Stenotrophomonas promotes gastrointestinal tumor progression via STING degradation in tumour cells and mitigated immune response

Domain: neuroscience
Published: 2026-01-26
Editorial outcome: minor_revision

Abstract
While numerous studies have reported that intratumor bacteria modulate tumor biological characteristics and therapeutic outcomes, the role of intracellular bacteria in gastrointestinal tumor (GIT) progression remains largely unexplored. Here, we show, through bioinformatics prediction and consecutive patient sample validation, that Stenotrophomonas is associated with tumor progression and poor prognosis in GIT. Using various murine tumor models, we find that intracellular Stenotrophomonas promotes GIT progression by decreasing IFNβ secretion and inhibiting MHC-II+ macrophages, thereby impairing the activity of effector CD8+ T cells. Mechanistically, Stenotrophomonas entry into tumor cells induces endoplasmic reticulum stress, degrades STING protein, and downregulates the type I IFN pathway response. Additionally, Stenotrophomonas contributes to resistance to immunotherapy in GIT, which can be reversed by levofloxacin. Our findings indicate that intracellular Stenotrophomonas within tumor cells plays an important role in promoting GIT progression and poor prognosis and could serve as a potential target to improve outcomes for patients with GIT.

Introduction
Gastrointestinal tumors (GIT), including adenocarcinomas of the esophagogastric junction, gastric cancer, and colorectal cancer, are common malignancies associated with high mortality rates worldwide. GIT, which interfaces with the external environment, frequently interacts with diverse microbiomes. Bacteria are closely associated with tumor progression and metastasis, as well as the modulation of immunotherapy and chemotherapy responses in GIT1–12. Bacterial interactions with GIT progression occur through both indirect mechanisms within the tumor microenvironment (TME) and direct intracellular pathways in tumor or immune cells. In indirect interactions, bacteria secrete proteins or directly bind to cell receptors on tumor or immune cells. For instance, C. maltaromaticum enhances intestinal vitamin D production, and S. thermophilus secretes β-galactosidase, both inhibiting tumor progression. Conversely, S. anginosus activates the TMPC-ANXA2-MAPK axis, bacteria lacking ureolytic function suppress macrophage function, mycobacteria indirectly induce de novo Treg cells, and C. jejuni’s cytolethal distending toxin promotes tumor occurrence or progression1–4,6,7. These intracellular bacteria are associated with tumor subtypes and responses to immunotherapy13. Furthermore, intracellular bacteria can regulate cellular cytoskeleton and viability through mechanical stress, promoting breast cancer metastasis14. Notably, Stenotrophomonas (SP), a common opportunistic pathogen, is a multidrug-resistant bacterium widely colonizing the human body15. It possesses migratory abilities, inducing mastitis through upregulation of the calcium-ROS-AMPK-mTOR-autophagy pathway16. SP has been detected intracellularly in hepatic stellate cells, promoting senescence-associated secretory phenotype and cirrhosis via the TLR4-driven NF-κB signaling pathway, thus contributing to hepatocarcinogenesis17. Additionally, SP upregulates HDAC5 expression, promoting lung cancer progression18. While most previous research has focused on intratumor rather than intracellular bacteria, further investigation of intracellular bacteria in GIT is warranted to elucidate their role in cancer progression and treatment response.

Recent studies on TME regulation have predominantly focused on macrophages and their immunosuppressive role in inhibiting T cell function19–22. However, the paradigm of macrophage function has evolved. While initially considered solely as cancer cell killers and cytotoxic T cell activators, recent research has shifted towards identifying the regulatory mechanisms governing macrophage functional plasticity23,24. The functional state of macrophages is intricately linked to the activation of the type I interferon (IFN) signaling pathway, which induces macrophages to secrete tumor necrosis factor-alpha (TNFα) and interleukin-12, activating cytotoxic T cells and promoting tumor cell elimination, while also reprogramming CD206+ macrophages into nitric oxide synthase-expressing or MHC-II+ macrophages to enhance antigen processing and presentation capabilities23–25. The activation of the type I IFN signaling pathway in tumor-associated macrophages is regulated by a complex network of molecules, including toll-like receptors, MYD88, MAVS, STING, IFNβ26–29. Macrophages-mediated antigen processing and cytotoxic T cell activation are impaired in the absence of IFNβ stimulation, resulting in an M2-like phenotype that exhibits immunosuppressive properties and promotes tumor progression21,26,30.

The Stimulator of Interferon Gene (STING) protein emerges as a critical mediator in bacterial defense, modulating host innate immune responses and augmenting adaptive immunity through type I IFN pathway activation31–40. Within the TME, microbiota-derived STING agonists, such as cyclic di-AMP, activate the type I IFN pathway in monocytes, inducing substantial IFNβ production that shifts macrophages from pro-tumor to anti-tumor states, activates dendritic cells (DC), recruits natural killer (NK) cells, and enhances overall antitumor responses41,42. S. typhimurium with cGAS expression activates the STING pathway via cGAMPs in macrophages or DCs and promotes antitumor immunotherapy43. Conversely, Lachnospiraceae-secreted butyric acid inhibits STING activation and IFNβ production in intratumor DCs, impairing the antitumor effects of ionizing radiation44. There remains a lack of research specifically investigating the role of intracellular bacteria in modulating STING protein and immune responses within the TME.

The involvement of intracellular bacteria in GIT and their impact on immune-regulation remains insufficiently defined. In this study, we analyze intracellular bacteria in GIT progression, identifying SP that degrade STING in tumor cells and its resulting mitigated immune response. By impacting macrophages, SP would eventually damage the killing function of CD8+ T cells and lead to poor prognosis of cancer patients. These results highlight their broader implications as viable targets for therapeutic intervention in GIT that may inform the development of more effective treatment strategies for this patient population.

Results
Intracellular SP is associated with tumor progression in GIT
The majority of studies have focused on the intersection of gut microbes and tumor therapy45–49. Recent studies have reported the presence of intracellular bacteria in various tumor types. However, investigations of intracellular bacteria in GIT remain limited, despite the direct contact of GIT with numerous bacteria, which increases the likelihood of bacterial infiltration into tumor cells13,14,50–52. To investigate the enrichment of intracellular bacteria in GIT, we analyzed five public datasets using a bulk microbiome pipeline, employing the Kraken2 Uniq method to match bacterial reference sequences (Fig. S1)13,53–55. Across all GIT subtypes, including adenocarcinoma of the gastroesophageal junction (AEG), gastric cancer (GC), and colorectal cancer (CRC), we observed abundant bacterial reads (Fig. 1A). We performed normalized comparisons to mitigate false positives and account for partial read degeneracy during sequence matching, which may result from the limited length of high-throughput sequencing reads. To standardize differences in sequencing library size, we applied two normalization metrics, reads per million microbiome reads and reads per million total reads, based on cell line data. By comparing the normalized read distributions of each species between cell lines and tumor samples, species showing similar or higher abundance in cell lines were classified as contaminants. This approach enabled the identification of several bacterial species unaffected by contamination (Fig. S2). Bacillus cereus, SP, Klebsiella pneumoniae, and Pseudomonas sp. CIP-10 is present at significantly higher abundances compared to other bacterial species in GIT (Fig. 1B). We conducted survival analysis and found that SP is significantly associated with poor prognosis (Fig. 1C). Furthermore, fluorescence in situ hybridization (FISH) confirmed the presence of SP within the GIT of our selected cohort (Fig. 1D and S3B). In the high-abundance group, SP is also significantly correlated with poor prognosis in our cohort, which includes patients with GC and AEG (Fig. 1E).

The detection of SP genomic signatures following the tissue dissociation process, which would be expected to remove extracellular bacteria, suggests that the SP population we identified was physically protected within host cells. This observation provided the critical rationale for our subsequent hypothesis that SP is an intracellular bacterium. To further confirm whether SP is located in tumor cells, we engineered a recombinant strain of SP expressing green fluorescent protein (Fig. S4A). We found that CD45- cells could be detected in SP-infected tissues, whereas T cells, DCs, NK cells, B cells, and macrophages were hardly detectable (Fig. S4A). Those cells were sorted from the tumor by flow cytometry, and quantitative real-time PCR (qPCR) analysis showed that CD45− cells exhibited a high abundance of SP in the SP group, while no other immune cells were detected in vivo (Fig. S4B). To confirm the identity of CD45⁻ cells, EpCAM was used as a tumor cell marker, which validated that CD45⁻EpCAM⁺ cells displayed a higher abundance of SP (Fig. S4C). We further conducted in vitro experiments, where qPCR and flow cytometry demonstrated SP infection in tumor cells (Figs. 1F,G and S4D). In addition, immunofluorescence (IF) indicated that SP is nearly localized within the cytoplasm of tumor cells (Fig. S5). Consistent with these findings, transmission electron microscopy (TEM) revealed that SP is present within tumor cells, primarily localized in the cytosol (Fig. 1H). Finally, we constructed a SP-infected tumor cell model, injected it into mice, and the tumor was dissociated and cultured on a bacterial plate, where SP could be cultured (Fig. S6A). Tumor areas with high abundance of SP tended to exhibit increased hypoxia (Fig. S6B). These data suggest that SP is an intracellular bacterium, predominantly distributed in the cytoplasm of tumor cells.

We next sought to validate that SP infection leads to poor prognosis in GIT. To investigate this, we constructed an SP-infected tumor model (Fig. 1I). We observed that tumors harboring SP exhibited accelerated progression compared to the mock group in MC38 cells (Fig. 1J). Consistently, intracellular SP significantly promoted tumor progression in B16F10, Hepa1-6, and CT26 cells (Fig. 1K–M). To address potential interference from extracellular SP in the simple mixed coculture system, we developed a more precise approach. We cocultured MC38 cells with GFP-labeled SP for 4 h and then isolated the SP-containing tumor cells using fluorescence-activated cell sorting (FACS) (Fig. S6C). Following FACS, we obtained two distinct populations: a positive group consisting of SP-GFP+ MC38 cells and a negative group comprising SP-GFP- MC38 cells. Positive and negative MC38 cells isolated by FACS were engrafted into the right flank of C57 mice to establish tumor model. The positive group exhibited significantly enhanced tumor progression compared to the negative group (Fig. 1N). These results provide compelling evidence that intracellular SP promotes tumor progression.

Intracellular SP induces an immunosuppressive TME by impairing MHC-II+ macrophages
Intracellular bacteria establish symbiotic relationships with host cells by modulating immune responses, enabling their survival within the host13,14,18,50–52,56. To investigate how intracellular SP promotes GIT progression by suppressing tumor immunity, we used multiplex immunofluorescence to analyze the TME patterns in AEG with high and low intracellular SP groups (Fig. 2A). The results revealed that the proportions of macrophages (CD68+ cells), Treg cells (FoxP3+CD4+ cells), and CD8+ T cells were similar between the high and low SP groups; however, MHC-II+ macrophages were significantly decreased in the high SP group among AEG patient samples, suggesting that the presence of high levels of SP suppresses MHC-II+ macrophage function (Fig. 2B). These findings suggest that intracellular SP may inhibit macrophage function. Furthermore, intracellular SP did not promote tumor progression in MC38 cell engrafted NCG mice, indicating that its effects are dependent on host immunity (Fig. 2C, D). To further explore this, we constructed a C57 mouse model with depleted CD4+ and CD8+ T cells through the administration of anti-CD4 and anti-CD8 antibodies (Fig. 2E). Flow cytometry and quantification revealed a high deletion rate of CD4+ and CD8+ T cells in the mouse spleen (Fig. S7A and S7B). In mice with deleted CD4+ and CD8+ T cells, intracellular SP similarly promoted tumor progression compared to the group without T cell deletion (Fig. 2F). Collectively, these results indicate that intracellular SP primarily inhibits tumor progression by modulating macrophage function, which in turn suppresses T cell activity. As anticipated, the tumor-promoting effect of intracellular SP was attenuated in macrophage-depleted mice (Figs. 2G and S7C). Consistent with these findings, APC (Min/+) mice administered SP via intragastric gavage developed more adenomas compared to PBS-treated controls (Fig. 2H–J). Moreover, compared to the PBS group, the SP-treated group exhibited a lower proportion of MHC-II⁺ macrophages and a higher proportion of CD206⁺ macrophages in APC (Min/+) mice (Figs. 2K and S7D). Collectively, our data indicate that intracellular SP promotes GIT progression by suppressing macrophage function, thereby inducing an immunosuppressive TME.

Intracellular SP within tumor cells decreases macrophage function by type I IFN related pathway
To further characterize macrophage regulation by intracellular SP in tumor cells, we performed single-cell RNA sequencing (scRNA-seq) of CD45+CD11b+ cells from the TME of MC38-engrafted mice using FACS. MC38 cells with or without intracellular SP were implanted in C57 mice, and tumor tissues were harvested after 7 days for scRNA-seq analysis. Unsupervised clustering identified 23 distinct clusters, classified into seven types of immune cells, including NK cells, monocytes and macrophages (Mono/Macro), fibroblasts, dendritic cells, CD8⁺ T cells, CD4⁺ T cells, and B cells (Fig. S8A–D). Consistent with SP regulating macrophage function, scRNA-seq analysis revealed that the proportion of DCs was much lower than that of the Mono/Macro cluster, and the antigen presentation module score in DCs was similar between the control and SP groups, while macrophage function in the SP group showed a suppressive trend compared to the control group (Fig. S8E and S8F). We focused on the Mono/Macro cluster, which was the most abundant in the scRNA-seq data, and re-clustering revealed 10 distinct clusters (Figs. 3A, S8G and S8H). Five of these clusters exhibited M1-like functions: the c3_Cd74 cluster indicated immune activation; the c4_H2-Aa cluster was associated with antigen presentation; the c7_S100a8 cluster and the c8_Il1b cluster displayed pro-inflammatory characteristics; and the c10_Isg15 cluster was linked to type I IFN-related responses (Fig. 3B and S8I). Three clusters exhibited M2-like functions: the c1_Mrc1 cluster displayed regulatory properties; the c2_Spp1 cluster was associated with angiogenesis; and the c5_Pf4 cluster also indicated angiogenesis. Additionally, the c9_Plac8 cluster exhibited IFN-related characteristics from monocytes, while the c6_Mki67 cluster displayed proliferative features.

