Deconjugating taurocholic acid with Bifidobacterium to mitigate obesity-driven cancer progression by restoring CD8+ T-cell infiltration

Yuan, Ruoshui; Li, Yuke; Wang, Yifei; Li, Qingxiang; Guo, Chuanbin; Wang, Lin

doi:10.1038/s41522-025-00809-4

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Published: 21 August 2025

Deconjugating taurocholic acid with Bifidobacterium to mitigate obesity-driven cancer progression by restoring CD8⁺ T-cell infiltration

Ruoshui Yuan¹,
Yuke Li²,
Yifei Wang¹,
Qingxiang Li¹,
Chuanbin Guo¹ &
…
Lin Wang¹

npj Biofilms and Microbiomes volume 11, Article number: 167 (2025) Cite this article

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Abstract

Obesity is linked to an increased cancer risk, and probiotics show promise in weight management. Here, we elucidate the precise mechanisms through which the probiotic Bifidobacterium breve (B. breve) modulates the immune response in obesity-associated tumours utilizing a Hepa1-6 cell-bearing hepatocellular carcinoma (HCC) model sensitive to high-fat diet (HFD)-induced obesity. HFD-induced obesity expedited HCC progression and fostered an immunosuppressive microenvironment. Treatment with B. breve enhanced cancer control by rescuing the local infiltration of antitumour immune cells. Elevated serum taurocholic acid (TCA) levels were negatively correlated with B. breve levels in obese HCC mice. TCA hindered the infiltration of CD8⁺ T cells into the tumour microenvironment and diminished their antitumour efficacy by blocking ERK phosphorylation. B. breve deconjugated TCA via its type 4 bile salt hydrolase (BSH), and this effect was diminished upon BSH inhibition by AAA-10. These results highlight the potential application of the probiotic B. breve in the multidisciplinary treatment of cancer in obese individuals.

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Introduction

Convincing evidence has suggested that overweight/obesity, whose prevalence has dramatically increased, is associated with a higher risk of at least thirteen types of cancers, including hepatocellular carcinoma, although the underlying biological mechanisms remain debated^1,2. The gut microbiota and obesity have a reciprocal relationship³, and an obesity-associated imbalance in the gut microbiota promotes cancer progression by affecting systemic immunity or disrupting local immunosurveillance^4,5. Emerging evidence has confirmed that certain commensal bacteria can protect against carcinogenesis or increase the efficacy of chemotherapy and immunotherapy^6,7. While probiotics have a significant market presence in weight management, their potential role in tumour regulation and the underlying mechanisms in obese individuals require further investigation.

An increasing number of studies have shown that microbiota-derived metabolites are key hubs for the crosstalk between the gut microbiome and tumours passing through the gut mucosal barrier. Short-chain fatty acids produced from the fermentation of complex carbohydrates and tryptophan-derivatized compounds generated by bacterial enzymatic processes are the most extensively studied classes of small-molecule metabolites. Despite their importance, these metabolites have a range of effects on immune cells, some of which are contradictory^8,9,10,11. Recently, emerging studies have focused on bile acids (BAs), which are host-microbe cometabolites that function as gut microbial messengers. BAs can have both beneficial and detrimental impacts, depending upon factors such as the specific type and metabolic status of affected tissues^12,13.

Excess body weight reshapes the tumour microenvironment (TME) by altering lipid and amino acid metabolism in immune cells, such as natural killer (NK) cells or cytotoxic CD8⁺ T cells^14,15. Our previous work focused on one probiotic, Bifidobacterium breve lw01, confirming its antitumour effect on head and neck squamous carcinoma (HNSCC). B. breve lw01 influences tryptophan metabolism, ameliorating the precancerous inflammatory intestinal environment and inhibiting tumorigenesis by guiding the differentiation of immature colonic macrophages^16,17.

In this study, we further investigated its impact on tumour control in a high-fat diet (HFD)-induced obesity rodent model and its underlying metabolic mechanisms. We found that taurocholic acid (TCA), a bile acid that is present at high levels in obese individuals, exacerbates tumour growth solely by suppressing the accumulation, local expansion and function of cytotoxic CD8⁺ T cells at the tumour site. Oral administration of B. breve lw01 decreased the serum TCA level, mitigating the tumour-promoting effects of obesity.

This study explored TME alterations in a HFD-induced obese tumour model, and we established a link between probiotics and tumour control in the context of HFD/obesity. These findings elucidate the mechanisms by which probiotics deconjugate immunosuppressive metabolites, thereby inhibiting tumour growth in obese individuals. This finding broadens the potential application of probiotics in overweight and obese populations with tumours and provides a mechanistic basis for these effects.

Results

High-fat diet-induced obesity accelerates tumour growth and disturbs organism homeostasis

A diet-induced obesity (DIO) model was used to verify the effects of obesity on tumour progression. Six-week-old male wild-type C57BL/6N mice were randomized to receive either a control diet (CD, 10% kcal) or a high-fat diet (HFD, 60% kcal) for 16 weeks (Fig. 1A). HFD-fed mice exhibited significantly increased body weight (Fig. 1B) and systemic obesity-associated metabolic changes, such as dyslipidaemia, hyperglycaemia and impaired glucose clearance, which was evaluated by an oral glucose tolerance test (OGTT) (Fig. 1C–E). HFD-fed mice presented increased liver and epididymal white adipose tissue (eWAT) weights along with severe liver steatosis (Fig. 1F, G).

**Fig. 1: HFD-induced obesity accelerates tumour development and disturbs organism homeostasis.**

After adaptation to a CD or HFD, syngeneic Hepa1-6 hepatocellular carcinoma cells were injected subcutaneously into the right flanks of the mice. The Hepa1-6 tumours grew more rapidly, and the tumour volume was significantly larger in the HFD-fed mice than in the CD-fed mice (Fig. 1H). Western blots showed that the expression levels of the pro-apoptotic protein cleaved Caspase-3 and the ratio of Bax/Bcl-2 were reduced in the HFD group, whereas the ratio of p-STAT3/STAT3 was increased (Fig. 1I), indicating the increased proliferation of tumour cells.

As a crucial factor, an HFD may also lead to changes in organism homeostasis and immune function by regulating gut microbial communities. We performed 16S ribosomal RNA sequencing of stool samples from the two groups to determine whether the HFD altered the gut microbiota. Compared with the CD group, the HFD group presented decreased community diversity and changes in the structure of the gut microbiota (Fig. 1J). The linear discriminant analysis effect size (LEfSe) analysis revealed that the abundances of Akkermansia, Clostridium, and Erysipelatoclostridium were increased, whereas the abundances of Bifidobacterium, Ruminococcus, and Parabacteroides were decreased in the HFD group (Fig. 1K). Consistent with previous reports that Bifidobacterium was depleted in HFD-fed mice¹⁸, the abundance of the Bifidobacterium genus in the gut was also lower in humans with metabolic disorders, such as overweight, diabetes mellitus and metabolic-associated fatty liver disease (MAFLD), than in healthy individuals (Supplementary Fig. 1A). Moreover, many human studies have confirmed the decreased abundance of Bifidobacterium in a variety of obesity-associated cancers (Supplementary Fig. 1B).

