The effects of dietary fat on gut microbial composition and function in ovarian cancer

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AlHilli, Naseer Sangwan, Alex Myers, Surabhi Tewari, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5904007/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2025 Read the published version in Journal of Ovarian Research → Version 1 posted 9 You are reading this latest preprint version Abstract Objectives : The gut microbiome (GM) is pivotal in regulating inflammation, immune responses, and cancer progression. This study investigates the effects of a ketogenic diet (KD) and a high-fat/low-carbohydrate (HF/LC) diet on GM alterations and tumor growth in a syngeneic mouse model of high-grade serous ovarian cancer (EOC). Methods : Thirty female C57BL/6J mice injected with KPCA cells were randomized into KD, HF/LC, and low-fat/high-carbohydrate (LF/HC) diet groups. Tumor growth was monitored with live, in vivo imaging. Stool samples were collected at the time of euthanasia and analyzed by 16SrRNA sequencing and shotgun metagenomic sequencing was performed to identify differential microbial taxonomic composition and metabolic function. Results : Our findings revealed that KD and HF/LC diets significantly accelerated EOC tumor growth compared to the LF/HC diet in a xenograft model. GM diversity was markedly reduced in KD and HF/LC-fed mice, correlating with increased tumor growth, whereas LF/HC-fed mice showed higher GM diversity. Metagenomic analyses identified distinct alterations in microbial taxa including Bacteroides , Lachnospiracae bacterium , Bacterium_D16_50, and Enterococcus faecalis predominantly abundant in HF/LC-fed mice, Dubsiella_newyorkensis predominantly abundant in LF/HC-fed, and KD fed mice showing a higher abundance of Akkermansia and Bacteroides . Functional pathways across diet groups indicated polyamine biosynthesis and fatty acid oxidation pathways were enriched in HF/LC-fed mice. Conclusions These results highlight the intricate relationship between diet, the gut microbiome, and tumor metabolism. The potential role of dietary interventions in cancer prevention and treatment warrants further investigation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION The gut microbiome (GM) houses a diverse and rich cohort of bacteria that play a central role in regulating metabolism, inflammation, and immune responses 1 – 3 . The intricate relationship between the GM and the host is now understood to play a crucial role in maintaining physiological balance and contributing to disease 4 – 6 . The GM directly impacts carcinogenesis through its interaction with diet, genetics, lifestyle, and environmental factors 7 , 8 . Metabolites produced by GM species, such as bile acids, have pro-inflammatory effects that contribute to DNA damage, genomic instability, and oncogenic signaling 8 , 9 . More importantly, decreased GM diversity is strongly associated with adverse treatment outcomes and poor responses to therapy in cancer patients. Understanding how dietary intake impacts GM and cancer progression is essential for exploring therapeutic opportunities targeting dietary interventions in cancer prevention. Diet is a predominant factor that directly influences the GM composition, and acute dietary changes produce rapid and sustained changes in GM composition and function 6 , 10 . For instance, under obesogenic dietary conditions, bile acids produced by the Firmicutes phylum have oncogenic effects. However, feeding a high-fiber diet or a Mediterranean-style diet yields short-chain fatty acid (SCFA) production by GM which is associated with tumor suppressive benefits 11 , 12 . A high-fat diet shifts the gut microbiome composition to one with an elevated Firmicutes to Bacteriodetes ratio thus disrupting homeostasis and increasing inflammation while reducing the availability of beneficial SCFA 13 . Poor diet quality and high fat intake strongly correlate with inflammation and adverse oncologic outcomes in many cancers, including ovarian cancer 14 , 15 . However, there remains uncertainty about the impact of dietary fat concentration and saturation on cancer progression. For instance, the ketogenic diet (KD) consisting of 80–90% of energy as fat with very low carbohydrate concentration has become a popular diet among cancer patients due to the proposed benefits of KD on tumor biology and anti-tumor response 16 – 18 . Furthermore, ketone bodies (e.g., beta-hydroxybutyrate) produced on a KD can significantly alter the GM, selectively inhibit microbial growth, and promote a reduced abundance of Bifidobacterium and an increased abundance of Akkermansia species 19 – 22 . Dietary intake has been increasingly recognized to influence tumor metabolism. For example, tumor cells increase glucose uptake with increased carbohydrate abundance in the diet, potentially fueling tumor growth through the Warburg effect. High-fat diets also alter lipid availability and composition, using fatty acids for energy and tumor growth. We have shown that treatment with KD (90% fat, 0% carbohydrate) in a syngeneic mouse model of epithelial ovarian cancer (EOC) induced tumor growth and caused significant upregulation of fatty acid metabolism 23 . Prior work has also demonstrated the role of fatty acids and adipocytes in promoting EOC growth and metastasis 24 , 25 . Herein, we explore the diet- microbiome- tumor axis in ovarian cancer and elucidate the effects of a high-fat diet on GM alterations and metabolomic readouts under KD and high-fat diet conditions in a syngeneic model of high-grade serous ovarian cancer. MATERIALS AND METHODS Cell Line KPCA EOC cell line, kindly provided by Dr. Robert Weinberg at the Massachusetts Institute of Technology, was utilized in these studies. KPCA tumors harbor mutations in KRAS, P53, CCNE, and AKT2 overexpression mimicking homologous recombination (HR) proficient high-grade serous EOC 26 . KPCA EOC cell lines were cultured in Dulbecco Modified Eagle Medium (DMEM) media containing heat-inactivated 5% FBS (Atlas Biologicals Cat # F-0500-D, Lot F31E18D1) and grown under standard conditions. For luciferase transduction, in short, HEK 293T/17 (ATCC CRL-11268) cells were plated and co-transfected with Lipofectamine 3000 (L3000015 Invitrogen), 3rd generation packaging vectors pRSV-REV #12253, pMDG.2 #12259, and pMDLg/pRRE #12251 (Addgene) and lentiviral vector directing expression of luciferase reporter pHIV-Luciferase #21375 4.5 µg (Addgene). Viral particles were harvested, filtered through a 0.45 µm Durapore PVDF Membrane (Millipore SE1M003M00) and added to each cell line’s culture media. Viral infections were carried out over 72 hours and transduced cells were selected by their resistance to 2 µg/mL puromycin (MP Biomedicals 0219453910). Animal Studies Female C57BL/6J (BL/6) mice were purchased from Jackson Laboratories (Bar Harbor, ME) at 6–8 weeks of age. Experimental animals were housed and handled in accordance with Cleveland Clinic Lerner Research Institute Institutional Animal Care and Use Committee approved protocol. Thirty C57BL/6J mice were injected intraperitoneally with murine KPCA-luc cells (5 x10 6 ) on day 0. On day 14, mice were randomized to three diet arms: KD, high fat/ low carbohydrate diet (HF/LC), and low fat, high carbohydrate (LF/HC) diet. All murine diets were irradiated and provided by Tekland Envigo 27 , 28 . HF/LC (RD.160239.PWD) diet consisted of 75% fat from Crisco, cocoa butter, and corn oil and 15% carbohydrates, while KD diet (RD. 160153.PWD) consisted of 90% fat from the same sources and 0% carbohydrates. LF/HC diet (TD.150345) consisted of 13% fat and 77%% carbohydrates. Diet macronutrient and micronutrient composition is shown in Table 1 . Weekly in vivo imaging system (IVIS) was performed between days 14 and 41. Weekly blood glucose and ketone levels were also assessed following a tail vein blood draw using a standard laboratory glucometer (Precision Xtra ®). Mouse weight was assessed weekly on a standard laboratory scale. At necropsy, plasma (cardiac puncture) and tumor tissue were collected. Fecal pellets were collected at the endpoint for microbial sequencing and analysis. Murine Body Condition Score Mouse wellbeing was assessed directly: while gently restraining a mouse by holding the base of its tail, the observer (blinded to the treatment group) used the thumb and index finger of the other hand to palpate the degree of muscle and fat over the sacroiliac region. A score from 1–5 was given to each mouse weekly following IP tumor cell injection based on previous literature 29 . The same observer was used for all tests. Based on established IACUC protocols #2018 − 2003, a body composition score of 2 or lower was defined as meeting endpoint criteria for euthanasia. Tumor Monitoring by 2D IVIS Imaging All bioluminescence images were taken with IVIS Lumina (PerkinElmer) using D-luciferin as previously described 30 . Mice received an intraperitoneal (IP)injection of D-luciferin (Goldbio LUCK-1G, 150 mg/kg in 150 uL) under inhaled isoflurane anesthesia. Images were analyzed (Living Image Software), and total flux was reported in photons/second/cm 2 /steradian for each mouse abdomen. All images were obtained with an automatic exposure. 16SrRNA gene amplicon sequencing Genomic DNA extraction, 16S rRNA gene amplification, sequencing, and bioinformatic analysis were performed as described previously 1 – 4 . Briefly, raw 16S amplicon sequence and metadata were demultiplexed using split_libraries_fastq.py script implemented in QIIME2 31 . Demultiplexed fastq file was split into sample-specific fastq files using split_sequence_file_on_sample_ids.py script from QIIME2. Individual fastq files without non-biological nucleotides were processed using Divisive Amplicon Denoising Algorithm (DADA) pipeline 32 .The output of the dada2 pipeline (feature table of amplicon sequence variants (an ASV table)) was processed for alpha and beta diversity analysis using phyloseq 33 , and microbiomeSeq ( http://www.github.com/umerijaz/microbiomeSeq ) packages in R. We analyzed variance (ANOVA) among sample categories while measuring the α-diversity measures using plot_anova_diversity function in microbiomeSeq package. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was performed on all principal coordinates obtained during CCA with the ordination function of the microbiomeSeq package. Shotgun Metagenomics sequencing and bioinformatics analysis Quality control of the metagenomic reads was conducted as described previously 1 , 5 – 8 . Briefly, raw reads were processed for low-quality based filtering using Trimmomatic pipeline 9 . Host-derived reads were excluded by mapping the reads to the reference human genome (version GRCh38.p14) using BBMap software (sourceforge.net/projects/bbmap/). Quality trimmed reads were processed for taxonomic and functional profiling using Metaphlan2 10 and Humann2 11 , respectively. Differential feature selection was performed using Fisher’s exact-t-test 12 . We assessed the statistical significance ( P < 0.05) throughout, and whenever necessary, we adjusted P -values for multiple comparisons according to the Benjamini and Hochberg method to control False Discovery Rate 13 while performing multiple tests on taxa and pathway abundances according to sample types. Differential network analysis was performed at the species level using NetComi set at the following parameters; filtTax=”highestVar”, filtTaxPar = list(highestVar = 40), measure=”sparcc”, normMethod = “mclr”, zeroMethod =”none”, sparsMethod = “none”, dissFunc = “signed”. Eigenvector centrality was used for defining hubs and scaling node sizes. Node colors represent clusters, which are determined using greedy modularity optimization. Clusters have the same color in both networks if they share at least two taxa. Nodes that are unconnected in both groups were removed Statistical Analysis Differential abundance test benchmarking was performed using DAtest package ( https://github.com/Russel88/DAtest/wiki/usage#typical-workflow ). Briefly, differentially abundant methods were compared with False Discovery Rate (FDR), Area Under the (Receiver Operator) Curve (AUC), Empirical power (Power), and False Positive Rate (FPR). Based on the DAtest’s benchmarking, we selected lefseq and anova as the methods of choice to perform differential abundance analysis. We assessed the statistical significance (P < 0.05) throughout, and whenever necessary, we adjusted P-values for multiple comparisons according to the Benjamini and Hochberg method to control the False Discovery Rate(Benjamini and Hochberg, 1995). Linear regression (parametric test), and Wilcoxon (Non-parametric) tests were performed on genera and species abundances against metadata variables using their base functions in R (version 4.1.2; R Core Team, 2021) (Team, 2021). Study Approvals All murine studies were completed in accordance with the Institutional Animal Care and Use Committee guidelines, approval # 2018 − 2003. All studies utilizing lentiviral particle generation were completed in accordance with the Institutional Biosafety Committee guidelines, approval #IBC0920. RESULTS KD and HF/LC diets induced the growth of KPCA ovarian tumors. To investigate the impact of KD vs. HF/LC diet on EOC growth, 30 female C57BL/6J (BL/6) mice were injected intraperitoneally with KPCA Luc cells (5x10 5 ). Two weeks after injection, mice were randomized to one of three diet arms (KD, HF/ LC diet, and LF/ HC diet (10 mice per arm). Glucose levels remained stable throughout the study, with no difference between groups (Fig. 1 A). By endpoint, mice treated with KD demonstrated a significant increase in circulating ketone levels compared to HF/LC and LF/HC diet-fed mice, confirming mice were in ketosis (p < 0.001) (Fig. 1 B). Body weight was significantly higher in HF/LC diet-fed mice relative to KD and LF/HC diet-fed mice (p < 0.001) (Fig. 1 C). Mice fed KD and HC/LC diet showed a marked increase in tumor growth (Fig. 1 D) compared to LF/HC diet-treated mice (P < 0.001). Figure 1 : KD and High Fat Diets induced ketones and tumor growth. Ketogenic diet and HF/LC diet in C57Bl/6 mice bearing KPCA tumors induces ketosis and tumor growth ( A ) Weekly circulating glucose levels in each diet arm remain stable throughout the study ( B ) Weekly circulating ketone levels in each diet arm with significant increase in KD fed mice noted ( C ) Mouse weight over the study course between the three diet groups HF/LC diet treated mice show significant increase in body weight compared to KD and LC/HF diet.. ( D ) Increase in tumor growth in mice fed KD and HF/LC diet relative to LF/HC diet fed mice (n = 10 mice per group, ANOVA *** p < 0.001, n.s.= not significant). Decreased gut microbial diversity in HF/LC and KD fed mice compared to LF/HC fed mice. Due to the known impact of diet on GM diversity, we sought to characterize differences between GM taxa among diet groups and elucidate alterations in GM species abundance in response to diet. Alpha diversity is shown in Fig. 2 A and 2 B. Overall, LF/HC diet-fed mice showed the highest alpha diversity while HF/LC and KD-fed mice showed a marked decrease in gut microbial diversity. Figure 2 B shows a significant leftward separation in microbial abundance between LF/HC control-fed mice and KD and HF/LC diet-fed mice. The key differentially abundant gut microbial species included Bacteroides thetaiotamicron , Lachnospiraceae bacterium , Bacterium_D16_50 and Enterococcus faecalis predominantly abundant in HF/LC-fed mice Fig. 2 C, and Dubosiella newyorkensis predominantly abundant in LF/HC-fed mice. KD-fed mice had a higher abundance of Akkermansia relative to HF/LC and LF/HC diet fed mice, while Bacteroides thetaiotamicron was highly abundant in HF/LC diet relative to the two other groups. Figure 2 : Alpha diversity and relative abundance of bacterial species KD, HF/LC, and LF/HC. (A) Box plot depicting the alpha diversity measured by the Simpson metric for the three groups. The box plot illustrates the distribution of alpha diversity scores within each group, highlighting median values, interquartile ranges, and potential outliers. (B) Principal Component Analysis (PCA) plot representing beta diversity among the three groups. Points on the PCA plot indicate the sample scores for each group, facilitating visualization of the variability and clustering of microbial communities across the different dietary conditions. (C) A stacked flow bar plot shows the relative abundance of microbial taxa across the KD, HF/LC, and LF/HC groups. The plot illustrates the composition of microbial communities, with different colors representing various taxa, allowing for a comparative analysis of community structure among the groups. The associations between gut microbial taxa and diet are illustrated in Fig. 3 A. Distinct clustering was noted for each diet group. A strong positive correlation was seen between both Lactobacillus johnsonii and Dubosiella newyorkenesis and LF/HC diet, while the same species were negatively correlated with HF/LC diet. A similar strong association was also noted between bacterium 1xD8-48, Neglecta, and Bifidobacterium-pseudolongum , and LF/HC diet compared to HF/LC diet. Within the HF/LC diet group, a significant positive correlation was noted between several species, including Clostridial bacterium , Enterococcus faecalis , Romboustia , and Turibacter sp - TS3. Enterohabdus-sp-p55 , Lachnospiraceae, Staphylococcus xylosus , and Lachnospiraceae bacterium - MD308 were significantly associated with KD. Receiver operator curves demonstrate high predictive accuracy for KD taxonomy group (AUC 0.89), HF/ LC group (AUC 0.79), and control (AUC 0.98) (Fig. 3 B). The statistical significance of the key species Lactobacillus johnsonii , Dubosiella newyorkenesis, Turibacter sp- TS3 and Enterohabdus-sp-p55 is shown in Fig. 3 C. As demonstrated, these species show high predictability in distinguishing between each of the diet groups Figure 3 : Differential Microbiome taxonomic composition across Groups. (A) Heatmap displaying the differentially abundant species across the three groups: KD, HF/LC, and LF/HC. The bubble size in the heatmap corresponds to the fraction of samples in which each species is present, while the color gradient indicates the abundance z-score, with warmer colors representing higher abundance. (B) Area Under the Curve (AUC) plot illustrating the specificity (y-axis) versus 1-specificity (x-axis) for the microbiome's predictive performance across the groups. Each colored line represents a specific group's performance, demonstrating the microbiome's discriminative ability to distinguish between them. (C) Box plot derived from Tukey's Honest Significant Difference (HSD) analysis, highlighting the statistical significance of key species identified in the heatmap. This plot assesses the capability of these species to differentiate between the KD, HF/LC, and LF/HC groups. Differential network analysis was used to visually represent functional associations between diet groups, as shown in Figs. 4 A-C. Eigenvector centrality was used to define hubs and scaling node sizes, with node size representing relative abundance. Associations between different subject groups were captured utilizing Aitchison’s distance. The analysis is restricted to samples and species with at least 1000 reads, with normalization performed to achieve fraction-based counts. To address the presence of zeros for clr transformation, “multiplicative imputation” is employed. Each dissimilarity matrix is scaled to a range of [0,1] and employs the k-nearest neighbor method (⁠=3) for sparsification. Colors of the nodes indicate clusters derived from hierarchical clustering using average linkage, where clusters sharing the same color have a minimum of 100 nodes in common. Hubs are depicted with bold borders and are defined as nodes with eigenvector centrality surpassing the 99% quantile of the empirical distribution. Edge thickness reflects the degree of similarity, and nodes are arranged closer together based on their compositional similarity, with unconnected nodes excluded from the visualization. Positive correlations are noted in green, and negative correlations are shown in red. Comparisons between HF/LC and KD groups revealed a significant positive association between the following bacterial species in KD: Bacteroides thetaiotamicron , Akkermansia muciniphilia , Lachnospiraceae bacterium and GGB30302 SGB432661. A similar pattern was noted in comparing KD and LF/HC diets. For LF/HC compared to HF/LC diets, significant positive associations between Bacterium D16 50, Lactococcus lactus , and Lachnospiraceae bacterium and HF/LC diet were detected. Figure 4 . Comparative differential network analysis across diet groups. . (A) the relationship between LF/HC and KD, (B) the connection between HF/LC and KD, and network (C) dissimilarity between LF/HC and HF/LC groups, utilizing consistent analytical parameters throughout. Functional analysis of GM reveals significant metabolic pathway alterations between diet groups . Functional pathway analysis was performed to identify bacterial metabolic alterations across LF/HC and HF/LC diets. As shown in Fig. 5 , a significantly higher abundance of super pathways of polyamine biosynthesis I and II, as well as fatty acid and beta-oxidation pathways and phospholipases, was observed in the HF/LC diet group. Similarly, the fucose degradation pathway was enriched in the KD group, while L-ornithine biosynthesis, the bifidobacterium shunt, the super pathway of L-alanine biosynthesis, and D-gluconate degradation were among the pathways significantly enriched in the LF/HC diet group (Fig. 5 A). Receiver operator curves demonstrate high predictive accuracy for functional composition across diet groups (Fig. 5 B). Finally, we used Tukey's Honest Significant Difference (HSD) test (as a post hoc test) to test the statical significance of individual pathways in differentiating all groups. As illustrated in Fig. 5 C, the polyamine biosynthesis significantly influenced tumor growth with marked differential abundance between HF/LC and LF/HC and KD groups. Figure 5 . Differential Microbiome functional composition across groups. (A) Heatmap displaying the differentially abundant functional pathways across the three groups: KD, HF/LC, and LF/HC. The bubble size in the heatmap corresponds to the fraction of samples in which each species is present, while the color gradient indicates the abundance z-score, with warmer colors representing higher abundance. (B) Area Under the Curve (AUC) plot illustrating the specificity (y-axis) versus 1-specificity (x-axis) for the microbiome's predictive performance across the groups. Each colored line represents a specific group's performance, demonstrating the microbiome's metabolic potential to distinguish between them. (C) Box plot derived from Tukey's Honest Significant Difference (HSD) analysis, highlighting the statistical significance of key functional pathways identified in the heatmap. This plot assesses the capability of these pathways to differentiate between the KD, HF/LC, and LF/HC groups. DISCUSSION Results of our investigation demonstrate that both KD and HF/LC diets induced significant growth in KPCA EOC tumors compared to the LF/HC diet. Despite stable