Abstract
Pancreatic cancer is an aggressive malignancy with a poor prognosis and
limited treatment options. Cdc‐like kinase 4 (CLK4), a kinase that
regulates alternative splicing by phosphorylating spliceosome
components, is implicated in aberrant splicing events driving
pancreatic cancer progression. In this study, we established a
computational model that integrates pharmacological interactions of
CLK4 inhibitors with an improved hit rate. Through this model, we
identified a novel CLK4 inhibitor, compound 150441, with a 50%
inhibitory concentration (IC[50]) value of 21.4 nm. Structure‐activity
relationship analysis was performed to investigate key interactions and
functional groups. Kinase profiling revealed that compound 150441 is
selective for CLK4. Subsequent in vitro assays demonstrated that this
inhibitor effectively suppressed cell growth and viability of
pancreatic cancer cells. In addition, it inhibited the phosphorylation
of key splicing factors, including serine‐ and arginine‐rich splicing
factor (SRSF) 4 and SRSF6. Cell cycle analysis further indicated that
the compound induced G2/M arrest, leading to apoptosis. RNA‐seq
analysis revealed that the compound induced significant changes in
alternative splicing and key biological pathways, including RNA
processing, DNA replication, DNA damage, and mitosis. These findings
suggest that compound 150441 has promising potential for further
development as a novel pancreatic cancer treatment.
Keywords: alternative splicing, CLK4, kinase inhibitor, pancreatic
cancer, structure‐based virtual screening
__________________________________________________________________
Pancreatic cancer is a highly aggressive malignancy with limited
treatment options. CLK4 regulates alternative splicing, contributing to
cancer progression. This study establishes a computational model to
identify CLK4 inhibitors, leading to compound 150441 (IC[50]: 21.4 nm).
It selectively inhibits CLK4, suppresses cancer cell growth, induces
G2/M arrest, and alters RNA splicing, highlighting its therapeutic
potential.
graphic file with name ADVS-12-2416323-g005.jpg
1. Introduction
Pancreatic cancer remains one of the most aggressive and high‐risk
malignancies to date. It is associated with extremely poor prognostic
outcomes and is characterized by a notably low survival rate.^[ [50]^1
, [51]^2 ^] Early‐stage pancreatic cancer is often asymptomatic, and
the disease typically only becomes apparent after the tumor invades
surrounding tissues or metastasizes to distant organs. As a result, the
5‐year survival rate for pancreatic cancer is only 2–9%.^[ [52]^3 ,
[53]^4 ^] Even with treatment, within 24 months postsurgery, 60% of
patients develop distant metastases, significantly contributing to
mortality.^[ [54]^1 , [55]^5 ^] In addition to surgical tumor
resection, current treatment guidelines incorporate chemotherapy.
Although chemotherapy can improve survival rates in patients with
pancreatic cancer, traditional chemotherapeutic agents currently in use
have several limitations. The most concerning limitation is their
limited efficacy, as the median overall survival of patients receiving
conventional chemotherapy does not exceed 2 years.^[ [56]^1 ^]
Additionally, traditional chemotherapeutic agents not only target
cancer cells but also affect normal cells, leading to unexpected side
effects. Therefore, it is necessary and urgent to develop targeted
therapies that can improve clinical outcomes by inhibiting specific
targets in pancreatic cancer cells and potentially have fewer side
effects.
RNA splicing is a crucial biological process where introns are removed
from precursor messenger RNAs (pre‐mRNAs), resulting in mature RNAs
composed of exons.^[ [57]^6 , [58]^7 ^] Among various splicing types,
alternative splicing (AS) is a key player in cancer development.
Studies reveal that many cancers exhibit aberrant AS compared to normal
tissues.^[ [59]^8 , [60]^9 ^] Aberrant AS can significantly impact
various hallmarks of cancer, including cell proliferation, apoptosis,
epithelial‐mesenchymal transition (EMT), and tumor immune evasion.^[
[61]^2 ^] Studies reveal that AS is regulated by splicing factors (SFs)
through interactions with pre‐mRNA molecules.^[ [62]^6 , [63]^7 ,
[64]^10 ^] Notably, some SFs are associated with pancreatic cancer,
such as serine‐ and arginine‐rich (SR) splicing factor 1 (SRSF1),
SRSF5, and SRSF6.^[ [65]^1 , [66]^2 ^] A study has shown that SRSF1 is
upregulated in pancreatic cancer cells, and knocking out SRSF1 inhibits
cell migration, invasion, and EMT.^[ [67]^11 ^] Phosphorylation of
SRSF5 regulates AS events in pancreatic cancer cells.^[ [68]^12 ^] In
addition, the activation of SRSF6 also contributes to the metastasis of
pancreatic cancer.^[ [69]^5 ^] These findings indicate the crucial
roles of AS and SFs in pancreatic cancer.
CLK4, or Cdc2‐like kinase 4, is a dual‐specificity kinase belonging to
the LAMMER kinase family.^[ [70]^5 , [71]^8 ^] CLK4 regulates the AS of
pre‐mRNA by phosphorylating serine‐ and arginine‐rich splicing proteins
within the spliceosome.^[ [72]^13 , [73]^14 ^] Therefore, the aberrant
AS in several cancers can be moderated by inhibiting CLK4 activity.
Moreover, higher CLK4 expression is associated with poorer patient
survival in triple‐negative breast cancer (TNBC). Silencing CLK4 in an
aggressive mesenchymal‐like subtype (MES) of TNBC reduces the
expression of genes involved in metastasis and lessens the invasive
behavior of these cancer cells.^[ [74]^15 ^] Therefore, CLK4 represents
a promising therapeutic target for development. Currently, several CLK4
inhibitors have been developed, including a few that have entered phase
I clinical trials. For example, SM08502 (Cirtuvivint), which inhibits
CLK4, showed efficacy across 17 colorectal cancer (CRC) cell lines and
inhibited the proliferation of six human gastric cancer cell lines with
various mutations.^[ [75]^16 ^] Furthermore, it exhibited significant
tumor growth inhibition in xenograft models of CRC and gastric
cancer.^[ [76]^16 ^] In addition, TG003, a discovery‐stage compound,
showed anticancer activity in prostate cancer cells and altered
splicing of cancer‐related genes.^[ [77]^17 ^] However, existing drugs
often exhibit off‐target activity, which can increase the risk of
adverse effects. Therefore, there is a need to develop a new CLK4
inhibitor with high selectivity for cancer treatment through targeting
the aberrant AS.
