Regulation by IFN-α/IFN-γ co-formulation (HerberPAG®) of genes involved in interferon-STAT-pathways and apoptosis in U87MG.

Interferons (IFNs) are proteins of the family of cytokines. Their antiproliferative function has been taken into account for several clinical therapies against malignant diseases. In this family, IFNs α and γ have demonstrated the highest antitumor effects. HerberPAG® is a new co-formulation with IFNs, α2b and γ. It has been obtained to increase the antiproliferative effect of individual IFNs and decrease their associated toxicity. Glioblastoma multiforme (GBM) is the most common primary brain tumor and one of the most deadly forms of cancer. The objective of the present work is to obtain insights into the regulation of Interferon-STAT-pathways and apoptosis in U87MG, at the transcriptional level. As a pharmacogenomic strategy we quantified mRNAs levels in vitro by quantitative PCR, using the cell line U87MG as a model. Some of the genes involved in the first steps of IFNs signaling pathways (stat1 and stat3) and apoptosis events (tp53, bax, bcl-2, bad, caspase3 (casp3), caspase8 (casp8) and caspase9 (casp9)) were studied. The detected mRNAs expression pattern for stat1and stat3 indicates a higher tumor suppressor activity of HerberPAG® compared to individuals IFNs. The up-regulation of tp53, bax, bad, casp3, casp8 and casp9 genes and the down regulation of bcl-2 gen, after the treatment with HerberPAG® show a pro-apoptotic function. HerberPAG® gene-induced profile shows an advantage in relation to IFN α2b and γ with a higher stat1 expression and a downregulation of bcl-2 which increases bax:bcl-2 ratio. The regulation of genes involved in IFN-STAT-pathways and apoptosis may be the first evidences to explain the increased antiproliferative properties of this co-formulation.

Selection of reference genes for use in quantitative reverse transcription PCR assays when using interferons in U87MG.

Relative gene quantification by quantitative reverse transcription PCR (qRT-PCR) is an accurate technique only when a correct normalization strategy is carried out. Some of the most commonly genes used as reference have demonstrated variation after interferon (IFN) treatments. In this work we evaluated the suitability of seven reference genes (RGs) [glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethylbilane synthase (HMBS), ß-2Microglobulin (B2M), ribosomal RNA subunits 18S and 28S, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ) and the RNA helicase (DDX5)] for use in qRT-PCR assays in the glioblastoma-derived cell line U87MG treated with IFNα, IFNγ or a co-formulated combination of both IFNs (HeberPAG); untreated cell lines were included as control. Data was analyzed using geNorm and NormFinder softwares. The expression stability of the seven RGs decreased in order of DDX5/GAPDH/HMBS, 18S rRNA, YWHAZ, 28S rRNA and B2M. qRT-PCR analyses demonstrated that DDX5, GAPDH and HMBS were among the best stably expressed markers under all conditions. Both, geNorm and NormFinder, analyses proposed same RGs as the least variables. Evaluation of the expression levels of two target genes utilizing different endogenous controls, using REST-MCS software, revealed that the normalization method applied might introduce errors in the estimation of relative quantities. We concluded that when qRT-PCR is designed for studies of gene expression in U87MG cell lines treated with IFNs type I and II or their combinations, the use of all three GAPDH, HMBS and DDX5 (or their combinations in pairs) as RGs for data normalizations is recommended.

A methodology based on molecular interactions and pathways to find candidate genes associated to diseases: its application to schizophrenia and Alzheimer's disease.

Experimental techniques for the identification of genes associated with diseases are expensive and have certain limitations. In this scenario, computational methods are useful tools to identify lists of promising genes for further experimental verification. This paper describes a flexible methodology for the in silico prediction of genes associated with diseases combining the use of available tools for gene enrichment analysis, gene network generation and gene prioritization. A set of reference genes, with a known association to a disease, is used as bait to extract candidate genes from molecular interaction networks and enriched pathways. In a second step, prioritization methods are applied to evaluate the similarities between previously selected candidates and the set of reference genes. The top genes obtained by these programs are grouped into a single list sorted by the number of methods that have selected each gene. As a proof of concept, top genes reported a few years ago in SzGene and AlzGene databases were used as references to predict genes associated to schizophrenia and Alzheimer's disease, respectively. In both cases, we were able to predict a statistically significant amount of genes belonging to the updated lists.