Abstract
Background
Qing-Jin-Hua-Tan decoction (QJHTD) is a classic traditional Chinese
medicine (TCM) prescription that first appeared in the ancient book
Yi-Xue-Tong-Zhi. QJHTD has shown effectiveness for treating chronic
obstructive pulmonary disease (COPD), although its mechanisms of action
are still perplexing. The molecular mechanisms underlying the curative
effects of QJHTD on COPD is worth exploring.
Methods
In vitro antiapoptotic and antiinflammatory activities of QJHTD were
evaluated using cell viability, proliferation, apoptosis rate, and
expression of IL-1β and TNF-α in BEAS-2B and RAW264.7 cells challenged
with cigarette smoke (CS) extract (CSE) and lipopolysaccharide (LPS).
In vivo therapeutic activities of QJHTD were evaluated using
respiratory parameters (peak inspiratory flow (PIFb) and peak
expiratory flow (PEFb) values), histopathology (mean linear intercept,
MLI), and proinflammatory cytokine (IL-1β and TNF-α) and cleaved
caspase-3 (c-Casp3) levels in the lung tissue of CS–LPS-exposed BALB/c
mice. Network pharmacology-based prediction, transcriptomic analysis,
and metabolic profiling were employed to investigate the signaling
molecules and metabolites pertinent to the anti-COPD action of QJHTD.
Results
Increased cell viability and proliferation with decreased apoptosis
rate and proinflammatory cytokine expression were noted after QJHTD
intervention. QJHTD administration elevated PEFb and PIFb values,
reduced MLI, and inhibited IL-1β, TNF-α, and c-Casp3 expression in
vivo. Integrated network pharmacology–transcriptomics revealed that
suppressing inflammatory signals (IL-1β, IL-6, TNF, IκB–NF-κB, TLR, and
MAPK) and apoptosis contributed to the anti-COPD property of QJHTD.
Metabolomic profiling unveiled prominent roles for the suppression of
apoptosis and sphingolipid (SL) metabolism and the promotion of choline
(Ch) metabolism in the anti-COPD effect of QJHTD. Integrative
transcriptomics–metabolomics unraveled the correlation between SL
metabolism and apoptosis. In silico molecular docking revealed that
acacetin, as an active compound in QJHTD, could bind with high affinity
to MEK1, MEK2, ERK1, ERK2, Bcl2, NF-κB, and alCDase target proteins.
Conclusion
The therapeutic effect of QJHTD on COPD is dependent on regulating
inflammatory signals and apoptosis-directed SL metabolism. These
findings provide deeper insights into the molecular mechanism of action
of QJHTD against COPD and justify its theoretical promise in novel
pharmacotherapy for this multifactorial disease.
Keywords: Qing-Jin-Hua-Tan decoction, Omics, Apoptosis, Inflammation,
Chronic obstructive pulmonary disease
1. Introduction
Increasing attention is being paid to chronic obstructive pulmonary
disease (COPD)—a weakening condition that has become the third leading
cause of morbidity and mortality for both genders worldwide [[37]1].
COPD is a common respiratory disease distinguished by airway limitation
and airflow obstruction. The lesions in this disease manifest as
persistent small airway inflammation and progress with incomplete
reversibility [[38]2]. Clinically, a series of typical signs, including
chronic cough, sputum production, wheezing, dyspnea, and even
respiratory failure, represent the outcomes of COPD progression with
worsening of lung function [[39]3]. When such alterations occur as
worsening dyspnea and increased sputum volume (specifically purulent
sputum), acute exacerbation of COPD can be diagnosed in the clinical
setting [[40]4]. Furthermore, these symptoms are linked to the
pathological severity of COPD; airway inflammation (infective or
non-infective) followed by an imbalance between lung injury and repair
determines disease prognosis. Therefore, attenuating airway
inflammation and improving lung recovery are expected to play
indispensable roles in COPD treatment. The recommended pharmacological
therapies for COPD involve the application of bronchodilators,
antiinflammatory agents, antiinfective agents, and combination agents
[[41]5]. These medications can produce significant effects on
alleviating respiratory symptoms, particularly in the short-term
treatment of COPD. However, several adverse reactions, such as
tachycardia, tremor, vomiting, diarrhea, hyperglycemia, hypertension,
fluid retention, and osteoporosis, restrict extensive applications of
those pharmacological modalities to COPD [[42]6]. Moreover, drug
resistance due to prolonged medications in patients may result in
disease relapse [[43]7]. Thus, the development of additional drug
regimens that are more effective and that have fewer side effects is
urgently needed.
Traditional Chinese medicine (TCM) formulas offer a promising
alternative to routine therapeutic drugs. Qing-Jin-Hua-Tan decoction
(QJHTD), a classical Chinese herbal prescription recorded in the
ancient (Ming dynasty's) Chinese medical book Yi-Xue-Tong-Zhi, has been
used to treat various lung disorders, including COPD, for almost 500
years. This formula comprises 11 medicinal herbs as listed in [44]Table
1. In TCM theory, the formula's traditional efficacy is described as
clearing lung heat and eliminating phlegm. These conventional terms can
be translated into the pharmacological activities of suppressing
inflammation, relaxing bronchial smooth muscle, facilitating
expectoration, alleviating coughs, and regulating immunity. In the
clinic, QJHTD and its modified prescriptions have been administered to
treat inflammatory bronchopneumopathies as diverse as acute/chronic
bronchitis, bronchiectasis, pneumonia, and COPD, which pertain to the
TCM syndrome of phlegm heat accumulation in the lung. This formula
holds tremendous potential for the treatment of COPD associated with
prolonged airway inflammation. Nevertheless, the molecular mechanism of
action of QJHTD in COPD remission remains elusive.
Table 1.
Composition of QJHTD.
Medicinal part/name Chinese name Origin (Latin scientific name) Portion
dosage (g)
Radix scutellariae (R. Scutellariae) Huangqin Scutellaria baicalensis
Georgi 5.595
Fructus gardeniae (F. Gardeniae) Zhizi Gardenia jasminoides J.Ellis
5.595
Rhizoma anemarrhenae (R. Anemarrhenae) Zhimu Anemarrhena asphodeloides
Bunge 3.73
Cortex mori (C. Mori) Sangbaipi Morus alba L. 3.73
Semen trichosanthis (S. Trichosanthis) Gualouren Trichosanthes
kirilowii Maxim. or Trichosanthes rosthornii Harms 3.73
Bulbus fritillariae thunbergii (B.F. Thunbergii) Zhebeimu Fritillaria
thunbergii Miq. 3.73
Radix ophiopogonis (R. Ophiopogonis) Maidong Ophiopogon japonicus
(Thunb.) Ker Gawl. 3.73
Exocarpium citri rubrum (E.C. Rubrum) Juhong Citrus reticulata Blanco
3.73
Sclerotium poriae cocos (S.P. Cocos) Fuling Poria cocos (Schw.) Wolf
3.73
Radix platycodonis (R. Platycodonis) Jiegeng Platycodon grandiflorus
(Jacq.) A.DC. 7.46
Radix glycyrrhizae (R. Glycyrrhizae) Gancao Glycyrrhiza uralensis
Fisch. 1.492
[45]Open in a new tab
In this study, the anti-COPD activities of QJHTD in vitro and in vivo
were validated. The molecular mechanisms of QJHTD therapy using network
pharmacology were further decrypted. Transcriptomics and metabolomics
in the in vivo exploration identified differentially activated pathways
and distinctively abundant metabolites (endogenous and exogenous),
respectively. Further relational analysis revealed drug-triggered and
gene-governed metabolic pathways. Finally, virtual molecular docking
hinted at the potential active components in the preparation of QJHTD.
The findings serve to decode the medicinal properties of QJHTD,
underpinning the clinical application of traditional medication to
complex respiratory disorders.