glucose levels across all groups, KD-fed mice exhibited elevated circulating ketone levels, confirming ketosis, and increased tumor growth. The HF/LC diet similarly promoted tumor growth, highlighting the potential oncogenic influence of high-fat intake regardless of carbohydrate restriction. Our results also demonstrated a generalized increase in gut microbial alpha diversity in mice fed an LF/HC diet and decreased diversity in mice fed an HF/LC and KD diet. The decrease in alpha diversity correlated with the increase in EOC tumor growth seen in our mouse models. It has been well established in prior studies that a lower gut microbial diversity is strongly associated with adverse treatment outcomes and poor response to therapy in patients with cancer 15 . The mechanisms that KD and increased dietary fat alter gut microbial abundance are complex and have not been comprehensively defined. One mechanism involves the increase in levels of the ketone body beta-hydroxybutyrate (β-HB) on a KD, which is correlated with a reduced abundance of Bifidobacterium 21 . Bifidobacteria species have been shown to have anti-proliferative and anti-apoptotic properties in colorectal cancer, and its abundance was associated with response to therapy in lung cancer-treated mice 10 , 22 , 34 . We indeed showed that mice fed KD had a lower abundance of Bifidobacteria which were overrepresented in LF/HC diet-fed mice that had a lower growth rate of EOC than KD-fed mice. We additionally noted a distinct clustering of bacterial taxa within each diet group, highlighting the discrete yet complex relationship between dietary intake and gut microbial composition. We noted that Lachnospiracae bacterium , Bacterium _D16 _50, and Enterococcus faecalis are predominantly associated with HF/LC-fed, and Dubsiella_newyorkensis predominantly associated with LF/HC-fed mice. Dubosiella is a genus within the family Lachnospiraceae , which is part of the Firmicutes phylum. It is the murine homologue of Clostridium, which has a probiotic immunomodulatory effect and is known for producing short-chain fatty acids that contribute to a balanced gut microbiome. On the other hand, increased abundance of Enterococcus fecalis has been previously shown to be associated with a high fat diet, which tends to be associated with inflammation, toxin-related damage, and cancer progression. E. faecalis , however, plays a controversial role in the development of colorectal cancer 35 . As demonstrated in the network plots, complex correlations between bacterial taxa and distinct clustering was noted, confirming prior findings demonstrating that a high-fat diet drives changes in gut microbiota with over underrepresentation of beneficial butyrate-producing bacteria 36 . Functional shotgun metagenomics analyses were performed to understand the mechanistic implications behind these associations. A critical pathway overrepresented in the HF/LC diet gut microbial analysis was the polyamine biosynthesis pathway. Other significant pathways included fatty acid elongation and fatty acid and beta-oxidation. In contrast, TCA cycle IV and fucose degradation were among the highly overrepresented metabolic pathways in KD. Polyamines play an essential role in cellular growth and differentiation and can influence antitumor immune response and modulate inflammation. Intake of polyamines and dysregulation of polyamine metabolism is a feature of many cancers, including colorectal cancer 37 . Furthermore, polyamine metabolism was found to regulate immune cells, including tumor-associated macrophages and T cells in hepatocellular carcinoma. Polyamine metabolism is regulated by MYC through the rate-limiting enzyme, ornithine decarboxylase. Moreover, the FDA-approved anti-protozoan drug, α-difluoromethylornithine (DFMO), inhibits ODC activity and induces polyamine depletion, leading to tumor growth arrest 38 . Herein, we show that polyamine biosynthesis was functionally enhanced by the gut microbiota of HF/LC-fed mice and was predictive of HF/LC dietary intake in mice. Whether this outcome is mediated through the immune microenvironment is imperative to investigate in future studies. On the other hand, fucose is a methylpentose, which was elevated in KD diet-fed mice stool samples, is found in glycoproteins and glycolipids. It is metabolized from dietary glycans by alpha fucosidase, which is prevalent in gut bacteria. Bacterium thetaiotamicron , associated with KD, is proficient at utilizing fucose for energy 39 . This suggests that fucose degradation by gut microbiota in mice fed KD may play a role in mediating tumor growth. Furthermore, the role of fatty acid beta-oxidation in the progression of tumor growth on HF/LC is noteworthy and has been demonstrated in prior studies 36 . Data from our group (unpublished) showed that an HF/LC diet was associated with the upregulation of the fatty acid oxidation gene, CPT1A. Fatty acid metabolism dysregulation is a characteristic feature of EOC and correlates with poor survival 40 . Thus, the effects of HF/LC on tumor growth may be potentially mediated through the utilization of available fatty acids and upregulation of fatty acid metabolism. Our findings underscore the complex interplay between diet, tumor metabolism, and the gut microbiome. We utilized advanced metagenomic analyses to validate and characterize the associations between diet and gut microbiota, shedding light on the intricate interactions between gut microbiome and metabolome. One of the main limitations of our study, however, is that we could not assess the effects of EOC growth on changes in gut microbial composition, as we did not control for tumor presence in our studies. Further correlations between gut, tumor, and plasma metabolites would solidify the results of our study. The pro-tumorigenic effects of KD and HF/LC diets raise important considerations for dietary recommendations in cancer patients. While KD has gained popularity for its potential anti-tumor effects, our data suggests it may not be universally beneficial and could potentially fuel tumor growth in specific contexts. These findings contribute to the growing body of evidence on the critical role of diet in cancer progression and the potential for dietary manipulation as a therapeutic strategy. Declarations Funding NIH K12CA076917 Clinical Oncology Training Program Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgements KPCA cell line was kindly provided by Dr. Robert Weinberg at the Massachusetts Institute of Technology Authors' contributions Conception and design: MA, OR, GC Collection and assembly of data: AM, ST, DJL Data analysis and interpretation: MA, NS, OR, NB, GC 16S RNA sequencing: NS Manuscript writing: MA Editing and final approval of manuscript: All authors. Clinical trial number: Not applicable Ethics approval and Institutional Review Board Statement The study was conducted according to guidelines of the Declaration of Helsinki and approved by IACUC at Cleveland Clinic (Protocol Informed consent statement Not applicable Conflicts of interests The authors declare that they have no competing interests. References Gopalakrishnan, V. et al. Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients. Science (1979) 359 , 97–103 (2018). Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359 , 104–108 (2018). Geller, L. T. et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357 , 1156–1160 (2017). AlHilli, M. M. & Bae-Jump, V. Diet and gut microbiome interactions in gynecologic cancer. Gynecol Oncol 159 , 299–308 (2020). Scott, A. J. et al. International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut 68 , 1624–1632 (2019). Gentile, C. L. & Weir, T. L. The gut microbiota at the intersection of diet and human health. Science (1979) 362 , 776–780 (2018). Scott, A. J. et al. International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut 68 , 1624–1632 (2019). Helmink, B. A., Khan, M. A. W., Hermann, A., Gopalakrishnan, V. & Wargo, J. A. The microbiome, cancer, and cancer therapy. Nat Med 25 , 377–388 (2019). Rea, D. et al. Microbiota effects on cancer: from risks to therapies. Oncotarget 9 , 17915–17927 (2018). AlHilli, M. M. & Bae-Jump, V. Diet and gut microbiome interactions in gynecologic cancer. Gynecol Oncol 159 , 299–308 (2020). Soldati, L. et al. The influence of diet on anti-cancer immune responsiveness. J Transl Med 16 , 75 (2018). Singh, R. K. et al. Influence of diet on the gut microbiome and implications for human health. J Transl Med 15 , 73 (2017). Beam, A., Clinger, E. & Hao, L. Effect of Diet and Dietary Components on the Composition of the Gut Microbiota. Nutrients 13 , (2021). Prentice, R. L. et al. Low-fat dietary pattern and cancer incidence in the Women’s Health Initiative Dietary Modification Randomized Controlled Trial. J Natl Cancer Inst 99 , 1534–43 (2007). Blank, M. M., Wentzensen, N., Murphy, M. A., Hollenbeck, A. & Park, Y. Dietary fat intake and risk of ovarian cancer in the NIH-AARP Diet and Health Study. Br J Cancer 106 , 596–602 (2012). Seyfried, T. N., Sanderson, T. M., El-Abbadi, M. M., McGowan, R. & Mukherjee, P. Role of glucose and ketone bodies in the metabolic control of experimental brain cancer. Br J Cancer 89 , 1375–1382 (2003). Klement, R. J., Brehm, N. & Sweeney, R. A. Ketogenic diets in medical oncology: a systematic review with focus on clinical outcomes. Med Oncol 37 , 14 (2020). Klement, R. J. & Sweeney, R. A. Impact of a ketogenic diet intervention during radiotherapy on body composition: II. Protocol of a randomised phase I study (KETOCOMP). Clin Nutr ESPEN 12 , e1–e6 (2016). Cabrera-Mulero, A. et al. Keto microbiota: A powerful contributor to host disease recovery. Rev Endocr Metab Disord 20 , 415–425 (2019). Paoli, A. et al. Ketogenic Diet and Microbiota: Friends or Enemies? Genes (Basel) 10 , 534 (2019). Ang, Q. Y. et al. Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. Cell 181 , 1263-1275.e16 (2020). Parisa, A. et al. Anti-cancer effects of Bifidobacterium species in colon cancer cells and a mouse model of carcinogenesis. PLoS One 15 , e0232930 (2020). AlHilli, M. M. et al. Unrestricted Ketogenic Diet Feeding Enhances Epithelial Ovarian Cancer Growth In Vivo. Nutrients 15 , (2023). Zhao, G., Cardenas, H. & Matei, D. Ovarian Cancer—Why Lipids Matter. Cancers (Basel) 11 , 1870 (2019). Nieman, K. M., Romero, I. L., Van Houten, B. & Lengyel, E. Adipose tissue and adipocytes support tumorigenesis and metastasis. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 1831 , 1533–1541 (2013). Iyer, S. et al. Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of Combination Immunotherapy. Cancer Discov 11 , 384–407 (2021). Ang, Q. Y. et al. Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. Cell 181 , 1263-1275.e16 (2020). Newman, J. C. et al. Ketogenic Diet Reduces Midlife Mortality and Improves Memory in Aging Mice. Cell Metab 26 , 547-557.e8 (2017). Ullman-Culleré, M. H. & Foltz, C. J. Body condition scoring: a rapid and accurate method for assessing health status in mice. Lab Anim Sci 49 , 319–23 (1999). Chambers, L. M. et al. Use of Transabdominal Ultrasound for the detection of intra-peritoneal tumor engraftment and growth in mouse xenografts of epithelial ovarian cancer. PLoS One 15 , e0228511 (2020). Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37 , 852–857 (2019). Callahan, M. J. et al. Primary fallopian tube malignancies in BRCA-positive women undergoing surgery for ovarian cancer risk reduction. J Clin Oncol 25 , 3985–90 (2007). McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One 8 , e61217 (2013). Clements, S. J. & R. Carding, S. Diet, the intestinal microbiota, and immune health in aging. Crit Rev Food Sci Nutr 58 , 651–661 (2018). de Almeida, C. V., Taddei, A. & Amedei, A. The controversial role of Enterococcus faecalis in colorectal cancer. Therap Adv Gastroenterol 11 , 1756284818783606 (2018). Xiao, L. et al. High-fat feeding rather than obesity drives taxonomical and functional changes in the gut microbiota in mice. Microbiome 5 , 43 (2017). Huang, C.