Cancer cells predominantly metabolize glucose by glycolysis to produce energy in order to meet their metabolic requirement, a phenomenon known as Warburg effect. Although Warburg effect is considered a peculiarity critical for survival and proliferation of cancer cells, the regulatory mechanisms behind this phenomenon remain incompletely understood. We report here that eukaryotic elongation factor-2 kinase (eEF-2K), a negative regulator of protein synthesis, has a critical role in promoting glycolysis in cancer cells. We showed that deficiency in eEF-2K significantly reduced the uptake of glucose and decreased the productions of lactate and adenosine triphosphate in tumor cells and in the Ras-transformed mouse embryonic fibroblasts. We further demonstrated that the promotive effect of eEF-2K on glycolysis resulted from the kinase-mediated restriction of synthesis of the protein phosphatase 2A-A (PP2A-A), a key factor that facilitates the ubiquitin-proteasomal degradation of c-Myc protein, as knockdown of eEF-2K expression led to a significant increase in PP2A-A protein synthesis and remarkable downregulation of c-Myc and pyruvate kinase M2 isoform, the key glycolytic enzyme transcriptionally activated by c-Myc. In addition, depletion of eEF-2K reduced the ability of the transformed cells to proliferate and enhanced the sensitivity of tumor cells to chemotherapy both in vitro and in vivo. These results, which uncover a role of the eEF-2K-mediated control of PP2A-A in tumor cell glycolysis, provide new insights into the regulation of the Warburg effect.
Complex gene regulatory networks ensure that important genes are expressed at precise levels. When gene expression is sufficiently perturbed it can lead to disease. To understand how gene expression disruptions percolate through a network, we must first map connections between regulatory genes and their downstream targets. However, we lack comprehensive knowledge of the upstream regulators of most genes. Here we developed an approach for systematic discovery of upstream regulators of critical immune factors - IL2RA, IL-2, and CTLA4 - in primary human T cells. Then, we mapped the network of these regulators' target genes and enhancers using CRISPR perturbations, RNA-Seq, and ATAC-Seq. These regulators form densely interconnected networks with extensive feedback loops. Furthermore, this network is highly enriched for immune-associated disease variants and genes. These results provide insight into how immune-associated disease genes are regulated in T cells and broader principles about the structure of human gene regulatory networks.
Identifying potential adverse drug reactions (ADRs) is critically important for drug discovery and public health. Here we developed a multiple evidence fusion (MEF) method for the large-scale prediction of drug ADRs that can handle both approved drugs and novel molecules. MEF is based on the similarity reference by collaborative filtering, and integrates multiple similarity measures from various data types, taking advantage of the complementarity in the data. We used MEF to integrate drug-related and ADR-related data from multiple levels, including the network structural data formed by known drug–ADR relationships for predicting likely unknown ADRs. On cross-validation, it obtains high sensitivity and specificity, substantially outperforming existing methods that utilize single or a few data types. We validated our prediction by their overlap with drug–ADR associations that are known in databases. The proposed computational method could be used for complementary hypothesis generation and rapid analysis of potential drug–ADR interactions.
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