Abstract
Drug repositioning research using transcriptome data has recently
attracted attention. In this study, we attempted to identify new target
proteins of the urotensin-II receptor antagonist, KR-37524
(4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)pip
erazine-1-carboxamide dihydrochloride), using a transcriptome-based
drug repositioning approach. To do this, we obtained KR-37524-induced
gene expression profile changes in four cell lines (A375, A549, MCF7,
and PC3), and compared them with the approved drug-induced gene
expression profile changes available in the LINCS L1000 database to
identify approved drugs with similar gene expression profile changes.
Here, the similarity between the two gene expression profile changes
was calculated using the connectivity score. We then selected proteins
that are known targets of the top three approved drugs with the highest
connectivity score in each cell line (12 drugs in total) as potential
targets of KR-37524. Seven potential target proteins were
experimentally confirmed using an in vitro binding assay. Through this
analysis, we identified that neurologically regulated serotonin
transporter proteins are new target proteins of KR-37524. These results
indicate that the transcriptome-based drug repositioning approach can
be used to identify new target proteins of a given compound, and we
provide a standalone software developed in this study that will serve
as a useful tool for drug repositioning.
Subject terms: Computational models, Gene ontology, Virtual drug
screening
Introduction
Novel drug development is still a time-consuming and expensive
task^[34]1,[35]2 because it is difficult to predict side effects or
toxicity in advance^[36]3. Even if a drug is successfully developed and
approved, its frequency of use decreases over time because of the
emergence of more efficient drugs and the occurrence of unexpected
resistance^[37]4. However, since approved drugs have already passed the
verification of in vivo toxicity and side effects through clinical
trials, drug repositioning or repurposing approaches can find potential
new target proteins and therefore improve drug usability^[38]5,[39]6.
This is more efficient and easier than the development of an entirely
new drug^[40]7,[41]8. For this reason, the drug repositioning approach
has recently been favored for new drug development.
Computational drug repositioning is an efficient approach for screening
new target proteins. Various computational drug repositioning methods
have been developed using transcriptome data to identify potential new
target proteins for drugs, such as comparing gene expression profile
changes between disease models and drug treatment
conditions^[42]9,[43]10, prediction of drug-protein
interactions^[44]11–[45]13, and network integration^[46]14,[47]15. In
addition, the Connectivity Map (CMap) database has provided a total of
564 gene expression profiles of 143 distinct bioactive small-molecule
perturbagens representing 453 individual instances since 2006^[48]16.
Many computational methods use CMap to predict unknown drug-disease
associations based on the inverse correlation of gene expression
patterns^[49]17,[50]18. The Library of Integrated Network-based
Cellular Signatures (LINCS) L1000 database extends CMap to include a
larger number of gene expression profiles^[51]19. The LINCS L1000
database provides gene expression profiles induced by approximately
33,000 small molecules in 99 cell lines, and is therefore highly useful
for new drug discovery and repositioning^[52]20,[53]21.
We previously developed novel urotensin-II receptor antagonists such as
KR-36676^[54]22 and KR-36996^[55]23,[56]24, which are derived from
benzo[b]thiophene-2-carboxamide. These compounds acted selectively
against the urotensin-II receptor with good affinity (Ki = 0.7 nM and
4.44 nM for KR-36676 and KR-36996, respectively) in cellular events
such as stress fiber formation and cellular hypertrophy. However, drug
development of these compounds has since been discontinued. This
decision was made after preliminary efficacy data from a
proof-of-concept program yielded unsatisfactory results and identified
cardiotoxicity issues. In contrast, KR-37524,
(4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino) phenyl)
piperazine-1-carboxamide dihydrochloride), which was developed in
tandem with KR-36676 and KR-36996, was found to be safe for toxicity
issues, including cardiotoxicity, although affinity (Ki = 37 nM) was
relatively low compared to that of other drugs^[57]25. In addition,
KR-37524 is an analogue of the piperazine-carboxamide family and these
analogs exhibit various biological activities such as platelet-derived
growth factor receptor (PDGFR) inhibitors^[58]26, monoacylglycerol
lipase (MAGL) inhibitor^[59]27, serotonin (5-HT1B) receptor
antagonists^[60]28, chemokine receptor (CCR) antagonists^[61]29,[62]30
or fatty acid amide hydrolase (FAAH) inhibitors^[63]31–[64]33.