In this study, we performed structure‐based virtual screening (SBVS) to
identify novel CLK4 inhibitors, followed by experimental validation of
the identified compounds. The workflow of this study is shown in
Figure [78] 1 . First, we identified the pharmacological interactions
of CLK4 and established a computational model. Pharmacological
interactions can be applied to improve the hit rate in SBVS.^[ [79]^14
, [80]^18 ^] Subsequently, compounds from the National Cancer Institute
(NCI) library were screened, and potential inhibitors were selected and
tested for their inhibitory activity by enzymatic assays. The compound
with the highest potency was selected for further exploration,
including a search for its analogs and a structure‐activity
relationship (SAR) analysis of these analogs. Validated inhibitors were
then evaluated for their effects on the viability and proliferation of
pancreatic cancer cells. The compound with the best potency was further
assessed for its ability to inhibit the phosphorylation of downstream
proteins (i.e., SRSFs) and its impact on the cell cycle. We also tested
the compound for selectivity against a panel of kinases from various
families. Finally, an RNA sequencing (RNA‐seq) analysis was conducted
to examine the global AS changes induced by the compound. In summary,
this study identified a novel inhibitor that serves as a crucial
starting point for developing potent CLK4 inhibitors for treating
pancreatic cancer.
Figure 1.
Figure 1
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Workflow of the study. Initially, a computational model for identifying
CLK4 inhibitors was first developed. Then, the model was applied to
identify potential inhibitors. Selected compounds were validated using
enzymatic assays. The effects of identified inhibitors on cancer cells
were further evaluated.
2. Result
2.1. Establishment of Pharmacological Model
We first evaluated the docking procedure by performing a redocking
analysis. Currently, only one structure of CLK4 (PDB ID: 6FYV) is
available, with a resolution of 2.46 Å and no missing residues in the
binding site. As such, it was selected for docking and screening
analysis. The redocking procedure demonstrated that the predicted
docking pose is similar to the conformation of the co‐crystallized
ligand (Figure [82]S1, Supporting Information). These results suggest
that 6FYV and the docking program are suitable for virtual screening.
To increase the hit rate of structure‐based virtual screening, we next
identified pharmacological interactions between inhibitors and the CLK4
binding site. Inhibitors with a 50% inhibitory concentraton (IC[50])
value of ≤ 1 µm were collected from ChEMBL. From this set, 60
inhibitors with diverse structures were selected and docked into the
binding site. The top 30 inhibitors were then selected based on their
docking score and were used to determine pharmacological interactions
(Figure [83] 2 ). An interaction involving at least 50% of the 30
inhibitors was considered a pharmacological interaction. Three
pharmacological hydrogen‐bonding interactions were identified,
including the interacting residues L244, E242, and K191
(Figure [84]2A). Notably, the first two residues, L244 and E242, were
located in the hinge region and formed hydrogen bonds with the
adenosine group of ATP (Figure [85]2C). The hydrogen‐bonding
interaction with residue K191 exhibited a 60% frequency among the
diverse inhibitors, which was not observed in ATP. The analysis also
identified seven pharmacological hydrophobic interactions involving the
residues A189, L244, V175, L167, L295, V324, and K191 (Figure [86]2B).
Notably, the first four residues formed a pocket that accommodates the
adenosine of ATP, while residues L167 and L295 make contact with the
ribose group of ATP (Figure [87]2C). These pharmacological interactions
play a critical role in the inhibitory action against CLK4.
Figure 2.
Figure 2
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Analysis of pharmacological interactions for CLK4. The analysis
includes two types of interactions: A) hydrogen‐bonding and B)
hydrophobic interactions. Interactions occurring with a frequency of
50% or higher are considered pharmacological interactions. C) Docking
pose of ATP. Hydrogen bonds are shown as green dashes. Binding site
residues are depicted as lines and labeled as shown. D) Comparison of
scoring functions using docking score alone and pharmacological
interaction score.
Next, we evaluated the performance of the pharmacological model in
identifying inhibitors. Thirty known CLK4 inhibitors and 990 compounds
randomly selected from the Available Chemical Directory (ACD)^[ [89]^19
^] were docked into CLK4. These compounds were ranked based on either
their docking score alone or their pharmacological interaction score.
For each compound, the pharmacological score, S(i), was calculated as
follows:
[MATH: Si=Ni+−0.01Di :MATH]
(1)
where N(i) represents the number of pharmacological interactions
compound i forms, and D(i) is the docking score of compound i. The
receiver operating characteristic (ROC) curve analysis showed that
pharmacological scoring yielded a better high area under the curve
(AUC) value compared with the docking scoring, indicating that the
pharmacological scoring provided better screening performance
(Figure [90]2D).
We further optimized the hit rate of the pharmacological model by
incorporating the analysis of inactive compounds. An additional 30
inactive CLK4 compounds with an IC[50] value of >10 µm were selected
and docked into the binding site. A comparative interaction analysis
between the inactive and active compounds was performed to identify
interactions with varying frequencies. The analysis revealed two
crucial hydrophobic interactions formed with the residue L244 and L167,
which showed the most significant differences (Figure [91]S2A,
Supporting Information). Subsequently, these two interactions were
integrated into the pharmacological model to evaluate their potential
to improve the model performance. In addition, different weights were
applied to these interactions and tested. For each compound, the
optimized pharmacological score, OS(i), was calculated using three
different equations:
[MATH: OSi=Si+1L167i+L244i
:MATH]
(2)
[MATH: OSi=Si+2L167i+L244i
:MATH]
(3)
[MATH: OSi=Si+3L167i+L244i
:MATH]
(4)
where S(i) represents the pharmacological score of compound i, L167(i)
is 1 if the compound forms a hydrophobic interaction with residue L167;
otherwise, L167(i) is 0, and L244(i) is 1 if the compound forms a
hydrophobic interaction with residue L244; otherwise, L244(i) is 0. The
compounds were ranked based on their optimized pharmacological scores,
and the performance of the three equations was evaluated
(Figure [92]S2B, Supporting Information). The results indicated that
Equation ([93]4) demonstrated the best performance, with an AUC of the
ROC curve of 0.81. In contrast, the AUC‐ROC was 0.77 when using the
pharmacological score alone. Therefore, Equation ([94]4) was selected
for further identification of potential CLK4 inhibitors.
2.2. Identification and Validation of Potential Inhibitors
Next, we applied the optimized pharmacological model to screen
compounds from the NCI library, which consists of ≈280,000 compounds.
Compounds that contain the PAINS structures, have quantitative estimate
of drug‐likeness (QED) scores below 0.25 or violate “Lipinski's and
Veber's Rules” were excluded. The remaining compounds were docked into
the binding site of CLK4 and ranked according to their pharmacological
scores, calculated using Equation ([95]4). The top 400 compounds were
clustered by their structure similarity. Representative compounds from
each cluster were selected and visually inspected. Based on their
availability and visual inspection, 15 compounds were ultimately
selected for testing. The selected compounds were then evaluated for
inhibitory activity at a concentration of 10 µm using LanthaScreen
Binding technology. The results revealed that compound 150441 exhibited
the highest inhibitory effect on CLK4 activity, with an inhibition rate
of 98% (Table [96]S1, Supporting Information). These findings suggest
that pharmacological interactions are useful for discovering CLK4
inhibitors and were successfully applied to identify a novel CLK4
inhibitor.
2.3. Interaction Analysis
We performed an interaction analysis to study the molecular
interactions of compound 150441. The docking results showed that
compound 150441 occupied the binding site of CLK4 (Figure [97] 3A;
Figure [98]S3, Supporting Information). The main scaffold of compound
150441 is a heterotetracyclic structure that includes an isoquinoline
(Group 1) and an indole ring (Group 2), along with a dimethylethylamine
side chain (Group 3) (Figure [99]3B). Group 1 forms a hydrogen bond
with residue L244. The docking pose of ATP shows that the adenine
scaffold of ATP occupies a position similar to Group 1 and forms the
same hydrogen bonds with L244 (Figure [100]2C). Moreover, Group 1 is
sandwiched by various residues, yielding hydrophobic interactions with
L167, V175, A189, V225, F241, L244, and L295 (Figure [101]3A). The
primary interactions established by Group 2 are hydrophobic, involving
the side chains of residues V175, K191, and V324. Group 3 forms a
hydrogen bond with residue E292 (Figure [102]3B). Together, these
interactions suggest that compound 150441 can bind to the CLK4 binding
site and further interfere with the activity of CLK4.
Figure 3.
Figure 3
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Docking pose and interactions of compound 150 441 in the CLK4 binding
site. A) Compound 150441 (salmon) shows favorable occupation of the
CLK4‐binding site (gray). Hydrogen bonds are represented as green dash
lines. Binding site residues are listed as shown. B) The 2D interaction
figure shows hinge residues (blue) interact with isoquinoline, interior
residues (pink) interact with indole, and exterior residues (lime)
interact with dimethylethylamine. Hydrogen bonds are represented as
green dash lines, and hydrophobic interactions are represented as gray
dot lines.
2.4. Structure‐Activity Relationship Analysis
To better understand the binding interactions of compound 150441, a
structure‐activity relationship (SAR) analysis was conducted. Fifteen
analogs were selected from the NCI library and tested for their
inhibitory activity (Table [104] 1 ). Compounds showing over 50%
inhibitory activity were further evaluated for their IC[50] values
(Table [105] 2 ; Figure [106]S4, Supporting Information). The analogs
were categorized into three classes based on their interactions and
inhibitory activities (Figure [107] 4 ). Class 1 includes compounds
150441, 164016, and 164017. The compounds in this class form hydrogen
bonds with residues E292 or E169 at the exterior site. Compounds 150441
and 164016 both contain an ethylamine functional group, allowing them
to form hydrogen bonds with residue E292. Compound 164017 has a
hydrogen‐bonding interaction with residue E169. In addition, compound
150 441 yields hydrophobic alkyl interactions with residues L167 and
V175, which may contribute to its slightly better inhibitory activity
compared to the other compounds.
Table 1.
Inhibitory activity of 150441 analogs.
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Table 2.
IC[50] values of identified inhibitors.
Compound IC[50] [nm]
150441 21.4 nm
164017 28.6 nm
164016 34.8 nm
152731 38.2 nm
69187 98.8 nm
150836 101.1 nm
322087 120.6 nm
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Figure 4.
Figure 4
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SAR analysis of 150441 and its analogs. A) Interaction profile of the
compounds. The compounds were categorized into three classes based on
their inhibitory activity. The most potent class is shown as green, and
the weakest class is shown as orange. Residues in the CLK4 binding
pocket are categorized into exterior, hinge, and interior sites. HB, A,
and M stand for hydrogen‐bonding interaction, alkyl hydrophobic
interaction, and mixed hydrophobic interaction, respectively. B)
Docking poses of the compounds. Hydrogen bonds are shown as green
dashes. Exterior, hinge, and interior residues are shown in lime, blue,
and pink, respectively.
Compounds in Class 2 lack the hydrogen bond with exterior residues E292
or E169, leading to lower activities compared to those in Class 1
(Figure [111]4). The main difference arises from the absence of an
alkylamine moiety in Class 2 compounds. For example, compound 152731,
lacking the ethylamine group compared to compound 150441, cannot form a
hydrogen bond with residue E292. Compounds 69187 and 150836 not only
lack the hydrogen bonds with exterior residues but also the mixed
hydrophobic interaction with residue V324. Compound 322087 lacks a
hydrophilic moiety in Group 3 and has fewer interactions with residues
A189 and F241, resulting in the lowest inhibitory activity in Class 2.
Furthermore, compound 150836 differs from compound 152731 by the
presence of a methoxy group, which reduces its inhibitory activity by
limiting interactions with the hinge residue L244.
Class 3 compounds exhibited the lowest inhibitory activities due to the
lack of a hydrogen‐bonding interaction with the hinge residue L244
(Figure [112]4). For example, compound 237069 differs from compound
69187 in Class 2 due to the presence of an oxygen atom, which hinders
the hydrogen‐bonding interaction with residue L244. As a result,
compound 237069 exhibits lower potency than compound 69187. Similarly,
compound 320613 differs from compound 322087 by the presence of a
formamide group, which disrupts the key hydrogen‐bonding interaction,
leading to reduced inhibitory activity. In summary, the interaction
analysis revealed the interactions critical for inhibitory potency,
including hydrogen bonds with residues E292, E169, and L244, as well as
hydrophobic interactions with residue V324.
2.5. Cytotoxic and Anti‐Proliferative Effects of Compounds
We selected compounds 150441, 164017, 164016, and 152731 to evaluate
their cytotoxicity and anti‐proliferation effect on pancreatic cancer
cell lines due to their enzyme inhibitory activities. In addition,
compound 322087 was included for testing due to its high IC[50] value.
We conducted experiments on two pancreatic cancer cell lines, Mia
PaCa‐2 and Panc‐1, both of which harbor common cancer‐related gene
mutations, including KRAS, TP53, and CDKN2A.^[ [113]^20 ^] These
mutations promote cancer cell proliferation, anti‐apoptosis, and drug
resistance. Furthermore, Panc‐1 cells exhibit a more aggressive
malignant phenotype compared to Mia PaCa‐2, characterized by the loss
of E‐cadherin expression and elevated levels of CDK56 and CD24.^[
[114]^21 , [115]^22 ^]
The results indicated that four of these compounds inhibited cell
viability and proliferation in both cell lines (Figure [116] 5A–D).
Among the tested compounds, 150441 demonstrated the highest potency
against cancer cell viability. It significantly reduced cancer cell
viability by over 50% at a concentration of 1 µm in both Mia PaCa‐2 and
Panc‐1 cell lines (Figure [117]5A), with IC[50] values of 0.61 and
0.92 µm, respectively (Table [118] 3 ). In addition, the compound
showed strong anti‐proliferative effects at 0.3 µm in Mia PaCa‐2 cells
and at 1 µm in Panc‐1 cells (Figure [119]5A), with 50% growth
inhibitory concentration (GI[50]) values of 0.18 and 0.67 µm,
respectively (Table [120]3). Furthermore, a colony formation assay to
evaluate cancer cell proliferation in a 2D culture system showed a
concentration‐dependent reduction in both pancreatic cancer cell lines,
with the most effective concentrations being 0.1 and 0.3 µm
(Figure [121]5E; Figure [122]S5A, Supporting Information). In addition,
compound 150441, demonstrating the strongest enzymatic inhibitory
activity with an IC[50] value of 21.4 nm, exhibited superior potency,
achieving over 80% cytotoxicity and more than 95% anti‐proliferative
effects at 10 µm in both pancreatic cancer cell lines. In comparison,
compound 322087, with lower enzyme potency, showed 40% cytotoxicity in
Mia PaCa‐2 and 10% in Panc‐1 at 10 µm, along with 80% antiproliferative
activity in Mia PaCa‐2 and 30% in Panc‐1. These findings suggest that
compound 150441 has the potential as a novel anticancer drug for
pancreatic cancer.
Figure 5.
Figure 5
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The anticancer effect of CLK4 inhibitors. A–D) Pancreatic cancer cells
were treated with compounds at 0.3, 1, 3, and 10 µm for 72 hours. Cell
viabilities were determined by
3‐(4,5‐Dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT)
assay, and antiproliferation effects were measured by sulforhodamine B
(SRB) assay. E) Colony formation analysis. Cells were incubated with
compound 150441 at 0.03, 0.1, 0.3, 1, and 3 µm for 12 days. F) Cells
were treated with 150441 at 1, 3, and 10 µm or SM08502 (SM) at 0.1 µm
for 2 hours, and the phosphorylation of SR protein was detected by
western blotting. SM08502 as a reference compound for SR protein
activity inhibition. These results were repeated in at least three
independent experiments. ^* p < 0.05, ^** p < 0.01 compared to the
control (ctrl., untreated) group.
Table 3.
Cell viability and anti‐proliferative effects of CLK4 inhibitors.
Compound name Mia PaCa‐2 Panc‐1
IC[50] (Mean ± SD, µm) GI[50] (Mean ± SD, µm) IC[50] (Mean ± SD, µm)
GI[50] (Mean ± SD, µm)
150441 0.61 ± 0.1 0.18 ± 0.1 0.92 ± 0.1 0.67 ± 0.1
152731 2.52 ± 0.2 7.67 ± 0.3 3.57 ± 0.4 3.10 ± 0.7
164016 1.02 ± 0.1 0.64 ± 0.1 1.66 ± 0.6 1.27 ± 0.1
164017 1.71 ± 0.1 1.21 ± 0.1 4.09 ± 0.2 1.60 ± 0.1
322087 >10 5.43 ± 0.2 >10 >10
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2.6. Inhibitory Effect of Compound 150441 on Splicing Regulatory Proteins
CLK4 regulates mRNA splicing by phosphorylating SR proteins, and
dysregulation of this process contributes to cancer progression.
Inhibiting CLK4 can modulate mRNA splicing and potentially inhibit
cancer growth. Therefore, we investigated the effect of compound 150441
on the phosphorylation levels of SR proteins. The compound was tested
on two pancreatic cancer cell lines, Mia PaCa‐2 and Panc‐1, at
concentrations of 1, 3, and 10 µm for 2 hours. The experimental results
showed that compound 150441 significantly inhibited the phosphorylation
of SRSF4 and SRSF6 in both cell lines at a concentration of 10 µm
(Figure [125]5F; Figure [126]S5B, Supporting Information). These
findings indicate that compound 150441 could affect SR protein activity
and subsequently influence the RNA splicing process.
2.7. Regulation of Cell Cycle and Induction of Apoptosis By Compound 150 441
Targeting CLK4
To validate the anticancer properties, we investigated the effect of
compound 150441 on the cell cycle distribution of Mia PaCa‐2 and Panc‐1
cell lines. These cells were exposed to 150441 at concentrations of
0.3, 1, 3, and 10 µm for 72 hours. The experimental results revealed a
significant concentration‐dependent increase in the proportion of cells
in the sub‐G1 phase for both cell lines (Figure [127] 6A–C).
Additionally, treatment with 150441 at concentrations of 3 and 10 µm
led to a substantial downregulation of the anti‐apoptotic proteins
Mcl‐1 and Bcl‐2, along with the activation of the caspase‐dependent
apoptotic pathway, as evidenced by an upregulation in cleaved‐caspase
levels (Figure [128]6D).
Figure 6.
Figure 6
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Cancer cell apoptosis and G2/M arrest while 150441 treatment. Flow
cytometry analysis was used to assess the distribution of pancreatic
cancer cell lines Mia Paca‐2 and Panc‐1 in various cell cycle phases
(sub‐G1, G0/G1, S, and G2/M). A,B) After treatment with indicated
concentrations of compound 150441 (0.3, 1, 3, and 10 µm) for
72 hours and C) the proportion of cells in the sub‐G1 phase. D) The
expression of apoptotic proteins, Mcl‐1, Bcl‐2, PARP, caspase 9, and
caspase 3, detected by western blotting. E) Cells were treated with
3 µm compound 150441 for 8, 16, 24, 48, and 72 hours, and the
proportion of cells in the G2/M phase. F) The expression levels of G2/M
regulatory proteins were assessed following treatment with 10 µm of
150441 for 16 hours. These results were repeated in at least three
independent experiments. ^* p < 0.05 compared to the control (ctrl.,
untreated) group.
Additionally, we found that treatment with 150441 at various time
points induced G2/M arrest, with the effect being most pronounced at
16 hours (Figure [130]6E). Previous research has shown that Aurora A
enhances PLK1 phosphorylation, which subsequently activates CDC25C
through phosphorylation at Ser216. This activation promotes the
formation of a checkpoint complex between cyclin B1 and CDK1, thus
regulating the transition from the G2 to the M phase.^[ [131]^23 ,
[132]^24 ^] Our current findings indicated that 150441 significantly
suppressed the expression of Aurora A and cyclin B1, while also
reducing the phosphorylation of PLK1, CDC25C, and CDK1
(Figure [133]6F). These results further confirmed that 150441 induced
G2/M arrest in cancer cells. Overall, the current results showed that
150441 promoted G2/M arrest and apoptosis in cancer cells by
downregulating key regulatory proteins and activating the caspase
pathway.
2.8. Selectivity Profile of Compound 150441
Selectivity is essential for kinase inhibitors to minimize off‐target
effects and reduce adverse reactions. Selective inhibitors also enable
researchers to better understand the specific functions of targets like
CLK4 in physiological and pathological pathways. Therefore, we further
evaluated the selectivity of compound 150441 using kinase profiling
analysis. The compound was tested at a concentration of 30 nm against a
panel of 60 kinases from various families using Thermo Fisher
SelectScreen kinase profiling services (Figure [134] 7 ). The results
showed that compound 150441 specifically targeted CLK4 with an
inhibition rate of 60%, suggesting that the compound is a selective
inhibitor of CLK4. In addition, CLKs comprise four isoforms: CLK1,
CLK2, CLK3, and CLK4. Among the isoforms, compound 150 441 exhibited
weaker inhibitory activity against CLK1, CLK2, and CLK3, with
inhibition percentages of 7.0%, 9.1%, and 1.8%, respectively
(Table [135]S2, Supporting Information). The evidence suggests that
compound 150441 is a highly selective CLK4 inhibitor.
