Summary By analyzing gene expression data in gliobastoma in combination with matched microRNA profiles, we have uncovered a post-transcriptional regulation layer of surprising magnitude, comprising over 248,000 microRNA (miR)-mediated interactions. These include ~7,000 genes whose transcripts act as miR ‘sponges’ and 148 genes that act through alternative, non-sponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of tumor initiation and subtype, including PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. RNA silencing of 13 microRNA-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3′ UTR-dependent manner and to increase tumor-cell growth rates. Thus, this miR-mediated network provides a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus.

Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverseengineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows "de novo" construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo "benchmarking" of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.

Mutations in the gene encoding the KMT2D (also called MLL2) methyltransferase are highly recurrent and occur early during tumorigenesis in diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL). However, the functional consequences of KMT2D mutations and their role in lymphomagenesis are unknown. Here we show that FL/DLBCL-associated KMT2D mutations impair KMT2D enzymatic activity, leading to diminished global H3K4 methylation in germinal-center (GC) B-cells and DLBCL cells. Conditional deletion of Kmt2d early during B cell development, but not after initiation of the GC reaction, results in an increase in GC B-cells and enhances B cell proliferation in mice. In mice overexpressing BCL2, which develop GC-derived lymphomas resembling human tumors, genetic ablation of Kmt2d leads to a further increase in tumor incidence. These findings suggest that KMT2D acts as a tumor suppressor gene whose early loss facilitates lymphomagenesis by remodeling the epigenetic landscape of the cancer precursor cells. Eradication of KMT2D-deficient cells may represent a rational therapeutic approach for targeting early tumorigenic events.