Phosphorylation networks regulating JNK activity in diverse genetic backgrounds

Abstract

Cellular signaling networks have evolved to enable swift and accurate responses, even in the face of genetic or environmental perturbation. Thus, genetic screens may not identify all the genes that regulate different biological processes. Moreover, although classical screening approaches have succeeded in providing parts lists of the essential components of signaling networks, they typically do not provide much insight into the hierarchical and functional relations that exist among these components. We describe a high-throughput screen in which we used RNA interference to systematically inhibit two genes simultaneously in 17,724 combinations to identify regulators of Drosophila JUN NH(2)-terminal kinase (JNK). Using both genetic and phosphoproteomics data, we then implemented an integrative network algorithm to construct a JNK phosphorylation network, which provides structural and mechanistic insights into the systems architecture of JNK signaling.

Fig. 1

Overview of FRET-based screen for JNK regulators. (A) Schematic of dJUN-FRET construct. (B) Representative image of dJUN-FRET transfected BG-2 cells and image analysis protocol. (C) Graph of Z scores for individual dsRNAs in KP screen. (D) List of 11 JNK suppressors and 13 enhancers identified in the KP screen. Genes are considered JNK regulators if two or more independent dsRNAs result in mean changes in dJUN-FRET activity above or below a Z score of +2.0 or −2.0, respectively. Circles represent the number of dsRNAs tested per gene; filled circles represent dsRNAs that contribute to the average Z score. Genes in bold indicate previously described JNK regulators (3).

Fig. 2

Overview of the ability of dsRNAs to enhance or suppress dJUN-FRET reporter activity in diverse sensitized backgrounds. Genes are considered hits in individual screens if two or more independent dsRNAs result in a mean dJUN-FRET reporter activity that is considered in the top or bottom 5% of each screen. Shading indicates 0.05 to 0.95 percentile in each screen. Blue boxes indicate the number of JNK enhancers in each screen; red boxes indicate the number of JNK suppressors.

Fig. 3

Information flow through the JNK network. Kinases with predicted substrates in the JNK signaling network are shown in various colors, and the corresponding phosphorylation sites are indicated with similarly colored boxes on the corresponding targets. Stacked boxes indicate instances where the same motif is predicted to be phosphorylated by multiple kinases. Colored lines indicate either that we have detected an epistatic interaction among kinases and substrates or that kinase and substrate have correlated scores across sensitized screens (Fig. 4). Curved arrows emanating from JNK represent instances for which we predict the existence of regulatory feedback in the JNK network. Additionally, we show components of the networks that we did not predict as either kinases or substrates, but that have well-characterized functional relations with particular components of the JNK phosphorylation network. For example, KSR (kinase suppressor of Ras), RAF (a family of serine-threonine protein kinases), and ERK are well-characterized components of the same pathway (26). Unless predicted as a kinase by NetworKIN, JNK regulators are colored according to whether they were identified as JNK enhancers (blue) or JNK suppressors (red) in this study. AKT, FOS, and JUN were not isolated in this screen. All other JNK regulators are listed in the colored boxes and were grouped according to GO annotation (www.flybase.org). AMPK, adenosine monophosphate (AMP)–activated protein kinase. Numbers in parentheses correspond to the number of JNK regulators identified in this study that make up each group.

Fig. 4

Determining functional interactions among kinases and substrates in the JNK network. Hierarchical clustering of average dJUN-FRET Z scores after inhibition by RNAi of components in the JNK phosphorylation network in unmodified (KP), as well as in backgrounds deficient in ERK, hippo, MLK, or puc. Functional interactions are defined by the detection of an epistatic interaction between kinase and substrate (white boxes) or when the average Z scores of kinases and substrate dsRNAs across all sensitized screens cluster together with a cluster distance metric (an average of uncentered Pearson correlation coefficients) greater than 0.67 (shaded boxes). For example, whereas typically ERK acts as a JNK suppressor, ERK RNAi in MLK-deficient background (asterisk) leads to a notable decrease in dJUN-FRET reporter activity, which suggests that the ERK can act upstream of JNK via predicted phosphorylation of MLK and JNKK. Alternatively, GSK3 is predicted to target MLK, JNKK, and Dlg1, but only Z scores for GSK3, MLK, or JNKK dsRNAs cluster across screens, which suggests that GSK3-mediated phosphorylation of MLK and JNKK, but not Dlg1, is functionally relevant to JNK signaling.