Aloe vera polysaccharides mitigate high-fat high-cholesterol diet-induced atherosclerosis in ApoE^−/− mice via regulation of lipid metabolism and gut microbiota†

Shenji Yang, Xiaojun Huang, Xiajialong Li, Jing Feng, Peng Chen, Zhikang Cao, Luyao He, Chaojun Li, Zhipeng Li* and Mingyong Xie*
State Key Laboratory of Food Science and Resources, China-Canada Joint Lab of Food Science and Technology (Nanchang), University, Nanchang 330047, China. E-mail: zhipengli@ncu.edu.cn; myxie@ncu.edu.cn

First published on 11th July 2025

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Introduction

Cardiovascular disease (CVD) accounts for approximately one-third of all deaths worldwide.¹ The clinical manifestations of CVD typically emerge after many years of progressive atherosclerosis (AS), which is the underlying pathological process. AS is characterized by the deposition of lipid-rich plaques within the arterial intima.² Foam cell formation is an important process in the development of AS, involving macrophages that take up excessive low-density lipoprotein (LDL) particles, subsequently transforming into lipid-laden foam cells.³ These foam cells accumulate within the arterial walls and promote plaque formation and arterial hardening.⁴

The potential health benefits of dietary fiber in reducing the risk of CVD have been hypothesized for over half a century. Evidence supporting the associations between dietary fiber and AS has accumulated through epidemiological observations and clinical trials.⁵ Polysaccharides are one of the primary components responsible for the health-promoting activities observed in many food materials.⁶ Glucomannan, a water-soluble polysaccharide, is a hemicellulose component found in the cell walls of certain plant species.⁷ It is primarily a straight-chain polymer, composed of β-(1-4)-linked D-mannose and D-glucose, with a small amount of branching.⁸ A meta-analysis of randomized controlled trials has shown that glucomannan supplementation can significantly reduce total cholesterol and LDL-C,⁹ which are key risk factors for AS.^10,11

Dendrobium officinale, Konjac, and Aloe vera are three common food materials in China that are rich in bioactive polysaccharides, primarily glucomannans.¹² The diversity of glucomannan depends on the ratio of glucose to mannose and the degree of acetylation in the chain.¹³ Both Dendrobium officinale polysaccharide (DOP) and Konjac glucomannan (KGM) have been reported to ameliorate hyperlipidemia.^14,15 They have also been shown to improve AS in high-fat diet (HFD)-fed ApoE^−/− mice and HFD-fed rabbits, respectively.^16,17 Although Aloe vera polysaccharide (AVP) has also been reported to improve inflammation and enhance gut barrier integrity in HFD-fed mice,¹⁸ its effects on AS are not clear. Furthermore, DOP, KGM, and AVP are glucomannans with different structural properties, and their relative effectiveness in improving AS is still unknown. Therefore, the effects of these three polysaccharides on AS, as well as their underlying mechanisms, require a comprehensive investigation.

In the present research, we investigated the atheroprotective activities of DOP, KGM, and AVP using an in vitro foam cell formation model and a high-fat, high-cholesterol diet (HFHCD)-fed ApoE^−/− mouse model. Both in vitro and in vivo studies consistently indicate that among the three polysaccharides examined, AVP exhibits the most pronounced atheroprotective effects. Our research emphasizes AVP's capacity to prevent foam cell formation, inhibit inflammatory responses and restore lipid metabolism homeostasis. Furthermore, metagenomic analysis highlights the involvement of gut microbiota as a mediator of these positive effects.

Methods

Materials and reagents

The stems of Dendrobium officinale, fresh Konjac tubers, and six-year-old Curacao Aloe vera fresh leaves were purchased from Yunnan Jiujindi Biotechnology Co., Ltd (Yunnan, China), Sichuan Yizhi Konjac Co., Ltd (Sichuan, China), and Fujian Jiangxia Aloe Development Co., Ltd (Fujian, China), respectively. Papain (catalog number: P164463-25g) and thermostable α-amylase (cat. no.: A109181) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd (Shanghai, China). Dialysis bags were purchased from Shanghai Yuanye Biotechnology Co., Ltd (molecular weight cutoff (MWCO): 8000–14000 Da; cat. no.: MD1477-5m, Shanghai, China). Fetal bovine serum (FBS) (cat. no.: C04001-500) was obtained from Shanghai Xiaopeng Biotechnology Co., Ltd (Shanghai, China). High-glucose DMEM culture medium (cat. no.:12100-500ml) and phosphate-buffered saline (PBS, pH = 7.2–7.4, cat. no.: P1020-500 ml) were purchased from Beijing Solarbio Technology Co., Ltd (Beijing, China). Oxidized low-density lipoprotein (ox-LDL, cat. no.: YB-002) was purchased from Guangzhou Yiyuan Biotechnology Co., Ltd (Guangzhou, China). The 96-well, 12-well, and 6-well cell culture plates and cell culture flasks were purchased from Corning Incorporated (cat no.: 3599, 3336, 3516 and 430641, respectively, New York, USA). Annexin V-FITC reagent (cat. no.: E-CK-A111) and the reactive oxygen species (ROS) fluorometric assay kit (green) (cat. no.: E-BC-K138-F) were purchased from Elabscience Biotechnology Co., Ltd (Wuhan, China). Oil Red O dye (cat. no.: O0625) was purchased from Sigma (Darmstadt, Germany), and 4% paraformaldehyde (cat. no.: BL539A-500 ml) was purchased from Lanjie Ke Technology Co., Ltd (Biosharp, Beijing, China). The fecal genomic DNA extraction kit (cat. no.: DP328) was purchased from Tiangen Biochemical Technology Co., Ltd (Beijing, China), and TRIzol (cat. no.: 15596018CN) was purchased from Thermo Fisher Scientific (Massachusetts, USA). The PrimeScript™ FAST RT reagent kit with gDNA Eraser and TB Green® Premix Ex Taq™ II (Tli RNaseH Plus) were purchased from Takara Bio Inc. (cat. no.: RR092A and RR820A, respectively, Japan). The mouse IL-1β ELISA kit (cat. no.: EK201B) was purchased from Hangzhou LianKe Biotechnology Co., Ltd (Hangzhou, China). The irradiated maintenance diet was purchased from Synergy Bioscience (cat. no.: AIN-93M; 9.4 kcal% fat, Nanjing, China), and the irradiated high-fat, high-cholesterol diet (HFHCD) was purchased from Research Diets, Inc. (cat. no.: D12109C, 40 kcal% fat, 1.25% cholesterol, 0.5% cholic acid, USA). Anhydrous ethanol, n-hexane, and isopropanol are all domestically produced analytical grade.

Preparation of DOP, KGM, and AVP

We prepared the polysaccharides using the methods developed in our previous research.¹² Briefly, n-hexane was first used to remove pigments and lipids from the materials, followed by water extraction to obtain the polysaccharides, which were then precipitated using ethanol. Starch and proteins in the polysaccharides were removed using α-amylase and papain, respectively. Small molecular substances were eliminated through dialysis (MWCO: 8000–14000 Da). The purified DOP, KGM, and AVP were obtained through freeze-drying, with neutral sugar contents of 94.20 ± 5.79%, 73.52 ± 4.77%, and 82.48 ± 4.98%, respectively. The measurement methods and results for neutral sugars, proteins, moisture, and other components in the samples are provided in the ESI.†

Cell culture

The RAW 264.7 cell line was obtained from Wuhan Punosai Life Science and Technology Co., Ltd (Wuhan, China). Cells were cultured using high-glucose Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% FBS in a humidified incubator at 37 °C with 5% CO₂. To induce foam cell formation, cells were seeded into 96-well plates at a density of 3.7 × 10⁵ cells per mL. After 24 h, the medium was replaced with serum-free high-glucose DMEM containing ox-LDL (80 μg mL⁻¹), and the cells were incubated for an additional 24 h. Subsequently, the medium was replaced with normal culture medium, and the cells were treated with varying doses of DOP, KGM, and AVP (10, 100, and 500 μg mL⁻¹) or a vehicle control for 24 h.

Oil Red O staining

Cells were washed several times with pre-cooled phosphate-buffered saline (PBS), followed by fixation with 4% paraformaldehyde for 30 minutes at room temperature. After fixation, the cells were washed with PBS and then rinsed once with pre-cooled 60% isopropanol. Subsequently, 60% Oil Red O staining solution was added to the cells and incubated at room temperature for 30 min. After staining, the Oil Red O solution was removed, and the cells were washed twice with pre-cooled 60% isopropanol to remove excess dye. Finally, the cells were thoroughly rinsed, and 200 μL of PBS was added to each well for observation of foam cell morphology and lipid accumulation. Images were captured using an Olympus CKX53 inverted microscope (Olympus, Japan). The positive areas in the images were quantified using ImageJ software 1.54 (National Institutes of Health, USA).

ROS assay

The level of ROS was measured using the reactive oxygen species fluorometric assay kit (green) according to the manufacturer's instructions. Briefly, cells were collected and resuspended in 10 μM 2,7-dichlorofluorescin diacetate (DCFH-DA). The cells were incubated at 37 °C in the dark for 1 h, with intermittent shaking to ensure sufficient contact between the probe and the cells. After incubation, the cells were collected by centrifugation at 1000g for 5–10 min and washed 2–3 times with serum-free cell culture medium to remove residual probe. The final cell pellet was resuspended in serum-free cell culture medium, and the fluorescence intensity was measured using a Thermo Scientific Varioskan Flash full-wavelength scanning multifunctional reader (Thermo Fisher Scientific, USA) (Ex/Em = 500 nm/525 nm).

CCK-8 assay

The Cell Counting Kit-8 (MedChemExpress, USA) was used to determine the influence of polysaccharides on the viability of ox-LDL-induced foam cells. After treatment with the polysaccharides, CCK-8 was added and incubated at 37 °C for 1 h. The absorbance was measured at 450 nm using the SpectraMax Plus384/190/VersaMax/340PC384 microplate reader (Meigu Molecular Instruments Co., Ltd, USA).

Flow cytometry

The cell pellet was resuspended in 500 μL of diluted 1× Annexin V binding buffer, and 5 μL of Annexin V-FITC reagent and 5 μL of nuclear DNA staining solution (propidium iodide, PI) were added. After gentle vortexing, the mixture was incubated at room temperature in the dark for 15–20 min. Upon completion of the reaction, the samples were immediately analyzed using a CytoFLEX S flow cytometer (Beckman Coulter, USA), and the results were analyzed using FlowJo (V 10.0). Annexin V-FITC single-positive cells were classified as early apoptotic cells, Annexin V-FITC and PI double-positive cells as necrotic or late apoptotic cells, and PI single-positive cells as naked nuclei.

RNA extraction

Cell samples are dissolved in TRIzol and transferred to sterile, enzyme-free tubes, followed by the addition of 200 μL of chloroform. After mixing for 15 s, the mixture is allowed to stand at room temperature for 2–3 min and then centrifuged at 12000g for 15 min at 4 °C. The upper aqueous phase is collected into a new centrifuge tube, and an equal volume of isopropanol is added. The mixture is inverted to mix and incubated at 4 °C for 10 minutes. Centrifugation is performed at 12000g for 10 minutes at 4 °C, resulting in RNA precipitation at the bottom or wall of the tube. The supernatant is discarded, and 1 mL of pre-chilled 75% ethanol is added. A brief vortexing for 5 seconds is performed to resuspend the pellet, followed by washing the pellet. Centrifugation is conducted at 5000g for 5 minutes at 4 °C. The supernatant is removed, and the pellet is allowed to air-dry for 5–10 min. An appropriate amount of ddH₂O is added to dissolve the RNA. The concentration and purity of the obtained RNA are measured using a NanoDrop 2000 micro-spectrophotometer (Thermo Fisher Scientific, USA).

Real-time polymerase chain reaction

Genomic DNA removal and RNA reverse transcription are performed using the PrimeScript™ FAST RT reagent kit with gDNA Eraser and T100™ Thermal Cycler PCR (Bio-Rad Laboratories, USA), while quantitative reverse transcription PCR (RT-qPCR) is conducted using TB Green® Premix Ex Taq™ II (Tli RNaseH Plus). The threshold cycle (CT) is measured using the QuantStudio™ 7 fluorescence quantitative PCR system (Thermo Fisher Scientific, USA), and the relative expression levels of target gene mRNA are calculated using the 2^−ΔΔCT method. All reagents, containers, and buffers used in this test are RNase-free. Primers were synthesized by Genewiz Biotechnology Co., Ltd (Shanghai, China). The sequences of the primers used are as follows: CD36, ATGGGCTGTGATCGGAACTG (F), GTCTTCCCAATAAGCATGTCTCC (R); LXR-α, CTCAATGCCTGATGTTTCTCCT (F), TCCAACCCTATCCCTAAAGCAA (R); ABCA1, GCTTGTTGGCCTCAGTTAAGG (F), GTAGCTCAGGCGTACAGAGAT (R); ABCG1, CTTTCCTACTCTGTACCCGAGG (F), CGGGGCATTCCATTGATAAGG (R); β-actin, GTGGGAATGGGTCAGAAGGA (F), TCATCTTTTCACGGTTGGCC (R).

Animals and diet

Seven-week-old SPF-grade ApoE^−/− and wild type (WT) C57BL/6 male mice, weighing 18–20 g, purchased from Jiangsu Jicui Pharmaceutical Biotechnology Co., Ltd (Jiangsu, China), were housed under a 12 h light/12 h dark cycle in a controlled room at a temperature of 25 ± 1 °C and a humidity of 50 ± 10%, allowing free access to food and water. After a one-week acclimatization period, the WT mice were fed an irradiated maintenance diet as the normal group, while the ApoE^−/− mice were randomly divided into the model and intervention groups and were fed an irradiated HFHCD. After eight weeks, the intervention groups began to be gavaged with DOP, KGM, or AVP (300 mg kg⁻¹ d⁻¹), while the normal and model groups received an equal volume of vehicle. Body weight, food intake, and water intake were recorded weekly, and the mice were sacrificed after twelve weeks of treatment. All animal and laboratory conditions in this study were conducted in accordance with the Guide for the Care and Use of Laboratory Animals (8th edition, 2011) published by the National Academies Press (US) for the use of laboratory animals. The animal experiment was approved by the Institutional Animal Care and Use Committee (IACUC) of Nanchang University (animal ethics approval no.: NCULAE-20221030024).

Histopathologic evaluation

Hematoxylin and eosin (H&E), Oil Red O, and Masson staining were performed on the aortic sinus, while immunohistochemical staining was conducted on the aortic arch, and Oil Red O staining was performed on the thoracic aorta. The sections were scanned using the Aperio LV1 pathological scanner (Leica, Germany), and images were collected using the accompanying software ImageScope x64 (v12.4.6.5003). The cumulative optical density (IOD) of immunohistochemical staining and the positive area ratios of the other staining images were analyzed using ImageJ 1.54 (National Institutes of Health, USA).

Determination of IL-1β levels in serum

According to the manufacturer's instructions, the levels of serum IL-1β were measured using a commercial detection kit, Mouse IL-1β ELISA kit (Hangzhou LianKe Biotechnology Co., Ltd, China). A Wellwash™ Versa Microplate Washer (Thermo Fisher Scientific, USA) was used to assist the assay.

Quantification of short-chain fatty acids

A certain amount of cecal content was weighed and placed into a centrifuge tube, followed by the addition of 9 times of PBS buffer (w/v = 1 : 9) and 2 steel balls (0.3 mm; Sevier, Wuhan, China). The mixture was homogenized at 70 Hz for about 1 min andcentrifuged at 13000 rpm for 5 min at 4 °C. The supernatant was transferred to a new sterile tube after passing through a 0.45 μm water filter membrane. 450 μL was added to 200 μL of 10% H₂SO₄ (v/v) and 400 μL of anhydrous ether, vortexed for 15 s, mixed and allowed to stand for 2 min, and centrifuged at 13000 rpm for 2 min at 4 °C. The supernatant was filtered using a 0.22 μm organic filter membrane, collected into vials, and analyzed using an Agilent 6890N Gas Chromatograph (Agilent Technologies Co., Ltd, USA).

RNA sequencing

Total RNA was extracted from mouse liver using the TRIzol reagent. Briefly, tissue blocks were placed directly into a mortar, and a small amount of liquid nitrogen was added. The tissues were continuously ground, with additional liquid nitrogen added periodically to prevent softening. The ground tissue samples (50–100 mg) were added to 1 mL of TRIzol and transferred into centrifuge tubes. RNA extraction was then carried out as per the standard procedure for cellular RNA extraction. Total RNA was analyzed by transcriptome sequencing at Novogene Co., Ltd (Beijing, China). The raw data underwent quality control using fastp (v23.1) to obtain high-quality clean data, which were subsequently used for differential gene expression and enrichment analyses.

Gene categorization and pathway enrichment analysis

Compared with the model group, the genes significantly regulated by AVP (FDR < 20%) were classified into three categories: category 1, genes that were also significantly regulated by the model group compared to the normal group (FDR < 20%), but in the opposite direction to AVP; category 2, genes that were also significantly regulated by the model group compared to the normal group (FDR < 20%), and in the same direction as AVP; and category 3, genes that were significantly regulated only by AVP and not by the disease model. We performed pathway enrichment analysis on the genes in category 1 that were significantly upregulated or downregulated by AVP using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool with default system parameters (https://davidbioinformatics.nih.gov/). An FDR of 5% was set as the threshold for significance.

Protein–protein interaction network analysis

Using default parameters, we constructed a protein–protein interaction (PPI) network for category 3 genes using STRING (version 12.0, https://string-db.org/). Gene ontology analysis was then performed to identify significantly enriched biological processes within the network. An FDR of 5% was set as the threshold for significance.

Metagenomic sequencing of fecal samples

Fecal DNA was extracted using a fecal genomic DNA extraction kit. The DNA samples were fragmented and subjected to end repair, A-tailing, adapter ligation, purification, and PCR amplification to complete library preparation and quality control. Metagenomic sequencing was then performed using a paired-end approach on the Illumina HiSeq PE150 platform at Novogene Co. Ltd (Beijing, China). After quality control and host DNA filtering of the raw data, scaffolds were assembled, followed by gene prediction and redundancy removal. Finally, the gene catalog was compared with reference databases for species identification and functional annotation.

Transkingdom network analysis

We calculated the Spearman correlations between AVP-regulated genes (FDR < 15%), gut microbes (p < 0.05), and phenotypes in pairwise combinations within both the model group and the AVP group. Fisher's method was then used to combine the p-values from the two groups, followed by correction of the combined p-values using the Benjamini–Hochberg (BH) method. Edges with opposite signs of correlation coefficients between the two groups were removed.

Edges were retained if FDR < 5% and they satisfied the principles of causality.¹⁹ Bi-partite betweenness centrality (BIBC) has previously been used to find nodes that control the information flow between two parts of a network.²⁰ We calculated the BIBC for each microbe between the gut microbiome subnetwork and the transcriptome subnetwork, and between the gut microbiome subnetwork and the phenotype subnetwork. Similarly, we calculated the BIBC for each gene between the gut microbiome subnetwork and the transcriptome subnetwork and between the transcriptome subnetwork and the phenotype subnetwork.

