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Background: Glioblastoma (GBM) is the most aggressive primary brain tumor and has a dismal prognosis. Previously, we identified that junctional adhesion molecule A (JAM-A), a cell adhesion molecule, is highly elevated in human GBM cancer stem cells and predicts poor patient prognosis. While JAM-A is also highly expressed in other cells in the tumor microenvironment, specifically microglia and macrophages, how JAM-A expression in these cells affects tumor growth has yet to be determined. The goal of this study was to understand the role of microenvironmental JAM-A in mediating GBM growth., Methods: Male and female wild-type (WT) and JAM-A-deficient mice were transplanted intracranially with the syngeneic glioma cell lines GL261 and SB28 and were assessed for differences in survival and microglial activation in tumors and in vitro. RNA-sequencing was performed to identify differentially regulated genes among all genotypes, and differences were validated in vitro and in vivo., Results: We found that JAM-A-deficient female mice succumbed to GBM more quickly compared with WT females and JAM-A-deficient and male WT mice. Analysis of microglia in the tumors revealed that female JAM-A-deficient microglia were more activated, and RNA-sequencing identified elevated expression of Fizz1 and Ifi202b specifically in JAM-A-deficient female microglia., Conclusions: Our findings suggest that JAM-A functions to suppress pathogenic microglial activation in the female tumor microenvironment, highlighting an emerging role for sex differences in the GBM microenvironment and suggesting that sex differences extend beyond previously reported tumor cell-intrinsic differences., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: In this paper we propose to use recent developments in knowledge representation languages and reasoning methodologies for representing and reasoning about signaling networks. Our approach is different from most other qualitative systems biology approaches in that it is based on reasoning (or inferencing) rather than simulation. Some of the advantages of our approach are, we can use recent advances in reasoning with incomplete and partial information to deal with gaps in signal network knowledge; and can perform various kinds of reasoning such as planning, hypothetical reasoning and explaining observations., Results: Using our approach we have developed the system BioSigNet-RR for representation and reasoning about signaling networks. We use a NFkappaB related signaling pathway to illustrate the kinds of reasoning and representation that our system can currently do., Availability: The system is available on the Web at http://www.public.asu.edu/~cbaral/biosignet