RNA velocity analysis of each cluster between control and SP groups revealed transitions from M1-like macrophages (c8_Il1b) to M2-like macrophages (c5_Pf4) in SP-treated samples (Fig. 3C). Quantification of cluster changes following SP group showed a significant decrease in M1-like macrophages and an increase in M2-like macrophages, with c4_H2-Aa, an antigen processing cluster, notably reduced (Figs. 3D,E, S8J and S8K). These results suggest SP suppresses macrophage function. To validate this finding, we established a co-culture model to assess macrophage function influenced by SP-treated MC38 cells (Fig. 3F). Compared to macrophages co-cultured with untreated MC38 cells, those exposed to SP-treated MC38 cells exhibited increased ARG1 and decreased CD86 expression, as measured by qPCR, and reduced IFNβ secretion, as determined by enzyme-linked immunosorbent assay (ELISA) (Fig. 3G). Flow cytometry analysis of co-cultured macrophages revealed a significant decrease in CD206-MHC-II+ macrophages and an increase in CD206+MHC-II− macrophages, confirming SP-mediated inhibition of macrophage function via tumor cells (Fig. 3H). Similar results were observed with varying tumor cell to macrophage ratios (Fig. S9A–D).

Previous studies have reported that IFNβ secretion is a host cell defense response to bacterial attack57–60. We hypothesize that SP inhibits macrophages by reducing tumor cell IFNβ secretion through weakening the cell defense response. To rule out direct macrophage interaction, we employed a 0.45µm transwell chamber co-culture model, which demonstrated macrophage inhibition by SP, regardless of the varying MC38 cell to macrophage ratios (Figs. 3I,J and S9E–H). These findings indicate that SP in tumor cells leads to macrophage inhibition.

We assessed the response of type I IFN in tumor and immune cells using qPCR (samples from Fig. S4A), and found that SP infection in tumor cells suppressed the expression of interferon-stimulated genes (ISGs), while immune cells showed no significant differences in vivo (Fig. S9I). Furthermore, in vitro experiments demonstrated that SP infection in tumor cells also suppressed ISGs expression, while immune cells sustained high ISGs expression (samples from Figs. S4D and S9J). This contrasting pattern of ISGs expression suppressed in tumor cells yet sustained in immune cells supports the model that SP compromises macrophage function primarily by blunting type I IFN production within tumor cells, rather than through direct action on immune cells. These findings also indicate that SP in tumor cells leads to macrophage inhibition.

Reassessment of macrophage clusters by gene signature in scRNA-seq revealed that c3_Cd74 and c4_H2-Aa clusters positively correlated with type I IFN pathway and antigen presentation, while c1_Mrc1 and c6_Mki67 clusters negatively correlated in the SP group (Fig. 3K). Furthermore, c3_Cd74 and c4_H2-Aa clusters exhibited M1-like macrophage characteristics, while c5_Pf4 and c1_Mrc1 clusters displayed M2-like macrophage features in the SP group (Fig. 3L). Notably, SP treatment significantly decreased c3_Cd74 and c4_H2-Aa clusters while increasing c1_Mrc1, c6_Mki67, and c5_Pf4 clusters (Fig. 3D, E). Since DCs were the primary antigen-presenting cells in the human body, SP was neither able to affect the proportion of DC recruitment, nor to suppress MHC-II expression on DCs, whereas MHC-II expression on macrophages was significantly decreased in vivo (Fig. S10). In vitro experiments, the SP-conditioned medium (SP-CM) group with IFNa2b treatment increased MHC-II expression on DCs (Fig. S11A, B). However, the SP-CM group did not reduce MHC-II+ DC compared to the CM group, and the SP-CM-cGAMP group was unable to increase MHC-II+ DC compared to the CM-cGAMP group, regardless of whether DCs were IFNAR1 knockout (Fig. S11B). These results suggest that SP has a limited effect on DC function. Nevertheless, the SP-CM group reduced MHC-II+ macrophages and could not be rescued by cGAMP treatment compared to the CM or CM-cGAMP groups (Fig. S11C). In contrast, the SP-CM with IFNa2b treatment group rescued MHC-II+ macrophages compared to the CM group in vitro (Fig. S11C). When macrophages were IFNAR1 knockout, these effects were abolished in vitro (Fig. S11C). This suggests that SP-mediated macrophage inhibition may decrease IFN-related macrophages through an IFNβ-dependent mechanism.

Intracellular SP impairs MHC-II+ macrophage via decreasing tumor cell secretion IFNβ
Consistent with our hypothesis, IFNAR1 is required for IFNβ-mediated macrophage activation. Therefore, we generated an IFNAR1-KO mouse model and found no significant difference in tumor progression between the SP-treated and control group (Fig. 4A). Furthermore, administration of human IFNa2b, a clinically approved drug belonging to the type I IFN family with homology to mouse IFNs, failed to promote tumor progression in the SP with IFNa2b group compared to the SP group61–64 (Fig. 4B). We harvested tumors from the experiment described in Fig. 4B to assess changes in the immune TME during SP-induced tumor progression (Fig. S12A). While the overall proportion of macrophages was comparable among the three groups, an intriguing observation was that the frequency of MHC-II⁺ macrophages was significantly lower in the SP group than in the control group, and this reduction was rescued by IFNα2b supplementation (Fig. 4C, D). Moreover, the proportions of NK cells, NKT cells, CD8+ T cells, CD4+ T cells, and Tregs, as well as macrophage activation markers CD86 and CD206, showed no significant differences between groups (Fig. S12B, C and S13A–D). However, TNFα+ macrophages were suppressed in the SP group (Fig. S13E). This finding aligns with our scRNA-seq results, which revealed that SP impairs the c4_H2-Aa macrophage cluster involved in antigen processing. Although the proportion of CD8+ T cells did not differ among the three groups, the cytotoxic function of CD8+ T cells, as measured by GranzB+ and IFNγ+ expression, was markedly inhibited in the SP group (Fig. S13F). These results indicate that SP impairs MHC-II+ macrophages, leading to suppressed CD8+ T cell cytotoxic function.

To further confirm that SP suppresses CD8+ T cell cytotoxic function, we co-cultured macrophages pretreated with CM with CD8+ T cells (Fig. S14). Macrophages pretreated with SP-CM significantly inhibited the expression of IFNγ+, GranzB+ and Ki67+ CD8+ T cells compared to the CM group (Fig. 4E). Additionally, SP-CM with cGAMP treatment failed to rescue CD8+ T cell cytotoxic function compared to the CM with cGAMP treatment (Fig. 4E). However, macrophages pretreated with SP-CM and IFNa2b treatment enhanced the expression of IFNγ+ and Ki67+ CD8+ T cells compared to the SP-CM group (Fig. 4E). Furthermore, IFNAR1-KO macrophages pretreated with SP-CM, with or without IFNa2b or cGAMP treatment, had little effect on CD8+ T cell cytotoxic function (Fig. 4F). To further confirm the critical role of MHC-II⁺ macrophages in this process, mice were treated with anti-MHC-II antibodies (Fig. 4G). As expected, when MHC-II function was inhibited, SP no longer exerted a significant effect on tumor progression or CD8+ T cell activity (Fig. 4G, H). In summary, our findings demonstrate that intracellular SP inhibits macrophage function by decreasing tumor cell secretion of IFNβ, resulting in immune suppression and limiting STING function.

Intracellular SP decreases IFNβ secretion via STING protein degradation
While we have established that intracellular SP induces immune suppression through the type I IFN pathway, the precise mechanism by which SP decreases IFNβ secretion requires further elucidation. To investigate this, we first conducted bulk RNA-seq analysis in the HCT116 cell. Results revealed significant downregulation of ISGs such as IFIT1, IFNB1, CXCL10, and ISG15 in the SP group compared to the control group, confirming that intracellular SP inhibits the type I IFN pathway response (Fig. 5A). To further characterize SP’s inhibitory function, we treated tumor cells with various IFN agonists in the presence or absence of SP (Fig. S15A). Notably, the expression of ISGs was markedly suppressed in the SP group treated with cGAMP, a STING agonist, while other IFN agonists showed no significant effect (Fig. 5B). In contrast, poly(I:C), which activates interferon responses through MAVS recognition, showed no significant differences in WB analysis, further highlighting that the observed effects are specifically induced by STING (Fig. 5C). Additionally, ELISA revealed reduced IFNβ secretion by tumor cells in the SP-treated, cGAMP-stimulated group (Fig. 5D). Collectively, these data support our conclusion that intracellular SP suppresses the type I IFN pathway by promoting STING protein degradation.

To further validate the finding above, we examined STING protein expression in OE19, MC38, SKGT4, and HCT116 cells treated with varying multiplicities of infection (MOI) of SP for 48h. WB analysis confirmed a decrease in STING protein levels across four cell lines while cGAS protein level showed no significant difference (Figs. 5E and S15B). Intriguingly, IF revealed colocalization of SP with the endoplasmic reticulum (ER), where STING protein is typically localized in tumor cells (Fig. 5F and S15C). qPCR result showed that SP with different MOI did not affect the gene expression of STING in HCT116 (Fig. S15D). This observation provides compelling evidence for the association of SP with STING degradation.

To corroborate these findings in vivo, we utilized STING knockout (STING-KO) MC38 cells in a C57 mouse model (Fig. S15E). As expected, no significant differences were observed in IFNβ secretion, and flow cytometry analysis revealed that the proportions of MHC-II⁺ and CD206⁺ macrophages also remained unchanged in SP treatment (Fig. 5G–I). Moreover, SP failed to promote tumor progression in STING-KO MC38 cells compared with wild-type controls (Fig. 5J). This result further emphasizes the critical role of STING in SP-mediated tumor progression.

Intracellular SP activates ERAD pathway degrading STING protein
To elucidate the mechanism by which SP, localized near the ER, degrades STING protein in tumor cells, we investigated major cellular protein degradation pathways65,66. While previous studies have reported that STING degradation typically occurs through the lysosomal pathway following its physiological function, abnormal cellular states such as ER stress can trigger alternative degradation pathways, particularly the ER-associated degradation (ERAD) pathway mediated by the proteasome67–72. Enrichment analysis of proteasome and lysosome-related pathways revealed that SP treatment in HCT116 cells activates the proteasome pathway while inhibiting the lysosomal pathway compared to the control group (Fig. 6A). Furthermore, TEM showed ER swelling in SP-treated tumor cells across multiple cell lines, indicating SP-induced ER stress (Figs. 6B and S16A). IF corroborated these findings, confirming ER swelling in SP-treated HCT116 cells compared to control group (Fig. 6C, E). These results collectively demonstrate that SP induces ER stress, potentially leading to STING protein degradation.

To determine whether SP-induced STING degradation occurs via the proteasomal or lysosomal pathway, we examined phosphorylated STING (p-STING) levels, which typically indicate lysosomal degradation following STING activation. IF analysis revealed lower p-STING expression in SP-treated tumor cells compared to controls, particularly upon cGAMP stimulation (Fig. 6D, F). The expression of the marker proteins (PERK, IRE1a, ATF6 and XBP1s) for ER stress was increased in the SP group (Fig. 6G). To distinguish between inhibition of STING synthesis and enhanced degradation, we employed the proteasomal inhibitor MG132 and the protein synthesis inhibitor cycloheximide (CHX). Treatment with MG132 rescued STING protein levels, confirming that SP induces STING degradation via the proteasome pathway (Fig. 6H). Other proteasome inhibitor CB-5083 effectively rescued SP-induced STING degradation in HCT116 cells, whereas lysosome inhibitors (BafA1 and chloroquine) showed no rescue effects (Fig. 6I, J).

A previous study reported that the ERAD pathway can degrade STING protein via SEL1L in the proteasome70. Co-immunoprecipitation (Co-IP) experiments suggested the interaction between SEL1L and STING protein in SP-treated tumor cells compared to control (Fig. 6K). Moreover, Co-IP revealed that MG132 increased the interaction between STING and SEL1L during SP treatment (Fig. 6K). Corroborating this finding, knockdown or knockout of SEL1L expression resulted in reduced STING degradation in the SP-treated group (Figs. 6L; S16B and S16C). Although SEL1L knockout failed to degrade the STING protein, it was shown to induce ER stress, demonstrating that SP-mediated degradation of STING protein through ER stress depended on SEL1L (Fig. 6M). ELISA results demonstrated that SP failed to suppress cGAMP-induced IFNβ secretion in SEL1L-knockout HCT116 cells, suggesting that SEL1L is essential for SP-mediated inhibition of STING-dependent type I interferon signaling (Fig. 6N). Intriguingly, STING protein levels were also reduced, and STING rarely colocalized with the ER in SP-treated cells even without cGAMP stimulation, suggesting that intracellular SP decreases basal STING protein levels rather than inhibiting STING activation (Figs. S16D and 16E). To further confirm the relationship between STING and the ER, we examined STING protein expression and its association with SP in the presence of ER stress attenuators (Fig. S16F). As anticipated, treatment with ER stress attenuators rescued the SP-induced STING degradation, supporting our conclusion.

