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Deciphering Risperidone-Induced Lipogenesis by Network Pharmacology and Molecular Validation

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Background Risperidone is an atypical antipsychotic that can cause substantial weight gain. The pharmacological targets and molecular mechanisms related to risperidone-induced lipogenesis (RIL) remain to be elucidated. Therefore, network pharmacology and further experimental validation were undertaken to explore the action mechanisms of RIL. Methods RILs were systematically analyzed by integrating multiple databases through integrated network pharmacology, transcriptomics, molecular docking, and molecular experiment analysis. The potential signaling pathways for RIL were identified and experimentally validated using gene ontology (GO) enrichment and Kyoto encyclopedia of genes and genomes (KEGG) analysis. Results Risperidone promotes adipocyte differentiation and lipid accumulation through Oil Red O staining and reverse transcription-polymerase chain reaction (RT-PCR). After network pharmacology and GO analysis, risperidone was found to influence cellular metabolism. In addition, risperidone influences adipocyte metabolism, differentiation, and lipid accumulation-related functions through transcriptome analysis. Intersecting analysis, molecular docking, and pathway validation analysis showed that risperidone influences the adipocytokine signaling pathway by targeting MAPK14 (mitogen-activated protein kinase 14), MAPK8 (mitogen-activated protein kinase 8), and RXRA (retinoic acid receptor RXR-alpha), thereby inhibiting long-chain fatty acid β-oxidation by decreasing STAT3 (signal transducer and activator of transcription 3) expression and phosphorylation. Conclusion Risperidone increases adipocyte lipid accumulation by plausibly inhibiting long-chain fatty acid β-oxidation through targeting MAPK14 and MAPK8.
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ORIGINAL RESEARCH
published: 18 April 2022
doi: 10.3389/fpsyt.2022.870742
Frontiers in Psychiatry | www.frontiersin.org 1April 2022 | Volume 13 | Article 870742
Edited by:
Ji-chun Zhang,
Jinan University, China
Reviewed by:
Mansi Wu,
Jinan University, China
Shilin Luo,
Central South University, China
*Correspondence:
Wei-Dong Li
liweidong98@tmu.edu.cn
Shen Li
lishen@tmu.edu.cn
These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Psychopharmacology,
a section of the journal
Frontiers in Psychiatry
Received: 07 February 2022
Accepted: 18 March 2022
Published: 18 April 2022
Citation:
Fu Y, Yang K, Huang Y, Zhang Y, Li S
and Li W-D (2022) Deciphering
Risperidone-Induced Lipogenesis by
Network Pharmacology and Molecular
Validation.
Front. Psychiatry 13:870742.
doi: 10.3389/fpsyt.2022.870742
Deciphering Risperidone-Induced
Lipogenesis by Network
Pharmacology and Molecular
Validation
Yun Fu 1†, Ke Yang 1†, Yepei Huang 1, Yuan Zhang 1, Shen Li 1,2
*and Wei-Dong Li 1
*
1Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China, 2Department of
Psychiatry and Psychology, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
Background: Risperidone is an atypical antipsychotic that can cause substantial
weight gain. The pharmacological targets and molecular mechanisms related to
risperidone-induced lipogenesis (RIL) remain to be elucidated. Therefore, network
pharmacology and further experimental validation were undertaken to explore the action
mechanisms of RIL.
Methods: RILs were systematically analyzed by integrating multiple databases through
integrated network pharmacology, transcriptomics, molecular docking, and molecular
experiment analysis. The potential signaling pathways for RIL were identified and
experimentally validated using gene ontology (GO) enrichment and Kyoto encyclopedia
of genes and genomes (KEGG) analysis.
Results: Risperidone promotes adipocyte differentiation and lipid accumulation through
Oil Red O staining and reverse transcription-polymerase chain reaction (RT-PCR). After
network pharmacology and GO analysis, risperidone was found to influence cellular
metabolism. In addition, risperidone influences adipocyte metabolism, differentiation,
and lipid accumulation-related functions through transcriptome analysis. Intersecting
analysis, molecular docking, and pathway validation analysis showed that risperidone
influences the adipocytokine signaling pathway by targeting MAPK14 (mitogen-activated
protein kinase 14), MAPK8 (mitogen-activated protein kinase 8), and RXRA (retinoic
acid receptor RXR-alpha), thereby inhibiting long-chain fatty acid β-oxidation by
decreasing STAT3 (signal transducer and activator of transcription 3) expression
and phosphorylation.
Conclusion: Risperidone increases adipocyte lipid accumulation by plausibly inhibiting
long-chain fatty acid β-oxidation through targeting MAPK14 and MAPK8.
Keywords: risperidone, lipogenesis, network pharmacology, molecular docking, lipolysis, beta oxidation
INTRODUCTION
Antipsychotic drugs are the cornerstone of the current treatment of schizophrenia. In addition,
antipsychotics can be used to treat other mental disorders, including bipolar disorder,
autism spectrum disorder, obsessive-compulsive disorder, and dementia (13). Compared with
first-generation antipsychotics (FGAs), second-generation antipsychotics (SGAs) have shown
Fu et al. Network Pharmacology for Risperidone
better improvements in adherence, cognitive function, negative
symptoms, and dyskinesia, making them first-line clinical agents
(4). However, the administration of SGA could generate several
reported adverse effects, such as weight gain, obesity, metabolic
disturbances, and hepatic and renal dysfunctions (57). These
side effects are high-risk factors for diabetes, cardiovascular
disease, and cerebrovascular disease (810), leading to poor
treatment compliance.
