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Animals : an Open Access Journal from MDPI logoLink to Animals : an Open Access Journal from MDPI
. 2026 Mar 6;16(5):831. doi: 10.3390/ani16050831

Intratumoral Staphylococcus pseudintermedius Promotes Proliferation and Migration of CMT-U27 Cells Through the TLR2/PI3K/Akt Signaling Pathway

Luting Luo 1, Jin Li 1, Meng Li 1,*
Editor: Juan Carlos Illera del Portal1
PMCID: PMC12985303  PMID: 41829039

Simple Summary

Increasing evidence suggests that intratumoral microorganisms and their metabolites can modulate cancer initiation and progression. However, the composition and functional roles of intratumoral bacteria in canine mammary tumors remain unclear. This study investigated the intratumor microbiome in dogs with mammary tumors. Amplicon sequencing revealed that both the species richness and diversity of intratumor microbiome in tumor tissues were significantly lower than in non-tumorous healthy mammary tissues. Statistical analysis indicated that Burkholderiales and Roseateles were significantly enriched in tumor tissues at different taxonomic levels. Subsequently, bacteria originating from five different genera were isolated from clinical mammary tumor tissues, among which Staphylococcus pseudintermedius was the most frequently isolated species. Functional assays demonstrated that Staphylococcus pseudintermedius promoted the proliferation and migration of canine mammary cancer cells by activating the TLR2/PI3K/Akt pathway. This study revealed a functional link between tumor-associated bacteria and cancer progression, providing further theoretical support for the close relationship between intratumor microbiome and canine mammary tumors.

Keywords: intratumor microbiome, bacterial isolation, Staphylococcus pseudintermedius, toll-like receptors pathway, PI3K/Akt pathway, canine mammary tumor

Abstract

Increasing evidence suggests that intratumoral microorganisms and their metabolites can modulate cancer initiation and progression. However, the composition and functional role of intratumoral bacteria in canine mammary tumors (CMTs) remain unclear. In this study, we investigated the functional significance of tumor-derived Staphylococcus in CMTs, focusing on its effects on the proliferation and migration of CMT-U27 cells. 16S rRNA sequencing revealed reduced alpha diversity in CMT tissues, with Staphylococcus pseudintermedius identified as the most frequently isolated species. Functional assays, including CCK-8, wound healing, RT-qPCR, and Western blot analyses, demonstrated that intratumoral Staphylococcus pseudintermedius significantly enhanced cellular proliferation and migration. Mechanistically, Staphylococcus pseudintermedius significantly upregulated the expression of TLR2, as well as the phosphorylation levels of PI3K, Akt and P70S6K. The inhibition of TLR2 using C29 suppressed the mRNA expression of VEGF, MMP9, MMP2, and EGFR. Collectively, these findings indicate that intratumoral Staphylococcus pseudintermedius promotes the proliferation and migration of CMT-U27 cells through activation of the TLR2/PI3K/Akt pathway, highlighting a functional link between tumor-associated bacteria and cancer progression.

1. Introduction

Advancements in high-throughput sequencing technology and a deepened understanding of cancer complexity led to the recognition that the interior of tumors was not sterile; instead, the microbiota was now acknowledged as an important component of the tumor microenvironment. Intratumoral microbiota profiles were also found to differ from those of paired or normal tissues in humans [1,2,3,4]. A study across seven human cancer types showed that the microbial composition varied by tumor type, with breast cancer tissues harboring a particularly rich and diverse microbiome [5]. However, research on the intratumoral microbiome in veterinary medicine remained limited. A significant difference in overall community diversity was detected between oronasal mucosal melanoma and normal oral tissues in dogs [6], and the bacterial richness within canine mammary tumor tissues was lower than that in tumor-adjacent tissues [7].

Recent reports suggested that the intratumoral microbiome was closely linked to tumor development and many bacteria were found to possess the potential to promote the growth and metastasis of cancer [8,9,10,11]. Bacteria directly influence the host through the secretion of virulence factors or the instigation of inflammatory factors, leading to malignant transformation of host cells; they also alter the tumor microenvironment by shaping it in either pro-tumorigenic or anti-tumorigenic directions [12].

Notably, studies have isolated Staphylococcus from breast tumor tissues in mice and humans [13,14,15]. Urbaniak et al. [14] observed that the relative abundance of Staphylococcus in breast cancer patients was significantly higher than in healthy controls, and isolating Staphylococcus epidermidis from normal adjacent tissues was able to induce DNA double-strand breaks in HeLa cells, thereby increasing cancer risk in humans. Staphylococcus epidermidis, as a potent inducer of inflammation, influenced the release of pro-inflammatory cytokines and chemokines, thereby promoting breast cancer progression [13]. In summary, these findings underscored that Staphylococcus was related to the development of breast cancer and further research to determine tumor-associated microbiome profile was crucial.

However, studies on the intratumoral microbiome of canine mammary tumors (CMTs) remain limited. A recent study showed that Staphylococcus could also be isolated from tissues of CMT [7], but the role of Staphylococcus in CMT remains unclear. Therefore, this study aimed to characterize the intratumoral microbiome in dogs with CMT and reveal the direct effects of intratumoral Staphylococcus pseudintermedius on host cells.

2. Materials and Methods

2.1. Sample Collection

Between April 2024 and February 2025, mammary tumor tissues were obtained from dogs with CMT at the Veterinary Teaching Hospital of Nanjing Agricultural University. The inclusion criteria were as follows: (1) a clinical diagnosis of mammary tumor based on physical examination; and (2) histopathological confirmation of mammary tumor following surgical excision. The exclusion criteria were as follows: (1) a history of other tumors, metabolic diseases, or immunosuppressive disorders; (2) ulcerated mammary tumors; (3) antibiotic use within one month prior to surgery; and (4) any previous anti-tumor therapy. For comparison, non-tumorous mammary tissues were collected from healthy experimental dogs undergoing routine ovariohysterectomy procedures. All procedures were approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University (20241209257). Surgically excised tissues were immediately transferred to a biosafety cabinet, placed in sterile Petri dishes and dissected with a sterile surgical instrument under aseptic conditions. The tumor epidermis was excised to obtain the internal tumor tissue and cut into fragments of 3 to 5 mm3. The processed samples were divided into two portions: one portion was used fresh for bacterial isolation, while the other was placed into sterile tubes and stored at −80 °C until further use.

2.2. 16S rRNA Gene Sequencing

Genomic DNA was extracted from tissue sample using the TIANamp Genomic DNA kit (TIANGEN, Beijing, China). The V4 variable region of the bacterial 16S rRNA gene was amplified using 515F/806R primers and sequencing libraries were constructed using fusion primers containing the Illumina adapter sequences. Polymerase chain reaction (PCR) amplicons were verified by agarose gel electrophoresis, and library quality was assessed using a Qubit® 4.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Sequencing was carried out on the Illumina Nextseq 2000 PE300 platform (Illumina, San Diego, CA, USA).

Raw reads were merged and quality-filtered, high-quality sequences were clustered into operational taxonomic units (OTUs) with ≥97% similarity using Usearch (v11.0.667), and singleton OTUs were removed [16]. The representative sequence for each OTU was annotated by being assigned with the SILVA database based on the RDP classifier algorithm [17]. An alpha diversity analysis including Chao 1 richness estimator (Chao 1), abundance-based coverage estimator, and Simpson and Shannon indexes was calculated using Mothur software (v3.8.31) [18]. Beta diversity was visualized via principal coordinate analysis (PCoA) based on Bray–Curtis distances, and an analysis of similarities (ANOSIM) was performed to assess intra- and inter-group differences. Moreover, significant differences in the relative abundance of taxa between groups were determined using linear discriminant analysis effect size (LEfSe) [19] and taxa with LDA > 4.0 at p < 0.05 were considered significantly enriched.

2.3. Isolations and Identification of Intratumoral Bacteria

Fresh tumor tissues were processed under aseptic conditions, as described above. For bacterial enrichment, tissue fragments were inoculated onto brain heart infusion (BHI) broth (Hopebio, Qingdao, China) and incubated at 37 °C under both aerobic and anaerobic conditions for 24–72 h. Once turbidity was observed in BHI broth, cultures were subcultured onto BHI agar plates to obtain isolated colonies. Colonies with a distinct morphology were stained by Gram staining and examined under a microscope. Pure culture was obtained through repeated streak plating. Single purified colonies were inoculated into 5 mL of BHI broth and incubated for 16–18 h to obtain the bacterial suspensions. Each suspension was divided into two portions. For strain preservation, 1 mL of culture was mixed with an equal volume of 50% sterile glycerol and stored at −80 °C. The remaining portion was used for DNA extraction and the extracted DNA was stored at −20 °C for subsequent analyses.