Therefore, we tried to identify new target proteins of KR-37524 instead
of the urotensin-II receptor by taking advantage of the toxicity-safe
KR-37524.
In this study, we identified the new target proteins of KR-37524 using
a transcriptome-based drug repositioning approach to expand the new
indication of KR-37524 (Fig. [65]1). To do this, we first generated the
gene expression profile changes induced by KR-37524 in each of the four
cell lines (A375, A549, MCF7, and PC3). We then compared the gene
expression profile changes induced by KR-37524 in each cell line to the
approved drug-induced gene expression profile changes provided in the
LINCS L1000 database and found approved drugs with similar gene
expression profile changes. Here, the similarity between the changes in
the two gene expression profiles was calculated using a connectivity
score. Specifically, we selected the top three approved drugs (12 drugs
in total) with the highest connectivity score for each cell line, and
selected their target proteins as potential target proteins of
KR-37524. Among them, seven potential target proteins were
experimentally validated through an in vitro binding assay, confirming
that the neurologically regulated serotonin transporter protein is a
new target for KR-37524. We also provide standalone software used in
the current study to generalize our strategy (i.e., transcriptome-based
drug repositioning).
Figure 1.
[66]Figure 1
[67]Open in a new tab
Overall scheme of this study. (a) Samples were prepared from four cell
lines treated with KR-37524 or dimethyl sulfoxide (DMSO). (b)
Microarray data of KR-37524 induced transcriptome changes were
generated from this sample, and the fold change values were calculated
to measure DEGs for the 978 landmark genes. (c) The gene expression
patterns were compared with the LINCS L1000 data to find similar
perturbagen signatures, and (d) the top three candidate target proteins
were selected using the Zhang score for each cell line. (e) Finally,
experimental validation was performed to confirm the affection of
KR-37524 on the candidate target protein. The protein structure was
drawn by Chimera software^[68]34 ([69]www.cgl.ucsf.edu/chimera).
Results
KR-37524-induced gene expression profile data analysis
First, we obtained gene expression profiles after treatment with DMSO
(control group) and KR-37524 (treatment group) at 10 μM for 6 h by
using microarray experiments in each of the four cell lines (i.e.,
A375, A549, MCF7, and PC3) (detailed in the Materials and Methods).
Since the experiments were performed in triplicate, the mean value of
gene expression for each gene was used. Then, we calculated the gene
expression profile changes in each cell line by dividing the gene
expression of the treatment group (KR-37524 treatment) by the gene
expression of the control group (DMSO treatment). Thus, when the
absolute value of the fold change (FC) was lower than 1, the expression
lower than that of the control group was marked with a negative (−)
sign.
Next, we analyzed the differentially expressed genes (DEGs) in each
cell line (p < 0.05, |FC |
[MATH: ≥ :MATH]
1.5). In A375 cells, there were 99 upregulated genes and 103
downregulated genes, and in A549 cells, there were 72 upregulated and
44 downregulated genes. In MCF7 cells, 110 upregulated genes and 62
downregulated genes were identified. Finally, in PC3 cells, there were
163 and 81 upregulated and downregulated genes, respectively. In all
cell lines, there were a total of three commonly upregulated genes,
while there were no commonly downregulated genes (Fig. [70]2 and Table
S1). Commonly upregulated genes were HMGCR
(3-hydroxy-3-methylglutaryl-CoA reductase), LPIN1 (Lipin 1), and SQLE
(Squalene epoxidase). HMGCR is the main target of statins, a class of
cholesterol-lowering drugs, LPIN1 controls fatty acid metabolism at
different levels, and SQLE is considered to be a rate-limiting enzyme
in steroid biosynthesis. All three genes are known to primarily
regulate the synthesis and metabolism of lipids, such as cholesterol.
Figure 2.