The probiotic Bifidobacterium breve lw01 attenuated HFD-promoted tumour growth

Previous studies conducted by our group revealed that a strain of Bifidobacterium breve purified from foetal stool samples (B. breve lw01) exhibits a remarkable antitumour capacity¹⁹. Thus, we wondered whether it could rescue the accelerated tumour growth in animals with long-term HFD consumption. Along with the HFD, 6-week-old male wild-type C57BL/6 N mice were administered B. breve lw01 suspended in PBS (HFD + B. breve) or only PBS as a control (HFD + PBS) by oral gavage for 16 weeks (Fig. 2A). Although no significant differences in body weight gain or eWAT weight were observed between the two groups (Fig. 2B, C), supplementation with B. breve reduced the liver weight and ameliorated liver steatosis, as revealed by a lower hepatic lipid content (Fig. 2C, D). In addition, oral gavage with B. breve optimized glucose homeostasis (Fig. 2E, F). Essentially, the B. breve gavage group presented a significant inhibition of tumour growth, and the average tumour volume was significantly smaller (Fig. 2G). Western blots also showed elevated expression levels of the proapoptotic protein cleaved Caspase-3 and the ratio of Bax/Bcl-2, as well as a decreased ratio of p-STAT3/STAT3 (Fig. 2H), suggesting that the proliferation of the tumour cells was inhibited.

**Fig. 2: Oral administration of *B. breve* lw01 attenuates the HFD-induced increase in tumour growth and improves glucose and lipid metabolism.**

We subsequently investigated the ability of B. breve supplementation to modulate the gut microbiota and lipid metabolism. The inoculation of HFD-fed mice with B. breve did not induce an increase in biodiversity but rather restructured the microbiota composition (Supplementary Fig. 2A–C). Briefly, treatment of HFD-fed mice with B. breve decreased the abundances of the order Erysipelotrichales, which is associated with glucose metabolism disorders, and the genus Betaproteobacteria, increased the abundances of the probiotic Ligilactobacillus, longevity-related Eisenbergiella and short-chain fatty acid-producing bacteria such as the Eubacterium xylanophilum group and UCG-005 (Supplementary Fig. 2D), indicating that B. breve could adjust the gut microbiota moderately. In contrast to our expectations, very few differences were observed in the constitution of serum lipids after B. breve treatment (Supplementary Fig. 2E–G), highlighting the dominant effect of the diet. Finally, PICRUSt2 was used to predict the levels of the Notch signalling pathway, which is associated with CD8⁺ T-cells and B-cell differentiation, and cytochrome P450, which is responsible for the metabolism of many physiologically effective substances; their levels were higher in the HFD + B. breve group than in the control group^20,21 (Supplementary Fig. 3A–C).

The probiotic B. breve ameliorates the HFD-mediated immunosuppressive microenvironment

We performed flow cytometry to profile tumour-infiltrating immune cell populations in tumours 14 days after implantation and to assess whether HFD feeding affects the immune landscape of Hepa1-6 tumours (Supplementary Fig. 4A). We observed large changes in the lymphocyte compartment: significantly reduced densities of infiltrating total CD3⁺ lymphocytes and CD8⁺ T cells and slight reductions in the ratios of CD4⁺ T cells, NK cells and NKT cells to total Hepa1-6 tumour cells in HFD-fed mice (Fig. 3A). Moreover, the ratio of cytotoxic CD8⁺ T cells (Tc) and helper CD4⁺ T cells (Th) among CD3⁺ lymphocytes was altered (Fig. 3B), suggesting that HFD feeding promoted an impaired capacity for antigen-specific cell killing. In addition to lymphocytes, a different gating strategy was applied to analyse the myeloid cell populations in Hepa1-6 tumours (Supplementary Fig. 4B). Among CD11b⁺ myeloid cells, the ratio of F4/80⁺ GR1^- CD11b⁺ tumour-associated macrophages (TAMs), particularly CD86⁺ proinflammatory (M1) TAMs, was the only value that decreased, although a slight inversion in the subsets of TAMs was observed (Fig. 3C, D). The ratios of CD11c⁺ dendritic cells and Ly6G⁺/Ly6C⁺ myeloid-derived suppressor cells (MDSCs) in the HFD group were comparable to those in the CD group (Fig. 3C). These findings were confirmed by immunochemistry (Fig. 3E) and revealed that lymphocytes, with the exception of Treg cells (Supplementary Fig. 4C), were the most dramatically impacted cells among the immune cell types by a HFD in the Hepa1-6 TME.

**Fig. 3: Landscape of the Hepa1-6 tumour immune microenvironment after the consumption of different diets and supplements.**

Oral gavage with B. breve alleviated the poor conditions to some extent. Both flow cytometry and immunochemistry revealed a significant recovery in the percentage of total T cells and their subsets, with the exception of Treg cells, along with slight increases in the numbers of NK cells, TAMs and M1 TAMs, without disturbing other immune cell types (Fig. 3F–J, Supplementary Fig. 4D). These results suggest that B. breve can reverse the HFD-induced immune suppression in Hepa1-6 tumours. Moreover, we demonstrated the tumour-suppressive and immunomodulatory capacity of B. breve in an orthotopic HCC model in genetically obese mice (Fig. 4A–K).

**Fig. 4: Oral administration of *B. breve* lw01 promotes tumour growth in genetic obese mice.**

The probiotic B. breve inhibits tumour growth by deconjugating taurocholic acid

The mechanisms underlying tumour promotion/suppression by the daily diet and gut probiotics were explored using liquid chromatograph mass spectrometry (LC‒MS/MS) to detect changes in the serum metabolites of the CD, HFD, and HFD + B. breve groups. As shown in Fig. 5A, clearly distinguishable clustering of the CD and HFD groups was revealed by principal component analysis (PCA), indicating that the serum metabolite profile changed significantly with the diet. Minimal adjustment was observed after the administration of B. breve. The three groups shared 228 common metabolites, including amino acids and their derivatives, bile acids (BAs), nucleotides and their metabolites, and organic acids and their derivatives. Moreover, each group presented unique metabolites, although the numbers were not comparable (Fig. 5B). We screened the differentially abundant metabolites through pairwise comparisons and identified 11 metabolites that were up- or downregulated between the CD and HFD groups; 5 of these metabolites were altered as a result of the B. breve intervention (Fig. 5C). Notably, substantial increases in the taurocholic acid (TCA) and taurodeoxycholic acid (TDCA) levels were observed in the HFD group, but were reduced by B. breve treatment (Fig. 5D). Similarly, we did observe an increased serum TCA concentrations in obese individuals and type 2 diabetes patients (Supplementary Fig. 5A, B) and circulating TCA levels can also be used as a prediagnostic marker for obesity-related tumours, such as hepatocellular carcinoma and colorectal cancer^22,23. Additionally, a negative correlation between the serum TCA concentration and the relative abundance of Bifidobacterium in the gut was observed (Fig. 5E, Supplementary Fig. 5C). We examined the mRNA expression of enzymes responsible for the synthesis of primary bile acids, conjugation to either taurine or glycine, and secretion in the liver, and only Cyp27a1 and Cyp7b1 in the ‘alternative’ pathway, which produces chenodeoxycholic acid (CDCA), were downregulated by B. breve lw01, whereas the expression of Cyp7A1, which initiates the ‘classical’ pathway that primarily produces cholic acid (CA), remained constant, and conjugation and excretion were not affected (Supplementary Fig. 5D). In vitro experiments with bacterial cultures showed that B. breve could deconjugate TCA to generate cholic acid (Fig. 5F). These results suggest that the oral administration of B. breve lw01 reduces serum TCA levels predominantly by degrading TCA rather than inhibiting its formation.

