxonomies units (OTUs), and the USPASE (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) channel was used for clustering. In each cluster, the most abundant marker sequence was selected as the representative sequence. Inter-group Venn analysis was performed using R 3.4.1 to identify unique and common OTUs. The biomarker characteristics of each group were screened using Metastats (20090414 version) (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e) and LEfSe software (1.0 version) (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). \u0026beta;-diversity comparison among groups was computed using Kruskal-Wallis H test in R. The coordinates of principal component analysis (PCA) were then calculated and plotted.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eTumor growth\u003c/h2\u003e\n\u003cp\u003eAfter 4T1 cells were inoculated for 3 to 7 days, tumor nodules started to appear at the site of inoculation and expanded. 15 days later, the tumors started to grow faster 15 days after implanted. At 21 days after inoculation, all mice were sacrificed and the tumors were dissected. The tumor was light yellow-pink in color, hard in texture, and adhered to the surrounding tissues. The maximum tumor diameter was 1.99 cm in C group and the maximum tumor volume was 2.72 cm\u003csup\u003e3\u003c/sup\u003eof the A group. There were no statistically significant between the A and C group (P\u0026thinsp;=\u0026thinsp;0.182) and the B and C group (P\u0026thinsp;=\u0026thinsp;0.405), but the difference between the A and B group was significant (P\u0026thinsp;=\u0026thinsp;0.013) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eGut microbiota\u003c/h2\u003e\n\u003cp\u003eThe A, B and C groupS had 397, 573 and 583 types of OTUs ,respectively. The corresponding species and abundance in the OTUs are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB. Based on the species abundance in the OTU list, Principal Component Analysis (PCA) was carried out to examine the compositional distance between groups using dimensionality reduction (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the weighted (P\u0026thinsp;=\u0026thinsp;1.5015e-10) and unweighted (P\u0026thinsp;=\u0026thinsp;5.5914e-05) \u0026beta; diversity analysis, the gut microbiota were significantly different among the three groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). The differences in gut microbiota were analyzed using LEFse analysis, and the specific flora of each group were identified. Interestingly, bifidobacteria was not the dominant species in the B group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eIFN-\u0026gamma; and IL-2 in both serum and tumors\u003c/h2\u003e\n\u003cp\u003eThe IFN-\u0026gamma; levels in the B and C group were both significantly higher than that in the A group (P\u0026thinsp;=\u0026thinsp;0.01, P\u0026thinsp;=\u0026thinsp;0.0009, respectively), and the IL-2 level of the B group was significantly higher than both the A group (P\u0026thinsp;=\u0026thinsp;0.0003) and C group (P\u0026thinsp;=\u0026thinsp;0.046) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eThe IFN-\u0026gamma; level of the B group was significantly higher than either the A group (P\u0026thinsp;=\u0026thinsp;0.0011) and C group (P\u0026thinsp;=\u0026thinsp;0.0099), and the IL-2 level of the B group was significantly higher than that of the A group (P\u0026thinsp;=\u0026thinsp;0.0010) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eAbundance of TILs\u003c/h2\u003e\n\u003cp\u003eThe example staining of tumor infiltrating lymphocytes is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA.There was no significant difference among the three groups in the ratio of TILs (P\u0026thinsp;=\u0026thinsp;0.883) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study analyzed the relationship between gut microbiota and immune response in breast cancer in a mouse model. We found that the group with the Bifidobacterium treatment had the smallest tumor size and the slowest growth, while the mice treated with antibiotics had the largest tumor size, suggesting that antibiotics and Bifidobacterium had opposite effects on tumor growth. Furthermore, the gut microbiota in the three mice groups were different in both weighted and unweighted β diversity. Our data reported here indicated that gut microbiota alteration can influence body\u0026rsquo;s anti-tumor immunity as evidence with IFN-γ and IL-2 level changes in both tumors and serum corresponding to the treatment.\u003c/p\u003e \u003cp\u003eThe regulatory role of microbiota in immune function was relatively established and, indeed, commensal bacteria have been shown to direct differentiation of T cells leading to expansion of specific molecular subsets, thereby influencing systemic inflammatory processes that was involved in T cell differentiation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Other studies highlighted a role for commensal bacteria in modulating the activation state of innate antigen-presenting cells (APCs), thereby impacting priming of systemic immune responses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Sivan A et al. replorted that the mice with Bifidobacterium detected in gastrointestinal microbiota showed slower tumor growth, higher CD8\u0026thinsp;+\u0026thinsp;T cell