Abstract
Introduction
Influenza A virus (IAV) infection is associated with high morbidity and
mortality and can ultimately lead to acute lung injury (ALI). In
traditional Chinese medicine, Maxing Shigan Decoction (MXSGD) can treat
exogenous wind-cold, toxic heat invading the lungs, and heat-toxicity
obstructing the lungs. However, the active components and underlying
mechanisms of MXSGD in IAV-induced diseases remain largely unexplored.
Therefore, we aimed to investigate the active constituents of MXSGD and
its underlying mechanism of action in ALI.
Methods
Bioactive components of MXSGD in rat serum were identified using
ultra-high-performance liquid chromatography and high-resolution mass
spectrometry (UPLC-HRMS). Blood-absorbed MXSGD components (i.e., the
constituents of MXSGD detectable in serum) in ALI were predicted
through network pharmacology and molecular docking analyses. A mouse
lung injury model was established using the influenza virus. The degree
of lung injury, viral load in lung tissues, serum levels of
inflammatory factors, gene expression levels of inflammation-related
factors in lung tissue, and macrophage polarization in the lungs were
then assessed.
Results and discussion
In the rat serum, 242 bioactive components were identified using
UPLC-HRMS. Moreover, 56 ingredients, including glycyrrhizin, amygdalin,
and ephedrine, were analyzed using network pharmacology, revealing 338
ALI-related targets and 99 core proteins in the protein–protein
interaction network. Gene Ontology and Kyoto Encyclopedia of Genes and
Genomes pathway analyses were conducted for core targets, and molecular
docking confirmed the binding affinity of the main identified targets
with their respective blood-absorbed components. Validation results
demonstrated that MXSGD significantly ameliorated lung injury,
mitigated lung congestion and inflammation, lowered viral load in mouse
lung tissue, promoted macrophage polarization, and downregulated the
expression of the PI3K/AKT pathway in IAV-infected mice. Overall, this
study revealed the mechanisms and active ingredients underlying the
therapeutic effects, highlighting of MXSGD its potential in treating
IAV-induced ALI and regulating the polarization of macrophages.
Keywords: Maxing Shigan Decoction, acute lung injury, UPLC-HRMS,
blood-absorbed components, macrophage polarization
1. Introduction
Influenza is a major cause of respiratory infections worldwide and is
associated with high morbidity and mortality rates, often leading to
complications such as pneumonia. Without timely treatment, these
infections can progress to acute lung injury (ALI) and acute
respiratory distress syndrome (ARDS), a leading cause of death in
severe cases. According to the World Health Organization, seasonal
influenza results in 3–5 million cases of severe illness and between
290,000 and 650,000 deaths annually ([40]1).
Traditional Chinese medicine (TCM) has demonstrated notable efficacy in
the treatment of infectious diseases ([41]2, [42]3). Certain TCMs and
their active components can effectively prevent and treat acute lung
damage, and the preventive benefits of TCM in mitigating lung injury
are garnering increasing recognition. For example, puerarin can
significantly mitigate ALI by activating liver X receptor alpha and
attenuating lipopolysaccharide (LPS)-induced inflammatory responses
([43]4). Moreover, treatment with the Huayu Lifei formula showed
decreased expression of tumor growth factor-beta (TGF-β), connective
tissue growth factor, and Smad3 proteins in rats ([44]5). Maxing Shigan
Decoction (MXSGD), derived from Shang Han Lun, is a classic TCM
treatise compiled by Zhang Zhongjing. It is commonly used to promote
lung ventilation, liminate evil heat, clear inflammation, and relieve
respiratory conditions such as cough, bronchitis, pneumonia, and
asthma. MXSGD is also widely used to treat syndromes characterized by
toxic heat invading the lungs and heat-toxicity obstructing lung
function ([45]6, [46]7). During the COVID-19 pandemic, MXSGD and its
derivatives were frequently included in Chinese health guidelines as
treatments for viral pneumonia ([47]8, [48]9). Notably, MXSGD exerts
inhibitory effects against multiple subtypes of the influenza virus,
including H1N1, H9N2, H6N2, and type B influenza. MXSGD is commonly
used to treat patients with pathogenic wind invasion at the body
surface and heat-toxicity obstructing the lungs. The 2018–2020
Guidelines for the Diagnosis and Treatment of Influenza, issued by the
National Health Commission of China, included this formula as a core
treatment for the syndrome characterized by heat-toxicity blocking the
lungs ([49]10–[50]13). The antiviral action of MXSGD may involve
suppressing pathogen proliferation, reducing virus-induced
inflammation, preventing inflammatory cytokine storms, and ameliorating
gut microbial dysbiosis ([51]14–[52]16). However, the complex
composition of the active compounds in MXSGD complicates efforts to
fully unravel the precise mechanisms underlying its therapeutic
effects, particularly in influenza-induced ALI.
Network pharmacology offers a systematic approach to predict the
potential mechanisms underlying the therapeutic effects of herbal
formulations by analyzing compound–target–disease interaction networks
through bioinformatics. This approach effectively explores the
relationships between TCM and diseases, aligning with the holistic
perspective of TCM. In the present study, using network pharmacology,
we aimed to examine the mechanisms underlying MXSGD activity in ALI,
predict the active compounds using ultra-high-performance liquid
chromatography-high-resolution mass spectrometry (UPLC-HRMS), and
validate the associated pathways through in vivo tests ([53] Figure 1
).
Figure 1.
[54]Flowchart illustrating the process of identifying therapeutic
targets. It includes data sources like UPLC-HRMS and PubChem, leading
to network construction with PPI, C-T, and C-T-P networks. Enrichment
analysis involves GO and KEGG pathways. Molecular docking images show
interactions of compounds with proteins. The experimental assessment
includes examining histopathological changes in lung tissues from mice
in each group, performing immunofluorescence staining for macrophage
polarization markers (CD80 and CD206), and measuring serum levels of
pro-inflammatory cytokines, among other assays.
[55]Open in a new tab
Workflow of systems pharmacology analysis and experimental assessment.
2. Materials and methods
2.1. Instruments, drugs, and reagents
2.1.1. Instruments
The following instruments were used: a low-temperature high-speed
centrifuge (Eppendorf Centrifuge 5430 R; Eppendorf, Hamburg, Germany),
Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA),
ACQUITY UPLC HSS T3 chromatographic column (2.1 mm ×100 mm, 1.8 µm),
Q-Exactive HFX mass spectrometer (Thermo Fisher Scientific), ELX-800
multifunctional microplate reader (Bio Tek, Winooski, VT, USA),
LightCycler 96 fluorescent quantitative PCR instrument (Roche, Basel,
Switzerland), tissue optical scanning microscope camera (ZEISS,
Oberkochen, Germany), Mini-PROTEAN vertical electrophoresis apparatus,
Mini Trans-Blot transfer apparatus, and SCG-W2000 chemiluminescence
imaging system (all from Servicebio, Hubei, China).
2.1.2. Drugs and reagents
The reagents utilized in liquid chromatography-tandem mass spectrometry
(LC-MS) included water (MS grade, MilliporeSigma, Burlington, MA, USA),
methanol and acetonitrile (MS grade, Thermo Fisher Scientific), and
formic acid (MS grade, Honeywell International Inc., Charlotte, NC,
USA).
MXSGD consists of four TCM components: Ephedrae Herba (Lot No.
2109170042), Armeniacae Semen Amarum (Lot No. 2022011201), Gypsum
fibrosum (Lot No. 2110151), and Glycyrrhizae Radix et Rhizoma (Lot No.
220201). All Chinese medicinal materials were purchased from the First
Affiliated Hospital of Hunan University of Traditional Chinese
Medicine, and their quality met the standards established by the
Chinese Pharmacopoeia (2020 edition). Oseltamivir phosphate capsules
(75 mg, Lot No. M1073; Roche) were purchased from the People’s Hospital
of Hunan Province. The virus strain A/PR/8/34 (H1N1, mouse
lung-adapted) was provided by the Molecular Virology Laboratory at
Hunan Normal University and amplified in chicken embryos in a BSL-2
laboratory at Hunan University of Chinese Medicine.
The reagents used in the validation experiments included influenza A
virus (IAV) nucleoprotein antibody (Cat No. GTX125989; GeneTex, Irvine,
CA, USA), anti-CD80 antibody (Cat No. ab254579; Abcam, Cambridge, UK),
anti-mannose receptor antibody (Abcam, Cat No. ab64693), HRP
peroxidase-conjugated goat anti-rabbit IgG (H+L, Cat No. GB21303;
Servicebio), Cy3 conjugated goat anti-rabbit IgG (H+L, Cat No. GB23303;
Servicebio), PI3K p85 alpha antibody (Cat No. AF6241; Affinity
Biosciences, Cincinnati, OH, USA), phospho-pan-AKT1/2/3 antibody (Cat
No. AF3262; Affinity Biosciences), pan-AKT1/2/3 antibody (Cat No.
AF6261; Affinity Biosciences), RIPA lysis buffer (Cat No. G2002-100ML;
Servicebio), phosphatase inhibitor (Cat No. G2007-1ML; Servicebio), BCA
protein quantification assay kit (Cat No. G2026-200T; Servicebio),
prestained protein marker VII (8–195 kDa; Cat No. G2087-250UL;
Servicebio), enhanced chemiluminescent reagent kit (Cat No.
G2161-200ML; Servicebio), RNA simple total RNA extraction kit (Cat No.
DP419; TIANGEN, Beijing, China), NovoScript Plus All-in-One 1st Strand
cDNA Synthesis SuperMix (gDNA Purge; Cat No. E047-01B; Novoprotein,
Shanghai, China), NovoScript SYBR qPCR Super Mixture (Cat No. E096-01A;
Novoprotein), mouse IL-1β enzyme-linked immunosorbent assay (ELISA) kit
(Cat No. ml301814; MLBIO, Shanghai, China), mouse IL-6 ELISA kit (Cat
No. ml063159; MLBIO), and mouse TNF-α ELISA Kit (Cat No. ml002095;
MLBIO).
