Systems biology approaches based discovery of a small molecule inhibitor targeting both c-Met/PARP-1 and inducing cell death in breast cancer

Article abstract of DOI:10.7150/jca.40758

Breast cancer is the second most common types of cancer worldwide. Molecular strategies have developed rapidly; however, novel treatments strategies with high efficacy and lower toxicity are still urgently demanded. Notably, biological networks estimated from microarray data and functional activity network analysis could be utilized to identify and validate potential targets. In this study, two microarray data (GSE13477, GSE31192) were firstly selected, and analyzed by multi-functional activity network analysis to generate the core protein-protein-interaction (PPI) network. Several potential targets were subsequently identified and c-Met and poly (ADP-ribose) polymerase-1 (PARP-1) were manually chosen as the key targets in breast cancer. Furthermore, virtual screening and molecular dynamics (MD) simulations were utilized to recognize novel c-Met/PARP-1 inhibitors in Specs products database. Three small molecules, namely, ZINC19909930, ZINC20032678 and ZINC13562414 were selected. Additionally, these compounds were synthesized, and two breast cancer cell lines, MDA-MB-231 and MCF-7 cells were used to validate our bioinformatic findings in vitro. MTT assay and Hoechst staining showed that ZINC20032678 significantly induced breast cancer cell death, which was mediated through apoptosis by flow cytometry. Furthermore, ZINC20032678 was shown to target the active sites of the both targets and recruitment of downstream apoptotic signaling pathways, eventually inducing breast cancer cell apoptosis. Collectively, our findings not only offer systems biology approaches based drug target identification, but also provide the new clues for developing novel inhibitors for future breast cancer research.

Full text of DOI:10.7150/jca.40758

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Journal of Cancer 2020, Vol. 11
2656
Ivyspring
International Publisher
Journal of Cancer
2020; 11(9): 2656-2666. doi: 10.7150/jca.40758
Research Paper
Systems biology approaches based discovery of a small
molecule inhibitor targeting both c-Met/PARP-1 and
inducing cell death in breast cancer
Tian Yu1*, Lijia Cheng1*, Xueling Yan1, Hang Xiong1, Jie Chen3, Gang He1, Hui Zhou1, Hongbo Dong1,


Guangya Xu1, Yong Tang1, 2 , Zheng Shi1
1. School of Medicine & Sichuan Industrial Institute of Antibiotics & Department of Respiratory and Critical Care Medicine, Affiliated Hospital/ Clinical
College of Chengdu University, Chengdu University, Chengdu 610015, China
2. School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
3. Central Laboratory of Clinical Medicine, Sichuan Academy of Medical Science and Sichuan Provincial People’s Hospital, Chengdu, 610000, China
*Co-first authors, who contributed equally.
 Corresponding authors: Dr. Yong Tang & Dr. Zheng Shi, School of medicine, Chengdu University & School of Acupuncture and Tuina, Chengdu University
of Traditional Chinese Medicine, Chengdu, Sichuan 610016, P.R. China, E-mail: tangyong@cdutcm.edu.cn & drshiz1002@hotmail.com
© The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
See http://ivyspring.com/terms for full terms and conditions.
Received: 2019.09.29; Accepted: 2020.01.28; Published: 2020.02.19
Abstract
Breast cancer is the second most common types of cancer worldwide. Molecular strategies
have developed rapidly; however, novel treatments strategies with high efficacy and lower
toxicity are still urgently demanded. Notably, biological networks estimated from microarray
data and functional activity network analysis could be utilized to identify and validate potential
targets. In this study, two microarray data (GSE13477, GSE31192) were firstly selected, and
analyzed by multi-functional activity network analysis to generate the core
protein-protein-interaction (PPI) network. Several potential targets were subsequently
identified and c-Met and poly (ADP-ribose) polymerase-1 (PARP-1) were manually chosen as
the key targets in breast cancer. Furthermore, virtual screening and molecular dynamics (MD)
simulations were utilized to recognize novel c-Met/PARP-1 inhibitors in Specs products
database. Three small molecules, namely, ZINC19909930, ZINC20032678 and
ZINC13562414 were selected. Additionally, these compounds were synthesized, and two
breast cancer cell lines, MDA-MB-231 and MCF-7 cells were used to validate our bioinformatic
findings in vitro. MTT assay and Hoechst staining showed that ZINC20032678 significantly
induced breast cancer cell death, which was mediated through apoptosis by flow cytometry.
