Fig. 1

miRNAs coordinately regulate signalling pathways despite mildly regulating individual targets. A and B – MDA-MB-231 cells were transfected with individual miRNAs from a library of 800 representing the global miRNome. 48 h post-transfection, total protein lysates were harvested and the expression of 62 target proteins was assessed by Reverse Phase Protein Assay (RPPA). After normalization for total protein content, the effect of miRNAs on target proteins was quantified by limma test. P-values were corrected for multiple testing with Benjamini-Hochberg method. Tabular results are available in Supplementary Table 2. A. Effects of miRNAs on the 62 probed targets are represented with a heatmap of all fold changes compared to negative controls in log2 scale (log2FC). Only miRNAs which caused at least one statistically significant interaction across the entire dataset were plotted, leading to 722 rows. The upper rug represents the prevalence of miRNA regulation for each target, weighted for the library size, separating the number of miRNAs significantly positively (+ve) or negatively (−ve) regulate each target. The second rug represents the average of statistically significant (q-value ≤0.001) regulation of each target, separating positive and negative regulations. The lower rugs represent from which pathway(s) of origin the targets derived from, as well as the putative effect of the target on the pathway. B. The regulatory activity of miRNAs is summarised in a violin plot containing all statistically significant (q ≤ 0.001) log2FC in protein expression. Full and dotted lines in the violins respectively represent the medians and the quartiles of the distributions. The horizontal lines in the plot represent the averages. C. Principles of PC score computation to transform the effect of a miRNA on a single target protein into a Pathway Coregulatory (PC) effect, integrating the function of the assayed protein on the signalling pathway. miRNA negatively or positively regulate (dark purple and green, respectively) the expression of a target with repressive or activating function (lilac and yellow, respectively). The combination of these two factors identifies the effect on the pathway as positive or negative (bright green or red, respectively). The cumulative effect of a miRNA is then summarized in a PC score classifying each miRNA as activator or repressor of a pathway. D. The distributions of computed PC scores for WNT/β-catenin (left), c-Met (middle), and integrin signalling (right). In each graph, numbers indicate the number of putative repressing or activating proteins probed associated to the pathway. E. Principles of bootstrapping for statistical testing. The miR-N matrix used to calculate the PC scores was randomized 10,000 times, for each a miRNA-specific random PC score was computed. Then, the experimental PC score was tested against the randomly generated ones. An experimental PC score was considered significant with a 5% alpha level. F. Venn diagrams display the number of miRNAs repressing (left) or activating (right) the signalling pathways with a significant effect after randomization test