A cell-based probabilistic approach unveils the concerted action of miRNAs

Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate the underlying principles of miRNA regulation in living cells. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under a paradigm of a transcriptional arrest. Based on an exhaustive matrix of mRNA-miRNA binding probabilities, and parameters extracted from our experiments, we developed a computational framework that captures the cooperative action of miRNAs in living cells. The framework, called COMICS, simulates the stochastic binding events between miRNAs and mRNAs in cells. The input of COMICS is cell-specific profiles of mRNAs and miRNAs, and the outcome is the retention level of each mRNA at the end of 100,000 iterations. The results of COMICS from thousands of miRNA manipulations reveal gene sets that exhibit coordinated behavior with respect to all miRNAs (total of 248 families). We identified a small set of genes that are highly responsive to changes in the expression of almost any of the miRNAs. In contrast, about 20% of the tested genes remain insensitive to a broad range of miRNA manipulations. The set of insensitive genes is strongly enriched with genes that belong to the translation machinery. These trends are shared by different cell types. We conclude that the stochastic nature of miRNAs reveals unexpected robustness of gene expression in living cells. By applying a systematic probabilistic approach some key design principles of cell states are revealed, emphasizing in particular, the immunity of the translational machinery vis-a-vis miRNA manipulations across cell types. We propose COMICS as a valuable platform for assessing the outcome of miRNA regulation of cells in health and disease. Author summary Alteration in miRNA expression occurs throughout cell differentiation, inflammation, viral infection, tumorigenesis, and other pathologies. Notwithstanding a rich body of experimental data intended to assess the outcome of miRNA alterations in cells, the underlying design principles remain obscure and fragmented. In this study, we develop a quantitative stochastic model that simulates the mRNA steady-state in view of alteration in miRNAs' abundance. We systematically analyzed the behavior of miRNA-mRNA regulation and confirm that the stochastic nature of miRNA regulation reveals unexpected robustness of cell behavior across cell types. Specifically, we expose the immunity of the translational machinery towards miRNA regulation. The developed platform, called COMICS compares the results of miRNA regulation across various cell types. Based on stochastic and probabilistic considerations, we provide a dynamic and flexible framework that quantifies the competition of miRNAs within cells in health and disease.