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1.  Optimized amplification and single-cell analysis identify GnRH-mediated activation of Rap1b in primary rat gonadotropes 
Identifying the early gene program induced by GnRH would help understand how GnRH-activated signaling pathways modulate gonadotrope secretory response. We previously analyzed GnRH-induced early genes in LβT2 cells, however these lack GnRH self-potentiation, a physiological attribute of gonadotropes. To minimize cellular heterogeneity, rat primary pituitary cultures were enriched for gonadotropes by 40–60% using a sedimentation gradient. Given the limited number of gonadotropes, RNA was amplified prior to microarray analysis. Thirty-three genes were up-regulated 40 minutes after GnRH stimulation. Real-time PCR confirmed regulation of several transcripts including fosB, c-fos, egr-2 and rap1b, a small GTPase and member of the Ras family. GnRH stimulated rap1b gene expression in gonadotropes, measured by a sensitive single cell assay. Immunocytochemistry revealed increased Rap1 protein in GnRH-stimulated gonadotropes. These data establish rap1b as a novel gene rapidly induced by GnRH and a candidate to modulate gonadotropin secretion in rat gonadotropes.
PMCID: PMC3919063  PMID: 22127306
GnRH; Rap1b; rat primary pituitary cultures; gonadotrope enrichment; early gene; single-cell gene expression
2.  Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows 
PLoS Computational Biology  2010;6(6):e1000828.
Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.
Author Summary
Elucidating the biochemical interactions in living cells is essential to understanding their behavior under various external conditions. Some of these interactions occur between signaling components with many active states, and their activity levels may be difficult to measure directly. However, most methods to reverse engineer interaction networks rely on measuring gene activity at steady state under various cellular stimuli. Such gene measurements therefore ignore the intermediate effects of signaling components, and cannot reliably convey the interactions between the signaling components themselves. We propose using the changes in activity of early genes shortly after the stimulus to infer the functional interactions between the unmeasured signaling components. The change in expression in such genes at these times is directly and linearly affected by the signaling components, since there is insufficient time for other genes to be transcribed and interfere with the early genes' expression. We present an algorithm that uses such measurements to reverse engineer the functional interaction network between signaling components, and also provides a means for testing these predictions. The algorithm therefore uses feasible experiments to reconstruct functional networks. We applied the algorithm to experimental measurements and uncovered known interactions, as well as novel interactions that were then confirmed experimentally.
PMCID: PMC2891706  PMID: 20585619

Results 1-2 (2)