Metabolomic, Lipidomic, Transcriptomic, and Metagenomic Analyses in Mice Exposed to PFOS and Fed Soluble and Insoluble Dietary Fibers

doi:10.1289/EHP11360

. 2022 Nov;130(11):117003.

doi: 10.1289/EHP11360. Epub 2022 Nov 4.

Metabolomic, Lipidomic, Transcriptomic, and Metagenomic Analyses in Mice Exposed to PFOS and Fed Soluble and Insoluble Dietary Fibers

Pan Deng^{1

2

3}, Jerika Durham^{2

4}, Jinpeng Liu⁵, Xiaofei Zhang⁵, Chi Wang⁵, Dong Li⁶, Taesik Gwag⁶, Murong Ma⁶, Bernhard Hennig^{2

7}

Affiliations

¹ Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China.
² Superfund Research Center, University of Kentucky, Lexington, Kentucky, USA.
³ Department of Pharmaceutical Sciences, University of Kentucky, Lexington, Kentucky, USA.
⁴ Department of Toxicology and Cancer Biology, College of Medicine, University of Kentucky, Lexington, Kentucky, USA.
⁵ Markey Cancer Center, University of Kentucky, Lexington, Kentucky, USA.
⁶ Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, Kentucky, USA.
⁷ Department of Animal and Food Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, Kentucky, USA.

PMID: 36331819
PMCID: PMC9635512
DOI: 10.1289/EHP11360

Metabolomic, Lipidomic, Transcriptomic, and Metagenomic Analyses in Mice Exposed to PFOS and Fed Soluble and Insoluble Dietary Fibers

Pan Deng et al. Environ Health Perspect. 2022 Nov.

. 2022 Nov;130(11):117003.

doi: 10.1289/EHP11360. Epub 2022 Nov 4.

Authors

Pan Deng^{1

2

3}, Jerika Durham^{2

4}, Jinpeng Liu⁵, Xiaofei Zhang⁵, Chi Wang⁵, Dong Li⁶, Taesik Gwag⁶, Murong Ma⁶, Bernhard Hennig^{2

7}

Affiliations

¹ Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China.
² Superfund Research Center, University of Kentucky, Lexington, Kentucky, USA.
³ Department of Pharmaceutical Sciences, University of Kentucky, Lexington, Kentucky, USA.
⁴ Department of Toxicology and Cancer Biology, College of Medicine, University of Kentucky, Lexington, Kentucky, USA.
⁵ Markey Cancer Center, University of Kentucky, Lexington, Kentucky, USA.
⁶ Department of Pharmacology and Nutritional Sciences, College of Medicine, University of Kentucky, Lexington, Kentucky, USA.
⁷ Department of Animal and Food Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, Kentucky, USA.

PMID: 36331819
PMCID: PMC9635512
DOI: 10.1289/EHP11360

Abstract

Background: Perfluorooctane sulfonate (PFOS) is a persistent environmental pollutant that has become a significant concern around the world. Exposure to PFOS may alter gut microbiota and liver metabolic homeostasis in mammals, thereby increasing the risk of cardiometabolic diseases. Diets high in soluble fibers can ameliorate metabolic disease risks.

Objectives: We aimed to test the hypothesis that soluble fibers (inulin or pectin) could modulate the adverse metabolic effects of PFOS by affecting microbe-liver metabolism and interactions.

Methods: Male C57BL/6J mice were fed an isocaloric diet containing different fibers: a) inulin (soluble), b) pectin (soluble), or c) cellulose (control, insoluble). The mice were exposed to PFOS in drinking water ( $3 μ g / g per day$ ) for 7 wk. Multi-omics was used to analyze mouse liver and cecum contents.

Results: In PFOS-exposed mice, the number of differentially expressed genes associated with atherogenesis and hepatic hyperlipidemia were lower in those that were fed soluble fiber than those fed insoluble fiber. Shotgun metagenomics showed that inulin and pectin protected against differences in microbiome community in PFOS-exposed vs. control mice. It was found that the plasma PFOS levels were lower in inulin-fed mice, and there was a trend of lower liver accumulation of PFOS in soluble fiber-fed mice compared with the control group. Soluble fiber intake ameliorated the effects of PFOS on host hepatic metabolism gene expression and cecal content microbiome structure.

Discussions: Results from metabolomic, lipidomic, and transcriptomic studies suggest that inulin- and pectin-fed mice were less susceptible to PFOS-induced liver metabolic disturbance, hepatic lipid accumulation, and transcriptional changes compared with control diet-fed mice. Our study advances the understanding of interaction between microbes and host under the influences of environmental pollutants and nutrients. The results provide new insights into the microbe-liver metabolic network and the protection against environmental pollutant-induced metabolic diseases by high-fiber diets. https://doi.org/10.1289/EHP11360.