2. Materials and methods
2.1. Drug preparation and constituent analysis
QJHTD samples (lyophilized powder, Lot No. Z200601) were kindly
provided by Ji-Ren Pharmaceutical Group Co., Ltd (Bozhou, China). This
recipe contains 11 herbal materials and their quantities, listed in
[46]Table 1, which were chosen according to the conversion of ancient
doses into modern routine administration. The whole prescription thus
amounted to 46.252 g in mass of the prepared slices. The final yield of
the dried extract was 29.2 %. Main components of QJHTD were analyzed
with baicalin (150 μg mL^−1) as the reference standard and its chemical
features were fingerprinted using high-performance liquid
chromatography (HPLC). The operating conditions were shown in [47]Table
2. The gradient elution is programmed in [48]Table 3.
Table 2.
Chromatographic conditions in analysis of QJHTD components.
Parameter Value
Sample injection volume 5 μL
Separating column Agilent Eclipse XDB C[18] column (250 mm × 4.6 mm,
5 μm)
Column temperature 30 °C
Mobile phase A acetonitrile
Mobile phase B 0.1 % phosphoric acid solution
Flow rate 1 mL min^−1
Detection wavelength 230 nm
Theoretical plate number ≥2500 (as calculated upon baicalin peak)
[49]Open in a new tab
Table 3.
Gradient elution for constituent detection of QJHTD extract.
Elution time (min) A: acetonitrile (%) B: 0.1 % phosphoric acid
solution (%)
0 5 95
5 6 94
25 15 85
35 21 79
37 24 76
48 25 75
53 26 74
56 28 72
65 100 0
76 100 0
[50]Open in a new tab
Dexamethasone (DEX; Lot No. C84-1434-1G, purity >99 %) as a positive
control was purchased from Dalian Meilun Biotech Co., Ltd (Dalian,
China).
All agents were thoroughly dispersed in culture medium or deionized
water at specific concentrations when used for in vitro or in vivo
activity testing.
2.2. Preparation and quality control of cigarette smoke extract (CSE)
BEAS-2B and RAW264.7 cells were stimulated by CSE and
lipopolysaccharide (LPS) to evaluate the in vitro therapeutic effect of
QJHTD on COPD [[51]8]. For CSE preparation, a commercial cigarette
(Wuniu, China Tobacco Sichuan Industrial Co. LTD, Sichuan, China)
containing 10 mg tobacco tar, 0.7 mg nicotine, and 11 mg carbon
monoxide was burnt to produce cigarette smoke (CS). The resultant smoke
was then bubbled through 20 mL of serum-free high-glucose DMEM (hgDMEM)
(Gibco, NY, USA) in a 50 mL conical tube under continuous negative
pressure using a vacuum pump. After cigarette burning-off and complete
dissolution of the smoke, the smoke solution was blended upside down
and subsequently passed through a 0.22 μm membrane filter (Millipore,
Billerica, MA, USA). This filtrate represented 100 % (relative
concentration) CSE, which was diluted with serum-free hgDMEM to the
required concentration. Fresh CSE was prepared for each experiment.
Following the same preparation procedure, the absorbance of CSE at
320 nm (OD[320]) was measured at five different time points to
determine its stability. The OD[320] values of freshly prepared CSEs
were 0.60, 0.62, 0.58, 0.59, and 0.59, yielding a mean ± SD of
0.60 ± 0.01. The coefficient of variation expressed as
[MATH: SDmean×100% :MATH]
was 1.67 % (i.e., less than 5 %). Thus, stable CSE was obtained through
the preparation method and was suitable for in vitro modeling.
2.3. Cell culture and In vitro COPD modeling
BEAS-2B and RAW264.7 cells were purchased from the American Type
Culture Collection [[52]9] and used for in vitro modeling of COPD. The
two cell lines were cultured under uniform conditions as reported
previously [[53]10]. During the logarithmic growth phase, BEAS-2B and
RAW264.7 cells were seeded in 96-well and 6-well plates at densities of
6 × 10^3 and 2 × 10^5 cells per well, respectively. After cell
adherence, CSE was combined with LPS for stimulation. At 24, 48, and
72 h post-irritant challenge, BEAS-2B cell viability was measured with
the MTT assay (Beyotime, Sichuan, China) to evaluate mimetic
tracheobronchial epithelial injury. At 6, 12, 24, 48, and 72 h after
CSE–LPS costimulation, extracellular inflammatory cytokines from
RAW264.7 cells were determined using the ELISA kits (4A Biotech,
Beijing, China) to assess mimetic airway inflammation. Together, the
exploration of in vitro COPD modeling uncovered the optimal stimulating
conditions.
2.4. Cell viability assessment
BEAS-2B cells in the logarithmic phase were seeded in a 96-well plate
and divided into control, model (irritant challenge), DEX (40 μM), and
QJHTD (12.5, 25, and 50 μg mL^−1) groups. The cells were pretreated
with the corresponding drugs or equivoluminal medium after cell
attachment. Following pretreatment for 1 h, the cells, except for
controls, were exposed to the irritant containing 5 % CSE and
1 μg mL^−1 LPS with the uninterrupted medication. After 24 h of
irritant challenge with concurrent treatment, the viability of BEAS-2B
cells was examined to evaluate the cell-protective activity of QJHTD.
Three replicates were used per test, and the experiment was repeated
three times.
2.5. EdU cell proliferation assay
According to the manufacturer's instructions for the EdU Imaging Kits
(Cy3) (APExBIO, TX, USA), BEAS-2B cells at a density of 6 × 10^3 cells
per well were incubated with EdU solution in 96-well plates after 24 h
of CSE–LPS coexposure along with medicinal intervention [[54]11].
Following cell fixation, membrane permeabilization, and click reaction
in turn, DNA synthesis in the cells was examined using the ImageXpress
Micro 4 Widefield High-Content Analysis System (Molecular Devices,
Sunnyvale, CA, USA) [[55]12] to assess cell proliferation activity. The
experiment was repeated three times.
2.6. Cell apoptosis detection using TUNEL staining
In the present study, the TUNEL kit (TransGen Biotech, Beijing, China)
was employed to observe apoptotic morphology. The procedure was as
reported previously [[56]10]. Fluorescence intensity was measured under
a fluorescence microscope (Nikon Eclipse Ts2R, NY, USA). The experiment
was repeated three times.
2.7. Fluorescence-activated cell Sorting (FACS) to confirm apoptosis
The FACS assay gave access to the quantitative analysis of apoptotic
cells. Briefly, after grouping, modeling, and intervention as described
above, BEAS-2B cells were harvested into 1.5 mL Eppendorf tubes and
rinsed with precooled PBS three times. The cells in each tube were
resuspended in 100 μL of 1 × binding buffer by gentle mixing.
Thereafter, 3 μL of Annexin V-FITC and 3 μL of PI were added, and the
cells were incubated for 10 min. The cell suspensions were transferred
to tubes containing 200 μL of 1 × binding buffer, with gentle blending
to test apoptotic cell percentage using a flow cytometer (BD
Biosciences, San Jose, CA, USA). The experiment was repeated three
times.
2.8. Proinflammatory cytokine measurement using ELISA
Since lung macrophages are implicated in bronchoalveolar inflammation,
particularly during acute exacerbation of COPD, we considered
RAW264.7 cells acceptable to model the key link in inflammatory damage
to small airways. Specifically, RAW264.7 cells were seeded into 6-well
plates at a density of 2 × 10^5 cells per well. Upon adherence, the
cells in each well were treated with the same drugs or medium. After
1 h, the cells separately underwent the same CSE–LPS costimulation and
corresponding medication as mentioned above. ELISA kits (4A Biotech,
Beijing, China) were used to determine the expression of the
proinflammatory cytokines IL-1β and TNF-α in the culture supernatant as
per the manual [[57]10]. Absorbance post reaction at 450 nm (OD[450])
was measured using a microplate reader (BioTek Cytation 3, MA, USA) and
transformed to protein content on the basis of the
OD[450]-versus-concentration standard curve. The experiment was
repeated three times.
