Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments

Abstract

Competitive endogenous (ce)RNAs cross-regulate each other through sequestration of shared microRNAs and form complex regulatory networks based on their microRNA signature. However, the molecular requirements for ceRNA cross-regulation and the extent of ceRNA networks remain unknown. Here, we present a mathematical mass-action model to determine the optimal conditions for ceRNA activity in silico. This model was validated using phosphatase and tensin homolog (PTEN) and its ceRNA VAMP (vesicle-associated membrane protein)-associated protein A (VAPA) as paradigmatic examples. A computational assessment of the complexity of ceRNA networks revealed that transcription factor and ceRNA networks are intimately intertwined. Notably, we found that ceRNA networks are responsive to transcription factor up-regulation or their aberrant expression in cancer. Thus, given optimal molecular conditions, alterations of one ceRNA can have striking effects on integrated ceRNA and transcriptional networks.

Fig. 1.

ceRNA interaction model. (A) Schematic graph depicting expression levels of the total pool of ceRNAs and miRNAs in a theoretical ceRNET. (B) Schematic outline of the considerations upon which the ceRNA model is based. Green rectangles are DNA molecules, which are transcribed with rates k_S, k_R1, and k_R2 to create miRNA molecules S (orange stars) and mRNA molecules R₁ and R₂ (blue and red pentagons), respectively. mRNA molecules form complexes with miRNAs (C₁ or C₂) with association rates k_1,+ or k_2,+ and dissociation rates k_1,− or k_2,−. Each complex degrades with rate g₁ or g₂, respectively, and miRNAs are recycled with the probability (1 − α), whereas mRNAs are always lost. Free molecular species (R₁, R₂, and S) can degrade with rates g_i (where i = {R₁, R₂, S}). Gray shapes represent degraded molecules. mRNA translation is not taken into account. (C) Prediction of the effect of altered ceRNA expression on a ceRNA network consisting of two ceRNAs and one miRNA. (D) The Pearson coefficient for ceRNA1 and ceRNA2 in C is shown.

Fig. 2.

Analysis of the ceRNA dosage effect in vitro. (A and B) The dosage effect predicted by the ceRNA model. ceRNA1 and -2 are expressed at different ratios (ceRNA2:ceRNA1 = 1:1–8:1 in A and ceRNA2:ceRNA1 = 1:1–1:8 in B) and the effect of silencing ceRNA2 by 50% (red bars) or 90% (blue bars) on ceRNA1 is shown. Parameters can be found in SI Materials and Methods. (C) Graph showing the VAPA:PTEN mRNA ratio in five cancer cell lines. (D) Combined expression of PTEN- and VAPA-targeting miRNAs. (E) Representative Western blot displaying the effect of VAPA silencing on PTEN expression and vice versa. P, PTEN; V, VAPA; NC, negative control. (F) Quantification of E. (G) The average percentages of ceRNA-mediated silencing of PTEN and VAPA in response to short interfering (si)RNA against VAPA and PTEN, respectively.

Fig. 3.

Analysis of the miRNA dosage in silico and in vitro. (A–C) Effect of varying miRNA expression levels on ceRNA cross-regulation. Shown are three plots representing the steady-state mean number of free molecules for ceRNA1, ceRNA2, and ceRNA3 and miRNA in a system of one miRNA and three ceRNAs as a function of ceRNA1 transcription rate. The values for the miRNA transcription rate are as follows: (B) k_s = 0.001 [1/s] (∼4 miRNA molecules per hour), (C) k_s = 0.0083 [1/s] (∼30 miRNA molecules per hour), and (D) k_s = 0.02 [1/s] (∼70 miRNA molecules per hour). The remaining parameters can be found in SI Materials and Methods. (D) Plot comparing the steady-state mean number of free molecules for ceRNA2 as a function of ceRNA1 transcription in the systems with varying miRNA expression rates shown in A–C. (E) Graph showing the VAPA:PTEN mRNA ratio in five cancer cell lines. (F) Combined expression of PTEN- and VAPA-targeting miRNAs. (G) Representative Western blot displaying the effect of VAPA silencing on PTEN expression and vice versa. P, PTEN; V, VAPA; NC, negative control. (H) Quantification of G.

Fig. 4.

Indirect interactions amplify ceRNA crosstalk. Silencing of ceRNA1 (black line) has a stronger effect on ceRNA2 (red line) when ceRNA1 indirectly interacts with it through ceRNA3 (blue line) (Upper) than if no indirect interaction exists (Lower). Schematic outlines of the two networks are shown in the Upper Right corner of each panel. Solid arrows depict direct interactions, whereas dashed arrows depict ceRNA1’s indirect interactions.

Fig. 5.

TF networks underlie ceRNA regulation. (A) Graph displaying the percentage of predicted ceRNAs of FOXO1 and its targets MET, FGFR4, IGF1R, and ALK that are deregulated in ARMS. The numbers in the bars represent the number of ceRNAs in each category. No ceRNAs are down-regulated. (B) Forty-one percent of genes up-regulated in ARMS are putative ceRNAs of 284 predicted FOXO1 transcriptional targets that are up-regulated in ARMS. (C) Graphs showing the correlation between the Pearson coefficients of 37 TFs and their targets or ceRNAs. Targets and ceRNAs were ranked according to their Pearson coefficient in the high- and low-miRNA-expression subsets and the difference in average ranking between targets and ceRNAs was calculated.