-Y. et al. Dietary Polyamines Intake and Risk of Colorectal Cancer: A Case-Control Study. Nutrients 12 , (2020). Kim, H. I. et al. Pharmacological targeting of polyamine and hypusine biosynthesis reduces tumour activity of endometrial cancer. J Drug Target 30 , 623–633 (2022). Pickard, J. M. & Chervonsky, A. V. Intestinal fucose as a mediator of host-microbe symbiosis. J Immunol 194 , 5588–93 (2015). Zhao, G., Cardenas, H. & Matei, D. Ovarian Cancer—Why Lipids Matter. Cancers (Basel) 11 , 1870 (2019). Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2025 Read the published version in Journal of Ovarian Research → Version 1 posted Editorial decision: Revision requested 31 Mar, 2025 Reviews received at journal 29 Mar, 2025 Reviews received at journal 12 Mar, 2025 Reviewers agreed at journal 07 Mar, 2025 Reviewers agreed at journal 19 Feb, 2025 Reviewers invited by journal 18 Feb, 2025 Editor assigned by journal 05 Feb, 2025 Submission checks completed at journal 05 Feb, 2025 First submitted to journal 25 Jan, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5904007","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411720059,"identity":"435cf9b7-2c13-48f9-8d69-124172e717cf","order_by":0,"name":"Mariam M. AlHilli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYDACdh6GAw8KGOQYGBgfEKmFGaglwYDBGMgyIF4LA1BLYgPRWvibeQ8CbdmW3t9+mIHh455awlokDvMlALXczp1xJpmBccaz40RYc5jHAKxlgwT/AWaeA8cI65CHakk3kGBmIE6LAVRLAlRLDWEthlAthiC/HJxx4ABhLXLHe4w/fKi4Lc/ffpjxwYcDdYS1oACgFYdJ1AIEpNoyCkbBKBgFIwEAALhaO4dhvL23AAAAAElFTkSuQmCC","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":true,"prefix":"","firstName":"Mariam","middleName":"M.","lastName":"AlHilli","suffix":""},{"id":411720060,"identity":"911583ab-683a-411f-82db-a9072acf3feb","order_by":1,"name":"Naseer Sangwan","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Naseer","middleName":"","lastName":"Sangwan","suffix":""},{"id":411720061,"identity":"94a65c2d-e69f-4774-a247-8882484848d1","order_by":2,"name":"Alex Myers","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Myers","suffix":""},{"id":411720063,"identity":"f7eb6e69-8896-48cc-ab50-4cdfd26b9f95","order_by":3,"name":"Surabhi Tewari","email":"","orcid":"","institution":"Cleveland Clinic Lerner College of Medicine of CWRU","correspondingAuthor":false,"prefix":"","firstName":"Surabhi","middleName":"","lastName":"Tewari","suffix":""},{"id":411720065,"identity":"b5ea45b0-aec4-46ef-8644-24c7f830acb9","order_by":4,"name":"Daniel J. Lindner","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"J.","lastName":"Lindner","suffix":""},{"id":411720066,"identity":"a33edc0a-cc55-42b0-a2ed-43f5130c1880","order_by":5,"name":"Gail A.M. Cresci","email":"","orcid":"","institution":"Cleveland Clinic Lerner College of Medicine of CWRU","correspondingAuthor":false,"prefix":"","firstName":"Gail","middleName":"A.M.","lastName":"Cresci","suffix":""},{"id":411720067,"identity":"2f5186ba-6ed8-411e-a5f6-ee8c64c377d0","order_by":6,"name":"Ofer Reizes","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Ofer","middleName":"","lastName":"Reizes","suffix":""}],"badges":[],"createdAt":"2025-01-26 01:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5904007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5904007/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13048-025-01731-1","type":"published","date":"2025-08-02T16:13:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75707434,"identity":"1f79d08f-b95a-4a10-bcbb-141cf0d91523","added_by":"auto","created_at":"2025-02-07 10:37:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":339570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKD and High Fat Diets induced ketones and tumor growth. \u003c/strong\u003eKetogenic diet and HF/LC diet in C57Bl/6 mice bearing KPCA tumors induces ketosis and tumor growth (\u003cstrong\u003eA\u003c/strong\u003e) Weekly circulating glucose levels in each diet arm remain stable throughout the study (\u003cstrong\u003eB\u003c/strong\u003e) Weekly circulating ketone levels in each diet arm with significant increase in KD fed mice noted (\u003cstrong\u003eC\u003c/strong\u003e) Mouse weight over the study course between the three diet groups HF/LC diet treated mice show significant increase in body weight compared to KD and LC/HF diet.. (\u003cstrong\u003eD\u003c/strong\u003e) Increase in tumor growth in mice fed KD and HF/LC diet relative to LF/HC diet fed mice (n = 10 mice per group, ANOVA *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, n.s.= not significant).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/250ad7d28e99a557bdeb2221.png"},{"id":75708872,"identity":"5c8fd276-c56f-40e3-80bb-3dab42d24b8b","added_by":"auto","created_at":"2025-02-07 10:45:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":996533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity and relative abundance of bacterial species \u003c/strong\u003eKD, HF/LC, and LF/HC. \u0026nbsp;(A) Box plot depicting the alpha diversity measured by the Simpson metric for the three groups. The box plot illustrates the distribution of alpha diversity scores within each group, highlighting median values, interquartile ranges, and potential outliers. \u0026nbsp;(B) Principal Component Analysis (PCA) plot representing beta diversity among the three groups. Points on the PCA plot indicate the sample scores for each group, facilitating visualization of the variability and clustering of microbial communities across the different dietary conditions. \u0026nbsp;(C) A stacked flow bar plot shows the relative abundance of microbial taxa across the KD, HF/LC, and LF/HC groups. The plot illustrates the composition of microbial communities, with different colors representing various taxa, allowing for a comparative analysis of community structure among the groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/d4fe743368d2604d001cf187.png"},{"id":75707439,"identity":"5eab3c19-266d-4f30-b75e-f80877bf4fcd","added_by":"auto","created_at":"2025-02-07 10:37:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":318190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Microbiome taxonomic composition across Groups.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Heatmap displaying the differentially abundant species across the three groups: KD, HF/LC, and LF/HC. The bubble size in the heatmap corresponds to the fraction of samples in which each species is present, while the color gradient indicates the abundance z-score, with warmer colors representing higher abundance. \u003cstrong\u003e(B)\u003c/strong\u003e Area Under the Curve (AUC) plot illustrating the specificity (y-axis) versus 1-specificity (x-axis) for the microbiome's predictive performance across the groups. Each colored line represents a specific group's performance, demonstrating the microbiome's discriminative ability to distinguish between them. \u003cstrong\u003e(C)\u003c/strong\u003e Box plot derived from Tukey's Honest Significant Difference (HSD) analysis, highlighting the statistical significance of key species identified in the heatmap. This plot assesses the capability of these species to differentiate between the KD, HF/LC, and LF/HC groups.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/cbc7200c78084da688d0b0d6.png"},{"id":75707438,"identity":"77d4d898-65f9-4df1-97b9-23e03723eaa1","added_by":"auto","created_at":"2025-02-07 10:37:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3132966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative differential network analysis across diet groups.\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e the relationship between LF/HC and KD, \u003cstrong\u003e(B)\u003c/strong\u003e the connection between HF/LC and KD, and network \u003cstrong\u003e(C)\u003c/strong\u003e dissimilarity between LF/HC and HF/LC groups, utilizing consistent analytical parameters throughout.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/65c94f329a03e3ffd5b351b6.png"},{"id":75707440,"identity":"3dbf0efb-f2e0-44bc-8502-2bf7ddef795e","added_by":"auto","created_at":"2025-02-07 10:37:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":345981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Microbiome functional composition across groups. (A)\u003c/strong\u003eHeatmap displaying the differentially abundant functional pathways across the three groups: KD, HF/LC, and LF/HC. The bubble size in the heatmap corresponds to the fraction of samples in which each species is present, while the color gradient indicates the abundance z-score, with warmer colors representing higher abundance. \u003cstrong\u003e(B)\u003c/strong\u003e Area Under the Curve (AUC) plot illustrating the specificity (y-axis) versus 1-specificity (x-axis) for the microbiome's predictive performance across the groups. Each colored line represents a specific group's performance, demonstrating the microbiome's metabolic potential to distinguish between them.\u003cstrong\u003e (C) \u003c/strong\u003eBox plot derived from Tukey's Honest Significant Difference (HSD) analysis, highlighting the statistical significance of key functional pathways identified in the heatmap. This plot assesses the capability of these pathways to differentiate between the KD, HF/LC, and LF/HC groups.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/29b7ca59a80889ebdc2146d0.png"},{"id":88268208,"identity":"f7698078-d95d-4521-9953-a16b34d5e3b1","added_by":"auto","created_at":"2025-08-04 16:50:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6887659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/2c1678cc-31ba-42df-bf7e-01a2a14cd7a9.pdf"},{"id":75708870,"identity":"a802bdb1-7668-4602-b327-4ac632016a74","added_by":"auto","created_at":"2025-02-07 10:45:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":154590,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5904007/v1/bb811b9143efc4f35b1b672c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects of dietary fat on gut microbial composition and function in ovarian cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe gut microbiome (GM) houses a diverse and rich cohort of bacteria that play a central role in regulating metabolism, inflammation, and immune responses \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The intricate relationship between the GM and the host is now understood to play a crucial role in maintaining physiological balance and contributing to disease\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The GM directly impacts carcinogenesis through its interaction with diet, genetics, lifestyle, and environmental factors \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Metabolites produced by GM species, such as bile acids, have pro-inflammatory effects that contribute to DNA damage, genomic instability, and oncogenic signaling \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. More importantly, decreased GM diversity is strongly associated with adverse treatment outcomes and poor responses to therapy in cancer patients. Understanding how dietary intake impacts GM and cancer progression is essential for exploring therapeutic opportunities targeting dietary interventions in cancer prevention.