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A G Hatzigeorgiou

Publications and source records attributed to A G Hatzigeorgiou.

4 recordsLinked to original sources
PubMed2006

Anti-apoptotic function of a microRNA encoded by the HSV-1 latency-associated transcript.

MicroRNAs (miRNAs) are a class of small RNA molecules that regulate the stability or the translational efficiency of target messenger RNAs (mRNAs). The latency-associated transcript (LAT) of herpes simplex virus-1 (HSV-1) is the only viral gene expressed during latent infection in neurons. LAT inhibits apoptosis and maintains latency by promoting the survival of infected neurons. No protein product has been attributed to the LAT gene and the mechanism by which LAT protects cells from apoptosis is not yet known. Here we show that a miRNA encoded by the HSV-1 LAT gene confers resistance to apoptosis. Neuroblastoma cells transfected with a fragment of the LAT gene show reduced susceptibility to cell death. The anti-apoptotic function of LAT has been mapped to a region within the first exon. We have identified and characterized a microRNA (miR-LAT) generated from the exon 1 region of the HSV-1 LAT gene. The LAT miRNA was found to accumulate in cells transiently transfected with the LAT gene fragment or infected with a wild-type strain of HSV-1. A mutant virus in which a 372-nucleotide fragment encompassing the mature miRNA was deleted neither protected the infected cells from apoptosis nor generated an miRNA. miR-LAT exerts its anti-apoptotic effect by downregulation of transforming growth factor (TGF)-beta 1 and SMAD3 expression, both of which are functionally linked in the TGF-beta pathway. Our results suggest that the miRNA encoded by the HSV-1 LAT gene regulates the induction of apoptosis in infected cells by modulation of TGF-beta signalling and thus contributes to the persistence of HSV in a latent form in sensory neurons.

Apoptosis↗
PubMed2001

DIANA-EST: a statistical analysis.

MOTIVATION: Expressed Sequence Tags (ESTs) are next to cDNA sequences as the most direct way to locate in silico the genes of the genome and determine their structure. Currently ESTs make up more than 60% of all the database entries. The goal of this work is the development of a new program called DNA Intelligent Analysis for ESTs (DIANA-EST) based on a combination of Artificial Neural Networks (ANN) and statistics for the characterization of the coding regions within ESTs and the reconstruction of the encoded protein. RESULTS: 89.7% of the nucleotides from an independent test set with 127 ESTs were predicted correctly as to whether they are coding or non coding. AVAILABILITY: The program is available upon request from the author. CONTACT: Present address: Department of Genetics, University of Pennsylvania, School of Medicine, 475 Clinical Research Building, 415 Curie Boulevard, Philadelphia, PA 19104-6145, USA. artemis@pcbi.upenn.edu.

Computational Biology↗
PubMed1999

Bacterial start site prediction.

With the growing number of completely sequenced bacterial genes, accurate gene prediction in bacterial genomes remains an important problem. Although the existing tools predict genes in bacterial genomes with high overall accuracy, their ability to pinpoint the translation start site remains unsatisfactory. In this paper, we present a novel approach to bacterial start site prediction that takes into account multiple features of a potential start site, viz., ribosome binding site (RBS) binding energy, distance of the RBS from the start codon, distance from the beginning of the maximal ORF to the start codon, the start codon itself and the coding/non-coding potential around the start site. Mixed integer programing was used to optimize the discriminatory system. The accuracy of this approach is up to 90%, compared to 70%, using the most common tools in fully automated mode (that is, without expert human post-processing of results). The approach is evaluated using Bacillus subtilis, Escherichia coli and Pyrococcus furiosus. These three genomes cover a broad spectrum of bacterial genomes, since B.subtilis is a Gram-positive bacterium, E.coli is a Gram-negative bacterium and P. furiosus is an archaebacterium. A significant problem is generating a set of 'true' start sites for algorithm training, in the absence of experimental work. We found that sequence conservation between P. furiosus and the related Pyrococcus horikoshii clearly delimited the gene start in many cases, providing a sufficient training set.

Algorithms↗