Grand Theft Transcription Factor: Reversing Tumor Cell Immortality by Transcription Factor Relocalization

Overview

Glioblastoma (GBM) is the most predominant malignant brain cancer in adults without prognosis improvement in decades (average survival interval of 14 -17 months). Nevertheless, 83% of GBM cases have mutations in the Telomerase Reverse Transcriptase promoter ( TERTp), responsible for maintaining telomeres. TERTp mutations create an ETS factor binding site, enabling the ETS transcription factor, GABP, to bind and reactivate TERT expression. While directly targeting telomerase has systematic toxicity, targeting GABP may allow for tumor-specific TERT silencing. Still, targeting transcription factors with small- molecule inhibitors is nearly impossible, so a novel approach is required. To address this, we engineered GABPB1L dominant negative ( B1L-DN) transgenes by removing the transactivation (TAD-DEL) or both the TAD and the nuclear localization signal (TAD -NLS-DEL) domains. We hypothesized that with only TAD deleted, TERT expression would decrease, but the protein still could enter the nucleus. However, the DN with both NLS and TAD deleted would not enter the nucleus, ensuring the decrease of TERT expression. To test this hypothesis, we transduced GBM cells with either B1L-DN or an empty vector and measured TERT expression by RT -qPCR and protein subcellular localization with immunofluorescence staining. We observed a 70 -80% decrease in TERT expression by cells expressing either dominant-negative. Furthermore, immunofluorescence staining showed that GABPA was bound to the TAD-NLS-DEL-DN and could not enter the nucleus, thus rendering GABPA futile. If we can deliver the modified TAD-NLS-DEL-DN with viruses specifically targeting cancer cells in a TERTp mutant patient, this could be a potential application to inhibit tumor growth and thereby reverse immortality. The Creation of SPIRo: An AI Based Origami Soft Robot with Multidimensional Locomotion for Gas Leak Detection Eddie Zhang The Harker School, San Jose, CA Co-researcher: Evan Zhang Methane, a super pollutant thirty times more potent than carbon dioxide, is responsible for one third of the global warming caused by greenhouse gasses. With millions of tons of methane released into the atmosphere from major and fugitive gas leaks every year, there is an urgent need to develop an accurate, efficient, and cost -effective method for inspecting gas pipes. This research presents the first AI based origami-inspired soft robot with multimodal ensemble learning for real -time gas leak detection in remote, complex, and upstream pipeline environments. Our compact, lightweight, and modularized soft robot SPIRo moves at a speed of 15 mm/s through individually actuated pneumatic McKibben artificial muscle actuators, which offer increased strength and ext ension distance. SPIRo utilizes curved magnetic feet to attach to a variety of pipe surfaces, and it has two metal oxide gas sensors and a thermal camera, which collect data about the pipeline environment. A multimodal deep feature fusion system with the d eep forest classifier is developed for improved accuracy, redundancy, and efficiency. Thus, SPIRo achieved an accuracy of 88% in a simulated gas leak testing environment and 77% on real field data of gas sensors collected from the METEC lab at Colorado State University, demonstrat ing the capabilities of SPIRo’s real time gas leak detection. Finally, SPIRo is being integrated with a portable air compressor and valve system to increase its range. SPIRo can also be applied to infrastructure assessments for structures such as nuclear facilities and chemical plants that are hazardous to access.

Competition history

• JSHS 2024 Category not listed

Resources

• National JSHS abstract book ↗

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