Furthermore, ZINC20032678 was shown to target the active sites of the both targets and
recruitment of downstream apoptotic signaling pathways, eventually inducing breast cancer cell
apoptosis. Collectively, our findings not only offer systems biology approaches based drug
target identification, but also provide the new clues for developing novel inhibitors for future
breast cancer research.
Key words: breast cancer, c-Met, PARP-1, systems biology, apoptosis, drug discovery
Introduction
Breast cancer is one of the most leading causes of
cancer death among women. It is reported that the
overall pathogenic incidence rate in women remains
generally stable, whereas the breast cancer incidence
has slight increased from 2004 to 2013 [1].Currently,
there are four main molecular subtypes of breast
cancer, referring as luminal, HER2, normal-like and
basal. Of note, targeted therapies have been raised
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great attention in science community. It can target
specific targets expressed in/on the surface of cancer
cells which are involved in carcinogenesis and/or
tumor growth [2]. For example, tamoxifen for
treatment of ER positive tumors and trastuzumab in
the treatment of HER2 positive tumors in breast
cancer have been extensively studied [3]. However, it
is worth knowing that most basal-like breast cancers
do not express ER, PR and HER2, the two subgroups
are not mutually exclusive. And, 80% of basal-like
breast cancers are triple-negative and 80% of triple-
negative breast cancers exert a basal-type phenotype
[4]. Therefore, fundamental breakthroughs, particu-
larity new therapeutic targets and new drugs are most
urgent needed in breast cancer research.
It is known to all that the entire DNA
microarrays has been used in cancer drug
development and facilitate clinical applications for
years [5]. Since measurement of the sequence and
expression of potential targets is greatly facilitated by
microarray technology, it is of great importance to
generate new clues to gene function which can help to
identify appropriate targets for therapeutic
intervention. Hitherto, a great deal of drug targets
discovery and validation have been well described by
microarray analysis [6, 7]. Additionally, DAVID
online database, an integrated biological knowledge
base and analytic tool, can extract biological meaning
from large gene/protein lists [8]. Moreover, STRING
database includes known and predicted PPIs which
stem from various databases [9]. Combining the
microarray data and functional activity network
analysis of differentially expressed genes by using
DAVID (including KEGG pathway and GO analysis)
and STRING were accelerate the drug target
development [10]. To our knowledge, a number of
hub proteins/targets were successfully identified by
using abovementioned approaches [11, 12].
In this study, two microarray data (GSE13477,
GSE31192) were firstly analyzed to obtain consensus
results of differently expressed genes. Then, the up-
regulated genes were selected to identify the
candidate genes by using GO, KEGG pathway and
STRING analysis. According to the network results
and consulting numerous references, c-Met and
PARP-1 were manually selected as the key targets in
breast cancer. Subsequently, molecular docking and
MD simulations were performed to recognize
valuable agents to target both c-Met/PARP-1. Then,
three small molecules, namely ZINC13562414,
ZINC20032678 and ZINC19909930 were identified as
potential inhibitors against both c-Met and PARP-1.
We further synthesized three compounds, and
cultured MDA-MB-231 and MCF-7 cells and treated
with these compounds. Eventually, the compound
ZINC20032678 effectively induced breast cancer cell
apoptosis through inhibition of both c-Met and
PARP-1 kinases activities. In general, our findings
would lead to comprehensive mechanistic insights
into identification of more ideal targets as well as
more novel potential drugs in future cancer drug
discovery.
Material and methods
Hub protein identification
In order to investigate if genes were
co-expressed or not, two microarray data (GSE13477;
GSE31192) under the determiners “Breast cancer”
AND “Homo sapiens” from the GEO Data Sets were
selected, and analyzed by using GEO2R tool
(https://www.ncbi.nlm.nih.gov/geo/geo2r/). Two
microarray data by using different text conditions
were selected. The aim of two chips is to identify
differential genes between normal tissues and cancer
(cells), while excluding the error of individual chip
experiments through cell chips and tissue chips. GO
analysis could provide comprehensive information on
gene function of individual products through onto-
logy. An Expression Analysis Systematic Explorer
(EASE) score < 0.1 was adopted to refine the GO terms
set in major clusters through Database for Annotation,
Visualization
Bioinformatics
and
Resources
Integrated
Discovery
version
6.8
(DAVID:https://david.ncifcrf.gov/).