2.2. Drug and sample preparation
2.2.1. Preparation of experimental drugs
The MXSGD formulation comprised 9 g of ephedra, 9 g of apricot kernel,
18 g of gypsum, and 6 g of licorice. The medicinal substances were
measured according to their compositional ratios during production. The
aqueous extract of MXSGD was obtained by first boiling ephedra,
following the traditional preparation described in “Shang Han Lun” and
based on our previous findings ([56]17). Ephedra was mixed with
distilled water at a 1:10 weight-to-volume ratio. The mixture was first
boiled at 100°C and then simmered for 25 min. Following the removal of
froth, gypsum, apricot kernel, and licorice were added to the mixture,
which was then boiled for an additional 30 min and subsequently
filtered. For the second extraction, distilled water at seven times the
original volume was added, brought to a boil at 100°C, simmered for 20
min, and filtered again. The two extracts were then combined and
concentrated to yield final crude drug concentrations of 0.605 g/mL and
4.265 g/mL for animal validation and serum preparation, respectively.
The extracts were shielded from light and stored at 4°C until use.
Oseltamivir phosphate capsules were completely dissolved in distilled
water to create a suspension with a concentration of 2.15 mg/mL.
2.2.2. Preparation of drug-containing serum
Forty-eight specific-pathogen-free (SPF) male Sprague Dawley rats
(180–220 g) were purchased from Hunan SJA Laboratory Animal Co., Ltd.
[Animal Quality Certificate No. SYXK (Xiang) 2019-0009; Animal
Experiment Ethical Approval No.: LL2021081101]. They were housed in the
Animal Experiment Center of the Hunan University of Chinese Medicine
and allowed to acclimate for two days. The experimental conditions
included a 12-h light/dark cycle, unlimited access to water and food,
and controlled temperature and humidity. Animals were randomly assigned
to two groups: the MXSGD and control groups, each including seven rats.
Following serum pharmacochemistry protocols, the MXSGD group received a
dosage adjusted based on body surface area to increase the
concentration of active drug components in the blood. The gavage dosage
was equivalent to 10 times the standard clinical dosage. Each rat in
the MXSGD group received a gavage dose of 8.53 g/day. The control group
received an equivalent volume of physiological saline, delivered daily
at 2 mL for 7 consecutive days. On day 7, after an 8-h fasting period
with unlimited access to water, the rats were anesthetized with sodium
pentobarbital, and blood was drawn from the abdominal aorta. Blood
samples were incubated at 4°C for 2 h and then centrifuged at 3,000 rpm
for 10 min. The obtained MXSGD-containing serum and blank serum were
stored at –20°C until use.
2.2.3. Preparation of the test solution
A total of 600 μL of MXSGD aqueous extract was mixed with 400 μL of
methanol and vortexed. To dilute the mixture, 200 μL of it was added to
1.4 mL of a 40% aqueous methanol solution. The mixture was vortexed
once more and centrifuged at 16,000 ×g for 15 min at 4°C. The
supernatant was collected as the test solution sample (MXSGD-PRE).
2.2.4. Preparation of serum samples
Approximately 200 μL of blank rat serum and 200 μL of MXSGD-containing
serum were each combined with 800 μL of methanol. The two solutions
were vortexed for 60 s, incubated at -20°C for 30 min, and subsequently
centrifuged at 16,000 ×g for 20 min at 4°C. The supernatant was
collected and vacuum-dried. The residue was dissolved in 100 μL of a
40% aqueous methanol solution, vortexed, and centrifuged again at
16,000 ×g for 15 min at 4°C. The supernatant was obtained, yielding a
blank serum sample (CONTROL) and a serum sample containing MXSGD
(MXSGD).
2.2.5. Preparation of blank serum and test solution samples
Next, 200 μL of blank rat serum was mixed with 33.4 μL of MXSGD aqueous
extract. Subsequently, 800 μL of methanol was added to the mixture,
which was then vortexed for 60 s. The solution was incubated at –20°C
for 30 min, followed by centrifugation at 16,000 ×g for 20 min at 4°C.
The supernatant was then harvested and vacuum-dried. The residue was
subsequently dissolved in 100 μL of a 40% aqueous methanol solution,
vortexed, and centrifuged again at 16,000 ×g for 15 min at 4°C. The
supernatant was obtained, yielding a sample consisting of the blank
serum combined with the test solution (CONTROL + MXSGD-PRE).
2.3. Application of UPLC-HRMS in pharmaceutical analysis
UPLC-HRMS was used to determine the chemical constituents of MXSGD that
reached the bloodstream, with each constituent accurately
characterized. Chromatographic separation was performed using a
Vanquish UHPLC system equipped with an ACQUITY UPLC HSS T3
chromatographic column (2.1 × 100 mm, 1.8 µm). An aqueous solution of
0.1% formic acid served as mobile phase A, and an acetonitrile solution
with 0.1% formic acid functioned as mobile phase B. Gradient elution
was conducted as follows: 0–17 min, 5–98% B; 17–17.2 min, 98–5% B; and
17.2–20 min, 5% B. The flow rate was set at 0.3 mL/min, with the column
temperature set to 35°C and an injection volume of 2 μL.
The Q-Exactive HFX mass spectrometer was integrated with the UHPLC
system, and MS was conducted in both positive and negative modes using
electrospray ionization. The data-dependent acquisition mode was
utilized to select the top 10 MS1 ions to acquire the MS/MS spectra.
Collision energies were established at normalized values of 20, 40, and
60, with a data acquisition range of m/z 90–1300. The spray voltages
were set to +3800 V and –3000 V. The sheath gas flow rate was
maintained at 45, with the capillary temperature set to 320°C and the
probe heater temperature to 370°C. XCMS software was used for peak
alignment, retention time correction, and peak extraction. The
resulting data obtained were compared with a standard spectrum database
for structural identification.
The correlation of the secondary mass spectrum (MS2) was predominantly
evaluated using the MS2 fragment score, which has a maximum value of 1
([57]18, [58]19). A higher score (i.e., > 0.7) indicates greater
reliability of the identification results ([59]20, [60]21).
Consequently, the parameters were set to maintain an MS1 difference of
less than 15 ppm and a high degree of MS2 fragment similarity.
2.4. Network pharmacology analysis of MXSGD in the treatment of ALI
2.4.1. Screening of the blood-absorbed components and action targets of MXSGD
Representative components of MXSGD that entered the bloodstream, as
detected via UPLC-HRMS, were selected as research subjects based on the
average ion abundance values. Component IDs were obtained from the
PubChem database ([61]https://pubchem.ncbi.nlm.nih.gov/). Probable
matching and docking in vivo drug targets were predicted using the
PharmMapper database ([62]http://www.lilab-ecust.cn/pharmmapper/), with
targets filtered based on a Norm Fit threshold of ≥ 0.8. In the
SwissTargetPrediction database
([63]http://www.swisstargetprediction.ch), targets were selected based
on a “probability” threshold of ≥ 0.2. Targets underwent additional
screening using the BATMAN-TCM database
([64]http://bionet.ncpsb.org.cn/batman-tcm/) with a score cutoff” ≥ 20
and a P-value ≥ 0.05. The target protein names were standardized using
the UniProt database ([65]https://www.uniprot.org/), and duplicate
targets were consolidated and eliminated, yielding the action targets
of the components of MXSGD that entered the bloodstream.
2.4.2. Screening of potential targets for ALI
The search term “Acute Lung Injury” was employed to extract pertinent
targets from the GeneCards ([66]http://www.genecards.org/), DisGeNET
([67]https://www.disgenet.org/), TTD ([68]https://db.idrblab.net/ttd/),
and OMIM databases ([69]http://www.omim.org/). Duplicate targets were
consolidated and eliminated, and the target nomenclature was accurately
corrected using the UniProt database to identify disease-associated
targets. Venny 2.1
([70]https://bioinfogp.cnb.csic.es/tools/venny/index.html) was
subsequently used to input the action targets of the components and
diseases, producing a Venn diagram. The intersecting targets indicated
the probable action sites of the MXSGD components in the treatment of
ALI.
2.4.3. Construction of component–target and protein–protein interaction
network diagrams
Based on the identified MXSGD components that entered the bloodstream,
target prediction outcomes, and associated illness targets, the C–T
network diagram of MXSGD was generated employing Cytoscape 3.7.1
software. The PPI network was constructed using the STRING database
([71]https://cn.string-db.org), with a high confidence threshold (≥
0.7) as the screening criterion. The resulting network was visualized
and analyzed using Cytoscape software (version 3.7.1). The network was
further evaluated using the “Network Analyzer” tool, and network
topology analysis was performed using CytoNCA (version 2.1.6). Key
targets were identified based on degree, betweenness, and closeness
centrality metrics, with values exceeding the respective medians.
2.4.4. Gene ontology and Kyoto encyclopedia of genes and genomes pathway
enrichment analyses
The potential targets were imported into the Database for Annotation,
Visualization and Integrated Discovery (DAVID) database
([72]https://davidbioinformatics.nih.gov/summary.jsp) to conduct GO
enrichment analysis for biological function and KEGG pathway enrichment
analysis. These analyses highlighted the molecular functions,
biological processes, cellular components, and metabolic pathways of
these genes. A significance criterion of P < 0.05 was set, and part of
the results were visualized using the Internet Bioinformatics tool
([73]https://www.bioinformatics.com.cn/).
2.4.5. Construction of the blood-absorbed components–key targets–signaling
pathways network
The blood-absorbed components of MXSGD, target prediction outcomes,
pathway analysis, and associated diseases were utilized to delineate
the relationships among drugs–ingredients, ingredients–targets,
targets–pathways, and pathways–diseases. A network diagram titled
“Blood-Absorbed Components –Key Targets–Signaling Pathways” (CTP) was
generated utilizing Cytoscape software (version 3.7.1).
2.4.6. Molecular docking
The top five potentially active components with a high degree from the
CTP network were selected for molecular docking with the top 10 core
targets. The structures of prospective active constituents were
acquired from the PubChem database. PDB format files for the principal
targets were retrieved from the PDB database
([74]https://www.rcsb.org). Hydrogenation, dehydration, and ligand
separation were performed using the AutoDock Tools software (version
1.5.7). Molecular docking was performed using AutoDock Vina software
(version 1.1.2), and the minimal binding energies were computed. The
docking findings were imported into PyMol for visualization and
graphing.