**Fig. 5: TCA has a tumour-promoting effect, and *B. breve* inhibits tumour growth by deconjugating TCA.**

Since we did not observe such significant changes and a consistent correlation with B. breve among other bile acids (Supplementary Fig. 5E, F), in subsequent experiments, we evaluated whether TCA could promote Hepa1-6 tumour development in mice independently of diet and the gut microbiota. We administered TCA via intraperitoneal injection daily to male C57BL/6 N mice fed a normal diet for 8 weeks to mimic the long-term effects of an HFD and eliminate interference from bile acid metabolism mediated by the gut microbiome, followed by tumour cell implantation (Fig. 5G). TCA treatment did not affect body weight gain or liver or eWAT weight, nor did it exacerbate liver steatosis or increase the adipocyte size (Supplementary Fig. 6A–C). Intriguingly, treatment with TCA alone accelerated tumour growth and increased tumour volume at the endpoint (Fig. 5H). TCA treatment specifically reduced the number of tumour-infiltrating total CD3⁺ lymphocytes, particularly CD8⁺ T cells (Fig. 5I–M, Supplementary Fig. 6D). This reduction in CD8⁺ T cells was confined to the TME, as no such changes were observed in the spleen or small intestine (Supplementary Fig. 6E–I). Overall, oral gavage of B. breve reduced serum TCA levels, which otherwise increased tumour growth.

The antitumor capacity of the probiotic B. breve depends on its bile salt hydrolase

Bile salt hydrolases (BSHs) are microbial enzymes found in Lactobacillus, Bifidobacterium, Enterococcus, Clostridium and Bacteroides and are responsible for hydrolysing conjugated BAs to generate unconjugated BAs, maintaining the bile acid balance and affecting host health.

First, we analysed the genome sequence and BSH-coding sequence of B. breve lw01. The complete genome is composed of a circular chromosome of 2,313,172 bp with a 58.80% GC content. It contains 1954 coding DNA sequences; 64 RNAs, including 55 tRNAs, 1 tmRNA, 6 rRNAs and 2 ncRNA genes; and 1 clustered regularly interspaced short palindromic repeat (CRISPR), 2 short open reading formats (sORFs) and 2 OriCs (Fig. 6A, Supplementary Table 1). The gene function annotation and enrichment analysis showed that the protein-coding genes of B. breve lw01 were involved mainly in various metabolic processes, such as carbohydrate and amino acid metabolism (Fig. 6B, C). According to the gene annotations, the BSH-encoding gene is a 954 bp sequence located between 1167014 bp and 1167967 bp on the positive strand (Fig. 6D, Supplementary Table 2). The alignment and phylogenetic analysis of genes annotated as Bsh in the Bifidobacterium-type strains included in this study revealed five clusters (Fig. 6E, Supplementary Table 3), B. breve and B. longum formed a cluster, B. animalis and some B. pseudocatenulatum strains formed another cluster, B. angulatum JCM 7096 and B. thermophilum 1543B clustered separately, and the remaining strains were clustered together. B. breve lw01 had the highest similarity with B. longum subsp. infantis 157F and the lowest similarity with B. thermophilum 1543B. According to a previous study that classified bacterial Bsh genes into several phylotypes based on sequence similarity²⁴, we conducted a sequence alignment and predicted the structure of BSH in the lw01 strain and found that B. breve lw01 encoded predicted type 4 BSH enzymes (Fig. 6F, G, Supplementary Table 4).

**Fig. 6: Bioinformatic analysis of the *B. breve* lw01 genome and its *Bsh* gene.**

To confirm if B. breve’ s BSH activity reduces tumour progression in obese mice, We used AAA-10, a gut-restricted BSH inhibitor²⁵, to confirm whether the BSH activity of B. breve reduces tumour progression in obese mice. AAA-10 inhibited BSH activity in bacterial cultures (Fig. 7A) and counteracted the antitumour effect of B. breve on HFD-fed mice (Fig. 7B, C). The depletion of BSH activity worsened liver steatosis without significant changes in the body, eWAT or liver mass (Fig. 7D-F). The TME resembled that of TCA-treated lean mice, with a reduced number of total CD3⁺ lymphocytes infiltrating the tumour, particularly CD8⁺ cytotoxic T cells (Fig. 7G–K). Thus, the BSH activity of B. breve not only ameliorates hepatic steatosis induced by a high-fat diet but also reduces abnormally elevated TCA levels, slowing tumour progression in obese mice.

**Fig. 7: Inhibiting the BSH activity of *B. breve* weakens its antitumour effect.**

Taurocholic acid exacerbates tumour growth in a CD8⁺ T-cell-dependent manner

Since CD8⁺ T cells were the only significantly affected population after TCA treatment, we hypothesized that CD8⁺ T cells might mediate the tumour-promoting effects of TCA. We excluded the possibility that TCA supported tumour cell survival by promoting proliferation or suspending apoptosis (Supplementary Fig. 7A–C). Depleting CD8⁺ T cells neutralized the tumour-promoting effect of TCA (Fig. 8A–C, Supplementary Fig. 7D), suggesting that TCA directly regulates CD8⁺ T cells. We examined the survival and proliferation of CD8⁺ T cells treated with TCA. Treatment with 1000 μM TCA for 24 h increased the apoptosis of CD8⁺ T cells, as measured by viability dye uptake via flow cytometry, but did not suppress their proliferation (Fig. 8D, E). We activated isolated CD8⁺ T cells with a-CD3/CD28 and IL-2 and challenged them with TCA at doses ranging from 0 to 1 mM to confirm the direct effect of TCA on CD8⁺ T cells. TCA-treated CD8⁺ T cells presented reduced expression of cytotoxic cytokines, including TNFα, IFNγ, GZMB and PRF1, at both the transcript and protein levels. TCA also slightly affected the expression of CXCR6, a chemokine receptor crucial for CD8⁺ T-cell survival and local expansion in the TME²⁶ (Fig. 8E, F). Similarly, TCA treatment also reduces the secretion of multiple cytotoxic cytokines from CD8⁺ T cells in the TME (Supplementary Fig. 7E). GEPIA confirmed a strong correlation between CXCR6 expression and CD8⁺ T-cell infiltration in human liver hepatocellular carcinoma (LIHC) (Supplementary Fig. 7F). Furthermore, high CXCR6 expression in LIHC was associated with a better prognosis (Supplementary Fig. 7G). In conclusion, TCA specifically suppresses CD8⁺ T-cell infiltration, survival and effector function in the TME.