2.9. Animal housing and In vivo COPD modeling
Animal experiments in the present study were authorized by the Center
for Experimental Animal of Southwest Medical University, and all
experimental procedures were approved by the Ethics Committee of
Southwest Medical University [[58]13] (license No. 20180309091). Male
BALB/c mice, aged 6–8 weeks and weighing 18–22 g, were purchased from
SpePharm (Beijing) Lab Animals Technology Co., Ltd (license No. SCXK
(Beijing) 2019-0010; China). The animals were housed under
well-controlled ambient conditions [[59]14]. Sterile water and standard
chow were provided ad libitum.
After 7 days of acclimation, the mice were randomly split into 6 groups
(8 mice per group): control, model, DEX (1 mg kg^−1), low-dose QJHTD
(1.12 g kg^−1), moderate-dose QJHTD (2.24 g kg^−1), and high-dose QJHTD
(4.48 g kg^−1). Before irritant challenge, pulmonary function in all
mice was detected at baseline. For COPD modeling, all the animals
except controls were submitted to LPS and CS irritation as described
previously [[60]15]. The animal experiment lasted 42 days ([61]Fig.
3A). On days 1 and 14, the mice for modeling were anesthetized with
sodium pentobarbital (50 mg kg^−1; Sigma‒Aldrich, MO, USA) and
intratracheally injected with LPS (20 μg per mouse). The rest of the
experimental duration was scheduled for mouse exposure to CS in a
self-made instrument. The daily CS stimulus was controlled three times
by burning six cigarettes, each for 30 min. One hour prior to the daily
modeling, the mice in each group were intragastrically administered the
corresponding drug or equivoluminal vehicle. During the in vivo
modeling with drug administration, the respiratory status, fur
glossiness, body mass, and survival rate of the mice were daily
observed and recorded. Pulmonary function parameters such as peak
inspiratory flow (PIFb) and peak expiratory flow (PEFb) of the mice
were detected every 7 days using the FinePointe Whole Body
Plethysmograph (WBP) system (DSI, MN, USA).
Fig. 3.
[62]Fig. 3
[63]Open in a new tab
In vivo therapeutic effects of QJHTD on COPD. A COPD modeling and QJHTD
administration in mice. B and C Effects of QJHTD treatment on the
pulmonary function parameters (PEFb and PIFb) of CS–LPS-exposed mice.
Data are presented as the mean ± SD (n = 8). D Effects of QJHTD
treatment on the body mass of CS–LPS-exposed mice. Data are presented
as the mean ± SD (n = 8). E and F Effect of QJHTD treatment on lung
histopathology in CS–LPS-exposed mice. Data are presented as the
mean ± SD (n = 3). G–J Effects of QJHTD treatment on the levels of
proinflammatory cytokines (IL-1β and TNF-α) and the apoptotic signaling
molecule (c-Casp3) in the lung tissue of CS–LPS-exposed mice. Data are
presented as the mean ± SD (n = 3). ##p < 0.01, ###p < 0.001 versus the
control group. *p < 0.05, **p < 0.01, and ***p < 0.001 versus the model
group.
After the final assessment of lung function, all mice were euthanized
to collect lung tissue and blood samples, which were stored in an
ultralow temperature freezer (Thermo Scientific, MA, USA) or fixed with
4 % paraformaldehyde solution (Biosharp, Hefei, China) for subsequent
analysis.
2.10. Pulmonary function test
In the in vivo study, the FinePointe WBP system was employed to test
pulmonary function in mice. On the basis of the operating instructions,
two time parameters, “Duration of Acclimation Period” and “Response
Time”, were set to 5 and 3 min, respectively. Then, the mice were
individually placed in the test chambers. The values of PIFb and PEFb
were measured to represent ventilatory capacity.
2.11. Lung histopathology
The lung tissue from the mice was collected and fixed in 4 %
paraformaldehyde solution for 24 h [[64]16]. To analyze lung
histopathology, we prepared paraffin-embedded sections of the lung
tissue, deparaffinized the sections, and stained the tissue with
hematoxylin plus 1 % eosin ethanol (H&E) solution. The stained
histopathological sections were observed under a microscope (Nikon
Eclipse TS100, Tokyo, Japan) and photographed.
The mean linear intercept (MLI), a measure of airspace enlargement
(emphysema), was quantified to interpret lung pathology in the COPD
mice. As reported by a previous study [[65]17], 20 equidistant parallel
pipelines were depicted on H&E-stained section images. Their
intersection points with air-cavity walls (represented as Ns) were
counted to calculate the MLI (=
[MATH: pipelinelengthNs :MATH]
) with three repetitions.
2.12. Immunohistochemistry (IHC)
We retrieved antigens in the samples after dewaxing. These samples were
then blocked with 3 % bovine serum albumin buffer and incubated
separately with primary antibody [anti-IL-1β (#12242, Cell Signaling
Technology, MA, USA), anti-TNF-α (Cat No. 60291-1-Ig, Proteintech, IL,
USA), or anti-cleaved-caspase (-c-Casp)-3 (#9661, Cell Signaling
Technology, MA, USA)] at 4 °C overnight. Following incubation with
horseradish peroxidase-labeled secondary antibody for 50 min, the
samples were counterstained with hematoxylin and sealed for imaging
under a microscope.
2.13. Network pharmacology-based prediction for molecular signaling
Network pharmacology aided in predicting the molecular mechanism of
QJHTD action against COPD. The approach involved target fishing,
molecular interaction networking, and pathway enrichment analysis,
which required the databases, such as TCMSP ([66]https://tcmsp-e.com/),
BATMAN-TCM ([67]http://bionet.ncpsb.org.cn/batman-tcm/), PubChem
([68]https://pubchem.ncbi.nlm.nih.gov/), SwissTargetPrediction
([69]http://www.swisstargetprediction.ch/), DisGeNET
([70]http://www.disgenet.org/), CTD ([71]http://ctdbase.org/),
GeneCards ([72]https://www.genecards.org/), STRING (version 11.0;
[73]https://string-db.org/), DAVID ([74]https://david.ncifcrf.gov/),
and KEGG ([75]https://www.genome.jp/kegg/), as well as the software
Cytoscape (version 3.7.1; [76]https://cytoscape.org/). The analytical
workflow was based on our previous report [[77]18].
2.14. RNA sequencing (RNA-seq) and analysis
Lung tissue samples from the mice with or without irritation were sent
to the Majorbio Biopharm Technology Co., Ltd. (Shanghai, China) for
total RNA extraction, complementary DNA (cDNA) library construction,
RNA-seq, quality control of sequencing data, differential gene
expression analysis, GO term classification, and KEGG pathway
enrichment analysis. The procedure was as reported previously [[78]19].
An adjusted p value less than 0.05 denoted significant enrichment. All
sequencing data were submitted to the GEO repository ([79]GSE223102).
2.15. Nontargeted LC–MS/MS-based metabolomics
Part of the previously collected lung tissue was delivered to Majorbio
for nontargeted metabolomic profiling, which included specimen
preparation, quality control of sampling, metabolite identification
using UPLC-TQ-TOF-MS/MS (AB SCIEX, CA, USA), metabolomics data
analysis, and KEGG-based metabolic pathway enrichment. The workflow was
based on the previous report [[80]20].
2.16. In silico molecular docking
Virtual molecular docking analysis was applied to surmise the
therapeutic potency of candidate molecules in QJHTD for COPD targeting.
The required databases included RCSB Protein Data Bank
([81]http://www.rcsb.org/) and PubChem. The tool for semiflexible
docking was AutoDock (version 4.2.6). The procedure was as reported
previously [[82]18].
2.17. Statistical analysis
All experimental data were statistically analyzed using Prism (version
8.0; GraphPad, CA, USA) and expressed as the mean ± standard deviation
(SD). Statistical significance was assessed using analysis of variance
and Tukey‒Kramer post hoc test. In all analyses, a p value < 0.05 was
considered significant.
3. Results
3.1. Characteristic fingerprint spectrum of QJHTD
The typical fingerprints of QJHTD were provided by the Tianjin
Institute of Pharmaceutical Research (Tianjin, China). As shown in
[83]Fig. 1A, eight shared peaks (peaks 1–8) were designated under
various batches of samples, characterizing the chemical profile of
QJHTD. [84]Fig. 1B shows the major chemical compositions of the samples
in this study. Of these fingerprints, peaks 2, 3, 5, 6, and 8
represented geniposide, mangiferin, hesperidin, baicalin (reference
substance, S), and wogonoside, respectively; peaks 1, 4, and 7 were
undefined. Molecular structures of the defined compounds in QJHTD are
shown in [85]Fig. 1C.
Fig. 1.
[86]Fig. 1
[87]Open in a new tab
Characteristic fingerprint spectrum of QJHTD.
A Integrated characteristic fingerprint peaks based on multibatch
testing. B Characteristic fingerprint peaks of the samples in the
present study. Peak 1, an undefined compound; peak 2, geniposide; peak
3, mangiferin; peak 4, an undefined compound; peak 5, hesperidin; peak
6, baicalin (S); peak 7, an undefined compound; peak 8, wogonoside. C
Molecular structures of the defined compounds in QJHTD.
3.2. QJHTD exerts cytoprotective and antiinflammatory effects on COPD In
vitro
Since airway epithelial cell dysfunction and inflammatory response are
the central events in COPD pathophysiology, we surveyed in vitro
phenotypes including cell viability, proliferation, and apoptosis, as
well as inflammatory markers, in the presence or absence of QJHTD
intervention to assess the therapeutic potential of QJHTD against COPD.
As shown in [88]Fig. 2A, CSE–LPS costimulation significantly inhibited
BEAS-2B cell viability, whereas QJHTD (25 and 50 μg mL^−1) and DEX were
conducive to the survival of the damaged BEAS-2B cells. In addition,
the EdU assay revealed a significant decrease in the fluorescence
intensity of the model compared with that of the control; the injured
cells with drug intervention exhibited remarkably higher fluorescence
intensities than model cells without treatment ([89]Fig. 2B and (C)),
suggesting that QJHTD improved the impaired proliferation of bronchial
epithelial cells. The TUNEL assay revealed that the fluorescence
intensity in the model group was strikingly higher than that in the
control group; QJHTD or DEX involvement markedly attenuated the
fluorescent signals from the treated cells as opposed to no drug
intervention in the model ([90]Fig. 2D and E). Likewise, the FACS assay
showed that irritation significantly elevated the apoptosis rate of
BEAS-2B cells, whereas QJHTD or DEX intervention evidently restrained
this detrimental alteration ([91]Fig. 2F and G). Taken together, these
results demonstrate a potent antiapoptotic property of QJHTD in the
CSE–LPS coirritated BEAS-2B cells.
Fig. 2.
[92]Fig. 2
[93]Open in a new tab
In vitro therapeutic effects of QJHTD on COPD.
A Effect of QJHTD intervention on the viability of CSE–LPS cochallenged
BEAS-2B cells. B and C Effect of QJHTD intervention on the
proliferation of CSE–LPS cochallenged BEAS-2B cells. D–G Effect of
QJHTD intervention on the apoptosis of CSE–LPS cochallenged BEAS-2B
cells. H and I Effects of QJHTD intervention on the expression of
proinflammatory cytokines (IL-1β and TNF-α) in CSE–LPS cochallenged
RAW264.7 cells. All data are expressed as the mean ± SD (n = 3).
###p < 0.001 versus the control group. *p < 0.05, ***p < 0.001 versus
the model group.
Regarding inflammatory cell phenotypes, the proinflammatory factors
IL-1β and TNF-α are deemed biomarkers for inflammatory activation owing
to their sensitivities in COPD progression [[94]21,[95]22]. ELISA
revealed that CSE–LPS cochallenge of RAW264.7 cells notably increased
IL-1β and TNF-α production in the extracellular medium, reflecting the
proinflammatory nature of the CSE–LPS combination. Compared with the
model group, the QJHTD (25 and 50 μg mL^−1)-treated and DEX-treated
groups exhibited a substantial reduction in secretory IL-1β and TNF-α
levels ([96]Fig. 2H and I), indicating that QJHTD mitigated the
respiratory inflammation evoked by CSE and LPS. Collectively, these
findings validate the cytoprotective and antiinflammatory effects of
QJHTD against COPD.
3.3. QJHTD administration improves respiratory function and alleviates lung
injury in mice with COPD
COPD modeling and QJHTD administration in mice are as shown in [97]Fig.
3A. PIFb and PEFb values were measured to estimate whether QJHTD
improved spontaneous ventilation in COPD mice [[98]23,[99]24]. Final
respiratory assessment in this study showed that combined exposure to
CS and LPS caused a robust decrease in mouse PIFb and PEFb values,
which manifested lung dysfunction following CS–LPS coexposure and was
representative of bronchial obstruction in COPD. QJHTD (2.24 or
4.48 g kg^−1) or DEX administration significantly reversed these
downward trends ([100]Fig. 3B and C), indicating that QJHTD relieved
airflow limitation in the COPD mice. Given the correlation between COPD
severity and histopathology, H&E staining offered morphological
evidence supporting QJHTD's effectiveness against COPD. As expected,
mice subjected to CS and LPS stimulation showed significantly greater
MLI than controls, which suggested that CS–LPS cochallenge impaired
bronchioloalveolar walls and contributed to the pathogenesis of
emphysema, thereby accounting for reduced PIFb and PEFb values. QJHTD
(2.24 and 4.48 g kg^−1)-treated and DEX-treated groups presented a
remarkable decline in MLI compared to the model group ([101]Fig. 3E and
F), implying that QJHTD affected the development of COPD on both
functional and structural scales. To profile pulmonary inflammatory
lesions, we analyzed IL-1β, TNF-α, and c-Casp3 expression levels in
mouse lung tissue using IHC. The positive areas labeled by these
markers were sharply expanded under exposure to CS and LPS, but
prominently shrunk after QJHTD (2.24 or 4.48 g kg^−1) or DEX treatment
([102]Fig. 3G–J), reconfirming that the therapeutic effects of QJHTD on
COPD are relevant to restraint of airway inflammation and
bronchoalveolar cell death. However, COPD progression is partly linked
to loss of body mass [[103]25]. We thus estimated whether QJHTD
administration affected the weight of COPD mice. In the present study,
mice undergoing CS and LPS stimulation showed a dramatic drop in body
mass. Intriguingly, high-dose QJHTD (4.48 g kg^−1) prevented body
weight loss, whereas DEX produced no significant impact ([104]Fig. 3D).
Collectively, the integrative in vivo activity against COPD typifies
the therapeutic potency of QJHTD, parallel to its performance in vitro.
3.4. Network pharmacology predicts the molecular mechanism of QJHTD action on
COPD is related to modulating inflammatory and apoptotic signaling
Next, we tentatively probed the therapeutic machinery conferring QJHTD
activity against COPD using network pharmacology. We searched 954 drug
targets (from the SwissTargetPrediction database) corresponding to 243
active compounds of QJHTD (from the TCMSP and BATMAN-TCM databases) and
401 therapeutic targets for COPD (integrating 668 therapeutic targets
from the database DisGeNET, 19323 from CTD, and 5019 from GeneCards).