We next investigated whether SEL1L is essential for SP-mediated suppression of macrophage function via IFNβ reduction. In coculture experiments, SP treatment in SEL1L-knockout MC38 cells failed to reduce the proportion of MHC-II+ macrophages or elevated CD206+ macrophages across varying coculture ratios of BMDMs and tumor cells, demonstrating that SEL1L is required for SP-dependent macrophage suppression (Fig. S17A). Consistent with the coculture experiment findings, in vivo experiments using SEL1L-knockout MC38 tumors revealed no significant difference in macrophage MHC-II expression between SP-treated and untreated groups (Fig. 6O, P). Notably, DC proportions and functionality remained unaffected by SP or SEL1L knockout, suggesting that SP mainly impairs macrophage function within the TME (Fig. S17B).

These results indicate that SP-induced STING degradation primarily targets basal STING levels and depends on the SEL1L-mediated proteasomal pathway. In conclusion, our findings demonstrate that intracellular SP activates the ERAD pathway via the SEL1L-proteasome axis to degrade STING protein, thereby modulating the type I IFN response in tumor cells.

Intracellular SP impairs immunotherapy efficacy by reducing MHC-II+ macrophages
We next investigated whether intracellular SP within tumor cells influences the efficacy of immunotherapy. The effect of anti-PD-1 therapy was significantly impaired in the SP with anti-PD-1 group (Fig. 7A). To elucidate the underlying mechanisms, we conducted flow cytometry analysis of tumors harvested from the experiment described in Fig. 7A. The proportions of CD45+ cells and macrophages were similar across all four groups (Figs. 7B and S18A, B). Compared to the control group, NKT cells were decreased in the SP and SP with anti-PD-1 groups, while NK cell populations remained similar (Fig. S18C). Assessment of macrophage function revealed that the SP and SP with anti-PD-1 groups secreted less TNFα compared to the control group, while the proportions of CD206+ or CD86+ macrophages showed no significant differences among groups (Fig. S18D–F). Notably, the proportion of MHC-II+ macrophages was similar between the SP and SP with anti-PD-1 groups (Fig. 7C). These results indicate that SP, even in combination with anti-PD-1 therapy, impairs macrophage function, particularly MHC-II+ macrophages, explaining the lack of therapeutic effect of anti-PD-1 in the SP group. The proportions of CD4+ T cells, CD8+ T cells, and Tregs showed no significant differences among the four groups, except for a reduction in CD8+ T cell infiltration in the SP with anti-PD-1 group (Fig. S19A and S19B). IFNγ+ CD8+ T cells were similar across all groups and Ki67+ CD8+ T cells were similar between the SP and SP with anti-PD-1 group (Fig. 7D, E). These findings suggest that T cell cytotoxic function is impaired in the SP and SP with anti-PD-1 groups, likely due to the inhibition of MHC-II+ macrophages. Importantly, the addition of IFNa2b to SP or SP with anti-PD-1 groups failed to promote tumor progression and restored the anti-tumor effect of anti-PD-1 (Fig. 7F). The therapeutic efficacy of the oxaliplatin plus anti-PD-1 combination in SP group was reduced compared to the anti-PD-1 alone group (Fig. 7G). In summary, these findings indicate that gastrointestinal tumors harboring intracellular SP are associated with poor treatment outcomes, primarily due to impaired immunotherapy efficacy. Overall, tumors containing intracellular SP exhibit resistance to immunotherapy by suppressing MHC-II⁺ macrophages, thereby weakening T cell cytotoxic function.

Ablation of intracellular SP by levofloxacin eliminates therapeutic resistance
Given that GIT with SP leads to therapeutic resistance and poor prognosis compared to tumors without SP, we investigated potential therapeutic strategies. Previous studies have reported that levofloxacin can ablate SP73,74. We observed significant suppression of SP growth under various concentrations of levofloxacin, with an optimal concentration of 5µg/mL (Fig. S20A). After mixing SP with tumor cells for 24h, the addition of levofloxacin inhibited intracellular SP across different cell lines (Fig. S20B). Results showed that levofloxacin decreased SP within tumor cells and alleviated ER swelling (Fig. 8A). We further conducted mouse rescue experiments, demonstrating that levofloxacin treatment mitigated SP-promoted MC38 tumor progression in C57 mice compared to the SP group without levofloxacin (Fig. 8B).

qPCR analysis of tumor tissues from Fig. 8B revealed a marked reduction in SP abundance following levofloxacin administration (Fig. S20C). As expected, levofloxacin reversed anti-PD-1 therapeutic resistance in tumors with SP (Fig. 8C). Flow cytometry analysis of tumors from the experiment in Fig. 8C revealed that levofloxacin treatment combined with anti-PD-1 therapy restored MHC-II expression in macrophages compared to anti-PD-1 monotherapy (Figs. 8D and S20D). These results indicate that levofloxacin may serve as a potential treatment strategy against SP-positive GIT.

Methods
Cell culture
OE19 (cat: CL-0754, Procell), SKGT4 (cat: CL-0874, Procell), HCT116 (CL-0096, Procell), DLD1 (cat: CL-0074, Procell), B16F10 (cat: CL-0319, Procell), CT26 (cat: CL-0071, Procell) and Hepa1-6 (cat: CL-0105, Procell) and MC38 (cat: iCell-m032, iCell) are purchased from company. DMEM or RPMI-1640 basic medium consisting of 10% fetal bovine serum (FBS, Procell, 164210-50) and 1% Penicillin-Streptomycin (Gibco, 15140122) cultures all cell line under 37°C with 5% CO2 atmosphere.

Bacteria culture
SP (cat: BNCC342633, BNCC) is purchased from BNCC, but SP-GFP is a generous gift from Professor Xiaoyu Hu at Jilin University16. SP was revived on LB (Beef powder, 3.0g; peptone, 10g; sodium chloride, 5.0g; distilled water, 1.0L; if solid medium, adding agar, 20.0g) medium at 37°C for 24h. An oscillating incubator at 180 round/minute in the aerobic environment cultures SP within liquid LB.
We determine the numbers of SP by OD600. We use PBS to gradient-dilute SP on Eppendorf (EP) tube and test each EP tube the value of OD600 by ultramicrospectrophotometer (KAIAO TECHNOLOGY, k5800). Each EP tubes are further gradient-diluted, and calculate the numbers of SP by microscope and the CFU of SP by inoculation plate. Linear fitting calculates 1 OD600 approximately equal to 1 × 109 CFU/mL87. Because we test the cultured SP directly and chose to underestimate the number of SP to reduce the disturbance caused by bacterial excess cells, so 1 CFU is viewed as 1 SP.

Mouse experiments
All mouse experiments are conducted in Guangzhou LingFu Top-Biotech. Co., LTD under permit number SYXK(Yue) 2023-0315, are conducted in line with the regulations for the Administration of Affairs Concerning Experimental Animals issued by the Ministry of Science and Technology of the People’s Republic of China. C57, nude, and BALB/c mice, aged 4-8 weeks, were purchased from Guangdong medical laboratory animal center. NCG and APC (min/+) mice, female, aged 4-8 weeks, are purchased from GemPharmatech (Guangdong, China).
For most experiments assessing tumor volume changes, tumor cells were pre-incubated with SP at an MOI of 0.01 for 4h prior to injection. 1×106 MC38 cells are subcutaneously injected to C57 mouse with age-matched female to construct subcutaneous GIT murine model. Other murine tumor model including CT26, B16F10 and Hepa1-6, are subcutaneously injected with similar conditions in corresponding mouse. NCG mouse is subcutaneously injected with 5×105 MC38 cells. For the spontaneous colorectal cancer murine model, APC (min/+) mice is used to illustrate SP promoting GIT progression. SP (1×108 CFU/per mouse) or PBS (100μL/per mouse) are administered by gavage in APC (min/+) 4-8 weeks mouse, on once per week. For treatment with antibiotics (oral administration), the control group is gavaged with 200μL PBS per mouse while the treatment group with the same volume of levofloxacin (5 mg/mL) per mouse). For depletion of macrophages, mice were intraperitoneally injected 200μL clodronate liposome (cat: 40337ES08, YEASEN) once 3 days before xenograft MC38 cells, and 100μL once a week after xenograft MC38 cells in C57 mouse. For deletion of CD4+ and CD8+ T cells, anti-CD4 and anti-CD8 antibodies (respectively 10 mg/kg per mouse) were intraperitoneally injected in C57 mouse. We recorded tumor volume every two or three days using a vernier caliper (tumor volume = 1/2 × length × width2). At the experimental endpoint, mice were euthanized by cervical dislocation following anesthesia.

qPCR assay
EZ-press RNA Purification Kit (cat: B0004D, EZBioscience) was used for total RNA extraction. The reverse transcribed system based on Hiscript III Reverse Transcriptase (cat: R302-01, Vazyme), and 1μg of RNA in 20μL system was applied to reverse cDNA. RuiBiotech Co synthesized all primers. BIORAD CFX96 Touch with a 2× ChamQ Universal SYBR qPCR Master Mix (Vazyme) was conducted. qPCR was performed in the reaction conditions: 95°C for 30 second(s), followed by 40 cycles of 95°C for 10s and 60°C for 30s. The data was presented as a fold change normalized to the control group. In technical duplicates, we conducted all qPCR for each sample with three biological replicates. Primers are provided in the Supplementary Table 1.

Western blot
The proteins were extracted from the cells using RIPA lysis buffer (cat: CW2333, Beyotime Biotechnology) with a protease inhibitor cocktail (cat: B14001, Selleck). Pierce BCA Protein Assay Kit (cat: 23227, Thermo Scientific) was used to measure the protein concentration of each sample. Total protein (30μg) was resolved using a 10% sodium dodecyl sulphate-polyacrylamide gel (SDS-PAGE) electrophoresis and transferred onto polyvinylidene difluoride (PVDF) membranes. 5% nonfat milk in PBST blocked the PVDF membrane at room temperature for 1h and incubated with primary antibodies overnight at 4°C. Membranes were incubated with secondary antibodies for 1h. After washing with PBST, proteins were visualized by Omni-ECL™ Femto Light Chemiluminescence Kit (cat: SQ201, EpiZyme) in a chemiluminescence imaging system ImageQuant LAS 500 (Cytiva, USA). Some proteins were detected by through Odyssey CLX. The primary antibodies included anti-β-tubulin (cat: ARG62347, Arigo), anti-GAPDH (cat: 2118, Cell Signaling Technology), anti-IRF3 (cat: 4302, Cell Signaling Technology), anti-phosphorylated IRF3 (cat: 4947, Cell Signaling Technology), anti-TBK1 (cat: 3504S, Cell Signaling Technology), anti-phosphorylated TBK1 (cat: 5483S, Cell Signaling Technology), anti-STING (cat: 13647, Cell Signaling Technology) and anti-phosphorylated STING (cat: 50907S, Cell Signaling Technology), anti-SEL1L (cat: 29801-1-AP, Proteintech), anti-Phospho-PERK (cat: 3179T, Cell Signaling Technology), anti-PERK (cat: 3192T, Cell Signaling Technology), anti-cGAS (cat: 79978, Cell Signaling Technology), anti-IRE1α (cat: 3294, Cell Signaling Technology), anti-ATF-6 (cat: 65880, Cell Signaling Technology), anti-XBP-1s (cat: 40435, Cell Signaling Technology). The secondary antibodies included Goat anti-Mouse IgG (H+L)-HRP (cat: RM3001, Beijing Ray Antibody biotech), Goat anti-Rabbit IgG (H+L)-HRP (cat: RM3002, Beijing Ray Antibody biotech), Anti-Mouse IgG (cat: ab150116, ab150113, ab150080, ab150077, Abcam). The working dilutions of all antibodies are listed in Supplementary Table 2.

Elisa
The cell supernatant was collected for testing IFNβ secretion through LumiKine™ Xpress hIFN-β 2.0 ELISA kit (cat: luex-hifnbv2, Invivogen) and LumiKine™ Xpress mIFN-β 2.0 ELISA kit (cat: luex-mifnbv2, Invivogen). OD450 was read using Multiskan GO Luminometer (Thermofisher Scientific, USA).

