WEB INFORMATION SYSTEM

FOR THE STUDY OF ORAL HEALTH

José Melo

, Joel P. Arrais

, Pedro Lopes

, Nuno Rosa

, Maria José Correia

Marlene Barros

and José Luís Oliveira

Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics

Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal

Health Sciences Department, Portuguese Catholic University, 3504-505 Viseu, Portugal

Keywords: Oral health, Data integration, Proteins, Diseases, Web services.

Abstract: The human oral cavity is a complex ecosystem where multiple interactions occur and whose comprehension

is critical to understand several disease mechanisms. In order to comprehend the composition of the oral

cavity at a molecular level, it is needed to compile and integrate the biological information resulting from

specific techniques, especially from proteomic studies of saliva. The objective for this work was to compile

and curate a specific group of proteins related to the oral cavity, providing a tool to conduct further studies

over the salivary proteome. Despite previous efforts to identify the protein components of saliva in healthy

individuals and in several oral and systemic disorders, a resource compiling and reviewing all of these

proteins is still lacking. In this paper we present a platform that integrates in a single endpoint all available

information for proteins associated with the oral cavity. The proposed tool allows researchers in the

biomedical sciences to explore organisms, proteins and diseases, constituting a unique tool to analyse

meaningful interactions for oral health.

1 INTRODUCTION

Information available online is increasing in an

accelerated way. In order to be processed, this

increased amount of data requires the constant

development of computer applications that must

adapt to increasingly complex requirements,

particularly those related to the integration of

heterogeneous data and composition of distributed

services.

Oral health is an area of research where these

problems are particularly relevant. Being a very

specific area of study, researchers are faced with

many problems in obtaining clinically relevant

information concerning the oral cavity using an easy

and transparent way. This information must be

stored and managed using tools that should provide

the user with functionalities to retrieve, store, and

search this data.

Usually, databases for molecular biology are

centred either on a specific organism, such as SGD

for Saccharomyces (Cherry et al., 1998), or on

specific research topic, such as STRING for protein-

protein interaction (Mering et al., 2003). In addition,

databases like Entrez (Maglott et al., 2005) or the

Universal Protein Resource (UniProt) (Bairoch et

al., 2005) play a major role as hubs of biomolecular

information, storing data from multiple topics and

several organisms.

Despite this effort to create long lasting hubs of

biomedical data has been very successful, one

should not ignore the major contribute that many,

more specific, databases provide to the actual state

of science. They are of special interest for small

communities that share common research interests.

The UMD-DMD database (Humbertclaude et al.,

2007), specialized on Duchenne Muscular

Dystrophy, is one example.

Aware of the redundancy of features shared by

many of those databases, and of the lack of technical

expertise from the curators, several frameworks have

been proposed to ease the task of deploying new

databases. Examples include LOVD (Fokkema et al.,

2005), specialized on annotating locus specific

databases, GMODWeb (D O'Connor et al., 2008) for

organism specific databases, or Molgenis (Swertz et

al., 2010) that allows deploying more generic

Melo J., P. Arrais J., Lopes P., Rosa N., Correia M., Barros M. and Oliveira J..

WEB INFORMATION SYSTEM FOR THE STUDY OF ORAL HEALTH.

DOI: 10.5220/0003785300970105

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2012), pages 97-105

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

biomedical databases. Despite the validity of those

frameworks, there is none focused on simulating the

behaviour of a single human organ or set of adjacent

organs. This need to partition the data of the

“whole” human system is relevant because, on the

one hand, it reduces the time and resources involved

in searching, processing and curating information,

and on the other, it facilitates the use of algorithms

to retrieve biologically meaningful results.

The main objective of this project was the

development of a web information system that can

collect genotypic information about oral health and

that can be useful both for researchers and dentists.

