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Analysis of sex differences in dietary copper-fructose interaction-induced alterations of gut microbial activity in relation to hepatic steatosis

Abstract

Background

Inadequate copper intake and increased fructose consumption represent two important nutritional problems in the USA. Dietary copper-fructose interactions alter gut microbial activity and contribute to the development of nonalcoholic fatty liver disease (NAFLD). The aim of this study is to determine whether dietary copper-fructose interactions alter gut microbial activity in a sex-differential manner and whether sex differences in gut microbial activity are associated with sex differences in hepatic steatosis.

Methods

Male and female weanling Sprague-Dawley (SD) rats were fed ad libitum with an AIN-93G purified rodent diet with defined copper content for 8 weeks. The copper content is 6 mg/kg and 1.5 mg/kg in adequate copper diet (CuA) and marginal copper diet (CuM), respectively. Animals had free access to either deionized water or deionized water containing 10% fructose (F) (w/v) as the only drink during the experiment. Body weight, calorie intake, plasma alanine aminotransferase, aspartate aminotransferase, and liver histology as well as liver triglyceride were evaluated. Fecal microbial contents were analyzed by 16S ribosomal RNA (16S rRNA) sequencing. Fecal and cecal short-chain fatty acids (SCFAs) were determined by gas chromatography-mass spectrometry (GC-MS).

Results

Male and female rats exhibit similar trends of changes in the body weight gain and calorie intake in response to dietary copper and fructose, with a generally higher level in male rats. Several female rats in the CuAF group developed mild steatosis, while no obvious steatosis was observed in male rats fed with CuAF or CuMF diets. Fecal 16S rRNA sequencing analysis revealed distinct alterations of the gut microbiome in male and female rats. Linear discriminant analysis (LDA) effect size (LEfSe) identified sex-specific abundant taxa in different groups. Further, total SCFAs, as well as, butyrate were decreased in a more pronounced manner in female CuMF rats than in male rats. Of note, the decreased SCFAs are concomitant with the reduced SCFA producers, but not correlated to hepatic steatosis.

Conclusions

Our data demonstrated sex differences in the alterations of gut microbial abundance, activities, and hepatic steatosis in response to dietary copper-fructose interaction in rats. The correlation between sex differences in metabolic phenotypes and alterations of gut microbial activities remains elusive.

Introduction

The prevalence of nonalcoholic liver disease (NAFLD) in the USA has increased rapidly in the past two decades, from 19 to 24%, which is close to the global prevalence of 25.24% [1, 2]. Based on the epidemiological data from obesity and type 2 diabetes in adults, the estimated prevalence of NAFLD will continue to increase up to 33.5% by 2030, and nonalcoholic steatohepatitis (NASH) will increase proportionately from 20% of NAFLD to 27%, ranking it as a top indication for liver transplantation [3, 4].

Of note, NAFLD and NASH exhibit age and sex differences, with a higher prevalence in men than in premenopausal women. Conversely, a higher rate of NAFLD was found among the postmenopausal women [5,6,7]. In agreement with this finding, sex differences also exist in the risk factors, such as obesity and type 2 diabetes [8, 9]. Biological sex differences are exhibited in many physiological phenomenon, including fat distribution, triglyceride storage in the liver and muscle [10], and fatty acid and glucose metabolism [11]. Therefore, understanding sex differences in physiology and pathophysiology is required for precision medicine.

Sex hormones and sex chromosome are two major factors driving sex differences [7]. The role of sex hormones has been demonstrated in both human and animal studies. For example, postmenopausal women with estrogen deficiency display a higher risk for NAFLD progression to fibrosis [12]. In contrast, liver injury was improved by hormone replacement therapy in postmenopausal women with type 2 diabetes [13]. Ovariectomized (OVX) female rats exhibit exacerbated hepatic steatosis when exposed to high-fat high-fructose diet (HFFD), which was reversed by estrogen replacement [14]. A four-core genotype mouse model (XX gonadal male and female, XY gonadal male and female) allows for the identification of whether sex differences arise from the sex chromosome complement. Using this approach, it was revealed that XX mice are prone to developing obesity and fatty liver in response to high-fat diet, regardless of sex hormones [15].

In addition to genetics and sex hormones, diet is a key environmental factor leading to sex differences in metabolic diseases [16]. Copper and fructose are two dietary factors known to be critical in the pathogenesis of NAFLD [17,18,19,20,21,22]. Sex differences in the metabolic effects of fructose and/or copper deficiency have been noted in rodents [23,24,25,26] as well as in humans [27, 28], with more harmful effects reported in males and more protective effects in females, which is consistent with the sex differences in NAFLD [7]. In fact, sex differences in fructose-induced metabolic effects are more complex and vary by tissue and organ [14, 29, 30]. Although sex hormones are one of the factors leading to sex differences in copper-fructose interaction-induced metabolic disorders [26], the underlying mechanisms are largely unknown.

A growing body of evidence has shown that gut microbiota play a causal role in driving the development of obesity, diabetes, and NAFLD [31,32,33,34]. Diet, as one of the most common environmental factors, shapes the gut microbiome [35]. Interestingly, diet-induced alterations of gut microbiota exhibit a sex-dependent phenotype [36, 37]. Previous studies have shown that distinct alterations of the gut microbiome are linked to specific metabolic traits [38] as well as to different stages of NAFLD [39, 40], leading to the hypothesis that sex differences in the gut microbiota are linked to distinct metabolic phenotypes or disease severity. Our previous studies have shown that dietary copper-fructose interactions shifted gut microbiota and correlated to the development of hepatic steatosis in male rats [41, 42]. Given that diet shapes the gut microbiome in a sex-specific manner [36], we aimed to determine whether dietary copper-fructose interaction alters gut microbiota and induces hepatic steatosis in a sex-dependent manner and whether sex differences in metabolic phenotype contribute to the distinct alterations of the gut microbiota.

Materials and methods

Animals and diets

Male and female weanling Sprague-Dawley rats (35–45 g) from the Harlan Laboratories (Indianapolis, IN) were fed (ad lib) an AIN-93G purified rodent diet with a defined copper content. The rats received either 1.5 mg/kg or 6.0 mg/kg of copper as marginal or adequate doses, respectively, for 8 weeks. Control animals were fed adequate copper with no added fructose. The animals were single housed in stainless steel cages without bedding in a temperature- and humidity-controlled room with a 12:12-h light–dark cycle. Animals had free access to either deionized water or deionized water containing 10% fructose (w/v). Fructose-enriched drinking water was changed twice a week. Food consumption and body weight were monitored on a weekly basis. After a 2-h fasting, all the animals were sacrificed under anesthesia with ketamine/xylazine (100/10 mg/kg I.P. injection). Blood was collected from the inferior vena cava, and citrated plasma was stored at − 80 °C for further analysis. Portions of liver tissue were fixed with 10% formalin for subsequent sectioning, while others were snap-frozen with liquid nitrogen. All studies were approved by the University of Louisville Institutional Animal Care and Use Committee, which is certified by the American Association of Accreditation of Laboratory Animal Care.