TIANamp Bacteria DNA Kit (TIANGEN, Beijing, China) was used to extract bacterial DNA, which would serve as the DNA template for PCR. The target fragment in the template was amplified using a bacterial 16S rRNA universal primer (27 F: 5′-AGAGTTTGATCCTGGCTCAG-3′, 1492 R: 5′-GGTTACCTTGTTACGACTT-3′). The reaction was set up as follows: 12.5 μL 2 × Es Taq MasterMix (Dye) (Cwbio, Taizhou, China), 1 μL DNA template, 1 μL forward primer (10 μM), and 1 μL reverse primer (10 μM), before adding up to 25 μL of ddH2O. PCR was performed under the following conditions: 95 °C for 5 min, 95 °C for 15 s, 60 °C for 15 s, and 72 °C for 30 s, with 35 cycles, before finally extending to 72 °C for 10 min. PCR products were visualized by electrophoresis on 1% agarose gels and the positive PCR products were then submitted to Sangon Biotech Co., Ltd. (Shanghai, China) for sequencing. Sequence similarity was performed using NCBI BLAST [https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 19 March 2025)] against the 16S rRNA sequences (Bacteria and Archaea) database, with a similarity threshold of >97% for taxonomic assignment at the genus level. Species identification of Staphylococcus pseudintermedius was performed by PCR using primers (F: 5′-TRGGCAGTAGGATTCGTTAA-3′, R: 5′-CTTTTGTGCTYCMTTTTGG-3′) according to a previously described method [20].

2.4. Bacteria and Cell

Staphylococcus isolated from clinical mammary tumor tissue was first characterized for antimicrobial susceptibility. Gentamicin and lysozyme were used during subsequent cell treatments to eliminate extracellular bacteria according to previously described methods [21]. Consequently, one Staphylococcus pseudintermedius strain confirmed to be sensitive to gentamicin by antimicrobial susceptibility testing was used in subsequent in vitro experiments. After overnight bacterial culture, the bacterial suspension was centrifuged at 5000 rpm for 5 min, and the pellet was washed three times with sterile PBS after discarding the supernatant. After the final wash, the pellet was resuspended in 1 mL of RPMI 1640 medium (BasalMedia, Shanghai, China) with thorough mixing. Bacteria were freshly prepared prior to each experiment.

The canine mammary carcinoma cell line CMT-U27 (CRL-3456) was purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). The CMT-U27 cell line was confirmed to be free of mycoplasma contamination and cultured in complete medium containing RPMI 1640 medium with 10% fetal bovine serum (FBS) (Vazyme, Nanjing, China), penicillin (100 U/mL)—streptomycin (0.1 mg/mL)—gentamicin (50 μg/mL) (Beyotime, Shanghai, China). The cell was cultured in a humidified incubator set at 37 °C with 5% CO2. The morphological characteristics and growth patterns of all cell lines were routinely monitored to ensure consistency in their maintenance.

2.5. Cell Viability Assay

CMT-U27 cells were seeded into 96-well plates. After adhesion, bacteria were added at different multiplicities of infection (MOI; ratio of bacteria to cells). The plates were incubated at 37 °C with 5% CO2 for 2 h. Following infection, bacteria was aspirated, and each well was treated with gentamicin (0.2 mg/mL, Solarbio, Beijing, China) and lysozyme (1 mg/mL, Solarbio, Beijing, China) for 2 h to eliminate extracellular bacteria. Wells were then washed with PBS, which was recorded as the 0 h time point. Complete culture medium was subsequently added, and cells were incubated for another 24 h.

To evaluate cell viability 24 h after bacterial infection, a Cell Counting Kit-8 (CCK-8) solution (Solarbio, Beijing, China) was added to each well at a volume equal to 10% of the culture medium. The plate was incubated in the dark for 1–4 h, and absorbance was measured at 450 nm using the Multiskan FC Enzyme Analyser (Thermo Fisher Scientific, Waltham, MA, USA). Relative cell viability was calculated according to the manufacturer’s instructions.

2.6. Immunofluorescence (IF) Assay

Bacteria were incubated with 10 μM CFDA-SE (MedChemExpress, Shanghai, China) in the dark for 30 min, followed by centrifugation, and three washes with pre-cooled PBS. The labeled bacteria were then added to CMT-U27 cells and incubated according to the protocols described in Section 2.5. After 24 h, cells were fixed with 4% paraformaldehyde (Xilong Scientific Co., Ltd., Shenzhen, China) and permeabilization with 0.5% Triton X-100 (Servicebio Technology Co., Ltd., Wuhan, China). Following washing with PBS, F-actin was stained by incubation with IF555-conjugated phalloidin (Servicebio Technology Co., Ltd., Wuhan, China) for 2 h at room temperature in the dark. Cell nuclei were subsequently counterstained with DAPI (Servicebio Technology Co., Ltd., Wuhan, China), and an antifade mounting medium was applied. Fluorescence images were captured using a fluorescence microscope (Nikon, Tokyo, Japan).

2.7. Colony-Forming Unit (CFU) Assay

Cells were pre-seeded in 6-well plates, and after 24 h of infection, each well was washed with PBS. Cells were treated with sterile 0.1% Triton X-100 (Beyotime, Shanghai, China) and trypsin solution (Solarbio, Beijing, China) and incubated at 37 °C for 5 min to lyse the cells and release intracellular bacteria. The resulting lysates were serially diluted in sterile PBS and aliquots of each dilution were plated and cultured on BHI plates. After incubating at 37 °C for 16–18 h, colonies were used to observe bacterial growth.

2.8. Wound Healing Assay

Cells were inoculated into 6-well plates at a density of 7 × 105 cells/well, and a scratch migration assay was performed the next day. Three wells were added the bacteria with an MOI of 25, and the remaining wells were added to RIPM 1640 medium as the control. After infection, gentamicin and lysozyme were added to eliminate extracellular bacteria. Then, cells were cultured using complete medium containing 1% FBS and cell migration images were taken at 0 h and 24 h, respectively. Scratch processing, image acquisition, and data analysis were performed as previously described [22]. Scratch wound width was calculated using Image J software (v 2.16.0).

Open wound area %=Area (24 h)Area (0 h)×100%

2.9. RNA Extraction and Illumina RNA-Sequencing

CMT-U27 cells were pre-seeded in 6-well plates. Three wells were infected with bacteria at an MOI of 25, and the remaining wells were added to RIPM 1640 medium and served as the controls. After 2 h of infection, gentamicin and lysozyme were added to eliminate extracellular bacteria. Then, after 24 h of incubation, cells in each well were harvested and total RNA was extracted by a Total RNA Extraction kit (Vazyme, Nanjing, China). RNA quality and integrity were assessed using the RNA Nano 6000 Assay Kit (Agilent Technologies, Santa Clara, CA, USA). rRNA-depleted RNA libraries were constructed using the NEB Next Ultra-Directional RNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA) and sequenced on an Illumina NovaSeq X plus (Illumina, San Diego, CA, USA).

Clean reads were aligned with the Canis lupus familiar (ensemble database release-115) reference genome using HISAT2 (v2.0.5). Gene expression levels were quantified, and a differential expression analysis was performed using R package DESeq2 (v1.20.0). Given the small background set of differentially expressed genes (DEGs), applying strict multiple testing corrections to enrichment analysis results would fail to identify any biologically meaningful pathways. To minimize potential loss of biological signals due to overly stringent multiple testing corrections, a nominal p  < 0.05 was used for subsequent analysis. Genes with |log2 (Fold Change)| ≥  1 and p < 0.05 were considered DEGs. A Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted using Cluster Profiler (v3.8.1), and p < 0.05 was considered statistically significant. A gene set enrichment analysis (GSEA) was performed using KEGG gene sets. Enrichment results with |normalized enrichment scores| (|NES|) > 1.0 and p < 0.05 were considered statistically significant.

2.10. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)

Total RNA was converted to cDNA using HiScript IV All-in-one Ultra RT SuperMix for qPCR (Vazyme, Nanjing, China), according to the manufacturer’s instructions. AceQ qPCR SYBR Green Master Mix (Vazyme, Nanjing, China) was used for quantitative analysis and performed on an ABI 7300 real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). The β-actin gene was used as the control gene, and the relative expression of each target gene was analyzed by the 2−ΔΔCt method. The primer sequences were provided in Table 1.

Table 1.

Primer sequence information for RT-qPCR analysis.

Genes Name Primer Sequence (5′→3′) Amplicon Length (bp) Reference
EGFR F: CGAGCACAAGGACAACATCG 288 [23]
R: CTCCACACATCGCTTTGGTG
VEGF F: GAATGCAGACCAAAGAAAGATAGAG 88 [24]
R: GATCTTGTACAAACAAATGCTTTCTC
MMP2 F: GGGACCACGGAAGACTATGA 68 [25]
R: ATAGTGGACATGGCGGTCTC
MMP9 F: TGAGAACTAATCTCACTGACAAGCA 75 [25]
R: GCTCGGCCACTTGAGTGTA
TLR2 F: GGTTGCATATTCCACACTTTTACTC 116 [26]
R: TGAGCAAGGAACCAGAAAGACC
β-actin F: GCATCGTGATGGACTCCGGT 86 [22]
R: CAGACGCAAGATGGCATGGG

Note: F: forward; R: reverse; EGFR, epidermal growth factor receptor; VEGF, vascular endothelial growth factor; MMP2, matrix metallopeptidase 2; MMP9, matrix metallopeptidase 9; TLR2, Toll-like receptor 2.

2.11. Western Blot (WB) Analysis

The total protein of cells was lysed using radio immunoprecipitation assay (RIPA) buffer (Solarbio, Beijing, China), which contained 1% PMSF and 1% phosphatase inhibitor (Biotek, Shanghai, China). Protein samples were quantified and adjusted with a Bicinchoninic Acid (BCA) kit (Epizyme, Shanghai, China). A total of 20 μg protein was separated by 10% SDS-PAGE (EpiZyme, Shanghai, China) and then transferred onto 0.22 μm polyvinylidene difluoride (PVDF) membranes (Merck Millipore, Darmstadt, Germany). Non-specific binding sites were blocked using 3% BSA for 1 h, and then the membranes were sequentially detected with specific primary antibodies and corresponding secondary antibodies. Protein bands were visualized with enhanced chemiluminescence solution (Abbkine Scientific Co., Ltd., Wuhan, China). The expression of target proteins was normalized using β-actin as the reference protein.

The following antibodies were used for WB: β-actin (1:10,000; #4970, Cell Signaling Technology [CST], Danvers, MA, USA), VEGFA (1:1000; #WL00009, Wanleibio, Shenyang, China), EGFR (1:5000; #T55112, Abmart, Shanghai, China), MMP2 (1:1000; #WL03224, Wanleibio, Shenyang, China), MMP9 (1:1000; #WL03096, Wanleibio), TLR2 (1:500; #R380745, Zenbio, Chengdu, China), Rac1 (1:1000; #WL02851, Wanleibio), PI3K p110 (1:2000; #WL03380, Wanleibio), p-PI3K p110 (1:1000; #WLA0482, Wanleibio), Akt (1:1000; #WL0003b, Wanleibio), p-Akt (Ser 473; 1:1000; #T40067, Abmart), p-P70S6K (1:1000; #WL04213, Wanleibio), HRP-conjugated goat anti-rabbit IgG (H  +  L) secondary antibody (1:5000; SA00001-2, Proteintech group [PTG], Rosemont, IL, USA), HRP-conjugated goat anti-mouse IgG (H  +  L) secondary antibody (1:5000; SA00001-1, PTG).

2.12. TLR2 Inhibitor Treatment

C29 (25 μM, MedChemExpress, Shanghai, China) was used to inhibit Toll-like receptor 2 (TLR2) in CMT-U27 cells for 1 h prior to bacterial infection [27], and subsequently incubated for 24 h, as described in Section 2.5. The collected cells were then subjected to RT-qPCR analysis. The final concentrations of DMSO in all cell wells were maintained at ≤1‰.

2.13. Statistical Analysis

Statistical analysis and data visualization were carried out using commercial software (GraphPad Prism 10.3.0; GraphPad Software, San Diego, CA, USA). The Shapiro–Wilk test was used to assess data normality. Normally distributed data were presented as mean ± standard deviation (SD). Student’s t test or Welch’s t-test were employed to detect the difference for normally distributed data. Non-normally distributed data were assessed using the Mann–Whitney U test. Values of p < 0.05 were considered significant.

3. Results

3.1. Characterization of Intratumoral Microbiome in Dogs with CMT

Fifteen fresh canine mammary tumor tissues (CMTT group, n = 15) were collected from female dogs diagnosed with mammary tumors, with a mean age of 11.87 ± 2.39 years. The breeds included Poodles (n = 6), mixed-breed dogs (n = 3), Bichon Frise (n = 2), Alaskan Malamutes (n = 2), Golden Retrievers (n = 1) and Samoyed (n = 1) (Table 2). Non-tumorous healthy mammary tissues (HT group, n = 6) were derived from six female experimental beagle dogs, with a mean age of 5.17 ± 1.17 years. Histological classification was performed according to the modified CMT classification system proposed by Goldschmidt et al. [28]. The tumor types included tubular carcinoma (n = 4), intraductal papillary carcinoma (n = 3), solid carcinoma (n = 3), mixed carcinoma (n = 3), ductal carcinoma (n = 1), and intraductal papillary adenoma (n = 1) (Table 2).

Table 2.

Bacteria isolated from clinical canine mammary tumor tissues.

Number Breed Age (Years) Spay Status Histopathology Isolated Bacteria (Genus)
Classification Grading
1 Samoyed 9 No intraductal papillary carcinoma I Staphylococcus
2 Poodles 9 No tubular carcinoma II Staphylococcus, Micrococcus
3 Mixed-breed 11 No solid carcinoma II Staphylococcus
4 Bichon Frise 9 No solid carcinoma II Staphylococcus, Brevibacterium
5 Poodles 16 No tubular carcinoma II Staphylococcus
6 Mixed breed 15 No solid carcinoma II Staphylococcus, Pseudomonas
7 Poodles 14 No mixed carcinoma II Enterococcus
8 Golden Retrievers 9 No tubular carcinoma II Staphylococcus, Pseudomonas, Enterococcus
9 Mixed-breed 13 No intraductal papillary carcinoma III Staphylococcus
10 Bichon Frise 11 No ductal carcinoma II Staphylococcus
11 Poodles 10 Yes mixed carcinoma I N/A
12 Alaskan Malamutes 12 Yes intraductal papillary carcinoma I Staphylococcus, Brevibacterium
13 Alaskan Malamutes 12 Yes intraductal papillary adenoma N/A Staphylococcus, Pseudomonas
14 Poodles 14 No tubular carcinoma I Staphylococcus
15 Poodles 14 No mixed carcinoma II Staphylococcus

A total of 1,963,154 raw sequenced sequences (raw reads) were obtained from the downstream sequencing of CMTT and HT groups, and a total of 1,902,443 valid sequences (clean reads) were obtained after splicing and quality control. Moreover, the coverage rate of each sample was more than 99.9%, indicating that this sequencing result could represent the real situation of the samples. A total of 425 OTUs were generated in both groups, with the number of OTUs common to both groups being 81 and 166 OTUs being specific to the CMTT group.

The alpha diversity analysis results are shown in Figure 1A. It can be seen that Chao 1 and Shannon indexes of the HT group were significantly higher than that of the CMTT group, and the Simpson index was significantly lower than that of the CMTT group, indicating that a significant difference in alpha diversity between the two groups, with the HT group having significantly higher species richness and diversity than the CMTT group. There was a significant difference in the structure of community composition between the two groups in terms of beta diversity, as described by PCoA based on Bray–Curtis distances (Figure 1B,C).

Figure 1.

Figure 1

Characterization of intratumoral microbiota in canine mammary tumors. (A) Alpha diversity analysis showing species richness and diversity in non-tumorous healthy mammary tissues (HT) and canine mammary tumor tissues (CMTT). Alpha diversity indices were significantly higher in the HT group than in the CMTT group. (B) Principal coordinate analysis (PCoA) based on Bray–Curtis distances. (C) Analysis of similarities (ANOSIM) comparing microbiota between both CMTT and HT groups. (D,E) Relative abundance of the top 6 phyla (D) and 10 genera (E) detected in samples. (F,G) Linear discriminant analysis effect size (LEfSe) analysis identifying taxa enriched in the CMTT group (blue) and HT group (purple). Statistical significance was indicated as p  <  0.05 (*) and p  <  0.001 (***); ns indicates no significant difference.