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Figures

Figure 1A is a set of one table and one timeline. The tabular representation has three rows and one column, namely, Diets. Row 1: Cellulose, Insoluble fiber control, and Fiber: 8 percent weight by weight. Row 2: Inulin, Soluble fibers, and Fat: 35 percent kilocalories. Row 3: Pectin with Protein: 20 percent kilocalories and Carbohydrate: 45 percent kilocalories. The timeline depicts the sample collection of mice exposed to P F O S with body weight per day by drinking water and maintained on the diet for 7 weeks. Figure 1B is a set of one bar graph and one tabular representation. The bar graph, plotting Liver weight to body weight ratio, ranging from 0.00 to 0.10 in increments of 0.02 (y-axis) across Control, Inulin, and Pectin (x-axis) for vehicle and P F O S. The tabular representation has three rows and three columns, namely, Source of variation, Percentage of total variation, and Lowercase p value. Row 1: Interaction, 1.607, and 0.1655. Row 2: Fiber, 3.917, and 0.0162. Row 3: P F O S, 74.90, and less than 0.0001. — **Figure 1.**
Effects of PFOS exposure and dietary interventions on liver/body weight ratio. (A) Study design. Seven-week-old C57BL/6 male mice ( $lowercase italic n equals 6 to 8 per group$ ) were placed on an irradiated diet supplemented with one of three fibers: cellulose (control), inulin, or pectin. The mice were exposed to PFOS at $3 micrograms per kilogram$ BW per day by drinking water and maintained on the diet for 7 wk. Body weight, food, and water intake were measured weekly. The mice were fasted 16 h before euthanasia, and tissues and plasma were collected at the end of the study. (B) The effects of PFOS and dietary fibers on liver/body weight ratio. Data were analyzed by two-way ANOVA followed by Tukey’s post hoc test for multiple comparisons. Bars represent $means \pm SEMs$ of 6–8 mice in each group. *** $lowercase italic p less than 0.001$ . No significant interactions between PFOS exposure and dietary fiber on liver/body weight ratio were observed. Detailed data of liver/body weight ratio are listed in Excel Table S11. Note: ANOVA, analysis of variance; BW, body weight; PFOS, perfluorooctane sulfonate; SEM, standard error of the mean.

Figure 3 is a set a six bar graphs, plotting 16 to 0 Ceramides, ranging from 0.00 to 0.25 in increments of 0.05; 18 to 0 Ceramides, ranging from 0.00 to 0.06 in increments of 0.02; 20 to 0 Ceramides, ranging from 0.00 to 0.04 in increments of 0.01; 22 to 0 Ceramides, ranging from 0.00 to 0.15 in increments of 0.05; 24 to 0 Ceramides, ranging from 0.00 to 0.15 in increments of 0.05; 24 is to 1 Ceramides, ranging from 0.0 to 0.6 in increments of 0.2 (y-axis) across uppercase c plus vehicle, uppercase c plus P F O S, uppercase i plus vehicle, uppercase i plus P F O S, uppercase p plus vehicle, and uppercase p plus P F O S (x-axis). — **Figure 3.**
Liver ceramide levels in mice exposed to PFOS and fed diets supplemented with different fibers (C: cellulose as control, I: inulin, P: pectin). Ceramides were analyzed using UHPLC-Q Exactive MS. Bars represent $means \pm SEMs$ of 6–8 mice in each group. Data were compared using two-way ANOVA and Tukey test for multiple comparisons, * $lowercase italic p less than 0.05$ ; ** $lowercase italic p less than 0.01$ ; *** $lowercase italic p less than 0.001$ ; and **** $lowercase italic p less than 0.0001$ . Detailed ceramide data are listed in Excel Table S12. Note: ANOVA, analysis of variance; PFOS, perfluorooctane sulfonate; SEM, standard error of the mean; UHPLC-Q exactive MS, ultra-high-performance liquid chromatography coupled with quadrupole-exactive mass spectrometer.

Figure 4 is a set of six bar graphs, plotting 16 to 0 Ceramides per S M ratio, ranging from 0.00 to 0.25 in increments of 0.05; 18 to 0 Ceramides per S M ratio, ranging from 0.0 to 0.5 in increments of 0.1; 20 to 0 Ceramides per S M ratio, ranging from 0.0 to 0.3 in increments of 0.1; 22 to 0 Ceramides per S M ratio, ranging from 0.0 to 0.4 in increments of 0.1; 24 to 0 Ceramides per S M ratio, ranging from 0.0 to 0.8 in increments of 0.2; and 24 is to 1 Ceramides per S M ratio, ranging from 0.0 to 0.5 in increments of 0.1 (y-axis) across uppercase c plus vehicle, uppercase c plus P F O S, uppercase i plus vehicle, uppercase i plus P F O S, uppercase p plus vehicle, and uppercase p plus P F O S (x-axis). — **Figure 4.**
Liver Cer/SM ratio in mice exposed to PFOS and fed diets supplemented with different fibers (C: cellulose as control, I: inulin, P: pectin). Bars represent $means \pm SEMs$ of 6–8 mice in each group. Data were compared using two-way ANOVA and Tukey test for multiple comparisons, * $lowercase italic p less than 0.05$ ; ** $lowercase italic p less than 0.01$ ; *** $lowercase italic p less than 0.001$ ; and **** $lowercase italic p less than 0.0001$ . Detailed data of liver Cer/SM ratio are listed in Excel Table S13. Note: ANOVA, analysis of variance; Cer, ceramide; PFOS, perfluorooctane sulfonate; SEM, standard error of the mean; SM, sphingomyelin.

Figures 5A to 5D are four bar graphs, plotting Plasma linear P F O S (micrograms per milliliter), ranging from 0 to 400 in increments of 100; Plasma branched P F O S (micrograms per milliliter), ranging from 0 to 80 in increments of 20; Liver linear P F O S (micrograms per gram), ranging from 0 to 800 in increments of 200; and Liver branched P F O S (micrograms per gram), ranging from 0 to 300 in increments of 100 (y-axis) across uppercase c plus P F O S, uppercase i plus P F O S, and uppercase p plus P F O S (x-axis). — **Figure 5.**
Plasma and liver levels of (A,C) linear and (B,D) branched PFOS in mice fed with cellulose- (control), inulin-, and pectin-supplemented diets. Bars represent $means \pm SEMs$ of 6–8 mice in each group and $p$ -values are labeled on the chart. Data were compared using one-way ANOVA and Tukey’s post hoc test for multiple comparisons. Detailed data of PFOS levels are listed in Excel Table S14. Note: ANOVA, analysis of variance; PFOS, perfluorooctane sulfonate; SEM, standard error of the mean.