Unlike other SGAs, such as clozapine and olanzapine,
risperidone is a derivative of benzisoxazole with multiple receptor
antagonist properties, making it more effective against negative
symptoms and more beneficial on both affective symptoms
and cognitive impairments (11). Risperidone-induced weight
gain is associated with a number of factors, including gene
polymorphisms (1214), exercise (1517), peripheral molecules
(18), and hyperphagia caused by regulating the expression
of melanocortin-4 receptor (MC4R), neuropeptide Y (NPY),
and agouti-related peptide (AgRP) (1921). It is also related
to increased adiponectin (APN) expression associated with
adipocyte differentiation, as well as the expression of adipogenic
genes such as peroxisome proliferator-activated receptor (PPAR)
(22,23). Risperidone upregulates fatty acid synthase (FASN) and
sterol regulatory element-binding protein 1 (SREBP1) expression
in hepatocyte cultures and mouse liver by targeting the hepatic
SREBP-1c/FASN couple, which is also one of the mechanisms
by which risperidone induces weight gain (24). Interestingly,
some studies have linked risperidone-induced weight gain
with lipolysis and considered that risperidone increases lipid
accumulation by altering lipolysis (25). Risperidone inhibits
leptin-mediated phosphorylation of STAT3-Y705, leading to
weight gain (26). Our previous work and other studies have
observed that risperidone promotes lipid accumulation (24,27).
However, no studies have comprehensively explained the drug
targets and molecular mechanisms.
Network pharmacology is commonly used to predict
drug-binding targets to explore the mechanisms of drug
action and the induction of drug side effects (28,29). In
contrast to traditional molecular mechanistic studies, network
pharmacology provides systems-level insights into drug-disease
interactions due to its “multi-gene, multi-target” nature.
Providing a detailed drug-target-pathway network helps to
assess the rationality and compatibility of drugs. At present,
it has been widely used in the study of drug mechanisms for
the treatment of Alzheimer’s disease, anxiety, and other mental
diseases (30,31).
To the best of our knowledge, no study has clarified
the mechanisms of risperidone-induced weight gain using
network pharmacology and molecular docking. Here,
in combination with transcriptomic sequencing, network
pharmacological target analysis, and molecular mechanisms,
we aimed to investigate the possible mechanisms of
risperidone-induced lipid accumulation through the fatty
acid β-oxidation pathway from the perspective of lipid
decomposition, which may provide a new understanding
and strategy for risperidone-induced weight gain. A detailed
schematic diagram of the workflow of this study is shown in
Figure 1.
MATERIALS AND METHODS
Cell Culture and Treatment
3T3-L1 embryonic fibroblasts (purchased from BeNa Culture
Collection, China) and adipose tissue mesenchymal stem cells
(AMSCs) [isolated as previously described by Liu et al. (32)] are
commonly used models to study adipocyte differentiation in vitro
for performing this experiment. Briefly, inguinal adipose tissue
dissected from 6-week-old mice (C57BL/6J) was washed three
times in PBS containing 2% of penicillin-streptomycin (Cat#
15070063, GIBCO, California, USA) and cut into fragments.
Then, the fragments were digested into single cells using
collagenase 1 (Cat# A004194, Sangon Biotech, China) for
1 h and finally cultured for 4 days in Dulbecco’s Modified
Eagle Medium (DMEM, Cat# SH30022.01, HyClone, Utah,
USA) containing 20% of fetal bovine serum (Cat# 11011-8611,
EveryGreen, China), 1% of L-glutamine (Cat# 56-8-59, Sigma,
Missouri, USA), and 1% of penicillin-streptomycin. AMSC and
3T3-L1 cells were cultured with DMEM containing 10% of fetal
bovine serum (FBS) and 1% of penicillin-streptomycin at 37C in
5% of CO2.
Preadipocyte 3T3-L1 cells and AMSCs were differentiated as
previously described by Hilgendorf et al. (33). Briefly, AMSC
and 3T3L1 cells were sequentially treated with MDI (0.5 mM
isobutyl-methylxanthine (Cat# I8450, Solarbio, China), 1 µM of
dexamethasone (Cat# D8040, Solarbio, China), and 10 ng/L of
insulin (Cat# 11070-73-8, Sigma, Missouri, USA) for 2 days,
10 ng/L of insulin for another 2 days, and 2.5 ng/L of insulin
for the last 2 days. For drug treatment, differentiated cells were
treated with dimethyl sulfoxide (DMSO, Cat# D8370, Solarbio,
China) and risperidone (Cat# HY-11018, MCE, China) for 48 h.
In addition, we used undifferentiated cells treated with the same
drugs as differentiated cells to explore the effect of risperidone
on differentiation.
Cell Viability Assay
The effect of risperidone on cell viability in 3T3-L1
preadipocytes was assessed using a Cell Counting Kit-8
(CCK-8, Cat# BS350B, Biosharp, China). Briefly, 5,000
cells were seeded in a 96-well plate overnight and treated
with gradient concentrations (0, 10, 30, 50, 70, and
100 µM) of risperidone for 48 h. Then, 10 µl of CCK-8
solution was added to each well and incubated for 1–
4 h. The results were measured by a microplate reader
(MULTISCKAN GO, Thermo Fisher Scientific, Rockford,
IL, USA) under a 450-nm extraction laser. Each set contained
three replicates.
Oil Red O Staining
The Oil Red O (ORO, Cat# O0625, Sigma, Missouri, America)
solution was prepared by dissolving 0.3 g of ORO powder in
60 ml of isopropanol and diluting with 40ml of distilled water.
Before staining, the solution was filtered through a filter to
obtain a clear solution. The cells were carefully washed twice
with PBS and fixed with 4% of paraformaldehyde for 20 min.