A comprehensive integrated resource of the saliva

proteins, currently missing in the field of oral

biology, would enable researchers to understand the

basic constituents, diversity, and variability of the

salivary proteome, allowing the definition and

characterization of the human oral physiome. This

goal was achieved at two levels: (1) Oralome for the

application developer, which consists in a

proprietary database, and a set of tools to retrieve

biomolecular information from the major platforms

like NCBI (National Center for Biotechnology

Information) and UniProt; (2) for the end-user, a

web portal (OralCard) directed to researchers and

dentists, with a set of tools for searching and

filtering data from the database, and the possibility

to add new information.

Through OralCard web portal, users are able to

perform their queries and search among a list of

provided results. For each entity, users will be able

to consult and analyze a list of dependencies and

information retrieved from other major databases.

To demonstrate the usefulness of this project we also

present its application in the oral cavity research

domain. A platform designed to integrate protein

data related to this field will be implemented. This

will include salivary proteins obtained in proteomic

studies by different research groups, as well as

proteins potentially produced and excreted by

microorganisms assigned to the oral cavity. The

ultimate goal is to present a tool for the community

that contains accurate, manually curated and updated

data regarding the oral cavity, to enable interactions

studies, categorization and exploration.

We expect this work to be a valuable resource

for investigators aiming to clarify the oral biology,

identify molecular disease markers, develop

diagnostic tests and improve prognostic, as well as

providing information for the design of biological

pathways setting the ground for the discovery of

new therapeutic agents.

2 MOTIVATION

The oral cavity consists of a complex ecosystem

where a variety of proteins from numerous origins

are present. Being able to estimate the impact of the

interactions among those proteins is crucial to

understand the underlying disease mechanisms and

hopefully to develop new treatment methods.

Saliva is the watery and usually frothy substance

produced in the oral cavity of humans and most

other animals. It is a unique clear fluid, composed of

a complex mixture of electrolytes, proteins, and

represented by enzymes, immunoglobulins and other

antimicrobial factors, such as mucosal glycoproteins,

traces of albumin and some polypeptides and

oligopeptides, of importance for oral health (de

Almeida et al., 2008).

Whole saliva is secreted mainly from three pairs

of major salivary glands: the parotid, the

submandibular and the sublingual glands.

Approximately 90% of total salivary volume results

from the activity of these three pairs of glands, with

the bulk of the remainder from minor salivary glands

located at various oral mucosal sites (Greabu et al.,

2009). Whole saliva also contains proteins from

gingival crevicular fluid, oral mucosa and oral

microbiota. The various components of saliva from

these sources, together with the plasma proteins that

appear in saliva, define the physiological behaviour

of the oral cavity, the oral physiome (Oralome).

Saliva is an ideal translational research tool and

diagnostic medium and is being used in novel ways

to provide molecular biomarkers for a variety of oral

conditions, such as oral cancer (Nagler, 2009,

Shpitzer et al., 2009), dental caries (Rudney et al.,

2009) and periodontitis (Rudney et al., 2009,

Gonçalves et al., 2010) , as well as systemic

disorders such as breast cancer (Streckfus et al.,

2008), Sjögren's syndrome (Hu et al., 2007),

diabetes mellitus (Rao et al., 2009), cystic fibrosis

(Livnat et al., 2010) and diffuse systemic sclerosis

(Giusti et al., 2007). The ability to analyse saliva to

monitor health and disease is a highly desirable goal

for oral health promotion and research (Seymour et

al., 2010, Wong, 2006). The most important

advantage in collecting saliva is that it is obtained in

a non-invasive way and is of easy access.