Flow cytometry analysis and cell sorting
Single cell suspensions were stained with fluorochrome-conjugated antibodies against markers based on the manufacturer’s instructions. Flow cytometry was performed on the CytoFLEX (Beckman, USA) and FACSymphony S6 (Becton Dickinson, USA). All data were analyzed by FlowJo V10 or CytExpert software.
Mix tumor cells were SP-GFP cocultured MC38 tumor cells, and were sorted out negative tumor cell (without intracellular SP) and positive tumor cell (with intracellular SP) by FACSymphony S6.
Tumor tissues were harvested and minced into fragments of 8mm3 and digested for 30 minutes at 37°C with 1mg/mL of collagenase IV (cat: 40510ES60, Yeason) and 0.2mg/mL of DNase I (cat: D5025, Lablead). A 70mm cell strainer filtered with the dissociated tissues. Single-cell suspensions were incubated in an Fc block (cat: 553141, Becton Dickinson) and Zombie Aqua™ Fixable Viability Kit (cat: 423101, Biolegend) for 15 min at room temperature, stained for surface markers at 4°C for 30min, washed with Cell Staining Buffer (cat: 420201, Biolegend) once. The intracellular staining used the Intracellular Fixation & Permeabilization Buffer Set (cat: 88-8824-00, Invitrogen™). The intranuclear staining used the eBioscience™ Foxp3/Transcription Factor Fixation/Permeabilization Concentrate and Diluent Kit (cat: 00-5521-00, Invitrogen™). These antibodies including Brilliant Violet 510™ anti-mouse CD45 (cat: 103108, BioLegend), APC anti-mouse/human CD11b (cat: 101228, BioLegend), Brilliant Violet 785™ anti-mouse F4/80 (cat: 123141, BioLegend), FITC anti-mouse NK-1.1 (cat: 108706, BioLegend), APC/Cy7 anti-mouse CD3 (cat: 100221. BioLegend), PE/Cy7 anti-mouse CD4 (cat: 100422, BioLegend), Alexa Fluor 700 Rat Anti-Mouse CD8a (cat: 557959, Becton Dickinson), Alexa Fluor® 647 anti-mouse/rat/human FOXP3 (cat: 320014, BioLegend), Pacific Blue™ anti-human/mouse Granzyme B (cat: 515408, BioLegend), Brilliant Violet 605™ anti-mouse IFN-γ (cat: 505840, BioLegend), PE anti-mouse Ki-67 (cat: 103105, BioLegend), PE/Cyanine7 anti-mouse CD206 (cat: 141720, BioLegend), Brilliant Violet 421™ anti-mouse CD86 (cat: 105123, BioLegend), Alexa Fluor® 647 anti-mouse TNF-α (cat: 506314, BioLegend), Alexa Fluor® 700 (or other channel) anti-mouse I-A/I-E (cat: 107622, 107616, 107625, 107613, BioLegend), and APC anti-mouse CD326 (Ep-CAM) (cat: 118213, BioLegend) were used to stain.

Immunofluorescence staining
Immunofluorescence staining was performed by Absin Biotech (Shanghai, China). All image acquisition and quantitative analyses were performed by Absin Biotech (Shanghai, China), blinded to sample grouping and experimental purpose.
To confirm the localization of SP within tumor cells, cocultured SP-GFP tumor cells grown on confocal dishes were stained with AF647-Phalloidin (Cat. A22287, Invitrogen) recognizing F-actin and counterstained with DAPI. For mechanistic studies, tumor cells with or without SP-GFP coculture were labeled with ER-Tracker™ Blue-White DPX (Cat. E12353, Invitrogen) to visualize the endoplasmic reticulum, fixed and permeabilized using the corresponding buffer, and stained for STING and phospho-STING with primary and fluorescent secondary antibodies. All fluorescence images were captured using a Zeiss LSM900 laser confocal microscope.

SP testing by FISH
16S rRNA was used to identify whether SP located in tumor by FISH. The FISH probe of SP (5′-GTCGTCCAGTATCCACTGC-3′) and the protocol of operation was referred to a previous study17. The intensity of the fluorescence signal was quantified using imageJ.

Transmission Electron Microscopy (TEM)
The cells were collected by electron microscope fixative (car: G1102, Servicebio). After a serial procession of agarose pre-embedding, fix, dehydrate, resin penetration and embedding, polymerization, ultra-thin section and staining, the samples were observed under TEM (HT7800, Hitachi) and take images.

Macrophages induction
Bone marrow cells were extracted from the femurs and tibias of C57 mice and depleted of erythrocytes by Red Blood Cell Lysis Buffer (cat: G2015-500ML, Servicebio). Seeding in 6-well plates at a density of 2×106 cells/well cultured for 7 days in DMEM medium containing 10% FBS and 20 ng/mL macrophage colony stimulating factor (M-CSF).

HE staining
FFPE was treated by dewaxing and hydration, rewarming and fixing, and hematoxylin staining. Then, we put sections into hematoxylin solution for 5min, treat the section with hematoxylin differentiation solution, and place the sections in 95% ethanol for 1min, eosin dye for 15s. Lastly, the section was conducted by dehydration and sealing.

Immunoprecipitation cell lysis buffer
The cells were washed with ice-cold PBS and lysed in immunoprecipitation buffer (cat: G2038-100ML, Servicebio) with a protease inhibitor mixture (cat: G2007, Servicebio) for 30min at 4°C. The lysates were centrifuged at 12,000 rpm for 10min at 4°C, and the protein was determined with the BCA kit. The proteins incubated with Protein A/G beads (cat: IP05, Millipore) shaken at 4°C for 1h, and then incubated 1μg antibody rotated overnight at 4°C. Immunoprecipitates were washed thoroughly with immunoprecipitation buffer and immunoblotted with the antibodies.

Microbiome detection
The microbiome abundance in each dataset was detected by the Kraken software suite (Kraken2 version 2.1.2, Bracken version 2.9)88. Raw reads were quality-controlled by fastp (version 0.23.4) to generate clean reads89. The “Standard” collection (version 26/9/2022) from Kraken index database which includes archaea, bacteria, viral, plasmid, human and UniVec_Core was used to annotate the clean reads. Annotated reads were then normalized to reads per million microbiome reads and reads per million total reads. Relative abundance of each microbiome was estimated by Bracken. The background microbiome data from the SAHMI was used to detect the enriched microbiomes in each dataset90,91. During our analysis, in addition to the microbiome genomes, the human genome, universe vectors and other sequences were also included in the database which may reduce the potential false positive rate. The background microbiome data from SAHMI was used in our analysis to reduce the potential false positive findings, which may reduce our findings if the background data had a high positive false rate. Six publicly available datasets were included in this study: GSE126304 (n=46) and PRJNA788008 (n=83) for AEG, GSE156451 (n=47) for CRC, GSE122401 (n=80), GSE179252 (n=38) and GSE184336 (n=231) for GC.

Single cell RNA seq analysis
The isolated single cells were loaded to the single cell chip for GEM generation using the 10x Chromium controller to construct single cell library. The single-cell 3′ cDNA libraries were constructed using the Chromium Single Cell 3′ Reagent Kit (V3 chemistry, 10x Genomics) and sequenced on Illumina NovaSeq 6000 platform.
The raw single cell fastq data were processed by Cell Ranger pipelines (10x Genomics, version 6.0.1) to generate the count matrix. Reference genome (version refdata-gex-mm10-2020-A) was used for read alignment92. Then quality control was performed by filtering cells expressing less than 1000 genes or more than 6000 genes. Cells with more than 10% transcripts from mitochondrial genes were also filtered out. Doublets were identified by R package DoubletFinder (version 2.0.3) and filtered from the count matrix93.
After quality control, R package Seurat (version 4.0.4) was applied for downstream analysis. Gene expression was normalized, and highly variable genes were identified by SCTransform function with default parameters. Based on highly variable genes, principal component analysis was performed by RunPCA function94. Function RunHarmony from R package harmony (version 0.1.0) was applied to remove the potential batch effects between batches or samples. After batch correction, we used the Elbow function to identify the significant corrected components for cell clustering95. The identified 34 PCs with resolution 1.2 and 16 PCs with resolution 0.6 were used for major cell clustering and the macrophage clustering respectively. For visualization, we performed the dimension reduction to generate the Uniform Manifold Approximation and Project (UMAP) via the RunUMAP function. During the major cell clustering, we annotated the cell clusters manually with canonical cell type-specific markers, including B cells (Ms4a1, Cd79a, Cd79b), T cells (Cd3d, Cd3e, Cd3g), CD4 T cells (Cd4), CD8 T cells (Cd8a), NK cells (Ncr1, Ncam1, Prf1, Klrd1), DC (Clec9a, Cd209a, Xcr1, Ccr7, Fscn1, Ccl22), monocytes or macrophages (Itgam, Lyz2, Cd63, Csf1r, Cd14), fibroblast (Dcn, Col3a1), endothelial cells (Eng, Kdr), epithelial cells (Krt8, Epcam). During the cell clustering of macrophages or monocytes, cell clusters were annotated based on the cluster markers and their functionality.

RNA velocity
To detect the potential cell transition between each cluster from different groups, we performed RNA velocity analysis via velocyto.py (version 0.17.17) and scVelo (version 0.3.1)96,97. Briefly, loom files containing the spliced and unspliced transcript count matrix were generated by velocyto.py with parameter “run10x”. Generated loom files were then loaded into scVelo and preprocessed by scVelo.pp.filter_ and_normalize function. The moments of normalized spliced and unspliced counts were calculated using the scVelo.pp.moments function with default parameters. RNA velocity was estimated using the scVelo.tl.velocity function with “dynamical” model and scVelo.tl.velocity_graph function was used to build the velocity graph. For visualization, the estimated RNA velocities were projected into the UMAP coordinates generated by RunUMAP from cell clustering step by the scVelo.pl.velocity_embedding_stream function.

Sample or group preference analysis
The ratio of observed to expected (Ro/e) were used to infer sample or group preference on cells in each cluster. For cells from each sample in each cluster, we constructed a contingency table to calculate the observed and expected number of cells. P value was calculated via the fisher.test function from R package stats (version 4.0.4) to indicate the significance of Ro/e.

Gene module score
The function “AddModuleScore” from R package Seurat were applied to calculate the gene module score of given gene sets to illustrate the functionality of cell clusters. In this study, four gene signature were used and converted to corresponding mouse genes during analysis: type 1 interferon (CXCL10, IFI16, IFI27, IFI30, IFI6, IFIH1, IFIT1, IFIT2, IFIT3, IFITM1, IFITM2, IFITM3, IFNA1, IFNA2, IFNA4, IFNAR1, IFNAR2, IFNB1, IFNE, IFNW1, IRF1, IRF2, IRF3, IRF5, IRF7, IRF9, ISG15, ISG20, JAK1, JAK2, OAS1, OAS2, SOCS1, STAT1, STAT2, STAT3, TMEM173, TYK2), antigen presentation (CD70, CD74, CD80, CD83, CD86, TNFSF9, HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5, B2M), M1 signature (IL23, TNF, CXCL9, CXCL10, CXCL11, CD86, IL1A, IL1B, IL6, CCL5, IRF5, IRF1, CD40, IDO1, KYNU, CCR7) and M2 signature (IL4R, CCL4, CCL13, CCL20, CCL17, CCL18, CCL22, CCL24, LYVE1, VEGFA, VEGFB, VEGFC, VEGFD, EGF, CTSA, CTSB, CTSC, CTSD, TGFB1, TGFB2, TGFB3, MMP14, MMP19, MMP9, CLEC7A, WNT7B, FASL, TNFSF12, TNFSF8, CD276, VTCN1, MSR1, FN1, IRF4)98.

RNA seq analysis and enrichment analysis
Raw fastq reads were quality-controlled by fastp. The clean reads were mapped to GRCh38 or mm10 reference genome to generate the expression matrix via STAR (version 2.7.2a) with default parameters99. Based on the expression matrix, differentially expressed genes were identified by R package DESeq2 (version 1.30.1)100. Gene set enrichment analysis (GSEA) on Gene ontology (GO) was performed by R package fgsea (version 1.16.0) according to the rank of log2FoldChange of each gene101.

Survival analysis
To estimate the effects of microbiomes or genes on prognosis, Kaplan–Meier survival analysis on single factor was performed via R package survival (version 3.2.7). During analysis, samples were grouped into high or low group based on the abundance of candidate microbiome or the expression level of candidate gene. The P value was calculated by the log-rank method in survival analysis.

Clinical data collection
All studies are performed in accordance with the Declaration of Helsinki principles under a protocol approved by the Ethics Committee of Guangdong Provincial People’s Hospital and written informed consent was obtained from all subjects.
We collect AEG, GC, and CRC paraffin-embedded tissue samples and clinical data from our hospital. GC patients derive from our previous study102. AEG patients are included with complete clinical data, paraffin-embedded tissue, and follow up information from 2011 to 2019 years. CRC is randomly selected 64 patients from 2012 to 2019 years. Combination AEG with GC is conducted survival analysis by SP high or low groups, excluding CRC patient because CRC patient prognosis is too different than AEG and GC.

Statistics and reproducibility
All experiments were independently repeated at least three times with similar results. Representative images shown in micrographs (FISH, TEM, IF) reflect reproducible findings across independent biological replicates.

