Explore the mechanism of mulberry leaf combined with gut microbiota to improve hyperlipidemia based on network pharmacology

Abstract

To elucidate the underlying mechanisms by which mulberry leaves, in conjunction with gut microbiota, ameliorate hyperlipidemia and identify key gut microbial taxa involved, a mulberry leaf/gut microbiota-active components-metabolites-target-pathway network was constructed.
Information on active constituents of mulberry leaves, metabolites generated by gut microbiota, and target proteins associated with hyperlipidemia were retrieved from multiple databases. Employing bioinformatics tools, relevant pathways and targets involved in the synergistic effect of mulberry leaves and gut microbiota on hyperlipidemia improvement were systematically analyzed. Through multi-level data analysis and integration of existing knowledge on the interplay between mulberry leaves, gut microbiota, and hyperlipidemia, the atherosclerosis signaling pathway was identified as the central pathway, with AKT1 as the core target protein. Based on metabolites produced by gut microbiota that act on AKT1, 29 specific gut microbial strains were found to exhibit synergistic action within the context of mulberry leaf-mediated hyperlipidemia alleviation. This culminated in the establishment of a comprehensive mulberry leaf/gut microbiota-active components-metabolites-target-pathway network. Moreover, molecular docking and visualization analyses demonstrated stable interactions between the selected gut microbiota-derived metabolites and the central target AKT1.
These findings provide a robust scientific basis for the application of mulberry leaf-based formulations combined with probiotics in the development of specialized medical foods targeting hyperlipidemia, grounded in the elucidated molecular mechanisms and identified key gut microbial players.

Full text

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
Discover Plants
Research
Explore themechanism ofmulberry leaf combined withgut
microbiota toimprove hyperlipidemia based onnetwork
pharmacology
ShiLongWang1,2 · JialiZhou1,2· ZhiXuZhang1,2· KaiLi1,2· LingZhu1,2· ZhuoMingYing1,2· JiaLingWang1,2·
DongboLiu1,2,3
Received: 11 June 2024 / Accepted: 9 September 2024
© The Author(s) 2024 OPEN
Abstract
Purpose To elucidate the underlying mechanisms by which mulberry leaves, in conjunction with gut microbiota, ame-
liorate hyperlipidemia and identify key gut microbial taxa involved, a mulberry leaf/gut microbiota-active components-
metabolites-target-pathway network was constructed.
Methodology Information on active constituents of mulberry leaves, metabolites generated by gut microbiota, and
target proteins associated with hyperlipidemia were retrieved from multiple databases. Employing bioinformatics tools,
relevant pathways and targets involved in the synergistic eect of mulberry leaves and gut microbiota on hyperlipidemia
improvement were systematically analyzed. Through multi-level data analysis and integration of existing knowledge on
the interplay between mulberry leaves, gut microbiota, and hyperlipidemia, the atherosclerosis signaling pathway was
identied as the central pathway, with AKT1 as the core target protein. Based on metabolites produced by gut microbiota
that act on AKT1, 29 specic gut microbial strains were found to exhibit synergistic action within the context of mulberry
leaf-mediated hyperlipidemia alleviation. This culminated in the establishment of a comprehensive mulberry leaf/gut
microbiota-active components-metabolites-target-pathway network. Moreover, molecular docking and visualization
analyses demonstrated stable interactions between the selected gut microbiota-derived metabolites and the central
target AKT1.
Conclusion These ndings provide a robust scientic basis for the application of mulberry leaf-based formulations com-
bined with probiotics in the development of specialized medical foods targeting hyperlipidemia, grounded in the elu-
cidated molecular mechanisms and identied key gut microbial players.
Keywords Mulberry leaf· Gut microbiota· Network pharmacology
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s44372- 024-
00018-x.
* Dongbo Liu, chinasaga@163.com; Shi Long Wang, 2787132204@qq.com; Jiali Zhou, 1071899218@qq.com; Zhi Xu Zhang, 532000632@
qq.com; Kai Li, 3080711643@qq.com; Ling Zhu, 2156373235@qq.com; Zhuo Ming Ying, 1585849446@qq.com; Jia Ling Wang,
3509915052@qq.com | 1College ofHorticulture, Hunan Agricultural University, Changsha, Hunan410128, China. 2State Key Laboratory
ofSubhealth Intervention Technology, Changsha410128, China. 3Hunan Provincial Engineering Research Center ofMedical Nutrition
Intervention Technology forMetabolic Diseases, Hunan Agricultural University, Changsha, Hunan410128, China.

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
1 Background
Hyperlipidemia, characterized by elevated levels of triglycerides (TG) and/or total cholesterol (TC) in plasma, often accompa-
nied by increased low-density lipoprotein cholesterol (LDL-C) and decreased high-density lipoprotein cholesterol (HDL-C),
is a primary complication of obesity and a leading contributor to cardiovascular diseases, primarily attributed to unhealthy
lifestyles and diets [1–5]. In recent years, with improving living standards and the proliferation of high-calorie diets, The global
prevalence of dyslipidemia is rapidly increasing [6]. The therapeutic goal for hyperlipidemia extends beyond ameliorating lipid
abnormalities to prevent atherosclerosis and thereby reduce morbidity and mortality from atherosclerosis-related diseases.
The ecacy of dietary interventions and various pharmacotherapies in managing hyperlipidemia has been substantiated
by extensive clinical research [7]. Restrictive diets can improve hyperlipidemic conditions but are challenging to adhere to
over extended periods. Statins, as cornerstones in primary and secondary prevention of atherosclerosis per contemporary
guidelines [8], have raised concerns due to their adverse eects, including muscle necrosis, hepatic dysfunction, and insulin
resistance [9–12], highlighting the need for a therapeutic approach that combines the safety of dietary therapy with the
ecacy of pharmacotherapy.
Morusalba L., or mulberry leaf, belonging to the Moraceae family, the earliest record of mulberry leaf (Morusalba L.) can
be traced back to "Shennong’s Herbal Classic," where it is classied as a secondary grade herb. Its medicinal use is rst docu-
mented in "Fifty-two Prescriptions for Diseases." According to traditional Chinese medicine, the mulberry leaf has a sweet,
bitter, and cold nature, and its meridian tropism includes the Lung and Liver. It is believed to have the therapeutic eects
of dispersing wind-heat, clearing and moistening the lungs, and improving vision by clearing liver heat. It is commonly
used for conditions such as wind-heat common cold, lung heat with dry cough, dizziness, headache, redness and blurred
vision, Modern literature and studies have shown that mulberry leaves contain a variety of chemical constituents, including
avonoids, phenolic acids, alkaloids, coumarins, amino acids, polysaccharides, among others. Flavonoids, alkaloids, and
polysaccharides are considered the primary bioactive components responsible for the medicinal properties of mulberry
leaves [13]. Mulberry leaves have been approved by the Chinese Ministry of Health as a category of "food-herb homology"
plant, which can be utilized both as medicinal material and as food. Mulberry leaves contain various bioactive compounds
including 1-deoxynojirimycin, catechins, and quercetin. These components exhibit anti-obesity and anti-hyperlipidemic
activities [14]. As a medicinal material classied under the principle of food-herb homology, is a food-herb crossover with
both food safety and medicinal ecacy. Modern pharmacological studies have found mulberry leaf to have a high median
lethal dose (LD50 > 15.0g·kg⁻1) and to exhibit safety across various genotoxicity assessments [15–17]. Moreover, mulberry
leaf aqueous extracts signicantly inhibit macrophage inammatory mediator secretion and autophagic pathways, thereby
alleviating obesity [18, 19]. In murine models, mulberry leaf demonstrates substantial hypoglycemic eects, modulating
blood lipids and gut microbiota [20]. However, research on the interplay between mulberry leaf and gut microbiota in lipid
regulation is scarce, with a limited understanding of the underlying mechanisms, hindering personalized dietary interven-
tions based on individual gut microbiome proles and the application of mulberry leaf in special medical purpose formula
foods.
Modulations on gut microbiota by traditional Chinese medicines (TCM) are emerging as a potential rationale govern-
ing the protable eects of drugs on hyperlipidemia. However, it is unclear how gut microbes regulate the progression of
hyperlipidemia [21]. Network pharmacology, an emerging discipline examining disease mechanisms and drug actions from a
holistic biological network perspective, integrates knowledge from medicine, biology, computer science, and bioinformatics
to systematically predict active components and mechanisms of action for traditional Chinese medicines [22]. Emphasizing
multi-target modulation of signaling pathways, network pharmacology is particularly suited for studying complex systems
like TCMs or chronic diseases [23]. This study employs network pharmacology to elucidate the synergistic mechanism of
mulberry leaf and gut microbiota in hyperlipidemia intervention, identifying key gut microbiota in this process. The ultimate
aim is to provide a scientic basis for developing a safe and ecacious category of mulberry leaf-based special medical
purpose formula foods.
This paper uses a series of publicly available databases and online analysis sites, whose functions and web addresses are
shown in Table1.

