Chronic platelet-derived growth factor receptor signaling exerts control over initiation of protein translation in glioma

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

EGFRvIII tumorigenicity requires PDGFRA co-signaling and reveals therapeutic vulnerabilities in glioblastoma

Focal amplification of epidermal growth factor receptor (EGFR) and its ligand-independent, constitutively active EGFRvIII mutant form are prominent oncogenic drivers in glioblastoma (GBM). The EGFRvIII gene rearrangement is considered to be an initiating event in the etiology of GBM, however, the mechanistic details of how EGFRvIII drives cellular transformation and tumor maintenance remain unclear. Here, we report that EGFRvIII demonstrates a reliance on PDGFRA co-stimulatory signaling during the tumorigenic process in a genetically engineered autochthonous GBM model. This dependency exposes liabilities that were leveraged using kinase inhibitors treatments in EGFRvIII-expressing GBM patient-derived xenografts (PDXs), where simultaneous pharmacological inhibition of EGFRvIII and PDGFRA kinase activities is necessary for anti-tumor efficacy. Our work establishes that EGFRvIII-positive tumors have unexplored vulnerabilities to targeted agents concomitant to the EGFR kinase inhibitor repertoire.

Genome-wide translation patterns in gliomas: An integrative view

Gliomas are the most frequent tumors of the central nervous system (CNS) and include the highly malignant glioblastoma (GBM). Characteristically, gliomas have translational control deregulation related to overactivation of signaling pathways such as PI3K/AKT/mTORC1 and Ras/ERK1/2. Thus, mRNA translation appears to play a dominant role in glioma gene expression patterns. The, analysis of genome-wide translated transcripts, together known as the translatome, may reveal important information for understanding gene expression patterns in gliomas. This review provides a brief overview of translational control mechanisms altered in gliomas with a focus on the current knowledge related to the translatomes of glioma cells and murine glioma models. We present an integrative meta-analysis of selected glioma translatome data with the aim of identifying recurrent patterns of gene expression preferentially regulated at the level of translation and obtaining clues regarding the pathological significance of these alterations. Re-analysis of several translatome datasets was performed to compare the translatomes of glioma models with those of their non-tumor counterparts and to document glioma cell responses to radiotherapy and MNK modulation. The role of recurrently altered genes in the context of translational control and tumorigenesis are discussed.

Physical and Molecular Landscapes of Mouse Glioma Extracellular Vesicles Define Heterogeneity

Cancer extracellular vesicles (EVs) are highly heterogeneous, which impedes our understanding of their function as intercellular communication agents and biomarkers. To deconstruct this heterogeneity, we analyzed extracellular RNAs (exRNAs) and extracellular proteins (exPTNs) from size fractionation of large, medium, and small EVs and ribonucleoprotein complexes (RNPs) from mouse glioblastoma cells by RNA sequencing and quantitative proteomics. mRNA from medium-sized EVs most closely reflects the cellular transcriptome, whereas small EV exRNA is enriched in small non-coding RNAs and RNPs contain precisely processed tRNA fragments. The exPTN composition of EVs and RNPs reveals that they are closely related by vesicle type, independent of their cellular origin, and single EV analysis reveals that small EVs are less heterogeneous in their protein content than larger ones. We provide a foundation for better understanding of segregation of macromolecules in glioma EVs through a catalog of diverse exRNAs and exPTNs.