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. 2025 Jul 30;10(31):34597–34607. doi: 10.1021/acsomega.5c03364

Integration of Metabolomics, Transcriptomics, and 16s rRNA Sequencing Reveals the Mechanism of Morus alba L. (Sangzhi) Alkaloids (SZ-A) in Improving Cholesterol Metabolism in Diabetic Rats

Caina Li †,*, Yuting Ma †, Shengyu Wen †, Liran Lei †, Yi Huan †, Jiayu Zhai †, Kejia Xu †, Hui Cao †, Lei Lei †, Cunyu Feng †, Yuling Liu †,§, Zhufang Shen †,‡, Quan Liu †,*, Shuainan Liu †,‡,*
PMCID: PMC12355231  PMID: 40821600

Abstract

Mulberry (Morus alba L.) is a plant widely used in the agricultural, food and pharmaceutical fields. Sangzhi alkaloids (SZ-A) are a series of alkaloids extracted from mulberry branches that are approved in China for treating type 2 diabetes mellitus (T2DM). T2DM is always accompanied by hypercholesterolemia, which increases the risk of macrovascular complications. However, the mechanism by which SZ-A influences cholesterol metabolism remains to be elucidated. In this study, SZ-A was orally administered to Zucker diabetic fatty rats at doses of 100 and 200 mg/kg once daily for 9 weeks. Cholesterol and bile acid levels in the blood and feces were determined using biochemical assays and targeted metabolomics, and the gut microbial profile was analyzed using 16s rRNA sequencing. Transcriptomic analysis and quantitative real-time polymerase chain reaction were used to explore the underlying genes involved in cholesterol metabolism. The results showed that repeated treatment with 200 mg/kg SZ-A significantly decreased the total cholesterol and low-density lipoprotein cholesterol levels in the blood and increased the fecal content of two conjugated bile acids, taurodeoxycholic acid (TDCA) and taurolithocholic acid (TLCA). The abundances of the beneficial bacteria Akkermansiaceae, Tannerellaceae, and Rikenellaceae were also increased, while the abundance of bile salt hydrolase-producing bacteria was modulated; notably, the abundances of Bacteroidaceae and Bifidobacteriaceae were increased and those of Clostridiaceae and Lactobacillaceae were decreased. Decreases in Clostridiaceae and Lactobacillaceae were negatively correlated with increases in fecal TLCA and TDCA, respectively, and increases in Akkermansiaceae, Tannerellaceae, and Rikenellaceae were positively correlated with increases in TLCA. SZ-A also significantly lowered the expression of aldo-keto reductase 1b7 and its upstream gene farnesoid X receptor (FXR) and increased the expression of cholesterol 7α-hydroxylase and small heterodimer partner. Taken together, repeated treatment with SZ-A ameliorated hypercholesterolemia partly by regulating bile acids production and excretion. Additionally, the gut microbiota and hepatic FXR signaling played important roles in this process.

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1. Introduction

Many parts of the mulberry (Morus alba L.) plant, including the leaves, fruit, and branches, are widely used in food, agriculture, and medicine. Branches, which are important byproducts of mulberry planting, are a widely used material in traditional Chinese medicine. Sangzhi alkaloids (SZ-A) are alkaloids extracted from mulberry branches and have been approved for treating type 2 diabetes mellitus (T2DM) as an α-glucosidase inhibitor. Many studies have demonstrated that SZ-A possesses broad pharmacological activity, including anti-inflammatory and antiatherosclerosis effects, and also that it promotes insulin secretion and regulates the gut microbiota. −

Dyslipidemia often occurs along with T2DM and is a vital risk factor for macrovascular complications in patients with T2DM, such as ischemic heart disease and stroke. As living standards have increased, hypercholesteremia has become a global health problem. Generally, cholesterol is derived from either diet or de novo biosynthesis primarily in the liver, and it is balanced by synthesis, absorption, storage, and elimination. Presently, interference with absorption and de novo biosynthesis have been employed to develop drugs for treating hypercholesterolemia, such as 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase inhibitor and Niemann–Pick C1-like 1 protein selective inhibitor. The biosynthesis of bile acids is another important cholesterol elimination pathway, which has been proposed to be a strategy to regulate cholesterol metabolism. The farnesoid X receptor (FXR) belongs to the nuclear receptor superfamily and participates in regulating the expression of many target genes involved in cholesterol metabolism and bile acid production. Cholesterol 7α-hydroxylase (CYP7A1) is the first and rate-limiting enzyme in the classical bile acid synthetic pathway, and the expression of CYP7A1 is critically regulated by FXR through small heterodimer partner (SHP) and the fibroblast growth factor 19 (FGF19)/ fibroblast growth factor receptor (FGFR4) pathway. Additionally, diet-induced dyslipidemia is closely related to gut microbiota. The FXR/FGF19 axis is responsible for the regulation of diet-induced dyslipidemia by gut microbiota-bile acid crosstalk.

Numerous studies have shown that the extracts of mulberry leaves and fruit significantly modulate lipid metabolism and ameliorate nonalcoholic fatty liver disease and alcohol-induced liver damage. − The present study aimed to explore the effects and underlying mechanism of the mulberry branch product, namely SZ-A, on hypercholesterolemia to provide scientific evidence for expanding research on mulberry.

2. Methods

2.1. Experimental Animals and Grouping

All animal experiments were approved by the Institutional Animal Care and Use Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College (Approval No. 00006265), and were conducted according to the relevant guidelines established by China (GB14925-2001 and MOST 2006a). The type of animals used in this study, namely, male ZDF-Leprfa/Crl rats (fa/fa) and control rats (fa/+) aged 7–9 weeks, were the same as in our previous experiment. They were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed under controlled temperature and humidity conditions, with a 12 h light/dark cycle. As previously described, the ZDF-Leprfa/Crl rats (fa/fa) were separated into three groups with ten rats per group: diabetic control (Con), SZ-A (100 mg/kg), and SZ-A (200 mg/kg). Eight control rats (fa/+) were used as normal controls (Nor). All rats were fed a special diet (Purina, 5008) and treated with either SZ-A (lot number: 201707008, provided by the Department of Research & Development, Beijing Wehand-bio-Pharmaceutical Co. Ltd., Beijing, China) dissolved in water or with water alone (5 mL/kg) once daily by gavage for 9 weeks.

2.2. Determination of TC, LDL-C, HDL-C, and TBA in Serum

Blood total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels were determined after treatment for 3 and 5 weeks, and high-density lipoprotein cholesterol (HDL-C) level was measured after treatment for 3 weeks (Biosino, Beijing, China). At the end of the experiment, all rats were euthanized by cervical dislocation following anesthesia with pentobarbital sodium (60 mg/kg), and blood and liver were collected. Serum was separated after centrifugation at 6000 rpm for 10 min and the levels of total bile acids (TBAs) were assayed (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).

2.3. Determination of TC in Liver

The frozen liver tissues (stored at −80 °C) were homogenized in cold physiological saline to prepare a 10% homogenate solution. The supernatant was collected by centrifugation at 3000 rpm for 5 min at 4 °C. The TC content in the supernatant was then measured (Biosino, Beijing, China).

2.4. Determination of Bile Acid and TC in Feces

Fecal samples were collected after treatment for 8 weeks, and a total of 33 bile acids were detected by Applied Protein Technology using SRM/MRM (APTBIO, Shanghai, China). Briefly, fecal samples were analyzed using sample processing and detection methods previously reported. The target bile acid standard was used to calibrate retention time, and a standard curve was generated to calculate the bile acid concentration in the samples. Meanwhile, the TC content in feces was also determined. The fecal samples were dried and ground into powder, after which organic reagents were used to extract lipids. The solvent was then dried, and the precipitate was redissolved in a 10% TritonX-100/isopropyl alcohol solution, after which the TC content was analyzed as described above.

2.5. Transcriptomic Analysis of Liver

The isolated liver tissues (stored at −80 °C) were subjected to RNA sequencing by Berry Genomics Co., Ltd. (n = 5). The raw data were processed as in our previous study. The valid data were analyzed using the edgeR package (1.20.0), and differentially expressed genes (DEGs) with p-values of <0.05 and |log2 (fold-change)| > 1 were considered significant. Venn and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A corrected p-value of <0.05 was used to indicate the significance of KEGG pathway enrichment.

2.6. Quantitative Real-Time Polymerase Chain Reaction

Total RNA was extracted from the liver using TRIzol reagent (Applygen Technologies Inc., Beijing, China) and cDNA was synthesized using the TransScript First-strand cDNA Synthesis SuperMix according to the manufacturer’s protocols (Transgen Biotech, Beijing, China). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed as previous described. The relative expression of target genes was calculated using the 2–ΔΔC t method and normalized to the expression of the β-actin housekeeping gene. The primers of the target genes are presented in Table .

1. Primers of Target Genes.

mRNAs forward primer 5′-3′ reverse primer 5′-3′
Akr1b7 TCTTGGGAAGCAGAAGAAGTGCC GCCTTCACAGCTTCCTTGACT
Fxr CTCAGAGCAGCAACCTGGTA CATGGAGGATAAAACGAGGCG
Cyp7a1 GGGCAGGCTTGGGAATTTTG AGTGAGCATTGGTCCCGAAG
Cyp27a1 GCTCCAGGCGCTGAACAA GGAGAGGCCTTTGTGGTCTC
Shp CTGTAGAATGGGGTCCGGTG TGGAGGTTTTGGGAGCCATC
β-actin GCAGGAGTACGATGAGTCCG ACGCAGCTCAGTAACAGTCC
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2.7. Western Blotting

Approximately 50 mg of liver was lysed in radio-immunoprecipitation assay lysis buffer with protease and phosphatase inhibitors (Applygen Technologies Inc., Beijing, China) and analyzed by Western blotting (WB). Mouse anti-FXR was purchased from Cell Signaling Technology (Catalog No. 72105, USA), and rabbit anti-HSP90 was obtained from Proteintech (Catalog No. 13171-1-AP, China).

2.8. Analysis of Gut Microbiota by 16s rRNA Sequencing and Correlation with Differential Bile Acids in Feces

The fecal samples, collected after treatment for 8 weeks, were also subjected to Illumina sequencing by Major Bio-Pharm Technology Co. Ltd. (Shanghai, China) using standard protocols, and the data were analyzed on the online Majorbio cloud platform (www.majorbio.com). Community compositional analysis was performed at the phyla and family levels, and the differential microbiota among groups were analyzed at the operational taxonomic unit (OTU) and family levels using the Kruskal–Wallis H test.

Correlations between the gut microbiota and some differential fecal bile acids, including TDCA, TLCA, 12-KLCA, and 7-KDCA, were analyzed using the online Majorbio cloud platform (www.majorbio.com) to evaluate the effect of the gut microbiota on cholesterol metabolism. R and p-values were calculated following Spearman’s rank correlation.

2.9. Statistical Analysis

All numerical data are expressed as the mean ± SD and analyzed by ordinary one-way ANOVA or nonparametric tests using GraphPad Prism 8.0.2 version. A p-value <0.05 indicates statistical significance.

3. Results

3.1. SZ-A Improves Hypercholesterolemia in Diabetic ZDF Rats

ZDF rats, who also have hypercholesterolemia, are commonly used T2DM animal models. Repeated treatment with 200 mg/kg SZ-A significantly and dose-dependently lowered blood TC, LDL-C, and HDL-C levels (Figure A–E) compared to diabetic ZDF rats (Con) but did not affect the hepatic TC content (Figure F). These results suggest that repeated treatment with SZ-A can improve hypercholesterolemia in diabetic ZDF rats.

1.

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SZ-A decreased blood TC, LDL-C, and HDL-C levels in diabetic ZDF rats. (A,B) Blood TC levels were determined after treatment for 3 (A) and 5 (B) weeks. (C,D) Blood LDL-C levels were determined after treatment for 3 (C) and 5 (D) weeks. (E) Blood HDL-C levels were determined after treatment for 3 weeks. (F) TC content was assayed in liver homogenate after treatment for 9 weeks. All data are expressed as the mean ± SD, n = 8–10. ***p < 0.001, **p < 0.01, *p < 0.05 vs Con group.

3.2. SZ-A Regulates Bile Acid Excretion in Diabetic ZDF Rats

The bile acid content in feces was analyzed to evaluate whether bile acid biosynthesis participated in ameliorating hypercholesterolemia induced by SZ-A. Repeated treatment with 200 mg/kg SZ-A increased fecal TDCA and TLCA content (p < 0.05, Figure A,B) and elevated the TMCA content by 103.8% (p > 0.05, Figure C) compared to the Con group. It also decreased the content of 7-KDCA and 12-KLCA (p < 0.05, Figure D,E) and showed a trend toward lowering TCDCA and CDCA contents (p > 0.05, Figure F,G). The results suggest that the mechanism of SZ-A in ameliorating hypercholesterolemia may lie in promoting the conversion of cholesterol to bile acids. Meanwhile, treatment with 200 mg/kg SZ-A decreased the fecal TC content (p < 0.01, Figure H) compared to the Con group, also indicating the conversion of cholesterol to bile acids. The fecal content of TC in ZDF rats (Con) was lower than in normal rats (Nor), suggesting a possible mechanism of hypercholesterolemia in ZDF rats.

