Computational Analysis of Regulatory Network in Psoriasis by Top-Down Approach: An Initiation towards Identifying a Novel Biomarker to Diagnose and Treat Psoriasis in Future

In the era of post genomics, computational analysis of gene regulation in Psoriasis by the Top-down approach of computational biology to understand the pathology of disease and identifying a novel biomarker is a challenging task to execute. The challenge was approached on the context of Top-down approach [Annotation of Psoriasis associated genes from PubMed, DisGeNET and OMIM with text mining of regulators (microRNAs and Transcription Factors)] to construct and analyze the network by the approach of systems biology for understanding the disease pathology and predicting a novel and potential regulators to enhance the discovery of identifying a novel biomarker for Psoriasis in future.


Introduction
Psoriasis is a disorder mediated by immune system by making certain faulty signals in the human body. It's still a belief that psoriasis can be developed under the specified condition 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 on every 28-30 days. In case of psoriasis, the skin cells mature in 3-6 days. Instead of being in shed, the cells in skin get pile 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 considers 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 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 dermis. Antigen-presenting cells in skin like Langerhans cells were believed to migrate from 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 T-cells 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 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 with 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 patients with HIV positive [4].

PubMed
PubMed is an online search engine with open access facility to refer MEDLINE for identifying references and abstracts on topics in biomedical and life sciences. The United States National Library of Medicine (NLM) at the National Institutes of Health maintains the database as part of the Entrez system to retrieve information. Most of the records in PubMed contain links to the complete article, in PubMed Central (Roberts 2001). Information regarding the indexed journals in MEDLINE can be found in the Catalog of NLM.

DisGeNET
DisGeNET is a platform of pattern discovery, designed for addressing the queries regarding the genetic imprint of human diseases. DisGeNET is one of the largest repositories of gene-disease associations (GDAs) in humans (Piñero et al. 2015). It offers a set of tools in bioinformatics to facilitate the data analysis by different users. It is maintained by the Integrative Biomedical Informatics (IBI) Group of the (GRIB)-IMIM/UPF at the Barcelona Biomedical Research Park (PRBB), Barcelona in Catalonia.

TargetScan
TargetScan [5] is a web server to predict the biological targets of miRNAs by searching for the presence of target sites that matches with the seed region of each miRNA. The target predictions of each miRNA are updated regularly. miRTarBase miRTarBase [6] is a curated database of miRNA based target interactions. At present, miRTarBase has accumulated more than fifty thousand interactions of miRNA with target (MTIs); the interactions were manually collected by surveying the literature after the processing of data mining of the text to filter research articles to functional studies of miRNAs in a systematic method. In general, the MTIs were also experimentally validated by a reporter assay, western blot, microarray and experiments on next-generation sequencing. The miRTarBase provides the most updated collection by comparing with the previously developed databases. RegNetwork [7] is a data base that contains five types (Transcription Factor-Transcription factor, Transcription Factor-Gene, Transcription Factor-microRNA, microRNA-Transcription Factor) of transcriptional and post-transcriptional 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 post-transcriptional 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 software (Shannon et al. 2003) 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.

PANTHER
PANTHER (Protein Analysis through Evolutionary Relationships) is a classification system to classify proteins (expressed genes) to facilitate analysis in a high-throughput method [8]. In PANTHER, proteins were classified according to their family (evolutionarily related proteins); molecular function (interaction of proteins at a biochemical level); biological process (larger network of proteins that interact to accomplish a process at the level of the cellular level, e.g. mitosis; pathway (explicit specification of the relationships between the interacting molecules).

Method (Top-down approach)
Obtain the list of genes associated with psoriasis from PubMed, DisGeNET and OMIM.
Obtain the list of miRNA associated with psoriasis related genes from miRTarbase and TargetScan.
Obtain the list of transcription factors associated with psoriasis related genes from RegNetworks.
Construct and analyze the network in Cytoscape.

Results and Discussions
In case of gene identification, the genes associated with psoriasis were retrieved from PubMed, DisGeNET and OMIM. Psoriasis associated gene search in PubMed resulted in 660 genes. Similarly, DisGeNET and OMIM resulted in 388 and 261 genes. Data mining was performed in the gene set by removing duplicates and search for genes to have a seed pair for miRNA in miRTarBase (Experimental miRNAs) and Target Scan (Predicted miRNAs). Data mining resulted in 104 unique genes from the 3 sets of data of which only 58 genes contain a seed pairing site for miRNA. In case of miRNA search for associated genes, it was observed that there is a similarity between the miRNAs in Target Scan (Predicted miRNAs) with the miRNAs in miRTarBase (Experimental miRNAs). Since there is a similarity in the miRNA search of associated genes with the predicted and validated miRNAs, genes are paired with miRNAs in a way that geneexperimental miRNA>gene-predicted miRNA. In case of top down approaches in regulatory analysis of genes are associated with psoriasis; the genes are paired with the associated miRNAs and Transcription Factors and the results were given in Table 1. hsa-miR-181b-5p; hsa-miR-182-5p; hsa-miR-362-5p; hsa-miR-500a-5p

Pathway analysis (Annotation)
The obtained genes from PubMed/DisGeNET/OMIM were subjected to pathway analysis in PANTHER by the principle of the Bonferroni correction for multiple testing and the result is illustrated as Pie Chart (Figure 1). In case of Pathway Analysis, the genes associated with Psoriasis follows the hierarchy of Inflammation mediated by Chemokine and Cytokine signaling pathway, Gonadotropin-releasing hormone receptor pathway, ECF receptor signaling pathway and CCKR signaling map. In case of Annotation analysis, it is evident that these regulators play a vital role in the pathways (Apoptosis Signaling pathway, CCKR Signaling, Gonadotropin releasing hormone receptor pathway, Interleukin Signaling pathway, JAK/STAT signaling pathway, Oxidative Stress response and PDGF signaling pathway) associated with Psoriasis and it was also clear from studies of text mining in PubMed; has-miR-103 and has-miR-107 is associated with the 3' UTR region of CDK5R1, hsa-miR-125a-3p is associated with the clinical implication of inflammatory skin and hsa-miR-138 is responsible for inhibiting the expression of RUNX3 in Psoriasis but till date there is no experimental evidence in PubMed to illustrate the role of hsa-miR-24 in Psoriasis.