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

Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types

Pelizaeus–Merzbacher-like disease is caused not only by a loss of connexin47 function but also by a hemichannel dysfunction

Autosomal recessive mutations in the GJA12/GJC2 gene encoding the gap junction protein connexin47 (C × 47) cause a form of Pelizaeus–Merzbacher-like disease (PMLD) with hypomyelination, nystagmus, impaired psychomotor development and progressive spasticity. We investigated the functional consequences of four C × 47 missense mutations (G149S, G236R, T265A, and T398I) and one C × 47 complex mutation (A98G_V99insT) by immunoblot analysis and immunocytochemistry in transfected communication-incompetent HeLa cells and in OLI-neu cells. All studied C × 47 mutants, except G236R, generated stable proteins in transfected HeLa cells and OLI-neu cells. The mutants T265A and A98G_V99insT were retained in the ER, T398I formed gap junctional plaques at the plasma membrane, and G149S showed both, structures at the plasma membrane and ER localization. Two-microelectrode voltage clamp analyses in Xenopus laevis oocytes injected with wild-type and mutant C × 47 cRNA revealed reduced hemichannel currents for G236R, T265A, and A98G_V99insT. In contrast, T398I revealed hemichannel currents comparable to wild-type. For C × 47 mutant T398I, our results indicate a defect in hemichannel function, whereas C × 47 mutants G149S, G236R, T265A, and A98G_V99insT are predicted to result in a loss of C × 47 hemichannel function. Thus, PMLD is likely to be caused by two different disease mechanisms: a loss of function and a dysfunction