Synergistic Effects of Fructose and Food Preservatives on Non-Alcoholic Fatty Liver Disease: From Gut Microbiome Alterations to Hepatic Gene Expression

Synergistic Effects of Fructose and Food Preservatives on Non-Alcoholic Fatty Liver Disease: From Gut Microbiome Alterations to Hepatic Gene Expression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synergistic Effects of Fructose and Food Preservatives on Non-Alcoholic Fatty Liver Disease: From Gut Microbiome Alterations to Hepatic Gene Expression Tomas Hrncir, Eva Trckova, Lucia Hrncirova This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4814043/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a growing global health problem closely linked to dietary habits, particularly high fructose consumption. This study investigates the combined effects of fructose and common food preservatives (sodium benzoate, sodium nitrite, and potassium sorbate) on the development and progression of NAFLD in a human-microbiota-associated mouse model. Results Our comprehensive analysis reveals that fructose and potassium sorbate synergistically increase liver damage, inflammation, and fibrosis, while altering liver function, lipid profiles, and intestinal permeability. Significant changes were observed in the composition of gut bacterial and fungal communities, accompanied by the induction of predominantly pro-inflammatory immune responses, particularly in the mesenteric lymph nodes. Gene expression analysis in the liver uncovered extensive transcriptional changes induced by fructose and modulated by preservatives, affecting key genes involved in lipid metabolism, oxidative stress, and inflammatory responses. Conclusions Our findings highlight the complex interplay between dietary components, gut microbiota, and host metabolism in the development of NAFLD. The study suggests potential risks associated with combined fructose and preservative consumption, particularly potassium sorbate. These results open new avenues for understanding and treating NAFLD through dietary intervention and microbiome modulation, emphasizing the need for further investigation into the impact of food additives on liver health. non-alcoholic fatty liver disease fructose food preservatives gut microbiome hepatic gene expression intestinal permeability inflammation metabolic dysregulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Non-alcoholic fatty liver disease (NAFLD) is a rapidly growing global health challenge, characterized by excessive accumulation of fat in the liver in the absence of significant alcohol consumption. The global prevalence of NAFLD is estimated to be 24% and rising, with the highest rates reported in the Middle East and South America (Younossi et al., 2023 ). NAFLD encompasses a spectrum of conditions ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), which can progress to fibrosis, cirrhosis, and hepatocellular carcinoma (Rinella, 2015 ). The disease is closely associated with symptoms of the metabolic syndrome, including obesity, insulin resistance, and dyslipidemia (Vanni et al., 2010 ). The pathogenesis of NAFLD is complex and multifactorial, involving interactions between genetic, environmental, and metabolic factors (Hrncir et al., 2021 ). However, there is increasing evidence that the gut microbiota plays a critical role in the development and progression of NAFLD (Betrapally et al., 2017 ). Alterations in gut microbial composition and function, known as dysbiosis, have been consistently observed in NAFLD patients (Shen et al., 2017 ). These changes are characterized by reduced microbial diversity, shifts in the balance of beneficial and harmful bacteria, and altered microbial metabolic activities (Aron-Wisnewsky et al., 2020 ). Recent research has highlighted the importance of environmental factors, particularly diet, in shaping the gut microbiome and influencing the pathogenesis of NAFLD (Hrncir, 2022 ). Of particular concern is the increased consumption of fructose and food additives in modern diets. Fructose, a major component of high fructose corn syrup and sucrose, has been implicated in inducing gut dysbiosis, increasing intestinal permeability, and directly affecting liver metabolism (Jensen et al., 2018 ). Similarly, certain food additives have been shown to alter gut microbial communities and potentially exacerbate NAFLD (Hrncirova et al., 2019 )(Chassaing et al., 2017 ). The gut-liver axis, a bidirectional communication system between the gastrointestinal tract and the liver, plays a crucial role in the pathophysiology of NAFLD (Hrncir et al., 2021 ). Disruption of this axis, through increased intestinal permeability and translocation of bacterial products, can lead to chronic low-grade inflammation and metabolic disturbances in the liver (Lebeaupin et al., 2015 ). Understanding the complex interplay between dietary factors, gut microbiota, and host metabolism is essential for developing effective strategies for the prevention and treatment of NAFLD (Chu et al., 2019 ). This study aims to investigate the synergistic effects of fructose and the common food preservatives on the development and progression of NAFLD. Using a multifaceted approach combining histological, biochemical, immunological, and genomic analyses, we aim to elucidate the underlying mechanisms by which these dietary components affect gut microbiota composition, intestinal barrier function, and liver physiology. Our findings may provide new insights into the pathogenesis of NAFLD and inform the development of novel diagnostic, preventive, and therapeutic approaches targeting the food-gut-liver axis, highlighting the critical role of dietary components, including food additives, in this interconnected system (Sharpton et al., 2019 ). 2. Materials and Methods 2.1. Experimental animals Wild-type C57BL/6 mice were obtained from Jackson Laboratories and housed under specific pathogen-free conditions. Germ-free C57BL/6 mice were generated and maintained in flexible film isolators in our gnotobiotic facility. Human gut microbiota-associated mice were generated by colonizing germ-free mice with a fecal sample obtained from a healthy human donor, following appropriate informed consent and screening procedures. These mice were then bred in the facility for several generations to establish stable colonization. All mice were maintained on a 12-hour light/dark cycle with ad libitum access to food and water. Mice in the breeding colonies as well as experimental mice were fed standard breeding diet (Cat. No. V1124, ssniff, Germany). Animal care and experimental procedures were approved by the Institutional Animal Care and Use Committee and conducted in accordance with institutional guidelines. 2.2. Fructose and preservative supplementation To induce NAFLD, 10% fructose (w/v) was administered from 3 weeks of age for 11 weeks. Fructose (Cat. No. F0127, Merck) was given ad libitum in the drinking water. The preservatives, namely sodium benzoate (E211; Cat. No. 71300, Merck), sodium nitrite (E250; Cat. No. 237213, Merck) and potassium sorbate (E202; Cat. No. 85520, Merck), were administered together with the fructose. Exposure to preservatives, normalized to mouse body weight and water consumption, was adjusted to the estimated maximum daily intake of additives in the European population (source: Report from the Commission on Dietary Food Additive Intake in the European Union, https://publications.europa.eu ). These were 4.8 mg/kg bw/day for benzoate, 0.36 for nitrite and 19.0 for sorbate. All solutions were freshly prepared each week and kept refrigerated until use. 2.3. Histological sample preparation and staining Liver tissue samples were fixed in 10% neutral buffered formalin, dehydrated through a graded ethanol series, cleared in xylene substitute (Neo-Clear, Merck) and embedded in paraffin according to standard protocols. Sections were cut at 5 µm thickness and mounted on glass slides. H&E staining was performed according to standard procedures. Briefly, sections were deparaffinized, rehydrated, stained with Mayer's hematoxylin, counterstained with eosin, dehydrated, cleared, and mounted. Masson's trichrome staining was performed according to the manufacturer's protocol (Diapath S.p.A., Italy). Deparaffinized and rehydrated sections were stained with Weigert's iron hematoxylin for 10 minutes, followed by picric acid solution for 4 minutes. The sections were then stained with Biebrich's scarlet acid fuchsin for 4 minutes and differentiated in phosphomolybdic acid solution for 10 minutes. Finally, the sections were counterstained with aniline blue for 4 minutes, dehydrated through a graded series of ethanol, cleared, and mounted. This procedure stains nuclei black, muscle fibers and cytoplasm red, and collagen fibers blue. 2.4. Biochemical Analyses Plasma levels of liver enzymes and lipids were measured using commercially available kits from Merck (Darmstadt, Germany). Alanine aminotransferase (ALT) activity was measured using the ALT Activity Assay Kit (Cat. No. MAK052). Aspartate aminotransferase (AST) activity was measured using the AST Activity Assay Kit (Cat. No. MAK055). Alkaline phosphatase (ALP) activity was measured using the ALP Activity Assay Kit (Cat. No. MAK447). Triglyceride levels were quantified with the Triglyceride Quantification Assay Kit (Cat. No. MAK266). Total and free cholesterol levels were determined using the Cholesterol Quantitation Kit (Cat. No. MAK043). All assays were performed according to the manufacturer's instructions. Absorbance measurements were recorded using a SPECTROstar Nano microplate reader (BMG LABTECH, Ortenberg, Germany). Standard curves were generated for each assay to calculate the concentrations of the respective analytes in the plasma samples. 2.5. Oral administration of FITC-dextran and measurement of plasma fluorescence to determine intestinal permeability Intestinal permeability was determined in vivo using FITC-labeled dextran (4 kDa, Merck). Mice were fasted for 4 hours before oral administration of 200 µL FITC-dextran solution (50 mg/ml in PBS) per 20 g body weight. After 4 hours, blood was collected from the submandibular vein and allowed to clot for 30 minutes at room temperature. Plasma was separated by centrifugation at 2,000 x g for 10 minutes at 4°C. Plasma samples were diluted with PBS (35 µL plasma in 175 µL PBS) and fluorescence was measured using a Qubit fluorometer with blue excitation at 470 nm and green emission at 510 and 580 nm. A standard curve was generated by serial dilution of FITC-dextran in a mixture of plasma and PBS (17% plasma in PBS) to determine the final concentration of FITC-dextran in the plasma samples. Higher concentrations of FITC-dextran in plasma indicate increased intestinal permeability. 2.6. Sequencing of the 16S rRNA gene and the ITS amplicon and bioinformatic analysis The composition of the microbial community was analyzed by sequencing the 16S rRNA gene and the ITS amplicon. DNA was extracted using the QIAamp PowerFecal DNA Kit (QIAGEN). Amplicon libraries targeting the V3-V4 region of the 16S rRNA gene (341f − 806bR primers) and the ITS1 region (ITS1F - ITS2 primers) were prepared. The libraries were sequenced on the Illumina MiSeq platform (2x300 bp) using MiSeq reagent V3. Bioinformatic analysis was performed using QIIME 2 version 2024.5 (Bolyen et al., 2019 ). Raw reads were demultiplexed and the quality of raw read quality was visualized using FastQC (Andrews, 2010 ). Primer sequences were removed using cutadapt (Martin, 2011 ). Reads were denoised, chimeras were removed, and amplicon sequence variants (ASVs) were identified using the DADA2 workflow(Callahan et al., 2016 ). For 16S data, a multiple sequence alignment was performed using MAFFT (Katoh, 2002 ) and a phylogenetic tree was constructed using FastTree (Price et al., 2009 ). Taxonomy was assigned to ASVs using a naive Bayes classifier trained on the SILVA database release 138 for 16S (Quast et al., 2013 ) or the UNITE database for ITS. Unassigned ASVs and contaminants from mitochondrial and chloroplast origin were removed. Alpha diversity metrics, including Shannon's diversity index, observed ASVs, Faith's phylogenetic diversity (for 16S only), and Pielou's evenness, were calculated after rarefaction. Beta diversity metrics, including Jaccard distance, Bray-Curtis dissimilarity, unweighted UniFrac, and weighted UniFrac (for 16S only), were calculated to compare community composition between sample groups. Differential abundance analysis was performed using ANCOM-BC2 (Lin & Peddada, 2020 ) with a significance threshold of W-statistic > 0.7 and FDR-adjusted p-value < 0.05. Taxonomic composition was visualized using interactive stacked bar plots. Additional statistical analyses and data visualization were performed using R version 4.1.0 (R Core Team, 2021 ) in RStudio version 2024.04.2 (RStudio Team, 2021 ). 2.7. Cell isolation and culture Harvested tissues (spleen, mesenteric lymph nodes, and liver) were placed in separate tissue culture dishes containing cold PBS. Each tissue was gently disrupted into a single cell suspension using the plunger of a 2 ml syringe. The resulting suspensions were filtered through 70 µm cell strainers (BD Biosciences) and washed twice with PBS by centrifugation at 400 x g for 5 minutes at 4°C. Red blood cells were lysed with ACK lysis buffer (150 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA, pH 7.2) for 5 minutes at room temperature, followed by two washes with PBS. Liver cell suspensions were further purified by gradient centrifugation on 33% Percoll (GE Healthcare) at 500 x g for 20 minutes at room temperature without brake. The interface containing lymphocytes was collected and washed twice with PBS. Isolated cells were resuspended in complete RPMI-1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM L-glutamine, 100 U/ml penicillin, and 100 µg/ml streptomycin. Cells were cultured at a density of 1 x 10^6 cells/ml in 24-well plates and stimulated with phorbol 12-myristate 13-acetate (PMA, 20 ng/ml) and ionomycin (500 ng/ml) for 5 hours at 37°C in a humidified incubator with 5% CO2. 2.8. Cytokine profiling by ELISA Cytokine levels were measured in cell supernatants using ThermoFisher ELISA kits according to the manufacturer's protocols. The cytokines quantified included IFN𝛾, TNFα, IL-6, IL-10, and IL-17A. Briefly, 96-well plates were coated overnight at 4°C with 100 µL/well of capture antibody diluted in coating buffer. After three washes with wash buffer (PBS + 0.05% Tween-20), the plates were blocked with 200 µL/well assay diluent for 1 hour at room temperature. Standards and samples were diluted in assay diluent as needed and 100 µL were added to the appropriate wells. Plates were incubated for 2 hours at room temperature. After washing 3 times, 100 µL of detection antibody diluted in assay diluent was added to each well and incubated for 1 hour. After three washes, 100 µL of diluted streptavidin-HRP was added to each well and incubated for 30 minutes. After five washes, 100 µL of TMB substrate solution was added to each well and the plates were allowed to develop for 10–30 minutes. The reaction was stopped by the addition of 100 µL stop solution (1M H2SO4). Absorbance was measured at 450 nm with wavelength correction at 620 nm. Cytokine concentrations were calculated from 4-parameter logistic standard curves. Samples were analyzed in duplicate. All samples below the lowest standard were assigned the value of the minimum detectable concentration for that assay. 