Figure 7.
Figure 7
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Selectivity profile of compound 150441. A) Inhibitory activity of
150441 on a panel of 60 kinases. B) A kinome tree composed of seven
families shows the kinase inhibitory activity of the compound. The
compound was tested at a concentration of 30 nm. Among all tested
kinases, only the inhibition percentage for CLK4 exceeded 50%,
indicated by a red dot.
2.9. RNA‐Seq Analysis of the Effect of Compound 150441
To investigate the AS events induced by compound 150441, the Mia PaCa‐2
pancreatic cancer cell line was treated with compound 150441 for 6 h,
followed by an RNA‐seq analysis. Differential splicing events were
identified using replicated multivariate analysis of transcript
splicing (rMATS), which categorized five major splicing types: skipped
exon (SE), alternative 5′ splice site (A5SS), alternative 3′ splice
site (A3SS), mutually exclusive exons (MXE), and retained intron (RI).
The results showed that SE was the most frequent splicing event,
affecting 1101 genes, followed by RI (723 genes), A3SS (236 genes),
A5SS (189 genes), and MXE (75 genes) (Figure [137] 8A). The global
increase in AS events caused by compound 150441 suggested the
production of numerous aberrant splicing products, likely disrupting
essential cellular functions required for cancer cell survival.
Figure 8.
Figure 8
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Compound 150441‐induced alternative splicing events in Mia PaCa‐2
cells. Cells were treated with 3 µm compound 150441 for 6 hours and
then subjected to RNA‐seq analysis. A) Distribution of splicing event.
The number of splicing events caused by compound 150441. B) Gene
enrichment analysis. Pathway enrichment of genes affected by
150441‐induced splicing events, analyzed using DAVID gene functional
classification. The top ten most enriched biological processes are
ranked by fold enrichment. C) Top ten genes in mitosis ranked by
|IncLevelDifference|. The genes most affected by 150441 in the mitosis
pathway based on splicing inclusion level differences. D) List of genes
with |IncLevelDifference| > 0.3 in mitosis.
To explore the biological processes potentially impacted by compound
150441‐induced splicing changes, we analyzed all detected splicing
events using DAVID. Enrichment analysis of the affected genes was
performed using the UP_KW_BIOLOGICAL_PROCESS category, and the top 10
significantly enriched pathways, ranked by fold enrichment, are listed
in Figure [139]8B. These results revealed that RNA processing, cell
cycle regulation, cell division, and DNA damage response were major
biological processes affected by compound 150441 treatment.
Interestingly, gene enrichment analysis revealed that the mitosis
pathway was associated with compound 150441‐induced splicing events,
which is consistent with our in vitro observations of an increased G2/M
population after compound 150 441 treatment (Figure [140]6). To
identify key genes involved in the mitotic pathway that were most
affected by compound 150441‐induced splicing changes, we used
|IncLevelDifference| (the absolute value of inclusion level
difference), a measure of the splicing inclusion level difference
between conditions. The top 10 genes, ranked by |IncLevelDifference|,
are shown in Figure [141]8C. Among these, MIS12, PSRC1, CCNK, and BIRC5
were identified as highly affected by 150441, with |IncLevelDifference|
values >0.3. The splicing events of these genes are shown in
Figure [142]8D and further visualized using sashimi plots
(Figure [143]S6, Supporting Information). These results suggest that
compound 150441 significantly altered the splicing of key mitotic
regulators, potentially contributing to its effect on cell cycle
arrest.
In summary, our RNA‐seq analysis demonstrated that the treatment with
compound 150441 led to widespread AS events in Mia PaCa‐2 cells, with
several genes implicated in mitotic regulation. These findings provide
mechanistic insight into the anticancer effects of compound 150441,
particularly through its modulation of splicing in mitotic regulators.
3. Discussion
Aberrant AS in pancreatic cancer promotes tumor growth, metastasis, and
drug resistance. In this study, we identified a novel therapeutic
inhibitor targeting the critical splicing regulatory kinase CLK4 for
treating pancreatic cancer. The inhibitor was discovered through an
optimized pharmacological model that incorporated pharmacological
interactions to enhance the hit rate. It exhibited high selectivity for
CLK4, suggesting it may result in fewer side effects. The inhibitor
effectively suppressed the activity of SR proteins, SRSF4 and SRSF6,
disrupting the RNA splicing process and inhibiting the growth and
proliferation of the pancreatic cancer cells. Moreover, the inhibitor
promoted cancer cell apoptosis by inducing G2/M cell cycle arrest. Our
study elucidates the application of the SBVS approach in developing
novel CLK4 inhibitors and provides a therapeutic strategy for targeting
RNA splicing in pancreatic cancer treatment.
CLK4 serves as a key regulatory kinase in the AS processes through the
activation of SRSF proteins.^[ [144]^25 , [145]^26 ^] CLK4 offers some
therapeutic advantages. First, CLK4 inhibition allows for selective
modulation of gene expression, enabling precise regulation of oncogenes
and tumor suppressor genes in pancreatic cancer. Second, inhibiting
CLK4 can address therapy resistance by disrupting splicing‐driven
mechanisms, such as WT1 and dCK splicing abnormalities, which are
associated with chemotherapy resistance in leukemia.^[ [146]^27 ^]
Third, CLK4 inhibitors may exhibit synergistic potential when combined
with other treatments, such as chemotherapy or immunotherapy, enhancing
therapeutic efficacy through complementary mechanisms. Finally, CLK4
inhibition has broader applicability, as splicing abnormalities have
been identified in other cancers, including MET gene splicing in lung
cancer and exon 7 splicing defects in androgen receptor genes in
prostate cancer.^[ [147]^28 , [148]^29 ^] Targeting CLK4 presents
versatile and impactful opportunities for advancing cancer therapies,
as it plays a pivotal role in RNA splicing across various cancer types.
To investigate the novel interactions generated by compound 150441, we
compared its interactions with those of other CLK4 inhibitors,
including the 30 known inhibitors, SM08502, and ML315.^[ [149]^16 ,
[150]^30 ^] The interaction profile revealed that these CLK4 inhibitors
mainly interact with residues in the hinge and interior sites. The key
differences for compound 150441 are its hydrogen bond with residue E292
in the exterior site and its hydrophobic interactions with residue V225
in the interior site (Figure [151]S7, Supporting Information). Neither
SM08502 nor ML315 exhibits these interactions, and only two and seven
of the 30 known inhibitors form interactions with E292 and V225,
respectively. Notably, SM08502 also interacts with exterior site
residues D288 and D325 through its functional groups, piperazine and
amide group. In contrast, ML315 lacks the moieties required for
interaction with the exterior site. The interaction comparison suggests
that compound 150441 forms the two unique interactions, and its
distinctive structural feature makes 150441 a promising starting point
for developing potent and selective CLK4 inhibitors. In addition, the
key interactions identified through the interaction and SAR analyses
are summarized in Table [152]S3 (Supporting Information). These consist
of ten pharmacological interactions from active compound analysis,
including hydrogen bonds with residues K191, E242, and L244, and
hydrophobic interactions with residues L167, V175, A189, K191, L244,
L295, and V324. Two additional key interactions were identified from
the SAR analysis, namely hydrogen bonds with residues E169 and E292.
