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Biomedical subjects

Roland Somogyi

Publications and source records attributed to Roland Somogyi.

5 recordsLinked to original sources
PubMed2006

Convergence of TCR and cytokine signaling leads to FOXO3a phosphorylation and drives the survival of CD4+ central memory T cells.

The molecular events involved in the establishment and maintenance of CD4+ central memory and effector memory T cells (TCM and TEM, respectively) are poorly understood. In this study, we demonstrate that ex vivo isolated TCM are more resistant to both spontaneous and Fas-induced apoptosis than TEM and have an increased capacity to proliferate and persist in vitro. Using global gene expression profiling, single cell proteomics, and functional assays, we show that the survival of CD4+ TCM depends, at least in part, on the activation and phosphorylation of signal transducer and activator of transcription 5a (STAT5a) and forkhead box O3a (FOXO3a). TCM showed a significant increase in the levels of phosphorylation of STAT5a compared with TEM in response to both IL-2 (P<0.04) and IL-7 (P<0.002); the latter is well known for its capacity to enhance T cell survival. Moreover, ex vivo TCM express higher levels of the transcriptionally inactive phosphorylated forms of FOXO3a and concomitantly lower levels of the proapoptotic FOXO3a target, Bim. Experiments aimed at blocking FOXO3a phosphorylation confirmed the role of this phosphoprotein in protecting TCM from apoptosis. Our results provide, for the first time in humans, an insight into molecular mechanisms that could be responsible for the longevity and persistence of CD4+ TCM.

Apoptosis↗
PubMed2004

Transcription-based prediction of response to IFNbeta using supervised computational methods.

Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNbeta) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.

Adolescent↗
PubMed2002

Spearman correlation identifies statistically significant gene expression clusters in spinal cord development and injury.

An important problem in the analysis of large-scale gene expression data is the validation of gene expression clusters. By examining the temporal expression patterns of 74 genes expressed in rat spinal cord under three different experimental conditions, we have found evidence that some genes cluster together under multiple conditions. Using RT-PCR data from spinal cord development and two sets of microarray data from spinal injury, we applied Spearman correlation to identify clusters and to assign P values to pairs of genes with highly similar temporal expression patterns. We found that 15% of genes occurred in statistically significant pairs in all three experimental conditions, providing both statistical and experimental support for the idea that genes that cluster together are co-regulated. In addition, we demonstrated that DNA microarray and RT-PCR data are comparable, and can be combined to confirm gene expression relationships.

Aging↗
PubMed2001

The dynamics of molecular networks: applications to therapeutic discovery.

Recent advances in biomedical research, genomics and bioinformatics have given the pharmaceutical and biotechnology industries new promises and expectations: providing effective cures for complex diseases, discovering and prioritizing drug targets more efficiently, eliminating toxic and ineffective compounds earlier and delivering the right drug therapy to the appropriate patients. Ultimately, the biomedical information generated today must be transformed into integrated predictive models that can be consulted for decision-making in drug discovery, efficacy and toxicity screening and individualized therapy. Here we describe how models that capture different aspects of network dynamics can be generated and applied in disease pathway identification, drug screening, diagnostics and individualized therapy.

Journal Article↗