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Simple Summary Glioblastoma multiforme (GBM) is a highly invasive brain tumor that typically has poor patient outcomes. This is due in part to aggressive tumor expansion within the brain parenchyma. This process is aided by assiduous glutamate release via the System xc- (SXC) cystine–glutamate antiporter. SXC is over-expressed in roughly half of GBM tumors where it is responsible for glutamate-mediated neuronal cell death and provides excess glutamate to fuel tumor-associated epilepsy. Available pharmacological inhibitors have some promise, although they lack specificity and have poor bioavailability. Therefore, identifying regulators of SXC may provide a superior avenue to target GBM. In this study, we identify tumor protein 53 (TP53) as a molecular regulator of SXC in GBM. Abstract Glioblastoma multiforme (GBM) is a deadly brain tumor with a large unmet therapeutic need. Here, we tested the hypothesis that wild-type p53 is a negative transcriptional regulator of SLC7A11, the gene encoding the System xc- (SXC) catalytic subunit, xCT, in GBM. We demonstrate that xCT expression is inversely correlated with p53 expression in patient tissue. Using representative patient derived (PDX) tumor xenolines with wild-type, null, and mutant p53 we show that p53 expression negatively correlates with xCT expression. Using chromatin immunoprecipitation studies, we present a molecular interaction whereby p53 binds to the SLC7A11 promoter, suppressing gene expression in PDX GBM cells. Accordingly, genetic knockdown of p53 increases SLC7A11 transcript levels; conversely, over-expressing p53 in p53-null GBM cells downregulates xCT expression and glutamate release. Proof of principal studies in mice with flank gliomas demonstrate that daily treatment with the mutant p53 reactivator, PRIMA-1Met, results in reduced tumor growth associated with reduced xCT expression. These findings suggest that p53 is a molecular switch for GBM glutamate biology, with potential therapeutic utility.

Rapid and widespread testing of severe acute respiratory coronavirus 2 (SARS-CoV-2) is essential for an effective public health response aimed at containing and mitigating the coronavirus disease 2019 (COVID-19) pandemic. Successful health policy implementation relies on early identification of infected individuals and extensive contact tracing. However, rural communities, where resources for testing are sparse or simply absent, face distinctive challenges to achieving this success. Accordingly, we report the development of an academic, public land grant University laboratory-based detection assay for the identification of SARS-CoV-2 in samples from various clinical specimens that can be readily deployed in areas where access to testing is limited. The test, which is a quantitative reverse transcription polymerase chain reaction (RT-qPCR)-based procedure, was validated on samples provided by the state laboratory and submitted for FDA Emergency Use Authorization. Our test exhibits comparable sensitivity and exceeds specificity and inclusivity values compared to other molecular assays. Additionally, this test can be re-configured to meet supply chain shortages, modified for scale up demands, and is amenable to several clinical specimens. Test development also involved 3D engineering critical supplies and formulating a stable collection media that allowed samples to be transported for hours over a dispersed rural region without the need for a cold-chain. These two elements that were critical when shortages impacted testing and when personnel needed to reach areas that were geographically isolated from the testing center. Overall, using a robust, easy-to-adapt methodology, we show that an academic laboratory can supplement COVID-19 testing needs and help local health departments assess and manage outbreaks. This additional testing capacity is particularly germane for smaller cities and rural regions that would otherwise be unable to meet the testing demand., Here, the authors report the development of a versatile academic, SARSCoV-2 RT-qPCR molecular diagnostic test that uses 3D printed technology for sample collection, is implemented in rural setting in the US state of Virginia and validated in its population.