2.

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SZ-A regulated the fecal content of bile acids in diabetic ZDF rats. (A–G) Fecal bile acid content, including TDCA (A), TLCA (B), TMCA (C), 7-KDCA (D), 12-KLCA (E), TCDCA (F), and CDCA (G) was determined after treatment for 8 weeks. (H) Fecal TC content was determined after treatment for 8 weeks. All data are expressed as the mean ± SD, n = 8–10. ***p < 0.001, **p < 0.01, *p < 0.05 vs Con group.

3.3. FXR Signaling Participates in Bile Acid Regulation following SZ-A Treatment in Diabetic ZDF Rats

RNA sequencing was conducted to analyze the DEGs in liver and explore the underlying molecular target of SZ-A in lowering blood TC. A total of 545,023,153 reads from 20 samples were mapped to the reference genome with mapped rates greater than 94% (File S1 in Supporting Information). The fragments per kilobase of exon model per million mapped fragments (FPKM) value was used to indicate the gene expression level, and Pearson’s correlation coefficient and principal component analysis (PCA) based on the FPKM values showed that the sample selection was representative (Figure S1A,B in Supporting Information). The differential analysis separately revealed 748, 306, and 291 DEGs in comparisons between the Nor group and the Con group, the SZ-A 100 mg/kg group and the Con group, and the SZ-A 200 mg/kg group and the Con group (File S2 in Supporting Information).

Venn analysis was performed to define the DEGs following treatment with SZ-A. Forty-eight common DEGs were found in comparisons between the Nor group and the Con group, the SZ-A 100 mg/kg group and the Con group, and the SZ-A 200 mg/kg group and the Con group (Figure A,B). The 48 common DEGs were subjected to KEGG pathway enrichment analysis, which showed that metabolic pathways were enriched (p = 0.004, p adjusted = 0.068, Figure C). Meanwhile, aldo-keto reductase 1b7 (AKR1B7), a primary molecule in the biosynthesis of bile acids, was enriched in metabolic pathways, and qRT-PCR confirmed that repeated treatment with SZ-A significantly lowered the gene expression of AKR1B7 in the liver of ZDF rats (Figure D).

3.

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Analysis of DEGs in livers of ZDF rats using RNA sequencing. (A) Venn analysis of the common DEGs in the SZ-A (100 mg/kg) group vs the Con group, the SZ-A (200 mg/kg) group vs the Con group, and the Con group vs the Nor group. (B) Heatmap of the 48 common DEGs. (C) KEGG pathway enrichment analysis. (D) The relative gene expression of hepatic AKR1B7. The numerical data are expressed as the mean ± SD, n = 5 for (A–C), 4–6 for D. ***p < 0.001, **p < 0.01 vs Con group.

Because AKR1B7 was reported to be a downstream molecular target of FXR and participate in the regulation of lipid and glucose metabolism, , we hypothesized that FXR might be a molecular target of SZ-A. The qRT-PCR results showed that 200 mg/kg SZ-A significantly decreased the gene expression of hepatic FXR (Figure A), which was consistent with the gene expression of AKR1B7. However, the WB results showed that SZ-A treatment increased the expression of hepatic FXR at the protein level to some extent (p = 0.053, Figure B). Meanwhile, the gene expression of CYP7A1, a crucial enzyme in the classical pathway of bile acid biosynthesis, was greatly increased following treatment with SZ-A (Figure C); however, the gene expression of CYP27A1, a crucial enzyme in the alternative pathways, was not affected (Figure D), while the gene expression of SHP, a transcriptional repressor of CYP7A1, was also increased (Figure E). Importantly, SZ-A dose-dependently and significantly increased blood bile acid levels (Figure F) but had no significant effect on the structure and function of hepatocytes, as assayed by hematoxylin–eosin staining and blood alanine aminotransferase (ALT) and aspartate aminotransferase (AST) measurements (Figure S2A–C in Supporting Information). These results suggest that repeated treatment with SZ-A enhanced the conversion of cholesterol to bile acids through FXR signaling to ameliorate hypercholesterolemia in diabetic ZDF rats.

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Effects of SZ-A on the proteins involved in bile acid biosynthesis. (A) The relative gene expression of hepatic FXR. (B) The relative protein expression of hepatic FXR. (C–E) The relative gene expression of hepatic CYP7A1 (C), CYP27A1 (D), and SHP (E). (F) Blood bile acid levels were determined after treatment for 9 weeks. The numerical data are expressed as the mean ± SD, n = 5–6 for (A–E), 8–10 for (F). ***p < 0.001, **p < 0.01, *p < 0.05 vs Con group.

3.4. SZ-A Regulates the Relative Abundance of Beneficial Bacteria and Bile Salt Hydrolase-Producing Bacteria in Diabetic ZDF Rats

The 16S rRNA sequencing was performed to evaluate whether gut microbiota is involved in bile acid metabolism. A total of 982,572 valid sequences were obtained from 37 samples after normalization according to the minimum sample sequence number, which was clustered into 887 OTUs (File S3 in Supporting Information). PCA with ANOSIM showed that the bacterial community in ZDF rats (Con) was significantly distinguished from that of normal rats (Nor). Treatment with SZ-A (100 and 200 mg/kg) significantly altered the bacterial profile compared to the Con group (Figure S3A in Supporting Information), but had no significant effect on the OTU number, Shannon, or Chao index values (Figure S3B–D in Supporting Information). Community composition analysis was performed to explore community differences related to SZ-A treatment, which showed that SZ-A significantly increased the abundance of beneficial bacteria, including Akkermansiaceae, Tannerellaceae and Rikenellaceae, as well as controversial bacteria Lachnospiraceae (OTU 524, 816, 150, and 610). Bile salt hydrolase (BSH)-producing bacteria Bacteroidaceae and Bifidobacteriaceae (OTU 157) were also increased, whereas the abundance of Enterococcaceae and BSH-producing bacteria Clostridiaceae and Lactobacillaceae (OTU 60) was greatly decreased following treatment with 200 mg/kg SZ-A (Figure A–C). SZ-A treatment also significantly increased the abundance of Verrucomicrobiota and greatly decreased the abundance of Cyanobacteria at the phylum level (Figure D). These results indicate that repeated treatment with SZ-A significantly regulates the gut microbial profile in ZDF rats and remodels the profile of BSH-producing bacteria.

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SZ-A modulated the gut microbial profile in diabetic ZDF rats. (A) Bacterial community at the family level. (B,C) Analysis of differential bacteria between the groups at the OTU (B) and family (C) levels using the Kruskal–Wallis test with FDR multiple test correction and the Tukey–Kramer posthoc test. (D) Bacterial community at the phylum level. n = 8–10. ***p < 0.001, **p < 0.01 and *p < 0.05 represent significant differences in the average relative abundance among groups.

3.5. Gut Microbiota Are Closely Related to the Modulation of Fecal Bile Acids following Treatment with SZ-A

Bacteroidaceae, Bifidobacteriaceae, Lactobacillaceae, and Clostridiaceae are the main sources of BSH, a primary enzyme that degrades conjugated bile acids to free bile acids and glycine or taurine. Thus, a correlation analysis between gut microbiota and four significantly changed fecal bile acids (TDCA, TLCA, 12-KLCA, and 7-KDCA) was performed. The results showed that increases in Bacteroidaceae and Bifidobacteriaceae were negatively correlated with decreases in 12-KLCA, and decreases in Lactobacillaceae and Clostridiaceae were negatively correlated with increases in TDCA and TLCA, respectively. Decreases in Clostridiaceae were also positively correlated with decreases in 12-KLCA and 7-KDCA. Meanwhile, the correlations between variations in the abundance of three beneficial bacteria, including Akkermansiaceae, Tannerellaceae, and Rikenellaceae, and bile acids were opposite from the observations in Clostridiaceae, and increases in Lachnospiraceae were positively correlated with increases in TDCA and TLCA (Figure A). The increases in TDCA and TLCA were also positively correlated with increases in bacteria in the phylum Verrucomicrobiota, and decreases in 12-KLCA and 7-KDCA were positively associated with decreases in bacteria in the phylum Firmicutes and negatively associated with increases in bacteria in the phylum Verrucomicrobiota (Figure B). These results suggest that regulating the gut microbiota and increasing the fecal excretion of conjugated bile acids, including TDCA and TLCA, may partly explain the decreases in blood TC levels by SZ-A in diabetic ZDF rats.

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Spearman’s correlations between the gut microbiota and the fecal content of TDCA, TLCA, 12-KLCA, and 7-KDCA. (A) Correlation analysis between bile acids and the gut microbiota at the family level. (B) Correlation analysis between bile acids and the gut microbiota at the phylum level. n = 8. R values are displayed in different colors in the graph. ***p < 0.001, **p < 0.01, and *p < 0.05 represent significant correlations between bile acids and the gut microbiota.

4. Discussion

As a newly approved antidiabetic drug, SZ-A was demonstrated to exert hypoglycemic action through extensive mechanisms, such as inhibiting α-glucosidase, enhancing glucose-stimulated insulin and glucagon-like peptide-1 (GLP-1) secretion, and improving islet β-cell function. , Many additional studies have reported that SZ-A also participates in lipid metabolism in many animal models with impaired glucose and lipid metabolism. SZ-A was reported to significantly lower blood and hepatic TC content in high-fat diet-induced obese mice and ameliorate obesity-linked adipose inflammation. , Wang et al. explored the effects of SZ-A on hepatic lipid metabolism in high-fat-diet/streptozotocin-induced diabetic mice and found that SZ-A also notably lowered blood TC and TG levels by regulating glycerophospholipid and choline metabolism, as well as increasing the levels of phosphatidylcholine and lysophosphatidylcholine metabolites. However, the underlying molecular target of SZ-A in improving lipid metabolism remains unknown. Our previous study showed that SZ-A significantly improved glucose metabolism and diabetic nephropathy in diabetic ZDF rats. This study further found that treatment with SZ-A significantly decreased blood TC, LDL-C, and HDL-C levels in diabetic ZDF rats, and the underlying mechanism could lie in promoting bile acid production and excretion by regulating hepatic FXR signaling and the gut microbial profile.

Bile acid is the primary component of bile, and plays a crucial role in the digestion and absorption of dietary lipids and fat-soluble vitamins, as well as in enhancing the secretion and excretion of bile. As the end-product of cholesterol catabolism, bile acid synthesized in the liver comprises most of the cholesterol turnover. Therefore, promoting bile acid biosynthesis from cholesterol becomes an important way to ameliorate hypercholesterolemia. TLCA, a kind of conjugated secondary bile acid, is involved in the protection of kidney function mediated by Roux-en-Y gastric bypass surgery in diabetic mice. TDCA, as a G protein-coupled receptor 19 agonist, is also a kind of conjugated secondary bile acid with physiological effects, such as ameliorating atopic dermatitis and inhibiting inflammasomal activation. , Notably, lithocholic acid is mainly excreted from the body in feces after amino acid conjugation and sulfation. Here, SZ-A increased blood bile acid levels and fecal TDCA and TLCA contents, suggesting the enhanced production of bile acids in diabetic ZDF rats, which may explain the amelioration of hypercholesterolemia.

The present study also found that the enhanced production of bile acids by SZ-A treatment was closely associated with FXR signaling. As a nuclear receptor, FXR plays an important role in maintaining glucose, bile acid, and lipid homeostasis. Singh et al. reported that obeticholic acid activated FXR to decrease circulating TC and HDL-C levels and enhance fecal cholesterol excretion in mice. Bile acids are natural ligands of FXR. Studies have demonstrated that both the activation and inactivation of hepatic FXR are accepted strategies for treating lipid metabolic disorders. , AKR1B7 is a member of the AKR1B family, which is a downstream molecule of FXR and participates in regulating lipid and glucose metabolism. Ge et al. found that AKR1B7 might be a therapeutic target for treating fatty liver disease associated with diabetes. Schmidt et al. reported that AKR1B7 metabolized 3-keto bile acids into less-toxic 3β-hydroxy bile acids. Tirard et al. showed that AKR1B7 was an important inhibitor of preadipocyte differentiation and may be involved in obesity. The transcriptomic analysis of liver, as well as qRT-PCR analysis, demonstrated that SZ-A treatment decreased the gene expression of hepatic AKR1B7 in diabetic ZDF rats. Based on the correlation between AKR1B7 and FXR, we detected the expression of FXR at the gene and protein levels and found that SZ-A treatment significantly decreased FXR expression at the gene level and increased its expression at the protein level to some extent, indicating the complicated involvement of FXR in bile acid biosynthesis and metabolism, but warranting further investigation.