**Fig. 8: TCA exacerbates tumour growth in a CD8⁺ T-cell-dependent manner.**

Taurocholic acid incapacitates CD8⁺ T cells via the MAPK/ERK pathway

RNA sequencing (RNA-seq) was performed on CD8⁺ T cells isolated from the subcutaneous tumours of the TCA-treated and untreated mice to understand how TCA regulates CD8⁺ T cells. The analysis of gene expression revealed that TCA treatment upregulated 279 genes and downregulated 222 genes (Fig. 9A). Specifically, the mRNA expression of transcription factors involved in CD8⁺ T-cell differentiation and function (Id2, Irf8, Runx3, and Eomes) and cosimulatory surface molecules (Il21r, Il2rb, and Tnfrsf9) was downregulated, whereas the expression of exhaustion-associated TFs (Prdm1, Id3, and Batf) and coinhibitory or terminally exhausted surface molecules (Klrg1 and Cd101) was upregulated in the TCA-treated CD8⁺ TILs compared with the PBS-treated controls^8,27,28,29 (Fig. 9B). The KEGG pathway enrichment analysis of the downregulated genes revealed the significant inhibition of pathways crucial for CD8⁺ T-cell activation, including the T-cell receptor signalling, MAPK, and TLR signalling pathways (Fig. 9C). The GO analysis also indicated impaired cell surface receptor signalling and T-cell-mediated cytotoxicity (Fig. 9D).

**Fig. 9: TCA incapacitates CD8⁺ T cells via the MAPK/ERK pathway.**

TCA, a bile acid, possibly interacts with receptors such as farnesoid X receptor (FXR), pregnant X receptor (PXR), constitutive androstane receptor (CAR), vitamin D receptor (VDR), Takeda G-protein coupled receptor (TGR5) and sphingosine-1-phosphate receptor 2 (S1PR2)^30,31. However, we only detected a 13-fold increase in the expression of S1pr2 compared with Vdr, while Fxr, Pxr, Car and Tgr5 were undetectable (Supplementary Fig. 8A). Gene Set Enrichment Analysis (GSEA) showed that TCA-treated CD8⁺ T cells had significantly decreased enrichment of the sphingolipid signalling pathway, which is linked to the MAPK pathway (Supplementary Fig. 8B). We hypothesized that TCA might inhibit the MAPK pathway by binding to S1PR2 on the cell membrane, thereby interfering with CD8⁺ T-cell effector function. Nevertheless, the S1PR2 antagonist JTE-013 failed to rescue and even worsened the expression of some cytotoxic cytokines. Similarly, the S1PR2 receptor agonist CYM5520 could hardly replicate the effects of TCA (Supplementary Fig. 8C–E). In addition, we analysed the expression of BA receptors on Hepa1-6 cells and found that the most abundant receptor was S1pr2, whereas Pxr was almost not expressed (Supplementary Fig. 8F). Compared with the immortalized murine hepatocyte cell line, AML12 cells, Hepa1-6 cells rarely expressed Fxr and Pxr but highly expressed Vdr, S1pr2, and especially Car (Supplementary Fig. 8G).

Although the Th17, Th1 and Th2 differentiation pathways were among the top two pathways in the KEGG enrichment analysis, the differentially expressed genes involved were also enriched mainly in the T-cell receptor and MAPK signalling pathways (Supplementary Fig. 9A). In vitro, TCA suppressed ERK1/2 activity in a dose-dependent manner by reducing ERK1/2 phosphorylation, indicating downregulation of the MAPK/ERK1/2 pathway, which is consistent with the results of the KEGG analysis of the RNA-seq data (Fig. 9C, E) and in vivo multiplex immunohistochemical staining (Supplementary Fig. 9C). TCA did not affect the NF-κB pathway, as P65 phosphorylation remained unchanged, despite both pathways being critical for differentiation, apoptosis/survival and cytotoxic function^32,33, nor did it impact the phosphorylation of ERK in Hepa1-6 cells (Fig. 9E, Supplementary Fig. 9D). Coadministration of C16-PAF, a potent MEK/ERK activator, rescued ERK1/2 phosphorylation and partially restored the expression of cytotoxic cytokines that were impaired by TCA (Fig. 9F–H). In summary, TCA constrains ERK activity and induces an anergic CD8⁺ TIL phenotype.

Discussion

Large cohort studies have shown that a linear trend towards increased cancer mortality with increasing BMI³⁴. Host microbiota maladaptation has become a predominant research hotspot for obesity-driven tumorigenesis and progression^4,5,12. In our study, we verified accelerated hepatocellular tumour growth and a greater tumour burden in a DIO mouse model. We observed a decreased abundance of probiotics, such as Bifidobacterium, in the guts of obese mice. Based on our previous work, we investigated the role of B. breve lw01 in controlling tumours in an HFD-fed mouse model and explored the detailed mechanisms involved. Unlike previous studies that focused on suppressing cancer cell proliferation directly³⁵, we performed a novel study exploring the participation of probiotics in the immunomodulation of obesity-related tumours (Supplementary Fig. 10). The administration of B. breve significantly inhibited the accelerated progression of HCC in both the HFD-induced obese and genetically obese models, resulting from the increased antitumour efficacy caused by the increased local infiltration of cytotoxic immune cells such as CD8⁺ T cells and proinflammatory TAMs.

In our study, B. breve lw01 manipulated energy metabolism, consistent with previous reports^36,37. It optimized glucose homeostasis and reversed liver steatosis caused by long-term HFD consumption. However, serum lipidomic changes were minimal, likely because B. breve only partially regulates energy storage³⁸. Unlike some B. longum and B. bifidum strains³⁹, B. breve lw01 does not prevent diet-induced weight gain, possibly due to strain specificity. Additionally, the gut microbiome structure only changed slightly after B. breve was administered, suggesting that dietary factors play a more predominant role.

The host genome and microbiome jointly produce a wide range of metabolites that act as critical signalling molecules and energy substrates, influencing the immune system beyond the intestinal mucosal barrier^40,41. These findings prompted us to investigate metabolites that mediate the tumour-promoting effects of HFD-induced obesity. The metabolomic analysis revealed increased levels of taurine-conjugated BAs, such as TCA and TDCA, which aligns with observations in humans with obesity-related metabolic syndrome⁴², alongside reduced levels of amino acids and hormones. However, TCA was the only metabolite that was significantly correlated with the abundance of Bifidobacterium. BAs, synthesized from cholesterol in the liver, undergo significant modifications by the gut microbiota. Primary BAs conjugated with glycine or taurine are hydrolysed by BSH in gut bacteria such as Bacteroides, Clostridium, and Bifidobacterium, followed by dehydroxylation and epimerization to form secondary BAs^43,44. The reduced abundance of Bifidobacterium in HFD-fed mice and increased BA biosynthesis during liver inflammation likely explain the elevated serum TCA level. Our study revealed that the administration of B. breve significantly reduced the serum TCA level and repressed tumour progression in obese mice, primarily because of the BSH activity of B. breve. Inhibiting BSH activity externally compromised the tumour-suppressing effect of B. breve. In this study, we further identified BSH subtypes in B. breve and explored the corresponding gene(s) and their predicted structures. The analysis of the structure‒activity relationship revealed the therapeutic potential of BSH-active bacteria for the treatment of obesity-related tumours.