2.5. Animal experiments
2.5.1. Establishment of animal models, grouping, and medication
Overall, 48 SPF-grade BALB/c mice (16–20 g), equally divided between
males and females, were purchased from Hunan SJA Laboratory Animal Co.,
Ltd. [Animal Quality Certificate No. SYXK (Xiang) 2020-0010; Animal
Experiment Ethical Approval No.: ZYFY20230703-04). The rearing
conditions comprised a 12-h light/dark cycle, unlimited access to water
and food, and controlled temperature and humidity levels. All animals
were randomly allocated to six groups: control, model, positive control
medicine (oseltamivir, Ose), and low-dose, medium-dose, and high-dose
MXSGD groups, with eight mice in each group. All groups, excluding the
control group, received 0.05 mL of a 1:640 hemagglutination titer
dilution of IAV (1:100) by nasal drops to establish the IAV infection
model. Twenty-four hours post-infection, each group received the
corresponding treatment via oral gavage at clinically equivalent doses,
using a body surface area-based dose variation algorithm. The
medication was administered once daily at a volume of 0.2 mL. In the
positive control treatment group, each mouse received 0.433 mg of
oseltamivir daily by gavage. The low, medium, and high MXSGD groups
received daily doses of 0.0605, 0.121, and 0.242 g of MXSGD,
respectively. An equivalent volume of physiological saline was
administered to the control and model groups. After 3 or 7 consecutive
days of treatment, the animals were weighed, and blood was collected
from the eyeball after anesthesia. Lung tissues of the mice were
excised and weighed, with a portion preserved in 4% paraformaldehyde
and another portion maintained in a –80°C ultra-low-temperature
freezer.
2.5.2. General observation of mice
At the end of the experiment, the body weights of the mice were
recorded. The lungs were harvested, and the remaining blood was
absorbed using filter paper prior to weighing and documenting the lungs
to compute the organ index. The lung index (%) was calculated using the
following formula:
[MATH:
Lung In
dex % = Lung Weig
ht g/Body Weight g ×
100 :MATH]
5.3. Pathological observation of lung tissue
Lung tissues preserved in 4% paraformaldehyde were subjected to
gradient alcohol dehydration, xylene clearing, paraffin embedding,
sectioning, baking, deparaffinization, hematoxylin-eosin staining, and
neutral resin mounting. Tissue samples were examined under a
microscope, and pathological changes were documented using photography.
2.5.4. Immunofluorescence detection of IAV nuclear protein levels in lung
tissue
Three mice per group were randomly selected for immunofluorescence
detection. After embedding, lung tissue samples were sectioned and
deparaffinized for antigen retrieval. The tissue sections were blocked
with 3% bovine serum albumin (BSA) for 30 min and then incubated with
IAV nucleoprotein antibody (1:400) overnight at 4°C. Subsequently, they
were incubated with a CY3-conjugated secondary antibody (1:300, red) at
room temperature for 50 min. Nuclear staining was then performed using
4′,6-diamidino-2-phenylindole (DAPI) (blue) in the dark at room
temperature for 10 min. Following the suppression of tissue
autofluorescence using an autofluorescence quencher, an
anti-fluorescence quenching mounting solution was used, and the samples
were examined under a fluorescence microscope. Image-Pro Plus 6.0 was
then used to analyze the acquired images and determine the mean optical
density value (IOD/area) for each field of view.
2.5.5. ELISA detection of cytokine levels in mouse serum
Frozen mouse serum was retrieved, and the levels of cytokines (IL-1β,
IL-6, and TNF-α) were measured according to the instructions of the
ELISA kit. After obtaining absorbance values, sample concentrations
were calculated using a standard curve.
2.5.6. Reverse transcription quantitative polymerase chain reaction of the
gene expression of macrophage polarization markers in pulmonary tissue
Total RNA was extracted from pulmonary tissue using the TIANGEN
RNAsimple Total RNA Kit. The extracted total RNA was then
reverse-transcribed into cDNA using the NovoScript^® Plus All-in-one
1st Strand cDNA Synthesis SuperMix (gDNA Purge). Subsequently, PCR
amplification was performed using the NovoStart^® SYBR qPCR SuperMix
Plus kit. Following the PCR reaction, melting curve analysis was
conducted, and the relative expression levels of target genes were
calculated using the 2−^ΔΔCT method, followed by statistical analysis.
The specific primers utilized are presented in [75]Table 1 .
Table 1.
Primer sequences.
Genes Primer sequences(5’—3’)
IL-6 F: GACTTCCATCCAGTTGCCTT
R: ATGTGTAATTAAGCCTCCGACT
IL-10 F: GGACAACATACTGCTAACCGACTC
R: TGGATCATTTCCGATAAGGCTTGG
iNOS F: TCACTCAGCCAAGCCCTCAC
R: TCCAATCTCTGCCTATCCGTCTC
FIZZ1 F: ATCGTGGAGAATAAGGTCAAGGAAC
R: CAAGCACACCCAGTAGCAGTC
β-actin F: ACATCCGTAAAGACCTCTATGCC
R: TACTCCTGCTTGCTGATCCAC
[76]Open in a new tab
2.5.7. Immunofluorescence detection of protein expression of macrophage
polarization markers in lung tissue
After embedding, lung tissue samples were sectioned and deparaffinized
to facilitate antigen retrieval. The tissue sections were blocked with
3% BSA for 30 min and incubated with an anti-CD80 antibody (1:2000)
overnight at 4°C. Subsequently, the sections were incubated with the
appropriate horseradish peroxidase-conjugated secondary antibody
(1:500, green) at ambient temperature for 50 min, followed by
incubation with tryptic soy agar in the dark at room temperature for 10
min. Following incubation, tissue sections were subjected to microwave
heating for antigen retrieval. An anti-mannose receptor antibody
(1:200) was then added, and the sections were incubated overnight at
4°C. Sections were then incubated with the appropriate CY3-conjugated
secondary antibody (1:300, red) in the dark at ambient temperature for
50 min. Following incubation, DAPI (blue) was used for nuclear
staining. Tissue autofluorescence was suppressed using an
autofluorescence quencher, followed by the application of an
anti-fluorescence quenching mounting medium. The specimens were
examined under a fluorescence microscope. Image-Pro Plus 6.0 was
employed to analyze the acquired images and determine the mean optical
density value (IOD/area) for each field of view.
2.5.8. Immunohistochemical detection of related protein expression in lung
tissues
Following sectioning and dewaxing of paraffin-embedded lung tissues,
antigen retrieval was performed using 1× citrate buffer (pH 6.0) as the
repair solution, followed by endogenous enzyme blocking with 3%
H[2]O[2]solution. Tissue sections were incubated with 10% goat serum
for blocking, then subjected to antibody incubation, DAB staining, and
hematoxylin counterstaining. After dehydration and mounting, protein
expression was detected using the PV-9000 universal two-step detection
kit, with positive signals observed under an optical microscope. Five
random microscopic fields were selected per sample section, and the
acquired images were analyzed using Image-Pro Plus 6.0 software to
calculate the mean optical density value (IOD/area) for each field.
2.6. Statistical analysis
Experimental data were processed and analyzed using SPSS 25.0 (SPSS
Inc., Chicago, IL, USA). Data are expressed as mean ± standard
deviation. For normally distributed data, comparisons among different
groups were performed using one-way analysis of variance, with pairwise
comparisons performed using the least significant difference test (for
equal variances) or the Games–Howell test (for unequal variances).
Non-parametric rank-sum tests were used for data that did not follow a
normal distribution. The Kruskal–Wallis H test was initially used to
assess overall differences, followed by the Mann–Whitney U test for
pairwise group comparisons. A P-value < 0.05 was considered
statistically significant.
3. Results
3.1. Identification of MXSGD components using UPLC-HRMS
Using UHPLC-HRMS, we collected data from the CONTROL, MXSGD, CONTROL +
MXSGD-PRE, and MXSGD-PRE serum samples, and compared their positive and
negative ion base peak chromatograms (BPCs) ([77] Figure 2 ). In the
positive and negative ion BPCs of MXSGD-PRE, high-abundance
chromatographic peaks were validated based on peak shape and confirmed
using the corresponding MS² spectra. Each identified peak was then
sequentially labeled and marked in the positive and negative ion
chromatograms ([78] Figure 3 ).
Figure 2.
[79]Panels A and B show the base peak chromatogram (BPC) plots in
positive and negative ion modes, respectively, depicting relative
abundance versus retention time (from 0 to 20 min). The observed peaks
reflect the compound concentration over time. Each plot contains four
distinct traces, color-coded from top to bottom as follows: black for
the MXSGD-PRE group, red for the CONTROL + MXSGD-PRE group, green for
the CONTROL group, and blue for the MXSGD group.
[80]Open in a new tab
BPC graphs of each sample group. (A) BPC graphs of each group in
positive ion mode. (B) BPC graphs of each group in negative ion mode.
From top to bottom: MXSGD-PRE, CONTROL + MXSGD-PRE, CONTROL, MXSGD.
Figure 3.
[81]Chromatograms A and B depict the relative abundance of the
MXSGD-PRE group in positive and negative ion mode, respectively, over a
0–20 min period. Both graphs display distinct abundance profiles
throughout the retention time range. Characteristic base peaks are
labeled in each plot.
[82]Open in a new tab
BPC graph of MXSGD-PRE (Marked Peaks). (A) BPC graph of MXSGD-PRE in
positive ion mode. (B) BPC graph of MXSGD-PRE in negative ion mode.
We then transferred the resulting data to a local database of standard
chromatograms relevant to TCM for MS² retrieval and comparison,
identifying 1,297 chemical constituents in the blood following the
administration of MXSGD. We identified 111 classes based on the
ClassyFire classification method ([83]22). Of them, the top 6 compounds
included benzenes and their substituted derivatives (15.80%),
carboxylic acids and their derivatives (22.12%), lipids (15.17%),
flavonoids (15.42%), organic oxides (15.55%), and isoprenoids (15.93%).
