Research Article
Computational Analysis of Pharmacogenomic Based Regulatory Network in Psoriasis: An Approach of Systems Biology to Initiate the Discovery of Systemic Biomarkers to Treat Psoriasis
Author Affiliations
Department of Bioinformatics , Sathyabama University, India
Corresponding Author
Harishchander Anandaram, Department of Bioinformatics, Sathyabama University, India
Citation
Anandaram H. Computational
Analysis of Pharmacogenomic Based Regulatory
Network in Psoriasis: An Approach of Systems
Biology to Initiate the Discovery of Systemic
Biomarkers to Treat Psoriasis. Syst Comput Biol J.
2018 Jan;1(1):101
© 2018 Anandaram H. This is
an open-access article distributed under the
terms of the Creative Commons Attribution 4.0
international License, which permits unrestricted
use, distribution and reproduction in any
medium, provided the original author and source
are credited.
Abstract
In the era of post genomics, performing a computational analysis to understand the
pharmacogenomic based regulation of Psoriasis with respect to the principles of data
mining and constructing a regulatory network with respect to the principles of systems
biology and analyzing the network with respect to the principles of test statistic remains
a challenging task to execute. The challenge was approached by identifying the associated
genes of Psoriasis from PharmGkb and it was followed by identifying the associated
regulators (MicroRNAs and Transcription Factors) from PharmacomiR/RegNetworks.
Finally the regulatory networks were analyzed by the statistical measures.
Introduction
Psoriasis is a disorder mediated by the immune system by making certain faulty signals
in the human body. It’s still a belief that psoriasis can be developed under the specified
conditions, i.e. “when the immune system signals the body to accelerate the growth of skin
cells. Normally, skin cells get matured from the surface of the skin for every 28-30 days. In
case of psoriasis, the skin cells mature in 3-6 days. Instead of being in the shed, the cells in
the skin get piled up to cause the visible lesions. It was also found that the genes that cause
psoriasis can determine the reaction of a person’s immune system. These genes can either
cause psoriasis or other conditions which are immune-mediated like Type-I Diabetes or
rheumatoid arthritis.
Pathophysiology of psoriasis involves the understanding of the occurrence of prominent
pathologies in the major components of skin i.e. the epidermis and the dermis. There are
two well established hypotheses about the process that occurs in the development of the
disease. The first hypothesis consider psoriasis as a disorder with excessive growth and
reproduction of skin cells. Here, the problem is viewed as a fault of the epidermis and its
keratinocytes. In the second hypothesis, the disease is viewed as an immune-mediated
disorder. Here, the excessive reproduction of skin cells is secondary to the factors produced
by the immune system [1,2]
In current research, the inflammatory mechanisms are immune based and maintained
by T cells in the dermis. Antigen-presenting cells in the skin like Langerhans cells were
believed to migrate from the skin to the regional lymph nodes to interact with T cells.
Presentation of an unidentified antigen to the T cells along with various co-stimulatory
signals triggers an immune response to lead to the activation of Tcells and release of
cytokines. Co-stimulatory signals were initiated by the interaction of adhesion molecules
on the antigen-presenting cells like Lymphocyte Function-Associated antigen (LFA)-3 and
the intercellular adhesion of molecules with their respective receptors (CD2 and LFA-1) on
T cells. These T cells are released into the circulation. Reactivation of T cells in the dermis
and epidermis with local effects of cytokines like Tumor Necrosis Factor (TNF) to lead to
the cell mediated immune responses, inflammation and epidermal hyper proliferation in
persons with psoriasis. The immune based model of psoriasis was supported on the basis
of the observation that the immunosuppressant medications can clear plaques in psoriasis.
However, the complete role of the immune system needs refinement in understanding. It
was recently reported that an animal model of psoriasis can be triggered in mice without
T cells. This concept is a paradox to researchers because the reduction in the count of
T-cell causes psoriasis [3] but the count of CD4-T-cell decreases with the progression of
HIV in psoriasis. As an additive, HIV is characterized by a strong profile of Th2 cytokine
but psoriasis vulgaris is characterized by a strong secretion pattern of Th1. It was also
hypothesized that the presence of diminished CD4-T-cell can cause an over-activation in
CD8-T-cells to exacerbate the cause of psoriasis in patientswith HIV positive [4].