2.9. RNA extraction and quality control Total RNA was extracted from mouse liver tissue samples stabilized in RNAlater buffer. Extraction was performed using the RNeasy Micro Kit (QIAGEN, Hilden, Germany) according to the manufacturer's protocol for "Purification of total RNA from animal and human tissue". Briefly, tissue samples were disrupted and homogenized in 600 µL RLT buffer containing 1% β-mercaptoethanol using a Precellys 24 Homogenizer (Bertin Corp., Rockville, MD, USA) with Precellys CK14 ceramic beads (2 cycles of 15 seconds at 6500 rpm with a 30-second break). After centrifugation, 300 µL of the clarified lysate was processed through RNeasy MinElute spin columns, including an on-column DNase digestion step. Total RNA was eluted in 14 µL nuclease-free water. RNA integrity and purity were assessed using an Agilent 2100 Bioanalyzer with the RNA 6000 Nano LabChip reagent set (Agilent, Palo Alto, CA, USA). 2.10. Microarray analysis Gene expression profiling was performed using Applied Biosystems GeneChip Clariom S mouse arrays. Sample preparation and microarray hybridization were performed according to the manufacturer's protocol (Applied Biosystems GeneChip Whole Transcript PLUS Reagent Kit). Briefly, 200 ng of total RNA was used to generate double-stranded cDNA. After purification, 20 µg of cRNA was synthesized by in vitro transcription and then used to generate single-stranded (ss) cDNA incorporating dUTP. The purified ss cDNA was fragmented using uracil DNA glycosylase and apurinic/apyrimidinic endonuclease 1 and terminally labeled with biotin. 3.8 µg of fragmented and labeled ss cDNA was hybridized to the arrays for 16 hours at 45°C with rotation at 60 rpm. Arrays were washed and stained using an Applied Biosystems GeneChip Fluidics Station 450, and scanned with an Applied Biosystems GeneChip Scanner 3000 7G system. Fluidics and scanning functions were controlled by the Applied Biosystems GeneChip Command Console v5.0 software. 2.11. Microarray data analysis Microarray data analysis was performed using the Transcriptome Analysis Console (TAC) 4.0.2 software (Applied Biosystems). Probe set signal values were calculated using the Signal Space Transformation-Robust Multi-Chip Analysis (SST-RMA) algorithm. Differential gene expression analysis was performed using the Linear Models for Microarray Data (LIMMA) method. Genes were considered differentially expressed if they met the following criteria: fold change 2 and false discovery rate (FDR) p-value < 0.05. Principal component analysis and hierarchical clustering were used to visualize sample relationships. 2.12. Statistical Analysis Data are presented as mean ± standard deviation (SD) unless otherwise noted. Statistical analyses were performed using GraphPad Prism 10 for macOS (GraphPad Software, San Diego, USA), QIIME2 software package (version 2024.5, www.qiime2.org ), Transcriptome Analysis Console (TAC) 4.0.2 software (Thermo Fisher Scientific, Boston, USA), and R studio (version 2024.04.2, Boston, MA, USA). One-way analysis of variance (ANOVA) followed by Tukey's post hoc test for multiple comparisons was used for comparisons between groups. For microbiome data analysis, alpha diversity metrics were compared using the Kruskal-Wallis test, while beta diversity was assessed using PERMANOVA with 999 permutations. Differential abundance analysis of bacterial taxa was performed using ANCOM-BC2 with a significance threshold of W-statistic > 0.7 and FDR-adjusted p-value 1 and FDR-adjusted p-value < 0.05. Significance levels are indicated as follows: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ****P ≤ 0.0001. Multiple testing corrections were applied where appropriate using the Benjamini-Hochberg method to control for false discovery rate. Sample sizes for each experiment were determined based on power analysis or prior experience to detect biologically relevant differences. 3. Results 3.1. Fructose and preservatives synergistically induce hepatic steatosis and inflammation Histological analysis of liver sections revealed significant differences between the treatment groups (Fig. 1 ). Water-treated control mice showed normal liver architecture without steatosis, inflammation or fibrosis (Fig. 1 a, b). In contrast, mice treated with fructose alone or in combination with preservatives showed varying degrees of hepatic steatosis, characterized by lipid droplets within the hepatocytes (Fig. 1 c-i). The combination of fructose and sorbate resulted in the most severe liver pathologies (Fig. 1 ch, i). These liver sections showed significant infiltration with mononuclear inflammatory cells, disrupted lobular architecture and hepatocyte ballooning degeneration (Fig. 1 ch, upper inset). Glycogen deposition was also observed, indicating altered liver metabolism (Fig. 1 ch, lower inset). Notably, early-stage fibrosis was evident in this group (Fig. 1 i), suggesting potential progression to advanced liver disease. These findings demonstrate that while fructose alone can induce hepatic steatosis, its combination with preservatives, particularly sorbate, exacerbates liver damage. The synergistic effects observed highlight the importance of considering the combined effects of dietary components and food additives on liver health. 3.2. Fructose consumption alters liver function and lipid metabolism, with food preservatives potentially modulating these effects. Fructose administration resulted in significant increases in ALT, triglycerides, and cholesterol levels compared to the water control (Fig. 2 ). The addition of food preservatives to fructose resulted in different effects. Notably, only the addition of sorbate resulted in increases in all three liver enzymes, i.e. ALT, AST, and ALP, whereas addition of benzoate and nitrite significantly increased only ALT. Triglyceride levels were elevated in all treated groups. Total cholesterol levels were moderately elevated in the fructose and the fructose-sorbate groups. Free cholesterol levels were significantly elevated in the fructose and fructose-nitrite groups and highly elevated in the fructose-sorbate group. These results suggest that the addition of food preservatives, especially sorbate, leads to an amplification of the negative effects of fructose on liver function and lipid metabolism. 3.3. Fructose and food additives synergistically increase intestinal permeability Intestinal permeability was determined by measuring plasma FITC-dextran concentration after oral administration (Fig. 3 ). Fructose treatment did not significantly increase intestinal permeability compared to the water control group (2.89 ± 0.15 µg/ml vs. 2.98 ± 0.18 µg/ml). However, when fructose was combined with food additives, permeability increased. The most pronounced increase in permeability was observed in the fructose plus sorbate group (3.25 ± 0.15 µg/ml, p < 0.01 vs. water control), followed by fructose plus benzoate (3.21 ± 0.14 µg/ml, p < 0.05). These results suggest that the combined consumption of fructose and preservatives increases intestinal permeability. 3.4. Fructose combined with sorbate alters fungal but not bacterial alpha diversity Analysis of alpha diversity metrics revealed different responses between bacterial and fungal communities to the treatments. Bacterial communities showed surprising resilience, with no significant differences in observed ASVs, Shannon diversity index, or Faith's phylogenetic diversity across any treatment groups (Fig. 4 a, c, e). In contrast, fungal communities showed significant sensitivity, but only to the fructose-sorbate treatment. This group showed significantly lower Shannon diversity (p = 0.005479, q = 0.054786) and Pielou's evenness (p = 0.027891, q = 0.069726) compared to the water control and other treatment groups (Fig. 4 d, f). Notably, fructose alone or in combination with benzoate or nitrite did not significantly alter fungal alpha diversity metrics. These results suggest that while bacterial community richness and diversity remained stable across all treatments, the combination of fructose and sorbate uniquely disrupted fungal community. 3.5. Fructose and food preservatives induce significant shifts in the composition of bacterial and fungal community Beta diversity analysis showed significant differences in bacterial and fungal community composition between treatment groups. For bacterial communities, principal coordinate analysis (PCoA) based on weighted UniFrac distances showed a clear separation between treatment groups (Fig. 5 a), which was confirmed by PERMANOVA (pseudo-F = 3.204613, p = 0.001). Pairwise comparisons revealed significant differences (q < 0.05) between all treatment groups, with a particularly strong differentiation observed between the water control and the treatment with fructose and sorbate (Fig. 5 c, e). Fungal communities also showed significant shifts in composition between treatments, as shown by PCoA based on Bray-Curtis dissimilarities (Fig. 5 b) and confirmed by PERMANOVA (pseudo-F = 1.620826, p = 0.001). Pairwise comparisons showed significant differences between most treatment groups (Fig. 5 d, f), although the strength of differentiation was generally lower than in the bacterial communities, as evidenced by higher q values and lower pseudo-F values. 3.6. Fructose in combination with benzoate or sorbate induces the greatest changes in bacterial taxa abundance Differential abundance analysis revealed that the effects on bacterial taxa varied considerably between treatments. At the phylum level, fructose alone induced significant changes in two phyla (an increase in Patescibacteria and a decrease in Proteobacteria), demonstrating its ability to modulate the gut microbiome. Interestingly, fructose combined with nitrite showed no significant effect on any phylum, suggesting a potential neutralizing effect of nitrite on fructose-induced changes. The most profound changes were observed in the fructose + benzoate and fructose + sorbate groups. The fructose + benzoate combination significantly affected four phyla, causing an increase in Verrucomicrobia and a decrease in Proteobacteria, Bacteroidota (formerly Bacteroidetes), and Firmicutes. The fructose + sorbate group induced changes in three phyla, with increases in Patescibacteria, Actinobacteria, and Desulfobacteria. At the genus level, a complex pattern of changes was observed. Significant increases were detected in several genera, including Akkermansia (especially in the fructose + benzoate group), Corynebacterium , Acinetobacter , Butyricicoccaceae, Lachnoclostridium , Lachnospiraceae, Blautia , and Oscillospiraceae. Conversely, significant decreases were observed in genera such as Romboutsia and Turicibacter (Fig. 6 g). These results highlight that while fructose alone can induce some changes in bacterial abundance, its combination with certain food additives, particularly benzoate and sorbate, leads to more extensive and diverse changes in fecal bacterial community structure. This suggests a synergistic effect between fructose and these additives in modulating the gut microbiome, which could have important implications for host health and metabolism. 3.7. Fructose and food additives selectively alter the abundance of fungal taxa Analysis of the fungal communities revealed selective and treatment-specific changes in taxa abundance. At the phylum level, significant changes were observed mainly in the Basidiomycota (Fig. 6 d). Notably, both fructose + benzoate and fructose + sorbate treatments induced a decrease in this phylum, suggesting a consistent impact of these combinations on fungal community structure. At the genus level, we observed a complex pattern of changes that varied by treatment. Fructose alone caused the most significant depletion of fungal genera, particularly Parastagonospora , Bensingtonia , Armillaria , and Xeromyces , while increasing the abundance of Rasamsonia . The fructose + benzoate combination also induced an increase in Rasamsonia . In contrast, fructose + nitrite increased Candida , Penicillium , and Ceratocystis . The fructose + sorbate treatment shared some effects with fructose + nitrite, causing increases in Penicillium and Ceratocystis . However, it also caused the most extensive depletion of fungal genera, including Cryptococcus , Filobasidium , Sporobolomyces , Cladosporium , Alternaria , and Armillaria . These distinct patterns of changes in fungal taxa highlight the specific and diverse effects of fructose alone and in combination with different food preservatives. The observed shifts in both bacterial and fungal communities suggest that these treatments, particularly the combinations of fructose with benzoate and sorbate, significantly affect the composition of gut microbiome. These changes could potentially influence host-microbe interactions and metabolic processes, highlighting the importance of considering both bacterial and fungal components when studying the effects of dietary factors on the gut microbiome. 3.8. Fructose and food preservatives induce organ-specific changes in cytokine profiles To investigate the effects of fructose and food preservatives on immune responses, we measured cytokine levels in the spleen, mesenteric lymph nodes (MLN), and liver of treated mice using ELISA (Fig. 7 ). Our results showed different patterns of cytokine production in the different organs and treatments. Fructose alone had no significant effect on any of the cytokines measured. The most pronounced effect was observed with the fructose-sorbate treatment, which stimulated the production of IFNγ, TNFα, and IL-6 in both spleen and MLN. In MLN, this treatment also stimulated the production of IL-10 and IL-17A. The fructose-benzoate treatment stimulated the production of IFNγ in the spleen and of IFNγ, TNFα, and IL-17A in the MLN. The fructose-nitrite treatment significantly increased cytokine production only in the MLN. Overall, these results show that fructose-preservative combinations, especially fructose + sorbate, induce a proinflammatory cytokine profile in multiple organs. This profile is characterized by increased levels of IFNγ, TNFα, IL-6 and IL-17A. The increase in IL-10 production could play a counter-regulatory role and potentially attenuate the pro-inflammatory response. The organ-specific nature of these changes, particularly the pronounced effects in the MLN, suggests that gut-associated lymphoid tissue plays a critical role in mediating immune responses to fructose and food additives. This finding emphasized the potential importance of the gut-immune axis in the physiological response to food components. The lack of detectable cytokine changes in the liver may be due to methodological limitations. The higher dilutions required for liver samples due to the lower numbers of isolated liver leukocytes may have resulted in cytokine levels falling below the detection limit of the ELISA method. These results provide important insights into the immunomodulatory effects of fructose-preservative combinations and highlights the need for further investigation of their potential impact on systemic and organ-specific immune responses. 3.9. Fructose induces significant changes in hepatic gene expression To investigate the effects of fructose consumption on hepatic gene expression, we performed RNA sequencing on liver samples from mice treated with water (control) or fructose. Principal component analysis (PCA) of the gene expression data revealed a clear separation between the control and fructose-treated groups along the first principal component (PCA1), which accounted for 50.1% of the variance (Fig. 4 a). This indicates that fructose treatment caused a significant shift in the overall hepatic transcriptome. Differential expression analysis identified 3 560 genes that were significantly altered by fructose treatment compared to the water control (fold change 2, FDR p-value < 0.05) (Fig. 4 b). Of these, 1,936 genes were upregulated and 1,624 were downregulated. The top differentially expressed genes are shown in the heatmap (Fig. 4 c), which highlights the different expression patterns between the water and fructose treatments. 