Based on the BPCs of MXSGD-PRE ([84] Figure 3 ), we conducted a
preliminary identification of the typical base peaks, revealing 26 main
active components of MXSGD, each with basic chemical formulas and clear
definitions. Of them, 17 components were detected in the blood, 9 were
not detected, and 5 remained unidentified ([85] Table 2 ).
Table 2.
Identification results of Characteristic Base Peaks of MXSGD from the
BPC.
Peak number No. m/z RT min ppm Compound name Score Class Into Blood or
None
1 M166T89 2 166.1228 1.49 1 Hordenine 0.9983 Benzene and substituted
derivatives None
2 M166T129 1 166.0861 2.15 17.6 Phenylalanine 0.9997 Carboxylic acids
and derivatives None
3 M134T175 3 134.0965 2.76 0.6 Phenylalanine,.alpha.-methyl- 0.9953
Phenylpropanoic acids Into Blood
4 M152T175 2 152.1069 2.91 0.5 (+/-)-Norephedrine 0.9974 Benzene and
substituted derivatives Into Blood
5 M166T194 2 166.1227 3.24 0.1 DL-Ephedrine 0.9983 Benzene and
substituted derivatives Into Blood
6 M180T211 180.1382 3.51 0 N-Methylephedrine 0.9832 Benzene and
substituted derivatives Into Blood
7 M192T257 192.0655 4.28 0.4 4-Keto-8-methoxy-1H-quinoline-2-carboxylic
acid 0.9981 Quinolines and derivatives None
8 M551T300 551.1761 5.01 NA NA NA NA Into Blood
9 M257T307 5 257.0804 5.12 0.3 Liquiritigenin 0.9996 Flavonoids Into
Blood
10 M419T383 3 419.1335 6.38 1 Liquiritin 0.9947 Flavonoids None
11 M839T561 839.4059 9.36 0.3 Licoricesaponin g2 0.9237 Prenol lipids
Into Blood
12 M453T600 5 453.3363 10.00 0 18.beta.-Glycyrrhetinic acid 0.9873
Prenol lipids Into Blood
13 M173T93 173.0084 1.54 3.1 Isocitric acid 0.9985 Carboxylic acids and
derivatives None
14 M520T147 1 520.1679 2.45 1.4
2-((6-O-.beta.-D-Glucopyranosyl-.beta.-D-glucopyranosyl)oxy)-2-phenylac
etamide 0.9033 Fatty Acyls Into Blood
15 M951T187 951.3007 3.11 2.2 Physanguloside A 0.8615 Organooxygen
compounds None
16 M293T208 4 293.1240 3.46 NA NA NA NA Into Blood
17 M165T220 4 165.0555 3.67 3.2 Benzenepropanoic acid, 4-hydroxy- 0.994
Phenylpropanoic acids Into Blood
18 M502T237 2 502.1575 3.94 0.9
((6-O-Hexopyranosylhexopyranosyl)oxy)(phenyl)acetonitrile 0.9311
Organooxygen compounds Into Blood
19 M379T267 2 379.1979 4.44 17.1 Clausarin 0.8776 Coumarins and
derivatives Into Blood
20 M340T275 5 340.1040 4.58 0.5 (R)-Prunasin 0.9854 Organooxygen
compounds Into Blood
21 M549T301 549.1624 5.01 1.1
Liguiritigenin-7-O-beta-D-apiosyl-4’-O-beta-D-glucoside 0.9792
Flavonoids None
22 M417T306 6 417.1196 5.10 1.9 Isoliquiritin 0.9862 Flavonoids Into
Blood
23 M245T319 5 245.0932 5.32 0.3 N-Acetyl-D-tryptophan 0.963 Carboxylic
acids and derivatives Into Blood
24 M463T327 463.0890 5.44 1.4 Spireoside 0.9979 Flavonoids Into Blood
25 M187T364 3 187.0972 6.07 1.7 Azelaic acid 0.991 Fatty Acyls None
10 M417T383 4 417.1197 6.38 1.5 Liquiritin 0.8948 Flavonoids None
26 M255T429 5 255.0665 7.15 1.7 Isoliquiritigen 0.987 Linear 1 3 -
diarylpropanoids Into Blood
27 M361T439 2 361.1874 7.32 NA NA NA NA Into Blood
28 M821T600 5 821.3982 10.00 2.9 Glycyrrhizinate dipotassium 0.9847
Prenol lipids Into Blood
29 M367T688 3 367.1191 11.47 1.1 Glycycoumarin 0.992 Isoflavonoids None
30 M353T724 3 353.1035 12.06 NA NA NA NA Into Blood
31 M351T773 4 351.0879 12.89 NA NA NA NA Into Blood
[86]Open in a new tab
3.2. Network pharmacology analysis of MXSGD
3.2.1. Prediction of potential targets for the treatment of ALI based on the
blood-absorbed components of MXSGD
Among the 1,297 components identified using UPLC-HRMS, we selected the
blood-absorbed components of MXSGD with high average ion abundance
values using the criteria of “into blood” and “mean ≥ e^8.” We then
intersected these components with the 17 main blood-absorbed components
obtained from the BPCs, resulting in 56 core blood-absorbed components
([87] Table 3 ). We used these 56 blood-absorbed components to retrieve
component targets from the BATMAN-TCM, SwissTargetPrediction, and
PharmMapper databases, removing duplicates and integrating 1,114 target
points for the blood-absorbed components of MXSGD.
Table 3.
Screening results of Core compounds that enter the blood of MXSGD.
Number Molecular Formula m/z RT min ppm Compound name Score Class Mean
1 C[10]H[15]NO 166.1227 3.24 0.1 DL-Ephedrine 0.9983 Benzene and
substituted derivatives 3.89E+10
2 C[10]H[15]NO 148.1120 3.23 0 (-)-Pseudoephedrine 0.9967 Benzene and
substituted derivatives 3.50E+10
3 C[10]H[13]NO[2] 134.0965 2.76 0.6 Phenylalanine,.alpha.-methyl-
0.9953 Phenylpropanoic acids 9.93E+09
4 C[11]H[17]NO 180.1382 3.51 0 N-Methylephedrine 0.9832 Benzene and
substituted derivatives 8.68E+09
5 C[9]H[13]NO 152.1069 2.91 0.5 (+/-)-Norephedrine 0.9974 Benzene and
substituted derivatives 3.32E+09
6 C[15]H[12]O[4] 257.0804 5.12 0.3 Liquiritigenin 0.9996 Flavonoids
2.08E+09
7 C[9]H[9]NO[3] 178.0505 4.16 2.8 3-Pyridinebutanoic acid,.gamma.-oxo-
0.9975 Keto acids and derivatives 1.71E+09
8 C[7]H[16]N[2]O[2] 144.1020 1.04 0.5 L-.beta.-Homolysine 0.9968
Carboxylic acids and derivatives 1.21E+09
9 C[20]H[27]NO[11] 502.1575 3.94 0.9
((6-O-Hexopyranosylhexopyranosyl)oxy)(phenyl)acetonitrile 0.9311
Organooxygen compounds 1.20E+09
10 C[14]H[17]NO[6] 340.1040 4.58 0.5 (R)-Prunasin 0.9854 Organooxygen
compounds 9.23E+08
11 C[21]H[22]O[9] 417.1196 5.10 1.9 Isoliquiritin 0.9862 Flavonoids
8.99E+08
12 C[30]H[46]O[4] 453.3363 10.00 0 18.beta.-Glycyrrhetinic acid 0.9873
Prenol lipids 8.78E+08
13 C[9]H[10]O[3] 165.0555 3.67 3.2 Benzenepropanoic acid, 4-hydroxy-
0.994 Phenylpropanoic acids 8.59E+08
14 C[7]H[16]O[3] 149.1171 4.08 0.1
1-(2-Methoxyethoxy)-2-methyl-2-propanol 0.772 Organooxygen compounds
8.49E+08
15 C[42]H[62]O[16] 823.4111 10.00 1 Glycyrrhizin 0.9988 Prenol lipids
6.63E+08
16 C[11]H[15]N[5]O[4] 136.0625 0.84 0.6 2’-O-Methyladenosine 0.9999
Purine nucleosides 6.25E+08
17 C[10]H[10]O[5] 209.0454 3.66 2.8
3-(3,4-dihydroxy-5-methoxyphenyl)prop-2-enoic acid 0.9923 Cinnamic
acids and derivatives 5.64E+08
18 C[12]H[22]O[11] 377.0861 0.79 0.8 .alpha.,.beta.-Trehalose 0.9818
Organooxygen compounds 4.86E+08
19 C[42]H[60]K[2]O[16] 821.3982 10.00 2.9 Glycyrrhizinate dipotassium
0.9847 Prenol lipids 4.58E+08
20 C[17]H[26]O[10] 211.0942 5.90 11.2 Methyl
(1S)-1-(.beta.-D-glucopyranosyloxy)-6-hydroxy-7-methyl-1,4a,5,6,7,7a-he
xahydrocyclopenta[c]pyran-4-carboxylate 0.9992 Prenol lipids 4.48E+08
21 C[15]H[14]O[6] 291.0862 3.80 1 Epicatechin 0.9987 Flavonoids
4.36E+08
22 C[9]H[11]N 117.0702 2.91 1.8 trans-2-Phenylcyclopropylamine 0.9991
Organonitrogen compounds 4.30E+08
23 C[20]H[23]NO 134.0602 2.44 0.5 N-Cyclohexyl-2,2-diphenylacetamide
0.9982 Benzene and substituted derivatives 4.15E+08
24 C[10]H[17]NO[3] 100.0761 2.11 4 N-Boc-2-piperidone 0.9996
Piperidines 3.71E+08
25 C[7]H[12]O[5] 175.0607 3.73 2.6 alpha-Isopropylmalate 0.9983 Fatty
Acyls 3.11E+08
26 C[42]H[62]O[17] 839.4059 9.36 0.3 Licoricesaponin g2 0.9237 Prenol
lipids 2.99E+08
27 C[11]H[15]NO[2] 194.1172 5.85 0.2 2-Amino-2-methyl-4-phenylbutyric
acid 0.9604 Carboxylic acids and derivatives 2.91E+08
28 C[27]H[30]O[14] 579.1711 4.88 0.4
(1S)-1,5-Anhydro-2-O-(6-deoxy-.alpha.-L-mannopyranosyl)-1-(5,7-dihydrox
y-2-(4-hydroxyphenyl)-4-oxo-4H-chromen-6-yl)-D-glucitol 0.9521
Flavonoids 2.90E+08
29 C[10]H[12]O[3] 137.0237 6.49 5.5 Isopropyl m-hydroxybenzoate 0.9989
Benzene and substituted derivatives 2.64E+08
30 C[10]H[11]NO[2]S 106.0655 2.48 3.4 2-Phenylthiazolidine-4-carboxylic
acid 0.9989 Carboxylic acids and derivatives 2.51E+08
31 C[14]H[14]N[2]O[5] 291.0975 5.33 0.3 N-Malonyltryptophan 0.8316
Carboxylic acids and derivatives 2.44E+08
32 C[26]H[28]O[14] 565.1555 4.47 0.4 4H-1-Benzopyran-4-one,
6-arabinopyranosyl-8-.beta.-D-glucopyranosyl-5,7-dihydroxy-2-(4-hydroxy
phenyl)- 0.9452 Flavonoids 2.39E+08
33 C[12]H[20]O[2] 137.1325 5.40 0.1 Neryl acetate 0.9984 Fatty Acyls
2.37E+08
34 C[26]H[30]O[11] 355.1176 12.05 0.3 Phellamurin 0.9872 Flavonoids
2.26E+08
35 C[15]H[12]O[4] 255.0665 7.15 1.7 Isoliquiritigen 0.987 Linear 1 3 -
diarylpropanoids 2.26E+08
36 C[6]H[11]NO[3] 128.0707 0.81 2.1 2-Amino-5-oxohexanoic acid 0.9693
Carboxylic acids and derivatives 2.18E+08