Figure 6A is a set of three scores plots titled Control, Inulin, and Pectin, plotting Component 2 (16.7 percent), ranging from negative 5 to 5 in increments of 5; Component 2 (17.9 percent), ranging from negative 5 to 5 in increments of 5; and Component 2 (31.7 percent), ranging from negative 10 to 10 in increments of 5 (y-axis) across Component 1 (35.5 percent), ranging from negative 5 to 5 in increments of 5; Component 1 (31.9 percent), ranging from negative 4 to 6 in increments of 2; and Component 1 (34.6 percent), ranging from negative 5 to 5 in increments of 5 (x-axis) for uppercase c plus Vehicle, uppercase c plus P F O S; uppercase i plus Vehicle, uppercase i plus P F O S; and uppercase p plus Vehicle, uppercase p plus P F O S, respectively. Figure 6B is a set of three dot graphs, plotting Docosahexaenoic acid, 5-Hydroxyisouric acid, Trihydroxypentanoylcarnitine, Methylmalonyl-carnitine, Uridine diphosphate, Acetylarginine, Glutathione, Aminobenzoic acid, Gly-Lys, Inosine-5′-monophosphate, Val-Pro, Phenylenediamine, Methylhistamine, P F O S, and Glutarylcarnitine (left) and uppercase c plus P F O S, uppercase c plus vehicle from low to high (right); Malonyl-carnitine, Glycylglutamine, 2-(S-Glutathionyl)acetyl glutathione, Aminobenzoic acid, Eicosatrienoic acid, 3-Methylglutarylcarnitine, Gly-Lys, S-Adenosylmethionine, Val-Pro, N-Methylhexanamide, Acetylarginine, Inosine-5′-monophosphate, Glutathione, Glutarylcarnitine, and P F O S (left) and uppercase i plus P F O S, uppercase i plus vehicle from low to high (right); and Gly-Lys, Eicosapentanoic acid, Uridine monophosphate, N(2)-succinyl-L-ornithine, Docosahexaenoic acid, Aminobenzoic acid, Glucose 6-phosphate, 2-Aminoadipic acid, Cystathionine, Fructose-1,6-diphosphate, Uridine-diphosphate, Ophthalmic acid, Inosine-5′-monophosphate, Glutarylcarnitine, and P F O S (left) and uppercase p plus P F O S, uppercase p plus vehicle from low to high (right) (y-axis) across variable in projection scores, ranging from 2 to 7 in unit increments; 2 to 10 in increments of 2; and 2 to 8 in unit increments (x-axis). Figure 6C is a set of three volcano plots, plotting negative log 10 of (lowercase p), ranging from 0 to 8 in increments of 2; 0 to 5 in unit increments; and 0 to 6 in unit increments across log 2 of (fold change), ranging from negative 4 to 6 in increments of 2; negative 2 to 8 in increments of 2; and negative 2 to 6 (x-axis). — **Figure 6.**
Metabolomic analysis of liver samples from mice exposed to PFOS and fed with diets supplemented with cellulose (control), inulin, or pectin. (A) Liver metabolite profiles were analyzed by PLS-DA and significant separations between PFOS and vehicle treatments were observed in all three dietary groups ( $lowercase italic n equals 6 to 8 per group$ ). (B) Main metabolites responsible for separation between vehicle- and PFOS-treated groups by PLS-DA were analyzed by variable in projection (VIP). Up arrow indicates the level of the metabolite was higher in PFOS-exposed mice than that in the vehicle group, down arrow indicates the level of the metabolite was lower in PFOS-exposed mice than that in the vehicle group. Detailed data of VIP scores are listed in Excel Table S15. (C) Analysis of statistically significant metabolites were identified by volcano plot ( $fold change greater than 2$ , and $lowercase italic p less than or equal to 0.05$ ). Detailed metabolomic data are listed in Excel Table S5. Note: FC, fold change; Gly, glycine; Lys, lysine; PFOS, perfluorooctane sulfonate; PLS-DA, partial least-squares discriminant analysis; Pro, proline; Val, valine.