Together, these interactions provide valuable insights for the
identification and design of CLK4 inhibitors.
Selectivity of kinase inhibitors is a crucial issue in drug discovery.
However, most CLK4 inhibitors exhibit off‐target inhibitory actions,
leading to unexpected side effects. For example, SM08502 (Cirtuvivint),
the first small‐molecule CLK inhibitor to enter clinical trials,
inhibits multiple kinases, such as MAP4K4, MINK1, and LRRK2.^[ [153]^31
^] Research suggests that LRRK2 inhibition could lead to the
development of intestinal‐immune diseases.^[ [154]^32 ^] Previous
studies have shown that targeting Msn kinases (MINK1 and MAP4K4) can
promote regenerative proliferation.^[ [155]^33 ^] ML315, another potent
CLK4 inhibitor with an IC[50] value of 68 nm, inhibits CLK1 (68 nm) and
CLK2 (231 nm). ML315 also inhibits kinases in other families, such as
CSNK1E, MAP3K1, PKNB, and PRKCE, which may lead to potential off‐target
effects. In comparison, the selectivity profiling results of compound
150441 showed that it is a selective CLK4 inhibitor without inhibiting
CLK1, CLK2, and CLK3. The compound also did not exhibit inhibitory
activity against members of other kinase families. These results
suggest that compound 150441 is less likely to cause adverse effects
due to its high selectivity.
CLK4 facilitates the splicing process by regulating the phosphorylation
of SRSF proteins.^[ [156]^25 , [157]^26 ^] Our findings demonstrate
that the CLK4 inhibitor, compound 150441, significantly reduced the
phosphorylation of SRSF4 and SRSF6 (Figure [158]5F). SRSF proteins
consist of 12 isoforms, SRSF1 to SRSF12, based on their structural
differences.^[ [159]^34 ^] Each isoform possesses unique
arginine/serine‐rich (RS) domain sequences and structural
configurations, which determine its affinity for CLK4 and its
sensitivity to CLK4 inhibition.^[ [160]^35 , [161]^36 ^] Differences in
the effects of CLK4 inhibitors on SRSF isoforms may arise from
variations in their chemical structure and selectivity. For example,
SM08502, which inhibits DYRK and CLK kinases, primarily targets SRSF5
and SRSF6,^[ [162]^16 ^] while ML167 mainly inhibits SRSF1.^[ [163]^15
^] In addition, SRSF isoforms may regulate different splicing targets.
For instance, SRSF1 regulates the splicing of genes, such as cyclin D1,
MNK2, and RON1,^[ [164]^37 , [165]^38 , [166]^39 ^] while SRSF2, SRSF4,
SRSF5, and SRSF6 are involved in the splicing of apoptosis‐related
genes.^[ [167]^40 , [168]^41 , [169]^42 , [170]^43 ^] SRSF6
predominantly controls the splicing of EMT‐related genes.^[ [171]^44 ^]
Therefore, the variations in CLK4 and SRSF protein expression levels in
cancer cells, along with the differences in the chemical structure and
selectivity of the compounds, lead to diverse inhibitory responses
among SRSFs. This complexity underscores the importance of selecting
the most suitable inhibitor to effectively target specific SRSF
proteins in therapeutic applications.
Compound 150441 induced G2/M arrest in cancer cells, ultimately leading
to apoptosis. The results indicated that compound 150441 effectively
inhibited the expression of Aurora A and cyclin B1 and the activation
of PLK1, CDK1, and CDC25C, causing cancer cell arrest in the G2 phase
(Figure [172]6). However, CLK4 does not directly participate in
regulating the cell cycle. Given the role of CLK4 in RNA splicing, CLK4
inhibition affects the splicing of cyclin B1, thereby reducing the
production of the cyclin B1 protein. This reduction prevents the
formation of the CDK1‐cyclin B1 complex necessary for regulating the
G2/M phase.^[ [173]^45 ^] Moreover, previous studies have shown that
the alterations in the splicing of CDC25C can disturb the proper
activation of CDK1, preventing cells from entering mitosis and thereby
maintaining them in the G2 phase.^[ [174]^46 , [175]^47 ^]
Consequently, the inhibition of CLK4 can significantly impact the
regulation of the G2/M phase by modifying the AS of key genes involved
in cell cycle progression. By influencing cyclin B1, CDK1, and other
critical regulators, CLK4 inhibition disrupts the proper transition
from the G2 to M phase, resulting in cell cycle arrest. This mechanism
presents potential therapeutic applications, particularly in cancer
treatment, by sensitizing cells to DNA damage and enhancing the
efficacy of treatments that depend on inducing G2/M arrest.
RNA‐seq results revealed that compound 150441 induced extensive
splicing alterations in various genes. Among these, MIS12, PSRC1, CCNK,
and BIRC5 exhibited the most significant splicing changes within the
mitosis pathway (Figure [176] 8 ). These splicing events may alter
wild‐type protein expression and produce isoforms that impair native
protein functions. MIS12 encodes the Mis12 protein, which plays a
critical role in kinetochore assembly during mitosis.^[ [177]^48 ^]
The splicing changes observed in MIS12 induced by compound 150441 may
impair the production of functional Mis12 protein, compromising its
ability to bind centromere‐specific proteins. This disruption could
lead to chromosome misalignment and delayed mitotic progression, and
reduced Mis12 levels are known to impair proper chromosome
segregation.^[ [178]^49 ^] Similarly, PSRC1 encodes DDA3, a
microtubule‐associated protein that regulates mitotic spindle dynamics
by interacting with Kif2a.^[ [179]^50 ^] Splicing alterations in PSRC1
likely alter DDA3 function, which has been linked to unaligned
chromosomes during mitosis.^[ [180]^51 ^] In addition, CCNK encodes
cyclin K, a regulator of chromosome segregation that interacts with
Aurora kinase to ensure accurate mitotic progression.^[ [181]^52 ^]
Splicing changes in CCNK induced by compound 150441 may impair cyclin K
function, further disrupting mitotic control. Collectively, the RNA‐seq
analysis suggested that compound 150441 likely induced mitotic arrest
through AS events in MIS12, PSRC1, and CCNK. These findings provide a
potential explanation for its anticancer mechanism via G2/M cell cycle
arrest, as observed in Figure [182]6. Interestingly, CLK inhibitors,
such as cpd‐2 and TG003, have been reported to impact the cell cycle.^[
[183]^53 , [184]^54 ^] TG003 promoted the splicing of CENPE into its
long isoform, leading to mitotic defects,^[ [185]^54 ^] while compound
150441 induced mitotic defects by AS in MIS12, PSRC1, AURKB, and
several cyclin regulators (Supporting information).