Over the past thirty years, there have been many

efforts to determine and identify the main salivary

proteins and peptides. Nevertheless, the fluctuating

nature of saliva from different individuals, huge

dynamic protein concentration ranges and the

protein detection limits of most proteomic

techniques have made the saliva proteome elusive to

HEALTHINF 2012 - International Conference on Health Informatics

Figure 1: Pipeline for biomedical data integration.

define (Helmerhorst and Oppenheim, 2007). Even

when healthy individual’s saliva is considered, with

multi-dimensional separations and advanced

bioinformatics search software tools, proteins

identified in different saliva proteomics experiments

are often inconsistent with each other except for the

most abundant proteins. To overcome the poor

coverage, potential bias, and complementary nature

of each experimental measurement of the human

saliva proteome, it is necessary for biomedical

researchers to collect and evaluate all reliable

publicly-available saliva protein data sets generated

from different analytical and computational

platforms for healthy individuals as well as in

disease conditions.

A comprehensive integrated resource of the

saliva proteins would provide a high amount of

comparative power for interpreting proteomics

profile changes in patient’s saliva, and may

supplement or compensate for limitations and biases

associated with the set of controls for a given study.

It would also improve the ability for finding protein

biomarkers that are known to occur in healthy

human saliva, for instance where a protein is

differentially expressed in a patient sample related to

the quantities observed in the study control.

Oralome will have as a vital component an

integrated database, by compiling and manually

reviewing all of the existing experimental data

performed on healthy individual samples as well as in

several oral and systemic diseases. It will include a

collection of microbial proteins expected to be present

in saliva due to their presence in the genomes of the

oral microbiota (Chen et al., 2010, Nelson et al.,

2010) and a subset of microbial proteins determined

experimentally (Xie et al., 2008).

We think OralCard will be a fundamental

resource to clarify human oral biology and to

establish protein biomarkers for salivary diagnostic

processes based on the analysis of saliva samples

both in health and in disease, exploring the Oralome

functionalities. With OralCard, clinical samples

from patient’s saliva may be better analysed

contributing to improved diagnostic methods and to

the development of more effective therapies.

3 METHODS

Our solution is based upon two major components:

(1) the Oralome for information picking, gathering

and storage; and (2) the OralCard, a portal with a

rich set of services for the end-user (Figure 1).

Oralome, our backend pipeline, comprises four

functional phases: data curation, modelling,

information extraction and publication.

For the OralCard portal frontend, we have

selected two iterations: establishment of a

relationship between the previously created database

and the enterprise tier, and the design of views for

the entities using web application frameworks.

3.1 Compilation and Curation of the

Saliva Proteome

Regarding specific domains, such as the oral cavity,

manual data curation is the key iteration where

systems restrictions are imposed. These are essential

to correctly focus the system and to establish a

common platform for further iterations. In here three

main activities are performed: bibliography review,

target identification and requirements analysis.

Reviewing bibliography consists on analysing state-

of-the-art work and deciding where the final system

uniqueness will be. Next, the target identification

process involves exploring both relevant

WEB INFORMATION SYSTEM FOR THE STUDY OF ORAL HEALTH

bibliography and databases, and filtering which data

and/or features should be available in the final

system. Once data and features are selected, a

careful requirements analysis process must be

conducted. This envisages getting a first sense on the

technological requirements related to the desired

features and integrated data.

By the time this work was done, to our

knowledge, there was no database to join the

proteomes of major and minor salivary glands. For

this reason, the first step was to compile this

information from different sources. The proteome

data of major salivary glands (parotid,

submandibular/sublingual) were obtained from the

Salivary Proteome Knowledge Base and from Yates

Lab, The Scripps Research Institute. The proteome

of human minor salivary gland secretion was

obtained from Oppenheim Laboratory, Henry M.

Goldman School of Dental Medicine, Boston

University. The proteins identified in different works

were compared and repeated entries eliminated.

Biological information is constantly being

updated. Since first publication of saliva proteomes,

many of the original identified proteins, catalogued

as different entries in biological databases, have

been merged with other ones and some were deleted

due to misidentification. Therefore, all information

concerning the identified proteins was manually

curated and updated. The update of the IPI

(International Protein Index) entries was carried out

with the “IPI History Search” (www.ebi.ac.uk/IPI)

tool. All other updates have been made using the

UniProt database.