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
Table 1 Information about the databases and online tools used in this article
Serial number name Function introduction Web link
1 Traditional Chinese Medicine Systems Pharmacology
Database and Analysis Platform, TCMSP The database documents 29,384 constituents found within
499 registered traditional Chinese herbal medicines as listed
in the Chinese Pharmacopoeia (2010 Edition), along with
3311 corresponding targets and 837 associated diseases.
It also provides 12 key pharmacokinetic parameters for the
herbal constituents, including oral bioavailability, drug-
likeness, drug half-life, Caco-2 permeability, and blood–brain
barrier penetration, among others. These data are utilized
for the screening and evaluation of active pharmaceutical
ingredients
https:// old. tcmsp-e. com/
2 PubChem PubChem is an open chemistry database at the National
Institutes of Health (NIH), PubChem records are contributed
by hundreds of data sources. Examples include: government
agencies, chemical vendors, journal publishers, and more
https:// pubch em. ncbi. nlm. nih. gov/
3 Shanghai NewPro BioTech Ltd.’s online tool for con-
verting 3D molecular models to Canonical SMILES
notation
The database is capable of computing the Canonical SMILES
notation for molecules based on their three-dimensional
(3D) models
https:// www. novop ro. cn/ tools/ mol2s miles. html
4 SwissTargetPrediction This website allows you to estimate the most probable macro-
molecular targets of a small molecule, assumed as bioactive.
The prediction is founded on a combination of 2D and 3D
similarity with a library of 370,000 known actives on more
than 3000 proteins from three dierent species
http:// swiss targe tpred iction. ch/
5 Online Mendelian Inheritance in Man, OMIM "Online Mendelian Inheritance in Man (OMIM) is a compre-
hensive and authoritative database that investigates the
relationship between human phenotypes and genotypes.
It catalogs all known mendelian disorders, along with
information on over 16,000 genes (encompassing more than
half of the known human genes). OMIM does not generate
these data but rather provides a highly systematic curation
and integration of published research ndings, which are
updated daily and are freely accessible.”
https:// omim. org/
6 GeneCards:The Human Gene Database GeneCards is a searchable, integrative database that provides
comprehensive, user-friendly information on all annotated
and predicted human genes. The knowledgebase automati-
cally integrates gene-centric data from ~ 150 web sources,
including genomic, transcriptomic, proteomic, genetic, clini-
cal and functional information
https:// www. genec ards. org/
7 gutmgene A comprehensive resource for gut microbe-metabolite, gut
microbe-host gene, and microbial metabolite-host gene
associations in Human and Mouse, derived from manual
literature extraction and metabolic reconstruction
http:// bio- compu ting. hrbmu. edu. cn/ gutmg ene/
8 Wei Sheng Xing The online tool is capable of analyzing data and visualizing it
through graphical representations https:// www. bioin forma tics. com. cn/

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
Table 1 (continued)
Serial number name Function introduction Web link
9 Search Tool for Retrieval of Interacting Genes/Proteins,
STRING It is a database specically designed for the storage and
retrieval of biological sequence data. This database encom-
passes a substantial amount of protein interaction data and
provides a rich suite of functionalities and tools to facilitate
data retrieval, analysis, and application for users
https:// cn. string- db. org/
10 MetaScape Metascape is an online platform that integrates over 40 gene
annotation databases, including GO (Gene Ontology), KEGG
(Kyoto Encyclopedia of Genes and Genomes), UniProt, Drug-
Bank, among others. It oers a comprehensive suite of ser-
vices encompassing gene annotation, gene set enrichment
analysis, protein interaction network analysis, drug analysis,
and more. The overarching aim of Metascape is to provide a
robust resource for the functional analysis of genomic data
https:// metas cape. org/
11 Kyoto Encyclopedia of Genes and Genomes, KEGG KEGG is a database resource for understanding high-level
functions and utilities of the biological system, such as the
cell, the organism and the ecosystem, from molecular-level
information, especially large-scale molecular datasets gener-
ated by genome sequencing and other high-throughput
experimental technologies
https:// www. genome. jp/ kegg/

