Sequential steps underlying neuronal plasticity induced by a transient exposure to gabazine.

Periods of intense electrical activity can initiate neuronal plasticity leading to long lasting changes of network properties. By combining multielectrode extracellular recordings with DNA microarrays, we have investigated in rat hippocampal cultures the temporal sequence of events of neuronal plasticity triggered by a transient exposure to the GABA(A) receptor antagonist gabazine (GabT). GabT induced a synchronous bursting pattern of activity. The analysis of electrical activity identified three main phases during neuronal plasticity induced by GabT: (i) immediately after termination of GabT, an early synchronization (E-Sync) of the spontaneous electrical activity appears that progressively decay after 3-6 h. E-Sync is abolished by inhibitors of the ERK1/2 pathway but not by inhibitors of gene transcription; (ii) the evoked response (induced by a single pulse of extracellular electrical stimulation) was maximally potentiated 3-10 h after GabT (M-LTP); and (iii) at 24 h the spontaneous electrical activity became more synchronous (L-Sync). The genome-wide analysis identified three clusters of genes: (i) an early rise of transcription factors (Cluster 1), primarily composed by members of the EGR and Nr4a families, maximally up-regulated 1.5 h after GabT; (ii) a successive up-regulation of some hundred genes, many of which known to be involved in LTP (Cluster 2), 3 h after GabT likely underlying M-LTP. Moreover, in Cluster 2 several genes coding for K(+) channels are down-regulated at 24 h. (iii) Genes in Cluster 3 are up-regulated at 24 h and are involved in cellular homeostasis. This approach allows relating different steps of neuronal plasticity to specific transcriptional profiles.

Origin of co-expression patterns in E. coli and S. cerevisiae emerging from reverse engineering algorithms.

BACKGROUND: The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of "physical" networks of interactions among genes or gene products. RESULTS: This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E. coli and S. cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor-binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E. coli. CONCLUSION: The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E. coli to S. cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies.