Metabolome–Microbiome Reprogramming Under Circadian Disruption Accelerates Mammary Tumorigenesis

Metabolome–Microbiome Reprogramming Under Circadian Disruption Accelerates Mammary Tumorigenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolome–Microbiome Reprogramming Under Circadian Disruption Accelerates Mammary Tumorigenesis Mrinmoy Sarkar, Olajumoke Ogunlusi, Trenton Stewart, Efrat Muller, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8369606/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Circadian rhythm disruption (CRD), common in shift work and jet lag, is associated with aggressive breast cancer, yet the mechanisms linking circadian misalignment to tumor progression remain poorly understood. We investigated how CRD reshapes tumor immunometabolism and the intratumoral microbiome to promote immune evasion and metastasis. Methods Using a murine model of mammary tumorigenesis maintained under standard light–dark (LD) or CRD conditions, we performed untargeted metabolomics, 16S rRNA sequencing, flow cytometry, and integrative microbiome–metabolome modeling (MIMOSA2). Pharmacologic inhibition of arginase-1 (ARG1) was evaluated using nor-NOHA. Human breast cancer datasets and tumor samples were analyzed to assess clinical relevance. Results CRD significantly increased tumor burden and lung metastasis and induced extensive metabolic reprogramming within the tumor microenvironment. Immunosuppressive metabolites—including kynurenic acid, adenosine, xanthurenic acid, lactate, spermidine, and argininosuccinic acid—were markedly elevated, converging on the arginine–polyamine axis and driving ARG1 upregulation. ARG1 inhibition restored CD8⁺ T-cell infiltration, reduced immunosuppressive myeloid populations, and significantly decreased lung metastases. CRD also altered the intratumoral microbiome, enriching Firmicutes, Bacteroidetes, and Bacilli—taxa capable of producing or accumulating polyamines. Integrative analysis identified microbiome-driven regulation of key metabolites, including kynurenine and citrulline. Human tumor analyses confirmed associations between these microbial signatures, immune-modulatory pathways, and poor survival. Conclusions CRD promotes breast cancer progression through coordinated metabolic–microbial dysregulation that establishes an immunosuppressive tumor microenvironment. Targeting ARG1 and immunometabolic pathways may offer therapeutic opportunities for circadian disruption–associated breast cancer. circadian rhythm disruption tumor microenvironment arginase 1 host immune regulation tumor metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Circadian rhythms regulate fundamental biological processes, including metabolism, immunity, and microbial composition ( 1 ). Although much research has focused on the gut microbiota and its interaction with host circadian rhythms, the microbiome within the tumor microenvironment (TME) remains an unexplored but crucial factor in cancer initiation and progression ( 2 , 3 ). Accumulating evidence indicates that the intratumoral microbiome exerts metabolic and immunomodulatory effects that can promote tumorigenesis and cancer progression, primarily through the modulation of oncogenic signaling pathways, inflammatory processes, and interactions with the tumor microenvironment. ( 4 , 5 ). CRD, which is caused by factors such as shift work, jet lag, or experimental disruption of the light‒dark (LD) cycle, has been linked to increased cancer risk and tumor progression ( 6 ). In addition to these systemic effects, CRD may also disrupt gut microbiome rhythms, leading to dysbiosis with altered microbial composition and metabolite availability ( 7 ). Such disturbances can perturb the immune–metabolic balance, and accumulating evidence shows that immunometabolic dysfunction and oxidative stress promote tumor development ( 8 ). Tumors may exploit CRD-induced rewiring of polyamine biosynthesis as an adaptive metabolic strategy ( 9 ). Epidemiological ( 10 , 11 ) and experimental ( 12 , 13 ) studies have established a strong link between circadian rhythm disruption (CRD) and elevated breast cancer risk, with long-term night shift work significantly increasing incidence rates ( 14 ). These findings in animal models corroborate these findings, showing that chronic CRD accelerates mammary tumor development ( 14 ). Taken together, the circadian clock tightly coordinates metabolic pathways at the transcriptomic and metabolomic levels, and disruption of circadian genes leads to profound metabolic disturbances ( 15 ). Despite this clear association, the specific metabolic and microbial pathways through which CRD promotes tumorigenesis remain largely unknown. To bridge this knowledge gap, we aim to investigate the crosstalk between the metabolome and microbiome in tumors subjected to CRD or a regular LD cycle (12:12). These results suggest that CRD promotes an immunosuppressive mammary TME. Building on our previous finding ( 13 , 16 ) of arginase 1 (ARG1) upregulation alongside the immune inhibitor receptor LILRB4a under CRD conditions, in this study, we used a mouse model of breast cancer to explore the role of ARG1 in sustaining immune suppression within the TME. Targeted modulation of ARG1 alleviated the immunosuppressive state, restoring aspects of antitumor immunity. These findings emphasize the critical interaction between microbial metabolites and host immune regulation in CRD-driven tumor progression. By focusing on the microbiome of the TME rather than the gut microbiota, we aim to provide novel insights into the relationships among CRD, tumor metabolism, and local microbial influences. Our findings highlight new avenues for therapeutic intervention in CRD-driven breast malignancies. Methods Animal husbandry Nulliparous female BALB/cJ mice (aged 5 weeks) were purchased from Jackson Laboratory. The mice were housed under 12 h light and 12 h dark (LD 12:12) at room temperature (25°C), and food and water were provided ad libitum. BALB/cJ mice (Jax stock #000651) were used to create a syngeneic mouse model by injecting 4T1 cells into these mice. All animal care and treatments were performed per the Institutional Animal Care and Use Committee of Texas A&M University under protocol # 2024–0095. CRD conditions The mice (9–10 for each group) were randomly assigned to either standard light‒dark (LD) conditions (12:12 LD cycle), circadian rhythm disruption (CRD) or jet lag (JL) conditions, which involved an 8-hour advance in the light phase every two days, as previously described ( 13 ). All the animals had ad libitum access to standard chow and water. Zeitgeber time (ZT) 0 was defined as light onset, whereas ZT12 corresponded to dark onset. For activity monitoring, both the LD and CRD groups were provided with running wheels (Columbus Instruments) for voluntary exercise, with wheel rotation continuously recorded over several weeks. ActogramJ (ImageJ) was used to analyze voluntary running activity patterns ( 13 ). 4T1 cell TNBC mouse model : The mouse TNBC model was prepared with minor modifications to the standard protocol ( 17 ). 4T1 cell lines were obtained from the American Type Culture Collection (ATCC, CRL-2539™), cultured and maintained in RPMI-1640 (ATCC, 30-2001) with high glucose, L-glutamine, and HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer systems under a 5% CO2 atmosphere at 37°C. The cell culture medium was supplemented with 10% fetal bovine serum (FBS) (Sigma‒Aldrich, F2442) and 1% penicillin and streptomycin (Sigma‒Aldrich, P4333). For each mouse, 10,000 cells suspended in sterile PBS were injected subcutaneously into the 4th mammary gland fat pad. After the development of palpable tumors, these mice were housed under LD or CRD conditions for 3–4 weeks before they were sacrificed to harvest the tumors. Tumor burden The tumors and mammary glands were harvested via aseptic techniques. The volume of each breast tumor was measured via a caliper every 3 days throughout the experiment. Tumor volume = (length × width^2)/2 was used to measure the volume of the tumors, whereas the equation tumor burden (%) = (tumor weight/mouse body weight)×100% was used to determine the tumor burden ( 13 ). Image-based metabolomics Frozen tissue samples were carefully sectioned at a thickness of 12 µm via a Thermo CryoStar NX50 cryostat and placed onto indium tin oxide (ITO)-coated conductive microscope slides. To support both histological evaluation and molecular imaging, serial sections were collected—one designated for mass spectrometry imaging (MSI) and the other for hematoxylin and eosin (H&E) staining. Before MSI acquisition, fiducial markers were added to the corners of each slide to assist with accurate alignment between the optical and MSI data. High-resolution optical images were then captured at 4800 dpi via an Epson Perfection V600 flatbed scanner. For matrix application, the tissue sections were coated with a solution of 1,5-diaminonaphthalene (DAN) at 10 mg/mL in 50% acetonitrile via an HTX M5 robotic sprayer. The matrix was applied over four uniform passes via the following parameters: a flow rate of 0.10 mL/min, track speed of 1200 mm/min, track spacing of 3 mm, nozzle height of 40 mm, nozzle temperature of 60°C, nitrogen pressure of 10 psi, and a crisscross spray pattern. These settings were selected to ensure a fine, consistent coating with minimal analyte delocalization, optimized specifically for metabolite imaging in negative ion mode. MSI data were acquired via a Bruker timsTOF fleX QTOF mass spectrometer in negative ion mode at a spatial resolution of 40 µm, with 300 laser shots accumulated per pixel. The acquisition parameters included a m/z range of 50–1000, a funnel 1 RF of 75.0 Vpp, a funnel 2 RF of 100.0 Vpp, a multipole RF of 150.0 Vpp, a collision energy of 10.0 eV, a collision RF of 500.0 Vpp, a transfer time of 35.0 µs, and a prepulse storage of 2.0 µs. The acquired ion images were visualized and analyzed via SCiLS Lab Pro 2024b software (Bruker). All the data were root mean square (RMS) normalized, after which the peaks of interest were manually selected from the averaged spectra. Putative metabolite identifications were generated via the MetaboScape 2024b (Bruker) plugin integrated with SCiLS, which is based on high mass accuracy within a tolerance of ± 10 ppm. Metabolomics For metabolomics analysis, equal fractions of breast tumor samples from mice under 12:12 light–dark (LD) or circadian rhythm disruption (CRD) conditions were collected and flash-frozen in liquid nitrogen. The tissues