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
2 Materials andmethods
2.1 Screening ofactive components frommulberry leaves, disease targets, andgut microbiota metabolite
targets
Utilize the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) to screen
for the active components of Mulberry leaves. Components are filtered based on oral bioavailability (OB) ≥ 30%
and drug-likeness (DL) ≥ 0.18. Subsequently, acquire the 3D models of these selected components. Through the
PubChem database (https:// pubch em. ncbi. nlm. nih. gov/) and Shanghai NewPro BioTech Ltd.’s online tool for convert-
ing 3D molecular models to Canonical SMILES notation (https:// www. novop ro. cn/ tools/ mol2s miles. html), obtain the
Canonical SMILES identifiers for these active ingredients. Predict the associated targets of these components using
the SwissTargetPrediction website (http:// swiss targe tpred iction. ch/). Collect hyperlipidemia-related targets from
both the Online Mendelian Inheritance in Man (OMIM, https:// omim. org/) and GeneCards (https:// www. genec ards.
org/) databases. Apply the median score method for screening and, after merging, identify intersecting targets to
ascertain those related to hyperlipidemia.Gather information on gut microbiota metabolites from the GutMGene
website (http:// bio- compu ting. hrbmu. edu. cn/ gutmg ene/) and predict their targets using SwissTargetPrediction.
This comprehensive approach integrates pharmacological data, computational predictions, and biological database
inquiries to systematically identify and evaluate the active components of Mulberry leaves and their potential impact
on hyperlipidemia via interaction with both host targets and gut microbiota metabolite target [24].
2.2 Screening forcommon targets ofmulberry leaf inconcert withgut microbiota metabolites
inthetreatment ofhyperlipidemia
Importing the targets related to mulberry leaf components obtained in Sect.1.1, hyperlipidemia-related targets,
and gut microbiota metabolite targets into the micro bioinformatics website (https:// www. bioin forma tics. com. cn/)
to construct a Venn diagram, we identified the common targets of mulberry leaf in conjunction with gut microbiota
metabolites acting on hyperlipidemia, These shared targets indicate a congruence between mulberry leaf compo-
nents and certain gut microbiota metabolites in pathways relevant to the treatment of hyperlipidemia.
2.3 Protein–protein interaction network analysis
The common targets of mulberry leaf in conjunction with gut microbiota metabolites acting on hyperlipidemia were
imported into the STRING database [25] (https:// cn. string- db. org/), with the menu configured for "Multiple proteins"
and the species specified as "Homo sapiens," yielding a Protein–Protein Interaction (PPI) network. This network was
further visualized utilizing both Cytoscape versions 3.9.1 and 3.7.1, augmented with the CentiScaPe 2.2 plugin. Can-
didate targets were derived through screening these common targets based on topological parameters, including
degree, closeness, and betweenness centrality. These candidate targets represent the most significantly associated
subset of shared targets between mulberry leaf components and certain gut microbiota metabolites relevant to the
treatment of hyperlipidemia. They constitute the most critical portion of targets in the synergistic approach of using
mulberry leaves and gut microbiota for the management of hyperlipidemia.
2.4 Functional prediction andsignal pathway analysis oftargets affected bythesynergistic action
ofmulberry leaf andgut microbiota metabolites inhyperlipidemia
The targets modulated by the combined action of mulberry leaf and gut microbiota metabolites in hyperlipidemia
were submitted to the MetaScape database [26] (https:// metas cape. org/) with the input and analyzed species set
to "H. sapiens". Comprehensive analyses were performed on signal pathways, biological processes, cellular compo-
nents, and molecular functions. Subsequently, for enhanced visual representation, the results were depicted using
the MicroScape web platform [27] (https:// www. bioin forma tics. com. cn/), We have identified the signaling pathways,
biological processes, cellular components, and molecular functions associated with this set of common targets. The

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
credibility and importance of the signaling pathways were ranked based on the degree of association among these
common targets and the number of targets involved in each pathway.
2.5 Targets enriched insignaling pathways
The top ten enriched signaling pathways identied in Sect.1.4 were queried in the Kyoto Encyclopedia of Genes and
Genomes [28] (https:// www. genome. jp/ kegg/), with the objective of identifying candidate targets enriched within these
signaling pathways, Through the analysis of targets involved in the top ten signaling pathways with the highest relevance,
we aim to identify a more signicant subset of these targets.
2.6 Identifying core targets andexploring theassociated gut microbiota
By integrating the candidate targets and their implicated signaling pathways, biological processes, cellular components,
and molecular functions derived from Sects. 1.3 and 1.4, we aimed to delineate the core pathways and central targets
through which the synergistic action of mulberry leaf and gut microbiota metabolites exert their eects on hyperlipi-
demia, the critical gut microbiome metabolites and microorganisms involved in the process of treating hyperlipidemia
through the synergistic action of mulberry leaves with the human gut microbiota will be identied by reverse targeting
from the central therapeutic target.
2.7 Constructing amulberry leaf/gut microbiota‑active components/metabolites‑targets‑pathways‑disease
network
Incorporating the targets and associated pathways inuenced by the synergistic action of mulberry leaves and gut
microbiota-derived metabolites on hyperlipidemia, along with the core targets’ corresponding gut microbial metabolites
and gut bacteria, into Cytoscape 3.9.1 for visualization, thereby constructing a "Drug-Components-Targets-Pathways-
Targets-Gut Microbiota" network.
2.8 Molecular docking validation
Key target proteins, as identied in Sect.1.5, underwent molecular docking with the identied gut microbiota metabo-
lites. The AutoDock program was employed for molecular docking of mulberry leaf active components with the target
proteins, yielding the lowest binding energy. Subsequently, the outcomes of the molecular docking were visually ana-
lyzed using the PyMOL software [29].
3 Results anddiscussion
3.1 Target screening outcomes
As illustrated in Table1, 29 active components of mulberry leaf were identied alongside their respective Canonical
SMILES, yielding comprehensive information on the active constituents of mulberry leaf. Through prediction, 947 targets
related to these components, 1374 targets associated with hyperlipidemia, were obtained. The gut microbiota metabolite
targets were referenced from the published work by Dong Joon Kim and Ki-Tae Suk etal. [24], extracted from information
on 208 gut microbiota metabolites sourced from the gutmgene database (http:// bio- compu ting. hrbmu. edu. cn/ gutmg
ene/) and further predicted using SwissTargetPrediction, leading to the identication of 947 gut microbiota metabolite
targets.
3.2 Common targets ofmulberry leaf andgut microbiota metabolites inmodulating hyperlipidemia
As depicted in Fig.1, the application of a Venn diagram identied 90 common targets for the combined action of mul-
berry leaf and gut microbiota metabolites in the context of hyperlipidemia modulation. This demonstrates the concord-
ance between mulberry leaf and metabolites of a subset of gut microbiota in their pathways toward the treatment of

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
hyperlipidemia through 90 shared targets. Enhancing the abundance of this particular subset of gut microbiota in the
human body may potentiate the therapeutic ecacy of mulberry leaf in treating hyperlipidemia.
3.3 Results ofprotein–protein interaction (PPI) network analysis
As shown in Fig.2, the computational analysis via the online platform yielded a PPI network comprising 90 nodes
interconnected by 807 edges. Following visualization and filtering using Cytoscape 3.9.1, nodes that surpassed the
established thresholds for degree (≥ 85.69), betweenness (≥ 24.94), and closeness centrality (≥ 0.00586) within the
PPI network were designated as candidate targets, resulting in the identification of 16 key functional targets. The
specifics of these parameters are detailed in Table2. Further network analysis of these 16 targets was conducted
in Cytoscape 3.7, where node size and color were adjusted according to degree centrality, with larger and darker
Fig. 1 Mulberry leaves com-
bined with gut microbiota
act on the common target of
hyperlipidemia