Then, the cells were rinsed with PBS and stained with ORO
solution for 20 min at room temperature (RT). Finally, the cells
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Fu et al. Network Pharmacology for Risperidone
FIGURE 1 | Schematic of the workflow.
were washed 2–5 times with distilled water until there was
no excess ORO staining solution. The cells were observed and
photographed under an Olympus IX71 inverted microscope
(Olympus, Japan).
cDNA Synthesis and qRT-PCR Analysis
Total RNA from control and risperidone-treated adipocytes
was extracted with TRIzol reagent (Cat# 260802, Life, New
York, USA) according to the instructions. RNA quality and
concentration were measured using a microplate reader at
260/280 nm. Total RNA (1 µg) from each sample was reverse
transcribed into complementary cDNA using a cDNA Synthesis
Kit (Cat# B24408, Bimake, China). After synthesis, cDNA was
diluted 10 times and subjected to qRT-PCR amplification using
an SYBR Green Real-Time PCR Master Mix (Cat# B21703,
Bimake, China) and gene-specific primer pairs (Adiponectin:
Forward: GGACTCTACTACTTCTCTTACC and Reverse:
CAGATGGAGGAGCACAGA; RAC1: Forward: TGTAG
CCGTATTCATTGTCA and Reverse: GTCGCACTTCAGG
ATACC; PPARg: Forward: TTATGGGTGAAACTCTGGGA
and Reverse: AATCAACTGTGGTAAAGGGC; and FASN:
Forward: GCCCGGTAGCTCTGGGTGTA and Reverse: TGCTC
CCAGCTGCAGGC). The mRNA expression was normalized to
18S rRNA and analyzed using the 211Ct method.
Transcriptome Sequencing and Analysis
Two replicated transcriptome libraries were prepared from
DMSO- and RIS-treated differentiated 3T3-L1 cells. A total of
four libraries were sequenced using the MGISEQ-2000 sequencer
(BGI, Shenzhen, China). For each RNA sample, cells were
collected from three replicates and pooled after RNA extraction.
Raw sequencing reads were cleaned by removing adaptor
sequences, reads containing poly-N sequences, and low-quality
reads. After mapping the data, normalization was performed,
and fragments per kilobase million (FPKM) mapped reads were
calculated using the DC.TOM platform. Raw RNA sequencing
data were deposited at the GEO repository (GSE198053, https://
www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE198053). The
significance levels of terms and pathways were corrected
with a rigorous threshold by Bonferroni, shown as the Q
value (0.05).
Network Pharmacology
The structure of risperidone (Drug Bank ID: DB00734) was
downloaded from DrugBank (https://go.drugbank.com/) in PDB
format and converted to mol2 format using Open Babel 2.4.1
software. The potential targets of risperidone were predicted by
PharmMapper (http://www.lilab-ecust.cn/pharmmapper/) using
the Human Protein Targets Database (34).
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Fu et al. Network Pharmacology for Risperidone
The Construction of Visualization Networks
The obesity-related genes were obtained from the GeneCards
database (https://www.genecards.org/) by the keyword “obesity.”
The cross-targets of risperidone-, transcriptome-, and obesity-
related genes were queried in the STRING database (version
11.0) to obtain the interaction network (confidence 0.4) and
the enrichment results of high-confidence candidates using
Cytoscape 3.9.1 software for visual optimization of the protein-
protein interaction (PPI) results. In addition, the gene ontology
(GO) and Kyoto encyclopedia of genes and genomes (KEGG)
enrichments were visualized using OmicStudio (https://www.
omicstudio.cn/tool), supported by the R language.
Molecular Docking
The binding sites and interaction forces for proteins and small
molecules can be predicted using molecular docking analysis.
First, the molecular structure of risperidone was obtained from
DrugBank (https://go.drugbank.com/) in PDB format, and the
protein structures of predicted targets (MAPK14, MAPK8,
RXRA, RXRB [retinoic acid receptor RXR-β], and AKT2 [RAC-
βserine/threonine-protein kinase]) were obtained from the PDB
database in PDB format (https://www.rcsb.org/). All water and
ligands were removed using PyMOL version 1.2R2 and saved in
PDBQT format for molecular docking, so our predictions were
performed under nonaqueous conditions. Next, we evaluated the
predicted posture of the interactions between small molecules
and predicted targets for network pharmacology using the
AutoDock 4.2 package. The molecular docking mode we used
was semiflexible, and the analysis settings for the grid box were
set as a cube of 62 ×56 ×64, 40 ×40 ×70, 60 ×52 ×68, 88 ×
56 ×88, and 60 ×90 ×50 (x, y, z) with 1.000 Å and centered at
the grid point of receptors MAPK14, MAPK8, RXRA, RXRB, and
AKT2, respectively. The models were visualized using PyMOL
1.2R2 version.
Protein Extraction and Western Blot
Analysis
Cells were washed 3 times with ice-cold PBS, lysed with RIPA
lysis buffer (Cat# E1013+, Applygen, China) containing protease
inhibitors (Cat# P1265, Applygen, China), phosphorylase
inhibitor (Cat# B15000, Bimake, China) and PMSF (Cat# P0100,
Solarbio, China) for 20 min, centrifuged at 12,000 rpm for
20 min at 4C, and then boiled with loading buffer (Cat#
E153-01, Genstar, China). Each sample was separated by SDS-
PAGE (10%) gel (Cat# E153, Genstar, China) and transferred
into a piece of polyvinylidene fluoride (PVDF, Cat# IPV00010,
Merck Millipore, MA, USA) membrane. Nonspecific binding
was blocked by soaking the membrane in Tris-buffered saline-
Tween (TBST) buffer that contained 5% of nonfat dry milk
(Cat# A600669-0250, Sangon Biotech, China) or bovine serum
albumin (BSA, Cat# C508113-0001, Sangon Biotech, China) for
2 h at RT. The membrane was incubated overnight with the
primary antibody at 4C, including anti-P-mapk14-T180/Y182
antibody (AP0526, 1:1,000 dilution, Abclonal, China), anti-
mapk14 antibody (A0227, 1:1,000 dilution, Abclonal, China),
anti-P-stat3-Y705 antibody (AP0705, 1:1,000 dilution, Abclonal,
China), anti-P-stat3-S727 antibody (AP0715, 1:1,000 dilution,
Abclonal, China), anti-stat3 antibody (A19566, 1:1,000 dilution,
Abclonal, China), anti-cpt1A antibody (A20746, 1:1,000 dilution,
Abclonal, China), anti-tubulin-βantibody (AF7011, 1:3,000
dilution, Affinity, USA), anti-mapk8 antibody (A2462, 1:1000
dilution, Abclonal, China), anti-rxrB antibody (A18119, 1:1,000
dilution, Abclonal, China), anti-rxrA antibody (A19015, 1:1,000
dilution, Abclonal, China), anti-akt2 antibody (A18019, 1:1,000
dilution, Abclonal, China), anti-actin-βantibody (A026, 1:25,000
dilution, Abclonal, China), and anti-ppar-gamma antibody
(A0270, 1:1,000 dilution, Abclonal, China). After washing with
TBST buffer, the membrane was incubated with goat anti-
rabbit (Cat# S0001, Affinity, USA) or goat anti-mouse HRP-
linked secondary antibody (Cat# S0002, Affinity, USA) at a
dilution of 1:10,000 for 2 h at RT. Chemiluminescence solution
(Cat# 201005-79, Advansta, California, USA) and medical X
film (Carestream, China) were used for detection, and ImageJ
software was used for analysis.
Statistical Analysis
Statistical analyses and figures were generated using GraphPad
Prism 9.0 (GraphPad Software Inc., CA, USA). The values in the
figures are presented as the mean ±standard error of the mean
(SEM). A p-value of <0.05 was considered statistically significant.
RESULTS
Risperidone Promotes Lipid Accumulation
in Both Undifferentiated and Differentiated
Adipocytes
To test whether risperidone could directly induce weight
gain by promoting lipid accumulation in adipocytes and
preadipocytes, differentiated and undifferentiated cells
(3T3-L1 and AMSC) were exposed to risperidone for 48 h.
After ORO staining, risperidone promoted adipocyte lipid
accumulation and preadipocyte differentiation (Figures 2A,B
and Supplementary Figures 1A,B). As shown in Figure 2C,
risperidone inhibited the growth of 3T3L1 preadipocytes in a
dose-dependent manner. Risperidone (100 µM) had little effect
on the cell survival of 3T3L1 preadipocytes; therefore, 100 µM
of risperidone was used in the following in vitro experiments.
We also detected the transcript levels of adipocyte-related
genes (Figure 2D) and found that risperidone promoted the
expression of Apn,Rac1 (Rac family small GTPase 1), and Fasn.
Taken together, risperidone promoted lipid accumulation in
undifferentiated and differentiated 3T3-L1 cells and AMSCs.
Prediction and Analysis of Risperidone
Binding Targets
To explore the mode of action of risperidone, the targets
of risperidone were predicted using network pharmacology
analysis. We obtained the structure of risperidone from the
Drug Bank (Figure 2E) and collected 236 predicted targets by
network pharmacology analysis (Supplementary Table 1). The
153 targets that met the criterion of Z >0 were considered
strongly correlated targets (Figure 2F) for further analysis. GO
enrichment analysis indicated that risperidone affected a series
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Fu et al. Network Pharmacology for Risperidone
FIGURE 2 | Risperidone promotes adipogenesis and network pharmacology analysis. Oil red O staining of (A) differentiated 3T3-L1 cells and (B) undifferentiated
3T3-L1 cells treated with DMSO or risperidone. (C) Risperidone toxicity tested by cell counting kit 8 (CCK8). (D) The mRNA levels of genes related to adipocyte
differentiation and lipid accumulation in differentiated 3T3-L1 cells treated with DMSO or risperidone measured by qRT-PCR. (E) Structure of risperidone. (F) Predicted
target proteins of risperidone. Bubble size and depth of color are proportional to the Z-score (Z >0). (G) GO enrichment analysis. (H) KEGG enrichment analysis of
predicted targets. D-NC, differentiated 3T3-L1 cells treated with DMSO; D-RIS, differentiated 3T3-L1 cells treated with risperidone; U-NC, undifferentiated 3T3-L1
cells treated with DMSO; U-RIS, undifferentiated 3T3-L1 treated with risperidone. **P<0.01, ***P<0.005.
of biological processes, including cellular processes, metabolic
processes, organic substance metabolic processes, primary
metabolic processes, cellular metabolic processes, biological
regulation, nitrogen compound metabolic processes, responses
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Fu et al. Network Pharmacology for Risperidone
to stimuli, regulation of biological processes, and regulation of
cellular processes (Figure 2G). In addition, in the KEGG pathway
analysis, 155 KEGG pathways were selected (P-adjust <0.05)
related to all predicted targets, including the drug metabolism-
cytochrome P450, metabolism of xenobiotics by cytochrome
P450, prolactin signaling pathway, chemical carcinogenesis,
platinum drug resistance, prostate cancer, non-small cell lung
cancer, EGFR tyrosine kinase inhibitors, adherens junctions,
PPAR signaling pathway, fluid shear stress and atherosclerosis,
arachidonic acid metabolism, adipocytokine signaling pathway,
and tyrosine metabolism (Figure 2H).
Risperidone Influenced Metabolic and
Lipid β-Oxidation in Adipocytes
To determine the molecular mechanisms underlying lipid
accumulation in response to risperidone treatment, differentiated
3T3-L1 cells treated with risperidone or DMSO were tested
by transcriptome sequencing (mRNA-seq, n=2). Compared
with controls, risperidone caused the upregulation of 2,860
genes and the downregulation of 2,720 genes (Figure 3A). A
total of 5,580 genes were used for GO and KEGG enrichment
analysis, indicating that risperidone affected a range of biological
processes, including cellular processes, metabolic processes,
and biological regulation (Figure 3B). In the KEGG pathway
analysis, risperidone affected the metabolic pathway, autophagy-
animal, FoXO signaling pathway, mitophagy-animal, insulin
signaling pathway, citrate cycle, HIF-1 signaling pathway, MAPK
signaling pathway, oxidative phosphorylation, mTOR signaling
pathway, AMPK signaling pathway, adipocytokine signaling
pathway, JAK-STAT signaling pathway, lipoic acid metabolism,
and fatty acid metabolism (Figure 3C). Based on these results, we
investigated whether the adipocytokine signaling pathway played
an important role in risperidone-induced lipid accumulation. On
the one hand, transcriptomic results showed that risperidone
downregulated 13 genes, including Nfkbie (NFKB inhibitor
epsilon), Cpt1a (carnitine palmitoyltransferase 1A), Ptpn11
(protein tyrosine phosphatase non-receptor type 11), Nfkb1
(nuclear factor kappa B subunit 1), Tnfrsf1b (TNF receptor
superfamily member 1B), Ikbkb (inhibitor of nuclear factor
kappa B kinase subunit β), Mapk8,Prkag1 (protein kinase AMP-
activated non-catalytic subunit gamma 1), Rxra,Acsl5 (acyl-
CoA synthetase long chain family member 5), Slc2a1 (solute
carrier family 2 member 1), Socs3 (suppressor of cytokine
signaling 3), and Stat3, and up-regulated 15 genes, including
Acsl1 (acyl-CoA synthetase long chain family member 1), G6pc
(glucose-6-phosphatase, catalytic), Pck2 (phosphoenolpyruvate
carboxykinase 2), Paqr3 (progestin and adipoQ receptor family
member 3), Prkaa2 (protein kinase AMP-activated catalytic
subunit alpha 2), Prkab2 (protein kinase AMP-activated non-
catalytic subunit β2), Prkab1 (protein kinase AMP-activated
non-catalytic subunit β1), Acsl3 (acyl-CoA synthetase long
chain family member 3), Akt3 (AKT serine/threonine kinase
3), Akt2,Irs2 (insulin receptor substrate 2), Rxrb,Adipor1
(adiponectin receptor 1), Acacb (acetyl-CoA carboxylase β), and
Stk11 (serine/threonine kinase 11) (Figure 3D). In addition, we
found that the major highly expressed genes in the adipocytokine
signaling pathway, such as Cpt1a,Ptpn11,Rxra,Slc2a1,Stat3,
and Prkag1, were downregulated after risperidone treatment
(Figure 3E). To explore the relationships among the core set
genes and provide a global view of network architecture, we set up
a PPI network analysis model using STRING online (combined
score 0.4) and Cytoscape software. The top three proteins with
relatively high connectivity degrees were STAT3 (node degree =
11), AKT2 (node degree =11), and IKBKB (node degree =11)
(Figure 3F).
Analysis of Intersecting Targets in Network
Pharmacology/Transcriptomics/Obesity-
Related Genes
To obtain high-confidence candidates, we first accessed 9,307
obesity-related genes from the GeneCards online database using
the key word “obesity.” After comparing the predicted targets
of risperidone, transcripts influenced by risperidone, obesity-
related genes, and 34 intersecting targets were considered high-
confidence candidates (Figure 4A). To explore the relationships
among the 34 targets, we built a PPI network model. The top
five proteins with higher degrees of connectivity were CASP3
(caspase-3) (node degree =14), MDM2 (E3 ubiquitin-protein
ligase mdm2) (node degree =12), MAPK8 (node degree =
11), GSK3B (glycogen synthase kinase-3 β) (node degree =11),
and MMP2 (72 kDa type IV collagenase) (node degree =11)
(Figure 4B). Interestingly, transcriptomic sequencing showed
that the top five targets of CASP3, MDM2, MAPK8, and GSK3B
were downregulated (Figure 4C). Furthermore, GO and KEGG
enrichment analyses of these 34 targets showed that risperidone
affected biological processes, including the fibroblast growth
factor receptor signaling pathway involved in orbitofrontal
cortex development, ventricular zone neuroblast division,
cellular detoxification of nitrogen compounds, the vitamin D
receptor signaling pathway, and the nitrobenzene metabolic
process (Figure 4D). In the KEGG analysis, risperidone affected
platinum drug resistance, apoptosis-multiple species, EGFR
tyrosine kinase inhibitor resistance, the AGE-RAGE signaling
pathway in diabetic complications, metabolism of xenobiotics
by cytochrome P450, adipocytokine signaling pathway, etc
(Figure 4E).
Molecular Docking for Risperidone and
Immunoblot Validation
To determine the potential binding of risperidone to
intersecting targets, molecular docking analysis was performed.
The adipocytokine signaling pathway was enriched in
network pharmacology (Figure 2H) and transcriptome
sequencing (Figure 3C), as well as obesity-related genes
(Figure 4E). We focused on factors in the adipocytokine
signaling pathway. As reported previously, the adipocytokine
signaling pathway is closely related to lipid metabolism
and obesity, so MAPK8, RXRA, RXRB, and AKT2, which
are involved in the adipocytokine signaling pathway
and were screened as intersecting targets in network
pharmacology/transcriptomic/obesity-related genes, were
considered candidate genes for molecular docking and
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Fu et al. Network Pharmacology for Risperidone
FIGURE 3 | Transcriptome sequencing analysis showed that risperidone influenced cell metabolism and the adipocytokine signaling pathway. (A) Volcano plot
showing gene transcription influenced by risperidone in differentiated 3T3-L1 cells (P<0.05, n=2). Upregulated genes are shown in the right panel (red circles,
2,080 genes), and downregulated genes are shown in the left panel (blue circles, 2,720 genes). (B) GO enrichment analysis. (C) KEGG enrichment analysis of genes
influenced by risperidone shown in (A).(D) Heat map of risperidone-influenced genes in the adipocytokine signaling pathway. (E) Expression circos map of
risperidone-influenced genes in the adipocytokine signaling pathway. (F) Interaction network of risperidone-influenced genes in the adipocytokine signaling pathway.
The bubble size and depth of color are proportional to the node degree. NC1 and NC2 were control samples, and RIS1 and RIS2 were risperidone-treated samples.
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Fu et al. Network Pharmacology for Risperidone
FIGURE 4 | Analysis of intersected targets in network pharmacology/transcriptome/obesity-related genes. (A) Intersected targets or genes of risperidone and
transcriptome- and obesity-related genes were considered high-confidence candidates. (B) Interaction network of high-confidence candidates described in (A).(C)
Heat map showing the expression level of high-confidence candidates in samples. (D) GO analysis. (E) KEGG analysis of high-confidence candidates.
verification. Moreover, we noticed that MAPK14 is a critical
upstream gene that regulates adipocytokine signaling pathways
and obtained a high Z-value in network pharmacological
predictions (which indicates a target with promising binding
capacity and functional importance). Therefore, MAPK14 was
also considered a candidate and verified in molecular docking.
The crystal structure of the intersecting targets was collected
from the PDB database with PDB IDs 6HWU, 4AWI, 7B9O,
and 1H9U for docking analysis with risperidone. The MAPK14
docking results showed that the hydrogen bond between
risperidone and the MAPK14 protein acted on one amino
acid residue, viz. MET-111 (Figure 5A). The MAPK8 docking
results showed that the hydrogen bond between risperidone
and the MAPK8 protein acted on two amino acid residues,
namely, ASP-176 and MET-179 (Figure 5B). The RXRA docking
results showed that the hydrogen bond between the risperidone
and RXRA proteins acted on one amino acid residue, namely,
ARG-1302 (Figure 5C). The RXRB docking results showed
that there was no hydrogen bond between the risperidone and
RXRB proteins (Figure 5D). The AKT2 docking results showed
that there was no hydrogen bond between the risperidone and
AKT2 proteins (Figure 5E). The minimal combinations of these
models are shown in Table 1. Briefly, the minimum lowest
binding energies of MAPK14, RXRB, RXRA, MAPK8, and
AKT2 for risperidone were 20.69, 16.79, 18.97, 23.52,
and 17.09 KJ/mol, respectively. Furthermore, we performed
docking comparisons of risperidone and the binding position of
ligands of the protein crystal (Supplementary Figures 2A–E),
and we found a partial overlap between risperidone and
ligands in MAPK14 (Supplementary Figure 2A) and MAPK8
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Fu et al. Network Pharmacology for Risperidone
FIGURE 5 | Interaction between risperidone and predicted targets in the adipocytokine signaling pathway using molecular docking analysis. Models of risperidone
(DRUGBANK ID DB00734) with (A) mitogen-activated protein kinase 14 (MAPK14, PDB ID 6HWU), (B) mitogen-activated protein kinase 8 (MAPK8, PDB ID 4AWI),
(C) retinoic acid receptor RXR-alpha (RXRA, PDB ID 7B9O), (D) retinoic acid receptor RXR-β(RXRB, PDB ID 1H9U), and (E) RAC-βserine/threonine-protein kinase
AKT2 (PDB ID 3E87).
Frontiers in Psychiatry | www.frontiersin.org 9April 2022 | Volume 13 | Article 870742
Fu et al. Network Pharmacology for Risperidone
TABLE 1 | Binding model information.
Receptors Length (Human/Mouse) Identical positions Identity Similar position Binding energy (1KJ/mol)
MAPK14 360/360 358 99.444% 1 20.69
RXRB 533/520 504 94.559% 9 16.79
RXRA 462/467 455 97.430% 6 18.97
MAPK8 427/384 379 88.759% 2 23.52
AKT2 481/481 472 98.129% 6 17.09
(Supplementary Figure 2B), suggesting that risperidone may
have a good binding activity to MAPK14 and MAPK8. To
verify the effects of risperidone on molecular docking targets of
MAPK14, MAPK8, RXRA, RXRB, and AKT2, immunoblotting
analysis was performed on differentiated 3T3-L1 cells after
treatment with DMSO or risperidone. The results showed that
MAPK14, MAPK8, RXRA, and RXRB were downregulated
in risperidone treatment, while the AKT2 was upregulated
(Figures 6A–F). We also detected the expression levels of these
molecular docking targets in undifferentiated 3T3-L1 cells and
got similar results (Figures 6G–L).
Risperidone Inhibited the Adipocytokine
Signaling Pathway and Might Inhibit the
Lipid β-Oxidation Protein CPT1A in vitro
To determine the effect of risperidone on the output of
adipocytokine signaling pathway, we detected the expression
levels of CPT1A and STAT3, which were crucial downstream
effective factors of MAPK8 that play important roles in long-
chain fatty acid β-oxidation and hepatic triglyceride metabolism.