Materials and Methods PharmGkb
The Pharmacogenomics Knowledge Base, PharmGKB is an interactive tool for
researchers involved in the analysis of genetic variation to affect a drug response. PharmGKB contain information about the genotype, molecular, and clinical
knowledge to get integrated into a pathway and users can browse the
knowledge base by drugs, diseases, genes, variants and pathways [5].
PharmacomiR
Pharmaco-miR [6] consists of miRNAs, genes and drugs. The
output consists of miRNA based pharmacogenomic data set or a list
of unique associations between miRNAs, genes and drugs.
RegNetworks
RegNetwork [7] is a database that contains five types
(Transcription Factor-Transcription factor, Transcription FactorGene, Transcription Factor-microRNA, microRNA-Transcription
Factor) of transcriptional and posttranscriptional regulatory
relationships for human and mouse. RegNetwork integrates the
curated regulations from various databases and the potential
regulations were inferred on the basis of Transcription Factor
Binding Sites (TFBSs). Transcription Factor (TF) and microRNA
(miRNA) in gene regulations. Recently, more regulatory relationships
in databases and literatures are available and it’s valuable for
studying the system of gene regulation by integrating the prior
knowledge of the transcriptional regulations between TF and target
genes along with the posttranscriptional regulations between miRNA
and targets. The conservation of knowledge about the binding site of
transcription factor (TFBS) can also be implemented to couple the
potential regulation between regulators and their targets.
Cytoscape
Cytoscape [8] software is used for network construction,
visualization and analysis in Bioinformatics with an open
source platform for visualizing the interactions in molecular
networks and integrating them with the profiles of gene expression.
Additional features in Cytoscape are available as plugins for network
and molecular profiling. Plugins may be developed using the Cytoscape
Method (Sequential data mining & Systems biology)
- Obtain the list of genes associated with psoriasis from PharmGkb.
- Analyze the list of associated genes with an impact on drug
efficacy of Psoriasis in a larger population study.
- Obtain the list of miRNA associated with psoriasis from
Pharmaco-miR.
- Obtain the list of transcription factors associated with psoriasis
from RegNetworks.
- Construct and analyze the network in Cytoscape.
In case of indirect approach in regulatory analysis of genes
associated with psoriasis; the genes are paired with the associated
miRNAs and Transcription Factors (Table 1).
Further the genes, miRNAs and proteins in indirect approach
were subjected to network analysis and the further details about the
statistical methods were given below
Network construction & analysis (Cytohubba)
Genes and their regulators were subjected to the analysis by
various statistical methods (Degree, Edge Percolated Component,
Maximum Neighborhood Component, Density of Maximum
Neighborhood Component, and Maximal Clique Centrality with
six centralities Bottleneck, EcCentricity, Closeness, Radiality,
Betweenness and Stress) to identify their connectivity.
In case of MCC analysis in direct approach; VDR, MTHFR, GSTP1,
ABCC1, TYMS and SLC19A1 were ranked from 1 to 6 respectively.
Then; CYP1A2 and HLA-B were ranked 7. Finally; MAX and MYC were
ranked 9. VDR polymorphism is associated with Psoriasis (Figure 1).
In case of DMNC analysis in direct approach; MTHFR, AHR, ARNT,
CUX1, E2F1, EP300, hsa-miR-103, hsa-miR-107, hsa-miR-125a-3p
and hsa-miR-138 were ranked 1. MTHFR polymorphism is a possible
factor for reducing the clinical severity of psoriasis (Figure 2).
In case of MNC analysis in direct approach; MTHFR, AHR, ARNT,
CUX1, E2F1, EP300, hsa-miR-103, hsa-miR-107, hsa-miR-125a-3p
and hsa-miR-138 were ranked 1. MTHFR polymorphism is a possible
factor for reducing the clinical severity of psoriasis (Figure 3).
In case of degree analysis in direct approach; VDR, MTHFR,
GSTP1, ABCC1, TYMS and SLC19A1 were ranked from 1 to 6
respectively. Then; CYP1A2 and HLA-B were ranked 7. Finally; MAX
and MYC were ranked 9. VDR polymorphism is associated with
Psoriasis (Figure 4).
