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. 2026 Jul 3;49(5):127. doi: 10.1007/s13402-026-01249-8

Enriched environment inhibits melanoma by reshaping gut microbiota and enriching Parabacteroides distasonis

Xin Li 1,#, Huimin Li 1,#, Qing Wang 2,#, Xiang Gao 3, Jian Gao 1, Min Du 1, Wei Zhang 1, Shunan Liu 1, Yiyi Liang 1, Qian Li 1, Yu Gan 1,✉, Hong Tu 1,✉
PMCID: PMC13624209  PMID: 42397471

Abstract

Purpose

The role of psychological factors in the holistic regulation of cancer has garnered significant attention. Psychological eustress induced by an enriched environment (EE) inhibits various cancers including melanoma, but underlying mechanisms remain largely unknown. The brain-gut axis may play a key role in modulating psychological factors and cancer progression. This study investigates the role of gut microbiota in EE-induced anti-melanoma effects.

Methods

C57BL/6 mice were housed in EE or standard environment (SE). Longitudinal fecal samples from EE and SE mice underwent 16S rRNA gene sequencing. SE melanoma-bearing mice received fecal microbiota transplantation (FMT) from EE or SE donors to assess the microbiota’s causal role in EE’s anti-tumor effects. Bacterial species upregulated by EE in tumor-bearing mice were identified;Parabacteroides distasonis (Pd) was selected for therapeutic administration. Immune cells in spleen and tumor were quantified by flow cytometry. Natural killer (NK) cell function was tested using anti-NK1.1 depletion.

Results

EE increased the alpha diversity of the gut microbiota and alleviated the dysbiosis caused by melanoma. FMT from EE mice to SE mice inhibited melanoma growth, suggesting that the gut microbiota contributes to EE’s anti-tumor effects. EE upregulated seven bacterial species in tumor-bearing mice, including Pd. Oral administration of Pd inhibited melanoma growth and increased intratumoral NK/NKT cell proportions and NK cell granzyme B expression. NK cell depletion abrogated Pd’s anti-tumor effect.

Conclusion

This study underscores the interconnectedness of psychological eustress, gut microbiota, and cancer, providing preclinical insights into holistic cancer treatments. Leveraging the brain-gut-cancer axis, targeting psychological factors and gut microbiota could offer a potential adjunctive strategy for melanoma management.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s13402-026-01249-8.

Keywords: Eustress, Gut microbiota, Parabacteroides distasonis, Melanoma

Introduction

Melanoma is the most aggressive and lethal form of skin cancer, with an increasing incidence [1]. Currently, treatment for advanced melanoma mainly involves targeted therapy using BRAF and MEK inhibitors. While emerging immunotherapies have further enhanced treatment effectiveness, the five-year survival rate for patients remains at around 50% [2]. This underscores the urgent need for additional therapeutic strategies.

It has long been postulated that psychological distress can facilitate cancer progression by modulating most hallmarks of cancer [3]. Epidemiological studies have revealed that individuals suffering psychological distress have a higher tendency to develop melanoma [4]. Melanoma patients who maintain “psychological health” experience better overall outcomes [5]. Recently, a clinical study demonstrated that emotional distress negatively impacts the effectiveness of immunotherapy, with pretreatment emotional distress emerging as a potential predictor of the response to neoadjuvant immune checkpoint blockade in melanoma patients [6]. In line with these clinical observations, animal studies have shown that psychological distress can exacerbate melanoma growth by impairing the host’s immune response, primarily through sustained activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system [7]. Specifically, stress-related hormones, such as adrenergic or glucocorticoid hormones, bind to receptors on tumor or immune cells, promoting malignant behavior in melanoma cells while suppressing the quantity and functionality of crucial immune effector cells, including CD8+ T cells and natural killer (NK) cells in melanoma mouse models [8–10].

While psychological distress promotes tumor progression, eustress exerts the opposite effect on cancer. An enriched environment (EE) is a well-established housing condition that induces eustress in rodents. Compared to a standard environment (SE), EE typically provides increased living space, enhanced opportunities for physical activity and social interactions, and greater sensory stimulation. Research has shown that EE exposure reduces anxiety levels, enhances learning and memory, delays the progression of neurodegenerative diseases, and improves recovery from brain injury [11]. Interestingly, EE has consistently been demonstrated to inhibit the growth of various types of cancer, including melanoma, colon cancer [12], glioma [13], pancreatic cancer [14, 15], lung cancer [14], and liver cancer [16]. It is worth noting that isolated components of EE, including physical enrichment, social interaction, or exercise alone, are insufficient to reproduce a comparable anti-tumor effect as observed under full EE conditions [12, 17], highlighting the role of psychological eustress in EE-related anti-tumor phenotype. However, the underlying mechanisms through which EE exerts its antitumor effects remain unclear, hindering the translation of these preclinical findings into clinical applications.

Mounting evidence underscores the important role of gut microbiota in cancers including melanoma [18, 19]. Recent research demonstrated that fecal microbiota transplantation (FMT) from immune checkpoint inhibitors (ICIs) responders or healthy donors can improve responses to ICIs therapy in melanoma [20–22]. In animal experiments, recent studies have reported that several common probiotics, including Lactobacillus reuteri [23], Bifidobacterium longum [24, 25], Escherichia coli [23], Lactobacillus rhamnosus [26], Eubacterium rectale [27], Roseburia intestinalis and Anaerostipes caccae [28], can inhibit melanoma either alone or in combination with ICIs. FMT or probiotic complementation is emerging as a novel approach for treating melanoma.

The gut-brain-microbiota axis is a bidirectional communication system and a key regulatory node in maintaining health [29]. While the brain regulates the gut microbiota, the commensal bacteria, in turn, can influence the brain and behavior [30]. Disruption of gut microbiota has been closely associated with various neurological disorders [31], such as Parkinson’s, Alzheimer’s disease, cognition impairment and stroke. On the contrary, the supplementation of psychobiotics has been found to assist in restoring mental health. It has been reported that Lactobacillus rhamnosus, Bifidobacterium longum NCC3001 and Lactobacillus reuteri attenuates depression and anxiety-like behavior, and Akkermansia muciniphila improves cognitive impairment [32]. As a classical eustress model, EE has been demonstrated to alter the gut microbiota in different disease models of nervous systems [33–39]. In Tcf4Het/+ApcMin/+ colon cancer mice model, EE was also found to be able to increase gut biodiversity [36]. However, the biological impact of such gut microbiota alteration remains uninvestigated. In this study, we demonstrated for the first time that EE-induced gut microbiota alterations play an important role in the anti-melanoma effect of EE. Additionally, we identified a novel EE-responsive bacteria Parabacteroides distasonis (Pd) that possess significant anti-melanoma activity. Our findings highlight the role of eustress and gut microbiota in modulating melanoma progression.

Materials and methods

Experimental animals

All animal experiments were conducted in accordance with protocols approved by the institutional animal care and use committee of Shanghai Shuyuan Biotechnology Co., Ltd. C57BL/6 mice were purchased from Sino-British SIPPR/BK Lab Animal Ltd. (Shanghai, China) and housed in a temperature- and humidity-controlled vivarium with a 12-hour light/dark cycle.

The sample size for the EE experiments was determined based on prior publications [12, 14, 40], while for the remaining tumor intervention studies, it was based on the melanoma literature. In all experiments, mice were stratified by body weight and randomly assigned into each group.

Environmental housing conditions

Detailed housing conditions for SE and EE groups are described in our previous study [14]. Briefly, SE mice were housed in standard laboratory cages (26 × 16 × 13 cm; four mice/cage). EE mice were housed in larger cages (61 × 43 × 21 cm; 12 mice/cage) containing running wheels, wooden toys, plastic tunnels, climbing structures, shelters, and nesting material. To maintain novelty and complexity, the objects in the EE cages were repositioned twice weekly and replaced with different objects weekly. Mice had ad libitum access to food and water.

Cell culture

The murine melanoma cell line B16F10 (American Type Culture Collection) was cultured in DMEM (Basal Medium, Shanghai, China) supplemented with 10% fetal bovine serum (FBS; Biosera, France) and 1% penicillin-streptomycin (Gibco, USA) at 37 °C in a humidified incubator with 5% CO2.

Mice tumor models

To investigate dynamic changes in the gut microbiota of EE and SE mice, male 3-week-old mice were randomly housed under EE or SE conditions. Fecal samples were collected at 0, 1, 3 and 5 weeks post SE or EE for 16S rRNA sequencing. The EPM test was performed at 3-week.

To investigate the changes of gut microbiota in the EE and the SE mice after tumor inoculation, male 3-week-old mice were randomly housed under EE or SE conditions for 3 weeks and then subcutaneously implanted with 1.5 × 105 B16F10 cells in the right flank. After tumor inoculation 2 weeks, SE and EE mice fecal samples were collected for 16S rRNA sequencing, respectively.