\u003c/p\u003e \u003cp\u003eDiet is a predominant factor that directly influences the GM composition, and acute dietary changes produce rapid and sustained changes in GM composition and function \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. For instance, under obesogenic dietary conditions, bile acids produced by the \u003cem\u003eFirmicutes\u003c/em\u003e phylum have oncogenic effects. However, feeding a high-fiber diet or a Mediterranean-style diet yields short-chain fatty acid (SCFA) production by GM which is associated with tumor suppressive benefits \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A high-fat diet shifts the gut microbiome composition to one with an elevated Firmicutes to Bacteriodetes ratio thus disrupting homeostasis and increasing inflammation while reducing the availability of beneficial SCFA\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePoor diet quality and high fat intake strongly correlate with inflammation and adverse oncologic outcomes in many cancers, including ovarian cancer \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, there remains uncertainty about the impact of dietary fat concentration and saturation on cancer progression. For instance, the ketogenic diet (KD) consisting of 80\u0026ndash;90% of energy as fat with very low carbohydrate concentration has become a popular diet among cancer patients due to the proposed benefits of KD on tumor biology and anti-tumor response \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Furthermore, ketone bodies (e.g., beta-hydroxybutyrate) produced on a KD can significantly alter the GM, selectively inhibit microbial growth, and promote a reduced abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e and an increased abundance of \u003cem\u003eAkkermansia\u003c/em\u003e species \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDietary intake has been increasingly recognized to influence tumor metabolism. For example, tumor cells increase glucose uptake with increased carbohydrate abundance in the diet, potentially fueling tumor growth through the Warburg effect. High-fat diets also alter lipid availability and composition, using fatty acids for energy and tumor growth. We have shown that treatment with KD (90% fat, 0% carbohydrate) in a syngeneic mouse model of epithelial ovarian cancer (EOC) induced tumor growth and caused significant upregulation of fatty acid metabolism \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Prior work has also demonstrated the role of fatty acids and adipocytes in promoting EOC growth and metastasis \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Herein, we explore the diet- microbiome- tumor axis in ovarian cancer and elucidate the effects of a high-fat diet on GM alterations and metabolomic readouts under KD and high-fat diet conditions in a syngeneic model of high-grade serous ovarian cancer.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eCell Line\u003c/h2\u003e\n \u003cp\u003eKPCA EOC cell line, kindly provided by Dr. Robert Weinberg at the Massachusetts Institute of Technology, was utilized in these studies. KPCA tumors harbor mutations in \u003cem\u003eKRAS, P53, CCNE, and AKT2\u003c/em\u003e overexpression mimicking homologous recombination (HR) proficient high-grade serous EOC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. KPCA EOC cell lines were cultured in Dulbecco Modified Eagle Medium (DMEM) media containing heat-inactivated 5% FBS (Atlas Biologicals Cat # F-0500-D, Lot F31E18D1) and grown under standard conditions. For luciferase transduction, in short, HEK 293T/17 (ATCC CRL-11268) cells were plated and co-transfected with Lipofectamine 3000 (L3000015 Invitrogen), 3rd generation packaging vectors pRSV-REV #12253, pMDG.2 #12259, and pMDLg/pRRE #12251 (Addgene) and lentiviral vector directing expression of luciferase reporter pHIV-Luciferase #21375 4.5 \u0026micro;g (Addgene). Viral particles were harvested, filtered through a 0.45 \u0026micro;m Durapore PVDF Membrane (Millipore SE1M003M00) and added to each cell line\u0026rsquo;s culture media. Viral infections were carried out over 72 hours and transduced cells were selected by their resistance to 2 \u0026micro;g/mL puromycin (MP Biomedicals 0219453910).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnimal Studies\u003c/h3\u003e\n\u003cp\u003eFemale C57BL/6J (BL/6) mice were purchased from Jackson Laboratories (Bar Harbor, ME) at 6\u0026ndash;8 weeks of age. Experimental animals were housed and handled in accordance with Cleveland Clinic Lerner Research Institute Institutional Animal Care and Use Committee approved protocol.\u003c/p\u003e\n\u003cp\u003eThirty C57BL/6J mice were injected intraperitoneally with murine KPCA-luc cells (5 x10\u003csup\u003e6\u003c/sup\u003e) on day 0. On day 14, mice were randomized to three diet arms: KD, high fat/ low carbohydrate diet (HF/LC), and low fat, high carbohydrate (LF/HC) diet. All murine diets were irradiated and provided by Tekland Envigo\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. HF/LC (RD.160239.PWD) diet consisted of 75% fat from Crisco, cocoa butter, and corn oil and 15% carbohydrates, while KD diet (RD. 160153.PWD) consisted of 90% fat from the same sources and 0% carbohydrates. LF/HC diet (TD.150345) consisted of 13% fat and 77%% carbohydrates. Diet macronutrient and micronutrient composition is shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Weekly \u003cem\u003ein vivo\u003c/em\u003e imaging system (IVIS) was performed between days 14 and 41. Weekly blood glucose and ketone levels were also assessed following a tail vein blood draw using a standard laboratory glucometer (Precision Xtra \u0026reg;). Mouse weight was assessed weekly on a standard laboratory scale. At necropsy, plasma (cardiac puncture) and tumor tissue were collected. Fecal pellets were collected at the endpoint for microbial sequencing and analysis.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMurine Body Condition Score\u003c/h3\u003e\n\u003cp\u003eMouse wellbeing was assessed directly: while gently restraining a mouse by holding the base of its tail, the observer (blinded to the treatment group) used the thumb and index finger of the other hand to palpate the degree of muscle and fat over the sacroiliac region. A score from 1\u0026ndash;5 was given to each mouse weekly following IP tumor cell injection based on previous literature \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The same observer was used for all tests. Based on established IACUC protocols #2018\u0026thinsp;\u0026minus;\u0026thinsp;2003, a body composition score of 2 or lower was defined as meeting endpoint criteria for euthanasia.\u003c/p\u003e\n\u003ch3\u003eTumor Monitoring by 2D IVIS Imaging\u003c/h3\u003e\n\u003cp\u003eAll bioluminescence images were taken with IVIS Lumina (PerkinElmer) using D-luciferin as previously described \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Mice received an intraperitoneal (IP)injection of D-luciferin (Goldbio LUCK-1G, 150 mg/kg in 150 uL) under inhaled isoflurane anesthesia. Images were analyzed (Living Image Software), and total flux was reported in photons/second/cm\u003csup\u003e2\u003c/sup\u003e/steradian for each mouse abdomen. All images were obtained with an automatic exposure.\u003c/p\u003e\n\u003ch3\u003e16SrRNA gene amplicon sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA extraction, 16S rRNA gene amplification, sequencing, and bioinformatic analysis were performed as described previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Briefly, raw 16S amplicon sequence and metadata were demultiplexed using split_libraries_fastq.py script implemented in QIIME2 \u003csup\u003e31\u003c/sup\u003e. Demultiplexed fastq file was split into sample-specific fastq files using split_sequence_file_on_sample_ids.py script from QIIME2. Individual fastq files without non-biological nucleotides were processed using Divisive Amplicon Denoising Algorithm (DADA) pipeline \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.The output of the dada2 pipeline (feature table of amplicon sequence variants (an ASV table)) was processed for alpha and beta diversity analysis using phyloseq \u003csup\u003e33\u003c/sup\u003e, and microbiomeSeq (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.github.com/umerijaz/microbiomeSeq\u003c/span\u003e\u003c/span\u003e) packages in R. We analyzed variance (ANOVA) among sample categories while measuring the \u0026alpha;-diversity measures using plot_anova_diversity function in microbiomeSeq package. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was performed on all principal coordinates obtained during CCA with the ordination function of the microbiomeSeq package.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eShotgun Metagenomics sequencing and bioinformatics analysis\u003c/h2\u003e\n \u003cp\u003eQuality control of the metagenomic reads was conducted as described previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Briefly, raw reads were processed for low-quality based filtering using Trimmomatic pipeline \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Host-derived reads were excluded by mapping the reads to the reference human genome (version GRCh38.p14) using BBMap software (sourceforge.net/projects/bbmap/). Quality trimmed reads were processed for taxonomic and functional profiling using Metaphlan2 \u003csup\u003e10\u003c/sup\u003e and Humann2 \u003csup\u003e11\u003c/sup\u003e, respectively. Differential feature selection was performed using Fisher\u0026rsquo;s exact-t-test\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. We assessed the statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) throughout, and whenever necessary, we adjusted \u003cem\u003eP\u003c/em\u003e-values for multiple comparisons according to the Benjamini and Hochberg method to control False Discovery Rate\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e while performing multiple tests on taxa and pathway abundances according to sample types. Differential network analysis was performed at the species level using NetComi set at the following parameters; filtTax=\u0026rdquo;highestVar\u0026rdquo;, filtTaxPar\u0026thinsp;=\u0026thinsp;list(highestVar\u0026thinsp;=\u0026thinsp;40), measure=\u0026rdquo;sparcc\u0026rdquo;, normMethod = \u0026ldquo;mclr\u0026rdquo;, zeroMethod =\u0026rdquo;none\u0026rdquo;, sparsMethod = \u0026ldquo;none\u0026rdquo;, dissFunc = \u0026ldquo;signed\u0026rdquo;. Eigenvector centrality was used for defining hubs and scaling node sizes. Node colors represent clusters, which are determined using greedy modularity optimization. Clusters have the same color in both networks if they share at least two taxa. Nodes that are unconnected in both groups were removed\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eDifferential abundance test benchmarking was performed using DAtest package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Russel88/DAtest/wiki/usage#typical-workflow\u003c/span\u003e\u003c/span\u003e). Briefly, differentially abundant methods were compared with False Discovery Rate (FDR), Area Under the (Receiver Operator) Curve (AUC), Empirical power (Power), and False Positive Rate (FPR). Based on the DAtest\u0026rsquo;s benchmarking, we selected lefseq and anova as the methods of choice to perform differential abundance analysis. We assessed the statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) throughout, and whenever necessary, we adjusted P-values for multiple comparisons according to the Benjamini and Hochberg method to control the False Discovery Rate(Benjamini and Hochberg, 1995). Linear regression (parametric test), and Wilcoxon (Non-parametric) tests were performed on genera and species abundances against metadata variables using their base functions in R (version 4.1.2; R Core Team, 2021) (Team, 2021).