Figure 7A is a stacked bar graph, plotting Number of Metabolites P F O S versus Vehicle, ranging from 0 to 140 in increments of 20 (y-axis) across control, inulin, and pectin (x-axis) for up and down. Figure 7B is a Venn diagram with three circles. The circle on the left is labeled, Inulin: P F O S versus vehicle, the circle on the right is labeled, Pectin: P F O S versus vehicle with 56 specific metabolites, and the circle on the top is labeled control: P F O S versus vehicle. At the center, the intersection area is labeled, Core: 23. Figure 7C is a heatmap, plotting lowercase a to lowercase f (rows) across Amino acid metabolism, Nucleotide metabolism, Fatty acid metabolism, Citric cycle, Dipeptide, Steroid, and Uncategorized (carbonyls, carboxylic acids, and alcohols) (columns). A color scale depicts specific metabolites ranges from negative 0.5 to 0.5 in increments of 0.5. — **Figure 7.**
Liver metabolome in mice exposed to PFOS and fed with diets supplemented with different fibers. (A) The total number of different hepatic metabolites in mice exposed to PFOS and treated with cellulose- (control), inulin-, and pectin-supplemented diets ( $lowercase italic n equals 6 to 8 per group$ ). (B) Venn diagrams depicting the distribution of metabolites in the liver of mice on the cellulose, inulin, or pectin diet in response to PFOS exposure. Shared elements constitute “core” metabolites and are associated with PFOS exposure regardless of diets. “Specific” metabolites are associated with PFOS exposure in the cellulose diet group only but not in inulin- or pectin-fed groups. (C) Heatmap of liver metabolomic profiling of mice fed with diets supplemented with cellulose (b vs. a), inulin (d vs. c), and pectin (f vs. e) compared with mice treated with vehicle. Log fold changes of profiled metabolites are shown. Metabolites are ordered within each category: amino acid metabolism, nucleotide metabolism, fatty acid metabolism, citric cycle, dipeptide, steroid, and uncategorized (carbonyls, carboxylic acids, and alcohols). Detailed metabolomic data are listed in Excel Table S5. Heatmap data are listed in Excel Table S16. Note: a, $cellulose + vehicle$ ; b, $cellulose + PFOS$ ; c, $inulin + vehicle$ ; d, $inulin + PFOS$ ; e, $pectin + vehicle$ ; f, $pectin + PFOS$ ; PFOS, perfluorooctane sulfonate.

Figure 8A is a Venn diagram with three circles. The circle on the left is labeled, Inulin: P F O S versus vehicle, the circle on the right is labeled, Pectin: P F O S versus vehicle, and the circle on the top is labeled control: P F O S versus vehicle. At the center, the intersection area is labeled, 835. Figure 8B is a set of three volcano plots titled Control diet P F O S versus Vehicle, Inulin diet P F O S versus Vehicle, and Pectin diet P F O S versus Vehicle, plotting negative log 10 of (lowercase p), ranging from 1.3 to 100 in increment of 98.7 and 100 to 200 in increment of 100; 1.3 to 20 in increment of 18.7 and 20 to 80 in increments of 20; and 1.3 to 50 in increment of 48.7 and 50 to 100 in increment of 50 (y-axis) across log 2 of (fold change), ranging from negative 14 to 14 in increments of 7; negative 11 to 11 in increments of 6; and negative 11 to 11 in increments of 6 (x-axis) for Up-regulated genes, Down-regulated genes, and No genes, respectively. Figure 8C is a set of three horizontal bar graphs titled Control diet P F O S versus vehicle, Inulin diet P F O S versus vehicle, and Pectin diet P F O S versus vehicle, plotting Glycosaminoglycan biosynthesis - heparan sulfate or heparin (8), Longevity regulating pathway (22), Cysteine and methionine metabolism (14), Glycerolipid metabolism (16), Signaling pathways regulating pluripotency of stem cells (28), Thyroid hormone synthesis (19), Cushing syndrome (32), Adipocytokine signaling pathway (19), Protein processing in endoplasmic reticulum (38), Primary bile acid biosynthesis (8), Maturity onset diabetes of the young (8), Glucagon signaling pathway (26), Cholesterol metabolism (17), Autophagy - animal (33), P P A R signaling pathway (24), Bile secretion (22), Insulin resistance (30), Insulin signaling pathway (37), A M P K signaling pathway (36), Complement and coagulation cascades (41), 2-Oxocarboxylic acid metabolism (8), beta-Alanine metabolism (11), Cysteine and methionine metabolism (16), Tryptophan metabolism (14), Ascorbate and aldarate metabolism (11), Biosynthesis of amino acids (23), P P A R signaling pathway (25), Carbon metabolism (34), Valine, leucine and isoleucine degradation (19), Steroid hormone biosynthesis (25), Citrate cycle (T C A cycle) (14), Fatty acid degradation (20), Fluid shear stress and atherosclerosis (43), Ferroptosis (19), Arachidonic acid metabolism (27), Drug metabolism - cytochrome P 450 (26), Metabolism of xenobiotics (27), Drug metabolism - other enzymes (35), Proteasome (24), Retinol metabolism (39), Glutathione metabolism (33), and Ribosome (81); Starch and sucrose metabolism (8), T G F-beta signaling pathway (15), Complement and coagulation cascades (17), Central carbon metabolism in cancer (14), Age-Rage signaling pathway in diabetic, ellipsis, Focal adhesion (32), E C M-receptor interaction (17), Prostate cancer (20), P I3 K-Akt signaling pathway (49), beta-Alanine metabolism (7), Fatty acid elongation (8), Pyruvate metabolism (9), Glyoxylate and dicarboxylate metabolism (8), Propanoate metabolism (8), Fatty acid metabolism (11), Proteasome (11), Carbon metabolism (20), Ribosome (23), Peroxisome (17), Inflammatory mediator regulation of T R P channels (20), Biosynthesis of unsaturated fatty acids (9), Folate biosynthesis (8), Porphyrin and chlorophyll metabolism (13), Valine, leucine and isoleucine degradation (16), P P A R signaling pathway (20), Tryptophan metabolism (14), Pentose and glucuronate interconversions (13), Ascorbate and aldarate metabolism (12), Linoleic acid metabolism (15), Arachidonic acid metabolism (22), Fatty acid degradation (19), Glutathione metabolism (24), Steroid hormone biosynthesis (28), Drug metabolism - cytochrome P 450 (27), Metabolism of xenobiotics by cytochrome P 450 (28), Drug metabolism - other enzymes (37), and Retinol metabolism (37); and Complement and coagulation cascades (30), Glycerolipid metabolism (12), beta-Alanine metabolism (8), Ferroptosis (10), Valine, leucine and isoleucine degradation (12), Oxidative phosphorylation (23), Ascorbate and aldarate metabolism (8), Proteasome (12), Parkinson disease (26), Fatty acid degradation (15), Linoleic acid metabolism (13), P P A R signaling pathway (21), Steroid hormone biosynthesis (20), Arachidonic acid metabolism (21), Drug metabolism - other enzymes (25), Metabolism of xenobiotics by cytochrome P 450 (23), Drug metabolism - cytochrome P 450 (23), Glutathione metabolism (24), Retinol metabolism (33), and Ribosome (49) (y-axis) across negative log 10 (Padj), ranging from 0 to 30 in increments of 5 (x-axis) across Up-regulated genes and Down-regulated genes. — **Figure 8.**
Liver transcriptome in mice exposed to PFOS and fed with diets supplemented with different fibers ( $lowercase italic n equals 5 per group$ ). (A) Venn diagrams depicting the distribution of transcriptions in the liver of mice on cellulose (control), inulin, or pectin diet in response to PFOS exposure. (B) Analysis of statistically significant transcripts identified by volcano plot. (C) Enrichment analysis of the differentially expressed genes to identify biological functions or pathways that differ between PFOS-exposed and control mice in three dietary groups. The horizontal axis is $log to the base 10$ (Padj) of the significantly enriched pathway and the vertical axis is the enriched pathway, and the number of differentially expressed genes is shown in the parentheses. Detailed transcriptomic data are listed in Excel Tables S2–S4. Note: AGE, advanced glycation end products; Akt, protein kinase B; AMPK, adenosine monophosphate-activated protein kinase; ECM, extracellular matrix; FC, fold change; Padj, adjusted $p$ -value; PFOS, perfluorooctane sulfonate; PI3K, phosphatidylinositol-3-kinase; PPAR, peroxisome proliferator-activated receptor; RAGE, receptor for advance glycation end products; TCA, tricarboxylic acid; TGF, transforming growth factor; TRP, transient receptor potential.