were homogenized in 80% methanol with internal standards and sonicated at 4°C. After centrifugation, the supernatant was collected for derivatization. Sample analysis was carried out via MS-Omics as follows. The analysis was carried out via a Thermo Scientific Vanquish LC coupled to a Q Exactive™ HF Hybrid Quadrupole-Orbitrap, Thermo Fisher Scientific. An electrospray ionization interface was used as an ionization source. Analysis was performed in positive and negative ionization modes. UPLC was performed via a slightly modified version of the protocol described by Doneanu et al. (UPLC/MS Monitoring of Water-Soluble Vitamin Bs in Cell Culture Media in Minutes, Water Application note 2011, 720004042en). The peak areas were extracted via Compound Discoverer 3.3 (Thermo Scientific). The identification of compounds was performed at four levels: Level 1: identification by retention times (compared with in-house authentic standards), accurate mass (with an accepted deviation of 3 ppm), and MS/MS spectra; and Level 2a: identification by retention times (compared with in-house authentic standards), accurate mass (with an accepted deviation of 3 ppm). Level 2b: identification by accurate mass (with an accepted deviation of 3 ppm), and MS/MS spectra; Level 3: identification by accurate mass alone (with an accepted deviation of 3 ppm). High-throughput 16S rRNA gene amplicon sequencing and data analysis Tumor tissues were collected and snap-frozen until microbiome analysis. Microbial DNA was extracted via the ZymoBIOMICS™ DNA Miniprep Kit (Zymo Research, USA), and its quality was assessed via a Qubit™ fluorometer (Thermo Fisher, USA). The full-length 16S rRNA gene (27F/1492R primers) was amplified via Kapa HiFi HotStart DNA polymerase (Roche, Switzerland), purified with Select-a-Size DNA Clean & Concentrator™ (Zymo Research, USA), and sequenced on the PacBio Sequel IIe System with HiFi reads in circular consensus sequencing mode. The raw reads were processed with DADA2 ( 18 ) for error correction and ASV inference, with taxonomic classification assigned via Uclust (QIIME v1.9.1) and the Zymo Research curated 16S database. Microbial composition was analyzed via QIIME v1.9.1, including α diversity metrics (Shannon, InvSimpson, Ace, Chao1) and β diversity via principal coordinate analysis (PCoA), with ANOSIM used for the statistical assessment of group differences ( 19 ). Significant taxa were identified via LEfSe where applicable ( 20 ). The quality controls included the ZymoBIOMICS® Microbial Community Standard for DNA extraction, the ZymoBIOMICS® Microbial Community DNA Standard for library preparation, and negative controls to assess background contamination. Bacterial abundance and survival analysis in human breast tumors The Bacteria in Cancer (BIC) database developed by Chen et al. (2022) is used to evaluate the expression of intratumoral bacteria in human breast cancer. The relative abundance values of Firmicutes and Bacilli, as provided by the database, were extracted and compared across samples. The clinical relevance of these bacterial species was used to evaluate survival in patients via the database’s predefined cutoffs and statistical pipelines. The results were downloaded from the software for visualization and interpretation. Finally, bacteria-associated biological functions were identified via fast gene set enrichment analysis (FGSEA), which uses the associations between mRNA expression and bacterial abundance as the gene rank. Cytokine array This was performed via the Proteome Profiler Mouse XL Cytokine Array Kit (R&D Systems, ARY028). The blood samples were kept undisturbed for 30 minutes at room temperature before being spun at 2000 × g for 10 minutes at 4°C. The serum was separated for cytokine array profiling according to the manufacturer’s protocol and was scanned via a ChemiDoc imaging system (Bio-Rad, 12003153). Flow Cytometry Dissociated tumor cells were counted and resuspended in 1 mL of cell staining buffer (CSB) (BioLegend, 420201). A Zombie NIR™ Fixable Viability Kit (BioLegend, 423105) was applied on ice for 20 minutes to assess cell viability. After centrifugation at 400 × g for 4 minutes, the cells were incubated with fluorochrome-conjugated antibodies, including Alexa Fluor® 700 anti-mouse CD4, Pacific Blue™ anti-mouse CD8a, Alexa Fluor® 488 anti-mouse CD86, PE anti-mouse CD163, Alexa Fluor® 594 anti-mouse CD45, PE anti-mouse/human CD11b, and FITC anti-mouse Ly-6C/Ly-6G. The cells were then fixed with 2% paraformaldehyde and permeabilized with 0.1% Triton X-100 in PBS, followed by intracellular staining with Alexa Fluor® 488-conjugated anti-mouse/rat/human FOXP3 for 20 minutes at 4°C in the dark. After staining, the cells were rinsed with FACS buffer (1% FBS in PBS) and stored in FACS buffer until analysis on a Cytek Aurora spectral flow cytometer. MIMOSA 2 analysis MIMOSA2( 21 ) is a metabolic model-based framework for relating variation in microbiome composition to paired metabolite measurements [ https://doi.org/10.1093/bioinformatics/btac003 ]. Briefly, MIMOSA2 uses the KEGG database [ https://doi.org/10.1093/nar/27.1.29 ] to predict the metabolic potential of a community of microbes, linking genomes to genes for metabolic reactions. It then compares the community-wide metabolic potential to actual metabolite levels observed and infers whether each metabolite variation can be well explained by changes in bacterial composition. Here, metabolite and microbial profiles of tumor samples (LD and CRD-induced) were input into the MIMOSA2 web app ( http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ ), with the following settings: 16S rRNA sequence variants (ASVs) are mapped to KEGG via precomputed genome inference from the PICRUSt model. Metabolite names were mapped to their respective KEGG compound IDs via MetaboAnalyst [ https://doi.org/10.1093/nar/gkae253 ]. Statistical analysis All the statistical analyses were performed via GraphPad Prism (v9.0) and R (v4.2.2). Data normality was assessed via the Shapiro‒Wilk test, and variance homogeneity was tested via Levene’s test. For comparisons between two groups, unpaired two-tailed Student’s t tests were used for normally distributed data, whereas the Mann‒Whitney U test was applied for nonnormally distributed datasets. For multiple-group comparisons, one-way or two-way ANOVA was performed with Tukey’s post hoc test for pairwise comparisons. Results CRD accelerates aggressive tumorigenesis via metabolic rewiring To investigate the impact of CRD on the TME, we used a murine model maintained under either standard light–dark (LD) or CRD conditions (Fig. 1 A). Consistent with our recently published study ( 13 ), CRD profoundly altered tumor behavior, leading to increased tumor burden and enhanced metastasis (Fig. 1 B-D). Untargeted metabolomic profiling revealed broad reprogramming of tumor metabolism under CRD. Notably, kynurenic acid (KA) was markedly upregulated in CRD tumors compared with LD controls (Fig. 1 E). Lactate levels were also elevated under CRD (Fig. 1 E). CRD tumors presented reduced D-galactose and glucosamine levels (Fig. 1 E). To assess global metabolic remodeling, we employed pathway enrichment and correlation-based network analyses via MetaboAnalyst and KEGG. Several pathways, including histidine metabolism, arginine biosynthesis, and nicotinate/nicotinamide metabolism, were significantly enriched under CRD (Fig. 2 A, B). Principal component analysis confirmed the distinct separation between LD and CRD tumors (Fig. 2 C), and heatmap clustering of metabolites further revealed discrete profiles (Fig. 2 D). These analyses suggest that CRD drives systemic metabolic rewiring that promotes both tumor growth and immune escape. In addition to these immunoregulatory metabolites, spermidine, N8-acetyl-spermidine, and argininosuccinic acid were also significantly upregulated in CRD tumors (Fig. 2 E–G). Collectively, these findings indicate that CRD reshapes tumor metabolism to create an immunosuppressive landscape. Elevated metabolites such as KA, XA, adenosine, lactate, spermidine, and argininosuccinic acid converge through distinct but complementary pathways to inhibit antitumor immunity (Supplementary Table 1). Spermidine orchestrates the CRD-induced immunosuppressive tumor microenvironment Spermidine, a polyamine critical for cell growth and autophagy, exerts potent immunomodulatory effects by dampening T-cell responses and inhibiting proinflammatory pathways (Fig. 3 A) ( 22 ). Our earlier study( 13 ) and the current study demonstrated that CRD enhances LILRB4a signaling, which in turn promotes ARG1 expression (Fig. 3 B, C). This convergence of polyamine accumulation and ARG1 upregulation underscores how CRD-induced metabolic alterations promote immune suppression. Given the central role of ARG1 in depleting arginine and maintaining immune suppression, we next tested whether ARG1 inhibition could restore antitumor immunity. Tumor-bearing mice were treated with nor-NOHA, a specific ARG1 inhibitor ( 23 ), under both LD and CRD conditions. Nor-NOHA treatment significantly reduced the number of metastatic foci in the lungs of CRD mice (Fig. 3 D, Supp Fig. 1 A) and partially restored the immune balance. Flow cytometry (gating strategy, Supp Fig. 2 ) revealed increased infiltration of cytotoxic CD8⁺ T cells and decreased frequencies of regulatory T cells, myeloid-derived suppressor cells, and M2 macrophages (Fig. 3 E-I, Supp Fig. 1 B-F). Interestingly, nor-NOHA inhibited the expression of Arg1 in CRD-induced tumors but not in LDs (Fig. 3 J, Supp Fig. 1 G) but did not significantly alter the primary tumor volume (Supp Fig. 1 H). These findings suggest that ARG1 inhibition primarily modulates the metastatic niche and immune function. CRD Alters Tumor Microbiome Composition We next investigated whether CRD-associated immunometabolic alterations were linked to intratumoral microbiome changes. Using 16S rRNA sequencing, we compared the microbiomes of tumors from LD and CRD mice. Distinct differences were observed at the phylum level (Fig. 4 A), with CRD tumors showing greater relative abundances of Firmicutes and Bacteroidetes than LD controls did. Firmicutes abundance was significantly elevated (Fig. 4 B). At the class level, CRD tumors were enriched with Bacteroidia, Sphingobacteria, and Bacilli (Fig. 4 C). Among these genera, the number of Bacilli significantly increased under CRD conditions (Fig. 4 D). To evaluate clinical relevance, we analyzed human breast tumor data from the Bacteria in Cancer (BIC) database ( 24 ). Firmicutes were significantly more abundant in tumor tissues than in adjacent normal tissues (Supp Fig. 3 A), and high Firmicutes abundance was associated with reduced overall survival (Fig. 4 E). These trends also held for Bacilli (Supp Fig. 3 B, Fig. 4 F). Pathway analysis via KEGG, GO, and Reactome revealed that Firmicutes and Bacilli were involved in pathways related to the cell cycle, DNA repair, and DNA replication (Supp Fig. 4 ). Firmicutes exert immunomodulatory effects through small glycoconjugates that can increase the production of cytokines, including IL-34 and IL-1β. Consistent with these findings, CRD mice presented elevated serum IL-1β (Fig. 4 G). Crosstalk between the Microbiome and Metabolites in CRD-Induced Tumors Finally, we applied MIMOSA2 ( 21 ), a metabolic model-based framework (Fig. 5 A), to evaluate how changes in microbial communities are related to metabolic alterations within CRD-induced mammary tumors. This approach allowed us to integrate microbial abundance with estimated metabolic potential and compare the results to experimentally measured metabolite levels. Notably, MIMOSA2 identified citrulline (Fig. 5 B, C) and kynurenine (Fig. 5 B) as metabolites whose variation was mostly explained by microbial composition variations. These findings are consistent with our metabolomic and microbiome data, and they lend mechanistic support to the hypothesis that CRD-induced microbial alterations actively shape the metabolic environment of tumors. Together, the MIMOSA2 findings support a functional microbiome‒metabolite axis in CRD tumors, where bacterial taxa may contribute to the generation or regulation of immunomodulatory metabolites that alter the tumor microenvironment. Discussion CRD has far-reaching consequences for metabolism, immunology, and microbial balance, all of which can have a significant impact on cancer progression. Our study revealed that CRD enhances mammary tumor growth by altering both the metabolic landscape of tumors and the microorganisms that inhabit them, resulting in a tumor microenvironment that inhibits immune protection. Our combined findings demonstrated that CRD promotes the accumulation of various immunosuppressive metabolites, including KA, spermidine, adenosine, and argininosuccinic acid, while enriching bacterial groups such as Firmicutes and Bacilli, which are known to influence host immunity. We also identified arginase-1 (ARG1) as a critical metabolic circuit that links these alterations to decreased cytotoxic T-cell function. Importantly, inhibiting ARG1 partially restored antitumor immune responses and decreased metastatic dissemination. These findings reveal how CRD alters the metabolic‒microbial balance within tumors and suggest new treatment approaches for minimizing the negative effects of circadian disturbance in patients with breast cancer. The increase in KA is consistent with other studies reporting how KA stimulates immune evasion by reducing T-cell responses, hence fostering tumor development ( 25 ). These findings highlight KA as a key immunosuppressive metabolite in the CRD-altered mammary TME, underscoring its potential as a biomarker in CRD-driven mammary tumorigenesis. Other immunoregulatory metabolites were also elevated in CRD tumors. The level of adenosine, which acts through A2A and A2B receptors, is significantly increased, which is consistent with its established role in dampening cytotoxic T-cell activity, promoting regulatory T cells, and driving M2 macrophage polarization ( 26 ). Consistent with this immunosuppressive profile, xanthurenic acid (XA) acts via the aryl hydrocarbon receptor (AHR) to suppress dendritic cell maturation and T-cell priming ( 25 ). Lactate levels are also elevated in CRD tumors, and they are known to promote immune suppression by skewing macrophages toward the M2 phenotype ( 27 ). CRD tumors exhibit reduced D-galactose and glucosamine, which are essential for glycosylation, antigen presentation, and T-cell receptor stability. These findings suggest that CRD shifts the balance toward immunosuppressive metabolites while depleting those critical for effective immune surveillance ( 28 ). The level of spermidine, which has been reported to promote M2 macrophage polarization through mitochondrial ROS-mediated activation of AMPK and HIF-1α signaling ( 29 ), is elevated in CRD tumors. The subsequent increase in argininosuccinic acid (L-arginine precursor) suggests the dysregulation of arginine metabolism, a key axis in immune suppression. L-arginine is an essential amino acid for T-cell proliferation and function; in the TME, L-arginine is targeted for breakdown by ARG1 (Fig. 1 E) ( 22 ). Our study (Fig. 2 ) demonstrated that the Bacteroidetes, Firmicutes, and Bacilli levels shifted considerably between LD- and CRD-induced tumor tissues. Previous studies have shown that Bacteroidetes can synthesize spermidine through alternative pathways, whereas many Firmicutes, although unable to produce polyamines, maintain active polyamine transport systems (e.g., potABCD) that allow them to scavenge spermidine and putrescine ( 30 ). Within Bacilli, Bacillus subtilis encodes a speD-mediated S-adenosylmethionine decarboxylase, enabling the conversion of putrescine to spermidine. These findings suggest that CRD-induced microbial shifts favor taxa capable of producing or accumulating polyamines, directly linking microbiome alterations to elevated spermidine levels in CRD tumors ( 30 ). These results highlight how circadian rhythm disruption drives tumor progression through coordinated metabolic and microbial changes ( Fig. 6 ) . Elevated levels of immunosuppressive metabolites converge with microbial enrichment to establish an immunosuppressive TME. This synergy promotes immune evasion, metastasis, and poor prognosis. Importantly, ARG1 inhibition partially reversed these effects, demonstrating the therapeutic potential of targeting metabolic–microbial crosstalk in CRD-associated breast cancer. Future work should dissect the causal pathways linking microbial metabolites to host immune suppression, paving the way for novel interventions in circadian disruption-driven malignancies. Conclusion This study demonstrated that CRD reshaped the mammary tumor microenvironment through coordinated metabolic and microbial reprogramming that favors immune suppression and metastatic progression. CRD significantly elevates the levels of immunosuppressive metabolites—including kynurenic acid, adenosine, xanthurenic acid, lactate, spermidine, and argininosuccinic acid—each of which acts through distinct pathways to dampen antitumor immunity. These converging pathways establish a robust immunosuppressive metabolic axis under CRD. CRD also altered the intratumoral microbiome, enriching Firmicutes, Bacteroidetes, and Bacilli—taxa capable of producing or accumulating polyamines such as spermidine. Human tumor analyses validated their associations with poor survival and immune-modulatory pathways. The increase in IL-1β in CRD mice provides a mechanistic link, as Firmicutes-derived factors can stimulate systemic inflammatory signaling and enhance MDSC recruitment. Importantly, pharmacologic inhibition of ARG1 with nor-NOHA restored CD8⁺ T-cell infiltration and reduced lung metastases, highlighting ARG1 as a key mediator of CRD-driven immune escape. In conclusion, CRD promotes tumor aggressiveness through integrated metabolic–microbial dysregulation, and targeting this immunometabolic network represents a promising therapeutic strategy for circadian disruption–associated breast cancer. Abbreviations LD Light and Dark CRD Circadian Rhythm Disruption LILRB4 leukocyte immunoglobulin-like receptor 4 TNBC Triple-negative Breast Cancer TME Tumor microenvironment ARG1 Arginase 1 Declarations Ethics statement Nulliparous female BALB/cJ mice (aged 5 weeks) were purchased from Jackson Laboratory and stored in the animal facility of Texas A&M University. The in vivo studies were approved by the Ethical Committee of Texas A&M University. All methods were performed in accordance with relevant guidelines and regulations. Data availability All the data generated or analyzed during this study are included in the manuscript and supporting files. Acknowledgments This work was funded by the Texas A&M University Faculty Development Grant (to T.R.S.), PRISE grant (to T.R.S.), Strategic Transformative Research Funds (STRP to T.R.S.), and 1R01GM163238. This work was supported, in part, by a Pilot grant from NIEHS P30ES029067 (to T.R.S.). Mass spectrometry imaging was performed at the University of Texas at the Austin Mass Spectrometry Imaging Facility, supported by the Cancer Prevention and Research Institute of Texas Awards RP190671 and RP240559. We would like to thank Zymo Research for their assistance in analyzing the microbiome data and Creative Proteomics for their assistance in analyzing the metabolomics data. We would like to thank Dr. Gus Wright for helping with the flow cytometry experiments and analysis. Author’s Contributions M.S. and O.O. performed experiments, analyzed the data, and wrote the original draft. T.S. performed experiments and analyzed the data. E.M did data curation and formal analysis, Erin Seeley did data curation and Formal analysis, TRS conceptualized the project, supervised, wrote the original draft, and secured funding. Ethics Declarations: Animal experiments were performed according to the use of laboratory animals approved by the Animal Care and Use Committee of Texas A&M University under protocol #2024--0095. All methods were performed in accordance with relevant guidelines and regulations. Competing interests The authors declare no competing interests. References Zeng Y, Guo Z, Wu M, Chen F, Chen L. Circadian rhythm regulates the function of immune cells and participates in the development of tumors. Cell Death Discovery. 2024;10(1). 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DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. Noecker C, Eng A, Muller E, Borenstein E. MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data. Bioinformatics. 2022;38(6):1615–23. Geiger R, Rieckmann JC, Wolf T, Basso C, Feng Y, Fuhrer T, et al. L-Arginine Modulates T-Cell Metabolism and Enhances Survival and Anti-tumor Activity. Cell. 2016;167(3):829–e4213. Li X, Zhu F, He Y, Luo F. [Arginase inhibitor nor-NOHA induces apoptosis and inhibits invasion and migration of HepG2 cells]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2017;33(4):477–82. Chen K-P, Hsu C-L, Oyang Y-J, Huang H-C, Juan H-F. BIC: a database for the transcriptional landscape of bacteria in cancer. Nucleic Acids Res. 2022;51(D1):D1205–11. Basson C, Serem JC, Hlophe YN, Bipath P. The tryptophan-kynurenine pathway in immunomodulation and cancer metastasis. Cancer Med. 2023;12(18):18691–701. Allard B, Allard D, Buisseret L, Stagg J. The adenosine pathway in immuno-oncology. Nat Reviews Clin Oncol. 