A
protein
interaction network of each gene and all protein
interaction networks of up-regulated genes and
down-regulated genes were constructed through
STRING. Subsequently, the selected genes were
carried up by using DAVID to get the KEGG pathway
and Gene Ontology results, and using STRING to get
the core PPI network, respectively. We further
imported the results into cytoscape to strip out the
core PPI network. Herein, the identification of the hub
protein schematic model was shown in Figure 1.
Molecular docking
The initial 3D X-ray crystal structures of c-Met
(3ZCL) and PARP-1 (3L3M) were downloaded from
the RCSB Protein Data Bank (PDB) (http://www.pdb.
org/pdb/home/home.do) [13]. 32791 candidate
compounds that were commercially available for
screening were collected from specs subsets (http://
zinc.docking.org/catalogs/specsnp) [14]. The c-Met/
PARP-1 and their ligands were prepared by USCF
chimera (Version1.8) by elimination of solvent
molecules as well as insertion of hydrogen atoms and
standard charges [15]. UCSF DOCK (version 6.5) was
used to identify compound libraries against a target
receptor to predict potential drugs [16, 17]. To ensure
the accuracy of docking, the specified parameters
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docking were confirmed by redocking the
co-crystallized ligand back into the binding site of the
receptor. Root mean square deviation (RMSD) serves
as a reference value for judging docking performance
[18, 19]. The top 100 ranked small molecular natural
products were rescored by amber score function [20,
21]. Generally speaking, the lower score of ligands
indicates the higher binding affinity to receptor.
the end, 25 ns MD simulations with a time step of 2 fs
at constant pressure (1 atm) and temperature (300 K)
were performed. During the MD simulation process,
all bond lengths were constrained by LINCS
algorithm and Particle Mesh Ewald (PME) method
was adopted to calculate the long-range electrostatic
interactions [27-29]. The trajectories of c-Met/PARP-1
bounded with different small molecules were
computed by backbone root mean square deviation
(RMSD, g_rms) of GROMACS utilities.
MD simulations
GROMACS package (Version 4.5) [22] was
adopted to perform the MD simulations. Amber
ff99SB force field and TIP3P water mode were
explored to convert the protein structure into the
topology [23]. Additionally, the ligands were charged
by AM1-BCC via antechamber program. Then,
parmchk program was utilized to check and revise the
missing force field parameters as well as converting
the parameters and topologies for ligands with
ACPYPE tools [24]. Simulations were carried out
under periodic boundary conditions (PBC) with a
dodecahedron periodic box setting the minimal
distance of 1.0 nm between the protein and edge of
the box, which was solvated by adding simple point
charge (SPC) water molecules [25]. Moreover, Na+
and Cl− were added to mimic a physiological NaCl
concentration of 0.15 M to neutralize the solvated
system. Subsequently, we employed the steepest
descent minimization algorithm for all atoms to
minimize the energy of system [26]. Afterwards, 100
ps NVT (constant number of particles, volume, and
temperature) and 100 ps NPT (constant number of
particles, pressure, and temperature) equilibrations
were performed for each system with position
restraints for receptor and potent small molecules. In
Chemical synthesis of candidate compounds
Synthesis of ZINC20032678 requires two key
intermediates a4 and dodecane-1, 12-diamine, which
were synthesized using the route described in Figure
2A (ZINC20032678). Dodecane-1, 12-diamine was
commercial availability. To prepare the intermediates
a4, compound a1 was treated with CS2 and heated in
the
water
to
furnish
the
corresponding
carbamodithioic acid a2. Compound a2 was oxidized
by HgCl2 to afford ethyl, 2-isothiocyanatoacetate a3.
Finally intermediate a4 could be obtained from a3
through cyclization in DMF at 120 C.
For ZINC19909930, our synthetic strategy started
from commercially available 3-bromopropan-1-ol
b1.Compound b1 was treated with phenol and then 1,
4-dibromobut-2-yne to get key Intermediate b3.
Compound b3 was reacted with piperazine to get
ZINC19909930 in good yield (Figure 2B).
As for compound ZINC13562414, our synthetic
strategy started from compound c1. Compound c1
was reacted with 1-(4-nitrophenyl) ethan-1-one to get
compound c2. The amino hydrogens of compound c2
were replaced by benzyl groups to get ZINC13562414
(Figure 2C).