3.2 Oral Cavity Data Integration

The orthogonal nature and innate heterogeneity

associated with life sciences resources have always

hampered easier developments regarding the

integration of distributed data. Furthermore, research

in this field has entered a cycle where computational

solutions lag one step behind technological

requirements in biology. This brought about a

growing disparity regarding bioinformatics software,

where few well-known and widely used resources,

such as UniProt or NCBI, co-exist with hundreds of

smaller independent tools.

Although the oral cavity presents a stricter scope,

it involves assorted life sciences fields, from

microorganisms to proteins or diseases. Establishing

new connections amongst these diverse entities

creates a high degree of complexity, thus requiring

the development of new ad-hoc data integration

software solutions. On the one hand, large

warehouses that might contain this domain-specific

information also contain plenty other resources.

Consequently, researchers are overwhelmed by huge

datasets, making their data of interest impossible to

find. For instance, discovering oral cavity

information amongst UniProt is a nightmarish task.

From a technological perspective, there are

miscellaneous strategies for solving data integration

problems. Though, they all rely on three elementary

concepts: warehousing, middleware and link

integration. Warehouse approaches intend to support

an efficient decision making process, requiring the

aggregation of all desired data in a huge central

dataset (Santos and Bernardino, 2008). Middleware-

based solutions rely on the development of specific

wrappers to mediate connections between users’

requests and original data servers (Barbosa et al.,

2002). At last, link-based integration attempts to

connect heterogeneous data types by creating graphs

or networks based on pointers between distinct data

units (Lopes et al., 2011). These approaches can be

distinguished by the way they treat aggregated data.

Warehouses replicate entire resources, creating a

truly integrated environment whereas middleware or

link-based solutions only provide a streamlined

access to data, resulting in virtual integration.

Although many examples can be found for each

integration strategy, the most common solutions

involve developing a hybrid architecture, where

some data is replicated whilst other are simply

connected through links and identifiers. This

approach was integrated into the Oralome project.

Once target resources and data were identified,

the modelling iteration started. This task consisted

on the design of a common information model to

support oral cavity data from distinct resources.

Before the actual data integration, a system

skeleton needed to be deployed. As mentioned

above in this article, there are several frameworks

designed for rapid prototyping of data portals for life

science projects, such as LOVD or GMODWeb. For

this specific task, we have chosen the Molgenis

framework for its agility on creating a database and

application, complete with data exploration web

workspace, REST and SOAP web services, and R

interface out of the box. For the data integration

process, Molgenis provides easy and direct data

input, whether through the web interface, through

any of the available services, or through a provided

database API. Therefore, custom data wrappers,

collecting data from miscellaneous resources, can be

easily implemented. Oralome required the

deployment of general-purpose wrappers, combining

external data in the newly deployed Molgenis

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instance. These wrappers allow for systematic

information extraction from resources such as

UniProt, NCBI or STRING, amongst others. These

resources provide several ways for information

retrieval, such as REST interfaces or APIs for Java

development.

Executing this streamlined data integration

workflow curated oral cavity data is collected and

re-organized in a publicly available web framework.

3.3 Oralome Development

Oralome will consist in a set of tools and a database

which will provide access to information related to

several entities, such as microorganisms, proteins,

diseases and pathways, integrating crucial data

regarding the oral cavity.

The upper entity is a microorganism which has

several proteins associated to it. A protein for itself

has others ids linked to it, such as OMIM (Online

Mendelian Inheritance in Man), KEGG (Kyoto

Encyclopedia of Genes and Genomes), PDB (Protein

Data Bank) and GO (Gene Ontology) terms. The

main subject for this tool consists in two groups of

proteins: (1) a subset of microbial proteins

determined experimentally, and (2) microbial

proteins expected to be present in saliva. Regarding

the first group, beside the information retrieved from

UniProt, Oralome will integrate information related

to the environment in which a protein was identified

(health or disease, regulation, age group, and the

particular source where it resides, for instance,

mucosa or tongue).