To elucidate the role of gut microbiota in EE-induced anti-melanoma effects, male 4-week-old mice were randomly assigned to two groups: SE-FMT and EE-FMT. Mice were administered a broad-spectrum antibiotic cocktail (1 g/L ampicillin, 0.5 g/L metronidazole, and 1 g/L neomycin; Abmole, USA) for three consecutive days, followed by a two-day rest period. Subsequently, 2 × 105 B16F10 cells were inoculated on the right side of mice after three consecutive days of FMT administration. And FMT administration was continued every two days until the end of the experiment.

To explore the protective effect of Pd on melanoma inhibition, male 5-week-old mice were randomly assigned to two groups: PBS (control) and Pd. The mice were orally administered with PBS or Pd, respectively, starting on day − 3 continuing until the end of the experiment. On day 0, mice were injected with 2 × 105 B16F10 cells in the right flank. The Pd-treated mice were oral gavaged daily with 1 × 109 colony forming units (CFU) bacteria in 100 µL sterile PBS, and the control group was given an equal amount of sterile PBS.

To deplete NK cells in mice, 100 µg of anti-NK1.1 antibody (Leinco, N123) was administered via intraperitoneal injection every four days. The treatment regimen commenced 1 day prior to the tumor inoculation and continued throughout the experimental period.

Tumor volumes were measured every 2–3 days using a caliper and calculated using the formula: V=width2 x length/2. At the experimental endpoint, mice were euthanized, and tumors were excised, weighed, and processed for subsequent analyses. Blinding was applied during outcome assessment and data analysis, with investigators unaware of group allocation.

Behavioral test: elevated plus maze

The elevated plus-maze (EPM) test was used to assess anxiety-related behavior in mice, as described previously [17]. The EPM apparatus consists of two open arms (5 cm x 35 cm), two closed arms (5 cm x 35 cm) with vertical walls (15 cm), and a center platform (5 cm x 5 cm) placed at 65 cm above the floor. Each mouse was placed on the center platform facing an open arm, and allowed to explore freely for 10 min. Behavior was video-recorded, and behavioral parameters were analyzed using SuperMaze software (version 2.0; Shanghai Xinruan Information Tech Co., Shanghai, China). Time spent in the open arms and the percentage of distance traveled in the open arms were used as indices of anxiety-like behavior. The apparatus was cleaned with 75% ethanol between each test.

Fecal collection

Feces were collected from each mouse housed individually in a clean, sterile cage. Subsequently, equal quantities of feces from each group of four mice were pooled to create a single fecal sample per group. Fecal samples were promptly frozen at -80°C within 2 h of collection for subsequent analysis.

Fecal microbiota transplantation

Mice housed in EE or SE conditions for 3 weeks served as donors for FMT. Freshly collected feces from donors were homogenized and suspended in sterile saline (9.0 g/L NaCl). The fecal suspension was filtered through a 40 μm cell strainer to remove large debris. All manipulations were performed on ice under the Anaerobic Workstation (DG250, Don Whitley Scientific). The filtrate was centrifuged at 2500 xg for 15 min at 4 °C, resuspended in MD preservation solution, and stored at -80 °C (1 mL of saline per 0.2 g of fecal pellet).

MD preservation solution: Maltodextrin and trehalose were dissolved in normal saline to final concentrations of 18.75% and 6.25% (w/v), respectively [41]. The preservation solution was filter-sterilized using a 0.22 μm polyethersulfone membrane (Merck, Germany) and degassed in a 90 °C water bath for 30 min.

Microbial strain

Pd was obtained from the BeNa Culture Collection (BNCC342474). The strain was cultured in tryptic soy agar/broth (BD236950/BD 211825, BD Difco™, USA) supplemented with 5% FBS (Biosera, France) under anaerobic conditions.

DNA extraction and 16S rRNA bioinformatic analyses

Total genomic DNA was extracted from fecal samples using the TIANamp Stool DNA Kit (TIANGEN, China). PCR amplification, libraries generation and Illumina NovaSeq sequencing were performed by Wekemo Tech Group Co Ltd. (Shenzhen, China). Briefly, the V3-V4 region of the 16S rRNA gene was amplified using barcoded primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’). The PCR reactions were performed using Phusion® High-Fidelity PCR Master Mix (New England Biolabs, USA) according to the manufacturer’s recommendations. PCR products were mixed with an equal volume of 1xTAE buffer and visualized by electrophoresis on a 2% agarose gel. Equimolar amounts of the PCR products were pooled and purified using a Universal DNA Purification Kit (TianGen, China).

Sequencing libraries were generated using NEB Next® Ultra DNA Library Prep Kit (New England Biolabs, USA) following the manufacturer’s recommendations and index codes were added. The library quality was assessed using the Agilent 5400 system (Agilent Technologies Co Ltd., USA). The library was then sequenced on an Illumina NovaSeq platform, generating 250 bp paired-end reads.

Raw data in FASTQ format were imported into a QIIME2-compatible format. Using the QIIME2 dada2 plugin, sequences were demultiplexed, quality filtered, trimmed, and denoised; paired-end reads were merged; and chimeric sequences were identified and removed to obtain a feature table of amplicon sequence variant (ASV) [42]. The QIIME2 feature-classifier plugin was then used to align ASV sequences to a pretrained GREENGENES 13.8 database to generate the taxonomy Table [43]. Bioinformatic analyses were performed using Wekemo Bioincloud (https://www.bioincloud.tech). Feature level alpha diversity indices were calculated to estimate the microbial diversity within an individual sample. Beta diversity differences among experimental groups were assessed using Bray-Curtis dissimilarity matrices. Group clustering patterns were visualized with principal coordinates analysis (PCoA) and partial least squares discriminant analysis (PLS-DA). Statistical significance of group differences was tested using PERMANOVA with 999 permutations, and the robustness of PLS-DA models was evaluated by permutation testing (n = 200).

Detection of Pd

Genomic DNA was extracted as described above. Quantitative PCR (qPCR) was performed on a QuantStudio 7 Flex real-time PCR system (Thermo Fisher Scientific, USA) using TB Green® Premix Ex Taq™ II (Takara Bio, Japan). Pd abundance was quantified using primers F (5′-GTTCCTTGGTCGTGGCTACA-3′) and R (5′-TGGACAAACGGATCAGGAGC-3′), standardized to 16S rRNA using primers 340 F (5′-ACTCCTACGGGAGGCAGCAGT-3′) and 514R (5′-ATTACCGCGGCTGCTGGC-3′), following the manufacturer’s instructions.

Flow cytometry

Tumors were harvested and weight was recorded. Single-cell suspensions were generated by digesting tumor tissue in a solution containing 0.05 mg/mL collagenase I and IV (Worthington Biochemical) and 0.01 mg/mL DNase I (Sigma-Aldrich) for 15 min at 37 °C. Samples were washed with cold 1x PBS containing 2% FBS and filtered through 70 μm cell strainers. After washing with cold 1x PBS, cells were stained with Ghost Dye Red 780 (Tonbo Bioscience, 13–0865) or Viability-BV421 (Tonbo Biosciences, 13–0863) at 4 °C for 15 min. Cells were then incubated with Fc Block (Tonbo Biosciences, 70–0161, clone 2.4G2) at room temperature for 10 min, followed by staining with antibodies against surface antigens at 4 °C for 30 min. The following surface antibodies were used: anti-CD45 (FITC, Invitrogen, MCD4501, clone 30-F11; BV510, Biolegend, 103137, clone 30-F11), anti-CD3 (APC, Biolegend, 100311, clone 145-2C11), anti-CD8 (Percp-cy5.5, Tonbo bioscience, 67–0081, clone 53 − 6.7), anti-CD4 (BV510, Biolegend, 100553, RM4-5), anti-CD11b (APC-cy7, BD bioscience, 557657, clone M1/70), anti-F4/80 (PE, Tonbo bioscience, 50-4801, clone BM8.1), anti-CD11c (FITC, Tonbo bioscience, 35–0114, clone N418), anti-Ly6G (Percp-ef710, BD bioscience, 560602, clone 1A8) and anti-NK1.1 (PE, Tonbo bioscience, 50-5941, clone PK136; PE-cy7, Invitrogen, 25-5941-81, clone PK136). For intracellular detection of Granzyme B, cells were fixed and permeabilized using the Transcription Factor Fixation/Permeabilization solution (Biogems, 92550-00 and 92160-00) after cell-surface labeling and then stained with anti-Granzyme B (PE, Biolegend 372208, clone QA16A02) for 30 min at 4 °C. Flow cytometry data were acquired on a FACS Celesta instrument (BD Biosciences, USA) and analyzed using FlowJo software (version 10.8.1; BD Biosciences, USA).