Liver enzyme and plasma biochemical assays

Liver enzymes assays were performed with commercially available kits: alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholesterol, triglyceride (TG) (Thermo Fisher Scientific Inc., Middletown, VA, USA), glucose (Millipore Sigma, St. Louis, MO, USA), and nonesterified fatty acids (NEFA) (Wako Chemicals, Richmond, VA, USA).

Histology

Formalin-fixed, paraffin-embedded liver sections were cut at 5-μm thickness and stained with hematoxylin and eosin (H&E).

Hepatic triglyceride assay

Liver tissues were homogenized in 50 mM sodium chloride solution. Hepatic total lipids were extracted with chloroform/methanol (2:1) according to the method described by Bligh and Dyer [43]. Hepatic triglyceride was determined by commercially available kit (Thermo Fisher Scientific Inc., Middletown, VA, USA).

16S ribosomal RNA (16S rRNA) gene library preparation and sequencing on the Illumina MiSeq

Fecal pellets were collected into sterile tubes at the end of the experiment and stored at − 80 °C. Microbial genomic DNA was extracted from frozen fecal samples using DNeasy PowerSoil kit (Cat#:12888-100, Qiagen, Germantown, MD, USA) according to the manufacturer’s instructions. The composition of fecal microbiota was analyzed using Illumina MiSeq technology targeting the variable V3 and V4 regions of 16S ribosomal RNA. 16S variable regions were amplified using 12.5 ng microbial genomic DNA. PCR conditions are as follows: 95 °C for 3 min; 25 cycles of 95 °C for 30 s, 55 °C for 30 s, and then 72 °C for 30 s; and 72 °C for 5 min. The primers used for 16S Amplicon PCR are as follows: Forward: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG; Reverse: 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC. Index PCR was performed to attach dual indices and Illumina sequencing adapters using the Nextera Index Kit (Cat#: FC-121-1012, Illumina, San Diego, CA, USA). Each step was followed by the PCR clean-up, using AMPure XP beads to obtain a purified library. After libraries were normalized, pooled, and denatured, sequencing was done using Illumina MiSeq Reagents kit v3 (600 cycles, read lengths up to 2 × 300 bp) (Cat#: MS-102-3003, Illumina, San Diego, CA, USA) on an Illumina MiSeq instrument.

Sequencing data analysis

Quality control of raw sequence files was performed using FastQC and further analyzed using QIIME 2 (version 2019.04) [44]. The workflow is shown in the schematic diagram (supplementary Figure 1). Briefly, the paired-end files per sample were merged and imported into a QIIME 2 artifact. The sequences reads were then demultiplexed and denoised into amplicon sequence variants (ASVs) (supplementary Table 8) using DADA2 in QIIME 2 which can identify more real variants and output fewer spurious sequences than other methods. The resulted feature table and representative sequences were used for the downstream analysis. Rarefaction curve using the observed operational taxonomy unit (OTU) and Shannon index generated by QIIME 2 were used as metrics of α-diversity [45]. Principal coordinate analysis (PCoA) was performed to compare microbial community structure between groups (β-diversity), using both weighted and unweighted UniFrac [46]. Heat map analysis of OTU abundance was performed using R software (https://www.r-project.org/). Linear discriminant analysis (LDA) effect size (LEfSe) method was used to find the most differentially abundant enriched microbial taxa between the different diets. The analysis was performed on Galaxy platform (http:/huttenhower.sph.harvard.edu/galaxy). The data generated from LEfSe analysis was shown by cladogram and histogram with LDA score > 2 and a significance of α < 0.05, as determined by Wilcoxon rank-sum test [47,48,49]. The 16S data set was used for metagenome predictions using the software package PICRUSt2 [50]. Predictions were based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways [51], and the output was based on the pathway mapping of the MetaCyc database [52]. A Venn diagram was used to show genus distribution between groups.

Short-chain fatty acid (SCFA) measurement by gas chromatography-mass spectrometry (GC-MS)

About 50 mg of cecal and fecal stool samples were weighed, and polar metabolites were extracted for GC-MS analysis using established methods as described previously [53].

Statistical analysis

Data were expressed as mean ± SD (standard deviation) and analyzed using two-way ANOVA to test the factors of copper, fructose, and their interactions (copper × fructose), followed by Tukey’s multiple comparison test. The Kruskal-Wallis test was used for pairwise comparison between treatment groups (α-diversity). Comparison of the mean distance matrix (β-diversity) between two treatment groups using PERMANOVA (a nonparametric method for multivariate analysis of variance) with permutation tests was based on UniFrac distance matrix (999 Monte Carlo permutations). Two-tailed nonparametric Spearman correlation was done with GraphPad Prism. Differences at p ≤ 0.05 were considered to be statistical significant.

Results

Characterization of dietary copper-fructose interaction on metabolic phenotypes in male and female rats

Male and female rats exhibit similar trends of changes in the body weight and body weight gain in response to dietary copper and fructose, with a generally higher level in male rats (Fig. 1, Tables 1 and 2). Two-way ANOVA analysis showed that the liver weight of female rats, but not male rats, was affected by dietary copper content within the 8-week period. The liver/body weight ratio was altered by both dietary copper and fructose. However, copper-fructose interaction was apparent only in female rats. While the variations of perigonadal white adipose tissue (WAT) weight as well as WAT/body weight ratios were related to dietary copper content in male rats, they were more likely to be affected by dietary fructose in female rats. The energy efficiency ratio (EER, %), i.e., the ratio of body weight gain and total energy intake [54, 55], was decreased by dietary fructose in both male and female rats compared to their controls, suggesting the metabolic effects of fructose may not be contributed to the calorie intake. Ad libitum feeding of fructose via drinking water led to a significant increase in water intake and a decrease in pellet food intake. Although there was a trend toward an increase in the total energy intake in rats fed with fructose compared to those without, the difference did not reach statistical significance in either males or females. Plasma triglyceride was significantly elevated in male rats fed with fructose regardless dietary copper. However, it was only significantly elevated in CuMF female rats compared to marginal copper diet (CuM) female rats. Plasma cholesterol levels were not significantly changed by dietary fructose or copper level in both male and female rats. Plasma NEFA was significantly increased in CuAF male rats compared to adequate copper diet (CuA) rats. In female rats, fructose feeding led to a trend of an increase in plasma NEFA levels. Plasma glucose level was significantly elevated by fructose feeding in female rats regardless of dietary copper level, whereas this effect was only observed in male CuA rats (Tables 1 and 2). Collectively, plasma lipids and glucose display distinct alterations in response to dietary copper and fructose between male and female rats.