Statistical analyses

All bar plots are presented as mean ± SD, with significance indicated at the top of each graph. A two-tailed parametric t-test was conducted to assess the significance of differences between two groups, with statistical significance defined as a p-value <0.05. GraphPad Prism 9.5.0 (GraphPad Software LLC, USA) was used to create the bar charts and perform the statistical analyses. Multiple comparisons were corrected for RNA-seq results using the Benjamini–Hochberg (BH) procedure, with a false discovery rate (FDR) cutoff of 20%. For pathway enrichment analysis, statistical significance was set at FDR < 5%.

Results

AVP inhibits ox-LDL-induced foam cell formation and alleviates oxidative stress by regulating cholesterol metabolism

The formation of foam cells is an important characteristic in the pathogenesis of AS, so we first investigated the regulatory effects of the three polysaccharides in this regard. We employed a widely adopted model, in which ox-LDL was used to induce foam cell formation in RAW 264.7 cells (Fig. 1A). We analyzed the effects of the polysaccharides on lipid deposition in macrophages using Oil Red O staining (Fig. 1B). The results show that DOP has no significant effect, and KGM exhibits a slight inhibitory effect, while AVP significantly inhibits lipid deposition in macrophages at all the three doses (Fig. 1C). Excessive lipid accumulation can disrupt the intracellular redox balance and lead to an increase in oxidative stress levels.²¹ We found that all the polysaccharides significantly reduced the levels of ROS (Fig. 1D). CD36 is a receptor for ox-LDL and facilitates the uptake of ox-LDL into macrophages.²² We found that ox-LDL stimulated the up-regulation of CD36, and treatment with these polysaccharides significantly inhibited CD36 expression (Fig. 1E). LXR-α is a nuclear receptor primarily involved in regulating cholesterol metabolism.²³ In macrophages, LXR-α activates specific target genes such as ABCG1 and ABCA1 to promote the efflux of cholesterol from cells, thereby reducing the accumulation of cholesterol within the cells.²⁴ Our results show that only AVP can significantly up-regulate the LXR-α expression (Fig. 1F). Moreover, it also shows a tendency to upregulate ABCG1 and ABCA1 (Fig. 1G and H).

AVP attenuates ox-LDL-induced apoptosis in RAW 264.7 cells

Increased accumulation of cholesterol and ROS in cells may induce apoptosis.²⁵ We assessed the impact of the polysaccharides on cell viability using the CCK-8 assay. The results show that DOP, KGM, and AVP can increase the viability of ox-LDL-challenged cells (Fig. 2A). It is important to note that the low concentration of AVP promoted cell viability, whereas the high concentration impaired it, potentially due to the cytotoxic properties at higher doses (Fig. 2A). Next, we used Annexin V to label early apoptotic cells and PI to stain late apoptotic cells (Fig. 2B). The flow cytometry results indicate that all the polysaccharides can increase the population of viable cells and reduce the proportion of necrotic cells (Fig. 2C and D). Only AVP can significantly reduce the level of early phase apoptotic cells (Fig. 2E).

AVP mitigates aortic plaque formation and coronary artery fibrosis in HFHCD-fed ApoE^−/− mice

To confirm the atheroprotective effects of the glucomannans in vivo, we induced AS in ApoE^−/− mice by feeding them an HFHCD for 20 weeks. Concurrently, subsets of mice were supplemented with DOP, KGM, and AVP daily via oral gavage for eight 8 weeks to observe the effects of these glucomannans on improving AS (Fig. 3A). The supplementation had no effect on the body weight, food intake, or water consumption of the mice (Fig. 3B and Fig. S1†). We performed Oil Red O staining on the aorta to evaluate whether the three polysaccharides affected the progression of the disease (Fig. 3C). Lipid accumulation in the aortic walls of mice treated with AVP was significantly lower than that in the model group (Fig. 3D). Next, we assessed the proliferation of fibrous tissue in the coronary arteries using Masson's trichrome staining (Fig. 3E) and determined the levels of α-SMA in the coronary arteries through immunohistochemistry (Fig. 3F). Among the three glucomannans, only AVP significantly decreased the area of fibrosis (Fig. 3G). High expression levels of α-SMA may indicate the active involvement of smooth muscle cells in the formation and development of plaques.²⁶ We found that both KGM and AVP could significantly reduce the levels of α-SMA (Fig. 3H). Moreover, mice treated with AVP showed significantly lower circulating IL-1β levels compared to those in the model group (Fig. 3I).

