Project description:Epigenomic profiling by ChIP-seq is a prevailing methodology used to investigate chromatin-based regulation in biological systems, such as human disease, yet the lack of an empirical methodology to normalize amongst experiments has limited the usefulness of this technique. Here we describe a “spike-in” normalization method that allows the quantitative comparison of histone modification status across cell populations using defined quantities of a reference epigenome. We demonstrate the utility of this method in measuring epigenomic changes following chemical perturbations and show how control normalization of ChIP-seq experiments enables discovery of disease-relevant changes in histone modification occupancy. ChIP-Seq of histone modifications H3K79me2 and H3K4me3 in human samples treated with EPZ-5676 with/without reference epigenome spike-in.
Project description:Epigenomic profiling by ChIP-seq is a prevailing methodology used to investigate chromatin-based regulation in biological systems, such as human disease, yet the lack of an empirical methodology to normalize amongst experiments has limited the usefulness of this technique. Here we describe a “spike-in” normalization method that allows the quantitative comparison of histone modification status across cell populations using defined quantities of a reference epigenome. We demonstrate the utility of this method in measuring epigenomic changes following chemical perturbations and show how control normalization of ChIP-seq experiments enables discovery of disease-relevant changes in histone modification occupancy.
Project description:Label-free quantification is a powerful method for studying cellular protein phosphorylation dynamics. However, whether current data normalization methods achieve sufficient accuracy has not been examined systematically. Here, we demonstrate that a large uni-directional shift in the phosphopeptide abundance distribution is problematic for global median centering and quantile-based normalizations and may mislead the biological conclusion from unlabeled phosphoproteome data. Instead, we present a novel normalization strategy, named pairwise normalization, which is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of pairwise normalization was validated by statistical methods, western blotting analysis, and by bioinformatics analysis. In addition, we demonstrate that the choice of normalization method influences the downstream analyses of the data and perceived pathway activities. Furthermore, we demonstrate that the developed normalization method, combined with pathway analysis algorithms, revealed a novel biological synergism between Ras signalling and PP2A inhibition by CIP2A.
Project description:Label-free quantification is a powerful method for studying cellular protein phosphorylation dynamics. However, whether current data normalization methods achieve sufficient accuracy has not been examined systematically. Here, we demonstrate that a large uni-directional shift in the phosphopeptide abundance distribution is problematic for global median centering and quantile-based normalizations and may mislead the biological conclusion from unlabeled phosphoproteome data. Instead, we present a novel normalization strategy, named pairwise normalization, which is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of pairwise normalization was validated by statistical methods, western blotting analysis, and by bioinformatics analysis. In addition, we demonstrate that the choice of normalization method influences the downstream analyses of the data and perceived pathway activities. Furthermore, we demonstrate that the developed normalization method, combined with pathway analysis algorithms, revealed a novel biological synergism between Ras signalling and PP2A inhibition by CIP2A.
Project description:HIV-1 and HIV-2 are two etiological agents of Acquired Immune Deficiency Syndrome (AIDS). Several differences exist between these two retroviruses in terms of geographical distribution, replication, transmission and progression to AIDS. The molecular reasons explaining these features are largely unknown. One reason could rely on host factors able to differently counteract HIV replication. Among these factors, cellular microRNAs (miRNAs) have recently emerged as playing crucial roles. One aspect of the complex interplay between HIV and host miRNAs is the ability of HIV-1 to modulate host miRNAs and thereby to create favorable conditions for its replication. Here, we sought to compare the miRNA modulations elicited by HIV-1 and HIV-2 using an unbiased experimental strategy based on miRNA arrays. Surprisingly, we observed that these two unrelated HIVs similarly modulated the host miRNA repertoire, when utilizing CD4 and CXCR4 for entry. However, these modulations were different from the changes triggered by HIV-1 using CD4 and CCR5. In accordance with the mode of action of miRNAs, our observations were confirmed at the mRNA level. We concluded that co-receptor utilization (CXCR4 or CCR5), as opposed to genomic organization and phylogeny, is a key event determining the modulations of the host miRNA repertoire.
Project description:The transcriptomic modulations leading to defense response in rice one hour after inoculation by Xanthomonas oryzae pv oryzae. Xoo and mock inoculated plant of cultivars IET8585 (bacterial leaf blight resistant) and IR-24 (bacterial leaf blight susceptible) were compared.
Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling