Marina Ioannou

Detection of DNA mutations using a capacitive micro-membrane array

The detection of DNA hybridization using capacitive readout and a biosensor array of ultrathin Si membranes is presented. The biosensor exploits the ability of the ultrathin membranes to deflect upon surface stress variations caused by biological interactions. Probe DNA molecules are immobilized on the membrane surface and the surface stress variations during hybridization with their complementary strands force the membrane to deflect and effectively change the capacitance between the flexible membrane and the fixed substrate. The sensor array comprises 256 such sensing sites thus allowing the concurrent sensing of multiple DNA mutations. The biosensor and its performance for the detection of complementary DNA strands are demonstrated using beta-thalassemia oligonucleotides. The experimental results show that the presented sensors are able to detect DNA hybridization and to discriminate single nucleotide mismatches.

Population-ethnic group specific genome variation allele frequency data: a querying and visualization journey.

National/ethnic mutation databases aim to document the genetic heterogeneity in various populations and ethnic groups worldwide. We have previously reported the development and upgrade of FINDbase (www.findbase.org), a database recording causative mutations and pharmacogenomic marker allele frequencies in various populations around the globe. Although this database has recently been upgraded, we continuously try to enhance its functionality by providing more advanced visualization tools that would further assist effective data querying and comparisons. We are currently experimenting in various visualization techniques on the existing FINDbase causative mutation data collection aiming to provide a dynamic research tool for the worldwide scientific community. We have developed an interactive web-based application for population-based mutation data retrieval. It supports sophisticated data exploration allowing users to apply advanced filtering criteria upon a set of multiple views of the underlying data collection and enables browsing the relationships between individual datasets in a novel and meaningful way.

A capacitive biosensor based on ultrathin Si membranes

A biosensor which takes advantage of surface stress changes during biological interactions and is able to translate them into a capacitive signal is presented. The sensor consists of an ultrathin silicon membrane on which receptor molecules are immobilized. During biomolecular interactions, the surface stress changes and the membrane deflects resulting in a change in device capacitance. The biosensor is part of a 16 times 16 array thus allowing for the making of larger assays with the concurrent sensing of multiple biological targets. First results using the biotin-steptavidin system are presented.

Multi-platform Data Integration in Microarray Analysis

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray plat forms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among platforms scale implemented by Shen et at. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.

A new gene expression signature related to breast cancer estrogen receptor status

The aim of this study is to identify a gene expression signature which is characteristic of ER status in breast cancer patients. To our knowledge, this is the first microarray study in Greece involving clinical samples. We identified 97 genes that are characteristic for ER status and can well distinguish the ER+ from the ER- samples. We shrank our list to a 11-gene list correlating to the same patient ER status. We found a significant overlap of these genes with published ER status characteristic signatures like the ones of West et.al. and of Vanpsilat Veer et. al.. This fact is very important given the minimal overlap of such genes reported by others . In order to obtain a molecular insight into how the expression of estrogen receptor activates cancer cells, we found associations with biological pathways. Interestingly, the vast majority of these genes are highly related to breast cancer.

Towards an era of epidemiological databases for autoimmune diseases

Nowadays, autoimmune diseases are among the leading causes of death for a remarkable number of patients all around the world. Recent studies have witnessed that the epidemiological indices for a specific disease can vary according to ethnic and geographical parameters. As a result, the genetic epidemiology of autoimmune diseases is a major matter of study for the worldwide scientific community. We have previously reported the development of dAUTObase (www.dAUTObase.org), a database recording solely epidemiological data of autoimmune diseases in various populations around the globe. Here, we present an important upgrade of the dAUTObase system focused on the development of new data visualization tools oriented to further assist the effective data querying and the mining process.

Genome-based Population Clustering: Nuggets of Truth Buried in a Pile of Numbers?

National/Ethnic population Mutation databases (NEMDBs) are online mutation depositories recording extensive information about the described genetic heterogeneity in populations and ethnic groups worldwide. FINDbase ( http://www.findbase.org ) is a database containing causative mutations and pharmacogenomic markers allele frequencies in various populations and ethnic groups. In this paper, we experiment with designing and applying new automated data mining techniques on the original FINDbase causative mutations data collection in an attempt to identify genomic relationships between populations. Furthermore, we have developed an interactive web-based visualization tool that enables users to correlate the information, determine the relationships and gain insight into the underlying data collection in a novel and meaningful way.

Multi-platform data integration in microarray analysis

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most challenging tasks is to develop robust statistical models to integrate their findings. We compare some recent methodologies on the field, with respect to ER status, and focus on a unified among platforms scale suggested by Parmigiani et al.(2002) and Shen et al.(2004), which is based on a Bayesian mixture model. Under this unified scale, we study the intensity similarities between four breast cancer data sets derived from various platforms. We evaluate our results with an independent data set in terms of ER sample clustering given the derived gene ER signatures of the integrated data. We found that intensity and fold-change variability similarities between different platform measurements can greatly assist the statistical analysis of independent microarray data sets.

Sensitivity investigations of surface stress capacitive DNA sensor

Investigations of the sensitivity and selectivity of a capacitive type biosensor array, consisting of a total of 256 biosensing elements, in the detection of single oligonucleotide mutations is presented. The biosensor takes advantage of surface stress changes during biological interactions and is able to translate them into a capacitive signal. The array is organized in a 16×16 matrix of distinct biosensing elements thus allowing for the concurrent sensing of multiple biological targets. In this work the sensing elements of the array are spotted with three different oligonucleotides (CD8, CD17 and CD19) and their hybridization is detected using 36nM PCR. Moreover tests with CD19 revealed the ability of the biosensor to detect the hybridization of the oligo with sample concentrations of 36, 18 and 9nM.