[71]Figure 2
[72]Open in a new tab
Venn diagram for the differentially expressed gene (DEG) lists in four
cell lines. (a) Upregulated genes and (b) downregulated genes. The
crossing areas show the commonly changed DEGs. Statistically
significant DEGs were defined using p < 0.05 and |FC|
[MATH: ≥ :MATH]
1.5 as cut-off. Venny website was used for drawing Venn diagrams
([73]https://bioinfogp.cnb.csic.es/tools/venny/index.html).
GO and KEGG pathway enrichment analysis
Gene ontology (GO) enrichment^[74]35 and Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathway^[75]36 enrichment analyses were performed
for gene sets with |FC|
[MATH: ≥ :MATH]
1.5 and p < 0.05 (i.e., DEGs) in each cell line (Tables S2 and S3). GO
enrichment analysis was performed using the Database for Annotation,
Visualization and Integrated Discovery (DAVID)^[76]37,[77]38. In this
study, we only considered the biological process (BP) sub-ontology
among three sub-ontologies (cellular component, biological process, and
molecular function). Significantly enriched BP GO terms (p < 0.05) were
extracted from each cell line, and the genes from each cell line were
merged to investigate the changes in the overall gene expression
profile induced by KR-37524. A total of 54 upregulated and 36
downregulated genes were used for GO enrichment analysis, and the
results are indicated in red and blue, respectively (Fig. [78]3). The
top-ranked BP GO terms of upregulated genes were cholesterol
biosynthetic process (22.2%), and the genes involved were SQLE, IDI1,
MVK, HMGCS1, INSIG1, MSMO1, DHCR24, HMGCR, DHCR7, HSD17B7, LSS, and
FDFT1. However, the overall number of downregulated genes was low, and
the highest-ranked BP GO terms included the positive regulation of
transcription from RNA polymerase II promoter (22.9%), transcription
from RNA polymerase II promoter (20.0%), positive regulation of cell
proliferation (20.0%), cell differentiation (14.3%), and cell
proliferation (11.4%) (Table S4).
Figure 3.
[79]Figure 3
[80]Open in a new tab
Results of GO enrichment analysis for Biological Process. The red and
blue bars represent the results of GO enrichment analysis using
upregulated genes and downregulated genes, respectively.
As a result of KEGG pathway enrichment analysis, the significantly
enriched pathways of 40 upregulated and 13 downregulated genes were
identified (p < 0.05) from the merged gene list of each cell line
(Fig. [81]4). The top-ranked pathway for upregulated genes was the
metabolic pathway (64.1%). In addition, biosynthesis of antibiotics
(33.3%) and steroid biosynthesis pathway (17.9%) were highly ranked.
Metabolic pathway genes included PI4K2B, IDI1, MTMR3, MVK, MSMO1,
HMGCR, HSD17B7, GK2, MTM1, FDFT1, AOC2, HMGCS1, IDH1, ACSL4, DHCR24,
LSS, SQLE, GNPDA1, PSAT1, FASN, CYP1A2, DHCR7, LPIN1, BCAT1, and
LDHAL6B. The top-ranked pathway for downregulated genes was
Huntington's disease pathway (61.5%) and included NDUFA13, NDUFS7,
NDUFA1, PPIF, CLTB, POLR2F, UQCR11, and POLR2I (Table S5).
Figure 4.
[82]Figure 4
[83]Open in a new tab
Results of KEGG pathway enrichment analysis. The red and blue bars
represent the results of KEGG pathway analysis using upregulated genes
and downregulated genes, respectively.
Selection of candidate target proteins using LINCS L1000 data search
To identify new target proteins of KR-37524, we compared the gene
expression profile changes induced by KR-37524 with each perturbagen in
the LINCS L1000 dataset. To compare the similarity, we calculated the
connectivity score developed by Zhang^[84]39 between the gene
expression profile changes of KR-37524 and each perturbagen in the
LINCS L1000 database. In each cell line, the three drugs with the
highest connectivity scores were selected (i.e., 12 drugs in total),
and the LINCS perturbagen ID was mapped to the DrugBank ID list for
comparison with approved drugs. The top three selected lists and their
corresponding drug names, gene IDs, and UniProt ID results are listed
in Table [85]1.
