WEB INFORMATION SYSTEM
FOR THE STUDY OF ORAL HEALTH
José Melo
1
, Joel P. Arrais
1
, Pedro Lopes
1
, Nuno Rosa
2
, Maria José Correia
2
,
Marlene Barros
2
and José Luís Oliveira
1
1
Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics
Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal
2
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
97
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
Copyright
c
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
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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
99
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
WEB INFORMATION SYSTEM FOR THE STUDY OF ORAL HEALTH
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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