At the phylum level, Proteobacteria, Firmicutes, Bacteroidota, Actinobacteriota and Fusobacteriota were the dominant species in the intratumoral microbiome of both groups. The relative abundance of Firmicutes, Bacteroidota and Fusobacteriota in the HT group was significantly higher than that of the CMTT group, and the relative abundance of Proteobacteria in the CMTT group was significantly higher than that of the HT group (Figure 1D). At the genus level, Roseateles, unclassified_Bacteria, Staphylococcus, Acinetobacter and Stenotrophomonas were the dominant bacteria in the CMTT group, and Roseateles, Stenotrophomonas, Escherichia-Shigella, unclassified_Enterobacteriaceae and Pseudomonas were the dominant bacteria in the HT group (Figure 1E).

We set the LDA value at >4.0, and plotted the evolutionary branching diagrams and histograms of the distribution of LDA values after sorting the results (Figure 1F,G). The intratumor microbiome was enriched with 4 and 28 species in CMTT and HT groups, respectively. The Proteobacteria, Burkholderiales, Comamonadaceae, and Roseateles were enriched in the CMTT group compared to the HT group.

In addition, intratumoral microbiota were analyzed in grade I (n = 4) and grade II (n = 9) CMT samples. No significant differences in alpha or beta diversity were observed between the two groups. Furthermore, LEfSe analysis did not identify any significantly enriched taxa (Supplementary Figure S1).

3.2. Bacteria Isolated from Mammary Tumor Tissue

Bacteria were isolated, cultured and identified by Sanger sequencing as originating from five different genera, including Staphylococcus spp. (n = 12), Micrococcus spp. (n = 1), Pseudomonas spp. (n = 3), Brevibacterium spp. (n = 2) and Enterococcus spp. (n = 2) (Table 2). Among the Staphylococcus isolates, seven isolates were identified as Staphylococcus pseudintermedius (Supplementary Figure S2).

3.3. Staphylococcus pseudintermedius Invaded CMT-U27 Cells and Undergoes Intracellular Internalization

CFDA-SE-labeled bacteria were detected within CMT-U27 cells by fluorescence microscope, indicating successful bacterial internalization (Figure 2A). Consistently, CFU assays confirmed bacteria entry and demonstrated that Staphylococcus pseudintermedius remained viable within CMT-U27 cells for at least 24 h after internalization (Figure 2B).

Figure 2.

Figure 2

Internalization of Staphylococcus pseudintermedius by CMT-U27 cells. (A). Immunofluorescence assay showing bacterial internalization (green, CFDA-SE; indicated by arrows). Actin and nuclei were stained in red (phalloidin) and blue (DAPI), respectively. Scale bars represent 20 μm. (B). Representative image of intracellular bacteria by colony-forming unit assay, indicating bacteria remained viable within CMT-U27 cells for at least 24 h.

3.4. Staphylococcus pseudintermedius Promoted the Proliferation and Migration of CMT-U27 Cells In Vitro

CCK-8 assay results showed that CMT-U27 cell viability was significantly increased 24 h after bacterial infection at MOIs ranging from 6.25 to 25 (Figure 3A). Consistently, wound healing experiments demonstrated that, compared to the control group, the migration of CMT-U27 cells was significantly increased in Staphylococcus group (Figure 3B,C). Moreover, RT-qPCR and WB analyses demonstrated significantly elevated mRNA and protein expression levels of VEGFA, EGFR, MMP2 and MMP9 in the Staphylococcus-treated group relative to controls (Figure 3D–F). These findings indicated that Staphylococcus pseudintermedius promoted the proliferation and migration of CMT-U27 cells in vitro.

Figure 3.

Figure 3

Effects of Staphylococcus pseudintermedius (25 MOI) on the proliferation and migration of CMT-U27 cells in vitro. (A) Cell viability assessed by CCK-8 assay under different MOIs. (B) Representative images of wound healing assays showing the migration of CMT-U27 cells. Scale bars represent 100 μm. (C) Quantification of cell migration based on the percentage of open wound area. (D) Relative mRNA expression levels of VEGF, EGFR, MMP2 and MMP9 in CMT-U27 cells. (E) Western blot analysis of VEGFA, EGFR, MMP2 and MMP9 protein expression in CMT-U27 cells. (F) Quantification of the relative protein expression levels of VEGFA, EGFR, MMP2 and MMP9 in CMT-U27 cells. β-actin was used as a loading control. Statistical significance was indicated as p  <  0.05 (*), p  <  0.01 (**) and p  <  0.001 (***); ns indicates no significant difference.

3.5. Transcriptome Analysis of Differentially Expressed Genes in CMT-U27 Cells Following Staphylococcus pseudintermedius Infection

As shown in Figure 4A, a total of 71 DEGs were identified in CMT-U27 cells following Staphylococcus pseudintermedius infection compared with the control group. KEGG pathway enrichment analysis revealed that these DEGs were significantly enriched in the Toll-like receptors signaling pathway in the Staphylococcus-treated group (Figure 4B). Furthermore, GSEA demonstrated significant enrichment of Toll-like receptors and PI3K/Akt signaling pathways in the Staphylococcus-treated group (Figure 4C,D). Collectively, these bioinformatics analyses suggested that intracellular Staphylococcus-associated transcriptional changes potentially involved Toll-like receptors-related signaling pathways and the PI3K/Akt signaling pathway.

Figure 4.

Figure 4

Transcriptome analysis of differentially expressed genes (DEGs) in CMT-U27 cells following Staphylococcus pseudintermedius (25 MOI) infection. (A) Volcano plot showing DEGs with |log2(Fold Change)|  ≥  1 and p  < 0.05. (B) KEGG pathway enrichment analysis of DEGs (p  < 0.05). (C,D) Gene set enrichment analysis (GSEA) of the Toll-like receptors (C) and PI3K/Akt (D) signaling pathways, with |normalized enrichment scores| (|NES|) > 1.0 and p < 0.05.

3.6. Staphylococcus pseudintermedius Promoted the Proliferation and Migration of CMT-U27 Cells Through TLR2/PI3K/Akt Signaling Pathway

Protein expression analysis revealed that TLR2 and Rac1, key components of the Toll-like receptors signaling pathway, were significantly upregulated following Staphylococcus treatment (p = 0.002 and p = 0.014, respectively). Additionally, the phosphorylation levels of PI3K, Akt and P70S6K were markedly increased in the Staphylococcus-treated group (p = 0.013, p = 0.011 and p = 0.035, respectively), indicating activation in PI3K/Akt signaling pathway (Figure 5A–E). These findings suggested that Staphylococcus pseudintermedius activated the TLR2/PI3K/Akt signaling in CMT-U27 cells.

Figure 5.

Figure 5

Staphylococcus pseudintermedius (25 MOI) promoted the proliferation and migration of CMT-U27 cells through TLR2/PI3K/Akt signaling pathway. (A,B) Western blot analysis of TLR2 and Rac1 (A), and PI3K, Akt, the phosphorylation of PI3K, Akt and P70S6K (B) in CMT-U27 cells. (C) The quantification of relative protein expression and phosphorylation levels of TLR2/PI3K/Akt pathway-associated proteins in CMT-U27 cells. The β-actin was used as a control for equal loading. (D,E) The ratios of phosphorylated to total PI3K (D) and Akt (E) in CMT-U27 cells. (F) CMT-U27 cells were pretreated using C29 (25 μM) and subsequently infected with Staphylococcus pseudintermedius (25 MOI). TLR2 mRNA expression levels were measured at the 24 h time point. (G) mRNA expression levels of VEGF, EGFR, MMP2 and MMP9 in CMT-U27 cells following C29 pretreament (25 μM) and Staphylococcus pseudintermedius infection (25 MOI) at the 24 h time point. Statistical significance was indicated as p  <  0.05 (*), p  <  0.01 (**) and p  <  0.001 (***); ns indicates no significant difference.