Table 1.
Comparison results of connectivity score using LINCS L1000 dataset.
Cell line Perturbagen ID Drug name Zhang score Targets
Gene symbol UniProt ID Name of target proteins*
A375 BRD-K15933101 Ropinirole 0.152284551
BRD-K40758068 Efavirenz 0.14029383
BRD-K70505054 Ranitidine 0.127356285 HRH2 [86]P25021 Histamine H2
receptor
A549 BRD-A64290322 Cyclosporine 0.151246937 PPP3R2, PPIA [87]Q96LZ3,
[88]P62937 Calcineurin subunit B type 2, Peptidyl-prolyl cis–trans
isomerase A
BRD-A79768653 Sirolimus 0.113739203 MTOR [89]P42345
Serine/threonine-protein kinase mTOR
BRD-K84937637 Sirolimus 0.111655751 MTOR [90]P42345
Serine/threonine-protein kinase mTOR
MCF7 BRD-A22032524 Amlodipine 0.19968588 CACNA1C, CACNA1I [91]Q13936,
[92]Q9P0X4 Voltage-dependent L-type calcium channel subunit alpha-1C,
Voltage-dependent T-type calcium channel subunit alpha-1I
BRD-A01320529 Salmeterol 0.194945701
BRD-K91263825 Nortriptyline 0.182943722 SLC6A2, SLC6A4, HTR2A
[93]P23975, [94]P31645, [95]P28223 Sodium-dependent noradrenaline
transporter, Sodium-dependent serotonin transporter,
5-hydroxytryptamine receptor 2A
PC3 BRD-K89732114 Trifluoperazine 0.158636237 DRD2, CALY, ADRA1A
[96]P14416, 9NYX4, [97]P35348 Dopamine D2 receptor, Neuron-specific
vesicular protein calcyon, Alpha-1A adrenergic receptor
BRD-A45889380 Quinacrine 0.155439425 PLA2G6, PLA2G4A, PLCL1 [98]O60733,
[99]P47712, [100]Q15111 85/88 kDa calcium-independent phospholipase A2,
Cytosolic phospholipase A2, Inactive phospholipase C-like protein 1
BRD-A29485665 Bicalutamide 0.131271152 AR [101]P10275 Androgen receptor
[102]Open in a new tab
*Filtered option: ‘antagonist or inhibitor, human protein,
pharmacological action = yes’ were used.
In this study, we focused on drugs that could inhibit target proteins.
The rationale behind this decision is that developing inhibitor drugs
is a common approach, and developing an activator is more difficult
than developing an inhibitor. Therefore, only human proteins that are
inhibitor targets by approved drugs (i.e., antagonists and/or
inhibitors) with known pharmacological action were considered in the
DrugBank database^[103]40. A total of 16 valid proteins targeted by 12
approved drugs were obtained such as histamine H2 receptor (HRH2),
calcineurin subunit B type 2 (PPP3R2), peptidyl-prolyl cis–trans
isomerase A (PPIA), serine/threonine-protein kinase mTOR (MTOR),
voltage-dependent L-type calcium channel subunit alpha-1C (CACNA1C),
voltage-dependent T-type calcium channel subunit αalpha-1I (CACNA1I),
sodium-dependent noradrenaline transporter (SLC6A2), sodium-dependent
serotonin transporter (SLC6A4), 5-hydroxytryptamine receptor 2A
(HTR2A), D(2) dopamine receptor (DRD2), neuron-specific vesicular
protein calcyon (CALY), α-1A adrenergic receptor (ADRA1A), 85/88 kDa
calcium-independent phospholipase A2 (PLA2G6), cytosolic phospholipase
A2 (PLA2G4A), inactive phospholipase C-like protein 1 (PLCL1), and
androgen receptor (AR). To identify the functions of 16 target
proteins, GO enrichment and KEGG pathway enrichment analyses were
performed, and a list with p < 0.05, as shown in Tables S6 and S7. BP
GO terms were evenly distributed and affected the nervous system, such
as the drug response to stimulation, memory, and synaptic transmission
(Fig. [104]5a). In the KEGG pathway enrichment analysis, the
distribution of calcium signaling pathways and serotonergic synapses
was highly involved in neurotransmission pathway (Fig. [105]5b). From
the results of GO enrichment and KEGG pathway enrichment analyses, it
was shown that filtered candidate groups of target proteins generally
inhibit the nervous system.