We recognized that a BSH-knockout B. breve strain is the gold standard for confirming its tumour-suppressive role. However, despite recent advances in CRISPR-based genome editing systems, the editing efficiency and genome accessibility remain dependent on the unique genomic and epigenetic landscapes of the target strain^45,46. Hence, in this study, we opted to use AAA-10—a highly recognized, gut-restricted bacterial BSH inhibitor—as an alternative approach to probe the function of BSH. In the future, we aim to investigate whether CRISPR-based systems can achieve efficient genome editing in our B. breve strain and subsequently validate the tumour-suppressive effects of the edited strain using animal models.

Many studies have shown that certain BAs, such as deoxycholic acid (DCA) and lithocholic acid (LCA), have tumour-promoting properties⁴⁷, whereas others, such as ursodeoxycholic acid (UDCA) and iso-DCA, have therapeutic benefits^48,49. Unlike DCA, which only promotes tumour growth in DIO mice¹², our study revealed that TCA accelerates tumour development in normal diet-fed mice implanted with HCC cells. Although high concentrations of TCA do not directly target tumour cells, long-term TCA exposure can directly promote tumour cell proliferation. We used a dose of 0.25 mg/kg (~7.43 μM) to mimic the serum TCA levels in DIO mice, which is much lower than the concentrations used in previous studies^50,51. However, according to an article published in Science in January 2025, in the tumour interstitial fluid of three murine models, the concentration of TCA can even reach as high as 1 mM. More importantly, researchers have reported that TCA was more abundant in tumour lesions within the liver than in healthy adjacent tissue⁵², which may explain the different levels of TCA between the serum and tumour lesions in our research. In vitro, TCA had no effect on tumour cell proliferation or apoptosis across a range of concentrations (0–500 μM). Recent studies suggest that TCA can epigenetically promote the immunosuppressive functions and glycolysis of MDSCs⁵³. Flow cytometry showed that at physiological concentrations, TCA limited CD8⁺ T-cell infiltration in the TME. In vitro experiments revealed that TCA reduced CXCR6 expression²⁶, induced CD8⁺ T-cell apoptosis and weakened their cytotoxic function, similar to chronic viral infection⁵⁴. This effect was associated with the downregulation of the MAPK/ERK signalling pathway. The ERK agonist C16-PAF partially reversed the impaired phenotype of CD8⁺ T cells caused by TCA treatment.

Although many studies have explored BA receptors, including FXR, TGR5, PXR, CAR, VDR and S1PR2, on liver and intestinal cells^31,43, knowledge of these receptors on immune cells is limited. TCA can promote M2-like macrophage polarization via FXR, and secondary BAs can enhance CD8⁺ T-cell functions through TGR5^55,56. However, RNA-seq did not detect the expression of FXR and TGR5 in tumour-infiltrating CD8⁺ T cells. We focused on S1PR2, which presented relatively high transcript levels. The S1PR2 antagonist did not rescue impaired CD8⁺ T cells or worsen cytotoxicity, whereas the SIPR2 agonist failed to replicate the effects of TCA. These findings suggest that S1PR2 may not be the receptor to which TCA binds on CD8⁺ T cells. We also analysed the downstream genes of other BA receptors but did not obtain any significant results (Supplementary Fig. 9B). Further research on BA receptors and their signalling pathways in immune cells is urgently needed.

This study focused on the underlying mechanism, encompassing genomic evidence and key metabolites of probiotics that participate in tumour immunomodulation in HFD-induced obesity and genetic obesity. From a practical clinical perspective, our research has focused primarily on the preventive effects of B. breve. In the future, we would like to further generalize and strengthen our findings by using a naturally occurring liver cancer model and explore whether probiotics exert a synergistic or counteracting effect with ICB on obese individuals to achieve more precise tumour management.

Methods

Mice and syngeneic tumour model

Male C57BL/6N mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. and used 1 week after they were received. All the mice were maintained under specific pathogen-free conditions. At the age of 6-week-old, they were randomly separated into two groups: those fed a control diet (CD, Mediscience MD17121) or a high-fat diet (HFD, Research Diets D12492, 60 kcal% fat) until euthanasia. For the model administered B. breve, 6-week-old C57BL/6 N mice were randomly chosen for each group administered 100 µl of B. breve (1 × 10⁹ CFUs/mouse) (HFD + B. breve) or 100 µl of PBS as a control (HFD + PBS) by oral gavage every day until euthanasia, and both groups were maintained on a high-fat diet. After 16 weeks differentiated feeding, the mice were subcutaneously injected with 1.2 × 10⁶ Hepa1-6 cells.

For the TCA experiment, 0.25 mg/kg TCA (Sigma‒Aldrich) or PBS (as a control) was administered to male C57BL/6N mice via intraperitoneal injection every day beginning at 6 weeks of age, and 8 weeks later, the mice were subcutaneously injected with 1.2 × 10⁶ Hepa1-6 cells.

For bacterial BSH inhibition, after 16 weeks of high-fat diet plus B. breve administration, the mice were split into two groups and gavaged once daily with either 200 μl of 90% normal saline/10% DMSO containing 20% SBE-β-CD (MCE) (w/v) or 200 μl of 90% normal saline/10% DMSO containing 20% SBE-β-CD (w/v) and AAA-10 (MCE) at a concentration of 3.5 mg/ml starting 3 days before the inoculation of Hepa1-6 cells until euthanasia.

The tumour size was measured every two days until the end point, and the tumour volume was calculated as length × width² × 0.5. The end point of the experiment was 14 days after tumour cell inoculation.

Male lep KO (ob/ob) mice were purchased from Cyagen Biosciences, Inc. For the orthotopic HCC model, 6-week-old C57BL/6N mice were randomly chose for each group and administered 100 µl of Bifidobacterium breve (1 × 10⁹ CFUs/mouse) (ob/ob+B. breve) or 100 µl of PBS as a control (ob/ob+PBS) by oral gavage every day until euthanasia; both groups were maintained on a control diet. After 1 week of differentiated feeding, 1 × 10⁶ Hepa1-6-luc cells were injected into the left liver lobe of each mouse. Bioluminescence was imaged with a Xenogen IVIS Kinetic imaging system (Caliper). The end point of the experiment was 9 days after tumour cell inoculation.

During the experiments, the body weights of the mice were monitored once a week. The mice were anaesthetized with 1% sodium pentobarbital, and blood was collected via ophthalmectomy, followed by euthanasia through cervical dislocation. After euthanasia, the livers, epididymal white adipose tissue (eWAT), distal ileum and spleen were excised.

All animal studies were performed in compliance with the regulations and guidelines of the Peking University Institutional Animal Care and Use Committee (No. LA2022173).

CD8⁺ T-cell depletion

For the depletion of CD8⁺ T cells, we injected mice with an anti-CD8 depletion antibody (Bio X Cell, BE0061, 2.43) or an isotype control antibody (Bio X Cell, BE0090, LTF-2) at a dose of 250 µg per mouse intraperitoneally starting on day -3 of tumour cell inoculation and every 3 days thereafter until the end of the study.