Ethics statement
All patients consented to an institutional review broad-approved protocol that allows comprehensive analysis of tissues samples (Ethics committee of Guangdong Provincial People’s Hospital).
All animal-related procedures received prior approval from the Ethics Committee of Guangdong Provincial People’s Hospital. Animals were housed and monitored under institutional ethical oversight, and all handling followed national and institutional guidelines for humane care, welfare protection, and regulatory compliance. No animal manipulation was initiated before formal protocol authorization. According to the Ethics Committee, the size of subcutaneous tumors should not exceed 2000 mm3. The experiment should be terminated in time when the tumors develop ulceration, necrosis, or severe infection.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents a novel and mechanistically detailed study demonstrating that intracellular Stenotrophomonas (SP) within tumor cells impairs anti-tumor immunity by degrading STING protein, thereby suppressing the type I interferon signaling pathway. The authors employ a broad range of techniques, including microscopy, flow cytometry, transcriptomics, and functional in vivo models, to build a coherent narrative linking bacterial localization to immunotherapy resistance. The study is timely and of potential translational relevance, especially given the increasing recognition of bacteria-tumor interactions in modulating the tumor microenvironment. The results are largely compelling and well supported by the data. However, several aspects of the methodology, mechanistic interpretation, and data presentation could benefit from clarification or further experimentation. For example, the contamination control methods are not fully described, some mechanistic conclusions (such as the role of MHC-II or chemotherapy resistance) appear premature, and certain controls (e.g., use of STING-deficient macrophages or validation of human IFNa2b in mice) are lacking or under-discussed. Strengthening these aspects would improve the rigor and impact of the study. Specific comments are provided below to help improve clarity and robustness.\n1.Simplified explanation of contamination control\nAlthough the authors mention normalization using \"reads per million microbiomes\" and \"reads per million total reads\" to control false positives, they do not clearly explain how contaminant species were distinguished from true intracellular bacteria, nor whether negative controls or tools like decontam were used. Please elaborate on how potential contamination was excluded. Were technical replicates, reagent blanks, or computational filtering tools (e.g., decontam) used?\n2.Premature conclusion on intracellular localization\nThe classification of SP as an “intracellular” bacterium seems to rely mainly on sequencing and FISH data in figure 1. Consider softening the claim of “intracellular” at this stage or adding a statement that further cellular validation is presented in the following sections.\n3.Survival analysis methods not described\nThe authors mention conducting a survival analysis but do not clarify the method used (e.g., Kaplan-Meier, Cox regression), nor whether confounding clinical factors were controlled in figure 1. Please specify the survival analysis method used and clarify whether clinical covariates (e.g., age, sex, TNM stage) were adjusted for in the analysis.\n4.“To further confirm that SP is located whether tumor cells...” This is a grammatically incorrect expression. The phrase “located whether tumor cells” lacks proper prepositional structure and is not standard English.\n5.The authors define CD45⁻ cells as tumor cells; however, CD45 negativity alone is insufficient to definitively identify tumor cells. Please clarify whether additional markers (e.g., EpCAM, GFP, or tumor-specific labeling) were used to validate the identity of these cells.\n6.“Not detectable” by flow cytometry does not necessarily mean “absent”—cells might be present in low abundance or poorly stained.\n7.“Tumor area with the high abundance of SP tended to exhibit increased hypoxic...”. \"hypoxic\" is an adjective and is used incorrectly in this sentence. It should likely be “hypoxia.”\n8.Since the model involves C57BL/6 mice with intact immune systems, it is important to discuss whether SP-induced tumor promotion is partly mediated by modulation of host immunity. Alternatively, did the authors test this model in immunodeficient mice as a control? While the authors demonstrate that SP-containing tumor cells promote accelerated tumor growth in vivo, it remains unclear whether this effect is solely due to intracellular bacterial influence on tumor cell-intrinsic pathways, or whether SP also modulates the host immune response.\n9.it is \"Figure 1N\" rather than \"Figures.\"\n10.The decrease in MHC-II⁺ macrophages suggests altered phenotype, but does not directly demonstrate functional suppression. Have the authors performed functional assays (e.g., cytokine secretion, antigen presentation) to validate macrophage impairment? (Figure 2)\n11.The interpretation that SP suppresses tumor immunity via macrophage-mediated T cell inhibition seems inconsistent with the finding that tumor progression is still enhanced after T cell depletion. This may suggest that SP also acts via T cell–independent mechanisms. Could the authors clarify this discrepancy? (Figure 2)\n12.The authors state that the proportions of macrophages, Treg cells, and CD8⁺ T cells were similar between the high and low SP groups based on multiplex immunofluorescence. However, this method may have limitations in quantifying immune cell subsets precisely, particularly in terms of cell density and spatial variability. Flow cytometry could provide a more quantitative and sensitive assessment of immune cell proportions. Have the authors considered validating these findings using flow cytometry, especially to confirm whether the frequencies of Tregs, CD8⁺ T cells, and macrophages are truly comparable between the two groups?\n13.The authors conclude that intracellular SP primarily inhibits tumor progression by modulating macrophage function, which in turn suppresses T cell activity. However, this mechanistic link remains speculative, as no functional blockade or genetic depletion of MHC-II was performed to directly demonstrate the role of antigen presentation by macrophages in mediating this effect. To strengthen this conclusion, the authors could consider experiments that directly impair MHC-II function in macrophages (e.g., antibody blockade, siRNA knockdown, or use of MHC-II–deficient mice).\n14.In the APC(Min/⁺) model, the authors administered SP by intragastric gavage and observed increased adenoma formation and reduced MHC-II⁺ macrophages. While this supports a tumor-promoting role for SP, it remains unclear whether the observed effects stem from direct intracellular colonization of tumor cells or from broader immune modulation triggered by gut exposure to SP.\nCould the authors clarify whether intragastric administration of SP alters systemic or local immune responses independently of tumor colonization? Were control experiments performed to evaluate gut barrier integrity, systemic inflammation, or innate immune activation following gavage? Distinguishing between direct intracellular effects and microbiota-mediated immune modulation is important for interpreting the mechanism.\n15.While the authors used a transwell model to minimize direct SP-macrophage contact, it is not fully clear whether all co-culture experiments ensured the absence of extracellular SP. Please clarify whether antibiotic treatment or sterilized SP-conditioned medium was consistently used to eliminate the influence of extracellular bacteria on macrophages and DCs. (Figure 3)\n16.The authors used human IFNa2b to rescue IFN signaling in mice. While IFNa2b is approved in clinical use, its cross-reactivity and bioactivity in murine immune cells (particularly macrophages) may differ from endogenous murine IFNβ. Please provide validation of IFNa2b efficacy in the murine system or cite literature demonstrating its functional equivalence. (Figure 4)\n17.While reduced CD8⁺ T cell cytotoxicity is observed in the SP group and rescued by IFNa2b, this remains a correlative observation. Could the authors further dissect whether this is directly mediated by altered macrophage MHC-II expression, or possibly other immunosuppressive cytokines or checkpoints induced by SP? (Figure 4)\n18.While the decrease in MHC-II⁺ macrophages is compelling, functional assays of antigen presentation (e.g., OT-II T cell proliferation, antigen-specific cytokine release) would provide direct evidence that antigen presentation is impaired. The authors are encouraged to consider these assays to reinforce their mechanistic claims. (Figure 4)\n19.The immunofluorescence data suggesting colocalization of SP with the ER are compelling. However, please provide representative images and quantitative analysis (e.g., Pearson’s or Manders’ coefficient) to support this conclusion.\n20.The absence of SP-mediated tumor promotion in STING-KO MC38 cells is an important observation. However, it would strengthen the conclusion if downstream markers (e.g., IFNβ secretion, MHC-II macrophage levels) were measured in these models to confirm the loss of SP function via STING pathway disruption.\n21.The ERAD pathway involves multiple components, including SEL1L, HRD1, and DERL1. Please clarify whether other ERAD-related genes are upregulated or physically interact with STING in SP-treated cells.\n22.While the authors observed that intracellular SP reduces the efficacy of both immunotherapy (anti-PD1) and chemotherapy (OXP), the rationale for including chemotherapy in this study is not clearly justified. Given that the primary mechanistic focus centers on immune suppression via the type I IFN-STING pathway, the extension to chemotherapy lacks mechanistic linkage and appears abrupt. Please clarify whether SP affects tumor-intrinsic chemotherapy resistance pathways or whether immune cell modulation contributes to the observed reduction in OXP efficacy.\n23.As levofloxacin is a broad-spectrum antibiotic, potential effects on the gut microbiome and systemic immunity should be discussed, particularly if translated to human therapy. Are there SP-specific approaches or narrower-spectrum alternatives?\n24.The authors are encouraged to strengthen the discussion by comparing their findings with previous studies on other tumor-associated bacteria, such as Fusobacterium nucleatum. This would help contextualize the novelty of SP-mediated STING degradation as a mechanism of immune suppression in GIT tumors, and clarify how SP differs in its tumor-promoting strategies.\n25.The authors propose that levofloxacin can eliminate intracellular SP and restore anti-PD1 efficacy, which is promising. However, the discussion could be enhanced by addressing the clinical feasibility and limitations of this approach, such as the potential for antibiotic resistance, disruption of the gut microbiome, and patient stratification based on SP status.", "reviewer_2": "The manuscript by Feng et al., describes the effect of Stenotrophomonas (SP) infection in promoting the progression of gastrointestinal tumor (GIT). SP specifically infects tumor cells and decreases STING expression by inducing ER stress, leading to the downregulation of the type I IFN pathway response in GIT. The decreased expression of IFNβ by tumor cells subsequently attenuates the presence of MHC-II+ macrophages and impairs effector CD8 T cell activity within the GIT microenvironment.\nThe research concept presented in this manuscript is interesting, well-designed, and generally well-written. The authors have clearly invested significant effort into developing and articulating their study. However, there remain some aspects that would benefit from additional supporting evidence or further clarification. Addressing these points would enhance the overall clarity and impact of the work. The following major and minor comments are intended to help improve the manuscript.\nMajor comments:\nQuestion 1. It is recommended to show the difference in SP levels between normal adjacent mucosa and tumor tissues in AEG and GC in Figure 1D.\nQuestion 2. Is there any association between SP levels and the clinicopathological characteristics of patients with AEG and GC? Additionally, could SP levels be an independent prognostic factor in GIT?\nQuestion 3. How did the authors select the five patients for fluorescent IHC in Figure 2A? It is recommended to increase the sample size to improve the robustness of the results. Additionally, could the authors please provide the detailed methodology used to assess the percentages of each immune cell type by fluorescent IHC, including the size and number of areas or spots analyzed?\nQuestion 4. Did the authors perform fluorescent IHC for M2-TAM markers, such as CD163 and CD206, in Figure 2?\nQuestion 5. In Figure S9J, the in vitro experiments show that SP infection suppresses the expression of ISGs in tumor cells, while enhancing the expression of ISGs in immune cells, including DC, macrophages, and T cells. The authors should briefly explain the rationale or underlying mechanisms for this opposite response in each cell type.\nQuestion 6. Did the authors examine the expression of cGAS in GIT lines treated with or without SP in Figure 5E?\nQuestion 7. It would be preferable to test whether ER stress inducers, such as thapsigargin and tunicamycin, decrease the expression of STING in GIT lines as a positive control (Figure 6). Additionally, although CB-5083 is used, it remains unclear whether SP-induced ER stress is involved in the downregulation of STING expression in GIT lines, as there is no direct evidence. If possible, the authors should investigate whether ER stress attenuators, such as 4-PBA and TUDCA, or knockdown/knockout of ATF-6 or IER-1-XPB1 pathways can rescue the SP-induced degradation of STING in GIT lines.\nQuestion 8. It is recommended that the authors include a discussion on the mechanism by which SP specifically infects tumor cells but not other cell types within the GIT.\nMinor Comments:\nQuestion 1. There are no explanations for Supplementary Figure S9K in the main text or the figure legend.\nQuestion 2. Why did the authors use ρ (rho) to indicate “phosphorylated” in the text?\nQuestion 3. In Figure 8A, there are no labels. It seems that the upper panels show GIT lines treated only with SP, while the bottom panels show the same cells treated with SP in combination with levofloxacin? If this interpretation is correct, please clarify it in both the figure and the legend.", "reviewer_3": "This manuscript presents a comprehensive study on how intracellular Stenotrophomonas (SP) targets the STING–IFNβ–macrophage axis to promote tumor progression and immunotherapy resistance in gastrointestinal tumors (GIT). The authors aim to provide mechanistic insights through a series of in vivo and in vitro experiments, ultimately establishing SP as a tumor-intrinsic immunomodulatory pathobiont. While the study is conceptually innovative, several key issues in the presented findings still require additional explanation or experimental validation.\n1. The authors report that STING agonist treatment in tumor cells induces significant IFN-β gene expression, and that the resulting conditioned medium affects macrophage MHC-II expression. However, direct evidence of intracellular IFN-β protein production—such as intracellular cytokine staining, ELISA, or IFNβ-promoter reporter assays—is still lacking. It is scientifically insufficient to designate tumor cells as the primary source of IFN-β based solely on mRNA expression data.\n2. The authors quantified the proportion of tumor cells harboring Stenotrophomonas DNA using FACS sorting and qPCR, and conclude that SP is exclusively present in tumor cells, not in immune cells. However, the evidence supporting this claim appears limited. A key question remains as to why Stenotrophomonas selectively infects tumor cells and not immune cells, and this has not yet been addressed with sufficient experimental support.\n3. Although the manuscript shows that tumor-conditioned medium leads to reduced MHC-II⁺ macrophages and decreased IFNγ production by CD8⁺ T cells, no direct functional experiments—such as MHC-II blockade, macrophage depletion and reconstitution, or T cell activation assays—appear to have been conducted to validate these causal relationships.