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. 2024 Oct 30;16(21):3722.
doi: 10.3390/nu16213722.

Synergistic Effects of Fructose and Food Preservatives on Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): From Gut Microbiome Alterations to Hepatic Gene Expression

Tomas Hrncir  1 , Eva Trckova  1 , Lucia Hrncirova  1
Affiliations

Synergistic Effects of Fructose and Food Preservatives on Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): From Gut Microbiome Alterations to Hepatic Gene Expression

Tomas Hrncir et al. Nutrients. .

Abstract

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) 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 MASLD. Methods: We utilized a human microbiota-associated mouse model, administering 10% fructose with or without preservatives for 11 weeks. Liver histology, hepatic gene expression (microarray analysis), biochemical markers, cytokine profiles, intestinal permeability, and gut microbiome composition (16S rRNA and Internal Transcribed Spacer (ITS) sequencing) were evaluated. Results: Fructose and potassium sorbate synergistically induced liver pathology characterized by increased steatosis, inflammation and fibrosis. These histological changes were associated with elevated liver function markers and altered lipid profiles. The treatments also induced significant changes in both the bacterial and fungal communities and disrupted intestinal barrier function, leading to increased pro-inflammatory responses in the mesenteric lymph nodes. Liver gene expression analysis revealed a wide range of transcriptional changes induced by fructose and modulated by the preservative. Key genes involved in lipid metabolism, oxidative stress, and inflammatory responses were affected. Conclusions: Our findings highlight the complex interactions between dietary components, gut microbiota, and host metabolism in the development of MASLD. The study identifies potential risks associated with the combined consumption of fructose and preservatives, particularly potassium sorbate. Our data reveal new mechanisms that are involved in the development of MASLD and open up a new avenue for the prevention and treatment of MASLD through dietary interventions and the modulation of the microbiome.

Keywords: food additives; fructose; gut microbiome; hepatic gene expression; inflammation; intestinal permeability; mycobiome; non-alcoholic fatty liver disease (NAFLD).

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Histological analysis of liver sections from mice treated with fructose in combination with preservatives. Representative H&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, and (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 combination with preservatives exhibit varying degrees of steatosis, characterized by the presence of lipid droplets within the hepatocytes. (ch) Livers from mice treated with fructose + sorbate show an extensive steatosis with ballooning degeneration of hepatocytes (black arrows), disruption of normal lobular architecture, an infiltration of mononuclear inflammatory cells (upper inset, blue arrows), glycogen deposition (lower inset, white arrows), and (i) early stages of fibrosis (left inset, yellow arrows).
Figure 2
Figure 2
Effects of fructose and food preservatives on liver function markers and lipid profiles in mice. 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, and **** p ≤ 0.0001.
Figure 3
Figure 3
Intestinal permeability measured by FITC–dextran absorption. Intestinal permeability was determined by the oral administration of FITC–dextran (4 kDa) and subsequent measurement of plasma fluorescence after 4 h. 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 < 0.05, ** p < 0.01. n = eight mice per group.
Figure 4
Figure 4
Analysis of alpha diversity of bacterial (16S V3-V4) and fungal (ITS) communities. Measures 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. Shannon diversity index represents both the richness and evenness of species, while Faith’s Phylogenetic diversity considers the phylogenetic differences between species. Higher values indicate greater diversity. Pielou’s evenness ranges from 0 to 1, with 1 indicating complete evenness in species abundance. 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. n = eight mice per group. Statistically significant differences (p < 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.
Figure 5
Figure 5
Beta diversity analysis of bacterial (16S V3-V4) and fungal (ITS) communities. (a,b) Principal Coordinate Analysis (PCoA) plots based on weighted UniFrac distances for bacteria and Bray–Curtis dissimilarities for fungi. Each point represents one sample, with colors indicating treatment groups. The closer the points, the more similar the community composition. (c–f) Heatmaps showing pairwise PERMANOVA results. Darker colors in (c,d) indicate lower q-values (stronger statistical significance), while darker colors in (e,f) indicate higher pseudo-F values (larger effect sizes). PERMANOVA analysis shows the significant effects of treatments on both bacterial and fungal communities. Pairwise comparisons also revealed significant differences (q < 0.05) between most treatment groups for both bacterial and fungal communities. Greater differentiation was observed for bacterial communities (lower q values and higher pseudo-F values). Data were analyzed using QIIME2 and plots were generated using R Studio.
Figure 6
Figure 6
Differential abundance analysis of bacterial (16S V3-V4) and fungal (ITS) communities. (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, where 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), where 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, where 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), where 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. n = eight mice per group. Bacterial communities showed significant changes in all phyla except Cyanobacteria, with notable changes at the genus level in Akkermansia, Acinetobacter, Butyricicoccaceae, Blautia, and many others. Fungal communities showed significant shifts, mainly in the Basidiomycota phyla, with genus-level changes observed in Rasamsonia, Candida, and Penicillium, among others. The data were analyzed using QIIME2 and graphs were generated using R studio.
Figure 7
Figure 7
Cytokine profiles of multiple organs in response to fructose and preservative treatments. 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 < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. n = eight mice per group.
Figure 8
Figure 8
Gene expression analysis in liver tissue across different fructose-based treatments. (a) PCA plot showing sample clustering based on treatment groups (three mice/group). Each dot represents a sample, and colors indicate different treatments. The percentage of variance explained by each principal component is shown on the axes. (b) 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 were filtered based on the following criteria: fold change < −2 or >2; FDR p-value < 0.05. (c) 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 was 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. (d) 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 < −2 or >2; FDR p-value < 0.05.
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