Experiments with cell lines and primary cells

The mouse hepatocellular carcinoma Hepa1-6 cell line was obtained from ATCC. The Hepa1-6-luc cell line was purchased from Shanghai Model Organisms Center, Inc. and cultured in DMEM (Gibco) supplemented with 10% foetal bovine serum (Excell) and 1% penicillin/streptomycin (Aoqing Biotech., AQ512) at 37 °C in a 5% CO₂/95% air incubator. The AML12 cell line was obtained from Wuhan Pricella Biotechnology Co., Ltd. and cultured in DMEM/F12 (Gibco) supplemented with 10% foetal bovine serum, 0.5% insulin transferrin selenium, 40 ng/ml dexamethasone and 1% penicillin/streptomycin.

For the in vitro assay involving TCA, Hepa1-6 cells were treated with TCA at doses ranging from 0 to 500 µM, and cell viability was assessed using a Cell Counting Kit-8 (CCK8, Aoqing Biotech., AQ308), and proliferation (Ki67, BioLegend, 652406) and apoptosis (Annexin V &7-AAD, BioLegend, 640906, 420403) were analysed by flow cytometry.

CD8⁺ T cells were isolated from the spleens of naïve mice and purified by MACS negative selection (Selleck, B90011). CD8⁺ T cells were then cultured in complete RPMI 1640 culture medium supplemented with 25 ng/ml recombinant mouse IL-2 (Abcam, ab259380), 1 μg/ml anti-CD3 (eBioscience™, 16-0032-85) and 2 μg/ml anti-CD28 (eBioscience™, 16-0281-85) for 2 days and subsequently stimulated with TCA at concentrations ranging from 0 to 1000 µM. The proliferation, apoptosis and function of CD8⁺ T cells were analysed by flow cytometry, Western blotting and qRT‒PCR.

Bacterial culture

B. breve lw01 was previously isolated by our group and was routinely cultured in MRS broth (Land Bridge) supplemented with raffinose (Solarbio) and 0.5% L-cysteine·HCl (Sigma‒Aldrich) in an anaerobic environment at 37 °C.

Oral glucose tolerance test

An oral glucose tolerance test was performed after 16 weeks of treatment. The mice were fasted by the removal of food and bedding for 8 h and then administered a glucose solution (2 g/kg) by oral gavage. Blood glucose levels at time 0 (fasting glucose level, measured before the glucose gavage) and at 15, 30, 60, and 120 min after glucose gavage were assessed in blood collected from the tip of the tail vein using a glucometer (Sinocare). The glucose level was plotted against time, and the area under the glucose curve (AUC) was calculated using the trapezoidal rule.

Serum biochemical analysis

Blood samples were collected in clean 1.5 ml Eppendorf tubes via ophthalmectomy, allowed to stand for 1 h at 4 °C to separate the components, and centrifuged. Then, the serum samples were stored at −80 °C. Serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) measurements were performed by the Department of Laboratory Animal Science (Peking University Health Scientific Center) using an automatic Mindray BS-350E Biochemistry Analyzer.

Faecal sample collection and DNA extraction

Freshly excreted stools from the mice from the CD or HFD, HFD + PBS or HFD + B. breve groups were collected in sterile tubes directly from the opening of the anal orifice and then stored at −80 °C. DNA was extracted from different samples using the CTAB method according to the manufacturer’s instructions. The total DNA was eluted in 50 μL of elution buffer and stored at −80 °C until measurement by PCR by LC-Bio Technology Co., Ltd., Hang Zhou, Zhejiang Province, China.

Faecal microbiota 16S rRNA sequencing and analysis

The bacterial 16S rRNA V3-V4 region was amplified by PCR using the primers to which adapter overhang sequences were added: 341 F (5’-CCTACGGGNGGCWGCAG-3’) and 805 R (5’-GACTACHVGGGTATCTAATCC-3’).

PCR amplification was performed in a total volume of 25 μL of reaction mixture containing 25 ng of template DNA, 12.5 μL of PCR Premix, 2.5 μL of each primer, and PCR-grade water to adjust the volume. The PCR conditions used to amplify the prokaryotic 16S fragments consisted of initial denaturation at 98 °C for 30 s; 32 cycles of denaturation at 98 °C for 10 s, annealing at 54 °C for 30 s, and extension at 72 °C for 45 s; and a final extension at 72 °C for 10 min. The PCR products were purified with AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified with a Qubit instrument (Invitrogen, USA). The amplicon pools were prepared for sequencing, and the size and quantity of the amplicon library were assessed using an Agilent 2100 Bioanalyzer (Agilent, USA) and the Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively.

The samples were sequenced on the Illumina NovaSeq PE250 platform according to the recommendations provided by LC-Bio. Paired-end reads were assigned to the samples based on their unique barcodes and were truncated by removing the barcode and primer sequences. Paired-end reads were merged using FLASH. Quality filtering of the raw reads was performed under specific filtering conditions to obtain high-quality clean tags according to fqtrim (v0.94). Chimeric sequences were filtered using Vsearch software (v2.3.4). After dereplication using DADA2, we obtained a feature table and feature sequence. Alpha diversity and beta diversity were calculated by normalization to the same sequences randomly. Then, according to the SILVA (release 138) classifier, feature abundance was normalized to the relative abundance of each sample. Alpha and beta diversities were calculated using alpha- and beta-group significance in the QIIME2 diversity plugin, respectively. The bioinformatic analysis was performed using the OmicStudio tools at https://www.omicstudio.cn/tool.

Serum metabolomic profiling

After the serum samples were thawed, they were vortexed for 10 s. Fifty microlitres of each sample was transferred to a centrifuge tube, mixed with 250 μL of 20% acetonitrile/methanol, vortexed for 3 min, and centrifuged at 12,000 r/min for 10 min at 4 °C. A total of 250 μL of the supernatant was transferred to a new centrifuge tube, and the supernatant was placed in a −20 °C freezer for 30 min. Then, the supernatant was centrifuged at 12,000 r/min for 10 min at 4 °C. After centrifugation, 180 μL of the supernatant was transferred to a protein precipitation plate for the subsequent LC‒MS analysis. All the metabolites were detected by MetWare Biotechnology, Inc., using the AB SciexQTRAP 6500 LC‒MS/MS platform.

Differentially abundant metabolites were screened and PCA was performed using the MetWare Cloud (https://cloud.metware.cn). A differentially abundant metabolite heatmap was generated using the GeneCloud tool at https://www.genescloud.cn/. Volcano plots and clustering correlation heatmaps with signs were generated using OmicStudio tools at https://www.omicstudio.cn.