\n4. While the authors propose that STING degradation occurs via the ERAD and proteasomal pathways, and present results showing STING restoration by proteasome inhibition, the distinction between this and lysosomal degradation remains unclear. The specific molecular mechanism underlying STING degradation thus requires further clarification."}, "author_response": "Response: The data used for microbiome discovery were publicly available, and therefore technical replicates or reagent blanks could not be added. We attempted to use the decontam tool to remove potential contaminant species, but this was not feasible due to the absence of DNA concentration data or negative controls required by decontam. To minimize potential contamination, we applied the assumption that “the abundance distribution of background and contaminant species differs from that of species enriched in biological samples.” The microbiome composition of common laboratory cell lines was considered to reflect background and contaminant species.\nTo account for variations in sequencing library size, we used “reads per million microbiome reads” (normalizing the reads of each species to the total microbiome reads) and “reads per million total reads” (normalizing the reads of each species to the total reads including both microbial and human-derived reads). By comparing the normalized read distributions of each species between cell lines and tumor samples, species showing similar distributions or higher abundance in cell lines were classified as contaminants.\nIn addition, we performed fluorescence in situ hybridization (FISH) using an SP-specific probe and observed variable SP abundance across clinical samples from patients, which further supports this conclusion. These methodological refinements and the corresponding data have been incorporated into the revised manuscript under the Methods section [line 944-961], and highlighted in green. The related data are temporarily publicly accessible at:\nhttps://dataview.ncbi.nlm.nih.gov/object/PRJNA1177527?reviewer=b01apsrifau5mkhci8tgqtvlrn.\n2.Premature conclusion on intracellular localization\nThe classification of SP as an “intracellular” bacterium seems to rely mainly on sequencing and FISH data in figure 1. Consider softening the claim of “intracellular” at this stage or adding a statement that further cellular validation is presented in the following sections.\nResponse: Thank you for highlighting this important suggestion. We added a statement for further cellular SP validation in the manuscript. This amendment is provided in Manuscript [line 195-215].\n3.Survival analysis methods not described\nThe authors mention conducting a survival analysis but do not clarify the method used (e.g., Kaplan-Meier, Cox regression), nor whether confounding clinical factors were controlled in figure 1. Please specify the survival analysis method used and clarify whether clinical covariates (e.g., age, sex, TNM stage) were adjusted for in the analysis.\nResponse: Kaplan-Meier with SP as single factor was used in the survival analysis. To get a better clarification, we performed additional CoxPH analysis to adjust the clinical covariates (age, sex, TNM or other factors if available) on both public cohort (Table 1) and our in-house cohort. The hazard ratio (HR [95%CI, P-value]) for public cohort and in-house cohort were 2.14 [0.85-5.2, 0.091] and 2.3 [1.20-4.3, 0.012] respectively (Forest 1 and Forest 2). These CoxPH results (Survival 1 and Survival 2) indicated that SP may be an independent prognosis factor in GIT, which was consistent with the KM survival analysis.\nTo estimate the effects of microbiomes or genes on prognosis, Kaplan-Meier survival analysis on single factor was performed via R package survival (version 3.2.7). During analysis, samples were grouped into high or low group based on the abundance of candidate microbiome or the expression level of candidate gene. The P value was calculated by the log-rank method in survival analysis.\n4.“To further confirm that SP is located whether tumor cells...” This is a grammatically incorrect expression. The phrase “located whether tumor cells” lacks proper prepositional structure and is not standard English.\nResponse: Sorry for our negligence and gratitude for your correction. This sentence will be corrected into “To further confirm whether SP is located in tumor cells...”, presented in manuscript [line 199].\n5.The authors define CD45⁻ cells as tumor cells; however, CD45 negativity alone is insufficient to definitively identify tumor cells. Please clarify whether additional markers (e.g., EpCAM, GFP, or tumor-specific labeling) were used to validate the identity of these cells.\nResponse: Thank you for your advice. EpCAM as a positive marker and CD45 as a negative marker were used to further confirm the identity of these cells. The results showed that CD45-EpCAM+ cells also showed more SP content in the SP group (Figure S4C). Related description is provided in manuscript [line 207-209].\n6.“Not detectable” by flow cytometry does not necessarily mean “absent”—cells might be present in low abundance or poorly stained.\nResponse: Thanks for your suggestion. We would like to describe it like “We found that CD45- cells could be detected in SP-infected tissues, whereas T cells, DCs, NK cells, B cells, and macrophages were hardly detectable” presenting in manuscript [line 199-205].\n7.“Tumor area with the high abundance of SP tended to exhibit increased hypoxic...”. \"hypoxic\" is an adjective and is used incorrectly in this sentence. It should likely be “hypoxia.”\nResponse: Thanks for correction, we are going to replace the wrong word into hypoxia. This correction is in revised manuscript [line 215].\n8.Since the model involves C57BL/6 mice with intact immune systems, it is important to discuss whether SP-induced tumor promotion is partly mediated by modulation of host immunity. Alternatively, did the authors test this model in immunodeficient mice as a control? While the authors demonstrate that SP-containing tumor cells promote accelerated tumor growth in vivo, it remains unclear whether this effect is solely due to intracellular bacterial influence on tumor cell-intrinsic pathways, or whether SP also modulates the host immune response.\nResponse: Thank you for this thoughtful comment. We examined tumor growth in NCG mice (Figure 2C–D) and observed no significant difference between the SP and Mock groups, suggesting that immune cells are required for SP to promote tumor growth. We also designed a macrophage depletion experiment and showed that SP did not promote tumor progression in the absence of macrophages. Consistently, in co-culture experiments with BMDMs and MC38 cells (Figure S9), SP increased the proposition of CD206⁺MHC-II- macrophages while the proposition of CD206-MHC-II⁺ macrophages, independent of transwell separation. These findings suggest that SP primarily modulates the tumor immune microenvironment through macrophages, in line with our observation that SP affects the IFNβ-related pathway and thereby regulates macrophage function.\nTaken together, our results support that its decisive impact on tumor growth is mediated through immune modulation.\nFurthermore, we found that SP could not reside and replicate in the immune cells (Figure S4). SP infection suppresses the expression of ISGs in tumor cells, while enhancing the expression of ISGs in immune cells, including DC, macrophages, and T cells (Figure S9I and J). These results indicated that SP promote the tumor progression via tumor cell-intrinsic pathways, but not directly modulate the host immune response. We have incorporated these results into the revised Discussion section to more clearly address this interplay.\n9.it is \"Figure 1N\" rather than \"Figures.\"\nResponse: Thanks for your correction! We have replaced all “Figures” with “Figure”.\n10.The decrease in MHC-II⁺ macrophages suggests altered phenotype, but does not directly demonstrate functional suppression. Have the authors performed functional assays (e.g., cytokine secretion, antigen presentation) to validate macrophage impairment? (Figure 2)\nResponse: Thank you for highlighting this important limitation. To evaluate macrophage function, we performed qPCR to determine whether the expression of macrophage related genes was altered. This analysis revealed that SP negatively impacted macrophage polarization. Simultaneously, we observed reduced IFNβ expression in the SP group. These results collectively suggest that SP infection leads to functional inhibition of macrophages (Figure 3G, 3I). Complementing the qPCR data, our flow cytometry results (Figure 3H ,3J) and single cell sequencing data (Figure 3A-E) also indicated adverse changes in macrophages under the influence of SP. Furthermore, IFNAR1-KO macrophages exhibited significantly inhibited T cell activation compared with WT macrophages (Figure 4K, 4L). These findings indicate that SP leads to T cell inhibition by affecting the interferon system within macrophages.\n11.The interpretation that SP suppresses tumor immunity via macrophage-mediated T cell inhibition seems inconsistent with the finding that tumor progression is still enhanced after T cell depletion. This may suggest that SP also acts via T cell–independent mechanisms. Could the authors clarify this discrepancy? (Figure 2)\nResponse: As you noted, T cell depletion attenuated but did not fully abolish the tumor-promoting trend of SP, indicating that T cells play the partial role in mediating pro-tumor effect by SP. Considering that macrophages exhibit both and anti-tumorigenic subtypes, our current research indicates that the presence of SP not only leads to inhibition of macrophages and T cells but also potentially drives macrophage polarization towards a pro-tumorigenic phenotype (Figure 4K-L, Figure S14). The shift from pro-tumorigenic into pro-tumorigenic phonotype in macrophages offers an explanation for this observed tumor progression even after T cell depletion. Furthermore, our T cell depletion strategy utilizing anti-CD4 and anti-CD8 antibodies, which means it can also consequently remove Tregs which represent an immunosuppressive subset within the CD4 T cell population. This depletion could lead to a degree of immune activation.\n12.The authors state that the proportions of macrophages, Treg cells, and CD8⁺ T cells were similar between the high and low SP groups based on multiplex immunofluorescence. However, this method may have limitations in quantifying immune cell subsets precisely, particularly in terms of cell density and spatial variability. Flow cytometry could provide a more quantitative and sensitive assessment of immune cell proportions. Have the authors considered validating these findings using flow cytometry, especially to confirm whether the frequencies of Tregs, CD8⁺ T cells, and macrophages are truly comparable between the two groups?\nResponse: We thank the reviewer for raising this valid methodological point. As our samples were derived from formalin-fixed paraffin-embedded (FFPE) tissue blocks, they were not amenable to flow cytometric analysis. In designing our study, we carefully considered this limitation and selected multiplex immunofluorescence (mIF) as the most appropriate alternative. This approach is well established in the field for robustly quantifying immune cell subsets and their spatial relationships in FFPE specimens. For instance, prior studies have successfully employed mIF to compare frequencies of Tregs, CD8⁺ T cells, and macrophages in clinical tumor samples, validating its reliability in contexts where fresh tissue is unavailable [1, 2]. While we acknowledge that mIF may have different sensitivity thresholds compared to flow cytometry, the consistency of our mIF-based observations with established immune profiling workflows supports the validity of our conclusion that the overall proportions of these immune subsets were comparable between the high- and low-SP groups.\n13.The authors conclude that intracellular SP primarily inhibits tumor progression by modulating macrophage function, which in turn suppresses T cell activity. However, this mechanistic link remains speculative, as no functional blockade or genetic depletion of MHC-II was performed to directly demonstrate the role of antigen presentation by macrophages in mediating this effect. To strengthen this conclusion, the authors could consider experiments that directly impair MHC-II function in macrophages (e.g., antibody blockade, siRNA knockdown, or use of MHC-II–deficient mice).\nResponse: We thank the reviewer for this insightful suggestion. To directly demonstrate the functional role of macrophage MHC-II in SP-mediated immune suppression, we performed additional in vivo experiments using anti-MHC-II blocking antibody. As shown in Figures 4G and 4H, when MHC-II was functionally blocked, the previously observed differences in both tumor progression and T cell function between control and SP-treated groups were completely abolished. These results provide direct experimental evidence that SP impairs T cell activity primarily through modulating MHC-II-mediated antigen presentation by macrophages, thereby strongly supporting our proposed mechanistic pathway.\n14.In the APC(Min/⁺) model, the authors administered SP by intragastric gavage and observed increased adenoma formation and reduced MHC-II⁺ macrophages. While this supports a tumor-promoting role for SP, it remains unclear whether the observed effects stem from direct intracellular colonization of tumor cells or from broader immune modulation triggered by gut exposure to SP.\nCould the authors clarify whether intragastric administration of SP alters systemic or local immune responses independently of tumor colonization? Were control experiments performed to evaluate gut barrier integrity, systemic inflammation, or innate immune activation following gavage? Distinguishing between direct intracellular effects and microbiota-mediated immune modulation is important for interpreting the mechanism.\nResponse: We will try to distinguish them in the revised discussion section [line 569-574], to clarify the relative contributions of systemic versus cell-intrinsic pathways. In the revised discussion section [line 569-574]: “In our results, intracellular SP promoted GIT progression primarily through direct intracellular colonization rather than systemic immune modulation. Consistent with this mechanism, SP infection enhanced adenoma formation and reduced the proportion of MHC-II⁺ macrophages in APC(Min/+) mice, indicating localized immune suppression driven by intracellular infection and blockade of IFN signaling”.\n15.While the authors used a transwell model to minimize direct SP-macrophage contact, it is not fully clear whether all co-culture experiments ensured the absence of extracellular SP. Please clarify whether antibiotic treatment or sterilized SP-conditioned medium was consistently used to eliminate the influence of extracellular bacteria on macrophages and DCs. (Figure 3)\nResponse: We thank the reviewer for raising this important methodological point. To eliminate the potential influence of extracellular bacteria on macrophages and DCs in our co-culture experiments, we employed a sterilized SP-conditioned medium approach (Figure S11, S14 and 4E in the revised manuscript). We found that sterilized SP-conditioned medium could mimic the effect of SP on the reduction of MHC-II expression on macrophage (Figure S11) and decreased IFNγ production by CD8⁺ T cells (Figure 4E). We consistently filtered the SP-conditioned medium through a 0.45 μm filter prior to all co-culture experiments. Given that the vast majority of intact bacteria are larger than 0.5 μm (Figure 1H), this method effectively removes extracellular SP. This approach ensured that the observed effects on macrophages were attributable to soluble factors derived from SP-infected tumor cells, rather than the direct SP-macrophage contact.