Available via license: CC BY 4.0
Content may be subject to copyright.
TITLE
Towards an integrated interactive database for the search of stratification biomarkes in Alkaptonuria
AUTHORS
Cicaloni V¹, Rossi A², Zazzeri M², Zugarini A², Santucci A¹, Bernini A¹, Spiga O¹.
¹ Dipartimento di Biotecnologie, Chimica e Farmacia. Università degli Studi di Siena
² Dipartimento di Ingegneria dell’Informazione e Scienze matematiche. Università degli Studi di
Siena
ABSTRACT
MOTIVATION Alkaptonuria (AKU) is a rare and genetic disease which causes discoloration of
bone (a process called ‘ochronosis’) and induces early-onset osteoarthritis. AKU data have not been
organized yet and the disease has no approved biomarkers. The ability to collect, integrate and
analyze relevant data streams is the core for developing a “Precision Medicine Ecosystem” AKU-
dedicated in which biological resources are shared between researchers, clinicians and patients.
Computational modeling can be a useful guide to generate an exhaustive and dynamic picture of the
individual and to identify the molecular interactions between biomarkers on which progressive
diseases are based.
METHODS It has been built a new integrated interactive database thanks to MySQL, the most
frequently chosen for use in web applications. In addition, data are statistically analyzed by R
software (www.r-project.org) based on Pearson’s correlation coefficient and P value. For a
biological interpretation of statistic results, Stitch (Kuhn M. et al, 2007) and KEGG (Kanehisa M. et
al, 2000) are used.
RESULTS For Precision Medicine (PM) application to AKU, the collection of as much data as
possible Alkaptonuria affected patients has been needed in order to organize them in an interactive
and integrated database and to find a data-processing system for AKU biomarkers discovery. The
database is an effective tool for registered researchers, clinicians and patients who could both easily
access all the current information, as well as being able to insert new data, refreshing or replacing
previous entries. Data are divided into different sections: genetic, proteic, biochemical,
histopathologic and clinical. As far as data analysis is concerned, it has been developed an
algorithm to build up a refreshable correlation matrix based on Pearson’s correlation coefficient
and P value which allows the monitoring of renewable correlations between the most recent data
PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2174v1 | CC BY 4.0 Open Access | rec: 28 Jun 2016, publ: 28 Jun 2016
inserted. Together with the mathematical and statistical interpretation, a biological explanation of
the results is needed in order to investigate on AKU biomarkers. This dynamic tool could be useful
for biomarkers investigation also in other osteoarticular diseases and it is a good starting point for
the creation of data management and analysis model appropriate for PM. Through the use of AKU-
dedicated database and this innovative analytic approach, it has been possible to become aware of
the failure of biomarkers clinically used and to improve the detenction of more exploitable
prognostic biomarkers for a more reliable AKU patients clinical monitoring.
PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2174v1 | CC BY 4.0 Open Access | rec: 28 Jun 2016, publ: 28 Jun 2016