2020;17(10):611–29. Zhao P, Wang S, Jiang J, Gao Y, Wang Y, Zhao Y, et al. Targeting lactate metabolism and immune interaction in breast tumor via protease-triggered delivery. J Control Release. 2023;358:706–17. Reily C, Stewart TJ, Renfrow MB, Novak J. Glycosylation in health and disease. Nat Rev Nephrol. 2019;15(6):346–66. Liu R, Li X, Ma H, Yang Q, Shang Q, Song L, et al. Spermidine endows macrophages anti-inflammatory properties by inducing mitochondrial superoxide-dependent AMPK activation, Hif-1alpha upregulation and autophagy. Free Radic Biol Med. 2020;161:339–50. Hanfrey CC, Pearson BM, Hazeldine S, Lee J, Gaskin DJ, Woster PM, et al. Alternative spermidine biosynthetic route is critical for growth of Campylobacter jejuni and is the dominant polyamine pathway in human gut microbiota. J Biol Chem. 2011;286(50):43301–12. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationDecember2025.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 Apr, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor invited by journal 09 Feb, 2026 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8369606","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603427832,"identity":"9effaa50-2192-4b59-a336-212b40da6228","order_by":0,"name":"Mrinmoy Sarkar","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Mrinmoy","middleName":"","lastName":"Sarkar","suffix":""},{"id":603427835,"identity":"b463e27d-afcc-4001-933c-25077de26bac","order_by":1,"name":"Olajumoke Ogunlusi","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Olajumoke","middleName":"","lastName":"Ogunlusi","suffix":""},{"id":603427836,"identity":"e82970f6-5e67-44a0-b540-3938bfc58681","order_by":2,"name":"Trenton Stewart","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Trenton","middleName":"","lastName":"Stewart","suffix":""},{"id":603427843,"identity":"e866e1cd-a7c5-4229-a5a2-0da3941ec7a7","order_by":3,"name":"Efrat Muller","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Efrat","middleName":"","lastName":"Muller","suffix":""},{"id":603427844,"identity":"cdbd2908-9252-4627-826a-5caf56d7ea5b","order_by":4,"name":"Erin H. Seeley","email":"","orcid":"","institution":"MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Erin","middleName":"H.","lastName":"Seeley","suffix":""},{"id":603427848,"identity":"2911d8be-7615-4867-865c-80c7c2094e62","order_by":5,"name":"Tapasree Roy Sarkar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYJACxgYGBjkDZoSAAV7lPFAtxmAtB0jRkriBgVgt9vyHn32cUXMvfTs78wHmDzXbEhvYm7dJ4LVFIs145oZjxbk7m9kSGA4cu53YwHOsjIAWBmPGB2wJuRsO8xgwHGADapHIMcOvhf/4Z8YH/xLSDcBa/gG1yL8hoIUhx5hxY1tCAljLwTaQLTwEtNzIKWac2ZdgCPLLgbN9t43beNKKLfBpYe8/vpmx51uCvDn/4YMPKr7dlu1nP7zxBj4tKOAAiGAjWvkoGAWjYBSMApwAAH5DSgpqKsAIAAAAAElFTkSuQmCC","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Tapasree","middleName":"Roy","lastName":"Sarkar","suffix":""}],"badges":[],"createdAt":"2025-12-15 19:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8369606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8369606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104413909,"identity":"4e9c5644-f86c-4925-8ada-ba29d08dde36","added_by":"auto","created_at":"2026-03-11 13:05:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1833682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRD reprograms tumor metabolism. (A) \u003c/strong\u003eSchematic of the experimental design: BALB/cJ mice were injected with 4T1 cells and housed under either a standard LD 12:12 cycle or circadian rhythm disruption (CRD). \u003cstrong\u003e(B)\u003c/strong\u003e The tumor burden (%) was significantly greater in CRD mice than in LD control mice (n=9--10). \u003cstrong\u003e(C)\u003c/strong\u003eQuantification of the number of metastatic foci per lung revealed a markedly increased number of metastases in CRD mice (n = 5). \u003cstrong\u003e(D)\u003c/strong\u003e Representative histology of lung sections showing metastatic nodules. \u003cstrong\u003e(E)\u003c/strong\u003e Elevated KA, adenosine, XA, lactate, and reduced sugars. Data are presented as the mean ± SEM; *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 represent the significance level from an unpaired t test.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/d70158a898cbda47208517af.png"},{"id":104413702,"identity":"b2953abc-40ba-4e53-a358-870dec58a8aa","added_by":"auto","created_at":"2026-03-11 13:05:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":838302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRD drives polyamine metabolism. (A) \u003c/strong\u003eNetwork diagram of enrichment analysis demonstratingsignificant perturbations in metabolic pathways. \u003cstrong\u003e(B)\u003c/strong\u003e Dot plot summarizing pathway enrichment, highlighting histidine metabolism, β-alanine metabolism, arginine biosynthesis, and nicotinate/nicotinamide metabolism. \u003cstrong\u003e(C)\u003c/strong\u003ePrincipal component analysis (PCA) showing separation between the LD and CRD metabolomes. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmaps showing the upregulation of spermidine and argininosuccinic acid. \u003cstrong\u003e(E–G)\u003c/strong\u003e Box plots of selected metabolites showing increased spermidine, argininosuccinic acid, and N8-acetylspermidine in CRD tumors compared with LD controls, implicating dysregulated polyamine and arginine metabolism in shaping theimmunosuppressive tumor microenvironment (n=6).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/1efd4f511bce68bbc4c499e2.png"},{"id":104413844,"identity":"555a1540-011d-45ad-89ea-988bec0788b1","added_by":"auto","created_at":"2026-03-11 13:05:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":667348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of arginase 1 treatment via nor-NOHA in CRD-induced tumors. (A)\u003c/strong\u003eDiagrammatic representation of the molecular pathway of polyamine synthesis driven by arginase 1. Spermidine synthesis is influenced by the urea cycle, where argininosuccinate is broken down to arginine, which is hydrolyzed to ornithine via arginase 1. Ornithine is diverted to the polyamine pathway by ornithine decarboxylase 1 (ODC1) to putrescine, which is converted to spermidine. The link between the urea cycle and spermidine synthesis is implicated in tumor immunosuppression through the activation of Treg cells and MDSCs and the promotion of M1-to-M2 macrophage polarization. \u003cstrong\u003e(B)\u003c/strong\u003e The transcript level of \u003cem\u003eLilrb4\u003c/em\u003e in LD and CRD tumors was analyzed via real-time PCR. (n=4). \u003cstrong\u003e(C)\u003c/strong\u003eThe transcript level of \u003cem\u003eArg1\u003c/em\u003e in LD- and CRD-induced mammary tumors was analyzed via real-time PCR. (n=3). There was a reduction in the number of metastatic foci in the lungs of CRD mice treated with nor-NOHA compared with CRD (control) mice (n=5) \u003cstrong\u003e(D)\u003c/strong\u003e. Nor-NOHA-treated CRD mice presented an increase in the number of CD8-positive cells \u003cstrong\u003e(E)\u003c/strong\u003e and a reduction in the number of regulatory T cells \u003cstrong\u003e(F)\u003c/strong\u003e and MDSCs \u003cstrong\u003e(G)\u003c/strong\u003e. Compared with those in control CRD tumors, the number of proinflammatory macrophages (M1) is increased, whereas the number of anti-inflammatory macrophages (M2) is decreased in non-NOHA-treated CRD tumors (H). The M1/M2 ratio shown in \u003cstrong\u003eI\u003c/strong\u003e is high in CRD tumors treated with nor-NOHA, which explains the shift in the TME to an antitumor state. \u003cem\u003eArg1\u003c/em\u003e transcript levels were significantly reduced in CRD tumors following nor-NOHA treatment \u003cstrong\u003e(J).\u003c/strong\u003e Data are presented as the mean ± SEM; *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 represent the significance level from an unpaired t test.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/019f4d95df241e41d1bfa83c.png"},{"id":104413774,"identity":"840037a7-391c-4a19-9229-953ef561bfa2","added_by":"auto","created_at":"2026-03-11 13:05:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":442219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRD reshaped the tumor microbiome. (A)\u003c/strong\u003e Phylum-level microbiome profiles of tumor tissues from LD 12:12 and CRD mice, illustrating overall compositional differences across Bacteroidetes, Firmicutes, and Proteobacteria. \u003cstrong\u003e(B)\u003c/strong\u003e Firmicutes were significantly more abundant in CRD tumors than in LD 12:12 tumors, indicating a phylum-level microbial shift associated with circadian disruption (n=5). \u003cstrong\u003e(C)\u003c/strong\u003e Within the Firmicutes phylum, the class Bacilli was markedly enriched in CRD tumors relative to their LD counterparts, highlighting a CRD-associated shift toward taxa with known immunomodulatory properties. \u003cstrong\u003e(D)\u003c/strong\u003e Quantitative comparison confirmed that Bacilli abundance was significantly elevated under CRD conditions (n=5).\u003c/p\u003e\n\u003cp\u003eAnalysis of the Bacteria in Cancer (BIC) database revealed that patients with relatively high Firmicutes \u003cstrong\u003e(E)\u003c/strong\u003e and Bacilli \u003cstrong\u003e(F)\u003c/strong\u003e levels presented a reduced overall survival probability.\u003cstrong\u003e (G)\u003c/strong\u003e Serum IL-1β levels were significantly elevated in CRD mice compared with those in LD control mice, which isconsistent with Firmicutes- and Bacilli-driven induction of proinflammatory cytokines that contribute to immunosuppression within the tumor microenvironment. Data are presented as the mean ± SEM; *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 represent the significance level from an unpaired t test.