Our immunoblotting results showed that risperidone not
only downregulated CPT1A and STAT3 expressions, it also
decreased MAPK14 and STAT3 phosphorylation in differentiated
and undifferentiated 3T3-L1 cells after DMSO or risperidone
treatments (Figures 7A–L), indicating that risperidone inhibited
signal transduction. As we know, lipid metabolisms, including
lipogenesis and lipolysis, were more activated in adipocytes
than in preadipocytes. To better understand the specificity of
the role of risperidone in the adipocytokine signaling pathway
and long-chain fatty acid β-oxidation, we performed the same
validation and analysis on undifferentiated and differentiated
3T3-L1 cells. The results showed that the expression and
corresponding phosphorylation levels of MAPK14, STAT3, and
CPT1A were upregulated when the cells differentiated into
adipocytes (Figures 8A–G).
DISCUSSION
To the best of our knowledge, we are the first to discover the
mechanisms of risperidone-induced lipid accumulation using
multiple crossover analyses, including network pharmacology,
transcriptomics, and disease databases. Furthermore, we
validated the candidate pathway and the adipocytokine signaling
pathway and established a series of binding models by molecular
docking to elucidate the mechanisms of risperidone-induced
weight gain.
In our study, risperidone directly promoted adipocyte
differentiation and lipid accumulation in both 3T3-L1 cells
and AMSCs. Furthermore, the mRNA expression levels of
Apn,Rac1, and Fasn were upregulated, while Pparg was
not significantly changed. These cellular results indicate
that risperidone treatment may directly affect adipose tissue
independent of the central nervous system and food intake,
which is consistent with previous studies (25,27). The lack
of significant upregulation of the mRNA expression levels of
Pparg may be the result of feedback regulation, as increased
Pparg protein expression was observed after risperidone
treatment (Supplementary Figures 3A,B). Through network
pharmacology analysis, we identified risperidone binding
targets and found that these proteins have important biological
functions, such as metabolic processes and responses to stimuli.
In addition, these targets were involved in the PPAR signaling
pathway and adipocytokine signaling pathway, which modulate
multiple biological processes within cells, including lipid
and glucose metabolism and energy balance (35). The above
results suggested that risperidone may affect adipogenesis and
adipocyte differentiation through the PPAR signaling pathway
and adipocytokine signaling pathway.
After transcriptomic analysis, risperidone affected the
transcription of related genes in the adipocytokine signaling
pathways, as predicted by network pharmacology analysis.
Among the downregulated genes, STAT3 and CPT1A are closely
associated with β-oxidation of long-chain fatty acids (36,37),
indicating that risperidone inhibits lipolysis. Furthermore, an
inactive adipocytokine signaling pathway was associated with
insulin resistance, increased food intake, and reduced energy
expenditure, which may explain risperidone-induced weight
gain due to hyperphagia (21).
We analyzed the top 3 proteins (AKT2, STAT3, and IKBKB)
in lipid metabolism from the protein-protein interaction and
KEGG enrichment analyses and the top 5 proteins (MAPK8,
MDM2, MMP2, GSK3B, and CASP3) in intersection analysis
with tight junctions (Supplementary Figure 4). In the KEGG
analysis, MAPK8, AKT2, STAT3, and IKBKB were enriched in
the adipocytokine signaling pathway, located downstream of
the leptin receptor, and strongly related to adipocyte volume
and number. Other proteins from these two analyses were
shown to be in pathways related to glycolipid metabolism as
well as endocrine regulation, which are closely associated with
lipogenesis, indicating that proteins have a synergistic effect
Frontiers in Psychiatry | www.frontiersin.org 10 April 2022 | Volume 13 | Article 870742
Fu et al. Network Pharmacology for Risperidone
FIGURE 6 | Immunoblotting analysis of molecular docking targets. (A) Expression level of the molecular docking targets in differentiated 3T3-L1 cells after treatment
with DMSO or risperidone. (BF) Gray degree analysis shows the expression level of targets in (A).(G) Expression level of the molecular docking targets in
undifferentiated 3T3-L1 cells after treatment with DMSO or risperidone. (HL) Gray degree analysis shows the expression level of targets in (G). Values are expressed
as the mean ±SEM (n=2). *P<0.05, **P<0.01; ns, no significant difference; SEM, standard error of mean; D-NC, differentiated 3T3-L1 cells treated with DMSO;
D-RIS, differentiated 3T3-L1 cells treated with risperidone; U-NC, undifferentiated 3T3-L1 cells treated with DMSO; U-RIS, undifferentiated 3T3-L1 treated with
risperidone.
on lipid accumulation to a certain extent. Synergy among
these proteins has also been supported. For example, GSK3β
expression promotes ESCC cell progression through STAT3 (38),
and GSK3βinhibition could block STAT3 signaling, reducing
pro-inflammatory responses (39). STAT3 activation could lead
to MMP2 expression through MAPK8, affecting cell migration
and viability (40,41). In addition, the stability of crucial
factors in regulatory pathways may affect signal transduction.
IKBKB could promote the stability of MDM2 (42), which
can interact with MAPSK3 to promote STAT3 degradation
(43). In conclusion, the proteins we screened have critical
functions in cellular biological processes and lipid metabolism
and may modulate the adipocytokine signaling pathway in a
synergistic mode.