Fig. 1
figure1

Body weight and calorie intake throughout the 8 weeks of the experiment. Male and female weanling Sprague-Dawley rats were fed with adequate or marginal copper diet and had free access to deionized water or deionized water containing 10% fructose (w/v) for 8 weeks as described in the “Materials and Methods” section. Data represent means ± SD (n = 7–8). Cu, copper; A, adequate copper diet; AF, adequate copper diet +10% fructose (w/v) in the drinking water; M, marginal copper diet; MF, marginal copper diet +10% fructose (w/v) in the drinking water

Table 1 Effects of dietary fructose and marginal copper deficiency on metabolic phenotypes in male rats
Table 2 Effects of dietary fructose and marginal copper deficiency on metabolic phenotypes in female rats

Hepatic manifestations in response to dietary copper-fructose interaction in male and female rats

Neither male nor female rats showed obvious liver injury in terms of plasma ALT and AST after being exposed to CuA or CuM diets with or without 10% fructose (w/v) for 8 weeks (Fig. 2a). Three of eight female rats fed with CuA plus fructose (CuAF) developed mild steatosis, characterized with macrosteatosis around the portal area. Only very mild microsteatosis could be visualized in either CuMF female rats or male rats fed with marginal copper diet and/or fructose (Fig. 2b). Consistently, hepatic triglyceride was significantly elevated in CuAF female rats compared to control rats (Fig. 2c). Compared to our previous study with AIN-76 diet (containing 49% sucrose) and 30% fructose (w/v) in the drinking water [21], the extent of hepatic steatosis is mild and no apparent liver injury was detected. Despite there being only mild steatosis induced under the current conditions, sex differences still were detected, with female CuAF rats showing hepatic steatosis.

Fig. 2
figure2

Effects of dietary copper-fructose interaction on plasma ALT, AST, liver histology, and fat accumulation. a Plasma ALT and AST. b Representative photos of liver histology using H&E staining. c Hepatic triglyceride. CuAF female rats had macrosteatosis (arrows) around the portal area. Microsteatosis (arrowheads) was observed in female CuMF rats as well as in some male rats as indicated. Data represent means ± SD (n = 7–8). Statistical significance was set at p ≤ 0.05. P values displayed are for the factors copper (Cu), fructose (F), and interaction (Cu × F) using two-way ANOVA followed with Tukey’s multiple comparisons test. A, adequate copper diet; AF, adequate copper diet +10% fructose (w/v) in the drinking water; M, marginal copper diet; MF, marginal copper diet +10% fructose (w/v) in the drinking water

Distinct alterations of fecal gut microbiota in response to dietary copper and fructose between male and female rats as analyzed by 16S rRNA sequencing

To examine whether copper-fructose interaction alters the gut microbiome in a sex-specific manner, we performed 16S rRNA sequencing of fecal stool DNA. In male rats, either fructose or CuM resulted in a trend of decrease in alpha-diversity in terms of the observed OTU. However, only the difference between CuA and CuAF reached statistical significance (CuA versus CuAF, p = 0.037), suggesting fructose feeding led to reduced species richness in male rats [56]. There were no significant differences between groups of female rats in terms of observed OTU, suggesting neither fructose nor CuM alters the species richness of the gut microbiota in female rats. There was no significant difference between groups of both male and female rats in terms of Shannon index (Fig. 3a, supplementary Table 1). Beta-diversity was evaluated by UniFrac analysis [46]. Unweighted UniFrac is a qualitative β-diversity measure, which detects the difference in the presence or absence of lineages of bacteria in different communities [57]. Unweighted UniFrac analysis demonstrated that the mean distance between groups CuA and CuAF, CuA and CuM, and CuA and CuMF were significantly different in male rats (p < 0.05) (Fig. 3b, right panel, supplementary Table 2). In female rats, unweighted UniFrac analysis showed significant differences were between groups CuM and CuMF, and CuA and CuMF (p < 0.05) (Fig. 3b, right panel, supplementary Table 2). The weighted UniFrac measure was used for detecting differences in abundance [57], and no significant differences were detected between the four treatment groups in male or female rats (Fig. 3b, left panel, supplementary Table 2). These results suggested that either dietary fructose (CuAF) or copper (CuM) or the combined effects (CuMF) alter bacterial communities in male rats. However, bacterial communities were altered by dietary copper (CuM) or copper plus fructose (CuMF) in female rats. Moreover, the baseline bacterial communities (CuA) were significantly different between male and female rats.

Fig. 3
figure3

Effects of dietary copper and fructose on gut bacterial diversity and abundance. a Alpha-diversity: alpha rarefaction curves with each treatment using observed OTU measure and Shannon index. b Beta-diversity: weighted and unweighted UniFrac. c Taxonomic composition (percentage) of the gut microbiota at the phylum level. Cu, copper; A, adequate copper diet; AF, adequate copper diet +10% fructose (w/v) in the drinking water; M, marginal copper diet; MF, marginal copper diet +10% fructose (w/v) in the drinking water. M (first letter in the group name), male; F (first letter in the group name), female

At the phylum level, fructose feeding led to a remarkable increase in the abundance of Bacteroidetes and Proteobacteria and a decrease in Firmicutes independent of dietary copper content. In male rats, only the abundance of Bacteroidetes and Proteobacteria was altered by dietary fructose, and the effect was less pronounced compared to female rats (Fig. 3c, supplementary Tables 3 and 4). In agreement with this, more families and genera under the phyla Bacteroidetes, Firmicutes, and Proteobacteria were altered in female rats compared to male rats. For example, Bacteroidaceae, Bacteroides, Lachnospiraceae, Erysipelotrichaceae, Allobaculum, Alcaligenaceae, and Sutterella were markedly shifted in female rats, but not in male rats. Even among the commonly changed taxa, such as Porphyromonadaceae, Parabacteroides, and Blautia, the factors leading to such changes are different between males and females, as shown by two-way ANOVA (supplementary Tables 3, 4, 5, 6 and Fig. 4). In addition to the sex differences in response to dietary fructose and marginal copper, the composition of gut microbiota is also different between male and female rats when exposed to adequate copper diet, which was considered as a normal control. A higher abundance of Firmicutes and a lower abundance of Bacteroidetes were observed in female rats than in male rats, leading to a higher Firmicutes/Bacteroidetes ratio in females rats (12.06 versus 7.47, female versus male), which was considered an obese phenotype contributing to increased capacity of energy harvesting from diet [58]. Sex differences also exist in the abundance of Lactobacillaceae and Lactobacillus (9.39 versus 20.72, female versus male), Clostridiaceae (15.99 versus 8.69, female versus male), Ruminococcaceae (20.9 versus 17.85, female versus male), and Lachnospiraceae (17.25 versus 11.86, female versus male).

Fig. 4
figure4

Relative abundance of gut microbiota at the genus level. Heatmap showing the abundance of 73 fecal gut microbes in a Male rats and b Female rats. Data represent means ± SD (n = 7–8). Statistical significance was set at p ≤ 0.05. P values displayed are for the factors copper (Cu), fructose (F), and interaction (Cu × F) by two-way ANOVA with Tukey’s multiple comparisons test. * versus CuA; # versus CuAF; $ versus CuM. A, adequate copper diet; AF, adequate copper diet +10% fructose (w/v) in the drinking water; M, marginal copper diet; MF, marginal copper diet +10% fructose (w/v) in the drinking water

Collectively, female rats exhibit more pronounced alterations of gut microbiota, and fructose plays a dominant role.