\n16.The authors used human IFNa2b to rescue IFN signaling in mice. While IFNa2b is approved in clinical use, its cross-reactivity and bioactivity in murine immune cells (particularly macrophages) may differ from endogenous murine IFNβ. Please provide validation of IFNa2b efficacy in the murine system or cite literature demonstrating its functional equivalence. (Figure 4)\nResponse: Regarding the use of human IFNα2b in mice, this is indeed a pertinent consideration. Previous studies have frequently explored the use of human-derived IFN in murine models to enhance clinical translatability [3,4]. These precedents support its utility for translational studies. In our experiments, the observed rescue of CD8⁺ T cell cytotoxicity and MHC-II expression (Figure 4) further indicate the biological activity of human IFNα2b in our murine system. Those literatures were added in the revised manuscript [line 354].\n17.While reduced CD8⁺ T cell cytotoxicity is observed in the SP group and rescued by IFNa2b, this remains a correlative observation. Could the authors further dissect whether this is directly mediated by altered macrophage MHC-II expression, or possibly other immunosuppressive cytokines or checkpoints induced by SP? (Figure 4)\nResponse: We thank the reviewer for this insightful comment, which helps us further clarify the mechanistic pathway. Our data indicate that the impaired CD8⁺ T cell cytotoxicity in the SP group is primarily mediated through altered macrophage function rather than other SP-induced immunosuppressive pathways. This conclusion is supported by our macrophage depletion experiment (Figure 2G), which demonstrated that the tumor-promoting effects of SP were completely abolished in the absence of macrophages, establishing macrophages as indispensable mediators in this process. We also performed additional experiments to directly link macrophages MHC-II expression to CD8⁺ T cell function (Figures 4G-H). The results showed that when MHC-II was blocked, SP infection no longer affected tumor progression or T cell function, providing direct evidence that MHC-II expression is required for SP-mediated immunosuppression. To more directly link MHC-II expression on macrophages to CD8⁺ T cell function, we performed MHC-II blockade experiments (Figures 4G-H). The results showed that when MHC-II was inhibited, SP infection no longer affected tumor progression or T cell function, providing direct evidence that MHC-II expression is required for SP-mediated immunosuppression.\nFurther supporting this mechanism, our co-culture experiments comparing WT and IFNAR1-KO macrophages revealed that the disparity in CD8⁺ T cell function between SP and control groups was more pronounced in the presence of WT macrophages than with IFNAR1-KO macrophages (Figure S14), indicating that IFNAR signaling in macrophages is critical for SP's effect on T cells. While our study does not directly demonstrate the precise interaction between MHC-II⁺ macrophages and CD8⁺ T cells, this mechanism is well-supported in the literature [5]. These findings strongly support a critical role for macrophage MHC-II-dependent antigen presentation in sustaining CD8⁺ T cell function within the tumor microenvironment, consistent with our proposed mechanism.\n18.While the decrease in MHC-II⁺ macrophages is compelling, functional assays of antigen presentation (e.g., OT-II T cell proliferation, antigen-specific cytokine release) would provide direct evidence that antigen presentation is impaired. The authors are encouraged to consider these assays to reinforce their mechanistic claims. (Figure 4)\nResponse: We thank the reviewer for this insightful suggestion regarding the functional assessment of antigen presentation. To directly address this point, we conducted in vivo experiments using an MHC-II blockade model. As shown in Figures 4G and 4H, when MHC-II was inhibited, SP infection no longer exerted a significant effect on tumor progression, and no notable changes in T cell function were observed. This result indicates that the presence of MHC-II is essential for SP to modulate T cell activity within the tumor microenvironment. These findings, together with prior established knowledge that MHC-II expression on myeloid cells is critically involved in activating and sustaining CD8⁺ T cell cytotoxicity [8], provide strong functional evidence linking SP-induced reduction of MHC-II⁺ macrophages to impaired T cell function. Although direct antigen presentation assays such as OT-II T cell proliferation would further reinforce our conclusions, the combination of our experimental data and the established mechanistic framework support the model that SP impairs antigen presentation capacity, ultimately leading to diminished antitumor immunity.\n19.The immunofluorescence data suggesting colocalization of SP with the ER are compelling. However, please provide representative images and quantitative analysis (e.g., Pearson’s or Manders’ coefficient) to support this conclusion.\nResponse: We sincerely thank the reviewer for this insightful comment. In response, we performed quantitative colocalization analysis using seven randomly selected confocal fields. The average Pearson’s correlation coefficient between SP-GFP and ER signals was 0.0642. Although this value appears low and might be interpreted as a lack of correlation, we believe this result reflects the biological and methodological characteristics of our infection model rather than the absence of colocalization.\nUnlike conventional protein–protein colocalization assays (e.g., between protein A and protein B within the same cellular compartment), bacterial localization relative to an organelle such as the ER cannot be fully captured by global Pearson’s correlation analysis. First, the multiplicity of infection of SP was 0.1, resulting in a limited number of bacteria internalized into tumor cells. Second, extracellular SP-GFP bacteria that did not enter the cells or associate with the ER were removed during methanol fixation and washing steps. Consequently, the remaining fluorescent signals predominantly represent ER-associated bacteria, while the overall pixel-based correlation across the entire cell area remains inherently low. Therefore, although the representative confocal images clearly demonstrate spatial proximity between SP-GFP and the ER, the numerical Pearson’s coefficient may underestimate this relationship. We have now included representative high-resolution confocal images in the revised manuscript to better illustrate the colocalization pattern.\nIn future studies, we will further optimize our quantification approach to more accurately assess bacterial–organelle interactions.\n20.The absence of SP-mediated tumor promotion in STING-KO MC38 cells is an important observation. However, it would strengthen the conclusion if downstream markers (e.g., IFNβ secretion, MHC-II macrophage levels) were measured in these models to confirm the loss of SP function via STING pathway disruption.\nResponse: We thank the reviewer for this insightful suggestion. To further validate the role of the STING pathway in SP-mediated effects, we performed additional flow cytometry and ELISA analyses in the STING-KO MC38/C57 mouse model. As shown in Figures 5G–I, SP treatment in STING-KO mice did not significantly alter the antigen-presenting capacity o, including MHC-II expression on macrophages, nor did it affect downstream IFNβ secretion. These results indicate that the absence of STING abrogates the immunomodulatory effects of SP. Together, these data confirm that SP influences macrophage function and subsequent T cell activity primarily through the STING pathway, as detailed in the main text [lines 414–420]. The lack of phenotypic changes in STING-KO models upon SP challenge further supports the central role of STING in mediating SP-driven tumor progression.\n21.The ERAD pathway involves multiple components, including SEL1L, HRD1, and DERL1. Please clarify whether other ERAD-related genes are upregulated or physically interact with STING in SP-treated cells.\nResponse: We thank the reviewer for this insightful question regarding the involvement of other ERAD components. To address this, we analyzed our transcriptomics data and found that the expression levels of key ERAD-related genes, including SEL1L, HRD1, and DERL1, were not significantly altered upon SP infection (see updated figure below). This suggests that transcriptional regulation of these core ERAD factors is not the primary mechanism driving STING degradation. Instead, as demonstrated in our co‑immunoprecipitation experiments (Figure 6K), SP treatment enhances the interaction between STING and SEL1L, rather than changes in gene expression, underlies the ERAD‑mediated degradation of STING. These results are fully consistent with our proposed mechanistic model.\n22.While the authors observed that intracellular SP reduces the efficacy of both immunotherapy (anti-PD1) and chemotherapy (OXP), the rationale for including chemotherapy in this study is not clearly justified. Given that the primary mechanistic focus centers on immune suppression via the type I IFN-STING pathway, the extension to chemotherapy lacks mechanistic linkage and appears abrupt. Please clarify whether SP affects tumor-intrinsic chemotherapy resistance pathways or whether immune cell modulation contributes to the observed reduction in OXP efficacy.\nResponse: We thank the reviewer for raising this important point regarding the inclusion of chemotherapy in our study. Emerging evidence strongly supports a role for intact STING–type I interferon signaling in mediating the efficacy of certain chemotherapeutic agents. For example, Ma et al. demonstrated that suppression of the STING pathway via aryl hydrocarbon receptor (AhR) activation diminishes chemotherapy response in bladder cancer [6]. Similarly, Liu et al. reported that low STING expression predicts poorer outcomes in multiple myeloma patients treated with bortezomib [7]. In our study, we found that intracellular SP infection promotes STING degradation and attenuates type I interferon signaling. Building on the established role of STING in chemotherapy sensitization, we propose that SP-induced STING impairment may represent a shared mechanism contributing to both immunotherapy resistance and reduced chemotherapy efficacy. Therefore, the inclusion of oxaliplatin was intended to evaluate the broader therapeutic implications of SP-driven STING suppression, linking a single microbial mechanism to dual resistance phenotypes in cancer treatment. This perspective not only extends the clinical relevance of our findings but also suggests that targeting SP or restoring STING signaling could potentially enhance the efficacy of both immunotherapeutic and chemotherapeutic regimens.\nIn response to this point and to sharpen the focus of our finding, we have removed the data concerning SP's impact on oxaliplatin (OXP) monotherapy from the revised manuscript. Our revised analysis now concentrates on the effect of SP on the combined regimen of oxaliplatin and anti-PD-1. Since the efficacy of combined chemotherapy and immunotherapy often relies on the successful initiation of an anti-tumor immune response, the impairment of the STING pathway by SP provides a plausible mechanistic basis for the observed reduction in the efficacy of the \"oxaliplatin + anti-PD-1\" combination.\n23.As levofloxacin is a broad-spectrum antibiotic, potential effects on the gut microbiome and systemic immunity should be discussed, particularly if translated to human therapy. Are there SP-specific approaches or narrower-spectrum alternatives?\nResponse: We sincerely thank the reviewer for raising this critical point regarding the translational implications of our findings. We fully acknowledge that levofloxacin, as a broad-spectrum antibiotic, may exert unintended effects on the gut microbiome and systemic immunity, which represents a limitation of the current therapeutic strategy. It is important to note that SP is an intracellular, multidrug-resistant pathogen, for which no highly specific antimicrobial agents are currently available. Therefore, broad-spectrum antibiotics with potent intracellular activity, such as levofloxacin, remain a necessary clinical choice for managing SP infections. Looking forward, we are actively planning to develop more specific therapeutic agents to target SP. These approaches aim to achieve selective eradication of SP within the tumor microenvironment while minimizing off-target effects, thereby preserving the gut microbiome and overall immune homeostasis.\n24.The authors are encouraged to strengthen the discussion by comparing their findings with previous studies on other tumor-associated bacteria, such as Fusobacterium nucleatum. This would help contextualize the novelty of SP-mediated STING degradation as a mechanism of immune suppression in GIT tumors, and clarify how SP differs in its tumor-promoting strategies.\nResponse: Thank you for this valuable suggestion. This discussion point has been incorporated into the discussion section of the revised manuscript [line 615-621].\n25.The authors propose that levofloxacin can eliminate intracellular SP and restore anti-PD1 efficacy, which is promising. However, the discussion could be enhanced by addressing the clinical feasibility and limitations of this approach, such as the potential for antibiotic resistance, disruption of the gut microbiome, and patient stratification based on SP status.\nResponse: We appreciate the reviewer's constructive suggestions and agree that this aspect warrants clarification in the manuscript. The supplementary description regarding this point has been incorporated into the manuscript [line 640-644].\nReference\n[1] Federico, L. et al. Distinct tumor-infiltrating lymphocyte landscapes are associated with clinical outcomes in localized non-small cell lung cancer. Ann Oncol 33, 42–56 (2022).\n[2] de Rodas, M. L. et al. Objective analysis and clinical significance of the spatial tumor infiltrating lymphocyte patterns in non-small cell lung cancer. Clin Cancer Res 30, 998–1008 (2024).\n[3] Harper, M. S. et al. IFN-α treatment inhibits acute Friend retrovirus replication primarily through the antiviral effector molecule Apobec3. J Immunol 190, 1583–1590 (2013).\n[4] He, Q., Hao, T., She, D. & Lv, P. IFNα2b in combination with oncolytic adenovirus enhances antitumor activity against melanoma. Int Immunopharmacol 165, 115435 (2025).\n[5] Kilian, M. et al. MHC class II-restricted antigen presentation is required to prevent dysfunction of cytotoxic T cells by blood-borne myeloids in brain tumors. Cancer Cell 41, 235-251.e9 (2023).\n[6] Ma, Z. et al. AhR diminishes the efficacy of chemotherapy via suppressing STING dependent type-I interferon in bladder cancer. Nat Commun 14, 5415 (2023).\n[7] Liu, Y. et al. Decreased STING predicts adverse efficacy in bortezomib regimens and poor survival in multiple myeloma. Clin Exp Med 25, 37 (2025).\nReviewer #2 (Remarks to the Author):\nThe manuscript by Feng et al., describes the effect of Stenotrophomonas (SP) infection in promoting the progression of gastrointestinal tumor (GIT). SP specifically infects tumor cells and decreases STING expression by inducing ER stress, leading to the downregulation of the type I IFN pathway response in GIT. The decreased expression of IFNβ by tumor cells subsequently attenuates the presence of MHC-II+ macrophages and impairs effector CD8 T cell activity within the GIT microenvironment.\nThe research concept presented in this manuscript is interesting, well-designed, and generally well-written. The authors have clearly invested significant effort into developing and articulating their study. However, there remain some aspects that would benefit from additional supporting evidence or further clarification. Addressing these points would enhance the overall clarity and impact of the work. The following major and minor comments are intended to help improve the manuscript.\nMajor comments:\nQuestion 1. It is recommended to show the difference in SP levels between normal adjacent mucosa and tumor tissues in AEG and GC in Figure 1D.\nResponse: We thank the reviewer for this valuable suggestion. As recommended, we have added experiment to illustrate the difference in SP levels between normal adjacent mucosa and tumor tissues in both AEG and GC, as indicated by the arrows. The figure clearly shows that SP levels are significantly lower in normal mucosa.\nQuestion 2. Is there any association between SP levels and the clinicopathological characteristics of patients with AEG and GC? Additionally, could SP levels be an independent prognostic factor in GIT?