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Fu et al. Network Pharmacology for Risperidone
FIGURE 7 | Immunoblotting analysis of effective factors in the adipocytokine signaling pathway. (A) Expression levels of effective factors in the adipocytokine signaling
pathway in differentiated 3T3-L1 cells after treatment with DMSO or risperidone. (B–F) Gray degree analysis shows the expression level of effective factors in (A).(G)
Expression levels of effective factors in the adipocytokine signaling pathway in undifferentiated 3T3-L1 cells after treatment with DMSO or risperidone. (H–L) Gray
degree analysis shows the expression level of effective factors in (G). Values are expressed as the mean ±SEM (n=2), *P<0.05, **P<0.01; ns, no significant
difference; SEM, standard error of mean; D-NC, differentiated 3T3-L1 cells treated with DMSO; D-RIS, differentiated 3T3-L1 cells treated with risperidone; U-NC,
undifferentiated 3T3-L1 cells treated with DMSO; U-RIS, undifferentiated 3T3-L1 treated with risperidone.
The β-oxidation of long-chain fatty acids is the main source of
cellular energy and occurs in both mitochondria and peroxisomes
(44). Mitochondria catalyze the β-oxidation of the bulk of short-,
medium-, and long-chain fatty acids derived from diet, and this
pathway constitutes the major process by which fatty acids are
oxidized to generate energy. CPT1A acts as a catalytic enzyme
by catalyzing the acyl transfer of long-chain fatty acid-CoA
conjugations onto carnitine, which is required for mitochondrial
uptake of long-chain fatty acids and subsequent β-oxidation
in mitochondria (45). STAT3 is a nuclear transcription factor
that is phosphorylated and activated by JAK2 (tyrosine-protein
kinase JAK2) to form an activated homologous dimer that
Frontiers in Psychiatry | www.frontiersin.org 12 April 2022 | Volume 13 | Article 870742
Fu et al. Network Pharmacology for Risperidone
FIGURE 8 | Immunoblot analysis of proteins in the adipocytokine signaling pathway. (A) Expression levels of effective factors in the adipocytokine signaling pathway in
differentiated and undifferentiated 3T3-L1 cells. (B–G) Gray degree analysis shows the expression level of effective factors in (A). Values are expressed as the mean ±
SEM (n=2), **P<0.01; ns, no significant difference; SEM, standard error of mean.
enters the nucleus to activate CPT1A transcription. Furthermore,
STAT3 is required for leptin signaling transduction and activation
(46). Our immunoblotting results showed that risperidone
decreased STAT3 and CPT1A expression as well as their
phosphorylation levels in differentiated and undifferentiated
3T3-L1 cells, which was not an indirect result of risperidone-
induced cell differentiation (Figures 7A,H). These results suggest
that risperidone can specifically inhibit the STAT3-CPT1A axis.
Our intersection analysis (Figure 4A) indicated that MAPK8,
AKT2, RXRA, and RXRB were high-confidence candidates
that also belong to the adipocytokine signaling pathway
upstream of STAT3. In addition, MAPK14 is an upstream
protein of the STAT3-CPT1A signaling pathway among the
top 10 targets with the highest Z-score predicted by network
pharmacology. Through molecular docking analyses (Figure 5)
and immunoblotting validation (Figures 6,7), it was concluded
that risperidone inhibited MAPK14 and STAT3 phosphorylation
and their downstream protein CPT1A by downregulating
MAPK14, MAPK8, RXRA, and RXRB, thereby inhibiting the
β-oxidation of fatty acids. It has been reported that MAPK14
is negatively correlated with lipid accumulation from MAPK14
knockout mice with increased peripheral fat (47). RXRA and
RXRB, members of the RXR family, can be activated by sterols
and are involved in a series of biological processes in cells, such
as cell differentiation and fatty acid oxidation (48,49). PPARs
are members of the nuclear hormone receptor superfamily and
have been implicated in the regulation of lipid metabolism
and adipocyte differentiation (35). To bind DNA and activate
transcription, PPARs must form heterodimers with RXR (50).
Furthermore, PPARA could induce CPT1A expression using
a PPARA agonist (51,52) or fasting, which may explain why
risperidone induces lipogenesis. Taken together, risperidone
promotes lipid accumulation by inhibiting fatty acid β-oxidation.
There were several limitations in our study. First, we may have
missed some of the predicted targets due to insufficient databases
of protein structures. Moreover, our results demonstrated
that MAPK8, RXRA, RXRB, and AKT2 were high-confidence
candidates and played important roles in the β-oxidation of
long-chain fatty acids. However, given that drug-target binding
does not necessarily lead to changes in target expression, our
screening criteria may be too stringent; thus, other targets may
be overlooked. In the future, we should not only consider the
intersection of these four targets but also pay attention to the 73
targets that are associated with obesity but do not change their
expression levels.
In summary, the adipocytokine signaling pathway, as a novel
signaling pathway, plays an important role in risperidone-
induced lipogenesis. In addition, these findings could provide a
more comprehensive understanding of the action of risperidone
and a new administration strategy for risperidone-induced
weight gain.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
Frontiers in Psychiatry | www.frontiersin.org 13 April 2022 | Volume 13 | Article 870742
Fu et al. Network Pharmacology for Risperidone
accession number(s) can be found below: https://www.ncbi.nlm.
nih.gov/geo/query/acc.cgi?acc=GSE198053.
AUTHOR CONTRIBUTIONS
W-DL and SL were responsible for the study concept and
design, drafting of the manuscript, and study supervision.
YF performed the experiments and prepared the initial
draft of the manuscript. KY, YH, and YZ performed the
experiments. All authors have contributed to and approved the
final manuscript.
FUNDING
This study was supported by the National Natural Science
Foundation of China (92046014, 81801323), Beijing-Tianjin-
Hebei Jointed Research Program (19JCZDJC64700), and
National Key R&D Program of China (2017YFC1001900).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyt.
2022.870742/full#supplementary-material
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Frontiers in Psychiatry | www.frontiersin.org 15 April 2022 | Volume 13 | Article 870742
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