LEfSe identified microbiota signature associated with dietary copper and fructose

To further identify more specific taxa changes in gut microbiome by dietary copper and fructose, LEfSe analysis was performed using 16S rRNA metagenomic data [47]. Fifteen and 26 differentially abundant taxa were identified with LDA score higher than 2 in male and female rats, respectively (Fig. 5a and b). The Proteobacteria and Bacteroidetes were enriched in the CuAF and CuMF group, respectively, in both male and female rats. No specific taxa were identified to be enriched in CuM male rats. The highest number of abundant taxa was in the CuMF group (7 of 15 in male and 12 of 26 in female). Sex differences in abundance also existed in CuA rats, which were considered as normal controls. Female CuA rats were characterized by enriched Firmicutes, particularly, Lachnospiraceae. Of note, while Porphyromonadaceae and Parabacteroides were enriched in CuMF male rats, they were also enriched in female CuAF rats, which is consistent with the mean abundance data analysis (supplementary Tables 3 and 4). Particularly, abundant beta-Proteobacteria and Erysipelotrichi in CuMF rats as well as abundant alpha-Proteobacteria in CuAF rats were identified in female rats. Thus, distinct abundant taxa were identified by LEfSe analysis between male and females. We further performed correlation analysis between liver fat content and the genera identified by LEfSe analysis in female CuAF rats. Unfortunately, the abundance levels of the genera are not correlated with the liver fat content (supplementary figure 2).

Fig. 5
figure5

Linear discriminant analysis (LDA) effect size (LEfSe) analysis identifies differentially abundant taxa induced by dietary copper and fructose. Cladogram and histogram with LDA score ≥ 2 showing the features with differential abundance of taxa between groups in a male rats and b female rats (Wilcoxon rank-sum test). c Venn diagram. Each circle’s diameter in the cladogram is proportional to the taxon’s abundance. From the outer circle to the inner circle, the circles represent phyla, class, order, family, and genus. Differentially abundant taxa in specific groups were represented in different colors with the exception that yellow represents non-significant in the cladogram. M, male; F, female; Cu, copper; A, adequate copper diet; AF, adequate copper diet +10% fructose (w/v) in the drinking water; M, marginal copper diet; MF, marginal copper diet +10% fructose (w/v) in the drinking water

To further explore the functional changes of gut microbiome in response to dietary copper and fructose, we performed PICRUSt2 analysis. In male rats, 40 significant differences in the functional profiles were identified by PICRUSt2 analysis between groups CuA and CuM, mainly involving fatty acid biosynthesis, electron carrier biosynthesis, lipopolysaccharide biosynthesis, and vitamin B6 biosynthesis, which were enriched in CuM male rats. Twenty-three significantly enriched pathways were predicted in male CuAF rats compared to male CuA rats. In female rats, 34 significant differences in the functional profiles were identified between CuA and CuMF groups, involving branched chain amino acid biosynthesis, fermentation, nucleotide biosynthesis and degradation, folate biosynthesis, and phospholipid biosynthesis, with lower abundance in CuMF rats (supplementary Table 7). Taken together, significant functional alterations of microbiota in female rats were induced mainly by the combined effects of copper and fructose (CuMF), whereas they were induced by either copper or fructose singly in male rats.

The Venn diagram plot showed 51 shared genera by four groups in both male and female rats. There are total 65 and 56 detected genera in male and female rats, respectively. Fructose and marginal copper led to reduced genera in male rats, but an increase in female rats. Six genera were not altered by fructose or marginal copper diet in male rats, but only two were not altered in female rats (Fig. 5c), suggesting more genera abundance changes occur in female rats.

Sex differences in fecal short-chain fatty acids in response to dietary copper-fructose interaction

To better understand the sex differences in microbial activities induced by dietary copper and fructose, we measured SCFAs by GC-MS in cecal and fecal contents. Acetate, propionate, and butyrate are the predominant SCFAs in cecal and fecal contents. Overall, the levels of total as well as individual SCFAs were higher in cecal contents than that in fecal contents in both male and female rats. While the level of total cecal SCFAs is higher in males, the level of total fecal SCFAs are comparable between male and female rats. Fructose feeding resulted in a decrease of total SCFAs in both cecal and fecal contents in CuA- and CuM-fed rats; however, a significant decrease was found in female CuMF rats. A similar trend of alterations in SCFAs, but to a lesser extent, was observed in male rats, as shown in Fig. 6a. Consistently, acetate, propionate, and butyrate were all markedly decreased in female CuMF rats (Fig. 6b). In addition, decreased total SCFAs was associated with the relatively increased proportion of acetate and decreased proportion of butyrate in both cecal (acetate to propionate to butyrate = 63.3:18.4:18.4 versus 66.9:19.5:13.6; CuA versus CuMF) and fecal stool (68.7:13.1:18.2 versus 73.7:16.6:9.7; CuA versus CuMF) of female CuMF rats. This effect was less prominent in male rats (Fig. 6c). Collectively, a substantial decrease of SCFAs was seen in female rats and profoundly so in the CuMF group. Two-way ANOVA showed that the alteration in SCFAs was most likely due to the additive effect of copper and fructose in female rats, but the decrease in SCFAs in male rats was only attributable to copper.

Fig. 6
figure6

Alterations of cecal and fecal SCFA levels induced by dietary copper and fructose. a Total SCFA levels. b SCFA levels (C2–C4). c Percentage of total SCFAs. Data represent means ± SD (n = 7–8). Statistical significance was set at p ≤ 0.05. P values displayed are for the factors copper (Cu), fructose (F), and interaction (Cu × F) by two-way ANOVA with Tukey’s multiple comparisons test. * versus CuA; # versus CuAF; $ versus CuM. Cu, copper; A, adequate copper diet; AF, adequate copper diet +10% fructose (w/v) in the drinking water; M, marginal copper diet; MF, marginal copper diet +10% fructose (w/v) in the drinking water. C2, acetic acid; C3, propionic acid; C4, butyric acid

Discussion

Copper-fructose interaction-induced metabolic effects exhibit sex dimorphism [23, 25]. Sex-specific alterations of gut microbiota in response to a specific diet have been demonstrated in a variety of studies [59,60,61]. Given that the gut microbiota play a causal role in driving the development of metabolic diseases, we aimed to determine whether sex-specific alterations of the gut microbiota are linked to hepatic steatosis. Our data showed that sex differences do exist in the gut microbiota, gut microbiota metabolites such as SCFAs, and hepatic steatosis following dietary copper and fructose exposure. Female rats exhibited more pronounced alterations in the abundance of various taxa than that did male rats at multiple taxa levels, including phylum, family, and genus. The number of distinct abundant taxa identified by LEfSe was also higher in female rats than in male rats. In addition, SCFAs were decreased to a greater extent in female rats compared to male rats, particularly in the CuMF group. Moreover, female rats with an adequate copper diet developed mild, but apparent steatosis after 8 weeks of added fructose feeding (CuAF), but female CuMF rats, which showed the most significantly altered gut microbial activity, did not. Therefore, the altered gut microbial activity does not correlate with the hepatic fat accumulation.