In case of EPC analysis in direct approach; MTHFR, VDR, SLC19A1,
GSTP1, MAX, MYC, ABCC1, hsa-miR-24, TYMS and USF1 were ranked
from 1 to 10 respectively. MTHFR polymorphism is a possible factor
for reducing the clinical severity of psoriasis (Figure 5).
In case of Bottleneck Analysis in direct approach; VDR, hsamiR-24 and MTHFR were ranked from 1 to 3. Then, MYC and GSTP1
were ranked 4. Further; ABCC1, TYMS and STAT1 were ranked from
6 to 8. Finally, YY1and HLA-B were ranked 9. VDR polymorphism is
associated with Psoriasis (Figure 6).
In case of Eccentricity Analysis in direct approach; JUN, MAX,
MYC, CREB1 and hsa-miR-24 were ranked
Then; MTHFR, SLC19A1, TYMS, VDR and GSTP1 were ranked 6.
JUN is associated with psoriasis because it’s involved in the epidermal
signaling (Figure 7).
In case of Closeness Analysis in direct approach; VDR, MTHFR,
MAX, hsa-miR-24 and JUN were ranked from 1 to 5 respectively. Then,
MYC and GSTP1 were ranked 6. ABCC1, TYMS and SLC19A1 were
ranked from 8 to 10 respectively. VDR polymorphism is associated
with Psoriasis (Figure 8).
In case of Radiality Analysis in direct approach; MAX, hsa-miR-24,
VDR, MTHFR, JUN, MYC, GSTP1, TYMS, SLC19A1 and ABCC1 were
ranked from 1 to 10 respectively (Figure 9).
In case of Betweenness Analysis in direct approach; VDR, MTHFR,
GSTP1, MAX, hsa-miR-24, ABCC1, TYMS, MYC, HLA-B and JUN were
ranked from 1 to 10 respectively. VDR polymorphism is associated
with Psoriasis (Figure 10).
In case of Stress Analysis in direct approach; MTHFR, VDR,
GSTP1, MAX, CYP1A2, ABCC1, hsa-miR- 24, HLA-B, MYC and SLC19A1
were ranked from 1 to 10. MTHFR polymorphism is a possible factor
for reducing the clinical severity of psoriasis (Figure 11).
In case of Clustering coefficient Analysis in direct approach;
MTHFR, AHR, ARNT, CUX1, E2F1, EP300 hsa-miR-103, hsa-miR-107,
hsa-miR-125a-3p and hsa-miR-138 were ranked from 1 to 10. MTHFR
polymorphism is a possible factor for reducing the clinical severity of
psoriasis (Figure 12) (Table 2).
Figure 1: MCC Analysis of genes and regulators in Direct Approach
(Cytohubba)
Figure 2: DMNC Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 3: MNC Analysis of genes and regulators in Direct Approach
(Cytohubba)
Figure 4: Degree Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 5: EPC Analysis of genes and regulators in Direct Approach
(Cytohubba)
Figure 6: Bottleneck Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 7: Eccentricity Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 8: Closeness Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 9: Radiality Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 10: Betweenness Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 11: Stress Analysis of genes and regulators in Direct
Approach (Cytohubba)
Figure 12: Clustering Coefficient Analysis of genes and regulators
in Direct Approach (Cytohubba)
Table 1: Associated genes, miRNAs and Transcription Factors of Psoriasis
Table 2: Network Analysis (Direct Approach)
Conclusion
Overall network analysis of pharmacogenomic based regulatory
network in psoriasis resulted in identifying 25 potential regulators of Psoriasis [20 Transcription Factors (VDR, MTHFR, GSTP1, ABCC1,
TYMS, SLC19A1, CYP1A2, HLA-B, MAX, MYC, AHR, ARNT, CUX1, E2F1
and EP300) and 5 miRNAs (hsa-miR-103, hsa-miR-107, hsa-miR125a-3p, hsa-miR-138 and hsa-miR-24)]. In a biological context,
these potential regulators of psoriasis have a maximum probability to
become a potential biomarker for Psoriasis and there was an identical
pattern in the comparative-network analysis to illustrate the fact that
there is a maximum probability for these potential regulators to be
considered to treat psoriasis in future
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