Figure 5.
[106]Figure 5
[107]Open in a new tab
(a) GO and (b) KEGG pathway enrichment analysis of potential target
genes.
Among the 16 predicted targets, eight targets were identified to be
capable of being used for the antagonist radioligand binding assays
(HRH2, CACNA1C, CACNA1I, SLC6A2, SLC6A4, HTR2A, DRD2, and ADRA1A).
CACNA1I was excluded because it is a gene belonging to the same family
as CACNA1C. Therefore, we conducted an in vitro binding assay with
seven predicted targets including H2 human histamine GPCR binding assay
for the histamine H2 receptor (HRH2), Ca v1.2 human calcium ion channel
binding assay for voltage-dependent L-type calcium channel subunit α-1C
(CACNA1C), NET human norepinephrine transporter binding assay for
sodium-dependent noradrenaline transporter (SLC6A2), SET human
serotonin transporter binding assay for sodium-dependent serotonin
transporter (SLC6A4), 5-HT2A human serotonin GPCR binding assay for
5-hydroxytryptamine receptor 2A (HTR2A), D2L human dopamine GPCR
binding assay for D(2) dopamine receptor (DRD2), and α-1A human
adrenoceptor GPCR binding assay for the α-1A adrenergic receptor
(ADRA1A).
Experimental validation of KR-37524 target proteins
KR-37524 was used to treat seven target proteins capable of antagonist
radioligand testing to confirm that KR-37524 acts on the target protein
we selected. As shown in Fig. [108]6, KR-37524 exhibited more than 50%
interaction with the sodium-dependent serotonin transporter (SLC6A4).
In contrast, less than 50% inhibition was observed for other targets at
10 µM KR-37524.
Figure 6.
[109]Figure 6
[110]Open in a new tab
Experimental validation of potential targets of KR-37524.
Discussion
Urotensin-II receptor is a notable target for various cardiovascular
diseases, such as heart failure, pulmonary hypertension, and
atherosclerosis. However, owing to the unsatisfactory proof-of-concept
and cardiotoxicity issues, numerous urotensin-II receptor antagonists
have been discontinued during the development of new drugs for
cardiovascular treatment. Unlike other discontinued drugs, we wanted to
find new possibilities for KR-37524, which is known to be safe for
toxicity issues. The drug repositioning approach involves expanding the
indication of the targeted drug as well as altering the indication for
a previously developed drug with a new function by changing the drug
target. In general, finding a new target protein requires understanding
not only the properties of the drug, but also the mechanism of the
target proteins, although it can be difficult to identify a new target
protein. Therefore, we applied a computational method to identify other
effective target proteins to increase the usability of KR-37524.
Primarily, we identified the gene expression profile changes through
microarray experiments after treatment with KR-37524 in four cancer
cell lines to understand the cellular response to drug treatment. Next,
we performed pathway enrichment analysis using the DEGs after treatment
with KR-37524. KR-37524 induced the gene expressions involved in
cholesterol synthesis and metabolism, and KR-37524 reduced the gene
expressions involved in cell differentiation and the nervous system.
The regulation of cholesterol synthesis is known to be regulated by
UTR^[111]41–[112]43. Although pathway enrichment analysis using
transcriptome data reveals the regulatory mechanisms of drugs at the
pathway level, it has limitations in identifying the direct inhibitory
targets of the compound.
Therefore, we used another approach to compare the experimental
information and microarray data from KR-37524 with that in the LINCS
L1000 database. The LINCS L1000 data measures the gene expression
levels of 978 landmark genes, and the gene expression levels of other
genes were estimated using a computational model. LINCS L1000 includes
gene expression profile changes induced by thousands of perturbagens in
various cell lines. Therefore, it was convenient to compare the gene
expression profile changes induced by KR-37524. This enables
comparative analyses of several experimental groups at the same time to
identify any differences between them. Identification of approved drugs
with similar gene expression profile changes in LINCS L1000 to that
induced by KR-37524 could indirectly determine a shared target protein
targeted by KR-37524. Since the LINCS L1000 dataset contains most of
the genes expressed in cells, this served our goal of finding other
target proteins; landmark genes were selected as genes that are widely
expressed in cells, while the expression of other genes that were not
directly measured in the analysis could be inferred^[113]19. Thus, by
comparing the LINCS L1000 dataset with the KR-37524 data, it was
possible to find drugs for a new target.
The connectivity score was used to identify the most similar gene
expression profile changes between the two datasets. In this study, we
used the Zhang score, an improved version of the connectivity score,
CMap^[114]39. Since we have already used the landmark gene to find
other targets in the cell, we applied the Zhang score, which assigns
more weight to the most differentially expressed genes. This method is
appropriate for searching for drugs with similar gene expression
profile changes, and drug candidates were selected by sorting by a high
score. In the DrugBank database, we searched for a list of drug-target
proteins and narrowed down those acting as inhibitors. As a result of
performing GO enrichment and KEGG pathway enrichment analysis using the
gene list of target proteins we selected, the drug information for the
nervous system, such as response to drug and negative regulation of
synaptic transmission, was retrieved. In the KEGG pathway enrichment
analysis, pathways such as calcium signaling and the serotonergic
synapse pathway were selected. Thus, we predicted that KR-37524 could
act on serotonin-related target proteins. Finally, receptor-binding
experiments confirmed that KR-37524 has a high affinity for human
serotonin transporters.
Our approach compared gene expression profile changes with the approved
drugs in the LINCS L1000 database. In this case, it is possible to
screen only well-known target proteins of approved drugs. Therefore,
structural modeling technique based on machine learning has been
recently proposed as a way to overcome this problem. Machine
learning-based technique is accurate enough to predict most human
protein structures^[115]44. This is expected to provide a great
opportunity for drug repositioning by predicting more diverse target
proteins.
Conclusion
We used a transcriptome-based drug repositioning approach to identify
new target proteins for KR-37524. Although other effects of KR-37524
were unknown, we were able to effectively infer the characteristics of
KR-37524 represented as gene expression profile changes affecting
potential target proteins from the analysis of transcriptome data. The
LINCS L1000 database provides drug-induced transcriptome data (i.e.,
gene expression profile changes induced by perturbagens) under a
variety of conditions, which we utilized to help identify new target
proteins. In particular, by taking only filtered data from specific
experimental conditions, gene expression profile changes could be
appropriately applied. The in vitro binding assay for KR-37524 against
seven candidate target proteins selected by connectivity score was
performed to confirm its potential as a new target. Consequently, the
serotonin transporter was identified as a novel target. This is
expected to provide a new function to KR-37524 in addition to the
indicated treatment for cardiovascular disease. The standalone
software, which was used for drug repositioning, effectively suggested
potential target proteins, and the LINCS L1000 database is easily
accessible to researchers. Therefore, our approach is expected to be
valuable for drug repositioning research.
Materials and methods
Materials
KR-37524 (CAS Registry No. 2228942-83-2),
(4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)
piperazine-1-carboxamide dihydrochloride), was synthesized at the
Research Center for Medicinal Chemistry, Korea Research Institute of
Chemical Technology (KRICT, Daejeon, Korea). Four cell lines, human
skin malignant melanoma cells (A375), human lung carcinoma cells
(A549), human breast carcinoma cells (MCF7), and human prostate
adenocarcinoma cells (PC3) were purchased from American Type Culture
Collection (ATCC, Rockville, MD, USA).