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
nodes representing higher degree values. Similarly, edge thickness and color were set based on binding affinity
scores, with thicker and darker lines signifying stronger interactions. Notably, the top four targets emerging from
this analysis were Estrogen Receptor 1 (ESR1), Protein Kinase B (AKT1), Peroxisome Proliferator-Activated Receptor
Gamma (PPARG), and Tumor Necrosis Factor Alpha (TNF), as illustrated in Fig.3, these four targets exhibit the high-
est degree of association among the 90 shared targets involved in the synergistic treatment of hyperlipidemia by
mulberry leaf and gut microbiota, potentially playing a critical role.
3.4 Outcomes offunctional predictions andsignal pathway analyses
As illustrated in Fig.4, the KEGG enrichment analysis revealed that the synergistic action of mulberry leaf and gut
microbiota in improving hyperlipidemia is prominently involved in pathways such as ’Pathways in cancer’, ’Lipid
and atherosclerosis’, ’PPAR signaling pathway’, and ’Insulin resistance’. The number of shared targets enriched in
these three signaling pathways is the highest (the size of the dots represents the number of enriched targets), and
the credibility of the ranking of these three pathways, based on the degree of association between the enriched
targets, is the highest (dot color represents the credibility of the pathway ranking). The GO enrichment results,
as shown in Fig.5, indicate that the biological processes primarily involved with the 90 shared targets through
which Mori leaf in conjunction with gut microbiota ameliorates hyperlipidemia mainly include:indicated that the
biological processes primarily implicated in the alleviation of hyperlipidemia by the combination include responses
to hormones, cellular lipid metabolism, cellular responses to hormone stimuli, and regulation of hormone levels,
among others. Regarding cellular components, the intervention mainly affects features like membrane rafts, micro-
domains, vesicles, and thin filament sides. In terms of molecular function, key aspects include nuclear receptor
activity, ligand-activated transcription factor activity, monohydroxy acid binding, and nuclear steroid receptor
binding, among others. These findings suggest that the intervention of mulberry leaf in conjunction with gut
microbiota in managing hyperlipidemia potentially achieves its effects through the regulation of lipid metabolism
and intercellular hormone levels.
Fig. 2 Mulberry leaf com-
bined with gut microbiota-
hyperlipidemia PPI network
map

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
Table 2 Components of mulberry leaf activity
Mol ID Molecule name OB (%) DL Canonical SMILES
MOL001771 Poriferast-5-en-3beta-ol 36.91 0.75 CCC(CCC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C
MOL002218 Scopolin 56.45 0.39 COC1=C(C=C2C(= C1)C=CC(= O)O2)OC3C(C(C(C(O3)CO)O)O)O
MOL002773 Beta-carotene 37.18 0.58 CC1=C(C(CCC1)(C)C)C=CC(=CC=CC(= CC=CC=C(C)C=CC=C(C)C=CC2=C(CCCC2(C)C)C)C)C
MOL003842 Albanol 83.16 0.24 CC1=C[C@@H]2c3c(O)cc(-c4cc5ccc(O)cc5o4)cc3O[C@]3(c4ccc(O)cc4O)Oc4cc(O)ccc4[C@@H](C1)[C@H]23
MOL003847 Inophyllum E 38.81 0.85 CC1C(OC2=C(C1=O)C3=C(C(= CC(= O)O3)C4=CC=CC=C4)C5=C2C=CC(O5)(C)C)C
MOL003850 26-Hydroxy-dammara-20,24-dien-3-one 44.41 0.79 C=C(CCC=C(C)CO)[C@H]1CC[C@]2(C)[C@@H]1CC[C@@H]1[C@@]3(C)CCC(= O)C(C)(C)[C@H]3CC[C@]12C
MOL003851 Isoramanone 39.97 0.51 CC(= O)[C@H]1CC[C@]2(O)[C@@H]3CC=C4C[C@@H](O)CC[C@@]4(C)[C@H]3C[C@@H](O)[C@@]12C
MOL003856 Moracin B 55.85 0.23 COC1=CC(= CC(= C1)O)C2=CC3=CC(= C(C=C3O2)OC)O
MOL003857 Moracin C 82.13 0.29 CC(= CCC1=C(C=C(C=C1O)C2=CC3=C(O2)C=C(C=C3)O)O)C
MOL003858 Moracin D 60.93 0.38 CC1(C=CC2=C(C=C(C=C2O1)C3=CC4=C(O3)C=C(C=C4)O)O)C
MOL003859 Moracin E 56.08 0.38 CC1(C=CC2=C(C=C(C=C2O1)O)C3=CC4=C(O3)C=C(C=C4)O)C
MOL003860 Moracin F 53.81 0.23 COC1=C(C=C2C(= C1)C=C(O2)C3=CC(= CC(= C3)O)O)OC
MOL003861 Moracin G 75.78 0.42 CC1=CCC2=C(C=CC3=C2OC(= C3)C4=CC(= CC(= C4)O)O)OC1
MOL003862 Moracin H 74.35 0.51 CC1=CCC2=C3C(= C(C=C2OC1)OC)C=C(O3)C4=CC(= CC(= C4)O)O
MOL003879 4-Prenylresveratrol 40.54 0.21 CC(= CCC1=C(C=C(C=C1O)C=CC2=CC=C(C=C2)O)O)C
MOL000433 FA 68.96 0.71 Nc1nc2ncc(CNc3ccc(C(= O)N[C@@H](CCC(= O)O)C(= O)O)cc3)nc2c(= O)[nH]1
MOL000729 Oxysanguinarine 46.97 0.87 CN1C2=C(C=CC3=CC4=C(C=C32)OCO4)C5=C(C1=O)C6=C(C=C5)OCO6
MOL000098 Quercetin 46.43 0.28 C1=CC(= C(C=C1C2=C(C(= O)C3=C(C=C(C=C3O2)O)O)O)O)O
MOL000358 Beta-sitosterol 36.91 0.75 CCC(CCC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C
MOL000422 kaempferol 41.88 0.24 C1=CC(= CC=C1C2=C(C(= O)C3=C(C=C(C=C3O2)O)O)O)O
MOL000449 Stigmasterol 43.83 0.76 CCC(C=CC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C
MOL001439 Arachidonic acid 45.57 0.2 CCC(C=CC(C)C1CCC2C1(CCC3C2CC=C4C3(CCC(C4)O)C)C)C(C)C
MOL001506 Supraene 33.55 0.42 CC(= CCCC(= CCCC(= CCCC=C(C)CCC=C(C)CCC=C(C)C)C)C)C
MOL003759 Iristectorigenin A 63.36 0.34 COC1=C(C=CC(= C1)C2=COC3=C(C2=O)C(= C(C(= C3)O)OC)O)O
MOL003975 Icosa-11,14,17-trienoic acid methyl ester 44.81 0.23 CCC=CCC=CCC=CCC CCC CCCCC(= O)OC
MOL006630 Norartocarpetin 54.93 0.24 C1=CC(= C(C=C1O)O)C2=CC(= O)C3=C(C=C(C=C3O2)O)O
MOL007179 Linolenic acid ethyl ester 46.1 0.2 CCC=CCC=CCC=CCC CCC CCC(= O)OCC
MOL007879 Tetramethoxyluteolin 43.68 0.37 COC1=C(C=C(C=C1)C2=CC(= O)C3=C(O2)C=C(C=C3OC)OC)OC
MOL013083 Skimmin (8CI) 38.35 0.32 C1=CC(= CC2=C1C=CC(= O)O2)OC3C(C(C(C(O3)CO)O)O)O

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
3.5 Targets enriched insignaling pathways
Upon querying the Kyoto Encyclopedia of Genes and Genomes, it was discovered that the targets implicated in
the amelioration of hyperlipidemia by the combination of mulberry leaf and gut microbiota were predominantly
enriched in pathways such as ’Pathways in cancer’, ’Lipid and atherosclerosis’, ’PPAR signaling pathway’, and ’Insulin
Fig. 3 Core target screening of mulberry leaves combined with gut microbiota for hyperlipidemia
Fig. 4 Analysis of KEGG-
enriched pathways of inter-
secting targets of mulberry
leaves combined with gut
microbiota in hyperlipidemia
(top 10)

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
Fig. 5 Intersecting targets of mulberry leaf combined with gut microbiota in hyperlipidemia. Point GO functional enrichment analysis
Table 3 Key targets of
mulberry leaves combined
with gut microbiota in the
intervention of hyperlipidemia
Serial number Candidate target Betweenness
centrality Closeness centrality Degree centrality
1 TNF 64 0.008695652 1109.865504
2 PPARG 64 0.008695652 1040.938253
3 AKT1 59 0.008264463 824.3088518
4 EGFR 41 0.007142857 306.1372388
5 MMP9 41 0.007092199 145.9612592
6 ESR1 40 0.007042254 159.5964282
7 PPARA 40 0.007194245 394.9657363
8 ICAM1 35 0.006756757 108.1403315
9 HSP90AA1 31 0.006622517 137.9476355
10 SCARB1 31 0.006622517 151.6992485
11 SERPINE1 31 0.006622517 149.627909
12 HMGCR 30 0.006622517 193.5508651
13 GCG 27 0.006451613 130.7358932
14 MAPK1 26 0.006410256 139.4605482
15 HNF4A 21 0.00617284 104.7202364
16 RXRA 21 0.006024096 104.0593689

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
resistance’, as summarized in Table3. Among these targets, AKT1, PPARG, and MMP9 were found to be associated
with the greatest number of pathways, with a graphical representation provided in Fig.6. Notably, AKT1 was linked
to pathways that ranked higher in significance, suggesting that the AKT1 target may play a pivotal role in the process
of mulberry leaf and gut microbiota intervention against hyperlipidemia.
3.6 Core targets andassociated gut bacteria
Based on the findings in Sects. 2.3, 2.4 and 2.5, and in conjunction with previous research on mulberry leaves [30–32],
the core pathway was determined to be Lipid and Atherosclerosis, with AKT1 as the primary target. In Sect.2.1,
information on the human gut microbiota and their corresponding microbial metabolites was obtained from the
GutMicrobe database. The targets of these microbial metabolites were predicted using SwissTargetPrediction. Con-
sequently, gut microbiota producing metabolites that act upon the primary target AKT1 were identified (see Table4).
These metabolites produced by the gut microbiota can potentially act through the Lipid and Atherosclerosis signaling
pathway at the AKT1 target, complementing the therapeutic effects of mulberry leaves on hyperlipidemia, thereby
enhancing the efficacy of mulberry leaves in treating this condition.
Fig. 6 Number of signaling
pathways involved in the
target
Table 4 Targets enriched by the top ten enriched signaling pathways
Signal path The targets involved
Pathways in cancer AGTR1, AKT1, CDK4, EGFR, ERBB2, ESR1, ESR2, F2, HSP90AA1, IL2, JAK2, KIT, MDM2,
MMP2, MMP9, NOS2, PIK3CA, PIK3CB, PPARD, PPARG, MAPK1, RXRA, RXRB, RXRG,
SMO, HSP90B1
Lipid and atherosclerosis AKT1, ERN1, HSP90AA1, ICAM1, JAK2, MMP3, MMP9, PIK3CA, PIK3CB, PPARG,
MAPK1, RXRA, RXRB, RXRG, SELE, TNF, HSP90B1, NLRP3
PPAR signaling pathway FABP4, FABP1, FABP2, PPARA, PPARD, PPARG, RXRA, RXRB, RXRG, SCD, NR1H3
Insulin resistance ACACB, AKT1, GYS1, INSR, PIK3CA, PIK3CB, PPARA, PYGL, PYGM, TNF, NR1H2, NR1H3
Adipocytokine signaling pathway ACACB, AKT1, JAK2, PPARA, RXRA, RXRB, RXRG, TNF
Bladder cancer CDK4, EGFR, ERBB2, MDM2, MMP2, MMP9, MAPK1
Transcriptional misregulation in cancer FLT1, IGFBP3, MDM2, MMP3, MMP9, MPO, PPARG, RXRA, RXRB, RXRG
Starch and sucrose metabolism GAA, GCK, GYS1, PYGL, PYGM, MGAM
Fat digestion and absorption SCARB1, FABP1, FABP2, PLA2G1B, NPC1L1
Bile secretion SCARB1, CFTR, HMGCR, ABCB1, RXRA, SLC9A1

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
3.7 Constructing aMorus Alba/gut microbiota‑active components/metabolites‑targets‑pathways network
As illustrated in Fig.7, a "Mulberry Leaf/Gut Microbiota-Active Components/Metabolites-Targets-Pathways" network was
constructed using Cytoscape version 3.9.1 to investigate the mechanisms underlying the intervention of mulberry leaf in
conjunction with gut microbiota metabolites on hyperlipidemia. Our ndings revealed that nineteen bioactive compo-
nents from mulberry leaves synergistically interact with gut microbiota-derived metabolites to modulate hyperlipidemia.
Furthermore, it was discovered that twenty-nine gut microbial species contribute substantially to this process through the
production of ten key metabolites.
3.8 Molecular docking verification results
As depicted in Table5, the core action targets implicated in the amelioration of hyperlipidemia by the combination of mul-
berry leaves and gut microbiota were subjected to molecular docking with the corresponding ten gut microbiota metabolites.
As depicted in Tabel 6, it is generally accepted that binding energies less than −5.0kcal/mol between active components
and target proteins denote favorable binding [33], whereas those under −7.0kcal/mol are considered indicative of strong
binding interactions. Remarkably, all ten gut microbiota metabolites exhibited binding energies below −7.0kcal/mol, sug-
gesting a robust binding anity with the AKT1 target. Visualization of the molecular docking outcomes is presented in
Fig.8, illustrating that Glycocholic acid, Apigenin, 3,9-Dihydroxybenzo[c]chromen-6-one, Kaempferol, Quercetin, Myricetin,
Naringenin chalcone, and Luteolin each formed hydrogen bonds with AKT1. Collectively, these ndings substantiate that the
ten metabolites produced by the 29 selected gut microbial species can eectively bind with the pivotal target AKT1, thereby
highlighting their synergistic role with mulberry leaves and gut microbiota in modulating hyperlipidemia.
4 Discussion
Dysbiosis of the gut microbiota is intimately linked to the development and progression of hyperlipidemia. An
abnormal microbial structure may lead to a decrease in the production of short-chain fatty acids (SCFAs), thereby
impacting the energy metabolic balance of the host [34]. Certain pathogenic bacteria produce pro-inflammatory
Fig. 7 Mulberry leaf/gut microbiota-active ingredient/metabolite-target-pathway network diagram