AVP significantly reverses the transcriptomic alterations in HFHCD-fed ApoE^−/− mice

As the results of both in vivo and in vitro experiments indicate that AVP can inhibit the pathogenesis of AS, we next examined its influence on the hepatic transcriptome of ApoE^−/− mice using RNA-seq (Fig. 4A and B). If a 20% FDR cut-off is used, AVP treatment regulated 769 genes, with 286 being up-regulated and 483 down-regulated (Table S1† and Fig. 4B). To analyze the relationship between AVP's regulation on the transcriptome and disease onset, we categorized the AVP-regulated genes into three groups (Fig. 4C). Category 1 includes genes that are regulated by both the model and AVP in opposite directions. For example, Tnfrsf1a, an important gene involved in inflammatory signaling, was upregulated in the model but downregulated by AVP (Fig. 4D); category 2 comprises genes that are regulated by the model and AVP in the same direction. For example, Lilr4b, which helps prevent excessive inflammation, was upregulated by the model and further upregulated by AVP (Fig. 4E); category 3 consists of genes that are not significantly regulated by the model. For example, Parp16, which is associated with cellular stress responses, was not regulated by the model but was significantly downregulated by AVP (Fig. 4F). Category 1 refers to genes whose expression changes, related to the pathological alterations in AS, are reversed by AVP. These genes may be of particular interest to our research. We analyzed the proportions of the three types of genes and found, as expected, that the number of genes in category 2 is significantly lower than that in category 1 (Fig. 4G). Among the 449 genes that are significantly regulated by both AVP and the model, 334 are regulated in opposite directions, while only 115 are regulated in the same direction, yielding a binomial test p = 8.0 × 10⁻²⁶ (Fig. 4H and Table S1†). These results indicate that from a perspective of gene expression, AVP significantly reverses the pathological changes associated with AS.

AVP reduces inflammation and stress responses by improving cholesterol metabolic balance in the liver

To explore the potential mechanisms by which AVP ameliorates the pathological state of AS, we performed pathway enrichment analysis using genes whose expression is reversed by AVP (category 1). We found that AVP significantly upregulated pathways including the fatty acid metabolic process, steroid metabolic process, bile acid and bile salt transport, and bile acid binding (FDR < 5%) (Fig. 5A). Cholesterol serves as a precursor for bile acids and various steroids, and effective transport of bile acids and salts helps maintain cholesterol balance. Glucuronidation facilitates the excretion of bile acids, and we observed that AVP also significantly upregulated the cellular glucuronidation pathway and enhanced glucuronosyltransferase activity (Fig. 5A). Specifically, genes involved in the synthesis, transport, and uptake of bile acids, such as Abcc2, Aqp9, Nr1h4 (FXR), and Slco1a1, were significantly downregulated in disease and upregulated by AVP (Fig. 5B).

Imbalance in liver cholesterol metabolism may lead to inflammation. We discovered that AVP significantly downregulated pro-inflammatory signaling pathways associated with toll-like receptors (TLRs) and NF-κB, as well as the production of cytokines (e.g., TNF and IL-6) and chemokines (FDR < 5%) (Fig. 5C). Of note, genes involved in inflammation, such as Cd14, Tlr7, Tlr8, and Il17a, were significantly upregulated in the model group compared to the normal group and downregulated in the AVP group compared to the model group (Fig. 5D).

Category 3 also includes genes that are significantly regulated by AVP; analyzing them may also reflect the regulatory effects of AVP. We constructed a PPI network using genes from category 3. The network consists of 311 nodes and 550 edges, with a PPI enrichment p = 2.93 × 10⁻¹¹ (Fig. 5E), indicating that these genes are not randomly distributed but are primarily from specific signaling pathways. We found significant enrichment of biological processes, such as response to stress (FDR = 8.96 × 10⁻⁶) and cellular response to DNA damage stimulus (FDR = 8.96 × 10⁻⁶), in the network (Table S2†). Most of genes in the pathways were downregulated by AVP compared to the model group (Fig. 5F). Inflammation induced by lipid metabolism disorders may lead to stress responses, indirectly indicating that AVP reduces stress levels by improving cholesterol metabolism and inflammation.

AVP significantly modulates the gut microbiome in HFHCD-fed ApoE^−/− mice

Although we observed the direct effects of AVP on foam cells in vitro, AVP, as a dietary fiber, may also function by regulating the gut microbiome. Therefore, we performed metagenomic sequencing using fecal samples and determined the composition of gut microbes (Table S3† and Fig. 6A). There were no changes in α-diversity after AVP treatment, as indicated by Shannon and Chao indices (Fig. 6B). We evaluated β-diversity using non-metric multidimensional scaling (NMDS) and observed significant difference between the AVP and model groups (Fig. 6C). Next, we analyzed the 114 microbial genes regulated both by the disease and AVP (p < 0.05). Interestingly, AS and AVP influenced these genes in the opposite directions (Fig. 6D). Out of the 114 genes, 97 were regulated in opposite directions, while only 17 were regulated in the same direction, with a binominal test p = 7.2 × 10⁻¹⁵ (Fig. 6E). For example, mgp, essential for glycosyl compound metabolism,²⁷ is significantly suppressed under AS conditions but markedly enhanced by AVP. Conversely, dltE, critical for bacterial cell wall formation,²⁸ is upregulated under AS but downregulated by AVP (Fig. S2A and B†). As short-chain fatty acids (SCFAs) play important roles in host metabolism,^29,30 we analyzed a group of microbes that have been reported to be SCFA producers.³¹ Interestingly, the microbe abundance was generally reduced in the model group but increased in the AVP group (Fig. 6F). For example, Bacteroides sp. CAG:661, a key propionate producer, was suppressed under AS but stimulated by AVP (Fig. S2C†). Consistently, higher levels of propionic acid and isobutyric acid were detected in the AVP group compared to the model group (Fig. 6G and H).

The gut microbiome partially mediates the atheroprotective effects of AVP

Out of the 325 microbes regulated by both the model and AVP, 276 were regulated in opposite directions, while only 49 were regulated in the same direction (Fig. 7A). These findings suggest that AVP predominantly reverses the changes in microbial abundance associated with the disease (p = 1.6 × 10⁻¹⁹). Moreover, microbes that were up-regulated, such as Roseburia, Faecalibacterium, and Prevotella, generally show a negative correlation with AS phenotypes, including arterial plaques and fibrosis. Conversely, down-regulated microbes, such as Clostridium, Staphylococcus, and Methanosarcina, tend to exhibit a positive correlation with these parameters (Fig. 7B and Table S4†). To model host–microbiota interactions under the influence of AVP, we constructed a transkingdom network comprising 781 nodes and 4183 edges, integrating the AVP-regulated microbes in the gut, genes in the liver, and AS parameters (Fig. 7C). The degree distribution of the network follows a power law (Fig. 7C). The network highlights the dense interactions between the gut microbiota, the host transcriptome, and metabolic parameters. Bi-partite betweenness centrality (BIBC) quantifies the importance of nodes in a bi-partite graph by measuring how frequently a node serves as a bridge along the shortest paths between nodes in different partitions.³² A higher BIBC score indicates a node's critical role in facilitating information transfer between the two partitions. We calculated the microbiome–transcriptome BIBC and the microbiome–phenotype BIBC for each microbe in the network (Table S5†) and observed a positive correlation between them (p = 4.67 × 10⁻¹⁶⁵, Fig. 7D). This suggests that microbes that influence host gene expression may be associated with AS phenotypes. We also noticed that some microbes with high BIBC, such as Prevotella and Bacteroides, have been previously reported to play roles in host metabolism^33,34 (Fig. 7E). Similarly, we also calculated the microbiome–transcriptome BIBC and transcriptome–phenotype BIBC for each gene. We observed that certain genes mediate both the interaction between the gut microbiome and the host transcriptome, and the linkage between gene expression and AS parameters (p = 7.11 × 10⁻⁸²) (Fig. 7F and Table S6†). For example, Cdip1, with very high BIBC in both paths, plays an important role in p53-mediated apoptosis.³⁵ Under cellular stress, such as DNA damage or oxidative stress, the expression of Cdip1 is upregulated, promoting the transmission of apoptotic signals. In this study, expression of Cdip1 is downregulated by AVP (Fig. 7G). Collectively, our transkingdom network analysis suggests that AVP may inhibit AS progression by modulating the gut–liver axis.

Discussion

This study explored the ameliorative effects and underlying mechanisms of three types of glucomannans on AS from various angles, including foam cell formation, plaque development, lipid metabolism, inflammation, and the gut microbiome. Both cell and animal studies indicate that among the three polysaccharides, AVP exhibits the most significant effects. Cell experiments demonstrate that AVP can effectively inhibit the uptake of ox-LDL by macrophages and promote cholesterol homeostasis by stimulating the LXR pathway. Consequently, AVP significantly inhibits foam cell formation and reduces oxidative stress levels. As cholesterol and other lipids accumulate, foam cells undergo stress responses that can lead to cellular dysfunction and apoptosis.³⁶ The apoptotic foam cells release a large amount of lipids and cellular debris, which can accumulate within the arterial walls, forming lipid cores. The accumulation of apoptotic cells and the formation of the lipid core may lead to the enlargement of atherosclerotic plaques.³⁷ Consistently, we found that AVP could significantly reduce the proportion of dying and dead cells and promote viability.

Next, we verified the results from cell experiments through animal studies. Our results indicate that AVP can significantly inhibit lipid accumulation in aortic walls. Under the condition of AS, smooth muscle cells (SMCs) in the coronary arteries shift from a contractile to a synthetic phenotype, expressing more α-SMA.³⁸ This enables SMCs to produce more extracellular matrix macromolecules, such as collagen. The increased deposition of collagen and other extracellular matrix proteins forms a fibrous cap over the lipid core to prevent plaque rupture.³⁹ Consistently, we found that mice treated with AVP exhibited significantly lower levels of fibrosis and α-SMA expression in their coronary arteries. This suggests that AVP can prevent the progression of AS.

The liver is the primary organ for lipid metabolism, responsible for the synthesis, breakdown, and transport of cholesterol, triglycerides, and lipoproteins.⁴⁰ Our transcriptomic results indicate that AVP can significantly reverse the gene expression changes associated with AS progression. It restores the homeostasis of cholesterol metabolism by reactivating the FXR pathway and promoting bile acid transport and secretion. Excessive dietary intake of cholesterol can accumulate in the liver, leading to steatohepatitis. CD14, as a co-receptor, collaborates with TLR4 to initiate inflammatory signaling pathways.⁴¹ We found that the expression of CD14 and pattern recognition receptors (PRRs), such as TLR7 and TLR8, was significantly reduced in the AVP-treated mice. Gene ontology enrichment analysis reveals that pathways related to immune system processes were significantly suppressed in the AVP group compared to the model group. Additionally, many genes involved in biological processes such as “response to stress” are significantly downregulated, which may be attributed to the effects of AVP in alleviating inflammation, reducing oxidative stress, and improving liver metabolic homeostasis.

As the coronary artery is directly affected by AS, studying its gene expression changes could provide valuable insights into the local molecular alterations in the vasculature, including the regulation of endothelial function, vascular inflammation, oxidative stress, and plaque stability, all of which are critical in the pathogenesis of AS.⁴² This approach could help identify specific pathways and gene expression changes modulated by AVP at the site of disease progression, offering a more localized understanding of its therapeutic effects. The direct regulatory effects of AVP on the coronary artery warrant further investigation.

In recent years, the regulatory role of the gut microbiota in immunity and metabolism has attracted widespread attention.^43–45 Changes in the gut microbiota play a crucial role in the progression of AS.⁴⁶ Given that AVP is essentially a dietary fiber, it may exert its physiological effects, at least in part, through modulation of the gut microbiota. Consistently, we found that AVP significantly reversed AS-induced alterations in the gut metagenome. SCFAs, primarily produced by the gut microbiota through the fermentation of dietary fiber, play a crucial regulatory role in CVDs.⁴⁷ We found that among the three polysaccharides, only AVP significantly increased the levels of propionate and isobutyrate in the cecum of mice. Transkingdom network analysis has been successfully used to model host–microbiota interactions, identify causal relationships from correlations, and predict key regulatory factors, such as critical microbial species or genes.^19,48 We calculated the BIBC for each gut microbe and DEG in the network. Gut microbes and genes with higher BIBC scores may play a significant role in mediating AVP functions and deserve further in-depth analysis in future research. Nevertheless, it is worth noting that although our integrative multi-omics network analysis, combining metagenomic and transcriptomic data, suggests that the gut microbiota partially mediates the effects of AVP, future studies utilizing antibiotic treatment or fecal microbiota transplantation approaches are essential to confirm the causal relationship between gut microbiota modulation and the observed atheroprotective effects.

The structural characteristics of glucomannans, including molecular weight, degree of acetyl substitution, and mannose-to-glucose ratio, significantly influence their biological activities. Among the three glucomannans tested, AVP has the highest degree of acetyl substitution and mannose-to-glucose ratio,⁴⁹ which correlates with its strongest activity observed in both cellular and animal experiments. These findings suggest that structural features may contribute to the atheroprotective effects of glucomannans, highlighting the need for further investigation into their structure–activity relationship. Moreover, it is important to note that, despite employing various methods to enhance the purity of polysaccharide samples, the neutral sugar content did not reach 100%. The presence of other substances may potentially interfere with the experimental outcomes. Addressing this limitation remains a critical challenge for future studies.

In conclusion, this is the first study demonstrating that AVP shows potential for improving AS. AVP enhances cholesterol metabolism, thereby reducing lipid buildup in macrophages and significantly inhibiting the oxidative stress and cell apoptosis associated with excessive lipid levels. In animal experiments, AVP can regulate the gut microbiota and restore cholesterol metabolism homeostasis in the liver, reducing inflammation, lipid deposition in vascular walls, and arterial plaque formation, which are typical symptoms of AS. These findings suggest that AVP may exert its atheroprotective effects through both microbe-dependent and independent mechanisms. Our study highlights the translational potential of AVP as a healthy dietary supplement for ameliorating AS.

Conflicts of interest

There are no conflicts of interest to declare.

Data availability

All data needed to evaluate the conclusions of this study are present in the paper and the ESI.† Raw omics data are available upon request.

Acknowledgements

This work was financially supported by the Key Research and Development Project of Jiangxi Province (20224BBF62002), the Natural Science Foundation of Jiangxi Province (20242BAB20323), the State Key Laboratory of Food Science and Technology Project (SKLF-ZZA-202211), the National Natural Science Foundation of China (32402112), and the General Program of Natural Science Foundation of Jiangxi Province (20224BAB205040).

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5fo01995h

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