Cell culture and sample preparation
A375 and A549 cells were cultured in RPMI-1640 supplemented with 10%
fetal bovine serum (FBS) and 1% penicillin–streptomycin–glutamine. MCF7
cells were cultured in Dulbecco's modified Eagle's medium (DMEM) with
100% FBS and 1% penicillin–streptomycin–glutamine. PC3 cells were
cultured in RPMI with 10% FBS, 1% penicillin–streptomycin–glutamine,
1 mM sodium pyruvate, and 10 mM
4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES).
The cells were first incubated for two weeks to stabilize after initial
seeding. KR-37524 dissolved in DMSO was stored at 10 nM at − 80℃. Cell
lines were seeded in six 60-mm dishes (DMSO × 3 and KR-37524 × 3).
Cells were plated at 8.45 × 10^5 cells for MCF7, 1.496 × 10^6 cells for
PC3, 1.1375 × 10^6 cells for A375, and 1.3 × 10^6 cells for A549 under
the culture conditions. After 24 h, DMSO and KR-37524 were diluted
1000-fold and used to treat the cells. The final concentrations of DMSO
and KR-37524 were 0.1% and 10 μM, respectively. After 6 h, total RNA
from these cells was isolated using 1 mL of TRIzol reagent. After cell
destruction by pipetting, the cells were stored in a deep freezer
(-80℃).
Microarray data analysis
Total RNA samples were assessed using the Clariom™ S Assay, Human
platform. cDNA was synthesized using the GeneChip WT (Whole Transcript)
Amplification kit, as described by the manufacturer. Sense cDNA was
then fragmented and biotin-labeled with TdT (terminal deoxynucleoridyl
transferase) using the GeneChip WT Terminal labeling kit. Approximately
5.5 μg of labeled DNA target was hybridized to the Affymetrix GeneChip
Array at 45 ℃ for 16 h. Hybridized arrays were washed and stained on a
GeneChip Fluidics Station 450 and scanned using a GCS3000 scanner.
Microarray data export processing and basic analysis were performed
using the Affymetrix^® GeneChip Command Console® Software version
6.0 + (AGCC,
[116]www.thermofisher.com/kr/ko/home/life-science/microarray-analysis/m
icroarray-analysis-instruments-software-services/microarray-analysis-so
ftware/affymetrix-genechip-command-console-software.html). The data
were summarized and normalized using the signal space
transformation-robust multichip analysis (SST-RMA) method implemented
in Affymetrix^® Power Tools version 2.11.4 (APT,
[117]www.thermofisher.com/kr/en/home/life-science/microarray-analysis/m
icroarray-analysis-partners-programs/affymetrix-developers-network/affy
metrix-power-tools.html). We exported the results with gene level
SST-RMA analysis. The statistical significance of the expression data
was determined using an independent t-test and fold change, in which
the null hypothesis was that no difference exists among groups. The
fold change and p value cut-off are 1.5 and 0.05, respectively.
The Database for Annotation, Visualization, and Integrated Discovery
(DAVID) functional annotation tools^[118]37,[119]38 were used to
calculate gene enrichment, pathways, and functional annotation analysis
for a significant probe list.
LINCS L1000 database search and candidate target protein selection
The LINCS L1000 database was used to compare the gene expression
profile changes induced by KR-37524 treatment with the gene expression
profile changes induced by thousands of perturbagens obtained from
various times, points, doses, and cell lines. The LINCS L1000 level 5
dataset, perturbation, signature, and landmark gene lists were
downloaded from the Gene Expression Omnibus (GEO; [120]GSE92742)
([121]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92742). The
LINCS L1000 level 5 dataset contains gene expression profile changes of
978 landmark genes, which were measured directly by the L1000 assay.
LINCS L1000 raw data were filtered by the cell line type and gene
expression level under the same conditions as those for KR-37524
treatment for 6 h at a dose of 10 μM. Fold change was calculated by
obtaining the mean value of the sample gene expression level per cell
line of KR-37524, and dividing this by the mean value of DMSO. The fold
change was then normalized using log[2] and compared with the LINCS
fold change of 978 landmark gene expression patterns. We used the
DrugBank drug list to find a list of approved drugs in the LINCS
database. We collected the simplified molecular input line entry system
(SMILES) of drugs from the LINCS and DrugBank, and calculated the
Tanimoto coefficient^[122]45 to list the drugs that matched perfectly
(Tanimoto coefficient = 1.0).