Serum lipidomic profiling

The serum sample was thawed on ice, incubated for ~10 s, and then centrifuged at 3000 rpm at 4 °C for 5 min. Fifty microlitres of each sample was homogenized with a 1 mL mixture (including methanol, MTBE and internal standard mixture). The mixture was swirled for 15 min. Then, 200 μL of water was added, the mixture was swirled for 1 min, and the mixture was centrifuged at 12,000 rpm at 4 °C for 10 min. A total of 500 μL of the supernatant was extracted and concentrated. The powder was dissolved in 200 μL of reconstitution solution and then stored at −80 °C. Finally, the solution was transferred to a sample bottle for LC‒MS/MS analysis. Lipid contents were detected by MetWare Biotechnology Inc. using the AB Sciex QTRAP 6500 LC‒MS/MS platform. The differentially abundant metabolite screen, heatmap construction and PCA were performed using the MetWare Cloud (https://cloud.metware.cn). A volcano plot was generated using OmicStudio tools at https://www.omicstudio.cn.

Measurement of bile acids in bacterial supernatants

An overnight B. breve culture was reinoculated into fresh MRSRC medium supplemented with 10 µM TCA or MRSRC medium with the same dose of TCA as the control, and both were cultured in an anaerobic environment at 37 °C for 36 h.

For in vitro bacterial BSH inhibition, an overnight B. breve culture was reinoculated into fresh MRSRC medium supplemented with 100 µM TCA and 100 nM AAA-10 or 100 nM DMSO as a control and cultured in an anaerobic environment at 37 °C for 24 h.

The mixture was subsequently centrifuged at 5000 × g for 2 min (room temperature), after which the supernatant was filtered through a 0.22 µm strainer on a clean bench and stored at −80 °C.

Fifty microlitre samples were extracted with 200 μL of methanol/acetonitrile (v/v = 2:8). A total of 10 μL of an internal standard mixture (1 μg/mL) was added to the extract for quantitation. The samples were incubated at −20 °C for 10 min to precipitate the protein. After centrifugation for 10 min (12,000 r/min, 4 °C), the supernatant was transferred to clean plastic microtubes. The extracts were evaporated to dryness and reconstituted in 100 μL of 50% methanol (v/v) for the subsequent LC‒MS/MS analysis. The bile acid contents were detected by MetWare Biotechnology, Inc., using the AB Sciex QTRAP 6500 LC‒MS/MS platform.

Cell preparation and flow cytometry

For the analysis of tumour-infiltrating immune cells, tumours were minced on ice and digested in RPMI 1640 medium containing 1.5 mg/ml collagenase I (Sigma‒Aldrich) at 37 °C for 1 h. The digested tumour tissues were filtered through 70 mm cell strainers (BD Biosciences). For the immune cell analysis, single-cell suspensions of the spleen were prepared using a semiautomated gentleMACS™ Dissociator (Miltenyi Biotec, 130-093-235) and then filtered through 70 mm cell strainers. For the analysis of lymphocytes in peripheral blood, blood samples were collected in heparin-coated tubes via ophthalmectomy, and lymphocytes were separated with a mouse peripheral blood lymphocyte isolation kit (Solarbio).

Viability dye (BioLegend) was used to exclude dead cells. For surface staining, the cells were stained with antibodies against surface antigens in staining buffer in the dark for 30 min at room temperature. For intracellular cytokine and nuclear staining, cells were first stimulated with a Cell Activation Cocktail with brefeldin A (BioLegend) and then fixed and permeabilized with a Mouse Foxp3 Buffer Set according to the manufacturer’s instructions (BD Pharmingen™). Apoptosis was measured in cells cultured in vitro by the staining cells with specific antibodies in Annexin V binding buffer in the dark for 15 min at room temperature. Flow cytometry was performed on an Aria Sorp instrument (BD Biosciences), and the cells were analysed with FlowJo_v10.8.1 software. The antigen-specific antibodies used are shown in Supplementary Table 5.

RNA sequencing analysis

Tumour-infiltrating CD8⁺ T cells were sorted with a FACS Aria Sorp cell sorter (BD Biosciences). Total RNA was extracted using the TRIzol method according to the manufacturer’s instructions (BGI-NGS-TQ-RNA-001), and the RNA-Seq analysis was performed at the Shenzhen Huada Genomic Institute. The sequencing data were filtered with SOAPnuke, after which clean reads were obtained and stored in FASTQ format. The clean data were mapped to the reference genome (mm10) using HISAT (v2.2.1). The clean data were mapped to the assembled unique gene with Bowtie2 (v2.4.5). The expression levels of genes were calculated using RSEM (v1.3.1). The differential expression analysis was performed using DESeq2 (v1.4.5) with a Q value ≤ 0.05 (or FDR ≤ 0.001). GO (http://www.geneontology.org/) and KEGG (https://www.kegg.jp/) enrichment analyses of annotated differentially expressed genes were performed with Phyper using a hypergeometric test. The significance levels of terms and pathways were corrected by the Q value with a rigorous threshold (Q value ≤ 0.05).

Histology and H&E and immunohistochemical staining

All the tissues were fixed with 10% (v/v) formalin for 24 h at room temperature, and then, the tissues were sequentially dehydrated in ethanol at increasing concentrations ranging from 50 to 100%. Dehydrated tissues were infiltrated with 100% xylene and embedded in paraffin. The tissues were subsequently sectioned at a thickness of 4 μm and stained with haematoxylin and eosin (H&E). For immunohistochemistry, antigen-specific antibodies (shown in Supplementary Table 5) were used. Multiplex immunohistochemistry was conducted using an Opal Polaris^TM 7-colour Manual IHC Kit (AKOYA, NEL861001KT) according to the manufacturer’s instructions. All the sections were observed under a microscope (Olympus BX53). Three representative fields separated from each tissue sample were randomly selected for statistical analysis.

Western blot

The tumour tissues were carefully dissected and stored in liquid nitrogen, and the tissues or cell pellets were lysed in RIPA buffer (Solarbio) containing proteinase inhibitors (Huaxingbio) and phosphatase inhibitors (Huaxingbio). After the protein concentration was quantified using a BCA assay (Thermo Fisher Scientific), equal amounts of protein samples were separated by electrophoresis on 8–15% SDS polyacrylamide gels and transferred to polyvinylidene difluoride membranes by wet blotting. The membranes were blocked with 5% nonfat dry milk for 1 h at room temperature and probed with antibodies (shown in Supplementary Table 5) separately at 4 °C overnight. After an incubation with peroxidase-conjugated secondary antibodies for 1 h at room temperature, enhanced chemiluminescence reagent was used to visualize the immunoreactive proteins.

Quantitative real-time PCR

RNA was extracted from mouse livers, Hepa1-6 cells, AML12 cells and CD8⁺ T cells using the TRIzol method. Reverse transcription was conducted using PrimeScript™ RT Master Mix (Takara), and real-time PCR was performed using a qRT‒PCR system (ROCGENE) with SYBR Green Master Mix (Yeasen) and gene-specific primers (shown in Supplementary Table 6).

B. breve lw01 genomic analysis

The full genome sequences of B. breve lw01 were annotated and visualized using Bakta (https://bakta.computational.bio/). The corresponding amino acid sequences were used for KEGG and GO analyses with eggNOG-Mapper (http://eggnog-mapper.embl.de/).