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/0d38dcafb37a3e3a54a39d74.png"},{"id":104413638,"identity":"643df787-0b50-4c9c-bcfb-a1e05145c44f","added_by":"auto","created_at":"2026-03-11 13:04:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":476723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMIMOSA reveals crosstalk between the microbiome and metabolome profile. (A)\u003c/strong\u003e Summary of the MIMOSA2 analysis pipeline. \u003cstrong\u003e(B)\u003c/strong\u003e Summary of the MIMOSA2 results. For each metabolite (columns), the upper bars represent thetotal variance explained by the model, and the heatmap below representsthe fraction of variation explained by each specific taxon. \u003cstrong\u003e(C)\u003c/strong\u003e L-Citrulline is the metabolite best explained by microbial composition (p=0.006), as identified by MIMOSA2. Scatter plots showing the relationships between the microbial community-level metabolic potential scores (computed by MIMOSA2, x-axis) and actual metabolite measurements (y-axis). The model fit is drawn as a dashed line.”\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/9a7eb691ec76fbdb85a1fb76.png"},{"id":104413561,"identity":"aa4dcd2e-6607-4e69-b310-2420193f402e","added_by":"auto","created_at":"2026-03-11 13:04:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2719082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRD promotes crosstalk between the microbiome and the metabolome, driving tumor progression and immune suppression. \u003c/strong\u003eSchematic representation of how a disrupted circadian clock enhances tumor initiation and growth through microbiome-metabolome interactions. Circadian disruption induces microbial dysbiosis characterized by high abundance of Firmicutes and Bacilli, and alterations in the host metabolome. Bidirectional crosstalk between the dysregulated microbiota and metabolites results in elevated levels of kynurenic acid, xanthurenic acid, adenosine, lactic acid, and spermidine. These changes perturb key metabolic pathways, including tryptophan, purine, glucose, polyamine, and urea metabolism. Collectively, these metabolic alterations reshape the tumor immune microenvironment, promoting a shift from an immune-inflamed “hot” tumor (enriched in M1 macrophages, dendritic cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells) toward an immune-suppressed “cold” tumor phenotype dominated by M2 macrophages and FOXP3\u003csup\u003e+\u003c/sup\u003e regulatory T cells, thereby facilitating tumor progression.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/d8ce75499977a4ffa6b22180.png"},{"id":104835392,"identity":"3e7a0dd1-852a-4d0d-849a-3971f735c418","added_by":"auto","created_at":"2026-03-17 17:44:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8832629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/f0e7f0cf-ff65-44ad-8dc6-c4be574c85cc.pdf"},{"id":104413643,"identity":"40b586a0-c50a-4eb2-a235-6e2ec46f6fd7","added_by":"auto","created_at":"2026-03-11 13:05:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1753931,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationDecember2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8369606/v1/fbe8da7acd2e943eb84bc778.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolome–Microbiome Reprogramming Under Circadian Disruption Accelerates Mammary Tumorigenesis","fulltext":[{"header":"Background","content":"\u003cp\u003eCircadian rhythms regulate fundamental biological processes, including metabolism, immunity, and microbial composition (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although much research has focused on the gut microbiota and its interaction with host circadian rhythms, the microbiome within the tumor microenvironment (TME) remains an unexplored but crucial factor in cancer initiation and progression (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Accumulating evidence indicates that the intratumoral microbiome exerts metabolic and immunomodulatory effects that can promote tumorigenesis and cancer progression, primarily through the modulation of oncogenic signaling pathways, inflammatory processes, and interactions with the tumor microenvironment. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCRD, which is caused by factors such as shift work, jet lag, or experimental disruption of the light‒dark (LD) cycle, has been linked to increased cancer risk and tumor progression (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In addition to these systemic effects, CRD may also disrupt gut microbiome rhythms, leading to dysbiosis with altered microbial composition and metabolite availability (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Such disturbances can perturb the immune\u0026ndash;metabolic balance, and accumulating evidence shows that immunometabolic dysfunction and oxidative stress promote tumor development (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Tumors may exploit CRD-induced rewiring of polyamine biosynthesis as an adaptive metabolic strategy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEpidemiological (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and experimental (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) studies have established a strong link between circadian rhythm disruption (CRD) and elevated breast cancer risk, with long-term night shift work significantly increasing incidence rates (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These findings in animal models corroborate these findings, showing that chronic CRD accelerates mammary tumor development (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Taken together, the circadian clock tightly coordinates metabolic pathways at the transcriptomic and metabolomic levels, and disruption of circadian genes leads to profound metabolic disturbances (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this clear association, the specific metabolic and microbial pathways through which CRD promotes tumorigenesis remain largely unknown. To bridge this knowledge gap, we aim to investigate the crosstalk between the metabolome and microbiome in tumors subjected to CRD or a regular LD cycle (12:12). These results suggest that CRD promotes an immunosuppressive mammary TME. Building on our previous finding (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) of arginase 1 (ARG1) upregulation alongside the immune inhibitor receptor LILRB4a under CRD conditions, in this study, we used a mouse model of breast cancer to explore the role of ARG1 in sustaining immune suppression within the TME. Targeted modulation of ARG1 alleviated the immunosuppressive state, restoring aspects of antitumor immunity. These findings emphasize the critical interaction between microbial metabolites and host immune regulation in CRD-driven tumor progression. By focusing on the microbiome of the TME rather than the gut microbiota, we aim to provide novel insights into the relationships among CRD, tumor metabolism, and local microbial influences. Our findings highlight new avenues for therapeutic intervention in CRD-driven breast malignancies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal husbandry\u003c/h2\u003e \u003cp\u003eNulliparous female BALB/cJ mice (aged 5 weeks) were purchased from Jackson Laboratory. The mice were housed under 12 h light and 12 h dark (LD 12:12) at room temperature (25\u0026deg;C), and food and water were provided ad libitum. BALB/cJ mice (Jax stock #000651) were used to create a syngeneic mouse model by injecting 4T1 cells into these mice. All animal care and treatments were performed per the Institutional Animal Care and Use Committee of Texas A\u0026amp;M University under protocol # 2024\u0026ndash;0095.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCRD conditions\u003c/h3\u003e\n\u003cp\u003eThe mice (9\u0026ndash;10 for each group) were randomly assigned to either standard light‒dark (LD) conditions (12:12 LD cycle), circadian rhythm disruption (CRD) or jet lag (JL) conditions, which involved an 8-hour advance in the light phase every two days, as previously described (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). All the animals had ad libitum access to standard chow and water. Zeitgeber time (ZT) 0 was defined as light onset, whereas ZT12 corresponded to dark onset. For activity monitoring, both the LD and CRD groups were provided with running wheels (Columbus Instruments) for voluntary exercise, with wheel rotation continuously recorded over several weeks. ActogramJ (ImageJ) was used to analyze voluntary running activity patterns (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e4T1 cell TNBC mouse model\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe mouse TNBC model was prepared with minor modifications to the standard protocol (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). 4T1 cell lines were obtained from the American Type Culture Collection (ATCC, CRL-2539\u0026trade;), cultured and maintained in RPMI-1640 (ATCC, 30-2001) with high glucose, L-glutamine, and HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer systems under a 5% CO2 atmosphere at 37\u0026deg;C. The cell culture medium was supplemented with 10% fetal bovine serum (FBS) (Sigma‒Aldrich, F2442) and 1% penicillin and streptomycin (Sigma‒Aldrich, P4333). For each mouse, 10,000 cells suspended in sterile PBS were injected subcutaneously into the 4th mammary gland fat pad. After the development of palpable tumors, these mice were housed under LD or CRD conditions for 3\u0026ndash;4 weeks before they were sacrificed to harvest the tumors.