37 C[22]H[22]O[9] 431.1337 6.49 0.2 Ononin 0.9997 Isoflavonoids
2.16E+08
38 C[10]H[18]O 137.1325 4.45 0.7 (+)-Isomenthone 0.996 Prenol lipids
1.97E+08
39 C[20]H[29]NO[12] 520.1679 2.45 1.4
2-((6-O-.beta.-D-Glucopyranosyl-.beta.-D-glucopyranosyl)oxy)-2-phenylac
etamide 0.9033 Fatty Acyls 1.87E+08
40 C[13]H[14]N[2]O[3] 245.0932 5.32 0.3 N-Acetyl-D-tryptophan 0.963
Carboxylic acids and derivatives 1.82E+08
41 C[11]H[14]O[4] 193.0861 3.71 1.1 Sinapyl alcohol 0.9881 Phenols
1.75E+08
42 C[9]H[10]O[3] 165.0552 5.20 3.3 DL-3-Phenyllactic acid 0.9342
Phenylpropanoic acids 1.69E+08
43 C[11]H[11]NO[3] 131.0490 4.16 0.8 Cinnamoylglycine 0.9993 Carboxylic
acids and derivatives 1.60E+08
44 C[15]H[21]NO[7] 328.1390 1.99 0.9 N-Fructosyl phenylalanine 0.8725
Carboxylic acids and derivatives 1.60E+08
45 C[26]H[30]O[13] 551.1762 6.09 0.4
4-(7-Hydroxy-4-oxo-3,4-dihydro-2H-chromen-2-yl)phenyl
2-O-(3,4-dihydroxy-4-(hydroxymethyl)tetrahydrofuran-2-yl)hexopyranoside
0.9819 Flavonoids 1.48E+08
46 C[30]H[44]O[4] 469.3312 8.56 0.3 Glabrolide 0.9808 Prenol lipids
1.41E+08
47 C[8]H[17]NO[2] 160.1332 0.84 0.3 5-aminovaleric acid betaine 0.9892
Fatty Acyls 1.40E+08
48 C[18]H[24]O[12] 433.1340 3.93 0.6 Licoagroside B 0.9994
Saccharolipids 1.33E+08
49 C[18]H[33]C[l]N[2]O[5]S 425.1780 8.09 21.9 Clindamycin 0.8949
Carboxylic acids and derivatives 1.31E+08
50 C[27]H[30]O[15] 595.1660 4.14 1.7 Vicenin-2 0.9789 Flavonoids
1.14E+08
51 C[21]H[20]O[6] 369.1334 10.50 0.3 Icaritin 0.9458 Flavonoids
1.14E+08
52 C[13]H[13]NO[4] 218.0457 4.26 0.8 Ethyl
4-hydroxy-7-methoxy-3-quinolinecarboxylate 0.9906 Quinolines and
derivatives 1.04E+08
53 C[13]H[16]O[9] 315.0726 2.79 8.5 Benzoic acid + 2O, O-Hex 0.8777
Organooxygen compounds 1.04E+08
54 C[24]H[28]O[4] 379.1979 4.44 17.1 Clausarin 0.8776 Coumarins and
derivatives 1.03E+08
55 C[7]H[10]O[4] 113.0601 5.71 2.2 Succinylacetone 0.9809 Keto acids
and derivatives 1.02E+08
56 C[15]H[12]O[5] 273.0749 5.84 0.5 Naringenin chalcone 0.9978 Linear 1
3 - diarylpropanoids 1.01E+08
[88]Open in a new tab
Following the consolidation and elimination of duplicates from all ALI
targets received from the GeneCards, DisGeNET, and OMIM databases, we
identified 3,037 genes associated with ALI. These genes were then
intersected with 1,114 target points of the blood-absorbed components
of MXSGD, revealing 338 overlapping targets.
3.2.2. Construction of the C–T network
We generated the C–T network using Cytoscape software ([89] Figure 4 )
and analyzed it using the Network Analyzer plugin. A higher degree
indicates that a node is connected to more nodes, reflecting a more
significant regulatory role in the overall network. The network
contained 1,172 nodes, including one Chinese herbal formula, 56
blood-absorbed components, 1,114 action targets, and 1 disease, as well
as 4,619 edges.
Figure 4.
[90]This network diagram displays the compound-target (C-T)
interactions of MXSGD. The central nodes are labeled “ALI” and “MXSGD”
and are surrounded by peripheral nodes marked with alphanumeric
identifiers. Node colors represent different categories: purple denotes
MXSGD-related entities, green indicates potential target molecules, and
blue corresponds to the 56 blood-absorbed constituents of MXSGD. In
such networks, a node with a higher degree (i.e., connected to more
nodes) is considered to play a more significant regulatory role.
[91]Open in a new tab
The C-T network of MXSGD. Purple nodes refer to MXSGD. Green nodes
refer to the potential targets. Blue nodes refer to the 56 constituents
absorbed into blood contained in MXSGD (M1-M56 represents the active
component of MXSGD with serial numbers 1–56 in [92]Table 3 ).
3.2.3. Construction and analysis of the PPI network
We imported the 338 selected intersection targets into the STRING
database to determine the interaction relationships between proteins.
Each circle represents a protein node participating in the
interactions, and each line represents the interactions between
targets. Larger circles indicate higher degree values, and thicker
lines indicate higher binding scores. The resulting PPI network
contained 304 nodes, with 34 targets not participating in the
interactions, and 916 interaction lines. The network yielded a median
degree of 4, a median betweenness of 182.669, and an average closeness
of 0.051 resulting in 99 core targets ([93] Figure 5A ).
Figure 5.
[94]Three panels present PPI and enrichment analysis of key targets for
MXSGD treating ALI. Panel A shows a network diagram in which circles
represent protein nodes participating in interactions, and lines
indicate interactions between targets. Larger circles indicate higher
degree values, and thicker lines represent higher binding scores. Panel
B displays three bubble charts showing the top 15 significantly
enriched terms across biological processes, molecular functions, and
cellular components. Panel C is a dot plot illustrating the top 20
significantly enriched KEGG pathways (dot size = count; color =
-log₁₀(p-value)).
[95]Open in a new tab
PPI and Enrichment analysis of the key targets. (A) Protein-protein
interaction (PPI) network of potential targets for MXSGD treatment of
ALI. (B) Top 15 significantly enriched terms in biological processes,
molecular functions, and cellular components. (C) Top 20 significantly
enriched terms in KEGG pathways.
Among these key targets, the primary ones included tumor necrosis
factor (TNF) and interleukin (IL)-1β, which are associated with
inflammation; HSP90AA1 and SRC, which are associated with cell
proliferation and carcinogenesis; epidermal growth factor receptor
(EGFR), which is linked to vascular endothelium; and AKT,
Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), and
phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha
(PIK3CA), which are critical components of the PI3K/AKT pathway. This
result indicates that MXSGD influences the regulation of inflammatory
responses, oxidative stress, autophagy, apoptosis, and vascular
function.
Further analysis of these key targets revealed that many of them were
closely associated with macrophage polarization. For example,
inflammation-related targets such as TNF, interferon gamma (IFNG), and
prostaglandin-endoperoxide synthase 2 (PTGS2) are involved in the
macrophage polarization process. Several signaling pathways associated
with key targets were also identified as critical pathways regulating
macrophage polarization, including AKT1, PIK3R1, PIK3CA, Phosphatase
and tensin homolog (PTEN), mitogen-activated protein kinase 1 (MAPK1),
MAPK14, inhibitor of nuclear factor kappa-b kinase subunit beta
(IKBKB), and conserved helix-loop-helix ubiquitous kinase (CHUK). These
key targets included transcription factors related to macrophage
polarization, such as peroxisome proliferator-activated receptor gamma
(PPARG) and JUN/FOS (AP-1 complex), and markers associated with
macrophage polarization, including nitric oxide synthase 2 (NOS2).
3.2.4. GO enrichment analysis
We performed GO analysis of the principal targets of the blood-absorbed
components of MXSGD for the treatment of ALI using DAVID 2021, with P <
0.05 set as the criterion, revealing a total of 915 GO items. The GO
enrichment analysis is primarily categorized into three domains:
biological processes (BP), cellular components (CC), and molecular
functions (MF). The BP-related entries were the most abundant, totaling
696, and predominantly related to the positive regulation of gene
expression, signal transduction, protein phosphorylation, negative
regulation of apoptotic process, positive regulation of transcription
and DNA-templated, and immunological responses. The CC-related entries
totaled 81, mainly involving the cytosol, cytoplasm, nucleus, and
nucleoplasm. The MF-related entries included 138 entries, primarily
focusing on protein binding, ATP binding, identical protein binding,
enzyme binding, and protein kinase activity and binding. These GO
entries were sorted by count values, and the top 15 entries for BP, CC,
and MF were selected and visualized using a bubble chart ([96]
Figure 5B ).
