SSR Inst. Int. J. Life. Sci.,
5(4): 2341-2348, July 2019
In silico
Modeling Personalized Therapy of Pulmonary Artery Hypertension
Xiaonan Ying1, Wenqin Li2, Yan
Wang3, Biaoru Li4*
1Student,
Department of Bioinformatics, Northeastern University, Boston, MA 02115, USA
2Student,
Department of Chemistry, University of California, Irvine, CA, USA
3MD, Department
of Internal Medicine, China Petroleum Kuerla Hospital, Kuerla, XinJiang, P.R.
China
4Senior
Scientist, Department of Pediatrics and GA Cancer Center, Children Hospital at
GA, Augusta, GA, USA
*Address for Correspondence: Dr. Biaoru Li, Senior
Scientist, (Tenured), CN4111 Building, Department of Pediatrics and GA Cancer
Center, Children Hospital at GA
E-mail: bli@augusta.edu
ABSTRACT- Background:
The pulmonary arteries are the blood vessels that
carry blood from the right side of the heart through the lungs. Pulmonary
arterial hypertension (PAH) is a progressive disorder characterized by higher
pressure of lungs pulmonary artery for no apparent reason. PAH is treatable
although there is no clear cure for the disease. Fortunately, human genomics have
been decoded in 2004 and several molecular targeting compounds related to PAH
were discovered, the uncured disease should give clinical scientists and
medical doctors new scenery to develop some new modules to administer patients.
Methods: Here we used a set of genomic data
from clinical PAH to combine traditional medication and molecular target
therapy so that integration modules will be used to the
clinical field. The integration model has primarily relied on system
biology including network, topology and gene-drug interaction database.
Results: In this research article, we firstly
analyzed using genomic expression signature from a set of clinical PAH genomic data and three known PAH pathways, and then we
combined current medications and molecular targeting therapy into the
integration model.
Conclusion: In the near
future, we will develop a second-generation model based on the module by using
individual clinical genomic data from different patients such as patient
genomic data, clinical information including patient symptom and laboratory
results.
Key
Words: Gene expression signature, Integration medicine,
Pulmonary arterial hypertension, Personalized therapy, Topology
INTRODUCTION- German
doctor E. Romberg first reported PAH in 1891, who described a patient with
thickening pulmonary artery but no heart or lung disease under autopsy [1].
Now we have known that PAH is a progressive disorder characterized by
hypertension in the lungs for no
apparent reason. Symptoms of PAH include dyspnea during exercise, chest pain
and fainting episodes. PAH patients may experience several years without a
diagnosis [2]. However, it is important to cure PAH because of
without optimal treatment, high blood pressure in the lungs causes the right
heart to work much harder, progressively and eventually, the patient heart muscle may result in
failure [3].
Since 2004 when human
genomics are
decoded, it gives an expectation for medical doctors to treat the uncured
disease. Furthermore, because several molecular targeting compounds related to
PAH were discovered, it will be possible that PAH should be cured [4].
Foremost, system biology and gene-drug interaction databases are emerging; an
integration model based on genomics will be possible for the combination
employment of different medication methods in the clinical disease. Here we
first studied a set of genomic databases from PAH for the integration model.
Secondly, some comprehensive pathways are studied to PAH network mechanism so
that we further set up a network construction by genomic expression signature
obtained from the public GEO database. Finally, we combine PAH networks with
current medication and PAH related to molecular targeting treatment, and
therefore this integration modeling will be optimal to treat PAH.
The manual
purpose will provide the possibility for effective treatment to this kind of
uncured disease. In near
future, we will continue to develop second-generation module with clinical
genomic data from an individual patient and his/her information relied on each
patient symptom and laboratory results. Our final purpose is that the feasible
module can be used for MD to treat
or medical care to maintain a reasonable quality of life according to personal genomics information, patient
symptom and lab results.
Materials and
Methods
Clinical
genomic sources-
There are
several public PAH genomic databases published in Gene
Expression Omnibus (GEO). After these databases in GEO are carefully
studied, we mainly select GSE113439 from GEO for our study
in silico model because the data
based on diagnosis were defined
by pre-capillary pulmonary hypertension. GSE113439
were studied from fifteen patients with PAH and eleven normal controls. The PAH
group included six patients with idiopathic PAH, four patients with PAH
secondary to connective tissue disease (CTD), four patients with PAH secondary
to congenital heart disease (CHD) and one patient with chronic thromboembolic
pulmonary hypertension (CTEPH) [5]. After collecting the genomic
data, NIH BRB Array Tool
(https://brb.nci.nih.gov/BRB-ArrayTools/download.html#) was plug into Excel for
genomic expression profile mining.