Another gene affected by compound 150441 through splicing alterations
is BIRC5, which encodes the antiapoptotic protein Survivin. As an
inhibitor of apoptosis protein, Survivin plays a critical role in
cellular stress responses during prolonged mitotic arrest.^[ [186]^55 ,
[187]^56 ^] Survivin has been shown to activate the DNA damage repair
machinery^[ [188]^57 ^] and is associated with chemoresistance in
PDAC.^[ [189]^58 ^] Compound 150441 induced intron retention in BIRC5,
which may impact the production of functional Survivin (Figure [190]S6,
Supporting Information). Notably, distinct splice variants of Survivin
exhibit distinct functions, with some isoforms acting as pro‐apoptotic
proteins.^[ [191]^59 ^] Therefore, the splicing alterations induced by
compound 150441 may reduce the anti‐apoptotic effect of Survivin. In
conclusion, the potential mechanisms of compound 150441 include
disrupting mitotic progression by altering the splicing of key mitotic
regulators, such as MIS12, PSRC1, and CCNK, and impairing survival
pathways through splicing alterations in BIRC5, which encodes Survivin.
These multiple effects are especially important because PDAC patients
show elevated Survivin expression, which is strongly associated with
increased drug resistance.^[ [192]^60 ^]
4. Conclusion
CLK4 represents a promising therapeutic target for the treatment of
pancreatic cancer. In this study, we developed an optimized
pharmacological model incorporating pharmacological interactions and
successfully identified novel CLK4 inhibitors. Among the inhibitors,
compound 150441 demonstrated the most potent inhibitory activity with
an IC[50] value of 21.4 nm. The SAR analysis revealed the key
functional groups and interactions contributing to its efficacy. Kinase
profiling further confirmed the selectivity of compound 150441 for
CLK4. Moreover, this compound inhibited the growth and survival of
pancreatic cancer cells by inducing G2/M phase cell cycle arrest. The
treatment with compound 150441 also inhibited the phosphorylation of
SRSFs. RNA‐seq analysis further demonstrated that compound 150441
induced widespread alternative splicing changes, particularly affecting
genes involved in RNA processing, DNA replication, DNA damage, and cell
cycle regulation. These findings suggest that compound 150441 is a
promising starting point for further optimization as a new pancreatic
cancer treatment.
5. Experimental Section
Molecular Docking and Identification of Pharmacological Interactions
Known CLK4 inhibitors with a IC[50] value of ≤1 µm were obtained from
the ChEMBL compound database. These compounds were clustered using
Pipeline Pilot,^[ [193]^61 ^] resulting in the selection of 60 diverse
compounds. The protein structure of CLK4 (PDB ID: 6FYV) was obtained
from the RCSB Protein Data Bank.^[ [194]^62 ^] Structures of the
protein and the compounds were prepared using the Maestro software
suite.^[ [195]^63 ^] The co‐crystal ligand was used as the centroid for
the docking grid. Compounds were docked into the CLK4 binding site
using Glide,^[ [196]^64 ^] and the top compounds were selected for a
further interaction analysis using Pipeline Pilot. In this study,
pharmacological interactions were defined as hydrogen‐bonding or
hydrophobic interactions that appeared in ≥50% of the docked CLK4
inhibitors.
Virtual Screening for Potential CLK4 Inhibitors
The NCI compound library, containing ≈280 000 compounds, was selected
for screening. Pipeline Pilot was used to filter out compounds with
poor drug properties. The initial screening involved a high‐throughput
screening (HTS) filter to remove poor candidates, including molecules
with non‐organic atom types and reactive structures. Compounds that
violated the “Lipinski and Verber Rules” or contained Pan Assay
Interference Structures (PAINS) were also removed. In addition,
compounds with a calculated QED score of ≤0.25 were removed. The
remaining compounds were docked into the CLK4 binding site. The final
pharmacological interaction scores were then calculated and ranked.
Finally, 15 compounds were selected for evaluation based on their
availability, inspection, and structure similarity.
Kinase Assay
Enzyme‐based assays were carried out by Thermo Fisher Scientific
(Waltham, MA, USA). Thermo Fisher Scientific utilizes LanthaScreen Eu
Kinase Binding Assay Technology
([197]www.thermofisher.com/lanthascreen), which is based on
fluorescence resonance energy transfer (FRET), to conduct the kinase
activity assays. Fluorescent receptor dyes are conjugated to the
kinase, and an Eu‐labeled anti‐tag antibody is added to detect the
phosphorylated fluorescein‐labeled substrate. The fluorescence‐labeled
kinase, antibody, and test compounds are combined in an appropriate
buffer solution. After incubation, the mixture is placed in a
fluorescent plate reader capable of detecting FRET signals. FRET
efficiency is calculated by comparing the ratio of the acceptor
emission to the donor emission. The selected compounds were evaluated
at specified concentrations, and IC[50] values were determined using
GraphPad Prism software (La Jolla, CA, USA). Each enzymatic activity
assay was performed in duplicate, following the quality control
guidelines of Thermo Fisher Scientific. Kinase profiling was carried
out using the SelectScreen kinase profiling service provided by Thermo
Fisher Scientific, with supplementary assays such as Adapta and Z'LYTE
included when available for the respective kinases. The detailed
protocols are available at the following links:
[198]www.thermofisher.com/adapta and
[199]https://www.thermofisher.com/z‐lyte.
Cell Culture
Pancreatic cancer cell lines, Mia PaCa‐2 and Panc‐1, were obtained from
the Bioresource Collection and Research Center (BCRC, Hsinchu, Taiwan).
These cells were cultured in Dulbecco's modified Eagle medium (DMEM)
medium supplemented with 10% (v/v) fetal bovine serum (FBS), 100
units/mL of penicillin, and 100 µg mL^−1 of streptomycin. Cultures were
maintained at 37 °C in a humidified incubator with 5% CO[2].
Cell Viability Analysis
Cells were seeded in 96‐well plates at specified densities: 3 × 10^3
cells per well for Mia PaCa‐2 cells and 5 × 10^3 cells per well for
Panc‐1 cells. After cells had become adhered, they were treated with
concentrations of 0.3, 1, 3, and 10 µm of the compounds for 72 hours.
The MTT reagent (0.5 mg mL^−1 in PBS) was then added to the medium at a
1:10 ratio, and the plates were incubated for 1 hour. Following
incubation, 100 µL of DMSO was added to each well to dissolve the MTT
metabolic products.^[ [200]^65 ^] The absorbance was measured at 550 nm
(Synergy HTX ELISA reader, Bioteck, CA, USA), and IC[50] values were
calculated based on cell viability.
Cell Proliferation Analysis
Mia PaCa‐2 cells were seeded at 3 × 10^3 cells per well, while Panc‐1
cells were seeded at 5 × 10^3 cells per well, both in 96‐well plates.