SCFAs are the end products of microbial fermentation of indigestible fiber, and they play a critical role in energy homeostasis and metabolism [62]. In our study, we found significantly decreased SCFAs, particularly butyrate, concomitant with the reduced butyrate producers, Lachnospiraceae and Ruminococcaceae [63], in CuMF female rats, implying that the most significantly altered gut microbial activities were in this group. We found mild hepatic steatosis in CuAF female rats; thus, it is unlikely that this hepatic steatosis is attributable to the metabolic effects of gut microbiota. Accelerated de novo lipogenesis (DNL) is known to contribute to fructose-induced hepatic steatosis [64, 65]. However, the underlying mechanisms are unclear. A recent study demonstrated a two-point mechanism leading to fructose-induced hepatic steatosis. One part is gut bacteria-derived acetate which serves as a substrate for acetyl-CoA synthesis via acyl-CoA synthetase short-chain family member 2 (ACSS2) in the liver. Second, fructose metabolism in hepatocytes activates a signal leading to lipogenic gene expression [66]. Interestingly, the most significantly changed SCFAs occurred in CuMF rats. We also observed this effect in our previous study when rats were exposed to a high-fructose diet via 30% fructose (w/v) in the drinking water and sucrose-enriched diet (AIN-76) [21]. This finding suggests that hepatic steatosis may be related to the amount of fructose intake. In support of this, a recent study demonstrated that dietary fructose is primarily metabolized in the small intestine and only excess fructose intake spills over to the colon microbiota and the liver [67]. Previous studies showed that either inhibition of fructose metabolism in the liver [68] or elimination of gut microbiota by antibiotics [69] protected against fructose-induced hepatic steatosis, indicating that fructose metabolism in both the liver and gut microbiota is required to facilitate the development of steatosis. When a large amount of fructose intake saturates the capacity of the small intestine metabolism, presumably excess fructose will proceed to the colon, the gut microbiota, and the liver. However, the priority of excess fructose to be distributed and metabolized in colon microbiota or the liver or other tissues is unclear when a modest amount of fructose was ingested. It has been shown that dietary copper-fructose interaction exacerbates copper deficiency-induced metabolic syndrome, likely due to impaired intestinal copper absorption because of excess fructose ingestion [21, 70]. Whether the extent of interaction relates to the relative amounts of copper and/or fructose, and subsequent metabolic effects, remains largely unknown and warrants further study.

Despite significantly changed gut microbiota and decreased SCFAs in CuMF rats, only a few of the female rats in the CuAF group developed modest steatosis, suggesting decreased SCFAs and the altered gut microbial activities were not sufficient to lead to hepatic steatosis in female CuMF rats. Of note, Porphyromonadaceae and Parabacteroides are two of the microbiota signatures associated with the CuAF diet in female rats, although with relatively low abundance (1.52%), which is different from male rats identified by LEfSe. Whether increased abundance of Porphyromonadaceae and Parabacteroides plays a causal role in fructose-induced hepatic steatosis needs to be examined.

Sex differences in fructose-induced metabolic effects are mixed [24, 71, 72]. In contrast to previous studies on copper-fructose interactions [23, 25, 26], our results showed that female rats are relatively sensitive to fructose-induced hepatic steatosis. The discrepancy may be attributed to several factors. First is the dose of copper and fructose. A lower dose of copper (0.6 ppm) and a higher dose of fructose (30–62%) were used in Field’s as well as in Morrell’s studies [23, 26]. It appeared that males are more sensitive to the deleterious effects of copper deficiency. In our study, marginal copper diet (1.5 ppm) and 10% fructose (w/v) in the drinking water were used, presumably leading to less-pronounced copper-fructose interactions and metabolic effects than previous studies [23, 26]. Second, the activities of fructose-metabolizing enzymes and intermediate metabolites differed by sex and copper level [73]. In fact, the activities of liver enzymes involved in lipogenesis were affected not only by the type of carbohydrate but also by the quantity [74]. Lastly, differences in facilities, diet components, and species as well as experimental durations may all contribute to discrepancy [25, 75, 76].

In support of our results, a previous study demonstrated that weanling female rats exhibit a higher rate of acetate incorporation into lipids in the liver compared to male rats [77], suggesting a higher lipogenic capacity in female rats. However, there is a different species driving the lipogenic enzyme activity in response to carbohydrate [74]. In human studies, the fructose-induced increase in hepatic DNL and decrease in fatty acid oxidation were more pronounced in men and premenopausal women than in postmenopausal women [28, 65, 78, 79]. Sex hormones are known factors regulating sex dimorphism of fructose-related metabolic effects [7]. However, the molecular underpinnings remain elusive. Recent studies showed that GLUT8 mediates distinct metabolic effects between males and females in response to dietary fructose [29, 30, 80]. GLUT8 is a dual-specificity glucose and fructose transporter, which was found to be abundantly expressed in both murine and human liver and intestine [30, 80, 81]. Interestingly, while GLUT8 mutation does not alter intestinal fructose absorption in male mice [29], it enhances intestinal fructose absorption in female mice, which was associated exacerbated hypertension, hyperinsulinemia, and hyperlipidemia in those animals when they were fed with high-fructose diet [30]. Conversely, GLUT8-deficient male mice are protected from high-fructose diet-induced dyslipidemia, glucose intolerance, and hypertension [29]. These studies revealed an important molecular mechanism underlying the tissue-specific and sex-specific divergence in response to fructose.

A potential limitation of the current study is the one time analysis of gut microbiota and hepatic steatosis. Although female rats displayed earlier development of steatosis, it is difficult to predict the ultimate severity of steatosis and disease progression. Since male rats exhibit decreased diversity of gut microbiome, and given that the microbial gene richness is associated with inflammation, insulin resistance, and dyslipidemia [82, 83], it is plausible that male rats develop steatosis with a prolonged duration on the experimental regime. Thus, long-term and multiple time points evaluation will provide more accurate profiles of disease progression in the context of sex difference. However, sex differences observed in animal studies are under strictly defined experimental conditions. Therefore, a caveat must be noted when extrapolating animal data to human, as humans have much more complex genetic and environmental factors than experimental animals.

Perspectives and significance

In summary, our current study provides evidence of sex-specific alterations in gut microbial abundance, activities, and hepatic steatosis in response to dietary copper-fructose interaction in a rat model. However, the correlation of sex differences in hepatic steatosis and alterations of gut microbial activities was not established in the current experimental condition. Future studies deciphering the molecular mechanisms as well as tissue-specific effects would help us better understand sex-specific responses to dietary copper-fructose interactions.

Conclusions

Our data demonstrated sex differences in the alterations of gut microbial abundance, activities, and hepatic steatosis in response to dietary copper-fructose interaction in rats. The correlation between sex differences in metabolic phenotypes and alterations of gut microbial activities remains elusive.