To further confirm the role of TLR2, we utilized the TLR2 inhibitor C29 and verified its inhibitory effect on TLR2 gene expression (Figure 5F). Subsequent experiments demonstrated that C29 significantly suppressed the Staphylococcus-induced upregulation of VEGF, EGFR, MMP2 and MMP9 at the mRNA level (Figure 5G). Collectively, these findings suggested that Staphylococcus pseudintermedius promoted the proliferation and migration of CMT-U27 cells through the TLR2/PI3K/Akt signaling pathway.

4. Discussion

Canine mammary tumors (CMTs) are common neoplasms, particularly in female unspayed dogs, and represent a valuable comparative model for human breast cancer. Accumulating evidence confirmed the existence of a distinct microbiota within breast tissue in both human and dogs [5,7,14,29]. As a vital component of the tumor microenvironment, the intratumoral microbiota of mammary tumors originated from a wide array of sources: bacteria might enter the mammary tumors through the oral cavity or the areola [30,31], while gut microbes could be transported to tumor sites via the bloodstream, influencing the intratumoral microbiome [32,33,34].

In the present study, the canine mammary tumor tissues (CMTT) group exhibited lower alpha diversity than the non-tumorous healthy mammary tissues (HT) group, which was consistent with previous findings in both humans and dogs [7,15,35]. At the phylum level, Proteobacteria, Firmicutes, Bacteroidota and Actinobacteriota represented the dominant components of the intratumoral microbiota in both groups, in agreement with earlier reports in human breast tumors [35,36,37]. Although the dominant phyla were similar between groups, the reduced alpha diversity observed in tumor tissues suggested a disruption of microbial ecology balance, which might reflect tumor-associated selective pressures within the mammary microenvironment. Additionally, studies have shown that humans and dogs living in the same household could share skin, gut and oral microbiomes, highlighting the potential for bacterial transfer [38,39]. This suggests that future cancer research might extend beyond individual patients, necessitating the consideration of both the pet and owner microbiomes.

Notably, the dominant bacterial genera differed between the two groups. The relative abundance of Roseateles was significantly higher in the CMTT group than in the HT group. Roseateles had been detected in diverse biological sites, including the intestine tract, human heart valves and urine, as well as in goat colostrum [40,41,42,43]. A study indicated that the relative abundance of Roseateles was significantly increased in gastric mucosal tissues from patients with chronic atrophic gastritis compared with those with chronic non-atrophic gastritis, suggesting a potential association with bacterial biodegradation capacity [44]. In addition, members of the order Burkholderiales were significantly enriched in the CMTT group compared with the HT group, and enrichment of this order was also observed in formalin-fixed paraffin-embedded tissue blocks from human colorectal cancer [45]. In non-small cell lung cancer, the presence of Burkholderiales in normal lung tissue was associated with improved recurrence-free survival [46]. Conversely, Burkholderiales in the gut microbiota was linked to an increased risk of oral cavity cancer [47], while another study reported a protective association with thyroid cancer [48]. These findings suggest that Burkholderiales might exert a tumor-dependent role across different cancer types. Given the complexity of tumor-associated microbial communities, the potential mechanisms underlying the role of Burkholderiales in CMT remain to be elucidated. Collectively, these findings raise the possibility that intratumoral bacteria might contribute to cancer progression. Indeed, Yao et al. [44] reported that microorganisms could participate in biodegradation and inflammation, and the modulation of sustained inflammation in tumors. Similarly, Xuan et al. [36] proposed that microbial dysbiosis might attenuate bacteria-driven immune stimulation and thereby promote breast cancer development.

It is noteworthy that the relative abundance of Staphylococcus was higher in the CMTT group than in the HT group (10.39% vs. 0.72%), although the difference was not statistically significant in the present study. Staphylococcus pseudintermedius was the most frequently isolated bacterial species from clinical tumor tissues in this study and accumulating evidence linked Staphylococcus to cancer development and progression [13,14]. Bromfield et al. reported that Staphylococcus, particularly Staphylococcus pseudintermedius, dominated the skin microbiota of dogs with cutaneous squamous cell carcinoma [49]. In a mouse model, elimination of the tumor microbiome could reduce lung metastases by more than 3-fold, while the re-administration of Staphylococcus led to an increase in lung metastases [15]. The study also found that Staphylococcus might migrate with breast cancer cells, promoting metastatic colonization [15]. On the basis of these observations, Staphylococcus pseudintermedius was selected for subsequent functional analysis.

Staphylococcus pseudintermedius has become a significant pathogen in dogs and cats, not only affecting the skin or skin infections but also serving as an opportunistic pathogen distributed throughout various other parts of the body. A recent report demonstrated that Staphylococcus pseudintermedius could invade canine epithelial cells [50] and Staphylococcus aureus could be internalized by human breast cancer cells and survive intracellularly [21], consistent with our experimental findings. Furthermore, the mRNA and protein expression levels of VEGFA, MMP9, MMP2 and EGFR—key factors associated with cell proliferation and migration—were significantly increased in Staphylococcus-treated cells. These results suggest that Staphylococcus pseudintermedius enhances the proliferation and migratory capacities of CMT-U27 cells. Consistent with our findings, the pro-proliferative and pro-metastatic effects of Staphylococcus or its products could also be observed in other tumor types, including lung cancer, colorectal cancer, melanoma and lymphoma [51,52,53,54,55].

Transcriptomic sequencing analysis further revealed these DEGs were primarily associated with the Toll-like receptors and PI3K/Akt signaling pathways. Consistent with these findings, WB analysis demonstrated that the Staphylococcus treatment significantly upregulated the expression of TLR2 and Rac1, as well as the phosphorylation levels of PI3K, Akt and P70S6K, indicating activation of the TLR2/PI3K/Akt signaling pathway in CMT-U27 cells. C29, a specific TLR2 inhibitor, has been reported to block hTLR2/1 and hTLR2/6 signaling, thereby suppressing TLR2-mediated signaling pathways [56]. In the present study, the inhibition of TLR2 with C29 significantly reduced the mRNA expression levels of VEGF, MMP9, MMP2, and EGFR. Taken together, these findings demonstrate that Staphylococcus pseudintermedius promotes the proliferation and migration of CMT-U27 cells through the TLR2/PI3K/Akt signaling pathway.

TLRs plays a critical role in microbial recognition by sensing pathogen-associated molecular patterns (PAMPs), including bacterial lipoproteins and peptidoglycan [57]. Accumulating evidence has shown that elevated TLR2 expression is closely associated with tumor progression and metastasis in several malignancies, such as breast, gastric and lung cancer [58,59,60]. Moreover, PAMPs have been reported to induce TLR2 activation—for example, bacterial peptidoglycan has been shown to promote the migration of breast cancer cells via TLR2 signaling [61] and Listeria monocytogenes has been shown to promote hepatocellular carcinoma growth via the TLR2 pathway [62]. The activation of TLR2 could trigger multiple downstream signaling cascades, among which the PI3K/Akt pathway is well-recognized for its role in regulating cell survival, proliferation, and migration [63]. In line with this, previous studies have demonstrated that bacterial internalization or exposure to their toxins could activate the PI3K/Akt pathway in tumor cells [64,65,66]. Taken together, our findings suggest that Staphylococcus-associated microbial signals could activate TLR2, and subsequently stimulate the PI3K/Akt signaling pathway, enhancing the proliferation and migration capacities of CMT-U27 cells.

Growing evidence, along with our findings, supports a functional link between the intratumoral microbiome and tumor progression [13,14,15], highlighting its potential as a novel target for microbiome-based diagnostic and therapeutic strategies in mammary tumors. A previous study has shown that mammary tumors could be distinguished from matched normal tissue based on their bacterial characteristics, achieving an accuracy rate of 84.78% [67]. Li et al. further identified several key intratumoral bacteria that were associated with prognosis and immunotherapeutic efficacy in breast cancer patients [68]. Furthermore, multiple studies demonstrated that targeting tumor-associated bacteria with antibiotics, or combining antibiotics with anticancer therapies, could slow tumor progression, prolong disease-free survival, and enhance treatment efficacy [8,69,70]. Further studies are warranted to clarify how targeted modulation of the intratumoral microbiome may influence tumor progression and therapeutic outcomes.

There were certain limitations that should be acknowledged. First, the sample size was relatively small, which might limit the generalizability of microbial profiles across diverse canine populations and tumor stages. Larger and multi-center studies are needed in the future to further validate these findings. In addition, the healthy control dogs were maintained under relatively uniform conditions with respect to age, living environment and diet; therefore, these samples might not fully represent the broader canine population. Furthermore, the present study was limited to a single cell line and lacked in vivo animal models, meaning it cannot fully reflect the complex interactions within the tumor microenvironment. Future studies incorporating three-dimensional organoid cultures and animal models are warranted to further elucidate the effects of Staphylococcus pseudintermedius on the growth and metastasis of canine mammary tumors. Future exploration should also focus on determining which specific bacterial metabolites or virulence factors of Staphylococcus pseudintermedius induce proliferative and metastatic responses.

5. Conclusions

This study systematically characterized the tumor-associated microbiota in canine mammary tumors and successfully isolated viable bacteria from clinical tumor tissues. Moreover, our findings demonstrated that Staphylococcus pseudintermedius enhanced the proliferation and migration of CMT-U27 cells through the activation of the TLR2/PI3K/Akt signaling pathway. These results provide new insights into the potential role of intratumoral bacteria in the progression of canine mammary tumors and highlight the importance of host–microbe interactions in tumor biology.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani16050831/s1: Figure S1: Characterization of intratumoral microbiota in canine mammary tumors between pathological grades. Figure S2: Identification of Staphylococcus pseudintermedius and the amplicon size for Staphylococcus pseudintermedius was 926 bp.

animals-16-00831-s001.zip (238.3KB, zip)

Author Contributions

L.L.: Conceptualization; Data curation; Writing—original draft; J.L.: Investigation and writing—original draft; M.L.: Conceptualization; Writing—review and editing; Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The animal study protocol was reviewed and approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University (20241209257).

Informed Consent Statement

The study’s purpose and objectives were explained to the dog’s owners, and informed consent was obtained from the owner of the animals.

Data Availability Statement

Raw sequencing data during the current study had been submitted to the NCBI database (PRJNA1399047) and the data used in this study were available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding Statement

This study was funded by Young Scientists Fund of the National Natural Science Foundation of China (No. 32402964), Jiangsu Qinglan Project (No. 840003) and Meng Li’s Nanjing Agricultural University Research Start-up Fund (No. 804127).

