Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray

Background: Many tools used to analyze microarrays in different conditions have been described. However, the integration of deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion based on biological functions exists to determine a threshold demonstrating that a gene is indeed differentially expressed. Methodology/Principal Findings: To improve transcriptomic analysis of microarrays, we propose a new statistical approach that takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed genes in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes. Conclusions/Significance: We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed

In vitro and in vivo evaluations of a mannosylated thalidomide derivative.

International audienc

Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray

Background: Many tools used to analyze microarrays in different conditions have been described. However, the integration of deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion based on biological functions exists to determine a threshold demonstrating that a gene is indeed differentially expressed. Methodology/Principal Findings: To improve transcriptomic analysis of microarrays, we propose a new statistical approach that takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed genes in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes. Conclusions/Significance: We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed

Targeting multiplicity: the key factor for anti-cancer nanoparticles.

International audienceIn this mini-review, we focus on different strategies to bring nanotools specifically to cancer cells. We discuss about a better targeting of tumor, combining the characteristics of tumor environment, the increase in nanoparticles life time, the biomarkers overexpressed on cancer cells and different physical methods for non invasive therapies. Here we detail the necessity of a synergy between passive and active targeting for an actual specificity of cancer cells

The level of ascorbate peroxidase is enhanced in benznidazole-resistant populations of Trypanosoma cruzi and its expression is modulated by stress generated by hydrogen peroxide

Ascorbate peroxidases (APX) are class I heme-containing enzymes that convert hydrogen peroxide into water molecules. The gene encoding APX has been characterized in 11 strains of Trypanosoma cruzi that are sensitive or resistant to benznidazole (BZ). Bioinformatic analysis revealed the presence of two complete copies of the T. cruzi APX (TcAPX) gene in the genome of the parasite, while karyotype analysis showed that the gene was present in the 2.000-kb chromosome of all of the strains analyzed. The sequence of TcAPX exhibited greater levels of similarity to those of orthologous enzymes from Leishmania spp than to APXs from the higher plant Arabidopsis thaliana. Northern blot and real-time reverse transcriptase polymerase chain reaction (RT-PCR) analyses revealed no significant differences in TcAPX mRNA levels between the T. cruzi strains analyzed. On the other hand, Western blots showed that the expression levels of TcAPX protein were, respectively, two and three-fold higher in T. cruzi populations with in vitro induced (17 LER) and in vivo selected (BZR) resistance to BZ, in comparison with their corresponding susceptible counterparts. Moreover, the two BZ-resistant populations exhibited higher tolerances to exogenous hydrogen peroxide than their susceptible counterparts and showed TcAPX levels that increased in a dose-dependent manner following exposure to 100 and 200 µM hydrogen peroxide