Abstract
Objectives: Jasminoidin (JA) and ursodeoxycholic acid (UA) have been
shown to exert synergistic effects on cerebral ischemia (CI) therapy,
but the underlying mechanisms remain to be elucidated. Objective: To
elucidate the synergistic mechanisms involved in the combined use of JA
and UA (JU) for CI therapy using a driver-induced modular screening
(DiMS) strategy. Methods: Network proximity and topology-based
approaches were used to identify synergistic modules and driver genes
from an anti-ischemic microarray dataset (ArrayExpress, E-TABM-662). A
middle cerebral artery occlusion/reperfusion (MCAO/R) model was
established in 30 Sprague Dawley rats, divided into sham, vehicle, JA
(25 mg/mL), UA (7 mg/mL), and JU (JA:UA = 1:1) groups. After 90 minutes
of ischemia, infarct volume and neurological deficit scores were
evaluated. Western blotting was performed 24 h after administration to
validate key protein changes. Results: Six, eleven, and four
drug-responsive On_modules were identified for JA, UA, and JU,
respectively. Three synergistic modules (Sy-modules, JU-Mod-7, 8, and
10) and 12 driver genes (e.g., NRF1, FN1, CUL3) were identified, mainly
involving the PI3K-Akt and MAPK pathways and regulation of the actin
cytoskeleton. JA and UA synergistically reduced infarct volume and
neurological deficit score (2.5, p < 0.05) in MCAO/R rats. In vivo
studies demonstrated that JU suppressed the expression of CUL3, FN1,
and ITGA4, while it increased that of NRF1. Conclusions: JU acts
synergistically on CI–reperfusion injury by regulating FN1, CUL3,
ITGA4, and NRF1 and inducing the PI3K-Akt, MAPK, and actin cytoskeleton
pathways. DiMS provides a new approach to uncover mechanisms of
combination therapies.
Keywords: combination therapy, synergistic effect, network topology,
modular analysis, cerebral ischemia
1. Introduction
Cerebral ischemic stroke is a common cause of death and disability. It
has high incidence, disability, and mortality rates [[40]1]. It is a
major problem that threatens human health and quality of life. Current
treatments primarily include thrombolytic therapy, such as recombinant
tissue plasminogen activator (rt-PA) [[41]2], endovascular thrombectomy
[[42]3,[43]4], anticoagulant/antiplatelet therapy [[44]5], and
neuroprotective agents [[45]6,[46]7,[47]8]. Increasing evidence shows
that combination therapy is more advantageous for ischemic stroke since
it improves the therapeutic effect without increasing side effects
[[48]9,[49]10]. An in-depth study of ischemic stroke showed that herbal
combination therapy is an effective treatment for cerebral ischemia
(CI) [[50]11,[51]12]; however, the mechanisms driving the synergistic
effects of combination therapy remain unclear. It is crucial to
understand the synergistic mechanisms of combination therapy, which
could provide valuable insights and improve the treatment of cerebral
ischemia. In recent years, several computational approaches were
developed to identify combination therapy targets. This includes
co-expression networks based on modular analysis that are based on
association genes or proteins and allow a deeper understanding of the
underlying mechanisms of drug use in complex diseases
[[52]13,[53]14,[54]15].
Ischemic stroke triggers complex pathological processes, including
excitotoxicity [[55]16], oxidative stress [[56]17], and inflammation
[[57]18], leading to neuronal damage and functional deficits [[58]19].
Single-target therapies often struggle to effectively control the
multiple mechanisms of injury associated with stroke [[59]20].
Therefore, multi-target synergistic treatment strategies have gained
increasing attention. Qingkailing injection, a widely used herbal
compound in stroke treatment, contains two main bioactive components:
jasminoidin (JA) and ursodeoxycholic acid (UA). JA, derived from the
traditional Chinese medicine Fructus Gardeniae, has shown potential in
addressing oxidative stress, inflammation, and neuronal apoptosis
[[60]21]. UA, derived from calculus bovis factitius, exhibits a range
of pharmacological effects, including immunosuppression, inhibition of
calcium influx, prevention of neurodegeneration, strong
anti-inflammatory activity, and neuroprotection through mitochondrial
stabilization and apoptosis inhibition [[61]22,[62]23]. Unlike
arbitrary combinations of antioxidants and anti-inflammatory agents,
Fructus Gardeniae and artificial bezoars have been historically
co-administered in traditional formulations, with their active
ingredients, JA and UA, suggesting the possibility of synergistic
effects. However, studies investigating the synergistic effects of JA
and UA remain limited, with a lack of in-depth mechanistic analysis.
Given the multi-faceted nature of stroke pathology, it is crucial to
explore the combined effects of JA and UA. While single-target
therapies often fail to fully address the complex biological processes
of stroke, JA and UA may offer broader neuroprotection by targeting
multiple signaling pathways simultaneously. Although their individual
efficacy has been demonstrated, the mechanisms underlying their
combined therapy are still under-explored. This research aims to fill
this gap by using a modular-pharmacology-based approach to explore the
combined effects of JA and UA, providing insights into their
synergistic mechanisms.
Despite the promise of combination therapies, existing studies often
lack a deep mechanistic understanding of the synergies involved. This
study seeks to address this gap by using a network topology approach to
identify key synergistic modules and driver genes. Network topology has
become an essential tool in pharmacology for decoding disease
mechanisms, accelerating drug discovery, and repositioning existing
drugs. For example, the BrainMI framework integrates brain functional
connectivity with molecular genetic networks to identify genes involved
in neurological disorders [[63]24]. Network topology methods have been
successfully applied in antiviral drug repositioning [[64]25],
drug-target prediction [[65]26], and traditional Chinese medicine
[[66]27]. By employing this network-driven approach (network proximity,
[MATH: SAB :MATH]
), network pharmacology reveals complex molecular interactions that
drive therapeutic synergies [[67]28].
This study used module-based separation measure of
[MATH: SAB :MATH]
(network proximity of drugs A and B) and network topology methods to
identify JU synergistic modules and driver genes based on the
anti-ischemic genome network. The driver-induced modular screening
(DiMS) strategy was used to elucidate the synergistic mechanism of JU
in CI treatment through further functional analysis and literature
verification.
2. Materials and Methods
2.1. Gene Expression Dataset
The microarray gene expression dataset containing 16,463 mouse cDNAs
(Inocyte Genomics, Inc., Santa Clara, CA, USA) was constructed in our
previous study and was uploaded to the Array Express database:
[68]http://www.ebi.ac.uk/arrayexpress,E-TABM-662 (accessed on 19 August
2022) [[69]29]. Sham, JA (25 mg /mL), UA (7 mg/mL), and JU (a
combination of jasminoidin and ursodeoxycholic acid at a ratio of 1:1)
were selected for analysis.
Experimental Design: Healthy adult male Kunming mice (12 weeks, 38–48
g) were used in this study. These mice were free of specific pathogens
and were housed at 25 °C under a 12 h light/dark cycle. A model of
middle cerebral artery occlusion (MCAO) was surgically established as
described in our previous studies [[70]29,[71]30]. Specifically, the
left middle cerebral artery was blocked using intracavitary filaments
for 1.5 h, followed by 24 h of reperfusion to induce
ischemia–reperfusion. Histological analysis and cerebral infarction
area measurements were then performed, as detailed in the cited
references.