CYP7A1 is a crucial enzyme in the classical bile acid biosynthetic pathway, while FXR is an important transcription factor in the bile acid-induced inhibition of Cyp7a1 transcription. Thus, FXR antagonists can increase downstream CYP7A1 expression to augment the conversion of cholesterol to bile acids and consequently, ameliorate hypercholesterolemia. Several FXR antagonists have been identified, which significantly increase the expression of CYP7A1, but the effects on cholesterol metabolism remain to be investigated. However, when bile acid levels increase, two main negative regulatory pathways, FXR/SHP and FXR/FGF19/FGFR4, start to decrease bile acid synthesis to maintain bile acid homeostasis and prevent damage to hepatocytes. In the present study, SZ-A decreased the gene expression of FXR and increased the gene expression of CYP7A1, which was consistent with the elevated blood bile acid levels in diabetic ZDF rats. However, it also increased the gene expression of SHP, indicating that SZ-A promoted bile acid synthesis through the classical pathway, while maintaining bile acid homeostasis through the negative FXR/SHP pathway. A previous study showed that mulberry leaves possessed antihypercholesterolemic action in high-fat diet-fed Sprague–Dawley rats and the underlying mechanism lay in promoting cholesterol and bile acid excretion through FXR- and CYP7A1-mediated pathways.

The gut microbiota also plays an important role in bile acid and lipid metabolism and is closely associated with the FXR signaling pathway. The intestinal microbiota initiates the metabolism of conjugated bile acids through a critical first step catalyzed by bacterial BSH. BSH can degrade conjugated bile acids to free bile acids and glycine or taurine and plays a crucial role in cholesterol management. Bacteroidaceae, Bifidobacteriaceae, Clostridiaceae, and Lactobacillaceae are BSH-producing bacteria. In the present study, SZ-A significantly increased the abundance of Bacteroidaceae and Bifidobacteriaceae (OTU 157) and decreased the abundance of Clostridiaceae and Lactobacillaceae (OTU 60), whereas Lactobacillaceae and Clostridiaceae were negatively correlated with fecal TDCA and TLCA contents, respectively, indicating that remodeling the profile of BSH-producing bacteria was involved in the effect of SZ-A to improve cholesterol metabolism.

Additionally, Lactobacillaceae is a probiotic with cholesterol-lowering activity, , while Clostridiaceae may be a potentially common microbial link to inflammatory arthritis. These findings suggest that balancing the abundance of beneficial bacteria and pathogenic bacteria also contributed to the amelioration of hypercholesterolemia. Akkermansiaceae, Tannerellaceae, and Rikenellaceae are proven to be beneficial bacteria. Akkermansia muciniphila, a bacteria belonging to Akkermansiaceae has been proven to improve glucose homeostasis and ameliorate metabolic disease in mice by secreting a GLP-1-inducing protein. Rikenellaceae and Tannerellaceae are SCFA-producing bacteria. Tavella et al. reported that elevations in Rikenellaceae abundance were associated with reduced visceral adipose tissue and healthier metabolic profiles in an elderly Italian population, and Zhao et al. reported that improving dyslipidemia and insulin resistance using adzuki bean hydrolysates was accompanied by an increased abundance of Tannerellaceae. In the present study, SZ-A significantly increased the abundance of beneficial bacteria, including Akkermansiaceae, Tannerellaceae, and Rikenellaceae, which was positively correlated with fecal TLCA and TDCA contents, further indicating that regulating the gut microbial profile participated in the SZ-A amelioration of hypercholesterolemia in diabetic ZDF rats.

As a group of alkaloid components, SZ-A mainly contains deoxynojirimycin (DNJ), fagomine (FAG), and 1,4-dideoxy-1,4-imino-D-arabinitol (DAB), which comprise 39.0%, 10.5%, and 7%, respectively. Liu et al. reported that 1-DNJ significantly lowered serum levels of TC and LDL-C, as well as serum ALT and AST levels in diabetic db/db mice. Ren et al. found that DNJ increased the abundance of Akkermansia and Bifidobacterium and decreased the abundance of Enterococcaceae in high-fat and streptozotocin-induced prediabetic mice, which was consistent with the results of the present study. However, fewer studies have reported on the FAG or DAB regulation of cholesterol metabolism. Thus, DNJ may be responsible for the cholesterol-lowering effect of SZ-A, but this remains to be validated in future work.