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
Table 5 Metabolites of gut microbiota acting on AKT1 targets and their metabolite information
Gut microbial metabolites Corresponding gut microbes producing the metabolites and their respective IDs in Gutmgene database
Luteolin Enterococcus sp. 45 (1343173,
gm0884)
Glycocholic acid Escherichia (561, gm0304) Paraprevotella (577309, gm0500) Akkermansia (239934, gm0029) Carnobacterium (2747, gm0156)
Bacteroides fragilis (817, gm0082) Mitsuokella multacida (52226,
gm0450) Butyricicoccus pullicaecorum
(501571, gm0135) Ruminococcus avefaciens (1265,
gm0590)
Icaritin bacterium MRG-PMF-1 (1477104,
gm0861)
Apigenin Blautia sp. MRG-PMF1 (gm1029)
3,9-Dihydroxybenzo[c]
chromen-6-one CEBAS 4A1 (gm1030) CEBAS 4A2 (gm1031) CEBAS 4A3 (gm1032) CEBAS 4A4 (gm1033)
Diosmetin Escherichia sp. 4 (1343180, gm0805)
Quercetin Bacteroides sp. 45 (gm1056) Bidobacterium dentium (1689,
gm0111) Bacillus sp. 46 (1266601, gm1132) Enterococcus sp. 45 (1343173,
gm0884)
Bacteroides ovatus (28116, gm0084) Bacteroides uniformis (820, gm0088) Enterococcus casseliavus (37734,
gm0738) Escherichia sp. 33 (1343178, gm1150)
Kaempferol Bidobacterium pseudocatenulatum
B7003 (gm1100) Bidobacterium longum subsp. infan-
tis B7875 (gm1101) Bidobacterium adolescentis B7304
(gm1102) Bidobacterium catenulatum B7377
(gm1103)
Bidobacterium breve B7824
(gm1104) Parabacteroides distasonis (823,
gm1177)
Myricetin Escherichia sp. 12 (1343175, gm1149) Enterococcus sp. 45 (1343173,
gm0884) Escherichia sp. 33 (1343178, gm1150)
Naringenin chalcone Eubacterium ramulus (39490,
gm0737)

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
factors, triggering chronic inflammatory responses that accelerate the process of atherosclerosis. Furthermore,
dysbiosis can also interfere with bile acid metabolism, subsequently influencing the absorption and excretion of
cholesterol [35].
Morusalba, recognized as a plant with dual use in medicine and food, exhibits significant potential for the develop-
ment of special medical purpose foods. Research has shown that the aqueous extract of M. alba can mediate the gut
microbe metabolic axis in mice effectively alleviating type 2 diabetes, while concurrently ameliorating lipid metabo-
lism [21]. Polysaccharides from M. alba have been demonstrated to improve obesity by promoting browning of white
adipose tissue and through a comprehensive regulatory effect on the gut microbiota composition [36]. However,
the mechanism by which M. alba improves hyperlipidemia in humans is intricate, involving not only alterations in
gut microbiota abundance but also the participation of certain gut bacteria in the cholesterol-lowering effect of
M. alba. The precise involvement and synergistic mechanisms of the gut microbiota in this process remain unclear.
This study employs a network pharmacology approach combined with molecular docking techniques to construct
a Morusalba/gut microbiota-active components/metabolites-targets-pathways network. Through this analysis, we
Fig. 8 Molecular docking diagram of some gut microbiota and core target AKT1. A–H represent the molecular docking diagrams for Glyco-
cholic acid, Apigenin, 3,9-Dihydroxybenzo[c]chromen-6-one, Kaempferol, Quercetin, Myricetin, Naringenin chalcone, and Luteolin, respec-
tively, with the AKT1 protein
Table 6 Binding energy of
candidate gut microbiota
metabolites and core target
AKT1 molecules kJ/mol
Gut microbial metabolites Binding energy in
molecular docking with
AKT1
Glycocholic acid− −19.79
Apigenin −19.33
Icaritin −19.246
3,9-Dihydroxybenzo[c]chromen-6-one −16.903
Diosmetin −16.485
Kaempferol −15.857
Quercetin −15.271
Myricetin −14.141
Naringenin chalcone −11.004
Luteolin −9.414