Figure 9 is a set of eight bar graphs, plotting Nqo1 read count, ranging from 0 to 1,000 in increments of 200; Gsst3 read count, ranging from 0 to 5,000 in increments of 1,000; Hmox1 read count, ranging from 0 to 800 in increments of 200; Sqstm1 read count, ranging from 0 to 20,000 in increments of 5,000; Mmp2 read count, ranging from 0 to 100 in increments of 20; Ctsl read count, ranging from 0 to 30,000 in increments of 10,000; Icam1 read count, ranging from 0 to 250 in increments of 50; and Vcam1 read count, ranging from 0 to 250 in increments of 50 (y-axis) across uppercase c plus vehicle, uppercase c plus P F O S, uppercase i plus vehicle, uppercase i plus P F O S, uppercase p plus vehicle, and uppercase p plus P F O S (x-axis). — **Figure 9.**
Hepatic expression of anti-atherogenesis (*Nqo1*, *Gstt3*, *Hmox1*, and *Sqstm1*) and pro-atherogenesis genes (*Mmp2*, *Ctsl*, *Icam1*, and *Vcam1*) determined by transcriptomics. Bars represent $means \pm SEMs$ of gene read count for 5 mice in each group. Data were compared using two-way ANOVA and Tukey’s post hoc test for multiple comparisons, * $lowercase italic p less than 0.05$ ; ** $lowercase italic p less than 0.01$ ; *** $lowercase italic p less than 0.001$ ; and **** $lowercase italic p less than 0.0001$ . Detailed transcriptomic data are listed in Excel Tables S2–S4. Note: ANOVA, analysis of variance; PFOS, perfluorooctane sulfonate; SEM, standard error of the mean.

Figure 10A is a set of three bar graphs, plotting Smpd3 read count, ranging from 0 to 400 in increments of 100; Cers2 read count, ranging from 0 to 8,000 in increments of 2,000; and Cers2 read count, ranging from 0 to 200 in increments of 50 (y-axis) across uppercase c plus vehicle, uppercase c plus P F O S, uppercase i plus vehicle, uppercase i plus P F O S, uppercase p plus vehicle, and uppercase p plus P F O S (x-axis). Figure 10B is an illustration that displays that Sphingomyelin with Sphingomyelinase and Sphingomyelin Phosphodiesterase 3 and Sphingosine with Ceramide Synthase and CerS2, CerS6, and Acyl chain-lengths specificity lead to Ceramide with C 20 to C 26 and C 16. — **Figure 10.**
Hepatic expression of ceramide biosynthesis-related genes determined by transcriptomics. (A) Gene expression of *Smpd3*, *CerS2*, and *CerS6* determined by transcriptomics. Bars represent $means \pm SEMs$ of gene read count for 5 mice in each group. (B) Biosynthetic pathways of ceramide that were modulated by PFOS exposure and dietary fibers. Data were compared using two-way ANOVA and Tukey’s post hoc test for multiple comparisons, * $lowercase italic p less than 0.05$ ; ** $lowercase italic p less than 0.01$ ; *** $lowercase italic p less than 0.001$ ; and **** $lowercase italic p less than 0.0001$ . Detailed transcriptomic data are listed in Excel Tables S2–S4. Note: ANOVA, analysis of variance; PFOS, perfluorooctane sulfonate; SEM, standard error of the mean; Smpd3, sphingomyelin phosphodiesterase 3.

Figure 11A is a set of six stacked bar graph, plotting Relative Abundance, ranging from 0 to 100 in increments of 50 (y-axis) across uppercase c plus vehicle, ranging as lowercase a 1, lowercase a 2, lowercase a 3, lowercase a 4, and lowercase a 5; uppercase c plus P F O S, ranging as lowercase b 1, lowercase b 2, lowercase b 3, lowercase b 4, and lowercase b 5; uppercase i plus vehicle, ranging as lowercase c 1, lowercase c 2, lowercase c 3, lowercase c 4, and lowercase c 5; uppercase i plus P F O S, ranging as lowercase d 1, lowercase d 2, lowercase d 3, lowercase d 4, and lowercase d 5; uppercase p plus vehicle, ranging as lowercase e 1, lowercase e 2, lowercase e 3, lowercase e 4, and lowercase e 5; and uppercase p plus P F O S, ranging as lowercase f 1, lowercase f 2, lowercase f 3, lowercase f 4, and lowercase f 5 (x-axis) for Cyanobacteria, Chordata, Actinobacteria, Proteobacteria, Firmicutes, Verrucomicrobia, and Bacteroidetes. Figure 11B is a box and whisker plot, plotting alpha diversity, ranging from 0 to 5 in unit increments (y-axis) across uppercase c plus vehicle, uppercase c plus P F O S, uppercase i plus vehicle, uppercase i plus P F O S, uppercase p plus vehicle, and uppercase p plus P F O S (x-axis). Figure 11C is an dot graph titled P Co A, plotting Axis 2 [23 percent], ranging from negative 30 to 20 in increments of 10 (y-axis) across Axis 1 [74.4 percent], ranging from negative 60 to 40 in increments of 20 (x-axis) for uppercase a 1 to uppercase a 5: uppercase c plus vehicle; uppercase b 1 to uppercase b 5: uppercase c plus P F O S; uppercase c 1 to uppercase c 5: uppercase i plus vehicle; uppercase d 1 to uppercase d 5: uppercase p plus vehicle; and uppercase f 1 to uppercase f 5: uppercase p plus P F O S. — **Figure 11.**
Cecal content microbial communities in mice exposed to PFOS and fed with diets supplemented with different fibers. Shotgun metagenomic analysis of cecal contents of mice fed with different diets containing cellulose (control), inulin (I), or pectin (P), and exposed to PFOS by drinking water ( $lowercase italic n equals 5 per group$ ). (A) The major bacterial phyla presented in mice cecum content samples, shown as relative abundance in each sample (Excel Table S6). (B) Alpha-diversity index of microbial communities at the genus level. Data were analyzed by two-way ANOVA and presented as a box and whisker plot. Detailed data of alpha-diversity index are listed in Excel Table S17. * $lowercase italic p less than 0.05$ (midline, median; box limits, upper and lower quartiles; whiskers, 10th and 90th percentiles). (C) Principal coordinates analysis (PCoA) at the genus level comparing microbial beta-diversity (Bray–Curtis distance) among different groups. Note: ANOVA, analysis of variance; PFOS, perfluorooctane sulfonate.

Figure 12A is a cladogram titled Control that presents data for a control diet. The data from the cladogram are as follows: P F O S: lowercase z: lowercase g underscore Barnesiella, lowercase a 0: lowercase f underscore Barnesiellaceae, lowercase a 1: lowercase f underscore Dysgonomonadaceae, lowercase a 2: lowercase g underscore Duncaniella, lowercase a 3: lowercase g underscore Muribaculum, lowercase a 4: lowercase g underscore Sodaliphilus, lowercase a 5: lowercase f underscore Muribaculaceae, lowercase a 6: lowercase g underscore Porphyromonas, lowercase a 7: lowercase f underscore Porphyromonadaceae, lowercase a 8: lowercase g underscore Alloprevotella, lowercase a 9: lowercase g underscore Prevotella, lowercase b 0: lowercase f underscore Prevotellaceae, lowercase b 3: lowercase g underscore Tannerella, lowercase b 4: lowercase s underscore Phocaeicolacoprophilus, lowercase b 5: lowercase g underscore Flavobacterium, lowercase b 6: lowercase f underscore Flavobacteriaceae, lowercase b 7: lowercase g underscore Chryseobacterium, lowercase b 8: lowercase f underscore Weeksellaceae, lowercase b 9: lowercase o underscore Flavobacteriales, lowercase c 0: lowercase g underscore Mucilaginibacter, lowercase c 1: lowercase f underscore Sphingobacteriaceae, and lowercase c 2: lowercase o underscore Sphingobacteriales. Vehicle: lowercase a: lowercase f underscore Actinomycetaceae, lowercase b: lowercase o underscore Actinomycetales, lowercase c: lowercase g underscore Corynebacterium, lowercase d: lowercase f underscore Corynebacteriaceae, lowercase e: lowercase f underscore Mycobacteriaceae, lowercase f: lowercase f underscore Nocardiaceae, lowercase g: lowercase o underscore Corynebacteriales, lowercase h: lowercase f underscore Microbacteriaceae, lowercase i: lowercase o underscore Micrococcales, lowercase j: lowercase g underscore Micromonospora, lowercase k: lowercase f underscore Micromonosporaceae, lowercase l: lowercase o underscore Micromonosporales, lowercase m: lowercase g underscore Nocardioides, lowercase n: lowercase f underscore Nocardioidaceae, lowercase o: lowercase f underscore Propionibacteriaceae, lowercase p: lowercase o underscore Propionibacteriales, lowercase q: lowercase f underscore Pseudonocardiaceae, lowercase r: lowercase o underscore Pseudonocardiales, lowercase s: lowercase g underscore Streptomyces, lowercase t: lowercase f underscore Streptomycetaceae, lowercase u: lowercase o underscore Streptomycetales, lowercase v: lowercase o underscore Streptosporangiales, lowercase w: lowercase g underscore Olsenella, lowercase x: lowercase f underscore Atopobiaceae, lowercase y: lowercase o underscore Coriobacteriales, lowercase b l: lowercase g underscore Alistipes, lowercase b 2: lowercase f underscore Rikenellaceae, lowercase c 3: lowercase g underscore Paenibacillus, lowercase c 4: lowercase f underscore Paenibacillaceae, lowercase c 5: lowercase o underscore Bacillales, lowercase c 6: lowercase g underscore Enterococcus, lowercase c 7: lowercase f underscore Enterococcaceae, lowercase c 8: lowercase o underscore Lactobacillales, lowercase c 9: lowercase g underscore Christensenella, lowercase d 0: lowercase f underscore Christensenellaceae, lowercase d 1: lowercase g underscore Acutalibacter, lowercase d 2: lowercase g underscore Caproiciproducens, lowercase d 3: lowercase g underscore Dysosmobacter, lowercase d 4: lowercase g underscore Faecalibacterium, lowercase d 5: lowercase g underscore Flavonifractor, lowercase d 6: lowercase g underscore Oscillibacter, lowercase d 7: lowercase g underscore Ruminococcus, lowercase d 8: lowercase g underscore Ruthenibacterium, lowercase d 9: lowercase g underscore Oscillospiraceae, lowercase e 0: lowercase s underscore Flintibactersp underscore K G M B 00164, lowercase e 1: lowercase g underscore Flintibacter, lowercase e 2: lowercase s underscore Intestinimonasbutyriciproducens, lowercase e 3: lowercase g underscore Intestinimonas, lowercase e 4: lowercase o underscore Eubacteriales, lowercase e 5: lowercase f underscore Rhodospirillaceae, lowercase e 6: lowercase o underscore Rhodospirillales, lowercase e 7: lowercase f underscore Alcaligenaceae, lowercase e 8: lowercase f underscore Comamonadaceae, lowercase e 9: lowercase g underscore Turicimonas, lowercase f 0: lowercase f underscore Sutterellaceae, lowercase f 1: lowercase o underscore Burkholderiales, lowercase f 2: lowercase o underscore Myxococcales, lowercase f 3: lowercase g underscore Citrobacter, lowercase f 4: lowercase f underscore Enterobacteriaceae, lowercase f 5: lowercase o underscore Enterobacterales, lowercase f 6: lowercase f underscore Synergistaceae, and lowercase f 7: lowercase o underscore Synergistales. Figure 12B is a cladogram titled Inulin that presents data for a inulin diet. The data from the cladogram are as follows: P F O S: lowercase a: lowercase o underscore Streptomycetales and lowercase b: lowercase o underscore Desulfovibrionales. Figure 12C is a cladogram titled Pectin that presents data for a pectin diet. The data from the cladogram are as follows: Vehicle: lowercase a: lowercase g underscore Paraprevotella, lowercase b: lowercase g underscore Staphylococcus, lowercase c: lowercase f underscore taphylococcaceae, and lowercase d: lowercase f underscore Sutterellaceae. — **Figure 12.**
Cladograms of cecal content microbiome generated using linear discriminant analysis (LDA) effect size (LEfSe) analysis. Relative abundances of taxa were compared between groups using LEfSe with the default $p$ -value ( $lowercase alpha equals 0.05$ ) and the LDA score of 2.0. Comparison results are presented for PFOS vs. vehicle in each dietary group. Colors distinguish taxa differences between PFOS and vehicle treatments. (A) control diet; (B) inulin diet; and (C) pectin diet. Note: PFOS, perfluorooctane sulfonate.

Figures 13A to 13C are horizontal bar graphs titled control, inulin, pectin, plotting g underscore Alistipes, g underscore Oscillibacter, g underscore Flavonifractor, g underscore Flintibacter, g underscore Dysosmobacter, g underscore Paenibacillus, g underscore Citrobacter, g underscore Intestinimonas, g underscore Acutalibacter, g underscore Streptomyces, g underscore Turicimonas, g underscore Olsenella, g underscore Micromonospora, g underscore Christensenella, g underscore Corynebacterium, g underscore Caproiciproducens, g underscore Ruminococcus, g underscore Flavobacterium, g underscore Mucilaginibacter, g underscore Chryseobacterium, g underscore Alloprevotella, g underscore Porphyromonas, g underscore Tannerella, g underscore Barnesiella, g underscore Sodaliphilus, g underscore Prevotella, g underscore Muribaculum, and g underscore Duncaniella; g underscore Faecalitalea, g underscore Ruminococcus, g underscore Streptomyces, g underscore Dysosmobacter, and g underscore Flavonifractor; and g underscore Paraprevotella and g underscore Staphylococcus (y-axis) across Linear discriminant analysis score, ranging from negative 5 to 5 in increments of 2 (x-axis) for P F O S and vehicle, respectively. — **Figure 13.**
Linear discriminant analysis (LDA) scores of differentially abundant taxa in cecal content between the PFOS- and vehicle-treated mice fed the (A) control diet, (B) inulin diet, or (C) pectin diet using the linear discriminant analysis effect size method. Only taxa meeting an LDA significant threshold of 2 and $uppercase italic p less than 0.05$ are shown. The mice were fed with a diet supplemented with dietary fibers. Detailed LDA score data are listed in Excel Table S18. Note: PFOS, perfluorooctane sulfonate.