Bsh gene collection and construction of the phylogenetic tree

The Bsh gene sequences of the Bifidobacterium species used in the present study were retrieved from NCBI Nucleotide database, and the Bsh gene sequences of lw01 were harvested according to the annotated location and verified using BLASTX. The Bsh gene sequences were aligned, and phylogenetic trees were built using the neighbour-joining method (Kimura2-parameter model) in MEGA software (v12.0). Interactive Tree of Life (iTOL) (https://itoL.embL.de/) was used to construct the phylogenetic tree by adjusting the labels and filling the colours as needed.

Comparisons of BSH structures

Potential 3D models of unknown structures were produced by Phyre2 (https://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index) based on an amino acid sequence alignment to known protein structures. The comparison of 3D structures and root-mean-square distance (RMSD) per-column spatial variation among the structures were calculated using Chimera (V1.18).

Statistical analysis

Statistical analyses were performed using GraphPad Prism 8. Tumour growth curves were analysed using two-way ANOVA. For other studies, if the variance between groups was similar, parametric tests, such as standard Student’s t test or one-way ANOVA with the Bonferroni post hoc correction, were used. In the case of a significantly different variance between groups, nonparametric tests, such as the Mann‒Whitney U test or the Kruskal‒Wallis test with Dunn’s post hoc test, were applied. The data in the bar graphs are presented as the means ± SEMs. Measurements were taken from distinct samples. For all the statistical analyses, p < 0.05 was considered statistically significant and is denoted as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Data availability

The relative abundances of the Bifidobacterium genus between patients with different diseases and healthy individuals were determined from a curated database of human gut metagenomes: https://gmrepo.humangut.info. The absolute quantification of serum TCA levels between patients with different obesity-related diseases and healthy individuals was performed from a curated database of the metabolomics workbench: https://www.metabolomicsworkbench.org/. Research numbers: ST001411 and ST001412. The full genome sequence of B. breve lw01 is available at GenBank under accession number CP034192. The other data generated in this study are available from the corresponding author upon reasonable request.

Abbreviations

AOI:: area of interest
B. breve :: Bifidobacterium breve
BA:: bile acid
BMI:: body mass index
BSH:: bile salt hydrolase
CAR:: constitutive androstane receptor
CD:: control diet
CFU:: colony-forming unit
DCA:: deoxycholic acid
DIO:: diet-induced obesity
eWAT:: epididymal white adipose tissue
FXR:: farnesoid X receptor
GEPIA:: Gene Expression Profiling Interactive Analysis
GO:: Gene Ontology
GSEA:: Gene Set Enrichment Analysis
HFD:: high-fat diet
H&E:: haematoxylin and eosin
ICB:: immune checkpoint blockade
KEGG:: Kyoto Encyclopedia of Genes and Genomes
LC‒MS/MS:: liquid chromatograph mass spectrometry
LEfSe:: linear discriminant analysis effect size
LIHC:: liver hepatocellular carcinoma
MDSC:: myeloid-derived suppressor cell
NK:: natural killer
OGTT:: oral glucose tolerance test
PXR:: pregnant X receptor
S1PR2:: sphingosine-1-phosphate receptor 2
TAM:: tumour-associated macrophage;
Tc:: cytotoxic CD8⁺ T cells
TC:: total cholesterol
TCA:: taurocholic acid
TDCA:: taurodeoxycholic acid
TG:: triglyceride
TGR5:: Takeda G protein-coupled receptor 5
Th:: helper CD4⁺ T cell
TIL:: tumour-infiltrating lymphocyte
TME:: tumour microenvironment
Treg:: regulatory T cell
VDR:: vitamin D receptor
WT:: wild type

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Acknowledgements

We sincerely thank the Central Laboratory of Peking University School and Hospital of Stomatology for providing convenient experimental instruments and operating platforms. We are also grateful to Wuhan MetWare Biotechnology, Inc., for the metabolomic analysis and data analysis; Hangzhou LC-Bio Technology, Co., Ltd., for 16S rRNA sequencing and data analysis; and BGI for RNA sequencing services and data analysis. The diagram of the mechanism was created with BioGDP.com. This study was supported by the National Key R&D Program of China (grant number 2022YFC2406300), the Beijing Natural Science Foundation Proposed Program (grant number 7232220), the National Natural Science Foundation of China (grant number 82203514), and the Clinical Medicine Plus X - Young Scholars Project of Peking University (grant number PKU2022LCXQ19). The funders played no role in the study design; data collection, analysis or interpretation; or writing of this manuscript.

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Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
Ruoshui Yuan, Yifei Wang, Qingxiang Li, Chuanbin Guo & Lin Wang
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China
Yuke Li

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Ruoshui Yuan
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Yuke Li
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Yifei Wang
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Qingxiang Li
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Chuanbin Guo
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Lin Wang
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Contributions

R.Y., C.G., and L.W. designed the project. R.Y. performed most of the experiments, analysed the data, and drafted the initial manuscript. Y.L., Q.L. and Y.W. assisted with the animal experiments and analysed the data. C.G. and L.W. supervised the research, revised the article, and are the corresponding authors.

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Correspondence to Chuanbin Guo or Lin Wang.

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Yuan, R., Li, Y., Wang, Y. et al. Deconjugating taurocholic acid with Bifidobacterium to mitigate obesity-driven cancer progression by restoring CD8⁺ T-cell infiltration. npj Biofilms Microbiomes 11, 167 (2025). https://doi.org/10.1038/s41522-025-00809-4

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Received: 24 February 2025
Accepted: 05 August 2025
Published: 21 August 2025
DOI: https://doi.org/10.1038/s41522-025-00809-4

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Abstract

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Introduction

Results

High-fat diet-induced obesity accelerates tumour growth and disturbs organism homeostasis

The probiotic Bifidobacterium breve lw01 attenuated HFD-promoted tumour growth

The probiotic B. breve ameliorates the HFD-mediated immunosuppressive microenvironment

The probiotic B. breve inhibits tumour growth by deconjugating taurocholic acid

The antitumor capacity of the probiotic B. breve depends on its bile salt hydrolase

Taurocholic acid exacerbates tumour growth in a CD8+ T-cell-dependent manner

Taurocholic acid incapacitates CD8+ T cells via the MAPK/ERK pathway

Discussion

Methods

Mice and syngeneic tumour model

CD8+ T-cell depletion

Experiments with cell lines and primary cells

Bacterial culture

Oral glucose tolerance test

Serum biochemical analysis

Faecal sample collection and DNA extraction

Faecal microbiota 16S rRNA sequencing and analysis

Serum metabolomic profiling

Serum lipidomic profiling

Measurement of bile acids in bacterial supernatants

Cell preparation and flow cytometry

RNA sequencing analysis

Histology and H&E and immunohistochemical staining

Western blot

Quantitative real-time PCR

B. breve lw01 genomic analysis

Bsh gene collection and construction of the phylogenetic tree

Comparisons of BSH structures

Statistical analysis

Data availability

Abbreviations

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Taurocholic acid exacerbates tumour growth in a CD8⁺ T-cell-dependent manner

Taurocholic acid incapacitates CD8⁺ T cells via the MAPK/ERK pathway

CD8⁺ T-cell depletion