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Approvals\u003c/h3\u003e\n\u003cp\u003eAll murine studies were completed in accordance with the Institutional Animal Care and Use Committee guidelines, approval # 2018\u0026thinsp;\u0026minus;\u0026thinsp;2003. All studies utilizing lentiviral particle generation were completed in accordance with the Institutional Biosafety Committee guidelines, approval #IBC0920.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eKD and HF/LC diets induced the growth of KPCA ovarian tumors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the impact of KD vs. HF/LC diet on EOC growth, 30 female C57BL/6J (BL/6) mice were injected intraperitoneally with KPCA Luc cells (5x10\u003csup\u003e5\u003c/sup\u003e). Two weeks after injection, mice were randomized to one of three diet arms (KD, HF/ LC diet, and LF/ HC diet (10 mice per arm). Glucose levels remained stable throughout the study, with no difference between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). By endpoint, mice treated with KD demonstrated a significant increase in circulating ketone levels compared to HF/LC and LF/HC diet-fed mice, confirming mice were in ketosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Body weight was significantly higher in HF/LC diet-fed mice relative to KD and LF/HC diet-fed mice (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Mice fed KD and HC/LC diet showed a marked increase in tumor growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) compared to LF/HC diet-treated mice (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eKD and High Fat Diets induced ketones and tumor growth.\u003c/b\u003e Ketogenic diet and HF/LC diet in C57Bl/6 mice bearing KPCA tumors induces ketosis and tumor growth (\u003cb\u003eA\u003c/b\u003e) Weekly circulating glucose levels in each diet arm remain stable throughout the study (\u003cb\u003eB\u003c/b\u003e) Weekly circulating ketone levels in each diet arm with significant increase in KD fed mice noted (\u003cb\u003eC\u003c/b\u003e) Mouse weight over the study course between the three diet groups HF/LC diet treated mice show significant increase in body weight compared to KD and LC/HF diet.. (\u003cb\u003eD\u003c/b\u003e) Increase in tumor growth in mice fed KD and HF/LC diet relative to LF/HC diet fed mice (n\u0026thinsp;=\u0026thinsp;10 mice per group, ANOVA *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n.s.= not significant).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDecreased gut microbial diversity in HF/LC and KD fed mice compared to LF/HC fed mice.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDue to the known impact of diet on GM diversity, we sought to characterize differences between GM taxa among diet groups and elucidate alterations in GM species abundance in response to diet. Alpha diversity is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. Overall, LF/HC diet-fed mice showed the highest alpha diversity while HF/LC and KD-fed mice showed a marked decrease in gut microbial diversity. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows a significant leftward separation in microbial abundance between LF/HC control-fed mice and KD and HF/LC diet-fed mice. The key differentially abundant gut microbial species included \u003cem\u003eBacteroides thetaiotamicron\u003c/em\u003e, \u003cem\u003eLachnospiraceae bacterium\u003c/em\u003e, Bacterium_D16_50 and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e predominantly abundant in HF/LC-fed mice Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, and \u003cem\u003eDubosiella newyorkensis\u003c/em\u003e predominantly abundant in LF/HC-fed mice. KD-fed mice had a higher abundance of \u003cem\u003eAkkermansia\u003c/em\u003e relative to HF/LC and LF/HC diet fed mice, while \u003cem\u003eBacteroides thetaiotamicron\u003c/em\u003e was highly abundant in HF/LC diet relative to the two other groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eAlpha diversity and relative abundance of bacterial species\u003c/b\u003e KD, HF/LC, and LF/HC. (A) Box plot depicting the alpha diversity measured by the Simpson metric for the three groups. The box plot illustrates the distribution of alpha diversity scores within each group, highlighting median values, interquartile ranges, and potential outliers. (B) Principal Component Analysis (PCA) plot representing beta diversity among the three groups. Points on the PCA plot indicate the sample scores for each group, facilitating visualization of the variability and clustering of microbial communities across the different dietary conditions. (C) A stacked flow bar plot shows the relative abundance of microbial taxa across the KD, HF/LC, and LF/HC groups. The plot illustrates the composition of microbial communities, with different colors representing various taxa, allowing for a comparative analysis of community structure among the groups.\u003c/p\u003e \u003cp\u003eThe associations between gut microbial taxa and diet are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Distinct clustering was noted for each diet group. A strong positive correlation was seen between both \u003cem\u003eLactobacillus johnsonii\u003c/em\u003e and \u003cem\u003eDubosiella newyorkenesis\u003c/em\u003e and LF/HC diet, while the same species were negatively correlated with HF/LC diet. A similar strong association was also noted between bacterium 1xD8-48, Neglecta, and \u003cem\u003eBifidobacterium-pseudolongum\u003c/em\u003e, and LF/HC diet compared to HF/LC diet. Within the HF/LC diet group, a significant positive correlation was noted between several species, including \u003cem\u003eClostridial bacterium\u003c/em\u003e, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, \u003cem\u003eRomboustia\u003c/em\u003e, and \u003cem\u003eTuribacter sp\u003c/em\u003e- TS3. \u003cem\u003eEnterohabdus-sp-p55\u003c/em\u003e, Lachnospiraceae, \u003cem\u003eStaphylococcus xylosus\u003c/em\u003e, and \u003cem\u003eLachnospiraceae bacterium\u003c/em\u003e- MD308 were significantly associated with KD. Receiver operator curves demonstrate high predictive accuracy for KD taxonomy group (AUC 0.89), HF/ LC group (AUC 0.79), and control (AUC 0.98) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The statistical significance of the key species \u003cem\u003eLactobacillus johnsonii\u003c/em\u003e, \u003cem\u003eDubosiella newyorkenesis, Turibacter sp- TS3 and Enterohabdus-sp-p55\u003c/em\u003e is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. As demonstrated, these species show high predictability in distinguishing between each of the diet groups\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cb\u003eDifferential Microbiome taxonomic composition across Groups. (A)\u003c/b\u003e Heatmap displaying the differentially abundant species across the three groups: KD, HF/LC, and LF/HC. The bubble size in the heatmap corresponds to the fraction of samples in which each species is present, while the color gradient indicates the abundance z-score, with warmer colors representing higher abundance. \u003cb\u003e(B)\u003c/b\u003e Area Under the Curve (AUC) plot illustrating the specificity (y-axis) versus 1-specificity (x-axis) for the microbiome's predictive performance across the groups. Each colored line represents a specific group's performance, demonstrating the microbiome's discriminative ability to distinguish between them. \u003cb\u003e(C)\u003c/b\u003e Box plot derived from Tukey's Honest Significant Difference (HSD) analysis, highlighting the statistical significance of key species identified in the heatmap. This plot assesses the capability of these species to differentiate between the KD, HF/LC, and LF/HC groups.\u003c/p\u003e \u003cp\u003eDifferential network analysis was used to visually represent functional associations between diet groups, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C. Eigenvector centrality was used to define hubs and scaling node sizes, with node size representing relative abundance. Associations between different subject groups were captured utilizing Aitchison\u0026rsquo;s distance. The analysis is restricted to samples and species with at least 1000 reads, with normalization performed to achieve fraction-based counts. To address the presence of zeros for clr transformation, \u0026ldquo;multiplicative imputation\u0026rdquo; is employed. Each dissimilarity matrix is scaled to a range of [0,1] and employs the k-nearest neighbor method (⁠=3) for sparsification. Colors of the nodes indicate clusters derived from hierarchical clustering using average linkage, where clusters sharing the same color have a minimum of 100 nodes in common. Hubs are depicted with bold borders and are defined as nodes with eigenvector centrality surpassing the 99% quantile of the empirical distribution. Edge thickness reflects the degree of similarity, and nodes are arranged closer together based on their compositional similarity, with unconnected nodes excluded from the visualization. Positive correlations are noted in green, and negative correlations are shown in red. Comparisons between HF/LC and KD groups revealed a significant positive association between the following bacterial species in KD: \u003cem\u003eBacteroides thetaiotamicron\u003c/em\u003e, \u003cem\u003eAkkermansia muciniphilia\u003c/em\u003e, \u003cem\u003eLachnospiraceae bacterium\u003c/em\u003e and GGB30302 SGB432661. A similar pattern was noted in comparing KD and LF/HC diets. For LF/HC compared to HF/LC diets, significant positive associations between \u003cem\u003eBacterium D16\u003c/em\u003e 50, \u003cem\u003eLactococcus lactus\u003c/em\u003e, and \u003cem\u003eLachnospiraceae bacterium\u003c/em\u003e and HF/LC diet were detected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cb\u003eComparative differential network analysis across diet groups.\u003c/b\u003e. \u003cb\u003e(A)\u003c/b\u003e the relationship between LF/HC and KD, \u003cb\u003e(B)\u003c/b\u003e the connection between HF/LC and KD, and network \u003cb\u003e(C)\u003c/b\u003e dissimilarity between LF/HC and HF/LC groups, utilizing consistent analytical parameters throughout.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional analysis of GM reveals significant metabolic pathway alterations between diet groups\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFunctional pathway analysis was performed to identify bacterial metabolic alterations across LF/HC and HF/LC diets. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, a significantly higher abundance of super pathways of polyamine biosynthesis I and II, as well as fatty acid and beta-oxidation pathways and phospholipases, was observed in the HF/LC diet group. Similarly, the fucose degradation pathway was enriched in the KD group, while L-ornithine biosynthesis, the bifidobacterium shunt, the super pathway of L-alanine biosynthesis, and D-gluconate degradation were among the pathways significantly enriched in the LF/HC diet group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Receiver operator curves demonstrate high predictive accuracy for functional composition across diet groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Finally, we used Tukey's Honest Significant Difference (HSD) test (as a post hoc test) to test the statical significance of individual pathways in differentiating all groups. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, the polyamine biosynthesis significantly influenced tumor growth with marked differential abundance between HF/LC and LF/HC and KD groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cb\u003eDifferential Microbiome functional composition across groups. (A)\u003c/b\u003e Heatmap displaying the differentially abundant functional pathways across the three groups: KD, HF/LC, and LF/HC. The bubble size in the heatmap corresponds to the fraction of samples in which each species is present, while the color gradient indicates the abundance z-score, with warmer colors representing higher abundance. \u003cb\u003e(B)\u003c/b\u003e Area Under the Curve (AUC) plot illustrating the specificity (y-axis) versus 1-specificity (x-axis) for the microbiome's predictive performance across the groups. Each colored line represents a specific group's performance, demonstrating the microbiome's metabolic potential to distinguish between them. \u003cb\u003e(C)\u003c/b\u003e Box plot derived from Tukey's Honest Significant Difference (HSD) analysis, highlighting the statistical significance of key functional pathways identified in the heatmap. This plot assesses the capability of these pathways to differentiate between the KD, HF/LC, and LF/HC groups.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eResults of our investigation demonstrate that both KD and HF/LC diets induced significant growth in KPCA EOC tumors compared to the LF/HC diet. Despite stable glucose levels across all groups, KD-fed mice exhibited elevated circulating ketone levels, confirming ketosis, and increased tumor growth. The HF/LC diet similarly promoted tumor growth, highlighting the potential oncogenic influence of high-fat intake regardless of carbohydrate restriction. Our results also demonstrated a generalized increase in gut microbial alpha diversity in mice fed an LF/HC diet and decreased diversity in mice fed an HF/LC and KD diet.\u003c/p\u003e \u003cp\u003eThe decrease in alpha diversity correlated with the increase in EOC tumor growth seen in our mouse models. It has been well established in prior studies that a lower gut microbial diversity is strongly associated with adverse treatment outcomes and poor response to therapy in patients with cancer \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The mechanisms that KD and increased dietary fat alter gut microbial abundance are complex and have not been comprehensively defined. One mechanism involves the increase in levels of the ketone body beta-hydroxybutyrate (β-HB) on a KD, which is correlated with a reduced abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBifidobacteria species\u003c/em\u003e have been shown to have anti-proliferative and anti-apoptotic properties in colorectal cancer, and its abundance was associated with response to therapy in lung cancer-treated mice \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. We indeed showed that mice fed KD had a lower abundance of \u003cem\u003eBifidobacteria\u003c/em\u003e which were overrepresented in LF/HC diet-fed mice that had a lower growth rate of EOC than KD-fed mice.\u003c/p\u003e \u003cp\u003eWe additionally noted a distinct clustering of bacterial taxa within each diet group, highlighting the discrete yet complex relationship between dietary intake and gut microbial composition. We noted that \u003cem\u003eLachnospiracae bacterium\u003c/em\u003e, \u003cem\u003eBacterium _D16\u003c/em\u003e_50, and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e are predominantly associated with HF/LC-fed, and \u003cem\u003eDubsiella_newyorkensis\u003c/em\u003e predominantly associated with LF/HC-fed mice. \u003cem\u003eDubosiella\u003c/em\u003e is a genus within the family \u003cem\u003eLachnospiraceae\u003c/em\u003e, which is part of the Firmicutes phylum. It is the murine homologue of Clostridium, which has a probiotic immunomodulatory effect and is known for producing short-chain fatty acids that contribute to a balanced gut microbiome. On the other hand, increased abundance of \u003cem\u003eEnterococcus fecalis\u003c/em\u003e has been previously shown to be associated with a high fat diet, which tends to be associated with inflammation, toxin-related damage, and cancer progression. \u003cem\u003eE. faecalis\u003c/em\u003e, however, plays a controversial role in the development of colorectal cancer \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. As demonstrated in the network plots, complex correlations between bacterial taxa and distinct clustering was noted, confirming prior findings demonstrating that a high-fat diet drives changes in gut microbiota with over underrepresentation of beneficial butyrate-producing bacteria \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFunctional shotgun metagenomics analyses were performed to understand the mechanistic implications behind these associations. A critical pathway overrepresented in the HF/LC diet gut microbial analysis was the polyamine biosynthesis pathway. Other significant pathways included fatty acid elongation and fatty acid and beta-oxidation. In contrast, TCA cycle IV and fucose degradation were among the highly overrepresented metabolic pathways in KD. Polyamines play an essential role in cellular growth and differentiation and can influence antitumor immune response and modulate inflammation. Intake of polyamines and dysregulation of polyamine metabolism is a feature of many cancers, including colorectal cancer \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Furthermore, polyamine metabolism was found to regulate immune cells, including tumor-associated macrophages and T cells in hepatocellular carcinoma. Polyamine metabolism is regulated by MYC through the rate-limiting enzyme, ornithine decarboxylase. Moreover, the FDA-approved anti-protozoan drug, α-difluoromethylornithine (DFMO), inhibits ODC activity and induces polyamine depletion, leading to tumor growth arrest \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Herein, we show that polyamine biosynthesis was functionally enhanced by the gut microbiota of HF/LC-fed mice and was predictive of HF/LC dietary intake in mice. Whether this outcome is mediated through the immune microenvironment is imperative to investigate in future studies.\u003c/p\u003e \u003cp\u003eOn the other hand, fucose is a methylpentose, which was elevated in KD diet-fed mice stool samples, is found in glycoproteins and glycolipids. It is metabolized from dietary glycans by alpha fucosidase, which is prevalent in gut bacteria. \u003cem\u003eBacterium thetaiotamicron\u003c/em\u003e, associated with KD, is proficient at utilizing fucose for energy \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This suggests that fucose degradation by gut microbiota in mice fed KD may play a role in mediating tumor growth. Furthermore, the role of fatty acid beta-oxidation in the progression of tumor growth on HF/LC is noteworthy and has been demonstrated in prior studies \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Data from our group (unpublished) showed that an HF/LC diet was associated with the upregulation of the fatty acid oxidation gene, CPT1A. Fatty acid metabolism dysregulation is a characteristic feature of EOC and correlates with poor survival \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Thus, the effects of HF/LC on tumor growth may be potentially mediated through the utilization of available fatty acids and upregulation of fatty acid metabolism.\u003c/p\u003e \u003cp\u003eOur findings underscore the complex interplay between diet, tumor metabolism, and the gut microbiome. We utilized advanced metagenomic analyses to validate and characterize the associations between diet and gut microbiota, shedding light on the intricate interactions between gut microbiome and metabolome. One of the main limitations of our study, however, is that we could not assess the effects of EOC growth on changes in gut microbial composition, as we did not control for tumor presence in our studies. Further correlations between gut, tumor, and plasma metabolites would solidify the results of our study.\u003c/p\u003e \u003cp\u003eThe pro-tumorigenic effects of KD and HF/LC diets raise important considerations for dietary recommendations in cancer patients. While KD has gained popularity for its potential anti-tumor effects, our data suggests it may not be universally beneficial and could potentially fuel tumor growth in specific contexts. These findings contribute to the growing body of evidence on the critical role of diet in cancer progression and the potential for dietary manipulation as a therapeutic strategy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNIH K12CA076917 Clinical Oncology Training Program\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cu\u003eData Availability\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKPCA cell line was kindly provided by Dr. Robert Weinberg at the Massachusetts Institute of Technology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: MA, OR, GC\u003c/p\u003e\n\u003cp\u003eCollection and assembly of data: AM, ST, DJL\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: \u0026nbsp;MA, NS, OR, NB, GC\u003c/p\u003e\n\u003cp\u003e16S RNA sequencing: NS\u003c/p\u003e\n\u003cp\u003eManuscript writing: MA\u003c/p\u003e\n\u003cp\u003eEditing and final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cu\u003eClinical trial number:\u003c/u\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics approval and Institutional Review Board Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to guidelines of the Declaration of Helsinki and approved by IACUC at Cleveland Clinic (Protocol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eInformed consent statement\u003c/u\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflicts of interests\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGopalakrishnan, V. \u003cem\u003eet al.\u003c/em\u003e Gut microbiome modulates response to anti\u0026ndash;PD-1 immunotherapy in melanoma patients. \u003cem\u003eScience (1979)\u003c/em\u003e \u003cstrong\u003e359\u003c/strong\u003e, 97\u0026ndash;103 (2018).\u003c/li\u003e\n\u003cli\u003eMatson, V. \u003cem\u003eet al.\u003c/em\u003e The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e359\u003c/strong\u003e, 104\u0026ndash;108 (2018).\u003c/li\u003e\n\u003cli\u003eGeller, L. T. \u003cem\u003eet al.\u003c/em\u003e Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e357\u003c/strong\u003e, 1156\u0026ndash;1160 (2017).\u003c/li\u003e\n\u003cli\u003eAlHilli, M. M. \u0026amp; Bae-Jump, V. Diet and gut microbiome interactions in gynecologic cancer. \u003cem\u003eGynecol Oncol\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 299\u0026ndash;308 (2020).\u003c/li\u003e\n\u003cli\u003eScott, A. J. \u003cem\u003eet al.\u003c/em\u003e International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis. \u003cem\u003eGut\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 1624\u0026ndash;1632 (2019).\u003c/li\u003e\n\u003cli\u003eGentile, C. L. \u0026amp; Weir, T. L. The gut microbiota at the intersection of diet and human health. \u003cem\u003eScience (1979)\u003c/em\u003e \u003cstrong\u003e362\u003c/strong\u003e, 776\u0026ndash;780 (2018).\u003c/li\u003e\n\u003cli\u003eScott, A. J. \u003cem\u003eet al.\u003c/em\u003e International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis. \u003cem\u003eGut\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 1624\u0026ndash;1632 (2019).\u003c/li\u003e\n\u003cli\u003eHelmink, B. A., Khan, M. A. W., Hermann, A., Gopalakrishnan, V. \u0026amp; Wargo, J. A. The microbiome, cancer, and cancer therapy. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 377\u0026ndash;388 (2019).\u003c/li\u003e\n\u003cli\u003eRea, D. \u003cem\u003eet al.\u003c/em\u003e Microbiota effects on cancer: from risks to therapies. \u003cem\u003eOncotarget\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 17915\u0026ndash;17927 (2018).