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Invited Perspective: PFOS-Pick Fiber, Oust Sulfonate.
Golonka RM, Vijay-Kumar M. Golonka RM, et al. Environ Health Perspect. 2022 Nov;130(11):111301. doi: 10.1289/EHP12012. Epub 2022 Nov 4. Environ Health Perspect. 2022. PMID: 36331817 Free PMC article. No abstract available.

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1. Tu P, Chi L, Bodnar W, Zhang Z, Gao B, Bian X, et al. . 2020. Gut microbiome toxicity: connecting the environment and gut microbiome-associated diseases. Toxics 8(1):19, PMID: , 10.3390/toxics8010019. - DOI - PMC - PubMed
1. Parthasarathy G, Revelo X, Malhi H. 2020. Pathogenesis of nonalcoholic steatohepatitis: an overview. Hepatol Commun 4(4):478–492, PMID: , 10.1002/hep4.1479. - DOI - PMC - PubMed
1. Wang C, Zhang Y, Deng M, Wang X, Tu W, Fu Z, et al. . 2019. Bioaccumulation in the gut and liver causes gut barrier dysfunction and hepatic metabolism disorder in mice after exposure to low doses of OBS. Environ Int 129:279–290, PMID: , 10.1016/j.envint.2019.05.056. - DOI - PubMed
1. Liao J, Liu Y, Yi J, Li Y, Li Q, Li Y, et al. . 2022. Gut microbiota disturbance exaggerates battery wastewater-induced hepatotoxicity through a gut-liver axis. Sci Total Environ 809:152188, PMID: , 10.1016/j.scitotenv.2021.152188. - DOI - PubMed
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[1] Tu P, Chi L, Bodnar W, Zhang Z, Gao B, Bian X, et al. . 2020. Gut microbiome toxicity: connecting the environment and gut microbiome-associated diseases. Toxics 8(1):19, PMID: , 10.3390/toxics8010019. - DOI - PMC - PubMed

[2] Tu P, Chi L, Bodnar W, Zhang Z, Gao B, Bian X, et al. . 2020. Gut microbiome toxicity: connecting the environment and gut microbiome-associated diseases. Toxics 8(1):19, PMID: , 10.3390/toxics8010019. - DOI - PMC - PubMed

[3] Parthasarathy G, Revelo X, Malhi H. 2020. Pathogenesis of nonalcoholic steatohepatitis: an overview. Hepatol Commun 4(4):478–492, PMID: , 10.1002/hep4.1479. - DOI - PMC - PubMed

[4] Parthasarathy G, Revelo X, Malhi H. 2020. Pathogenesis of nonalcoholic steatohepatitis: an overview. Hepatol Commun 4(4):478–492, PMID: , 10.1002/hep4.1479. - DOI - PMC - PubMed

[5] Wang C, Zhang Y, Deng M, Wang X, Tu W, Fu Z, et al. . 2019. Bioaccumulation in the gut and liver causes gut barrier dysfunction and hepatic metabolism disorder in mice after exposure to low doses of OBS. Environ Int 129:279–290, PMID: , 10.1016/j.envint.2019.05.056. - DOI - PubMed

[6] Wang C, Zhang Y, Deng M, Wang X, Tu W, Fu Z, et al. . 2019. Bioaccumulation in the gut and liver causes gut barrier dysfunction and hepatic metabolism disorder in mice after exposure to low doses of OBS. Environ Int 129:279–290, PMID: , 10.1016/j.envint.2019.05.056. - DOI - PubMed

[7] Liao J, Liu Y, Yi J, Li Y, Li Q, Li Y, et al. . 2022. Gut microbiota disturbance exaggerates battery wastewater-induced hepatotoxicity through a gut-liver axis. Sci Total Environ 809:152188, PMID: , 10.1016/j.scitotenv.2021.152188. - DOI - PubMed

[8] Liao J, Liu Y, Yi J, Li Y, Li Q, Li Y, et al. . 2022. Gut microbiota disturbance exaggerates battery wastewater-induced hepatotoxicity through a gut-liver axis. Sci Total Environ 809:152188, PMID: , 10.1016/j.scitotenv.2021.152188. - DOI - PubMed

[9] Peters A, Nawrot TS, Baccarelli AA. 2021. Hallmarks of environmental insults. Cell 184(6):1455–1468, PMID: , 10.1016/j.cell.2021.01.043. - DOI - PMC - PubMed

[10] Peters A, Nawrot TS, Baccarelli AA. 2021. Hallmarks of environmental insults. Cell 184(6):1455–1468, PMID: , 10.1016/j.cell.2021.01.043. - DOI - PMC - PubMed

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Metabolomic, Lipidomic, Transcriptomic, and Metagenomic Analyses in Mice Exposed to PFOS and Fed Soluble and Insoluble Dietary Fibers

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Metabolomic, Lipidomic, Transcriptomic, and Metagenomic Analyses in Mice Exposed to PFOS and Fed Soluble and Insoluble Dietary Fibers

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