Widespread Rewiring of Genetic Networks upon Cancer Signaling Pathway Activation

Abstract

Cellular signaling networks coordinate physiological processes in all multicellular organisms. Within networks, modules switch their function to control signaling activity in response to the cellular context. However, systematic approaches to map the interplay of such modules have been lacking. Here, we generated a context-dependent genetic interaction network of a metazoan's signaling pathway. Using Wnt signaling in Drosophila as a model, we measured >290,000 double perturbations of the pathway in a baseline state, after activation by Wnt ligand or after loss of the tumor suppressor APC. We found that genetic interactions within the Wnt network globally rewired after pathway activation. We derived between-state networks that showed how genes changed their function between state-specific networks. This related pathway inhibitors across states and identified genes required for pathway activation. For instance, we predicted and confirmed the ER-resident protein Catsup to be required for ligand-mediated Wnt signaling activation. Together, state-dependent and between-state genetic interaction networks identify responsive functional modules that control cellular pathways.

Keywords: APC; Wnt; epistatic mapping; genetic interactions; signaling.

Graphical abstract

Figure 1

State-Dependent Genetic Interactions of Wnt Signaling Enable the Estimation of Signed Functional Relations (A) Schematic workflow for state-dependent genetic interaction analysis. (B) Wnt pathway activity z-scores in S2R⁺ cells of 28,950 independent double-stranded RNAs (dsRNAs) covering 14,331 genes of the Drosophila genome. The scores of the two independent dsRNA designs against core Wnt pathway components known to affect the activity positively (green) or negatively (red) are labeled. (C and D) Combinatorial RNAi to quantify genetic interactions within the Wnt pathway. Single knockdown phenotypes were estimated from phenotypes of two independent dsRNAs in 144 different genetic backgrounds each (error bars show SE of median determined by bootstrapping). The white dashed bar indicates the expected combinatorial knockdown effect for each gene pair using a multiplicative neutrality function. The measured combinatorial phenotype illustrates the median of the four possible combinations of two independent dsRNA designs against each gene. The difference between the expected and measured combinatorial phenotype is quantified as a π-score for each genetic interaction (yellow if positive, blue if negative at false discovery rate <1%). The data are presented at log₂ scale. Examples show measurements in the Wnt-active state, for all three states see Figures S3A, S3B, S3D, and S3E. (E) Reproducibility of π-scores with rescreened (four replicates each) query genes evi/wls and Axn, which cover the secreting cell and the destruction complex. Further rescreened query genes are illustrated in Figure S2E. Correlations are highly significant (p < 2.2e−16). (F) Genetic interaction (π) scores between 14 core pathway components in Drosophila S2R⁺ and 1182-4H cells (PCC, 0.78; p < 2.2e−16). Gene pairs from (C) and (D) and Figure S3C are highlighted. (G–I) Genetic interaction scores in the three Wnt pathway states: baseline state, and after induction by Wg expression or Apc and Apc2 knockdown (low Wg levels). Candidate and query genes have the same order in each panel.

Figure 2

Between-State and State-Specific Functional Relations Visualize Change of Gene Function and Rewiring of the Core Wnt Signaling Network (A–D) State-specific correlation between genetic interaction profiles of the destruction complex components Axn and CkIα (both states: p < 2.2e−16, Pearson product-moment correlation) (A), dsh and nkd (baseline: p = 0.88; Wnt-active: p = 8.5e−14) (B) or receptor complex components LRP5/6/arr and dsh (baseline: p = 0.02; Wnt-active: p < 2.2e−16) (D) in the baseline and Wnt-active state. Each profile contains 72 quantitative genetic interaction (π) scores. (C) Correlation coefficients from (A), (B), and (D) in relation to the 56,280 measured coefficients in each respective state. (E and F) State-specific correlation-based networks connecting core Wnt pathway components involved in ligand secretion and receptor complex binding (green), negative feedback (orange), the destruction complex (red), or target gene transcription (dark green) in baseline (E) or Wnt-active (F) states. The similarity or dissimilarity of each gene pair was estimated by computing Pearson correlation coefficient (PCC) between the genetic interaction profiles. Genes with an absolute PCC > 0.65 were connected. The edge width represents the absolute value of the coefficient; the color indicates positive (blue) or negative (red) correlation. (G–I) Between-state correlation between genetic interaction profiles of Axn and dsh (p = 1.08e−9) (G), or Axn and nkd (p = 2.13e−6) (H). (I) Correlation coefficients from (G) and (H) in relation to the 112,896 measured coefficients between baseline and Wnt-active state. (J) Between-state correlation-based network connecting core Wnt pathway components involved in ligand secretion and receptor complex binding (green), negative feedback (orange), the destruction complex (red), or target gene transcription (dark green) between baseline and Wnt-active state. Genes with an absolute PCC > 0.5 were connected.

Figure 3

Rewiring after Pathway Activation Partially Caused by Change in Gene Function (A) Distribution of self-correlation of all tested genes between baseline and Wnt-active (left) or Apc-loss (right) state. (B) Self-correlation (x axis) and maximal association (y axis) after pathway induction. The maximal association represents the highest absolute PCC of each gene in the Wnt-active state. The color illustrates positive (red) or negative (green) single knockdown Wnt signaling phenotypes in the Wnt-active state. (C) Zoomed-in networks illustrate state-specific similarity (PCC > 0.65) between selected genes with a low autocorrelation. (D) Change of genetic interaction profiles of JNK signaling transcription factors Jra and kay from baseline to Wnt-active state. (E) Self-correlation of Jra and kay between baseline and Wnt-active state.

Figure 4

Plasticity of Functional Relations in Modules after Pathway Activation (A) State-specific cumulative rank-based distance to the six destruction complex components Axn, CkIα, slmb, sgg, skpA, and lin19. (B) Between-state cumulative rank-based distance from the destruction complex. The rank of the PCC of each gene with a destruction complex component was computed and ranks were summed per gene. (C) Comparison of baseline-specific similarity (PCC between genetic interaction profiles) with baseline and Wnt-active state similarity of baseline state Axn with other destruction complex components (red) or negative feedback regulators (nkd, Notum) (orange). (D) Between-state relations among components of stable functional modules. Stable functional relations exceeded a connection specificity index (CSI) of 0.9 in the baseline and respective activated state. Self-correlation of all tested genes is illustrated in black; the dark-blue line represents the self-correlation of genes in stable modules. (E) Between-state relations among components state-specific functional modules. State-specific relations exceeded a CSI of 0.9 in either the baseline or activated state but not in the respective other state (CSI < 0.7). Self-correlation of all tested genes is illustrated in black. Curves were compared using a one-sided Wilcoxon rank-sum test.

Figure 5

A Ligand Induction-Specific Network of Wnt Signaling (A) Correlation-based network connecting components of Wnt secretion and receptor binding with potential co-regulatory modules. “Bait” genes required for ligand secretion and receptor binding (green nodes) were selected and potentially novel components (light-blue nodes) were called if they shared a CSI > 0.9 in the Wnt-active state. Potential negative regulators (orange nodes) were added if they shared a between-state similarity (PCC > 0.5) with the destruction complex (red nodes) and a Wnt-active state-specific dissimilarity (PCC < −0.65) with at least one “bait” gene. The network was further extended for genes sharing a CSI > 0.9 with potentially novel positive or negative regulators (gray nodes). Genes were connected if their state-specific profiles shared an absolute PCC > 0.65 or if their between-state shared an absolute PCC > 0.5 (dashed lines). Positive and negative correlations are shown as blue and red lines, respectively. Per state, the modules of positive regulators (green, light blue), negative regulators (orange), and the destruction complex (red) were separately arranged using a force-directed layout algorithm. The edge width represents the absolute PCC. (B) State-specific (if |PCC| > 0.65) and between-state (if |PCC| > 0.5) relations of Catsup with the ligand secretion machinery and negative regulators. (C) Catsup single depletion effect on Wnt signaling activity upon increasing Wg concentrations. (D and E) Knockdown of Catsup in third instar wing imaginal disc leads to Wg accumulation in the Wg-producing cells. Total Wg staining intensity (red) was quantified using FIJI (n > 6 wing discs). enGal4, UAS-GFP/UAS-Catsup RNAi, posterior to the right (GFP positive). Scale bars, 20 μm. (F and G) Knockdown of Catsup leads to depletion of extracellular Wg. Extracellular Wg (Wg ex) was stained on living third instar imaginal disc without cell permeabilization. Staining intensity (red) was quantified using FIJI (n > 6 wing discs). enGal4, UAS-GFP/UAS-Catsup RNAi, posterior to the right (GFP positive). Scale bars, 20 μm.

Figure 6

Genes Resuming a Destruction Complex-like Role in Wnt Signaling in Different Pathway Activation Modes (A) Putative inhibitors of the Wnt target genes transcription machinery after ligand and Apc-loss-mediated pathway activation. (B and C) Putative inhibitors of the Wnt target gene transcription machinery exclusively after ligand or Apc-loss-mediated pathway activation. “Bait” genes required for target gene transcription (green nodes) were selected and potentially novel components (light-blue nodes) were called if they shared a CSI > 0.9 in both active pathway states (A) or one but not the other active state (B and C). Potential negative regulators (orange nodes) were added if they shared a between-state similarity (PCC > 0.5) with the destruction complex (red nodes) and a state-specific dissimilarity (PCC < −0.65) with at least one “bait” gene. Genes were connected if their state-specific profiles shared an absolute PCC > 0.65 or if their between-state shared an absolute PCC > 0.5 (dashed lines). Positive and negative correlations are shown as blue and red lines, respectively. Per state, the modules of positive regulators (green, light blue), negative regulators (orange), and the destruction complex (red) were separately arranged using a force-directed layout algorithm. The edge width represents the absolute PCC. (D) Lpt knockdown effect on Wnt signaling activity (upper panel) and the genetic interaction of Lpt with destruction complex components (lower panel) in the baseline state. The single knockdown phenotype was estimated from phenotypes of two independent dsRNAs in 144 different genetic backgrounds each (error bars show SE of median determined by bootstrapping). Error bars of the genetic interactions show the median absolute deviation of four independent genetic interaction scores for each gene pair.