For the Oralome tool development we chose the

Molgenis framework for generating all the necessary

tools and features needed to start fulfilling our

database and to rapidly view this data in an easy way.

Molgenis consists in a framework written in

Java, which accepts two XML files as input: a

database and a user interface descriptor files. Using

the first file, users can specify how the database will

be structured, its entities and relations; the second

file specifies the layout for the web interface.

Molgenis generates a Java model and a database API

which are used to deploy the related SQL tables,

web services and web interface into a web server

(Figure 2).

In order to start using this framework we needed

to have preinstalled a MySQL server to store its

database, an Apache Tomcat web server for

deploying web services and a simple user interface,

and the Java Development Kit for generation of SQL

and HTML code.

Along with the Oralome application, we

Figure 2: Molgenis architecture.

developed tools and wrappers to obtain specific

information on each of the elements that build up the

system (proteins, diseases, pathways, and others).

For this, we carried out a first survey of sources

where this information would be available, and built

a runnable script to update the Oralome database.

This data fetch is made easier using Molgenis. It

bundles a Database API that has the advantage of

hiding complex SQL commands.

To import and filter the information needed in

our database, we used Java as the programming

language because it is highly compatible with the

most APIs provided by the major external services

resources (UniProt, KEGG, and NCBI Entrez

Utilities).

3.4 OralCard Development

In order to take advantage of the Oralome

functionalities, we proposed a tool that would enable

searching over the oral cavity database and showing

different and customized views for each entity. This

led to the OralCard web application, a fundamental

resource for salivary diagnostic processes of protein

biomarker studies of health and disease based on the

analysis of saliva samples.

For information retrieval from Oralome, we

decided to use Hibernate, an object-relational

mapping tool for Java. This architecture is illustrated

in the following diagram (Figure 3).

WEB INFORMATION SYSTEM FOR THE STUDY OF ORAL HEALTH

101

JEE SERVER

Enterprise

Information

System Server

Enterprise Information

System Tier

Business Tier

Client Tier

HTML CSS

AJAXJavaScript

Figure 3: OralCard web application architecture.

OralCard frontend was developed using Stripes,

a web framework that makes easier the development

of Java web applications, by introducing some

useful tools.

Stripes enabled us to take full control over

URLs, making easy the task of accessing an entity

by only knowing its id. For instance, researchers can

have direct access to the protein P22894 (Neutrophil

collagenase), introducing the address http://

bioinformatics.ua.pt/oralcard/proteins/view/P22894.

By using the Stripes framework, we were able to

improve the user interface, introducing frameworks

such as jQuery and jQueryUI. These tools contributed

to present information in a more user friendly way,

taking control over tables and AJAX interactions.

Finally, the OralCard web application takes

advantage of the CSS benefits. It is designed to

separate the document content written in JSP from

the document presentation, including elements such

as the page layout, used colours and fonts.

4 RESULTS

4.1 Oralome Functionalities

The web interface feature provided by Oralome

reflects data contained in the database. It is available

online at http://bioinformatics.ua.pt/oralome.

Both oral cavity researchers and developers can

use this tool. Oralome provides two distinct entry

points, each fitting a particular user type needs.

This workspace enables searching, filtering,

browsing and viewing all collected data in a typical

web interface. Moreover, researchers can combine

data and download it for personal usage.

Along with the web interface for researchers,

Oralome encompasses a set of web services for

programmatic data access. Oralome’s API is

available for R, HTTP, REST and SOAP. These

interfaces enable any developer to build custom

solutions using any development framework,

consuming Oralome’s curated data related to the oral

cavity.

In addition to the described oral cavity data

exploration scenarios, Oralome allows the

deployment of an unlimited number of customized

applications. Using any the remote API interfaces,

developers can use Oralome data to enrich already

existing applications or to develop new ones.