activity, and better response to PD-L1 inhibitor treatment than the mice without Bifidobacterium (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It has been also reported that antibiotics induced gut microbiota dysbiosis and promoted tumor initiation and development (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Additionally, antibiotics were found to inhibit mitochondrial function in mice, leading to further damage of the gut epithelial cells (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Therefore, antibiotics can alter gut microbiota and subsequently affect immune homeostasis. All these changes ultimately increase the release of microbial products and the activities of inflammatory cells, such as tumor-associated macrophages to promote tumor growth and metastasis through activating cellular signalling pathway (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur work has some limitations. For example, firstly, the specific flora of each group were identified using LEFse analysis, but bifidobacteria was not the dominant species in its treated group. The reason for this is uknown but this might be due to that bifidobacterium medicament rather than more active bifidobacterium lyophilized powder was used in our study. Secondly, because a relative small sample size of our study, some findings, e.g. the TILs among the three groups, was not significantly. Therefore, the role of gut microbiota on tomur microenviroment requires further study.\u003c/p\u003e \u003cp\u003eThe translational value of our study is unknown. However, interestingly, it was found that the microbiota disruption paired the response of subcutaneous tumors to CpG-oligonucleotide immunotherapy and platinum chemotherapy (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Furthermore, in the antibiotics-treated or germ-free mice, tumor-infiltrating myeloid-derived cells responded poorly to the therapies (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Nevertheless, our current study and other studies may suggest that the use of wide specrum antibiotics should be avoided during the course of treatments including surgery, immunotherapy and chemotherapy as necessary.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eour study demonstrated that gut microbiota alteration may promote tumor growth, suggesting that the presence or absence of certain gut microbiota might affect the body's anti-tumor immunity. Howeverr, underlying mechanisms of our study are largely unknown and warrant further study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe present study was supported by grants from Hebei Science and Technology Planning (grant no. 16967788D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003eAll data in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCotillard, A., Kennedy, S. P., Kong, L. 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Science 342, 967-970.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiota, Meta 16S DNA sequencing, IFN-γ, IL-2, Tumor infiltrating lymphocytes","lastPublishedDoi":"10.21203/rs.3.rs-3079798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3079798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo explore the effects of gut microbiota intervention on anti-tumor immune response, Balb/c mice were divided into three groups that were administered one of the following: antibiotics (mixture of ampicillin, streptomycin and polymyxin), bifidobacterial or sterile water via oral gavage. Their feces were collected for Meta 16S DNA sequencing. The differences in gut bacteria composition among three groups through β diversity analysis. To determine the effects of microbiota intervention on tumor growth, the mice were inoculated with 4T1 breast cancer cells, and the tumor growth was measured after tumor formation. IFN-γ and IL-2 levels in tumors and serum were determined by ELISA, and HE staining was used to quantify lymphocytes infiltrated into tumors. The tumor growth in the antobiotics was significantly faster than that of the bifidobacterial group (P\u0026thinsp;=\u0026thinsp;0.013). β diversity analysis showed significant differences in gut microbiota composition among the three groups (weighted P\u0026thinsp;=\u0026thinsp;1.5015e-10; unweighted P\u0026thinsp;=\u0026thinsp;5.5914e-05). The IFN-γ and IL-2 levels in tumors and serum were higher after bifidobacterial administration than those after antibiotics and water treatment (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Bifidobacteria modulating gut microbiota inhibited breast cancer growth which was likely associated with anti-tumor immune responses.\u003c/p\u003e","manuscriptTitle":"Bifidobacteria modulating gut microbiota inhibits breast cancer growth via anti-tumor immune response in mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-26 19:37:17","doi":"10.21203/rs.3.rs-3079798/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":"a9b93a7e-fb0c-460b-b9ec-1ed24a1f386e","owner":[],"postedDate":"June 26th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-22T09:29:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-26 19:37:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3079798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3079798","identity":"rs-3079798","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}