Figure 1. The schematic model of data mining and virtual screening process. We systematically combined two microarray datasets and analyzed these data by using GO and
pathway enrichment analysis to predict hub proteins in breast cancer.
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Journal of Cancer 2020, Vol. 11
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Figure 2. Synthetic route of three compounds. (A) Synthetic Approach to ZINC20032678. Reagent and conditions: (a) CS2, water, ambient temperature, 48 h; (b) HgCl2,
methylbenzene, 5h; (c) DMF, 120 ºC, 6h; (d) CH3CHO, NH2(CH2)12NH2, 6h. (B) Synthetic Approach to ZINC19909930. Reagent and conditions: (a) 18-crown-6-ether, K2CO3,
acetone, 12h; (b) NaOH, 1.4-dibromobut-2-yne, N-Benzyl-N-triethylammonium chloride, benzene, 70 ºC, 15h; (c) piperazine, NaOH, acetone. (C) Synthetic Approach to
ZINC13562414. Reagent and conditions: (a) 1-(4-nitrophenyl)etana-1-one, KOH/K2CO3, Water, 0.5h; (b) (bromomethyl)benzene, NaOH, DMF, 4h.
molecule ZINC20032678 as followings: 0.1 μmol/L, 1
μmol/L and 10 μmol/L ZINC20032678 after 18 h.
Cell culture
The MDA-MB-231 and MCF-7 cells were
purchased from American Type Culture Collection
(ATCC, Manassas, VA, USA). The cells were cultured
in Dulbecco’s Modified Eagle Medium (DMEM) with
10% fetal bovine serum (FBS) and 1% penicillin/
streptomycin, and maintained in a 5% CO2/37℃
incubator, respectively. Cells were using with 0.25%
trypsin, DMEM, FBS and antibiotics were purchased
from GIBCO (Carlsbad, CA, USA).
Cisplatin was used as positive control and untreated
cells were set as blank control. Each group was set
three wells. Forty-eight hours later, cells were stained
with Hoechst kit from Beyotime (Haimen, Jiangsu,
China). Cell counting was carried out using the
software ImageJ from National Institutes of Health,
which is available at http://rsbweb.nih.gov. The
corresponding cell death rates were calculated
according to Hoechst staining.
(4,5-dimethyl-2-thiazolyl)-2,5-diphenyltetrazol
ium bromide (MTT) assay
Annexin V-FITC/PI stained fluorescence-
activated cell sorter (FACS)
5103 MDA-MB-231 and MCF-7 cells were
seeded in 96-well plate, respectively. Eighteen hours
post-seeding, cells were treated with different
concentrations of ZINC20032678 as followings: 0.1
μmol/L, 1 μmol/L and 10 μmol/L ZINC20032678.
Cisplatin was used as positive control, and untreated
cells were set as blank control. Each group was set
three wells. Forty-eight hours post-drug treatments,
the viable cells were stained by adding 20 μl of 5
mg/ml MTT solution per 100 μl of growth medium.
After incubating for 4 h at 37℃, the media were
removed and 150 μl DMSO was added to dissolve the
formazan. The absorbance of each well was measured
by microplate reader and viable cells are presented as
a percent of the control.
To explore whether the cell death was caused by
apoptosis, MDA-MB-231 cells was chosen to perform
Annexin V-FITC/PI stained FACS. After incubation
of cells with 0.1, 1 and 10 μmol/L ZINC20032678 for
48 h in 6-well plate, the cells were harvested through
trypsinization, and washed twice with PBS. The cells
were centrifuged at 3000 r/min for 5 min, then the
supernatant was discarded and the pellet was
resuspended in 1×binding buffer at a density of
1.0×105/ml. The sample solution of 100 μl was
transferred to a 5 ml culture tube, and incubated with
5 μl FITC-conjugated annexin V (Abcam, USA) and 5
μl PI (Abcam, USA) for 15 min at room temperature in
the dark. 400 μl 1×binding buffer was added to each
sample tube, and the samples were analyzed by FACS
(Mindray, China).
Hoechst staining
1105 MDA-MB-231 and MCF-7 cells were
seeded in 6-well plate, respectively. The cells were
treated with different concentrations of small
Western blot
Proteins were extracted from MDA-MB-231 cells
treated with 0, 0.1, 1 and 10 μmol/L ZINC20032678
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