MicroRNAs (miRNAs) are integral elements in the post-transcriptional control of gene expression. After the identification of hundreds of miRNAs, the challenge is now to understand their specific biological function. Signalling pathways are ideal candidates for miRNA-mediated regulation owing to the sharp dose-sensitive nature of their effects. Indeed, emerging evidence suggests that miRNAs affect the responsiveness of cells to signalling molecules such as transforming growth factor-beta, WNT, Notch and epidermal growth factor. As such, miRNAs serve as nodes of signalling networks that ensure homeostasis and regulate cancer, metastasis, fibrosis and stem cell biology.

Although specific microRNAs (miRNAs) can be upregulated in cancer, global miRNA downregulation is a common trait of human malignancies. The mechanisms of this phenomenon and the advantages it affords remain poorly understood. Here we identify a microRNA family, miR-103/107, that attenuates miRNA biosynthesis by targeting Dicer, a key component of the miRNA processing machinery. In human breast cancer, high levels of miR-103/107 are associated with metastasis and poor outcome. Functionally, miR-103/107 confer migratory capacities in vitro and empower metastatic dissemination of otherwise nonaggressive cells in vivo. Inhibition of miR-103/107 opposes migration and metastasis of malignant cells. At the cellular level, a key event fostered by miR-103/107 is induction of epithelial-to-mesenchymal transition (EMT), attained by downregulating miR-200 levels. These findings suggest a new pathway by which Dicer inhibition drifts epithelial cancer toward a less-differentiated, mesenchymal fate to foster metastasis.

The gene regulatory circuitry through which pluripotent embryonic stem (ES) cells choose between self-renewal and differentiation appears vast and has yet to be distilled into an executive molecular program. Here we developed a data-constrained, computational approach to reduce complexity and derive a set of functionally validated components and interaction combinations sufficient to explain observed ES cell behavior. This minimal set, the simplest version of which comprises only 16 interactions, 12 components and 3 inputs, satisfies all prior specifications for self-renewal and furthermore predicts unknown and non-intuitive responses to compound genetic perturbations with an overall accuracy of 70%. We propose that propagation of ES cell identity is not determined by a vast interactome, but rather can be explained by a relatively simple process of molecular computation.