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
identified 29 gut microbiota species that act in concert with M. alba in improving hyperlipidemia. Furthermore, we
utilized molecular docking to validate the interaction of metabolites produced by these 29 gut microbiota species, in
conjunction with M. alba-derived metabolites, with AKT1, a central target implicated in the hyperlipidemia-alleviating
process. This investigation elucidates the cooperative engagement and mechanisms of the gut microbiota in the
context of M. alba’s therapeutic effects against hyperlipidemia.
In our study, we uncovered that the primary mechanism by which mulberry leaf in conjunction with gut microbiota
metabolites impacts hyperlipidemia operates through key targets—AKT1, TNF, and PPARG—of the atherosclerosis sign-
aling pathway. AKT1, or Protein Kinase B, plays a pivotal role in lipid metabolism within the atherosclerosis pathway,
inuencing fatty acid synthesis, cholesterol metabolism, and very low-density lipoprotein (VLDL) secretion [37]. Dys-
regulation of AKT1 signaling in hyperlipidemic patients may lead to lipid metabolism disorders, facilitating foam cell
formation and plaque progression [38]. Our research revealed that active components in mulberry leaf, including Moracin
C, 4-Prenylresveratrol, quercetin, kaempferol, Norartocarpetin, Linolenic acid ethyl ester, and Tetramethoxyluteolin, col-
laborate with metabolites from the 29 gut microbiota species to exert eects on AKT1.Tumor Necrosis Factor alpha (TNF),
a prominent pro-inammatory cytokine, upregulates the expression of enzymes related to cholesterol synthesis in cells,
thereby promoting foam cell formation and potentially impeding reverse cholesterol transport by inhibiting lipoprotein
lipase (LPL) activity, exacerbating dyslipidemia [39]. The observed action of mulberry leaf and gut microbiota on the TNF
target aligns with the anti-inammatory properties attributed to mulberry leaf in traditional Chinese medicine. Peroxi-
some Proliferator-Activated Receptor gamma (PPARG), a nuclear hormone receptor, upon activation, enhances adipocyte
dierentiation and fatty acid storage while increasing lipoprotein lipase activity, facilitating cholesterol esterication
and clearance from macrophages, thereby reducing foam cell formation [40]. Moreover, PPARG suppresses the expres-
sion of genes involved in cholesterol synthesis in the liver, leading to decreased plasma low-density lipoprotein (LDL)
levels. These interconnected targets illustrate that the process by which mulberry leaf in synergy with gut microbiota
improves hyperlipidemia involves multi-targeted, multi-tiered eects, highlighting the complexity and holistic nature
of their combined therapeutic action.
Among the gut microbiota that synergize with mulberry leaf in ameliorating hyperlipidemia, Bidobacterium ado-
lescentis has been shown to protect mice against diet-induced obesity [41]. Bacteroides fragilis promotes maturation of
the human immune system, suppresses inammation, and modulates gut microbiota composition. Bacteroides ovatus,
whose colony counts are highly correlated with cancer incidence, produces indole-3-acetic acid which mitigates insulin
resistance in mice [42]. Akkermansia spp., known to reverse obesity induced by high-fat diets, alleviate metabolic endo-
toxemia, and eectively reduce insulin resistance [43], have been proven in animal experiments to be enriched in the
human gut by mulberry leaf intake [14]. These observations suggest the presence of a mechanism in which mulberry
leaf collaborates with select gut microbiota to enhance the benecial impact on hyperlipidemia, indicating a synergistic
interplay between mulberry leaf and specic gut bacterial strains in this process.
5 Conclusion andfuture prospect
In summary, the ameliorative effect of mulberry leaves on hyperlipidemia in humans is a complex process involving
multiple layers, pathways, and targets. This study, through the application of network pharmacology and molecu-
lar docking techniques, has identified and validated that 29 specific metabolites produced by gut microbiota can
interact with mulberry leaves to modulate the AKT1 target in the atherosclerosis signaling pathway associated with
hyperlipidemia. By enhancing the abundance of these beneficial gut microbiota in hyperlipidemic patients, the effi-
cacy of mulberry leaves in improving hyperlipidemia can be augmented. Based on these findings, there is potential
for exploring personalized nutritional intervention strategies, such as selecting appropriate probiotic supplements
based on individual gut microbiota profiles in hyperlipidemic patients to enhance the effectiveness of mulberry leaf
interventions. These insights could provide scientific rationale and novel approaches for developing special medical
purpose formulated foods combining mulberry leaves with probiotics for the management of hyperlipidemia. How-
ever, it should be noted that this research is currently limited to theoretical network pharmacology and molecular
docking validation, and further in-depth verification will be required to provide more robust scientific evidence for
the personalized treatment of hyperlipidemic patients using a combination of traditional Chinese medicine and
probiotics.

Vol.:(0123456789)
Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x Research
6 Declarations
By submitting my article I agree to pay the APC in full if my article is accepted for publication (unless it is covered by an
institutional agreement or journal partner, or a full waiver has been granted).I declare that the authors have no compet-
ing interests as dened by Discover, or other interests that might be perceived to inuence the results and/or discus-
sion reported in this paper. The results/data/gures in this manuscript have not been published elsewhere, nor are they
under consideration (from you or one of your Contributing Authors) by another publisher. I conrm the corresponding
author has read the journal policies and submit this manuscript in accordance with those policies. All of the material is
owned by the authors and/or no permissions are required. Data is provided within the manuscript or supplementary
information les.
Acknowledgements This work was supported by the Chenzhou National Sus-tainable Development Agenda Innovation Demonstration Zone
Construction Project (2023sfq38), National Key Research and Development Program of China (Grant No. 2023YFD2100300) and the Major
Special Project of Hunan Provincial Science and Technology (Grant No. 2017SK1020).
Author contributions W.S.L. and Z.J.L. conceived and designed the study. Z.Z.X. and L.K. performed the experiments and collected the data.
Z.L. and Y.Z.M. analyzed and interpreted the data. W.J.L. and L.D.B. drafted the initial manuscript. All authors, including W.S.L., Z.J.L., Z.Z.X., L.K.,
Z.L., Y.Z.M., W.J.L., L.D.B., and E.F., critically revised the manuscript for important intellectual content. E.F. also contributed to the preparation
of Figs.1, 2 and 3. All authors have read and approved the nal version of the manuscript for publication.
Data availability Data is provided within the manuscript or supplementary information les.
Declarations
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which
permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to
the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modied the licensed material. You
do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party
material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If
material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco
mmons. org/ licen ses/ by- nc- nd/4. 0/.
References
1. Li Y, etal. The role of Gynostemma pentaphyllum in regulating hyperlipidemia. Am J Chin Med. 2023;51(04):953–78.
2. Shi T, etal. Nonalcoholic fatty liver disease: pathogenesis and treatment in traditional Chinese medicine and western medicine. Evid-Based
Complement Altern Med. 2020.
3. Lamloum SN, Soliman HA, Rashad RA, etal. The modulation eect of green tea and pumpkin oils on hyperlipidemia, oxidative stress,
and hematological abnormalities in an experimental multiple sclerosis rat model. Clin Phytosci. 2024; 10(1). https:// doi. org/ 10. 1186/
S40816- 024- 00365-Y.
4. Yang M-H, Wang C-H, Chen H-L. Green, oolong and black tea extracts modulate lipid metabolism in hyperlipidemia rats fed high-sucrose
diet. J Nutr Biochem. 2001;12(1):14–20.
5. Sousa DAJ, Pereira P, Allgayer CDM, etal. Evaluation of DNA damage in Wistar rat tissues with hyperlipidemia induced by tyloxapol. Exp
Mol Pathol. 2017;103(1):51–5. https:// doi. org/ 10. 1016/j. yexmp. 2017. 06. 009.
6. Wang S, etal. Prevalence and associated factors of dyslipidemia in the adult Chinese population. PLoS ONE. 2011;6(3):e17326.
7. Nakashima Y. Treatment of hyperlipidemia. Intern Med. 2000;39(10):765–72.
8. She J, Tuerhongjiang G, Guo M, etal. Statins aggravate insulin resistance through reduced blood glucagon-like peptide-1 levels in a
microbiota-dependent manner. Cell Metab. 2024;36(2):408-421.e5.
9. Bellosta S, Corsini A. Statin drug interactions and related adverse reactions: an update. Expert Opin Drug Saf. 2018;17(1):25–37.
10. Ezgi D, Olga P. Targeted strategy in lipid-lowering therapy. Biomedicines. 2022;10(5):1090–1090.
11. HuiYin Y, Sharina H, Nusaibah RA, etal. Pharmacogenomics of response to statin treatment and susceptibility to statin-induced adverse
drug reactions in Asians: a scoping review. Asian Biomed. 2023;17(3):95–114.
12. Xiangcheng F, Jun H, Feng Z, etal. Red yeast rice: a functional food used to reduce hyperlipidemia. Food Rev Intl. 2023;39(8):4965–91.
13. Ma G, etal. Phytochemistry, bioactivities and future prospects of mulberry leaves: a review. Food Chem. 2022;372:131335.
14. Li Y, etal. The gut microbiota-produced indole-3-propionic acid confers the antihyperlipidemic eect of mulberry-derived 1-deoxyno-
jirimycin. mSystems. 2020;5(5):10–1128.

