Global modeling of transcriptional responses in interaction networks.

MOTIVATION: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms between tissues, and potentially as yet unknown processes. The same method can also be applied to cell-biological conditions in one or more tissues. RESULTS: We introduce a novel approach for organism-wide discovery and analysis of transcriptional responses in interaction networks. The method searches for local, connected regions in a network that exhibit coordinated transcriptional response in a subset of tissues. Known interactions between genes are used to limit the search space and to guide the analysis. Validation on a human pathway network reveals physiologically coherent responses, functional relatedness between tissues and coordinated, context-specific regulation of the genes. AVAILABILITY: Implementation is freely available in R and Matlab at http://www.cis.hut.fi/projects/mi/software/NetResponse

Accurate Identification of Closely Related Mycobacterium tuberculosis Complex Species by High Resolution Tandem Mass Spectrometry.

Rapid and accurate differentiation of Mycobacterium tuberculosis complex (MTBC) species from other mycobacterium is essential for appropriate therapeutic management, timely intervention for infection control and initiation of appropriate health care measures. However, routine clinical characterization methods for Mycobacterium tuberculosis (Mtb) species remain both, time consuming and labor intensive. In the present study, an innovative liquid Chromatography-Mass Spectrometry method for the identification of clinically most relevant Mycobacterium tuberculosis complex species is tested using a model set of mycobacterium strains. The methodology is based on protein profiling of Mycobacterium tuberculosis complex isolates, which are used as markers of differentiation. To test the resolving power, speed, and accuracy of the method, four ATCC type strains and 37 recent clinical isolates of closely related species were analyzed using this new approach. Using different deconvolution algorithms, we detected hundreds of individual protein masses, with a subpopulation of these functioning as species-specific markers. This assay identified 216, 260, 222, and 201 proteoforms for M. tuberculosis ATCC 27294™, M. microti ATCC 19422™, M. africanum ATCC 25420™, and M. bovis ATCC 19210™ respectively. All clinical strains were identified to the correct species with a mean of 95% accuracy. Our study successfully demonstrates applicability of this novel mass spectrometric approach to identify clinically relevant Mycobacterium tuberculosis complex species that are very closely related and difficult to differentiate with currently existing methods. Here, we present the first proof-of-principle study employing a fast mass spectrometry-based method to identify the clinically most prevalent species within the Mycobacterium tuberculosis species complex.

The effects of acute and long-term lithium treatments on trkB neurotrophin receptor activation in the mouse hippocampus and anterior cingulate cortex.

As brain-derived neurotrophic factor (BDNF) and its receptor trkB are linked to the etiology and treatment of mood disorders, we examined the effects of acute and long-term treatment of mood-stabilizer lithium on trkB activation and signaling and BDNF levels in the mouse anterior cingulate cortex (AC) and hippocampus (HC). The trkB activity was measured using specific antibodies against the phosphorylated trkB catalytic domain (pY705/6) and the shc binding site (pY515). In the AC, both acute and long-term LiCl treatment enhanced the pY705/6 of trkB. In contrast, acute or long-term LiCl treatment did not significantly alter the pY705/6 of trkB in the HC. Interestingly, however, acute LiCl treatment significantly reduced the phosphorylation of cAMP related element binding protein (CREB), a major intracellular target of trkB, in the HC. Moreover, pY515 of trkB in the AC and HC was not altered by any of the treatment. Also, prolonged LiCl treatment had no significant effects on BDNF levels or CREB activation in either the AC or HC. The present results suggest that acute and long-term lithium treatment induces trkB activation in the AC but not in the HC. The activation of CREB is, however, significantly reduced in the HC after acute LiCl treatment.

Associative clustering for exploring dependencies between functional genomics data sets.

High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.

Effects of antidepressant drug imipramine on gene expression in rat prefrontal cortex.

We have investigated gene expression changes produced by acute and chronic daily treatment with a prototypical antidepressant, imipramine, using DNA microarrays. The analysis of similarities in gene expression patterns among functionally related genes revealed four expression profile cluster areas that showed a highly significant overrepresentation of several functional classes. Genes encoding for proteins involved in cAMP metabolism, postsynaptic membrane proteins, and proto-oncogenes were overrepresented in different cluster areas. Furthermore, we found that serine proteases as a group were similarly regulated by chronic antidepressant treatment. Our data suggest that cAMP metabolism, synaptic function, and protein processing by serine proteases may be important targets of antidepressant treatment and potential objects for antidepressant drug development.