3.2.5. KEGG pathway analysis
We conducted KEGG analysis of the potential treatment targets of ALI by
MXSGD using DAVID (2021; with P < 0.05 as the criterion), revealing 169
enriched KEGG pathways. The top 20 pathways are displayed in a bubble
chart in [97]Figure 5C .
Most of the targets were enriched in signaling pathways associated with
cancer, the PI3K/AKT signaling pathway, lipid metabolism,
atherosclerosis, human cytomegalovirus infection, the MAPK signaling
pathway, and Alzheimer’s disease.
3.2.6. Construction of the CTP network
We constructed the CTP network using Cytoscape software ([98] Figure 6A
) and analyzed it using the Network Analyzer plugin. Higher node degree
values indicate a greater number of connected nodes, reflecting
enhanced regulatory function within the entire network. The network
contained 176 nodes, including 1 Chinese herbal formula, 56
blood-absorbed components, 99 key targets, 20 signaling pathways, and 1
disease, as well as 1,124 edges. These findings highlight the
characteristic therapeutic approach of TCM formulas, which act through
a multi-component, multi-target, multi-pathway mechanism.
Figure 6.
[99]Panel A displays a network graph illustrating interactions among
multiple nodes, with purple nodes representing MXSGD, blue nodes
indicating the 99 key targets, green nodes on the right showing
signaling pathways, and purple nodes on the left denoting the 56
blood-absorbed constituents. Panel B shows structural bioinformatics
visualizations that highlight molecular interactions with proteins,
depicting binding sites for compounds such as pseudoephedrine and
norephedrine with proteins including TGFBR2, AKT1, and PIK3R2. Panel C
contains a circular heatmap displaying affinity values for various
compounds—annotated with names like DL-Ephedrine and Phellamurin—where
the values represent negative binding energies.
[100]Open in a new tab
CTP network and molecular docking results. (A) CTP network of MXSGD.
Purple nodes refer to MXSGD. Blue nodes refer to the 99 key targets.
Green nodes on the right refer to the signal pathways. Purple nodes on
the left refer to the 56 constituents absorbed into blood contained in
MXSGD (M1-M56 represents the active component of MXSGD with serial
numbers 1–56 in [101]Table 3 ). (B) Representative docking complex of
key targets and compounds. (C) Pie chart of docking scores of 10 key
targets combining to 5 active compounds in MXSGD. Binding energy values
in [102]Figure 6C are negative.
3.2.7. Molecular docking
We selected degree-ranking compounds in the CTP network, including
DL-ephedrine, (-)-pseudoephedrine, (+/-)-norephedrine, phellamurin, and
trans-2-phenylcyclopropylamine, for molecular docking validation with
EGFR, CDK2, MAPK1, GSTP1, MAPK8, TGFBR2, PIK3R2, AKT1, PIK3R1, and
PIK3CA. The minimum binding energies for each protein-ligand complex
were calculated. A binding energy below zero indicates spontaneous
binding between ligand molecules and receptor proteins, and a binding
energy below –1.2 kcal/mol suggests strong binding activity between the
molecules. The smaller the binding energy, the stronger the binding
ability, indicating better docking.
The four docking results with the best binding activity are visualized
in [103]Figure 6C . The four pharmacologically relevant blood-absorbed
components exhibited good binding to target proteins. Specifically, the
binding energies of (-)-pseudoephedrine and TGFBR2, (-)-pseudoephedrine
and AKT1, (+/-)-norephedrine and TGFBR2, (+/-)-norephedrine and PIK3R2,
and DL-ephedrine and TGFBR2 in the blood-absorbed components were all
less than –1.2 kcal/mol. Among these, (+/-)-norephedrine exhibited the
lowest binding energy with TGFBR2, followed by (+/-)-norephedrine with
PIK3R2, indicating that (+/-)-norephedrine exhibited the strongest
binding activity with TGFBR2 and PIK3R2. The docking results depicting
the best visual binding activity are presented in [104]Figure 6B .
3.3. Effects of MXSGD on IAV-induced lung injury mouse models
3.3.1. Protective effects of MXSGD on IAV-induced ALI
We then investigated the effects of MXSGD in vivo using IAV-induced
lung injury mouse models. Histological analysis revealed intact
alveolar morphology and septal structure in control mice. However, the
model control group exhibited significant histopathological changes in
lung tissue ([105] Figure 7A ), characterized by pulmonary congestion
and edema, extensive infiltration of lymphocytes and macrophages, and
large necrotic solid lesions. Compared to that in the model group, lung
tissue histopathological damage in the MXSGD treatment groups
significantly improved on days 3 and 7 post-treatment, as evidenced by
reduced congestion and edema, decreased inflammatory cell infiltration,
and a smaller area of necrotic solid lesions. These improvements were
the most pronounced in the oseltamivir (positive treatment group) and
medium-dose MXSGD groups, which exhibited clear alveolar outlines and
reduced inflammatory cell infiltration.
Figure 7.
[106]Histological analysis image of lung tissues from a study. Panel A
shows stained tissue sections at different magnifications (one hundred
times and four hundred times) over three and seven days comparing
control, model, Ose, and varying MXSGB doses (low, medium, high). Panel
B is a bar graph of the pulmonary index over time. Panel C presents a
bar graph of the relative expression of NP. Panel D displays
fluorescence images showing NP, DAPI staining, and merged images.
[107]Open in a new tab
Effects of MXSGD on lung tissue induced by IAV. (A) Pathological
changes in lung tissue of mice in each group (HE). (B) Comparison of
lung index of mice in each group (
[MATH: x¯ :MATH]
± s, n = 8). (C) Average fluorescence intensity of NP in each group (
[MATH: x¯ :MATH]
± s, n = 3, × 400). (D) Immunofluorescence staining of NP in lung
tissue. Compared to the control group, *P < 0.05, **P < 0.01; compared
to the model group, ^# P < 0.05, ^## P < 0.01.
The lung index data ([108] Figure 7B ) indicated that the model group
exhibited a significant increase in the lung index on both days 3 and 7
post-IAV infection. In contrast, subsequent treatment with MXSGD led to
a reduction in the lung index across all treatment groups to varying
extents. Specifically, the lung index in the model group significantly
increased compared to that in the control group (P < 0.01). Moreover,
both the oseltamivir and medium-dose MXSGD groups exhibited a
significant reduction in the lung index compared to the model group (P
< 0.01 or P < 0.05).
3.3.2. MXSGD decreases IAV levels in lung tissue
Our immunofluorescence analysis ([109] Figures 7C, D ) revealed
significantly increased nuclear protein (NP) levels in lung tissues in
the model groups on days 3 and 7 post-IAV infection relative to those
in the control group (P < 0.01). However, following therapeutic
intervention with MXSGD, the NP levels in all treatment groups
decreased to varying degrees. On days 3 and 7 post-infection, the
oseltamivir group demonstrated the significant decrease compared to the
model group (P < 0.01).
3.4. Validation of the potential mechanisms underlying the therapeutic
effects of MXSGD in lung injury models induced by IAV
The pathophysiological mechanism of ALI involves numerous cells and
effector cells, among which macrophages are the most important cells in
the innate immune system. A growing body of evidence indicates that,
beyond their role in immune defense, pulmonary macrophages also play an
important role as effector cells in regulating the local inflammatory
microenvironment and inflammatory response in lung tissue ([110]23).
Macrophages exhibit high plasticity and heterogeneity, polarizing into
classical activated M1 macrophages and alternatively activated M2
macrophages depending on the microenvironment. These two subtypes have
opposing functions: M1 macrophages promote inflammation, whereas M2
macrophages suppress it ([111]24).
Our network pharmacology analysis revealed that the blood-absorbed
components of MXSGD ameliorate ALI through key target proteins and
signaling pathways significantly associated with macrophage
polarization. Notably, among the 99 identified key targets, core
mediators such as TNF, IL6, IL1B, and IFNG play dual roles as central
inflammatory cytokines and key regulators of macrophage polarization.
Furthermore, several identified genes encode signature and effector
molecules critical for macrophage polarization. For example, Ptgs2
encodes cyclooxygenase-2 (COX-2), while Nos2 produces inducible nitric
oxide synthase (iNOS). Additionally, these key targets encompass
essential transcription regulators such as PPARG, JUN, and FOS.
3.4.1. MXSGD induces changes in pro-inflammatory cytokine levels in serum
Based on the above findings, we subsequently investigated whether MXSGD
confers protection against IAV-induced ALI by modulating macrophage
polarization. To this end, we measured the levels of inflammatory
cytokines in mouse sera. The results are presented in [112]Figure 8A .
Figure 8.
[113]Bar graphs and fluorescence microscopy images in multiple panels.
Panel A shows cytokine levels (IL-1β, IL-6, TNF-α) in serum across
different groups at two time points (3 and 7 days). Control and
experimental groups are color-coded. Panel B presents mRNA expression
of IL-6 and iNOS over the same intervals. Panel C displays mRNA
expression of IL-10 and FIZZ1. Panel D includes fluorescence images
illustrating CD80, CD206, and DAPI staining, merged images, and graphs
showing relative expression. Magnification at four hundred times.
[114]Open in a new tab
Verification of the potential mechanism of MXSGD in a lung injury model
induced by influenza. (A) Effects of MXSGD on the levels of
pro-inflammatory cytokines in serum (
[MATH: x¯ :MATH]
± s, n = 5). (B) The mRNA Expression of IL-6 and iNOS in lung tissue (
[MATH: x¯ :MATH]
± s, n = 4). (C) The mRNA Expression of IL-10 and FIZZ in lung tissue (
[MATH: x¯ :MATH]
± s, n = 4). (D) Immunofluorescence staining of macrophage polarization
markers CD80 and CD206 in lung tissue (
[MATH: x¯ :MATH]
± s, n = 4). Compared to the control group, *P < 0.05, **P < 0.01;
compared to the model group, ^# P < 0.05, ^## P < 0.01.
