We compared the transcriptomes of Saccharomyces cerevisiae cells growing under steady-state conditions on 21 unique sources of nitrogen. We found 506 genes differentially regulated by nitrogen and estimated the activation degrees of all identified nitrogen-responding transcriptional controls according to the nitrogen source. One main group of nitrogenous compounds supports fast growth and a highly active nitrogen catabolite repression (NCR) control. Catabolism of these compounds typically yields carbon derivatives directly assimilable by a cell's metabolism. Another group of nitrogen compounds supports slower growth, is associated with excretion by cells of nonmetabolizable carbon compounds such as fusel oils, and is characterized by activation of the general control of amino acid biosynthesis (GAAC). Furthermore, NCR and GAAC appear interlinked, since expression of the GCN4 gene encoding the transcription factor that mediates GAAC is subject to NCR. We also observed that several transcriptional-regulation systems are active under a wider range of nitrogen supply conditions than anticipated. Other transcriptional-regulation systems acting on genes not involved in nitrogen metabolism, e.g., the pleiotropic-drug resistance and the unfolded-protein response systems, also respond to nitrogen. We have completed the lists of target genes of several nitrogen-sensitive regulons and have used sequence comparison tools to propose functions for about 20 orphan genes. Similar studies conducted for other nutrients should provide a more complete view of alternative metabolic pathways in yeast and contribute to the attribution of functions to many other orphan genes.

The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning (“Causal Reasoning Analytical Framework for Target discovery”—CRAFT). Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in three pre-clinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. CRAFT is applicable to disease settings other than epilepsy.

MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods.

Epilepsy affects around 50 million people worldwide, and in about 65 % of patients, the etiology of disease is unknown. MicroRNAs are small non-coding RNAs that have been suggested to play a role in the pathophysiology of epilepsy. Here, we compared microRNA expression patterns in the hippocampus using two chronic models of epilepsy characterised by recurrent spontaneous seizures (pilocarpine and self-sustained status epilepticus (SSSE)) and an acute 6-Hz seizure model. The vast majority of microRNAs deregulated in the acute model exhibited increased expression with 146 microRNAs up-regulated within 6 h after a single seizure. In contrast, in the chronic models, the number of up-regulated microRNAs was similar to the number of down-regulated microRNAs. Three microRNAs—miR-142-5p, miR-331-3p and miR-30a-5p—were commonly deregulated in all three models. However, there is a clear overlap of differentially expressed microRNAs within the chronic models with 36 and 15 microRNAs co-regulated at 24 h and at 28 days following status epilepticus, respectively. Pathway analysis revealed that the altered microRNAs are associated with inflammation, innate immunity and cell cycle regulation. Taken together, the identified microRNAs and the pathways they modulate might represent candidates for novel molecular approaches for the treatment of patients with epilepsy.Electronic supplementary materialThe online version of this article (doi:10.1007/s12031-014-0368-6) contains supplementary material, which is available to authorized users.