\u003c/li\u003e\n\u003cli\u003eAlHilli, M. M. \u0026amp; Bae-Jump, V. Diet and gut microbiome interactions in gynecologic cancer. \u003cem\u003eGynecol Oncol\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 299\u0026ndash;308 (2020).\u003c/li\u003e\n\u003cli\u003eSoldati, L. \u003cem\u003eet al.\u003c/em\u003e The influence of diet on anti-cancer immune responsiveness. \u003cem\u003eJ Transl Med\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 75 (2018).\u003c/li\u003e\n\u003cli\u003eSingh, R. K. \u003cem\u003eet al.\u003c/em\u003e Influence of diet on the gut microbiome and implications for human health. \u003cem\u003eJ Transl Med\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 73 (2017).\u003c/li\u003e\n\u003cli\u003eBeam, A., Clinger, E. \u0026amp; Hao, L. Effect of Diet and Dietary Components on the Composition of the Gut Microbiota. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003ePrentice, R. L. \u003cem\u003eet al.\u003c/em\u003e Low-fat dietary pattern and cancer incidence in the Women\u0026rsquo;s Health Initiative Dietary Modification Randomized Controlled Trial. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e \u003cstrong\u003e99\u003c/strong\u003e, 1534\u0026ndash;43 (2007).\u003c/li\u003e\n\u003cli\u003eBlank, M. M., Wentzensen, N., Murphy, M. A., Hollenbeck, A. \u0026amp; Park, Y. Dietary fat intake and risk of ovarian cancer in the NIH-AARP Diet and Health Study. \u003cem\u003eBr J Cancer\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 596\u0026ndash;602 (2012).\u003c/li\u003e\n\u003cli\u003eSeyfried, T. N., Sanderson, T. M., El-Abbadi, M. M., McGowan, R. \u0026amp; Mukherjee, P. Role of glucose and ketone bodies in the metabolic control of experimental brain cancer. \u003cem\u003eBr J Cancer\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 1375\u0026ndash;1382 (2003).\u003c/li\u003e\n\u003cli\u003eKlement, R. J., Brehm, N. \u0026amp; Sweeney, R. A. Ketogenic diets in medical oncology: a systematic review with focus on clinical outcomes. \u003cem\u003eMed Oncol\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 14 (2020).\u003c/li\u003e\n\u003cli\u003eKlement, R. J. \u0026amp; Sweeney, R. A. Impact of a ketogenic diet intervention during radiotherapy on body composition: II. Protocol of a randomised phase I study (KETOCOMP). \u003cem\u003eClin Nutr ESPEN\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e1\u0026ndash;e6 (2016).\u003c/li\u003e\n\u003cli\u003eCabrera-Mulero, A. \u003cem\u003eet al.\u003c/em\u003e Keto microbiota: A powerful contributor to host disease recovery. \u003cem\u003eRev Endocr Metab Disord\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 415\u0026ndash;425 (2019).\u003c/li\u003e\n\u003cli\u003ePaoli, A. \u003cem\u003eet al.\u003c/em\u003e Ketogenic Diet and Microbiota: Friends or Enemies? \u003cem\u003eGenes (Basel)\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 534 (2019).\u003c/li\u003e\n\u003cli\u003eAng, Q. Y. \u003cem\u003eet al.\u003c/em\u003e Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 1263-1275.e16 (2020).\u003c/li\u003e\n\u003cli\u003eParisa, A. \u003cem\u003eet al.\u003c/em\u003e Anti-cancer effects of Bifidobacterium species in colon cancer cells and a mouse model of carcinogenesis. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, e0232930 (2020).\u003c/li\u003e\n\u003cli\u003eAlHilli, M. M. \u003cem\u003eet al.\u003c/em\u003e Unrestricted Ketogenic Diet Feeding Enhances Epithelial Ovarian Cancer Growth In Vivo. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, (2023).\u003c/li\u003e\n\u003cli\u003eZhao, G., Cardenas, H. \u0026amp; Matei, D. Ovarian Cancer\u0026mdash;Why Lipids Matter. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1870 (2019).\u003c/li\u003e\n\u003cli\u003eNieman, K. M., Romero, I. L., Van Houten, B. \u0026amp; Lengyel, E. Adipose tissue and adipocytes support tumorigenesis and metastasis. \u003cem\u003eBiochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids\u003c/em\u003e \u003cstrong\u003e1831\u003c/strong\u003e, 1533\u0026ndash;1541 (2013).\u003c/li\u003e\n\u003cli\u003eIyer, S. \u003cem\u003eet al.\u003c/em\u003e Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of Combination Immunotherapy. \u003cem\u003eCancer Discov\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 384\u0026ndash;407 (2021).\u003c/li\u003e\n\u003cli\u003eAng, Q. Y. \u003cem\u003eet al.\u003c/em\u003e Ketogenic Diets Alter the Gut Microbiome Resulting in Decreased Intestinal Th17 Cells. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 1263-1275.e16 (2020).\u003c/li\u003e\n\u003cli\u003eNewman, J. C. \u003cem\u003eet al.\u003c/em\u003e Ketogenic Diet Reduces Midlife Mortality and Improves Memory in Aging Mice. \u003cem\u003eCell Metab\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 547-557.e8 (2017).\u003c/li\u003e\n\u003cli\u003eUllman-Culler\u0026eacute;, M. H. \u0026amp; Foltz, C. J. Body condition scoring: a rapid and accurate method for assessing health status in mice. \u003cem\u003eLab Anim Sci\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 319\u0026ndash;23 (1999).\u003c/li\u003e\n\u003cli\u003eChambers, L. M. \u003cem\u003eet al.\u003c/em\u003e Use of Transabdominal Ultrasound for the detection of intra-peritoneal tumor engraftment and growth in mouse xenografts of epithelial ovarian cancer. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, e0228511 (2020).\u003c/li\u003e\n\u003cli\u003eBolyen, E. \u003cem\u003eet al.\u003c/em\u003e Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. \u003cem\u003eNat Biotechnol\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 852\u0026ndash;857 (2019).\u003c/li\u003e\n\u003cli\u003eCallahan, M. J. \u003cem\u003eet al.\u003c/em\u003e Primary fallopian tube malignancies in BRCA-positive women undergoing surgery for ovarian cancer risk reduction. \u003cem\u003eJ Clin Oncol\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 3985\u0026ndash;90 (2007).\u003c/li\u003e\n\u003cli\u003eMcMurdie, P. J. \u0026amp; Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e61217 (2013).\u003c/li\u003e\n\u003cli\u003eClements, S. J. \u0026amp; R. Carding, S. Diet, the intestinal microbiota, and immune health in aging. \u003cem\u003eCrit Rev Food Sci Nutr\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 651\u0026ndash;661 (2018).\u003c/li\u003e\n\u003cli\u003ede Almeida, C. V., Taddei, A. \u0026amp; Amedei, A. The controversial role of Enterococcus faecalis in colorectal cancer. \u003cem\u003eTherap Adv Gastroenterol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1756284818783606 (2018).\u003c/li\u003e\n\u003cli\u003eXiao, L. \u003cem\u003eet al.\u003c/em\u003e High-fat feeding rather than obesity drives taxonomical and functional changes in the gut microbiota in mice. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 43 (2017).\u003c/li\u003e\n\u003cli\u003eHuang, C.-Y. \u003cem\u003eet al.\u003c/em\u003e Dietary Polyamines Intake and Risk of Colorectal Cancer: A Case-Control Study. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eKim, H. I. \u003cem\u003eet al.\u003c/em\u003e Pharmacological targeting of polyamine and hypusine biosynthesis reduces tumour activity of endometrial cancer. \u003cem\u003eJ Drug Target\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 623\u0026ndash;633 (2022).\u003c/li\u003e\n\u003cli\u003ePickard, J. M. \u0026amp; Chervonsky, A. V. Intestinal fucose as a mediator of host-microbe symbiosis. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e194\u003c/strong\u003e, 5588\u0026ndash;93 (2015).\u003c/li\u003e\n\u003cli\u003eZhao, G., Cardenas, H. \u0026amp; Matei, D. Ovarian Cancer\u0026mdash;Why Lipids Matter. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1870 (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5904007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5904007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: The gut microbiome (GM) is pivotal in regulating inflammation, immune responses, and cancer progression. This study investigates the effects of a ketogenic diet (KD) and a high-fat/low-carbohydrate (HF/LC) diet on GM alterations and tumor growth in a syngeneic mouse model of high-grade serous ovarian cancer (EOC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Thirty female C57BL/6J mice injected with KPCA cells were randomized into KD, HF/LC, and low-fat/high-carbohydrate (LF/HC) diet groups. Tumor growth was monitored with live, in vivo imaging. Stool samples were collected at the time of euthanasia and analyzed by 16SrRNA sequencing and shotgun metagenomic sequencing was performed to identify differential microbial taxonomic composition and metabolic function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Our findings revealed that KD and HF/LC diets significantly accelerated EOC tumor growth compared to the LF/HC diet in a xenograft model. GM diversity was markedly reduced in KD and HF/LC-fed mice, correlating with increased tumor growth, whereas LF/HC-fed mice showed higher GM diversity. Metagenomic analyses identified distinct alterations in microbial taxa including \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eLachnospiracae bacterium\u003c/em\u003e, Bacterium_D16_50, and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e predominantly abundant in HF/LC-fed mice, \u003cem\u003eDubsiella_newyorkensis\u003c/em\u003e predominantly abundant in LF/HC-fed, and KD fed mice showing a higher abundance of \u003cem\u003eAkkermansia\u003c/em\u003eand \u003cem\u003eBacteroides\u003c/em\u003e. Functional pathways across diet groups indicated polyamine biosynthesis and fatty acid oxidation pathways were enriched in HF/LC-fed mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e These results highlight the intricate relationship between diet, the gut microbiome, and tumor metabolism. The potential role of dietary interventions in cancer prevention and treatment warrants further investigation.\u003c/p\u003e","manuscriptTitle":"The effects of dietary fat on gut microbial composition and function in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:37:38","doi":"10.21203/rs.3.rs-5904007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-31T13:31:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T03:52:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-12T19:13:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243067914397471734333323431820224020861","date":"2025-03-08T03:22:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168148255110142216229951498802582757099","date":"2025-02-19T10:04:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-18T19:26:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-05T18:57:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-05T09:54:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2025-01-26T01:44:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bef4b7b-54e5-4aa5-a87f-600f6c8f2755","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:40:54+00:00","versionOfRecord":{"articleIdentity":"rs-5904007","link":"https://doi.org/10.1186/s13048-025-01731-1","journal":{"identity":"journal-of-ovarian-research","isVorOnly":false,"title":"Journal of Ovarian Research"},"publishedOn":"2025-08-02 16:13:04","publishedOnDateReadable":"August 2nd, 2025"},"versionCreatedAt":"2025-02-07 10:37:38","video":"","vorDoi":"10.1186/s13048-025-01731-1","vorDoiUrl":"https://doi.org/10.1186/s13048-025-01731-1","workflowStages":[]},"version":"v1","identity":"rs-5904007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5904007","identity":"rs-5904007","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}