Department of Biological sciences and Engineering, MANIT, Bhopal (M.P) INDIA

Corresponding author Email: kmpbiomanit@gmail.com

Article Publishing History

Introduction

Vitiligo is a progressive depigmentation disorder affecting nearly 1% of the world population (Guerra et al., 2010). Vitiligo disorder is characterized by progressive skin depigmentation phenomena, which is induced and maintained by the loss of melanin – producing cells at the cutaneous level. Vitiligo is known to be a skin disorder with complex etiology. Its pathogenesis and the inner causes are still unclear but, in recent years, scientific research identified various pathways and processes for its onset and progression. There are several proposed hypotheses for this depigmenting disorder which includes autoimmunity auto-cytotoxic/ metabolic mechanisms, (Reimann et al., 2012), and impaired melanocyte migration and/or proliferation (Westerhof and d’Ischia, 2007, Speeckaert et al., 2015).

Studies also show the important role played by genetic susceptibility in the origin of vitiligo. These mechanisms are neither mutually exclusive, noradequate enough to explain the disease etiology on their own. An integrated viewpoint can be formed by incorporating all the different causal factors that could contribute to some extent to melanocyte destruction into a ‘convergence theory’ (Le Poole et al., 1993), (Schallreuter et al., 2008, Jin et al., 2016, Spritz, 2011). Vitiligo involves a complex framework of interactions between various proteins, pathways and processes (Laddha et al., 2013). An attempt has been made here to bring together these disease proteins on a common platform to display their related properties. Further protein-protein interaction (PPI) data for each of these disease proteins were extracted to generate a comprehensive vitiligo interectome map (Malhotra et al., 2017).

Information regarding the interectome analysis is also made readily available on this portal.A better understanding of the disease could provide new targets for prevention or treatment of vitiligo (Passeron and Ortonne, 2012). An ideal drug target should comprehend following properties: favourable assayability for high throughput screening, ability to modify a disease, less effect on alteration of physiological conditions or other diseases, differential expression across the body, the availability of a biomarker, etc. Experimentally evaluating all proteins for their targetability is an overwhelming task (Gashaw et al., 2011, Dey-Rao and Sinha, 2017).

Hence, it’s not experimentally possible to analyse and prioritize all the drug targets in a laboratory. This necessitates employinga computational technique for evaluation of proteins on the standards of potent drug targets (Sakharkar and Sakharkar, 2007, Costa et al., 2010, Kandoi et al., 2015). Thus targetability analysis of these proteins will aid in shortlisting prospective therapeutic drug targetsfrom the entire proteome with high
specificity.

There is no exclusive database for vitiligo disease proteins available till date. All related information is dispersed in various published literature and online databases and is, therefore, extremely difficult to access. For assisting the vitiligo researchers, all scattered data has been compiled together and adedicated and comprehensive vitiligo proteins repository – vitiDB – is developed.

Methodology

Data Collection and Compilation

The organization of data was done systematically as primary and derived data [Figure-1]. The primary data consists of the vitiligo disease proteins related information. The secondary data includes disease proteins interaction data and its analysis features. Alongside the targetability related parameters were also computed and added to the resource.

Figure 1: Architecture of vitiDB

Disease Protein Information

In order to build a comprehensive resource for vitiligo proteins an extensive search was carried out across various online databases like NCBI (2017), Uniprot (2015), AnyGene [AnyGenes® http://www.anygenes.com/index.php], Harmonizome (Rouillard et al., 2016), vitivar [http://vitivar.igib.res.in/genes], GeneCards (Rebhan et al.,
1998), DOlite (Du et al., 2009), DisGeNET (Pinero et al., 2015), OMIM (Hamosh et al., 2005), Disease Ontology (Schriml et al., 2012) and Human Phenotype Ontology (Kohler et al., 2014). Along with these databases, information was also manually curated from published literature, microarray experiments and SNP analysis. Gene susceptibility studies (Passeron and Ortonne, 2005), (Picardo et al., 2015) were also considered while collating the disease genes for vitiligo.

Vitiligo Interectome Data

PPI data for the disease proteins were extracted from eight different databases. These includes BioGrid (Chatr-Aryamontri et al., 2015), MINT (Ceol et al., 2010), STRING (Szklarczyk et al., 2017), Reactome (Croft et al., 2014), DIP (Xenarios et al., 2002), IntAct (Kerrien et al., 2012), Spike (Paz et al., 2011) and GeneMania (Zuberi et al., 2013). All this data was merged and the unique interactions were employed for construction of unidirectional vitiligo interaction map (Malhotra et al., 2017) in Cytoscape (Kohl et al., 2011).