Following IAV infection, the model group exhibited a significant
increase in IL-1β, IL-6, and TNF-α levels on days 3 and 7 (P < 0.01).
However, following treatment with oseltamivir and MXSGD, the serum
levels of IL-1β, IL-6, and TNF-α significantly decreased in all
drug-treated mouse groups compared to the model group at both time
points (P < 0.01 or P < 0.05). These results demonstrate that treatment
with MXSGD reduces the serum levels of pro-inflammatory cytokines
induced by IAV in mice.
3.4.2. MXSGD alters the mRNA expression of Il-6, iNos, Il-10, and Fizz in
lung tissue
We then analyzed the gene expression of various molecules in the lung
tissue of mice from different groups ([115] Figure 8B ). They included
IL-6, a classic pro-inflammatory cytokine; iNOS, an enzyme induced by
inflammatory signaling, which synergizes with pro-inflammatory
cytokines to amplify inflammatory responses and serves as a marker of
M1 macrophages; IL-10, a classic anti-inflammatory cytokine; and FIZZ1,
a secretory protein involved in inflammatory regulation and a marker of
M2 macrophages.
Compared to that in the control group, Il-6 expression was
significantly increased in the model group on both days 3 and 7
post-IAV intervention (P < 0.01). Compared to that in the model group,
the mRNA expression of Il-6 was decreased in the oseltamivir group and
all MXSGD dose groups. On day 3, the most pronounced reduction occurred
in the oseltamivir group (P < 0.05). On day 7, we observed significant
decreases in the oseltamivir group and the medium-dose MXSGD group (P <
0.05; [116]Figure 8B ).
Compared to that in the control group, iNos expression was
significantly increased in the model group on both days 3 and 7
post-IAV infection (P < 0.05). All MXSGD -treated groups exhibited
marked reductions in iNos expression compared to the model group. On
day 3 post-infection, the most significant decrease occurred in the
oseltamivir group (P < 0.05). On day 7, both the oseltamivir group and
all MXSGD treatment groups demonstrated statistically significant
reductions in iNos expression (P < 0.05 or P < 0.01; [117]Figure 8B ).
The expression of Il-10 was downregulated in the model group on days 3
and 7 post-IAV infection compared to those in the control group;
however, this decrease was not statistically significant. Compared to
that in the model group, Il-10 expression was upregulated in all
drug-treated groups. Specifically, on day 3 post-infection, the most
significant increase occurred in the oseltamivir group (P < 0.05). On
day 7, Il-10 expression was significantly upregulated in the
oseltamivir group and all MXSGD treatment groups (P < 0.05 or P < 0.01;
[118]Figure 8C ).
On days 3 and 7 post-IAV infection, Fizz1 expression decreased in the
model group compared to that in the control group, though without
statistical significance. All drug-treated groups exhibited increased
Fizz1 expression compared to that in the model group. On day 3
post-infection, the low-dose and medium-dose MXSGD groups exhibited the
most significant elevation (P < 0.05 or P < 0.01). On day 7, all MXSGD
dose groups exhibited significant upregulation in Fizz1 expression
compared to the model group (P < 0.01; [119]Figure 8C ). These findings
suggest that MXSGD can regulate the gene expression of macrophage
polarization markers (iNos and Fizz1) in the lung tissue of
IAV-infected mice.
3.4.3. MXSGD affects macrophage polarization in lung tissue
Based on our preliminary experimental findings, and to further
investigate the mechanistic relationship between MXSGD and macrophage
polarization, we selected the following groups for subsequent analysis:
control, model, oseltamivir, and MXSGD medium-dose groups. We
subsequently characterized macrophage phenotypes in lung tissue using
immunofluorescence double staining.
In terms of M1 macrophages, immunofluorescence revealed that the model
group exhibited a significant increase in the expression of the M1
macrophage marker CD80 on both days 3 and 7 post-IAV infection,
compared to the control group (P < 0.01; [120]Figure 8D ). Conversely,
CD80 expression levels decreased to different extents in all MXSGD
groups compared to those in the model group. On day 3 post-infection,
the oseltamivir group exhibited a significant decrease in CD80
expression (P < 0.05). By day 7 post-infection, both the oseltamivir
and MXSGD groups exhibited a significant decrease in CD80 expression (P
< 0.01).
In terms of M2 macrophages, immunofluorescence revealed notable
alterations in the M2 macrophage marker CD206. Compared to that in the
control group, there were no notable alterations in CD206 levels in the
model group on day 3 post-infection; however, on day 7, CD206
expression significantly increased (P < 0.01; [121]Figure 8D ). The
oseltamivir and MXSGD groups exhibited a significant increase in CD206
levels on both days 3 and 7 compared to the model group (P < 0.01).
This finding indicates that the macrophages were successfully
repolarized from M1 to M2.
Overall, these findings suggest that MXSGD can mitigate the
inflammatory response and pulmonary damage following influenza
infection by modulating macrophage polarization.
3.4.4. MXSGD modulates protein expression in the PI3K/AKT pathway
Macrophage polarization is regulated by multiple signaling pathways. In
the present study, analysis of key targets revealed that several
target-associated pathways critically govern this process, notably the
PI3K/AKT, MAPK, and NF-κB pathways. Among them, the PI3K/AKT signaling
cascade plays a central regulatory role in pulmonary injury
pathologies. Thus, to validate these network pharmacology predictions,
we performed IHC analysis of key proteins in the PI3K/AKT pathway on
lung tissues from four experimental groups: control, model,
oseltamivir, and MXSGD medium-dose groups.
Compared to that in the control group, PI3K protein expression in the
model group was significantly upregulated in lung tissues on both days
3 and 7 following IAV infection (P < 0.01; [122]Figure 9 ). However,
both the oseltamivir and MXSGD groups exhibited a significant reduction
in PI3K expression levels, compared to the model group (P < 0.01).
Figure 9.
[123]Two-panel image depicting protein expression and quantification in
lung tissue. Panel A shows immunohistochemistry images of PI3K, AKT,
and phosphorylated AKT in control, model, Ose, and MXSGD groups at 3
and 7 days, magnified 400 times. Panel B displays bar graphs of the
mean density of PI3K, AKT, and phosphorylated AKT across these groups,
with significant differences indicated by asterisks and hashtags, for
both 3 and 7 days.
[124]Open in a new tab
Expression of PI3K/AKT pathway-related proteins in lung tissues of mice
across experimental groups. (A) Alterations in PI3K, AKT, and p-AKT
Expression in Lung Tissues of Mice from Different Groups. (B)
Comparison of Mean Optical Density (MOD) Values for PI3K, AKT, and
p-AKT in Lung Tissues Across Groups (
[MATH: x¯ :MATH]
± s, n = 5). Compared to the control group, *P < 0.05, **P < 0.01;
compared to the model group, ^# P < 0.05, ^## P < 0.01.
Similarly, AKT protein expression in the model group was significantly
elevated on days 3 and 7 post-IAV infection compared to that in the
control group (P < 0.01; [125]Figure 9 ). However, the drug-treated
groups exhibited reduced AKT expression compared to the model group. On
day 3, AKT expression levels in both the oseltamivir and MXSGD groups
were significantly lower than those in the model group (P < 0.01). By
day 7, AKT expression significantly decreased in the oseltamivir group
than that in the model group (P < 0.05).
We observed a consistent trend for phosphorylated AKT (p-AKT)
expression ([126] Figure 9 ). Specifically, the model group displayed a
significant increase in p-AKT levels at both time points compared to
the control group (P< 0.01), whereas therapeutic intervention with
oseltamivir or MXSGD resulted in a significant decrease in p-AKT
expression, compared to the model group (P < 0.01).
4. Discussion
IAV is a prevalent cause of respiratory infections in humans, and
severe IAV-induced lung infection can lead to ALI by inducing
macrophages to secrete inflammatory mediators. Notably, ALI is a
significant contributor to mortality associated with IAV infection
([127]25). Although lung-protective ventilation and neuromuscular
blockers are effective in treating ALI, the mortality rate remains
approximately 40%, primarily owing to multiple organ failure induced by
inflammatory mediators ([128]26–[129]28). Consequently, mitigating the
intense acute inflammatory response in individuals with ALI or ARDS is
crucial for improving outcomes. MXSGD is a TCM formula used to treat
influenza infections; however, the specific components mediating the
therapeutic actions of MXSGD and its underlying mechanisms remain
unknown. Therefore, in the present study, we used UPLC-HRMS to
determine the blood-absorbed components of MXSGD and investigate the
potential mechanisms underlying the therapeutic effects of MXSGD in ALI
using network pharmacology and molecular docking techniques. Our
findings demonstrated that MXSGD alleviates ALI through mechanisms
involving viral inhibition, regulation of the inflammatory response,
immune modulation, apoptosis, and oxidative stress.
In the present study, we analyzed blood-absorbed components, key
targets, and signaling pathways and identified DL-ephedrine,
pseudoephedrine, norephedrine, phellamurin, and 18β-glycyrrhetinic acid
(18β-GA) as the components with the highest degree values in the
network, indicating their central roles in the therapeutic effect.