The Venn diagram ([105]Fig. 4A) exhibited 136 intersections of the two
target sets for QJHTD treatment of COPD. These intersections and the
active compounds were used to construct a
drug–ingredient–target–disease association network consisting of 359
nodes and 3286 edges ([106]Fig. 4B). Moreover, we employed the above
intersections to build a PPI network through the STRING database. The
PPI network was optimized and topologically analyzed using Cytoscape
([107]Fig. 4C). The results indicated 26 hub targets/proteins,
including TP53, STAT3, IL-6, MAPK1, TNF, and CXCL8, under the
conditions of degree >18, betweenness >0.00293113, and closeness
>0.40878378 ([108]Table S1). The information on the hub proteins was
then input into the DAVID database for GO term and KEGG pathway
enrichment analyses. In GO term enrichment analysis, the identified hub
proteins were linked with 435 terms in biological process (BP)
([109]Fig. 5A), 24 in cellular component (CC) ([110]Figs. 5B), and 29
in molecular function (MF) ([111]Fig. 5C) categories. Of the
significantly enriched terms, the representatives in BP were
exemplified as follows: regulation of cell death, positive regulation
of biosynthetic process, regulation of apoptosis, positive regulation
of gene expression, and regulation of cell proliferation ([112]Fig.
5A). KEGG pathway enrichment analysis unveiled latent molecular
pathways, including the NOD-like receptor (NLR) signaling pathway, MAPK
signaling pathway, Jak-STAT signaling pathway, Toll-like receptor (TLR)
signaling pathway, and VEGF signaling pathway ([113]Fig. 5D). These
results suggest that the modulation of inflammatory (such as NLR, MAPK,
TLR, and VEGF) and cell death-related signaling cascades is involved in
the anti-COPD action of QJHTD.
Fig. 4.
[114]Fig. 4
[115]Open in a new tab
Prediction of constituent–target interplay for the action of QJHTD
against COPD.
A Intersections between QJHTD targets and therapeutic targets for COPD.
B Drug (QJHTD)–constituent–target–disease (COPD) association network. C
Protein–protein interaction analysis to identify the hub targets of
QJTHD in COPD treatment.
Fig. 5.
[116]Fig. 5
[117]Open in a new tab
Prediction of GO functions and KEGG pathways related to the action of
QJHTD against COPD.
A–C GO terms in biological process (BP), molecular function (MF), and
cellular component (CC) domains annotating QJHTD activity against COPD.
D KEGG pathway enrichment for the potential molecular mechanism of
QJHTD activity against COPD.
3.5. Transcriptome analysis identifies DEGs and signaling pathways involved
in the Anti-COPD Effect of QJHTD
Relative to the virtual prediction, RNA-seq provides practical insight
into the transcriptome of cells or tissue that decrypts the DEGs in
lung tissue of the COPD mice receiving QJHTD treatment (at high/optimal
dose, 4.48 g kg^−1) in the present study. Homogenization of mRNA
abundance data showed that TPM values in all samples were not
significantly different ([118]Fig. S1), indicating that the mRNA
expression data were applicable to differential expression analysis.
DEG clustering in the three groups is presented as a heat map in
[119]Fig. 6A and as a three-circle Venn diagram in [120]Fig. 6B. DEGs
in the model-versus-control and QJHTD-versus-model comparisons were
enumerated using a Venn diagram ([121]Fig. 6C) and stacked bar chart
([122]Fig. 6D) and visualized as volcano plots ([123]Fig. 6E and F),
according to the screening criteria of p value < 0.05 and
|log[2](FC)| ≥ 1. A total of 5924 DEGs were identified between the
model and control groups, involving 3075 upregulated and 2849
downregulated genes. Meanwhile, 216 DEGs were identified between the
QJHTD and model groups, involving 90 upregulated and 126 downregulated
genes ([124]Fig. 6C and D; [125]Table S2). Subsequently, we implemented
GO term and KEGG pathway enrichment analyses to expound DEG functions
and deduce the molecular mechanism of action of QJHTD against COPD.
Functional annotation in BP showed that the DEGs between the model and
control groups were enriched in such terms as regulation of natural
killer cell (NKC) activation, leukocyte migration involved in
inflammatory response, regulation of acute inflammatory response, and
regulation of tumor necrosis factor biosynthetic process ([126]Fig.
7A). These descriptions characterize the active phase or acute
exacerbation of chronic inflammation, conforming to the pathophysiology
of COPD. The DEGs between the QJHTD and model groups were enriched in
the following processes: regulation of immune response, innate immune
response, regulation of response to external stimulus, and positive
regulation of defense response ([127]Fig. 7B). These results suggest
that the efficacy of QJHTD in treating COPD relies on immune adjustment
and antiinflammatory interference. The pathway enrichment profile
disclosed that the DEGs between the model and control groups
participated in a series of molecular signaling cascades, such as the
phospholipase D (PLD) signaling pathway, NF-kappa B (NF-κB) signaling
pathway, Fc epsilon RI (FcεRI) signaling pathway, and NKC-mediated
cytotoxicity ([128]Fig. 7C). Analogously, the DEGs between the QJHTD
and model groups contributed to pathways including the NF-κB signaling
pathway, PLD signaling pathway, FcεRI signaling pathway, and
NKC-mediated cytotoxicity ([129]Fig. 7D). These findings imply that the
molecular routes relevant to NF-κB, PLD, and FcεRI, as well as
NKC-mediated cytotoxicity (ultimately leading to cell apoptosis), are
instrumental in the therapeutic mechanism of QJHTD against COPD.
Fig. 6.
[130]Fig. 6
[131]Open in a new tab
RNA-seq-dependent differential gene expression analysis of the lung
tissue of mice with COPD receiving QJHTD regimen.
A Cluster test of gene expression patterns in the control (C1–C3),
model (M1–M3), and QJHTD (Q1–Q3) groups. B Expressed gene counts in the
control, model, and QJHTD groups. C DEG counts in the
model-versus-control and QJHTD-versus-model comparisons. D–F Analysis
of upregulated and downregulated DEGs in the model-versus-control and
QJHTD-versus-model comparisons.
Fig. 7.
[132]Fig. 7
[133]Open in a new tab
GO function and KEGG pathway enrichment analyses based on the gene sets
of intergroup comparisons. A and B GO terms annotating DEG biofunctions
in the model-versus-control and QJHTD-versus-model comparisons. C and D
KEGG pathways contributing to the modulation of COPD pathogenesis by
QJHTD.
Furthermore, we investigated whether QJHTD positively or negatively
tuned molecular signaling for the remission of COPD. Intersections of
the downregulated (upregulated) genes in the model-versus-control group
comparison and the upregulated (downregulated) genes in the
QJHTD-versus-model group comparison were used for GO function and KEGG
pathway enrichment reanalyses. The functional annotations in BP showed
that the processes (or signaling cascades) reinforced in the model
group relative to those in the control group and concurrently weakened
in the QJHTD group relative to those in the model group embraced
positive regulation of IL-6, TNF, and IL-1β production, as well as
positive regulation of MAPK (specified as ERK1, ERK2, and JNK), I-kappa
B (IκB) kinase/NF-κB, apoptosis, and TLR4 signaling pathways ([134]Fig.
8A). The processes (or signaling cascades) that were inhibited in the
model group relative to those in the control group and enhanced in the
QJHTD group relative to those in the model group included negative
chemotaxis, lung alveolus development, and positive regulation of cell
proliferation and differentiation ([135]Fig. 8B). KEGG pathway
enrichment analysis showed that the molecular pathways that were
upregulated in the model group versus the control group and
simultaneously downregulated in the QJHTD group versus the model group
included the NF-κB, TNF, and TLR signaling pathways, and apoptosis
([136]Fig. 8C). The pathways that were downregulated in the model group
versus in the control group and concurrently upregulated in the QJHTD
group versus the model group included the calcium signaling pathway,
adrenergic signaling in cardiomyocytes, and circadian entrainment
([137]Fig. 8D). Collectively, the landscape of pathway activity
regulation suggests that suppressing the molecular routes of IL-1β,
IL-6, TNF, IκB–NF-κB, TLR, MAPK (involving ERK1/2 and JNK), and
apoptosis is prominent in the anti-COPD action of QJHTD, which closely
mirrored our speculation based on network pharmacology.
