Inferring the role of transcription factors in regulatory networks

<p>Abstract</p> <p>Background</p> <p>Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays.</p> <p>Results</p> <p>We evaluate our approach in several settings of increasing complexity. First, we generate artificial expression data on a transcriptional network of <it>E. coli </it>extracted from the literature (1529 nodes and 3802 edges), and we estimate that 30% of the regulations can be annotated with about 30 profiles. We additionally prove that at most 40.8% of the network can be inferred using our approach. Second, we use this network in order to validate the predictions obtained with a compendium of real expression profiles. We describe a filtering algorithm that generates particularly reliable predictions. Finally, we apply our inference approach to <it>S. cerevisiae </it>transcriptional network (2419 nodes and 4344 interactions), by combining ChIP-chip data and 15 expression profiles. We are able to detect and isolate inconsistencies between the expression profiles and a significant portion of the model (15% of all the interactions). In addition, we report predictions for 14.5% of all interactions.</p> <p>Conclusion</p> <p>Our approach does not require accurate expression levels nor times series. Nevertheless, we show on both data, real and artificial, that a relatively small number of perturbation experiments are enough to determine a significant portion of regulatory effects. This is a key practical asset compared to statistical methods for network reconstruction. We demonstrate that our approach is able to provide accurate predictions, even when the network is incomplete and the data is noisy.</p

Vitamin C promotes widespread yet specific DNA demethylation of the epigenome in human embryonic stem cells

Vitamin C (ascorbate) is a widely used medium supplement in embryonic stem cell culture. Here, we show that ascorbate causes widespread, consistent, and remarkably specific DNA demethylation of 1,847 genes in human embryonic stem cells (hESCs), including important stem cell genes, with a clear bias toward demethylation at CpG island boundaries. We show that a subset of these DNA demethylated genes displays concomitant gene expression changes and that the position of the demethylated CpGs relative to the transcription start site is correlated to such changes. We further show that the ascorbate-demethylated gene set not only overlaps with gene sets that have bivalent marks, but also with the gene sets that are demethylated during differentiation of hESCs and during reprogramming of fibroblasts to induced pluritotent stem cells (iPSCs). Our data thus identify a novel link between ascorbate-mediated signaling and specific epigenetic changes in hESCs that might impact on pluripotency and reprogramming pathways. © AlphaMed Press

Changes in the Spinal Cord Proteome of an Amyotrophic Lateral Sclerosis Murine Model Determined by Differential In-gel Electrophoresis *S⃞

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by loss of motor neurons resulting in progressive paralysis. To date, more than 140 different mutations in the gene encoding CuZn-superoxide dismutase (SOD1) have been associated with ALS. Several transgenic murine models exist in which various mutant SOD1s are expressed. We used DIGE to analyze the changes in the spinal cord proteome induced by expression of the unstable SOD1 truncation mutant G127insTGGG (G127X) in mice. Unlike mutants used in most other models, G127X lacks SOD activity and is present at low levels, thus reducing the risk of overexpression artifacts. The mice were analyzed at their peak body weights just before onset of symptoms. Variable importance plot analysis showed that 420 of 1,800 detected protein spots contributed significantly to the differences between the groups. By MALDI-TOF MS analysis, 54 differentially regulated proteins were identified. One spot was found to be a covalently linked mutant SOD1 dimer, apparently analogous to SOD1-immunoreactive bands migrating at double the molecular weight of SOD1 monomers previously detected in humans and mice carrying mutant SOD1s and in sporadic ALS cases. Analyses of affected functional pathways and the subcellular representation of alterations suggest that the toxicity exerted by mutant SODs induces oxidative stress and affects mitochondria, cellular assembly/organization, and protein degradation