Availability of data and materials

The 16S rRNA raw sequence reads are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with BioProject accession: PRJNA641690; BioSample accession: SAMN15358594 (https://www.ncbi.nlm.nih.gov/sra).

Abbreviations

NAFLD:

Nonalcoholic fatty liver disease

SD rat:

Sprague-Dawley rat

CuA:

Adequate copper diet

CuM:

Marginal copper diet

F:

Fructose

SCFAs:

Short-chain fatty acids

LDA:

Linear discriminant analysis

LEfSe:

Linear discriminant analysis effect size

NASH:

Nonalcoholic steatohepatitis

OVX:

Ovariectomized

HFFD:

High-fat high-fructose diet

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

H&E:

Hematoxylin and eosin

16S rRNA:

16S ribosomal RNA

OTUs:

Operational taxonomy units

ASVs:

Amplicon sequence variants

PCoA:

Principal coordinate analysis

WAT:

White adipose tissue

EER:

Energy efficiency ratio

DNL:

De novo lipogenesis

ACSS2:

Aacyl-CoA synthetase short-chain family member 2

C2:

Acetic acid

C3:

Propionic acid

C4:

Butyric acid

References

  1. 1.

    Lazo M, Hernaez R, Eberhardt MS, Bonekamp S, Kamel I, Guallar E, Koteish A, et al. Prevalence of nonalcoholic fatty liver disease in the United States: the Third National Health and Nutrition Examination Survey, 1988-1994. Am J Epidemiol. 2013;178:38–45.

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64:73–84.

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Estes C, Razavi H, Loomba R, Younossi Z, Sanyal AJ. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology. 2018;67:123–33.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Younossi ZM, Marchesini G, Pinto-Cortez H, Petta S. Epidemiology of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis: implications for liver transplantation. Transplantation. 2019;103:22–7.

    PubMed  Article  Google Scholar 

  5. 5.

    Fraser A, Longnecker MP, Lawlor DA. Prevalence of elevated alanine aminotransferase among US adolescents and associated factors: NHANES 1999-2004. Gastroenterology. 2007;133:1814–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Hashimoto E, Tokushige K. Prevalence, gender, ethnic variations, and prognosis of NASH. J Gastroenterol. 2011;46(Suppl 1):63–9.

    PubMed  Article  Google Scholar 

  7. 7.

    Lonardo A, Nascimbeni F, Ballestri S, Fairweather D, Win S, Than TA, Abdelmalek MF, et al. Sex Differences in nonalcoholic fatty liver disease: state of the art and identification of research gaps. Hepatology. 2019;70:1457–69.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, Gourdy P. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 2020;63:453–61.

    PubMed  Article  Google Scholar 

  9. 9.

    Reue K. Sex differences in obesity: X chromosome dosage as a risk factor for increased food intake, adiposity and co-morbidities. Physiol Behav. 2017;176:174–82.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Beaudry KM, Devries MC. Sex-based differences in hepatic and skeletal muscle triglyceride storage and metabolism (1). Appl Physiol Nutr Metab. 2019;44:805–13.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Link JC, Reue K. Genetic basis for sex differences in obesity and lipid metabolism. Annu Rev Nutr. 2017;37:225–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Klair JS, Yang JD, Abdelmalek MF, Guy CD, Gill RM, Yates K, Unalp-Arida A, et al. A longer duration of estrogen deficiency increases fibrosis risk among postmenopausal women with nonalcoholic fatty liver disease. Hepatology. 2016;64:85–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    McKenzie J, Fisher BM, Jaap AJ, Stanley A, Paterson K, Sattar N. Effects of HRT on liver enzyme levels in women with type 2 diabetes: a randomized placebo-controlled trial. Clin Endocrinol (Oxf). 2006;65:40–4.

    CAS  Article  Google Scholar 

  14. 14.

    Chukijrungroat N, Khamphaya T, Weerachayaphorn J, Songserm T, Saengsirisuwan V. Hepatic FGF21 mediates sex differences in high-fat high-fructose diet-induced fatty liver. Am J Physiol Endocrinol Metab. 2017;313:E203–e212.

    PubMed  Article  Google Scholar 

  15. 15.

    Chen X, McClusky R, Chen J, Beaven SW, Tontonoz P, Arnold AP, Reue K. The number of x chromosomes causes sex differences in adiposity in mice. PLoS Genet. 2012;8:e1002709.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Link JC, Chen X, Prien C, Borja MS, Hammerson B, Oda MN, Arnold AP, et al. Increased high-density lipoprotein cholesterol levels in mice with XX versus XY sex chromosomes. Arterioscler Thromb Vasc Biol. 2015;35:1778–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Aigner E, Strasser M, Haufe H, Sonnweber T, Hohla F, Stadlmayr A, Solioz M, et al. A role for low hepatic copper concentrations in nonalcoholic fatty liver disease. Am J Gastroenterol. 2010;105:1978–85.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Aigner E, Theurl I, Haufe H, Seifert M, Hohla F, Scharinger L, Stickel F, et al. Copper availability contributes to iron perturbations in human nonalcoholic fatty liver disease. Gastroenterology. 2008;135:680–8.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Abdelmalek MF, Suzuki A, Guy C, Unalp-Arida A, Colvin R, Johnson RJ, Diehl AM. Increased fructose consumption is associated with fibrosis severity in patients with nonalcoholic fatty liver disease. Hepatology. 2010;51:1961–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Ouyang X, Cirillo P, Sautin Y, McCall S, Bruchette JL, Diehl AM, Johnson RJ, et al. Fructose consumption as a risk factor for non-alcoholic fatty liver disease. J Hepatol. 2008;48:993–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Song M, Schuschke DA, Zhou Z, Chen T, Pierce WM Jr, Wang R, Johnson WT, et al. High fructose feeding induces copper deficiency in Sprague-Dawley rats: a novel mechanism for obesity related fatty liver. J Hepatol. 2012;56:433–40.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Song M, Schuschke DA, Zhou Z, Chen T, Shi X, Zhang J, Zhang X, et al. Modest fructose beverage intake causes liver injury and fat accumulation in marginal copper deficient rats. Obesity (Silver Spring). 2013;21:1669–75.

    CAS  Article  Google Scholar 

  23. 23.

    Fields M, Lewis C, Scholfield DJ, Powell AS, Rose AJ, Reiser S, Smith JC. Female rats are protected against the fructose induced mortality of copper deficiency. Proc Soc Exp Biol Med. 1986;183:145–9.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Galipeau D, Verma S, McNeill JH. Female rats are protected against fructose-induced changes in metabolism and blood pressure. Am J Physiol Heart Circ Physiol. 2002;283:H2478–84.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Morrell A, Tripet BP, Eilers BJ, Tegman M, Thompson D, Copie V, Burkhead JL. Copper modulates sex-specific fructose hepatoxicity in nonalcoholic fatty liver disease (NALFD) Wistar rat models. J Nutr Biochem. 2020;78:108316.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Fields M, Lewis CG, Beal T, Scholfield D, Patterson K, Smith JC, Reiser S. Sexual differences in the expression of copper deficiency in rats. Proc Soc Exp Biol Med. 1987;186:183–7.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Bantle JP, Raatz SK, Thomas W, Georgopoulos A. Effects of dietary fructose on plasma lipids in healthy subjects. Am J Clin Nutr. 2000;72:1128–34.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Couchepin C, Le KA, Bortolotti M, da Encarnacao JA, Oboni JB, Tran C, Schneiter P, et al. Markedly blunted metabolic effects of fructose in healthy young female subjects compared with male subjects. Diabetes Care. 2008;31:1254–6.

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    DeBosch BJ, Chen Z, Finck BN, Chi M, Moley KH. Glucose transporter-8 (GLUT8) mediates glucose intolerance and dyslipidemia in high-fructose diet-fed male mice. Mol Endocrinol. 2013;27:1887–96.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    DeBosch BJ, Chi M, Moley KH. Glucose transporter 8 (GLUT8) regulates enterocyte fructose transport and global mammalian fructose utilization. Endocrinology. 2012;153:4181–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Cani PD, Osto M, Geurts L, Everard A. Involvement of gut microbiota in the development of low-grade inflammation and type 2 diabetes associated with obesity. Gut Microbes. 2012;3:279–88.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Harvie R, Walmsley R, Schultz M. "We are what our bacteria eat": The role of bacteria in personalizing nutrition therapy in gastrointestinal conditions. J Gastroenterol Hepatol 2017;32:352–7.

  33. 33.

    Le Roy T, Llopis M, Lepage P, Bruneau A, Rabot S, Bevilacqua C, Martin P, et al. Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice. Gut. 2013;62:1787–94.

    PubMed  Article  CAS  Google Scholar 

  34. 34.

    Ussar S, Griffin Nicholas W, Bezy O, Fujisaka S, Vienberg S, Softic S, Deng L, et al. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metabolism. 2015;22:516–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63.

    CAS  Article  Google Scholar 

  36. 36.

    Bolnick DI, Snowberg LK, Hirsch PE, Lauber CL, Org E, Parks B, Lusis AJ, et al. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat Commun. 2014;5:4500.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Zhang H, Wang Z, Li Y, Han J, Cui C, Lu C, Zhou J, et al. Sex-based differences in gut microbiota composition in response to tuna oil and algae oil supplementation in a D-galactose-induced aging mouse model. Front Aging Neurosci. 2018;10:187.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Holmes E, Li JV, Marchesi JR, Nicholson JK. Gut microbiota composition and activity in relation to host metabolic phenotype and disease risk. Cell Metab. 2012;16:559–64.

    CAS  Article  Google Scholar 

  39. 39.

    Boursier J, Mueller O, Barret M, Machado M, Fizanne L, Araujo-Perez F, Guy CD, et al. The severity of nonalcoholic fatty liver disease is associated with gut dysbiosis and shift in the metabolic function of the gut microbiota. Hepatology. 2016;63:764–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Loomba R, Seguritan V, Li W, Long T, Klitgord N, Bhatt A, Dulai PS, et al. Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease. Cell Metab. 2017;25:1054–62 e1055.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Song M, Li X, Zhang X, Shi H, Vos MB, Wei X, Wang Y, et al. Dietary copper-fructose interactions alter gut microbial activity in male rats. Am J Physiol Gastrointest Liver Physiol. 2018;314:G119–g130.

    PubMed  Article  CAS  Google Scholar 

  42. 42.

    Wei X, Song M, Yin X, Schuschke DA, Koo I, McClain CJ, Zhang X. Effects of dietary different doses of copper and high fructose feeding on rat fecal metabolome. J Proteome Res. 2015;14:4050–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37:911–7.

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC, Gevers D, Knight R. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet. 2011;13:47–58.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biology. 2011;12:R60.

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Cech M, Chilton J, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46:W537–w544.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Goecks J, Nekrutenko A, Taylor J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11:R86.

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46:D633–d639.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Prodhan MAI, Shi B, Song M, He L, Yuan F, Yin X, Bohman P, et al. Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics. Analyst. 2019;144:4331–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Pitynski-Miller D, Ross M, Schmill M, Schambow R, Fuller T, Flynn FW, Skinner DC. A high salt diet inhibits obesity and delays puberty in the female rat. Int J Obes (Lond). 2017;41:1685–92.

    CAS  Article  Google Scholar 

  55. 55.

    Ma T, Liaset B, Hao Q, Petersen RK, Fjaere E, Ngo HT, Lillefosse HH, et al. Sucrose counteracts the anti-inflammatory effect of fish oil in adipose tissue and increases obesity development in mice. PLoS One. 2011;6:e21647.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Nipperess DA. The rarefaction of phylogenetic diversity: formulation, extension and application. In: Pellens R, Grandcolas P, editors. Biodiversity conservation and phylogenetic systematics: preserving our evolutionary heritage in an extinction crisis. Cham: Springer International Publishing; 2016. p. 197–217.

    Google Scholar 

  57. 57.

    Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73:1576–85.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.

    PubMed  Article  Google Scholar 

  59. 59.

    Min Y, Ma X, Sankaran K, Ru Y, Chen L, Baiocchi M, Zhu S. Sex-specific association between gut microbiome and fat distribution. Nat Commun. 2019;10:2408.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    Lee SM, Kim N, Yoon H, Nam RH, Lee DH. Microbial changes and host response in F344 rat colon depending on sex and age following a high-fat diet. Front Microbiol. 2018;9:2236.

    PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Zhuang P, Shou Q, Wang W, He L, Wang J, Chen J, Zhang Y, et al. Essential fatty acids linoleic acid and alpha-linolenic acid sex-dependently regulate glucose homeostasis in obesity. Mol Nutr Food Res. 2018;62:e1800448.

    PubMed  Article  CAS  Google Scholar 

  62. 62.

    Canfora EE, Jocken JW, Blaak EE. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat Rev Endocrinol. 2015;11:577–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Vital M, Howe AC, Tiedje JM. Revealing the bacterial butyrate synthesis pathways by analyzing (meta) genomic data. MBio. 2014;5:e00889.

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Parks EJ, Skokan LE, Timlin MT, Dingfelder CS. Dietary sugars stimulate fatty acid synthesis in adults. J Nutr. 2008;138:1039–46.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Tran C, Jacot-Descombes D, Lecoultre V, Fielding BA, Carrel G, Le KA, Schneiter P, et al. Sex differences in lipid and glucose kinetics after ingestion of an acute oral fructose load. Br J Nutr. 2010;104:1139–47.

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Zhao S, Jang C, Liu J, Uehara K, Gilbert M, Izzo L, Zeng X, et al. Dietary fructose feeds hepatic lipogenesis via microbiota-derived acetate. Nature. 2020;579:586–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Jang C, Hui S, Lu W, Cowan AJ, Morscher RJ, Lee G, Liu W, et al. The small intestine converts dietary fructose into glucose and organic acids. Cell Metab. 2018;27:351–61 e353.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Softic S, Gupta MK, Wang GX, Fujisaka S, O'Neill BT, Rao TN, Willoughby J, et al. Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. J Clin Invest. 2017;127:4059–74.

    PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Bergheim I, Weber S, Vos M, Kramer S, Volynets V, Kaserouni S, McClain CJ, et al. Antibiotics protect against fructose-induced hepatic lipid accumulation in mice: role of endotoxin. J Hepatol. 2008;48:983–92.

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Fields M, Holbrook J, Scholfield D, Smith JC Jr, Reiser S. Effect of fructose or starch on copper-67 absorption and excretion by the rat. J Nutr. 1986;116:625–32.

    CAS  PubMed  Article  Google Scholar 

  71. 71.

    Busserolles J, Mazur A, Gueux E, Rock E, Rayssiguier Y. Metabolic syndrome in the rat: females are protected against the pro-oxidant effect of a high sucrose diet. Exp Biol Med (Maywood). 2002;227:837–42.

    CAS  Article  Google Scholar 

  72. 72.

    Galipeau DM, Yao L, McNeill JH. Relationship among hyperinsulinemia, insulin resistance, and hypertension is dependent on sex. Am J Physiol Heart Circ Physiol. 2002;283:H562–7.

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Millo H, Werman MJ. Hepatic fructose-metabolizing enzymes and related metabolites: role of dietary copper and gender. J Nutr Biochem. 2000;11:374–81.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Bailey E, Taylor CB, Bartley W. Effect of dietary carbohydrates on hepatic lipogenesis in the rat. Nature. 1968;217:471–2.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Ferrere G, Wrzosek L, Cailleux F, Turpin W, Puchois V, Spatz M, Ciocan D, et al. Fecal microbiota manipulation prevents dysbiosis and alcohol-induced liver injury in mice. J Hepatol. 2017;66:806–15.

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Hyer MM, Dyer SK, Kloster A, Adrees A, Taetzsch T, Feaster J, Valdez G, et al. Sex modifies the consequences of extended fructose consumption on liver health, motor function, and physiological damage in rats. Am J Physiol Regul Integr Comp Physiol. 2019;317:R903–r911.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Taylor CB, Bailey E, Bartley W. Changes in hepatic lipigenesis during development of the rat. Biochem J. 1967;105:717–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Pramfalk C, Pavlides M, Banerjee R, McNeil CA, Neubauer S, Karpe F, Hodson L. Sex-specific differences in hepatic fat oxidation and synthesis may explain the higher propensity for NAFLD in men. J Clin Endocrinol Metab. 2015;100:4425–33.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Macdonald I. Influence of fructose and glucose on serum lipid levels in men and pre- and postmenopausal women. Am J Clin Nutr. 1966;18:369–72.

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Debosch BJ, Chen Z, Saben JL, Finck BN, Moley KH. Glucose transporter 8 (GLUT8) mediates fructose-induced de novo lipogenesis and macrosteatosis. J Biol Chem. 2014;289:10989–98.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Doege H, Schurmann A, Bahrenberg G, Brauers A, Joost HG. GLUT8, a novel member of the sugar transport facilitator family with glucose transport activity. J Biol Chem. 2000;275:16275–80.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Cotillard A, Kennedy SP, Kong LC, Prifti E, Pons N, Le Chatelier E, Almeida M, et al. Dietary intervention impact on gut microbial gene richness. Nature. 2013;500:585–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Verger EO, Kayser BD, et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016;65:426–36.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

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Acknowledgements

We thank Sabine Waigel for the technical support with 16S rRNA sequencing. We thank Jane Frimodig, Kimberly Head, and Yali Wang for the technical support in the sample collection. We thank Marion McClain for carefully reading this manuscript. Sequencing was performed with assistance of the UofL Genomics Facility and Bioinformatics, which are supported by NIH/NIGMS Phase III COBRE P30 GM106396 (Donald Miller), NIH/NIGMS KY-INBRE P20GM103436 (Martha Bickford), the James Graham Brown Foundation, and user fees.

Funding

This study was supported in part by NIH Grants U01AA026934, U01AA026936, U01AA026980, and R01AA023681; an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM113226; and the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Award Number P50AA024337 (all to CJM). Support was also provided by the Jewish Heritage Fund for Excellence Pilot Grant Program at the University of Louisville School of Medicine (MS); R01DK115406, R21AA025724, and R21AI128206 (ZD); T35ES014559 (RAP, CJM); and the Veterans Administration 1I01BX002996 (CJM). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Contributions

MS contributed in the design of the research, data analysis and interpretation, and manuscript preparation. MS, FY, XM, XY, and XZ participated in the data collection and analysis. XL and ECR, did the data analysis. RAP and ZD took part in the data interpretation and had intellectual contribution to the manuscript preparation. CJM did the overall research direction and support. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Ming Song.

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Animal study was approved by the University of Louisville Institutional Animal Care and Use Committee (IACUC).

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Supplementary Table 1.

The Kruskal-Wallis test results between treatment groups (α-diversity).

Additional file 2: Supplementary Table 2.

The permutation tests of the mean distance matrix (β-diversity).

Additional file 3: Supplementary Table 3.

Mean abundance of gut microbiome taxa in male rats. Supplementary Table 4. Mean abundance of gut microbiome taxa in female rats. Numbers listed under study groups are percentages. Data are expressed as means ± SD (n = 7–8) and analyzed by two-way ANOVA testing factors of copper (Cu), fructose (F), and interactions (Cu × F), followed by Tukey’s multiple comparison test. Statistical significance was set to p ≤ 0.05. P values are displayed for the factors Cu, F, and Cu × F. NS, p > 0.05. CuA, adequate copper diet; CuM, marginal copper diet; CuAF, adequate copper diet +10% fructose drinking; CuMF, marginal copper deficient diet + 10% fructose drinking. * versus CuA; # versus CuAF; $ versus CuM. p_Phyla, f_Families, and s_species with average abundance greater than 1% in any of the groups are listed. Unknown, 16S rRNA sequence distinct from any known genera in this family/species.

Additional file 4: Supplementary Table 5.

Full bacteria name listed in Fig. 4a in male rats. Supplementary Table 6. Full bacteria name listed in Fig. 4b in female rats.

Additional file 5: Supplementary Table 7.

PICRUSt2 analysis results.

Additional file 6: Supplementary Table 8.

ASV table.

Additional file 7: Supplementary Figure 1.

Schematic diagram of QIIME 2 workflow. Supplementary Figure 2. Correlation of liver triglyceride with signature gut bacteria in CuAF rats. Correlation of liver triglyceride with signature gut bacteria in CuAF rats. Data represent means ± SD (n = 7). Statistical significance was set at p ≤ 0.05. P values displayed are for Spearman correlation test.

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Song, M., Yuan, F., Li, X. et al. Analysis of sex differences in dietary copper-fructose interaction-induced alterations of gut microbial activity in relation to hepatic steatosis. Biol Sex Differ 12, 3 (2021). https://doi.org/10.1186/s13293-020-00346-z

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Keywords

  • Copper
  • Fructose
  • Gut microbiota
  • Sex
  • Nonalcoholic fatty liver disease