Finally, we are concerned that increased blood bile acid levels could induce hepatotoxicity. However, in the present study, we found that SZ-A had no effect on blood ALT or AST levels and ameliorated portal inflammation and the destruction of liver tissue in diabetic ZDF rats. Furthermore, the increase in the gene expression of SHP indicated the start of the negative feedback regulation of bile acid biosynthesis and the protection of hepatocytes. Thus, we speculate that the elevations in blood bile acids can explain the conversion of bile acids from cholesterol.

In summary, repeated treatment with SZ-A significantly ameliorated hypercholesterolemia in diabetic ZDF rats by regulating bile acid production and excretion, and the gut microbiota, as well as hepatic FXR signaling, were shown to play important roles in this process.

Supplementary Material

ao5c03364_si_001.xlsx (1.4MB, xlsx)

Acknowledgments

We are grateful to the manufacturer (Guangxi Wehand-bio Pharmaceutical CO., Ltd) and suppliers of SZ-A (Beijing Wehand-bio Pharmaceutical CO., Ltd). We thank LetPub (www.letpub.com.cn) for its linguistic assistance during the preparation of this manuscript.

Glossary

Abbreviations

Akr1b7

Aldo-keto reductase 1b7

ANOSIM

analysis of similarities

BSH

bile salt hydrolase

CDCA

chenodeoxycholic acid

CYP7A1

cholesterol 7α-hydroxylase

DEGs

differentially expressed genes

FGF19

fibroblast growth factor 19

FGFR4

fibroblast growth factor receptor

FPKM

fragments per kilobase of exon model per million mapped fragments

FXR

Farnesoid X receptor

KEGG

Kyoto Encyclopedia of Genes and Genomes

LDL-C

low-density lipoprotein cholesterol

OTUs

operational taxonomic units

PCA

principal component analysis

RT-PCR

real-time polymerase chain reaction

SHP

small heterodimer partner

SZ-A

Sangzhi alkaloids

SRM/MRM

selective reaction monitor/multiple reaction monitoring

T2DM

type 2 diabetes mellitus

TC

total cholesterol

TCDCA

taurochenodeoxycholic acid

TDCA

taurodeoxycholic acid

TLCA

taurolithocholic acid

TMCA

tauromuricholic acid

12-KLCA

12-keto-lithocholic acid

7-KDCA

7-keto-deoxycholic acid

Data supporting the findings of this study are publicly available in the article, and the 16s rRNA sequencing and transcriptomic analysis data can be accessed in the NCBI database under accession numbers PRJNA1271689 and PRJNA1271614, respectively.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c03364.

  • Mapped rate of sequencing reads, Pearson’s correlation, principal component analysis (PCA) and differential expressed genes in the transcriptomic analysis of liver; hematoxylin–eosin staining of liver; blood level of alanine aminotransferase and aspartate aminotransferase; OTUs, PCA, Shannon and Chao indices in the bacterial 16s rRNA sequencing (XLSX)

∥.

C.L. and Y.M. contribute equally to the work. Conceptualization: C.N.L., and S.N.L.; investigation and data analysis: Q.L., Y.T.M., S.Y.W., L.R.L., Y.H., J.Y.Z., K.J.X., H.C., L.L., and C.Y.F.; academic support: Y.L.L., Z.F.S., and S.N.L.; manuscript writing and editing: C.N.L., Y.T.M., and S.N.L.; funding acquisition: C.N.L., S.N.L., Y.H., and L.L.

The work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2024ZD0531900/2024ZD0531903), the National Natural Science Foundation of China (No. 82474136, 82474243, 82373922, 82200883) and the CAMS Initiative for Innovative Medicine (CAMS-I2M) (No. 2021-I2M-1–026).

The authors declare no competing financial interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ao5c03364_si_001.xlsx (1.4MB, xlsx)

Data Availability Statement

Data supporting the findings of this study are publicly available in the article, and the 16s rRNA sequencing and transcriptomic analysis data can be accessed in the NCBI database under accession numbers PRJNA1271689 and PRJNA1271614, respectively.

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