Vol:.(1234567890)
Research Discover Plants (2024) 1:19 | https://doi.org/10.1007/s44372-024-00018-x
15. Li Y, Zhang X, Liang C, etal. Safety evaluation of mulberry leaf extract: acute, subacute toxicity and genotoxicity studies. Regul Toxicol
Pharmacol. 2018;95:220–6.
16. Heo H-S, etal. Evaluation of general toxicity and genotoxicity of the silkworm extract powder. Toxicol Res. 2013;29:263–78.
17. Tsai CM, Wang CC, Tsai NI, etal. Improving the Eects of Mulberry Leaves and Neochlorogenic Acid on Glucotoxicity-Induced Hepatic
Steatosis in High Fat Diet Treated db/db Mice. J Agric Food Chem. 2024. https:// doi. org/ 10. 1021/ ACS. JAFC. 3C090 33.
18. Ying-Hui L, Xin M, Di-Yi Z, etal. Mechanism of eective components of Mori Folium in alleviating insulin resistance based on JNK signaling
pathway. Zhongguo Zhong yao za zhi Zhongguo zhongyao zazhi China J Chinese Mater Medica. 2019;44(5):1019–25.
19. Rim ML, Eun JK, Ju JP, etal. Protective role of fermented mulberry leave extract in LPS-induced inammation and autophagy of RAW2647
macrophage cells. Mol Med Rep. 2020;22(6):4685–95.
20. Li R, etal. Mulberry leaf and its eects against obesity: a systematic review of phytochemistry, molecular mechanisms and applications.
Phytomedicine. 2024;128:155528. https:// doi. org/ 10. 1016/j. phymed. 2024. 155528.
21. Yan D, etal. Morusalba L. water extract changes gut microbiota and fecal metabolome in mice induced by high-fat and high-sucrose diet
plus low-dose streptozotocin. Phytothera Res. 2022;36(3):1241–57.
22. Zhao L, etal. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. J
Ethnopharmacol. 2023;309:116306.
23. Song Y, etal. Investigating the multi-target pharmacological mechanism of Hedyotis diusa willd acting on prostate cancer: a network
pharmacology approach. Biomolecules. 2019;9(10):591.
24. Kwang KO, Jun SY, Been SL, etal. The convergent application of metabolites from Avena sativa and gut microbiota to ameliorate non-
alcoholic fatty liver disease: a network pharmacology study. J Transl Med. 2023;21(1):263–263.
25. Wang Z, Jiang Z, Xiao P, etal. The mechanisms of baicalin ameliorate obesity and hyperlipidemia through a network pharmacology
approach. Eur J Pharmacol. 2020;878:173103.
26. Yan Y, Yuan N, Chen Y, etal. SKP alleviates the ferroptosis in diabetic kidney disease through suppression of HIF-1α/HO-1 pathway based
on network pharmacology analysis and experimental validation. Chin Med. 2024;19(1):31–31.
27. Bardou P, Mariette J, Escudié F, etal. jvenn: an interactive Venn diagram viewer. BMC Bioinformat. 2014;15:293. https:// doi. org/ 10. 1186/
1471- 2105- 15- 293.
28. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
29. He M, Gao J, He M, etal. Exploring the mechanism of crataegus in treating hyperlipidemia based on network pharmacology and molecular
docking. Pharmaceut Res. 2024;43(2):115–21. https:// doi. org/ 10. 13506/j. cnki. jpr. 2024. 02. 003.
30. Sikai C, Miaomiao X, Feng G, etal. Evaluation of mulberry leaves’ hypoglycemic properties and hypoglycemic mechanisms. Front Phar-
macol 2023;141045309–1045309.
31. Ruilin L, Zihan X, Shuqin L, etal. Mulberry leaf polysaccharides ameliorate obesity through activation of brown adipose tissue and modu-
lation of the gut microbiota in high-fat diet fed mice. Food Funct. 2021;13(2).
32. Li L, Zhang S, Xu F, etal. Research progress on comprehensive utilization of mulberry leaves. Food Indus Sci Technol. 2022;43(2):397–404.
https:// doi. org/ 10. 13386/j. issn1 002- 0306. 20201 00105.
33. Zong Y, Ding ML, Jia KK, etal. Exploration of active compounds in da yuan yin for the treatment of COVID-19 using network pharmacology
and molecular docking approaches. Chin Herbal Med. 2020;51(04):836–44.
34. Zhai QX, Tian FW, Wang G, etal. Research progress on intestinal microbes and human health. Food Sci. 2013;34(15):337–41.
35. Guo HL, Shao YY, Meng HBL, etal. Advances in research on the relationship between intestinal ora and diseases. Microbiol China.
2015;42(02):400–10. https:// doi. org/ 10. 13344/j. micro biol. china. 140474.
36. Yao S, Junyu L, Shujuan Z, etal. Mulberry leaves ameliorate obesity through enhancing brown adipose tissue activity and modulating
gut microbiota. Food Funct. 2019;10(8):4771–81.
37. Tang QJ, Chen SH, Pan DD, etal. Pharmacological eects and mechanisms of atractylodes extract on metabolic hyperlipidemia in rats.
China J Chin Mater Med. 2015;40(09):1803–7.
38. Gong YZ, Sun SW, Liao DF. Interaction and regulation between cellular inammatory response and lipid metabolism. Chin J Atheroscle-
rosis. 2017;25(06):623–9.
39. Wang XG, Zhao X, Li Y, etal. Advances in the pathogenesis and treatment of hyperlipidemia. J Liaoning Univ Tradit Chin Med.
2020;22(12):196–200. https:// doi. org/ 10. 13194/j. issn. 1673- 842x. 2020. 12. 044.
40. Liu BW, Zeng CL. Anti-atherosclerotic eect of high-density lipoprotein. Chin J Atherosclerosis. 1994;1994(01):44–50.
41. Qingbo Xu, Yang Yu, Li Y, etal. Advances in research on the relationship between gut microbiota and type 1 diabetes mellitus. J Pract
Med. 2023;39(02):142–7.
42. Lin Y, Wenrui X, Guoxin H, etal. Advances in research on the interaction between chinese medicines and gut microbiota and its correla-
tion with host metabolic immune homeostasis. Chin Herbal Med. 2022;53(08):2526–38.
43. Saeed K, etal. Akkermansia muciniphila exerts lipid-lowering and immunomodulatory eects without aecting neointima formation in
hyperlipidemic APOE*3-Leiden.CETP mice. Mol Nutr Food Res. 2020;64(15):e1900732.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional aliations.