Statistics

Statistical parameters are reported in the figure legends. Differences were considered statistically significant at P < 0.05. The statistical analysis was performed with GraphPad Prism (GraphPad Software, United States).

Results

Enriched environment modulates the gut microbiota of intact mice

As one of the most notable benefits of EE for rodents is the reduction of anxiety levels, we first assessed anxiety-like behavior in mice using the elevated plus-maze (EPM) test (Fig. 1A and B). Mice housed in EE for three weeks exhibited significantly lower anxiety levels, as evidenced by the longer cumulative time spent and the greater percentage of distance traveled in the open arms (Fig. 1C-E), indicating that eustress was successfully induced under our experimental conditions.

Fig. 1.

Fig. 1

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EE alters the composition of gut microbiota in mice. (A) Schematic of EE and SE conditions. (B) Experimental timeline and fecal samples collection strategy. (C) Mice were tested for anxiety-like behavior with the EPM after 3 weeks housing in SE or EE. Representative travel pathways of an SE (left) and an EE (right) mouse on the EPM. (D-E) Comparison of time spent in open arm (D) and the percentage of distance traveled in open arm (E) between SE and EE mice (n=12/group). (F) Venn diagram of the ASV in SE and EE. (G-H) Alpha diversity of the gut microbiota from the SE and EE groups, showing by Chao1 (G) and Shannon (H) indices (n=12/group). (I) Principal coordinate analysis (PCoA) of the microbiota based on Bray-Curtis for SE and EE (Permanova, P=0.02; permutations=999). Data are presented as the mean ± SEM. Statistical analyses were performed using unpaired two-tailed Student’s t-tests for (D), (E), and Kruskal–Wallis test for (G) and (H). *, P < 0.05, **, P < 0.01

To explore the alterations in gut microbiota composition between EE and SE mice, we collected fecal samples at 0-, 1-, 3-, and 5-weeks post-EE and SE exposure for 16S rRNA sequencing (Fig. 1B). After filtering out low-quality reads, the optimized reads from 16S rRNA sequencing ranged from 59,835 to 75,111 (Table S1). A total of 5,351 amplicon sequence variant (ASV) were identified from the fecal samples (Table S2). Rarefaction curves generated from the ASV demonstrated a high sampling coverage (~ 99%), indicating sufficient sequencing depth for further analysis (Table S1 and S2). A combined analysis of the 16S rRNA sequencing data across the four time points revealed that 3,247 ASV were unique to the EE group, 1,333 ASV to the SE group, and 771 ASV were shared between the two groups (Fig. 1F). Alpha diversity, as measured by the Chao1 index, was significantly higher in the EE group compared to the SE group (Fig. 1G). Although the Shannon index did not reach statistical significance between the two groups, a trend towards a higher diversity was observed in the EE group compared to the SE group (P = 0.053, Fig. 1H). Principal Coordinates Analysis (PCoA) indicated that the gut microbiota composition of the EE group clusters was more heterogeneous and significantly different from that of the SE group (Fig. 1I).

Next, we investigated the temporal alteration in gut microbiota composition between the EE and SE mice at four different time points. As shown in Fig. 2A, the number of ASV increased with age in both EE and SE mice. Notably, EE mice exhibited more ASV at weeks 1, 3 and 5 compared to SE mice. While SE mice exhibited a slow but steady growth in gut microbiota composition, EE mice experienced a rapid expansion of their gut microbiota soon after exposure to the rich housing condition. The alpha diversity based on Chao1 and Shannon indices showed a similar pattern of change as ASV (Fig. 2B and C). These findings indicate that the housing environment, as well as the age, influences the establishment of gut microbiota in the early life of mice, with different spatiotemporal characteristics between EE and SE mice. Beta diversity, assessed by PCoA based on Bray-Curtis distances (Fig. 2D) and partial least squares discriminant analysis (PLS-DA) (Fig.S1A and B), demonstrated increasing separation of the EE group from the SE group between weeks 1, 3, and 5. Principal component analysis (PCA) based on ASV abundance indicated a significant difference in microbiota composition at week 3, while samples from week 0 and week 1 exhibited higher similarity in composition (Fig. 2E). These results indicate that the third week after EE exposure marks a critical period for the influence of EE on gut microbiota dynamics.

Fig. 2.

Fig. 2

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The dynamic alteration of gut microbiota in EE and SE intact mice. A. The number of ASV for SE and EE mice at different time points (n = 3/group). B-C. Chao1 index (B) and Shannon index (C) of SE and EE mice at different time, showing alpha-diversity (n = 3/group). D. Principal coordinate analysis (PCoA) based on Bray–Curtis distance showing dynamic changes in gut microbial composition across different time points in SE and EE mice (Permanova, P = 0.001; permutations = 999). E. Scores of the first principal component (PC1) from PCA analyses of ASV abundance at each time point. Data are presented as the mean ± SEM. Statistical analyses were performed using unpaired two-tailed Student’s t-tests for comparisons between SE and EE mice at each time point in (A), (B), (C), and (E). *, P < 0.05, **, P < 0.01

EE mitigates gut microbiota imbalance caused by melanoma implantation

Tumor implantation usually leads to an imbalance in the host gut microbial community [44, 45]. To investigate the influence of EE on gut dysbiosis caused by melanoma, we reared mice bearing melanoma under either EE or SE conditions (Fig. 3A). The results showed that mice housed in EE exhibited a markedly slower rate of tumor growth compared to those housed in SE (Fig. 3B). At the time of sacrifice, the EE group exhibited significantly reduced tumor volume (SE: 782.70 ± 131.60 mm³ vs. EE: 301.20 ± 89.72 mm³, P < 0.05) and tumor weight (SE: 0.67 ± 0.09 g vs. EE: 0.34 ± 0.07 g, P < 0.01) compared to the SE group (Fig. 3B-D).

Fig. 3.

Fig. 3

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EE mitigates gut microbiota imbalance caused by melanoma implantation. (A) Experimental design and fecal samples collection strategy. The horizontal coordinate indicates the time of housing in EE. (B) Tumor growth curve based on tumor volumes measured every three days after inoculation. (C) Representative melanoma tumors for SE (top) and EE (bottom) are shown. (D) Comparison of tumor weights between the SE and EE (n = 12/group). (E) Principal coordinate analysis (PCoA) of the microbiota based on Bray-Curtis for among three groups (Permanova, P = 0.004; permutations = 999). F-G. Shannon index (F) and Chao1 index (G) for SE and EE mice at different time, showing alpha-diversity (n = 3/group). Data are presented as the mean ± SEM. Statistical analyses were performed using the two-way ANOVA for (B), unpaired two-tailed Student’s t-tests for (D), and Kruskal–Wallis test for (F) and (G). *, P < 0.05, **, P < 0.01, ***, P < 0.001, ns, not significant

Mice feces at the two weeks after inoculation of melanoma cells (i.e. five weeks exposure to EE or SE, designated as mEE5w or mSE5w) were collected and undergone 16s rRNA sequencing (Table S1). Fecal samples from age-matched intact mice under SE condition were used as the control (designated as SE5w). In melanoma-bearing mice, PCoA based on Bray-Curtis distances (Fig. 3E) and PLS-DA (Fig.S2A and B) showed distinct clustering among the intact SE5w, mSE5w, and mEE5w groups. Alpha diversity, as assessed by the Shannon index, showed a significant reduction in SE mice post-tumor implantation compared to intact SE mice. Interestingly, EE intervention markedly alleviated the tumor-induced decrease in alpha diversity (Fig. 3F). Chao1 index also showed a similar trend in alpha diversity among these three groups, although the difference did not reach statistical significance (Fig. 3G). These results indicated that while melanoma implantation disrupts the gut microbiota, EE appears to exert a protective effect in maintaining eubiosis (a balanced microbial state).

Fecal microbiota transplantation with feces from EE mice inhibits melanoma growth

To investigate whether the gut microbiota is involved in the anti-melanoma effects of EE, we conducted a FMT experiment. Donor mice were housed under either EE or SE conditions. Recipient mice housed under standard conditions throughout the experiment were received either fecal bacterial suspension from EE donors (EE-FMT) or SE donors (SE-FMT). The experimental process is shown in Fig. 4A. Our results demonstrated a significant inhibition of melanoma growth in the EE-FMT group. Specifically, the EE-FMT group exhibited significantly reduced tumor volumes (SE-FMT: 555.60 ± 150.80 mm³ vs. EE-FMT: 374.50 ± 62.23 mm³, P < 0.05) and tumor weights (SE-FMT: 0.59 ± 0.05 g vs. EE-FMT: 0.43 ± 0.05 g, P < 0.05) at necropsy compared to the SE-FMT group (Fig. 4B-D). These data suggested that the gut microbiota contributes to the tumor-suppressive effect of EE.

Fig. 4.