The connectivity score (Zhang score) was calculated to find LINCS L1000
data similar to the gene expression profile changes induced by
KR-37524. To compare gene expression patterns, Lamb et al. created a
CMap^[123]16, converted the gene expression patterns of known chemicals
into a database, and then calculated an order according to the
expression level of test DEGs. Based on this, Zhang et al. developed a
simple and more accurate and sensitive method to calculate the
connection between two gene expression profile changes^[124]39. The
connectivity score has a value between − 1 and 1, where 1 indicates the
maximum positive connection strength with the reference profile,
whereas − 1 indicates that two experimental perturbations had the
maximum inverse correlation. All program scripts used in this
calculation are available in the web repository
([125]https://bitbucket.org/krictai/lincs_search).
In vitro binding assay for target validation
The receptor binding affinity of KR-37524 was determined by profiling
services at Eurofins Cerep (Test No.: FR095-0019211, US034-0011488;
I’Evescault, France) using radioligand binding assays for seven
distinct human receptors and transporters; α-1A adrenergic receptor
(ADRA1A), D(2) dopamine receptor (DRD2), histamine H2 receptor (HRH2),
sodium-dependent noradrenaline transporter (SLC6A2),
5-hydroxytryptamine receptor 2A (HTR2A), sodium-dependent serotonin
transporter (SLC6A4), and voltage-dependent L-type calcium channel
subunit α-1C (CACNA1C). To evaluate the percentage (%) inhibition of
specific binding, all radioligand binding assays were performed in 96
well plates at 37℃ in binding buffer (25 mM HEPES, 100 mM NaCl, 2 mM
MgCl[2], and 1 mM 3-[(3-cholamidopropy) dimethyl
ammonio]-1-propanesulfonate [CHAPS] at pH 7.4 [NaOH]). The human
recombinant receptor membranes of the α-1A adrenergic receptor
(ADRA1A), sodium-dependent noradrenaline transporter (SLC6A2),
5-hydroxytryptamine receptor 2A (HTR2A), and sodium-dependent serotonin
transporter (SLC6A4) were used in the CHO cell membrane, and D(2)
dopamine receptor (DRD2), histamine H2 receptor (HRH2), and
voltage-dependent L-type calcium channel subunit α-1C (CACNA1C) were
used in the HEK293 cell membrane overexpressed by each receptor. The
specific radiolabeled ligands of α-1A adrenergic receptor (ADRA1A),
D(2) dopamine receptor (DRD2), histamine H2 receptor (HRH2),
sodium-dependent noradrenaline transporter (SLC6A2),
5-hydroxytryptamine receptor 2A (HTR2A), sodium-dependent serotonin
transporter (SLC6A4), and voltage-dependent L-type calcium channel
subunit α-1C (CACNA1C) were [^3H]prazosin, [^3H]methyl-spiperone,
[^125I]APT, [^3H]nisoxetine, [^3H]ketanserin, [^3H]imipramine, and
[^3H]PN200-110, respectively. Nonspecific binding was defined in the
presence of 0.1 mM epinephrine, 10 μM butaclamol, 100 μM tiotidine,
1 μM desipramine, 1 μM ketanserin, 10 μM imipramine, and 1 μM
isradipine, respectively. Ligand binding was determined by filtration
of the assay mixture over GF/C Whatman filters (Cytiva, Marlborough,
MA, USA). After washing the filters, liquid scintillation counting was
performed to quantify the radioactivity. Compound binding was
calculated as the % inhibition of the binding of a radioactively
labeled ligand specific to each target. In each experiment, and when
applicable, the respective reference compound was tested concurrently
with KR-37524, and the data were compared with historical values
determined at Eurofins. The experiment was performed in accordance with
the Eurofins validation standard operating procedure. The receptor
binding assays were performed in triplicate, and each result was
determined in three independent experiments.
Supplementary Information
[126]Supplementary Information.^ (62.7KB, docx)
Acknowledgements