3.10. Key genes associated with the pathogenesis of NAFLD were significantly modulated by fructose Significant changes were found in the expression of several genes known to be associated with the development of NAFLD (Supplementary Table 1). In particular, genes involved in lipid metabolism were strongly upregulated, including Me1 (22-fold), which provides NADPH for fatty acid biosynthesis, and Acsl1 (4.2-fold), which activates long-chain fatty acids. The insulin receptor gene (Insr) was also upregulated 20-fold, possibly indicating developing insulin resistance. Genes associated with oxidative stress, a key feature of NAFLD, were also affected. Aox1, which is involved in the production of reactive oxygen species (ROS), was upregulated 17-fold. Conversely, Aldh3a2, which protects against lipid peroxidation, was increased 6.2-fold, suggesting a possible compensatory mechanism. Interestingly, some changes appeared to be potentially protective. Csad, which is involved in the biosynthesis of taurine, was upregulated 25-fold; taurine deficiency is associated with NAFLD. In addition, Abca1, which is involved in cholesterol efflux, was upregulated 11-fold. The inflammatory response also appeared to be modulated, with the chemokines Cxcl1 and Ccl9 being downregulated by 4.4- and 10-fold, respectively. This unexpected decrease in proinflammatory markers warrants further investigation. 3.11. Food preservatives modulate fructose-induced gene expression changes PCA revealed that although all fructose-treated groups clustered separately from the water control, there were significant differences between the fructose-only and fructose plus preservative groups (Fig. 4 a). This suggests that the preservatives had additional effects on gene expression beyond those induced by fructose alone. Differential expression analysis showed that each preservative uniquely modulated the fructose-induced gene expression profile (Fig. 4 c). Compared to fructose alone, sodium benzoate altered the expression of 394 genes, sodium nitrite of 133 genes, and potassium sorbate of 662 genes (fold change 2, p-value < 0.05) (Fig. 4 d). Of particular interest, several cytochrome P450 enzymes showed dramatic changes in expression upon preservative treatment (Supplementary Table 1). For example, Cyp4a12b and Cyp14a12a were strongly downregulated in the fructose-sorbate group compared to fructose alone (by -717-fold and − 2499-fold, respectively), while Cyp2b9 was strongly upregulated (by 6356-fold). These enzymes play a crucial role in xenobiotic and lipid metabolism, suggesting that preservatives may significantly affect these processes in the context of fructose-induced metabolic changes. Other notable genes affected by preservatives included Elovl3 (involved in fatty acid elongation), which was down-regulated 1835-fold, and Sult3a1 (involved in lipid metabolism), which was up-regulated 2267-fold in the fructose plus sorbate group. These changes emphasize the potential of food preservatives to affect lipid metabolism in the fructose-exposed liver. In summary, our results show that fructose consumption induces widespread changes in hepatic gene expression that are consistent with the NAFLD development. Furthermore, we show for the first time that common food preservatives can significantly modulate these fructose-induced changes, which could alter the course of NAFLD progression. These findings emphasize the need for further investigation into the combined effects of fructose and food additives on liver health. 4. Discussion This study provides new insights into the synergistic effects of fructose and common food preservatives on the development and progression of non-alcoholic fatty liver disease (NAFLD). Our findings demonstrate that while fructose alone can induce hepatic steatosis and alter liver function, its combination with certain preservatives, particularly potassium sorbate, exacerbates liver damage and triggers extensive changes in the composition of gut microbiota and host immune responses. The histological analysis revealed that fructose in combination with sorbate induced the most severe liver pathology, characterized by significant steatosis, infiltration of inflammatory cells, and early-stage fibrosis. This synergistic effect was also supported by biochemical analyses, which showed that the fructose-sorbate combination led to the most pronounced increases in liver enzymes (ALT, AST, ALP) and lipid profiles. These findings are consistent with previous studies demonstrating the hepatotoxic potential of fructose (Jensen et al., 2018 ), but more importantly, they highlight the potential for common food preservatives to amplify these effects. Our study of intestinal permeability revealed an intriguing pattern. While fructose alone did not significantly increase intestinal permeability, its combination with preservatives, especially sorbate and benzoate, led to a significant increase. This finding suggests that preservatives may enhance the ability of fructose to disrupt intestinal barrier function, which could facilitate the translocation of bacterial products and contribute to liver inflammation (Mouries et al., 2019 ). The mechanism behind this synergistic effect needs further investigation but could be related to changes in tight junction proteins or changes in the mucus layer composition. The microbiome analysis provided several important findings. While bacterial alpha diversity remained stable across all treatments, fungal diversity was significantly reduced by the fructose-sorbate combination. This differential impact on the bacterial and fungal communities underscores the importance of considering both components of the gut microbiome in NAFLD research, an aspect that has often been overlooked in previous studies (Lang et al., 2020 ). The observed reduction in fungal diversity could have important implications for metabolic health, given the emerging role of the mycobiome in regulating host metabolism (Sokol et al., 2017 ). Beta-diversity analyzes revealed significant compositional shifts in the composition of both bacterial and fungal communities between treatments. The fructose-sorbate and fructose-benzoate combinations induced the most extensive changes, particularly in the bacterial phyla Verrucomicrobia, Proteobacteria, and Bacteroidota. The increase in Verrucomicrobia, mainly caused by the genus Akkermansia , is particularly noteworthy given their potential protective role in metabolic disorders (Depommier et al., 2019 ). Akkermansia muciniphila is usually perceived as beneficial bacterium in the context of metabolic diseases. However, it is important to note that Akkermansia is a mucolytic bacterium whose overgrowth might compromise intestinal mucosal barrier. Its increase in the fructose-benzoate group was more than 4-fold. Analysis of differential abundance at the genus level revealed a complex pattern of changes, with certain treatments promoting the growth of potentially pathogenic genera while depleting others associated with metabolic health. For example, the increase in Candida abundance observed with all fructose-based treatments could have negative implications given recent findings that NAFLD patients have an altered fecal mycobiome and enhanced systemic immune responses to Candida albicans (Demir et al., 2022 ). Our cytokine profiling experiments provided valuable insights into the immunomodulatory effects of the combination of fructose and preservatives. The most striking finding was the organ-specific nature of these effects, with the mesenteric lymph nodes (MLN) showing the most pronounced changes. This suggests a crucial role of the gut-associated lymphoid tissue in mediating immune responses to these dietary components. In particular, the fructose-sorbate combination induced a pro-inflammatory cytokine profile characterized by increased levels of IFNγ, TNFα, IL-6, and IL-17A. This pro-inflammatory milieu could contribute to the exacerbation of liver damage observed in this group (Tilg et al., 2021 ). The gene expression analysis revealed extensive transcriptional changes induced by fructose and further modulated by preservatives. The upregulation of genes involved in lipid metabolism (e.g. Me1, Acsl1) and oxidative stress (e.g. Aox1) is consistent with the known mechanisms of fructose-induced liver damage (Softic et al., 2020 ). However, the observed upregulation of potentially protective genes (e.g. Csad, Abca1) suggests the activation of compensatory mechanisms that should be further investigated. Importantly, our study shows for the first time that common food preservatives can significantly modulate fructose-induced changes in hepatic gene expression. The dramatic changes in the expression of cytochrome P450 enzymes upon treatment with preservatives, particularly sorbate, suggest that these additives can profoundly affect xenobiotic and lipid metabolism in the context of fructose-induced metabolic changes. This finding has important implications for understanding the potential interactions between dietary components and environmental toxins in NAFLD pathogenesis. Several limitations of this study should be considered. First, while our mouse model provides valuable insights, the translatability to human NAFLD requires further investigation. Second, the mechanisms underlying the synergistic effects of fructose and preservatives, particularly at the molecular level, need further investigation. Finally, the long-term consequences of the observed changes in gut microbiota composition, immunological function, and hepatic gene expression need to be investigated in longitudinal studies. In summary, our results highlight the complex interplay between dietary fructose, food preservatives, gut microbiota, and host metabolism in the context of NAFLD. The synergistic effects observed with certain preservatives, particularly sorbate, emphasize the need to consider the combined effects of different dietary components in the pathogenesis of NAFLD. These findings open new avenues for exploring potential therapeutic strategies targeting the gut-liver axis and suggest that dietary guidelines for the management of NAFLD may need to consider not only macronutrient composition but also the presence of common food additives. Declarations Ethics approval and consent to participate: All animal procedures were approved by the Institutional Animal Care and Use Committee (approval number 46/2020). Written informed consent was obtained from the human donor for the collection and use of gut microbiota samples in this study. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research was funded by the Czech Science Foundation (20-09732S and 22-12533S) and the Institutional Research Concept (RVO: 61388971). Author Contribution Conceptualization, T.H.; methodology, validation, formal analysis, investigation, curation, writing—original draft preparation, writing—review and editing, visualization, T.H., L.H. and E.T.; supervision, project administration, and funding acquisition, T.H. The first two authors contributed equally. All authors have read and agreed to the published version of the manuscript. Acknowledgement We would like to express our sincere gratitude to our dedicated laboratory technicians, Radka Stribrna, Lenka Cizkova and Eliska Kozlova, for their invaluable technical expertise and unwavering support throughout this project. Data Availability The original contributions presented in the study are available in the article/supplementary materials. The raw metagenomics and gene expression data have been deposited in the ASEP (Academic Science Electronic Publications) repository, maintained by the Library of the Czech Academy of Sciences. These datasets can be accessed through the ASEP online catalogue (https://asep.lib.cas.cz/arl-cav/en/search/). For specific dataset identifiers or access instructions, please refer to the metadata records associated with this study in ASEP. If you encounter any difficulties accessing the data, please contact the corresponding author. References Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data [Computer software]. http://www.bioinformatics.babraham.ac.uk/projects/fastqc Aron-Wisnewsky, J., Vigliotti, C., Witjes, J., Le, P., Holleboom, A. G., Verheij, J., Nieuwdorp, M., & Clément, K. (2020). Gut microbiota and human NAFLD: Disentangling microbial signatures from metabolic disorders. Nature Reviews Gastroenterology & Hepatology , 17 (5), 279–297. https://doi.org/10.1038/s41575-020-0269-9 Betrapally, N. S., Gillevet, P. M., & Bajaj, J. S. (2017). Gut microbiome and liver disease. Translational Research , 179 , 49–59. https://doi.org/10.1016/j.trsl.2016.07.005 Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology , 37 (8), 852–857. https://doi.org/10.1038/s41587-019-0209-9 Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods , 13 (7), 581–583. https://doi.org/10.1038/nmeth.3869 Chassaing, B., Van De Wiele, T., De Bodt, J., Marzorati, M., & Gewirtz, A. T. (2017). Dietary emulsifiers directly alter human microbiota composition and gene expression ex vivo potentiating intestinal inflammation. Gut , 66 (8), 1414–1427. https://doi.org/10.1136/gutjnl-2016-313099 Chu, H., Duan, Y., Yang, L., & Schnabl, B. (2019). Small metabolites, possible big changes: A microbiota-centered view of non-alcoholic fatty liver disease. Gut , 68 (2), 359–370. https://doi.org/10.1136/gutjnl-2018-316307 Demir, M., Lang, S., Hartmann, P., Duan, Y., Martin, A., Miyamoto, Y., Bondareva, M., Zhang, X., Wang, Y., Kasper, P., Bang, C., Roderburg, C., Tacke, F., Steffen, H.-M., Goeser, T., Kruglov, A., Eckmann, L., Stärkel, P., Fouts, D. E., & Schnabl, B. (2022). The fecal mycobiome in non-alcoholic fatty liver disease. Journal of Hepatology , 76 (4), 788–799. https://doi.org/10.1016/j.jhep.2021.11.029 Depommier, C., Everard, A., Druart, C., Plovier, H., Van Hul, M., Vieira-Silva, S., Falony, G., Raes, J., Maiter, D., Delzenne, N. M., de Barsy, M., Loumaye, A., Hermans, M. P., Thissen, J.-P., de Vos, W. M., & Cani, P. D. (2019). Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: A proof-of-concept exploratory study. Nature Medicine , 25 (7), 1096–1103. https://doi.org/10.1038/s41591-019-0495-2 Hrncir, T. (2022). Gut Microbiota Dysbiosis: Triggers, Consequences, Diagnostic and Therapeutic Options. Microorganisms , 10 (3), 578. https://doi.org/10.3390/microorganisms10030578 Hrncir, T., Hrncirova, L., Kverka, M., Hromadka, R., Machova, V., Trckova, E., Kostovcikova, K., Kralickova, P., Krejsek, J., & Tlaskalova-Hogenova, H. (2021). Gut Microbiota and NAFLD: Pathogenetic Mechanisms, Microbiota Signatures, and Therapeutic Interventions. Microorganisms , 9 (5), 957. https://doi.org/10.3390/microorganisms9050957 Hrncirova, Machova, Trckova, Krejsek, & Hrncir. (2019). Food Preservatives Induce Proteobacteria Dysbiosis in Human-Microbiota Associated Nod2-Deficient Mice. Microorganisms , 7 (10), 383. https://doi.org/10.3390/microorganisms7100383 Jensen, T., Abdelmalek, M. F., Sullivan, S., Nadeau, K. J., Green, M., Roncal, C., Nakagawa, T., Kuwabara, M., Sato, Y., Kang, D.