Fig. 4

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Fecal microbiota transplantation with feces from EE mice inhibits melanoma growth. (A) Experimental design. (B) Representative melanoma tumors for the SE-FMT group (top) and EE-FMT group (bottom) were shown. (C) Tumor growth curve based on tumor volumes measured every two days after inoculation (n=8/group). (D) Comparison of tumor weights between the SE-FMT and EE-FMT groups (n=8/group). Data are presented as the mean ± SEM. Statistical analyses were performed using the two-way ANOVA for (C) and unpaired two-tailed Student’s t-tests for (D). *, P < 0.05

Parabacteroides distasonis is a eustress-responsive bacterium that inhibits melanoma growth

To identify which microbiota taxa were significantly altered by EE, we compared the differential microbiota compositions at the species level between mSE5w and mEE5w mice. Seven bacterial species, namely Bacteroides acidifaciens (Ba), Bacteroides uniformis (Bu), Parabacteroides distasonis (Pd), Ruminococcus gnavus (Rg), Clostridium cocleatum (Cc), Eubacterium dolichum (Ed), and Pelobacter carbinolicus (Pc), were significantly upregulated by EE (Kruskal-Wallis test P < 0.05, Fig. 5A). In contrast, no bacterial species exhibited a decrease in the mEE5w mice.

Fig. 5.

Fig. 5

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Pd is a eustress-responsive bacterium that inhibits melanoma growth. (A) The seven bacterial species with differential relative abundances between the mSE5w and mEE5w groups identified by 16S rRNA sequencing. (B) The relative abundance of Pd were measured by qPCR in mSE5w and mEE5w groups (n=6/group). (C) The relative abundance of Pd were measured by qPCR in SE-FMT and EE-FMT groups (n=6/group), fecal samples were collected two days after FMT. (D) Flow diagram of the experimental approach. (E) Representative melanoma tumors for PBS (up) and Pd (bottom) are shown. (F) Tumor growth curve based on tumor volumes measured every two days after inoculation (n=7/group). (G) Comparison of tumor weights between the PBS and Pd groups. Data are presented as the mean ± SEM. Statistical analyses were performed using the Kruskal–Wallis test for (A), unpaired two-tailed Student’s t-tests for (B), (C), and (G), and two-way ANOVA for (F). *, P < 0.05, **, P < 0.01, ***, P < 0.001

Because the relative abundances of the top three bacterial taxa in mice are all lower than 0.01%, the corresponding sequencing signals approached the detection limit and became highly susceptible to background noise and stochastic variation [46]. Therefore, we selected Pd for subsequent functional investigation. The significant increase of Pd in mEE5w mice and EE-FMT mice was confirmed by qPCR (Fig. 5B and C). To evaluate its effects on melanoma, we administered Pd orally to tumor-bearing mice (Fig. 5D). At the end of the experiment, Pd group had an obvious reduction in tumor volume (PBS: 705.60 ± 82.87 mm3 vs. Pd: 418.80 ± 59.74 mm3, P < 0.05) and tumor weight (PBS: 0.67 ± 0.06 g vs. Pd: 0.42 ± 0.08 g, P < 0.05) compared with the PBS group (Fig. 5E-G).

NK cells play an important role in the anti-melanoma effect of Pd

Given the established immunomodulatory properties of probiotics, we subsequently investigated changes in immune cell populations within the spleens and tumor tissues of Pd-treated mice (gating strategy: Fig.S3A). Flow cytometry analysis revealed that Pd treatment significantly increased the proportions of intratumoral NK and NKT cells (Fig. 6A-C). Furthermore, the mean fluorescence intensity (MFI) of granzyme B was significantly elevated in NK cells, but not in NKT cells (Fig. 6D and E). Additionally, neutrophil proportions were significantly reduced (Fig. 6F and G). No significant changes were observed in the proportions of CD8⁺ T cells, CD4⁺ T cells, macrophages, or dendritic cells (DCs) (Fig.S3B). In the spleen, none of the aforementioned immune cell populations showed significant alterations in quantity (Fig.S3C). Collectively, these results demonstrate that the immunomodulatory effect of Pd exhibits pronounced tissue specificity, being restricted to the tumor microenvironment, and distinct cellular selectivity, characterized by enhanced abundance and cytotoxic function of NK cells.

Fig. 6.

Fig. 6

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Effects of Pd treatment on the tumor immune microenvironment in mice. A. Representative flow cytometry plots showing the percentage of tumor-infiltrating NK and NKT cells in Pd- or PBS-treated mice. B-C. Statistical histograms showing percentage of NK (B) and NKT (C) cells (n = 7/group). D-E. Flow cytometry analysis of Granzyme B expression in tumor-infiltrating NK cells and NKT cells from melanoma-bearing mice treated with PBS or Pd. Representative flow cytometry panels showing Granzyme B expression are presented in D (left, NK cells) and E (left, NKT cells). The right panels of D and E compare the mean fluorescence intensity (MFI) of Granzyme B in NK cells and NKT cells between PBS and Pd groups (n = 7/group). F. Representative flow cytometry plots showing the percentage of tumor-infiltrating neutrophils in Pd- or PBS-treated mice. G. Statistical histograms showing percentage of neutrophils (n = 7/group). Data are presented as the mean ± SEM. Statistical analyses were performed using the unpaired two-tailed Student’s t-tests for (B), (C), (D), (E) and (G). *, P < 0.05, **, P < 0.01, ns, not significant

To further validate the critical role of NK cells in mediating the anti-melanoma effect of Pd, we performed in vivo NK cell depletion using an anti-NK1.1 antibody (Fig. 7A). Flow cytometry confirmed efficient depletion of NK cells (Fig. 7B). Critically, NK cell depletion abrogated the tumor-suppressive activity of Pd (PBS + αNK1.1 vs. Pd + αNK1.1; P > 0.05; Fig. 7C-E), demonstrating that NK cell-mediated immunity is indispensable for the anti-melanoma efficacy of Pd.

Fig. 7.

Fig. 7

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NK cells play an important role in the anti-melanoma effect of Pd. (A) Schematic diagram of NK cell depletion in the melanoma mice. (B) Representative flow cytometry plots of NK cells in the spleen confirmed the success of NK cell depletion. (C) Representative melanoma tumors from mice treated with PBS or Pd and administered anti-NK1.1 antibody or isotype control are shown. (D) Comparison of the growth curves among the four groups (n = 8/group). (E) Comparison of tumor weights among the four groups (n = 8/group). Data are presented as the mean ± SEM. Statistical analyses were performed using the two-way ANOVA for (D) and one-way ANOVA for (E). **, P < 0.01, ***, P < 0.001, ns, not significant

Discussion

This study demonstrated that EE maintains gut microbiota homeostasis and buffers tumor‑induced gut dysbiosis. FMT experiment revealed that EE‑associated gut microbiota exerts anti‑melanoma effects. In addition, Pd, a bacterial species enriched under EE conditions, inhibits melanoma progression by enhancing intratumoral NK‑cell immune responses, providing a novel candidate for microbiota‑based intervention against melanoma.

An increased number of gut bacteria species and higher alpha diversity are commonly associated with a healthier state of the host [47]. Research had revealed a significant negative correlation between depression, anxiety or neurodegenerative disorders and alpha diversity of the gut microbiota [48, 49]. The effects of eustress on gut microbiota are dependent on the disease models [33–35], [37, 50]. In the context of tumors, our findings align with previous observations in colorectal cancer [36], where EE enhances gut microbiota diversity. In this study, our longitudinal observations found that mice exhibited significant alterations in gut microbial alpha diversity as early as 1 week after exposure to EE, with the most pronounced changes occurring at third week. Further prolongation of EE exposure did not amplify this effect. Similar temporal trends were observed at the ASVs number. These findings indicate that a three-week exposure to EE is sufficient to establish a novel steady state of the gut microbiota.

To date, only one study has reported that EE normalizes the gut microbiota composition in cancer model [36]. However, this study did not explore whether the normalization of gut microbiota in EE mice is a causal factor in tumor suppression or merely a consequence of reduced tumor burden. FMT has been widely used as a tool for elucidating the microbiome’s role in diseases. Utilizing this approach, we demonstrated for the first time that EE can convey an anti-melanoma effect to SE mice via fecal transfer, thereby establishing that gut microbiota mediates the antitumor phenotype induced by eustress. It is noteworthy that although in many preclinical studies, FMT has been shown to confer beneficial effects in various types of cancers [51], including melanoma, numerous factors will constrain its clinical application. Currently, there is an increasing emphasis on the transplantation of specific microbial constituents in cancer therapy. Given that EE exerts its tumor-suppressive effects through the modulation of gut microbiota, it is reasonable to postulate that the bacteria upregulated by EE may possess anti-tumor potential. By comparing the differential bacterial species between EE and SE mice bearing melanoma, we identified that seven bacteria were significantly upregulated by EE. Based on the existing literature, with the exception of Pd and Rg, which has been documented as probiotics in cancer [52–57], the others lack direct evidence of involvement in cancer treatments. Notably, Cc, Ed, and Pc are key genera responsible for butyric acid production, which plays an essential role in sustaining host health and maintaining intestinal homeostasis, thus, their functions in anti-melanoma warrant further investigation. It is interesting to note that no bacterial taxa were downregulated by EE, indicating that the antitumor effect of EE is primarily attributed to the induction of beneficial probiotics rather than a reduction in pathogenic bacteria. Future research should focus on validating these findings in clinical settings and exploring the therapeutic potential of psychobiotics interventions in conjunction with standard cancer therapies.

Pd is an obligately anaerobic, gram-negative, rod-shaped bacterium that commonly colonizes the gastrointestinal tract. It should be emphasized that Pd is a commensal bacterium present in both humans and mice. Reduced abundance of Pd has been previously reported in bladder cancer [58, 59] and colorectal cancer [60], while its enrichment has been associated with immunotherapy responsiveness in patients with non-small cell lung cancer [61–63]. It is also important to point out that the Pd strain used for gavage in this study was originally isolated from humans. Collectively, this evidence suggests that Pd may possess cross-species biological activity, and its translational potential clearly warrants further clinical evaluation.

Mechanistically, we found that Pd’s immunomodulatory effects exhibit pronounced tissue specificity confined to the tumor microenvironment and distinct cellular selectivity. Pd treatment increased NK and NKT cell proportions while reducing neutrophil infiltration, but selectively enhanced only intratumoral NK cell cytotoxicity. These findings collectively underscore the essential role of NK cells in Pd-mediated anti-melanoma activity. The conclusion was further supported by NK cell depletion experiments. Notably, oral administration of Pd significantly enhanced NK cell infiltration and activity, which is consistent with previous reports demonstrating that NK cell-mediated immunity represents a key mechanism through which EE inhibits melanoma progression [64, 65]. Nonetheless, the precise mechanism by which Pd regulates NK cell antitumor responses remains unclear. Recent studies have shown that the gut microbiota regulates host anti-tumor immunity through multiple bioactive metabolites [66]. Pd generates short-chain fatty acids, including acetate, propionate, and butyrate that may enhance NK-cell anti-tumor activity [67, 68], potentially through G protein-coupled receptor-mediated ERK1/2, p38 MAPK, and PI3K-AKT signaling pathways that promote NK-cell recruitment and cytotoxicity [18, 19, 69, 70]. In addition, Pd can also generate tryptophan-derived metabolites, such as indole-3-lactic acid and indole-3-acetic acid [66]. These metabolites activate the aryl hydrocarbon receptor signaling and enhance NK-cell effector functions [71]. In the future, we will focus on identifying the specific metabolites that mediate the connection between Pd and NK cell function in the context of melanoma.

This study has several limitations. First, compared with metagenomic sequencing, 16S rRNA sequencing has inherent limitations in taxonomic resolution and functional prediction. Future studies integrating metagenomic and metabolomic approaches are needed to provide deeper mechanistic insights. Second, this study used pooled fecal samples for 16S rRNA sequencing. While pooling samples offers cost-effectiveness and high-throughput analysis, it sacrifices individual variation, and hinders the longitudinal tracking of specific subjects. Third, only male mice and a single melanoma model (B16F10) were used in this study. This may not fully capture the heterogeneity of melanoma, including differences in tumor immunogenicity and the tumor microenvironment, which could limit the generalizability of our findings.

In summary, our study delineates the impacts of EE on gut microbiota, highlighting the crucial role of gut microbiota in partially mediating the anti-melanoma phenotype induced by eustress. Our findings suggest that psychological support or probiotic supplementation could represent potential strategies for melanoma prevention and treatment.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (1.6MB, docx)

Author contributions

H.T. and Y.G. designed the experiments. X.L., H.M.L, Q.W., X.G., J.G., M.D., Y.Y.L., W.Z., S.N.L and Q.L. performed the experiments. X.L., H.M.L and Q.W. analyzed data. H.T., X.L., and Y.G. wrote the manuscript. H.T. and Y.G. co-supervised the study and reviewed the manuscript. All authors had full access to all data and accepted responsibility to submit the manuscript for publication.

Funding

The study was supported by the National Natural Science Foundation of China (82173381 to H.T., 82003249 to Q.W., and 82203173 to Q.L.), Natural Science Foundation of Shanghai (24ZR1465400 to M.D.) and the State Key Laboratory of Systems Medicine for Cancer (ZZ94-2307, ZZ94-2524 to H.T.).

Data availability

16S rRNA sequencing data are available in the China National Center for Bioinformation (https://ngdc.cncb.ac.cn) under accession number PRJCA035961.

Declarations

Ethical approval

The animal experiments were conducted in accordance with protocols approved by the institutional animal care and use committee of Shanghai Shuyuan Biotechnology Co., Ltd.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Xin Li, Huimin Li and Qing Wang contributed equally to this work.

Contributor Information

Yu Gan, Email: ganyu@shsci.org.

Hong Tu, Email: tuhong@shsci.org.

References

  • 1.G.V. Long, S.M. Swetter, A.M. Menzies, J.E. Gershenwald, R.A. Scolyer, Cutaneous melanoma. Lancet. 402, 485–502 (2023) [DOI] [PubMed] [Google Scholar]
  • 2.B. Routy, T. Jackson, L. Mahlmann, C.K. Baumgartner, M. Blaser, A. Byrd, N. Corvaia, K. Couts, D. Davar, L. Derosa, H.C. Hang, G. Hospers, M. Isaksen, G. Kroemer, F. Malard, K.D. McCoy, M. Meisel, S. Pal, Z. Ronai, E. Segal, G.D. Sepich-Poore, F. Shaikh, R.F. Sweis, G. Trinchieri, van den M. Brink, R.K. Weersma, K. Whiteson, L. Zhao, J. McQuade, H. Zarour, Zitvogel, L. Melanoma and microbiota: Current understanding and future directions. Cancer Cell. 42, 16–34 (2024) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.A. Eckerling, I. Ricon-Becker, L. Sorski, E. Sandbank, Ben-Eliyahu, Stress and cancer: mechanisms, significance and future directions. Nat. Rev. Cancer. 21, 767–785 (2021) [DOI] [PubMed] [Google Scholar]
  • 4.S. Sinnya, B. De’Ambrosis, Stress and melanoma: increasing the evidence towards a causal basis. Arch. Dermatol. Res. 305, 851–856 (2013) [DOI] [PubMed] [Google Scholar]
  • 5.P.C. Trask, K.A. Griffith, The identification of empirically derived cancer patient subgroups using psychosocial variables. J. Psychosom. Res. 57, 287–295 (2004) [DOI] [PubMed] [Google Scholar]
  • 6.M. Dieng, R.L. Morton, D.S.J. Costa, P.N. Butow, S.W. Menzies, S. Lo, G.J. Mann, A.E. Cust, Kasparian, N. A. Benefits of a brief psychological intervention targeting fear of cancer recurrence in people at high risk of developing another melanoma: 12-month follow-up results of a randomized controlled trial. Br. J. Dermatol. 182, 860–868 (2020) [DOI] [PubMed] [Google Scholar]
  • 7.W. Tian, Y. Liu, C. Cao, Y. Zeng, Y. Pan, X. Liu, Y. Peng, F. Wu, Chronic Stress: Impacts on Tumor Microenvironment and Implications for Anti-Cancer Treatments. Front. Cell. Dev. Biol. 9, 777018 (2021) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.A. Sommershof, L. Scheuermann, J. Koerner, M. Groettrup, Chronic stress suppresses anti-tumor T(CD8+) responses and tumor regression following cancer immunotherapy in a mouse model of melanoma. Brain Behav. Immun. 65, 140–149 (2017) [DOI] [PubMed] [Google Scholar]
  • 9.M.J. Bucsek, G. Qiao, C.R. MacDonald, T. Giridharan, L. Evans, B. Niedzwecki, H. Liu, K.M. Kokolus, J.W. Eng, M.N. Messmer, K. Attwood, S.I. Abrams, B.L. Hylander, E. Repasky, A. beta-Adrenergic Signaling in Mice Housed at Standard Temperatures Suppresses an Effector Phenotype in CD8(+) T Cells and Undermines Checkpoint Inhibitor Therapy. Cancer Res. 77, 5639–5651 (2017) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.A. Glasner, R. Avraham, E. Rosenne, M. Benish, O. Zmora, S. Shemer, H. Meiboom, S. Ben-Eliyahu, Improving survival rates in two models of spontaneous postoperative metastasis in mice by combined administration of a beta-adrenergic antagonist and a cyclooxygenase-2 inhibitor. J. Immunol. 184, 2449–2457 (2010) [DOI] [PubMed] [Google Scholar]
  • 11.J. Nithianantharajah, A.J. Hannan, Enriched environments, experience-dependent plasticity and disorders of the nervous system. Nat. Rev. Neurosci. 7, 697–709 (2006) [DOI] [PubMed] [Google Scholar]
  • 12.L. Cao, X. Liu, E.J. Lin, C. Wang, E.Y. Choi, V. Riban, B. Lin, M.J. During, Environmental and genetic activation of a brain-adipocyte BDNF/leptin axis causes cancer remission and inhibition. Cell. 142, 52–64 (2010) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.S. Garofalo, G. D’Alessandro, G. Chece, F. Brau, L. Maggi, A. Rosa, A. Porzia, F. Mainiero, V. Esposito, C. Lauro, G. Benigni, G. Bernardini, A. Santoni, C. Limatola, Enriched environment reduces glioma growth through immune and non-immune mechanisms in mice. Nat. Commun. 6, 6623 (2015) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Y. Song, Y. Gan, Q. Wang, Z. Meng, G. Li, Y. Shen, Y. Wu, P. Li, M. Yao, J. Gu, H. Tu, Enriching the Housing Environment for Mice Enhances Their NK Cell Antitumor Immunity via Sympathetic Nerve-Dependent Regulation of NKG2D and CCR5. Cancer Res. 77, 1611–1622 (2017) [DOI] [PubMed] [Google Scholar]
  • 15.Y. Liang, M. Du, X. Li, J. Gao, Q. Li, H. Li, J. Li, X. Gao, H. Cong, Y. Huang, X. Li, L. Wang, J. Cui, Y. Gan, H. Tu, Upregulation of Lactobacillus spp. in gut microbiota as a novel mechanism for environmental eustress-induced anti-pancreatic cancer effects. Gut Microbes. 17, 2470372 (2025) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.C. Liu, Y. Yang, C. Chen, L. Li, J. Li, X. Wang, Q. Chu, L. Qiu, Q. Ba, X. Li, H. Wang, Environmental eustress modulates beta-ARs/CCL2 axis to induce anti-tumor immunity and sensitize immunotherapy against liver cancer in mice. Nat. Commun. 12, 5725 (2021) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.G. Li, Y. Gan, Y. Fan, Y. Wu, H. Lin, Y. Song, X. Cai, X. Yu, W. Pan, M. Yao, J. Gu, H. Tu, Enriched environment inhibits mouse pancreatic cancer growth and down-regulates the expression of mitochondria-related genes in cancer cells. Sci. Rep. 5, 7856 (2015) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.R.C. Simpson, E.R. Shanahan, R.A. Scolyer, G.V. Long, Towards modulating the gut microbiota to enhance the efficacy of immune-checkpoint inhibitors. Nat. Rev. Clin. Oncol. 20, 697–715 (2023) [DOI] [PubMed] [Google Scholar]
  • 19.J. Bautista, J.A. Villegas-Chavez, D. Bunces-Larco, R. Martin-Aguilera, A. Lopez-Cortes, The microbiome as a therapeutic co-driver in melanoma immuno-oncology. Front. Med. (Lausanne). 12, 1673880 (2025) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.B. Routy, J.G. Lenehan, W.H. Jr. Miller, R. Jamal, M. Messaoudene, B.A. Daisley, C. Hes, K.F. Al, L. Martinez-Gili, M. Puncochar, S. Ernst, D. Logan, K. Belanger, K. Esfahani, C. Richard, M. Ninkov, G. Piccinno, F. Armanini, F. Pinto, M. Krishnamoorthy, R. Figueredo, P. Thebault, P. Takis, J. Magrill, L. Ramsay, L. Derosa, J.R. Marchesi, S.N. Parvathy, A. Elkrief, I.R. Watson, R. Lapointe, N. Segata, S.M.M. Haeryfar, B.H. Mullish, M.S. Silverman, J.P. Burton, Maleki Vareki, S. Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial. Nat. Med. 29, 2121–2132 (2023) [DOI] [PubMed] [Google Scholar]
  • 21.V. Gopalakrishnan, C.N. Spencer, L. Nezi, A. Reuben, M.C. Andrews, T.V. Karpinets, P.A. Prieto, D. Vicente, K. Hoffman, S.C. Wei, A.P. Cogdill, L. Zhao, C.W. Hudgens, D.S. Hutchinson, T. Manzo, M. Petaccia de Macedo, T. Cotechini, T. Kumar, W.S. Chen, S.M. Reddy, S. Sloane, R. Galloway-Pena, J. Jiang, H. Chen, P.L. Shpall, E.J. Rezvani, K. Alousi, A.M. Chemaly, R.F. Shelburne, S. Vence, L.M. Okhuysen, P.C. Jensen, V.B. Swennes, A.G. McAllister, F.M. Riquelme Sanchez, E. Zhang, Y. Le Chatelier, E. Zitvogel, L. Pons, N. Austin-Breneman, J.L. Haydu, L.E. Burton, E.M. Gardner, J.M. Sirmans, E. Hu, J. Lazar, A.J. Tsujikawa, T. Diab, A. Tawbi, H. Glitza, I.C. Hwu, W.J. Patel, S.P. Woodman, S.E. Amaria, R.N. Davies, M.A. Gershenwald, J.E. Hwu, P. Lee, J.E. Zhang, J. Coussens, L.M. Cooper, Z.A. Futreal, P.A. Daniel, C.R. Ajami, N.J. Petrosino, J.F. Tetzlaff, M.T. Sharma, P. Allison, J.P. Jenq, PD-1 immunotherapy in melanoma patients. Science. 359, 97–103 (2018). R. R. & Wargo, J. A. Gut microbiome modulates response to anti- [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.D. K. Hadi, K. J. Baines, B. Jabbarizadeh, W. H. Miller, R. Jamal, S. Ernst, D. Logan, K. Belanger, K. Esfahani, A. Elkrief, S. N. Parvathy, M. S. Silverman, B. Routy, S. Maleki Vareki, and J. G. Lenehan, Improved survival in advanced melanoma patients treated with fecal microbiota transplantation using healthy donor stool in combination with anti-PD1: final results of the MIMic phase 1 trial, J. Immunother. Cancer 13 (2025) [DOI] [PMC free article] [PubMed]
  • 23.M. J. Bender, A. C. McPherson, C. M. Phelps, S. P. Pandey, C. R. Laughlin, J. H. Shapira, L. Medina Sanchez, M. Rana, T. G. Richie, T. S. Mims, A. M. Gocher-Demske, L. Cervantes-Barragan, S. J. Mullett, S. L. Gelhaus, T. C. Bruno, N. Cannon, J. A. McCulloch, D. A. A. Vignali, R. Hinterleitner, A. V. Joglekar, J. F. Pierre, S. T. M. Lee, D. Davar, H. M. Zarour, and M. Meisel, Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment, Cell 186, 1846–1862.e1826 (2023) [DOI] [PMC free article] [PubMed]
  • 24.A. Sivan, L. Corrales, N. Hubert, J.B. Williams, K. Aquino-Michaels, Z.M. Earley, F.W. Benyamin, Y.M. Lei, B. Jabri, M.L. Alegre, E.B. Chang, T.F. Gajewski, Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science. 350, 1084–1089 (2015) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.V. Matson, J. Fessler, R. Bao, T. Chongsuwat, Y. Zha, M.L. Alegre, J.J. Luke, T.F. Gajewski, The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 359, 104–108 (2018) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.C. N. Spencer, J. L. McQuade, V. Gopalakrishnan, J. A. McCulloch, M. Vetizou, A. P. Cogdill, M. A. W. Khan, X. Zhang, M. G. White, C. B. Peterson, M. C. Wong, G. Morad, T. Rodgers, J. H. Badger, B. A. Helmink, M. C. Andrews, R. R. Rodrigues, A. Morgun, Y. S. Kim, J. Roszik, K. L. Hoffman, J. Zheng, Y. Zhou, Y. B. Medik, L. M. Kahn, S. Johnson, C. W. Hudgens, K. Wani, P. O. Gaudreau, A. L. Harris, M. A. Jamal, E. N. Baruch, E. Perez-Guijarro, C. P. Day, G. Merlino, B. Pazdrak, B. S. Lochmann, R. A. Szczepaniak-Sloane, R. Arora, J. Anderson, C. M. Zobniw, E. Posada, E. Sirmans, J. Simon, L. E. Haydu, E. M. Burton, L. Wang, M. Dang, K. Clise-Dwyer, S. Schneider, T. Chapman, N. A. S. Anang, S. Duncan, J. Toker, J. C. Malke, I. C. Glitza, R. N. Amaria, H. A. Tawbi, A. Diab, M. K. Wong, S. P. Patel, S. E. Woodman, M. A. Davies, M. I. Ross, J. E. Gershenwald, J. E. Lee, P. Hwu, V. Jensen, Y. Samuels, R. Straussman, N. J. Ajami, K. C. Nelson, L. Nezi, J. F. Petrosino, P. A. Futreal, A. J. Lazar, J. Hu, R. R. Jenq, M. T. Tetzlaff, Y. Yan, W. S. Garrett, C. Huttenhower, P. Sharma, S. S. Watowich, J. P. Allison, L. Cohen, G. Trinchieri, C. R. Daniel, and J. A. Wargo, Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response, Science 374, 1632–1640 (2021) [DOI] [PMC free article] [PubMed]
  • 27.N. Liu, L. Chen, M. Yan, Q. Tao, J. Wu, J. Chen, X. Chen, W. Zhang, C. Peng, Eubacterium rectale Improves the Efficacy of Anti-PD1 Immunotherapy in Melanoma via l-Serine-Mediated NK Cell Activation. Res. (Wash D C). 6, 0127 (2023) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.A. Montalban-Arques, E. Katkeviciute, P. Busenhart, A. Bircher, J. Wirbel, G. Zeller, Y. Morsy, L. Borsig, J. F. Glaus Garzon, A. Muller, I. C. Arnold, M. Artola-Boran, M. Krauthammer, A. Sintsova, N. Zamboni, G. E. Leventhal, L. Berchtold, T. de Wouters, G. Rogler, K. Baebler, M. Schwarzfischer, L. Hering, I. Olivares-Rivas, K. Atrott, C. Gottier, S. Lang, O. Boyman, R. Fritsch, M. G. Manz, M. R. Spalinger, and M. Scharl, Commensal Clostridiales strains mediate effective anti-cancer immune response against solid tumors, Cell Host & Microbe 29, 1573–1588.e1577 (2021) [DOI] [PubMed]
  • 29.J. Bautista, E. Bazantes-Rodríguez, H. Cedeño, C. Anrango-Flores, F. Carrera-Cruz, Z. Cisneros-Nieto, and A. López-Cortés, Reprogramming human health through the microbiome and precision medicine, Crit. Rev. Microbiol. 1–15 (2026) [DOI] [PubMed]
  • 30.J.F. Cryan, T.G. Dinan, Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012) [DOI] [PubMed] [Google Scholar]
  • 31.L. H. Morais, H. L. Schreiber, and S. K. Mazmanian, The gut microbiota–brain axis in behaviour and brain disorders, Nat. Rev. Microbiol. 19, 241–255 (2021) [DOI] [PubMed]
  • 32.M. You, N. Chen, Y. Yang, L. Cheng, H. He, Y. Cai, Y. Liu, H. Liu, G. Hong, The gut microbiota-brain axis in neurological disorders. MedComm (2020). 5, e656 (2024) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.L. Lupori, S. Cornuti, R. Mazziotti, E. Borghi, E. Ottaviano, M.D. Cas, G. Sagona, T. Pizzorusso, P. Tognini, The gut microbiota of environmentally enriched mice regulates visual cortical plasticity. Cell. Rep. 38, 110212 (2022) [DOI] [PubMed] [Google Scholar]
  • 34.C. Gubert, C.J. Love, S. Kodikara, J.J. Mei Liew, T. Renoir, Le K.A. Cao, A.J. Hannan, Gene-environment-gut interactions in Huntington’s disease mice are associated with environmental modulation of the gut microbiome. iScience. 25, 103687 (2022) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Y. Singh, M. El-Hadidi, J. Admard, Z. Wassouf, J.M. Schulze-Hentrich, U. Kohlhofer, L. Quintanilla-Martinez, D. Huson, O. Riess, Casadei, N. Enriched Environmental Conditions Modify the Gut Microbiome Composition and Fecal Markers of Inflammation in Parkinson’s Disease. Front. Neurosci. 13, 1032 (2019) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.B.D. Bice, M.R. Stephens, S.J. Georges, A.R. Venancio, P.C. Bermant, A.V. Warncke, K.E. Affolter, J.R. Hidalgo, M.L. Angus-Hill, Environmental Enrichment Induces Pericyte and IgA-Dependent Wound Repair and Lifespan Extension in a Colon Tumor Model. Cell. Rep. 19, 760–773 (2017) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.S.G. Higarza, S. Arboleya, J.L. Arias, M. Gueimonde, N. Arias, Akkermansia muciniphila and environmental enrichment reverse cognitive impairment associated with high-fat high-cholesterol consumption in rats. Gut Microbes. 13, 1–20 (2021) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.A. Orock, A.C. Johnson, E. Mohammadi, Greenwood-Van Meerveld, B. Environmental enrichment reverses stress-induced changes in the brain-gut axis to ameliorate chronic visceral and somatic hypersensitivity. Neurobiol. Stress. 28, 100590 (2024) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.R. Manzo, L. Gallardo-Becerra, S. Diaz de Leon-Guerrero, T. Villaseñor, F. Cornejo-Granados, J. Salazar-Leon, A. Ochoa-Leyva, G. Pedraza-Alva, and L. Pérez-Martínez, Environmental enrichment prevents gut dysbiosis progression and enhances glucose metabolism in high-fat diet-induced obese mice, Int. J. Mol. Sci. 25 (2024) [DOI] [PMC free article] [PubMed]
  • 40.Z. Meng, T. Liu, Y. Song, Q. Wang, D. Xu, J. Jiang, M. Li, J. Qiao, X. Luo, J. Gu, H. Tu, Y. Gan, Exposure to an enriched environment promotes the terminal maturation and proliferation of natural killer cells in mice. Brain Behav. Immun. 77, 150–160 (2019) [DOI] [PubMed] [Google Scholar]
  • 41.S.D. Burz, A.L. Abraham, F. Fonseca, O. David, A. Chapron, F. Beguet-Crespel, S. Cenard, Le K. Roux, O. Patrascu, F. Levenez, C. Schwintner, H.M. Blottiere, C. Bera-Maillet, P. Lepage, J. Dore, C. Juste, A Guide for Ex Vivo Handling and Storage of Stool Samples Intended for Fecal Microbiota Transplantation. Sci. Rep. 9, 8897 (2019) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.B.J. Callahan, P.J. McMurdie, M.J. Rosen, A.W. Han, A.J. Johnson, S.P. Holmes, DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 13, 581–583 (2016) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.N.A. Bokulich, B.D. Kaehler, J.R. Rideout, M. Dillon, E. Bolyen, R. Knight, G.A. Huttley, Gregory Caporaso, J. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 6, 90 (2018) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.E.M. Park, M. Chelvanambi, N. Bhutiani, G. Kroemer, L. Zitvogel, J.A. Wargo, Targeting the gut and tumor microbiota in cancer. Nat. Med. 28, 690–703 (2022) [DOI] [PubMed] [Google Scholar]
  • 45.X.Q. Wu, T.K. Lee, W. Influence of the gut microbiota on the response to immunotherapy in hepatocellular carcinoma. Hepatoma Res. 11, xx (2025) [Google Scholar]
  • 46.G.B. Gloor, J.M. Macklaim, V. Pawlowsky-Glahn, J.J. Egozcue, Microbiome Datasets Are Compositional: And This Is Not Optional. Front. Microbiol. 8, 2224 (2017) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.C.A. Lozupone, J.I. Stombaugh, J.I. Gordon, J.K. Jansson, R. Knight, Diversity, stability and resilience of the human gut microbiota. Nature. 489, 220–230 (2012) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.D. Radjabzadeh, J.A. Bosch, A.G. Uitterlinden, A.H. Zwinderman, M.A. Ikram, van J.B.J. Meurs, A.I. Luik, M. Nieuwdorp, A. Lok, van C.M. Duijn, R. Kraaij, Amin, N. Gut microbiome-wide association study of depressive symptoms. Nat. Commun. 13, 7128 (2022) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.N.M. Vogt, R.L. Kerby, K.A. Dill-McFarland, S.J. Harding, A.P. Merluzzi, S.C. Johnson, C.M. Carlsson, S. Asthana, H. Zetterberg, K. Blennow, B.B. Bendlin, F.E. Rey, Gut microbiome alterations in Alzheimer’s disease. Sci. Rep. 7, 13537 (2017) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.F. Marrocco, M. Delli Carpini, S. Garofalo, O. Giampaoli, De E. Felice, Di M.A. Castro, L. Maggi, F. Scavizzi, M. Raspa, F. Marini, A. Tomassini, R. Nicolosi, C. Cason, F. Trettel, A. Miccheli, V. Iebba, G. Alessandro, C. Limatola, Short-chain fatty acids promote the effect of environmental signals on the gut microbiome and metabolome in mice. Commun. Biol. 5, 517 (2022) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.D. Chen, J. Wu, D. Jin, B. Wang, H. Cao, Fecal microbiota transplantation in cancer management: Current status and perspectives. Int. J. Cancer. 145, 2021–2031 (2019) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.L. Zhai, C. Huang, Z. Ning, Y. Zhang, M. Zhuang, W. Yang, X. Wang, J. Wang, L. Zhang, H. Xiao, L. Zhao, P. Asthana, Y. Y. Lam, C. F. W. Chow, J. D. Huang, S. Yuan, K. M. Chan, C. S. Yuan, J. Y. Lau, H. L. X. Wong, Z. Bian, and X. Z. Liu, Ruminococcus gnavus plays a pathogenic role in diarrhea-predominant irritable bowel syndrome by increasing serotonin biosynthesis, Cell Host & Microbe 31, 33–44.e35 (2023) [DOI] [PubMed]
  • 53.X. Zhang, D. Yu, D. Wu, X. Gao, F. Shao, M. Zhao, J. Wang, J. Ma, W. Wang, X. Qin, Y. Chen, P. Xia, and S. Wang, Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance, Cell Host & Microbe 31, 418–432.e418 (2023) [DOI] [PubMed]
  • 54.G.Y. Koh, A.V. Kane, X. Wu, J.W. Crott, Parabacteroides distasonis attenuates tumorigenesis, modulates inflammatory markers and promotes intestinal barrier integrity in azoxymethane-treated A/J mice. Carcinogenesis. 41, 909–917 (2020) [DOI] [PubMed] [Google Scholar]
  • 55.G.Y. Koh, A. Kane, K. Lee, Q. Xu, X. Wu, J. Roper, J.B. Mason, J.W. Crott, Parabacteroides distasonis attenuates toll-like receptor 4 signaling and Akt activation and blocks colon tumor formation in high-fat diet-fed azoxymethane-treated mice. Int. J. Cancer. 143, 1797–1805 (2018) [DOI] [PubMed] [Google Scholar]
  • 56.H. Zhu, M. Li, D. Bi, H. Yang, Y. Gao, F. Song, J. Zheng, R. Xie, Y. Zhang, H. Liu, X. Yan, C. Kong, Y. Zhu, Q. Xu, Q. Wei, H. Qin, Fusobacterium nucleatum promotes tumor progression in KRAS p.G12D-mutant colorectal cancer by binding to DHX15. Nat. Commun. 15, 1688 (2024) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.J. Yu, L. Feng, Z. Luo, J. Yang, Q. Zhang, C. Liu, D. Liang, Y. Xie, H. Li, J. Gong, Z. He, and P. Lan, Interleukin-10 deficiency suppresses colorectal cancer metastasis by enriching gut Parabacteroides distasonis, J. Adv. Res. (2024) [DOI] [PMC free article] [PubMed]
  • 58.W. Li, W. Shangguan, W. Huang, J. Zhao, Y. Zhu, M. Xie, Y. Yu, Q. Yang, J. Zheng, L. Yang, Q. Sun, L. Li, X. Shi, Y. Xiao, H. Huang, B. Cheng, P. Wu, Gut Parabacteroides distasonis-derived Indole-3-Acetic Acid Promotes Phospholipid Remodeling and Enhances Ferroptosis Sensitivity via the AhR-FASN Axis in Bladder Cancer. Adv. Sci. (Weinh). 12, e04688 (2025) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.B. Wang, Y. Qiu, M. Xie, P. Huang, Y. Yu, Q. Sun, W. Shangguan, W. Li, Z. Zhu, J. Xue, Z. Feng, Y. Zhu, Q. Yang, P. Wu, Gut microbiota Parabacteroides distasonis enchances the efficacy of immunotherapy for bladder cancer by activating anti-tumor immune responses. BMC Microbiol. 24, 237 (2024) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.J. Yu, L. Feng, Z. Luo, J. Yang, Q. Zhang, C. Liu, D. Liang, Y. Xie, H. Li, J. Gong, Z. He, P. Lan, Interleukin-10 deficiency suppresses colorectal cancer metastasis by enriching gut Parabacteroides distasonis. J. Adv. Res. 76, 467–479 (2025) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.H. Zhou, R. Sun, X. Nie, L. Xia, H. Dong, Y. Liu, S. Hou, W. Dong, X. Zhu, Y. Yao, G.P. Zhao, S. Lu, Y. Wang, C. Yang, A clinic-responder-derived defined microbial consortium enhances anti-PD-1 immunotherapy efficacy in mice. Nat. Microbiol. 11, 993–1007 (2026) [DOI] [PubMed] [Google Scholar]
  • 62.D. Dora, G. J. Weiss, Z. Megyesfalvi, G. Gálffy, E. Dulka, A. Kerpel-Fronius, J. Berta, J. Moldvay, B. Dome, and Z. Lohinai, Computed tomography-based quantitative texture analysis and gut microbial community signatures predict survival in non-small cell lung cancer, Cancers (Basel) 15 (2023) [DOI] [PMC free article] [PubMed]
  • 63.J. Huang, D. Liu, Y. Wang, L. Liu, J. Li, J. Yuan, Z. Jiang, Z. Jiang, W. W. Hsiao, H. Liu, I. Khan, Y. Xie, J. Wu, Y. Xie, Y. Zhang, Y. Fu, J. Liao, W. Wang, H. Lai, A. Shi, J. Cai, L. Luo, R. Li, X. Yao, X. Fan, Q. Wu, Z. Liu, P. Yan, J. Lu, M. Yang, L. Wang, Y. Cao, H. Wei, E. Leung, and L. Y. Zhao, Ginseng polysaccharides alter the gut microbiota and kynurenine/tryptophan ratio, potentiating the antitumour effect of anti-programmed cell death 1/programmed cell death ligand 1 (anti-PD-1/PD-L1) immunotherapy, Gut 71, 734–745 (2022) [DOI] [PMC free article] [PubMed]
  • 64.S.M. Bergin, R. Xiao, W. Huang, C.R.T. Judd, X. Liu, A.G. Mansour, N. Queen, K.J. Widstrom, M.A. Caligiuri, L. Cao, Environmental activation of a hypothalamic BDNF-adipocyte IL-15 axis regulates adipose-natural killer cells. Brain Behav. Immun. 95, 477–488 (2021) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.R. Xiao, S. Ali, M.A. Caligiuri, L. Cao, Enhancing Effects of Environmental Enrichment on the Functions of Natural Killer Cells in Mice. Front. Immunol. 12, 695859 (2021) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.J. Duan, Q. Li, Y. Cheng, W. Zhu, H. Liu, F. Li, Therapeutic potential of Parabacteroides distasonis in gastrointestinal and hepatic disease. MedComm (2020). 5, e70017 (2024) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.M. Pérez, B. Buey, P. Corral, D. Giraldos, and E. Latorre, Microbiota-derived short-chain fatty acids boost antitumoral natural killer cell activity, J. Clin. Med. 13 (2024) [DOI] [PMC free article] [PubMed]
  • 68.K.D. Miller, S. O’Connor, K.A. Pniewski, T. Kannan, R. Acosta, G. Mirji, S. Papp, M. Hulse, D. Mukha, S.I. Hlavaty, K.N. Salcido, F. Bertolazzi, Y.V.V. Srikanth, S. Zhao, K.E. Wellen, R.S. Shinde, D.T. Claiborne, A. Kossenkov, J.M. Salvino, Schug, Z. T. Acetate acts as a metabolic immunomodulator by bolstering T-cell effector function and potentiating antitumor immunity in breast cancer. Nat. Cancer. 4, 1491–1507 (2023) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.M.J. Sun, J.H. Xing, Q.S. Yan, B.S. Zou, Y.J. Wang, T.M. Niu, T. Yu, H.B. Huang, D. Zhang, S.M. Zhang, W.S. Sun, R.N. Zou, C.F. Wang, C.W. Shi, The Acetic Acid Produced by Lactobacillus Species Regulates Immune Function to Alleviate PEDV Infection in Piglets. Probiotics Antimicrob. Proteins. 17, 2962–2979 (2025) [DOI] [PubMed] [Google Scholar]
  • 70.J. Bautista, A. Lopez-Cortes, Biohacking the human gut microbiome for precision health and therapeutic innovation. Front. Microbiol. 17, 1776983 (2026) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.J.H. Shin, L. Zhang, O. Murillo-Sauca, J. Kim, H.E. Kohrt, J.D. Bui, J.B. Sunwoo, Modulation of natural killer cell antitumor activity by the aryl hydrocarbon receptor. Proc. Natl. Acad. Sci. U S A 110, 12391–12396 (2013) [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

Supplementary Material 1 (1.6MB, docx)

Data Availability Statement

16S rRNA sequencing data are available in the China National Center for Bioinformation (https://ngdc.cncb.ac.cn) under accession number PRJCA035961.

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