Cytoscape is a highly versatile program for the analysis, operation and visualization of large networks. Later, the topological properties (Degree, Betweeness centrality, Topological coefficient, Clustering coefficient, Closeness centrality, Radiality, Stress, Neighbourhood connectivity and Average shortest path length) were calculated for all the proteins in the map. These calculation were done in Network Analyzer (Assenov et al., 2008), an inbuilt plugin with cytoscape that is used for comprehensive analysis of network topologies.

Targetability statistics for disease proteins

The key disease proteins were analysed for their ability to be a good therapeutic target (Rask-Andersen et al., 2011). This was done under five heads namely Druggability (Druggable Genome (Hopkins and Groom, 2002), can SAR druggability (Bulusu et al., 2014), DrugBank (Law et al., 2014), DGI druggability (Griffith et al., 2013), PDTD (Gao et al., 2008) and Domain DrugEBIlity (EMBL) [https://www.ebi.ac.uk/chembl/drugebility/]) Assayability (Sigma Aldrich database, Brenda (Schomburg et al., 2017), Ki database [https://kidbdev.med.unc.edu/databases/kidb.php], PDB (Berman et al., 2000) or Binding DB (Gilson et al., 2016)), Essentiality (OGE (Chen
et al., 2012), MGD (Eppig et al., 2007), Human Phenotype project (Kohler et al., 2014), Part et al. (Park et al., 2008) or Georgi et al. (Georgi et al., 2013)), Vulnerability (Holme et al., 2002) and Secretability (Narayanan, 2015).

Database architecture and web interface development

All the collated data were entered in excel files which were converted into csv files. These files were later imported into MySQL database. MySQL, an object-relational open source database management system, was employed to manage the data at the back-end. There were 11 tables in the database. The database was launched using Apache HTTP server online on Linux Platform and on local machine via WAMP server 2.0. For the backend support i.e., database interfacing scripts, we have used PHP programming language. It queries backend database to retrieve information. The front end was designed using HTML 5.

Results and Discussion

Implementation

The database is sectioned into three categories: Vitiligo Protein Repository, Vitiligo Interectome map and Targetability Analysis. All the information can be browsed under the sections mentioned below (Figure-2).

Figure 2: Schematic representation of vitiDB web interface

Vitiligo Protein Repository

A total of 333 disease proteins were identified. Their related information is made available from this section under the following features:

Basic Information- This includes Gene name, Protein name, Uniprot id, status, Length of the protein, gene name synonyms and also the source from which this protein is obtained as vitiligo disease protein. This source can be related to protein or pathway in which the protein is involved like database, microarray experimentation, autoimmunity, susceptibility studies, apoptosis, melanogenesis (Singh et al., 2013) or oxidative
stress.

Kinetic properties – Enzyme related information is displayed here under the headings EC number, Catalytic activity, KineticsPathway, Enzyme regulation, Active site, Binding site, Site or Function.

Ontology Data – Gene Ontology details and identifiers are made available.

PubmedDetails – Pubmed ids for the corresponding protein is mentioned.

Cross-reference to other online databases – The vitiligo protein entries are linked to other biological databases like PDB, SMR, Protein Model Portal, PDBsum, DisProt, STRING, MINT, IntAct, DIP, BioGrid, Guide to PHARMACOLOGY, DrugBank, ChEMBL, BindingDB, KEGG, GeneID, Ensembl, BRENDA, BioCyc, Reactome, InterPro, PRINTS, PROSITE, Pfam, DisGeNET, GeneCards, PharmGKB or GeneWiki.

Vitiligo Interaction Map

The disease proteins interaction network consists of 4845 nodes and 107416 unique interactions. The interacting partners along with the topological properties are available for each of 4845 proteins. Further, based on the network topology analysis, the proteins were classified as backbone and core proteins and are labeled accordingly in the database under the section Classification of proteins.

Targetability Analysis of Disease Proteins

A comprehensive description of all the features pertaining to the drug target identification was fetched from various sources and displayed under this head. The proteins that are in alignment with most of the parameters are entitledto be the prospective target for the vitiligo disorder. The later updates will aim at making the targetability analysis available for all the human proteins.

Concluding Remarks

The increasing amount of data on protein interactions, drug target features,and the knowledge of drug-like properties can act as a very lucrative starting point for vitiligo drug hunt. Vitiligo sufferers still have to face ostracism and discrimination around the world. This is a modest attempt to aid in its treatment. The enriched data and search utilities available at vitiDB might assist other online databases in providing all-inclusive information about the drugs, targets and target identification process aimed at further research and drug discovery efforts.

Availability

vitiDBis a free database that can be accessed at http://vitidb.com

Acknowledgments

The authors thankfully acknowledge the help, support and guidance provided by Dr. Ajay Pandey, Department of Mechanical Engineering, MANIT, Bhopal.

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