Footnotes

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References

  • 1.Zhou B., Sun C., Huang J., Xia M., Guo E., Li N., Lu H., Shan W., Wu Y., Li Y., et al. The biodiversity Composition of Microbiome in Ovarian Carcinoma Patients. Sci. Rep. 2019;9:1691. doi: 10.1038/s41598-018-38031-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Peters B.A., Hayes R.B., Goparaju C., Reid C., Pass H.I., Ahn J. The Microbiome in Lung Cancer Tissue and Recurrence-Free Survival. Cancer Epidemiol. Biomark. Prev. 2019;28:731–740. doi: 10.1158/1055-9965.EPI-18-0966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Okuda S., Shimada Y., Tajima Y., Yuza K., Hirose Y., Ichikawa H., Nagahashi M., Sakata J., Ling Y., Miura N., et al. Profiling of host genetic alterations and intra-tumor microbiomes in colorectal cancer. Comput. Struct. Biotechnol. J. 2021;19:3330–3338. doi: 10.1016/j.csbj.2021.05.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Xue C., Gu X., Shi Q., Ma X., Jia J., Su Y., Bao Z., Lu J., Li L. The interaction between intratumoral bacteria and metabolic distortion in hepatocellular carcinoma. J. Transl. Med. 2024;22:237. doi: 10.1186/s12967-024-05036-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Nejman D., Livyatan I., Fuks G., Gavert N., Zwang Y., Geller L.T., Rotter-Maskowitz A., Weiser R., Mallel G., Gigi E., et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science. 2020;368:973–980. doi: 10.1126/science.aay9189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Blacklock K.L.B., Donnelly K., Lu Y., Pozo J.D., Glendinning L., Polton G., Selmic L., Tanis J.B., Killick D., Parys M., et al. Oronasal mucosal melanoma is defined by two transcriptional subtypes in humans and dogs with implications for diagnosis and therapy. J. Pathol. 2025;265:245–259. doi: 10.1002/path.6377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zheng H.H., Du C.T., Yu C., Tang X.Y., Huang R.L., Zhang Y.Z., Gao W., Xie G.H. The Relationship of Tumor Microbiome and Oral Bacteria and Intestinal Dysbiosis in Canine Mammary Tumor. Int. J. Mol. Sci. 2022;23:10928. doi: 10.3390/ijms231810928. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Bullman S., Pedamallu C.S., Sicinska E., Clancy T.E., Zhang X., Cai D., Neuberg D., Huang K., Guevara F., Nelson T., et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science. 2017;358:1443–1448. doi: 10.1126/science.aal5240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Ma Y., Chen H., Li H., Zheng M., Zuo X., Wang W., Wang S., Lu Y., Wang J., Li Y., et al. Intratumor microbiome-derived butyrate promotes lung cancer metastasis. Cell Rep. Med. 2024;5:101488. doi: 10.1016/j.xcrm.2024.101488. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Rubinstein M.R., Wang X., Liu W., Hao Y., Cai G., Han Y.W. Fusobacterium nucleatum Promotes Colorectal Carcinogenesis by Modulating E-Cadherin/β-Catenin Signaling via its FadA Adhesin. Cell Host Microbe. 2013;14:195–206. doi: 10.1016/j.chom.2013.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Li Y., Zhang R., Fu C., Jiang Q., Zhang P., Zhang Y., Chen J., Tao K., Chen W.-H., Zeng X. Intratumoral microbiome promotes liver metastasis and dampens adjuvant imatinib treatment in gastrointestinal stromal tumor. Cancer Lett. 2024;601:217149. doi: 10.1016/j.canlet.2024.217149. [DOI] [PubMed] [Google Scholar]
  • 12.Li Q. Bacterial infection and microbiota in carcinogenesis and tumor development. Front. Cell. Infect. Microbiol. 2023;13:1294082. doi: 10.3389/fcimb.2023.1294082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Bernardo G., Le Noci V., Ottaviano E., De Cecco L., Camisaschi C., Guglielmetti S., Di Modica M., Gargari G., Bianchi F., Indino S., et al. Reduction of Staphylococcus epidermidis in the mammary tumor microbiota induces antitumor immunity and decreases breast cancer aggressiveness. Cancer Lett. 2023;555:216041. doi: 10.1016/j.canlet.2022.216041. [DOI] [PubMed] [Google Scholar]
  • 14.Urbaniak C., Gloor G.B., Brackstone M., Scott L., Tangney M., Reid G. The Microbiota of Breast Tissue and Its Association with Breast Cancer. Appl. Environ. Microbiol. 2016;82:5039–5048. doi: 10.1128/AEM.01235-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Fu A., Yao B., Dong T., Chen Y., Yao J., Liu Y., Li H., Bai H., Liu X., Zhang Y., et al. Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell. 2022;185:1356–1372.e1326. doi: 10.1016/j.cell.2022.02.027. [DOI] [PubMed] [Google Scholar]
  • 16.Edgar R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. 2013;10:996–998. doi: 10.1038/nmeth.2604. [DOI] [PubMed] [Google Scholar]
  • 17.Quast C., Pruesse E., Yilmaz P., Gerken J., Schweer T., Yarza P., Peplies J., Glöckner F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596. doi: 10.1093/nar/gks1219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Schloss P.D., Westcott S.L., Ryabin T., Hall J.R., Hartmann M., Hollister E.B., Lesniewski R.A., Oakley B.B., Parks D.H., Robinson C.J., et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009;75:7537–7541. doi: 10.1128/AEM.01541-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W.S., Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Sasaki T., Tsubakishita S., Tanaka Y., Sakusabe A., Ohtsuka M., Hirotaki S., Kawakami T., Fukata T., Hiramatsu K. Multiplex-PCR Method for Species Identification of Coagulase-Positive Staphylococci. J. Clin. Microbiol. 2010;48:765–769. doi: 10.1128/JCM.01232-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhao H., Zhang L., Du D., Mai L., Liu Y., Morigen M., Fan L. The RIG-I-like receptor signaling pathway triggered by Staphylococcus aureus promotes breast cancer metastasis. Int. Immunopharmacol. 2024;142:113195. doi: 10.1016/j.intimp.2024.113195. [DOI] [PubMed] [Google Scholar]
  • 22.Wang J., Li M., Li M. Newcastle disease virus LaSota strain induces apoptosis and activates the TNFα/NF-κB pathway in canine mammary carcinoma cells. Vet. Comp. Oncol. 2023;21:520–532. doi: 10.1111/vco.12915. [DOI] [PubMed] [Google Scholar]
  • 23.Jermnak U., Supsavhad W., Kunakornsawat S., Jaroensong T., Watcharasit P., Visitnonthachai D., Pairor S., Phaochoosak N. Anti-cancer potentials of Gynura procumbens leaves extract against two canine mammary cancer cell lines. Vet. Med. Sci. 2022;8:69–84. doi: 10.1002/vms3.684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Lim G.H., An J.H., Park S.M., Youn G.H., Oh Y.I., Seo K.W., Youn H.Y. Macrophage induces anti-cancer drug resistance in canine mammary gland tumor spheroid. Sci. Rep. 2023;13:10394. doi: 10.1038/s41598-023-37311-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Aresu L., Giantin M., Morello E., Vascellari M., Castagnaro M., Lopparelli R., Zancanella V., Granato A., Garbisa S., Aricò A., et al. Matrix metalloproteinases and their inhibitors in canine mammary tumors. BMC Vet. Res. 2011;7:33. doi: 10.1186/1746-6148-7-33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Takahashi K., Yoshimatsu R., Kaida Y., Hasegawa T., Ohmori K. Toll-like receptor 2 activation induces C-C motif chemokine ligand 5 production in canine keratinocytes. Vet. Dermatol. 2025;36:117–126. doi: 10.1111/vde.13330. [DOI] [PubMed] [Google Scholar]
  • 27.Kim J.Y., Lee E.Y., Kim J.H., Seo E.J., Eom S.Y., Seo J.H. Diesel exhaust particles disrupt blood-retina barrier integrity via TLR2 and TLR4 activation. BMB Rep. 2025;58:300–306. doi: 10.5483/BMBRep.2025-0013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Goldschmidt M., Peña L., Rasotto R., Zappulli V. Classification and grading of canine mammary tumors. Vet. Pathol. 2011;48:117–131. doi: 10.1177/0300985810393258. [DOI] [PubMed] [Google Scholar]
  • 29.Urbaniak C., Cummins J., Brackstone M., Macklaim J.M., Gloor G.B., Baban C.K., Scott L., O’Hanlon D.M., Burton J.P., Francis K.P., et al. Microbiota of human breast tissue. Appl. Environ. Microbiol. 2014;80:3007–3014. doi: 10.1128/AEM.00242-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Ahn J., Chen C.Y., Hayes R.B. Oral microbiome and oral and gastrointestinal cancer risk. Cancer Causes Control. 2012;23:399–404. doi: 10.1007/s10552-011-9892-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Chan A.A., Bashir M., Rivas M.N., Duvall K., Sieling P.A., Pieber T.R., Vaishampayan P.A., Love S.M., Lee D.J. Characterization of the microbiome of nipple aspirate fluid of breast cancer survivors. Sci. Rep. 2016;6:28061. doi: 10.1038/srep28061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Avtanski D., Reddy V., Stojchevski R., Hadzi-Petrushev N., Mladenov M. The Microbiome in the Obesity-Breast Cancer Axis: Diagnostic and Therapeutic Potential. Pathogens. 2023;12:1402. doi: 10.3390/pathogens12121402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Riquelme E., Zhang Y., Zhang L., Montiel M., Zoltan M., Dong W., Quesada P., Sahin I., Chandra V., San Lucas A., et al. Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes. Cell. 2019;178:795–806.e712. doi: 10.1016/j.cell.2019.07.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Xie Y., Xie F., Zhou X., Zhang L., Yang B., Huang J., Wang F., Yan H., Zeng L., Zhang L., et al. Microbiota in Tumors: From Understanding to Application. Adv. Sci. 2022;9:e2200470. doi: 10.1002/advs.202200470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Esposito M.V., Fosso B., Nunziato M., Casaburi G., D’Argenio V., Calabrese A., D’Aiuto M., Botti G., Pesole G., Salvatore F. Microbiome composition indicate dysbiosis and lower richness in tumor breast tissues compared to healthy adjacent paired tissue, within the same women. BMC Cancer. 2022;22:30. doi: 10.1186/s12885-021-09074-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Xuan C., Shamonki J.M., Chung A., Dinome M.L., Chung M., Sieling P.A., Lee D.J. Microbial dysbiosis is associated with human breast cancer. PLoS ONE. 2014;9:e83744. doi: 10.1371/journal.pone.0083744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Klann E., Williamson J.M., Tagliamonte M.S., Ukhanova M., Asirvatham J.R., Chim H., Yaghjyan L., Mai V. Microbiota composition in bilateral healthy breast tissue and breast tumors. Cancer Causes Control. 2020;31:1027–1038. doi: 10.1007/s10552-020-01338-5. [DOI] [PubMed] [Google Scholar]