This key Oralome feature envisages the usage of

collected curated oral cavity information in

education scenarios or by medical dentists. Oralome

web framework’s openness will enable the creation

of an entire application ecosystem built around

expertly curated information, aiming the delivery of

improved dental health care.

4.2 OralCard Interface

OralCard web application is available online at

http://bioinformatics.ua.pt/oralcard.

The researcher is first presented with a home

page where he can insert search items. The protein

customized view offers several tools. The Data tab

presents the PANTHER classification system web

application; the Structure tab provides a list of PDB

structures related to the protein (Figure 4), where the

user can download and visualize the related models;

the Interactions tab features a STRING tool to

visualize specific network diagrams containing

related genes; the References tab contains all the

publication related to the protein, and links to

PubMed; it also features views for the related

diseases, gene ontologies and for the sources of the

oral cavity in which the protein was studied.

5 EVALUATION

As an application example, we used the Oralome

tool to evaluate how saliva proteins contribute to or

reflect impaired healing of oral cavity tissues in

diabetic patients (Lamster et al., 2008). Then we

identified all the salivary proteins whose amount is

changed in patients with diabetes mellitus.

Subsequently, in order to understand which salivary

proteins molecular functions were altered in

diabetes, we found that binding and catalytic activity

functions have changed more evidently. That led to

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Figure 4: Structure view for the Neutrophil collagenase protein.

the search for pathways in which the binding

molecular function could be acting, in other words,

which signalling pathways could be involved. Our

results indicate that, the blood coagulation pathway

(directly related to healing) was one in which the

proteins altered in diabetes mellitus patient’s saliva

were involved. Then traced molecular networks

reflecting the interactions between proteins involved

in this pathway. We found that two key molecules in

this network are two blood coagulation cascade

inhibitors.

This methodology has allowed us to identify

molecular reasons for the impaired healing of oral

tissues in diabetic patients, as well as some key

molecules in this process, which may be good

molecular markers useful for diagnostic and even

good targets for therapeutic agents.

6 CONCLUSIONS

In this paper we described a pipeline for creating

web-based databases specialized in a set of adjacent

human organs. We believe that the proposed work is

of major importance to research projects that need

an easy and agile solution to share the result of

studies.

The information of the oral cavity is dispersed

through different databases focused on more general

systems. In addition to being dispersed, data is not

always standardized, which makes their integration

and comprehensive study a colossal task. This work

resulted in the development of an integrated

database which comprises a comprehensive

catalogue and characterization of the human oral

proteome. It aims at becoming a fundamental

resource to clarify human oral biology and in

establishment of protein biomarker for salivary

diagnostic processes.

We presented Oralome as a set of tools

combining a database, web services and user

interface, useful for joining specific results from

several major databases, such as UniProt or NCBI.

This framework generated a database, web services,

and a web application where users can access the

downloaded data.

Finally we proposed OralCard as an example of a

web application that takes advantages of the

Oralome functionalities. It works as a search engine

where the researcher can input any looked-for search

item, concerning to the field of oral biology. It uses

the Oralome database and some of the new web

standards for presenting specific information

regarding a protein (JavaScript, AJAX and CSS).

Some of the advantages of the Oralome approach

are the time and resources reduction involved in

searching, processing and curating information, as

facilitating the use of algorithms to retrieve

biologically meaningful results. As a particular

example of Oralome use, OralCard offer the

advantage in gathering several major external tools

in one single web application, becoming easier and

more confortable to researchers accessing important

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103

information regarding one specific protein. Using

the oral cavity as a particular case study we have

shown how it can be used to obtain a fully functional

tool that enables both interaction’s categorization

and exploration.

We believe this project will be a valuable

resource for investigators to clarify the oral cavity

biology, identify molecular disease markers, to

develop diagnostics tests and improve prognostic, as

well as providing invaluable help in discovering new

therapeutic agents.

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