-H., Tolan, D. R., Sanchez-Lozada, L. G., Rosen, H. R., Lanaspa, M. A., Diehl, A. M., & Johnson, R. J. (2018). Fructose and sugar: A major mediator of non-alcoholic fatty liver disease. Journal of Hepatology , 68 (5), 1063–1075. https://doi.org/10.1016/j.jhep.2018.01.019 Katoh, K. (2002). MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research , 30 (14), 3059–3066. https://doi.org/10.1093/nar/gkf436 Lang, S., Duan, Y., Liu, J., Torralba, M. G., Kuelbs, C., Ventura-Cots, M., Abraldes, J. G., Bosques-Padilla, F., Verna, E. C., Brown, R. S., Vargas, V., Altamirano, J., Caballería, J., Shawcross, D., Lucey, M. R., Louvet, A., Mathurin, P., Garcia-Tsao, G., Ho, S. B., … Schnabl, B. (2020). Intestinal Fungal Dysbiosis and Systemic Immune Response to Fungi in Patients With Alcoholic Hepatitis. Hepatology (Baltimore, Md.) , 71 (2), 522–538. https://doi.org/10.1002/hep.30832 Lebeaupin, C., Proics, E., de Bieville, C. H. D., Rousseau, D., Bonnafous, S., Patouraux, S., Adam, G., Lavallard, V. J., Rovere, C., Le Thuc, O., Saint-Paul, M. C., Anty, R., Schneck, A. S., Iannelli, A., Gugenheim, J., Tran, A., Gual, P., & Bailly-Maitre, B. (2015). ER stress induces NLRP3 inflammasome activation and hepatocyte death. Cell Death & Disease , 6 (9), e1879. https://doi.org/10.1038/cddis.2015.248 Lin, H., & Peddada, S. D. (2020). Analysis of compositions of microbiomes with bias correction. Nature Communications , 11 (1), 3514. https://doi.org/10.1038/s41467-020-17041-7 Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal , 17 (1), Article 1. https://doi.org/10.14806/ej.17.1.200 Mouries, J., Brescia, P., Silvestri, A., Spadoni, I., Sorribas, M., Wiest, R., Mileti, E., Galbiati, M., Invernizzi, P., Adorini, L., Penna, G., & Rescigno, M. (2019). Microbiota-driven gut vascular barrier disruption is a prerequisite for non-alcoholic steatohepatitis development. Journal of Hepatology , 71 (6), 1216–1228. https://doi.org/10.1016/j.jhep.2019.08.005 Price, M. N., Dehal, P. S., & Arkin, A. P. (2009). FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution , 26 (7), 1641–1650. https://doi.org/10.1093/molbev/msp077 Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research , 41 (Database issue), D590–D596. https://doi.org/10.1093/nar/gks1219 R Core Team. (2021). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/ Rinella, M. E. (2015). Nonalcoholic fatty liver disease: A systematic review. JAMA , 313 (22), 2263–2273. https://doi.org/10.1001/jama.2015.5370 RStudio Team. (2021). RStudio: Integrated Development Environment for R (2024.04.2) [Computer software]. RStudio. http://www.rstudio.com/ Sharpton, S. R., Maraj, B., Harding-Theobald, E., Vittinghoff, E., & Terrault, N. A. (2019). Gut microbiome-targeted therapies in nonalcoholic fatty liver disease: A systematic review, meta-analysis, and meta-regression. The American Journal of Clinical Nutrition , 110 (1), 139–149. https://doi.org/10.1093/ajcn/nqz042 Shen, F., Zheng, R.-D., Sun, X.-Q., Ding, W.-J., Wang, X.-Y., & Fan, J.-G. (2017). Gut microbiota dysbiosis in patients with non-alcoholic fatty liver disease. Hepatobiliary & Pancreatic Diseases International , 16 (4), 375–381. https://doi.org/10.1016/S1499-3872(17)60019-5 Softic, S., Stanhope, K. L., Boucher, J., Divanovic, S., Lanaspa, M. A., Johnson, R. J., & Kahn, C. R. (2020). Fructose and Hepatic Insulin Resistance. Critical Reviews in Clinical Laboratory Sciences , 57 (5), 308–322. https://doi.org/10.1080/10408363.2019.1711360 Sokol, H., Leducq, V., Aschard, H., Pham, H.-P., Jegou, S., Landman, C., Cohen, D., Liguori, G., Bourrier, A., Nion-Larmurier, I., Cosnes, J., Seksik, P., Langella, P., Skurnik, D., Richard, M. L., & Beaugerie, L. (2017). Fungal microbiota dysbiosis in IBD. Gut , 66 (6), 1039–1048. https://doi.org/10.1136/gutjnl-2015-310746 Tilg, H., Adolph, T. E., & Moschen, A. R. (2021). Multiple Parallel Hits Hypothesis in Nonalcoholic Fatty Liver Disease: Revisited After a Decade. Hepatology , 73 (2), 833–842. https://doi.org/10.1002/hep.31518 Vanni, E., Bugianesi, E., Kotronen, A., De Minicis, S., Yki-Järvinen, H., & Svegliati-Baroni, G. (2010). From the metabolic syndrome to NAFLD or vice versa? Digestive and Liver Disease: Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver , 42 (5), 320–330. https://doi.org/10.1016/j.dld.2010.01.016 Younossi, Z. M., Golabi, P., Paik, J. M., Henry, A., Van Dongen, C., & Henry, L. (2023). The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review. Hepatology (Baltimore, Md.) , 77 (4), 1335–1347. https://doi.org/10.1097/HEP.0000000000000004 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1geneexpression.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-4814043","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339714028,"identity":"ce20ee2b-d587-4277-a75e-065141ffaaf2","order_by":0,"name":"Tomas Hrncir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYNCCCjDJeICxgQjFPGDyDBCzMTCQoIWxjRQt9uzdiR9/zqvN45dvPnDg5w4GeX6xAwRs4Tm7WZp32/FiyTa2hIO9ZxgMZ85OIKBFIneDNOO2Y4kbjvEYHOBtY0gwuE1Yy+afP+dAtBz8S6SWbRK8DTVgLYeJs+XM2W3WPMcOJM5sS0s4LHtGgrBf2Nt7N9/8UVOX2M98+ODDtzts5PmlCWiBgsMwhgRRykGgjmiVo2AUjIJRMAIBAM2iR/4ZUyy8AAAAAElFTkSuQmCC","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Tomas","middleName":"","lastName":"Hrncir","suffix":""},{"id":339714029,"identity":"e0b1b6ea-a1dc-422e-bf61-06db3b104a6e","order_by":1,"name":"Eva Trckova","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Trckova","suffix":""},{"id":339714030,"identity":"fe100d50-810a-422a-8d28-d15676be5b2f","order_by":2,"name":"Lucia Hrncirova","email":"","orcid":"","institution":"Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lucia","middleName":"","lastName":"Hrncirova","suffix":""}],"badges":[],"createdAt":"2024-07-27 17:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4814043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4814043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63376218,"identity":"12e2c2ee-5638-4065-94df-22a4c6e68645","added_by":"auto","created_at":"2024-08-27 12:48:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2192511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistological analysis of liver sections from mice treated with fructose in combination with preservatives.\u003c/strong\u003e Representative H\u0026amp;E and Masson’s trichrome stained liver sections from (a, b) control mice and mice treated with (c, d) fructose, (e, f) fructose + benzoate, (g, h) fructose + nitrite, (ch, i) fructose + sorbate. Scale bars represent 100 μm. (a, b) Water-treated control liver shows normal hepatic architecture with no evidence of steatosis, inflammation or fibrosis. (c-i) Livers from mice treated with fructose alone and in the combination with preservatives exhibit varying degrees of steatosis characterized by the presence of lipid droplets within the hepatocytes. (ch) Liver from mice treated with fructose + sorbate show a significant increase in infiltration with mononuclear inflammatory cells, disruption of normal lobular architecture, steatosis with ballooning degeneration of hepatocytes (upper inset), glycogen deposition (lower inset) and (i) early stages of fibrosis.\u003c/p\u003e","description":"","filename":"Figure1histology.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/58ea1344cd79caba0cf6f284.jpg"},{"id":63375610,"identity":"11aae7e1-eb44-4d39-8322-093a3fd67199","added_by":"auto","created_at":"2024-08-27 12:40:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":236373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of fructose and food preservatives on liver function markers and lipid profiles in mice.\u003c/strong\u003e Plasma levels of liver enzymes and lipids were measured. (a) Alanine aminotransferase (ALT), (b) aspartate aminotransferase (AST), (c) alkaline phosphatase (ALP), (d) triglycerides, (e) total cholesterol, and (f) free cholesterol. Data are expressed as mean ± SD. Statistical analysis was performed by one-way ANOVA followed by Tukey's multiple comparison test. Asterisks indicate significant differences compared with the water control group: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ****P ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure2biochemistry.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/fb8e1837f033e8c57d4b7810.jpg"},{"id":63375607,"identity":"e4da977c-f373-41c2-96c7-5c65e210d831","added_by":"auto","created_at":"2024-08-27 12:40:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntestinal permeability measured by FITC-dextran absorption.\u003c/strong\u003e Intestinal permeability was determined by oral administration of FITC-dextran (4 kDa) and subsequent measurement of plasma fluorescence after 4 hours. Mice were treated with water (control), fructose alone or fructose in combination with benzoate, nitrite, or sorbate. Data are presented as mean plasma FITC-dextran concentrations (μg/ml) ± standard deviation. Statistical significance was determined by one-way ANOVA followed by Dunnett's post hoc test. *p \u0026lt; 0.05, **p \u0026lt; 0.01. n = 8 mice per group.\u003c/p\u003e","description":"","filename":"Figure3permeability.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/0aa8665aa9ef9b34b6700b0e.jpg"},{"id":63376219,"identity":"a59a9d02-deb2-47aa-b7a7-e4130f2242cf","added_by":"auto","created_at":"2024-08-27 12:48:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":725883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of alpha diversity of bacterial (16S V3-V4) and fungal (ITS) communities.\u003c/strong\u003eMeasures of bacterial alpha diversity: (a) Observed ASVs, (c) Shannon diversity index and (e) Faith's phylogenetic diversity. Measures of alpha diversity for fungi: (b) Observed ASVs, (d) Shannon diversity index and (f) Pielou's evenness. Each point represents a single sample, with boxplots showing the median and interquartile range. The colors indicate different treatment groups: Water (control), fructose, fructose + benzoate, fructose + nitrite and fructose + sorbate. Statistically significant differences (p \u0026lt; 0.05) between groups are indicated by p-values and q-values (FDR-corrected p-values). Bacterial alpha diversity metrics showed no significant differences between treatment groups. In contrast, fungal communities showed significant differences in Shannon diversity (p = 0.005479, q = 0.054768) and Pielou’s evenness (p = 0.027891, q = 0.069726) for the fructose-sorbate group compared to the control group, suggesting that the treatments had a stronger effect on fungal community structure than on bacterial community structure. Data were analyzed using QIIME2 and graphs were generated using GraphPad Prism 10.\u003c/p\u003e","description":"","filename":"Figure4alphadiversity.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/804c90128de4aba99a57c001.jpg"},{"id":63376221,"identity":"c0ee783b-9201-4a55-b27b-f84189abfbc2","added_by":"auto","created_at":"2024-08-27 12:48:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":911336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeta diversity analysis of bacterial (16S V3-V4) and fungal (ITS) communities. (\u003c/strong\u003ea, c, e) Bacterial beta diversity analysis: (a) Principal coordinate analysis (PCoA) plot based on weighted UniFrac distances. Each point represents a single sample, with 95% confidence ellipses shown for each treatment group. (c) Heatmap of pairwise PERMANOVA q-values (FDR-corrected p-values) between treatment groups. (e) Heatmap of pairwise PERMANOVA pseudo-F values between treatment groups. (b, d, f) Fungal beta diversity analysis: (d) Principal coordinate analysis (PCoA) plot based on Bray-Curtis dissimilarities. Each point represents a single sample, with 95% confidence ellipses shown for each treatment group. (d) Heatmap of pairwise PERMANOVA q-values (FDR-corrected p-values) between treatment groups. (f) Heatmap of pairwise PERMANOVA pseudo-F values between treatment groups. Colors in the PCoA panels indicate different treatment groups: Water (control), fructose, fructose + benzoate, fructose + nitrite, and fructose + sorbate. PERMANOVA results: The bacterial communities showed significant differences between groups (pseudo-F = 3.204613, p = 0.001), as did the fungal communities (pseudo-F = 1.620826, p = 0.001). Pairwise comparisons revealed significant differences (q \u0026lt; 0.05) between most treatment groups for both bacterial and fungal communities, with greater differentiation observed for bacterial communities (lower q values and higher pseudo-F values). Data were analyzed using QIIME2 and plots were generated using R Studio.\u003c/p\u003e","description":"","filename":"Figure5betadiversity.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/b3d01e46ed82bb82bba2c268.jpg"},{"id":63375609,"identity":"8392156d-15b5-42f7-845c-35e42207a48d","added_by":"auto","created_at":"2024-08-27 12:40:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":875188,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance analysis of bacterial (16S V3-V4) and fungal (ITS) communities. \u003c/strong\u003e(a, c, e, g) Bacterial community analysis: (a) Stacked bar graph showing the relative abundance of bacterial phyla across treatment groups. (c) Heatmap showing differential abundance at the phylum level. Colors represent log fold changes (LFC) relative to the water control group. (e) Stacked bar graph showing the relative abundance of the top 20 bacterial genera across treatment groups. (g) Heatmap showing differential abundance at the genus level for the top 20 taxa with the most significant changes (based on q-values). Colors represent log fold changes (LFC) relative to the water control group. (b, d, f, h) Fungal community analysis: (b) Stacked bar plot showing relative abundance of fungal phyla across treatment groups. (d) Heatmap showing differential abundance at the phylum level. Colors represent log fold changes (LFC) relative to the water control group. (f) Stacked bar graph showing the relative abundance of the top 20 fungal genera across treatment groups. (h) Heatmap showing differential abundance at the genus level for the top 20 taxa with the most significant changes (based on q-values). Colors represent log fold changes (LFC) relative to the water control group. For both bacterial and fungal heatmaps, statistical significance is indicated by asterisks: * q ≤ 0.05, ** q ≤ 0.01, *** q ≤ 0.001. Treatment groups are indicated as water (control), fructose, fructose + benzoate, fructose + nitrite, and fructose + sorbate. Bacterial communities showed significant changes in all phyla except Cyanobacteria, with notable changes at the genus level in \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, Butyricicoccaceae, \u003cem\u003eBlautia\u003c/em\u003e, and many others. Fungal communities showed significant shifts mainly in the Basidiomycota phyla, with genus-level changes observed in \u003cem\u003eRasamsonia\u003c/em\u003e, \u003cem\u003eCandida\u003c/em\u003e, and \u003cem\u003ePenicillium\u003c/em\u003e, among others. The data were analyzed using QIIME2 and graphs were generated using R studio.\u003c/p\u003e","description":"","filename":"Figure6differentialabundance.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/11a6f6ffa5f307f5b8540e09.jpg"},{"id":63376748,"identity":"bb0b819b-c3d2-4b84-af58-747fa88d2a16","added_by":"auto","created_at":"2024-08-27 12:56:10","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":294414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytokine profiles in multiple organs in response to fructose and preservative treatments.\u003c/strong\u003e Cytokine levels were measured in the spleen, mesenteric lymph nodes (MLN), and liver tissues using ELISA. The mice were treated with water (control), fructose alone, or fructose in combination with preservatives (benzoate, nitrite, or sorbate). Quantified cytokines include (a) IFNγ, (b) TNFα, (c) IL-17A, (d) IL-6, and (e) IL-10. Data are presented as mean ± SD in pg/ml. Statistical significance was determined using two-way ANOVA followed by Tukey's post-hoc test. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001. n = 8 mice per group.\u003c/p\u003e","description":"","filename":"Figure7cytokines.