  • 38.Ito Y., Nagasawa M., Koyama K., Ito K., Kikusui T. Comparative analysis based on shared amplicon sequence variants reveals that cohabitation influences gut microbiota sharing between humans and dogs. Front. Vet. Sci. 2024;11:1417461. doi: 10.3389/fvets.2024.1417461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Song S.J., Lauber C., Costello E.K., Lozupone C.A., Humphrey G., Berg-Lyons D., Caporaso J.G., Knights D., Clemente J.C., Nakielny S., et al. Cohabiting family members share microbiota with one another and with their dogs. eLife. 2013;2:e00458. doi: 10.7554/eLife.00458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Cai Y., Li X., Chen S., Liu Q., Lu H., Xie J., Li W., Chen G. Characterization and Optimization of Fermentation Conditions of Roseateles sp. L2-2, a Novel Chitin-Degrading Bacterium from the Intestine of Odorrana margaretae. Microorganisms. 2025;13:2033. doi: 10.3390/microorganisms13092033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Sinitskaya A., Kostyunin A., Asanov M., Khutornaya M., Klyueva A., Poddubnyak A., Tupikin A., Kabilov M., Sinitsky M. Bacterial Diversity in Native Heart Valves in Infective Endocarditis. Biomedicines. 2025;13:245. doi: 10.3390/biomedicines13010245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lee Y., Park J.Y., Lee E.H., Yang J., Jeong B.R., Kim Y.K., Seoh J.Y., Lee S., Han P.L., Kim E.J. Rapid Assessment of Microbiota Changes in Individuals with Autism Spectrum Disorder Using Bacteria-derived Membrane Vesicles in Urine. Exp. Neurobiol. 2017;26:307–317. doi: 10.5607/en.2017.26.5.307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Niyazbekova Z., Yao X.T., Liu M.J., Bold N., Tong J.Z., Chang J.J., Wen Y., Li L., Wang Y., Chen D.K., et al. Compositional and Functional Comparisons of the Microbiota in the Colostrum and Mature Milk of Dairy Goats. Animals. 2020;10:1955. doi: 10.3390/ani10111955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Yao H., Liu T., Chen Y., She L., Wu T., Liu D., Deng Y., Han Y., Chen K., Deng J., et al. Dysregulated gastric microbial communities and functional shifts in chronic atrophic versus non-atrophic gastritis: A Helicobacter pylori-Negative observational study. BMC Gastroenterol. 2025;25:304. doi: 10.1186/s12876-025-03900-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Al Bataineh M.T., Dash N.R., Mysara M., Saeed O., Alkhayyal N., Talaat I.M., Bendardaf R., Saber-Ayad M. Metagenomic analysis of gut microbiota in colorectal adenocarcinoma in the MENA region. Front. Cell. Infect. Microbiol. 2025;15:1634631. doi: 10.3389/fcimb.2025.1634631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Peters B.A., Pass H.I., Burk R.D., Xue X., Goparaju C., Sollecito C.C., Grassi E., Segal L.N., Tsay J.J., Hayes R.B., et al. The lung microbiome, peripheral gene expression, and recurrence-free survival after resection of stage II non-small cell lung cancer. Genome Med. 2022;14:121. doi: 10.1186/s13073-022-01126-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Sun Z., Bai C., Hao D., Jiang X., Chen J. Gut microbiota and oral cavity cancer: A two-sample bidirectional Mendelian randomization study. Front. Oncol. 2024;14:1389678. doi: 10.3389/fonc.2024.1422009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Zhou J., Zhang X., Xie Z., Li Z. Exploring reciprocal causation: Bidirectional mendelian randomization study of gut microbiota composition and thyroid cancer. J. Cancer Res. Clin. Oncol. 2024;150:75. doi: 10.1007/s00432-023-05535-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Bromfield J.I., Zaugg J., Straw R.C., Cathie J., Krueger A., Sinha D., Chandra J., Hugenholtz P., Frazer I.H. Characterization of the skin microbiome in normal and cutaneous squamous cell carcinoma affected cats and dogs. mSphere. 2024;9:e0055523. doi: 10.1128/msphere.00555-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Pietrocola G., Gianotti V., Richards A., Nobile G., Geoghegan J.A., Rindi S., Monk I.R., Bordt A.S., Foster T.J., Fitzgerald J.R., et al. Fibronectin Binding Proteins SpsD and SpsL Both Support Invasion of Canine Epithelial Cells by Staphylococcus pseudintermedius. Infect. Immun. 2015;83:4093–4102. doi: 10.1128/IAI.00542-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Yu H., Du Y., He Y., Sun Y., Li J., Jia B., Chen J., Peng X., An T., Li J., et al. Lactate production by tumor-resident Staphylococcus promotes metastatic colonization in lung adenocarcinoma. Cell Host Microbe. 2025;33:1089–1105.e1087. doi: 10.1016/j.chom.2025.06.013. [DOI] [PubMed] [Google Scholar]
  • 52.Hattar K., Reinert C.P., Sibelius U., Gökyildirim M.Y., Subtil F.S.B., Wilhelm J., Eul B., Dahlem G., Grimminger F., Seeger W., et al. Lipoteichoic acids from Staphylococcus aureus stimulate proliferation of human non-small-cell lung cancer cells in vitro. Cancer Immunol. Immunother. 2017;66:799–809. doi: 10.1007/s00262-017-1980-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Giese M.A., Ramakrishnan G., Steenberge L.H., Dovan J.X., Sauer J.D., Huttenlocher A. Staphylococcus aureus lipid factors modulate melanoma cell clustering and invasion. Dis. Model. Mech. 2024;17:dmm050770. doi: 10.1242/dmm.050770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Geng L., Fan Z., Chen R., Cho K.-C., Liu Y., Cheng Y., Yang J., Zhang Y., Wei X., Gong L., et al. The Nα-acetyl-L-lysine/Loxl2/H2O2 promotes intestinal tumor growth in Drosophila and cell proliferation in human colorectal cancer. Cell Rep. 2025;44:116126. doi: 10.1016/j.celrep.2025.116126. [DOI] [PubMed] [Google Scholar]
  • 55.Zeng Z., Vadivel C.K., Gluud M., Namini M.R.J., Yan L., Ahmad S., Hansen M.B., Coquet J., Mustelin T., Koralov S.B., et al. Keratinocytes Present Staphylococcus aureus Enterotoxins and Promote Malignant and Nonmalignant T Cell Proliferation in Cutaneous T-Cell Lymphoma. J. Investig. Dermatol. 2024;144:2789–2804.e2710. doi: 10.1016/j.jid.2024.04.018. [DOI] [PubMed] [Google Scholar]
  • 56.Mistry P., Laird M.H., Schwarz R.S., Greene S., Dyson T., Snyder G.A., Xiao T.S., Chauhan J., Fletcher S., Toshchakov V.Y., et al. Inhibition of TLR2 signaling by small molecule inhibitors targeting a pocket within the TLR2 TIR domain. Proc. Natl. Acad. Sci. USA. 2015;112:5455–5460. doi: 10.1073/pnas.1422576112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Akira S., Uematsu S., Takeuchi O. Pathogen recognition and innate immunity. Cell. 2006;124:783–801. doi: 10.1016/j.cell.2006.02.015. [DOI] [PubMed] [Google Scholar]
  • 58.Xie W., Wang Y., Huang Y., Yang H., Wang J., Hu Z. Toll-like receptor 2 mediates invasion via activating NF-κB in MDA-MB-231 breast cancer cells. Biochem. Biophys. Res. Commun. 2009;379:1027–1032. doi: 10.1016/j.bbrc.2009.01.009. [DOI] [PubMed] [Google Scholar]
  • 59.Wang S., Yao Y., Rao C., Zheng G., Chen W. 25-HC decreases the sensitivity of human gastric cancer cells to 5-fluorouracil and promotes cells invasion via the TLR2/NF-κB signaling pathway. Int. J. Oncol. 2019;54:966–980. doi: 10.3892/ijo.2019.4684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Velasco W.V., Khosravi N., Castro-Pando S., Torres-Garza N., Grimaldo M.T., Krishna A., Clowers M.J., Umer M., Tariq Amir S., Del Bosque D., et al. Toll-like receptors 2, 4, and 9 modulate promoting effect of COPD-like airway inflammation on K-ras-driven lung cancer through activation of the MyD88/NF-ĸB pathway in the airway epithelium. Front. Immunol. 2023;14:1118721. doi: 10.3389/fimmu.2023.1118721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Xie W., Huang Y., Xie W., Guo A., Wu W. Bacteria Peptidoglycan Promoted Breast Cancer Cell Invasiveness and Adhesiveness by Targeting Toll-Like Receptor 2 in the Cancer Cells. PLoS ONE. 2010;5:e10850. doi: 10.1371/journal.pone.0010850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Huang B., Zhao J., Shen S., Li H., He K.L., Shen G.X., Mayer L., Unkeless J., Li D., Yuan Y., et al. Listeria monocytogenes promotes tumor growth via tumor cell toll-like receptor 2 signaling. Cancer Res. 2007;67:4346–4352. doi: 10.1158/0008-5472.CAN-06-4067. [DOI] [PubMed] [Google Scholar]
  • 63.Ersahin T., Tuncbag N., Cetin-Atalay R. The PI3K/AKT/mTOR interactive pathway. Mol. Biosyst. 2015;11:1946–1954. doi: 10.1039/C5MB00101C. [DOI] [PubMed] [Google Scholar]
  • 64.Cai M., Fan W., Li X., Sun H., Dai L., Lei D., Dai Y., Liao Y. The Regulation of Staphylococcus aureus-Induced Inflammatory Responses in Bovine Mammary Epithelial Cells. Front. Vet. Sci. 2021;8:683886. doi: 10.3389/fvets.2021.683886. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Guo L., Wang Z., Zhu C., Li J., Cui L., Dong J., Meng X., Zhu G., Li J., Wang H. MCC950 inhibits the inflammatory response and excessive proliferation of canine corneal stromal cells induced by Staphylococcus pseudintermedius. Mol. Immunol. 2022;152:162–171. doi: 10.1016/j.molimm.2022.11.001. [DOI] [PubMed] [Google Scholar]
  • 66.Kierbel A., Gassama-Diagne A., Mostov K., Engel J.N. The phosphoinositol-3-kinase-protein kinase B/Akt pathway is critical for Pseudomonas aeruginosa strain PAK internalization. Mol. Biol. Cell. 2005;16:2577–2585. doi: 10.1091/mbc.e04-08-0717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Hogan G., Eckenberger J., Narayanen N., Walker S.P., Claesson M.J., Corrigan M., O’Hanlon D., Tangney M. Biopsy bacterial signature can predict patient tissue malignancy. Sci. Rep. 2021;11:18535. doi: 10.1038/s41598-021-98089-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Li J., Zhang Y., Cai Y., Yao P., Jia Y., Wei X., Du C., Zhang S. Multi-omics analysis elucidates the relationship between intratumor microbiome and host immune heterogeneity in breast cancer. Microbiol. Spectr. 2024;12:e0410423. doi: 10.1128/spectrum.04104-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Geller L.T., Barzily-Rokni M., Danino T., Jonas O.H., Shental N., Nejman D., Gavert N., Zwang Y., Cooper Z.A., Shee K., et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science. 2017;357:1156–1160. doi: 10.1126/science.aah5043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Wang M., Rousseau B., Qiu K., Huang G., Zhang Y., Su H., Le Bihan-Benjamin C., Khati I., Artz O., Foote M.B., et al. Killing tumor-associated bacteria with a liposomal antibiotic generates neoantigens that induce anti-tumor immune responses. Nat. Biotechnol. 2024;42:1263–1274. doi: 10.1038/s41587-023-01957-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

animals-16-00831-s001.zip (238.3KB, zip)

Data Availability Statement

Raw sequencing data during the current study had been submitted to the NCBI database (PRJNA1399047) and the data used in this study were available from the corresponding author upon reasonable request.


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