Topology analysis
for personalized therapy modeling- As
our previous reports, after we got the genomic expression profiles, we first
studied this genomic expression profiles under the topology model. In details,
the specific PAH gene expression profile after BRB tool mining was input into
Cytoscape to observe abnormal expression (gene expression signature, GES) for
the genomic characteristics. Based on our previous publications, we selected
three indexes, betweenness Centrality (BC), which was a short pathway between two
proteins (node), Connectivity Degree (CD), which is a protein linking other
protein number and Cluster Coefficient (CC), which means side-way to a protein.
Furthermore, the topology formula selected in the network combined to the
vessel endothelium pathway, all
GES and vessel pathway laid the foundation for the establishment of operational
therapeutic targets.
After we studied the GES topology, we were also collected three pathways related with PAH: Nitric oxide pathway (NO), which help to control blood pressure by opening arteries (also known as dilation) when needed; Endothelin pathway, which is almost the opposite of nitric oxide. It increases blood pressure and makes the blood vessels firm; Prostacyclin pathway, which helps with dilation and it helps prevent the vessels from getting blocked [6-10].
GES and three
pathways related to PAH be also input into a drug-bank in the Drug Genomic
Interaction Database (DGIdb) to define targeted therapeutic drug and the
targeting molecule. As our researches described above, we were also studied an
index from each compound with higher BC and lower CC and CD. These targets
indicate as a higher targeting for abnormal cells with a lower toxicity
for normal cells. Eventually, a list of compounds from
drug-bank established to link genes, especially include FDA-approved drug and
molecular therapeutic antibodies and small molecule therapeutics. This led to
the establishment of compartment and drug response networks based on the
abnormal genome expression characteristics obtained from PAH for the further
purpose of personalized medication.
Topology
analysis for targeting treatment- Three pathways have been discovered
for PAH networks as introduced above, NO pathway, endothelin pathway, and prostacyclin pathway.
When patients become PAH, the three pathways with their regulation will become
major factors to involve in the disease [11-13]. We were applied for
the list of molecular targets approved by FDA with targeting genes to combine
into the network including three pathways and list of molecular targets.
Topology analysis for current medicine- Moreover,
routine clinical health care for PAH focuses on an oxygen intake to increase
NOS, decrease sodium to decrease vessel constriction, digoxin to increase vessel dilation and diltiazem
and dihydropyridine to regulate calcium function [13]. Therefore, the
topology analysis from current medication includes gene expression and their
treatment.
Topology analysis for integration module- As
eventual combination for integration model, we merged all networks from genomic
data including their therapeutic targets; three pathways with suppressing and
activation related medication.
Support
analysis- In order to support the
module of the selected pathways for current drugs and targeted molecule therapy
for personalized therapy, python scripts with their compartment to
simulate to assay a drug. The python scripts were established as our
previously reported, they are used to simulate PAH drugs to support the module
and analyze the matched therapeutic targets including traditional medicine and
targeting treatment in the PAH network for targeted gene expression and the
discovered therapeutic molecules. The design principle is that network with a
dynamic model based on differential equations including qualitative
relationships and directed responses as our previous report.
RESULTS- Construction and topology
establishment from GES- Recently,
therapeutic targeting was going to focus on topology based on genomic
expression profile to discover drug targeting, small molecule targeting, Ab
targeting and RNA-interfering therapy. Base on the conception from our
long-term data analysis and experimental support, although 15 specimens from
patients with PAH to 11 control specimens were obtained from GEO data, we need
further refine a gene profile for construction for feasible therapeutic
targets. After gene expression mining and analysis, 427 genes
were refined for further study as Fig.
1.
.png)
Fig. 1: A
Gene expression mining
and protocol was performed by BRB platform
Our laboratory
has spent more than twenty years to study different topology parameters relied
on our experimental assays such as quantitative rtPCR and Western blot.
Although most of the parameters can be used in different cell-lines, animal and
human beings in different labs, as our previous studies, both BC and DC majorly
play an important role from clinical specimens while DC is likely to be toxic
for normal cells such as normal lymphocytes due to their system-wide influence,
thus the high BC value indicates a significant targeting node from abnormal
cells and low DC and CC means very few branches without their system-wide
influence to cause normal cell dysfunction. After gene expression profile was
set, we firstly studied the GES, GES results. Due to study PAH, we additionally
input angiogenesis pathway and three PAH pathways into Cytoscape, a
construction from both PAH GES and angiogenesis pathway/three PAH pathways were
established as Fig. 2. The uncovered nodes (or genes or proteins) were loaded
into the DGIdb to mine drugs, small molecule and other molecular therapy
agents. The resulting node and drug candidates with their index (BC, DC, and
CC) were configured by the construction map as Fig. 3 because FDA has approved
for several drugs of targeting therapy, we have not further studied new
targeting, in details, in the DGIdb for clinical module purpose.
.png)
.png)
.png)
.png)
.png)
.png)