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/8918eef17fb9af612b23bdf3.jpg"},{"id":63375616,"identity":"a52feba9-43a6-41d0-b4ec-9f8dd099c316","added_by":"auto","created_at":"2024-08-27 12:40:11","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":523697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression analysis in liver tissue across different fructose-based treatments\u003c/strong\u003e. (a) PCA plot showing sample clustering based on treatment groups. Each dot represents a sample, and colors indicate different treatments. The percentage of variance explained by each principal component is shown on the axes. (\u003cstrong\u003eb\u003c/strong\u003e) Volcano plot showing the differentially expressed genes between the water and fructose treatment groups. The x-axis represents the fold change in expression, while the y-axis shows the -log10 of the FDR p-value. Each dot represents a gene, with red dots indicating significantly up-regulated genes and green points indicating significantly down-regulated genes. The genes have been filtered based on these criteria: fold change \u0026lt; -2 or \u0026gt; 2, FDR p-value \u0026lt; 0.05. (\u003cstrong\u003ec\u003c/strong\u003e) Hierarchical clustering heatmap of top 40 differentially expressed genes between water and fructose-based treatments. The colors indicate the expression level (signal), with genes selected based on the lowest FDR p-value. Clustering is performed on both genes and samples using Euclidean distance metric and the complete linkage method (maximum distance between pairs of objects in clusters). The length of the dendrogram branches represents the degree of similarity between clusters, with shorter branches indicating more closely related objects. (\u003cstrong\u003ed\u003c/strong\u003e) Venn diagram showing the overlap of differentially expressed genes across all fructose-preservative treatment comparisons. Numbers indicate unique and shared differentially expressed genes between groups. Gene filter criteria: Fold change \u0026lt; -2 or \u0026gt; 2, FDR p-value \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure8geneexpression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/0478f60638844e39f996fa9d.jpg"},{"id":63481903,"identity":"803515c4-4e41-4f39-ac61-a55cbd62199f","added_by":"auto","created_at":"2024-08-28 15:07:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6847222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/7439ffb1-a0d2-4dc5-9828-907e7af8fb83.pdf"},{"id":63375612,"identity":"36c71d44-db23-49c0-b9b6-73fd3a0ff771","added_by":"auto","created_at":"2024-08-27 12:40:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48291,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1geneexpression.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4814043/v1/89dd55048fe139ee81d7c21d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synergistic Effects of Fructose and Food Preservatives on Non-Alcoholic Fatty Liver Disease: From Gut Microbiome Alterations to Hepatic Gene Expression","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is a rapidly growing global health challenge, characterized by excessive accumulation of fat in the liver in the absence of significant alcohol consumption. The global prevalence of NAFLD is estimated to be 24% and rising, with the highest rates reported in the Middle East and South America (Younossi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NAFLD encompasses a spectrum of conditions ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), which can progress to fibrosis, cirrhosis, and hepatocellular carcinoma (Rinella, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The disease is closely associated with symptoms of the metabolic syndrome, including obesity, insulin resistance, and dyslipidemia (Vanni et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathogenesis of NAFLD is complex and multifactorial, involving interactions between genetic, environmental, and metabolic factors (Hrncir et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there is increasing evidence that the gut microbiota plays a critical role in the development and progression of NAFLD (Betrapally et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Alterations in gut microbial composition and function, known as dysbiosis, have been consistently observed in NAFLD patients (Shen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These changes are characterized by reduced microbial diversity, shifts in the balance of beneficial and harmful bacteria, and altered microbial metabolic activities (Aron-Wisnewsky et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent research has highlighted the importance of environmental factors, particularly diet, in shaping the gut microbiome and influencing the pathogenesis of NAFLD (Hrncir, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Of particular concern is the increased consumption of fructose and food additives in modern diets. Fructose, a major component of high fructose corn syrup and sucrose, has been implicated in inducing gut dysbiosis, increasing intestinal permeability, and directly affecting liver metabolism (Jensen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, certain food additives have been shown to alter gut microbial communities and potentially exacerbate NAFLD (Hrncirova et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)(Chassaing et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe gut-liver axis, a bidirectional communication system between the gastrointestinal tract and the liver, plays a crucial role in the pathophysiology of NAFLD (Hrncir et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Disruption of this axis, through increased intestinal permeability and translocation of bacterial products, can lead to chronic low-grade inflammation and metabolic disturbances in the liver (Lebeaupin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Understanding the complex interplay between dietary factors, gut microbiota, and host metabolism is essential for developing effective strategies for the prevention and treatment of NAFLD (Chu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to investigate the synergistic effects of fructose and the common food preservatives on the development and progression of NAFLD. Using a multifaceted approach combining histological, biochemical, immunological, and genomic analyses, we aim to elucidate the underlying mechanisms by which these dietary components affect gut microbiota composition, intestinal barrier function, and liver physiology. Our findings may provide new insights into the pathogenesis of NAFLD and inform the development of novel diagnostic, preventive, and therapeutic approaches targeting the food-gut-liver axis, highlighting the critical role of dietary components, including food additives, in this interconnected system (Sharpton et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental animals\u003c/h2\u003e \u003cp\u003eWild-type C57BL/6 mice were obtained from Jackson Laboratories and housed under specific pathogen-free conditions. Germ-free C57BL/6 mice were generated and maintained in flexible film isolators in our gnotobiotic facility. Human gut microbiota-associated mice were generated by colonizing germ-free mice with a fecal sample obtained from a healthy human donor, following appropriate informed consent and screening procedures. These mice were then bred in the facility for several generations to establish stable colonization. All mice were maintained on a 12-hour light/dark cycle with ad libitum access to food and water. Mice in the breeding colonies as well as experimental mice were fed standard breeding diet (Cat. No. V1124, ssniff, Germany). Animal care and experimental procedures were approved by the Institutional Animal Care and Use Committee and conducted in accordance with institutional guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Fructose and preservative supplementation\u003c/h2\u003e \u003cp\u003eTo induce NAFLD, 10% fructose (w/v) was administered from 3 weeks of age for 11 weeks. Fructose (Cat. No. F0127, Merck) was given ad libitum in the drinking water. The preservatives, namely sodium benzoate (E211; Cat. No. 71300, Merck), sodium nitrite (E250; Cat. No. 237213, Merck) and potassium sorbate (E202; Cat. No. 85520, Merck), were administered together with the fructose. Exposure to preservatives, normalized to mouse body weight and water consumption, was adjusted to the estimated maximum daily intake of additives in the European population (source: Report from the Commission on Dietary Food Additive Intake in the European Union, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://publications.europa.eu\u003c/span\u003e\u003cspan address=\"https://publications.europa.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These were 4.8 mg/kg bw/day for benzoate, 0.36 for nitrite and 19.0 for sorbate. All solutions were freshly prepared each week and kept refrigerated until use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Histological sample preparation and staining\u003c/h2\u003e \u003cp\u003eLiver tissue samples were fixed in 10% neutral buffered formalin, dehydrated through a graded ethanol series, cleared in xylene substitute (Neo-Clear, Merck) and embedded in paraffin according to standard protocols. Sections were cut at 5 \u0026micro;m thickness and mounted on glass slides. H\u0026amp;E staining was performed according to standard procedures. Briefly, sections were deparaffinized, rehydrated, stained with Mayer's hematoxylin, counterstained with eosin, dehydrated, cleared, and mounted. Masson's trichrome staining was performed according to the manufacturer's protocol (Diapath S.p.A., Italy). Deparaffinized and rehydrated sections were stained with Weigert's iron hematoxylin for 10 minutes, followed by picric acid solution for 4 minutes. The sections were then stained with Biebrich's scarlet acid fuchsin for 4 minutes and differentiated in phosphomolybdic acid solution for 10 minutes. Finally, the sections were counterstained with aniline blue for 4 minutes, dehydrated through a graded series of ethanol, cleared, and mounted. This procedure stains nuclei black, muscle fibers and cytoplasm red, and collagen fibers blue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Biochemical Analyses\u003c/h2\u003e \u003cp\u003ePlasma levels of liver enzymes and lipids were measured using commercially available kits from Merck (Darmstadt, Germany). Alanine aminotransferase (ALT) activity was measured using the ALT Activity Assay Kit (Cat. No. MAK052). Aspartate aminotransferase (AST) activity was measured using the AST Activity Assay Kit (Cat. No. MAK055). Alkaline phosphatase (ALP) activity was measured using the ALP Activity Assay Kit (Cat. No. MAK447). Triglyceride levels were quantified with the Triglyceride Quantification Assay Kit (Cat. No. MAK266). Total and free cholesterol levels were determined using the Cholesterol Quantitation Kit (Cat. No. MAK043). All assays were performed according to the manufacturer's instructions. Absorbance measurements were recorded using a SPECTROstar Nano microplate reader (BMG LABTECH, Ortenberg, Germany). Standard curves were generated for each assay to calculate the concentrations of the respective analytes in the plasma samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Oral administration of FITC-dextran and measurement of plasma fluorescence to determine intestinal permeability\u003c/h2\u003e \u003cp\u003eIntestinal permeability was determined in vivo using FITC-labeled dextran (4 kDa, Merck). Mice were fasted for 4 hours before oral administration of 200 \u0026micro;L FITC-dextran solution (50 mg/ml in PBS) per 20 g body weight. After 4 hours, blood was collected from the submandibular vein and allowed to clot for 30 minutes at room temperature. Plasma was separated by centrifugation at 2,000 x g for 10 minutes at 4\u0026deg;C. Plasma samples were diluted with PBS (35 \u0026micro;L plasma in 175 \u0026micro;L PBS) and fluorescence was measured using a Qubit fluorometer with blue excitation at 470 nm and green emission at 510 and 580 nm. A standard curve was generated by serial dilution of FITC-dextran in a mixture of plasma and PBS (17% plasma in PBS) to determine the final concentration of FITC-dextran in the plasma samples. Higher concentrations of FITC-dextran in plasma indicate increased intestinal permeability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Sequencing of the 16S rRNA gene and the ITS amplicon and bioinformatic analysis\u003c/h2\u003e \u003cp\u003eThe composition of the microbial community was analyzed by sequencing the 16S rRNA gene and the ITS amplicon. DNA was extracted using the QIAamp PowerFecal DNA Kit (QIAGEN). Amplicon libraries targeting the V3-V4 region of the 16S rRNA gene (341f \u0026minus;\u0026thinsp;806bR primers) and the ITS1 region (ITS1F - ITS2 primers) were prepared. The libraries were sequenced on the Illumina MiSeq platform (2x300 bp) using MiSeq reagent V3.\u003c/p\u003e \u003cp\u003eBioinformatic analysis was performed using QIIME 2 version 2024.5 (Bolyen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Raw reads were demultiplexed and the quality of raw read quality was visualized using FastQC (Andrews, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Primer sequences were removed using cutadapt (Martin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Reads were denoised, chimeras were removed, and amplicon sequence variants (ASVs) were identified using the DADA2 workflow(Callahan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For 16S data, a multiple sequence alignment was performed using MAFFT (Katoh, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and a phylogenetic tree was constructed using FastTree (Price et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaxonomy was assigned to ASVs using a naive Bayes classifier trained on the SILVA database release 138 for 16S (Quast et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) or the UNITE database for ITS. Unassigned ASVs and contaminants from mitochondrial and chloroplast origin were removed. Alpha diversity metrics, including Shannon's diversity index, observed ASVs, Faith's phylogenetic diversity (for 16S only), and Pielou's evenness, were calculated after rarefaction. Beta diversity metrics, including Jaccard distance, Bray-Curtis dissimilarity, unweighted UniFrac, and weighted UniFrac (for 16S only), were calculated to compare community composition between sample groups. Differential abundance analysis was performed using ANCOM-BC2 (Lin \u0026amp; Peddada, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with a significance threshold of W-statistic\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and FDR-adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Taxonomic composition was visualized using interactive stacked bar plots. Additional statistical analyses and data visualization were performed using R version 4.1.0 (R Core Team, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in RStudio version 2024.04.2 (RStudio Team, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Cell isolation and culture\u003c/h2\u003e \u003cp\u003eHarvested tissues (spleen, mesenteric lymph nodes, and liver) were placed in separate tissue culture dishes containing cold PBS. Each tissue was gently disrupted into a single cell suspension using the plunger of a 2 ml syringe. The resulting suspensions were filtered through 70 \u0026micro;m cell strainers (BD Biosciences) and washed twice with PBS by centrifugation at 400 x g for 5 minutes at 4\u0026deg;C. Red blood cells were lysed with ACK lysis buffer (150 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA, pH 7.2) for 5 minutes at room temperature, followed by two washes with PBS. Liver cell suspensions were further purified by gradient centrifugation on 33% Percoll (GE Healthcare) at 500 x g for 20 minutes at room temperature without brake. The interface containing lymphocytes was collected and washed twice with PBS. Isolated cells were resuspended in complete RPMI-1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM L-glutamine, 100 U/ml penicillin, and 100 \u0026micro;g/ml streptomycin. Cells were cultured at a density of 1 x 10^6 cells/ml in 24-well plates and stimulated with phorbol 12-myristate 13-acetate (PMA, 20 ng/ml) and ionomycin (500 ng/ml) for 5 hours at 37\u0026deg;C in a humidified incubator with 5% CO2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Cytokine profiling by ELISA\u003c/h2\u003e \u003cp\u003eCytokine levels were measured in cell