After allowing the cells to adhere, they were exposed to concentrations
of 0.3, 1, 3, and 10 µm of the compounds for 72 hours. Following
treatment, cells were fixed with 10% trichloroacetic acid (TCA) for
15 min and rinsed with distilled water. Subsequently, 100 µL of 0.4%
SRB dye was applied for staining, and cells were then washed with 1%
acetic acid.^[ [201]^66 ^] Finally, 100 µL of 10 mm Tris‐base was added
to each well, and the absorbance was read at 515 nm to calculate GI[50]
values based on cell proliferation (Synergy HTX ELISA reader, Bioteck,
CA, USA).
Western Blot Analysis
Cells were treated with the compound at the indicated concentrations
and incubation times. After treatment, cell lysates were extracted
using RIPA buffer containing phosphatase inhibitors (2 mm Na[3]VO[4],
1 mm NaF, and 20 mm NaP[2]O[4]) and an EDTA‐free protease inhibitor.
The lysates were centrifuged at 14 000 rpm for 15 min at 4 °C, and
total protein content was measured using a BCA protein assay kit. For
sample preparation, lysates were mixed with 5X sample buffer (312.5 mm
Tris, pH 6.8, 10% sodium dodecylsulfate (SDS), 50% glycerol, 0.05%
bromophenol blue, and 10% 2‐mercaptoethanol) and incubated at 95 °C for
10 min.
For the western blot analysis, equal amounts of protein were separated
by SDS‐PAGE and transferred onto PVDF membranes. The membranes were
then blocked with 5% nonfat milk in TBST buffer for 1 hour at room
temperature. Target proteins were detected by incubating the membranes
with primary antibodies in TBST overnight at 4 °C. The following day,
the membranes were washed with TBST and incubated with HRP‐conjugated
secondary antibodies for 1 hour at room temperature. Primary antibodies
phospho‐SR proteins (MABE50) and GAPDH (MAB374) were purchased from
Millipore (Bedford, MA, USA). The Mcl‐1 antibody (SC‐819) was purchased
from Santa Cruz (Santa Cruz, CA, USA), and Cyclin B (BD554178) and PLK1
(Thr210) (BD558400) were obtained from BD Biosciences (Qume Drv, USA).
The antibodies against Bcl‐2 (2876S), PARP, caspase 9 (#9502), caspase
3 (#9662), cleaved caspase 3 (#9661), Aurora A (#4718), CDC25C (Ser216)
(#9528), and CDK1 (Tyr15) (#9111) were purchased from Cell Signalling
Technology (MA, USA). The secondary HRP‐conjugated anti‐rabbit IgG
(111‐035‐003) and anti‐mouse IgG (115‐035‐003) were obtained from
Jackson ImmunoResearch (PA, USA). Protein expression was visualized
using an enhanced chemiluminescence detection kit and detected using an
eBLOT machine (Shanghai, China). Relative protein expression levels
were quantified with Image J software (National Institutes of Health
(NIH), Bethesda, MD, USA).
Cell Cycle Analysis
The cell cycle was analyzed using flow cytometry. Cells (3 × 10⁵
cells/well) were plated in 6‐cm dishes with 3 mL of culture medium and
exposed to various concentrations of the compound for specified time
intervals. Following treatment, cells were harvested, washed with cold
PBS, and fixed in 70% ice‐cold ethanol at −20 °C for 30 min. After
fixation, cells were centrifuged to remove ethanol and subsequently
stained with 0.5 mL of propidium iodide (PI) staining buffer, which
contained 80 µg mL^−1 PI, 100 µg mL^−1 RNase A, and 1% Triton X‐100 in
PBS, for 20 min. The cell cycle distribution was then determined using
a BD Accuri™ Flow Cytometer and its accompanying software (Becton
Dickinson, Mountain View, CA, USA).
RNA Sequence Analysis
Mia PaCa‐2 cells were exposed to 3 µm of the compound for 6 hours,
after which total RNA was isolated according to the Direct‐zol RNA Kit
protocol (Zymo Research, CA, USA). The RNA purity and concentration
were measured using SimploNamoE‐Biochrom Spectrophotometers (Biochrom,
MA, USA). Sequencing libraries were generated from 1 µg of total RNA
using the KAPA mRNA HyperPrep Kit (KAPA Biosystems, Roche,
Switzerland). The quality of the libraries was evaluated using the
Qubit 2.0 Fluorometer (Thermo Scientific, MA USA) and the Agilent
Bioanalyzer 2100 system. Finally, sequencing was performed on the
Illumina NovaSeq 6000 platform. Differential AS events were analyzed
with rMATS.^[ [202]^67 ^] Splicing events were identified using splice
junction exon counts (JCECs), with significant events defined as those
with a false discovery rate (FDR) <0.05 and related reads ≥10 in either
splicing form. For pathway enrichment analysis, DAVID (The Database for
Annotation, Visualization, and Integrated Discovery)^[ [203]^68 ^] was
used to identify biological processes enriched in the genes affected by
compound‐induced splicing changes.
Statistical Analysis
All biological experiments were repeated at least three times to ensure
reproducibility and reliability of the results. The data are expressed
as the mean ± standard deviation (SD) or as a percentage of the control
group, depending on the nature of the data. For statistical analysis, a
Student's t‐test was used to compare the differences between the
treatment and control groups. In some cases, a two‐tailed Student's
t‐test was applied to determine the significance of differences. A
p‐value of <0.05 was considered statistically significant.
Additionally, a one‐way analysis of variance (ANOVA) was performed to
analyze the data. When the ANOVA indicated significant differences
between groups, Tukey's post‐hoc test was used to identify which pairs
of groups showed statistically significant differences. Parameters with
p < 0.05 were deemed statistically significant.
Conflict of Interest
The authors declare no conflict of interest.
Author Contributions
C.‐L.Y. and Y.‐W.W. contributed equally to this work. C.‐L.Y., Y.‐W.W.,
S.‐L.P., and K.‐C.H. were responsible for designing the experiments and
writing the manuscript. C.‐L.Y., T.E.L., and T.‐Y.S. performed
molecular docking and analyzed the results. Y.‐W.W., M.‐C.L., and
S.‐C.Y. analyzed the results of kinase profiling. Y.‐W.W., Y.‐H.Y., and
H.‐J.T. established the experimental models of SR protein
phosphorylation, and regulatory protein expression, and analyzed the
data. H.‐J.T. and J.‐H.H. performed the NGS and gene enrichment
analysis. S.‐Y.H., M.‐C.Y., and H.‐P.H. c provided resources, and
funding, and oversaw the investigation. S.‐L.P. and K.‐C.H. conceived
and supervised the project. All authors have read and agreed to the
published version of the manuscript.
Supporting information
Supporting Information
[204]ADVS-12-2416323-s002.docx^ (7.5MB, docx)
Supplemental Table 1
[205]ADVS-12-2416323-s001.xlsx^ (442.7KB, xlsx)
Acknowledgements