\nResponse: To clarify the potential association, we calculated the correlation (continuous value) or difference on median (discrete value) between available clinical pathological characteristics by Pearson correlation test and Kruskal-Wallis rank sum test respectively on public cohorts and found no obvious associations (all P-values > 0.1, Table 1). We then performed the CoxPH analysis on SP adjusted by multiple variances (including age, sex, stage, et al.) to examine the prognostic role of SP in GIT (Forest 1). Despite the P-value (0.091) was > 0.05, the hazard ratio (HR [95% CI]) of 2.14 [0.89-5.2] indicted the prognostic role of SP on GIT. When using as a single factor, SP exhibited a HR value of 2.2 [1.1, 4.7] with P-value 0.033 (Survival 1). Besides, CoxPH analysis on our in-house cohort shown the similar results (multiple variances model: HR = 2.3 [1.20-4.3], P-value = 0.012. Single variance model: HR = 1.90 [1.03-3.54], P-value = 0.04) (Forest 2, Survival 2). Taken together, there is no obvious association between SP levels and the clinical pathological characteristics of patients. It may be a prognostic factor in GIT.\nQuestion 3. How did the authors select the five patients for fluorescent IHC in Figure 2A? It is recommended to increase the sample size to improve the robustness of the results. Additionally, could the authors please provide the detailed methodology used to assess the percentages of each immune cell type by fluorescent IHC, including the size and number of areas or spots analyzed?\nResponse: We appreciate the reviewer’s thoughtful comments regarding sample selection and methodology. The five patients selected for fluorescent IHC in Figure 2A were chosen based on a clear separation between low- and high-SP groups within our cohort. We acknowledge that the sample size is limited. However, similar sample sizes (five or fewer patients) have been successfully employed in previous high-quality mechanistic studies [1,2], supporting the feasibility of this approach for exploratory analysis. Importantly, the initial observations from fluorescent IHC were consistently validated in our subsequent functional experiments investigating the role of SP in modulating macrophage behavior (Figure 3F–J), thereby strengthening the robustness and biological relevance of our findings.\nThe slides were air-dried and subsequently imaged using the Pannoramic MIDI II whole-slide scanner (3DHISTECH). Image analysis was performed with Indica Halo software. The process is provided by Absin Biotech (Shanghai, China) as follows. For each patient, five to eight non-overlapping regions of interest (ROIs) encompassing both tumor and stromal compartments were selected. After spectral unmixing, single-cell segmentation was conducted based on DAPI nuclear staining. The proportion of each immune cell subset was calculated as the number of marker-positive cells divided by the total nucleated cells within each ROI, and the mean value per patient was used for subsequent statistical analysis. All procedures related to image acquisition and quantitative analysis were carried out by Absin Biotech (Shanghai, China) in a blinded manner, without knowledge of sample grouping or experimental objectives [lines 780–791].\nQuestion 4. Did the authors perform fluorescent IHC for M2-TAM markers, such as CD163 and CD206, in Figure 2?\nResponse: Thank you for this suggestion. As recommended, we have performed fluorescent immunohistochemistry to evaluate the M2-like tumor-associated macrophage marker CD206. The results revealed a significant increase in CD206⁺ macrophages in the SP-treated group compared to the PBS control group (Figure S7D). This finding further supports our conclusion that SP infection promotes M2-like polarization of macrophages within the tumor microenvironment.\nQuestion 5. In Figure S9J, the in vitro experiments show that SP infection suppresses the expression of ISGs in tumor cells, while enhancing the expression of ISGs in immune cells, including DC, macrophages, and T cells. The authors should briefly explain the rationale or underlying mechanisms for this opposite response in each cell type.\nResponse: We agree with the reviewer that this contrasting response is an important observation. The opposite ISG expression patterns in tumor cells versus immune cells upon SP infection can be explained by their distinct intrinsic immune capacities and the intracellular fate of SP. In immune cells (such as DCs, macrophages, and T cells), which possess an intact innate immune system, SP is efficiently recognized, thereby triggering a robust IFN signaling cascade and subsequent upregulation of interferon-stimulated genes as a defensive response (Figure S9J). This activation effectively limits SP survival within these cells.\nBy contrast, tumor cells often exhibit impaired IFN pathway activity and deficient antitumor immune surveillance [3]. These intrinsic deficiencies not only permit SP to reside intracellularly but also allow SP to further suppress host IFN responses, leading to the downregulation of ISGs. Our data confirm that SP infection significantly suppresses IFN-β expression in tumor cells at both transcriptional and protein levels (Figures 5A–D), corroborating the dampened ISG signature.\nThus, the divergent ISG responses reflect the fundamental difference in immune competence between professional immune cells and immunodeficient tumor cells, which determines both the intracellular survival of SP and the direction of interferon-stimulated transcriptional output.\nQuestion 6. Did the authors examine the expression of cGAS in GIT lines treated with or without SP in Figure 5E?\nResponse: We measured cGAS expression levels across different groups using Western Blotting (Figure S15B), and the results are shown in the figure. It can be observed that the addition of SP did not lead to a significant change in cGAS levels. This finding has been described in manuscript [line 408-409].\nQuestion 7. It would be preferable to test whether ER stress inducers, such as thapsigargin and tunicamycin, decrease the expression of STING in GIT lines as a positive control (Figure 6). Additionally, although CB-5083 is used, it remains unclear whether SP-induced ER stress is involved in the downregulation of STING expression in GIT lines, as there is no direct evidence. If possible, the authors should investigate whether ER stress attenuators, such as 4-PBA and TUDCA, or knockdown/knockout of ATF-6 or IER-1-XPB1 pathways can rescue the SP-induced degradation of STING in GIT lines.\nResponse: We agree that this series of experiments is necessary. By adding the ER stress attenuators 4-PBA and TUDCA, we found that this treatment successfully rescued the downstream responses induced by SP, primarily evidenced by the preservation of STING (Figure S16F). This experimental evidence provides additional support for our conclusion, and we mentioned these findings in manuscript [line 466-468]\nQuestion 8. It is recommended that the authors include a discussion on the mechanism by which SP specifically infects tumor cells but not other cell types within the GIT.\nResponse: We appreciate this excellent suggestion. We have already added this part of the content to the revised manuscript [line 562-579].\nMinor Comments:\nQuestion 1. There are no explanations for Supplementary Figure S9K in the main text or the figure legend.\nResponse: Thank you for pointing out this omission. Supplementary Figure S9K was intended to show the expression of IFNβ in SP-infected macrophages compared to mock-treated macrophages under the in vitro conditions described in Figure S4C. However, upon re-evaluation, we concluded that this dataset, while generated, does not constitute a central finding for the narrative of our manuscript. Therefore, to maintain focus and streamline the supplementary information, we have removed this panel from the revised manuscript. We appreciate your careful review.\nQuestion 2. Why did the authors use ρ (rho) to indicate “phosphorylated” in the text?\nResponse: Sorry for our carelessness. We will change ρ (rho) to p in the paper.\nQuestion 3. In Figure 8A, there are no labels. It seems that the upper panels show GIT lines treated only with SP, while the bottom panels show the same cells treated with SP in combination with levofloxacin? If this interpretation is correct, please clarify it in both the figure and the legend.\nResponse: Thank you for the thoughtful comments. The top part of each picture represents the control group that GIT lines treated only with SP, while the bottom part shows the experimental group that has been treated with levofloxacin (5μg/mL). And we will clarify it in the figure legend [line 1455-1460].\nReference\n[1] Dobersalske, C. et al. Cranioencephalic functional lymphoid units in glioblastoma. Nat Med 30, 2947–2956 (2024).\n[2] Stockem, C. F. et al. Final clinical analysis of pre-operative ipilimumab and nivolumab in locally advanced urothelial cancer and exploration of tumor-draining lymph node composition: The NABUCCO trial. Eur J Cancer 229, 115731 (2025).\n[3] Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397-404.e9 (2016).\nReviewer #3 (Remarks to the Author):\nThe manuscript presents a comprehensive study on how intracellular Stenotrophomonas (SP) targets the STING–IFNβ–macrophage axis to promote tumor progression and immunotherapy resistance in gastrointestinal tumors (GIT). The authors aim to provide mechanistic insights through a series of in vivo and in vitro experiments, ultimately establishing SP as a tumor-intrinsic immunomodulatory pathobiont. While the study is conceptually innovative, several key issues in the presented findings still require additional explanation or experimental validation.\n1.The authors report that STING agonist treatment in tumor cells induces significant IFN-β gene expression, and that the resulting conditioned medium affects macrophage MHC-II expression. However, direct evidence of intracellular IFN-β protein production—such as intracellular cytokine staining, ELISA, or IFNβ-promoter reporter assays—is still lacking. It is scientifically insufficient to designate tumor cells as the primary source of IFN-β based solely on mRNA expression data.\nResponse: We thank the reviewer for raising this important point. We have performed the experiments to directly evaluate IFN-β production at the protein level. Following the initial transcriptomic analysis, we provide Western blot and ELISA data, which consistently show that STING agonist treatment induces significant IFN-β protein expression in tumor cells (Figure 5A-E, Figure 6N). Together, these multi-level results from mRNA quantification to protein detection firmly establish that SP infection robustly suppresses IFN-β expression, thereby impairing the activation of downstream interferon-stimulated genes (Figure 5A-D).\n2. The authors quantified the proportion of tumor cells harboring Stenotrophomonas DNA using FACS sorting and qPCR, and conclude that SP is exclusively present in tumor cells, not in immune cells. However, the evidence supporting this claim appears limited. A key question remains as to why Stenotrophomonas selectively infects tumor cells and not immune cells, and this has not yet been addressed with sufficient experimental support.\nResponse: Thank you for this insightful comment. The preferential localization of SP in tumor cells over immune cells can be explained by fundamental differences in their intrinsic immune defenses. Substantial evidence indicates that immune cells possess an intact innate immune system, enabling them to rapidly mount a defensive response against bacterial invasion, thereby preventing SP from establishing an intracellular presence. Consistent with this, our data revealed that SP infection triggers significant upregulation of the IFN pathway in immune cells (Figure S9J), highlighting their capacity to detect and react to the bacteria. In contrast, tumor cells frequently exhibit deficiencies in critical immune signaling pathways, including IFN-mediated responses (Figure 5A-D), and often lack functional tumor suppressor mechanisms [1]. Such immunological impairments are known to facilitate intracellular survival for certain bacteria. Therefore, the robust innate immunity in immune cells coupled with the relative immunodeficiency of tumor cells, collectively explains why SP preferentially persists within tumor cells rather than immune cells.\n3. Although the manuscript shows that tumor-conditioned medium leads to reduced MHC-II⁺ macrophages and decreased IFNγ production by CD8⁺ T cells, no direct functional experiments—such as MHC-II blockade, macrophage depletion and reconstitution, or T cell activation assays—appear to have been conducted to validate these causal relationships.\nResponse: Thank you for raising this important point. To directly investigate the causal relationships, we performed the following functional experiments. First, macrophage depletion in C57BL/6 mice demonstrated that SP failed to promote tumor progression in the absence of macrophages (Figure 2G), establishing the necessity of macrophages in SP-mediated tumor growth. Furthermore, to specifically examine the role of MHC-II + macrophages, we conducted in vivo antibody-mediated blockade of MHC-II. The results showed that when MHC-II on macrophages was inhibited (Figure 4G, H), SP no longer exerted a significant effect on tumor progression, and no notable changes in T cell responses were observed. Together, these functional experiments provide direct evidence supporting the causal role of MHC-II + macrophages in SP-induced tumor progression and T cell modulation.\n4. While the authors propose that STING degradation occurs via the ERAD and proteasomal pathways, and present results showing STING restoration by proteasome inhibition, the distinction between this and lysosomal degradation remains unclear. The specific molecular mechanism underlying STING degradation thus requires further clarification.\nResponse: Thank you for this insightful comment. STING predominantly resides in the endoplasmic reticulum. Upon detection of cytosolic double-stranded DNA, cGAS produces cGAMP, which binds to and activates STING, leading to its conformational change and oligomerization. In the conventional pathway, activated STING is typically ubiquitinated and targeted for lysosomal degradation [3]. However, under certain pathological conditions, STING can be aberrantly processed via the ER-associated degradation machinery and subsequently degraded by the proteasome [lines 423–436]. In our study, several lines of evidence support the involvement of the proteasomal pathway in SP-induced STING degradation. However, lysosomal inhibition using chloroquine and bafilomycin A1 failed to restore STING protein levels following SP treatment (Figure 6J), indicating that the lysosomal route is not the primary mechanism. Taken together, these findings support the conclusion that SP-induced STING degradation occurs predominantly via the ERAD-proteasome pathway.\nReference\n[1] Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397-404.e9 (2016).\n[2] Kilian, M. et al. MHC class II-restricted antigen presentation is required to prevent dysfunction of cytotoxic T cells by blood-borne myeloids in brain tumors. Cancer Cell 41, 235-251.e9 (2023).\n[3] Kuchitsu, Y. et al. STING signalling is terminated through ESCRT-dependent microautophagy of vesicles originating from recycling endosomes. Nat Cell Biol 25, 453–466 (2023)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed my concerns. I have no further comments.", "reviewer_2": "I would like to thank the authors for the response, which addresses all my questions and concerns. The revised manuscript has been significantly improved. I find the manuscript acceptable from my perspective.", "reviewer_3": "The authors have diligently addressed the reviewers' comments, and additional experiments helped address many of the key concerns regarding the study's main conclusions. Nevertheless, the rationale for the approach to controlling potential microbial community contamination needs further clarification. Therefore, the authors should briefly note in the review that the inherent limitations of the decontamination strategy are due to the lack of negative controls and reagent blanks, and should more clearly state that the newly added FISH and tissue-level analyses are intended to cross-validate the sequencing-based results."}, "author_response": ""}]

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