Figure 1.
Response of early and late genes and their correlation to signaling activity.
A. The normalized pERK level (Y axis) as a function of GnRH concentration (X axis) as determined by ERK ELISA assays at 5 minutes following activation of LβT2 gonadotropes. B. The expression level of cfos mRNA (Y axis) as a function of GnRH concentrations (X axis) as determined by quantitative PCR at 45 minutes (solid line) and 5 hours (dashed line) following GnRH stimulation of LβT2 gonadotropes. C–F. The expression level of several genes is plotted vs. the activity levels of pERK at 45 minutes (squares) and 5hrs (triangles) following activation of LβT2 gonadotropes by a number of different concentrations of GnRH. Both time-points are fitted to a linear curve (solid and dashed lines, respectively). C and D. The early genes such as cfos and mkp1 exhibit a good linear correlation to pERK activity at 45 minutes, and much weaker correlation at 5 hours. E and F. Later genes such as pacap and follistatin exhibit no correlation to pERK activity at 45 minutes and show weak correlation at 5 hours. Error bars are standard error of mean. n = 4 per point.
Figure 2.
The flow chart describes the methodology proposed in Plato's Cave Algorithm. The algorithm is made of three stages: Data acquisition; Reverse Engineering that is repeated several times for statistical significance; and Post-processing where the results of several experiments are integrated.
Figure 3.
Weight Matrix Derivation – choosing good predictors of signaling activity.
A. The expression levels (in arbitrary units) of four genes under five perturbations (red bars), and their levels without any perturbation (green bars); B. The Z-values for each gene under each perturbation. High Z-values are obtained for genes with statistically significant change (red bars), and low Z-values are obtained when the change can be attributed to noise (blue bars); C. The information score of each gene. When the change in gene activity is specific to one perturbation the information score is high (red bars), and otherwise it is low (blue bars); D. The final weight attributed to each gene for each perturbation. A gene can only get a high weight (red bars) if it has both a high Z-value and a high information score. The values used for the gene expression are not drawn from any experiment and were generated merely to illustrate the methodology.
Figure 4.
Synthetic interaction network.
A. The biochemical interaction network for the synthetic network, including the four signaling components (S1–S4), and the 10 early genes they affect (G1–G10); B. The network of functional interactions between the four signaling components in the synthetic network, as inferred by PLACA. The inferred functional interactions convey the correct biochemical network; C. The heat map of the change in gene activity in all genes (X axis), as obtained from a set of simulations where each signaling component (Y axis) was perturbed. The heat map reveals which genes were involved in inferring each functional interaction.
Figure 5.
Applying PLACA to experimental results in the gonadotrope.
A. The clustered heat map of the log fold change in gene activity for 21 genes (X axis), as obtained from a single experiment (experiment #6), where LβT2 gonadotropes were treated with one of six chemical inhibitors acting on signaling components (Y axis). The fold change is the gene activity in the presence of both inhibitor and GnRH divided by the gene activity with GnRH alone; B. The inferred functional network in the Gonadotrope. PLACA was applied to the experimental data from five independent experiments. The functional network represents signaling components that present a statistically significant functional interaction. The interactions between ERK and PKC, between JNK and Src, and between EGFR and CaMKII were previously seen experimentally, and we have found experimental evidence for the validity of the functional inhibition of EGFR by JNK.
Figure 6.
JNK represses EGF-Stimulation of klf4.
A. Experimental results showing that JNK repression enhances EGF-stimulation of klf4. LβT2 cells were pre-incubated with SP600125 (JNK Inhibitor) for 30 minutes and treated with different concentration of EGF for 45 minutes. The levels of klf4 were measured by quantitative PCR and rps11 was used for normalization. EGF alone causes a slight induction of klf4, and inhibition of JNK results in a more significant induction. With concurrent JNK inhibition and EGF stimulation, a synergistic effect can be seen; B. The proposed biochemical interaction network involving JNK, EGF, EGFR, and klf4 that mediates the functional repression of EGFR by JNK.