supernatants using ThermoFisher ELISA kits according to the manufacturer's protocols. The cytokines quantified included IFN\u0026#120574;, TNFα, IL-6, IL-10, and IL-17A. Briefly, 96-well plates were coated overnight at 4\u0026deg;C with 100 \u0026micro;L/well of capture antibody diluted in coating buffer. After three washes with wash buffer (PBS\u0026thinsp;+\u0026thinsp;0.05% Tween-20), the plates were blocked with 200 \u0026micro;L/well assay diluent for 1 hour at room temperature. Standards and samples were diluted in assay diluent as needed and 100 \u0026micro;L were added to the appropriate wells. Plates were incubated for 2 hours at room temperature. After washing 3 times, 100 \u0026micro;L of detection antibody diluted in assay diluent was added to each well and incubated for 1 hour. After three washes, 100 \u0026micro;L of diluted streptavidin-HRP was added to each well and incubated for 30 minutes. After five washes, 100 \u0026micro;L of TMB substrate solution was added to each well and the plates were allowed to develop for 10\u0026ndash;30 minutes. The reaction was stopped by the addition of 100 \u0026micro;L stop solution (1M H2SO4). Absorbance was measured at 450 nm with wavelength correction at 620 nm. Cytokine concentrations were calculated from 4-parameter logistic standard curves. Samples were analyzed in duplicate. All samples below the lowest standard were assigned the value of the minimum detectable concentration for that assay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. RNA extraction and quality control\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from mouse liver tissue samples stabilized in RNAlater buffer. Extraction was performed using the RNeasy Micro Kit (QIAGEN, Hilden, Germany) according to the manufacturer's protocol for \"Purification of total RNA from animal and human tissue\". Briefly, tissue samples were disrupted and homogenized in 600 \u0026micro;L RLT buffer containing 1% β-mercaptoethanol using a Precellys 24 Homogenizer (Bertin Corp., Rockville, MD, USA) with Precellys CK14 ceramic beads (2 cycles of 15 seconds at 6500 rpm with a 30-second break). After centrifugation, 300 \u0026micro;L of the clarified lysate was processed through RNeasy MinElute spin columns, including an on-column DNase digestion step. Total RNA was eluted in 14 \u0026micro;L nuclease-free water. RNA integrity and purity were assessed using an Agilent 2100 Bioanalyzer with the RNA 6000 Nano LabChip reagent set (Agilent, Palo Alto, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Microarray analysis\u003c/h2\u003e \u003cp\u003eGene expression profiling was performed using Applied Biosystems GeneChip Clariom S mouse arrays. Sample preparation and microarray hybridization were performed according to the manufacturer's protocol (Applied Biosystems GeneChip Whole Transcript PLUS Reagent Kit). Briefly, 200 ng of total RNA was used to generate double-stranded cDNA. After purification, 20 \u0026micro;g of cRNA was synthesized by in vitro transcription and then used to generate single-stranded (ss) cDNA incorporating dUTP. The purified ss cDNA was fragmented using uracil DNA glycosylase and apurinic/apyrimidinic endonuclease 1 and terminally labeled with biotin. 3.8 \u0026micro;g of fragmented and labeled ss cDNA was hybridized to the arrays for 16 hours at 45\u0026deg;C with rotation at 60 rpm. Arrays were washed and stained using an Applied Biosystems GeneChip Fluidics Station 450, and scanned with an Applied Biosystems GeneChip Scanner 3000 7G system. Fluidics and scanning functions were controlled by the Applied Biosystems GeneChip Command Console v5.0 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Microarray data analysis\u003c/h2\u003e \u003cp\u003eMicroarray data analysis was performed using the Transcriptome Analysis Console (TAC) 4.0.2 software (Applied Biosystems). Probe set signal values were calculated using the Signal Space Transformation-Robust Multi-Chip Analysis (SST-RMA) algorithm. Differential gene expression analysis was performed using the Linear Models for Microarray Data (LIMMA) method. Genes were considered differentially expressed if they met the following criteria: fold change \u0026lt; -2 or \u0026gt;\u0026thinsp;2 and false discovery rate (FDR) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Principal component analysis and hierarchical clustering were used to visualize sample relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Statistical Analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) unless otherwise noted. Statistical analyses were performed using GraphPad Prism 10 for macOS (GraphPad Software, San Diego, USA), QIIME2 software package (version 2024.5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.qiime2.org\u003c/span\u003e\u003cspan address=\"http://www.qiime2.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Transcriptome Analysis Console (TAC) 4.0.2 software (Thermo Fisher Scientific, Boston, USA), and R studio (version 2024.04.2, Boston, MA, USA). One-way analysis of variance (ANOVA) followed by Tukey's post hoc test for multiple comparisons was used for comparisons between groups. For microbiome data analysis, alpha diversity metrics were compared using the Kruskal-Wallis test, while beta diversity was assessed using PERMANOVA with 999 permutations. Differential abundance analysis of bacterial taxa was performed using ANCOM-BC2 with a significance threshold of W-statistic\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and FDR-adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For gene expression data, differential expression analysis was conducted using the limma-voom pipeline in R, with significance thresholds of |log2 fold change| \u0026gt; 1 and FDR-adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Significance levels are indicated as follows: *P\u0026thinsp;\u0026le;\u0026thinsp;0.05, **P\u0026thinsp;\u0026le;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026le;\u0026thinsp;0.001, and ****P\u0026thinsp;\u0026le;\u0026thinsp;0.0001. Multiple testing corrections were applied where appropriate using the Benjamini-Hochberg method to control for false discovery rate. Sample sizes for each experiment were determined based on power analysis or prior experience to detect biologically relevant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Fructose and preservatives synergistically induce hepatic steatosis and inflammation\u003c/h2\u003e \u003cp\u003eHistological analysis of liver sections revealed significant differences between the treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Water-treated control mice showed normal liver architecture without steatosis, inflammation or fibrosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). In contrast, mice treated with fructose alone or in combination with preservatives showed varying degrees of hepatic steatosis, characterized by lipid droplets within the hepatocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec-i). The combination of fructose and sorbate resulted in the most severe liver pathologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ech, i). These liver sections showed significant infiltration with mononuclear inflammatory cells, disrupted lobular architecture and hepatocyte ballooning degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ech, upper inset). Glycogen deposition was also observed, indicating altered liver metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ech, lower inset). Notably, early-stage fibrosis was evident in this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei), suggesting potential progression to advanced liver disease.\u003c/p\u003e \u003cp\u003eThese findings demonstrate that while fructose alone can induce hepatic steatosis, its combination with preservatives, particularly sorbate, exacerbates liver damage. The synergistic effects observed highlight the importance of considering the combined effects of dietary components and food additives on liver health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Fructose consumption alters liver function and lipid metabolism, with food preservatives potentially modulating these effects.\u003c/h2\u003e \u003cp\u003eFructose administration resulted in significant increases in ALT, triglycerides, and cholesterol levels compared to the water control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The addition of food preservatives to fructose resulted in different effects. Notably, only the addition of sorbate resulted in increases in all three liver enzymes, i.e. ALT, AST, and ALP, whereas addition of benzoate and nitrite significantly increased only ALT. Triglyceride levels were elevated in all treated groups. Total cholesterol levels were moderately elevated in the fructose and the fructose-sorbate groups. Free cholesterol levels were significantly elevated in the fructose and fructose-nitrite groups and highly elevated in the fructose-sorbate group. These results suggest that the addition of food preservatives, especially sorbate, leads to an amplification of the negative effects of fructose on liver function and lipid metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Fructose and food additives synergistically increase intestinal permeability\u003c/h2\u003e \u003cp\u003eIntestinal permeability was determined by measuring plasma FITC-dextran concentration after oral administration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Fructose treatment did not significantly increase intestinal permeability compared to the water control group (2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 \u0026micro;g/ml vs. 2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 \u0026micro;g/ml). However, when fructose was combined with food additives, permeability increased. The most pronounced increase in permeability was observed in the fructose plus sorbate group (3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 \u0026micro;g/ml, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 vs. water control), followed by fructose plus benzoate (3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 \u0026micro;g/ml, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggest that the combined consumption of fructose and preservatives increases intestinal permeability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Fructose combined with sorbate alters fungal but not bacterial alpha diversity\u003c/h2\u003e \u003cp\u003eAnalysis of alpha diversity metrics revealed different responses between bacterial and fungal communities to the treatments. Bacterial communities showed surprising resilience, with no significant differences in observed ASVs, Shannon diversity index, or Faith's phylogenetic diversity across any treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, c, e). In contrast, fungal communities showed significant sensitivity, but only to the fructose-sorbate treatment. This group showed significantly lower Shannon diversity (p\u0026thinsp;=\u0026thinsp;0.005479, q\u0026thinsp;=\u0026thinsp;0.054786) and Pielou's evenness (p\u0026thinsp;=\u0026thinsp;0.027891, q\u0026thinsp;=\u0026thinsp;0.069726) compared to the water control and other treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, f). Notably, fructose alone or in combination with benzoate or nitrite did not significantly alter fungal alpha diversity metrics. These results suggest that while bacterial community richness and diversity remained stable across all treatments, the combination of fructose and sorbate uniquely disrupted fungal community.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Fructose and food preservatives induce significant shifts in the composition of bacterial and fungal community\u003c/h2\u003e \u003cp\u003eBeta diversity analysis showed significant differences in bacterial and fungal community composition between treatment groups. For bacterial communities, principal coordinate analysis (PCoA) based on weighted UniFrac distances showed a clear separation between treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), which was confirmed by PERMANOVA (pseudo-F\u0026thinsp;=\u0026thinsp;3.204613, p\u0026thinsp;=\u0026thinsp;0.001). Pairwise comparisons revealed significant differences (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between all treatment groups, with a particularly strong differentiation observed between the water control and the treatment with fructose and sorbate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, e).\u003c/p\u003e \u003cp\u003eFungal communities also showed significant shifts in composition between treatments, as shown by PCoA based on Bray-Curtis dissimilarities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and confirmed by PERMANOVA (pseudo-F\u0026thinsp;=\u0026thinsp;1.620826, p\u0026thinsp;=\u0026thinsp;0.001). Pairwise comparisons showed significant differences between most treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, f), although the strength of differentiation was generally lower than in the bacterial communities, as evidenced by higher q values and lower pseudo-F values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Fructose in combination with benzoate or sorbate induces the greatest changes in bacterial taxa abundance\u003c/h2\u003e \u003cp\u003eDifferential abundance analysis revealed that the effects on bacterial taxa varied considerably between treatments. At the phylum level, fructose alone induced significant changes in two phyla (an increase in Patescibacteria and a decrease in Proteobacteria), demonstrating its ability to modulate the gut microbiome. Interestingly, fructose combined with nitrite showed no significant effect on any phylum, suggesting a potential neutralizing effect of nitrite on fructose-induced changes. The most profound changes were observed in the fructose\u0026thinsp;+\u0026thinsp;benzoate and fructose\u0026thinsp;+\u0026thinsp;sorbate groups. The fructose\u0026thinsp;+\u0026thinsp;benzoate combination significantly affected four phyla, causing an increase in Verrucomicrobia and a decrease in Proteobacteria, Bacteroidota (formerly Bacteroidetes), and Firmicutes. The fructose\u0026thinsp;+\u0026thinsp;sorbate group induced changes in three phyla, with increases in Patescibacteria, Actinobacteria, and Desulfobacteria. At the genus level, a complex pattern of changes was observed. Significant increases were detected in several genera, including \u003cem\u003eAkkermansia\u003c/em\u003e (especially in the fructose\u0026thinsp;+\u0026thinsp;benzoate group), \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, Butyricicoccaceae, \u003cem\u003eLachnoclostridium\u003c/em\u003e, Lachnospiraceae, \u003cem\u003eBlautia\u003c/em\u003e, and Oscillospiraceae. Conversely, significant decreases were observed in genera such as \u003cem\u003eRomboutsia\u003c/em\u003e and \u003cem\u003eTuricibacter\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eThese results highlight that while fructose alone can induce some changes in bacterial abundance, its combination with certain food additives, particularly benzoate and sorbate, leads to more extensive and diverse changes in fecal bacterial community structure. This suggests a synergistic effect between fructose and these additives in modulating the gut microbiome, which could have important implications for host health and metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Fructose and food additives selectively alter the abundance of fungal taxa\u003c/h2\u003e \u003cp\u003eAnalysis of the fungal communities revealed selective and treatment-specific changes in taxa abundance. At the phylum level, significant changes were observed mainly in the Basidiomycota (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Notably, both fructose\u0026thinsp;+\u0026thinsp;benzoate and fructose\u0026thinsp;+\u0026thinsp;sorbate treatments induced a decrease in this phylum, suggesting a consistent impact of these combinations on fungal community structure. At the genus level, we observed a complex pattern of changes that varied by treatment. Fructose alone caused the most significant depletion of fungal genera, particularly \u003cem\u003eParastagonospora\u003c/em\u003e, \u003cem\u003eBensingtonia\u003c/em\u003e, \u003cem\u003eArmillaria\u003c/em\u003e, and \u003cem\u003eXeromyces\u003c/em\u003e, while increasing the abundance of \u003cem\u003eRasamsonia\u003c/em\u003e. The fructose\u0026thinsp;+\u0026thinsp;benzoate combination also induced an increase in \u003cem\u003eRasamsonia\u003c/em\u003e. In contrast, fructose\u0026thinsp;+\u0026thinsp;nitrite increased \u003cem\u003eCandida\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, and \u003cem\u003eCeratocystis\u003c/em\u003e. The fructose\u0026thinsp;+\u0026thinsp;sorbate treatment shared some effects with fructose\u0026thinsp;+\u0026thinsp;nitrite, causing increases in \u003cem\u003ePenicillium\u003c/em\u003e and \u003cem\u003eCeratocystis\u003c/em\u003e. However, it also caused the most extensive depletion of fungal genera, including \u003cem\u003eCryptococcus\u003c/em\u003e, \u003cem\u003eFilobasidium\u003c/em\u003e, \u003cem\u003eSporobolomyces\u003c/em\u003e, \u003cem\u003eCladosporium\u003c/em\u003e, \u003cem\u003eAlternaria\u003c/em\u003e, and \u003cem\u003eArmillaria\u003c/em\u003e. These distinct patterns of changes in fungal taxa highlight the specific and diverse effects of fructose alone and in combination with different food preservatives. The observed shifts in both bacterial and fungal communities suggest that these treatments, particularly the combinations of fructose with benzoate and sorbate, significantly affect the composition of gut microbiome. These changes could potentially influence host-microbe interactions and metabolic processes, highlighting the importance of considering both bacterial and fungal components when studying the effects of dietary factors on the gut microbiome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Fructose and food preservatives induce organ-specific changes in cytokine profiles\u003c/h2\u003e \u003cp\u003eTo investigate the effects of fructose and food preservatives on immune responses, we measured cytokine levels in the spleen, mesenteric lymph nodes (MLN), and liver of treated mice using ELISA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Our results showed different patterns of cytokine production in the different organs and treatments. Fructose alone had no significant effect on any of the cytokines measured. The most pronounced effect was observed with the fructose-sorbate treatment, which stimulated the production of IFNγ, TNFα, and IL-6 in both spleen and MLN. In MLN, this treatment also stimulated the production of IL-10 and IL-17A. The fructose-benzoate treatment stimulated the production of IFNγ in the spleen and of IFNγ, TNFα, and IL-17A in the MLN. The fructose-nitrite treatment significantly increased cytokine production only in the MLN. Overall, these results show that fructose-preservative combinations, especially fructose\u0026thinsp;+\u0026thinsp;sorbate, induce a proinflammatory cytokine profile in multiple organs. This profile is characterized by increased levels of IFNγ, TNFα, IL-6 and IL-17A. The increase in IL-10 production could play a counter-regulatory role and potentially attenuate the pro-inflammatory response. The organ-specific nature of these changes, particularly the pronounced effects in the MLN, suggests that gut-associated lymphoid tissue plays a critical role in mediating immune responses to fructose and food additives. This finding emphasized the potential importance of the gut-immune axis in the physiological response to food components. The lack of detectable cytokine changes in the liver may be due to methodological limitations. The higher dilutions required for liver samples due to the lower numbers of isolated liver leukocytes may have resulted in cytokine levels falling below the detection limit of the ELISA method. These results provide important insights into the immunomodulatory effects of fructose-preservative combinations and highlights the need for further investigation of their potential impact on systemic and organ-specific immune responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Fructose induces significant changes in hepatic gene expression\u003c/h2\u003e \u003cp\u003eTo investigate the effects of fructose consumption on hepatic gene expression, we performed RNA sequencing on liver samples from mice treated with water (control) or fructose. Principal component analysis (PCA) of the gene expression data revealed a clear separation between the control and fructose-treated groups along the first principal component (PCA1), which accounted for 50.1% of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This indicates that fructose treatment caused a significant shift in the overall hepatic transcriptome.\u003c/p\u003e \u003cp\u003eDifferential expression analysis identified 3 560 genes that were significantly altered by fructose treatment compared to the water control (fold change \u0026lt; -2 or \u0026gt;\u0026thinsp;2, FDR p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Of these, 1,936 genes were upregulated and 1,624 were downregulated. The top differentially expressed genes are shown in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), which highlights the different expression patterns between the water and fructose treatments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Key genes associated with the pathogenesis of NAFLD were significantly modulated by fructose\u003c/h2\u003e \u003cp\u003eSignificant changes were found in the expression of several genes known to be associated with the development of NAFLD (Supplementary Table\u0026nbsp;1). In particular, genes involved in lipid metabolism were strongly upregulated, including Me1 (22-fold), which provides NADPH for fatty acid biosynthesis, and Acsl1 (4.2-fold), which activates long-chain fatty acids. The insulin receptor gene (Insr) was also upregulated 20-fold, possibly indicating developing insulin resistance.\u003c/p\u003e \u003cp\u003eGenes associated with oxidative stress, a key feature of NAFLD, were also affected. Aox1, which is involved in the production of reactive oxygen species (ROS), was upregulated 17-fold. Conversely, Aldh3a2, which protects against lipid peroxidation, was increased 6.2-fold, suggesting a possible compensatory mechanism.\u003c/p\u003e \u003cp\u003eInterestingly, some changes appeared to be potentially protective. Csad, which is involved in the biosynthesis of taurine, was upregulated 25-fold; taurine deficiency is associated with NAFLD. In addition, Abca1, which is involved in cholesterol efflux, was upregulated 11-fold. The inflammatory response also appeared to be modulated, with the chemokines Cxcl1 and Ccl9 being downregulated by 4.4- and 10-fold, respectively. This unexpected decrease in proinflammatory markers warrants further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.11. Food preservatives modulate fructose-induced gene expression changes\u003c/h2\u003e \u003cp\u003ePCA revealed that although all fructose-treated groups clustered separately from the water control, there were significant differences between the fructose-only and fructose plus preservative groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This suggests that the preservatives had additional effects on gene expression beyond those induced by fructose alone. Differential expression analysis showed that each preservative uniquely modulated the fructose-induced gene expression profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Compared to fructose alone, sodium benzoate altered the expression of 394 genes, sodium nitrite of 133 genes, and potassium sorbate of 662 genes (fold change \u0026lt; -2 or \u0026gt;\u0026thinsp;2, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eOf particular interest, several cytochrome P450 enzymes showed dramatic changes in expression upon preservative treatment (Supplementary Table\u0026nbsp;1). For example, Cyp4a12b and Cyp14a12a were strongly downregulated in the fructose-sorbate group compared to fructose alone (by -717-fold and \u0026minus;\u0026thinsp;2499-fold, respectively), while Cyp2b9 was strongly upregulated (by 6356-fold). These enzymes play a crucial role in xenobiotic and lipid metabolism, suggesting that preservatives may significantly affect these processes in the context of fructose-induced metabolic changes.\u003c/p\u003e \u003cp\u003eOther notable genes affected by preservatives included Elovl3 (involved in fatty acid elongation), which was down-regulated 1835-fold, and Sult3a1 (involved in lipid metabolism), which was up-regulated 2267-fold in the fructose plus sorbate group. These changes emphasize the potential of food preservatives to affect lipid metabolism in the fructose-exposed liver.\u003c/p\u003e \u003cp\u003eIn summary, our results show that fructose consumption induces widespread changes in hepatic gene expression that are consistent with the NAFLD development. Furthermore, we show for the first time that common food preservatives can significantly modulate these fructose-induced changes, which could alter the course of NAFLD progression. These findings emphasize the need for further investigation into the combined effects of fructose and food additives on liver health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides new insights into the synergistic effects of fructose and common food preservatives on the development and progression of non-alcoholic fatty liver disease (NAFLD). Our findings demonstrate that while fructose alone can induce hepatic steatosis and alter liver function, its combination with certain preservatives, particularly potassium sorbate, exacerbates liver damage and triggers extensive changes in the composition of gut microbiota and host immune responses.\u003c/p\u003e \u003cp\u003eThe histological analysis revealed that fructose in combination with sorbate induced the most severe liver pathology, characterized by significant steatosis, infiltration of inflammatory cells, and early-stage fibrosis. This synergistic effect was also supported by biochemical analyses, which showed that the fructose-sorbate combination led to the most pronounced increases in liver enzymes (ALT, AST, ALP) and lipid profiles. These findings are consistent with previous studies demonstrating the hepatotoxic potential of fructose (Jensen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but more importantly, they highlight the potential for common food preservatives to amplify these effects.\u003c/p\u003e \u003cp\u003eOur study of intestinal permeability revealed an intriguing pattern. While fructose alone did not significantly increase intestinal permeability, its combination with preservatives, especially sorbate and benzoate, led to a significant increase. This finding suggests that preservatives may enhance the ability of fructose to disrupt intestinal barrier function, which could facilitate the translocation of bacterial products and contribute to liver inflammation (Mouries et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The mechanism behind this synergistic effect needs further investigation but could be related to changes in tight junction proteins or changes in the mucus layer composition.\u003c/p\u003e \u003cp\u003eThe microbiome analysis provided several important findings. While bacterial alpha diversity remained stable across all treatments, fungal diversity was significantly reduced by the fructose-sorbate combination. This differential impact on the bacterial and fungal communities underscores the importance of considering both components of the gut microbiome in NAFLD research, an aspect that has often been overlooked in previous studies (Lang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The observed reduction in fungal diversity could have important implications for metabolic health, given the emerging role of the mycobiome in regulating host metabolism (Sokol et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeta-diversity analyzes revealed significant compositional shifts in the composition of both bacterial and fungal communities between treatments. The fructose-sorbate and fructose-benzoate combinations induced the most extensive changes, particularly in the bacterial phyla Verrucomicrobia, Proteobacteria, and Bacteroidota. The increase in Verrucomicrobia, mainly caused by the genus \u003cem\u003eAkkermansia\u003c/em\u003e, is particularly noteworthy given their potential protective role in metabolic disorders (Depommier et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e is usually perceived as beneficial bacterium in the context of metabolic diseases. However, it is important to note that \u003cem\u003eAkkermansia\u003c/em\u003e is a mucolytic bacterium whose overgrowth might compromise intestinal mucosal barrier. Its increase in the fructose-benzoate group was more than 4-fold.\u003c/p\u003e \u003cp\u003eAnalysis of differential abundance at the genus level revealed a complex pattern of changes, with certain treatments promoting the growth of potentially pathogenic genera while depleting others associated with metabolic health. For example, the increase in \u003cem\u003eCandida\u003c/em\u003e abundance observed with all fructose-based treatments could have negative implications given recent findings that NAFLD patients have an altered fecal mycobiome and enhanced systemic immune responses to \u003cem\u003eCandida albicans\u003c/em\u003e (Demir et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur cytokine profiling experiments provided valuable insights into the immunomodulatory effects of the combination of fructose and preservatives. The most striking finding was the organ-specific nature of these effects, with the mesenteric lymph nodes (MLN) showing the most pronounced changes. This suggests a crucial role of the gut-associated lymphoid tissue in mediating immune responses to these dietary components. In particular, the fructose-sorbate combination induced a pro-inflammatory cytokine profile characterized by increased levels of IFNγ, TNFα, IL-6, and IL-17A. This pro-inflammatory milieu could contribute to the exacerbation of liver damage observed in this group (Tilg et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe gene expression analysis revealed extensive transcriptional changes induced by fructose and further modulated by preservatives. The upregulation of genes involved in lipid metabolism (e.g. Me1, Acsl1) and oxidative stress (e.g. Aox1) is consistent with the known mechanisms of fructose-induced liver damage (Softic et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the observed upregulation of potentially protective genes (e.g. Csad, Abca1) suggests the activation of compensatory mechanisms that should be further investigated.\u003c/p\u003e \u003cp\u003eImportantly, our study shows for the first time that common food preservatives can significantly modulate fructose-induced changes in hepatic gene expression. The dramatic changes in the expression of cytochrome P450 enzymes upon treatment with preservatives, particularly sorbate, suggest that these additives can profoundly affect xenobiotic and lipid metabolism in the context of fructose-induced metabolic changes. This finding has important implications for understanding the potential interactions between dietary components and environmental toxins in NAFLD pathogenesis.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be considered. First, while our mouse model provides valuable insights, the translatability to human NAFLD requires further investigation. Second, the mechanisms underlying the synergistic effects of fructose and preservatives, particularly at the molecular level, need further investigation. Finally, the long-term consequences of the observed changes in gut microbiota composition, immunological function, and hepatic gene expression need to be investigated in longitudinal studies.\u003c/p\u003e \u003cp\u003eIn summary, our results highlight the complex interplay between dietary fructose, food preservatives, gut microbiota, and host metabolism in the context of NAFLD. The synergistic effects observed with certain preservatives, particularly sorbate, emphasize the need to consider the combined effects of different dietary components in the pathogenesis of NAFLD. These findings open new avenues for exploring potential therapeutic strategies targeting the gut-liver axis and suggest that dietary guidelines for the management of NAFLD may need to consider not only macronutrient composition but also the presence of common food additives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e All animal procedures were approved by the Institutional Animal Care and Use Committee (approval number 46/2020). Written informed consent was obtained from the human donor for the collection and use of gut microbiota samples in this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Czech Science Foundation (20-09732S and 22-12533S) and the Institutional Research Concept (RVO: 61388971).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, T.H.; methodology, validation, formal analysis, investigation, curation, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, visualization, T.H., L.H. and E.T.; supervision, project administration, and funding acquisition, T.H. The first two authors contributed equally. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our sincere gratitude to our dedicated laboratory technicians, Radka Stribrna, Lenka Cizkova and Eliska Kozlova, for their invaluable technical expertise and unwavering support throughout this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in the study are available in the article/supplementary materials. The raw metagenomics and gene expression data have been deposited in the ASEP (Academic Science Electronic Publications) repository, maintained by the Library of the Czech Academy of Sciences. These datasets can be accessed through the ASEP online catalogue (https://asep.lib.cas.cz/arl-cav/en/search/). For specific dataset identifiers or access instructions, please refer to the metadata records associated with this study in ASEP. If you encounter any difficulties accessing the data, please contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndrews, S. (2010). \u003cem\u003eFastQC: a quality control tool for high throughput sequence data\u003c/em\u003e [Computer software]. http://www.bioinformatics.babraham.ac.uk/projects/fastqc\u003c/li\u003e\n\u003cli\u003eAron-Wisnewsky, J., Vigliotti, C., Witjes, J., Le, P., Holleboom, A. G., Verheij, J., Nieuwdorp, M., \u0026amp; Cl\u0026eacute;ment, K. (2020). Gut microbiota and human NAFLD: Disentangling microbial signatures from metabolic disorders. \u003cem\u003eNature Reviews Gastroenterology \u0026amp; Hepatology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(5), 279\u0026ndash;297. https://doi.org/10.1038/s41575-020-0269-9\u003c/li\u003e\n\u003cli\u003eBetrapally, N. S., Gillevet, P. M., \u0026amp; Bajaj, J. S. (2017). Gut microbiome and liver disease. \u003cem\u003eTranslational Research\u003c/em\u003e, \u003cem\u003e179\u003c/em\u003e, 49\u0026ndash;59. https://doi.org/10.1016/j.trsl.2016.07.005\u003c/li\u003e\n\u003cli\u003eBolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodr\u0026iacute;guez, A. M., Chase, J., \u0026hellip; Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. \u003cem\u003eNature Biotechnology\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(8), 852\u0026ndash;857. https://doi.org/10.1038/s41587-019-0209-9\u003c/li\u003e\n\u003cli\u003eCallahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., \u0026amp; Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. \u003cem\u003eNature Methods\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), 581\u0026ndash;583. https://doi.org/10.1038/nmeth.3869\u003c/li\u003e\n\u003cli\u003eChassaing, B., Van De Wiele, T., De Bodt, J., Marzorati, M., \u0026amp; Gewirtz, A. T. (2017). Dietary emulsifiers directly alter human microbiota composition and gene expression ex vivo potentiating intestinal inflammation. \u003cem\u003eGut\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(8), 1414\u0026ndash;1427. https://doi.org/10.1136/gutjnl-2016-313099\u003c/li\u003e\n\u003cli\u003eChu, H., Duan, Y., Yang, L., \u0026amp; Schnabl, B. (2019). Small metabolites, possible big changes: A microbiota-centered view of non-alcoholic fatty liver disease. \u003cem\u003eGut\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(2), 359\u0026ndash;370. https://doi.org/10.1136/gutjnl-2018-316307\u003c/li\u003e\n\u003cli\u003eDemir, M., Lang, S., Hartmann, P., Duan, Y., Martin, A., Miyamoto, Y., Bondareva, M., Zhang, X., Wang, Y., Kasper, P., Bang, C., Roderburg, C., Tacke, F., Steffen, H.-M., Goeser, T., Kruglov, A., Eckmann, L., St\u0026auml;rkel, P., Fouts, D. E., \u0026amp; Schnabl, B. (2022). The fecal mycobiome in non-alcoholic fatty liver disease. \u003cem\u003eJournal of Hepatology\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(4), 788\u0026ndash;799. https://doi.org/10.1016/j.jhep.2021.11.029\u003c/li\u003e\n\u003cli\u003eDepommier, C., Everard, A., Druart, C., Plovier, H., Van Hul, M., Vieira-Silva, S., Falony, G., Raes, J., Maiter, D., Delzenne, N. M., de Barsy, M., Loumaye, A., Hermans, M. P., Thissen, J.-P., de Vos, W. M., \u0026amp; Cani, P. D. (2019). Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: A proof-of-concept exploratory study. \u003cem\u003eNature Medicine\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(7), 1096\u0026ndash;1103. https://doi.org/10.1038/s41591-019-0495-2\u003c/li\u003e\n\u003cli\u003eHrncir, T. (2022). Gut Microbiota Dysbiosis: Triggers, Consequences, Diagnostic and Therapeutic Options. \u003cem\u003eMicroorganisms\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 578. https://doi.org/10.3390/microorganisms10030578\u003c/li\u003e\n\u003cli\u003eHrncir, T., Hrncirova, L., Kverka, M., Hromadka, R., Machova, V., Trckova, E., Kostovcikova, K., Kralickova, P., Krejsek, J., \u0026amp; Tlaskalova-Hogenova, H. (2021). Gut Microbiota and NAFLD: Pathogenetic Mechanisms, Microbiota Signatures, and Therapeutic Interventions. \u003cem\u003eMicroorganisms\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 957. https://doi.org/10.3390/microorganisms9050957\u003c/li\u003e\n\u003cli\u003eHrncirova, Machova, Trckova, Krejsek, \u0026amp; Hrncir. (2019). Food Preservatives Induce Proteobacteria Dysbiosis in Human-Microbiota Associated Nod2-Deficient Mice. \u003cem\u003eMicroorganisms\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(10), 383. https://doi.org/10.3390/microorganisms7100383\u003c/li\u003e\n\u003cli\u003eJensen, T., Abdelmalek, M. F., Sullivan, S., Nadeau, K. J., Green, M., Roncal, C., Nakagawa, T., Kuwabara, M., Sato, Y., Kang, D.-H., Tolan, D. R., Sanchez-Lozada, L. G., Rosen, H. R., Lanaspa, M. A., Diehl, A. M., \u0026amp; Johnson, R. J. (2018). Fructose and sugar: A major mediator of non-alcoholic fatty liver disease. \u003cem\u003eJournal of Hepatology\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(5), 1063\u0026ndash;1075. https://doi.org/10.1016/j.jhep.2018.01.019\u003c/li\u003e\n\u003cli\u003eKatoh, K. (2002). MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(14), 3059\u0026ndash;3066. https://doi.org/10.1093/nar/gkf436\u003c/li\u003e\n\u003cli\u003eLang, S., Duan, Y., Liu, J., Torralba, M. G., Kuelbs, C., Ventura-Cots, M., Abraldes, J. G., Bosques-Padilla, F., Verna, E. C., Brown, R. S., Vargas, V., Altamirano, J., Caballer\u0026iacute;a, J., Shawcross, D., Lucey, M. R., Louvet, A., Mathurin, P., Garcia-Tsao, G., Ho, S. B., \u0026hellip; Schnabl, B. (2020). Intestinal Fungal Dysbiosis and Systemic Immune Response to Fungi in Patients With Alcoholic Hepatitis. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(2), 522\u0026ndash;538. https://doi.org/10.1002/hep.30832\u003c/li\u003e\n\u003cli\u003eLebeaupin, C., Proics, E., de Bieville, C. H. D., Rousseau, D., Bonnafous, S., Patouraux, S., Adam, G., Lavallard, V. J., Rovere, C., Le Thuc, O., Saint-Paul, M. C., Anty, R., Schneck, A. S., Iannelli, A., Gugenheim, J., Tran, A., Gual, P., \u0026amp; Bailly-Maitre, B. (2015). ER stress induces NLRP3 inflammasome activation and hepatocyte death. \u003cem\u003eCell Death \u0026amp; Disease\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(9), e1879. https://doi.org/10.1038/cddis.2015.248\u003c/li\u003e\n\u003cli\u003eLin, H., \u0026amp; Peddada, S. D. (2020). Analysis of compositions of microbiomes with bias correction. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 3514. https://doi.org/10.1038/s41467-020-17041-7\u003c/li\u003e\n\u003cli\u003eMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. \u003cem\u003eEMBnet.Journal\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), Article 1. https://doi.org/10.14806/ej.17.1.200\u003c/li\u003e\n\u003cli\u003eMouries, J., Brescia, P., Silvestri, A., Spadoni, I., Sorribas, M., Wiest, R., Mileti, E., Galbiati, M., Invernizzi, P., Adorini, L., Penna, G., \u0026amp; Rescigno, M. (2019). Microbiota-driven gut vascular barrier disruption is a prerequisite for non-alcoholic steatohepatitis development. \u003cem\u003eJournal of Hepatology\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(6), 1216\u0026ndash;1228. https://doi.org/10.1016/j.jhep.2019.08.005\u003c/li\u003e\n\u003cli\u003ePrice, M. N., Dehal, P. S., \u0026amp; Arkin, A. P. (2009). FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(7), 1641\u0026ndash;1650. https://doi.org/10.1093/molbev/msp077\u003c/li\u003e\n\u003cli\u003eQuast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., \u0026amp; Gl\u0026ouml;ckner, F. O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(Database issue), D590\u0026ndash;D596. https://doi.org/10.1093/nar/gks1219\u003c/li\u003e\n\u003cli\u003eR Core Team. (2021). \u003cem\u003eR: A language and environment for statistical computing\u003c/em\u003e [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eRinella, M. E. (2015). Nonalcoholic fatty liver disease: A systematic review. \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e313\u003c/em\u003e(22), 2263\u0026ndash;2273. https://doi.org/10.1001/jama.2015.5370\u003c/li\u003e\n\u003cli\u003eRStudio Team. (2021). \u003cem\u003eRStudio: Integrated Development Environment for R\u003c/em\u003e (2024.04.2) [Computer software]. RStudio. http://www.rstudio.com/\u003c/li\u003e\n\u003cli\u003eSharpton, S. R., Maraj, B., Harding-Theobald, E., Vittinghoff, E., \u0026amp; Terrault, N. A. (2019). Gut microbiome-targeted therapies in nonalcoholic fatty liver disease: A systematic review, meta-analysis, and meta-regression. \u003cem\u003eThe American Journal of Clinical Nutrition\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e(1), 139\u0026ndash;149. https://doi.org/10.1093/ajcn/nqz042\u003c/li\u003e\n\u003cli\u003eShen, F., Zheng, R.-D., Sun, X.-Q., Ding, W.-J., Wang, X.-Y., \u0026amp; Fan, J.-G. (2017). Gut microbiota dysbiosis in patients with non-alcoholic fatty liver disease. \u003cem\u003eHepatobiliary \u0026amp; Pancreatic Diseases International\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 375\u0026ndash;381. https://doi.org/10.1016/S1499-3872(17)60019-5\u003c/li\u003e\n\u003cli\u003eSoftic, S., Stanhope, K. L., Boucher, J., Divanovic, S., Lanaspa, M. A., Johnson, R. J., \u0026amp; Kahn, C. R. (2020). Fructose and Hepatic Insulin Resistance. \u003cem\u003eCritical Reviews in Clinical Laboratory Sciences\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(5), 308\u0026ndash;322. https://doi.org/10.1080/10408363.2019.1711360\u003c/li\u003e\n\u003cli\u003eSokol, H., Leducq, V., Aschard, H., Pham, H.-P., Jegou, S., Landman, C., Cohen, D., Liguori, G., Bourrier, A., Nion-Larmurier, I., Cosnes, J., Seksik, P., Langella, P., Skurnik, D., Richard, M. L., \u0026amp; Beaugerie, L. (2017). Fungal microbiota dysbiosis in IBD. \u003cem\u003eGut\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(6), 1039\u0026ndash;1048. https://doi.org/10.1136/gutjnl-2015-310746\u003c/li\u003e\n\u003cli\u003eTilg, H., Adolph, T. E., \u0026amp; Moschen, A. R. (2021). Multiple Parallel Hits Hypothesis in Nonalcoholic Fatty Liver Disease: Revisited After a Decade. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 833\u0026ndash;842. https://doi.org/10.1002/hep.31518\u003c/li\u003e\n\u003cli\u003eVanni, E., Bugianesi, E., Kotronen, A., De Minicis, S., Yki-J\u0026auml;rvinen, H., \u0026amp; Svegliati-Baroni, G. (2010). From the metabolic syndrome to NAFLD or vice versa? \u003cem\u003eDigestive and Liver Disease: Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 320\u0026ndash;330. https://doi.org/10.1016/j.dld.2010.01.016\u003c/li\u003e\n\u003cli\u003eYounossi, Z. M., Golabi, P., Paik, J. M., Henry, A., Van Dongen, C., \u0026amp; Henry, L. (2023). The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(4), 1335\u0026ndash;1347. https://doi.org/10.1097/HEP.0000000000000004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"non-alcoholic fatty liver disease, fructose, food preservatives, gut microbiome, hepatic gene expression, intestinal permeability, inflammation, metabolic dysregulation","lastPublishedDoi":"10.21203/rs.3.rs-4814043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4814043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is a growing global health problem closely linked to dietary habits, particularly high fructose consumption. This study investigates the combined effects of fructose and common food preservatives (sodium benzoate, sodium nitrite, and potassium sorbate) on the development and progression of NAFLD in a human-microbiota-associated mouse model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur comprehensive analysis reveals that fructose and potassium sorbate synergistically increase liver damage, inflammation, and fibrosis, while altering liver function, lipid profiles, and intestinal permeability. Significant changes were observed in the composition of gut bacterial and fungal communities, accompanied by the induction of predominantly pro-inflammatory immune responses, particularly in the mesenteric lymph nodes. Gene expression analysis in the liver uncovered extensive transcriptional changes induced by fructose and modulated by preservatives, affecting key genes involved in lipid metabolism, oxidative stress, and inflammatory responses.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings highlight the complex interplay between dietary components, gut microbiota, and host metabolism in the development of NAFLD. The study suggests potential risks associated with combined fructose and preservative consumption, particularly potassium sorbate. These results open new avenues for understanding and treating NAFLD through dietary intervention and microbiome modulation, emphasizing the need for further investigation into the impact of food additives on liver health.\u003c/p\u003e","manuscriptTitle":"Synergistic Effects of Fructose and Food Preservatives on Non-Alcoholic Fatty Liver Disease: From Gut Microbiome Alterations to Hepatic Gene Expression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 12:40:05","doi":"10.21203/rs.3.rs-4814043/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2197f4d3-db5d-459a-9c92-7a8ed1114725","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T14:59:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 12:40:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4814043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4814043","identity":"rs-4814043","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}