Ephedra is widely used in TCM to treat diseases such as bronchial
asthma, fever, cough, and colds ([130]29). DL-ephedrine is an isomer of
the alkaloid ephedrine derived from ephedra. Ephedrine can effectively
facilitate bronchial dilatation, mitigate inflammatory reactions, and
inhibit cough reflexes. It can also suppress airway hyperreactivity in
asthmatic mice by modulating the TGF-β1/Smads and TGF-β1/NF-κB
signaling pathways, thereby enhancing airway remodeling and diminishing
lung inflammation ([131]30, [132]31). In rats with knee osteoarthritis,
ephedrine can suppress the NF-κB signaling pathway by activating the
AMPK pathway, thus inhibiting inflammatory responses and ameliorating
cartilage damage ([133]32). Pseudoephedrine is a significant
constituent of ephedra, and compared to ephedrine, it exhibits a
diminished vasoconstrictive action and a reduced influence on the
central nervous system ([134]33). The combination of pseudoephedrine
and emodin can inhibit inflammatory pathways, alleviate pulmonary
edema, decrease M1 macrophage polarization, and increase M2 macrophage
polarization, significantly mitigating LPS-induced ALI in rats
([135]34). Norephedrine possesses extensive applications in the
treatment of various diseases. Clinically, oral norepinephrine exerts a
modest decongestant effect in individuals with a cold; therefore, it is
extensively utilized for the common cold ([136]35). Phellamurin, a
flavonoid glycoside found in plants, decreases the viability of
osteosarcoma cells and promotes apoptosis by inhibiting the
PI3K/AKT/mTOR pathway ([137]36). 18β-GA is the main metabolite of
glycyrrhetinic acid, which is the primary active component of licorice.
18β-GA exhibits a range of biological effects, including hepatic
protective, anti-cancer, kidney protective, antiviral, antibacterial,
and anti-inflammatory activities ([138]37). Moreover, 18β-GA can
downregulate the gene expression of Icam-1, Tnf-Α, Cox-2, and iNos in
LPS-induced RAW 264.7 cells by decreasing NF-κB expression and
inhibiting its nuclear translocation. It also downregulates the gene
and protein expression levels of TNF-α, IL-6, and MCP-1 in the culture
supernatant of human pulmonary artery smooth muscle cells stimulated by
platelet-derived growth factor BB, demonstrating substantial
anti-inflammatory efficacy ([139]38, [140]39). Notably, DL-ephedrine,
pseudoephedrine, and norepinephrine are all derived from ephedra,
highlighting the pivotal role of ephedra in mediating the therapeutic
effects of MXSGD against IAV infections.
The PPI network diagram illustrates several interactions among the
identified targets. Our PPI network revealed 99 key targets. A higher
degree of connectivity suggests a greater probability that MXSGD exerts
its therapeutic effects against ALI through those specific targets.
Among the 99 key targets, a substantial number were closely associated
with macrophage polarization. For example, TNF-α, encoded by Tnf, is a
classical pro-inflammatory factor that drives M1 macrophage
polarization by activating multiple signaling pathways ([141]40).
IFN-γ, encoded by Ifng, promotes M1 polarization while suppressing M2
polarization via STAT pathway activation ([142]41). COX-2, produced by
Ptgs2, is implicated in inflammatory M1 polarization ([143]42). The
signaling pathways linked to these targets include the PI3K/AKT
pathway, involving AKT1, PIK3R1, PIK3CA, and PTEN ([144]43, [145]44);
the MAPK pathway, involving MAPK1 and MAPK14 ([146]45, [147]46); and
the NF-κB pathway, mediated by IKBKB and CHUK ([148]47). Notably, all
these pathways are critical in the regulation of macrophage
polarization. Furthermore, these key targets also encompassed
transcription factors associated with macrophage polarization,
including PPARG ([149]48, [150]49), JUN, and FOS ([151]50), as well as
macrophage polarization-related targets such as NOS2. We then
integrated the 99 key targets identified in the PPI network with the
results from the CTP network and identified significant targets: EGFR,
CDK2, MAPK1, GSTP1, MAPK8, TGFBR2, PIK3R2, AKT1, PIK3R1, and PIK3CA.
Notably, all these targets exhibited strong binding affinities for
blood components in the molecular docking analysis. GO and KEGG
analyses indicated that the prospective targets of MXSGD in the
treatment of ALI are predominantly enriched in the PI3K/AKT, MAPK, and
nuclear receptor signaling pathways. For example, the PI3K/AKT
signaling pathway is essential for cell survival, initiation of
inflammatory responses, and oxidative stress in pulmonary diseases
([152]51–[153]53). This pathway affects the progression of various
respiratory disorders, including ALI, ARDS, chronic obstructive
pulmonary disease, asthma, and novel coronavirus pneumonia ([154]51,
[155]54–[156]58). The primary targets linked to the regulation of the
PI3K/AKT signaling pathway, PIK3R1 and PIK3R2, encode the regulatory
subunits p85α and p85β of PI3K, respectively, while PIK3CA encodes the
catalytic subunit P110α of PI3K ([157]59, [158]60). Moreover, the
PI3K/AKT signaling pathway is closely associated with macrophage
polarization. The activation of this pathway induces macrophage
repolarization from the pro-inflammatory M1 phenotype to the
anti-inflammatory M2 phenotype, thereby exerting anti-inflammatory
effects ([159]61). For example, grape seed proanthocyanidin alleviates
LPS-induced ALI by inducing M1-to-M2a macrophage repolarization through
the PI3K/AKT pathway ([160]62). These results, along with network
pharmacology and molecular docking findings, indirectly corroborate the
accuracy of the network pharmacology-predicted targets.
Patients with ALI exhibit considerable accumulation of inflammatory
cells in the lungs, which release inflammatory cytokines. These
cytokines, in turn, activate various signaling pathways, forming a
complex signaling network. Continuous stimulation by external antigens
triggers an escalating pulmonary inflammatory response, which
eventually becomes uncontrolled, leading to lung tissue damage
([161]63–[162]65). To further validate the network pharmacology and
molecular docking results, we established an ALI mouse model induced by
IAV and administered MXSGD to evaluate its effects on lung tissue
pathology and inflammatory cytokines. Our findings demonstrated that
MXSGD mitigated lung tissue damage, decreased viral load in the lungs,
and downregulated the expression of IAV-induced inflammatory markers,
thereby attenuating the pulmonary inflammatory response. Therefore,
MXSGD exhibits favorable therapeutic efficacy.
Our network pharmacology analysis revealed that a significant
proportion of key therapeutic targets of MXSGD against ALI are
associated with macrophage polarization. Macrophages are key initiators
of the inflammatory response, and alveolar macrophages play a central
role in the pathogenesis of ALI, contributing to both inflammation and
tissue repair ([163]66). Alveolar macrophages function as
antigen-presenting cells to trigger innate immunity and polarize into
different phenotypes based on the local or systemic inflammatory
microenvironment. After polarization, they can release large amounts of
pro-inflammatory cytokines to drive the inflammatory response or
anti-inflammatory cytokines to mediate the repair of damaged lung
tissue. Therefore, we investigated macrophage polarization in mouse
lung tissue treated with MXSGD. The gene expression of pro-inflammatory
cytokines, including Il-6 and iNos, was significantly downregulated
following treatment with MXSGD. In contrast, the expression of the
anti-inflammatory cytokines Il-10 and Fizz1 was markedly upregulated
following MXSGD treatment. Therefore, we performed immunofluorescence
double staining of macrophage polarization markers in mouse lung tissue
to further elucidate the effects of MXSGD on macrophage polarization in
ALI. Our results revealed that MXSGD significantly downregulated the
expression of the pro-inflammatory M1 phenotype marker CD80 in
IAV-infected lung tissue while simultaneously promoting the expression
of the anti-inflammatory M2 phenotype marker CD206. These findings
collectively suggest that MXSGD exerts protective effects against lung
injury by modulating macrophage polarization, facilitating the
transition from M1 to M2 phenotypes. Notably, this mechanistic insight
is fully consistent with our network pharmacology predictions.
As mentioned previously, the PI3K/AKT signaling pathway plays a pivotal
role in macrophage polarization, and substantial evidence highlights
its regulatory functions. For example, aloe-emodin derivatives suppress
macrophage inflammatory mediator release via PI3K-AKT/NF-κB signaling
([164]67). Moreover, Xibining mitigate macrophage inflammation by
regulating the PI3K/AKT pathway ([165]68). Notably, our network
pharmacology analysis revealed that the therapeutic targets of MXSGD
for ALI were mainly enriched in PI3K/AKT pathway targets.
Immunohistochemical analysis of lung tissues further confirmed that
MXSGD intervention significantly downregulated IAV-induced PI3K/AKT
pathway activation, validating the network pharmacology predictions.
Nevertheless, whether MXSGD modulates macrophage polarization through
PI3K/AKT suppression, as well as the precise mechanistic interplay
between PI3K/AKT signaling and macrophage polarization in
MXSGD-mediated ALI treatment, remains to be fully elucidated.
This study has certain limitations. First, as we did not quantify
pharmacokinetic parameters, such as bioavailability and half-life, for
the identified components, the main active components driving
macrophage polarization and their direct molecular targets remain
unclear. Second, although we preliminarily validated the involvement of
the PI3K/AKT pathway, the specific upstream and downstream signaling
molecules, tandem nodes, and their mechanism interactions need to be
systematically defined. Therefore, future studies should screen the
active, blood-absorbed components of MXSGD that target macrophage
polarization to assess the necessity and sufficiency of PI3K/AKT
signaling in cellular and animal models, as well as to elucidate the
downstream regulatory mechanisms. Such studies will precisely identify
the therapeutic mechanisms of MXSGD, provide strong scientific support
for clinical optimization of the treatment of severe influenza-induced
lung injury, and lay the foundation for rational drug combinations and
TCM-based drug development.
In summary, we confirmed the significant efficacy of MXSGD in
alleviating influenza-induced ALI and associated inflammation. Notably,
through integrated network pharmacology and in vivo validation, we
established macrophage polarization as a pivotal therapeutic mechanism.
This discovery elucidates the scientific basis of MXSGD against
influenza-associated ALI from an immunomodulatory perspective while
providing mechanistic insights into the multi-component, multi-target,
multi-pathway synergistic paradigm of TCM. We also identified the
bioactive blood-absorbed components of MXSGD, offering a
pharmacodynamic foundation for modern research and clinical
applications.
5. Conclusion
In this study, we examined the blood-absorbed components of MXSGD,
using network pharmacology and bioinformatics, to investigate the
effective pharmacological components, key targets, and potential
regulatory mechanisms underlying the therapeutic effects of MXSGD
against ALI. Our findings provide new insights into the underlying
mechanisms of MXSGD in treating ALI. However, further investigations
are required to determine the mechanisms by which MXSGD regulates
macrophage polarization to suppress inflammatory responses in the
treatment of ALI.
Acknowledgments