Fig. 8.
[138]Fig. 8
[139]Open in a new tab
Molecular pathways underlying the anti-COPD property of QJHTD.
A BPs upregulated in the model-versus-control and downregulated in the
QJHTD-versus-model comparison. B BPs downregulated in the
model-versus-control and upregulated in the QJHTD-versus-model
comparison. C Pathways upregulated in the model-versus-control and
downregulated in the QJHTD-versus-model comparison. D Pathways
downregulated in the model-versus-control and upregulated in the
QJHTD-versus-model comparison.
3.6. Metabolome profiling Unveils metabolite signature in mice with COPD
receiving QJHTD treatment
Accumulating evidence supports the correlation of metabolic alterations
with COPD pathophysiology and treatment [[140]26]. Moreover,
traditional herbs or medicinal formulas have been proven effective in
redressing dysmetabolism in multiple diseases [[141]27]. Considering
this, we attempted to explore the metabolite signature in QJHTD
interference (at high/optimal dose, 4.48 g kg^−1) with COPD at the
molecular level. LC‒MS/MS analysis revealed the metabolite composition
of mouse lung tissue. Total ion chromatograms of quality control
samples in positive and negative ion modes verified the performance of
the LC‒MS/MS system, reflecting the expected reliability of measurement
([142]Fig. 9A and B). The permutation test plot showed an appropriate
PLS-DA prediction model without overfitting based on the Y-intercept of
the Q^2 regression line <0.05 ([143]Fig. 9E). PLS-DA score plots in the
positive and negative ion modes showed minor intragroup differences and
sharp intergroup distinctions, indicating that COPD modeling remarkably
affected metabolites in mouse lung tissue and that QJHTD altered
metabolic patterns in the COPD mice ([144]Fig. 9C and D). A total of
732 identified metabolites were subsumed into 13 chemical categories
using HMDB ([145]Fig. 9F). The maximal portion of the classification
pie was occupied by lipids and lipid-like molecules, totaling 355
compounds and accounting for 48.50 % of all components, indicating the
latent dominance of lipids and lipid metabolism in COPD progression and
QJHTD action. A cluster heat map was used to visualize the functional
relevance and abundance discrepancy of metabolites in the control,
model, and QJHTD groups ([146]Fig. 9G). Certain compounds such as
imperialine, baicalin, peimine, oroxylin A, and acacetin were highly
abundant in the QJHTD group but of low content in the control and model
groups. Therefore, these molecules potentially function as active
components of QJHTD in COPD treatment. Differential metabolite (DMB)
analysis ([147]Fig. 10A; [148]Tables S3 and S4) revealed 272 DMBs (101
in the positive and 171 in the negative ion mode) between the model and
control groups, and 74 DMBs (36 in the positive and 38 in the negative
ion mode) between the QJHTD and model groups. These DMBs were
chemically classified using the KEGG compound database. Six chemical
categories were detected in the model-versus-control group comparison
and two were detected in the QJHTD-versus-model group comparison
([149]Fig. 10B and C). The category phospholipids was shared in the two
comparisons, which highlighted the regulatory roles of lipids,
specifically phospholipids, in the anti-COPD action of QJHTD. We
further analyzed the up- and downregulated DMBs in QJHTD treatment of
COPD, according to the filter criterion of fold change (FC) > 1,
signifying upregulation of the DMBs in the model (QJHTD)-versus-control
(model) comparison, or FC < 1, denoting the contrary modulation. The
results were visualized as volcano plots, exhibiting 178 upregulated
and 94 downregulated DMBs in the model-versus-control comparison, as
well as 50 upregulated and 24 downregulated DMBs in the
QJHTD-versus-model comparison ([150]Fig. 10D–G). Nine upregulated DMBs
in the model-versus-control comparison and downregulated DMBs in the
QJHTD-versus-model comparison overlapped, including sphingosine (Sph),
agavasaponin C’, 2-ethyl-2,5-dihydro-4,5-dimethylthiazole, kynurenine,
fusarin C, indolelactic acid, 1-heneicosanoyl-glycero-3-phosphate,
lucidenolactone, and asparaginyl-phenylalanine ([151]Fig. 10H). In
addition, nine downregulated DMBs in the model-versus-control
comparison and upregulated DMBs in the QJHTD-versus-model comparison
overlapped, encompassing pentacosanoic acid,
1-nitro-5-hydroxy-6-glutathionyl-5,6-dihydronaphthalene, lysoPC(18:3
(9Z,12Z,15Z)), HDMBOA-Glc, S-(formylmethyl)glutathione, lysoPC(22:5
(7Z,10Z,13Z,16Z,19Z)), p-salicylic acid, gravacridonediol, and
fructosamine ([152]Fig. 10I). Together, these endogenic compounds
formed a metabolic bridge between COPD development and QJHTD action.
Fig. 9.
[153]Fig. 9
[154]Open in a new tab
Metabolite outline in metabolic profiling of mouse lung tissue. A and B
Total ion chromatograms (TICs) of quality control samples in the cation
and anion modes. C and D PLS-DA score plots in the cation and anion
modes showing the classification of mouse lung tissue samples. E
Permutation test of the PLS-DA model. F HMDB categorization
(superclass) of 732 compounds in the metabolomic dataset. Numbers in
parentheses represent metabolite counts in the superclass terms. G
Cluster heatmap visualizing metabolite annotations in the control
(C1–C7), model (M1–M7), and QJHTD (Q1–Q7) groups.
Fig. 10.
[155]Fig. 10
[156]Open in a new tab
DMB metabolomics analysis of mouse lung tissue. A Venn diagram of DMB
counts in the model-versus-control and QJHTD-versus-model comparisons.
B and C KEGG compound classification of DMBs in the
model-versus-control and QJHTD-versus-model comparisons. D–G Volcano
plots in the positive and negative ion modes visualizing DMBs in the
model-versus-control and QJHTD-versus-model comparisons. H and I
Quantitative differences in endogenous metabolites between the control,
model, and QJHTD groups. Data are presented as the mean ± SD (n = 7).
###p < 0.001 versus the control group. *p < 0.05, **p < 0.01,
***p < 0.001 versus the model group.
Furthermore, KEGG enrichment analysis of up- (down-)regulated DMBs in
the model-versus-control comparison and concurrently down-
(up-)regulated DMBs in the QJHTD-versus-model comparison was
implemented to identify metabolism-related pathways that mediated the
action of QJHTD on COPD. The activities of two pathways, namely,
apoptosis and sphingolipid (SL) metabolism, were reinforced in the
model-versus-control comparison but suppressed in the
QJHTD-versus-model comparison ([157]Fig. 11A and D). Activity of the
pathway choline (Ch) metabolism in cancer was restrained in the
model-versus-control comparison and concurrently boosted in the
QJHTD-versus-model comparison ([158]Fig. 11B and C). These findings
imply that suppression of apoptosis and SL metabolism with promotion of
Ch metabolism partly supports the anti-COPD properties of QJHTD,
rehighlighting the regulatory significance of SL metabolism.
Fig. 11.
[159]Fig. 11
[160]Open in a new tab
Pathway enrichment analysis of DMBs in intergroup comparisons. A and D
Metabolic pathways upregulated in the model-versus-control and
downregulated in the QJHTD-versus-model comparison. B and C Metabolic
pathways downregulated in the model-versus-control and upregulated in
the QJHTD-versus-model comparison. E Nexus between sphingolipid
metabolism and apoptosis. *p < 0.05, **p < 0.01, and ***p < 0.001.
3.7. Integrated Transcriptome–Metabolome analysis reveals the role of SL
metabolism in QJHTD action against apoptosis
Comprehensive analysis of metabolomic and transcriptomic data
illustrated the link between transcriptional and metabolic controls by
QJHTD. The apoptosis pathway was emphasized in that it was enriched
with the differential metabolite Sph and emerged at the nexus of the
signaling cascades and metabolic pathways ([161]Fig. 11A and D). Using
the KEGG pathway IDs of apoptosis (map 04210) and Sph-associated
cascades (map 00600, map 01100, and map 04071), we parsed the KEGG
pathway maps and edited the regulative network of Sph-originated
apoptosis signaling by QJHTD. As shown in [162]Fig. 11E, Sph-induced
apoptosis relies on the direct or indirect transmutation of Sph into
ceramide (Cer) in lysosomes. Precisely, Sph is converted into Cer
through ceramide synthetase (CerS) catalysis or transformed into
sphingosyl-phosphocholine (SPCh), sphingomyelin (SM), and Cer in turn
under successive catalysis by sphingosine cholinephosphotransferase
(SCPT), acyl-CoA:sphingosine N-acyltransferase (ASNAT, equivalent of
CerS), and sphingomyelin phosphodiesterase (SMPDE, equivalent of
sphingomyelinase, SMase). Furthermore, alkaline ceramidase (alCDase)
catalyzes the transformation of Cer to Sph. Ceramide
cholinephosphotransferase (CCPT) activation enables Cer to convert to
SM, while sphingomyelin synthase (SMS) catalyzes the bidirectional
conversion between Cer and SM. Lysosomal Cer activates the aspartic
protease cathepsin, thereby provoking Bax/Bak-mediated mitochondrial
outer membrane permeabilization (MOMP) by upregulating the Bid–Bax/Bak
signaling axis and inhibiting Bcl2/BclX[L] activity. MOMP drives the
release of cytochrome-C (CytC) which participates in apoptosome
formation by complexing Apaf 1 and pro-Casp9. Active Casp9 cleaves
downstream pro-caspases such as pro-Casp 7, pro-Casp3, and pro-Casp 6
to execute cell death. In this diagram, targeting Sph
transmutation-relevant enzymes is expected to be part of QJHTD action
against bronchoalveolar cell apoptosis in COPD owing to the apparent
discrepancies in Sph content and apoptotic factor expression between
the COPD mice treated with and without QJHTD.
3.8. Discovery of exogenous compounds in lung tissue Ascertains the potential
of active ingredients of QJHTD for COPD treatment
Following the metabolic landscape, extrinsic compounds in the lung
tissue of the QJHTD-treated mice, including acacetin, deoxyloganic
acid, peimine, oroxylin A, and baicalin, were uncovered. To trace
acacetin and baicalin as representative components in the QJHTD
preparation, we qualitatively examined these two compounds in QJHTD
drug-containing serum and QJHTD samples using LC‒MS (Shimadzu, Japan).
Both acacetin and baicalin were detected in the drug-containing serum
and the preparation, indicating that these compounds are likely
associated with the anti-COPD properties of QJHTD in vitro and in vivo
([163]Fig. 12A–D). Virtual molecular docking allows for inferring the
binding of small molecules with target proteins. Thus, we used it to
predict the therapeutic potential of acacetin and baicalin against
COPD. Herein, we docked acacetin and baicalin on the signaling proteins
MEK1, MEK2, ERK1, ERK2, Bcl2, Bax, TLR4, and NF-κB, as well as the
metabolic enzyme alCDase. The mean binding energy (mBE) of 10
conformations in one docking study denoted the overall affinity between
the ligand and target. The mBE values in acacetin binding to MEK1,
MEK2, ERK1, ERK2, Bcl2, NF-κB, and alCDase were less than −5 (absolute
values > 5), signifying that acacetin may bind those targets with high
affinity ([164]Fig. 13A). The mBE values for acacetin binding to Bax
and TLR4 exceeded −5 (absolute values < 5), implying low-affinity
interactions between acacetin and these two proteins. In contrast,
baicalin showed low binding affinity to all these proteins due to mBE
values exceeding −5 (absolute values < 5), suggesting that baicalin may
not target those proteins ([165]Fig. 13B). Structurally, the
high-affinity interactions of acacetin with the seven targets were
visualized, and the docking parameters are presented ([166]Fig. 13C–I;
[167]Table 4). These docking parameters portrayed the optimal
interaction patterns apt to excellent binding energies. We thus suppose
that acacetin harnesses MEK1/2–ERK1/2 signaling, Bcl2 function, NF-κB
signaling, and alCDase activity. In other words, acacetin, as an active
component, contributes to the antiinflammatory, antiapoptotic, and
Sph-anabolism-suppressive effects of QJHTD. Baicalin may exert a
curative impact on COPD via alternative routes.
Fig. 12.
[168]Fig. 12
[169]Open in a new tab
Potential active components in QJHTD.
A and B Retention time and mass-to-charge ratio (m/z) of baicalin and
acacetin in mass spectrum analysis. C and D Mass spectrum analyses of
QJHTD preparation and QJHTD-containing serum.
Fig. 13.
[170]Fig. 13
[171]Open in a new tab
Interactions of potential active components with target proteins.
A and B Mean binding energy (mBE) of baicalin and acacetin to targets
relevant to inflammation, apoptosis, and sphingolipid metabolism. C–I
Visualization of acacetin binding to the detected targets with mBE
values below −5.
Table 4.
Docking parameters in the high-affinity interactions of acacetin with
seven targets.
Target protein Bond-forming residue Hydrogen-bond length Mean binding
energy (mBE)
MEK1 ILE-216 2.6 Å −7.44
SER-212 2.2 Å; 1.7 Å
MEK2 SER-216 2.1 Å; 2.2 Å −5.74
ERK1 MET-125 1.9 Å −6.31
ASP-123 2.2 Å
ERK2 GLN-105 2.1 Å −5.87
GLU-71 2.1 Å
LYS-54 2.3 Å
TYR-36 2.0 Å
Bcl2 ARG-146 1.8 Å −5.11
NF-κB VAL-444 2.5 Å −5.54
TYR-601 2.8 Å
SER-357 2.2 Å
alCDase PHE-140 2.2 Å −5.57
VAL-165 2.6 Å; 2.6 Å
GLY-164 2.5 Å; 1.7 Å
ARG-333 2.0 Å
Sulfuric acid (H[2]SO[4]) 3.0 Å
[172]Open in a new tab
4. Discussion
COPD is a complex disease that implicates multiple pathologies at the
organ and cell levels. Hierarchically, cellular injuries disrupt airway
homeostasis, inducing structural abnormalities and obstructive
ventilatory defects [[173]28]. In the present study, the combined use
of in vitro, in vivo, and in silico assays permitted gathering
multidimensional evidence for COPD-like phenotypes and the efficacy of
QJHTD regimen against them. BEAS-2B cells and RAW264.7 cells irritated
by CSE and LPS were regarded as rational to mimic airway epithelial
cell death and cellular inflammatory response, respectively [[174]29].
Male BALB/c mice exposed to CS and LPS were used to imitate COPD
development and lung pathology [[175]30]. The in vitro and in vivo
phenotypes from COPD treatment with QJHTD provided cues for mechanistic
illustration using network pharmacology, transcriptomics, and
metabolomics. Active compounds from the QJHTD extract were identified
via comparative LC‒MS analysis of the preparation, drug-containing
serum, and lung tissue. The latent activities of these compounds were
speculated using molecular docking. The results of all these analyses
offered insight into lung distribution-based drug discovery from
traditional medicines, accurately deciphering their pharmacodynamic
material bases against COPD.