\u003c/p\u003e\n\u003ch3\u003eTumor burden\u003c/h3\u003e\n\u003cp\u003eThe tumors and mammary glands were harvested via aseptic techniques. The volume of each breast tumor was measured via a caliper every 3 days throughout the experiment. Tumor volume = (length \u0026times; width^2)/2 was used to measure the volume of the tumors, whereas the equation tumor burden (%) = (tumor weight/mouse body weight)\u0026times;100% was used to determine the tumor burden (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eImage-based metabolomics\u003c/h3\u003e\n\u003cp\u003eFrozen tissue samples were carefully sectioned at a thickness of 12 \u0026micro;m via a Thermo CryoStar NX50 cryostat and placed onto indium tin oxide (ITO)-coated conductive microscope slides. To support both histological evaluation and molecular imaging, serial sections were collected\u0026mdash;one designated for mass spectrometry imaging (MSI) and the other for hematoxylin and eosin (H\u0026amp;E) staining. Before MSI acquisition, fiducial markers were added to the corners of each slide to assist with accurate alignment between the optical and MSI data. High-resolution optical images were then captured at 4800 dpi via an Epson Perfection V600 flatbed scanner. For matrix application, the tissue sections were coated with a solution of 1,5-diaminonaphthalene (DAN) at 10 mg/mL in 50% acetonitrile via an HTX M5 robotic sprayer. The matrix was applied over four uniform passes via the following parameters: a flow rate of 0.10 mL/min, track speed of 1200 mm/min, track spacing of 3 mm, nozzle height of 40 mm, nozzle temperature of 60\u0026deg;C, nitrogen pressure of 10 psi, and a crisscross spray pattern. These settings were selected to ensure a fine, consistent coating with minimal analyte delocalization, optimized specifically for metabolite imaging in negative ion mode. MSI data were acquired via a Bruker timsTOF fleX QTOF mass spectrometer in negative ion mode at a spatial resolution of 40 \u0026micro;m, with 300 laser shots accumulated per pixel. The acquisition parameters included a m/z range of 50\u0026ndash;1000, a funnel 1 RF of 75.0 Vpp, a funnel 2 RF of 100.0 Vpp, a multipole RF of 150.0 Vpp, a collision energy of 10.0 eV, a collision RF of 500.0 Vpp, a transfer time of 35.0 \u0026micro;s, and a prepulse storage of 2.0 \u0026micro;s. The acquired ion images were visualized and analyzed via SCiLS Lab Pro 2024b software (Bruker). All the data were root mean square (RMS) normalized, after which the peaks of interest were manually selected from the averaged spectra. Putative metabolite identifications were generated via the MetaboScape 2024b (Bruker) plugin integrated with SCiLS, which is based on high mass accuracy within a tolerance of \u0026plusmn;\u0026thinsp;10 ppm.\u003c/p\u003e\n\u003ch3\u003eMetabolomics\u003c/h3\u003e\n\u003cp\u003eFor metabolomics analysis, equal fractions of breast tumor samples from mice under 12:12 light\u0026ndash;dark (LD) or circadian rhythm disruption (CRD) conditions were collected and flash-frozen in liquid nitrogen. The tissues were homogenized in 80% methanol with internal standards and sonicated at 4\u0026deg;C. After centrifugation, the supernatant was collected for derivatization. Sample analysis was carried out via MS-Omics as follows. The analysis was carried out via a Thermo Scientific Vanquish LC coupled to a Q Exactive\u0026trade; HF Hybrid Quadrupole-Orbitrap, Thermo Fisher Scientific. An electrospray ionization interface was used as an ionization source. Analysis was performed in positive and negative ionization modes. UPLC was performed via a slightly modified version of the protocol described by Doneanu et al. (UPLC/MS Monitoring of Water-Soluble Vitamin Bs in Cell Culture Media in Minutes, Water Application note 2011, 720004042en). The peak areas were extracted via Compound Discoverer 3.3 (Thermo Scientific). The identification of compounds was performed at four levels: Level 1: identification by retention times (compared with in-house authentic standards), accurate mass (with an accepted deviation of 3 ppm), and MS/MS spectra; and Level 2a: identification by retention times (compared with in-house authentic standards), accurate mass (with an accepted deviation of 3 ppm). Level 2b: identification by accurate mass (with an accepted deviation of 3 ppm), and MS/MS spectra; Level 3: identification by accurate mass alone (with an accepted deviation of 3 ppm).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHigh-throughput 16S rRNA gene amplicon sequencing and data analysis\u003c/h2\u003e \u003cp\u003eTumor tissues were collected and snap-frozen until microbiome analysis. Microbial DNA was extracted via the ZymoBIOMICS\u0026trade; DNA Miniprep Kit (Zymo Research, USA), and its quality was assessed via a Qubit\u0026trade; fluorometer (Thermo Fisher, USA). The full-length 16S rRNA gene (27F/1492R primers) was amplified via Kapa HiFi HotStart DNA polymerase (Roche, Switzerland), purified with Select-a-Size DNA Clean \u0026amp; Concentrator\u0026trade; (Zymo Research, USA), and sequenced on the PacBio Sequel IIe System with HiFi reads in circular consensus sequencing mode. The raw reads were processed with DADA2 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) for error correction and ASV inference, with taxonomic classification assigned via Uclust (QIIME v1.9.1) and the Zymo Research curated 16S database. Microbial composition was analyzed via QIIME v1.9.1, including α diversity metrics (Shannon, InvSimpson, Ace, Chao1) and β diversity via principal coordinate analysis (PCoA), with ANOSIM used for the statistical assessment of group differences (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Significant taxa were identified via LEfSe where applicable (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The quality controls included the ZymoBIOMICS\u0026reg; Microbial Community Standard for DNA extraction, the ZymoBIOMICS\u0026reg; Microbial Community DNA Standard for library preparation, and negative controls to assess background contamination.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBacterial abundance and survival analysis in human breast tumors\u003c/h3\u003e\n\u003cp\u003eThe Bacteria in Cancer (BIC) database developed by Chen et al. (2022) is used to evaluate the expression of intratumoral bacteria in human breast cancer. The relative abundance values of Firmicutes and Bacilli, as provided by the database, were extracted and compared across samples. The clinical relevance of these bacterial species was used to evaluate survival in patients via the database\u0026rsquo;s predefined cutoffs and statistical pipelines. The results were downloaded from the software for visualization and interpretation. Finally, bacteria-associated biological functions were identified via fast gene set enrichment analysis (FGSEA), which uses the associations between mRNA expression and bacterial abundance as the gene rank.\u003c/p\u003e\n\u003ch3\u003eCytokine array\u003c/h3\u003e\n\u003cp\u003eThis was performed via the Proteome Profiler Mouse XL Cytokine Array Kit (R\u0026amp;D Systems, ARY028). The blood samples were kept undisturbed for 30 minutes at room temperature before being spun at 2000 \u0026times; g for 10 minutes at 4\u0026deg;C. The serum was separated for cytokine array profiling according to the manufacturer\u0026rsquo;s protocol and was scanned via a ChemiDoc imaging system (Bio-Rad, 12003153).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFlow Cytometry\u003c/h2\u003e \u003cp\u003eDissociated tumor cells were counted and resuspended in 1 mL of cell staining buffer (CSB) (BioLegend, 420201). A Zombie NIR\u0026trade; Fixable Viability Kit (BioLegend, 423105) was applied on ice for 20 minutes to assess cell viability. After centrifugation at 400 \u0026times; g for 4 minutes, the cells were incubated with fluorochrome-conjugated antibodies, including Alexa Fluor\u0026reg; 700 anti-mouse CD4, Pacific Blue\u0026trade; anti-mouse CD8a, Alexa Fluor\u0026reg; 488 anti-mouse CD86, PE anti-mouse CD163, Alexa Fluor\u0026reg; 594 anti-mouse CD45, PE anti-mouse/human CD11b, and FITC anti-mouse Ly-6C/Ly-6G. The cells were then fixed with 2% paraformaldehyde and permeabilized with 0.1% Triton X-100 in PBS, followed by intracellular staining with Alexa Fluor\u0026reg; 488-conjugated anti-mouse/rat/human FOXP3 for 20 minutes at 4\u0026deg;C in the dark. After staining, the cells were rinsed with FACS buffer (1% FBS in PBS) and stored in FACS buffer until analysis on a Cytek Aurora spectral flow cytometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eMIMOSA 2 analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eMIMOSA2(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) is a metabolic model-based framework for relating variation in microbiome composition to paired metabolite measurements [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btac003\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btac003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Briefly, MIMOSA2 uses the KEGG database [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/27.1.29\u003c/span\u003e\u003cspan address=\"10.1093/nar/27.1.29\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] to predict the metabolic potential of a community of microbes, linking genomes to genes for metabolic reactions. It then compares the community-wide metabolic potential to actual metabolite levels observed and infers whether each metabolite variation can be well explained by changes in bacterial composition. Here, metabolite and microbial profiles of tumor samples (LD and CRD-induced) were input into the MIMOSA2 web app (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/\u003c/span\u003e\u003cspan address=\"http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the following settings: 16S rRNA sequence variants (ASVs) are mapped to KEGG via precomputed genome inference from the PICRUSt model. Metabolite names were mapped to their respective KEGG compound IDs via MetaboAnalyst [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae253\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed via GraphPad Prism (v9.0) and R (v4.2.2). Data normality was assessed via the Shapiro‒Wilk test, and variance homogeneity was tested via Levene\u0026rsquo;s test. For comparisons between two groups, unpaired two-tailed Student\u0026rsquo;s t tests were used for normally distributed data, whereas the Mann‒Whitney U test was applied for nonnormally distributed datasets. For multiple-group comparisons, one-way or two-way ANOVA was performed with Tukey\u0026rsquo;s post hoc test for pairwise comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCRD accelerates aggressive tumorigenesis via metabolic rewiring\u003c/h2\u003e \u003cp\u003eTo investigate the impact of CRD on the TME, we used a murine model maintained under either standard light\u0026ndash;dark (LD) or CRD conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Consistent with our recently published study (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), CRD profoundly altered tumor behavior, leading to increased tumor burden and enhanced metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D). Untargeted metabolomic profiling revealed broad reprogramming of tumor metabolism under CRD. Notably, kynurenic acid (KA) was markedly upregulated in CRD tumors compared with LD controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Lactate levels were also elevated under CRD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). CRD tumors presented reduced D-galactose and glucosamine levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess global metabolic remodeling, we employed pathway enrichment and correlation-based network analyses via MetaboAnalyst and KEGG. Several pathways, including histidine metabolism, arginine biosynthesis, and nicotinate/nicotinamide metabolism, were significantly enriched under CRD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Principal component analysis confirmed the distinct separation between LD and CRD tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and heatmap clustering of metabolites further revealed discrete profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These analyses suggest that CRD drives systemic metabolic rewiring that promotes both tumor growth and immune escape. In addition to these immunoregulatory metabolites, spermidine, N8-acetyl-spermidine, and argininosuccinic acid were also significantly upregulated in CRD tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u0026ndash;G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCollectively, these findings indicate that CRD reshapes tumor metabolism to create an immunosuppressive landscape. Elevated metabolites such as KA, XA, adenosine, lactate, spermidine, and argininosuccinic acid converge through distinct but complementary pathways to inhibit antitumor immunity (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSpermidine orchestrates the CRD-induced immunosuppressive tumor microenvironment\u003c/h2\u003e \u003cp\u003eSpermidine, a polyamine critical for cell growth and autophagy, exerts potent immunomodulatory effects by dampening T-cell responses and inhibiting proinflammatory pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Our earlier study(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and the current study demonstrated that CRD enhances LILRB4a signaling, which in turn promotes ARG1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). This convergence of polyamine accumulation and ARG1 upregulation underscores how CRD-induced metabolic alterations promote immune suppression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the central role of ARG1 in depleting arginine and maintaining immune suppression, we next tested whether ARG1 inhibition could restore antitumor immunity. Tumor-bearing mice were treated with nor-NOHA, a specific ARG1 inhibitor (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), under both LD and CRD conditions. Nor-NOHA treatment significantly reduced the number of metastatic foci in the lungs of CRD mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and partially restored the immune balance. Flow cytometry (gating strategy, Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed increased infiltration of cytotoxic CD8⁺ T cells and decreased frequencies of regulatory T cells, myeloid-derived suppressor cells, and M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-I, Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-F). Interestingly, nor-NOHA inhibited the expression of \u003cem\u003eArg1\u003c/em\u003e in CRD-induced tumors but not in LDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ, Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) but did not significantly alter the primary tumor volume (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). These findings suggest that ARG1 inhibition primarily modulates the metastatic niche and immune function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCRD Alters Tumor Microbiome Composition\u003c/h2\u003e \u003cp\u003eWe next investigated whether CRD-associated immunometabolic alterations were linked to intratumoral microbiome changes. Using 16S rRNA sequencing, we compared the microbiomes of tumors from LD and CRD mice. Distinct differences were observed at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), with CRD tumors showing greater relative abundances of Firmicutes and Bacteroidetes than LD controls did. Firmicutes abundance was significantly elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). At the class level, CRD tumors were enriched with Bacteroidia, Sphingobacteria, and Bacilli (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Among these genera, the number of Bacilli significantly increased under CRD conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate clinical relevance, we analyzed human breast tumor data from the Bacteria in Cancer (BIC) database (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Firmicutes were significantly more abundant in tumor tissues than in adjacent normal tissues (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and high Firmicutes abundance was associated with reduced overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). These trends also held for Bacilli (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Pathway analysis via KEGG, GO, and Reactome revealed that Firmicutes and Bacilli were involved in pathways related to the cell cycle, DNA repair, and DNA replication (Supp Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Firmicutes exert immunomodulatory effects through small glycoconjugates that can increase the production of cytokines, including IL-34 and IL-1β. Consistent with these findings, CRD mice presented elevated serum IL-1β (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCrosstalk between the Microbiome and Metabolites in CRD-Induced Tumors\u003c/h2\u003e \u003cp\u003eFinally, we applied MIMOSA2 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), a metabolic model-based framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), to evaluate how changes in microbial communities are related to metabolic alterations within CRD-induced mammary tumors. This approach allowed us to integrate microbial abundance with estimated metabolic potential and compare the results to experimentally measured metabolite levels. Notably, MIMOSA2 identified citrulline (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, C) and kynurenine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) as metabolites whose variation was mostly explained by microbial composition variations. These findings are consistent with our metabolomic and microbiome data, and they lend mechanistic support to the hypothesis that CRD-induced microbial alterations actively shape the metabolic environment of tumors. Together, the MIMOSA2 findings support a functional microbiome‒metabolite axis in CRD tumors, where bacterial taxa may contribute to the generation or regulation of immunomodulatory metabolites that alter the tumor microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRD has far-reaching consequences for metabolism, immunology, and microbial balance, all of which can have a significant impact on cancer progression. Our study revealed that CRD enhances mammary tumor growth by altering both the metabolic landscape of tumors and the microorganisms that inhabit them, resulting in a tumor microenvironment that inhibits immune protection. Our combined findings demonstrated that CRD promotes the accumulation of various immunosuppressive metabolites, including KA, spermidine, adenosine, and argininosuccinic acid, while enriching bacterial groups such as Firmicutes and Bacilli, which are known to influence host immunity. We also identified arginase-1 (ARG1) as a critical metabolic circuit that links these alterations to decreased cytotoxic T-cell function. Importantly, inhibiting ARG1 partially restored antitumor immune responses and decreased metastatic dissemination. These findings reveal how CRD alters the metabolic‒microbial balance within tumors and suggest new treatment approaches for minimizing the negative effects of circadian disturbance in patients with breast cancer.\u003c/p\u003e \u003cp\u003eThe increase in KA is consistent with other studies reporting how KA stimulates immune evasion by reducing T-cell responses, hence fostering tumor development (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings highlight KA as a key immunosuppressive metabolite in the CRD-altered mammary TME, underscoring its potential as a biomarker in CRD-driven mammary tumorigenesis. Other immunoregulatory metabolites were also elevated in CRD tumors. The level of adenosine, which acts through A2A and A2B receptors, is significantly increased, which is consistent with its established role in dampening cytotoxic T-cell activity, promoting regulatory T cells, and driving M2 macrophage polarization (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Consistent with this immunosuppressive profile, xanthurenic acid (XA) acts via the aryl hydrocarbon receptor (AHR) to suppress dendritic cell maturation and T-cell priming (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Lactate levels are also elevated in CRD tumors, and they are known to promote immune suppression by skewing macrophages toward the M2 phenotype (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). CRD tumors exhibit reduced D-galactose and glucosamine, which are essential for glycosylation, antigen presentation, and T-cell receptor stability. These findings suggest that CRD shifts the balance toward immunosuppressive metabolites while depleting those critical for effective immune surveillance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe level of spermidine, which has been reported to promote M2 macrophage polarization through mitochondrial ROS-mediated activation of AMPK and HIF-1α signaling (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), is elevated in CRD tumors. The subsequent increase in argininosuccinic acid (L-arginine precursor) suggests the dysregulation of arginine metabolism, a key axis in immune suppression. L-arginine is an essential amino acid for T-cell proliferation and function; in the TME, L-arginine is targeted for breakdown by ARG1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated that the Bacteroidetes, Firmicutes, and Bacilli levels shifted considerably between LD- and CRD-induced tumor tissues. Previous studies have shown that Bacteroidetes can synthesize spermidine through alternative pathways, whereas many Firmicutes, although unable to produce polyamines, maintain active polyamine transport systems (e.g., potABCD) that allow them to scavenge spermidine and putrescine (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Within Bacilli, \u003cem\u003eBacillus subtilis\u003c/em\u003e encodes a speD-mediated S-adenosylmethionine decarboxylase, enabling the conversion of putrescine to spermidine. These findings suggest that CRD-induced microbial shifts favor taxa capable of producing or accumulating polyamines, directly linking microbiome alterations to elevated spermidine levels in CRD tumors (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results highlight how circadian rhythm disruption drives tumor progression through coordinated metabolic and microbial changes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Elevated levels of immunosuppressive metabolites converge with microbial enrichment to establish an immunosuppressive TME. This synergy promotes immune evasion, metastasis, and poor prognosis. Importantly, ARG1 inhibition partially reversed these effects, demonstrating the therapeutic potential of targeting metabolic\u0026ndash;microbial crosstalk in CRD-associated breast cancer. Future work should dissect the causal pathways linking microbial metabolites to host immune suppression, paving the way for novel interventions in circadian disruption-driven malignancies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThis study demonstrated that CRD reshaped the mammary tumor microenvironment through coordinated metabolic and microbial reprogramming that favors immune suppression and metastatic progression. CRD significantly elevates the levels of immunosuppressive metabolites\u0026mdash;including kynurenic acid, adenosine, xanthurenic acid, lactate, spermidine, and argininosuccinic acid\u0026mdash;each of which acts through distinct pathways to dampen antitumor immunity. These converging pathways establish a robust immunosuppressive metabolic axis under CRD. CRD also altered the intratumoral microbiome, enriching Firmicutes, Bacteroidetes, and Bacilli\u0026mdash;taxa capable of producing or accumulating polyamines such as spermidine. Human tumor analyses validated their associations with poor survival and immune-modulatory pathways. The increase in IL-1β in CRD mice provides a mechanistic link, as Firmicutes-derived factors can stimulate systemic inflammatory signaling and enhance MDSC recruitment. Importantly, pharmacologic inhibition of ARG1 with nor-NOHA restored CD8⁺ T-cell infiltration and reduced lung metastases, highlighting ARG1 as a key mediator of CRD-driven immune escape. In conclusion, CRD promotes tumor aggressiveness through integrated metabolic\u0026ndash;microbial dysregulation, and targeting this immunometabolic network represents a promising therapeutic strategy for circadian disruption\u0026ndash;associated breast cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Light and Dark\u003c/p\u003e\n\u003cp\u003eCRD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Circadian Rhythm Disruption\u003c/p\u003e\n\u003cp\u003eLILRB4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;leukocyte immunoglobulin-like receptor 4\u003c/p\u003e\n\u003cp\u003eTNBC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Triple-negative Breast Cancer\u003c/p\u003e\n\u003cp\u003eTME\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eARG1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Arginase 1\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003cstrong\u003e statement\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNulliparous female BALB/cJ mice (aged 5 weeks) were purchased from Jackson Laboratory and stored in the animal facility of Texas A\u0026amp;M University. \u003cem\u003eThe in\u003c/em\u003e\u003cem\u003e vivo\u003c/em\u003e studies were approved by the Ethical Committee of Texas A\u0026amp;M University. All methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\n\u003cp\u003eAll the data generated or analyzed during this study are included in the manuscript and supporting files.\u003c/p\u003e\n\n\u003ch3\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis work was funded by the Texas A\u0026amp;M University Faculty Development Grant (to T.R.S.), PRISE grant (to T.R.S.), Strategic Transformative Research Funds (STRP to T.R.S.), and 1R01GM163238. This work was supported, in part, by a Pilot grant from NIEHS P30ES029067 (to T.R.S.). Mass spectrometry imaging was performed at the University of Texas at the Austin Mass Spectrometry Imaging Facility, supported by the Cancer Prevention and Research Institute of Texas Awards RP190671 and RP240559. We would like to thank \u003cstrong\u003eZymo Research\u003c/strong\u003e for their assistance in analyzing the microbiome data and Creative Proteomics for their assistance in analyzing the metabolomics data. We would like to thank Dr. Gus Wright for helping with the flow cytometry experiments and analysis.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003ch5\u003eM.S. and O.O. performed experiments, analyzed the data, and wrote the original draft. T.S. performed experiments and analyzed the data. E.M did data curation and formal analysis, Erin Seeley did data curation and Formal analysis, TRS conceptualized the project, supervised, wrote the original draft, and secured funding. \u003c/h5\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics Declarations: \u003c/strong\u003eAnimal experiments were performed according to the use of laboratory animals approved by the Animal Care and Use Committee of Texas A\u0026amp;M University under protocol #2024--0095. All methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZeng Y, Guo Z, Wu M, Chen F, Chen L. Circadian rhythm regulates the function of immune cells and participates in the development of tumors. 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LILRB4 regulates circadian disruption-induced mammary tumorigenesis via noncanonical WNT signaling pathway. Oncogene. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen J, Pedersen JE. Night shift work and breast cancer risk \u0026ndash; 2023 update of epidemiologic evidence. J Natl Cancer Cent. 2025;5(1):94\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckel-Mahan KL, Patel VR, Mohney RP, Vignola KS, Baldi P, Sassone-Corsi P. Coordination of the transcriptome and metabolome by the circadian clock. Proc Natl Acad Sci U S A. 2012;109(14):5541\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgunlusi O, Sarkar M, Chakrabarti A, Boland DJ, Nguyen T, Sampson J et al. Disruption of Circadian Clock Induces Abnormal Mammary Morphology and Aggressive Basal Tumorigenesis by Enhancing LILRB4 Signaling. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Zhang JJ, Huang X-Y. 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Nat Rev Nephrol. 2019;15(6):346\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Li X, Ma H, Yang Q, Shang Q, Song L, et al. Spermidine endows macrophages anti-inflammatory properties by inducing mitochondrial superoxide-dependent AMPK activation, Hif-1alpha upregulation and autophagy. Free Radic Biol Med. 2020;161:339\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanfrey CC, Pearson BM, Hazeldine S, Lee J, Gaskin DJ, Woster PM, et al. Alternative spermidine biosynthetic route is critical for growth of \u003cem\u003eCampylobacter jejuni\u003c/em\u003e and is the dominant polyamine pathway in human gut microbiota. J Biol Chem. 2011;286(50):43301\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"circadian rhythm disruption, tumor microenvironment, arginase 1, host immune regulation, tumor metabolism","lastPublishedDoi":"10.21203/rs.3.rs-8369606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8369606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCircadian rhythm disruption (CRD), common in shift work and jet lag, is associated with aggressive breast cancer, yet the mechanisms linking circadian misalignment to tumor progression remain poorly understood. We investigated how CRD reshapes tumor immunometabolism and the intratumoral microbiome to promote immune evasion and metastasis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing a murine model of mammary tumorigenesis maintained under standard light\u0026ndash;dark (LD) or CRD conditions, we performed untargeted metabolomics, 16S rRNA sequencing, flow cytometry, and integrative microbiome\u0026ndash;metabolome modeling (MIMOSA2). Pharmacologic inhibition of arginase-1 (ARG1) was evaluated using nor-NOHA. Human breast cancer datasets and tumor samples were analyzed to assess clinical relevance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCRD significantly increased tumor burden and lung metastasis and induced extensive metabolic reprogramming within the tumor microenvironment. Immunosuppressive metabolites\u0026mdash;including kynurenic acid, adenosine, xanthurenic acid, lactate, spermidine, and argininosuccinic acid\u0026mdash;were markedly elevated, converging on the arginine\u0026ndash;polyamine axis and driving ARG1 upregulation. ARG1 inhibition restored CD8⁺ T-cell infiltration, reduced immunosuppressive myeloid populations, and significantly decreased lung metastases. CRD also altered the intratumoral microbiome, enriching Firmicutes, Bacteroidetes, and Bacilli\u0026mdash;taxa capable of producing or accumulating polyamines. Integrative analysis identified microbiome-driven regulation of key metabolites, including kynurenine and citrulline. Human tumor analyses confirmed associations between these microbial signatures, immune-modulatory pathways, and poor survival.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCRD promotes breast cancer progression through coordinated metabolic\u0026ndash;microbial dysregulation that establishes an immunosuppressive tumor microenvironment. Targeting ARG1 and immunometabolic pathways may offer therapeutic opportunities for circadian disruption\u0026ndash;associated breast cancer.\u003c/p\u003e","manuscriptTitle":"Metabolome–Microbiome Reprogramming Under Circadian Disruption Accelerates Mammary Tumorigenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 12:28:00","doi":"10.21203/rs.3.rs-8369606/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-13T14:21:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T19:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9421609787762048877932496405503181209","date":"2026-03-13T05:56:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126603610440454350034804484884484228465","date":"2026-03-06T15:27:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T09:12:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-09T09:47:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T03:58:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T03:57:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-15T19:48:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87aeccaf-9754-45f8-b772-a13cbbaa4e24","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T12:28:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 12:28:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8369606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8369606","identity":"rs-8369606","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}