Of note, airway inflammation that is marked by proinflammatory
cytokines intensifies with COPD progression, affecting other
manifestations of the disease [[176]31]. Apoptosis of airway epithelial
cells is deemed equally imperative in COPD pathogenesis, owing to its
close correlation with inflammatory outcomes and aggravation rather
than simply being a result of CS–LPS irritation [[177]32]. Hence, it is
essential to comprehensively understand QJHTD efficacy against COPD in
view of its complex pathology. Here, we discovered the combined
occurrence of airway inflammation and epithelial cell apoptosis, as
evidenced by enhanced cytokine expression and apoptotic phenotypes in
vitro and in vivo. Moreover, CSE–LPS challenge of BEAS-2B cells
repressed their viability/proliferation, hinting at the link between
lung dysfunction and airway epithelial repair deficiency. The resultant
lower values of PIFb and PEFb in the COPD mice versus the control group
were based on integration of these CS/CSE–LPS-induced lesions,
consistent with the histopathological status of enlarged alveoli
(increased MLI), decreased alveolar septa, and extensive inflammatory
cell infiltration plus exudation. Similar to previous reports on the
study of TCM preparations against this disease [[178]33,[179]34], our
findings verified that QJHTD exerted beneficial effects on respiratory
function and lung histology in subjects with COPD because of its
antiinflammatory, antiapoptotic, and cell
viability-/proliferation-promoting activities. This deduction opened a
new path to explore the putative mechanism of action of QJHTD against
COPD, that is, inhibition of inflammatory and apoptotic signaling
pathways.
Classical inflammatory signaling involves ILs, TNFs, TLRs, NF-κB, and
MAPK activities, and apoptotic signaling involves Bax, Bcl2, Casp9, and
Casp3 expression. Crosstalk between the two pathways underlies the
inflammation–apoptosis interplay and candidate targets for QJHTD
treatment. Targeting these signaling molecules promises to remit airway
inflammation and obstruction. A network pharmacology-based strategy
predicted that the pathways related to NLR, MAPK, TLR, VEGF, and cell
death mediated the therapeutic action of QJHTD on COPD. Transcriptome
analysis using RNA-seq revealed that molecular signals such as IL-1β,
IL-6, TNF, IκB–NF-κB, TLR, MAPK (involving ERK1/2 and JNK), and
apoptosis were intensified in COPD mice compared with controls but
weakened in the QJHTD group versus the model group. Together, these
results underscore the synergistic modulation of proinflammatory
cytokines, MAPK signaling, TLR4–NF-κB cascade, and apoptotic factors by
QJHTD.
Endogenous metabolism, another aspect of pathogenesis, severity
biomarkers, and feasible medication guidance in chronic lung
inflammation, has garnered much attention [[180]35,[181]36].
QJHTD-mediated metabolic regulation of COPD lung tissue embodied the
restraint on apoptosis and SL metabolism, as opposed to the boost of Ch
metabolism. In patients with COPD, enhanced production of Sph as a
central metabolic intermediate of SLs/SM activates Casp3 and
facilitates PKCδ KD release, leading to cell apoptosis
[[182]37,[183]38]. Likewise, Sph elevation elicits IL-1β secretion
dependent on the NLRP3 inflammasome from macrophages [[184]39].
Therefore, we deduce that QJHTD can inhibit the switch from SLs/SM to
Sph and consequently reduce the levels of metabolite-derived
apoptotic/inflammatory mediators on the basis of our findings that lung
Sph level was raised due to CS–LPS challenge but lowered after QJHTD
treatment. Furthermore, Ch is instrumental in COPD development,
especially in bronchospasm for acetylcholine (ACh) synthesis and
release [[185]40]. Dampening cholinergic activity is a potent strategy
for relieving airway obstruction. In this study, the levels of two Ch
metabolites, lysoPC(18:3 (9Z,12Z,15Z)) and lysoPC(22:5
(7Z,10Z,13Z,16Z,19Z)), were decreased in COPD lung tissue but increased
post QJHTD administration. We infer that QJHTD intervention may
transform Ch into lyso-phosphatidylcholines (lyso-PtdChs) and
indirectly block Ch conversion to ACh, mitigating bronchospasm in COPD.
Integrated transcriptomic–metabolomic analysis uncovered the
antiapoptotic mechanism of QJHTD compounds partly by targeting
Sph-metabolizing enzymes, as QJHTD intervention affected three
functional modules, namely, Sph transmutation/cycle, MOMP, and
apoptosis-executing cascade, to suppress cell death. This result
underlines the implication of SL metabolism in apoptosis signaling and
denotes the Sph conversion-targeting antiapoptotic property of QJHTD.
Nevertheless, these results fail to explain the antiinflammatory
activity of QJHTD pertinent to acting on endogenous metabolism,
implying that the antiinflammatory effect of this formula may be
independent of metabolic control.
Intriguingly, the representative components acacetin and baicalin from
certain medicinal plants were identified in the lung tissue of
QJHTD-treated mice. Besides, these two compounds were discerned in the
serum from the same mice as well as in the QJHTD preparation used for
cellular tests. Thus, acacetin and baicalin may function as active
ingredients favoring the in vitro and in vivo therapeutic efficacy of
QJHTD against COPD [[186]41]. Mechanistically, acacetin could bind
MEK1/2, ERK1/2, Bcl2, NF-κB, and alCDase according to the results of
our docking analysis, which elucidates the signaling modulation by
QJHTD and indirectly testifies to the role of acacetin as an effective
compound in this preparation. However, molecular docking exhibited low
affinity between baicalin and target proteins, indicating that baicalin
may exert its action on COPD via distinct signaling cascades, as
previously reported [[187][42], [188][43], [189][44]]. Overall,
acacetin and baicalin may act synergistically via complementary
molecular routes to assist in the anti-COPD properties of QJHTD.
5. Conclusion
In summary, the present investigation confirmed that QJHTD involvement
suppressed airway epithelial cell apoptosis and inflammatory cell
activation both in vitro and in vivo, which was central to COPD
remission and connoted the putative action mechanisms upon the
apoptotic and inflammatory cascades. Network pharmacology-based
prediction and transcriptome analysis emphasized IL-1β, IL-6, TNF,
IκB–NF-κB, TLR, MAPK, and apoptosis, detailing QJHTD-triggered
molecular signaling. Metabolome profiling, integrated
transcriptomic-metabolomic analysis, and virtual molecular docking
uncovered the promising active compounds such as baicalin and acacetin
in the preparation, as well as the functional cohesion between
QJHTD-targeted SL metabolism and apoptosis pathway. These findings
provide comprehensive insight into the mechanism of action of QJHTD,
highlighting its potential as an alternative to current treatment
guidelines.
Ethics statement
For comprehensive information on ethical guidelines for journal
publication, we can refer to the Publishing & Research Ethics section
and the Ethics Model on the Researcher Academy. Animal experiments in
this study were authorized by the Center for Experimental Animal of
Southwest Medical University, and all experimental procedures were
approved by the Ethics Committee of Southwest Medical University
(license No. 20180309091).
Funding
This study was funded by the Sichuan Hospital Association Research Fund
for Young Pharmacists (Grant No. 22007), the National Natural Science
Foundation of China (Grant No. 81804221), the National Major Science
and Technology Project of the Ministry of Science and Technology of
China (Grant No. 2018ZX09721004-006), and the Sichuan Province Science
and Technology Program (Grant No. 2019YJ0473).
Institutional review board statement
This study was conducted in accordance with ARRIVE guidelines and
approved by Ethics Committee of Southwest Medical University (license
No. 20180309091).
Informed consent statement
Not applicable.
Data availability statement
Data to support the study are contained within the article and
Supplementary Materials. Data associated with this study has been
deposited into a publicly available repository GEO repository
([190]GSE223102).
CRediT authorship contribution statement
Jing Yang: Writing – original draft, Funding acquisition,
Conceptualization. Xin Shen: Methodology, Investigation. Mi Qin:
Methodology, Investigation. Ping Zhou: Methodology, Investigation.
Fei-Hong Huang: Methodology, Investigation. Yun You: Supervision. Long
Wang: Formal analysis, Data curation. Jian-Ming Wu: Supervision,
Resources, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgments